esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Results and Analysis
### Experiment Data Set
#### 3.1.2 Multi-Source Trajectory Data Set  
In addition to the taxi vehicle GPS dataset, we validated the proposed algorithms using the multi-source trajectory dataset. The multi-source trajectory dataset is derived from the Routes 2019 subset of the MOPSI dataset ([[http://cs.uef.fi/mopsi/data](http://cs.uef.fi/mopsi/data)]([http://cs.uef.fi/mopsi/data](http://cs.uef.fi/mopsi/data)), accessed on 5 July 2022), which contains approximately 3 million trajectory data points [54]. Trajectories encompass a variety of activities, such as walking, biking, hiking, jogging, driving, taking the bus, and others. Some of these trajectory points record life routines, such as going to work or shopping. The dataset is particularly suitable for migratory pattern mining, person activity identification, and location-based similarity. In the experiments of this paper, we select multi-source trajectory data points within the urban area for analysis, mainly including trajectory point data of vehicles, walking, jogging and biking. The spatial distribution of the multi-source data trajectory points is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Taxi GPS Data Sets** & **Latitude and Longitude Span** & **Number of Data Points** \\ \hline Aracaju (Brazil) & \(0.14\times 0.16\) & 16,513 \\ Beijing (China) & \(0.90\times 0.90\) & 17,387 \\ Chongqing (China) & \(0.60\times 0.36\) & 19,149 \\ Rome (Italy) & \(0.35\times 0.50\) & 20,254 \\ San Francisco (USA) & \(0.10\times 0.10\) & 21,826 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary information of Taxis GPS data sets.  
Figure 3: The spatial distribution of the multi-source data trajectory points.
## 1 Introduction
In this paper, we present a novel solution method for the simultaneous optimization of location planning of charging stations and vehicle scheduling for BEBs in public transport to open up potentials for cost savings in comparison with a sequential planning. To do so, we develop a solution approach based on _Variable Neighborhood Search_ (VNS), which has been successfully applied to real-world combinatorial optimization problems in a variety of application areas [10]. We propose a heuristic solution approach because the E-VSP and the location planning problem are both difficult to solve, especially with regard to larger instances. Following [PERSON] and [PERSON] [11] and [PERSON] and [PERSON] [12], both problems are NP-hard. Simultaneous problem solving is expected to be no less difficult [13]. Within our solution approach we incorporate complete as well as partial charging procedures of the vehicle batteries. By a computational study, we prove the need for simultaneous optimization as opposed to sequential planning. We show that simultaneous problem solving is necessary because sequential planning generally leads to either infeasible vehicle rotations or to significant increases in costs. Further on, we discern that the incorporation of partial charging procedures leads in principle to major cost savings. This paper is outlined as follows: In Section 2 we provide an overview of existing work about scheduling of electric vehicles and location planning of charging stations for BEBs. In Section 3 we define the problem to be solved formally. Following this, we introduce the metaheuristic solution method in Section 4. In Section 5 we perform comprehensive computational experiments and analyze the results in order to make key statements. We provide conclusions and present potentials for further research in Section 6.
## 2 Materials and Methods
Following dimensionality reduction, unsupervised machine learning techniques were applied, specifically the K-Means clustering algorithm. This technique groups data points based on similarity, minimizing intra-cluster variance while maximizing inter-cluster separation. Several studies indicate that applying K-Means in PCA-transformed subspaces enhances computational efficiency [33; 34]. Despite its simplicity, K-Means has been shown to perform competitively against more complex clustering methods [35]. Additionally, research by [36] suggests that there is no universal solution for K-Means, reinforcing the importance of empirical validation. Key Steps in K-Means Implementation:  
* Step 1. Setting a Random Seed (Ensures reproducibility). * Step 2. Centroid Initialisation (Centroids are randomly assigned as initial cluster representatives). * Step 3. Cluster Assignment (Each data point is assigned to the nearest centroid based on Euclidean distance). * Step 4. Centroid Update (New centroids are recalculated as the arithmetic mean of points within each cluster). * Step 5: Iteration Until Convergence (Steps 3 and 4 are repeated until centroids stabilise, or a predefined iteration limit is reached). The optimal number of clusters (k) was determined using the elbow method, which evaluates intra-cluster variance (Within-Cluster Sum of Squares, WCSS) as a function of k. The optimal k is selected at the inflexion point where adding more clusters does not significantly improve compactness. After clustering, the composition of each group was examined based on the qualitative variables from the initial dataset. This analysis provided valuable insights into the characteristics of faculty members with lower commitment, helping to identify key retention factors and patterns that influence long-term faculty engagement.
## 2 Study Area and Data
### Data
#### In Situ Observation Data  
The in-situ forest fire observation data provided by the Korea Forest Service were used for training and validation of the proposed models. The observation data contain the time from the start of a forest fire to the extinguishment, as well as information on the location, area, and cause of the forest fire. In this study, forest fire cases occurred from 2014 to 2019 were used, resulting in a total of 3118 cases. It should be noted that only eight of these cases caused large damaged areas more than 100 ha, while most resulted in damaged areas less than 5 ha.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 5 Numerical Evaluations and Discussion
### Doppler Shift  
In Figure 7, we plot \(f_{0}(t)\) and the \(f_{mn}(t)\) with the maximal absolute value as the example. It can be seen see that after optimization (i.e., using \(\phi_{nm}^{\text{opt}}(t)\) in (23)), the DS at time \(t\), \(f_{mn}(t)\), can be aligned to \(f_{0}(t)\), which means that the Doppler spread is reduced to zero. Specifically, when the moving distance is 400 m, \(\max\{|f_{mn}(t)|\}\) without optimization is 380 Hz, but \(\max\{|f_{mn}(t)|\}\) with optimization is reduced to 295 Hz, the same as \(f_{0}(t)\). Similarly, when the moving distance is 700 m, \(\max\{|f_{mn}(t)|\}\) without optimization is 400 Hz, and \(\max\{|f_{mn}(t)|\}\) with optimization is just 365 Hz. This result validates Remark 3. Besides, it can be observed that when the vehicle is far enough from the BS and the RIS, i.e., the moving distance is smaller (larger) than 100 m (900 m), the \(f_{0}(t)\) and \(\max\{|f_{mn}(t)|\}\) are nearly the same and stationary. This is because \(d_{0}(t)\approx d_{\text{BS}}^{mm}+d_{mn}^{\text{MR}}(t)\) at this time. Besides, it is noteworthy that the positive and negative signs of the DS only reflect its deviation from the carrier frequency \(f_{c}\). In other words, the positive and negative DS has the same influence on the system.  
Figure 6: Ergodic SE for one vehicle pass with distance-dependent Rician factor (\(\varrho=13\), \(t=0.03\)) [20], and Fixed Rician factors (\(\varrho=0\), \(\iota=0\); \(\varrho=5\), \(t=0\); \(\varrho=10\), \(\iota=0\)[19]). The moving distance of the vehicle is from 2450 m to 2550 m.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 2 Methods  
This section summarizes the approach adopted to collect and review sources as part of this review of the literature. In summary, a list of key terms was developed and searched on the Web of Science. Once the results were recovered from the database, the titles and abstracts were reviewed according to the inclusion criteria. If a result met the inclusion criteria, it was included in the review.  
The first step in this review was to define a list of key terms found in Table 1. This list was cross-referenced with reviews regarding similar topics and expanded based on the specific goals of this work [4; 5]. The terms were separated into four sets, each defining various aspects of the search scope. The first set defined which sectors were to be reviewed. In this review, all sectors were considered within scope, i.e., residential, commercial, and industrial. Set two defines the level of aggregation of the buildings under consideration. This review includes studies at all levels, including individual buildings, districts, communities, the entire grid, and microgrids. Set three specifies various terms that could be used to refer to flexibility. Finally, set four includes terms that could be used to refer to estimation studies. Each set is combined through the \"AND\" operator.  
Filters were used to limit the search to the title, abstract, and author keywords. The search was applied to years after 1990 and excluded meetings, dissertations, editorial materials, and clinical trials. The database search was performed on 2 April 2024, on the Web of Science platform; 1402 results were retrieved, which were refined to 64 studies after a review of the titles and abstracts of each search result. A paper was included in the review when it was clear (through the title and abstract) that the goal of the paper was to quantify the demand-side flexibility of electricity resources within buildings. After flagging applicable papers based on these criteria, they were read and reviewed to track the chosen categories of information. In addition to those collected through this process, other known and relevant sources were included. Trends are presented in the Results Section, while takeaways are presented in the Discussion and Conclusion Sections.
## 3 Results and Discussion
### Antibacterial Activity  
The MIC and MBC of these preparations were evaluated through broth microdilution assay, bringing scalar concentrations of the substances into contact with the bacterial strains as described above.  
The antimicrobial activity of edible oil obtained from the berries of _P. lentiscus_ has been demonstrated [19]. In our experiments, notwithstanding the most abundant SFA in oil, were palmitic acid and stearic acid, which have shown remarkable antibacterial properties towards Gram-positive and Gram-negative bacteria [80]; LO alone was not able to inhibit bacterial growth, in accordance with those obtained from [PERSON] et al., 2017 [15], who did not appreciate the _S. aureus_ strains' sensitivity to _P. lentiscus_ berry oil.  
Divergent results regarding levels of antimicrobial activity were reported in the studies testing LO and their extracts. For example, a high antimicrobial activity was demonstrated in regard to the leaves from plants growing in different regions, whereas the fruits from Tunisia and Sardinia showed a limited effect against bacteria and yeasts [81]. These differences were explained by some factors such as the oil's geographic origin, climatic conditions, and soil composition and plant nutrition [81].  
When planktonic staphylococci were assayed for LVX susceptibility, each strain showed a peculiar antimicrobial activity. Interestingly, bacterial strains did not demonstrate appreciable sensitivity to empty NEs but the profiles changed when these nanocarriers were loaded with the antibiotic (Table 3). In fact, when bacterial strains were incubated with NEsL for 24 h, similar MIC and MBC values, with respect to free LVX, were detectable. These results are consistent with numerous studies that have discovered MIC and MBC values either equivalent to or much higher than those of their free-form antibiotics [82]. As reported by the authors, one possible explanation was the incomplete release of antibiotics from nano-antibiotics, resulting in an insufficient number of free antibiotics that are able to effectively exert their action.  
Our observation, showing retained MIC values with respect to LVX alone, led us to suppose the ability of NEsL to release antibiotics in adequate quantities in the bacterial growth medium and at the same time carefully preserve its activity.
# Simulating 3D Human Postural Stabilization in Vibration and Dynamic Driving
## 3 Results  
### Salom Validation  
High postural feedback gains were needed to stabilize the trunk and to obtain realistic head motions in the slalom (Figure 2). Lower postural gains resulted in excessive trunk and head rotation, and eventually the body simply rolled and fell (Figure 1--right). Without active leg control, the body rolled and fell even earlier. Hip and knee joint feedback control helped to reduce pelvis roll and pitch, spine control reduced trunk rotation and neck control reduced head rotation. In addition to such a reactive postural stabilization, we implemented a constant hip and knee extension activity, pushing the trunk into the seat back and pushing the thighs into the seat. This pushing activity reduced trunk pitch and strongly reduced trunk roll (Figure 2). The reduced trunk roll can be attributed to the seat back friction being more effective due to pushing. This was confirmed in an additional simulation with pushing of legs but without seat back friction, which again led to excessive trunk rotation and falling.  
With high postural control gains and pushing of legs, head roll fitted the measured data well, while trunk and pelvis roll were overestimated by the model. Head yaw followed the measured yaw with a short delay. As described in the Introduction, three head-neck control strategies were compared. As expected, head-in-space control yielded an unrealistic  
Figure 2: Slalom validation for body **rotation**. Model prediction of head (**upper**), trunk (**middle**) and pelvis (**lower**) orientations. Blue line: experimental; dotted green line: best model fit with high postural gains and with hips and knees pushing the body into the seat back; solid green line: high gains but no pushing; red line: lower postural gains fitted towards the vibration experiment.  
low head yaw motion where the controller tried to minimise head yaw while the vehicle was turning. Good results were found with head-on-trunk control (Figure 2), while very similar results were found with head-in-car control. Thus, our current results cannot discriminate the realism of the latter two control strategies. The results for acceleration show a good fit with high postural gains (Figure 3) and are not very sensitive to postural control gains and leg pushing.
# Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
## 3 Forgetful Data Structures
### Forgetful Random Forest  
The forgetful random forest is based on the forgetful decision tree described above in Section 3.1. Each random forest contains \(nTree\) forgetful decision trees. The update and rebuild algorithms for each decision tree in the random forest are the same as those described in Section 3.1 except:Only a limited number of features are considered at each split, increasing the chance of updating (rather than rebuilding) subtrees during recursion, thus saving time by avoiding the need to rebuild subtrees from scratch. The number of features considered by each decision tree is uniformly and randomly chosen within the range \(\left(\left\lfloor\sqrt{nFeatures}\right\rfloor+1,nFeatures\right)\), where \(nFeatures\) is the number of features in the dataset. Further, every node inside the same decision tree considers the same features.
* The update function for each tree will randomly and uniformly discard old data without replacement, instead of discarding data based on time of insertion. Because this strategy does not give priority to newer data, this strategy increases the diversity of the data given to the trees.
* To decrease the correlation between trees and increase the diversity in the forest, we give the user the option to choose the leveraging bagging [11] strategy to the data arriving at each random forest tree. The size of the data after bagging is \(W\) times the size of original data, where \(W\) is a random number with an expected value of 6, generated by a \(Poisson(\lambda=6)\) distribution. To avoid the performance loss resulting from too many copies of the data, we never allow \(W\) to be larger than 10. Each data item in the expanded data is randomly and uniformly selected from the original data with replacement. We apply bagging to each decision tree inside the random forest.  
When the overall random forest accuracy decreases, our method discards trees that suffer from particularly poor performance. If the decrease is large, then forgetful random forests discard many trees. If the decrease is small, then our method discards fewer. The discarded trees will be replaced with the same data but different features. We will not discard any tree unless we have enough confidence (which is \(1-tThresh\)) that the accuracy has decreased based on a two-sample \(t\)-test. The detailed pseudocode and accompanying explanation of the forgetful random forests are in Appendix C.
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 3 Proposed Method
### Graph Generation Module  
The raw data need to be processed to build a graph data sample before the data can be used for graph neural network learning. Graph generation consists of two key components: adjacency matrix \(A\) and node feature matrix \(X\). In order to obtain the node feature matrix  
Figure 3: Flowchart of the proposed method.  
Figure 2: MPGCTN structure diagram.  
\(X\), the features are first captured from the input data using a 1-D CNN. The mapping for extracting features can be represented as follows:  
\[f=F\big{(}x_{\text{input}}\big{)} \tag{10}\]  
where \(f\) represents the extracted features, \(F\) represents the 1-D CNN, and \(x_{\text{input}}\) represents the input data.  
[PERSON] et al. (2019) proposed a graph generation layer (GGL) to obtain the adjacency matrix \(A\). Features can obtain the adjacency matrix \(A\) through the GGL and use the input matrix to construct an example graph. The process is shown in Figure 4.  
The feature matrix is first input into the multilayer perceptron (MLP). Subsequently, the adjacency matrix is obtained through matrix multiplication between the MLP features and their transpositions. Finally, utilizing a top-k sorting mechanism, the first \(k\)-closest neighbors of each node are selected. Thus, the adjacency matrix can be derived using the following formula:  
\[\begin{cases}\tilde{X}=MLP(X)\\ A=\text{normalize}(\tilde{X}\tilde{X}^{T})\\ \overline{A}=\text{Top}_{k}(A)\end{cases} \tag{11}\]  
where \(A\) is the constructed adjacency matrix, and \(\tilde{X}\) is the output of the MLP. \(\overline{A}\) is a sparse adjacency matrix that returns the indices of the maximum values of \(k\) of \(A\) in rows, which decreases the computational load and produces a sparse adjacency matrix.
## 4 Open Issues
### Federated-Enabled Privacy Protection  
Security and privacy are critical to all computing paradigms. Although cryptographic techniques such as differential privacy and homomorphic encryption have been used to improve system security and privacy, they may reduce the output accuracy and increase computational overhead. Information sharing and collaboration among multiple providers make the (Com)\({}^{2}\)INet paradigm introduce security and privacy concerns. The core issue is how to achieve a balance between privacy protection and model performance in distributed data collaboration, and how to reduce overhead of computation and communication while ensuring privacy remains an open issue and deserves further study. Federated-enabled mechanism only shares model parameters instead of raw data [33], which is an important research topic for privacy protection and reducing data transmission costs.
## 4 Results and Discussion
The model's effectiveness was assessed while adversarial samples (generated via AFGSM) were being imposed. Table 4 presents the Fooling Rate (%) for both the GAN and LSTM models under adversarial attacks. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Consensus Methodology Approaches**} \\ \hline  & **Deterministic** & **Leader-Based** & **Leader-Free** \\ \hline Error Rate \% & 2.03 & 1.86 & **0.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Blockchain Reliability relying on different Consensus Methodology Approaches. Figure 10: Agents (UAV’s) trajectory prediction confirmation probability. In accordance with Table 4, there is a direct correlation between the increase in the number of iterations and the rise in the fooling rate. Both the LSTM and GAN models are sensitive to adversarial samples generated through AFGSM. After five iterations, the LSTM slightly outperforms the GAN in terms of fooling rate (59.9% vs. 61.3%). In both cases, the trajectory prediction models could be deemed unsuccessful against adversarial attacks. Consequently, defense strategies were devised, including Adversarial Retraining (AR) for white-box attacks and Generative Adversarial Networks (GANs) secured by Blockchain Ledger Technology (BLT) for black-box attacks. Table 5 illustrates the effectiveness of the defense mechanisms in relation to their fooling rates when confronted with black-box attacks. Table 5 outlines the outcomes confirming that the AR defense implemented on the LSTM (AR-LSTM) exhibited the lowest resilience (9.8% fooling rate after five iterations) when confronted with black-box adversarial samples. In contrast, the GAN defense method recorded nearly double the efficacy (4.8% fooling rate after five iterations) in comparison to Adversarial Retraining. The GAN defense methodology enhanced by Blockchain Ledger Technology (GAN-BLT) demonstrated the highest performance, causing a 2.8% fooling rate after five iterations. The incorporation of BLT notably amplified the efficiency of the GAN defense methodology. Nevertheless, BLT could yield even greater effectiveness if the occurrence of transaction execution failures during data registration in new blocks was reduced.