esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### Hyperspectral Imaging Technology
#### 2.2.1 Data Acquisition Methods and Characteristics  
Hyperspectral images are obtained by an imaging spectrometer, which is a complex and sophisticated optical system that includes several subsystems and components. The main components of the sensor are a scan mirror, fore-optics, spectrometers, detectors, onboard calibrators and electronic units. The fore-optics of the system receive light, which is dispersed by a spectrometer and converted from photons to electrons by the detector to yield an electronic signal. This electronic signal is then amplified, digitised and recorded by the electronic unit. The instrument performance and preprocessing data results are the main factors that aid in acquiring high-accuracy surface reflectance data. Such an instrument is characterised by its field-of-view (FOV), spectral range, spatial and spectral resolution and sensitivity. Data preprocessing includes geometric rectification, calibration and atmospheric correction [80].
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 3 Optimization Model
### Objective Functions
#### 3.1.2 Weight  
For a good mobile photovoltaic-diesel-storage microgrid system, especially in special operations teams, ensuring its flexibility is extremely important. Lightweight and sufficient electricity have always been a pair of conflicting variables: if we want to ensure a sufficient supply of electricity, the number of batteries and power generation components cannot be reduced, which will inevitably increase the burden of the system. Therefore, the weight of the system should be considered in the evaluation index. It is also necessary to find a plan to achieve the maximum balance between the total weight of carried components and the continuous power supply. The definition of this indicator is shown in the following formula:  
\[Weight_{total}=\sum N_{pv}\times Wei_{pv}+\sum N_{bat1}\times Wei_{bat1}+\sum N_{ bat2}\times Wei_{bat2}+\sum N_{dg}\times Wei_{dg} \tag{9}\]  
where \(N_{pv}\), \(N_{bat1}\), \(N_{bat2}\), \(N_{dg}\) are the number of photovoltaic panels, primary batteries, secondary batteries, and barrel diesel, while \(Wei_{pv}\), \(Wei_{bat1}\), \(Wei_{bat2}\), \(Wei_{dg}\) are the unit weights of photovoltaic panels, primary batteries, secondary batteries, and barrel diesel.
## 4 Experiments and Results
### Results Analysis  
In this section, the implementation and evaluation of a wormhole attack are described, along with the identification of the attack, the attack detection rate, Packet loss ratio, energy consumption, average residual energy, and attack detection time. The proposed method is compared against the PCC-RPL mechanism, which uses an MRHOF optimization problem to choose routes. The comparison is based on the packet loss ratio and energy usage, as MRHOF does not offer wormhole discovery. The evaluation results show improved performance of the proposed method in terms of the selected parameters.
# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 4 Results and Analysis
### Analysis of Rubber Tread Size Measurement Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**Measured Value**} & \multicolumn{2}{c}{**True Value**} & \multicolumn{2}{c}{**Absolute Error**} & \multicolumn{2}{c}{**Relative Error**} \\  & \multicolumn{2}{c}{**(mm)**} & \multicolumn{2}{c}{**(mm)**} & \multicolumn{2}{c}{**(mm)**} & \multicolumn{2}{c}{**(\%)**} \\ \hline \multirow{4}{*}{Thickness} & Group 1 & 25.54 & 25.98 & 0.44 & 1.69 \\  & Group 2 & 25.75 & 26.11 & 0.36 & 1.38 \\  & Group 3 & 25.44 & 25.98 & 0.54 & 2.08 \\  & Group 4 & 25.19 & 25.88 & 0.69 & 2.67 \\  & Group 5 & 25.36 & 25.95 & 0.59 & 2.27 \\ \hline \multirow{4}{*}{Width} & Group 1 & 36.80 & 37.00 & 0.2 & 0.54 \\  & Group 2 & 36.80 & 37.00 & 0.2 & 0.54 \\  & Group 3 & 36.95 & 37.00 & 0.05 & 0.14 \\  & Group 4 & 36.90 & 37.00 & 0.1 & 0.27 \\  & Group 5 & 36.90 & 37.00 & 0.1 & 0.27 \\ \hline \multirow{4}{*}{Length} & Group 1 & 148.46 & 150.63 & 2.17 & 1.44 \\  & Group 2 & 148.32 & 149.76 & 1.44 & 0.96 \\  & Group 3 & 147.34 & 148.82 & 1.48 & 0.99 \\  & Group 4 & 148.42 & 150.31 & 1.89 & 1.26 \\  & Group 5 & 147.65 & 149.38 & 1.73 & 1.16 \\ \hline \end{tabular}
\end{table} 
Table 8: Measurement results. Figure 20: Error distribution (unit: mm). As depicted in Figure 20, the absolute error in the five groups of three-dimensional tread size measurement experiments is within 2.17 mm, with minimal fluctuation. The relative error in three-dimensional size measurement is less than 2.67%, aligning with the highest-precision testing standards specified by national standards. This confirms the stable and reliable measurement effect.
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 1 Introduction
The UC employs the proposed ASLB scheme to determine the state of the RCs and dispatch flow requests to the most suitable RC to compute flow policies. The policies are then deployed to the switch to achieve quick request processing and a low response time. The UC also keeps track of the load information of the APs; once an AP becomes overloaded the UC shifts the WD to a least-loaded AP. Implementation of SDN into Wi-Fi networks sets a tradeoff between performance and programmability. An SDN controller requires an expert level of programming skills to be monitored before reaching an optimal throughput performance. To the best of our knowledge this is the first standardized method of achieving load balancing in the data plane and control plane of a SD-Wi-Fi. The major contributions of the study are:  
1. We propose an algorithm to balance the wireless traffic load in the data plane of an SD-WiFi. 2. We propose an algorithm to balance the wireless traffic load in the control plane of an SD-WiFi. 3. We simulate the high density SD-Wi-Fi in an OMNeT++ simulator supported by OpenFlow standards. The related work on control plane load balancing and data plane load balancing is discussed in Section 2. The methodology, including two algorithms, is explained in Section 3. The experimental details are described in Section 4. Performance evaluation is presented in Section 5 and finally we conclude the paper in Section 6. The table of abbreviations and symbols used in the study are shown in Tables 1 and 2, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations** & **Description** \\ \hline \hline ASLB & Adaptive Symmetrical Load Balancing Scheme \\ \hline SDN & Software Define Networking \\ \hline AP & Access Point \\ \hline UC & Universal Controller \\ \hline RC & Regional Controller \\ \hline CAPWAP & Control and Provisioning of Wireless Access Points \\ \hline AC & Access Controller \\ \hline WLANS & Wireless Local Area Networks \\ \hline WD & Wireless Device \\ \hline RSSI & Received Signal Strength Indicator \\ \hline DCF & Distributed Coordinated Function \\ \hline OUC & Over-Utilized Controller \\ \hline UUC & Under-Utilized Controller \\ \hline NUC & Non-Utilized Controller \\ \hline AHP & Analytical Hierarchical Process \\ \hline ACH & Adaptive connection and Hand off \\ \hline LIFO & Last In First Out \\ \hline FIFO & First In First Out \\ \hline COLBAS & Co-operative Load Balancing Scheme \\ \hline HFA & HoneyBee Foraging Algorithm \\ \hline GA & Genetic Algorithm \\ \hline AT & Active time \\ \hline PT & Passive time \\ \hline MPD & Mean Probe Delay \\ \hline CMAS & Channel Measurement-based Access Selection \\ \hline RRM & Round-Robin Mechanism \\ \hline WRR & Weighted Round-Robin Approach \\ \hline VoIP & Voice over Internet Protocol \\ \hline ESS & Extended Services Set \\ \hline BSS’s & Basic Service Sets \\ \hline DASA & Distributed Antenna Selection Algorithm \\ \hline DL-SINR & Downlink Signal-to-Interference-plus-Noise Ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of Abbreviations.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 3 The Proposed Algorithm
### Estimation of Illumination Component and Reflection Component  
In this section, an illuminance map estimation (IME) algorithm is proposed based on the neighborhood information of pixels. Specifically, the pixel's value is updated using a weighted sum with the neighboring pixels, followed by the morphological closure algorithm and guided filter for refinement. The specific steps are as follows.  
Let \(N_{i}\) be the \(i\)-th pixel's neighborhood, \(M_{i}\) the maximum value of R, G, and B at the position \(i\), \(j\) the pixel's position inside \(N_{i}\), \(w_{j}\) the weight value of position \(j\). The neighborhood-based illuminance map is derived from:  
\[I_{i}=0.5\times M_{i}+0.5\times\sum_{j}(N_{i}\{j\}\times w_{j}). \tag{5}\]  
Considering the fact that the greater the difference among pixel values, the lower the weight that should be constructed, or conversely, the greater the weight value at the position \(j\), the smaller the difference between \(N_{i}\) and \(M_{i}\) is, we designed the following weight:  
\[w_{j}=\left\{\begin{array}{ll}\frac{1}{|N_{i}(j)-M_{i}|},&if\quad|N_{i}-M_{i }|\
eq 0\\ \min(M+0.1,1)\,&else\end{array}\right., \tag{6}\]  
where \(M\) is the maximum weight in the neighborhood. We normalize the weights to obtain the final \(w_{j}\). The above idea is similar to the inverse distance weight (IDW) method [40; 41; 42].  
Figure 3: Flow chart of the algorithm for the proposed model.  
Then, we use the morphological closure algorithm [27; 43] and Guided filter [44] to refine the illumination map. The neighborhood-based illuminance map from Equation (5) is preliminarily refined by:  
\[I_{mor}=\frac{I\bullet P}{255}, \tag{7}\]  
where \(P\) denotes the structuring element and \(\bullet\) is the closing operation. Dividing by 255 is used to map the range to [0, 1] for the downstream operations. The final refined illumination map is obtained by:  
\[I_{end}=\sum_{j\in\omega_{i}}W_{\{i,j\}}(g)\times I_{mor}(m,n), \tag{8}\]  
where \(g\) is the value component of the guidance image in HSV space and \(W\) is the filter kernel:  
\[W_{\{i,j\}}(g)=\frac{1}{|\omega_{i}|^{2}}{\sum}_{\{i,j\}\in\omega_{i}}\Bigg{(}1 +\frac{(g_{i}-m_{i})\big{(}g_{j}-m_{i}\big{)}}{\sigma_{i}^{2}+\varepsilon} \Bigg{)}. \tag{9}\]  
Here \(\omega_{i}\) is the window centered at the \(i\)-th pixel and \(|\omega_{i}|\) is the number of pixels within \(\omega_{i}\); \(m_{i}\) and \(\sigma_{i}^{2}\) are the mean and variance of guidance image within \(\omega_{i}\), respectively, and \(\varepsilon\) is a regularization parameter.  
According to Equation (1), the reflection component is obtained by:  
\[R=\exp(\log S-\log\ I_{end}) \tag{10}\]
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 3 Methods
### Manual Feature Extractions  
Manual statistical features were extracted from preprocessed fNIRS signals, offering several benefits. These features capture essential aspects of fNIRS data, such as the mean, variance, and kurtosis, providing a compact and interpretable representation. This is particularly valuable when dealing with complex brain signals, as statistical features can highlight relevant patterns and trends that may be critical for accurate classification or regression tasks. Additionally, manual feature extraction can mitigate the curse of dimensionality and enhance model performance and robustness, particularly in situations where data are limited or noisy. Overall, this approach ensures that the most informative aspects of the fNIRS signals are effectively utilized, facilitating better understanding and improved outcomes in various neuroimaging studies and applications. A total of 11 features per channel were extracted with 24 channels for oxyhemoglobin (\(\Delta\)HbO2) and deoxyhemoglobin (\(\Delta\)HHb). The features used in our experiment were inspired by those extracted in [18], as shown in Table 1. Compared to [18], all features in our study were extracted exclusively from the on-event period, which refers to the time intervals during which specific pain-related stimuli were applied.  
The extracted manual features are then encoded through two fully connected layers to capture their essential characteristics. These encoded features are fused with features derived from the fNIRS signal, specifically the outputs from the EEG Conformer model, as presented in Figure 3. This fusion of manually extracted and Conformer-extracted features enables a more comprehensive representation of the underlying data, facilitating improved performance in subsequent tasks.  
Figure 3: EEG Conformer is employed to encode spatial and temporal information through convolution networks and global features through multi-head attention module [26]. Refer to Table 2 for the detail of convolution layers.
## 3 Method
### Hierarchical Modeling
#### 3.2.1 Eye-Tracker Experimentation
Real-time eye-movement data were transmitted to the computer, where fixation points, fixation times, fixation paths, and changes in pupil diameter were analyzed. The resulting images displayed the participant's visual attention distribution. As shown in Figure 5, the eye-movement hotspot map illustrated participants' attention distribution in the playground scene, with red indicating the most concentrated areas of gaze and fixation and yellow and green representing areas with less fixation.
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
## 4 Case Study: Tracking of Carts in a Large and High-Traffic RFID-Enabled Air Mail Centre  
In this section, we present a case study of developing an RFID infrastructure to enable tracking and tracing capabilities of carts at a large and high-traffic AMC in an Asian city.
## 2 Numerical Methods
### Noise Propagation Method
The third term shows the attenuation due to air absorption, which is proportional to the absorption coefficient \(\alpha\) and the propagation length \(D\). \(\Delta L\) is the relative SPL differences caused by factors such as ground reflection/absorption (flat/irregular terrain), atmospheric refraction, wind and turbulence and sound barriers, etc. It does not include the effects of geometric attenuation and air absorption. The \(\Delta L\) varies along the propagation path with these factors, which is beneficial to clearly manifest the effects of propagation. Most of the discussions and results in Section 3 are dealing with this relative pressure level which is abbreviated as RSPL. The values of \(\alpha\) depend on sound frequency, air temperature, pressure and humidity. In the following studies, unless specified separately, a relative humidity of 70% and temperature of 10 \({}^{\circ}\)C are assumed. The wave equation is solved based on each frequency. The ANSource and wake information of given wind turbines from BPM/AD computations are combined into the ANPropagation solver. For each frequency, the corresponding ANSource, known as the starting field, is individually applied as the initial condition to the computational domain. Detailed descriptions about starting field formulation can be found in publication from [PERSON] [11]. The horizontal size of the simulation range is determined by the location of the receiver. However, the size of the vertical domain is flexible such that the height of domain can vary with the simulated frequency. The maximum vertical size of the grid should be less than \(\Delta x=\Delta z=\lambda/8\) so that a sound wave can be resolved with at least eight grid points. As a result, the domain height is inversely proportional to frequencies for a fixed number of vertical grids. Besides, a larger CFD domain should be used in the simulation such that the flow field can fully cover the noise propagation domain.
# A Review of Blockchain Technology Applications in Ambient Assisted Living
## 3 Results
### Patient Monitoring and Intervention
In [28], the authors propose an IoT based on blockchain integration architecture, a rich-thin client IoT technique for addressing the challenges associated with the restricted IoT capacities when adopting blockchain in remote monitoring and healthcare processes. In this context, the smart contracts can assure a seamless and automatic solution platform connecting a range of IoT devices relevant for remote follow-up [66]. Smart contracts are a crucial feature of blockchain technology that enables it to be used in a variety of ambient assisted living systems [14; 85]. However, the smart contract concept, its operation, and how it can be used in ambient assisted living are still poorly understood [48]. Nevertheless, the main barrier to integrating IoT monitoring devices with blockchain in the context of ambient assisted living systems is scalability [62]. The researchers of [24] provide an overview of blockchain technology and explore prominent consensus methods utilized in the healthcare processes. However, as the authors pointed out in [36], there are issues to be solved, including scalability and standardization. Research has been conducted to improve the scalability of IoT to blockchain integration [73]. This is a relevant aspect of ambient monitoring and assistive services [37]. In [44], the authors propose a blockchain framework that is described as more accurate, precise, and efficient than other popular methods of storing and accessing patient records among personnel, medical stakeholders, and facilities. In [18], the authors discuss the evolution of healthcare, identifying the research gaps such as the relocation of care from hospital to home and ambient assisted care [5] that we consider to be a relevant use case for joining Wireless Body Area Networks with blockchain [74]. Improved scalability of a permissioned blockchain framework has been described in [51] using Hyperledger Fabric as an infrastructure for the blockchain network. The authors of [31] investigated a composite scalability concept which can be seen differently depending on the grade of innovation we want to achieve. The notion of blockchain scalability is discussed, including techniques and ideas for increasing core blockchain functionality and blockchain-based applications in domains such as remote care [76; 81]. Blockchain and fog computing is being used in care IoT to provide safe and trustworthy transactions [95]. An Extended Signature-Based Encryption technique is proposed for healthcare IoT device authentication, as well as authorization [52]. The authors claim the suggested architecture and algorithm may offer safe transaction and transmission. Finally, [54] looks at blockchain to IoT systems and how to make them more scalable. On-chain and off-chain methodologies are contrasted, and suggestions are made to help designers create scalable blockchain-based IoT medical systems.
## 2 Materials and Experimental Procedure  
The chemical compositions (wt. %) of superalloy nimonic 80A used in this study were as follows: C--0.069, Mn--0.630, Cr--2, Fe--1.260, Ti--2.070, Al--0.680, Si--0.550, S--0.001. Twenty nimonic 80A specimens with a diameter of 10 mm and a height of 12 mm were processed from the same extrudedbillet by wire-electrode cutting. A thermo-mechanical simulator, Gleeble-3500, with a high speed heating system, a servo hydraulic system, a digital control system and a data acquisition system, was used for compression testing. It is common to be used for simulating both mechanical and thermal process at a wide range during hot deformation. Twenty specimens were resistance heated to a proposed deformation temperature with a heating rate of 5 \({}^{\circ}\)C/s and then held at that temperature for 180 s by thermo-coupled-feedback-controlled AC current to obtain a homogeneous temperature field. Afterwards, all twenty-four specimens were compressed to a true strain 0.9163 (a fixed height reduction of 60%) at five different temperatures of 1050 \({}^{\circ}\)C, 1100 \({}^{\circ}\)C, 1150 \({}^{\circ}\)C, 1200 \({}^{\circ}\)C and 1250 \({}^{\circ}\)C, and four different strain rates of 0.01 s\({}^{-1}\), 0.1 s\({}^{-1}\), 1 s\({}^{-1}\) and 10 s\({}^{-1}\)[25]. After each compression, the deformed specimen was immediately quenched into water to retain the high temperature microstructures.  
During the compression process, the variations of strain and stress were continuously monitored by the computer equipment with the automatic data acquisition system. Generally, the true stain and true stress were derived from the nominal stress-strain relationship based on the following formula: \(\sigma_{\rm T}=\sigma_{\rm N}(1+\varepsilon_{\rm N})\), \(\varepsilon_{\rm T}=\ln(1+\varepsilon_{\rm N})\), where \(\sigma_{\rm T}\) is true stress, \(\sigma_{\rm N}\) is nominal stress, \(\varepsilon_{\rm T}\) is true strain and \(\varepsilon_{\rm N}\) is nominal strain.
# Ising-Based Kernel Clustering +
## 2 Ising-Based Combinatorial Clustering
### Problems  
Ising-based combinatorial clustering has the potential to obtain the global optimal clustering results. However, conventional Ising-based combinatorial clustering cannot handle data with irregular distributions. This is because the conventional method uses the Euclidean distances as the degree of similarity. When the input data are mapped on the Euclidean space, combinatorial clustering can handle linearly separable data. However, conventional combinatorial clustering cannot handle non-linear separable data. Thus, irregular data are defined as the data for which linear decision boundaries cannot be drawn. Euclidean-based clustering methods assume that each cluster has equal variance, and decision boundaries are drawn linearly. Therefore, irregular data with heterogeneous variances among clusters or with no radial spread from the cluster center cannot be appropriately clustered.  
One way to perform clustering of irregular data is to apply a kernel method to irregular data. The kernel method can perform clustering of irregular data by mapping the data onto a high-dimensional feature space.  
In the case of quasi-optimal clustering methods, kernel clustering is widely used to obtain clustering results for any distributed data. However, Ising-based combinatorial clustering using the kernel method that can handle irregular data has not been proposed yet. Thus, it is required to consider the QUBO formulation for kernel clustering.  
In addition, the preprocess for Ising-based computation takes a long time to calculate the coefficients of the QUBO problem. Ising-based clustering methods also require the calculation of the QUBO coefficients in Equation (5). To make matters complicated, Ising  
Figure 1: An overview of one-hot encoding.  
-based kernel clustering has to transform data onto the high-dimensional feature space. Thus, Ising-based kernel clustering requires more complex preprocessing calculations than conventional Ising-based combinatorial clustering.
## 2 Methods
### Evaluation Metrics  
#### 2.3.1 Primary Evaluation Metric: ROUGE-L  
ROUGE-L (Longest Common Subsequence) [24] was selected as our primary evaluation metric based on several considerations. It demonstrates cross-lingual suitability by being less sensitive to exact lexical matching, which is crucial for cross-lingual evaluation where direct word overlap may be limited. The metric preserves sequence order information, capturing content flow and organization. ROUGE-L represents an established standard widely adopted in summarization research, ensuring comparability with existing work. Additionally, it provides balanced precision-recall assessment, considering both content coverage and conciseness.  
The mathematical formulation for ROUGE-L is:  
\[\text{ROUGE-L}=\frac{(1+\beta^{2})\times(R_{\text{lcs}}\times R_{\text{lcs}})}{ R_{\text{lcs}}+\beta^{2}\times P_{\text{lcs}}} \tag{3}\]  
where \(R_{\text{lcs}}=\frac{\text{LCS}_{\text{length}}}{\text{reference}_{\text{ length}}}\) (recall), \(P_{\text{lcs}}=\frac{\text{LCS}_{\text{length}}}{\text{candidate}_{\text{ length}}}\) (precision), \(\beta^{2}=1\) for equal weighting of precision and recall, and \(\text{LCS}_{\text{length}}\) represents the length of the longest common subsequence.