esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Dynamic Fog Service Placement Approach
## 3 Service Placement Orchestrator Implementation
### Data Synchronization  
Currently, synchronization among orchestrator agents and communication among fog nodes is implemented as a three-way communication topology. Upwards communication is bound for a vertical synchronization with a parent agent by going one level up within the hierarchy. Downwards communication is meant for sending messages down by one level to a child agent. This is completed by sending ACL INFORM messages within internal fog node agents. Sideways communication is horizontal communication among nearby fog nodes, and it is being completed by Synchronization agents. All the fog nodes need to keep their data about resources and statuses updated to make their informed decisions for the best possible service placement when starting new services. This is also necessary when currently available services are getting relocated due to resource or security restrictions. All the horizontal communication now is being completed via Wi-Fi, but it can also be completed via Bluetooth, ZigBee, or even Ethernet. Please see Figure 4 for more details.  
To keep an agent list synchronized, it is updated using the AMS Service component. This component allows us to launch a search based on certain constraints and descriptions. It monitors agent registration, deregistration, and tracks them. A GetAgentList message is sent to all known orchestrators in the fog nodes to retrieve a list. Meanwhile, there is a list of possible orchestrators with their IP addresses stored, but their presence in the fog network is confirmed with a response. Please see Figure 5 for more details.
# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## Materials and Methods
### Measurement of Seeder Row Unit Vibration  
Alongside the evaluation of the seeding and plant emergence efficiency, the seeding row unit vibration dynamics during the trial were observed and analyzed. Seeding unit vibrations are a contributing factor that increases the likelihood of seeding errors. The vibrations directly affect the relationship between the various seed-releasing momentums and the loosening of seeds on the seeding plate. The data acquisition process was carried out using the QuantumX MX440A (HBM, Inc., Darmstadt, Germany), a high-resolution universal measurement module. A portable, single-axis accelerometer system (B12/200, HBM) was used to record the vertical acceleration of the seeding row unit. The data acquisition frequency was 300 Hz. Time domain vibration analysis is provided to quantify the strength of a vibration profile, such as peak-to-peak and root mean square (RMS). The peak-to-peak value is significant because it represents the wave's highest displacement, whereas the RMS value is the most meaningful measure of amplitude because it considers the wave's entire time history and provides an amplitude value that is directly linked to the energy content. Moreover, the fast Fourier transform (FFT) provides a representation of vibration amplitude as a function of frequency, enabling an understanding of how the operating speed affects the excitation of vibrations in the seeder structure.
## 7 Experimentation and Analysis
### Validation of Resource Optimization Methods
#### 7.2.3 The Effect of the Number of Iterations on System Overhead  
Figure 12 shows the convergence effect of the SOA, GLSOAG, PSO, and BSA strategies. At the beginning of the iteration, the convergence trend of SOA, PSO, and BSA is overall  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Implications** \\ \hline \(T_{i,trans}^{j}\) & Upstream transmission delay of the device \(i\) \\ \(T_{i,exe}^{j}\) & Processing latency at the MEC side \\ \(T_{j,mce}^{j}\) & Task \(i\) Total delay from offloading to MEC processing \\ \(f_{i,mce}\) & Arithmetic resources allocated by the MEC server for task \(i\) \\ \(f_{mce,total}\) & MEC total computing power resources \\ \(E_{i,trans}^{j}\) & Task \(i\) Offloading Transmission Energy Consumption to the MEC \\ \(F_{i,mce}^{j}\) & Task \(i\) Offload to Edge Layer Processing Total Overhead \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(Cont.\)  
Figure 11: Performance of the number of tasks under different architectures.  
gentle, while the GLSOAG converges faster, and the optimal solution can be approached around the 180 th iteration because the GLSOAG adopts Gaussian mapping processing for the initialization of the population, which makes its pre-exploration global exploration more efficient. In addition, the four algorithms show slight oscillations at 10-60 iterations before resuming the converged state, indicating that the algorithms all have good global search capabilities. Overall, the resource optimization method based on the GLSOAG has a significant advantage over the methods based on SOA, PSO, and BSA in terms of convergence speed and accuracy.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 6 Security Analysis
### Information Integrity  
The integrity of information collected for \(CS\) is a critical concern. In the disaster area, any false data could lead to aggravate the situation in the area or put the rescuers' lives in danger. The malicious adversaries can violate the integrity by compromising drones or intercepting the exchanged messages and falsifying them.
## 2 Related Work
### D SLIC Algorithm for Image Segmentation  
The SLIC is a gradient-ascend-based segmentation algorithm, which generates superpixels by clustering pixels with the distance measure according to their proximity and color similarity in the image plane [38]. An example is shown in Figure 1. Figure 1a shows the origin image, and Figure 1b shows the corresponding superpixel segmentation result.  
The distance measure is constructed by a 5D vector including the color vector (\(L\), \(a\), \(b\)) and the pixel position coordinates (\(x\), \(y\)). Instead of the Euclidean distance, the 2D SLIC algorithm introduced a normalized distance measure considering superpixel size. Suppose the size of an image \(I\) is \(W\) by \(H\), then the number of pixels \(N\) is described as Equation (1).  
\[N=W\times H, \tag{1}\]  
This algorithm specifies \(K\) superpixels with approximately equal size as input. For image \(I\) with \(N\) pixels, the size of each superpixel is \(N/K\). Therefore, the grid interval \(S\) of superpixels is computed as Equation (2).  
\[S\approx\sqrt{N/K}, \tag{2}\]  
where \(S\) denotes the size of the superpixel. For the distance measure normalization, the value of \(S\) is used to determine weight coefficients of different feature components.  
Choose \(K\) superpixel cluster centers \(C_{k}=\left[L_{k},a_{k},b_{k},x_{k},y_{k}\right]^{T}\) (\(k=\left[1,K\right]\)) at regular grid intervals \(S\). The computational formula of distance measure is shown in Equation (3).  
\[D_{S} = d_{Lab}+md_{xy}/S\] \[d_{Lab} = \sqrt{\left(L_{k}-L_{i}\right)^{2}+\left(a_{k}-a_{i}\right)^{2}+ \left(b_{k}-b_{i}\right)^{2}}\, \tag{3}\] \[d_{xy} = \sqrt{\left(x_{k}-x_{i}\right)^{2}+\left(y_{k}-y_{i}\right)^{2}}\]  
where \(D_{S}\) denotes the normalized distance measure; \(d_{Lab}\) denotes the Euclidean distance of the color vector \(\left(L_{k},\ a_{k},\ b_{k}\right)\) and \(\left(L_{i},\ a_{i},\ b_{i}\right)\) in CIELAB color space; \(d_{xy}\) denotes the Euclidean distance of pixel \(\left(x_{k},\ y_{k}\right)\) and pixel \(\left(x_{i},\ y_{i}\right)\) in the image plane; \(m\) is a variable to control the compactness of a superpixel. The greater the value of \(m\), the more important spatial proximity and the more compact the cluster. Experience indicates that the optimal range of values for \(m\) is [1, 20], which can get a good tradeoff between color similarity and spatial proximity [38].  
Figure 1: An example of superpixel segmentation: (**a**) The origin image; (**b**) Overlay display of the origin image and the corresponding superpixel segmentation result.
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 2 Literature Review
### CAPTCHA Types  
Online users have relied on traditional CAPTCHAs to distinguish between humans and bots for years. However, recent advancements in machine learning have introduced new security vulnerabilities, allowing bots to bypass these challenges more effectively.  
According to [PERSON] et al. [9], machine learning models are used to solve English text-based and numerical CAPTCHAs. Vulnerabilities were found in standard text CAPTCHAs against adversarial machine learning attacks, with the best model achieving 88% accuracy. While computer vision tasks are more secure, they can still be attacked as shown by [PERSON] et al. [10]. Their study collected a large image dataset to train deep neural networks for segmenting and recognizing visual CAPTCHA images. Their model achieved over 79.2% to 98.6% accuracy, defeating visual CAPTCHAs. Similarly, [PERSON] [11] employed Convolutional Neural Networks (CNNs) to recognize CAPTCHAs with high efficiency and accuracy.  
Therefore, the rapid advancements in visual recognition technologies challenge the effectiveness of traditional CAPTCHA systems. This has prompted researchers to explore new techniques aimed at enhancing CAPTCHA security [12]. In general, visual reasoning CAPTCHAs have been proposed as a more advanced alternative, requiring users to identify specific items in images based on text prompts. Meanwhile, audio CAPTCHAs have shown promise in the early stages as a potential method for preventing attacks [13].  
CAPTCHAs that rely on images are a promising substitute for traditional text-based CAPTCHAs. Systems like CAPTCHAsStar utilize human abilities in recognizing shapes and provide better user experience and strength against automated attacks [14]. Interactive CAPTCHAs, such as iCAPTCHA, employ multi-step user interactions to thwart third-party human attacks by generating time discrepancies between genuine users and human solvers [15]. Game-based CAPTCHAs, where users must solve AI problems using drag-and-drop or click methods, are viewed as very secure but could be at risk from image processing methods and relay attacks [16]. Video CAPTCHAs require users to track moving objects among decoys, making it difficult for automated and relay attacks to succeed [17].  
While CAPTCHAs have been successful in preventing automated attacks, the rise of human-assisted CAPTCHA solving poses a notable danger. As pointed out in [9], this method involves recruiting external parties to manually solve CAPTCHAs, undermining their core function of distinguishing between humans and machines. Although various approaches offer initial security benefits, the fact that existing solutions remain vulnerable highlights the ongoing need for stronger defenses. Thus, the proposed approach helps to enhance the defense level.  
In this section, we have reviewed several common CAPTCHA schemes explored in prior literature. Table 1 summarizes the key CAPTCHA types that were discussed.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Research Idea and Test Scheme Design  
As depicted in Figure 4, the technical workflow of the RF-SNSCNN-CA model in this study encompasses four significant modules: the data processing module, the model training module, the iterative calculation module, and the model application module.  
The data processing module entails the acquisition and processing of multi-source heterogeneous data. This encompasses three types of driving factor data, namely natural factors, economic factors, and social factors, along with two phases of land use data. Following essential cleaning and processing steps, the data undergo vector to raster conversion, rasterization, and resolution normalization to create a dataset with consistent resolution and coordinates. In the model training module, a partial sampling approach is employed on the dataset for training purposes. In the iterative calculation module, predictions are made using the complete dataset.  
Figure 3: Driving factors of 21 districts in Chongqing city.  
The model training module consists of two main components: RF-based development suitability extraction and SNSCNN neighborhood feature extraction. Initially, the driver data and land use change data from the observation period are sampled to create a training dataset. This dataset is then used as input for training the RF and SNSCNN models, which construct the development suitability extractor and the neighborhood effect extractor, respectively. The land use change data are generated by overlaying the 2010 and 2017 land use data, resulting in a new layer. A value of 1 in this layer indicates a raster that has transformed from non-urban to urban, while 0 represents a non-urban raster that has not undergone a transformation. To address the imbalanced distribution of urban and non-urban rasters, the sample dataset consists of 10,000 samples labeled as 1 and 30,000 samples labeled as 0. The development suitability and neighborhood features are then extracted using the driver data as input for the development suitability extractor and the neighborhood effect extractor, respectively. SNSCNN serves as the neighborhood effect extractor, and the Softmax classifier of the SNSCNN model extracts the classification probability, specifically for the classification of 1, as the neighborhood effect.  
The iterative computation module consists of model validation and model application. In the model validation phase, the trained RF-SNSCNN-CA model is employed to simulate the predicted land use changes for the final year (2017) based on the base year data (2010). The simulated results are then compared with the actual land use distribution data from 2017 to validate the effectiveness of the model proposed in this paper. Following the validation, the model application phase utilizes the 2017 land use data as input, and the established model is utilized to predict the land use status for the year 2024. In Figure 4 we use gray to show non-urban areas and red to show urban areas.
# Current Context and Research Trends in Linear DC-DC Converters
## 4 Dominant Characteristics of LDOs
### Low Quiescent Current  
High efficiency and energy savings can be achieved with a lower quiescent current. Especially in portable devices, long battery life is somewhat dependent on low quiescent current. Figure 5[21] shows the typical quiescent values of commercially available LDOs.
# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 2 Methodology
### Experimental Set-Up and Data Acquisition  
In order to set up a hand gesture classifier model, a recording of EMG signals from five different gestures was performed, which can be seen in Figure 1. These gestures were selected based on basic daily hand patterns, including two of the fundamental grasping motions, the power grip (Figure 1b) and the pinch grip (Figure 1c), which is a precision handling maneuver [43].  
The EMG data was acquired using a custom four-channel EMG armband [44]. The EMG armband acquired the EMG signal in each channel with dry electrodes, then it conditioned the signal so a microcontrollers ADC could read it and finally it sended packages containing the four channels information via serial communication. The data of the four EMG channels were received by a personal computer (PC) in which the preprocessing and development of the model were performed.  
Each gesture was recorded for 20 s with a sampling frequency of 1 KHz, allowing to obtain 20,000 samples per gesture. The sampling frequency was selected following the Nyquist sampling theorem, considering a maximum frequency for the EMG signal of 500 Hz [45; 46]; it was also taken into consideration the computational cost of acquiring the four EMG channels and the maximum serial transmission speed of the microcontroller. An adequate selection of the sampling frequency is key to acquire all the signal components and perform a better mapping of the signal with the proposed classifier.  
The acquired samples were divided into 100 windows of 200 samples; collecting a total of 500 examples to build the dataset. The 200 samples window was selected based on a literature review on hand prosthesis requirements and EMG window length selection; where a prosthesis requires an acting speed of 500 ms or less, in order to work smoothly [47; 48; 49; 50]. Taking into consideration that the sample time is 1 ms, 200 sample windows allow an actuation speed of around 200-300 ms considering a serial communication with the computational model, which is within the required speed; moreover, researchers have found that window lengths of 200 ms or more allow to have reduced classification errors when working with EMG signals [45; 51].  
The dataset was randomly shuffled and then split into two parts: 80% of the shuffled dataset was used for training, and the remaining 20% was used as a validation set to perform hyper-parameter tuning and validation of the training.  
Figure 1: Gestures: (**a**) Open hand, (**b**) Power grip, (**c**) Pinch grip, (**d**) Point to, (**e**) Rock you.  
It is important to note that the acquisition of the EMG signals was done following all the ethical guidelines set by law and the institution. The data was taken from eight test subjects with an age range between 20 and 30 years. All participants were healthy people. The experiments were approved by EIA University Ethical Committee (Code P201912-04), and also an informed consent was obtained from the participants.
## 3 WSN Protocols
### DEC (Deterministic Energy-Efficient Clustering)  
The deterministic energy-efficient clustering (DEC) protocol effectively enhances energy efficiency and extends the lifespan of Wireless Sensor Networks. This protocol enhances communication and data transfer by arranging the nodes into clusters. The goal of the DEC protocol is to minimise energy consumption by minimising the data transmission overhead and extending the functional lifetime of sensor nodes that rely on battery power [34; 46]. The DEC protocol divides the network activities into several rounds, taking two phases: the set-up and steady-state phases. The set-up phase of the protocol  
Figure 5: Results of implementing the standard TEEN protocol using MATLAB: cluster heads are marked as bold plus signs, other nodes are marked as coloured rhombuses, and the base station is marked as a red circle in the centre.  
involves three key activities: selecting the cluster head, choosing cluster heads randomly, and establishing [47].  
In the steady-state stage, data transmission will be completed, and the base station will communicate with all cluster heads in the network. This protocol's set-up stage structure differs from that of other protocols because this stage is categorised into three primary components, as mentioned. However, as the number of rounds in the DEC protocol increases, certain limitations hinder the network's overall efficiency [47]. For example, it can be seen that the nodes become inactive sooner than they did in LEACH, SEP, and TEEN. Therefore, finding a balance between the stability of the network and the number of active rounds is a significant challenge [47]. Figure 6 shows the implementation of a standard DEC protocol using MATLAB.
## 4 Results and Discussion
### Importance of Musical Elements for Different Feature Sets  
The importance of musical elements for different feature sets was compared and analyzed according to the corresponding relationship between acoustic features and musical elements, combined with human auditory perception. The importance coefficients of different musical elements, including timbre, loudness, pitch, and rhythm for each feature set, were calculated as the average values of the importance coefficients of acoustic features belonging to each musical element. The reason for introducing the average value of importance is to avoid distortion by the number of statistical features or multi-dimensional features. Although the MFCC reflected the timbre characteristic of the audio signal to a certain extent and should be classified as a timbre feature, the MFCC of a single dimension had no corresponding relationship in timbre perception. Therefore, the timbre features were divided into MFCC and other timbre features.  
The importance of different musical elements on the valence of Western and Chinese classical music is compared in Figure 7a. For Western classical music, the musical elements, ranked in order of the importance from high to low, are pitch, rhythm, MFCC, and timbre, whereas the ranking for Chinese classical music elements is pitch, rhythm, timbre, loudness, and MFCC. Although pitch and rhythm are very important for both forms of classical music, loudness is specifically significant for Chinese classical music. As shown in Figure 7b, the musical elements that affect the arousal perception of Western classical music are very different from those for Chinese classical music. Pitch and timbre are the most important musical elements for Western classical music, whereas all musical elements have a significant impact on Chinese classical music, especially rhythm.  
Based on our cross-cultural dataset, Figure 7c indicates that pitch and rhythm have a greater influence on valence perception, whereas timbre has a greater influence on arousal perception. This result is consistent with our usual experience of listening to music. By comparison, from the results shown in Figure 7d of emotion perception between Western and Chinese classical music, it can be seen that pitch is the most important musical element for Western classical music, whereas almost all musical elements are equally important for Chinese classical music. Furthermore, it suggests that the features related to rhythm and loudness of Chinese classical music are more important than those of Western classical music. A possible reason is that Western classical music has a strict and perfect rhythm theory, with most repertoires following a regular rhythm and speed, whereas Chinese classical music places more emphasis on personalization and personification. Some Chinese classical music does not even have a fixed rhythm, and the dynamic fluctuation is very large, which leads to the fact that rhythm and loudness affect people's emotional perception of Chinese classical music to a large extent.
## 5 Discussion
### Limitations  
The proposed framework represents an effort to demonstrate extended reality's potential in safety management, and through the implementation examples, the proposal'sreliability has been demonstrated. However, in light of the research objectives and the results shown, the limitations of this work must be considered.
* While various uses and tools are shown, the developments have been focused on specific technologies, software and equipment. Although the focus of the research is to describe a technologically neutral framework, it is necessary to deploy its functionalities in diverse technologies and tools to identify limitations or greater potentials through other unidentified tools (or new technologies or future equipment).
* The proposed uses for the technologies throughout the project life cycle are framed as part of a holistic vision for construction safety management. However, the proposal has not been deployed in a complete project, which would guarantee its effectiveness and measure its results. Therefore, being able to generate such an ideal scenario, or in principle, to simulate or partially incorporate the uses in a real, entire project, would represent an interesting opportunity to evaluate the integration and effectiveness of the proposal.
* In general, massive tests of the implemented functionalities are required to analyse the effectiveness of the proposed tools in the field, to measure and analyse their impact on safety management and to establish indicators to monitor the relationship with accident reduction.
* This study does not include quantitative validation through safety performance indicators, which remains a limitation. Future work should incorporate measurable KPIs to assess the impact of the framework in operational settings.