esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Storage Options and Associated Costs  
In this work, we consider the BESS systems provided by the combination of the Victron Energy Inverters with the Pylontech lithium batteries (Victron Energy, [[https://www.victronenergy.com/live/battery_compatibility:pylontech_phantom](https://www.victronenergy.com/live/battery_compatibility:pylontech_phantom)]([https://www.victronenergy.com/live/battery_compatibility:pylontech_phantom](https://www.victronenergy.com/live/battery_compatibility:pylontech_phantom))). This combination was selected for two reasons: (1) The modular design enables the combination of different inverter sizes and battery capacities. For example, it is possible to increase the nominal capacity by adding up to eight modules of 2.4 kWh without changing the inverter. (2) The degradation characteristics are aligned with our degradation model, i.e., 6000 cycles with 90% DoD (Pylontech, [[http://www.pylontech.com.cn/pro_detail.aspx?id=114&cid=23](http://www.pylontech.com.cn/pro_detail.aspx?id=114&cid=23)]([http://www.pylontech.com.cn/pro_detail.aspx?id=114&cid=23](http://www.pylontech.com.cn/pro_detail.aspx?id=114&cid=23))).  
The main characteristics and price of the different BESS systems are presented in Table 4. Note that the C-Rate of the battery modules is 0.5, i.e., it takes 2 h to fully charge or discharge the battery module. Note also that the prices presented do not consider the Value Added Tax (VAT) that may change depending on the country.  
With the objective of selecting the best-suited BESSs and control strategies for each UPAC, many inverter/battery combinations were considered. The different combinations considered are presented in Table 5.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{2}{c}{**Inverter/Charger (Victron Energy)**} & \multicolumn{4}{c}{**Battery (Pylontech US2000)**} \\
**Model** & **Power** & **Price** & **Min.** & **Power** & **Energy** & **Price** \\  & **(kW)** & **\(\mathbf{\xi}\)** & **Modules** & **(kW)** & **(kWh)** & **\(\mathbf{\xi}\)** \\ \hline Multiplus 48/1200/13 & 1.0 & 747.00 & 1 & 1.2 & 2.4 & 752.07 \\ Multiplus 48/3000/35 & 2.4 & 1178.00 & 2 & 2.4 & 4.8 & 1504.14 \\ Multiplus 48/5000/70 & 4.0 & 1791.00 & 4 & 4.8 & 9.6 & 3008.28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inverter/Battery specifications considered in this work.  
Figure 5: Hourly distribution of the active power demand each UPAC.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
###### Abstract  
The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud-edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud-edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments.  
video big data analytics; edge AI and federated learning; real-time video processing; hybrid cloud-edge computing; privacy-preserving AI +
Footnote †: journal: Applied sciences
## 2 Methods
### Symbiotic Organisms Search-Least Square Support Vector Machine
#### 2.2.1 Lssvm  
SVM is one of the most used predictive models modified into LSSVM by the researchers. SVM used quadratic programs as loss functions, whereas LSSVM uses the least square linear system as a loss function [32]. This algorithm is a statistical learning theory, and it differs from SVM in a way that it needs equality constraints while operating the least square cost function. The optimization constraints can be explained using the following formulations:  
\[J_{p}(w,\epsilon)=\frac{1}{2}w^{T}w+\gamma\frac{1}{2}\sum_{k=1}^{N}e_{k}^{2} \to minimise, \tag{1}\]  
\[y_{k}=w^{T}\phi(x_{k})+b+\epsilon_{k},\ k=1,2.\ldots.,N, \tag{2}\]  
where \(w\)\(\epsilon\) R is a vector of undetermined parameters. \(\phi(.)\) is a function with non-linearity mapping the input space to high feature space. \(e_{k}\)\(\epsilon\)\(R\) are error variables, and \(\gamma\) is a regularization constant and is always greater than zero.  
\[y(x)=\sum_{k=1}^{N}\alpha_{k}K(x_{k},x_{1})+b, \tag{3}\]  
Equation (3) shows function estimation by the LSSVM model where the solution to the linear system in Equation (4) is \(\alpha_{k}\) and \(b\). The kernel function defined here is Radial Basis Function, and it is defined as follows:  
\[K(x_{k},x_{1})=\exp\biggl{(}\frac{\|x_{k}-x_{1}\|^{2}}{2\sigma^{2}}\biggr{)}, \tag{4}\]  
where \(\sigma\) is a parameter of the kernel function. \(\gamma\) and \(\sigma\) need to be specified for better predictive results from the model.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 4 Performance Analysis
### Simulation Results
#### 4.3.2 Node Density Variation
This is because of the high packet collision and packet drops due to buffer overflow at intermediate relay nodes resulting in  
Figure 6: Performance comparison of ILBFC with LBF, DBD and BLR versus number of nodes, while keeping the maximum speed of the node equals 50 km/hr and the source transmission rate equals 400 kbps. (**a**) PDR versus number of nodes; (**b**) Average ETE delay versus number of nodes; (**c**) PRC versus number of nodes. fewer data packets being forwarded by intermediate relay nodes. This further reduces the chances of formation of multiple relay nodes at single hop level leading to low PRC. Again, the significance of road intersection information in enhancing the unicast efficiency can be seen when comparing LBF and ILBFC performance. ILBFC which has an average PRC value of 2.9, with its better winner relay management based on \(\delta_{eff}\), results in relatively lower PRC with rising node density as compared to LBF which has an average PRC value of 4.6.
# Impacts of Climate Change on Blue Carbon Stocks and Fluxes in Mangrove Forests
## 3 Climate Change Impacts
### Sea-Level Rise (SLR)
For the RCP4.5 scenario where sea-level is forecast to rise by 0.4-0.7 m by 2100, global gains and losses in mangrove blue carbon stocks will likely be much less at \(\approx\)\(-\)0.3-0.5 Pg C (Figure 1C) with CO\({}_{2}\) emissions of 1.1 Pg under high 'coastal squeeze' (Figure 1D). Under the four modeled scenarios (RCP8.5 and RCP4.5) at both low (P300) and high (P5) 'coastal squeeze', Lovelock and Reef [58] found that the influence of SLR on mangrove blue carbon stocks was variable between the SLR and coastal squeeze scenarios  
Figure 1: Impacts of sea-level rise on mangrove blue carbon stocks and fluxes. Estimates of changes in mangrove forest cover (**A,C**) under coastal adaptation scenarios that are either high (dark blue) or low (light blue). The projected influence on net blue carbon emissions and removals until 2100 (**B,D**) are estimated for RCP8.5 scenario (**A,B**) and RCP4.5 (**C,D**). Negative values are reductions in global cover or carbon emissions. Modified from source: [58]. Open access under the CC By-NC-ND license 4.0. (Figure 2). Both gains and losses of mangrove area occur in different regions and nations for net change in carbon stocks in all scenarios. For RCP8.5 in low and high coastal squeeze scenarios (Figure 2A,B), Australia, Mexico, and the United States have highest potential for soil carbon accumulation, while Indonesia has the potential for the highest CO\({}_{2}\) emissions in both coastal squeeze scenarios. For RCP4.5, gains in mangrove carbon stocks and emissions are much lower (Figure 2C,D) than in the RCP8.5 scenario because tidal inundation is limited, although many of the same countries are in the top 10 for both gains and losses. For RCP8.5, the 10 countries with the highest probability to protect blue carbon if coastal squeeze is minimized are Mexico, Indonesia, United States, Nigeria, Cuba, Mozambique, Fiji, Nicaragua, Malaysia, and Senegal, while the greatest negative effect is on island nations, especially in Oceania. The countries most vulnerable to blue carbon losses include Indonesia, Nigeria, Cuba, United States, Malaysia, Mexico, Australia, Philippines, Cameroon, and Papua New Guinea. Globally, blue carbon gains and losses may be huge, on the order of \(-\)70-489 Tg C under RCP8.5 and \(-\)27-28 Tg C under RCP4.5 (Figure 2).
## Precise Point Positioning with the BeiDou Navigation Satellite System  
**[PERSON], [PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
GNSS Research Center, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China;  
E-Mails: [EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (Q.Z.); [EMAIL_ADDRESS] (J.G.); [EMAIL_ADDRESS] (X.S.); [EMAIL_ADDRESS] (X.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-150-714-110-60; Fax: +86-276-877-8971.  
_Received: 28 November 2013; in revised form: 23 December 2013 / Accepted: 31 December 2013 / Published: 8 January 2014_  
**Abstract:** By the end of 2012, China had launched 16 BeiDou-2 navigation satellites that include six GEOs, five IGSOs and five MEOs. This has provided initial navigation and precise pointing services ability in the Asia-Pacific regions. In order to assess the navigation and positioning performance of the BeiDou-2 system, Wuhan University has built up a network of BeiDou Experimental Tracking Stations (BETS) around the World. The Position and Navigation Data Analyst (PANDA) software was modified to determine the orbits of BeiDou satellites and provide precise orbit and satellite clock bias products from the BeiDou satellite system for user applications. This article uses the BeiDou/GPS observations of the BeiDou Experimental Tracking Stations to realize the BeiDou and BeiDou/GPS static and kinematic precise point positioning (PPP). The result indicates that the precision of BeiDou static and kinematic PPP reaches centimeter level. The precision of BeiDou/GPS kinematic PPP solutions is improved significantly compared to that of BeiDou-only or GPS-only kinematic PPP solutions. The PPP convergence time also decreases with the use of combined BeiDou/GPS systems.  
**Keywords:** BeiDou navigation satellite system; Position and Navigation Data Analyst (PANDA); BeiDou Experimental Tracking Stations (BETS); Precise Point Positioning (PPP)
# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 4 Experiments
### Results and Analysis
#### 4.4.3 Effect Shown  
As shown in Figure 13, the segmentation results of each model. The pixels surrounded by the green curve are manually marked pixels belonging to cracks. The red part is the pixel inferred by the model as belonging to the crack. It should be noted that the segmentation result of the model is a binary image, that is, the pixels not belonging to cracks are marked as 0 and the pixels belonging to cracks are marked as 1. In order to obtain the display effect as shown in the figure, we used the segmented binary image as a mask. Corresponding to the position of 1 in the mask, the R channel of the original image is set to 255, while the G channel and B channel are both set to 0. As can be seen from Figure 13, the segmentation result of the proposed model was the best. As shown in Figure 13a, for cracks with large area, several models can segment cracks well. It should be pointed out that the segmentation result of the proposed model was closer to that of manual annotation. In addition, it can be seen in Figure 13a that FCN-VGG, FCN-Resnet-18 and FCN-Resnet-50 did not segment cracks with small area well, especially FCN-VGG. The same situation also occurs in Figure 13b,d. The reason is that excessive down-sampling leads to loss of details, thus the original semantic information cannot be decoded. Compared with other models, the proposed model has one less down-sampling operation, then the dilated convolutions with different dilation rates and a multi-branch fusion strategy were designed so that the receptive field was expanded while the detail information was preserved, which alleviated the problem of detail loss to some extent. As shown in Figure 13b, for the proposed model, some fine cracks could also be segmented. It should be pointed out that the proposed model also had incomplete segmentation of fine cracks, as shown in Figure 13d, which also shows that the proposed model had the problem of missing details, but it was not as serious as other models. As shown in Figure 13c, for scenes with relatively complex backgrounds, each model had the problem of wrong segmentation. The reason is that our dataset is relatively simple and lacks training samples under complex background, thus causing erroneous segmentation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **PA** & **MPA** & **MIoU** & **FWIoU** \\ \hline FCN-Resnet-50 & 95.53 & 91.36 & 82.01 & 92.20 \\ FCN-Resnet-18 & 96.10 & 89.75 & 82.79 & 92.90 \\ FCN-VGG & 96.41 & 90.27 & 83.90 & 93.40 \\ The proposed model & 96.84 & 92.55 & 86.05 & 94.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of all model.
# Study of Self-Heating and High-Power Microwave Effects for Enhancement-Mode p-Gate GaN HEMT
## 3 Results and Discussion
### Thermal Characteristics
#### 3.2.1 Self-Heating Effect  
Figure 7a depicts the thermal distribution of the device. Specifically, the hot spot is located in the channel region near the junction of the gate and the passivation layer. Figure 8a shows that the hot spot is the area with the largest potential gradient, where the electric field strength is also the greatest. This can be explained well in theory due to the  
Figure 5: The electron mobility (blue and green line, left Y-axis) and \(\Delta\)mobility (red line, right Y-axis) in the enhancement-mode p-Gate AlGaN/GaN HEMT of different operation modes.  
output simulation bias conditions. When performing output characteristic simulation, the source voltage is set to 0 V, the gate voltage is set to 2 V, and the drain voltage is scanned from 0 V to 18 V. The voltage drop between the gate and the drain is much bigger than the voltage drop between the gate and the source. The electric field is proportional to the voltage across the two electrodes due to the same gate-source distance Lgs and gate-drain distance Lgd. Thus, the maximum electric field strength is located the drain area, which is consistent with the distribution result of Figure 8b.  
Figure 6: Diagram of drain current with drain voltage for various values of gate voltage with self-heating (solid line, left Y-axis), without self-heating (dotted line, left Y-axis), and \(\Delta\)current (solid line with hollow circle, right Y-axis).  
Figure 7: (a) Internal thermal distribution of the device under self-heating effect; (b) thermal distribution near the hot spot.  
The action of a strong field leads to a larger current density, resulting in more Joule heat. An increase in temperature leads to a decrease in mobility, as shown in Figure 9a, which is a very good explanation for the NDC phenomenon mentioned above. In addition, the resistivity will also increase with the temperature, the current will decrease, and the rate of temperature rise will slow down which can be clearly demonstrated in the HPM effect discussed below. Figure 9b shows self-heating temperature variation over time; it is found that the temperature rise rate increases with time in the initial stage under self-heating conditions. At a certain time node, the temperature increases almost at a relatively constant rate.
## 5 Experimental Results
### Performance Evaluation Metrics  
To evaluate the performance of the proposed IDS and compare it with the performance levels of other IDSs, the following performance evaluation metrics were employed [12; 13; 49]:  
1. Accuracy (AC)  
Accuracy was defined as \"the ratio of correct predictions to the total number of predictions\" as in (3):  
\[\text{AC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{3}\]  
Figure 3: CS in GA.  
The Detection Rate (DR)  
The DR is defined as \"the ratio of the number of correctly detected attacks to the total number of attacks\" as in (4):  
\[\text{DR}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
The False Alarm Rate (FAR)  
The FAR is defined as \"the ratio of the number of normal patterns that have been classified as attacks (false positives) to the total number of normal patterns\" as in (5):  
\[\text{FAR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{5}\]  
where  
TP (true positive) is the number of correctly identified attacks.  
TN (true negative) is the number of correctly identified normal patterns.  
False positive (FP), also known as false alarm, is the number of normal patterns that have been erroneously classified as attacks.  
False negative (FN) is the number of actual attacks that have been erroneously classified as normal patterns. These attacks can be detected by using an IDS.  
To better assess the performance of the proposed IDS, researchers performed performance comparisons with IDSs that performed feature filtration using 41 features.
## 2 Materials and Methods
### The Chemical Compositions of FPs
#### 2.4.3 Molecular Weight Analysis  
The molecular weight of FPs and their distribution were determined using the technique of high-performance size exclusion chromatography equipped with a multi-angle laser light scattering instrument and a refractive index detector (HYSEC-MALLS-RI, WY-ATT Company, Santa Barbara, CA, USA) [28]. FPs were prepared as a 5 mg/mL solution and passed through a 0.22 \(\upmu\)m filter membrane. A 0.15 mol/L NaCl solution containing 0.02% NaN\({}_{3}\) (filtered through a 0.22 \(\upmu\)m membrane and degassed by ultrasonication) was used as the mobile phase. Shodex OHpak SB-806 HQ and Shodex OHpak SB-804 HQ (Shora Denko Scientific Instruments Shanghai Co., Ltd., Shanghai, China) were used as the columns for chromatographic separation. The injection volume was 100 \(\upmu\)L and the flow rate was 0.5 mL/min. The data were collected and processed by ASTRA 6.
# A TEE-Based Federated Privacy Protection Method: Proposal and Implementation
## 3 Method Introduction
### Protection Objectives  
To solve several security problems that federated learning are facing, such as data poisoning, backdoor attacks, and gradient leakage, the key point is how to protect the integrity of the input data and the confidentiality of the gradient. This study aims to provide full-link privacy protection based on TEE (Intel SGX), which could achieve the following protection goals (1)-(4):  
Figure 2: Vertical federation computing process based on TEE privacy protection improvement.  
1. Input protection: the inputs include training data, network parameters, and network structure.
2. Modeling process protection: model checkpoint and communication encryption.
3. Output protection: the outputs include model protection and model checkpoint protection.
4. Transmission security: adopting the method of dynamically creating certificates, combined with the remote authentication technology of SGX, ensures that participants and third parties cannot steal the transmitted messages.
## 4 Analysis  
Two analytical tools are provided to estimate the number of sensing rounds of the proposed schemes. For the CASA approach, a neural network model is built up to approximate the desire performance. For the DASA approach, a probabilistic model using the concept of geometry and the Lindeberg Theorem [29] are applied to abstract the properties of the algorithm. Moreover, based on the analysis, the sensor lifetime and cluster lifetime is further explored to show how the operations of the proposed schemes may prolong the network lifetime.
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 4 Practical Evaluation of Transport-Layer Options
### Orchestration Traffic Performance with Background Data Traffic
In this scenario, where the reliability of data transmission is to be implemented as required by orchestration-related application processes, the unreliable delivery service of UDP may provide a suitable option to disseminate the orchestration information.
# Simultaneous Analysis of Heavy Metal Concentration in Soil Samples
###### Abstract  
The application of Fourier transform infrared spectroscopy to the simultaneous analysis of heavy metal concentration in soil samples was demonstrated in this paper. Two spectral techniques, namely, attenuated total reflectance (ATR) and diffuse reflectance (DRIFT), were applied and the whole infrared spectral region, i.e., far IR, mid IR, and near IR were considered in this work. Spectral data with reference to the results of laboratory analysis enabled the development of calibration partial least squares (PLS) models. The PLS models for the ATR near IR were characterized by a good fit and good prediction abilities. According to the results obtained, the most accurate description and prediction were realized in the case of mid/far and near IR for the mercury and nickel concentration in soil. Application of far IR slightly improved the prediction possibilities of the model. The construction of PLS models based on the Fourier-transform infrared (FT-IR) spectra enables the perception of FT-IR spectroscopy as a supplementary method that is useful in the estimation and monitoring of the contamination level in soils.  
soil pollution; FTIR spectra; partial least squares PLS; far-IR; rapid estimation Article