esa-sceva/satcom-chunk-collection huggingface dataset:
# AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring-Modeling Approach
## Results of Feature Selection Methods for Gram-Negative Dataset using 10 Features
### Testing AMP-GSM on Different Benchmark Datasets, Comparative Evaluation with Existing Approaches  
[PERSON] et al. proposed a model consisting of a deep neural network (DNN) structure including convolution and LSTM layers [33]. The features are represented with a sequence-to-vector conversion, in which peptide sequences are encoded into uniform numerical vectors of length 200. They tested their proposed DNN on Dataset 2, where the main characteristics of this dataset are presented in the Materials and Methods section and further details can be found in [33].  
Table 9 compares the performance metrics of one of the DNN models proposed in [33] with the AMP-GSM model and with other feature selection methods for Dataset 2. The methods being compared are listed in column 1 of Table 9, along with the five performance metrics listed in columns 3 through column 7. The results in bold in Table 9 represent the best results for a particular metric. According to Table 9, the AMP-GSM model performs the best in terms of accuracy, sensitivity, F1 measure and AUC metrics.  
[PERSON] et al. proposed a model consisting of a random forest classifier and feature selection methods [35]. They used amino acid composition, amino acid index, dipeptide composition, physico-chemical properties, and distribution of amino acid patterns as peptide features. They compare their results with commonly used machine learning models, such as SVM, k-NN, and extremely randomized trees (ERT).  
Table 10 compares the performance metrics of the method proposed in [35] with AMP-GSM and other feature selection methods for Dataset 3. The methods being compared are listed in column 1 of Table 10 along with the four performance metrics listed in columns 3 through column 6. The results in bold in Table 10 indicate the best results for a particular metric. AMP-GSM considerably outperforms CMIM, IG, and mRMR feature selection methods for predicting AIPs. There is a significant difference for all performance metrics.
## 4 Experiments and Results
### Analysis of Profile Parameters  
To assess the robustness of our multi-profile fuzzy-RL broadcast under parameter uncertainty, we conducted a Monte Carlo sensitivity analysis inspired by [28; 29]. We focus on two key components of Method B:  
1. Fuzzy-logic membership weights. We selected one representative fuzzy parameter, the membership weight of \"Neighbor Number\" (\(w_{\text{NN}}\)), and generate 100 perturbed weight sets by varying \(w^{\prime}_{\text{NN}}\) uniformly within \(\pm 20\%\) of its baseline. For each perturbation, the remaining weights are re-normalized so that \[\sum_{k}w^{\prime}_{k}=1,\quad w^{\prime}_{j}=w_{j}+\frac{w_{j}}{1-w_{\text{NN }}}(w^{\prime}_{\text{NN}}-w_{\text{NN}}),\quad j\
eq\text{NN}.\] (6) Each perturbed set defines a slightly different fuzzy profile \(\mathcal{P}^{\prime}\), which produces a perturbed fuzzy state for the RL agent.
2. RL learning rate \(\alpha\). We likewise sampled the Q-learning rate \(\alpha^{\prime}\) over 100 evenly-spaced values in \([0.1,0.9]\), holding all other RL and fuzzy-logic settings fixed.  
For each Monte Carlo trial (100 runs per sweep), we executed a 500 s virtual-time simulation at three densities (40, 160, and 280 nodes) and recorded four metrics: Redundancy, Overhead, Coverage, and PDR. This yielded two \(100\times 4\) result matrices: one for fuzzy-weight perturbations, and one for \(\alpha\) variations.  
Figure 4 shows the radial Coverage plots under fuzzy-weight perturbations. Each concentric ring corresponds to one method (D-FL+RL, RL-PT, NFRL, Prop A, Prop B-P1, Prop B-P2, Prop B-P3), with a radius equal to the Coverage percentage. The narrow bands (about \(\pm 2\%\) around baseline) confirm that all profiles remain reliable despite \(\pm 20\%\) shifts in \(w_{\text{NN}}\). Similar radial plots for Redundancy, Overhead, and PDR (omitted for brevity) exhibit comparable stability. These results demonstrate that our multi-profile selection logic and RL-based BC/DNB optimization are insensitive to reasonable parameter perturbations, confirming the practical robustness of Method B.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.4 Belgium  
The Belgium case presents two different contexts to be analysed: Flanders and Wallonia. The Flemish pilot site involves all the motorways that form part of the core network in Flanders. The area concerned is the Belgium site falling in the InterCor deployment framework. In the Flemish pilot, only Day-1 services are investigated, while the communication technology analysed concerns the implementation of cellular networks that are tested in combination with the HERE Location Cloud and the local Traffic Management Centre (TMC). The aim is to develop a cloud-based 'virtual infrastructure' connecting road users with the TMC while allowing the TMC to directly interact with end users. The implementing bodies are: the Flemish Department of Mobility and Public Works, Tractebel Engineering SA, ITS Belgium, and HERE Technologies. In accordance with the declared timeline, all preparatory phases (i.e., use case definition, system setup, fine-tuning, and driver acquisition) have been completed. The next phases consist in carrying out preliminary test drives and making the whole system operational, which will perform trial activities for the whole of 2019. Finally, the ex-post evaluation will be carried out in 2020.  
As regards Wallonia test activities, the services and communication technologies tested as well as the planned timeline are essentially the same as the Flemish case, except for the fact that ITS-G5 is also investigated in Wallonia. The partners involved are: SOFICO (technically assisted by the Public Service of Wallonia/Directorate of Road Traffic Management), Tractebel Engineering SA, and ITS Belgium.
## 3 Results and Discussions
### Expression Change of Genes Related to Glutamate Metabolism Pathways  
Our data indicated that the pathways associated with glutamate metabolism were altered in the liquid-state fermented cells, compared with the solid-state fermented cells.  
In liquid-state fermented cells, the gene \(glnA\) encoding glutamine synthetase was down-regulated by 5.63 times, and the encoded enzyme catalyzed the conversion of glutamate into glutamine. Gene \(ansAB\) encoding L-asparaginase was down-regulated by 2.71 times, and this enzyme catalyzed the conversion of glutamate and glutamine. Two genes (\(carA\) and \(carB\)) encoding the small and large subunit of carbamyl-phosphate synthase were down-regulated by 5.31 and 6.15 times, respectively, and this enzyme catalyzed the conversion of glutamine to carbamyl-phosphate. Gene \(glnS\) encoding glucosamine-fructose-6-phosphate aminotransferase was down-regulated by 6.21 times, and the enzyme encoded by this gene catalyzed the conversion of L-glutamine into D-glucosamine-6-phosphate. Overall, the transition from glutamate to glutamine and many glutamate metabolism pathways were inhibited. However, genes \(gltB\) and \(gltD\) encoding the small and large chains of glutamate synthase (NADPH/NADH) were up-regulated by 12.06 and 12.16 times, and the encoded glutamate synthase catalyzed the conversion of 2-oxy-glutamate into glutamate, respectively. Gene \(gdhA\) encoding glutamate dehydrogenase was up-regulated by 3.62 times, and this glutamate dehydrogenase catalyzed the cross-conversion between 2-oxy-glutamate and glutamate, in which ammonia was released. Generally, glutamate was accumulated in the cells to produce \(\gamma\)-aminobutyric acid (GABA) under the catalysis of glutamate decarboxylase, thus relieving the acidic stress in the cell. This result was consistent with previous reports on GABA production as an important strategy for LAB to respond to acid stress [63; 69].
# Class Retrieval of Detected Adversarial Attacks
## 1 Introduction  
Deep learning is an ubiquitous machine learning approach which has been successfully used in many applications to find a practical solution for complicated problems, e.g., medical image segmentation [1] and self-driving cars [2]. Recently, deep learning has emerged as a cornerstone for safety and security-critical applications, like autonomous vehicles [3] and malware detection [4]. It even supports other applications, e.g., facial recognition, which may effect our daily lives.  
One of the recent obstacles of using deep learning are adversarial attacks, which were first introduced in [5]. The authors in [5] present a case of a malevolent attacker who can exploit the high-dimensional inputs by slightly modifying the pixels approaching the border of the high-dimensional geometrical manifold of the original class [6]. Although the attacks can be very strong, reaching more than 90% confidence for misclassified classes, the modification which has been applied to the original image is imperceptible to the naked eye. Minor perturbation over the entire image is the first adversarial attack which has been introduced by [PERSON] [7]. Many threatening results have surfaced urging the research community to find some defense mechanisms, e.g., Ref. [8] demonstrates a universal perturbation fooling a classifier on any image, Ref. [9] showed the possibility of fooling a classifier with a 3D printed object.  
The most commonly applied defenses against adversarial attacks depend on one of three approaches: adversarial training [10], modifying the network [11] or a detection-based approach [12]. Adversarial training slows down the training and it has also been demonstrated by [8] that you can form an adversarial attack against a network which has been trained with adversarial training. Most defenses which rely on modifying the network are too computationally infeasible to be applied on larger networks or they only work against specific attacks [13]. Detection-based approaches lack any notion of security in safety and time-critical applications where an immediate decision has to be taken without any delay, e.g., object detection in self-driving cars. Detection of adversarial attacks can be a good first step, but, on its own, it is not enough to ensure safety in practical applicationssince detection will only render the autonomous system in complete doubt, preventing the AI from making a sound and reliable decision. The ultimate safe solution for adversarial attacks, in the case of accurate detection, is recovering the original class. We will introduce a novel algorithm restoring the original class of the attacked image. We will demonstrate the effectiveness of our algorithm on three different datasets MNIST, CIFAR10 and 10 randomly selected classes from ImageNet.
## 6 Treatment  
### Conservative Treatment  
Currently, the gold standard for the treatment of displaced femoral neck fractures is surgical intervention. Surgical treatment is considered essential in most cases due to its ability to restore hip function, reduce pain, and prevent complications such as nonunion or avascular necrosis of the femoral head. However, there are certain exceptional situations where non-surgical treatment may be considered appropriate. These cases are generally rare and are determined based on specific clinical factors, including the following:  
1. **Short life expectancy:** In patients with a limited life expectancy, the risks associated with surgery may outweigh the potential benefits. In such cases, where the patient may not survive long enough to fully recover from surgery, conservative management might be preferred to avoid unnecessary complications.
2. **Chronic fractures with signs of consolidation:** In cases where the fracture has occurred long ago (inveterate fractures), and there are signs of partial or complete consolidation (healing) of the fracture, non-surgical management may be considered. This could be appropriate when the bone is healing in a satisfactory position and there is no need for surgical realignment.
3. **Patients who are bedridden:** For patients who are continuously bedridden and unable to bear weight, the need for surgical intervention might be reduced. In these cases, the focus would be on pain management and maintaining mobility to the extent possible, with surgery being deemed unnecessary due to the patient's inability to benefit from functional recovery post-surgery.
4. **Patient refusal of surgical treatment:** In some cases, patients may refuse surgery for personal, medical, or psychological reasons. When a patient makes an informed decision to decline surgical treatment, non-surgical management, including pain control and rehabilitation, may be pursued as an alternative, provided the patient understands the risks and limitations of this approach.  
In these exceptional cases, the decision to opt for non-surgical treatment requires careful consideration of the individual patient's health status, prognosis, and preferences, and should be made collaboratively between the patient and the healthcare team.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 4 Results
### Path Simulation Comparison
#### 4.2.5 Complex Scenario  
As shown in Figure 10, a complex warehouse environment is established, and the positions of the robot's initial and target points are randomly generated. The comparative algorithms are DSA-EGA, LM-GWO, LAPGWO, and CWSO. The evaluation indicators are the average path length, planning time, path smoothness, and average number of iterations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Indicators** & **GWO** & **DSA-EGA** & **LM-GWO** & **LAPGWO** & **CWSO** \\ \hline Average Path Length (m) & 18.14 & 17.36 & 17.51 & 16.24 & 17.12 \\ \hline Single-Planning Time (s) & 3.161 & 2.749 & 2.863 & 2.514 & 2.681 \\ \hline Path Smoothness (Curvature) & 0.47 & 0.19 & 0.23 & 0.12 & 0.17 \\ \hline Average Number of Iterations & 307 & 53 & 81 & 22 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the simple scenario.  
Figure 9: Operation result diagram.  
As can be seen from Figure 11 and Table 6, the paths planned by the LAPGWO algorithm have significantly fewer turning points, are smoother, have the shortest average path length, and maintain a certain safe distance from obstacles. The average computation time of the LAPGWO algorithm is improved by 8.3%, 10.5%, and 4.2%, respectively, compared with the DSA-EGA algorithm, the LM-GWO algorithm, and the CSWO algorithm. Path smoothness increased by 37.2%, 55.1%, and 18%, respectively. The search time, the number of iterations, and the path length of the method proposed in this paper are all superior to those of other algorithms.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 5 Experiment Results  
In order to evaluate the performance of the proposed method, we construct an experimental scenario oriented to the decomposition of remote sensing tasks based on the self-developed Common Satellite Toolkit (CSTK) simulation platform. The CSTK platform calculates the real-time positions and orbits of the satellites within a 3D scenario for the satellite's TLE data and assigns a unique computation, storage, and observation payload resource to each satellite for mission execution.
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 3 Joint Optimization  
Equation (13) shows that the joint optimization problem (13) is nonconvex with respect to (\(\mathbf{H}\), \(\theta\), \(\mathbf{P}\), and \(\mathbf{PL}\)). In order to deal with it more effectively, a joint optimization algorithm is proposed. Algorithm 1 illustrates the algorithm. This problem is solved by alternating minimization, in which the joint optimization problem (13) is decoupled into two subproblems, namely, the height and elevation subproblem with given power and path loss subproblem with given height and elevation. It is solved by using the gradient descent method and interior point method. In each iteration, the optimization variables are updated effectively by giving the last iteration, and the convergence of the algorithm is guaranteed.
# Digital Health Technology for Stroke Rehabilitation in Canada: A Scoping Review
###### Abstract  
(1) Background: Digital Health Technology (DHT) is an emerging method for stroke rehabilitation that could potentially be very effective in solving different problems in the therapeutic process. This study aims to explore the use of DHT for stroke rehabilitation in Canada, providing insights into how current technologies have been implemented and identifying gaps to inform future decision-making in clinical, research, and policy settings in the Canadian setting. (2) Methods: We followed the Arksey and O'Malley framework for scoping reviews. The original search was created in Medline (Ovid) and translated to PsycINFO (Ovid), Scopus, and CINAHL with Full Text (EBSCOhost). To locate grey literature, we searched Canadian Theses and Google. The search yielded 163 articles, of which we included 14 (8.6%) in the review. (3) Results: Fourteen studies published between 2010 and 2022 in Canada varied in design: 4 qualitative (28.6%), 4 randomized clinical trials (RCTs) (28.6%), 2 mixed methods (14.3%), and other types. The main goals included assessing intervention effectiveness (35.7%), client (28.6%) and clinician (28.6%) perceptions of technology, and feasibility (21.5%). Most studies focused on upper extremity (UE) function (85.71%), with some addressing walking speed (7.1%) and sitting balance (7.1%). Research mainly targeted the chronic phase of stroke (64.3%). Studies were conducted in home (50%) and institutional settings (42.9%). Technologies included sensors (50%), virtual reality (VR) (42.9%), games (28.6%), telerabilitation (28.6%), and robots (14.3%). (4) Conclusions: This scoping review offers key insights into the use of DHTs for stroke rehabilitation in Canada, highlighting the types of technologies, their effectiveness, and the facilitators and barriers to adoption. These technologies show promise in improving patient outcomes, and their integration into Canadian healthcare systems presents a significant opportunity to enhance stroke rehabilitation.  
digital health technology; stroke rehabilitation; Canada
## 2 Study Area  
This study takes place on the island of El Hierro, Canary Islands, Spain. El Hierro is a composed oceanic volcano with the oldest subaerial rocks having an age of 1.12 Ma. Its youngest subaerially exposed rocks are represented by the Rift Series, with a maximum age of 0.16 Ma [25]. Over the past 33,000 years, onshore eruptions have reoccurred approximately once every 1000 years [26]. The latest, ongoing phase of volcanism began around 2.5 ka [27]. Recently a period of intense seismic activity spanned from July 2011 [28] to 2014 [29]. During this period, an offshore eruption started on 10 October 2011 and finished in March 2012 [30]. A more detailed description of the geology of the island has recently been presented elsewhere [31; 32].  
The characteristic three-point star morphology of El Hierro is a result of several enormous flank collapses (Figure 1). Until now, seven debris avalanches have been identified: TInor (<880 ka), Las Playas I (545-176 ka), Las Playas II (176-145 ka), El Julan (>158 ka), El Golfo A (176-133 ka), El Golfo B (87-39 ka), and Punta del Norte (unknown age) [27; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42].  
In addition to the previously mentioned flank collapses, a giant San Andres Landslide (SAL) has been identified on the northeastern part of the island. The SAL is a deep-seated gravitational slope deformation [43] or a slump [44]. It has been supposed that the landslide underwent a single-event development at some point between 545 ka and 176 ka [45] and was inactive since then. Recent research shows, however, that there were at least two distinguishable separate slip events. The first one between 545 ka and 430 ka, and the second one between 183 ka and 52 ka [46]. Moreover, it has been proven that there is a continuous creep with a rate up to 0.5 mm\(\cdot\)a\({}^{-1}\), suggesting that the landslide mass may be moving steadily to the east and southeast [47; 32; 48]. Numerical stability modelling [49] showed that creep movements are possible due to unconsolidated sediments at the sea bottom, and destabilization of the landslide might be possible during periods of intense seismic activity.  
This creep has been monitored by high-precision (\(\pm\)0.007 mm) 3D dilatometer gauges [3] with automatic data processing [50]. There are two devices installed on the landslide detachment plane in the upper scap and on the left side of the landslide, respectively (Figure 1). The instrument records data based on optical-mechanical interferometry via the generation of moire patterns, which result from the bending and interference of light rays as they pass through two specially designed optical grids [51].
# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 3 MTI Filter Based on the Eigenvector Method
In order to obtain the best filtering effect, it is necessary to accurately estimate the clutter. Therefore, the accuracy of the clutter estimation is directly related to the performance of the designed filter. However, complex environments may lead to variable clutter characteristics and the clutter characteristics of different range cells may differ significantly, which increases the difficulty of obtaining accurate a priori knowledge, resulting in poor adaptive clutter suppression effect. How to accurately obtain the clutter information to adjust the filter parameters is a key issue in adaptive filter design. Deep reinforcement learning (DRL) provides another new attempt to accurately obtain the sea clutter information and adjust the filter parameters. DRL combines the techniques of deep learning (DL) and reinforcement learning (RL) [25]. The framework is shown in Figure 1, introducing the intelligent environment perception method of DL. Each range cell to be detected (agent) has the ability of online perception learning of complex environments and can obtain the optimal strategy through interactive learning with the environment, so as to realize intelligent decision-making and optimization of complex tasks. Compared with the traditional method of obtaining accurate clutter characteristics and designing adaptive filters, the clutter suppression method based on DRL simply estimates the relevant parameters of the sea clutter power spectrum as initial parameters and establishes a reward mechanism corresponding to the SCNR. Through the RL process and the environment interactive learning, according to the positive incentive of the reward mechanism, the optimal filter parameters are obtained to achieve a better clutter suppression effect.