esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 1 Introduction
### Our Contributions  
Our contribution is a route selection mechanism that enables a source node to select a route to a destination node independently in the presence of PUs' activities via either: (a) direct communication with the MC BS to improve the route performance; or (b) D2D communication among nodes in the SCs to offload traffic from the MC to alleviate traffic congestion in MCs and improve the overall spectrum efficiency. Our experiment is performed on a testbed that consists of USRP/GNU radio units connected to RP3, which serves as the decision-making engine of the source node, rather than in simulation. This helps to improve the accuracy of network performance measurement compared to that in simulation, which generally applies some assumptions, whereby: (a) nodes in simulation may be homogeneous, rather than heterogeneous with different processing and transmission capabilities, resources, and so on; and (b) the absence of the ambient environment that affects the quality of communication due to the presence of ambient signals (or interference) and white noise. This paper is an extension to our previous paper [16]. Although our focus is on route selection in this paper, in [16], the focus is on the testbed and to investigate and compare network performance, such as software and hardware processing delays, of a single two-hop route in both traditional and our proposed testbeds with fewer nodes and routes.
# Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
## 2 Methods
### A unified View of Data Decomposition Models for ML  
Given a linear subspace \(\mathcal{U}\subset\mathds{R}^{N}\), every vector data sample (one-dimensional signal) \(\mathbf{x}\in\mathcal{U}\) is decomposed into a sum of \(I\) components if it can be written as:  
\[\mathbf{x}=\sum_{i=1}^{I}\alpha_{i}\boldsymbol{\phi}_{i}+\mathbf{r}= \boldsymbol{\Phi}\boldsymbol{\alpha}+\mathbf{r}, \tag{2}\]  
where \(\alpha_{i}\in\mathds{R}\), the set of vectors \(\{\boldsymbol{\phi}_{i}\in\mathds{R}^{N}\}\) (\(i=1,2,\ldots,I\)) and \(\mathbf{r}\in R^{N}\) are the coefficients, the generatorsGiven a dataset composed of \(J\) vector samples \(\mathbf{x}_{j}\in\mathbb{R}^{N}\), \(j=1,2,\ldots,J\), by arranging them as columns of matrix \(\mathbf{X}\in\mathbb{R}^{N\times J}\), we can write the following matrix factorization equation (see Figure 1a):  
\[\mathbf{X}\approx\mathbf{\Phi}\mathbf{A}, \tag{3}\]  
where \(\mathbf{\Phi}\in\mathbb{R}^{N\times R}\) and \(\mathbf{A}\in\mathbb{R}^{R\times J}\) has entries \(\alpha_{i,j}\). Such a compact representation of datasets can be accomplished in several ways. For example, by using a subspace of a lower dimension (\(R<N\)) or using a sparse matrix \(\mathbf{A}\) meaning that each of the signals are approximated using few non-zero coefficients, compared to the size of the space \(N\). In the following subsections we describes these possible models.
# Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete
## 2 Theoretical Formulation
### Constitutive Equations  
#### 2.3.1 Creep Strain-Stress Relation  
The strain stress relation is given by the sum of the partial derivative of the thermodynamic potential with respect to the creep strain components, i.e., \(\sigma_{ij}=\rho\frac{\partial\Psi}{\partial\xi_{ij}^{\prime}}+\rho\frac{ \partial\Psi}{\partial\sigma_{ij}^{\prime}}\). Then, by using Equation (35), the Leibniz rule, and changing \(\tau\) and \(\tau^{\prime}\) equal to \(t\), such that \(H_{t}(\tau^{\prime}-t)=0\), when \(\tau=t\), and \(H_{t}(\tau-t)=0\), when \(\tau^{\prime}=t\). Finally, by setting \(\tau=\tau^{\prime}\), treating the positive orthogonal projection \(P_{ijkl}^{e+}\) as constant, and assuming the symmetry of the damage tensor, one finds  
\[\begin{array}{ll}\sigma_{ij}=\rho\frac{\partial\Psi}{\partial\xi_{ij}^{ \prime}}+\rho&\frac{\partial\Psi}{\partial\xi_{ij}^{\prime}}\\ &=\int_{\tau=0}^{t}\hat{\lambda}(t,\tau)d\varepsilon_{kk}^{\
u}(\tau)\delta_{ ij}+2\int_{\tau=0}^{t}\hat{\mu}(t,\tau)d\varepsilon_{ij}^{\
u}(\tau)+2\int_{ \tau=0}^{t}\hat{\lambda}_{1}(t,\tau)D_{kk}P_{mmij}^{e+}d\varepsilon_{ll}^{ \
u+}(\tau)\\ &+2\int_{\tau=0}^{t}\hat{\lambda}_{2}(t,\tau)D_{mm}P_{klkl}^{e+}d\varepsilon_{ ll}^{\
u+}(\tau)+\int_{\tau=0}^{t}\hat{\lambda}_{3}(t,\tau)D_{kl}P_{mmij}^{e+}d \varepsilon_{ll}^{\
u+}(\tau)\\ &+\int_{\tau=0}^{t}\hat{\lambda}_{3}(t,\tau)D_{kl}P_{klij}^{e+}d\varepsilon_{ mm}^{\
u+}(\tau)+2\int_{\tau=0}^{t}\hat{\lambda}_{4}(t,\tau)D_{lm}P_{kmij}^{e+}d \varepsilon_{kl}^{\
u+}(\tau)+\hat{k}_{f}e_{kk}^{f}\delta_{ij}\end{array} \tag{36}\]  
where the relaxation functions are dependent on the aging term \(v(t)\) at loading time \(\tau\), i.e., \(v(\tau)\), and the non-aging relaxation functions evaluated at loading duration \((t-\tau)\). In mathematical terms: \(\hat{\lambda}(t,\tau)=v(\tau)\lambda(t-\tau)\), \(\hat{\mu}(t,\tau)=v(\tau)\mu(t-\tau)\), and \(\hat{\lambda}_{1}(t,\tau)=v(\tau)\chi_{1}(t-\tau)\) (with \(I=1,2,3,4\)).
# GDPR and Large Language Models: Technical and Legal Obstacles
## 2 Detailed Analysis of GDPR Articles Challenging for LLMs
### Article 17--Right to Ensure ('Right to Be Forgotten')  
LLMs encode information within their neural network weights during training, which makes it extremely difficult to remove specific data points without altering the entire model. In addition, personal data is often intertwined with non-personal data, further complicating the isolation and removal process. Current machine unlearning methods, although promising, are not yet scalable for models with billions of parameters (e.g., GPT-4) [31; 37; 56].  
Several approaches have been proposed to tackle these issues. One idea is to develop algorithms for machine unlearning that allow models to forget specific data points without requiring full retraining. Alternatively, one could retrain the model from scratch after excluding the requested data, although this process is both resource-intensive and time-consuming. Other potential solutions include designing modular architectures that can isolate and remove specific data segments or implementing differential privacy techniques during training to limit the influence of any single data point [31; 54].  
Real-world examples further illustrate these challenges. For instance, Google has faced numerous right-to-erasure requests aimed at removing personal data from its search indices, highlighting the difficulties of applying Article 17 [8] to systems that aggregate and process vast amounts of data [63]. Similarly, although not publicly documented, hypothetical scenarios involving requests to remove personal data from OpenAI's GPT models underscore the technical hurdles associated with compliance [37].
## 4 Wettability
### The Effect of Surface Roughness on Wettability  
Numerous studies have investigated the effect of surface roughness on the wettability of various minerals, and the results show that the contact angle of particle surfaces is influenced by their surface roughness [114]. [PERSON] [115] was the first scientist to study the impact of surface roughness on the static contact angle. It was observed that the apparent contact angle decreases for hydrophilic materials and increases for hydrophobic materials as the surface roughness increases. Similar findings have been reported in investigations involving various minerals, including calcite [1], glass beads [35; 103], malachite [2], graphite [38], quartz [59], hematite [59], apatite [24], and dolomite [24]. And the results are shown in Table 4 (natural hydrophilic surfaces) and Table 5 (hydrophilic surfaces treated with collectors). For natural hydrophilic surfaces (\(\theta<90^{\circ}\)), similar results were reported by [PERSON] et al. [33], [PERSON] et al. [69], [PERSON] et al. [79], [PERSON] et al. [30], [PERSON] et al. [116], [PERSON] et al. [117], [PERSON] et al. [118], [PERSON] [119], [PERSON] et al. [58], and [PERSON] et al. [120], that the increasing surface roughness led to the increase in wettability (decrease in the contact angle) of both coking coal and anthracite surfaces (Figure 6). For natural hydrophilic surfaces (\(\theta<90^{\circ}\)) treated with collectors, similar results were reported by [PERSON] et al. [38], [PERSON] et al. [35], [PERSON] et al. [2], [PERSON] et al. [121], [PERSON] et al. [3; 39; 41; 59], [PERSON] et al. [44], [PERSON] et al. [1], and [PERSON] et al. [84], that increasing nanoscale roughness improved the hydrophobicity of the hematite, quartz, and magnesite particle surfaces (Figure 7). The angle changed from \(97.3^{\circ}\) to \(108.0^{\circ}\) as the Ra value increased from \(0.667\) nm to \(1.350\) nm in the presence of kerosene. The advancing contact angle increased in the presence of a surfactant (HTAB). The contact angle of the malachite surface increased from \(113.3^{\circ}\) to \(132.8^{\circ}\) as the Ra value rose from \(0.427\) um to \(3.620\) um in the presence of NaOL.
# Simulation of the Influence of Wing Angle Blades on the Performance of Counter-Rotating Axial Fan
## 4 Analysis of Optimization Results
### Total Pressure Distribution on Blade Surface  
The pressure distribution on the blade surface can effectively reflect the power capacity of the fan blade. Figure 6 shows the total pressure distribution curves of the first and second blades of the wing angle blade fan and prototype fan at the rated flow point. The prototype blades and wing angle blades are, respectively, analyzed at 25%, 50% and 75% relative blade heights. The relative position R = x/L is defined, where x is the distance from any point on the blade to the leading edge of the blade [16], and L is the chord length of the blade, mm. To the left of the black dotted line is the leading edge of the blade.  
In Figure 6a, as the relative blade height increases, the pressure of the pressure surface also increases, which also reflects that the upper half of the blade is the main working area. The total pressure distribution of the wing angle blade and the prototype blade on the suction surface and pressure surface is roughly the same. Additionally, the maximum pressure is located at the front edge of the blade. At a relative blade height of 75%, the maximum pressure of the angle blade is 6082.19 Pa, which is an increase of 22.15% compared to the maximum pressure of the prototype blade of 4979.35 Pa.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Position of** & **Tip Offset** & **Position of** & **Tip Offset Distance of** & **Efficiency** \\
**Test Serial** & **First-Stage Blade** & **Distance of** & **Second-Stage** & **Second-Stage Blades** & **\#\%** \\
**Number** & **Angle** & **First-Stage Blade** & **Blade Angle** & **B/mm** & **\#\%** \\
**A/mm** & **B/mm** & **C/mm** & & & \\ \hline
1 & 100 & 10 & 100 & 10 & 76.69 \\
2 & 100 & 15 & 110 & 15 & 76.36 \\
3 & 100 & 20 & 120 & 20 & 76.94 \\
4 & 110 & 10 & 110 & 20 & 76.53 \\
5 & 110 & 15 & 120 & 10 & 76.45 \\
6 & 110 & 20 & 100 & 15 & 76.38 \\
7 & 120 & 10 & 120 & 15 & 76.51 \\
8 & 120 & 15 & 100 & 20 & 76.32 \\
9 & 120 & 20 & 110 & 10 & 76.31 \\ \(k_{1}\) & 77.66 & 76.58 & 76.46 & 76.48 & \\ \(k_{2}\) & 76.45 & 76.38 & 76.40 & 76.42 & \\ \(k_{3}\) & 76.38 & 76.54 & 76.63 & 76.60 & \\ Range & 0.28 & 0.20 & 0.23 & 0.18 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design flow point efficiency data processing and range analysis.  
From Figure 6b, it can be seen that the total pressure of the secondary stage impeller blade at the pressure surface of the blade is stronger than that of the prototype blade at the same blade height, which indicates that the functional force of the blade angle blade is better than that of the prototype blade, and the maximum total pressure is also located at the pressure surface of the leading edge of the blade. The maximum pressures at 25%, 50%, and 75% of the blade height are 10,289.05 Pa, 12,575.33 Pa, 11,613.50 Pa, respectively, which are 2.3%, 9.3% and 8.1% higher compared to the prototype blade at the same height.
## 2 System Model of NGSO Satellites Equipped with Multi-Beam Antennas
### Downlink Analysis Model
The sum of all the interference \(I_{\text{sum}}\) can be figured out as  
\[I_{\text{sum}}=\sum_{M=1}^{M_{\text{sum}}}\sum_{N=1}^{N_{\text{sum}}}I_{MN} \tag{4}\]  
where \(M_{\text{sum}}\) and \(N_{\text{sum}}\) are the numbers of interfering satellites and interfering beams in a satellite, respectively, and \(I_{MN}\) stands for the augmentation interference the ground station receives from the \(N\)th beam of the \(M\)th satellite.
# Surface Ripple Formation by Bombardment with Clusters: Influence of Mass
## 3 Results and Discussion
### Atomic Redistribution  
The formation of surface ripples in the collisional phase may be due to atomic redistribution. Horizontal and vertical displacements, \(d_{x}\) and \(d_{z}\), respectively, are measures of the redistribution effect and are plotted in Figure 5a,b, respectively, as a function of the angle of incidence. The vertical displacement may explain the larger pulse amplitude in Figure 2 except for the grazing angles and light ions. In fact, Figure 5a shows how the vertical displacements for intermediate angles are ordered with the mass of the atom, except for light atoms, where the differences are quite small. The normal incidence and close angles do not interest us because they are situations in which the flat surface is experimentally stable [7]. Heavy atoms displace more atoms toward the surface at intermediate angles to the point of creating a well-defined maximum at 50\({}^{\circ}\). In turn, the horizontal displacement curve has a fairly defined maximum at 50\({}^{\circ}\) for all atoms except Xe. For this atomic species, the maximum moves towards 60\({}^{\circ}\). Some authors [10] multiply the displacement by the volume associated with a deposited atom and add it for all the displaced atoms. These authors consider that the maximum in this quantity allows for obtaining the critical angle, above which the formation of surface ripples occurs. In our case, we have verified that the horizontal displacement curves are completely similar to the displacement-volume curves calculated by these authors. That is, the volume deposited does not confer any special characteristics to the displacement curves, especially with regard to the position of the maximum. Accordingly, heavy atoms, such as Xe, would have a higher critical angle.  
Figure 5: (**a**) Vertical displacement \(d_{z}\) and (**b**) horizontal displacement \(d_{x}\) as a function of the angle of incidence for bombardment with different clusters.  
Horizontal displacements justify the enhancement of surface morphology for intermediate angles. That is, heavier atoms would be associated with greater horizontal displacements for these angles (Figure 5b), and, in turn, the surface patterns would be enhanced. In the case of He, the horizontal displacement becomes very small. Therefore, the formation of ridges is due to the rise of atoms from the solid, except for grazing angles, since it is not observed in that case.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### Perceptually Uniform Color Spaces
#### 2.1.2 IctCp  
\(ICtCp\) has a better chrominance and luminance decorrelation and has a better hue linearity than the classical \(Y^{\prime}CrCb\) color space [24]. This color space is calculated in three steps:  
* First, the linear \(RGB\) values (in the BT.2020 gamut) are converted into \(LMS\) values which correspond to the quantity of light absorbed by the cones: \[\begin{cases}L=&0.41210938\times R\ +0.52392578\times G\quad+0.06396484 \times B\\ M=&0.16674805\times R\ +0.72045898\times G\quad+0.11279297\times B\\ S=&0.02416992\times R\ +0.07543945\times G\quad+0.90039062\times B\end{cases}\] (6)
* Second, the inverse EOTF PQ [6] is applied to each \(LMS\) component: \[\begin{cases}L^{\prime}&=EOTF_{PQ}^{-1}(L)\\ M^{\prime}&=EOTF_{PQ}^{-1}(M)\\ S^{\prime}&=EOTF_{PQ}^{-1}(S)\end{cases}\] (7)
* Finally, the luminance component \(I\) and the chrominance components \(Ct\) and \(Cp\) are deduced as follows: \[\begin{cases}I=&0.5\times L^{\prime}\ +0.5\times M^{\prime}\\ Ct=&1.61376953\times L^{\prime}\ -3.32348632\times M^{\prime}\quad+1.70971679 \times S^{\prime}\\ Cp=&4.37817382\times L^{\prime}\ -4.37817383\times M^{\prime}\quad-0.13256835 \times S^{\prime}\end{cases}\] (8)  
The \(ICtCp\) color space [6] is particularly well adapted to video compression and more importantly to the PQ EOTF as defined in BT.2100 [6].
## 4 Scenario and Implementation of Test-bed  
In this study, a base station and five nodes are used in order to monitor data transmission and traffic conditions in a normal environment. The data and traffic are analyzed when a node attempts to keep sending a specific message to other nodes. Figure 2 shows the structure of the test-bed.  
Sensor 3 requests sensing data of each sensor per 0.1 second and sends the data to BS. Here, the address of the sensor requested is randomly selected among 1, 2, and 4. Also, it requests abnormal data (a command that can not be responded) from nodes, causing traffic and load on the entire sensor nodes.  
The total number of nodes is five and BS allows a PC to monitor. Node 1 and Node 2 generate traffic per 0.5 second by means of data transmission while Node 4 and 5 generate traffic only there is a  
Figure 1: DoS detection system based on global interdependent behaviors.  
change in sensing values, Node 3 requests sensing values from Node 1, 2, and 4 while constantly sending data to BS.
## 2 Experiment Setup  
Figure 1a depicts a photo of the VCSEL-based OWC system, showcasing its functionality over a transmission distance of 2.5 m. The essential instruments and devices governing the OWC system are highlighted in Table 1. Figure 1a is complemented by Figure 1b, which illustrates a schematic diagram presenting the PAM generation and decoding process employed within the IM/DD system. It begins with the offline generation of a random bit sequence using MATLAB, which is subsequently mapped to PAM symbols through gray coding. These PAM symbols undergo upsampling and processing via a non-return-to-zero (NRZ) rectangular pulse-shaping filter. The resulting signal is then loaded to an arbitrary waveform generator (AWG) for the creation of an analogue waveform, which, in turn, drives the VCSEL directly through a bias tee (Tektronix PSPL5542, 50 GHz).  
The VCSEL output undergoes collimation using an aspheric lens (Thorlabs ACL2520U). At the receiver, the optical signal is collected through an aspheric lens and is focused onto a PIN PD module (818-BB-45A). To facilitate optimal OWC link alignment and receiver optical power adjustment, both the VCSEL and PD are mounted on 3D-axis stages (Thorlabs NanoMax 300). The received signal is then captured by an oscilloscope. Then, standard digital signal processing (DSP) techniques are employed, including synchronisation, digital filtering, and equalisation. Finally, the transformed PAM symbols are de-mapped into the received bitstream, facilitating the evaluation of the bit-error-rate (BER) performance.  
The model that would best describe the output signal for the IM/DD OWC link assuming that most of the system non-linearity comes from the VCSEL is defined as [22; 23]:  
\[y(t)=\mathcal{G}_{pd}\mathcal{G}_{\textit{oavc}}f_{{}_{L-l-V}}\big{(}(V_{bias }+s(t))\otimes h_{\textit{resel}}(t)\big{)}\otimes h_{\textit{enc}}(t)\otimes h _{\textit{pd}}(t)+n(t), \tag{1}\]  
where \(\mathcal{G}_{pd}\) and \(\mathcal{G}_{\textit{oavc}}\) denote the gains from the photodetector and OWC link, respectively; \(f_{{}_{L-l-V}}(.)\) denotes the non-linear transfer function that converts the input voltage signal to the optical signal; \(V_{bias}\) denotes the bias voltage to drive the VCSEL, and \(s(t)\) denotes the bipolar input voltage signal; \(h_{\textit{resel}}(t)\), \(h_{\textit{env}}(t)\) and \(h_{\textit{pd}}(t)\) denote the impulse responses for the VCSEL, OWC link and photodetector, respectively; \(n(t)\) denotes the noise. This memory-based model is particularly useful for high-speed OWC links with significant system non-linearities.
## 3 Results
### Experimental Analysis of Detection Model
#### 3.2.2 Comparative Model Experiment  
To investigate the advantages of the improved model in detection, this study tested its performance under different image conditions. YOLOv8n-WSE-pest was compared against YOLOv8n, SSD, and Faster-RCNN. To ensure the reliability of the comparison results, the datasets used for the different models were consistent. The comparison results, as shown in Figure 14, were conducted on four insect categories: _Toxoptera aurantii_, _Xyleborus fonicatus Eichhoff_, _Empoasca pirisuga Matumura_, and _Malthodes discicollis [PERSON]_. The testing  
Figure 13: Grad-CAM results for pest identification.  
scenarios included multiple objects, single objects, occluded objects, missing subjects, and interfering objects.  
From the experimental results, it can be concluded that YOLOv8n-WSE-pest demonstrates good recognition accuracy under different lighting conditions, varying numbers of targets, and whether the subject is complete or not. The improved version of the model consistently outperforms other models in terms of accuracy across all conditions, while the original YOLOv8n model can recognize the subject in the majority of cases. Regarding the scenarios depicted in Figure 13(a) with small and multiple targets and occluded subjects, as well as Figure 13(b) with missing subjects, the improved model shows superior performance under these conditions, while SSD and Faster-RCNN fail to detect some of the targets. In Figure 13(c), where the subject is interfered with by other insect species, and Figure 13(d), where the image has high brightness, YOLOv8n-WSE-pest also exhibits a higher recognition accuracy compared to the other comparative models.  
The AP values can be obtained by using precision and recall as the \(x\)-axis and \(y\)-axis, respectively, and calculating the area under the curve. The AP values and mAP values of the comparative models for the four insect categories are shown in Table 4. In terms of the _Taxoptera aurantii_ category, this model achieved an AP improvement of 12.63%, 7.66%, and 3.18% compared to Faster-RCNN, SSD, and YOLOv8n, respectively. For _Xyleborus fornicatus_ [PERSON], the model showed AP improvements of 14.33%, 9.88%, and 2.58% compared to the three models, respectively. Similarly, for _Emposaca pirisuga_ [PERSON], the improvements were 15.06%, 9.08%, and 1.7%, and for _Malthodes disciollis_ [PERSON], the improvements were 15.37%, 9.05%, and 1.26%. The model also outperformed the comparative models in terms of the mAP, with improvements of 14.34%, 8.85%, and 2.18%,  
Figure 14: Comparison of actual image detection.  
respectively. The numerical visualization is shown in Figure 15, where YOLOv8n-WSE-pest achieved the highest AP values and mAP values among the four models.
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### QoS Metrics
#### 3.3.1 Delay  
The link delay between two vehicles is the transmission time over their interconnecting link. It can be calculated using Equation (4) proposed in [46]:  
\[T_{delay}=T_{r}-T_{s} \tag{4}\]  
where \(T_{r}\) is the reception time of the message and \(T_{s}\) is its sending time.  
The vehicle delay is the mean of all its links delays with the neighbouring vehicles and is calculated using Equation (5):  
\[T_{delay}(u)=\frac{\sum T_{delay}}{card(N(u))} \tag{5}\]  
where \(card(N(u))\) is the number of neighbours of vehicle \(u\).