esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The PT-JPL model was used to simulate ET and its components in the HLS, and the impact of vegetation restoration and climate change on ET were explored. The main conclusions are as follows:  
1. During 2000-2018, the ET, Ei, and Es of the HLS increased at a rate of 1.33, 0.87, and 2.99 mm/a, respectively, and the Et decreased at a rate of 2.52 mm/a under the vegetation-change scenario. The ET, Et, Ei, and Es increased at a rate of 0.57, 0.47, 0.04, and 0.04 mm/a, respectively.
2. Vegetation restoration led to a significant increase in the evaporation water consumption of the HLS. Vegetation restoration increased the annual ET from 331.26 mm (vegetation-unchanged scenario) to 338.85 mm (vegetation-change scenario), an increase of 2.3% from 2000 to 2018. The change rates in ET, Et, Ei, and Es directly caused by vegetation change were 0.76, -1.95, 0.83, and 2.95 mm/a, respectively.
3. PRE made a positive contribution to ET primarily in the northern and central regions. TMP showed a negative contribution in the central and southern regions. The high value of VPD is mainly concentrated in the southern region, which has a significant negative contribution to ET. TMP was the main factor affecting its change, and the dominant influence area accounted for 37.2%. PRE and VPD were identified as the main factors affecting ET changes.  
P.H.: conceptualization, methodology, software, formal analysis, writing--original draft. G.Y.: conceptualization, modifying figures. Y.L.: conceptualization, methodology, project administration, funding acquisition, supervision. X.C.: data curation. Z.W.: data curation. H.S.: data curation. E.H.: data curation. T.X.: data curation. Y.Z.: data curation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Open Foundation of the State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau (No. F2010121002-202316); the National Natural Science Foundation of China (No. 42107512); the Inner Mongolia Academy of Forestry Sciences Open Research Project, Hohhot 010010, China, Project NO. KF2024 MS04; the Key R&D Plan of Shaanxi Province (No. 2024 SF-YBXM-621); and the Special project of science and technology innovation plan of Shaanxi Academy of Forestry Sciences (No. SXLK2022-02-7 and SXLK2023-02-14). We also acknowledge data support from Loess plateau science data center, National Earth System Science Data Sharing Infrastructure, National Science and Technology Infrastructure of China ([[https://loess.geodata.cn/](https://loess.geodata.cn/)]([https://loess.geodata.cn/](https://loess.geodata.cn/))).  
Data Availability Statement:Data are contained within the article.  
Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 1 Introduction
Lattice strain is the extent to which crystal defects cause the distribution of lattice constants and lattice dislocations [41; 44]. Due to polycrystalline aggregates within a particle, crystallite and particle sizes are typically not comparable [45]. Some efficient methods to study microstructural parameters and structural characteristics have been published in the literature. The microstructural parameters analyzed in this study utilized a comparison of the Williamson-Hall (W-H) and Sherrer methods. The Williamson-Hall method is generally considered more accurate because it accounts for both the size and strain of the crystallites, providing a more comprehensive analysis of the structural parameters. However, the accuracy can depend on the assumptions made in the models used, such as the uniform deformation model (UDM), uniform deformation stress model (USDM), and uniform defect energy density model (UDEDM), which can yield different strain values [46]. As a counterpart, the Warren-Averbach method, now known as the Scherrer method, is currently widely used. It is a simple approach that relates the peak width only to the size of the crystallites. However, it does not account for lattice strain caused by defects like point defects, grain boundaries, and stacking faults. This approach showed that the constant Ks in the equation of this method is 1.84 for two-dimensional peaks and 0.89 for three-dimensional peaks [47]. These two calculation methods are used to determine the microstructural parameters based on XRD spectra. In the present work, fullerene (C\({}_{60}\)) films were proposed using UCBD, a solution process, and analyzed by X-ray diffraction (XRD), infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS). The microstructural properties and parameters are studied based on the characterization results.
# A Survey on Satellite Communication System Security
## 4 Attacks in Satellite Communication Systems  
As SCSs continue to evolve, they play a crucial role in both civilian and military purposes. Consequently, various forms of attacks are being directed at SCSs. In this section, we will explore attacks on SCSs, with the overall structure depicted in Figure 4.
# Detection of HTTP DDoS Attacks Using NFStream and TensorFlow
## 3 Methodology  
In the theoretical sections above, we defined some of the basic concepts and methods of machine learning used in network analysis. With this theoretical background, a methodology needs to be developed for further research, as is mentioned in the article \"Methodology for Detecting Cyber Intrusions in e-Learning Systems during COVID-19 Pandemic\" [77]. The methodology of machine learning-based network traffic analysis for anomaly detection can be divided into the following parts [78]:  
* Data collection: Collecting relevant data about HTTP network traffic. This can include data from intercepted packets, HTTP application log files and other sources of network traffic data.
* Data pre-processing: The collected data needs to be pre-processed, i.e., cleaned up and normalized. This can include removing any duplicates, filling in missing values and normalizing the data into a consistent format.
* Feature extraction: Relevant features need to be extracted from the pre-processed data. This may include data such as IP addresses, port numbers, protocol types, etc.
* Model training: Building and training a machine learning model from the pre-processed data.
* Model evaluation: Evaluating the performance of the trained model on test data. This can include the use of various metrics, such as model accuracy, F1 score, etc.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Error Rate** & **Accuracy** & **Precision** & **F1** \\ \hline LSTM & 2.004\% & 97.996\% & 98.108\% & 97.997\% \\ \hline CNNLSTM & 4.108\% & 95.896\% & 97.534\% & 95.831\% \\ \hline
3 LTSN & 3.505\% & 96.495\% & 97.688\% & 96.437\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance Comparison among Different RNN models.
## 4 Enabling Technologies for FSO Communication
### Robust Beam and Multi-Modulation Compatible System Design  
The robust beam and multi-modulation compatible laser design focuses on enhancing optical communication systems by integrating a laser capable of supporting multiple modulation formats. This design improves signal integrity and system flexibility while reducing the complexity and size of the communication hardware. By enabling seamless switching between various modulation techniques, it optimizes performance for diverse communication scenarios, ensuring robust and efficient data transmission.
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 3 Methodology
### Assumptions  
The simulation is based on the 2020 operational data from the Ragland site, considering the following key assumptions:  
* Energy demand: The mine's annual electricity consumption is 145,195 MWh, requiring 34.2 million liters of diesel. The daily average electricity consumption of the site is shown in Figure 5.
* Wind resource: Wind speed data from 2020 indicates monthly average speeds between 6 and 9 m/s (see Figure 6) with an annual average of 8.5 m/s at the wind turbine hub height (92 m).
* Ambient temperature: The average temperature on site is \(-\)15 \({}^{\circ}\)C, which affects air density (1.367 kg/m\({}^{3}\) instead of the standard 1.225 kg/m\({}^{3}\)).
* Wind farm expansion: The existing two wind turbines (Enercon E-82) are expected to be supplemented with newer E-115 EP3 E4 turbines, each with a rated power of 4260 kW (92 m hub height) and a power curve illustrated in Figure 7.
* Battery storage: The selected VRB ENERGY all-vanadium flow battery is installed in a heated facility to mitigate cold-related efficiency losses.  
Figure 5: Daily electricity consumption of the 25 kV network in 2020.
## 1 Introduction  
There is a great demand for digitalizing healthcare sectors, especially with the advancements in wireless technologies and electronic devices. Healthcare 4.0 is the latest paradigm in healthcare systems incorporating modern technologies, including the Internet of Things (IoT), artificial intelligence (AI), and cloud computing [1; 2]. It refers to adapting health-management systems, which can be achieved via cloud computing and mobile communications. Clinical images can be continuously evaluated with greater precision and accuracy through digitalization [3].  
Quality healthcare is a fundamental human right; it is not always provided adequately worldwide. Chronic diseases, including heart disease and diabetes, have substantially increased due to global economic changes. The most significant risk to human health comes from chronic disorders. Also, if a disease were to spread, it would cause an influx of patients to the hospitals, burdening medical facilities' capacity to care for everyone. For instance, the global spread of COVID-19 is straining medical facilities around the globe [4].  
Healthcare 4.0 refers to integrating digital technologies, data analytics, and AI in healthcare [1]. This includes telehealth, electronic health records, AI-driven diagnostics, and personalized medicine [5]. AI-driven diagnostics can provide accurate and timely diagnoses, improving treatment outcomes. Telehealth services can improve access to healthcare and reduce the burden on physical facilities [6].  
The Internet of Medical Things (IoMT) is another paradigm expected to revolutionize the healthcare sector and provide novel healthcare services [7]. The IoMT is expected to continue expanding with advancements in sensor technology, connectivity, and data analytics. More medical devices will be connected, allowing for real-time monitoring, remote patient care, and early detection of health issues. It can improve patient outcomes by enabling continuous monitoring, personalized treatments, and remote care. Real-time data transmission can facilitate early intervention, reducing complications and hospital readmissions [8].  
By leveraging technology, healthcare systems can become more proactive, personalized, and efficient. However, data security, privacy, and implementation challenges should also be addressed to maximize the benefits of these advancements [1,5].  
This review work investigates the current state-of-the-art healthcare systems, including Healthcare 4.0 and IoMT. It provides a deeper understanding of Healthcare 4.0 and IoMT. The main contributions of this review are summarized as follows.
## 2 Related Works
### Specific Technical Approaches  
#### 2.2.1 Simulation-Based Studies  
In our research, a key objective was to meet the requirements related to downlink latency while simultaneously achieving sufficiently high throughput. Numerous studies have proposed various strategies to optimize both aspects, with many of them conducted in simulation environments. A common approach to minimizing latency involves modifying the standard resource reservation mechanism--for example, by enabling immediate message transmission at the cost of a higher risk of collision. This technique was explored in [13; 14; 15; 16]. Furthermore, in [14], the authors proposed eliminating queuing to exclude buffering effects on latency and recommended using higher numerologies to shorten the slot duration. In [17], an alternative to traditional network slicing was proposed, where the focus was placed on services and functions rather than resource allocation, specifically in the context of 5G autonomous vehicular networks. In addition to adapting grant-free access, ref. [16] also presented a technique for non-orthogonal resource sharing between URLLC traffic and latency-tolerant services.  
To address the second goal, which is ensuring high uplink throughput, several recent studies have proposed innovative physical layer and network management techniques. In [18], the authors introduced a multi-layer non-orthogonal multiple access architecture for 5G NR that enables the simultaneous transmission of several signal layers with reduced transmitter complexity and latency. Their link-level simulations demonstrated a clear throughput gain over traditional orthogonal multiple access schemes, even under imperfect interference cancellation. Other works have focused on intelligent adaptation of modulation and coding. For instance, ref. [19] proposed a machine learning-based adaptive modulation model for MIMO-OFDM systems. Using a deep neural network, the model dynamically adjusts transmission parameters based on channel conditions, leading to significantly better throughput in complex environments when compared to rule-based AMC algorithms. Another notable direction is the use of carrier aggregation and dual connectivity. In [20], the authors developed a reinforcement learning algorithm for energy-aware scheduling of component carriers and uplink power control. This method increased average user throughput while simultaneously minimizing delay and energy consumption, confirming that the combination of CA and DC can be highly effective for uplink-intensive applications. Finally, ref. [21] addressed network-level optimization through deep reinforcement learning-based slicing. The proposed method dynamically allocated frequency resources to different service slices, maximizing overall network throughput while preserving QoS for latency-sensitive applications. This approach outperformed static slicing strategies and demonstrated the benefits of intelligent spectrum sharing in multi-service 5G environments.
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 4 Cloud-Edge Collaborative Online Scheduling Algorithm
### Task Allocation Strategy  
Based on the above analysis of the three cases of container processing of microservices, the waiting time of microservice \(\mathrm{v_{i}^{m}}\) when it is assigned to device \(\mathrm{e_{n}}\) for execution can be calculated by Equation (5).  
\[\mathrm{WT_{i,n}^{m}}=\left\{\begin{matrix}\mathrm{EFT_{i,n}^{k}},&c_{\mathrm{ State}}=1\\ 0,&c_{\mathrm{State}}=0\wedge\mathrm{A}<\mathrm{M}\\ \min_{\mathrm{v_{i}^{k}\in WQ_{n}}}\Big{\{}\mathrm{EFT_{j,n}^{t}}\Big{\}},&c_{ \mathrm{State}}=0\wedge\mathrm{A}=\mathrm{M}\end{matrix}\right. \tag{5}\]  
\(\mathrm{EFT_{i,n}^{k}}\) denotes the earliest end time of microservice \(\mathrm{v_{i}^{k}}\) at device \(\mathrm{e_{n}}\). Microservice \(\mathrm{v_{i}^{k}}\) is the queue-ending microservice of container \(\mathrm{c_{i}}\) wait queue \(\mathrm{WQ_{n}^{c}}\) in device \(\mathrm{e_{n}}\), and \(\mathrm{map}(\mathrm{v_{i}^{k}})=\mathrm{map}(\mathrm{v_{i}^{m}})=\mathrm{c_{ i}}\). \(\mathrm{WQ_{n}}\) denotes the set of queue-ending microservices of the waiting queue of all containers in device \(\mathrm{e_{n}}\).  
There are two constraints in task scheduling: priority and capability. The priority constraint means that the microservice cannot execute before the data from its predecessor microservice are transferred to this microservice. The capability constraint means that the microservice must withstand waiting time on the device until the device has the capability to execute this microservice.  
The priority constraint:  
\[\mathrm{EST_{i,n}^{m}}\geq\max_{\mathrm{v_{i}^{m}\in Pred(v_{1}^{m})}}\mathrm{ EFT_{i,tar(v_{i}^{m})}^{m}}+\frac{\mathrm{w_{i^{\prime},i}}}{\mathrm{u_{tar(v_{i}^{m}),n}}} \tag{6}\]  
\(\mathrm{EST_{i,n}^{m}}\) is the earliest execution time of \(\mathrm{v_{i}^{m}}\) at device \(\mathrm{e_{n}}\). The minimum value of this time can be obtained by traversing each edge device with the cloud. \(\mathrm{tar(v_{i^{\prime}}^{m})}\) is the device to which microservice \(\mathrm{v_{i^{\prime}}^{m}}\) is assigned.  
The capability constraint:  
\[\mathrm{EST_{i,n}^{m}}\geq\mathrm{WT_{i,n}^{m}} \tag{7}\]  
The earliest execution time of microservice \(\mathrm{v_{i}^{m}}\) in device \(\mathrm{e_{n}}\) is  
\[\mathrm{EST_{i,n}^{m}}=\max(\max_{\mathrm{v_{i}^{m}\in Pred(v_{1}^{m})}}\mathrm{ EFT_{i^{\prime},tar(v_{i}^{m})}^{m}}+\frac{\mathrm{w_{i^{\prime},i}}}{\mathrm{u_{ tar(v_{i}^{m}),n}}},\mathrm{WT_{i,n}^{m}}) \tag{8}\]  
The ending time is  
\[\mathrm{FT_{i,n}^{m}}=\mathrm{EST_{i,n}^{m}}+\mathrm{r_{i,n}^{m}} \tag{9}\]  
Each microservice is assigned to the device that can be executed earliest according to Equation (10).  
\[\mathrm{tar(v_{i}^{m})}=\min_{\mathrm{e_{n}}\in\mathcal{E}}\mathrm{EST_{i,n}^{m}} \tag{10}\]
## 5 Attributes and Characteristics of FL
### Data Privacy
#### 5.1.3 Differential Privacy in FL  
Federated learning's decentralized approach trains models directly at the data sources, reducing the data's exposure and risks. Differential privacy adds controlled randomness to the data or model training process, ensuring that the outputs are indistinguishable, whether individual data are included or not [16]. Secure aggregation, as highlighted by [67], uses cryptographic methods to combine model updates while protecting individual contributions. Combining FL with differential privacy offers robust privacy guarantees and effective data utilization. However, balancing data privacy with model accuracy remains challenging, and ongoing research aims to refine such techniques to meet stringent privacy standards while maintaining model performance. This balance is crucial as FL evolves to address contemporary regulatory requirements [5].
## 2 Materials and Methods
### Determination of Volatile Compounds  
The volatile compounds were determined using a method described in a previous study [33]. The volatile substances present in the beer were determined using headspace solid-phase microextraction gas chromatography (HS-SPME-GC-MS) (Thermo Fisher Scientific Shier Technology Company, Waltham, MA, USA). First, 5 mL of degassed beer was poured into a 20 mL brown headspace bottle; 2 g of sodium chloride and 10 \(\upmu\)L of 50 mg/L sec-caprylic alcohol internal standard solution were added, and the samples were extracted with a SPEM fiber extraction head of 50 \(\upmu\)m/30 \(\upmu\)m CAR/DVB/PDMS, incubated at 55 \({}^{\circ}\)C for 15 min, and extracted and adsorbed for 30 min. Following extraction, the extraction head was quickly inserted into the GC inlet and desorbed at 250 \({}^{\circ}\)C for 0.5 min. GC-MS was used to identify the volatile substances in the beer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Q1** & **Q3** & **RT** & **ID** & **DP** & **CE** \\ \hline
218 & 88 & 3.95 & VB5\_1 & \(-\)54 & \(-\)19 \\
218 & 146 & 3.95 & VB5\_2 & \(-\)54 & \(-\)22 \\
204 & 168 & 2.40 & VB6\_1 & \(-\)52 & \(-\)15 \\
204 & 150 & 2.40 & VB6\_2 & \(-\)52 & \(-\)25 \\
124 & 80 & 1.66 & VB3\_1 & 80 & 30 \\
124 & 80 & 1.66 & VB3\_2 & 80 & 31 \\
124 & 53 & 1.66 & VB3\_3 & 80 & 39 \\
265 & 122 & 1.74 & VB1\_1 & 90 & 22 \\
265 & 144 & 1.74 & VB1\_2 & 90 & 24 \\
377 & 243 & 4.80 & VB2\_1 & 29 & 34 \\
377 & 172 & 4.80 & VB2\_3 & 33 & 48 \\ \hline \hline \end{tabular}  
* Q1—parent ion (\(n/z\)); Q2—daughter ion (\(m/z\)); RT—etention time (ms); ID—vitamin ID, multiple groups of test results of the same vitamin (such as VB) under different Q3 and CE conditions will be distinguished by suffix numbers (\_1, \_2, etc.); DP—delustering potential (V); CE—collision energy (V).  
\end{table}
Table 2: The parameters of positive+negative ion mode for five B vitamins.  
The chromatographic conditions were as follows: carrier gas: high-purity helium; column flow: 1.2 mL/min; sample inlet temperature: 250 \({}^{\circ}\)C. The initial temperature was 40 \({}^{\circ}\)C, maintained for 1 min, and then increased to 180 \({}^{\circ}\)C at 3 \({}^{\circ}\)C /min and lastly increased to 230 \({}^{\circ}\)C at 20 \({}^{\circ}\)C /min and maintained for 15 min; split mode, split ratio: 4.2:1.  
The mass spectrometry conditions were as follows: ionization mode: electron ionization (EI); single four-stage rod mass analyzer; transmission line temperature: 230 \({}^{\circ}\)C; ion source temperature: 260 \({}^{\circ}\)C; mass range: 29-350 amu; scanning time: 0.2 s.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 3 Experimental Results and Analysis  
In this section, to demonstrate the superiority of our proposed SSD-SAR-BS, we conducted quantitative and visual comparison experiments, where several state-of-the-art despeckling methods were used for comparison. Both synthetic speckled and real SAR data were employed for the analysis.
# Surface Defect Detection Methods for Industrial Products: A Review
## 2 Traditional Feature-Based Machine Vision Algorithm for Surface Defect Detection
### Shape Feature-Based Method  
The method which is based on the shape effectively uses the target of interest in the image for retrieval. Among them, the contour-based method is the main type of method. The contour-based method obtains the shape parameters of the image by describing the outer boundary features of the object; the representative methods are Hough transform and Fourier shape descriptor.  
Hough transform uses the global features of the image to connect the edge pixels to form the closed boundary of the region, and its theoretical basis is the duality of point to line. Literature [34] proposes a method for detecting bottle surface defects; in the ROI extraction stage, fast Hough transform was used to detect the boundary line of light source. By using Gabor filter and Hough transform, literature [35] realized the detection of linear defects (such as indentation and bump) on the middle surface of E-TPU. Literature [36]realized surface defect detection of small camera lens based on circle Hough transform, polar coordinate transformation, weighted Sobel filter, and SVM.  
The Fourier shape descriptor uses the Fourier transform of the object boundary as the shape description and uses the closure and periodicity of the region boundary to transform a two-dimensional problem into a one-dimensional problem. Literature [37] proposed a method for detecting and locating small defects in aperiodic images, which is based on global Fourier image reconstruction and template matching. Literature [38] proposed a detection method for cutting defects on the surface of magnets; the method uses Fourier transform and Hough transform to reconstruct the image of the magnet surface, and obtains defect information by comparing the gray difference between the reconstructed image and the original image.  
In addition to the above three types of features, some other features, such as spatial relationship features, can also be used for the surface defect detection of industrial products. Because the surface of industrial products mostly contains a variety of information, it is usually not enough to use only a single feature or a single class of features. Therefore, in practical applications, multiple features and multiple classes of features are often used in combination.