esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 USPs of Pathogenic Bacteria
### Porphyromonas gingivialis  
The oral opportunistic pathogen _Porphyromonas gingivialis_ is a Gram-negative anaerobic bacterium of the phylum Bacteroidetes that is predominantly responsible for chronic periodontitis and the expression of various potentially virulent proteases that have the ability to hydrolyze iron-binding proteins, immunoglobins, complement factors and collagen [86]. To survive within the oral cavity and adapt to its variations, which include temperature change, availability of nutrients, the presence of other occupant bacteria or host cells, pH and oxygen tension, _P. gingivialis_ requires stress response mechanisms to be able to detect the changes associated with these environmental factors. Studies have shown that apart from heat shock proteins (HSPs), UspA is expressed to assist the pathogen to endure heat stress [87; 88]. _P. gingivialis_ colonizes different micro-environments and its transition to and from various locations subjects it to environmental stress. A series of stress-related proteins are therefore expressed, most of which are HSPs and Clp proteins. Among these, ClpB and ClpL ATPases act as chaperones whose duty comprise protein remodeling and reactivation. Knockout studies have hypothesized that UspA either compensates for ClpB loss or permits the protein to execute more specific roles in _P. gingivialis_[87]. However, the most notable role of UspA in _P. gingivialis_ was demonstrated by [PERSON] colleagues, who revealed the transcriptional and translational up-regulation of UspA during biofilm formation,whose structures are a well-known defense mechanism against environmental stress [89]. Buttressing this was the display of greater resistance by wild type _P. gingivalis_ strains to H2O2 stress and faster growth rates in the presence of unfavorable temperatures and the antibiotic tetracycline in comparison to _uspA_-deficient mutants. The study concluded that the _uspA_ gene is an important feature in biofilm development, which plays a role in the resistance of various organisms, including _P. gingivalis_, to environmental stress. Although the mechanism of action for UspA was not defined, it is suggested that the protein performs its role indirectly [90; 91]. Concurring with the results of the above study, whole-genome profiling by DNA microarray analysis also noted the conspicuous up-regulation of PG0245, a _uspA_ gene induced in response to exposure of _P. gingivalis_ to NO stress, suggesting its additional role in NO resistance [90; 92]. Consistent with this report was that of [PERSON] and co-workers, who also detected the elevated expression of the _PG0245_ gene in response to a microaerophilic state during the mid-exponential growth phase [93]. Such findings reveal the importance of USPs to the survival of this important pathogen in enduring various microenvironments.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 1 Introduction
### Motivation  
From the above literature, the techniques and analysis that have been used to improve system performance of CR-NOMA have predominantly been restricted to only CBSM [62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76].
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 4 Receiver Design
### Communication Receiver
 \tag{8}\]  
The overall process of match filtering and information decoding at the communication receiver is shown in Figure 4.  
Furthermore, by performing the simple ratio test on the output of the filter in Equation (6), we obtain  
\[\widehat{B}_{l}(\tau)= \begin{cases}0\quad if\quad f_{0}(t),\quad|y_{k,l}| \geqslant T\\ 1\quad if\quad f_{1}(t),\quad|y_{k,l}|\leqslant T\end{cases}  \tag{9}\]  
where \(T\) is the threshold constant of frequency separation for orthogonality. It is important to note that multiple waveforms are selected at a time, and they change on a pulse-to-pulse basis. The data rate is given as a product of the number of bits per pulse and the PRF, i.e.,  
\[\text{Data Rate}=\text{PRF}\times\text{bits per composite symbol}. \tag{10}\]  
The probability that 00 is transmitted and 01 is received can be written as \(P(01|00)\). Similarly, when 10 is received, it can be written as \(P(10|00)\) and the overall equation with error function can be modelled as  
\[P(00|00)=1-\{P(01|00)+P(10|00)+P(11|00)\}. \tag{11}\]
# BioSuiteT: A Unified Tool for Biological Sequence Analysis
## 2 Materials and Methods
### Validation of BioSuiteT
#### 2.6.5 Statistical Results Validation  
BioSuiteT relies on established statistical methods for validating results, which are inherently integrated into the analysis functions via trusted libraries. These methods are not custom-developed or theoretical; instead, they are implemented through standard packages.  
* Hypothesis testing: In differential analysis (e.g., gene expression), BioSuiteT applies statistical tests (\(t\)-test, ANOVA) with False Discovery Rate correction using the Benjamini-Hochberg procedure. The adjusted \(p\)-values are computed as \[p_{adjusted}=p\cdot\frac{m}{range(p)},\] where \(m\) is the number of tests. These calculations are performed internally by the statistical libraries used and are not re-implemented within BioSuiteT.
* PCA (Principal Component Analysis): BioSuiteT includes PCA for dimensionality reduction, based on spectral decomposition of the covariance matrix: \(\sum=X^{T}X\) This method is executed using existing numerical libraries that handle the decomposition and transformation steps.  
PCA was selected for dimensionality reduction in BioSuiteT due to its versatility, efficiency, and minimized reliance on data assumptions. This makes it particularly useful for exploratory analyses, as it can be applied to unlabeled datasets. Methods such as Linear Discriminant Analysis require predefined class labels. All validation techniques mentioned are thus implemented through well-established external libraries and are integrated into the functional workflows of BioSuiteT.
## 2 Methods
### Data Preprocessing  
In our experiment, we used the EEG Motor Movement /Imagery (M/I) Dataset provided by PhysioNet [14; 15]. This dataset includes 1500 EEG recordings from 109 subjects, each undergoing 14 experimental runs: two baseline runs (eyes open/closed) and four M/I tasks (repeated thrice). The tasks involved left/right fist movements (Task1), imagining left/right fist movements (Task2), both fist/foot movements (Task3), and imagining both fist/foot movements (Task4). The EEG signal was sampled at 160 Hz and each task contained 19,680 time points.  
To preprocess the data, first, we applied a five-order Butterworth filter to the extracted raw EEG data to isolate the 6-35 Hz frequency band, which is most relevant for motor imagery tasks [16]. Butterworth filter is a type of linear time-invariant system that can produce a very flat passband and slow roll-off. It is the most widely used filter in EEG signal processing due to its maximally flat frequency response in the passband [17].  
After that, we divided the EEG recordings into small segments (or \"clips\") which contained 164 time steps, each corresponding to a single motor imagery task. To increase the diversity of the data, we employed the SegPool strategy, where we randomly combined different clips from different tasks to form a single input sequence for the model. In different tasks, the length of the splicing clips also varied, e.g., two clips were randomly spliced and recombined in the binary classification tasks, while four clips were processed in the same way in the four-category classification tasks.
## 4 Performance Validation
The TLOS and VLOS of the OGCNN-RWD technique under distinct epochs are given in Figure 5. The figure infers that the OGCNN-RWD approach has demonstrated improved performance with the least values of TLOS and VLOS. Visibly, the OGCNN-RWD method has reduced VLOS outcomes. The lesser values indicate the effectual detection performance of the proposed model. Figure 4: TACY and VACY outcome of OGCNN-RWD system: (**a–i**) Epoch 100–500. Figure 5: TLOS and VLOS outcomes of OGCNN-RWD system: (**a–i**) Epoch 100–500. A brief precision-recall examination of the OGCNN-RWD method under distinct epochs is shown in Figure 6. The figure designates that the OGCNN-RWD algorithm has higher precision-recall values under two class labels. A clear ROC investigation of the OGCNN-RWD system under distinct epochs is portrayed in Figure 7. The results represent that the OGCNN-RWD algorithm has exhibited its capability in classifying different two-class labels. To assure the improved outcomes of the OGCNN-RWD approach, a brief comparative investigation is made in Table 3[21; 22]. Figure 8 investigates the comparative examination  
Figure 6: Precision–recall outcome of OGCNN-RWD system. Figure 7: ROC outcome of OGCNN-RWD system. of the OGCNN-RWD technique in terms of \(accu_{y}\). The experimental values indicate that the OGCNN-RWD technique reaches a maximum \(accu_{y}\) of 99.64% while the DWOML, bagging, AdaBoost-M1, ROF, DT, and RF models result in a minimum \(accu_{y}\) of 99.09%, 98.47%, 96.13%, 95.79%, 97.63%, and 98.83%, respectively. Figure 9 inspects the comparative investigation of the OGCNN-RWD algorithm in terms of \(sens_{y}\) and \(spec_{y}\). Based on \(sens_{y}\), the OGCNN-RWD technique reaches a maximum \(accu_{y}\) of 99.64% while the DWOML, bagging, AdaBoost-M1, ROF, DT, and RF methods result in minimal \(sens_{y}\) of 99.43%, 93.66%, 94.50%, 96.77%, 97.82% and 98.79%, respectively. Likewise, based on \(spec_{y}\), the OGCNN-RWD technique reaches a maximum \(spec_{y}\) of 99.64% while the DWOML, bagging, AdaBoost-M1, ROF, DT, and RF approaches result in minimum \(spec_{y}\) of 99.17%, 96.06%, 94.60%, 97.38%, 98.12% and 98.26%, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(Accu_{y}\) & \(Sens_{y}\) & \(Spec_{y}\) \\ \hline OGCNN-RWD & 99.64 & 99.64 & 99.64 \\ \hline DWOML Model [21] & 99.09 & 99.43 & 99.17 \\ \hline Bagging [22] & 98.47 & 93.66 & 96.06 \\ \hline AdaBoost-M1 [22] & 96.13 & 94.50 & 94.60 \\ \hline Rotation Forest (ROF) [22] & 95.79 & 96.77 & 97.38 \\ \hline Decision Tree (DT) [22] & 97.63 & 97.82 & 98.12 \\ \hline Random Forest (RF) [22] & 98.83 & 98.79 & 98.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative outcome of OGCNN-RWD approach with existing systems. Figure 8: \(Accu_{y}\) outcome of OGCNN-RWD system with existing systems. These results show the enhanced performance of the OGCNN-RWD technique over other models.
## Materials and Methods
### Experimental Region and Data
#### 2.1.2 Field Experimental Site  
The field measurements used in this study were collected during HiWATER (Heihe Watershed Allied Telemetry Experimental Research), which was conducted in 2015 [44]. There were five ground observation stations in the study area: Daman, Huazhaizi, Shenshawo, Zhangye, and Gebi stations. The underlying surface and geographic information at the five sites are described in Figure 1 and Table 1.  
An automatic weather station (AWS) was equipped with sensors for collecting data including temperature, wind speed and direction, air pressure, humidity, precipitation, soil moisture profile, solar radiation, four-component radiation, soil heat flux, and infrared temperature every 10 min over the whole year of 2015. All variables every 10 min were cumulated into the data of one day, then the daily data were interpolated into a map at a resolution of 30 m covering the study area with Kriging Interpolation.  
Fluxes of sensible and latent heat used in the model evaluation were measured at Daman and Huazhaizi stations. The eddy covariance (EC) sensors were installed at the height 4.5 m above the ground. EC data were sampled at a frequency of 10 Hz and turbulent fluxes were recorded every 30 min. The Gaussian fitting method was used to interpolate the missing data [45]. The closure error of the EC measurements at the daily scale, in the case of the energy imbalance of EC system, was calculated based on the energy balance ratio (EBR) to correct the daily flux data [46].
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation
### The Structure of Federated Filtering Algorithm
#### 2.1.3 LEO/INS Sub-Filter  
The structure of the LEO/INS sub-filter is similar to that of the GNSS/INS sub-filter. The difference is that the parameters of the LEO constellation model are different from those of the GNSS constellation. The main difference lies in the constellation orbit height, the number of orbital planes, and other parameters. See Section 2.3 for details.
## 3 MFA Algorithm Design
### Authentication Process  
The novelty of the proposed MFA mechanism lies in an algorithm which is based on image selection and user-established relations.  
The complete authentication process is described thoroughly below, beginning with Step 1.  
Step 1: User enters username and password in the screen, shown in Figure 6, for the first authentication factor.  
Step 2: If the user enters the correct credentials, twelve images are picked at random and are displayed on the screen, as shown in Figure 7; four of these images are picked from the group of images that the user uploaded in the set-up process and the rest of them are picked from a generic image database unaffiliated with the user's images. This is done to create confusion and test the user to increase the entropy of the mechanism. The user must select all 4 images which belong to him/her correctly. No image can be selected twice. If the user fails to select the correct images, authentication fails.  
Figure 6: Login screen.  
Figure 7: Image-Selection screen.  
Step 3: Once the user successfully selects the four images, the screen displays only two out of the four images, as seen on Figure 8a. Now, the user must select both images, and, once this is done, a modal appears on the screen, as seen on Figure 8b. The user must select the type of relation that exists between both images. The user must press the \"AUTHENTICATE\" button to validate the relation. If said relation is not correct the authentication attempt fails, otherwise, the user is authenticated successfully.
## 6 Evaluation and Experimental Results  
The fundamentals of cryptographic techniques used in IoT should respect the constraints of sensor nodes and the evaluation criteria are mainly the complexity of code, packets' sizes, processing time, and energy consumption. In the rest of this paper, we demonstrate that we respect all of these criteria. Our network setup and our evaluations were conducted on FIT IoT-LAB [10] that provides a large-scale infrastructure facility and experimental platform suitable for testing small wireless sensor devices and heterogeneous communicating objects. The FIT IoT-LAB is a real experimental testbed that allows users to implement a real experimental scenario since it embeds many Access Points in the building. It provides full control of network nodes and direct access to the gateways to which nodes are connected, allowing researchers to monitor several network-related metrics. FIT IoT-LAB features over 2000 wireless sensor nodes spread across six different sites in France. For our experimentation, we chose nodes from the site of Grenoble. The IoT-LAB hardware infrastructure consists of a set of IoT-LAB nodes. A global networking backbone provides power and connectivity to all IoT-LAB nodes and guarantees the out-of-band signaling network needed for power and command purposes and monitoring feedback. Various hardware platforms are available on FIT IoT-LAB: WSN430 Node, M3 Node, A8 Node, B-L07 ZZ-LRWAN1 LoRa kit, and so forth. We have executed our experimentations on A8 Nodes because they allow running a high-level OS like Linux and Contiki. Another central reason for this hardware choice is that our code implementation of the HIP DEX protocol is written in C++ and executed with the Makefile method for Linux operating system. It is our implementation described in our previous work in [9] where we added necessary libraries for the compression (explanation is below in this section). We have modified the code so that it enables handshakes in 6 LoWPAN architectures. A8 node's components are described in Table 3. Protocols used at the different network layers are detailed in Table 4. We evaluated the handshake delays explained in the following sections, in a WSN based-IPv4-IoT, and a WSN based-6 LoWPAN-IoT architecture. In the second one, we configured three A8-M3 nodes: the first node as Initiator, the second node as Responder, and the third one as 6 LoWPAN bridge router (CTTP). Both the initiator and the responder are configured as CoAP servers.
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
###### Abstract  
The proposed mixed-integer linear programming (MILP) model can be flexibly applied to a wide range of multi-energy systems to assess their techno-economic performance under optimal scheduling. By relying solely on the billing structure, it is particularly well-suited for real-world distributed applications, such as urban districts that do not actively participate in electricity markets.  
In the context of urban decarbonization, multi-energy microgrids (MEMGs) are gaining increasing relevance due to their ability to enhance synergies across multiple energy vectors. This study presents a block-based MILP framework developed to optimize the operations of a real MEMG, with a particular focus on accurately modeling the structure of electricity and natural gas bills. The objective is to assess the added economic value of integrating a battery energy storage system (BESS) under the assumption it is employed to provide implicit flexibility--namely, bill management, energy arbitrage, and peak shaving. Results show that under assumed market conditions, tariff schemes, and BESS costs, none of the analyzed BESS configurations achieve a positive net present value. However, a 2 MW/4 MWh BESS yields a 3.8% reduction in annual operating costs compared to the base case without storage, driven by increased self-consumption (+2.8%), reduced thermal energy waste (-6.4%), and a substantial decrease in power-based electricity charges (-77.9%). The performed sensitivity analyses indicate that even with a significantly higher day-ahead market price spread, the BESS is not sufficiently incentivized to perform pure energy arbitrage and that the effectiveness of a time-of-use power-based tariff depends not only on the level of price differentiation but also on the BESS size. Overall, this study provides insights into the role of BESS in MEMGs and highlights the need for electricity bill designs that better reward the provision of implicit flexibility by storage systems.  
multi-energy systems; energy management system; optimization; battery optimal sizing; implicit flexibility; energy arbitrage; peak shaving; time-of-use tariff licenses/by/4.0/.
## 3 Result and Discussion  
### Two-Phase system  
For the ellipsoid of revolution \(b_{1}=b_{2}\), then the aspect ratio can be introduced as \(\rho=b_{1}/b_{3}\), \(\rho\geq 1\). In this paper we consider \(b_{3}=2.38\) nm, so the total thickness corresponds to 14 graphite interlayer distances. The formation of the percolation paths depends on the concentration of filler, and also the distribution of the inclusions. The distribution effect as studied for the system filled with ellipsoids of \(\rho=5\). We made 10,000 observations for \(n=3\), 7000 for \(n=4\) and 3000 for \(n=5\). The percolation probability function is presented in Figure 4.  
The empiric probability functions are governed by the Weibull probability function, \(W(\lambda,k)=1-e^{-(x/\lambda)^{k}}\)[32]. The parameters of equation are follows: \(\lambda=0.115\), \(k=8.797\) for \(n=3\); \(\lambda=0.113\), \(k=12.092\) for \(n=4\), and \(\lambda=0.113\), \(k=14.90\) for \(n=5\). The Weibull function describes \"time-to-failure\" and it is typical for percolation processes [33; 34; 35]. All the probability functions for different cell size are closely collapsed, but the diffusion is slightly decreased from \(n=3\) to \(n=5\), that corresponds to the rise of \(k\). This means that the percolation threshold is independent of the UC. The diffusion difference may be explained by the finite volume fraction of the single ellipsoid inside the unit cell. On the other hand, the calculation time increases drastically with UC rise, so the UC size of \(n=3\) is optimal for the calculation of the composites with ellipsoids of higher aspect ratio.  
Figure 5 shows the dependence of the percolation threshold on the ellipsoids aspect ratio. As expected, the percolation threshold decreases with the aspect ratio rise. The resulting dependence is in good agreement with experimental data [36; 37; 38] and theoretical modeling [17; 39].
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging  
The measurement principle used for imaging using lidar is time-of-flight (TOF), where depth is measured by counting time delays in events in light emitted from a source. Thus, lidar is an active, non-contact range-finding technique, in which an optical signal is projected onto an object we call the target and the reflected or backscattered signal is detected and processed to determine the distance, allowing the creation of a 3D point cloud of a part of the environment of the unit. Hence, the range \(R\) or distance to the target is measured based on the round-trip delay of light waves that travel to the target. This may be achieved by modulating the intensity, phase, and/or frequency of the transmitted signal and measuring the time required for that modulation pattern to appear back at the receiver. In the most straightforward case, a short light pulse is emitted towards the target, and the arrival time of the pulse's echo at the detector sets the distance. This pulsed lidar can provide resolutions around the centimeter-level in single pulses over a wide window of ranges, as the nanosecond pulses used often have high instantaneous peak power. This enables to reach long distances while maintaining average power below the eye-safety limit. A second approach is based on amplitude modulation of a continuous wave (AMCW), so the phase of the emitted and backscattered detected waves are compared enabling to measure distance. A precision comparable to that of the pulsed technique can be achieved but only at moderate ranges, due to the short ambiguity distance imposed by the 2 \(\pi\) ambiguity in frequency modulation. The reflected signal arriving at the receiver coming from distant objects is also not as strong as in the pulsed case, as the emission is continuous, which makes the amplitude to remain below the eye-safe limit at all times. Further, the digitization of the back-reflected intensity level becomes difficult at long distances. Finally, a third approach is defined by frequency-modulatedcontinuous-wave (FMCW) techniques, enabled by direct modulation and demodulation of the signals in the frequency domain, allowing detection by a coherent superposition of the emitted and detected wave. FMCW presents two outstanding benefits ahead of the other techniques: it achieves resolutions in range measurement well below those of the other approaches, which may be down to 150 \(\upmu\)m with 1 \(\upmu\)m precision at long distances, although its main benefit is to obtain velocimetry measurements simultaneously to range data using the Doppler effect [17]. The three techniques mentioned are briefly discussed in the coming subsection.