esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Routing Layer in WMSN
### Future Research Directions  
In general (as we believe), protocols inspired in ant colonies and game theory will keep being published. They will look good on paper, specially with all those formulas. Nevertheless, nobody will consider seriously to put them into real use because deep down everybody will suspect that less glamorous but more down to earth approaches are the way to go. In addition, many of those protocols have a too long adaptation time to routing topology changes to be feasible to many scenarios.  
Multipath routing is the way to go in WMSNs because these wireless networks need to exploit the network bandwidth to its limit and sometimes in short bursts. Of course, that makes everything more complicated, affects transport layer, and introduces many new problems. Nevertheless, we foresee this to be the approach that will win in the long run. But, the future proposals will have to be designed as integral solutions that cover routing, transport layer, security and sometimes even MAC layer.  
Geographical routing has been touted by many as the routing that suits best WMSNs [46, 47, 48]. Nevertheless, we foresee exactly the opposite. This is due to the fact that these mechanisms require that nodes know their geographical positions (most of the times assuming they have a positioning system like GPS) being arguably an unfeasible requirement for many scenarios. In addition, these routing protocols have no security provisions at all. Thus, very simple attacks can be devised against them. Moreover, in WMSNs bandwidth is such a big issue, that more complex routing protocols that discover better routes are going to be required.  
Quality of Service is clearly a needed feature in many WMSNs, specially if there is some sort of real time streaming. Nevertheless, there will be many scenarios in which the use of different message priorities are going to give more or less the same performance with less complexity. Expect that sooner or later the general recommendation in this issue will be: If you do not really need QoS, use message priorities.
## 2 Materials and Methods
### Ultrasonic Characterization  
#### 2.2.1 Phase and Group Velocity  
The longitudinal group velocities were measured using a contact-based transducer (Olympus) in pulse-echo setup [41]. The transducer frequency was 5 MHz and the active area was 12 mm (0.5 inch). The shear group velocities were measured using two transducers in the through transmission ultrasound (TTU) setup as shown in Figure 1b. The shear transducer frequency was 2.25 MHz and the active area was 12 mm (0.5 inch). A mass loaded setup was used to obtain consistent loading pressure, which reduces the measurement error. Five measurements were carried out along each principal direction of the sample by  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Oxygen and moisture & \textless{}20 ppm \\ Laser power & 1000 W \\ Speed & 14.4 mm/s \\ Powder feed rate & 12.3 g/min \\ Powder particle size & 16–44 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Process parameters used in the present study.  
Figure 1: **(a)** Sample geometry after machining, D3 is the build direction. The hatched region shows the 300 mm\({}^{2}\) region where all the ultrasonic measurements were carried out on each face of the sample. **(b)** The through-transmission setup that was used for velocity measurement. **(c)** The immersion setup that was used for attenuation and backscatter coefficient measurement. A, B, C refer to the frontwall, backwall and 2 nd backwall. For the backscatter measurements, a focused transducer was used.  
moving the transducer within a 300 mm\({}^{2}\) region. This region was chosen at the center of the sample as shown in hatched region in Figure 1a to ensure no edge effects interfered with the measurements. Data capture was carried out at 1 GHz using an 8-bit oscilloscope, and a Panametrics 5055 pulser-receiver. The group velocities were calculated using the time of flights of the two backwall echoes, which were extracted from the recorded waveforms using Hilbert transforms. The same approach was used for shear velocity measurements, except for a fused silica reference, which was used to obtain the time of flight difference in the TTU setup. More details can be found in the supplementary material.  
The phase velocity as a function of frequency was obtained using the procedure outlined in Ref. [32]. Typically, the phase velocity in the 5-15 MHz range for metallic structures will be invariant of the frequency [42]. Therefore, we used a 1 MHz, 12 mm (0.5 inch) diameter transducer in pulse-echo mode to capture the phase velocity in the 0.1 MHz to 2.5 MHz range. Three measurements were carried out along each principal direction of the sample by moving the transducer within a 300 mm\({}^{2}\) region, and the response was averaged. More details of the phase velocity measurement are given in the supplementary material. The experimental setup is similar to Figure 1b, albeit using only one transducer instead of two.
# Control of _Francisella tulareis_ Virulence at Gene Level: Network of Transcription Factors
## 7 Response Regulators Involved in Two-Component Systems Work as Transcription Factors
### Biofilm-Regulating Francisella Protein Response Regulator--BfpR  
Recently, a response regulator that is involved in biofilm formation in _F. novicida_ was characterized and was denoted BfpR (Biofilm-regulating Francisella protein regulator). The presence of two domains, a REC- and a DNA-binding, was confirmed, suggesting it is a typical response regulator with transcription factor activity that is part of a TCS in Francisella. BfpR is encoded by the FTN_1452 gene and it is encoded in an operon with the FTN_1453 sensor kinase, thus, constituting another TCS in Francisella. BfpR has a negative effect on biofilm production, but it is a positive regulator of antimicrobial peptide resistance. The study also suggested that BfpR could be a negative regulator of _igIC_ expression [66].
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 3 The Dispatch of Power Systems Containing VPPs
### The Dispatch Model Containing VPPs  
The schematic diagram of VPPs participating in the dispatch is shown in Figure 8.  
The VPP, as an autonomous entity, connects to the power grid just like other conventional power plants (CPP), such as thermal power plants (TPP), hydraulic power plants (HPP), geothermal power plants (GPP) and nuclear power plants (NPP). After the process of output prediction, the VPP uploads its output and other relevant information to the dispatch center and the electricity market through information and communication technology and receives instructions from the system.  
Energy conservation and emissions reduction are the main objectives of the power system in the dispatch considering F-H weather. On the premise of satisfying the needs of the total load, the system preferentially takes in power generated by units with low emission and restricts (or even shuts down) units with high emission to some degree according to the operational constraints and economic characteristics of the units. In addition, several economic incentive measures are taken to encourage the development of new clean energy, such as wind power and photovoltaic power. Therefore, the VPP demonstrates its priority in dispatch via its advantage of low pollution and policies of fiscal subsidies. Generally, the higher the AQI level is, the more obvious the VPP's priority.
## 4 Results and Discussion
### Combined Uni + Bi + Tri Features  
FM with three feature types (uni + bi + tri) outperformed bigram- or trigram-only models but scored worse than the top unigram results. In terms of results, this combo feature set resulted in the best from those models prior, with an SVM yielding the best performance at 92.48%, next to logistic regression, which achieved 91.26%, and the two other classifiers were not able to produce such accuracy values, as Naive Bayes and Random Forest produced 82.98% and 89.88%, respectively. The overlap of the n-grams provided additional context, so they likely did help the models learn bigrams as well as individual offensive words but did not add much value beyond unigrams alone.  
For feature-wise comparison, the findings highlight the usefulness of unigram features to determine the presence of offensiveness in Urdu language data, whereas SVM remains the best model throughout all different types of features (shown in Table 2). For SVM, bigrams and trigrams have some value but less than that of unigrams. This tells us that unigram-based methods work very well on the pattern of offensive language in Urdu text and always do in all situations where SVM also works very well on a sparse matrix with high dimensions as well.
## 5 Experimental Proof of Two Cache Timing Attacks
### Cache Timing Side-Channel Attacks
#### 5.1.3 Flush+Reload  
The _Flush+Reload_ attack targets the first round of the _AES-128 T-table_ running in the secure world of the processing system. Before implementing the attack, we perform a pre-profiling step to define the threshold to use for each frequency in order to distinguish a cache miss from a cache hit.  
The main purpose of a _Flush+Reload_ attack on the _AES-128 T-table_ is to determine which index of the \(T_{0}\) is accessed by the encryption process in order to recover the key. To do so, the proposed _Flush+Reload_ attack scenario uses three main steps:  
* Step 1: The malicious master interface evicts the cache line containing one of the 256 elements of the table \(T_{0}\).
* Step 2: The master interface triggers encryption by sending a plaintext with a fixed pi byte.
* Step 3: Once the master interface receives the ciphertext, the interface issues a read request targeting the address of the element evicted in step 1 which counts the number of clock cycles elapsed between the handshake of the read address channel and the handshake of the read data channel. If the number of clock cycles is below the threshold, the master interface can deduce that the elements of \(T_{0}\) evicted in step 1 have been accessed by the cipher.  
In order to find the byte \(k_{0}\) of the key for the _Flush+Reload_ attack, we used the technique presented in [12]. This technique helps find the upper five bits of the byte \(k_{0}\) and reduce the key search space to 48 bits.  
The cache access patterns in \(T_{0}\) table presented in Figure 10 are created by running the three steps presented above 256 - 256 time in order to scan the whole T0 table (256 elements) and try all possible values of the byte \(p_{i}\) (256 possible values for a byte). In Figure 10a, the diagonal pattern of black squares reveals that \(k_{0}\) = 0 \(\times\) 00. The diagonal pattern is due to the fact that the intermediate state S1 of the _AES-128 T-table_ has accessed the value \(T_{0}\): [0 \(\times\)00 \(\oplus\)\(p_{i}\)] = \(T_{0}\)[\(p_{i}\)].  
Figure 10: Cache access pattern to \(T_{0}\) table using a _Flush+Reload_ attack from the programmable logic part, (**a**) frequency = 250 MHz, (**b**) frequency = 100 MHz.  
Figure 9: Threat model used for the _Flush+Reload_ and the _Exicl+Time_ attacks.  
Figure 10b shows the cache access pattern to table \(T_{0}\) with a frequency of 100 MHz applied to the master interface and \(k_{0}\) = 0 \(\times\) 0\(F\). This pattern shows that it is possible to implement cache attacks even with only one clock cycle to distinguish between a cache miss and a cache hit.
## 6 Discussion
### Limitations
From these measurements, the per-component energy-per-FLOP coefficients (\(\alpha_{\text{CPU}},\alpha_{\text{GPU}}\)) will be estimated via linear regression of power versus achieved FLOPS under varying utilization levels. Network service-time distributions will be characterized by fitting empirical inter-arrival and service intervals to known distributions (e.g., Weibull or log-normal) based on timestamped packet-exchange logs. Model parameters--service rates \(\mu_{i}\), energy coefficients, and network delay statistics--will then be calibrated by minimizing the mean absolute percentage error (MAPE) between predicted and observed metrics:  
\[\text{MAPE}\ =\ \frac{100\%}{N}\sum_{k=1}^{N}\left|\frac{y_{k}^{\text{ pred}}-y_{k}^{\text{obs}}}{y_{k}^{\text{obs}}}\right|.\]  
Finally, Kubernetes will be used to orchestrate containerized replicas of each node type, effectively scaling the testbed to emulate larger network sizes. By comparing model forecasts against these scaled experiments, we will identify and quantify systematic deviations--such as thermal throttling effects or network-fan-in bottlenecks--and incorporate correction factors into the decision-support system. This rigorous calibration and validation procedure will anchor the theoretical framework in real-world observations, ensuring that the system's recommendations remain accurate and robust across both laboratory and production scales. Another limitation is the need for additional factors. The current model does not consider factors such as external instability, changing user needs, or technological advancements. These factors can influence the long-term efficiency and sustainability of the chosen configuration, making it important for future models to incorporate such variables for a more comprehensive analysis [91]. Additionally, while the FFGA Algorithm demonstrated good performance in solving optimization tasks, it has its own limitations. Specifically, it does not always guarantee the identification of a global optimum [92], especially given the complexity involved in searching through multi-criteria solution spaces. To address this, future work may need to explore more advanced optimization approaches, such as multi-grid algorithms or deep learning techniques, which could enhance prediction accuracy for optimal configurations. Finally, there are scalability challenges that need attention. The tests conducted for LLC \"RITS\" used a relatively limited network model, and in order to scale the system for larger networks or more complex problem sets [93], more advanced infrastructure will be required. Furthermore, the algorithms must be improved to handle big data and multitasking efficiently, ensuring that the system can scale effectively to meet the needs of larger, more dynamic environments.
## 2 Mechanism
### Principle of the 3-DOF Upper-limb Exoskeleton
The length of the upper-arm and the forearm can refer to the human body, but the 3-DOF upper-limb exoskeleton is a non-anthropomorphic structure. The length of the upper-arm \(O_{1}A_{1}\) and forearm \(A_{1}B_{1}\) is determined by the workspace of the 5-DOF upper-limb exoskeleton and the angle \(\alpha\) between the forearm of the exoskeleton and the sagittal plane, shown in Figure 4a, so that the 3-DOF upper-limb exoskeleton has the same workspace as the 5-DOF upper-limb exoskeleton. In order for the 3-DOF upper-limb exoskeleton to reach the workspace of the 5-DOF upper-limb exoskeleton, the distance between the end-effector and the shoulder should be the same as that of the 5-DOF upper-limb exoskeleton. The lengths of the upper-arm and forearm of the 5-DOF upper-limb exoskeleton are 330 mm and 370 mm, respectively. In the sagittal plane of the shoulder joint, the distance between the end-effector of the 5-DOF upper-limb exoskeleton and the shoulder joint is shown in Figure 5a. In the sagittal plane of the shoulder joint, the distance between the end-effector of the 3-DOF upper-limb exoskeleton and the shoulder joint is described in Figure 5b. \(O_{0}\). and \(O_{1}\) represent the shoulder, \(A_{0}\) and \(A_{1}\) represent the elbow, \(B_{0}\) and \(B_{1}\) represent the end-effector, \(\theta_{0}\) and \(\theta_{1}\) represent the angle between the upper-arm and forearm, \(\alpha\) represents the angle between the upper-arm and the sagittal plane, \(O_{0}A_{0}\) and \(O_{1}A_{1}\) represent the upper-arm, and \(A_{0}B_{0}\) and \(A_{1}B_{1}\) represent the forearm. Figure 4: Working configuration of the 3-DOF upper-limb exoskeleton. (**a**) Top view; and (**b**) right view. Figure 3: Kinematic configuration and coordinates of the 3-DOF upper-limb exoskeleton. In Figure 5a, \(O_{0}B_{0}\) and \(\beta\) can be calculated as follows:  
\[\cos\theta_{0}=\frac{O_{0}{A_{0}}^{2}+A_{0}{B_{0}}^{2}-O_{0}{B_{0}}^{2}}{2\times O _{0}A_{0}\times A_{0}B_{0}} \tag{1}\]  
\[\cos\beta=\frac{O_{0}{B_{0}}^{2}+A_{0}{B_{0}}^{2}-O_{0}{A_{0}}^{2}}{2\times O_{0 }B_{0}\times A_{0}B_{0}} \tag{2}\]  
As shown in Table 2, \(62^{\circ}\leq\theta_{0}\leq 180^{\circ}\), and using Equations (1) and (2), the result is derived, where \(\max(\beta)\) is \(54^{\circ}\) and \(O_{0}B_{0}\) and \(O_{1}B_{1}\) have the same range of variation of \([362.3,\ 700]\). In Figure 5b, \(O_{1}B_{1}\) and \(\alpha\) can be calculated as follows:  
\[\cos\theta_{1}=\frac{O_{1}{A_{1}}^{2}+A_{1}{B_{1}}^{2}-O_{1}{B_{1}}^{2}}{2 \times O_{1}A_{1}\times A_{1}B_{1}} \tag{3}\]  
\[\cos\alpha=\frac{O_{1}{B_{1}}^{2}+A_{1}{B_{1}}^{2}-O_{1}{A_{1}}^{2}}{2\times O _{1}B_{1}\times A_{1}B_{1}} \tag{4}\]  
As shown in Table 3, \(62^{\circ}\leq\theta_{1}\leq 180^{\circ}\), \(O_{0}B_{0}=O_{1}B_{1}\), and using Equations (3) and (4), the result is derived, where \(\max(\alpha)\) is \(53^{\circ}\), \(O_{1}A_{1}\) is \(328\), and \(A_{1}B_{1}\) is \(372\).
# Protograph LDPC Code Design for Asynchronous Random Access +
## 4 Numerical Results  
For the numerical results, we drop the simplified surrogate channel model and focus on two more realistic RA channels. The first set of numerical results makes use of a physical layer abstraction where the interference is Gaussian, but not necessarily constant over the \(\alpha n_{s}\) symbols of a modulated codeword. In the second set of numerical results, the complete physical layer is implemented, and hence, the Gaussian assumption on the interference is dropped. While the abstracted physical layer model is a common approach to get first-level insights into the system performance [27], (long) physical layer simulations serve as a confirmation of the results. If the identified trends are still present, physical layer results may give evidence of the validity of the surrogate channel model adopted for the code design.  
The assumptions common to both sets of numerical results are summarized in the following. Users transmit \(d=2\) replicas according to the asynchronous RA protocol described in Section 2.1. Replicas are affected by multiple access interference and by AWGN noise, with \(E_{s}/N_{0}=6\) dB. All replicas are received with the same normalized power \(\mathsf{P}=1\), thanks to perfect power control. For simplicity, we normalized all durations to the packet length \(t_{p}\). The VF duration was set to \(200\,t_{p}\), while the receiver decoding window was \(W=600\,t_{p}\). The receiver decoding window shift was set to \(\Delta W=20\,t_{p}\).  
The performance metric we used to compare the performance of different error correcting codes in the asynchronous random access setting is the PLR \(\mathsf{p}_{l}\), as a function of the channel load \(\mathsf{G}\). The PLR is the average probability that a user cannot be correctly recovered at the receiver, at the end of the SIC process.
## 3 Image Processing
### Median Filtering and Improved Subtraction  
The obtained gray-level image is processed with improved subtraction processing. Subtraction processing typically eliminates the slight variation such as shadings or irregularly noticeable components. Thus, it is for elimination of the noise as well as the illuminated components from cracks. Figure 4 illustrates how the subtraction finds cracks against the other objects including shading. Illuminance of the original gray-level visualizes objects in the image, and the median filtering removes out sharp illuminance changes that represent small noises or cracks and remains smoothed background image representing concrete surface, large objects, and shading. Then, the subtraction of the original  
Figure 3: Image segmentation by K-means clustering (K = 3). (**a**) Original image; (**b**) Image segmented by K-means clustering (K = 3).  
image from the filtered image removes out the background image while remaining objects with sharp illuminance. Since the crack is visualized by very low illuminance profile with shape change, the crack object can be highlighted from the subtracted image, if combined with subsequent image processing to remove out small noises.  
The median filtering converts the gray-level image to a smoothed one. The subtracted image is obtained by the Equation (3).  
\[I_{s}(x_{i})=\max\left\{\begin{array}{c}median_{x_{j}\in R_{i}}I(x_{j})-I(x_ {i})\\ 0\end{array}\right\} \tag{2}\]  
where \(I_{s}\) (\(x_{i}\)) is the intensity of the pixel \(x_{i}\), \(R_{i}\) is the neighborhood of that pixel, and \(I(x_{j})\) is the intensity of the pixels in \(R_{i}\). When the result of the subtraction is a negative number, the result is represented with 0.  
In addition, the subtraction result from the Equation (2) was used to get an enhanced image by a manually decided threshold value \(T\). The thresholding operation is processed by the Equation (4).  
\[I_{IS}(x,y)=\max\left\{\begin{array}{c}2\times I_{s}(x,y)\quad\text{if}\quad I _{s}(x,y)>T\\ I_{s}(x,y)\quad\text{if}\quad I_{s}(x,y)\leq T\end{array}\right\} \tag{3}\]  
where \(I_{IS}(x,y)\) and \(I_{s}(x,y)\) are the results of the improved and the original subtraction method, respectively. The threshold value T is determined manually. By this process, the illuminance difference between cracks and noises is larger and it improves the noise reduction for the next step.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 3 Results
### Tree Crown Area  
A total of 124 tree crown trimming polygons were delineated (Figure 13). Tree crown area values ranged from 7 m\({}^{2}\) to 838 m\({}^{2}\), with an average of 152 m\({}^{2}\) across the study sites (Table 5). This average tree crown size corresponds approximately to the pixel size of a Sentinel sensor at a 10 m spatial resolution.
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 4 Structure of Power QKD Network  
The traditional power system constructs a network security protection system through security equipment such as firewalls, intrusion detection systems, VPNs and encryption devices, which ensures the secure transmission of data to a certain extent. However, new network attack methods are emerging in an endless stream, which poses new challenges to the security, reliability and timeliness of control/management instruction transmission. In practice, some businesses rely on offline keys to update keys, and the update frequency is slow, which affects the security level of information transmission. Considering that the traditional key online transmission has the hidden danger of being stolen, resulting in insufficient security and timeliness of online key distribution, and further affecting the security performance of the power system. In view of the above, this section proposes a security protection mode based on power QKD technology. At the same time, a new generation of data security transmission architecture based on QKD for dispatching automation, distribution automation, electricity information collection and video conference are designed.