esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning
## 3 The Proposed Deep Multi-User Reinforcement Learning
### Channel Cooperative Module  
According to the previous step, each user obtains the channel number to be accessed by selecting the subscript with the largest probability value, then the access channels of the \(N\) users form the action matrix \(A\), as shown in (6).  
By observing the action matrix \(A\), the observation matrix \(obs\) can be calculated. The observation matrix is mainly composed of the \(ACK\) signal, instant reward, and the remaining capacity of the channel. If the channel selected by the current user to access is the same channel selected by other users, the \(ACK\) signal and instant rewards received by these users are both 0. If they are not the same, it means that there is no collision. Then the \(ACK\) signal and instant reward are both set as 1. When the value of \(ACK\) signal is 1, the remaining capacity of channel is set to 0, otherwise it is set to 1. Then, \(ACK\) signals and action matrix are generated according to (5) and (6). By this, the next channel state \(next\_state\) can be calculated. That is, the cooperation information is integrated into the channel state to form channel cooperation.
###### Abstract  
Multibeam planar arrays are investigated as shared apertures for dual functionality in millimeter-wave (mmWave) joint communication and sensing (JCAS), providing time division duplex communication and full-duplex sensing with steerable beams. The conventional uniform planar arrays (CUPA)s have limited angular resolution, whereas the sparse planar arrays (SPA)s are often very costly to implement. In order to have a low-cost aperture with high angular resolution, we propose to design a sparse tiled planar array (STPA) shared aperture. Our proposed solution is modular tiling and uniform at the subarray level but sparse at the aperture level. The modular tiling and sparse design of a planar array are non-convex optimization problems; however, we exploit the fact that the more irregularity of the antenna array geometry, the less the side lobe level (SLL). In a JCAS scenario, we compare the performance of STPA, CUPA. and SPA, regarding the _spectral efficiency_ of a line-of-sight (LoS) included communication link, _detection loss rate_, and _detection accuracy rate_ for sensing, and the _blockage time_ in case of an overlapping communication and sensing beam. The SPA for comparison has the same size and beamwidth as STPA, but less average SLL and less modular design. The results show that the same spectral efficiency is achieved in the communication link for CUPA, SPA, and STPA. The effect of a smaller beamwidth of the STPA and SPA is reflected in the lower detection loss rate of them compared to that of the CUPA, but the side lobes of these sparse solutions result in errors in the association of the detected and true targets and hence a reduction in the detection accuracy. In such a multibeam solution for JCAS, it is critical to study blockage time, and we show that the STPA and SPA have a 40% shorter blockage time compared to the CUPA when a blocker moves across the LoS of the communication link. Therefore, STPA is a trade-off solution between CUPA and SPA, since it has uniformly distributed antennas within the subarrays as in CUPA, but a sparse solution in the whole aperture as in SPA, which guarantees the same beamwidth and sensing performances as a SPA.  
6  
S parse Tiled Planar Array: The Shared Multibeam Aperture for Millimeter-Wave Joint Communication and Sensing  
1 Faculty of Electrical Engineering, University of Twente, 7522 NB Enschede, The Netherlands;  
a.b,[EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (Y.M.)  
* Correspondence: [EMAIL_ADDRESS]
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 3 Methodology
### Environment and Airspace User Cost-Efficiency KPAs  
Further KPAs in which the Ad Hoc separation concept could provide some benefits are the environmental and airspace user cost-efficiency KPAs. In particular, the environmental impact was analyzed based on three indicators: flown distance reduction, fuel consumption reduction, and \(CO_{2}\) emissions reduction. The airspace user cost-efficiency KPA is quantified through the reduction in strategic delays.  
For this purpose, based on the previous FTS, the number of \(LOS\) that would not occur under the Ad Hoc separation concept was considered. If the separation was 5 NM, the ATCO would provide a resolution instruction to at least one of the aircraft involved in the conflict to prevent the \(LOS\) from occurring. The values of distance, fuel consumption, \(CO_{2}\) emissions, and delay related to such resolutions are computed. Thus, it is possible to define the average, maximum, and minimum values of distance (Equation (1)), fuel consumption (Equation (2)), \(CO_{2}\) emissions (Equation (3), [43]), and delay (Equation (4)) per \(LOS\) that could be saved with ADSM.  
\[\Delta\textit{Distance reduction per LOS saved}=\textit{sector flight distance}_{actual}-\textit{sector flight distance}_{scheduled} \tag{1}\]  
\[\Delta\textit{fuel reduction per LOS saved}=\textit{actual fuel}-\textit{ scheduled fuel} \tag{2}\]  
\[\Delta\textit{CO}_{2}\textit{ reduction per LOS saved}=\Delta\textit{fuel per conflict saved}*3.15 \tag{3}\]  
\[\textit{Delay reduction per LOS saved}=\textit{sector flight time}_{actual}-\textit{sector flight time}_{scheduled} \tag{4}\]  
It is worth noting that the outcomes achieved in terms of flight distance reduction, fuel reduction, \(CO_{2}\) emissions reduction, and delay reduction are a result of minimizing ATCO interventions in aircraft trajectories by reducing the number of \(LOS\) through the Ad Hoc separation application.
## 5 Results
### Impact of Sequence Length  
Figure 5 presents graphs of evaluation metrics as a function of sequence length. The metrics precision, recall, PR AUC, and ROC AUC were calculated for all the classifiers and the respective window lengths: 3, 5, 10, 15, 20, 30, 50, 100.  
In general, a larger window length contributes to decreased values for the evaluation metrics. This is due to the extended semantic context of a sequence, which makes the task of anomaly detection more difficult for binary classifiers. The individual features responsible for anomalies are less important in longer sequences, as a result of the transformation of template vectors to a single sequence vector through averaging. However, there are some exceptions to this pattern. For example, we observed an increase in the precision and F1-score metrics for the DT and AB algorithms and a window length of 15. For the RF and MLP classifiers, we also observed an increase in the values of the precision and F1-score metrics, but for a window length of five. For the GNB algorithm, as the window length increases, we do not see a consistent trend of change in the values of all metrics. On the contrary, in this case, the changes are spiking, and for some metrics, we even see an increasing trend. In summary, the length of the window should be chosen according to this, as it is one of the factors determining the performance of the model.  
Figure 4: Visualized embeddings of BGL log templates (each circle denotes one template).
## 5 Results and Discussion
### Scenario 3: WT-Battery System (\(S=[0\;\;s_{2}\;\;s_{3}]\))  
The summary of TLBO, EDE, SSA, and EESA results for the WT-battery hybrid system at various \(LPS^{pmx}\) values is given in Table 6. In Table 6, all algorithms achieved the same results in terms of average rank values. Thus, all algorithms were ranked equally with one final rank value. The configurations achieved for four \(LPS^{pmx}\) values are given as:  
\(S=[0\;\;50\;\;3552]\) at \(LPS^{pmx}=0\%\),  
\(S=[0\;\;50\;\;3552]\) at \(LPS^{pmx}=0.5\%\),  
\(S=[0\;\;49\;\;3362]\) at \(LPS^{pmx}=1\%\), and  
\(S=[0\;\;49\;\;3362]\) at \(LPS^{pmx}=3\%\).  
The configurations achieved at \(LPS^{pmx}\) values of 0% and 0.5% were the same; thus, this resulted in an equal amount of TAC of $124,750. Similarly, at \(LPS^{pmx}\) values of 1% and 3%, EESA achieved the same TAC of $118,690 with configuration \(S=[0\;\;49\;\;3362]\). The hourly WTs' power produced along with the energy storage level achieved by EESA during a year at four \(LPS^{pmx}\) values is depicted in Figure 10 for the WT-battery system. As the WTs were the highest for \(LPS^{pmx}\) values of 0% and 0.5%, i.e., \(N^{wt}=50\), therefore they also produced a larger amount of energy, as shown in Figure 9(a). At \(LPS^{pmx}\) values of 1% and 3%, WTs were low (\(N^{wt}=49\)). Figure 9(b) shows the hourly expected amount of stored energy for the WT-battery system. The LOL occurred when \(LPS^{pmx}\) values were set to 1% and 3%.  
The breakdown of the TAC values of the WT-battery at various \(LPS^{pmx}\) is given in Table 7. The illustration is provided in Figure 11. Unlike the case of the PV-battery system, where the maintenance cost of PVs was ignored, here the WTs' maintenance cost was considered according to the value as given in Table 1. Similar to the previous two scenarios, the breakdown of TAC for the WT-battery system showed that the major portion of the cost was caused by the battery units.  
Figure 11: Breakdown of the TAC values of the WT-battery system at various \(LPS^{pmx}\).  
Figure 10: Hourly WTs’ power produced and energy storage level of the WT-battery system by EESA during a year at various \(LPS^{pmx}\) values.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 1 Introduction
Indeed, the commercial use of small satellites has started thanks to recent advances in miniaturization and integration. Many fields have started benefiting from small satellites: scientific research, technology demonstrations, Earth observations, biological experiments/pharmaceuticals, telecommunications, military applications, etc. Small satellites and the \"NewSpace\" at Horizon 2020 offer unique opportunities in terms of constellation deployment providing larger simultaneous spatio-temporal coverage of the Earth, which is fundamental for Earth energy imbalance measurements (impacts of aerosols and clouds that are highly variable spatially and temporally).
## 4 Experimental Verification
### Visualization of Test Set Classification Process  
In this section, taking Working Condition 1 as an example, the features of the test set in the original data, the data after FFT signal feature extraction, and the data in the last implied layer are reduced to two dimensions and visualized respectively by the T-SNE dimensionality reduction technique, as shown in Figure 17.  
It can be seen that the original vibration signal is very disorganized and it is difficult to distinguish it correctly. After FFT, the features have been clearly distinguished after using the proposed network model. The results show that the proposed network model not only has a strong classification ability in the training set, but also still maintains a strong fault diagnosis discriminative ability in the actual test.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 3 Difficulties in Locating Skyline Geolocation in Hilly Areas
### Detailed Information of Skyline Affected by Dense Vegetation  
In hilly area of low altitude, the vegetation on the surface is mainly composed of trees and shrubs. The vegetation has seasonal variations, which cause great interference to the feature of the skyline. Figure 4 illustrates the effects of vegetation on a terrain surface. Although the vegetation does not affect the general trend of the skyline, the vegetation has different effects on the details of the skyline.
## 4 Results and Discussion
### Evaluation Metrics  
To comprehensively evaluate the model's performance in the signal prediction task, two types of metrics were used: classification metrics for direction prediction, and regression metrics for return value forecasting. The classification metrics included Precision, Recall, and F1-Score, which assessed the model's accuracy and completeness in identifying directional signals. In addition, regression performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE), which reflect the deviation between predicted and actual signal magnitudes.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{24}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{25}\]  
\[\text{F1-Score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{26}\]  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2} \tag{27}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}|, \tag{28}\]  
Here, \(TP\) denotes true positives, \(FP\) false positives, \(FN\) false negatives, \(y_{i}\) the observed value, \(\hat{y}_{i}\) the predicted value, and \(N\) the number of samples. These metrics jointly captured the model's effectiveness in both directional classification and magnitude estimation, thus offering a comprehensive assessment of predictive quality.
## 1 Introduction
Both the joint-approach and the probabilistic model permit finding a lower-dimensional representation for a set of documents, compared to the simple document-term matrix. Moreover, the outputs of the two methodologies are disjointed groups of documents with similar contents. In order to compare the results, ESCAPE provides different visualization techniques to help the analyst with interpreting the ESCAPE results. The proposed engine has been tested through different real-text datasets characterized by a variable document length and a different lexical richness. The experiments performed by ESCAPE underline its capability to autonomously spot groups of documents on the same subject, avoiding the user having to set the parameters of the various algorithms and to select the most appropriate weighting scheme. This paper introduces a novel self-tuning methodology tailored to textual data collection to democratize the data science on corpora. The main objective is masking the complexity of data-driven methodologies by allowing non-expert users to easily exploit complex algorithms in the proper way without knowing the technical details. The innovative aspects of the proposed approach are the following:  
1. The introduction of an automated data analytics pipeline that compares different algorithms and solutions tailored to textual data collection without requiring technical knowledge;
2. The automation of the discovery of unsupervised and relevant topics processes, together with their characterization in a given corpus of documents;
3. An integration of innovative and tailored self-tuning techniques to drive the automatated choice of optimal parameters for each algorithm;
4. A novel self-assessment approach of the obtained results seeks the best weighting schema;The implementation of different human-readable visualization techniques intended to facilitate understanding of the results, even for non-expert users. This paper is organized as follows. Section 2 discusses the state-of-the-art methodologies. Section 3 presents the ESCAPE engine, while Sections 4 and 5 show, in detail, its main building components and the self-tuning algorithms used. Section 6 thoroughly display the experiments performed on six real-text corpora, and also includes a comparison with state-of-the-art methods. Considerations about the obtained results are presented in Section 7. Finally, Section 8 draws conclusions and presents future developments of this work.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 6 Results
### Research Gaps and Challenges
#### 6.1.1 Early Phases IoT Botnets  
As has been explained in Section 3.3.2, the IoT botnet's lifecycle has three phases, scanning, propagation and attack. Throughout these phases, there is communication amongst the bots and the C&C, and among the bots. From Figure 7, it is clear that most of the studies developed detection techniques for IoT botnets in the late phase, when they are launching and triggering attacks on the targets, whereas they could be detecting botnets in earlier phases, for instance, when the attacker starts the scanning or propagation activities. Therefore, researchers need to further investigate the detection of IoT botnets in their early phases before triggering attacks; this would reduce the illegal utilization of the devices' resources, and thereby disrupt or deny the services of the IoT network [52; 96].
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 2 Related Work
### Challenges of Routing Protocols  
There are many technical challenges in designing DTN routing protocols such as long delay, high latency, limited resources, frequent disconnection and opportunistic or predictable connections. In several research projects, the performance of some of these DTN routing protocols has been analyzed and compared in terms of data delivery rates and latency.  
[PERSON] and [PERSON] [17] compared some of the popular routing protocols namely Epidemic, MaxProp, PROPHET and Spray and Wait (SAW). They studied the impact of varying buffer sizes and varying \"L\" copy parameter in SAW on the performance of three metrics, namely Delivery Ratio, Overhead Ratio, and Average Delay by using the Opportunistic Networking Environment (ONE) simulator. [PERSON], et al. [21] investigated and compared three different DTN routing protocols: Direct Delivery, Epidemic Routing and Spray and Wait (SAW) by focusing on the average packet delivery probability and average latency. They asserted that Direct Delivery has the lowest packet delivery probability and this routing is not appropriate for real-time applications. These studies investigated and analyzed the performance of DTN routing protocols in terms of various buffer sizes and number of nodes. The mainstream DTN routing protocols assumed to be unconstrained in energy resources, hence they do not adjust their behaviour based on the available energy.  
The main purpose of our study is to address the issues of energy-aware and energy-efficient DTN routing protocols, which have not been widely investigated. Our study addresses the fact that energy considerations are critically important in energy-constrained deployments and can substantially improve data delivery rates.
## 7 System Performance Optimization  
When the channel interference protection method is used to protect users' input sensitive information, in order not to affect the normal communication of the public wireless network, the system needs to be optimized in two ways: on the one hand, after switching the channel of the safe wireless transmitter, it should not affect the normal network service of the target public APs, so that the network service of the people who have already accessed the target public WiFi is not affected; on the other hand, there should exist no channel interference between the safe wireless transmitter and the normal public wireless transmitters.  
The strength of channel interference between two neighbor wireless transmitters is related to the distance between them and the channel spacing between their channel. The smaller the channel spacing of their channel is, the stronger the channel interference between them. However, when two wireless transmitters are far away from each other, even when they work on adjacent channels, there will not exist channel interference between them.  
From the analysis above, the safe wireless transmitter needs to be far away from normal public wireless transmitters and to be near the target public APs, so that after channel switching, the channel interference will not exist between the former, and it will only exist between the latter. Thus, we need to locate the distance between the safe wireless transmitters and all the public wireless transmitters, and based on the localization results, the system will give a proper position for the safe wireless transmitters when the WiGuard protection system is used.
## 3 Pathogenic Mechanisms of _P. gingivalis_
### Immune Evasion Strategies  
To establish chronic infection in the gingival pocket, _P. gingivalis_ employs strategies to evade or subvert host immune defenses. Its pathogenicity is linked to several virulence factors--such as gingipains, hemagglutinin, lipopolysaccharidesaccharides (LPSs), and fimbiae that aid in colonization and proliferation within the gingival creviec (24). _P. gingivalis_ can manipulate innate immunity, particularly Toll-like receptors (TLRs) and the complement system, thereby suppressing host immune responses and influencing adaptive immunity (25). A key mechanism involves TLR4: _P. gingivalis_ inhibits antimicrobial peptide (\(\beta\)-defensin) expression and releases LPS-containing vesicles that antagonize TLR4, weakening antimicrobial defense within the polymicrobial biofilm (26). Unlike TLR4, TLR2 is not antagonized directly; instead, _P. gingivalis_ promotes disruptive crosstalk between TLR2 and other innate receptors to attenuate host responses. These immune evasion mechanisms are summarized in Table 1(27).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mechanism** & **Effect Molecules** \\ \hline Counteraction of oxidative damage; resistance to environmental oxidative stress & Rubnerythrin (nonheme iron protein), alkyl hydroperoxide \\  & and oxidative killing by phagocytes \\  & reductase, FeoB2 \\  & Inherent resistance to complement-mediated lysis \\  & Hijacking complement regulatory proteins (C4b) & HrgpA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subversion of innate immunity by _P. gingivalis_. LPS (lipopolysaccharide), A-LPS (anionic LPS), HrgpA (histidine-rich gingipain A), Kgp (lysine-specific gingipain), RgpB (Arg-gingipain B), FeoB2 (ferrous iron transport protein), TLR (Toll-like receptor), CD (complement regulatory protein), IRAK-M (interleukin receptor-associated kinase M), CXCR (chemokine receptor), CR (complement receptor).
# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 2 Mobile Device 3D Measurement Technology  
As shown in Figure 1, research on body measurement has primarily been based on 2D image analysis. These studies have focused on methods that detect the positions of body parts using pose estimation techniques and calculate circumferences based on them. In particular, high-resolution pose estimation models like HRNet are effective in accurate keypoint detection, but due to the limitations of 2D images, 3D depth information is not reflected, restricting the accuracy of measurements [12]. Research on 2D image-based body measurement has primarily relied on pose estimation and keypoint detection. The HRNet model excels at accurately detecting keypoints of body parts while maintaining high-resolution representations, allowing precise identification of the locations of various body parts. However, these methods have limitations in that they do not consider depth information, thereby failing to accurately reflect the 3D structure of the body. For example,  
Figure 1: An example of human pose estimation using 2D images.  
they may not account for depth variations of the body or distortions due to various postures, which can reduce measurement accuracy [13]. Additionally, [14] proposed a method to track body movements through real-time pose estimation, but it is primarily focused on motion analysis and is not suitable for static body circumference measurements [15]. Attempted 3D pose estimation, but still did not fully incorporate depth information from 2D images, resulting in limitations in accuracy.