esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
###### Abstract  
Flooding is one of the catastrophic natural hazards worldwide that can easily cause devastating effects on human life and property. Remote sensing devices are becoming increasingly important in monitoring and assessing natural disaster susceptibility and hazards. The proposed research work pursues an assessment analysis of flood susceptibility in a tropical desert environment: a case study of Yemen. The base data for this research were collected and organized from meteorological, satellite images, remote sensing data, essential geographic data, and various data sources and used as input data into four machine learning (ML) algorithms. In this study, RS data (Sentinel-1 images) were used to detect flooded areas in the study area. We also used the Sentinel application platform (SNAP 7.0) for Sentinel-1 image analysis and detecting flood zones in the study locations. Flood spots were discovered and verified using Google Earth images, Landsat images, and press sources to create a flood inventory map of flooded areas in the study area. Four ML algorithms were used to map flash flood susceptibility (FFS) in Tarin city (Yemen): K-nearest neighbor (KNN), Naive Bayes (NB), random forests (RF), and eXtreme gradient boosting (XGBoost). Twelve flood conditioning factors were prepared, assessed in multicollinearity, and used with flood inventories as input parameters to run each model. A total of 600 random flood and non-flood points were chosen, where 75% and 25% were used as training and validation datasets. The confusion matrix and the area under the receiver operating characteristic curve (AUROC) were used to validate the susceptibility maps. The results obtained reveal that all models had a high capacity to predict floods (AUC > 0.90). Further, in terms of performance, the tree-based ensemble algorithms (RF, XGBoost) outperform other ML algorithms, where the RF algorithm provides robust performance (AUC = 0.982) for assessing flood-prone areas with only a few adjustments required prior to training the model. The value of the research lies in the fact that the proposed models are being tested for the first time in Yemen to assess flood susceptibility, which can also be used to assess, for example, earthquakes, landslides, and other disasters. Furthermore, this work makes significant contributions to the worldwide effort to reduce the risk of natural disasters, particularly in Yemen. This will, therefore, help to enhance environmental sustainability.  
flash flooding; remote sensing data; machine learning algorithms; Tarin city; Yemen 202214, 4050. [[https://doi.org/10.3390/rs14164050](https://doi.org/10.3390/rs14164050) [https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://doi.org/10.3390/rs14164050](https://doi.org/10.3390/rs14164050) [https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 2 QUIC Protocol
The connection initiates from the user side by sending a hello message containing all the required information. (i.e., STK, SCFG, new connection ID, and the client's Diffie-Hellman values). Next, the sent message is verified on the server side. If the verification is successful, then the encrypted connection will be established. Otherwise, the server will reject the connection, and 1-RTT is required again. Figure 1, which is adapted from [4], illustrates both connection steps. QUIC traffic is currently widely used in most Google services, supported by most web browsers, and is on the way to IETF standardization [12]. According to [3], around 7% of Internet traffic is QUIC. Other advantages of QUIC traffic include its performance, security, and stream multiplexing [3]. On the other hand, QUIC may suffer from some limitations. For instance, it was found that the CPU utilization in QUIC was around 3.5 higher than in TLS/TCP [3]. This could be due to the structure of QUIC and some operations that are involved in the protocol, such as the cryptographic process, transferring UDP packets, and keeping track of the internal states of QUIC [3].
# Sea Surface Temperature in Global Analyses: Gains from the Copernicus Imaging Microwave Radiometer
## 1 Introduction
Background error covariances are specified a priori and are parameterised using two components, one representing errors due to mesoscale ocean features and one capturing larger scale errors such a synoptic weather systems. The uncertainty associated with each retrieved SST is used with the errors assumed to be uncorrelated. The new analysis is then \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} **Channel** \\ **(GHz)** \\ \end{tabular} & \(\mathbf{U_{\text{total}}}\) & \(\mathbf{U_{\text{NEAT}}}\) & \(\mathbf{U_{\text{orb-stab}}}\) & \(\mathbf{U_{\text{life-stab}}}\) & \(\mathbf{U_{\text{pl-cal}}}\) \\ \hline
1.41 & 0.5 & 0.3 & 0.2 & 0.2 & 0.2 \\
6.9 & 0.4 & 0.2 & 0.1 & 0.2 & 0.2 \\
10.65 & 0.45 & 0.3 & 0.1 & 0.2 & 0.2 \\
18.7 & 0.6 & 0.4 & 0.2 & 0.2 & 0.2 \\
36.5 & 0.8 & 0.7 & 0.2 & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Uncertainty components for each of the CIMR channels. The columns give the total uncertainty, as well as the radiometric, orbital stability, lifetime-stability and pre-launch calibration contributions to the total uncertainty budget. calculated using the Nucleus for European Modelling of the Ocean Variational Data Assimilation (NEMOVAR) system [13] to minimize the cost function:  
\[f=\frac{1}{2}\delta\mathbf{x}^{\mathrm{T}}\mathbf{B}^{-1}\delta\mathbf{x}+ \frac{1}{2}\delta\mathbf{y}^{\mathrm{T}}\mathbf{R}^{-1}\delta\mathbf{y} \tag{1}\]  
where \(\delta\mathbf{x}\) is the difference between the new analysis field and the background state, \(\mathbf{B}\) is the background error covariance matrix, \(\delta\mathbf{y}\) is the difference between the newly measured SSTs and those in the new analysis field and \(\mathbf{R}\) is the observation error covariance matrix [14].
## 5 Case Study  
### Case Description  
The proposed TNEP approach is tested on the modified IEEE RTS-24 system; the test system data is available in [24; 25]. In this paper, the uncertainties considered in TNEP are load demand and wind generation. The basic scenario set for clustering analysis consists of 8760 hourly data of load demand and wind power output. The hourly load data is simulated by adding a 5% disturbance that follows the normal distribution to the base load data [24]. The hourly wind power data is converted from the actual annual hourly wind speed data, which is derived from the EWITS study [19].  
In the modified IEEE RTS-24 system, all the loads are assumed to be 2.8 times of the original values, while all the generation capacities are assumed to be 2.5 times of the original values. Three new buses, Bus-25, Bus-26, and Bus-27, are added, each with a wind capacity of 1000 MW; they can be integrated into the system by the right-of-way 16-25, 7-26, and 1-27. The network topology of the modified IEEE RTS-24 system is shown in Figure 5.  
The dashed line in Figure 5 represents the new right-of-ways. A total of 44 right-of-ways are allowed to build new transmission lines, the number of which should be no more than four in each right-of-way.  
The parameters are set as follows: the range of cluster number is set to [2,500]; the repetition \(s_{n}\) for cluster number analysis is set at 40, the repetition \(s_{m}\) for scenario sampling is set at 5000; penalty factor \(\beta\) is set as \(10^{6}\)\(\$\)/MW; the population number is set as 60 based on [5; 8]; the maximum number of iterations is set as 1500, which is a relatively large value to guarantee the optimality of the Pareto front.
# Low-Cost Software Evaluation of Communication Delays in Networked Industrial Systems
## 3 Measurement System Architecture
### Baseline (\(\mathcal{T}_{base}\)) Configuration  
The system itself can impair the collection of measurements by introducing some kind of either deterministic or random delay along the packet transmission path. To cope with this aspect, DUT is replaced by a deterministic component with negligible delay (e.g., a simple cable), as in the baseline configuration (\(\mathcal{T}_{base}\)) shown in the upper half of Figure 2. In this situation, _SW_ is instructed to implement the path highlighted in red by programming two VLANs as  
\[\textit{VLAN}_{1}=\{n_{\textit{SW}}^{S},n_{LOOP}^{SW}\},\textit{VLAN}_{2}=\{n _{\textit{SW}}^{LOOP},n_{R}^{SW}\}.\]  
Figure 2: Measurement system in the \(\mathcal{T}_{base}\) (upper) and \(\mathcal{T}_{DUT_{1}}\) (lower) configurations. Relevant network paths are highlighted in red.  
As is well known, port-based VLANs are used to create groups of isolated ports on a switch. Thus, thanks to \(VLAN_{1}\), a packet arriving at port \(n_{SW}^{S}\) may only egress from \(n_{LOOP}^{SW}\), while all the other active ports of the switch are ignored. The same is true for \(VLAN_{2}\) with respect to ports \(n_{SW}^{LOOP}\) and \(n_{R}^{SW}\).  
\(\mathcal{T}_{base}\) is needed for evaluating the impact of the measuring system itself with respect to DUT latency. Possible delays affecting \(\mathcal{T}_{base}\) are due to hardware component timings, whereas we are mostly interested in measuring the latency of DUTs driven by sophisticated software. For this purpose, it is worth noting that, in practice, for many devices of interest the impact of the baseline (tenth of nanoseconds) could be negligible when compared to the expected DUT latencies in the order of several microseconds. Section 4 shows how to take \(\mathcal{T}_{base}\) delays into account and Section 6 provides the experimental results regarding this aspect.
## 4 Case Study
### Identification Results of Dry Beach Area  
Eight satellite images of the tailings pond were obtained in 2022 and 2023, with one image captured throughout each quarter of the year. Acquiring satellite imaging data for the past two years can assure their veracity. As shown in Figure 14, water levels, vegetation cover, and surface characteristics of tailing ponds vary seasonally. The YOLOv5-seg model was utilized to detect the dry beach area in eight satellite images, and the results of the recognition are displayed in Figure 14.  
Figure 12: Site location of the tailings pond. (**a**) Site location; (**b**) satellite image of the tailings pond; (**c**) terrain and landform of tailings pond.  
Figure 13: Layout of monitoring sites A, B, and C.
# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 3 Result and Discussion  
### Detection Results of Unscaled Networks  
In actual engineering practice, the superiority of an algorithm is usually measured by the mean Average Precision (mAP) and the test speed FPS. mAP, the average value of AP of each class of objects, is the average value of AP obtained by multiple verification set individuals, which measures the overall detection accuracy of the algorithm. FPS, which is the frame rate that can be processed per second, is used to measure the processing speed of the algorithm.  
Figure 6 shows the detection results of MobilenetV3-YoloV4-1.0 (1.0 means \(\alpha=1.0\), that is, the channel number scaling factor is 1.0) on the dataset of this article. Figure 6a-d corresponds to the damage types in order of tear, breakdown, damage, and surface wear; it can be seen that this algorithm can better realize the detection of multiple damage types.  
Table 2 shows the results achieved by multiple models quantitatively, including the prediction accuracy (AP), mean Average Precision(mAP) and prediction speed (FPS) of the various algorithms for the various damage types. The data in Table 2 can be divided into three parts: The first part is the result using 7 current mainstream target detection algorithms, including Two-stage target detection algorithm: Faster R-CNN, One-stage detection algorithms: SSD and YOLO, then a key point based prediction algorithm: Centernet. It can be seen that the Resnet50 based Centernet algorithm has reached the highest average prediction accuracy of 95.05% with a fastest detection speed of 32.4 FPS; The second part is the detection results obtained in Ref. [3]. Among them, the EfficientNetB0 based EfficientNet-B0-Yolov3 has achieved the fastest detection speed of 41.91 FPS, and the EfficientNet-B4-Yolov3 has achieved the highest detection accuracy with 97.26%. Compared with the original Yolov3 algorithm, the accuracy is increased by 10.4%, with the speed 45.9%. The third part is the detection result of 3 lightweight neural networks that combines Mobilenet and Yolov4 network proposed in this paper. Among them, the MobilenetV1-YoloV4-1.0 has achieved the fastest prediction speed of 51.12 FPS, MobilenetV3-YoloV4-1.0  
Figure 6: The detection results of the target detection algorithm on the dataset: (**a**) tear, (**b**) breakdown, (**c**) damage, (**d**) surface wear.  
achieved the highest prediction accuracy of 93.08%, compared with the detection speed of 24.39 FPS with an accuracy of 90% obtained by the Yolov4 network, it can be seen that the lightweight algorithms shown in Table 2 has achieved an improvement on the highest prediction accuracy by increasing 3.4% and 109% in prediction speed compared to the original Yolov4, but it should be noted that the fastest prediction speed and the highest prediction accuracy are not achieved by the same lightweight network.
## 3 Results and Discussion
### Supercooling during the solidification Process  
Supercooling during the solidification process for all samples is shown in Figure 5a. The listed values are based on 5 repeat cycles that are shown in Figure 4a. Compared to the supercooling temperature of a blank sample of 11 \({}^{\circ}\)C, the supercooling of the copper foam (CF#0s) is only 7.7 \({}^{\circ}\)C, which indicates that the copper foam can reduce the supercooling. The results agree with those in previous studies [20]. The more exciting phenomenon is that the electrodeposition patterned copper foam with micro/nanostructures can further reduce supercooling. With an increase in the deposition time, supercooling is reduced. The supercooling temperatures for CF#195s, CF#255s, and CF#300s are approximately 5.6 \({}^{\circ}\)C, 4.8 \({}^{\circ}\)C, and 4.6 \({}^{\circ}\)C, respectively. The lowest supercooling (from CF#300s) was 6.4 \({}^{\circ}\)C lower than that of the blank sample, which was a reduction of almost 60%.  
The delay time is summarized in Figure 5b. Here, the delay time is the time between the sample reaching 0 \({}^{\circ}\)C (point A for blank in Figure 4b and beginning solidification (point C for blank in Figure 4b. The shorter was the delay time, the faster the sample could enter the solidification state. It took approximately 465 s for the blank sample to enter the solidification state. Using copper foam (CF#0s), the delay time was reduced to approximately 325 s. The electrodeposition patterned copper foam with micro/nanostructures can further reduce the delay time. With an increase in deposition time, the delay time is reduced. The delay time for CF#195s, CF#255s, and CF#300s was approximately  
Figure 4: Temperature profiles during the cool storage phase-change process test cycles (**a**) temperature profiles of five cycles experiment (**b**) temperature profiles of the 3 rd cycle.  
235, 204, and 198 s, respectively. The shortest delay time (from CF#300s) was 267 s shorter than that of the blank sample, which was a reduction of almost 60%. The obtained result clearly showed that the electrodeposition patterned copper foam with micro/nanostructures could further reduce both the supercooling and delay time.
## 1 Introduction
In references [13] and [14], the template of each color plane, as shown in Tables 14 in Reference [14], is constructed not only by its neighborhood pixels, but also considering the two pixels at the same position in the other two-color components, thus the quality of the reconstructed color image is improved to a certain extent. Since the correlation between the color components proposed in reference [13] and [14] is a weak correlation, there is still room to improve the reconstructed color image quality for color halftone images. Armed with all the neighborhoods of each color plane, we propose a novel color inverse halftoning method by exploiting the correlation of multi-color components. The proposed method uses all existent values in the same patterns of three-color component tables to estimate the nonexistent values in color table. In order to increase the estimation speed and accuracy, the extreme learning machine (ELM) is employed to learn the relation between color template patterns and their corresponding color contone values, which can not only improve the fitting precision of nonexistent values but also has fast transformation speed. Experimental results show that the proposed method achieves a better image quality when compared to the previously published method. The remaining part of this paper is organized as follows. Section 2 gives an overview of our proposed method. In Section 3, we detail our proposed method. The experiment is demonstrated in Section 4 by comparing it with other methods. Finally, conclusions are addressed in Section 5.
## 2 Background
### Mental Health Detection on Social Media  
The COVID-19 pandemic has intensified the reliance on social media as a support system, highlighting the medium's potential for public health surveillance [17]. The significance of social media transcends personal communication and has become essential for research. As of April 2023, the global social media active user base has reached 4.8 billion people [18]. Platforms such as Reddit, Twitter, and Facebook are now integral to mental health research, facilitating the discovery of behavioral patterns that might elude conventional methods [19].  
The rise of social media has created new avenues for supporting individuals with mental health issues [20]. These individuals utilize social media at comparable rates to the broader population, often seeking anonymity and refuge from social isolation [21; 22; 23]. The accessible and perceived safe space of online environments provides a platform for unjudged expression and connections often avoided offline [24; 25; 26]. This is particularly relevant to those with limited real-life social interactions [27].  
Younger generations have voiced that social media helps alleviate feelings of isolation, with a preference for sharing their experiences online over traditional clinical settings [28]. Early research has shown that individuals with schizophrenia are more inclined to share their experiences on social media [29; 30], with others indicating a general trend towards seeking online support over in-person help for mental health concerns [31]. The aggregation of user-generated content on forums such as Reddit has further expanded the scope for detecting a range of mental health issues, from depression to self-harm, through various analytical methodologies [32; 33].  
Collectively, these insights establish social media as a critical resource for understanding and supporting mental health, encouraging researchers to leverage this data to pre-emptively identify and address mental health risks, potentially averting more severe outcomes [34; 35; 36].
## 3 An Automated Framework for DSM
### Data Preprocessing
#### 3.1.2 Storage  
The storage of data is very important. A well-designed data relationship table is significant for the platform, as all the visualization and analysis functions are based on the database.  
First, the geometric model data for monitoring sensors and dam sections include information such as name, global ID, geometric parameters, material, spatial location, etc. These data are parsed using visualization techniques and then stored in the database. Second, the storage of monitoring data mainly involves the basic information of monitoring equipment, calculation parameters, monitoring type, monitoring time, status, manufacturer,  
Figure 1: The automated framework.  
etc. Then, the monitoring data including deformation, water level, temperature, water pressure, stress, and strain values are stored. Finally, the relevant data from the calculations are stored, including the time of the calculation, the point number, the calculated value, and the threshold values for the various instruments. The data are stored in different tables, and each table is associated with others using keywords. The keyword design and description of some tables are shown in Table 1.
## 2 Theoretical Foundations  
### The Control Equations of the Fluid Domain  
The flow of steam within the mold channels exhibits turbulent characteristics, with heat transfer governed by the energy conservation law. Due to the presence of sudden expansion and contraction geometric features in the heating plate's flow channels, the SST k-\(\omega\) turbulence model is employed to simulate vapor-mediated heat transfer, thereby improving the computational accuracy of turbulence-heat transfer coupling. The governing equations describing the turbulent flow and heat transfer processes of steam in the mold channels are formulated below, providing thermal boundary conditions for the solid domain [11; 12],  
\[\frac{\partial(\rho\mathbf{u})}{\partial t}+\
abla\cdot(\rho\mathbf{u}\otimes \mathbf{u})=-\
abla p+\
abla\cdot\mathbf{\tau}+\rho\mathbf{g} \tag{1}\]  
\[\frac{\partial(\rho E_{total})}{\sigma t}+\
abla\cdot(\rho\mathbf{u}E_{total}) =\
abla\cdot(k_{\text{steam}}\
abla T)+S_{energy} \tag{2}\]  
\[\frac{\partial(\rho k)}{\partial t}+\
abla\cdot(\rho\mathbf{u}k)=\
abla[(\mu+ \sigma_{k}\mu_{t})\
abla k]+P_{k}-\beta^{*}\rho\omega k \tag{3}\]  
\[\frac{\partial(\rho\omega)}{\partial t}+\
abla\cdot(\rho\mathbf{u}\omega)= \
abla\cdot[(\mu+\sigma_{\omega}\mu_{t})\
abla\omega]+\frac{\gamma}{v_{t}}P_{ k}-\beta\rho\omega^{2}+2(1-F_{1})\frac{\rho\sigma_{\omega 2}}{\omega}\
abla k\cdot\
abla\omega \tag{4}\]  
where \(\rho\) is the density of steam, \(\mathbf{u}\) is the velocity vector, \(p\) is the static pressure of the fluid, \(\mathbf{\tau}\) is the viscous stress tensor, \(\mathbf{g}\) is the gravitational acceleration vector, \(E_{total}\) is total energy (including internal and kinetic energy), \(k_{\text{steam}}\) is the thermal conductivity of steam, \(S_{energy}\) is the energy source term, \(k\) is the turbulent kinetic energy, \(\mu\) is the dynamic viscosity of the fluid, \(\sigma_{k}\) is the turbulent Prandtl number, assigned a value of 1, \(\mu_{t}\) is turbulent viscosity, \(P_{k}\) is the turbulent kinetic energy production term, \(\beta^{*}\) is dissipation coefficient, set to 0.09, \(\omega\) is the specific dissipation rate, \(\sigma_{\omega}\) is the turbulent Prandtl number for \(\omega\), \(\gamma\) is the production term coefficient with a value of 0.44, \(v_{t}\) is the turbulent kinematic viscosity, \(\beta\) is the dissipation coefficient, fixed at 0.075, \(F_{1}\) is the blending function, and \(\sigma_{\omega 2}\) is the second set of Prandtl numbers in the blending model, assigned a value of 1.168.