esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, and differently from the approach proposed in [9], the packets' priorities are assigned based on the sensing relevancies of the active source nodes, instead of the payloads' relevancies for the reconstruction of the original visual data. In many cases, source nodes may have different relevancies for the monitoring functions of the applications, where the significance of each source node is a function of the expected targets to be monitored (what is being covered), instead of the deployed network characteristics [13]. In the proposed mechanism, packets originated from the most relevant source nodes are more likely to be preserved, resulting in lower impact to the overall monitoring quality but still saving energy. In fact, as monitoring quality depends on what is viewed by source nodes instead of how good is the received visual data, our proposed relaying solution may perform better than mechanisms based only on payloads' relevancies, as in [9]. Besides the exploitation of the novel concept of sensing relevance in wireless image sensor networks, we also consider 2D DWT coding over original raw images as a second level of prioritization. When incoming packets are originated from a source node whose sensing relevance indicates that those packets must be silently dropped for a determined residual energy level, packets carrying the highest relevant DWT subband will be preserved, assuring a minimal quality for the corresponding active source node. Although less energy savings are expected when compared with the relaying approach based only on sensing relevance, visual information from all source nodes will be always received, potentially assuring higher overall monitoring quality. We performed extensive simulations over both approaches in order to highlight the expected energy savings of the proposed energy-efficient relaying mechanism over a traditional relaying paradigm. The remainder of this paper is organized as follows. Section 2 presents some related works. Section 3 brings fundaments of packet relaying and formulates the concept of sensing relevance in visual sensor networks. The statements and definitions of the proposed mechanism are described in Section 4. Simulation results and performance analyzes are presented in Section 5, followed by conclusions and references.
# _Applied_
## 3 Methodology
### Datasets  
Sound classifiers using ML techniques are used for automatic sound recognition. To be able to recognize a specific type of sound, these classifiers need to be trained. For such training, audio datasets of labelled sounds are necessary.  
The well-known open source datasets for ESR used in the experiments are presented in Table 1. Not all classes in these datasets are directly urban related, but they contain enough representative information to be used for urban sound classification. Notice that altering these datasets by removing the audio clips that are not related to urban sounds makes comparison with other work impossible. Furthermore, the datasets are heterogeneous enough to be representative for realistic audio environments.  
Figure 1: Different steps of the audio classification.  
* **BDLib**: This dataset consists of 12 classes representing real-life situations. It was created by the authors in [20], collecting audio from sources like BBC Complete Sound Effects Library, Sony Pictures Sound Effects Series and the online sound repository called Freesound [21]. Every class contains ten different audio files of 10 s each.
* **UrbanSound**: This large dataset of 12 GB contains around 18.5 h of labelled audio [22]. It is composed of ten classes, all representing only urban sounds. Just like BDLib, UrbanSound was made by manually filtering and labelling every recording from the online sound repository called Freesound [21]. There is also a larger subset of 4-s audio clips called UrbanSound8K, based on this dataset, but it is not considered here.
* **ESC**: ESC-50, presented in [23], is an annotated collection of more than 2000 short audio clips belonging to 50 different classes. The classes of this dataset are not all related to urban sound, but also include environmental recordings such as animal sounds, domestic sounds or natural soundscapes. In this work, the smaller version ESC-10 is used, which contains 10 audio categories.
# Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System
###### Abstract
Recent studies have also suggested fixed-time controllers, which guarantee convergence within a specified time regardless of initial conditions, despite the robustness and finite-time convergence characteristics of sliding mode controllers [25; 26]. These methods may be very promising for this work's future development. The present study applies the SMC method to elastic cable winch systems to enhance tracking and position control performance. Control engineering studies aim to find practical methods to solve problems. Electromechanical systems, in particular, involve numerous uncertainties, unknown parameters, and disturbances during operation. Thus, all these factors must be considered in controlling design. In this applied engineering field, experimental studies are more convincing than pure simulation analyses. Fortunately, today's technological advances offer experimental methods such as hardware-in-the-loop simulation or animation for real-time testing and analysis of a controller. Real-time control systems play a critical role in experimental studies. For example, using dSPACE, various control methods have been implemented in real time, verifying the performance of artificial intelligence-based asynchronous motor drives [27]. In this context, the opportunities provided by real-time control systems not only enhance the reliability of experimental studies but also optimize design processes, significantly contributing to the field of control engineering. The contribution of this study to the literature stands out in two main aspects. First, a realistic model with four degrees of freedom was developed by considering the elastic cable stretching between the winch drum and the guide pulleys and in the vertical direction. Second, through the real-time implementation of the developed controller, the SMC controller has demonstrated superior performance with ESMC compared to the PD method. This paper presents the mathematical modeling, control design, simulation, and experimental results of an elastic cable winch system. An experimental setup was designed on a smaller scale compared to actual crane mechanisms. While cable deformations are typically negligible in real-world applications due to high load-to-cable weight ratios, this study specifically aims to examine in detail the effects of system elasticity on control performance using elastic polyester cables and light loads. This approach will make significant contributions to developing control strategies for innovative applications requiring multiple elastic cables, such as mobile compact winch systems and unmanned aerial vehicle payload transport. The remainder of the paper is structured as follows: Section 2 presents the detailed mathematical modeling of an elastic cable crane system. Section 3 discusses the design principles and analysis of the SMC controller developed for the system. Section 4 covers the results of the experimental study and their discussion. Finally, Section 5 provides an overall evaluation of the study and offers recommendations for future research.
# Analysis of Road Sign-Related Factors Affecting Driving Safety with Respect to City Size
## 4 Results
### Analysis of Road Sign-Related Factors Affecting Driving Safety
#### 4.4.1 Analysis of Participants' Responses
The following variables were insignificant in this case: 3 years or less of driving experience, average weekly driving time, and use of navigation system. As all road sign satisfaction variables are continuous variables based on an ordinal scale, and their influences can be compared in terms of their odds ratios. The odds ratio of daytime recognition satisfaction was 1.547 (significant at the 0.1% level), exhibiting the highest influence, closely followed by that of guide information sufficiency at 1.449 (significant at the 0.1% level). It can be interpreted that driving safety improved by 54.7% in daytime road sign recognition satisfaction and 44.9% by sufficiency of guidance information. In terms of influence on driving safety, the two aforementioned variables were followed by comprehensibility of guide information (odds ratio = 1.222, significant at the 0.1% level), route accuracy (odds ratio = 1.215, significant at the 0.1% level), and location/frequency adequacy (odds ratio = 1.137, significant at the 5% level). In the context of current road sign information improvements required for road signs devised by the Road Sign Advisory Committee, the variable of customized guidance for users of different types of mobility was significant (odds ratio = 1.118, significant at the 5% level). In the context of future road sign development requirements, the variable of real-time road information guidance based on the situation (important events, accidents, construction zones, etc.) was found to be significant (odds ratio = 1.160, significant at the 1% level). In terms of facility maintenance measures, the variables of both facility safety management (odds ratio = 1.234, significant at the 0.1% level) and integrated installation of road facilities (odds ratio = 1.124, significant at the 5% level) were found to be significant.
# Gelatin-Siloxane Hybrid Monoliths as Novel Heavy Metal Adsorbents
## 3 Results and Discussion
### Resistance of Gelatin Beads to Shaking in Water Solutions
#### 3.6.2 pH Changes during Metal Adsorption  
pH is an important factor affecting the performance of the metal ion sorption process, which can affect both the form of the metal ion in the solution and the surface properties of the sorbent. The latter is dependent on the competition between metal ions and protons in available binding sites or results from ion exchange. We examined the influence of contact time on the pH of solutions during adsorption. The results are presented in Figure 9. Typically, during the adsorption process of metal ions the concentration of hydrogen ions increases however, in our studies, we observed that the solution became more basic. Such behavior is due the sodium carbonate used for the synthesis of hybrid monoliths. During the experiment sodium carbonate was leached from the hybrids and changed pH. Therefore, the initial pH value was controlled and adjusted during the metal adsorption process. In order to avoid the precipitation of metal hydroxides, which can occur when the pH of the solution is too high, 0.1 M of HNO\({}_{3}\) was added to the metal solutions before adsorption experiment. Lowering the initial pH to 2.6 resulted in a reduction in the final pH to about 5-6. This value was sufficient to avoid the precipitation of metal hydroxides. On the other hand, if the initial pH was too low, rapid destruction of the monoliths was observed.  
Figure 8: Photographs of the GS 21 hybrids after adsorption of metal ions: (**a**) copper(II), (**b**) lead(II), (**c**) cadmium(II).  
Figure 9: Effect of contact time on the pH of solutions during adsorption of Cu(II) (\(\blacklozenge\)), Pb(II) (\(\blacksquare\)) and Cd(II) (\(\blacktriangle\)) by hybrid monoliths GS 2:1: initial Cu(II) concentration 10 ppm, mass of adsorbent monoliths 0.32 \(\pm\) 0.02 g, initial pH 4 and temperature 23 \(\pm\) 1 \({}^{\circ}\)C.
## 2 Materials and Methods
### Security Requirements According to ISO/IEC62443  
The primary purpose of the IEC 62443 series is to provide a framework for addressing existing and potential cybersecurity vulnerabilities in industrial systems. For the whole system lifecycle and all layers of network infrastructures, it provides security risk management [13]. These include partitioning the system into security zones, specifying a security level for each zone, and describing security capabilities that enable a component to be integrated at a particular security level (SL). The IEC 62443 standard includes thirteen documents organized into four major categories: General, Policies and Procedures, System, and Component [14]. The first two categories include concepts, use cases, policies, and processes related to industrial control system security (ICS security). Technical requirements for networks and components are defined by the other two groups, namely, System and Component [15]. The standard proposed to build a set of distinct security boundaries is called a security zone; each has a set of common security requirements defined to achieve a particular security target. Another type of zone proposed by this standard is called a conduit, which is responsible for aggregating communication channels and building secure mediums between zones [16].
# Microheater Topology for Advanced Gas Sensor Applications with Carbyne-Enriched Nanomaterials
## 3 Results
The power consumption of the rectangular microheater is 10.65 mW due to the 4.8-fold higher resistance of the rectangular meander compared to the small one. The basic sensors parameters and the effects of the microheater topology on them are summarized in Table 1.  
Given the new information, the observed behavior between the accuracy and sensitivity of the sensors with different microheater designs suggests that the interaction between the microheater's heating pattern and the sensing element plays a critical role. The meander microheater likely provides a more uniform temperature distribution across the sensing area, contributing to a higher accuracy. In contrast, with localized heating, the spot high-density rectangular filled microheater results in a non-uniform temperature distribution, affecting the accuracy negatively. The meander microheater exhibits more con  
Figure 6: Hysteresis (recovery) of the sensor response (**a**) for the small meander microheater; (**b**) for the large meander microheater; (**c**) for the dense rectangular-shaped microheater. trolled and predictable heat transfer dynamics, leading to a more consistent and accurate response to changes in gas concentration. Meanwhile, the spot high-density rectangular filled microheater probably introduces unpredictable or non-linear heat transfer behavior, impacting the accuracy to a greater extent. The small meander-shaped microheater exhibits slower response times and reduced sensitivity to changes in temperature. Its thermal design delays the heating and cooling process, impacting the sensor's ability to detect rapid changes in gas concentration, which can be the reason for the lower sensitivity. It seems that the dense rectangular microheater positively affects the sensor's ability to accurately detect small fluctuations in gas concentration. Some results available in the literature related to the microheaters' parameters are summarized and presented in Table 2 for comparative purposes, considering the best results achieved in this work. In most of the studies, although the film for the microheater has similar properties (thickness, material composition) and operational temperature, the size of the microheater is larger and the design is more complex. As a result, the power consumption is also higher than that of the proposed structures at a similar hysteresis.
## 4 Test Systems  
To apply the methodology of this case study, a 32-node, 32-line grid distribution network, referred to as the IEEE 33-node system, was tested. This system has a 12.66 kV voltage at the substation node, an active power consumption of 3715 kW, and a reactive power of 2300 kvar at its peak hour. In addition, the total network losses during the peak hour are 210.9876 kW [2]. For this study case, the distribution system was divided into three different zones: (i) commercial zone (red), (ii) industrial zone (blue), and (iii) residential zone (green). Similarly, this test system's radial and meshed versions were considered to solve the D-STATCOM placement and sizing problem. Figures 2 and 3 show the electrical diagram of the IEEE 33-node test system in its radial and meshed versions [46], respectively. Note that, for the case of the meshed network, nodes 12, 18, 22, 25, 29, and 33 are active, thus forming closed cycles.  
Figure 1: Diagram of the information exchange in the MATLAB–GAMS interface.  
Table 2 provides the resistance and reactance values of the distribution lines, as well as the demand of bus \(j\), which make up this 33-bus IEEE test system. Note that the values of the active and reactive power correspond to the peak values.  
Similarly, Table 3 shows the electrical parameters of the distribution lines that allow the test system to be connected in a meshed topology (dotted lines).  
As mentioned above, the proposed networks (Figures 2 and 3) have three zones with different load types, so each of these areas has a different daily demand curve. Figure 4 contains the daily demand data for these load types.  
Finally, Table 4 presents the parameters used to evaluate the objective function in Equation (3). In addition, the basic voltage and power data of the grid are shown. A number of these values are taken from [2].  
Figure 4: Load curves: industrial, commercial, and residential.  
Figure 3: Electrical diagram of IEEE 33-node meshed test system.  
Figure 2: Electrical diagram of the IEEE 33-node radial test system.
## 4 Results and Discussions
### Results of Clustering Algorithms  
#### 4.1.1 K-Means  
The K-means clustering algorithm was used to generate the groupings, implemented through a function of Python's scikit learn library [33].  
Based on the dataset provided, the algorithm applies the \"elbow method\" and returns, in output, the graph from which it is possible to extract the ideal number of Clusters to generate, equal to 3 (Figure 3).  
This algorithm created 3 homogeneous clusters, characterized respectively by 10 (\"cluster 0\" in red), 29 (\"cluster 1\" in blue) and 16 (\"cluster 2\" in green) students (Figure 4).  
Figure 4: Representation of a two-dimensional projection (video tutorials viewing frequencies, login frequency) of the groupings that emerged from the clustering phase based on the different features selected.  
Figure 3: Elbow method graph.  
In output, the graph represents the vectors associated with the different clusters on a two-dimensional plane of two of the 10 features used and the text containing the name of the grouping, followed by a list of numbers, in which each number identifies each student.  
The results of K-Means are 3 clusters:  
* Cluster 0: (4, 8, 12, 22, 25, 29, 30, 34, 43, 46)
* Cluster 1: (0, 1, 2, 3, 5, 6, 7, 10, 11, 14, 15, 21, 23, 24, 26, 27, 28, 31, 33, 35, 36, 38, 39, 40, 41, 45, 47, 51, 52)
* Cluster 2: (9, 13, 16, 17, 18, 19, 20, 32, 37, 42, 44, 48, 49, 50, 53, 54)
# The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA
## 5 Experimental Results
### Hyperparameter Search Ranges  
In this work, we only consider convolutional neural networks (CNNs) as they contain many hyperparameters to tune. Therefore, it becomes more challenging to find good hyperparameter combinations than, e.g., multilayer perceptrons. Consequently, the tuning of CNNs benefits more from an efficient evaluation strategy than tuning some simpler neural network architectures. Moreover, CNNs demonstrated good performance even in the presence of various hiding and/or masking countermeasures, and numerous SCA works consider only them, see, e.g., [8; 9; 10; 30].  
Convolutional neural networks commonly consist of convolutional layers, pooling layers, and fully connected layers. The convolution layer computes the output of neurons connected to local regions in the input, each computing a dot product between their weights and a small region connected to the input volume. Pooling decreases the number of extracted features by performing a down-sampling operation along the spatial dimensions. Finally, the fully connected layer computes the hidden activations or the class scores.  
Table 1 provides the selected ranges for the hyperparameter tuning processes. These selected ranges result in a search space containing \(2.7\times 10^{9}\) possible combinations. As we can see, we allow CNNs to contain up to eight hidden layers, combining convolution and dense layers. A pooling layer always follows each convolution layer. As the ASCADf and ASCADr datasets contain 50,000 and 200,000 profiling traces, respectively, larger models would tend to overfit.  
Figure 1: Fast GE vs. empirical GE (ES = Early Stopping). In the left figure, we mark with a dot the epoch when GE is the lowest. Notice that FGE provides very similar results to empirical GE, making it an adequate choice for choosing when to stop the training process. In the right figure, we notice that FGE provides faster GE convergence, especially when the number of attack traces is limited.
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 4 System  
As shown in Figure 1, we introduce the general framework of our method. The input is a multivariate network \(G_{in}=(V,E,A)\), where \(V\) is a node set, \(E\) is an edge set and \(A=\{attr_{1},...,attr_{m}\}\) is the attributes for every node. The output is the layout of the whole of the multivariate network. The general framework consists of two major modules: data preprocessing and network layout module. After data preprocessing, every node has a label and it belongs to an attribute community, then we can use FDAC to start the network layout. In our method, the data preprocessing module mainly consists of three steps: _user select node of interest_, _user select attribute of interest_ and _node attribute community distribution_.  
On this basis, we developed a visualization system for applying the FDAC method. As shown in Figure 2, our system consists of three parts: (1) the **focus selection view** is used for the user to select the node of interest. (2) the **attribute selection view** is used for the user to select the attribute of interest and distribute the node attribute community (3) the **layout result view** is used to display multivariate network layouts.  
Figure 1: The general framework of our approach.
## 4 Problem Formulation
### System Model  
As depicted in Figure 2, our system consists of the following four primary entities: Attribute Authority (AA), Data Owner (DO), Cloud Service Provider (CSP), and Data User (DU). A brief overview of each entity is provided as follows:  
* **AA**: The AA is a trusted entity that generates both the public and private parameters required for the system. Additionally, it validates the user's attribute set and issues the corresponding private key. The AA ensures that only legitimate users who satisfy the defined access control policies can access and process the video data, significantly enhancing both the accuracy and security of video anomaly detection.
* **DO**: The DO is responsible for uploading their data to the cloud service provided by the CSP. When uploading data, the DO clearly defines an access control policy tailored specifically to the video anomaly detection scenario and encrypts the data accordingly. A well-defined access control policy prevents unauthorized users from viewing sensitive anomalous events, thereby preserving the security and confidentiality of the anomaly detection process.
* **CSP**: The CSP stores the encrypted data uploaded by the DO. In this scheme, the CSP is assumed to be \"curious but honest\", meaning it might have an interest in the stored video data but will strictly follow the defined access control protocol, ensuring no unauthorized decryption or data leakage occurs.
* **DU**: Each DU possesses a private key corresponding to its attribute set. Only when a non-revoked DU initiates a keyword-based query on video data can they successfully download and decrypt the latest encrypted video from the cloud, given their attributes precisely match the access control policy defined in the ciphertext.