esa-sceva/satcom-chunk-collection huggingface dataset:
# Protein Dynamics in F-like Bacterial Conjugation
## 3 Structures Involved in Pilin Processing, Pilus Extension, and Retraction
### Pilus Assembly and Extension Proteins
#### 3.2.1 Pilus Tip Formation  
Five proteins are involved in the formation of the pilus tip: TraE, -C, -G, -K and -L. TraE is a mainly hydrophobic protein found within the IM, thought to be involved in the process of pilus tip formation as well as the assembly of the IM complex [37; 55]. Mutations in _traE_ result in the prevention of pilus tip formation [14]. TraE has been purified from the complex in sizes from 130-261 aa, but is predominantly found as a 188 aa protein [14]. It is thought that TraE may contain a leader sequence that is cleaved prior to insertion into the IM [37; 62]. This predominantly hydrophobic protein forms an N-terminal membrane anchor from residues Val12-Ile33 [37]. According to Phyre2, the structure of TraE is thought to span the membrane a single time and is composed of mainly \(\alpha\)-helices in the N-terminal region and \(\beta\)-strands in the C-terminal region, with a low amount of disorder throughout [20]. TraL is a mainly hydrophobic protein localized within the IM thought to limit the number of F pili produced by a cell [14; 37; 74]. The TraL family of proteins range in size from 93 to 105 aa with a shared homology to VirB3 from the Ti plasmid and TrbD from the RP4 plasmid [75; 76]. TraL from the F plasmid is 91 aa and is predicted to localize within the IM [77]. The predicted structure of TraL contains two TM \(\alpha\)-helices bracketed by reverse turns, followed by regions of \(\beta\)-structure connected by a reverse turn [62]. TraC will be discussed in further detail as its ATPase activity is crucial for providing energy to the T4 SS for pilus extension. The NTD of TraG is embedded within the IM and has an unknown role in the assembly of the pilus tip. It is predicted to have three-to-five TM-spanning helices in the first 445 aa with the remaining CTD as a periplasmic region participating in other conjugative roles [37; 78; 79]. TraK is a predominantly hydrophilic protein that is localized in the periplasm, which is thought to interact with the lipoprotein TraV to form the envelope-spanning core complex into which the assembled pilus is extended [80]. Both proteins were shown to be involved in the initiation of pilus formation through M13K07 phage sensitivity assays of protein mutants [81].
## 4 Simulation Evaluation
### Impact of the Number of Sensor Nodes
When there is high density, BOSS tends to forward data packets by recovery routing, although there exist appropriate nodes in the greedy area, since an intermediate sender cannot receive any CTSs from neighbors due to their own collisions. Figure 11 shows the packet delivery ratio (PDR) among the four different protocols. As the node density increases, the PDR of each protocol also increases. This is because each protocol has a shorter  
Figure 10: Total number of perimeter packets _vs._ the number of sensor nodes. Figure 9: Total number of packets _vs._ the number of sensor nodes. path as the node density increases, as shown in Figure 8. Therefore, we can find that PDR is strongly related to the number of hops. In general, the proposed protocol shows higher PDR than BLR and BGR. The reason is that our protocol fully uses a forwarding candidate area, like the beacon-based protocols; however, other protocols use only a limited area that consists of mutually-communicable sensor nodes. These protocols can guarantee desired data throughputs for the destination only when the network has enough sensor nodes. On the other hand, BOSS shows a similar performance as the proposed protocol; however, this decreases when there is a high node density, due to the previously mentioned collision problem. Figure 12 shows the end-to-end delay for each protocol. Except the case of BOSS, the graph shows that the density is strongly correlated with the end-to-end delay; because BOSS requires full-contention time whenever a packet is forwarding to a next-hop node. In the case of other protocols, the lower densities show longer end-to-end delay. In fact, routing in perimeter mode is the main source of long delays. Similarly, in this simulation, a large part of the end-to-end delay is from perimeter routing. However, although the proposed protocol has smaller perimeter packets than other protocols, we observe that the proposed protocol has more delay than the others with low densities. This is the case when there are no appropriate forwarding candidates in the greedy area, including both the mutually-communicable area and the rest of the area. In this case, both BLR and BGR turn into perimeter mode after \(T_{max}\) (ms); however, the proposed protocol requires \(2\times T_{max}\) (ms) because of its two-phase contention process. However, as the node density increases, each protocol can find a similar route, and then, the end-to-end delay of each protocol also becomes short. Figure 11: The packet delivery ratio _vs._ the number of sensor nodes.
# Problematizing the Adoption of Formal Methods in the 4 IR-5 IR Transition
## 3 Literature Review
### Formal Methods in Software Engineering
#### 3.1.1 Advantages of Formal Methods  
FMs, through the use of a formal specification, assist a specifier in writing a precise specification of a system, thereby eliminating possible ambiguities. Often these also involve investigating the consequences of a specification. Natural- and semi-formal specifications are known to be ambiguous [30], hence our interest in formal specifications.  
The following example illustrates the value of formalizing natural language statements, i.e., the utility of FMs.  
**Example 1**.: _Consider the following statements in the security policy of a company [30]:_  
1. _All usernames and password combinations must be unique._
2. _Every user must have a password._  
From (1) we infer that two or more different users, e.g., U1 and U2, may have the same password, say P1, at any given point in time. Hence, (U1, P1) and (U2, P1) is a valid state of the system.  
From (2) we may infer two possible formulations:  
* (all x exists y (y is the password of x)) (a), or
* (exists y all x (y is the password of x)) (b).  
Now, (b) \(\rightarrow\) (a), but not vice versa. Logically speaking, statement (b) is too strong and is not what was intended--(b) states that there is one specific password for all users. This will not work since all users will know each other's password. Formulation (a) is what is intended by the policy statements, and it is only through formalizing the statements that the possible ambiguities emerge.  
Further advantages to using FMs are illustrated in Section 4.1.
# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 3 Experiments and Results  
Our experimental environment is the seventh floor of the XianSu Building at the Yaohu campus of Jiangxi Normal University, which includes a corridor (3 m \(\times\) 21.5 m); four laboratories, specifically, rooms 3705 (7 m \(\times\) 7 m), 3707 (7 m \(\times\) 7 m), 3712 (7 m \(\times\) 7 m) and 3713 (7 m \(\times\) 7 m); and a corner. We arrange eight TL-WR885N routers on the wall at a height of 2.1 m as the Wi-Fi RSSI signal access points (AP1-AP8). The mobile terminals used in the experiment include a standard mobile terminal (Huawei mate10 pro), test mobile terminal 1 (Huawei mate8) and test mobile terminal 2 (Huawei P8), all of which contain Wi-Fi sensors.
# Research on Compressed Sensing Spectrometry Based on Electro-Optical Transmittance Coding
## 3 Experiment
### Compressed Sensing Spectrum Measurement Experiment
The full width at half maximum (FWHM) of the laser spectrum measured by the compressed sensing spectrum measurement system is 1.2 nm, which is slightly less than 0.8 nm of HR4000 measurements. This similarity further demonstrates that the FWHM of the spectrum measurement system designed in this paper is small. The system has a high spectral resolution, and the theoretical spectral resolution can reach approximately 0.4 nm for a wide spectral range (0.38-0.78 \(\upmu\)m) with 1024 spectral channels. Further, the spectrum of the lighting fluorescent lamp in the laboratory was measured. The experimental results are shown in the Figure 9.  
The experimental result exhibits good agreement with that of HR4000, and the relative error of spectral reconstruction is 7.65%, which further proves that the proposed spectral measurement system scheme produces high precision measurements and spectrum reconstructions.
## 4 Simulation Results and Discussion  
In this part, several numerical examples are given to illustrate the performance of the BMST-RSPC-PNC system. These examples demonstrate the capabilities of the pro posed scheme. In all the simulations, QPSK modulation is employed, and the channel is modeled as a fast-fading Rician channel, where \(\mu_{\mathcal{K},1}=10.0\) dB, \(\mu_{\mathcal{K},2}=3.0\) dB, and \(\sigma_{\mathcal{K},1}=\sigma_{\mathcal{K},2}=0.0\) dB. Given that the combined transmission power from the two senders and the noise variance is \(2\sigma^{2}\), the signal-to-noise ratio (SNR) is equal to \(\frac{1}{\sigma^{2}}\). The decoding delay \(d\) is fixed at 20. The parameters in Algorithm 3 are set as \(J_{max}=18\) and \(\delta_{h}=10^{-5}\). The detailed simulation configurations are presented in Table 1.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 8 Ship Classification Results
### Dataset Partitioning  
It is essential to divide the dataset into multiple subsets. The most common approach involves splitting the data into two or three parts: a training set and a test set, or a training, validation, and test set.  
The training set is used to teach the model by providing input-output examples, specifically, for pairs of independent variables \(\mathbf{x}\) and the dependent variable \(y\). Through this, the model learns the underlying patterns and relationships.  
The validation set is used to monitor the model's performance during training. It helps evaluate the generalization of the model to new data and prevents overfitting by tuning parameters and selecting the best model.  
The test set contains data that the model has never seen during training or validation. It is used for the final evaluation, providing an unbiased estimate of the model's real-world performance.  
There is ongoing debate over how best to split the data. A common rule of thumb is to allocate 70% of the data for training, and 15% each for validation and testing [34].  
To better evaluate a model's stability and generalization, cross-validation techniques are often used. In \(K\)-fold cross-validation, the dataset is divided into \(K\) folds. For each of the \(K\) epochs, one fold is used as the test set, while the remaining \(K-1\) folds are used for training and validation. This process ensures that every data point is used for testing exactly once.  
If the model's performance is consistent across all \(K\) epochs, it suggests that the model is stable and performs similarly on unseen data. The final model performance is typically taken as the average of the \(K\) results.
## 4 Results and Analysis
### Learning Analysis  
Figure 8 illustrates the class-wise malware prediction of the proposed DAMN and its variants. It can be seen that malware from the same family (i.e., Swizzor.gen!I and the Swizzor.gen!E) substantially affects the performance of deep networks. The malware from homogeneous features is more complex to identify. Here, our proposed model can learn even such hard-to-distinguish features to understand the disparity between close malware.Our proposed auxiliary feature branches MHA and FSAM help our proposed network learn and refine salient information from malware images.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Network Varient** & **Backbone** & **FSAM** & **MHA** & **Param. (M)** & **Comp. (GFlops)** & **Accuracy (\%)** & **Precision** & **Recall** & **F1-Score** \\ \hline Base & X & X & X & 3.76 & 0.6605 & 98.07 & 0.9660 & 0.9638 & 0.9799 \\ FSAM & Y & X & X & 4.01 & 0.6728 & 98.93 & 0.9732 & 0.9780 & 0.9889 \\ SAMUX & Y & Y & X & 4.02 & 0.6748 & 99.04 & 0.9880 & 0.9865 & 0.9906 \\ DAMN (Proposed) & Y & Y & Y & 4.08 & 0.6782 & **99.36** & **0.9892** & **0.9879** & **0.9924** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study with different network variants of the proposed DAMN. Our novel components significantly improve the classification score by adding a small overhead.  
Figure 7: Training procedures of proposed DAMN and its variants. (**a**) Validation accuracy vs. epoch. (**b**) Training loss vs. epoch.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 6 HWLNets in IoT Applications
### Security  
In wireless communications a major security concern is the introduction of an eavesdropper into the channel. Types of man in the middle attacks reconnect users to malicious networks or use specialized programs to read unencrypted data. The use of virtual private networks solves this problem to some extent. These security barriers are made on a network or application level. Therefore, researchers are focused on physical layer security. Secure key generation uses the existing random distribution of channels (RSS) [92] to guarantee secure key distribution. In securing data transmission, we can enlarge the SINR difference in an eavesdropper's link in contrast to the legitimate users by reducing RSS or increasing noise/interference [93].  
Reducing transmission power via optimization of resource allocation and interference alignment for legitimate users thus will impair the possibility of eavesdropping.  
Adding artificial noise to the sub space of channels is also an option, so that legitimate users are not affected by this addition and eavesdropper's SINR performance is diminished. The above-mentioned models with channel coding can definitely boost security [94].  
LiFi physical properties have numerous security effects, for example:light does not traverse outside of rooms or buildings, thus making it difficult for an outside attacker to get access to the channel;
* LiFi short range due to light properties means eavesdroppers have to be very close in order to spy;
* field of view and line of sight are imperative in VLC with 80% of the signal being in direct LoS, thus making eavesdropping minimal due to limited resources.  
Researchers analysed the security performance of LiFi systems [95], proving that the hexagonal positioning of LiFi AP has the highest security capabilities and matrix slightly worse.  
Quantum key distribution enables photon encoding in zero or one status or both at the same time using polarization. Quantum mechanics, such as a no-cloning principle, enables the generation of random keys between two users. Such a system was proposed in [96] using small handheld devices with an encrypted data rate of 30 kb/s and a length of fifty centimetres.  
A number of studies have shown that HWLNets require less power while performing network security tasks than standalone ones [97]. Research on dual hop HWLNets have shown that LiFi signal energy can be used to relay data in the RF spectrum [98] with additional research on finding minimum transmission power that has enough security.  
Another approach proposed an algorithm based on the Observe, Orient, Decide, Act (OODA) and Cyber Kill Chain (CKC) strategies to annul impersonation attacks on networks. The researchers used the RF device identification system in combination with the SDN architecture to achieve conformance with the IEEE 802.11 standard, resulting in the detection of 97.5% of attacks on the network in real-time scenarios [99]. This model is used in hybrid networks and improves security in the WiFi part, leading to an overall better performance of the whole network.
## 7 Discussion
### Effects of Disjoint Level Threshold  
The use of very high levels of disjoint threshold has two main effects: a) restricting the number of disjoint routes, as it will depend on the topological connectivity of the traffic network. In the MLT network, the Dj-kSP calculation for 6 routes with minWeight and 60% disjoint level returns in some O/D tuples just 2 or 3 matching paths. If we lighten the disjoint constraint to 30%, we obtain 5 or even 6 paths, which provide the required path variety. These path flows are used to generate the TWM maps that will be used later for route selection by the routing agents.
# Designed on 0.18 um CMOS Process Small Size Broadband Millimeter Wave Chip Antenna
## 2 Materials and Methods
### Surface Current Analysis  
The Figure 13 shows the current distribution of the antenna at 64 GHz. The chip antenna proposed in this article uses a GSG probe to feed. The current flows evenly through the feed line and power divided into two triangular patch antennas. The current is mainly distributed on both sides of the power divider and triangular patch antenna. As can be seen from the figure, the current distribution of the power divider is average and consistent. After the triangular patch antenna is connected, the current is mainly distributed on both sides of the triangular patch antenna, making the antennas at both ends generate optimal resonance.  
Figure 11: Diagram of different numbers of circular slot.  
Figure 12: Reflection coefficient of different numbers of circular slot.
## 3 Data and Methods  
### Data Sources and Pretreatment  
#### 3.1.1 Data Sources  
According to its availability, the statistical data were obtained from the _Xuzhou Statistical Yearbook_ from 2005 to 2014 [27], including the area, population of Xuzhou central area, and the consumption and yield of bio-productive land.  
Figure 1: Location of the study area.  
There are two kinds of MODIS data used in this study: Net Primary Production (MOD17A3) and Land Cover Type (MCD12Q1). The data are from Earthdata, NASA ([[https://earthdata.nasa.gov/](https://earthdata.nasa.gov/)]([https://earthdata.nasa.gov/](https://earthdata.nasa.gov/))) [28]. The MODIS Net Primary Productivity product (MOD17A3) defines the rate at which all plants in an ecosystem produce net useful chemical energy. The spatial resolution is 1 km \(\times\) 1 km, the remote sensing parameters are from Terra satellite, and the calculation result is given in yearly production. The valid values of the MOD17A3 data range from 0 to 65500 and the scale factor is 0.0001. The MODIS Land Cover Type product contains five classification schemes, and the spatial resolution is 500 m \(\times\) 500 m. In this study, the Land Cover Type 2 was selected to reclass the land use cover.
## 5 Control Algorithms for UAV Networking
### Decentralized Control Algorithm
#### 5.2.2 Artificial Potential Field (APF)
Although traditional APF suffers from some limitations such as local minima and orbital wobble, recent studies have proposed many improvements. They include the incorporation of rotating force fields, leader-follower models, consensus algorithms, and sliding mode control (SMC). These improvements enable UAVs to not only maintain the desired formation but also flexibly adapt to complex environments, such as urban areas, crowded spaces, and dynamic obstacle conditions. Furthermore, integrating APF with distributed control models and cluster optimization improves the efficiency of UAV coordination, especially in missions requiring high UAV density such as light shows or search and rescue. Overall, APF remains a promising approach to UAV formation control, and its combination with modern methods such as machine learning and intelligent control promises to open up many development opportunities in the future.
## 2 Thermal Barrier Coating
### Thermal Properties of Thermal Barrier Coatings
#### 2.4.2 Thermal Conductivity by Material  
Materials that can be used in TBCs must satisfy several necessary conditions. The first requirement is low thermal conductivity, the second is mechanical stability with temperature, and the third is the effect of pores inside the material [77]. Polycrystalline oxides used as TBCs have a thermal conductivity of 1-30 W/m-K, depending on the temperature [77; 78]. Among them, zirconia (ZrO\({}_{2}\)), which shows small thermal conductivity change with temperature and has a thermal conductivity of approximately 2 W/m-K, is mostly used [79].  
\(\bullet\) Yttria stabilized zirconia  
The phase stability of pure zirconia is low because of the phase changes during the heating and cooling processes. Thus, YSZ is used by adding yttria (Y\({}_{2}\)O\({}_{3}\)). In addition, the higher the amount of yttria used, the lower the thermal conductivity of TBC; this is because of the phonon scattering due to point defects [80]. As the content of yttria in zirconia increases, the thermal conductivity decreases by more than 50%. Because of the thermal repeatability in practical use, zirconia with 7 wt.% yttria, 7 YSZ, is mostly used, which was developed by NASA in the 1970s via burner league tests.  
\(\bullet\) Zirconia with rare earth  
Currently, research on zirconia M\({}_{2}\)Zr\({}_{2}\)O\({}_{7}\) (M is a rare earth element) including rare earths is being actively conducted [81]. Rare earths are used because they increase the durability of TBCs, increase the acceptable temperature of turbine entry temperature, and use smaller thicknesses than conventional TBCs. The thermal conductivity of TBC using rare earths is low owing to the short phonon mean free path.  
EB-PVD TBCs with a thickness of 250 \(\mu\)m, including 4 mol% rare earth metals, such as Gadolinia, Neodymia, and Ytterbia, have thermal conductivities of approximately 1 W/m-K at room temperature, and those of 0.88, 1.00, and 1.02 W/m-K at 500 \({}^{\circ}\)C, respectively [79].
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 4 Case Study: Santa Maria Delle Grazie
### Load Test  
The load test was conducted through three loading and unloading cycles under a quasi-static regime to optimally detect AE events. Loads were applied using a system of sandbags positioned vertically above the arch at points D1 to D5, each aligned with one of the arch's voussoirs to distribute the load incrementally. Load increments were controlled manually under quasi-static conditions. Five displacement transducers were installed at the arch's intrados along the span, corresponding to key positions near the springing points, haunches, and crown. The displacement transducers used were mechanical dial gauge comparators (accuracy: 0.01 mm), selected for their robustness and ease of use in field conditions. Each comparator was affixed using stainless steel brackets bonded to the masonry surface with reversible adhesive to avoid damage. The transducers were positioned along the intrados of the arch to record vertical displacement at representative locations. Their layout is illustrated in the updated experimental setup schematic (Figure 4), and corresponding data are reported in Table 2.  
Figure 6: Installation of acoustic sensors: (**a**) schematic representation of the installation with magnetic clamps; (**b**) close-up of a sensor installed on the intrados of the arch (the yellow box highlights the sensor locations); (**c**) sensor setup and acquisition system.  
Considering the high degree of uncertainty regarding the potential initiation of micro- and/or macro-cracks and their associated emission frequencies, the first cycle was designed with numerous loading steps. This procedure also enabled the calibration of the system by setting the appropriate threshold value and accurately identifying background noise.  
The first loading cycle was thus performed with nine loading steps and two unloading steps. The limited number of unloading steps was due to the absence of AE detection during the loading phase. In the first cycle, a maximum load of 11.25 kN was reached, and no significant responses were observed in terms of displacements and deformations.  
It was therefore decided to proceed with the second cycle by reducing the number of loading steps and increasing the maximum load to 20.00 kN. The second cycle comprised five loading steps and two unloading steps. The third cycle was conducted with three loading steps of greater magnitude, each reaching a maximum load of 20.00 kN, and two unloading steps.