esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Distortion of Direct Longitudinal Waves  
The experiments demonstrated that, in all tested samples undergoing different heat-treatment times, profiles of detected acoustic pulses exhibited systematic modifications with increasing laser power densities applied for the photo-excitation of acoustic waves. As an example, we present in Figure 4 detected profiles of surface displacement and evaluated profiles of particle velocity in the direct longitudinal acoustic pulse detected in the sample subjected to thermal aging for 2 h.  
Profiles of the particle velocity presented in the right part of Figure 4 clearly reveal the following tendencies in the acoustic pulse evolution with increasing laser power: (a) There is an accumulating delay of the pulse peak arrival and (b) there is an accumulating delay in the end time of the first positive (compressional) phase of the particle velocity profile. Both these tendencies are known manifestations of the influence of the hysteretic nonlinearity of the materials on the propagation of the acoustic pulses, as theoretically predicted for longitudinal waves in micro-inhomogeneous media [57] and convincingly experimentally observed for torsional pulses in the chains of mechanical  
Figure 4: The transformation of profiles of the particle displacement (**left**) and of the particle velocity (**right**) in longitudinal acoustic pulses arriving to the rear (detection) surface of the plate directly from the front (generation) surface of the plate with increasing laser power densities applied for the excitation of the LSCP layer.  
Figure 3: Signals with and without application of the liquid-suspended carbon particles (LSCP) layer for optoacoustic conversion at the same generation point.  
beads [58]. Theories of longitudinal acoustic pulse propagation in hysteretic media [57; 59] have been earlier applied for the explanation of the broadening, without the formation of any shock fronts, of longitudinal acoustic pulses launched in annealed aluminum by an electromagnetic-induction type transducer in comparison with those launched in non-annealed aluminum [60]. The explanation of the nonlinear transformation of the profile of unipolar acoustic pulses in aluminum as a function of the annealing temperature, applied during the preparation of the specimens, for a fixed distance of the propagation was based on the assumption that the parameter of hysteretic quadratic nonlinearity, \(h\), increases with the rise of the annealing temperature. Note that this assumption is in accordance with established dependence on annealing process characteristics of the parameter \(h\) in copper [21; 61]. The theory in [57] has also provided insight in the observed nonlinear transformation of laser-generated longitudinal acoustic pulses in cracked rocks [62].
## 4 Evaluation
### Performance-Based Evaluation  
To determine whether the adaptive system improved learning efficiency, we analyzed three key performance metrics:  
* Time Spent on Exercises: How long students took to complete exercises before and after UI adjustments.
* Error Rate: The average number of incorrect submissions per exercise.
* Success Rate: The percentage of exercises successfully completed without requiring external help.  
These metrics provided quantitative evidence of the system's impact on learning effectiveness. To strengthen the statistical reliability of the findings, each experiment was repeated five times with randomized session data per learner category (Fast, Moderate, and Slow). The reported values in Tables 2-5 and Figures 1-4 reflect the mean outcomes across these runs, accompanied by standard deviation to illustrate variability. This multi-run approach ensures that the observed improvements are consistent and not incidental, thereby providing more robust evidence of the system's effectiveness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learner Category** & **Phase 1: Avg. Time per Exercise (min)** & **Phase 2: Avg. Time per Exercise (min)** & **Time Reduction (\%)** \\ \hline Fast Learners & \(5.24\pm 0.11\) & \(5.00\pm 0.07\) & 4.6 \\ Moderate Learners & \(11.96\pm 0.11\) & \(10.64\pm 0.11\) & 11.0 \\ Slow Learners & \(17.56\pm 0.11\) & \(14.84\pm 0.11\) & 15.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average time spent per exercise before and after reinforcement learning-based fuzzy weight adjustments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learner Category** & **Phase 1: Excess Rate (\%)** & **Phase 2: Success Rate (\%)** & **Improvement (\%)** \\ \hline Fast Learners & \(92.28\pm 0.33\) & \(94.46\pm 0.21\) & 2.4 \\ Moderate Learners & \(78.88\pm 0.26\) & \(85.10\pm 0.29\) & 7.9 \\ Slow Learners & \(55.36\pm 0.36\) & \(71.16\pm 0.43\) & 28.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Success rate improvement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Learner Category** & \(p\)**-Value** & **Significant (\(p\) \(<\) 0.05)** \\ \hline \multirow{3}{*}{Time spent in exercises} & Fast & \(2.40\times 10^{-2}\) & Significant \\  & Moderate & \(1.69\times 10^{-4}\) & Significant \\  & Slow & \(6.30\times 10^{-7}\) & Significant \\  & Fast & \(3.88\times 10^{-4}\) & Significant \\ Error Rate & Moderate & \(2.25\times 10^{-4}\) & Significant \\  & Slow & \(4.60\times 10^{-7}\) & Significant \\  & Fast & \(3.06\times 10^{-6}\) & Significant \\ Success Rate & Moderate & \(4.63\times 10^{-8}\) & Significant \\  & Slow & \(9.63\times 10^{-11}\) & Significant \\ \hline \hline \end{tabular}
\end{table}
Table 5: Paired \(t\)-test results for performance metrics by learner category.  
Figure 3: Time spent on exercises before vs. after RL optimization.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 6 Types of Information Transmitted
### Transmission of Medical Images  
Image transmission is another vital issue in telemedicine. For efficient medical diagnosis, relevant images transmitted from a patient to a remote physician need to have acceptable resolution and use standard file formats such as JPEG, TIFF, BMP, or Digital Imaging and Communications in Medicine (DICOM) [2]. The images are transmitted to the destination station via the File Transfer Protocol (FTP) or TCP/IP. Among the techniques used for capturing medical images are ultrasound, digital X-Rays, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) scanning. The work of [126] demonstrated that after using a wireless CT scan to obtain images of neurosurgical emergency cases, a neurosurgical consultant can reliably render telemedical services to the patient.
## 2 Materials and Methods
### Surface Sensors  
The SCH sensors were AISI 316 grade 2B stainless steel coupons (2 cm in diameter and 1 mm thick) coupled to a base through the action of neodymium magnets and coated with epoxy paint. These bases, which were also made of stainless steel, could hold three coupons simultaneously, thus facilitating their weekly analysis for three consecutive weeks. The supports were welded to the areas to be evaluated (Figure 1). This tool enabled the coupons to be in the same conditions as the rest of the surfaces in that area. The sensors were then subjected to the same contamination and cleaning/disinfection protocol as the surfaces to be sampled [17], allowing the natural biofilms that may have formed on the surfaces to be analyzed. To this effect, [PERSON] et al. indicated that stainless steel coupons are suitable for analyzing the natural microbiota of the surrounding environment and they have been used in different subsequent studies [18]. More concretely, a variation of just 5.1% on species richness between the sensors and the sinks (the real study material of their research) was demonstrated.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Processing Plant** & **ID** & **Surface** \\ \hline \multirow{4}{*}{A} & 1 & \multirow{2}{*}{\
\begin{tabular}{c} Sump in the deboning room \\ Slicer A \\ \end{tabular} } \\  & 2 & \multirow{2}{*}{
\begin{tabular}{c} Slicer A \\ \end{tabular} } \\  & 3 & \\ \hline \multirow{4}{*}{B} & 4 & Floor of the carcasses airing room \\  & 5 & Storage cabinet for tools \\  & 6 & Floor of the work room \\  & 7 & Floor of the fresh meat carts cleaning room \\  & 8 & Floor of the cured meat carts cleaning room \\  & 9 & Slicer B \\  & 10 & Iberian sausage transportation carts \\  & 11 & Slide of vacuum machine \\  & 12 & Floor of the heat treatment room \\  & 13 & Sink \\ \hline \hline \end{tabular}
\end{table}
Table 1: Work surfaces from Plants A and B where the SCH sensors were installed (coded from 1 to 13) [16].
# Late-Stage Optimization of Modern ILP Processor Cores via FPGA Simulation
## 6 Related Work  
Many studies have been conducted using FPGAs to accelerate simulation systems. Several of these works focus on accelerating simulation times by moving detailed resource modeling into the FPGA system [25; 26]. The RAMP [27] develops a general framework for FPGA based hardware and software CMP research. Other works such as such as ATLAS [28] and HAsim [29] try to develop a full-featured multicore platform for use in computer architecture research studies. FPGA works for direct RTL-based design space exploration, such as Fabscalar [18] and BOOM [30], have also been conducted, and these efforts have a few similarities to our work. Fabscalar uses a new RTL generation design flow, which is not suitable for realistic processor RTL designs because they are not generated with a template. Furthermore, the Fabscalar FPGA-sim system has a noticeable lack of hardware support for full system simulation. The RISC-V based BOOM [30] introduced a new RTL language (Chisel), which makes it possible to generate RTL model from a programmatic description. Its two-issue version (RISC-V BOOM-2w) can run 50 MHz on a FPGA (Zynq ZC706). It is a good start for implementing design options for processors, but such design flow has not been widely adopted in industry for the lack of commercial EDA tool support. On the contrary, our methodology augments conventional realistic RTL designs (such as Verilog design) to facilitate late-stage exploration, which is compatible with the conventional design flow on existing or new RTL cores.  
Companies such as Intel and IBM implement their commercial processor into the FPGA system [6; 7; 8; 9; 10]. However, these works achieve unsatisfactory speed for out-of-order processors. With a big step in speed improvement, this work identifies the potential usage of FPGA simulation in late-stage optimization. Another related direction is to design a customized processor directly on the FPGA chip. This customization can achieve high processor frequency. Recent work on out-of-order soft processors with simplified architectural features can achieve up to 200 MHz frequency [31; 32]. Compared with other designs [24; 25; 26; 27; 28; 29; 30; 31; 32], though the term of late-stage optimization has not been introduced before, many chip designers have already done similar work through conventional design flow. They may also do several optimization work while they find the performance goal is not achievable after simple evaluation on emulator or conventional FPGA system. However, compared with the proposed late-stage optimization, this flow may suffer from two major problems: high design effort and long optimization round. Such work will lend substantial experience in achieving superior speed in FPGA simulation.
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 2 Methodology
### Internal Graph Convolution Network
Subfigure (**B**) shows the dynamic graph calculated by similarity of traffic patterns. Figure 5: Internal graph convolutional network based on dynamic graph generation. Figure 3: Association between nodes. Subfigure (**A**) shows the distribution of each sensor in a road network. Subfigure (**B**) shows the correlation degree changing over time. we select the traffic flow as the metric and calculate the correlation degree of each node, using the Pearson correlation coefficient to generate the dynamic graph:  
\[p_{i,j}^{s}=\frac{Z\sum{x_{l}^{s,j}x_{l}^{s,j}}-\sum{x_{l}^{s,j}}\sum{x_{l}^{s,j}} }{\sqrt{Z\sum{\left({x_{l}^{s,j}}\right)^{2}}-\left({\sum{x_{l}^{s,j}}\right)^{ 2}}}\sqrt{Z\sum{\left({x_{l}^{s,j}}\right)^{2}}-\left({\sum{x_{l}^{s,j}}\right) ^{2}}}} \tag{3}\]  
\[\mathbf{A}_{i,j}^{s,c}=\mathbf{A}_{j,i}^{s,c}=\left\{ \begin{array}{ll}p_{i,j }^{s}&,\quad p_{i,j}^{s}\
eq 0\\ 0&,\quad p_{i,j}^{s}=0\end{array} \right. \tag{4}\]  
Equation (3) shows the formula for the Pearson correlation coefficient, where \(x_{l}^{s,j}\) and \(x_{l}^{s,j}\) are the parameters of lane \(i\) and lane \(j\) at moment \(l\), respectively, and \(Z\) is the amount of data for each lane. \(A^{s,c}\) in Equation (4) is the dynamic graph matrix of the same-intersection lanes and \(p_{i,j}^{s}\) is the correlation degree between lane \(i\) and lane \(j\) at the same intersection. Similarly, we can derive the dynamic graph matrix \(A^{d,c}\) for different-intersection lanes. When performing a graph convolution operation on same-intersection lanes, we utilize the dynamic matrix \(A^{s,c}\) to generate a convolution kernel \(g_{\theta}^{s,dy}(\Lambda)\) and convolute \(X_{s}^{l}\) at moment \(l\):  
\[F_{s}=g_{\theta}^{s,dy}(\Lambda)X_{l}^{s}=\sum_{k=0}^{K-1}\partial_{k}T_{k}^{s,dy}(\tilde{\Lambda})X_{l}^{s} \tag{5}\]  
When performing a graph convolution operation on different-intersection lanes, we utilize the convolutional kernels \(g_{\theta}^{d,dy}(\Lambda)\) and \(g_{\theta}^{d,st}(\Lambda)\) generated by the dynamic matrix \(A^{d,c}\) and the static adjacency matrix, respectively, to convolute \(X_{d}^{l}\):  
\[F_{d}=\left((g_{\theta}^{d,dy}(\Lambda)+g_{\theta}^{d,st}(\Lambda))X_{l}^{d} \right)\odot W \tag{6}\]  
\[g_{\theta}^{d,dy}(\Lambda)+g_{\theta}^{d,st}(\Lambda){=}\sum_{k=0}^{K-1} \partial_{k}\left(T_{k}^{d,st}(\tilde{\Lambda})+T_{k}^{d,st}(\tilde{\Lambda})\right) \tag{7}\]  
where \(W\) is the trainable weight matrix and \(\odot\) denotes the element-wise product. The final result of the internal graph convolution network is calculated by the fusion of \(F_{s}\) and \(F_{d}\) at moment \(l\):  
\[\xi_{l}^{sp}{=}relu\left(F_{s}*W_{l}^{sp}+b_{l}^{sp}+(F_{d}*V_{l}^{sp}+c)\right) \tag{8}\]  
where \(W_{l}^{sp}\) and \(V_{l}^{sp}\) are convolution kernels and \(b_{l}^{sp}\) and \(c\) are biases corresponding to the kernels.
## 4 Experimental Results
### A Comparison to Other FBP-Related Work  
We compared different FBP models based on three distinct datasets, namely the SCUT-FBP, SCUT-FBP5500, and MEBeauty datasets. The proposed method was compared with existing studies on these datasets, and the results are presented in Table 9. Our proposed FBP methodology outperformed the state-of-the-art techniques, as evidenced by the PC, RMSE, and MAE values. \"N/A\" is used to indicate that these studies do not provide the values of MAE and RMSE. While values in bold signify superior performance.  
In evaluating the SCUT-FBP dataset, the proposed fine-tuned FIAC-Net integrated with the proposed ensemble average lossl stands out as a top performer. It achieved a slightly higher PC of 0.9101, lower MAE of 0.1859, and lower RMSE of 0.2591 compared to most of the other investigated methods. Notably, the authors in [22] guided the regression task via pairwise ranking, which works well for small data, and it achieved a higher PC of 0.95, MAE of 0.2885, and RMSE of 0.2314, but our proposed model surpasses it in terms of lower error rates. Similarly, the proposed model exhibited an exceptional performance compared to other methods on the SCUT-FBP 5500 and MEBeaty datasets. It outperformed other compared approaches, showcasing superior performance in predicting facial-beauty scores. Our approach demonstrated competitive results on the SCUT-FBP5500 and MEBeauty datasets, achieving an MAE of 0.2028 and 0.4263, respectively. This performance is competitive to the FBP models presented in [28; 49], which achieved MAEs of 0.2009 and 0.365, respectively. These results highlight the effectiveness of our approach in this particular context.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 2 Background and Related Work
### Related Work
#### 2.2.2 Texture Properties-Based Methods  
There is a clear correlation between the CU size selected and the texture complexity of the video frame. For example, there is a tendency to use larger CUs in homogeneous regions of a video frame; whereas less homogeneous regions tend to use smaller CUs (e.g., Figure 1). Following a similar principle, [PERSON] et al. [26] propose to use gradient information computed using horizontal and vertical Sobel kernels to compute the CU split decision. Further to that, texture complexity computed from directional gradients is compared with a QP dependent adaptive threshold to determine the partitioning structure for a given content in [27]. A similar content-adaptive threshold determination is followed in [28] when determining the early termination of the recursive CU partitioning process. [PERSON] et al. in [29] and [PERSON] et al. in [30] follow similar approaches that consider texture homogeneity and directional edge complexities, respectively, to determine the CU size. Texture features of the spatial CUs and co-located CU are used in [31] for fast CU size decisions in 3D-HEVC. Furthermore, texture edges identified using Sobel kernels are utilised to early predict the angular prediction direction in HEVC intra-prediction in [32]. However, in general, kernel-based gradient computation used in these algorithms is seen as a time-consuming operation resulting in a limited encoding time reduction. Therefore, [PERSON] et al. in [33] propose to use local range as a texture complexity measurement when determining the CU size for given video content. However, the predetermined threshold and rigid decision trees make this algorithm less content-adaptive leading to inefficient CU size decisions for arbitrary sequences.
## 3 Results & Discussion
### Case II  
In this case, the relationship between the KPI \(B(t)\) and the PIs \(A_{1}(t)\), \(A_{2}(t)\), \(A_{3}(t)\) is \(B(t)=A_{1}(t)*A_{2}(t)\) + \(A_{3}(t)\). The evolution of the PIs trends is, as follows:  
1. \(A_{1}(t)\) is constant in time;
2. \(A_{2}(t)\) is constant in time; and,
3. \(A_{3}(t)\) increases in time.  
It is evident from the contribution diagram of Figure 10 that there is success in predicting that the contribution of the third factor should be high. The same happens if \(A_{3}(t)\) decreases in time. It seems that the algorithm runs successfully, regardless of the operations that are performed among the KPIs.  
Figure 9: Case I. Top: Evolution of KPI, Middle: Evolution of PIs, Bottom, Contribution of each PI.
## 3 Noise Measurement and Reduction in Mode-Locked Lasers
### Tining Jitter
#### 3.1.2 Timing Jitter Reduction Methods
Changing the voltage applied to the electro-optic crystal changes the refractive index of the crystal, leading to a cavity length modulation above 100 kHz or even close to 1 MHz bandwidth. The shortcoming of the electro-optic modulators is that their tuning range in repetition rate is limited. In this case, piezoelectric ceramics can act as low-speed actuators with a large tuning range while electro-optical modulators act as high-speed actuators with a small tuning range. This combination enables a high-speed, long-term stable phase and reliable locking. One thing that should be noted is that the crosstalk between repetition rate modulation and carrier-envelope phase modulation would be an issue and may restrain the noise performance of fully stabilized OFCs.
## 4 Results and Discussion
### Geophysical Logging Response Characteristics of Coal Quality
#### 4.3.3 Logging Response Characteristics of Volatile Matter  
Volatile matter in coal represents the fraction released as gases during high-temperature heating. Its yield displays a parabolic relationship with apparent resistivity and density (Figure 8a,c), a pattern influenced by the content, properties, and structure of organic and inorganic components, as well as coalification processes [14; 74]. Increased sulfide mineral content reduces coal seam apparent resistivity [75], while advancing coalification decreases inorganic mineral content, elevating resistivity [64; 76]. Density logging values correlate with coal lithotype, rank, ash composition, and external factors such as borehole diameter variations and fracture development [72].  
Figure 7: Relation between ash yield and logging parameters in coal. **(a)** The relationship between ash yield and apparent resistivity; **(b)** The relationship between ash yield and lateral resistivity; **(c)** The relationship between ash yield and bulk density; **(d)** The relationship between ash yield and natural gamma; **(e)** The relationship between ash yield and short-spaced gamma-gamma; **(f)** The relationship between ash yield and acoustic transit time.  
Volatile matter yield positively correlates with lateral resistivity, natural gamma (GR), short-spaced gamma-gamma (CGS), and acoustic transit time (AC) (Figure 8b,d-f). This relationship stems from reduced mineral content and higher organic matter resistivity in bituminous coals with increasing metamorphism [14; 77]. GR intensity rises proportionally with radioactive particle abundance in coal [14], predominantly hosted in pyrite and sulfur-rich organic matter [78; 79; 80]. Consequently, volatile matter correlates positively with lateral resistivity and GR. Concurrently, pore-fracture systems in medium- to low-rank bituminous coals elevate CGS and AC values [73].
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 4 Charging Type
### Wireless Charging: Charging Type
#### 4.2.1 Categorization of Wireless Charging Based on the Charging Standards  
Wireless charging can be classified into various charging standards such as Qi, SAE, A4 WP, and IEC charging, and can be operated with different power supplied and charging times based on the different coupling techniques. The coupling techniques are as follows [98,99]:  
* Qi wireless charging standard The Qi charging standard was developed by the Wireless Power Consortium and can be utilized to charge an EV up to a certain distance of 4 cm. It comprises two essential components, i.e., base stations and mobile devices. Base stations are generally attached to an electric grid to provide the power supply for wireless charging. In addition, it contains a power transmitter that induces the electromagnetic field at the receiving end of mobile devices to perform wireless power transfer between two devices, i.e., the base station and mobile device. Moreover, the base station comprises a flat surface on which a number of mobile devices can be placed for the wireless charging procedure. However, the Qi charging standard supports power transfer to a certain extent, as once mobile devices are fully charged, base stations can stop transmitting the power through electromagnetic induction. Power transfer in the Qi charging standard provides a low power of 5 W and high power of 120 W. One more critical aspect needs to be considered to ensure efficient power transmission between the base station and mobile device, i.e., placement of the components. Components can be placed according to two methods categorized as guided positioning and placement anywhere. Guided positioning works on the principle that the user should be adequately guided to position the mobile device at the correct place between the sender and receiver magnetic coil. W With placement anywhere, a user can randomly position their mobile device on the base station in a broader interface area with the help of multiple transmission magnetic coils.