esa-sceva/satcom-chunk-collection huggingface dataset:
# Livestock Management on Grazing Field: A FANET Based Approach
###### Abstract  
In recent times, designated grazing areas/fields or routes for livestock grazing are usually defined. Hence, their herding activities' success relies on data extracted from aerial photographs. As such, a direct and cost-effective way of monitoring livestock for perimeter coverage and in other natural situations is required. This paper presents a coverage solution involving multiple interacting unmanned aerial vehicles. The presented approach is built on a graph, with geographic coordinates set such that several Unmanned Aerial Vehicles (UAVs) can successfully cover the area. The maximum flying time determines the number of UAVs employed for coverage. The proposed solution is thought to solve some practical problems encountered during the execution of the task with actual UAVs. It is suitable for long-term monitoring of animal behavior under various weather conditions and observing the relationship between livestock distribution and available resources on a grazing field. The simulation was carried out using MATLAB and aerial images from Google Earth.  
livestock management; FANET operations; grazing field; area surveillance
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 3 Data Analysis
### Ship Specifications  
The ship analyzed in this study was based on the series 62 craft. Table 1 and Figure 2 present its specifications. The drafts and trim angles of the hulls in their initial condition were set for all simulation conditions before the nodes were distributed to calculate the seakeeping performance.  
\begin{table}
\begin{tabular}{c c} \hline
**Hull Dimensions** & **Size** \\ \hline Hull length (\(L\)) (m) & 34 \\ Hull width (\(B\)) (m) & 7.6 \\ Draft (\(T\)) (m) & 1.7 \\ Displacement volume V (m\({}^{3}\)) & 171 \\ Block coefficient (\(C_{B}\)) & 0.461 \\ Prismatic coefficient (\(C_{P}\)) & 0.685 \\ Transverse metacentric height (\(\overline{GM}_{T}\)) (m) & 1.723 \\ Longitudinal metacentric height (\(\overline{GM}_{L}\)) (m) & 59.3 \\ Longitudinal center of gravity (\(LCG\)) (m) & 13.1 \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the series 62 craft.
## 4 Simulations and Results
### Case I  
In this case, we are considering a spectral white space that is not used by the PU and is available for the SU. The total number of OFDM subcarriers used by SU is 16 modulated with BPSK, whose power is normalized to \(|d_{n}|^{2}=1\). The performance of the proposed technique is compared with different existing techniques, including CC [14], CC using GA and DE [17], ACC [18], and ASW [21]. In CC techniques, two CC\(s\) have been used either side of the data subcarriers, whereas in SW techniques all subcarriers are used for weighting. Figures 4 and 5 show the comparison of the proposed technique with the existing techniques in terms of PSD. It can be observed from Figures 4 and 5 that the existing techniques give a maximum of 36 dB improvement, while the proposed technique gives 92 dB improvement compared with the original OFDM spectrum. The proposed technique gives 58 dB improvement compared with the spectrum of OFDM using existing techniques.  
Figure 4: The PSD comparison between the proposed technique and existing techniques including SW, CC (Brandes), CC (GA), and CC (DE).  
The comparison of the proposed technique with the existing ones is also given in Table 1, which shows the effectiveness of the proposed technique.
# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 4 Results
### Performance Evaluation Parameters  
The **silhouette score** is a metric that measures the degree of similarity between an object and its own cluster, relative to other clusters. The range of values is from \(-1\) to \(1\)  
Figure 3: Workflow of the proposed model.  
A high value implies that the object is well-suited to its own cluster and not well-suited to surrounding clusters. It is a valuable measure for evaluating the accuracy of clustering when the actual labels are unknown.  
The **Calinski-Harabasz score**, sometimes referred to as the Variance Ratio Criterion, quantifies the ratio between the total dispersion among different clusters and the dispersion inside each individual cluster. A higher score signifies more distinct and well-defined clusters. This score is valuable for assessing the degree of compactness and distinctiveness of clusters. Greater scores suggest that the clusters exhibit high density and significant separation, which is advantageous for achieving effective clustering.  
The **Davies-Bouldin score** quantifies the mean resemblance between each cluster and its most resembling cluster, with resemblance being determined by the ratio of distances within a cluster to distances between clusters. Lower scores are indicative of superior clustering. This score is used to evaluate the degree of compactness and separation of clusters. A lower Davies-Bouldin score indicates that the clusters are highly segregated and clearly distinguishable from one another.  
The **Within Cluster Sum of Squares (WCSS)** is a measure that estimates the sum of squared distances between each data point and the centroid of the cluster it belongs to. It quantifies the level of density in clusters. Smaller WCSS values imply that the data points are near their centroids, indicating that the clusters are tightly packed and distinct from each other.  
Essentially, these metrics offer varying viewpoints on the excellence of clustering. A clustering algorithm that achieves a high silhouette score, high Calinski-Harabasz score, low Davies-Bouldin score, and low WCSS is considered to have superior clustering results, showing that the clusters are well-defined, compact, and isolated.  
The **variance** score quantifies the dispersion of the datapoints from the average. A high variance score is an indicator that the selected features exhibit a broad spectrum of values.
# A State of the Art Review of Systems of Linear Inequalities and Related Observability Problems
## 6 Examples of Applications
### Application to Artificial Intelligence Problems  
It has been shown that the general solution of bounded linear systems of inequalities are polytopes. Thus, these systems can be used to define mixtures of many models which enlarge the sets of solutions in an advantageous way. One example is that described in [37], where Gaussian mixture models are used. Another interesting example can be seen in [38], where a loss function called Gaussian mixture centers is proposed to reduce variances in a convolutional neural network machine learning model.  
In addition, since Bayesian methods are methods in which the parameters are random, again the mixtures appear, giving these systems of linear inequalities an important role to model the mixtures. An example where this technique can be used is available in [39].
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.1.1 Support Vector Machines (SVMs)
Analyzing the method advantages and disadvantages, it can be said that SVM models remains a powerful ML tool, especially regarding the high accuracy and robust performance in the case of complex classification and regression tasks.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 4 Performance Evaluation and Lessons Learnt
### Results and Discussion
#### 4.2.4 Summary of Results  
Table 1 shows the comparison of the accuracy level of the various machine learning models. From the results, CNN outperformed other ML models, like LR, NB, SVM, and KNN. Figures 13 and 14 are the training and validation accuracy and loss, respectively, of the CNN model. The result shows that CNN outperformed other algorithms.  
Figure 12: Error histogram distribution curve of the RP-LIDAR range data.  
Figure 13: Training and validation accuracy of the CNN model.
## 2 Materials and Methods
### Functional Assessment  
Preoperatively (t0) each patient was evaluated with instrumental activities of daily living (IADLs) scales [19]. IADLs are used to measure the activities that allow an individual to live independently in a community, thereby improving quality of life. [PERSON]'s IADL Scale [19] is composed of 8 items, and assesses a person's ability to perform tasks such as using a telephone or handling finances. Responses to each of the eight items in the scale are coded as 0 (unable or partially able) or 1 (able), and the responses are summed. The summary score ranges from 0 (low functioning, dependent) to 8 (high functioning, independent).  
Preoperatively (t0), at hospital discharge (t1), at 15 days after surgery (t2), and at one month (t3) after surgery, each patient was evaluated with the visual analogue scale (VAS), while the Vail Hip Score (VHS) was used for patients who underwent THA, and the Western Ontario and McMaster Universities (WOMAC) questionnaire was used for patients who underwent TKA.  
The VAS was used as a subjective measure of pain perception. A VAS score > 3 at the last follow-up indicated residual pain [20].  
The VHS is a self-administered sensitive scale. It includes 10 questions focused on pain, stiffness, gait, and function. The scale has a maximum of 100 points divided on the basis on the assessed items. Pain includes three questions with a total of 50 points; stiffness includes two questions with a total of 20 points; gait includes one question with a total of 5 points; and function includes four questions with a total of 25 points [21]  
The WOMAC questionnaire is a self-administered disease-specific validated outcome measure; this was used to assess pain (5 items), stiffness (2 items), and physical function disability (17 items) in patients suffering from knee osteoarthritis [22]. The WOMAC questionnaire provides either single domain scores or a total score (0-100); lower scores are associated with lower levels of pain and stiffness and better function.  
Patient satisfaction was assessed, and patients were asked if they would undergo the same type of surgery again.
## 4 Solving FJSPLSP-LTCC Using DRL
### Crgpo-Tkl  
This study adopts an enhanced PPO algorithm, named CRGPPO-TKL, to train the collaborative scheduling agent. PPO is a policy gradient method that collects data through interaction with the environment and updates the agent's policy using stochastic gradient ascent [50]. By employing a clipped surrogate objective and importance sampling, PPO maintains stable policy updates even in high-dimensional combinatorial optimization problems such as machine assignment, AGV selection, charging decisions, and charger selection in the proposed FjSPLP-LTCC. Unlike value-based methods that only handle discrete action spaces, PPO's actor-critic architecture allows separate policy heads to process both discrete and continuous actions. As one of the most widely used RL algorithms, PPO has proven its effectiveness in various manufacturing scenarios. However, PPO still suffers from several limitations. The clipping mechanism, while central to PPO, is sensitive to its clipping range: A narrow clip range may discard many samples due to out-of-range probability ratios (gradient truncation), reducing data efficiency. A wide clip range may lead to policy updates that exceed the trust region, violating the principle of monotonic improvement. To address these issues, [PERSON] and [PERSON] [51] proposed Candidate Ratio Guided PPO (CRGPPO), which introduces candidate actions sampled with Gaussian noise. By computing candidate ratios, CRGPPO enables a better assessment of policy divergence and adjusts the clip range adaptively for more stable learning. However, the method is designed for continuous action spaces and introduces randomness that may destabilize training. To address these issues for the discrete decision space of FjSPLP-LTCC, we propose a modified version called CRGPPO-TKL, with two key enhancements:  
1. Candidate Action Generation  
Since all actions designed in Section 4.1.2 are discrete, we replace the Gaussian noise-based sampling used in CRGPPO with stochastic sampling from the policy's discrete distribution, making it suitable for the current action space. \[\pi_{\theta}(a|s)=\text{Categorical}(p_{1},p_{2},\ldots,p_{A_{\text{max}}}) \tag{30}\]  
\[a_{t}^{(i)}\sim\pi_{\theta}(\cdot|s_{t}) \tag{31}\]  
To balance exploration capability and computational efficiency, we preset the number of candidate actions sampled from the policy distribution to 5 per decision step. This fixed sampling size ensures sufficient coverage of the action space while avoiding excessive computational overhead. Although categorical sampling lacks continuous perturbation mechanisms such as Gaussian noise, the PPO framework incorporates an entropy regularization term to preserve policy stochasticity. This is particularly beneficial in the early training phase, as it promotes diverse action sampling and mitigates the risk of premature convergence to suboptimal policies. Dynamic Adjustment of the Clipping Range  
To reduce training instability, we introduce a target KL divergence mechanism to adjust the clip range dynamically.
# Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students' Performance
## 4 Proposed Methodology and Results
### Results Comparison  
We have performed three different experiments on our research problem and accuracies of the experiments have been compared. Using Without-SMOTE data, we achieved the highest accuracy with experiment 2 by Random Forest (RF); using with-SMOTE data, the highest accuracy was also achieved with experiment 2 by Long Short Term Memory (LSTM), as shown in Figure 15 and the compared results in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Exp 3** & \multicolumn{3}{c}{**Without-SMOTE**} & \multicolumn{3}{c}{**With-SMOTE**} \\ \hline
**Algorithms** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline DT & 0.65 & 0.61 & 0.62 & 0.83 & 0.83 & 0.83 \\ LSTM & 0.69 & 0.68 & 0.68 & 0.88 & 0.87 & 0.87 \\ MLP & 0.65 & 0.64 & 0.65 & 0.86 & 0.86 & 0.86 \\ NB & 0.52 & 0.48 & 0.49 & 0.69 & 0.69 & 0.69 \\ RF & 0.76 & 0.58 & 0.59 & 0.89 & 0.88 & 0.88 \\ SVM & 0.72 & 0.63 & 0.64 & 0.88 & 0.88 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Exp2 Scores (Precision, Recall, F1-Score).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Without-SMOTE** & **With-SMOTE** \\ \hline  & Criterion = gini, \\ DT & min\_samples\_leaf = 2, \\  & min\_samples\_split = 5, \\  & splitter = best \\  & Activation = relu, batch\_size = 7, \\ LSTM & epochs = 110, last\_act = softmax, \\  & solver = Nadam, unit-LSTM = 30, \\  & dropout = 0.2 [(1+0) LSTM, Dense] \\  & Activation = tanh, alpha = 0.001, \\ MLP & hidden\_layer\_sizes = (93,93,93), \\  & max\_iter = 100, solver = adam \\ NB & Default \\  & min\_samples\_leaf = 2, \\ RF & min\_samples\_split = 2, \\  & n\_estimators = 200 & n\_estimators = 120 \\ SVM & C = 2, Gamma = 0.1, kernel = rbf \\ \hline \hline \end{tabular}
\end{table}
Table 7: Exp2 Scores (Precision, Recall, F1-Score).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Experiment 1**} & \multicolumn{2}{c}{**Experiment2**} & \multicolumn{2}{c}{**Experiment3**} \\ \hline
**Algorithms** & **Without-SMOTE** & **With-SMOTE** & **Without-SMOTE** & **With-SMOTE** & **Without-SMOTE** & **With-SMOTE** \\ \hline DT & 63.7 & 79 & 75 & 86 & 72.5 & 83.2 \\ LSTM & 74.4 & 86.4 & 78.8 & **96.9** & 75 & 64.4 \\ MLP & **78.6** & 86 & 78.8 & 88.8 & 72.5 & 86 \\ NB & 41.9 & 66.8 & 38.1 & 80.1 & 58.1 & 69.2 \\ RF & 75 & 87.1 & 80 & 86.5 & 77.5 & **88.8** \\ SVM & 74.4 & **98.3** & 78.1 & 88.1 & **78.1** & 88.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experiments Comparison (Accuracies).
###### Abstract  
This study aimed to test whether alveolar bone gain (width and osteodensity) in mandible implant sites, augmented by the screw-guided bone regeneration (S-GBR) technique and the porcine-derived xenograft, differ from that of the mandible sites reconstructed with S-GBR and the bovine-derived xenograft. Materials and method. A total of 42 edentulous patients (mean age 54.60 \(\pm\) 14.90) were randomly divided into a test group (S-GBR + porcine-derived xenograft) and a control group (S-GBR + bovine-derived xenograft). The immediate implants were placed simultaneously with grafting procedures. The implant site width was measured before teeth extractions and 6 months after the augmentation procedures. Osteodensity bone values were evaluated immediately after the implant placement and at 6 months follow-up. The CBCT sections and Sidexis XG/DVT (Densply/Sirona) software were used to evaluate the changes of the width in the augmented alveolar bone. The NNT Viewer/CT (NewTom) software was used to record the osteodensity changes. Statistical comparisons between the groups were performed by the Mann-Whitney and \(t\)-tests (significance level \(p<0.05\)). Results. The mean values of the bone gain at 6 months follow-up (control group vs. test group) were as follows: width (4.107 mm vs. 4.1624 mm); osteodensity (276.83 HU vs. 254.24 HU). The statistical analysis found the absence of statistically significant differences between the two groups regarding the bone gain (width and osteodensity) at 6 months after the augmentation and the immediate implant placement. **Conclusions.** The horizontal bone and osteotomy density gains in the porcine-derived group were similar and without significant statistical differences when compared with the implant sites reconstructed with the bovine-derived xenografts group at 6 months postoperatively. The reconstruction of the mandibular alveolar bone by the S-GBR technique and the porcine-derived xenografts is a valid bone regeneration strategy for edentulous patients with moderate/severe horizontal resorption of the mandibular alveolar bone.  
dental implants; bone resorption; S-GBR; xenograft; bone gain +
Footnote †: journal: Applied Sciences
## 2 MG Concept and Requirements
### MG Communication Requirements  
To detect efficient and appropriate communication technology for MG, we need to ascertain the specifications and constraints of information exchange in each supervisory level. Figure 3 shows a comparison of AMI, EMS, and DMS characteristics based on data rates, reliability, coverage, delay, complexity, security, and benefits per cost. This figure ensures each control level of MG has its communication requirements. While all control levels require a highly reliable and secure communication, their data rates and delay characteristics increase according to their coverage characteristic. The other important specification for communication infrastructure is backup power, which ensures the performance of communication during any outage and is proportional to the critical mission of each level control from zero for AMI, as interacting with meters increases to one hour for EMS, and 72 hours for DMS. In the SG environment, there is a hierarchical communication level including HAN, FAN, and WAN. This classification can be followed by MG. While HAN contains loads such as smart appliances and electric vehicles (EV), which can be automated and programmable through home energy management systems (HEMS) to provide AS for utility grid such as DR, FAN coordinates among DER, ESS, operators, and energy marketing entities, hence needing higher bandwidth than HAN. At the highest level, WAN embraces information exchange between EMS and DMS when MG works in grid-connected mode. Communication specifications of applications in MG are summarized in Table 2[33].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Communication Level** & **Application** & **Bandwidth** & **Latency** \\ \hline \multirow{4}{*}{HAN} & HEMS & 9.6–56 kbps & 200 ms–2 s \\ \cline{2-4}  & EV Charging & 9.6–56 kbps & 2 s–5 min \\ \cline{2-4}  & V2G & 9.6–56 kbps & 2 s–5 min \\ \hline \multirow{2}{*}{FAN} & AMI & node:10–100 kbps backhaul: 500 kbps & 2–15 s \\ \cline{2-4}  & DER and ESS & 9.6–56 kbps & 20 ms–15 s \\ \hline \multirow{2}{*}{WAN} & DR & 14–100 kbps & 500 ms–several min \\ \cline{2-4}  & DMS & 9.6–100 kbps & 100 ms–2 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Communication specification application in MG.  
Figure 2: DMS supervisory level domain.
###### Abstract  
One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm's flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns.  
hand motion recognition; electromyography; Pareto optimization; assisted living Article
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 7 Discussion and Evaluation  
The implementation in this work is based on leveraging SSI, Blockchain, and tamper-resistant key management, with the aim of simplifying and securing the banking identity in decentralized mode. In the design, we considered security and easy integration with existing banking platforms, with benefits arising from security standards such as PCI-DSS and from the use of HSM. Each customer will be able to control and gain from sharing his identity and financial data without relying on a centralized system or third parties. The Veramo SDK and Ethereum Blockchain allow the management of DIDs, which are sets of characteristics and claims that uniquely identify persons, entities, or simply things. In addition, users can share claims and attested proofs with others with minimal attributes required for each case. This ensures high privacy requirements are met with the concept of zero-knowledge proof and selective disclosure, which exists in our work by relying on the Veramo framework. Our work aims to control and maintain the trust between participants in the open banking context based on SSI implementation, and achieves privacy, security, and a high level of trust, while balancing the user experience, by utilizing the customer banking cards to securely manage the private keys and on-chain identity operations. In addition, it is able to manipulate the required characteristics, such as key recovery and portability, via the use of a client mobile app, enables interoperability by relying on DID and VC specifications, and addresses the ability to be integrated with the banking ecosystem.  
In contrast to using hardware tokens, such as in the work presented in [22], to achieve a high level of assurance we rely on existing banking cards, which are usually part of every banking customer's life, and provide the required level of trust and security (controlled by PCI-DSS and related standards), while remaining portable and easy to use. This can be extended for many other valued services, such as fast KYC and password-less access to banking mobile applications and online banking, on top of secure interaction with open banking and financial services.