esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Main Features of the Considered Design of the SHG with Ferrite Magnets  
As Figure 1 shows, the SHG has two laminated steel stator-rotor stack combinations (SRSCs). In the axial direction, the excitation magnetic flux is transmitted through the non-laminated stator housing, in which the stator laminated stacks are installed, and through the rotor sleeve, on which the laminated rotor stacks are installed. The three-phase armature winding is placed in 54 stator slots and has 12 poles with the number of slots per pole and per phase \(q=54/(12.3)=1.5\).  
The excitation winding is placed in an axial gap between the two generator SRSCs. The laminated rotor stacks are of salient pole design. The rotor has no windings and has low-energy ferrite magnets in the slots of the rotor stacks. Each rotor lamination has six teeth. The tooth shift of the two rotor laminations relative to each other is 30 mechanical degrees.  
Figure 2 shows the scheme for rectifying the output current of the generator to the network of the car. The generator feeds the network of the car, and also recharges the battery. The rectifier uses controlled transistor switches that provide vector control of the generator armature winding current. The concentric coil of the excitation winding is located between the pairs of rotor and stator stacks and is fixed on the supporting non-magnetic core. The DC-link voltage (close to the maximum amplitude of the line-to-line voltage of the generator) must not exceed 116 V. The semiconductor converter also includes a single-phase breaker for powering the excitation winding of the generator.  
At the preliminary calculations, it was established that without parallel paths, each layer of the armature winding should contain one turn. However, this led to large eddy current losses in the armature winding, so the number of parallel paths was chosen to be six, that is, equal to the number of pole pairs.  
Figure 1: SHG approximate geometry: (**a**) Cross-section and stator armature winding configuration (1/4 of the generator cross-section is shown, the other parts are symmetrical); (**b**) General view. One half of a stator cutout is shown. Rotor is shown without cutout. The armature winding placed in the stator slots is not shown.  
Figure 2: Scheme of rectifying the output current of the generator to the car network. Capital letters denote the phases A, B, C of the generator.
# A Polymer Inclusion Membrane for Sensing Metal Complexation in Natural Waters
## 1 Introduction
prepared a PIM that consisted of CTA, D2 EHPA, and the plasticizer di-octyl phthalate (DOP) to selectively separate Cu(II) from the digested e-waste in the presence of heavy metal ions [16]. PIMs have recently shown their effectiveness as sensors for the determination of metal bioavailability in different media. [PERSON] et al. developed a new tool for the determination of Zn free metal ion concentration using a PIM made of 70% PVC and 30% D2 EHPA [17] in a hydroponic media in which potato plants (_Solanum tuberculum_) were also grown for purposes of comparison. It was found that the presence of different ligands--such as ethylenediaminetetraacetic acid (EDTA), humic acid (HA), and citrate--greatly influenced the measured Zn flux through the membrane due to the formation of metal complexes in the feed phase. [PERSON] et al. developed a passive sampler for Cu, Ni, Co, and Cd in surface waters using a membrane made of 60% PVC and 40% D2 EHPA using a 1.0 M HNO\({}_{3}\) solution as a receiving phase [18]. Similarly, a PIM made of 50% CTA, 20% NPOE, and 30% of the ionic liquid Cyphos 104 was investigated as a simple tool to monitor Zn in polluted river water [19]. The comparison of the metal measured by the PIM sensor and the metal accumulated by biofilm grown in the same water showed the suitability of the membrane to perform metal bioavailability studies. In the present study, we investigate the use of a PIM containing the extractant D2 EHPA as a tool for a rough estimation of the degree of divalent metal complexation in natural waters. The sensor developed is similar to that reported in [17], where the following assumptions were considered: fast kinetics for the interfacial reactions and fast diffusion of the metal in the sample solution under the selected stirring conditions. The accumulation of both Zn and Cu ions has been investigated using a nitric acid solution as a receiving phase, and the effect of ligands or possible interfering ions have been evaluated, among other parameters. Finally, the novel sensor has been used for the evaluation of Zn complexation in river water affected by abandoned mine drainage.
# A Survey on Cybersecurity in IoT
## 1 Introduction
The present survey distinguishes itself by addressing the cybersecurity challenges in IoT through a more comprehensive and integrative approach compared to the surveys summarized in Table 1. Unlike prior works that focus narrowly on specific aspects such as blockchain, ML applications, or industry-specific IoT environments, this survey provides a broader scope by synthesizing diverse cybersecurity dimensions, including device authentication, data integrity, privacy, network security, and the role of AI in enhancing security. Furthermore, it delves into emerging technologies and methodologies, such as federated learning, post-quantum cryptography, and decentralized identity management systems, which are only briefly touched upon or overlooked in existing studies. By offering an extensive analysis of both technical and operational challenges, this work serves as an essential resource for developing scalable, adaptive, and future-proof IoT security solutions. In conclusion, this submission contributes to the field of IoT cybersecurity in several significant ways:  
* It provides a comprehensive overview of the unique cybersecurity challenges specific to IoT environments. * It examines advanced strategies for IoT device authentication, data integrity, and privacy protection. * It analyzes the security implications of various IoT communication protocols and network architectures. * It explores the application of AI and ML in enhancing IoT cybersecurity measures. * It identifies key areas for future research to address ongoing and emerging IoT security challenges. The remaining paper is illustrated in Figure 1 and structured as follows. Section 2 notes the importance of device authentication and identity management. Next, Section 3 analyzes the challenges of data integrity and privacy in the IoT. Section 4 refers to network security and communication protocols. Moreover, Section 5 outlines the integration of AI
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 7 Analysis Model  
Writing formal specifications in either formal language may not ensure the model's completeness. Even so, the established specification's errors can be identified and removed by analyzing the model rigorously using programming tool support. Using the VDM-SL toolbox and its various facilities, the specifications of the model are validated and verified. Validation ensures that the established system meets the consumer requirements, while verification guarantees that the constructed system achieves the requirements defined previously. To demonstrate correctness, the toolbox includes many techniques, such as syntax checking, type checking, integrity examiner, generating C++ code, and pretty printer. The model analysis of forest fire detection and counteraction system is shown in Figure 4, and Figure 5 shows the model analysis of the failure detection and recovery.  
Figure 4: Proof of formal analysis for forest fire detection and counteraction system.  
Figure 5: Proof of formal analysis for failure detection and recovery model.  
Formal specifications of both modules are syntactically analyzed through a syntax checker and semantically analyzed through a type checker. The pretty printer examines the specification for contradictions. Proving the correctness, the pretty printer generates the entire specifications that are included in this paper.  
The integrity checker is often used to review the formal specification for internal contradictions, such as violations in invariants and pre/post conditions. In terms of VDM-SL predicates, the integrity properties are generated through integrity examiner that must be true; otherwise, there are potential specification issues.  
All of the integrity properties for the established formal specification were found to be correct. The results of the analysis are presented in Table 2, and Table 3, where in the first column the static and dynamic model's variables are given, and in other columns the analysis is shown. The correctness of the specifications is shown by the (\(\checkmark\)) symbol.
# Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation
## 2 Materials and Methods
### Source Localization  
We applied low-resolution precision electromagnetic brain tomography (eLORETA) to solve the inverse problem and localize the sources of neuronal activity according to EEG data at each of the predetermined points (voxels) in the brain volume [22; 23; 24].  
LORETA is low-resolution brain electromagnetic tomography. This method solves the inverse problem: converting EEG measurements into information about the distribution of neural sources power into a brain volume. This method belongs to the class of nonparametric methods [25], which are based on the assumption that a separate current dipole (a source) is assigned to each of tens of thousands of elements of the tessellation of the cerebral cortex, while the orientation of the dipole is determined by the local normal to the surface. In this case, the inverse problem is linear, since the only unknowns are the amplitudes of the dipoles. Exact low-resolution brain electromagnetic tomography (eLORETA) is 3D, regularized, and minimum norm-weighted inverse solution with theoretically accurate zero error localization, even in the presence of structured biological or measurement noise [22; 25]. The \"Colin27\" brain MRI averaged template [26] was used to develop a three-layer (brain, skull, and scalp) head model based on a boundary element method (BEM) [27; 28]. The sources space inside the brain consisted of 11,865 voxels. The location of the EEG electrodes corresponded to the template head shape.  
We analyzed the source characteristics in the predefined time-frequency domain of interest, selected on the basis of sensor-level analysis. To do this, we reassigned the EEG signals to the total average, subtracted the mean, and filtered with a fourth-order Butterworth \([f_{L},f_{H}]\)-Hz band-pass filter, where \(f_{L}\) and \(f_{H}\) define the frequency domain of interest. Then, we performed time-lock averaging across the TOI1 trials and computed the covariance matrix. The inverse solution yielded estimates of the source power in each voxel, averaged over the selected TOI window. Finally, we normalized the obtained estimates of the power \(P\) of each source to the power of 40-s EEG resting state EEG as \((P-P_{rest})/P_{rest}\). We used the automated anatomical labeling (AAL) brain atlas [29] to map the location of sources to the anatomical brain regions.
## 4 The DCPVP Protocol
### Phase C: Cluster Building Phase  
As discussed before, the CH introduces itself by sending a packet to its neighbors. All nodes in its radio range hear the packets and are aware of the voting result, so when they receive the CH-Packet they respond by sending a packet to the CH (Join-Packet). The CH accepts their join request based on their weight from less to greater. In other words, the No accepted nodes in cluster are the No lower nodes in the weight-sorted table. The reason for priority voting is that often, after primary clustering, there are some nodes that are not in any cluster, because of the limited size of clusters, so in a second step, the nodes which have the highest vote and were not elected in the previous step introduce themselves as CH and the previous process will repeat. Finally, if there is a node which is neither organized nor received any packets, it introduces itself as a CH (outlying nodes). The benefit of this iterative method is that we don't have overlapping clusters. For example when node Y becomes a CH in a specific region it certainly has received the highest number of votes, which means that all neighbor nodes know Y as the most weighted node. The elected CH forms a cluster and accepts nearby nodes into its cluster. Also in some regions where the density of nodes is high and the cluster size is limited, the cluster is filled and some nodes will not be organized. In this case according to weight-based acceptance, the remaining nodes are the high weighted nodes of the previous step. These nodes elect the most weighted node among themselves as CH. Finally, after organizing all the nodes, the elected CHs form a timing table for their cluster members and the network goes into steady-state phase [34].
# Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection
## 3 Tracking Algorithm Design
### Re-Detection Module
For the candidate sample set \(x\) of the current frame, its coding coefficients \(\langle\hat{Z},\hat{E}\rangle\) can be obtained by Equation (7), where \(\hat{Z}=[\hat{Z}_{+},\hat{Z}_{-}]\) and \(\hat{E}=[\hat{E}_{+},\hat{E}_{-}]\). The template set \(A\) consists of the target templates \(A_{+}\) and background templates \(A_{-}\), that is, \(A=[A_{+},A_{-}]\). So, for the i-th candidate sample \(x_{i}\), the reconstruction error between it and target templates can be expressed as \(\varepsilon_{p}=\left\|x_{i}-A_{+}\hat{Z}_{+}^{i}-\hat{E}_{+}^{i}\right\|_{2}^ {2}\), and the reconstruction error between it and the background template is expressed as \(\varepsilon_{n}=\left\|x_{i}-A_{-}\hat{Z}_{-}^{i}-\hat{E}_{-}^{i}\right\|_{2}^ {2}\). Then, the likelihood score of the candidate sample \(x_{i}\) can be measured as follows:  
\[score=exp\bigg{\{}-\frac{\varepsilon_{p}}{\sigma(\varepsilon_{n}+\gamma)} \bigg{\}}. \tag{9}\]  
Here, \(\sigma\) and \(\gamma\) are set to 0.2 and \(10^{-32}\) respectively. Obviously, the higher the score, the more reliable the candidate. Then, we obtain the position of the candidate which hasthe highest likelihood score and adopt the correlation filter obtained in Section 3.1 to get a new response map. After that, we re-estimate the reliability of the response map by adopting the reliability estimation method described in Section 3.3. If the result is reliable, we replace it with the original tracking result. If not, we take the original tracking result.
# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
## 3 Pointer Meter Recognition Method
### Comparison with Existing Technology  
Previous studies have used a number of different methods and techniques to address the recognition and reading extraction problems of square pointer meters. These methods are shown in Table 1.  
In past research, scholars mainly focused on exploring circular pointer meters and water meters, but research on the square pointer meters mentioned in this paper is relatively limited. There are obvious differences in shape and structure between the square pointer meter and the traditional circular pointer meter, so it is of great significance to study it deeply.  
When locating instruments, commonly used technologies include Faster-RCNN, Yolov3, ORB algorithm, and Mask RCNN. This paper uses the newly proposed Yolov7 model, which significantly improves the accuracy of target detection.  
In terms of identifying pointers and readings, previous studies such as [PERSON] et al. [2], [PERSON] et al. [8], and [PERSON] et al. [11] directly used Hough transform to extract pointers, but this method is easily affected by complex backgrounds. However, this paper adopts the method of segmenting the pointer area by using image segmentation technology first, and then performing Hough transform, which greatly improves the robustness of instrument recognition and reduces the interference to complex backgrounds. Since the water meter has only 10 different states, [PERSON] et al. [3] use a classification method to distinguish different readings. However, this method is not suitable for complex ammeters or voltmeters. Conversely, [PERSON] et al. [13] only used the angle method to determine the reading of the pointer instrument, but in fact the pointer has a width, which may lead to errors in the reading. For this reason, this paper adopts the method of thinning first and then angle method, which can effectively extract the central axis of the pointer and improve the accuracy of reading. [PERSON] et al. [18] used the key point detection method to determine the pointed center point and pointer endpoint of the meter, but due to the long-term use of the meter, the position of the key point may be blocked by stains, which seriously affects the accurate recognition rate of the meter center and pointer endpoint.  
In addition, this paper also proposes the use of OCR technology to identify the instrument model and scale value, and take the maximum value of the scale value as the range of the instrument, which greatly improves the identification robustness in instruments of different models and ranges.  
This paper proposes a series of new methods for square pointer instruments, including using the Yolov7 model for target detection, combining image segmentation and Hough transform, extracting pointer readings by thinning first and then angle method, and using OCR technology to identify instruments' model and range. The application of these methods greatly enhances the accuracy and robustness of instrument recognition, making up for the shortcomings of previous studies.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 3 Case Study
### Effect of Axial Force on Wall Deflection and the Spatial Correlation Between Monitoring Points  
The linear correlation between axial forces and wall deflection is then explored, as shown in Figure 9, utilizing the Pearson correlation coefficient for analysis. Based on  
Figure 6: Project location.  
the categorization by [PERSON] [51], correlations are classified as very weak (0 to 0.2), weak (0.2 to 0.4), moderate (0.4 to 0.6), strong (0.6 to 0.8), and very strong (0.8 to 1). The Pearson correlation coefficient is used to determine the edges in the graph. A relationship is established between two nodes if their correlation is above a strong threshold. The very strong correlation observed between the measured deflections at the four monitoring points, particularly between wall deflection 6 and wall deflection 4, reaches a correlation coefficient of 0.99. This indicates a highly consistent behavior in the structural response at these locations and suggests a closely related spatial relationship among these monitoring points. However, the majority of the correlations between axial forces are very weak, which can be attributed to the fact that the servo axial forces are actively controlled to adjust wall deflections. These forces vary independently from each other as they are adjusted to stabilize or modify structural responses at different points, leading to low correlation values when compared inter-axially. In addition, the correlation between the servo axial forces and the four deflection monitoring points shows a trend where \"axial force 6\" > \"axial force 4\" > \"axial force 3\" > \"axial force 2\". This phenomenon might seem puzzling, as the #2 steel support, being the first installed, continues to influence wall deflection yet exhibits the weakest correlation.  
Based on these concerns, the non-linear correlation between servo axial forces and wall deflection is analyzed using the Spearman correlation coefficient, as shown in Figure 10. Notably, axial forces of 0 and the corresponding wall deflections are excluded. The results show that most axial forces and wall deflections exhibit weak to moderate correlations. This analysis demonstrates that servo axial forces have the potential to control wall deflections and also highlights the need for coordination among multiple supporting axial forces.  
Figure 8: Axial forces and wall deflections during the excavation process.  
Figure 7: Construction layout of the servo support system.
## 3 Proposed Optimal Orchestration Process
### Framework Overview and Technical Contributions
#### 3.1.1 Enhanced Sub-Slice (eSS) Architecture  
The eSS architecture comprises three interconnected modules that work in concert to enable efficient resource allocation and network optimisation. At its core, the Traffic Prediction Module implements Online AutoRL, incorporating both historical data analysis and real-time pattern recognition. This dual approach enables the system to maintain high prediction accuracy while adapting to emerging traffic patterns.  
The module's effectiveness is quantified through several key performance metrics, including root mean square error (RMSE) for prediction accuracy and model convergence time. Our experimental results demonstrate that the Traffic Prediction Module achieves an average RMSE of 3.8% for short-term predictions (within 5 min) and 6.5% for longer-term predictions (up to 1 h), with model convergence typically achieved within 850 ms. These results represent a significant improvement over traditional prediction methods, which typically show RMSE values of 8.2% and 12.4% for short-term and long-term predictions, respectively.  
The Slice Orchestrator functions as the central intelligence of the eSS architecture, employing LazyPredict to dynamically select and optimise machine learning algorithms. Through extensive testing, we identified that Random Forest algorithms consistently achieved the highest performance for our use case, with an average accuracy of 94.2% in resource allocation decisions. This performance is particularly notable compared to traditional static allocation methods, which typically achieve an accuracy rate of 78.5%. For short-term predictions (within 5 min), the system achieved a root mean square error (RMSE) of 3.8%, while longer-term predictions (up to 1 h) maintained an RMSE of 6.5%.  
The Resource Allocator module completes the eSS architecture by implementing cloud-native scaling principles. Our implementation demonstrates particular efficiency in resource utilisation, maintaining an average utilisation rate of 85%, while ensuring sufficient headroom for demand spikes. The module's response time to resource allocation requests averages 50 ms, with 99% of requests processed within 100 ms, meeting the stringent requirements of ultra-reliable low-latency communication (URLLC) applications.  
The three main components are illustrated in Figure 2.  
Figure 2: System architecture diagram showing the components and interactions.