esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on MARS Dataset  
Following our analysis of iLIDS-VID, we extend our evaluation to the MARS dataset [6], one of the largest and most complex video-based person re-identification databases. MARS presents unique challenges, with its brief video sequences, numerous distractors, and significant variations in capture conditions. These characteristics rigorously test GAF-Net, pushing its limits in scenarios that closely resemble real-world conditions. Unlike iLIDS-VID, MARS revealed significant limitations in gait information extraction. Our analysis shows that while appearance features were obtainable for all tracklets, gait features were extractable for only 85.6% of test-set tracklets, with a notable disparity between the gallery (82.7%) and query (99%) of the tracklets. This disparity highlights the challenges inherent in real-world scenarios, such as occlusions, suboptimal camera angles, and brief video sequences. Considering these constraints and the significantly larger scale of MARS compared to iLIDS-VID, we focus our evaluation on two backbone architectures, namely OSNet [16] and MGH [7]. These architectures offer a balance between performance and efficiency more suitable for the extensive MARS dataset. We exclude PiT from this evaluation due to its higher computational demands, which are less practical for the scale of MARS. Both feature-level and score-level fusion approaches are explored, with particular attention to  
Figure 3: Impact of the fusion factor value (\(\alpha\); varying from 0 to 1) on the rank-1 accuracy.  
conditional concatenation, which is especially relevant, given the inconsistent availability of skeleton-based gait information in this dataset.  
This study demonstrates GAF-Net's adaptability and effectiveness in scenarios where gait feature extraction is not always feasible, providing insights into the robustness of our approach in complex, real-world Re-ID tasks, specifically through the lens of the OSNet and MGH architectures.
## 5 Attacks on Edge-Based IoT
### Protocol-Based Attacks  
Protocol-based attacks in EC-IoT target vulnerabilities in communication protocols to compromise the security and integrity of the network. One common type of attack is the abuse of IoT communication protocols, leading to threats like AR-DDoS attacks.  
These attacks exploit protocols such as constrained application protocols (CoAPs), simple service discovery protocols (SSDPs), and simple network management protocols (SNMPs) to disrupt services and compromise the availability of IoT systems [39].  
Additionally, attacks can target the data transmission protocols used in IoT environments. Communication technologies like cellular networks, WiFi, ZigBee, and Bluetooth follow IoT or data transmission protocols such as the hypertext transfer protocol (HTTP) and message queuing telemetry transport (MQTT). Attackers may exploit vulnerabilities in these protocols to intercept or manipulate data, leading to unauthorized access or data breaches [35]. Also, attacks at the network layer of IoT systems can involve routing attacks, DoS attacks, and attacks on neighbor discovery protocols. In routing attacks, malicious devices redirect messages to incorrect paths, while DoS attacks flood the network with excessive data to cause congestion and resource exhaustion. Attacks on neighbor discovery protocols aim to disrupt the discovery process and compromise network integrity [42].
## 2 Related Work  
### Detection of CG Images  
Early methods for the classification of CG images and NIs are mostly based on hand-crafted features which can be extracted from either the spatial or a transformed domain of images. In the spatial domain, different kinds of discriminative features were proposed by researchers which were related to physics-motivated information [14], image color [15], statistics of image edges [16], intrinsic image noises [17], hybrid ones [18], etc. Regarding features in a transformed domain, various image transformations were studied from which discriminative features were extracted. Examples include features from the wavelet domain [19; 20], the Fourier transform of wavelet coefficients [21], the contourlet and ridgelet transformed domains [22], etc. Handcrafted features are in general easily understandable and explainable; however, the design of such features is quite laborious and the forensic performance often remains sub-optimal.  
Since the publication of the landmark paper of [PERSON] [23], methods based on the deep learning paradigm [24] have set up new state-of-the-art results for almost every task related to image analysis. There is no exception for the CG forensics problem. Neural networks are trained and discriminative representations are learned in an end-to-end manner for the classification of CG images and NIs, which bypasses the laborious design of handcrafted features and usually leads to better forensic performance. Convolution Neural Networks (CNNs) with specific designs were proposed, with the objective to cope better with the CG forensics problem. Examples of such specific CNN designs include a special statistics pooling layer in [25], cascaded filtering layers in [6], a high-pass pre-processing layer in [26], a two-stream structure in [27] and [28], a specially designed correlation module in [29], a multiscale texture learning module in [30], and a combination of shallow and deep features in [31]. Besides conventional CNNs, other network architectures were exploited for CG forensics, including the Capsule network [32], the recurrent neural network [33], and a recursive network with attention guidance [34]. In general, deep-learning-based methods outperform handcrafted-feature-based methods, mainly due to the powerful learning capacity of deep neural networks.  
As far as we know, all existing methods for CG forensics follow the conventional supervised learning framework. In this paper, we present a first study in the literature on CG image detection based on the self-supervised learning mechanism which is in general more flexible and more useful in practical application scenarios.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 2 Materials and Methods
### Spatiotemporal Analysis of Drought  
#### 2.2.1 Meteorological Drought Assessment  
For the assessment of meteorological drought, the Standardized Precipitation Index (SPI) was used due to its effectiveness, applicability, and suitability at different time scales for many case studies, e.g., [28; 33; 34]. Considering the hydrological year (October to September), SPI was calculated at four different time scales, i.e., SPI-3 (October-December), SPI-6 (October-March), SPI-9 (October-June), and SPI-12 (October-September). Further  
Figure 1: Location map of the study area.  
more, for the calculation of SPI at different time scales, the following expression was used for cumulative precipitation \((R_{i,K})\).  
\[R_{i,K}=\sum_{j=1}^{3k}P_{i,j}\;i=1,2,3,\ldots,n\;j=1,2,\ldots,12\;and\;k=1,2,3,4 \tag{1}\]  
where \(\left(P_{i,j}\right)\) corresponds to monthly precipitation, \(j\) denotes the particular month of the hydrological year, \(I\) indicates the hydrological year, and k reveals the timescale (e.g., SPI-3, \(k\) = 1 indicating 3-month time scale October-December, and similarly \(k\) = 4 is for October to September).  
SPI computation includes fitting distributions to precipitation data \((R_{i,K})\) and estimating a probability density function (PDF) and cumulative distribution function (CDF). These functions are further transformed into standardized distributions with unit standard deviations and zero means yielding the SPI value using the expression below (Equation (2)). The SPI value could range from \(<\)\(-\)2 to \(>\)\(+\)2 [35], whereas the positive value of SPI indicates a wet condition while the negative value represents a drought condition. Moreover, in this study, the drought thresholds were as follows: weak drought (\(-1.0\leq\) index \(<0\)), moderate drought (\(-1.49\leq\) index \(<-1.0\)), severe drought (\(-1.99\leq\) index \(<-1.50\)), and extreme drought (index \(\leq-2\)).  
\[\text{SPI}_{i,k}=\frac{R_{i,k}-R_{i,k}}{S_{k}} \tag{2}\]  
where \(S_{k}\) and \(\overline{R}_{k}\) are the standard deviation and average value of the precipitation, respectively.
# CFD Assessment of Car Park Ventilation System in Case of Fire Event
## 2 Materials and Methods
### Simulation Setup in OpenFOAM
#### 2.3.2 Boundary Conditions  
In the context of CFD analysis, boundary conditions refer to specific regions that exhibit distinct flow behavior, which is described by a predefined set of parameters based on the selected turbulence model. This study focuses on defining boundary conditions using the \(k-\varepsilon\) model, where initial values for pressure (\(p\)), velocity (\(U\)), turbulent kinetic energy (\(k\)), and rate of dissipation of turbulent kinetic energy (\(\epsilon\)) need to be specified. Three different types of boundary conditions are considered: inlet/outlet (representing supply/exhaust shafts), parking entrance, and wall surfaces. The characteristics of these boundary conditions are summarized in Table 3.  
Within the OpenFOAM simulation, the gas-solid interface (in wall boundary conditions) is defined by carefully crafted wall conditions that encompass a spectrum of physical phenomena, including elements, such as viscous shear, pressure gradients, and thermal effects. These dynamic wall conditions actively shape the gas flow patterns in proximity to solid surfaces, ensuring an accurate replication of the intricate details of the interaction. Moreover, the chosen RANS turbulence model equations adeptly capture the complex flow dynamics taking place in the vicinity of the gas-solid interface. This RANS model takes into account the intricate processes of turbulence eddy generation and dissipation, serving as a pivotal mechanism for facilitating the exchange of momentum and energy between the gas and solid phases.  
It is important to note that the pressure (\(p\)) boundary condition applied to the inlet/outlet shafts is based on data stored in the \"file: fanCurve\" directory. This data encompasses flux values for the fan across different static pressures, extracted from the manufacturer's provided fan characteristics curve and listed in Table 4.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 3 Results
### Validation of the Drying Model
#### 3.2.2 Simulation of the Moisture Content during the Drying Process  
Figure 12 shows the comparison between the experimental and predicted moisture contents during solar drying of paddy rice in the ISD. Rewetting during night or rainy hours was not observed as the fans were operating continuously. The target moisture content was reached with the ISD between 27 and 29.5 h according to the experimental values. From the experimental data, it can be seen that increased fluctuation of weather conditions strongly influences the drying behavior of paddy rice. Nevertheless, the simulation model could effectively predict the moisture content changes during drying, particularly under good weather conditions, as shown in batch 3.
# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 4 High Level Architecture for Proposed Digital Twin Use Cases in Agriculture
These guidelines are not just followed; they are dynamically adapted to suit the real-time conditions of the farm, ensuring the advice provided is both reliable and contextually relevant. On the other hand, agents play a crucial role in the DT framework, particularly in decision-making, task allocation, and resource management. These intelligent agents utilise the data gathered by microservices to make not only decisions but also recommendations. A comprehensive knowledge graph is built by a variety of microservices, such as those collecting weather data, nutrient recommendations, and decision-making services. This interconnected data representation enables agents to coordinate system activities effectively. This ensures the maintenance of the most accurate model of the field's state. The agents are focused on developing an efficient plan for the field. Although this plan is not a built-in feature of the DT, it is significantly influenced by the data collected and analysis from the DT. This field plan directly impacts nutrient levels, which in turn affects the growth stages of the crops. Efficient management of the DT, coupled with the use of intelligent agents for decision support, enables us to recommend strategies that maintain optimal field conditions,enhancing both the productivity and efficiency of agricultural practices. The agents can also analyse patterns, predict outcomes, and allocate tasks efficiently across the farming operation. Their capabilities include assessing the best planting strategies, optimising irrigation schedules, and managing resource allocation to maximise yield and efficiency. Overall, the proposed architecture depicted in Figure 2 enables farmers to utilise a DT that integrates cloud computing, fog computing, and edge computing, thereby simplifying and improving farm management. This approach significantly enhances farm management and decision-making through a comprehensive, multi-layered computational framework. The incorporation of agents in this architecture allows for autonomous data collection and processing, ensuring timely and accurate information. Microservices, meanwhile, offer modular and scalable solutions, facilitating the flexible integration of various farm data sources and services. The combined use of cloud, fog, and edge computing layers ensures efficient data processing: cloud computing handles complex, non-time-sensitive tasks; fog computing provides localised data analysis; and edge computing enables real-time data capture directly from farm devices. Collectively, these technologies form a digital replica of the farm that includes all important factors, thereby streamlining decision-making. In this way, the proposed architecture offers numerous advantages in overall farming processes compared to the existing agricultural systems.
# Ultrasonic Attenuation Characteristics of Glass-Fiber-Reinforced Polymer Hull Structure
## 4 Test Methods  
### Ultrasonic Inspection  
To increase the measurement accuracy of the ultrasonic NDE of the GFRP laminate, it is recommended to use a low-frequency probe (0.50-2.25 MHz) [5; 6]. In addition, pulse echo ultrasonic testing is recommended because only the measurement of one side of the hull structure is possible with the NDE method, owing to the dimensions of the structure and the hull structure characteristics [14; 23]. Accordingly, in this study, traditional pulse echo ultrasonic testing was conducted, and A-scan inspection was performed using a probe [30] with a diameter of 12.70 mm and frequency of 1.00 MHz. Glycerin was used as the couplant.  
The measurement points were set at 2 cm intervals along the width and length, and A-scans were performed at a total of 250 measurement points, with 5 hull plates and 50 measurement points per hull plate (Figure 3). As is shown in Figure 4, the attenuation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Hull Plate 1** & **Hull Plate 2** & **Hull Plate 3** \\ \hline Design \(Gc\) (\%) & 30.00 & 40.00 & 45.00 \\ Laminate schedule & CSM \(\times\) 10 & CSM \(\times\) 13 & CSM \(\times\) 12 \\ Design thickness (mm) & 10.50 & 9.62 & 7.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design details of specimens in the \(Gc\) variation group.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Hull Plate 2** & **Hull Plate 4** & **Hull Plate 5** \\ \hline Design \(Gc\) (\%) & 40.00 & 40.00 & 40.00 \\ Laminate schedule & CSM \(\times\) 13 & CSM \(\times\) 21 & CSM \(\times\) 26 \\ Design thickness (mm) & 9.62 & 15.54 & 19.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design details of the thickness variation group specimens.  
Figure 2: Prototype hull plates fabricated according to the design plan.  
characteristics at each measurement point were examined by analyzing the amplitude difference between the front and back surfaces of the GFRP hull plate through multiple back reflection signals.  
Attenuation describes the decrease in ultrasound intensity with distance. In this study, the attenuation of ultrasonic waves through the GFRP hull plate was determined from the ratio between the amplitudes of the first and second echo heights. Here, the wave path length (L) was the total distance traveled by the ultrasonic wave during the first and second echoes, which was approximately twice the GFRP hull plate thickness.  
The attenuation coefficient (\(\alpha\)) [32], which is defined as the attenuation per path length, can be expressed as  
\[\alpha=(20\text{log}A_{A}/A)/2T, \tag{1}\]  
where \(\alpha\) is measured in dB/mm; \(A_{A}\) and \(A\) are the amplitudes of the first and second back reflections, respectively, measured as percentages; and \(T\) is the laminate thickness (of each measurement point) in mm.
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 4 Performance Evaluation
### Multi-Classifier Performance Evaluation  
In the performance evaluation of the multi-classifier, the proposed scheme selected either the rule-based or deep learning discrimination method based on attributes using machine learning. The machine-learning models used in the multi-classifier include a total of four classification schemes: support vector classification (SVC), linear SVC, random forest, and naive Bayes. Through the multi-classifier performance evaluation, the most appropriate multi-classifier scheme was determined. To measure the accuracy of the proposed method, performance was evaluated by calculating precision, recall, and F1-measure. The performance evaluations of the classifiers were conducted as follows. First, we test the optimal hyperparameter for each classifier based on exactly the same training set. The training set was designated as 80% of the dataset. After that, the performance evaluation for each keyword was conducted based on the optimized model. For example, the hyperparameters of the SVC model are as follows: C, gamma, and the kernel are derived to be 0.1, 0.001, and rbf, respectively. The alpha value of the naive Bayse model is set to 1.0.  
For the input of the multi-classifier, two methods were compared: one that represents attributes in a binary form (1 and 0) and another that embeds attributes into vector values using word2 vec(W2V) for each attribute. The first method transforms values based on the presence or absence of an attribute. If the attribute is present, it is represented as one, and if absent, it is represented as zero for classifier training. Figure 14 shows the results of the multi-classifier performance evaluation based on binary attribute embedding. All four classification schemes displayed similar precision; however, the F1-measure of SVC and random forest showed values >0.7, indicating approximately 8% higher performance than linear SVC and naive Bayes schemes.  
The second method involves evaluating the performance of a multi-classifier based on W2V, one of the most renowned feature embedding schemes. Figure 15 shows the results of the multi-classifier performance evaluation based on W2V. The performance evaluation results showed that the random forest scheme exhibited outstanding precision and an F1-measure value of 0.98. This was 28% higher than the results of the random forest scheme embedded using 1 s and 0 s. Additionally, SVC also displayed an F1-measure value of 0.98, which was 23% better than that of the previous method. In conclusion, for the multi-classifier, use of values transformed through W2V for training is more suitable rather than relying solely on the presence or absence of attributes.  
Figure 14: Comparison of multi-classifier performance based on binary attribute embedding.
## 5 Practical Dependability Assessment
### Experimental Parameter Settings
We use the PRR available in Reference [9] to define the values of the membership function of each link based on the fact that the higher is the PRR, the lower is the probability of the link to fail. It is important to remark that, according to [PERSON] et al. [36], the relation between the qualitative natural language of the failure probability and its mathematical representation through the membership function (defined by fuzzy function parameters \(a\), \(b\), \(c\), \(d\)) must be stated by experts' belief of the most expected value that a failure event may occur. That way, the association between these parameters was a relatively subjective choice of us, the authors, based on our familiarity, considerable training in and knowledge of this application field. The same was applied to the association between these parameters and the PRR. Thereby, we consider that a PRR under 50% indicates a down link (nonoperational link). The fuzzy function was proportionally generated over the range of 50-100% and the link failure rates (\(\lambda_{lR}\)) were computed through Equations (11) and 12, according to [PERSON] et al. [36]. These rates, as well as the other parameters of the fuzzy model approach, are shown in Table 2. Once a communication failure occurs, due to the transient nature of links, the connection from a node to the gateway node could be reestablished naturally, eventually, or by the re-routing or self-healing features of routing protocols, which lead us to consider in this scenario the link repair rate \(\mu_{lR}=4\), which means about 15 min to reestablish. \[ \begin{split}& ADT\ =\ \left[18(a+b-c-d)^{2}\right]^{-1}\times \left[(a+2b-2c-d)\times\right.\\ &\times\left.\left((2a+2b)^{2}+(c+d)\left(-3a+2c-d\right)-2c\left( 3b+d\right)-4 ab\right)\right]\\ &\left.\lambda_{lR}\ =\ \left\{\begin{array}{cc}10^{-\left[((1-ADT)/ADT)^{ 1/3}\times 2.301\right]},&ADT\
eq 0\\ 0,&ADT\ =\ 0.\end{array}\right.\end{split}  \tag{12}\]
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 2 Methodology
### Interpolating Data  
To reduce the effect of over sampling along flight and tight lines, we applied a block-averaging approach similar to that proposed by [31] in the data system (Figure 1). We have first divided the survey area into a regular grid of adjacent horizontal blocks along the \(x\) and \(y\) directions, respectively. To the horizontal center of each block, we assign the averaged gravity-gradient data and vertical coordinate (\(z\)-coordinates) of the observation points within the corresponding block. The result is a new dataset on a regular grid of points, with block-averaged gravity-gradient data and vertical coordinates.