esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Correlated Multi-Objective Energy-Efficient Routing Scheme for SDWSN-IoT
#### 3.2.2 Components and Architecture of the Proposed DOS-RL for the SDWSN-IoT Scheme
The inclusion of route lifetime information ensures that a node does not use the same route indefinitely but periodically uses newly updated route information. By default, NCs are programmed to send topology update messages (TUs) to the RNs at regular intervals. However, in certain scenarios, such as unexpected packet loss, TUs may be sent as urgent route update messages from the RNs.
## 4 Proposed Framework
### Layer 4--Anomaly Detection  
The fourth layer (anomaly detection), contains the machine learning module represented in the architecture of Figure 5 and can run several state-of-the-art algorithms for detecting anomalies [33]. In order to reduce the processing overhead on cluster Kubernetes, the machine learning module was configured on a host dedicated to this type of workload. Due to the large number of operations with matrices performed by Deep Learning models and the high demand for computational power, graphics processors (GPUs) are indicated for training these models in a more acceptable time [17; 39]. Thus, a host with one or more GPUs is recommended for running the machine learning module. In the host configuration used in this work, the GNU/Linux Operating System (Kernel 5.19.8) was used with Python and its dependencies installed to run the Neural Network models. For integration between the machine learning module and the cluster Elasticsearch in layer 3, the existing requirement is that the communication be performed through the Elasticsearch API for reading and writing data. In Python-based environments, there is a library that integrates with Elasticsearch [33]. Several Python libraries were used to implement the functions performed by the machine learning module in this layer. Among them, the following libraries stand out: Keras [40] and Tensorflow [41]. The joint use of Keras and Tensorflow libraries facilitated the implementation and training of DL models as well as the conduction of tests. Other libraries like Pandas and Scikit-learn were also used in the other processing steps of the pipeline executed by the machine learning module. Still, to make use of the computational power of the GPUs produced by the manufacturer NVIDIA, it is necessary to use the cuDNN library made available by the company for performance optimization in Deep Learning. In a study where the cuDNN library was presented, the authors achieved a 36% increase in the performance of a Neural Network model in addition to the reduction in [42] memory consumption. According to the flowcharts represented by Figures 2-4, the machine learning module can operate in two distinct modes: training (the training phase of the framework) and detection (the testing and production phases of the framework). In both training and detection modes, the machine learning module interacts with the Elasticsearch cluster to obtain the datasets via API. To facilitate the interaction between the machine learning module and the Elasticsearch cluster, a Python function called read_from_elastic (used in the INPUT lines in Algorithms 1 and 2) was developed using the elasticsearch_dsl library and made available through a custom functions file that is used in other operations in the machine learning module's processing pipeline. Details of the file containing the developedfunctions can be found in the public github repository [36]. To facilitate understanding of the machine learning module's operating modes, Algorithms 1 and 2 detail the training mode and detection mode of the machine learning module, respectively.
## 3 Materials and Methods
### Data Processing and Resistivity Interpretation  
The VES data acquired were subjected to a series of processing steps to facilitate their interpretation, summarized in four steps. In the first step, the VES diagrams were first smoothed to eliminate all outliers. They were then inverted using Geosoft's Winsev software, which allows each diagram to be broken down into well-defined electrical levels in terms of thickness and resistivity. The models of the VES diagrams were calibrated by lithological logs of the three existing hydrogeological boreholes (Figure 3). In the second step, the geoelectric levels of the VES models of each profile (Figure 3) were correlated horizontally. Consequently, four geoelectrical sections were drawn up to follow the evolution of the resistivity and the thickness of the formations crossed in both the vertical and lateral directions. Then, the third step was to interpolate the VES data using the inverse distance weighting (IDW) method. Four thematic maps were produced, including two iso-resistivity maps, a bedrock-depth map and a thickness map of the main reservoir. Finally, based on the main geoelectrical characteristics extracted from the geoelectrical sections and maps, coupled with the available geological information, the fourth step concerned the elaboration of the groundwater prospectivity map. Figure 4 shows the methodological flowchart applied in this work, described in the four steps above.  
Figure 3: Position plan of VES profiles on digital terrain model.
## 4 Simulation, Results, and Discussion
### Comparative Analysis of Localization Accuracy
As evidenced by the research examined, the IWOA significantly reduces the average positioning error to 2.14 cm in the absence of PD rotation. However, it should be noted that the PSO algorithm presented in this study exhibits improved precision in finding a target. Compared to previous findings, it is clear that while the IWOA presents a robust approach, particularly in mitigating the challenges posed by PD rotation, the PSO algorithm excels by potentially offering greater accuracy across a range of heights, as reflected in the provided graph. This graph illustrates that the PSO maintains a relatively stable error rate regardless of height alterations, contrasting with the IWOA, which, although competitive at lower heights, exhibits a sharp increase in error beyond the 2 m mark. This comparison underscores the  
Figure 4: Localization error [cm] vs. the height of the receiver [m]. importance of algorithm selection based on the specific requirements of the localization task at hand. These results demonstrate the effectiveness of the PSO algorithm in achieving more accurate localization compared to other methods. The lower average error obtained with the PSO algorithm indicates its superior performance in estimating the position of the target object. In the simulation carried out, the average execution time of the PSO algorithm was 54 ms. In comparison, the conventional method that solves Equation (17) showed an average duration of 0.033 ms, while the non-PSO approach took 0.43 ms on average, and the multilevel modulation method presents an average duration of 42 ms. Therefore, it can be observed that the trade-off for improved localization accuracy is the increased computational overhead associated with employing the PSO algorithm in this particular scenario. Furthermore, Figure 5 shows the real and estimated positions when the receiver was placed at a height of 1.5 m above the floor. In this simulation, 76.85% of the estimates achieved an error of less than 1 cm, while 91.73% of the estimates demonstrated an error below 2 cm. These findings substantiate the algorithm's ability to deliver precise estimations with regard to the target's location. However, as expected, positions close to the corners exhibited higher estimation errors. In particular, the largest error at this height occurred at position (5.0, 3.0) m, measuring 3.48 cm.
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 9 Conclusions  
Effective non-repetitive routing among nodes in a network is an important function in communication networks. In order to achieve that, pruning the links of the network would be helpful. However, this operation comes with a caveat: it makes the network worse in terms of maintaining the freshness of messages as the defining QoS of the network. In our paper, we studied the case that if a link removal affects the AoI of the messages insignificantly or has no effect, we prune that link. The pruning can be applied to the \(k\) least significant links in terms of their impact on the AoI of the messages transmitted in the system. We studied the effect of pruning a number of links on the AoI to reduce the redundancy of the messages that would be received at the destination many times while transmitted only once. We introduced an efficient reduction method designed for series-parallel networks with links of exponentially distributed wait times. In addition, we considered the deterministic case and presented the pruning technique when link removal would not affect the AoI. We showed a pruning algorithm that determines the least significant link in the network, in terms of its effect on the AoI, and prunes it. Lastly, we presented a comprehensive simulation to study the effect of pruning the links on the AoI of the network and the redundancy of messages received by the destination. The main limitation of this work can be concluded in narrowing the study exclusively to SP networks. A potential future direction would be to study the effect of pruning for more general networks.  
Conceptualization, A.S. and J.W.; methodology, A.S. and J.W.; software, A.S.; validation, A.S.; formal analysis, A.S.; investigation, A.S.; resources, A.S. and J.W.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.S. and J.W.; visualization, A.S.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by NSF grants CPS 2128378, CNS 2107014, CNS 2150152, CNS 1824440, CNS 1828363, and CNS 1757533.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 4 Information Fusion Method Based on the Factor Graph  
The factor graph is a graphic model that encodes the conditional probability density among unknown variable nodes and the received measurements. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the factor graph without considering the relationship between the sensor update frequency and the fusion period.
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Battery-Based Supply Techniques
#### 2.2.3 Laser Beam Inflight Recharging  
The swapping approach has shown promise in extending the operational time of battery-powered UAVs by utilizing ground stations for battery replacement or recharging. However, an alternative approach that has been proposed is wireless recharging, as discussed in the previous literature [51, 85]. This method involves a ground station equipped with a primary power source, which generates a laser beam transmitted to the UAV while it is in flight. The UAV has an embedded optical receiver that converts the light into electricity, effectively powering the UAV. With wireless recharging, UAVs can remain airborne indefinitely without the need to land for battery recharging. When recharging is necessary, the UAV can access an aerial power link area to receive energy. This approach enhances
## 4 Experiment
### Objective Evaluation  
Although the main purpose of this paper was to generate appropriate background music for a given video, that is, to evaluate the matching degree of music and video in subjective experiments, we also paid attention to the quality of the generated music itself. In an objective evaluation, in order to measure the melody of the music, MusDr [22] was used to calculate the following indicators to measure the quality of the music itself, including pitch class histogram entropy (H), grooving pattern similarity (GS) and structureness indicator (SI):  
1. The pitch class histogram entropy measures the tonality of the music by the entropy of the pitch. The calculated method is shown in Formula (7), mentioned in Jazz Transformer; here \(P\) = 11. The clearer the music piece, the lower the histogram entropy; \[H(\vec{h})=-\sum_{i=0}^{p}h_{i}\log_{2}(h_{i}),\] (7)
2. The grooving pattern similarity computes the music rhythmicity, music with higher melodies has higher scores;
3. The structureness indicator measures the apparent repetition over a given length of time. The closer this indicator is to the music by composers, the better.  
In the experiment, five songs generated by the models were fed into MusDr for index computation. For the proposed model, the music of night scene was used to detect the indicators as this kind of background music had great scores in the subjective evaluation. A higher value was betteer for the sum of H and GS while a smaller value was better for the gap between the SI of the model generated music and the standard value. The standard value was obtained by taking three pieces of music from each of the five scenes and averaging the output index of MusDr. Compared with the music generated by Jazz Transformer [22], CMT [6] and VMSI [28], the sum value and the gap were improved as shown in Table 3. Here, \(CMT_{1}\), representing the control attribute, was only added in training while \(CMT_{2}\) means the attribute worked both in the training and inference step. Although the music generated by our model did not get the highest score, the sum of H and GS reached the top. That means it is effective to learn rhythm-related and note-related music features separately with two linear Transformers training jointly.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **It Is by AI** & **It Is by Human** & **Want to Use?** \\ \hline Generated music & 89\% (62\%) & 11\% (35\%) & 56\% (75\%) \\ Composers’ music & 5\% (23\%) & 95\% (64\%) & 78\% (63\%) \\ \hline \end{tabular}
\end{table}
Table 2: Audience’s distinction between the two kinds of music and their willingness to use the generated music as background music (the percentages in the table represent how recognizable the music generated by the model was when voted by the expert group and those in brackets were from people with no musical background.)
## 2 Materials and Methods
### Analysis of FMT Bacterial Composition Using Next-Generation Sequencing (NGS)
#### 2.3.2 Taxonomic Annotation  
Effective tags were analysed using the Uparse software (Uparse v7.0.1090) [24]. Sequences with \(\geq\)97% similarity were clustered into operational taxonomic units (OTUs). For species annotation, representative sequences from each OTU were aligned to reference sequences from the SILVA138 Database using Mothur software (Version v.1.43.0) [25]. The phylogenetic relationships among all OTU representative sequences were obtained using MUSCLE software (Version 3.8.31) [26].
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 2 Literature Review
### Feature Importance
#### 2.5.2 SHapley Additive Explanation (SHAP)  
SHAP is another popular method used to interpret model predictions for increased model transparency. It is based on coalitional game theory, which fairly distributes the payout among the features. SHAP measures each feature's contribution towards the model output [75]. The two great advantages of SHAP include: (a) global interpretability, which provides a good explanation of each feature's contribution (either positive or negative) to the model predictions across all permutations. (b) Local interpretability, which provides transparency through the local interpretability of each observation. Each observation \(x\) is assigned a corresponding SHAP value that can be applied to any model. SHAP computes the contribution of each feature to the predictions as follows:  
\[g(z^{\prime})=\phi_{0}+\Sigma_{j=1}^{M}\phi_{j}z^{\prime}_{j} \tag{2}\]where \(g\) represents the explainable model of deep learning model \(f\). \(\mathbf{z^{\prime}}\in\{\mathbf{0},\mathbf{1}\}^{\mathbf{M}}\) represents simplified features, also known as a coalition vector, where \(0\) means the feature value in the new data is not available in the original data, and \(1\) means the feature value in the new data is available in the original data. \(M\) represents the maximum coalition size, and \(\phi_{j}\in R\) represents the feature attribution, quality, or impact of feature j on predictions. A large value of \(\phi_{j}\) presents a strong positive impact of a feature on predictions.  
Depending on the model type, SHAP provides various explainers [76]; some of the commonly used explainers are as follows: (a) TreeExplainer can be used to explain tree-based classifiers such as XGBoost, random forest, etc. (b) KernelExplainer can be used to explain any function, (c) DeepExplainer only explains deep neural networks, and (d) GradientExplainer can also be used to explain neural networks frameworks, such as TensorFlow, Keras, and Pytorch [77].
## 2 Results  
### Bioinformatic Analysis of Aliggpps and Alldi  
Putative _Aliggpps_ and _AIIDI_ genes encoded proteins of 417 and 256 amino acids, respectively, with no transmembrane domains predicted by TMHMM (Figure 1a,b). The phylogenetic tree indicated that GGPPS and IDI of _Aurantioichytrium limacinum_ MYA-1381 exhibited high homology with the geranylgeranyl pyrophosphate synthase and isopentenyl diphosphate isomerase of _Hondaea fermentalgiana_ (Figure S1a,b).  
A reliable 3D model of AlGGPPS was constructed based on the X-ray crystal structure of the human geranylgeranyl pyrophosphate synthase (hGGPPS) mutant (Y246D; PDB ID 6C56) [51], which shared 51.36% sequence identity with AlGGPPS. The structure overlay of the AlGGPPS model and its template hGGPPS mutant (gray) also demonstrated similarity (Figure 2a). Furthermore, docking conformations of GGPPS-FPP (\(-\)6.3 kcal/mol) and GGPPS-IPP (\(-\)5.5 kcal/mol) were confirmed with affinity scores. The molecular docking prediction showed that two substrates (IPP and FPP) were both docked into the catalytic pocket of AlGGPPS, and close together (Figure 2b). Three amino acid residues (R95, Q300, K327) were hydrogen-bonded to FPP (Figure 2c). Another three amino acid residues (N141, R143, H172) were also observed to bind IPP through the formation of hydrogen bonds (Figure 2d). The result indicated the potential catalytic function of AlGGPPS by condensing the IPP with FPP. As for AIIDI, the best-scoring protein model and structure overlay were constructed, using the human IPP isomerase I (gray; hIPPI; PDB ID 26k) [52] with 52.47% sequence identity as its template (Figure 3a). The docking conformation of IDI-IPP (\(-\)6.0 kcal/mol) was also confirmed, indicating that substrate IPP was docked into the active pocket (Figure 3b). Four amino acid residues (K57, S108, K132, E189) formed hydrogen bonds with IPP, strengthening binding of the substrate (Figure 3c). Results facilitated the revelation of the potential catalytic function and conserved catalytic mechanisms of AIIDI.
# Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
## 3 Attack Threat Model Taxonomy
### Adversarial Goal  
Considering whether the adversarial example needs to become the excepted output after passing through the system, we group the attack into two different attacks.  
Non-targeted: Non-targeted means that an arbitrary result that is different from the original output can be generated after the adversarial example passes through the attacked system. For example, in [13; 15; 19; 76], the attacker only requires the system is to recognize as other text content. Non-target adversarial examples are usually generated in two ways: (1) perform several targeted attacks, obtain the least interference attack from the results; (2) maximize the possibility of misclassification.  
Targeted: The adversarial example is required to output a desired result through the system. In other words, for the input \(x\) and the network model \(f(\cdot)\), the output \(\tilde{y}=f(x+\delta)\) is the expected result. Using a simple example to illustrate, the correct text information of the audio we originally entered is \"Hello\", but the output converts it into \"World\" after the perturbation is added. Correspondingly, the non-target attack only requires output is not \"Hello\". During the past few years, many related works [4; 32; 33] have emerged to take advantage of the targeted adversarial example to finish the task of attack.  
Targeted adversarial examples help the attacker obtain the desired output, especially in critical situations such as identifying a specific person whose information to tamper with. However, the targeted adversarial examples require more calculations to solve than the non-target adversarial examples. The degree and calculation time are longer, which also puts forward higher requirements for the attack. Non-target adversarial examples are easier to implement because they have more options and space to directly aim at the output.