esa-sceva/satcom-chunk-collection huggingface dataset:
# AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0
## 4 Theoretical Perspectives
### Key Technologies Enabling the Evolution from Industry 4.0 to 5.0
#### 4.5.2 Optimization Technologies  
Optimization tools play a critical role in balancing complex and often competing objectives such as cost, energy use, waste reduction, and production agility.  
Multi-Objective Optimization and Edge Computing  
Modern industrial challenges require balancing multiple priorities simultaneously. Multi-objective optimization (MOO) algorithms address this by generating optimal trade-offs across competing goals, such as energy efficiency and throughput [40; 59].  
Edge computing, which processes data locally rather than relying solely on cloud infrastructure, enhances real-time responsiveness and reduces latency. Together, MOO and edge computing create systems that are both efficient and highly adaptive [59; 60].  
Life Cycle Assessment (LCA) Models  
LCA tools help evaluate the environmental impact of products and processes from cradle to grave. When integrated with AI, they provide real-time insights into resource usage, emissions, and recyclability [61; 62]. This enables manufacturers to make sustainable choices at every stage of the product life cycle, aligning operations with circular economy principles and global climate targets.
# A Survey on Network Optimization Techniques for Blockchain Systems
## 5 Network Optimization Techniques Used in Blockchain Networks
### Data Layer
In their approach, peers maintain a pool of transactions, and, hence, when any other peer receives a hash of a transaction, it should be able to recover the full transaction from the pool. Therefore, the authors replaced all transactions in a block with its hash, hence making it smaller and occupying a smaller bandwidth when transmitted. In the unlikely case that all peers have similar transaction pools, their approach will drastically reduce the requirements to transmit blocks. However, when there are dissimilarities in transaction pools, this approach is likely to consume more bandwidth than the normal blockchain operation and will, therefore, not scale well with peers or transactional load.
## Materials and Methods
### CMF Generation
#### 2.2.2 Generation of the CMF with a Spectral Range Regime  
To generate the CMF (in the device described above), the alternating component was formed using a signal which already contained a frequency spectrum comprising 12.6 Hz. This spectrum was obtained by averaging all channels from five EEG recordings from male and female volunteers aged between 25 and 54 years who signed informed consent for study participation. Each of the five EEGs was composed of 19 active channels, through which brain wave activity was recorded in accordance with the guidelines of the International Federation of Clinical Neurophysiology (IFCN) [13]. The resultant averaged signal was amplified proportionally to a maximum of 0.8 V (Figure 2). Each recording was performed for 5 min and repeated eight times to achieve the desired 40 min exposure to the CMF.  
The signal shown in Figure 2a was used to form the CMF alternating component. The signal spectrum contained 12.6 Hz band (Figure 2b). The amplitude of the CMF alternating component was selected to be approximately 100 nT (this value roughly corresponds to half-width of histogram of EEG signal distribution shown in Figure 2c).  
Figure 2: Electrophysiological signal used to form the alternating component of the combined collinear magnetic fields (CMF): (**a**) fragment of averaged electroencephalogram (EEG) used for modulation of alternating component of the CMF; (**b**) power spectrum of averaged EEG shown in panel (**a**); (**c**) distribution histogram of averaged EEG shown in panel (**a**).
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
## 2 Coal Samples and Experiments
### MIP, SEM, and Permeability Test  
MIP, SEM, and permeability measurements were performed for all coal samples to understand the internal relationship between PSD and permeability. MIP was performed using an Autopore IV 9500 instrument (Micromerities Instrument Co., Ltd., Norcross, GA, USA) by the GB/T21650.1-2008 [19]. The mercury injection pressure was 0.01-413.46 MPa, and the corresponding pore size was 130 \(\upmu\)m to 3 nm. The size of coal samples used in the experiment was 3-6 mm.  
The SEM test was carried out using a Quanta 250 field emission scanning electron microscope (FEI Company, Hillsboro, OR, USA). The coal samples were prepared by the PECS-II-685 argon ion polishing instrument (Gatan, Inc., Pleasanton, CA, USA). Pores and fissures larger than 20 nm on the coal surface can be observed.  
The permeability test used pulse-decay permeability of PDP-200 (Core Laboratories, Tulsa, OK, USA), which is widely used in the permeability test of coal with low to ultra-low permeability. The measurements ranged from 10\({}^{-5}\) mD to 10 mD. It is particularly worth noting that the experimental coal samples were dried with nitrogen as the working medium, and the permeabilities were tested at the same confining pressure and pore pressure of 2 MPa.
# Crowded Space: A Review on Radar Measurements for Space Debris Monitoring and Tracking
## 5 Other Countries' Radar Measurements
During the ODERACS experiment, the Don-2N radar was able to track the smallest family of spheres with a diameter of 5.1 cm. An estimation of the radar's sensitivity is reported on page 15 of [69]. Among others noteworthy facilities of the Russian network we cite the Dnestr-M/Dnepr radars located in Olengorsk, Balkhash (Kazakhstan), Mishelevka, Sevastopol (Ukraine), and Mukachevo (Ukraine), the Dayal-type radar in Pechora and Gabala (Azerbaijan), and, finally, the Volga-type radar in Baranovichi (Belarus) [72]. Like the Don-2N, these radars were once part of the early warning antiballistic missile network within the Soviet Union, and, except for the Dnestr-M/Dnepr radars, they are all large phased-array radars [72]. Similarly to the Russian radar network, only fragments of information are retrievable about the People's Republic of China's (PRC) radars for debris measurements. As pointed out in [73], there are several antennas spread across China that could be employed in space debris detection, including large parabolic reflectors and phased arrays. In [6], it is postulated that some of the phased array radars used for LEO debris tracking are located in Kashi, Kunming, Hainan, Jiangxi, Changchun, Xuanhua (Hebei province), Henan province, and in an unspecified location in the north-western part of the country. Moreover, as already discussed, there is evidence of Chinese space debris activity in [68], where the RT-25 in Shanghai and Urumqi, belonging to the VLBI network, were used as receivers in a space debris experiment.
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 3 Dynamic and Automated Resource Allocation Based on VDN and VDNO
### Automated Resource Allocation and Management by Vdno  
VDNO provides a location- and load-aware end-to-end orchestration mechanism to manage and utilize service resources efficiently, including computing, storage, and networking resources in the distributed edge cloud environment. In VDNO, orchestration is performed on demand by user requests to allocate physical and virtual resources dynamically. Figure 6 and Table 1 represent the overall  
Figure 5: VDN framework: core engines, subsystems, datastore, and external systems.  
system architecture, principal components, and basic process of VDNO, which interacts with both Kubernetes [27] and VDN systems to assign the required resources to the KREONET-S infrastructure. Here, Kubernetes works as a container manager, the VDN system works as a virtual network manager, and KREONET-S works as a wide-area SDN infrastructure, assuming that multiple (containerized) cloud computing nodes are located at the distributed MEC sites.  
According to the abovementioned orchestration components and basic process, when a user (or device) requests for the creation of a virtual network slice integrated with computing and storage service resources, a specific MEC site is selected and (containerized) service nodes are allocated among multiple distributed MEC sites. The container manager, Kubernetes, manages the allocation of the containerized MEC nodes (also known as PODs), which are recognized by ONOS as soon as they are allocated and connected to an OpenFlow network device. Next, the MEC nodes are included  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Roles** \\ \hline Orchestrator (VDNO) & Dynamic creation and allocation of containerized cloud resources with on-demand virtual network slices generated and connected \\ \hline Container Manager & Dynamic provisioning of containerized service resources at the specific MEC site selected by VDNO on the basis of optimal location and service resource availability \\ \hline Virtual Network Manger (VDN) & Processing the requests from VDNO to provision virtual network slices by interworking with ONOS for granular flow control and management \\ \hline SDN Controller (ONOS) & Management of abstracted network resources and flow control requested by the VDN system \\ \hline KREONET-S network devices and MEC nodes & Wide-area SDN infrastructure and MEC servers \\ \hline \hline \end{tabular}
\end{table}
Table 1: VDNO components and roles. ONOS, Open Network Operating System.  
Figure 6: Integrated VDN orchestrator architecture and process.  
and abstracted in the global network topology view as the controllable end-hosts. After this process, the VDN system provisions a virtual network slice including the connected and ONOS-aware MEC nodes with the required and guaranteed dedicated network performance.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model  
A trust model is based on some trust factors. The main purpose of a trust-based model plays a significant role in considering particular trust factors to design an effective solution. In this research, a trust model is designed for providing security in RPL, and the trust factors are considered accordingly. The trust factors form up the quantifiable trust metrics. For the proposed SMTrust, the mobility of nodes is considered, which constitutes the mobility-based trust metrics.  
The proposed design of the SMTrust model for the security of RPL mainly consists of two phases: trust formation and attack detection. The trust formation includes trust metrics identification, trust metrics calculation, trust index computation, trust rating, and trust monitoring. The second phase includes attack detection and isolation of malicious nodes. A preliminary model and workflow of SMTrust is explained in [21].
# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 3 Methods
### Pooling Compensation  
The pooling compensation mechanism proposed in this paper uses skip links to compensate for the loss of information in the encoder-decoder structure due to the compression. The main theoretical basis is the re-used feature implemented by skip links [40], on which the attention mechanism is used to enhance the desired detail regions while suppressing other irrelevant regions.  
[PERSON] et al. [41] and [PERSON] et al. [42] pointed out that pooling will bring about the loss of information, which may affect the final effect of the model. In previous studies, such as in the field of image segmentation, FCN [43] used skip links to add rich details to the segmentation results, but did not explain it theoretically. These methods did not highlight the specific needs of rich foreground target details, neither do methods such as Unet [44] and the foreground segmentation network FgSegNetV2 [9]. In other words, these methods do not achieve relevant feature enhancement and irrelevant feature suppression, while irrelevant features may have an impact on the accuracy of results. The motivation for designing the pooling compensation mechanism in this paper is based on this existing problem.  
We believe that the loss of detailed information caused by pooling can be alleviated by pooling compensation. We propose the pooling compensation mechanism (Figure 3) to enhance the detailed information required for classification and suppress irrelevant information. We first perform the convolution operation of \(k\times k\) on the feature map \(X\) before the maximum pooling operation to obtain the feature map \(SX\). We then obtain the corresponding weight matrix \(S\) through the Sigmoid activation process, and finally input the feature. The image \(X\) and the weight matrix \(S\) are multiplied to obtain the weighted output feature map \(Y\), as shown in Equation (2). The importance of each pixel on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & **Operator** & **Output** \\ \hline \(h\times w\times k\) & linear \(1\times 1\) conv2d & \(h\times w\times(tk)\) \\ \(h\times w\times(tk)\) & linear \(3\times 3\) dwise s = s & \(\frac{h}{s}\times\frac{w}{s}\times(tk)\) \\ \(\frac{h}{s}\times\frac{w}{s}\times(tk)\) & \(1\times 1\) conv2d, ReLU & \(\frac{h}{s}\times\frac{w}{s}\times k\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Single linear bottleneck block transforming from \(k\) to \(k^{\prime}\) channels, with stride s, and reduction factor t.  
input feature map for subsequent classification is obtained by the learning. Hence, we can enhance the feature details of the pooling loss and suppress the irrelevant regions.  
\[Y=\sigma(\,f^{k\times k}\,X\,)\,X \tag{2}\]  
where \(\sigma\) is the sigmoid activation operation, and \(f^{k\times k}\) represents the \(k\times k\) convolution operation.
## 2 Methodology
### Denoise Methods
#### 2.5.3 Evaluation Index of Noise Reduction  
The signal-to-noise ratio (SNR) represents the power ratio of signal \(P_{S}\) to the noise \(P_{n}\) in logarithm form with a base of 10, which is used to evaluate the performance of the denoising algorithm.  
\[SNR=10\lg\frac{P_{s}}{P_{n}} \tag{37}\]  
where lg represents the logarithm with a base of 10.  
Figure 3: Flowchart of the CEEMDAN-IWT denoising algorithm.  
In addition, the mean square error (MSE) can evaluate the differences between the true value \(x_{i}\) and the predicted value \(y_{i}\).  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}{(x_{i}-y_{i})^{2}} \tag{38}\]
## 6 Underwater Object Localization Service Case Study Relying on Multi-Sensor Data Fusion
### Application Model  
The application model is a representation of the system's functions and application components (in this example, the MeDON information system). The model in (Figure 13) illustrates several associations that connect application and business functions. The significance of the triggering links between two functions is conveyed by these associations. It explains what a call between two functions means. For instance, in accordance with the assignment relationship between functions and components, the SmartSensorSystemA component calls the InformA application function when sensor1A detects a dolphin. The various triggering relationships throughout the application functions then dictate the order of execution. The following two activities, ComputeCoordinatesA and StoreCoordinatesA, are carried out by the FusionSystemA with the resources allocated by the application function SystemResourceReservationA. Next, using the function CoordinatesTransmission to B, the coordination data are transferred from the active DataFusion component to the following DataFusion component, and so on. This process is repeated until the coordinates are determined with greater accuracy while accounting for the detection data gathered by the various Smart Sensors.  
Figure 12: Business view from the underwater object (dolphin) localization model [16]—ArchiMate Business Layer.
# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## Validation Guidelines and Technical Specifications
### Progress on the Development of Sampling Design and Scale Approach  
Obtaining ground truth at the pixel scale requires a well-designed sampling scheme, high quality data sets and rational statistical inference of measured samples. For homogeneous pixels, a few samples are adequate for capturing the dominant information for a pixel grid. If a heterogeneous pixel is presented, additional samples are usually desired to reach an unbiased estimation with a satisfactory accuracy [161]. Hence, how to use a limited number of sampling points to optimise their spatial distribution and therefore acquire reliable ground truth at the pixel scale is a question to be answered. This topic is investigated based on the characteristics of land surface, e.g., spatial autocorrelation and homogeneity. In general, if there is a lack of spatial autocorrelation of ground objects, classical sampling methods (e.g., simple random sampling, systematic sampling, and stratified sampling) can be used. Otherwise, more complicated spatial sampling methods, such as the MSN spatial sampling optimisation scheme [162; 163], are recommended. The following section focuses on the progress of the development of the sampling design and upscaling approach over heterogeneous land surfaces, as this issue is considered in Component 1 in Figure 1.
# Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
## 2 Non-Invasive Monitoring Techniques  
Non-intrusive monitoring techniques, which means that the monitoring device does not have any contact with the tested system, mainly include (laser) ultrasonic monitoring, optical fiber sensing, Eddy current monitoring, magnetic flux leakage monitoring, microwave monitoring, terahertz (THz) monitoring, thermography method, radiograph monitoring, acoustic emission, etc. Due to space limitations, this article will only focus on the representative non-invasive monitoring technologies, of which the summaries are given in Table 1. The detailed introduction is as follows.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Monitoring** & **Principle** & **Data** & **Metages** & **Disadvantages** & **Application** & **Error or Accuracy** \\ \hline Ultrasonic measurement [29; 30] & Highly sensitive to structural damage; high permeability to materials & Depth of corrosion pits and thickness of corrosion layer & Real-time, suitable for both internal and external corrosion & Not appropriate for small, thin materials & Large-scale testing of oil & Error less than 15\% \\ \hline \hline \multirow{4}{*}{
\begin{tabular}{c} Infrared thermography \\ [31; 32] \\ \end{tabular} } & Recording & & & & & \\ \cline{1-1}  & electromagnetic waves emitted by objects and establishing a clear relationship between temperature and material & Defect size, depth, and thermal characteristics & Reliable, fast, direct, and wide testing range & Required heating and cooling structures; not suitable for thick materials; costly & Extreme temperatures and environments & Affected by changes in the thermal characteristics of objects and environmental conditions \\ \cline{1-1}  & and material corrosion & & & & & \\ \cline{1-1}  & defects & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of various non-invasive monitoring methods and their characteristics.
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 2 Background
### Literature Review
These features include improved health services, enhanced quality of life, more effective medical research, and better experiences for medical professionals and patients. This study further highlights the growing role of 5G technology in addressing epidemiological concerns. The paper also covers a variety of technical obstacles and opportunities associated with the development of 5G-powered healthcare solutions. [PERSON] et al. [50] presented an IoT with a cloud-based clinical decision support system for illness prediction and severity level monitoring by integrating 5G services and blockchain technology. The proposed framework gathers patient data through medical devices linked to the patient, which are kept on a cloud server with pertinent medical documents. Blockchain and 5G technology enable the secure transfer of patient data at a rapid transmission rate with an adequate reaction time. In addition, a neural network (NN) classifier is used to forecast illnesses and their severity, and different classifiers are used to validate the proposed model. The above discussion highlights the importance of the latest ICT technologies in improving the healthcare sector; however, we did not find any study that explicitly uses these technologies to provide 24/7 healthcare facilities to patients living in remote/rural areas with fewer healthcare facilities. Table 1 provides a comparison of existing studies to better understand the current state of the art. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Problem Discussed** & **Solution Proposed** & **Research Methodology Used** & **Gaps** \\ \hline
[14] & Improving digital healthcare & Explore the applications of 5G wireless transmission technology in healthcare & Review of existing studies & Validation is missing \\ \hline
[43] & This investigation sought to better understand the barriers to mental health care for rural individuals & Understand the barriers rural residents face in obtaining mental healthcare & Semi-structured interviews & The sample size (8) is very small for generalizing results \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing studies.
# Evaluation of Algal Control Measures in Eutrophic Reservoir Based on Aquatic Ecosystem Models
## 2 Materials and Methods
### Model Setup and Simulation Scenarios  
#### 2.5.1 Initial Model Parameters  
In the simulation of the annual growth and succession of the main algal species in the Shanzai Reservoir, in order to reflect the succession of the main algal species in the Shanzai Reservoir as realistically as possible, it is necessary to use the actual water quality situation of the Shanzai Reservoir as a basis. For measurable indicators, the average of the measured data from various monitoring stations within the reservoir can be used as the initial value for the entire reservoir. For indicators that lack monitoring data or are difficult to monitor, default values of the various state variables in the model can be used as the initial values for simulation.  
The model comprised two inlet boundaries and one outlet boundary, as depicted in Figure 3. The total number of cells in the unstructured mesh model was 10,541, with a typical horizontal size of 40 m \(\times\) 40 m and no vertical stratification. The initial values of various indicators in the model were set as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**State Variable** & **Description** & **Initial Value** & **Unit** \\ \hline BAC & Blue algae carbon content & 0.1 & g C/m3 \\ BAN & Blue algae nitrogen content & 0.014 & g N/m3 \\ BAP & Blue algae phosphorus content & 0.002 & g P/m3 \\ BACH & Blue algae chlorophyll content & 0.001 & g CH/m3 \\ Diatoms Si & Diatoms silicon content & 0.01 & g Si/m3 \\ Zooplankton C & Zooplankton carbon & 0.003 & g C/m3 \\ DC & Detritus carbon content & 0.5 & g C/m3 \\ DN & Detritus nitrogen content & 0.3 & g N/m3 \\ Detritus P & Detritus phosphorus content & 0.02 & g P/m3 \\ Detritus Si & Detritus silicon content & 0.1 & g Si/m3 \\ Dissolved oxygen & Dissolved oxygen & 11.05 & g DO/m3 \\ Ammonium & Ammonium nitrogen & 4.05 & g N/m3 \\ Nitrate & Nitrate nitrogen & 1.6 & g N/m3 \\ Phosphate & Phosphate & 0.1 & g P/m3 \\ Silicate & Silicate & 0.1 & g Si/m3 \\ \hline \end{tabular}
\end{table}
Table 1: State variables and initial value settings.  
Figure 3: Domain and grid partitioning in the model.