esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Motivation  
Among the key targets of Smart Grid's operation is to bring forth new opportunities for the end consumers [1, 2]. In traditional power systems, the consumers have zero or limited information about the actions that take place in electricity markets [3]. In order to upgrade the role of the consumer in the new landscape of power systems, it is essential to measure the load consumption and implement tools for information retrieval [4, 5]. Smart metering infrastructure provides discrete time interval metering and, generally, more detailed data in terms of time resolution is available [6, 7]. The processing of the collected load data can lead to the determination of consumers' load profiles [8]. The term \"load profiling\" refers to the formulation of representative load curves over a given time period of a single consumer or groups of consumers [9, 10, 11]. The representative load curves or load profiles are actually the averaged load curves that have been grouped together in the same cluster. It should be noted that criteria such as voltage level, demographic parameters, type of economic activity, location and others are not sufficient enough to support a solid consumer classification [12]. This fact is recognized by current research leading to the examination of alternative methods to form consumer classes and derive the load profiles of each class [13].  
Clustering is an unsupervised machine learning tool with proven performance in a wide variety of problems [14, 15, 16]. In recent years, many researchers have proposed clustering algorithms in the field of load profiling. A clustering algorithm is a tool for data processing and information retrieval. The data processing may refer to non-typical data detection and discarding. The load data are grouped together based on their similarity. The load profiles of the load data clusters are actually a descriptive model of the recorded load data. The amount of data can be represented by a reduced set of typical load curves or load profiles. Therefore, clustering-based load profiling can serve as the basic tool for processing of smart meter data. This fact is recognized in power systems community leading to an intense research effort to test algorithms for load data clustering [17]. However, while the load profiling literature is rich with implementations of clustering algorithms, there is no study that provides a general framework that reaches safe conclusions for algorithm selection. The aim of the present paper is not only to provide a detailed comparative analysis of the most commonly used algorithms in the literature and based on this analysis and to identify and discuss the benefits and drawbacks of each algorithm category but to rank the algorithms of the literature from the most to less efficient.
# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
## 3 Numerical Analysis
### Analysis of Noise Suppression by ETC-IC  
ETC-IC improves the quality of imaging results by modifying the integration limits of the traditional cross-correlation imaging conditions. This method reduces the influence of parts of the wavefield that do not contribute to the imaging process. Specifically, this involved truncating the source and receiver wavefields based on the first-arrival travel times at each point in the model. Figure 12 shows the results of applying the first-arrival travel time limits to the receiver wavefield in the CRRTM at different depths.  
Apart from the correct reflection axis, most of the other parts of the wavefield were excluded by the excitation time, thereby not affecting the final imaging results.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 6 H\({}_{2}\) Generation in DORs
### Reservoir Engineering Aspects  
#### 6.2.1 Mode of Application  
A typical ISC is considered. The process has some advantages over SF including higher thermal efficiency, relatively small heat loss to the overburden, and no heat losses in the wellbore, and it can be applied in deeper and high-pressure reservoirs. Decisive factors for the success of the process include the highest possible homogeneous permeability. As previously stated, heterogeneous permeability distribution would lead to unfavorable, uncontrolled fire progression; therefore, homogeneous formations are advantageous. To enable the injection of a catalyst, an alternating injection of water/steam and gas (i.e., air) must occur. The water stands as the carrier of the catalyst, which is added to the injected water in powder form. The process is thus similar to \"wet combustion\" since water and gas are injected simultaneously. The surface installation at the production side requires separators for separating both the oil, water, and gaseous phase as well as the storage tanks and gas and water treatment facilities. The gas produced has to be further processed in order to capture H\({}_{2}\) from other gases like CO\({}_{2}\), CO, H\({}_{2}\)S, etc. Separated H\({}_{2}\) and CO\({}_{2}\) should be conditioned for transport to the final destination. Figure 6 shows a simplified schematic of the surface installations in the case of ISC application to generate H\({}_{2}\) in addition to oil. Due to related experience, ISC application for both EOR and H\({}_{2}\) generation is technically feasible, with a TRL of 5-6 considering that a similar (prototype) system has not yet been demonstrated in the relevant environment. The main challenge is to develop an efficient process for both incremental oil and H\({}_{2}\) generation.
## 5 The Relationship between Energy-Efficiency and Interference-Optimality  
As shown in [24; 28; 29], the energy required to transmit a data packet from node \(u\) to node \(v\) relates to the distance between these two nodes, which can be expressed as:  
\[P_{uv1}\propto r^{\gamma}, \tag{38}\]  
where \(r\) is the Euclidean distance between node \(u\) and node \(v\), \(\gamma\) is the distance-power gradient which depends on the characteristics of the communication medium (\(2\leq\gamma\leq 4\), \(\gamma\geq 2\) for outdoor propagation modes [29]); \(P_{uv1}\) is the power needed for the communication link between node \(u\) and node \(v\).  
As shown in Figure 15, if the transmission ranges of node \(u\) and node \(v\) are reduced, then node \(u\) and node \(v\) cannot communicate with each other directly. As a result, node \(n\) will be chosen as the relay node, where node \(n\) is the neighbor node of both node \(u\) and node \(v\). Thus the power needed to transmit packets from node \(u\) to node \(v\) relates to:  
\[P_{uz2}\propto r_{1}{}^{\gamma}+r_{2}{}^{\gamma}, \tag{39}\]  
where \(r_{1}\) is the Euclidean distance between node \(u\) and node \(n\), \(r_{2}\) is the Euclidean distance between node \(n\) and node \(v\); \(P_{uz2}\) is the power needed for the communication link between node \(u\) and node \(v\) by using the relay node \(n\).  
For exploring the probability of reducing the energy consumption by adjusting the transmission range, we define the Energy-efficient Dominating Sets (EDS) as follows:  
\[\left\{\begin{array}{l}P_{uv1}\geq P_{uv2}\\ r_{1}+r_{2}\geq r\\ 0<r_{1}\leq r\\ 0<r_{2}\leq r\\ \end{array}\right., \tag{40}\]  
The first constraint in Inequation (40) guarantees the energy consumption is smaller to previous values; the second constraint makes sure that the network is still connected after the transmission range adjustment; the third and the fourth constraints guarantee that the transmission ranges of each node are smaller to previous values.
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
## 3 Materials and Methods
### Training Supervised Machine Learning Models  
After dividing the data into training and test sets, the next step involves training various machine learning models on the training set and evaluating their performance on the test set. This process helps in selecting the most efficient model for our specific task. We initially used cross-validation to assess the performance of each supervised learning model. A 10-fold cross-validation with the KFold method (KFold(n_splits = 10)) was applied to estimate the mean accuracy of the model over different subsets of the training data. To prevent overfitting, we implemented early stopping during training and pruned the feature set based on importance scores, reducing it from 9 to 7 by excluding less impactful features like Agent ID while retaining critical ones like temporal patterns and source IP reputation. These measures, combined with SMOTE for class imbalance, ensure the model's generalizability and reliability in dynamic Security Operations Center (SOC) environments.
## 3 Results and Discussion
### Principal Component Analysis of Food Samples After Irradiation  
Figures 6-10 show discrimination plots of data sets obtained by processing chromatograms of irradiated and non-irradiated samples of turkey, beef, chicken, fish, and potato, respectively. A visual analysis of the sample projections on the principal component planes shows a noticeable clustering of the irradiated and control samples, demonstrating the informative nature of the accumulated data set and the ability to differentiate irradiated samples from non-irradiated ones. The first two principal components in the case of each product contain sufficient information, so the plots are plotted in the coordinates of the first two principal components. Figure 6 shows a plot of the scores of turkey samples irradiated with different doses compared to non-irradiated samples. The first two principal components (PC1 and PC2) explain 43.1% and 19.7% (a total of 62.8%) of the information.  
Figure 6: PCA plot for the discrimination of control samples and samples exposed to ionizing radiation for turkey.  
Figure 8: PCA plot for the discrimination of control samples and samples exposed to ionizing radiation for chicken.  
Figure 7: PCA plot for the discrimination of control samples and samples exposed to ionizing radiation for beef.  
Figure 9: PCA plot for the discrimination of control samples and samples exposed to ionizing radiation for fish.  
The main contribution to the separation of irradiated and non-irradiated beef samples is made by aldehydes. First, the number of aldehydes increases from the non-irradiated to irradiated beef samples, and second, the concentration of most of them also increases. Although this relationship is not linear, the total concentration of all identifiable aldehydes is almost 40% lower in the non-irradiated sample than in the sample exposed to a minimum dose of 0.25 kGy. In salmon samples, aldehydes, which are the lipid oxidation products, also predominate among all identified volatile compounds, and the overall concentration of aldehydes increases with an increase in the irradiation dose. While most VOCs demonstrate erratic behavior in response to different irradiation doses, especially in the case of poultry meat and potatoes, aldehydes, which are lipid and protein oxidation products, are more predictable and, therefore, particularly interesting for further research involving machine learning algorithms.  
Since most of the aldehydes identified in the food products showed an increase in concentration with an increase in irradiation dose, and these aldehydes are oxidation products of fats and proteins, their content can serve as a potential marker for selecting doses that ensure no physical and chemical changes occur in the product during irradiation. The use of PCA for processing aldehyde content data in food products of different chemical compositions irradiated with different doses will help determine specific doses for each product type that avoid intensive lipid and protein oxidation while enabling the identification of irradiated food products.
## 4 Proposed System
### Trajectory Update and Sharing Revocation  
After vehicles initially upload their trajectory data to SM, they can update their trajectory data whenever they want to change preference driving routes. Remind, in trajectory sharing phase in Section 4.3, \(V_{d}\)'s trajectory \(T_{d}\) is uploaded to the cloud in the encrypted form of \(C\) in \(trj_{d}\), and then \(C\) is transformed to a re-encrypted ciphertext \(C_{j}\) by the SM to be shared with an \(sRSU_{j}\in SR_{d}\). Suppose that \(V_{d}\)'s trajectory \(T_{d}\) is changed to different driving locations \(T^{\prime}_{d}=\{loc^{\prime}_{1}\rightarrow...\to loc^{\prime}_{l}\}\) but \(V_{d}\) still wants to share the changed trajectory with the current sRSUs in \(SR_{d}\). In this case, the operation required to \(V_{d}\) is just to generate a ciphertext \(C^{\prime}\) for the changed trajectory \(T^{\prime}_{d}\) in accordance with the procedures of Section 4.3, and update \(TM_{d}\) to include \(C^{\prime}\) on the cloud storage. Then, SM can transform \(C^{\prime}\) to \(C^{\prime}_{j}\) for each \(sRSU_{j}\) using the existing re-encryption keys \(RK_{d}\) in \(RSM_{d}\).  
Sometimes, however, a vehicle will not wish to share the updated trajectory data with some sRSUs any more if the vehicle changes its interested socialspots. That is, a vehicle needs to revoke unwilling trajectory sharing. Suppose that \(V_{d}\) does not want to share updated trajectory data with an \(sRSU_{j}\). In the proposed system, \(V_{d}\) can revoke trajectory sharing with \(sRSU_{j}\) by updating \(V_{d}\)'s private and public key pair and re-encryption keys for sRSUs except the \(sRSU_{j}\) as follows:  
1. Renew its private key as \(sk_{V_{d}}=(vsk_{d0},a^{\prime}_{d},b^{\prime}_{d})\gets SetProKey(vsk_{d0})\) and public key as \(pk_{V_{d}}=(g^{a^{\prime}_{d}}_{3},g^{b^{\prime}_{d}})\gets SetPubKey(sk_{V_{ d}})\) by choosing new random values \(a^{\prime}_{d},b^{\prime}_{d}\in Z^{*}_{d}\).
2. Change the socialspot RSUs list as \(SR_{d}\) to \(SR^{\prime}_{d}\) by adding new sRSUs and deleting revoked sRSUs.
3. Set \(RK^{\prime}_{d}=\{rk_{V_{d}\to sRSU_{i}}\mid sRSU_{i}\in SR^{\prime}_{d}\}\) by running \(rk_{V_{d}\to sRSU_{i}}\gets clReKeyGen(sk_{V_{d}},pid_{d0},sRSU_{i},\)\(pk_{V_{d}},pk_{sRSU_{i}})\) for each \(sRSU_{i}\).
4. Replace the existing \(RSM_{d}\) uploaded in enrolment phase of Section 4.2 with the updated \(RSM^{\prime}_{d}\) including \(RK^{\prime}_{d}\) and \(SR^{\prime}_{d}\).
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### K-Nearest Neighbors  
It is a simple supervised algorithm in which, based on the neighboring data points, crop classification can be performed. Here, the soil features, weather conditions, and production rate shall be the feature points for KNN to classify crops and identify nearest neighbors based on the suitable K value chosen. While training the dataset, a new data point is chosen, and the Euclidean distance is calculated from it to the nearest data points.  
The Euclidean distance formula \(D\) is shown in Equation (5), where the coordinate values of the 1 st point are \(x_{1},y_{1}\) and \(x_{2},y_{2}\) for the 2 nd point.  
\[Distance\ D=\sqrt{\left[\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2 }\right]} \tag{5}\]  
So, by finding the distance matrix and applying voting majority, it is decided that the new data point belongs to the class. The number of neighboring points is determined by the K value. The main benefit of KNN is that new data points can be added, as it will not learn from the training dataset. Here, choosing a K value is a crucial task.
## 3 Discussion
### Synergies and Adaptation to Enterprises with Limited Resources  
To validate the presence of synergies between concepts, the integration among concepts could be tested for each of the proposed trajectories in a business environment. This approach would allow for defining how different modularity concepts can integrate coherently.  
For enterprises with limited resources, adopting these strategies requires a gradual approach due to limited resources. Developing an implementation sequence is crucial to address this context. In cases of high product variety, it becomes essential to determine which solutions to prioritize, given the multitude of tools that can be implemented. Prioritization through an analysis of expected benefits could guide enterprises with limited resources in developing a strategy adapted to their contexts and constraints.  
The question of adaptability feasibility in enterprises with limited resources remains unanswered in the literature.
## 5 New Technologies in Greenhouses
### Computational Modeling and Simulation
#### 5.1.2 Challenges and Considerations in Greenhouses  
Despite the many benefits provided by computational modeling and simulation in greenhouses, there are also several challenges and important considerations to be taken into account. These challenges can impact the implementation and effectiveness of these advanced systems, requiring careful attention and proper planning. The main challenges are as follows:  
* Complexity and Initial Cost: Implementing computational modeling systems can be complex and expensive, requiring advanced hardware and software, as well as qualified professionals to configure and operate the models [(263)].
* Maintenance and Update: Computer models and simulation systems require regular maintenance and technology updates to ensure ongoing accuracy and efficiency. This includes sensor calibration and algorithm updating [(254)].
* Qualification and Training: Operators and managers need to be trained to understand and effectively use modeling and simulation technologies. Lack of training can lead to underutilization of these advanced tools [(264)].
* Data Integration: Collecting and integrating accurate, real-time data from sensors and other devices is crucial to the effectiveness of models. Any discrepancies in the data can affect the accuracy of simulations and subsequent decisions [(265)].  
These benefits and challenges highlight the importance of computational modeling and simulation in optimizing greenhouse operations, providing a solid foundation for developing more efficient and sustainable agricultural practices.
## 4 Results
### Mesh Sensitivity Analysis
#### 4.1.3 Air Concentration and Cell Size  
The air concentration near the impact point was analyzed as part of the mesh dependency analysis. Figure 7 illustrates the air concentration results obtained from the two models compared to the number of cells in the jet cross-section, along with experimental data collected by [PERSON] [12].  
In Figure 7a, which shows the air concentration at r = 0.05 m and z = 0.20 m, it is clear that mesh refinement does not significantly affect the twoPhaseEulerFoam solver. Its results are close to the measurements taken by [PERSON] [12]. Conversely, the interFoam solver exhibits a substantial mesh dependency due to the limitations of the VoF model. For meshes with more than 24 cells in the jet diameter, the air concentration values from this solver drop to zero. Figure 7b shows a similar trend for both solvers at r = 0.10 m and z = 0.20 m.  
Overall, when comparing the numerical results with the experimental data, it becomes evident that the twoPhaseEulerFoam solver provides closer values to the experimental measurements.
## 2 Materials and Methods
### Motion Configurations Tested and Design of the Simulations
#### 2.3.2 First Set, S1: Range and Speed of Motion Patterns  
In the S1 set, different ranges of motion (ROMs) were defined for each configuration, as follows:  
1. A: ROM1 = (+10, \(-15\)) deg, ROM2 = (+20, \(-30\)) deg, ROM3 = (+30, \(-50\)) deg;
2. B: ROM1 = 0 mm, ROM2 = 2 mm, ROM3 = 4 mm;
3. C: ROM1 = 0 mm, ROM2 = 2 mm, ROM3 = 4 mm;
4. D: ROM1 = (+10, \(-15\)) deg, ROM2 = (+20, \(-30\)) deg, ROM3 = (+30, \(-50\)) deg.  
In particular, the range of motion for configuration A only refers to the flexion/extension angle, being the only DoF available. For configurations B and C (2 DoFs), the default range (+20, \(-30\)) deg was selected for the flexion/extension, whereas different ROMs were chosen for the translational DoF (horizontal displacement for B, lateral displacement for C configuration). The range of motion of flexion/extension for configuration D was varied similarly to configuration A, whereas the ROMs of the three translational DoFs were set as constants and equal to 2 mm. For all the configurations, positive flexion/extension angles refer to dorsiflexion, negative angles to plantarflexion.  
The speed of each motion pattern was defined indirectly by tuning of the gait cycle duration \(t_{\text{cyc}}\). Three values of cycle duration were considered, equal to 1.6 s, 1.2 s, and 0.8 s, that correspond to increasing speed values labeled as V1, V2, and V3.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
###### Abstract  
The city of Hamburg has decided to electrify its bus fleets. The two public transportation companies in this city expect to operate up to 1500 buses by 2030. In order to accomplish this ambitious goal, both companies need to build an appropriate charging infrastructure. They have both decided to implement the centralized depot charging concept. Buses can therefore charge only at the depot and do not have the possibility for opportunity charging at intermediate stations. The load profile of such a bus depot is highly dependent on the charging schedule of buses. Without an intelligent scheduling system, the buses charge on demand as soon as they arrive to the depot. This can lead to an unevenly distributed load profile with high load peaks, which is problematic for the local grid as well as for the equipment dimensioning at the depot. Charging scheduling on large-scale bus depots is a relatively new and poorly researched topic. This paper addresses the issue and proposes two algorithms for charging scheduling on large-scale bus depots with the goal to minimize the peak load. The schedules created with the proposed algorithms were both tested and validated in the Bus Depot Simulator, a cosimulation platform used for bus depot simulations.  
electric bus depots; charging scheduling; load peak minimization +
Footnote †: journal: Applied Sciences
## 5 Road Friction Estimation
### Pre-Processing of Input Data  
To achieve the proposed estimation strategy, the input vector of ANN is specifically chosen and described by \(\mathbf{x}=\left[V_{0}\ d\overline{V}_{x}\ \overline{\sigma}_{i}\ \ \hat{m}_{v}\right]^{T}\in\Re^{4\times 1}\), which are, \(V_{0}\) is the initial velocity at the braking moment, \(d\overline{V}_{x}\) is the average velocity derivative (deceleration) of vehicle, \(\overline{\sigma}_{i}\) (for \(i\) = FL, FR, RL, RR) is the average slip ratio of each wheel, and \(\hat{m}_{v}\) is the estimated mass. These average values, \(d\overline{V}_{x}\) and \(\overline{\sigma}_{i}\), are calculated every twenty intervals. In other words, the average data are obtained every 0.02 s if the sampling time of the time-domain simulation process is set by 0.001 s. These \"twenty\" intervals are selected from the try-and-error method to achieve the best and reasonably quick estimation performance for other associated control systems.  
The average velocity derivative \(d\overline{V}_{x}\), one of the input data, is computed by,  
\[d\overline{V}_{x}=\left\{\begin{array}{cc}\frac{1}{N}\sum_{t=1}^{N}(V_{x,t}- V_{x,t-1})\ /\ \Delta t,\ 1\ \text{km}/\text{h}<V_{x}<0.97\ V_{0}\\ \frac{d}{dV_{x}}\,\ V_{x}<1\ \text{km}/\text{h}\end{array}\right. \tag{15}\]  
where \(N\) = 20 intervals and \(V_{0}\) is the vehicle velocity at the moment of braking. In addition, \(\Delta t\) indicates the sampling time.  
The average slip ratio of each wheel \(\overline{\sigma}_{i}\) (for \(i\) = FL, FR, RL, RR) are also calculated by,  
\[\overline{\sigma}_{i}=\left\{\begin{array}{cc}\frac{1}{N}\sum_{t=1}^{N}( \sigma_{it}-\sigma_{it-1}),\ 1\ \text{km}/\text{h}<V_{x}<0.97\ V_{0}\\ \overline{\sigma}_{i}\,\ V_{x}<1\ \text{km}/\text{h}\end{array}\right. \tag{16}\]  
Figure 4: Proposed ANN structure (four inputs, three hidden layers, and one output).  
Again, it should be remarked that the average values, (15) and (16), are updated every twenty intervals, which implies that the road friction coefficient is updated every twenty intervals over the entire process.