esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application in Determination of Rock-Mass Properties
### Strength of Rock Masses  
Construction materials used in mining and civil construction are categorized according to their strength properties. This basic quality information of the material is used in engineering and design for various construction purposes. Although rock is considered a construction material, in rock engineering, no such precise strength characterization of the rock mass is applied. Although the various utilizations of rocks and rock masses have different purposes and are subjected to various problems, the suitability and quality of the rock mass depends largely on its strength properties. Reliable determination of rock-mass strength is crucial for underground excavation [11; 57; 99; 100]. The development [12; 101] and update of the _GSI_ (geological strength index) system [102] and its incorporation in the Hoek-Brown empirical failure criterion [103] is mainly for the reliable calculation of \(\sigma_{cm}\). Like the empirical failure criteria, the use of empirical classification systems for the calculation of \(\sigma_{cm}\) is a common practice in rock engineering through correlation. The proposed correlations between the \(\sigma_{cm}\) and the \(RMR\) or \(Q\) values can also be divided into two groups:  
* The \(\sigma_{cm}\) calculated independently of the \(\sigma_{c}\), Table 6.
* The \(\sigma_{cm}\) calculated dependently of \(\sigma_{c}\), Table 7.
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 3 Early Endosome-to-Golgi Transport of STx2
### Similarities and Differences in the Transport of STx2B and STxB/STx1B
Secondly, both the above membrane fusion events require the HOPS (homotypic fusion and vacuole protein sorting) tethering complex [62; 64; 65], but ATG7 is required for the formation of autophagosomes [67], and the SNARE syntaxin 17 is required for the fusion of autophagosomes, but not late endosomes, with lysosomes [62]. Studies revealed that inhibition of autophagosome formation, by depletion of ATG7, or autophagosome-lysosome fusion, by depletion of syntaxin 17, has no effect on the early endosome-to-Golgi trafficking of STx2B [20]. However, depletion of the HOPS complex protein Vps39, which inhibits fusion of lysosomes with late endosomes, as well as autophagosomes, robustly blocks the transport of STx2B from early endosomes to the Golgi, and reroutes STx2B to late endosomes for degradation (Figure 4A,B and Table 2) [20]. Put together, the above results imply that the early endosome-to-Golgi transport of STx2B depends on the fusion of late endosomes with lysosomes, but not on the autophagy pathway (Figure 4A,B). The mechanisms via which late endosome-lysosome fusion modulate early endosome-to-Golgi trafficking of STx2B are still being determined. Available preliminary evidence suggests that recruitment of retromer to early endosomal membranes is altered when late endosome-lysosome fusion is inhibited, providing a possible reason for the observed block in the early endosome-to-Golgi trafficking of STx2B [20]. Figure 3: Crystal structures of STxB/STx1B and STx2B (Protein Data Bank (PDB): 1R4Q and 1R4P, respectively) with their \(\beta\)4–\(\beta\)5 loops.
# A Secure and Efficient Group Key Management Scheme for Clusters of String Inverters
## 3 System Model
Whenever an inverter joins, the current group key must be distributed to joining inverters. Whenever an inverter leaves, or is forced to leave the group, a new group-key has to be generated and distributed to group members but leaving ones in order to guarantee the forward security requirement. KMS is the component responsible to perform such tasks. KDS also features a group key refresh to reduce the amount of encrypted material for an effective analytical attack. Both during a leave and a key refresh operation, the KDC generates the new group key in a cryptographically secure random way. In this paper we focused on KMS and did not treat IDS and GMS any further. The interested reader may find initial readings about IDS and GMS in [76; 77; 78; 79]. In Table 1 we introduced key notations. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(\mathrm{Cert_{CA}}\) & X.509 v3 Certificate of Root Certification Authority \\ \(\mathrm{Cert_{i}}\) & X.509 v3 Certificate of Inverter \(i\) \\ CRL & Certificate Revocation List \\ G & Multicast Group \\ GC & Group Controller \\ GMS & Group Membership Service \\ \(\mathrm{H_{K_{G}}}(\cdot)\) & Message Authentication Code \\ HSM & Hardware Security Module \\ IDS & Intrusion Detection Service \\ IV & Initialization Vector \\ JS & Joining Set \\ KDC & Key Distribution Centre \\ KDS & Key Distribution Supervisor \\ \(\mathrm{K_{G}}\) & Symmetric Group Key \\ KMS & Key Management Service \\ LS & Leaving Set \\ N & Multicast Group Size \\ n & Size of the Cluster of String Inverters \\ n\({}_{j}\) & Cardinality of the Set of Joining Inverters \\ n\({}_{r}\) & Cardinality of the Set of Remaining Inverters \\ (\(\mathrm{privK_{i},\,pubK_{i}}\)) & Private-Public Key Pair of Inverter \(i\) \\ UID\({}_{i}\) & Unique Identifier of Inverter \(i\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key notations.
# Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence
## 2 Methods  
An overall research flowchart of this study is presented in Figure 1. First, a two-stage hybrid spatio-temporal clustering method based on co-clustering was proposed to divide the dataset into internally homogeneous spatio-temporal clusters. Then, within each cluster, a heterogeneous ensemble learning approach was developed to combine four heterogeneous prediction models for land subsidence prediction. In practice, some land subsidence outliers are useful for the early monitoring of ground geological disasters. Indeed, errors and meaningful outliers cannot be well distinguished. Therefore, in this study, we did not remove outliers from the dataset.
## 4 Numerical Simulation  
### Mathematical Calculation with Asymmetric Channels  
Based on the asymmetric protocol description, [PERSON] generates their classical bits according to the phase differences between [PERSON] and [PERSON] in the same time slot. To obtain the secure key rate, we apply the same denotations in previous sections that [PERSON] and [PERSON] send pulses with intensities \(\mu_{a}\), \(\mu_{b}\), and the distance between [PERSON] ([PERSON]) and [PERSON] is \(l_{a}(l_{b})\). In our scheme, the channel transmittance between [PERSON] ([PERSON]) and [PERSON] is \(\eta_{a}=\eta_{d}\times 10^{-al_{a}/10}(\eta_{b}=\eta_{d}\times 10^{-al_{b}/10})\), where \(\eta_{d}\) is the detection efficiency of [PERSON]'s detectors and \(\alpha\) is the attenuation coefficient of the ultra-low fiber. In addition, let us suppose that \(p_{d}\) is the dark count rate of one detector. For two detectors used by [PERSON], we derive the total dark count rate as \(2p_{d}\) and the error rate of background \(e_{0}=\frac{1}{2}\).  
In [PERSON]'s laboratory, after the BS (see Figure 1), the optical intensities received by detector 1 and detector 2 are given by \(D_{1}=(\frac{\sqrt{\mu_{a}\eta_{a}}}{2}+\frac{\sqrt{\mu_{b}\eta_{b}}}{2}\cos \theta)^{2}\) and \(D_{2}=(\frac{\sqrt{\mu_{a}\eta_{a}}}{2}-\frac{\sqrt{\mu_{b}\eta_{b}}}{2}\cos \theta)^{2}\), where \(\theta\) denotes the relative phase between [PERSON]'s and [PERSON]'s weak coherent states. In our asymmetric protocol, we have \(\theta\in\{0,\pi\}\). Thus, the detection probability of each detector is: \(Q_{1}=1-(1-p_{d})e^{-D_{1}}\), \(Q_{2}=1-(1-p_{d})e^{-D_{2}}\).  
The gain of the whole system for [PERSON]'s detections can be calculated by \(Q_{\mu}=Q_{1}(1-Q_{2})+Q_{2}(1-Q_{1})+Q_{1}Q_{2}\) and the error rate of the total gain can be derived by \(E_{\mu}Q_{\mu}=e_{d}Q_{1}(1-Q_{2})+(1-e_{d})Q_{2}(1-Q_{1})+\frac{1}{2}Q_{1}Q_ {2}\), where \(e_{d}\) is the misalignment error rate of detectors.
## 3 Proposed System
### Detail Implementation Flow
#### 3.3.1 Phase One  
* Data Loading and Preprocessing  
The MovieLens 1M dataset is utilized for performance evaluation. It contains roughly 1 million ratings given by users to movies, with rating scores ranging from 1 to 5. A crucial aspect of this dataset is that it includes user demographics and movie details, which correlate to the rating scale each user applies. Additionally, these ratings encompass approximately 3900 movies and were submitted by 6040 MovieLens users who joined the system in the year 2000. First, User data consist of variables such as userID, movieID, gender, age, occupation, and the user's zip code. Second, Movie data include movieID, title, and genres. The last one is the Rating file, which is composed of the interaction rating of the userID and the movieID with the specific timestamp variable.  
* Embedding Generation  
Obtaining embedding provides the ability to learn more detail as the continuous vector representation that captures latent features of users and movies. They represent users and movies in a shared, lower-dimensional latent space, facilitating more efficient and effective recommendations. The generated users and movie embeddings are stored in dictionaries for quick retrieval, especially during the process of recommendation. In our case, singular value decomposition (SVD) is performed on the user-movie matrix to generate the embedding of the movies and users. It refers to the matrix factorization technique commonly used in collaborative filtering. Moreover, it plays an important role in state representation, which will be discussed in the next section.  
* Train-Test Split and Data Preparation  
We have divided the available user-movie interactions into training and testing sets. This division is important for performance evaluation, especially the recommendation on unseen data (related to tests on user cold starts). Three-quarters (75%) of the dataset is used for training the recommendation model, and the remaining 25% is for evaluating its performance on unseen data. Ensuring that the model is tested on a separate set helps to assess its generalized ability and provides an unbiased estimate of its performance. The dataset is prepared to include the information such as userIDs, movieIDs, ratings, and  
Figure 2: The implementation flow of our proposed system.  
timestamps. Users with a positive rating count greater than 10 are selected for the training set. Both the training and testing datasets are filtered to ensure that they contain only unique IDs, with no duplication.
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 3 Experimental Results and Discussion
### Discussion  
To analyze deeply the performance of the comparison and proposed algorithms, the change situation is divided into four types, i.e., complete changing of forest-bareland, incomplete changing of forest-bareland, other changes, and unchanged. Here, complete changing means that dense forests change to bareland without green vegetation, and incomplete changing means that sparse forests change to bareland without green vegetation or that dense forests change to bareland with secondary vegetation. Typical examples of the detection results of these four types under each algorithm are shown in Figure 13a-d. For the complete changing type and unchanged type, most algorithms can achieve relatively perfect detection, except for the spectral-based and GLCM algorithms, as shown in Figure 13a,d. The main reason is the lack of sufficient description of the features of the superpixels. As the spatial resolution increases, the number of pixels in the superpixels increases, and the missing phenomenon improves, as shown in the third and fourth columns of Figure 13a. For the incomplete changing type, the distinguishability of the superpixel changes is reduced, which increases the difficulty of the change detection. The LBP family all have missing detection in the Sentinel-2 data, and the LETRIST and LDZP also exhibit this phenomenon in the Skysat data, as shown in the fifth and sixth columns of Figure 13b. The main reason is that the LBP family's ability to describe global texture features is not good enough, and the feature dimension is too high. For other changes, the LBP family has severe false detection compared with the statistics-based approach, i.e., GLCM-based, as shown in Figure 13c. In addition, algorithms with feature filtering are more effective and stable than those without feature filtering, as shown in the eighth and ninth columns of Figure 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment Name** & \begin{tabular}{c} **Missing Detection** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **False Detection** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{c} **Overall Accuracy** \\ **Rate** \\ \end{tabular} & **Kappa Coefficient** \\ \hline Comparison 1– & 5.43\% & 9.17\% & 92.45\% & 0.85 \\ Spectral-based & 5.00\% & 6.52\% & 94.34\% & 0.88 \\ Comparison 3– & 19.57\% & 5.00\% & 88.68\% & 0.77 \\ Comparison 4– & 15.22 & 7.50\% & 89.15\% & 0.78 \\ Comparison 5– & 4.35\% & 6.67\% & 94.34\% & 0.89 \\ Comparison 6– & 5.43\% & 2.5\% & 96.23\% & 0.92 \\ GLCM-SBS & 4.35\% & 1.67\% & 97.17\% & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Change detection accuracy of Skysat in different experiments.
# Flexible, Brain-Inspired Communication in Massive Wireless Networks
## 2 System Model
### Digital Communication Power Consumption Model
Based on real values of power consumption of \(P_{\text{ADC}}\) and \(P_{\text{DAC}}\) presented in Figure 4, the power consumption model of the converters have been proposed using _Curve Fitting Toolbox_ being part of MATLAB software. The approximations of the power consumption of the ADC and DAC as a function of \(f_{\text{s}}\) (presented in Figure 4) are given by: \(P_{\text{ADC}}=7.719\cdot 10^{-6}f_{\text{s}}^{0.6036}\) and \(P_{\text{DAC}}=8.219\cdot 10^{-5}f_{\text{s}}^{0.447}\), respectively. The power consumption of the signal processing (e.g., encoding, decoding, the channel estimation process) is usually modeled as \(P_{\text{COMP}}=\xi R\), where \(R\) is the achieved link-throughput, while \(\xi\) is the computational efficiency in W/ (bit/s). This relatively simple model not only has been applied in many papers focusing on the energy efficiency optimization [21; 22; 23], but it has also been confirmed by measurements in [4]. In the case of \(P_{\text{PA}}\), we assume the same class and the same power consumption model as in Section 2.1. Moreover, for the purpose of diverse comparisons, let us also consider a simpler digital modulation scheme, such as uncoded Amplitude-Shift Keying (ASK) modulation, which is commonly used in key fobs or in devices for controlling other electronics. The block diagram of the transmitter and receiver using ASK modulation is presented in Figure 5. It can be observed that the input signal is sampled with \(f_{\text{s}}\), quantized using \(N_{\text{b}}\) bits, and the signal at the output of DCA is shaped in the raised-cosine pulse shaping filter with roll-off factor \(\beta\). Thus, the occupied bandwidth for the ASK modulation, \(B_{\text{sigD}}^{\text{ASK}}\), can be calculated by Equation (6) for \(M=2\) and \(R_{\text{cod}}=1\). Intuitively, digital communication with ASK modulation (the block diagram shown in Figure 5) can be more energy efficient than the QAM-based encoded system (the one from Figure 3) due to a smaller number of components. On the other hand, the transmission power required to achieve a given quality can be higher. Figure 3: The transmitter and receiver block diagram of digital transmission schemes with QAM modulation. The total consumption power of the digital communication with ASK modulation is given by:  
\[P_{\text{tot}}^{\text{ASK}}=P_{\text{CD}}^{\text{ASK}}+P_{\text{PA}}+P_{\text{ADC }}+P_{\text{DAC}}, \tag{9}\]  
where \(P_{\text{CD}}^{\text{ASK}}\) is the power consumed by the transmitter and receiver components such as mixers, filters, oscillator, etc., and \(P_{\text{CD}}^{\text{ASK}}\leq P_{\text{CD}}^{\text{QAM}}\).
## 4 Experimental Validations  
In order to verify the FEA results, a prototype of the proposed machine after optimization was manufactured, as shown in Figure 13. The holes were adopted on the rotor iron to reduce the weight and inertia. Figure 14 shows the testing platform, which contained the 200 kW dynamometer machine to provide the electromagnetic load, a YOKOGAWA power analyzer, an industrial condensing unit, DSP controller, etc. Water cooling was also employed.  
Figure 15 and Table 3 compares the FEA calculated and measured torque results which were obtained at the rated operation (235 A) and peak torque output operation (540 A), respectively. It should be emphasized that the calculated and measured waveforms exhibited different torque ripple frequencies due to the relatively high sample time of the torque transducer, and the measured torque ripples were smaller than the calculated results. Overall, satisfying agreement was achieved between FEA results and experimental measurements, with the discrepancies of near 4%, considering the manufacturing and measuring tolerances. Figure 16 shows the FEA calculated and measured efficiency map. The efficiency was measured to cover the whole torque-speed region when the DC bus voltage was 540 V. The iron loss and copper loss were counted in the FEA calculations, which were carried out when the temperature was 80 \({}^{\circ}\)C, while in the experimental measurements, the iron loss, copper loss, and mechanic loss were counted in, and the measured temperature in stator was near 80 \({}^{\circ}\)C when the cooling water was 20 \({}^{\circ}\)C. As can be seen, good agreement was achieved. It was found that nearly 80% of the total operation region had an efficiency above 90%, and 45% of the total operation region had an efficiency above 95%.  
Figure 14: Experimental platform and instruments.  
Figure 13: The stator and rotor of the IMPSM prototype.  
Figure 16: Comparison of the FEA calculated and measured efficiency map. (**a**) FEA calculated; (**b**) Measured results.  
Figure 15: Comparison of the FEA calculated and measured torque output. (**a**) Rated operation when 235 A is applied. (**b**) Peak torque output operation when 540 A is applied.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method  
### RS Data Preprocessing  
The ENVI software was used to perform geometric correction was for MODIS, Landsat 8, HY-1C, and HJ-1 images. GF-1 comes with a Rational Polynomial Coefficient (RPC) file, so we used ENVI for RPC orthographic correction.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 4 DR Model Considering Real-Time Tariff  
In China, TOD tariff is the main measure of Demand Side Management (DSM). It started in the 1980s, and has now spread nationwide. Generally, it is divided into the three periods peak, flat and low, which correspond to high, medium and low prices, respectively. Real-time tariff is one of the vital indices of price type DR. Compared with incentive type DR, price type DR adjusts the running time of time-shifting load, which is friendlier to consumers. This method restrains consumers' increasing demand and gives consumers economic benefits back at the same time. Thus, power supply corporations and consumers are willing to implement the policy.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 9 Conclusions and Future Directions
### Explainability, Safety, and Human Trust  
As cobots move into safety-critical domains such as manufacturing, healthcare, and public spaces, explainability, safety, and human trust become essential system requirements [87; 90; 126; 137]. Without user trust, collaborative robots risk underutilization or unsafe operation, undermining their value in human-centered workspaces. Explainable AI (XAI) methods like LIME and SHAP provide model-agnostic interpretability, helping engineers and operators understand predictions of complex perception models [126; 137]. In robotics contexts, saliency mapping and attention visualization have been applied to visuomotor policies to clarify which visual inputs drive cobot actions [126].  
For safety assurance, frameworks such as SafeROS and VerifAI enable the formal verification of perception and planning components against safety requirements [90; 91]. These tools help identify policy violations or unsafe behaviors before deployment.  
International standards reinforce these needs. ISO/TS 15066 specifies safety limits and risk assessments for human-robot collaboration, while the EU AI Act introduces risk-based requirements for transparency, human oversight, and robustness in AI systems [90]. The IEEE P7000 series provides structured guidance for embedding ethical considerations and explainability throughout system design [91].  
Integrating these tools and frameworks into cobot development pipelines is critical for ensuring systems are not only effective and adaptive, but also safe, interpretable, and trustworthy for human partners.  
Future systems must incorporate transparency, fail-safes, and ethically-guided autonomy to ensure safety, trustworthiness, and regulatory compliance. Explainability should be tightly coupled with real-time monitoring and adaptive control systems. Real-time monitoring, transparency, and fail-safe systems must be tightly integrated with decision-making pipelines.
# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 4 Task 2. Story Modeling
### Organizing and Summarizing Feature Data
#### 4.1.1 Classification  
Multiple features are extracted from images, and images are enriched with metadata extracted from different sources. These metadata belong to various categories and classes.  
For example, some are words of places and some are human names; some are separate objects and items in the image. These metadata must be classified into various categories to organize our final feature data. Several existing classification methods are suitable for different data types; in this article, some of the most useful ones are described [66].
* **Decision Tree.** Decision trees are one of the classification methods that classify the data based on several conditions, each including one or more features of the data. It is a tree model, in that each node contains a condition that divides the training data into two or more parts, and the number of leaf nodes will determine the number of classes.
* **Probabilistic Methods.** A model created using probabilistic classifiers estimates the probability of the relation between the feature and its class. Naive Bayes and Logistic Regression are two popular probabilistic classifiers.
* **Support Vector Machines.** Support Vector Machines classify the data into two classes and are suitable for binary classification tasks. In the feature space for the data, this method tries to fit a hyperplane that separates the two data classes, so that the hyperplane has the maximum margin with the data of both categories.
* **Neural Networks.** Neural networks are also one type of classifier. Different architectures of neural networks with different output and input layers could be used based on the type of data and the number of classes to nonlinearly classify the data into different categories.