esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Framework  
The assessment process is divided into four main parts:  
1. Daily precipitation data processing: Data gathered from meteorological stations over multiple years are employed, and various distribution functions are applied for fitting, where the optimal distribution function is chosen to calculate the return period values for each station in the study area. Interpolation is then performed to generate distribution maps of the EAP under different return periods, which are subsequently used for prediction by the following models.
2. Additional data processing: First, it is necessary to collect data related to the topography, geology, hydrology, and other relevant factors of the study area, and a landslide inventory is compiled for the region. In this study, 14 conditioning factors from the research area were collected and subjected to multicollinearity analysis to select the appropriate factors.
3. Model training: A set of negative samples equal in number to the 2064 landslide points was created, excluding points located within water systems. These negative samples, together with the positive samples, form the training and validation datasets for the model. The random forest (RF), XGBoost, multilayer perceptron (MLP), k-nearest neighbor (KNN), support vector machine (SVM), and gradient boosting machine (GBM) models are trained. A total of 70% of the landslide points are used for training, while the remaining 30% are used for validation.
4. Landslide analysis and mapping: In this phase, the importance of each conditioning factor is calculated. In this study, various methods, including SHAP values and the mean decrease in Gini, are employed to compare the different models and assess the importance of the multifactorial variables.  
This framework (Figure 2) describes the steps that were carried out to assess landslide susceptibility in the study area.  
Figure 2: Framework of this study.
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results
### Storage Design
#### 3.1.3 Cavity Size  
In salt domes, due to their great thickness, unlike in stratified salt deposits, there are no space restrictions. The dimensions of the cavity are usually established by maintaining a height/diameter ratio equal to 5 [12; 23], i.e., in this case, for a diameter of 48 m, a height of 240 m.  
The volume of the capsular shape is determined with Equation (1).  
\[\mathrm{V}=\pi\times\mathrm{R}^{2}\times(\mathrm{H}-\mathrm{D})+\frac{4}{3} \times\pi\times\mathrm{R}^{3} \tag{1}\]  
The volume obtained for the cavity, 515,355 m\({}^{3}\), is within the usual values considered by other authors of 500,000 m\({}^{3}\)-750,000 m\({}^{3}\)[11; 12].
# Practical Inner Product Encryption with Constant Private Key +
## 3 Proposed Inner Product Encryption Scheme  
Our IPE scheme consists of four algorithms: **Setup**, **KeyGen**, **Encrypt** and **Decrypt**. The details of the proposed scheme are explained in the following.  
* \(\mathbf{Setup}(1^{\lambda},1^{\ell})\). Given the security parameters \((1^{\lambda},1^{\ell})\), where \(\lambda,\ell\in\mathbb{N}\), the algorithm performs as follows. 1. Choose bilinear groups \(\mathbb{G},\mathbb{G}_{T}\) of prime order \(p>2^{\lambda}\). Let \(P\) and \(g_{T}=e(P,P)\) be the generator of \(\mathbb{G}\) and \(\mathbb{G}_{T}\), respectively. 2. Set the predicate vector space and the attribute vector space to \(\mathbb{Z}_{p}^{\ell}\). 3. Choose \(\mathbf{s}=(s_{1},s_{2},\ldots,s_{\ell})\xleftarrow{\mathbb{S}}\mathbb{Z}_{p}^ {\ell}\). 4. Compute \(\widehat{\mathbf{h}}=(g_{T}^{s_{\ell}})_{i=1}^{\ell}=(\widehat{h}_{1},\ldots, \widehat{h}_{\ell})\). 5. Output the system parameter \(\mathtt{params}=(P,g_{T},\widehat{\mathbf{h}})\), and the master secret key \(\mathtt{msk}=\mathbf{s}\).
* \(\mathbf{Encrypt}(\mathtt{params},\mathbf{x},M)\). Given the system parameter \(\mathtt{params}\), a vector \(\mathbf{x}=(x_{1},x_{2},\ldots,x_{\ell})\in\mathbb{Z}_{p}^{\ell}\), and a message \(M\in\mathbb{G}_{T}\), the algorithm performs as follows. 1. Choose \(r,\delta\xleftarrow{\mathbb{S}}\mathbb{Z}_{p}\). 2. Compute \(\mathbb{C}_{0}=rP\), and \(\widehat{\mathbb{C}}_{0}=g_{T}^{r}\). 3. Compute \(\mathbb{C}_{i}=\widehat{h}_{i}^{r}\cdot g_{T}^{\delta x_{i}}\cdot M\) for \(i=1\) to \(\ell\). 4. Output the ciphertext \(\mathbb{C}_{\mathbf{x}}=(\mathbb{C}_{0},\widehat{\mathbb{C}}_{0},\mathbb{C}_{1 },\mathbb{C}_{2},\ldots,\mathbb{C}_{\ell})\).
* \(\mathbf{KeyGen}(\mathtt{params},\mathtt{msk},\mathbf{y})\). Given the system parameter \(\mathtt{params}\), a master secret key \(\mathtt{msk}\), and a vector \(\mathbf{y}=(y_{1},y_{2},\ldots,y_{\ell})\in\mathbb{Z}_{p}^{\ell}\), where \(\sum_{i=1}^{\ell}y_{i}\
eq 0\), the algorithm performs as follows. 1. Choose \(k\xleftarrow{\mathbb{S}}\mathbb{Z}_{p}\). 2. Compute \(\mathbb{K}_{0}=kP\), and \(\mathbb{K}_{1}=\langle\mathbf{s},\mathbf{y}\rangle+k\mod p\). 3. Output the private key \(\mathbb{K}_{\mathbf{y}}=(\mathbb{K}_{0},\mathbb{K}_{1})\).
* \(\mathbf{Decrypt}(\mathtt{params},\mathtt{c}_{\mathbf{x}},\mathbb{K}_{\mathbf{y}})\). Given the system parameter \(\mathtt{params}\), a ciphertext \(\mathbb{C}_{\mathbf{x}}\), and the private key \(\mathbb{K}_{\mathbf{y}}\), where \(\mathbf{y}=(y_{1},y_{2},\ldots,y_{\ell})\) the algorithm performs as follows. 1. Compute \(\mathbb{D}_{0}=e(\mathbb{K}_{0},\mathbb{C}_{0})\). 2. Compute \(\mathbb{D}_{1}=\prod_{i=1}^{
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 3 Material and Methods
### Optical Satellite Data  
The study area is located at the South West of France, near the city of Toulouse (1\({}^{\circ}\)10\({}^{\prime}\) E, 43\({}^{\circ}\)27\({}^{\prime}\) N). It is 24 km \(\times\) 24 km area where about 60% of the soil correspond to arable surfaces. The area has a temperate continental climate with hot and dry summer--average temperature of about 22.4 \({}^{\circ}\)C and rainfall of about 38 mm per month. Figure 5 displays a satellite image of the area in false color from 14 July 2006.  
The satellite dataset is composed of 46 Formosat-2 images acquired at 8 meter spatial resolution during the year 2006. Figure 6 shows the distribution of the acquisitions. Note that Formosat-2's characteristics are similar to the new Sentinel-2 satellites that provide 10 m spatial resolution images every five days.  
For each Formosat-2 image, only the three bands Near-Infrared (760-900 nm) (NIR), Red (630-690 nm) (R) and Green (520-600 nm) (G) are used. The blue channel has been discarded since it is sensitive to atmospheric artifacts.  
Each image has been ortho-rectified to ensure the same pixel locations throughout the time series. In addition, the digital numbers from the row images have been converted to top-of-canopy reflectance by the French Space Agency [(60)]. This last step corrects images from atmospheric effects, and also outputs cloud, shadow and saturation masks. The remaining steps of the data preparation--temporal resampling, feature extraction and feature normalization--are presented in Section 3.3.  
Figure 5: Formosat-2 image in false color (near infra-red, red, green) from 14 July 2006. Green and blue squares will be inspected visually in the experiments.  
Figure 6: Acquisition dates of the Formosat-2 image time series.
# Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training
## 3 Local Back-Propagation Algorithm  
One of the key distinctions between the FF algorithm and our proposed LBP method lies in the design of the loss computation. As shown in Figure 2, FF requires the generation of positive and negative samples for each input, and computes a goodness score for each layer, where the goodness increases for positive samples and decreases for negative samples. This score, typically based on the squared norm of the activation vector, is then used to maximize the difference between positive and negative samples. Consequently, FF mandates custom data construction and specialized loss functions that deviate from standard deep learning practices. In contrast, LBP adopts a more generalizable and modular approach. Each cell is trained independently using standard unsupervised learning objectives, such as reconstruction loss in AE or adversarial loss in GAN. Since the loss is computed locally within each layer, our method eliminates the need for label-conditioned sample engineering and allows compatibility with common input formats and standard loss functions like mean squared error or binary cross-entropy. This makes LBP more accessible and suitable for integration into conventional deep learning pipelines. We propose the LBP learning approach, which leverages unsupervised deep learning models. By incorporating this strategy, we remove the input and loss function constraints previously present in FF, thus enabling the use of standard input formats and loss calculations commonly employed in general deep learning methods. Through this algorithm, our aim is to preserve the learning directionality characteristic of FF while ensuring compatibility with existing deep learning frameworks. We anticipate that this proposed method will prove highly effective in circumstances where the physical separation of layers poses challenges for applying BP. Algorithm 1 outlines the detailed training procedure for the proposed Local Back-Propagation method. The training process consists of two main phases. In the first phase (lines 1-13), each cell is trained independently in an unsupervised manner, sequentially from the first cell to the last. Each cell receives the latent representation output from the preceding cell as input, thus maintaining a forward sequential dependency. Within each cell, an unsupervised model, such as an Auto-Encoder, reconstructs the input data. The reconstruction loss is calculated independently per cell, and the cell's parameters are updated accordingly. After training a cell, the learned latent representation is passed forward to serve as the input for the next cell, while the trained cell itself can be unloaded from GPU memory to optimize memory usage. Figure 2: Loss computation comparison between Forward-Forward and our proposed LBP framework. Unlike the FF algorithm, which requires label-specific sample construction and goodness-based loss, LBP enables per-layer unsupervised loss using standard inputs and reconstruction objectives.
## 5 Automatic Monitoring and Control Method for Middle Rock Pillars
### Association between Devices  
The displacement monitoring device consists of three parts: microwave radar, spherical camera, and automatic laser rangefinder. These three parts need to be connected to form a monitoring system.  
First, the radar set up a defense area and warning line, and the influence range of the microwave radar was classified. The main function area of the radar was within the range of the defense area and warning line, and it was also the key area of monitoring. The microwave radar was then associated with the spherical camera to visualize the radar's defense area in the camera. To facilitate remote monitoring using the spherical camera, the camera was connected to the remote computer through a wireless network connection. After sensing the displacement change in the measuring point in the defense area, the radar displayed the view in the spherical camera. To quantify the change in the displacement value, the radar was associated with the laser rangefinder, and the displacement change perceived by the radar was transmitted to the laser rangefinder to utilize the data. The correlation between the above devices enabled the realization of a real-time visual comprehensive monitoring system. The section layout of the test equipment is shown in Figure 9.  
Figure 8: Automatic monitoring overall scheme layout.
## 3 The Multiple Workflows Allocation Model
### Cloud Resource Model  
In a considered IaaS cloud environment, K computational VMs are created on the available physical host offering services to the cloud users and represented as a set, VM = {VM1, VM2, VM3,...VMk}. As part of this setup, the right instance types, such as compute-optimized VMs, are chosen based on the tasks' demand. Enough CPU cores and memory must be ensured for each VM to maintain stable performance. Storage configurations should also match the size and frequency of data access. Another critical factor is the network bandwidth, especially for workflows where tasks depend on each other and need to communicate with each other often. Auto-scaling capabilities allow the system to adjust VM instances based on workload fluctuations dynamically, ensuring efficient resource use. By carefully setting up virtual machines, cloud resources can be used to get the best workflow performance. The virtual machines in the model have been considered with the characteristics as follows:  
* K number of heterogeneous VMs.
* Virtual machines are created for compute-intensive workflows.
* The processing capacity (PCk) of VMk is in MIPS.
* Initial ready time (RT\({}^{1}\)k), which measures the previous workload of VMk.
* Machine distances (MDk) measure of the distance (number of hop counts) between machines VMk and VMr.
* E (N \(\times\) n\({}_{i}\)\(\times\) K) is the expected time to compute matrix, where n\({}_{i}\) is the number of tasks in wf\({}_{i}\).
## 5 Conclusions  
In this article, a new DWOML-RWD method was introduced for cyberattack detection in the IoT environment. The presented DWOML-RWD model was mainly developed for the recognition and classification of goodware/ransomware. In the presented DWOML-RWD technique, the EKHO algorithm was used to perform the feature selection process. For ransomware detection, DWO with an ELM classifier was used. The design of the DWO algorithm aids in the optimal parameter selection of the ELM model. The experimental validation of the DWOML-RWD method can be examined on a benchmark dataset. The experimental outcomes highlight the superiority of the DWOML-RWD model over other approaches. Thus, the DWOML-RWD approach can be utilized for ransomware detection in a real-time IoT-cloud environment. In the future, the DWOML-RWD model can be extended to the integration of hybrid deep learning models.  
Conceptualization, K.A.A. and D.H.E.; methodology, K.T.; software, A.Y.; validation, R.A., O.A. and A.M.; formal analysis, A.Y.; investigation, K.T.; resources, M.A.D.; data curation, O.A.; writing--original draft preparation, K.A.A., D.H.E. and K.T.; writing--review and editing, A.M., A.Y., O.A.; visualization, R.A.; supervision, K.A.A.; project administration, M.A.D.; funding acquisition, D.H.E. All authors have read and agreed to the published version of the manuscript.  
Princess [PERSON] Abdulrahman University Researchers Supporting Project number (PNURSP2022R238), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work (Grant Code: 22 UQU4331004 DSR03).  
Not applicable.  
Data Availability Statement: Data sharing does not apply to this article as no datasets were generated during the current study.  
Figure 11: FNR and FPR analysis of DWOML-RWD approach with existing algorithms.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 3 Materials and Methods
### Method: Comparative Analysis  
Different feature detector and descriptor combinations were compared in order to choose the best combination for image stitching applications. Inlier ratio, stitched image quality and execution time are the three parameters used for comparison.
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
## 3 Modeling for V2A Charging Mode  
The aim of this paper is to develop a generic model that can support the various charging activities of V2A applications. Therefore, all the charging statuses, including charging, V2H/V2B/V2V, and V2G should be determined at each timeslot. Meanwhile, for the development of the proposed model, the following assumptions are used: (1) The EV vehicles studied are pure EVs; (2) reactive power exchange is not considered at the current stage; (3) the price for EV charging and other loads is different from the V2G power price for incentive purposes [17], i.e., the power load price \(\theta_{L,t}\) and the V2G power price \(\theta_{V2G,t}\) are two different price signals; (4) the charging/discharging power is assumed to be the rated power of the EV battery.  
In addition, EV charging decisions must be given on the basis of predefined objectives. In this work, the power consumers are supposed to be price followers, i.e., EVs or the load consumption unit would make decisions about EV charging in response to price signals released by power system operators.
## 4 Results and Findings
### Yearly Publications by Top 10 Journals  
Though the \"yearly publications by top-ten Journals\" data offer essential insight into the structure of cybersecurity, ML, and DL, we focused on the data related to 2016 to 2025. The data include publications from ten prominent journals: _Journal of Applied Sciences, Computers and Electrical Engineering, Computers and Security, Computer, Materials and Continua, Electronics, Future Internet, IEEE Access, IEEE Internet of Things Journal, International Journal of Advanced Computer Science and Applications_, and _Sensors_ as presented in Figure 9.  
Figure 8: Authors co-authorship network.  
Based on the information from the analysed journals, this general trend presupposes an increase in publications starting from 2018. This means there was a moderate increase in interest and demand for conducting cybersecurity and ML/DL research. The upward trend demonstrates the significance of these fields in meeting today's technological phenomena, with more writers contributing their articles to quality journals.  
Some journals, such as _IEEE Access_ and _Sensors_, saw activity increase sharply in 2019-2020. This could be attributed to heightened awareness of the uses of ML/DL in cybersecurity, especially since industries began ramping up their digitisation and the call for effective shielding of IT systems became keener during this period.  
_IEEE Access_ appears to have had a significantly high number of published articles recently, which supports the argument that this journal is at the forefront of transmitting new knowledge. In the same way, yet still slightly below the top 15, _Sensors_ also produces a considerable amount of content, which can be attributed to the rising popularity of IoT and intelligent security networks involving sensors.  
On the other hand, the _Future Internet_ and _Computers and Security_ journal may have a lower growth rate. This indicates that more focused areas of cybersecurity and ML/DL activities may receive fewer submissions than IoTs and applied sciences.  
Most of the journals in the sample exhibit an overall increase in publications. Still, it is also evident that some journals act as if they are reaching their troughs or, at best, slow plateaus following their years of highest productivity. For instance, _Computers and Electrical Engineering and Electronics_ from Switzerland show sectors where publications stop growing or shrink over time. This could mean an oversaturation in the explored research issues or that researchers have switched preferences to other journals that publish more modern or highly significant studies.
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 4 Overview and Fundamental Results
### Hexagon Tiling Coloring  
This section introduces a hexagon tiling coloring scheme, which will be utilized later in Section 5 to locally partition the wireless devices in a given Smart Grid wireless mesh network into a series of testing groups.  
The definition of the problem to design a hexagon tiling coloring algorithm is as follows:  
The distance between two hexagons is defined as the Euclidean distance between any two closest points \(p_{1}\) and \(p_{2}\) in \(h_{1}\) and \(h_{2}\) respectively.  
It can be observed in Figure 2 that in a hexagonal tiling, the centers in all the hexagons are located on a triangular lattice. As a result, a new coordinate system with \(60^{\circ}\) inclined axes in the 2D plane is considered along with two unit vectors \(\overset{\rightarrow}{i}\ (\frac{\sqrt{3}}{2},0)\) and \(\overset{\rightarrow}{j}\ (\frac{\sqrt{3}}{4},\frac{3}{4})\) as described in Figure 2. In the 2D plane, each center in every hexagon \(h\) coincides with the integral coordinates. In addition, each center of hexagon \(h\) is represented as \(h(i,j)\) with the coordinates \((i,j)\). \(d_{c}(h_{1},h_{2})=\frac{\sqrt{3}}{2}\sqrt{(i_{1}-i_{2})^{2}+(i_{1}-i_{2})(j_ {1}-j_{2})+(j_{1}-j_{2})^{2}}\) denotes the Euclidean distance between two centers of different hexagons \(h(i_{1},j_{1})\) and \(h(i_{2},j_{2})\).
## 5 Conclusions  
In this work, we designed an improved magnetic coupling bistable piezoelectric energy harvester. The electromechanical coupling dynamics model was established, the nonlinear equation system was solved by the Newton-Raphson iteration method, the distribution of potential wells was improved by adjusting the magnet spacing, and the influences of excitation frequency and excitation amplitude on the vibration characteristics of BPEH and IBPEH were compared and analyzed by numerical simulation. Finally, an experimental platform was designed and built to verify the simulation results. The following main conclusions are drawn from the simulation and experiment:  
1. IBPEH has a wider operating frequency bandwidth. In particular, when the excitation amplitude is 14 m/s\({}^{2}\), the operating frequency bandwidth of BPEH is only 3.2 Hz, while that of IBPEH is 6.1 Hz, which is a 93.55% improvement.
2. For BPEH, adding magnets and properly adjusting the magnet spacing can make the potential well of the system shallow, and thus optimize the potential well. Compared with BPEH, IBPEH relieves the low excitation threshold to achieve large amplitude cross-hole periodic motion and can achieve dense high-energy output at low excitation amplitude.
3. IBPEH can remove the restriction of BPEH potential well depth and improve the power output. In the whole range of excitation frequency and amplitude, the voltage generated by IBPEH is always higher than that of BPEH under the same excitation conditions. This further proves that the change in nonlinearity can affect the collection efficiency of piezoelectric energy harvesters.  
Conceptualization, X.Z. and H.T.; Methodology, H.T.; Software, H.T. and J.P.; Validation, X.C. and H.X.; Writing--original draft preparation, H.T.; Writing--review and editing, F.Z.; Visualization, Y.G.; Supervision, H.T. and M.H.; Project administration, X.Z.; Funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant no. 51974228), the Shaanxi Coal Joint Fund Project (grant no. 2021 JLM-03), The National Green Manufacturing System Integration Project (grant no. 2017-327), the Shaanxi Innovative Talent Plan Project (grant no. 2018 TD-032), and the Key R&D project in Shaanxi (grant no. 2018 ZDCXL-GY-06-04).  
Not applicable.  
The authors acknowledge the support from the students [PERSON], [PERSON], [PERSON], and [PERSON] for their assistance in the modeling and experiments.  
The authors declare no conflict of interest.