esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### PHR-NFT System Architecture
#### 3.3.1 User Layer  
The system serves many users, including patients, physicians, hospitals, MoH, doctors, pharmacies, insurance providers, and labs. PHRs can be gathered and occasionally shared by doctors and patients. Using NFTs, the system gives patients control and authority over their medical records. Doctors wishing to view or amend a patient's medical record  
Figure 2: PHR-NFT system architecture.  
must undergo an authentication process. The safe and controlled exchange of medical information is ensured by the fact that only authorized individuals can access these records.
# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 3 System Design
### Overview of System Design  
The proposed system is based on a blockchain network, which is composed of a two-tier structure to allow reputation data to propagate at local and interlocal levels. While the proposed system, as described herein, utilizes blockchain, any decentralized database would also work with the proposed trust management system. The comparison of the performance of different methods of data storage and transmission is a key topic for potential future work.  
The blockchain network is designed to store reputation scores, determined from communication data, without replacing or facilitating communications themselves. At the local tier, information pertaining to V2X transactions are shared between vehicles and mined by nearby RSUs on a blockchain network, shown in Figure 1. After being stored, the RSUs process the data to create a reputation assessment digest of individual vehicles. On the regional tier, this digest is communicated between RSUs, from which the resulting transaction is mined by base stations (BSs) and stored on the unary blockchain network, which spans across an entire geographical land mass. The proposed system aims to alleviate or reduce computational and storage concerns by implementing a hierarchical blockchain structure. Within this architecture, computationally capable RSUs and base stations are responsible for mining and appending data to the blockchain ledger, as well as abstracting these functionalities from vehicles. Furthermore, blockchain networks on the local tier are divided by geographic region to store only relevant, proximate data. These regions are dynamically defined by the RSUs, which are also responsible for handling movement of vehicles between networks. RSUs may redistribute vehicles if the number of nodes across networks becomes too unbalanced; however, frequent travel across boundaries induces networks to remain relatively static despite vehicle movement.
## 3 Influence Metrics
### Quality of Service Metrics Analysis
#### 4.3.2 Calculation of Friends Nodes upon Contact  
In our proposal, the concept of a friend refers to a node that has connected with another node and is likely to reconnect within a relatively short time, based on the principle of temporal locality. To implement this idea of temporal locality, we introduce the friend concept, which involves setting a timer for each pair of connecting devices. This timer is activated after the nodes establish a connection. If they reconnect before the timer expires, we consider them friends who frequently connect. On the other hand, if the timer has expired by the time they encounter each other again, they are still friends but do not connect frequently.  
The concept of a friend represents a list of nodes to which the given node has previously connected. This list is closely related to the adjacency matrices described in Algorithm 1, as both implementations rely on node connections. However, unlike the adjacency matrices, the friends list is not reset with each new matrix. Instead, it is continually updated throughout the simulation as the node forms new connections.  
It is important to note that the friends list is not utilized during the training phase (when running Algorithm 2). Instead, it is used during testing, which will be explained further in Algorithm 3.  
Moreover, the concept of a friend can be utilized to assess the extent to which these friends adhere to the notion of temporal locality. For instance, a connection counter between them can be employed within the context of their unexpired timer. In these simulations, we use the concept of friends as a list of nodes to which a specific node has connected throughout the entire simulation.  
```
Input:\(N1\)LNF (List of N1 friends), \(N1\) and \(N2\) (new contact between two nodes) Output:\(N1\)LNF (a new version of N1 friends), \(T\)(N1,N2) (timeout between \(N1\) and \(N2\)) and TL(N1,N2) (temporal locality between \(L1\) and \(L2\)) begin  MAX_TIMEOUT = 20,000; ifN2 in N1 LNFthen  elapsed_time = (current_time - T(N1,N2)); if elapsed_time < MAX_TIMEOUTthen  TL(N1, N2) \(\leftrightarrow\)1; else  add N2 to N1 LNF;  T(N1,N2) = current_time; returnN1 LNF, T(N1,N2), TL(N1,N2) repeat with the input:N2 LNF, N1 and N2
```  
**Algorithm 2** Calculation of Friend Nodes on contact  
The complexity is also O(1) because this algorithm simply updates the list of friends (LNF) of a node and vice versa when it connects to another. The space complexity is O(N2) because the friend list of each node is not renewed during the simulation, as is the case with TM adjacency matrices during training. Instead, a friend is added to each contact in case they both meet and connect for the first time.
## 2 Offline Handwritten Text Recognition
### Image Preprocessing  
The cursive style varies depending on the writer and has unique characteristics from one person to another. Not only that, but external factors also directly influence the way of writing, such as the writing instrument used, the emotional state of the writer, and the time available [6]. This great variability leads to the application of image processing techniques to normalize these irregularities [8, 21]. In these techniques, contrast correction, slant correction and image standardization are commonly used [21, 46].  
The first strategy, contrast correction, is adopted to reduce lighting irregularities in the image, such as light peaks and excessive shadows. Thus, techniques such as illumination compensation are efficient for this scenario, since they also remove background and foreground noises through lighting [47]. The second, slant correction or dislant, aims to normalize the vertical inclination of the characters. The method detects the slant angle of the writing on the vertical axis and then applies a geometric image transformation to adjust the angle detected [48]. The last strategy, standardization (or Z-score normalization), aims to rescale the image features (pixels) to result in an image with pixels with mean and variance values equal to 0 and 1, respectively [49].  
Figure 1: Workflow of the offline handwritten text recognition system.
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 2 Related Works  
The processing of radar data remains a challenge due to the low density and noisiness of the captured information. This severe limitation in resolution can lead to the issue that objects are only captured partially, leading to the limited classification abilities of current algorithms and incorrect bounding box sizes. For this reason, some methods include segmentation or focus only on localization [10]. To counteract this sparsity, some datasets overlap data from multiple sensors [11; 12]. In addition to this inherent sparsity, conventional radar data (2D + 1D) is not able to represent the height of an object, making it difficult to separate static objects from environmental clutter and introducing errors when calibrating with respect to other sensors [13]. Additionally, ghost targets can be difficult to distinguish, with some research focusing exclusively on the detection of these radar errors [6]. While modern 4D radar sensors are able to capture height, data remain sparse. In addition to these difficulties, there are various representation methods for radar data, leading to a variety of deep learning methods.
## 2 Numerical Simulation Calculation Model
### Model Control Equations
#### 2.1.1 Cohesive Pore Pressure Unit
(4) Cohesive cell expansion guidelines  
The cohesive cell uses stiffness degradation to describe the damage evolution process of the cell, and its expression is  
\[T=(1-D)\overline{T} \tag{8}\]  
The damage factor is calculated by the formula  
\[D=\frac{\delta_{f}(\delta_{m}-\delta_{0})}{\delta_{m}\Big{(}\delta_{f}-\delta_ {0}\Big{)}} \tag{9}\]  
(5) Fluid flow equation in the unitary damage zone  
The fluid flow in the damage area of the cohesive cell is divided into the flow along the tangential direction and the normal filtration loss perpendicular to the upper and lower surfaces of the cohesive cell, as shown in Figure 4.  
Assuming that the fluid inside the cohesive cell is an incompressible Newtonian fluid, the equation for its tangential flow is  
\[q=-\frac{w^{3}}{12\mu}\
abla p \tag{10}\]  
where \(q\) is the tangential flow rate in units of m\({}^{3}\)/s, \(\
abla p\) is the pressure gradient along the length of the cohesive unit in units of Pa/m, and \(w\) is the fracture width in units of m.  
The normal filtration loss on the upper and lower surfaces of the cohesive cell can be described as  
\[q_{t}=c_{t}\Big{(}p_{f}-p_{t}\Big{)} \tag{11}\]  
\[q_{b}=c_{b}\Big{(}p_{f}-p_{b}\Big{)} \tag{12}\]  
where \(c_{t}\) and \(c_{b}\) are the filtration loss coefficients of the upper and lower surfaces, respectively, and the units are m\({}^{3}\)/(Pa\(\cdot\)s). The equation for the conservation of fluid mass in the cohesive cell is  
\[\frac{\partial w}{\partial t}+\
abla\cdot q+(q_{t}+q_{b})=Q(t)\delta(x,y) \tag{13}\]  
where \(Q(t)\) is the fracturing fluid injection rate, and the units are m\({}^{3}\)/s. Figure 4: Cohesive unit damage zone fluid flow diagram.
## 2 Materials and Methods  
### Synthesis of Samples  
**Preparation of Ni-MOF arrays:** A homogeneous green solution was first prepared by dissolving 2.24 mmol (372 mg) of terephthalalic acid (Aladdin, Shanghai, China) and 1.39 mmol (330 mg) of NiCl\({}_{2}\)-6H\({}_{2}\)O (Aladdin) in 32 mL of N,N-dimethylformamide (DMF) (Aladdin) under continuous stirring. To this solution, 2 mL of deionized water and 2 mL of ethanol (Aladdin) were added, followed by thorough mixing to ensure uniformity. The resulting mixture was then transferred into a Teflon-lined autoclave, into which a carbon paper (CP) (Macklin, Shanghai, China) substrate was immersed to facilitate the growth of the nanorod arrays. The autoclave was sealed and heated in an oven at 120 \({}^{\circ}\)C for 12 h. After the reaction, the system was allowed to cool naturally to room temperature. The product was collected and repeatedly washed with DMF and ethanol to remove residual organic species. Finally, the obtained green powder was dried at 60 \({}^{\circ}\)C for 1 h, yielding well-defined Ni-MOF arrays.  
**Preparation of Ni-MOF@ZIF-L arrays:** A ZIF-L suspension was first prepared by separately dissolving 11.76 mmol (0.966 g) of 2-methylimidazole (Aladdin) and 1.24 mmol (0.36 g) of Zn(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O (Aladdin) in 50 mL of deionized water each, followed by thorough mixing of the two solutions. The as-synthesized Ni-MOF was then immersed into the ZIF-L suspension and allowed to react at room temperature for 24 h under continuous stirring. After completion of the reaction, the resulting light green product was collected and washed repeatedly with deionized water and ethanol to remove unreacted species and impurities. Finally, the purified sample was dried under vacuum at 60 \({}^{\circ}\)C for 1 h to obtain the Ni-MOF@ZIF-L composite material.
# The Impact of Intermittent Androgen Suppression Therapy in Prostate Cancer Modeling
## 2 Model and Method
### The New Model  
In the BK model, the assumption of similarity in the levels of serum androgen and intracellular androgen may not hold consistently throughout the treatment. This motivates the following model, which directly takes into account body androgen production and its diffusion to intracellular androgen. Additionally, previous work by [PERSON] et al. [7] extrapolated serum androgen data with an exponential growth/decay function. The serum androgen values are then fed into a five-compartmental model, known as the PKN model. Extending upon this idea, we explicitly include a compartment that governs the dynamics of serum androgen. The following model is presented as an extension of the BK model:  
\[\frac{dx_{1}}{dt}=\underbrace{\mu_{1}\left(1-\frac{q_{1}}{Q}\right)x_{1}}_{ \text{growth}}-(\underbrace{D_{1}(Q)+\delta_{1}x_{1}}_{\text{death}})\ x_{1}- \underbrace{\lambda(Q)x_{1}}_{\text{cell transformation}} \tag{7}\]  
\[\frac{dx_{2}}{dt}=\underbrace{\mu_{2}\left(1-\frac{q_{2}}{Q}\right)x_{2}}_{ \text{growth}}-(\underbrace{D_{2}(Q)+\delta_{2}x_{2}}_{\text{death}})\ x_{2}+ \underbrace{\lambda(Q)x_{1}}_{\text{cell transformation}} \tag{8}\]  
\[\frac{dQ}{dt}=\underbrace{m(A-Q)}_{\text{androgen diffusion: }A\to Q}- \underbrace{\mu_{1}(Q-q_{1})x_{1}+\mu_{2}(Q-q_{2})x_{2}}_{\text{cells uptake of androgen}} \tag{9}\]  
\[\frac{dA}{dt}=\underbrace{\gamma_{2}+\gamma_{1}(A_{0}-A)}_{\text{androgen production suppression of production}} \tag{10}\]  
\[\frac{dP}{dt}=\underbrace{bQ}_{\text{baseline PSA production tumor's PSA production degradation}}\underbrace{-eP}_{\text{}} \tag{11}\]  
\[D_{i}(Q)=d_{i}\frac{R_{i}}{Q+R_{i}},\ \ \ \lambda(Q)=c\frac{K}{Q+K} \tag{12}\]  
In contrast to the BK model, the variable \(A\) in this model represents serum androgen. While the treatment is on, the rate of change of \(A\) is strongly influenced by a negative exponential term, indicating the effect of the total androgen blockage drug. While the treatment is off, the production of \(A\) takes the form of a logistic-type growth, where \(A_{0}\) is the maximum serum androgen, which is akin to the carrying capacity in the logistic equation. The uptake rate of intracellular androgen, \(Q\), depends on the current level of serum androgen. This is a biological improvement over the BK model as, after the patient is taken off treatment, it takes time for the serum androgen to recover to its maximum. As a consequence, the intracellular androgen level will take more time to recover its normal level.
# Rain Removal of Single Image Based on Directional Gradient Priors
## 3 Proposed Deraining Method  
In this section, we propose a rain removal method based on directional gradient priors. On the basis of the convolutional sparse coding model, this method constructs two gradient sparse prior terms to solve the problem of incomplete rain removal in the original model, so as to obtain better rain removal results. The main process of this method is as follows.  
1. Aiming at the existing problems in the rain removal results, a rain removal model based on gradient direction prior is proposed.
2. By analyzing the changes of vertical and horizontal gradients in the rain layer, the process of constructing two gradient sparse prior terms is given.
3. To obtain more abundant rain stripe information, the multi-scale dictionary used to encode the rain layer in the sparse prior terms is constructed and initialized.
4. To accurately solve the proposed model, the ADMM method is utilized to update the multiscale dictionary and coding coefficients to obtain the final accurate rain layer and rain removal result.  
The specific implementation of the above process is described below.
# Improved Application of Hyperspectral Analysis to Rock Art Panels from El Castillo Cave (Spain)
## 2 Materials and Methods
### Pigment Analysis
#### 2.5.5 Spectral Angle Mapper  
The spectral angle mapper (SAM) is a fully automated method used to compare spectral signatures of the image with spectral libraries [32]. For the SAM classification to be effective [33], the image data needs to be converted to apparent reflection. The algorithm determines the similarity between the two spectral signatures by calculating the spectral angle between them, treating them as vector units in the spectral space with spectral dimensionality equal to the number of bands.  
The evaluation was carried out empirically by selecting a sample of pixels from the thematic map obtained (classified image) and comparing the assigned class with the actual class determined from reference data, obtained in the field sampling. In this way, the percentage of pixels in each class that have been correctly classified can be estimated, as well as the proportion of errors due to confusion between the different classes.
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 3 Results
### Comparison of Fracture Network Parameters and Petrophysical Data  
To study possible associations between the physical properties and fracture network parameters, cross plots of select variables were made. The fracture length index \(E^{*}\), the constant \(F\), and the fractal dimensions of the system (\(D\)) and as determined at fracture midpoints (\(D\)) were plotted against the specimen depth, foliation direction, density, porosity, resistivity, relative dielectric permittivity, P- and S-wave velocities, P/S-ratio, Poisson's ratio, and Young's modulus (Figure 11).  
The fracture length index \(E^{*}\) showed no clear association with the depth of the specimen or foliation direction. Density showed slightly higher values of \(E^{*}\) in VGN as compared to PGR but no general trends. Porosity vs. fracture length showed a weak general negative trend, whereas resistivity vs. fracture length appeared to have a general positive trend. Relative dielectric permittivity showed a positive trend, except for two outliers in the data. The P- and S-wave velocities, P/S-ratio, Poisson's ratio, and Young's modulus showed no dependency on fracture length.  
Interestingly, the constant \(F\), which is only supposed to be a function of specimen size, showed a clear dependence on density and thus rock type. This observation could not be explained. \(F\) also showed similar trends for porosity, resistivity, and relative permittivity to fracture length. The rest of the parameters showed no trends. Neither \(D_{f}\) nor \(D\) showed an association with specimen depth or foliation direction. Both separated the two rock types based on density. \(D_{f}\) showed a more similar distribution of values, with gneiss showing large outlier values that granite was missing. \(D\), on the other hand, showed clearly higher values for granite compared to gneiss.  
\(D_{f}\) versus porosity seemed to show a weak positive trend, whereas \(D\) showed no similar behaviour. Resistivity showed no trends, but relative permittivity had a negative trend for both \(D_{f}\) and \(D\). P- and S-wave velocities showed no clear trends, but the P/S-ratio and Poisson's ratio seemed to have broad negative trends with respect to \(D_{f}\). The Young's modulus did not appear to have a trend with respect to \(D_{f}\), and no trends appeared for any of the elasticity parameters with respect to \(D\).  
Figure 10: Results of the discrete fracture network (DFN) simulations. Composition of the models (volumetric content of PGR) are blue = 0%, green = 25%, yellow = 50%, orange = 75%, red = 100%. Remainder of the modelled volume is VGN.
# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
## 4 Conclusions  
This paper proposes a two-stage substation thermal-visible-image registration method. Firstly, the original image pair is binarized, and a fast initial registration is performed using an improved FAST algorithm to reduce the difference in scale. Secondly, a registration framework is proposed, with ADU-PIIFD as the core. By comparing the comprehensive registration indexes in spaces with a different scale, the construction scheme of the image pyramid is determined to achieve a more precise transformation, enhance the accuracy of registration, and significantly improve the adaptability to different scenes. According to the analysis of experimental results, the method proposed in this paper shows an excellent registration performance in various complex scenes. Even in the case of extremely blurred images, the registration error can be controlled within five pixels, and the robustness is excellent. In the field of fire safety, this concept is supposed to have promising potential in the field of fire safety.  
Conceptualization, W.S. and H.G.; methodology, W.S.; software, W.S. and C.L.; validation, W.S. and H.G.; formal analysis, W.S.; investigation, W.S. and H.G.; resources, H.G.; data curation, W.S. and C.L.; writing--original draft preparation, W.S.; writing--review and editing, W.S. and H.G.; visualization, W.S. and C.L.; supervision, H.G.; project administration, W.S. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data are contained within the article.  
The authors declare no conflict of interest.
## 3 Methodology
### Datasets
#### 3.2.3 Increasing Volume  
Increasing volume is a data augmentation technique used in machine learning, particularly in audio signal processing, to artificially enhance the training dataset by adjusting the amplitude or loudness of the original audio signals. The process involves scaling the amplitude of the audio waveform, effectively amplifying the volume. Algorithm 3 outlines the pseudocode for implementing the volume-increasing augmentation technique.  
```
0:
0: augmented_audio: Numpy array representing the augmented audio data.
1: gain_factor \(\leftarrow\) 2 //Apply Volume Increase:
2: augmented_audio \(\leftarrow\) audio_data \(\times\) gain_factor //Clip Values to Ensure Valid Audio Range:
3: augmented_audio \(\leftarrow\) np.clip(augmented_audio, \(-\)1.0, 1.0)
4: return augmented_audio
```  
**Algorithm 3** Data augmentation: Increasing volume  
In this study, volume increase was applied uniformly across the entire audio signal, including both speech and silence segments. Specifically, the augmentation was implemented by multiplying the amplitude of the entire waveform by a fixed factor of 2.0. This ensures a consistent increase in loudness without selectively amplifying speech, while keeping the relative signal structure intact.  
An alternative approach could involve selectively amplifying only the speech segments while maintaining the background noise or silence at its original level. However, our method was chosen to preserve the overall acoustic balance and to examine how global volume augmentation affects speech emotion recognition. Additionally, applying a uniform scaling factor allows for a direct comparison between different volume levels, unlike a mixed approach where speech and silence are modified differently. To determine the most effective scaling factor, we tested various values, including 1.5, 2.0, and 2.5. The results showed that scaling by 2.0 provided the highest accuracy across both the Saudi Dialect and BAVED datasets, leading to the final choice of this factor in our experiments.
## 2 Results and Discussion
### Optimization of HS-SPME Procedure
#### 2.1.2 Effect of Extraction Temperature  
The HS-SPME extraction procedure was performed without heating the sample as well as with heating at 25, 40, 50, 60 and 70 \({}^{\circ}\)C. For almost all analytes, extraction temperature of 60 \({}^{\circ}\)C resulted in higher signal intensity. A further increase in temperature above 60 \({}^{\circ}\)C resulted in signal intensity reduction. High extraction temperature is known to release more volatiles in the headspace and assist in the extraction procedure. However, adverse effects, such as decrease of partition coefficients, which prevent the extraction, can also take place [23]. Therefore, 60 \({}^{\circ}\)C was selected as the optimum HS-SPME extraction temperature. Figure 2 shows the effect of extraction temperature on the intensity of selected analytes.