esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation Metrics  
The performance of the implemented models was assessed using accuracy, inference latency, and resource usage as key metrics. Specifically, Top-1 and Top-5 accuracy were employed to evaluate classification performance. Top-1 accuracy is defined as the percentage of instances where the model's highest probability prediction matches the true label, while Top-5 accuracy measures the percentage of instances where the true label is among the model's top 5 highest probability predictions. These metrics are expressed as:  
\[\text{Top-1 Accuracy}\;(\%)=\frac{1}{N}\sum_{i=0}^{n}1(\hat{y}_{i}=y_{i}) \times 100, \tag{1}\]  
Figure 3: Conceptual pipeline of the Xilinx Vitis-AI.  
\[\text{Top-5 Accuracy }(\%)=\frac{1}{N}\sum_{i=0}^{n}1\left(\hat{y}_{i}\in P_{i}^{5} \right)\times 100, \tag{2}\]  
where \(N\) is the total number of samples, \(\hat{y}_{i}\) is a predicted label, \(y_{i}\) is a true label, and \(P_{i}^{5}\) is a set of the top 5 predicted labels for the \(i\)-th sample.  
To provide a baseline, the performance of FPGA-based implementations was compared against CPU-based inference using the same VGG16 and VGG19 models. Additionally, the resource utilization of the FPGA was analyzed to evaluate the efficiency of the hardware in handling computational workloads. This multifaceted evaluation highlights the advantages of FPGA acceleration for real-time, resource-constrained applications such as autonomous vehicles and IoT.
## 2 Materials and Methods  
The investigated GaN/Al0.2 Ga0.8N HEMT structures on Al2O3 substrate (wafer No. TG2196) is depicted in Figure 1A. It was grown along \(c-\)axis by the metal organic chemical vapor deposition [11]. The two-dimensional electron gas (2 DEG) sheet concentration was \(n_{\mathrm{s}}=9\times 10^{12}\) cm\({}^{-2}\) and Hall mobility measurements gave \(\mu_{\mathrm{H}}=1400\) cm\({}^{2}/(\mathrm{V}\cdot\mathrm{s})\) and 4200 cm\({}^{2}/(\mathrm{V}\cdot\mathrm{s})\) at 293 and 77 K, respectively. Residual doping level was estimated to be below \(10^{17}\)cm\({}^{-3}\)[12]. Two electrical ohmic contact stripes of 0.1 mm width and 2 mm length were formed at 2 mm distance from each other.  
Large active area allowed to investigate the emission using a conventional far-infrared Fourier transform spectroscopy. The sample was kept in a closed-loop liquid helium cryostat with a TPX optical window. It was pumped electrically with voltage pulses of \(t_{\mathrm{P}}=2.1\) ms duration at modulation frequency of 7 Hz and bias amplitude up to \(U_{\mathrm{P}}=90\) V. The emitted radiation was collected with a short focal length off-axis parabolic mirror and directed to the input of Fourier spectrometer (\"Thermo Scientific Nicolet-8700\") equipped with a SolidStateTM beam splitter and DTGS pyroelectric detector, operating in a step-scan mode. A lock-in technique was employed to demodulate the detected signal. Using this set-up, we were able to record emission spectra at the cryostat temperatures (measured at the cold finger) of 4-300 K in 50-600 cm\({}^{-1}\) (1.5-18 THz) spectral range.  
Figure 1: The layer composition of investigated AlGaN/GaN HEMT structure (**A**). Sample resistance dependencies on cryostat temperature at \(U_{\mathrm{P}}=5\) V, 60 V and 90 V (**B**). Schematics of spectroscopic experimental setup (**C**).
## 7 Involvement of Cementum in ORR  
### The Hardness of Cementum  
As a new attempt, we focused on the relationship between root resorption and cementum hardness from a different perspective. The cementum, which covers the root surface, is composed of thin calcified tissue produced by cementoblasts. The apical third of the root is covered with cellular cementum, whereas the coronary third of the root is covered with noncellular cementum. Active cellular cementum depends on blood circulation. Generally, the mineralized surface of the cementum is more resorbed in the apical part of the root following orthodontic tooth movement than in other regions in humans. Hardness may be a major factor affecting the structure of cementum, particularly during root resorption caused by orthodontic forces.  
According to the group of [PERSON] et al. [41; 42; 43; 44], substantial individual variations in the occurrence, surface extension, and depth of root resorption have been reported in some studies [41; 42; 43; 44]. The hardness (soft, moderate, and hard) and tissue mineral density (TMD) of the cementum in the apical region of the root have been measured using the dynamic micro-indentation method and X-ray \(\upmu\)CT analysis, respectively. The cementum hardness values were lower in the soft and moderate groups than in a hard group [5], and the X-ray \(\upmu\)CT analysis revealed that the teeth in the hard group had higher TMD values than those in the other two groups [6]. Therefore, the hardness of the cementum may be related to the occurrence of ORR. Further studies are necessary to investigate which cementum hardness and TMD contribute to ORR incidence clinically, in animal or human models.
## 1 Introduction
### The Importance of Energy Storage Systems
This means that the VRFB energy storage technology has become a real, competitive alternative to Li-ion-based (lithium titanate, lithium iron phosphate, nickel manganese cobalt oxide, lithium nickel cobalt aluminum) storage systems [52; 53; 54]. Table 1 shows the expected energy installation cost reference values for 2030 [42]. In the recent period, the market perception of utility-scale Li-ion energy storage systems in the USA has changed somewhat. In the summer of 2019, there was a major fire at a 2-MW lithium-ion battery system in the state of Arizona (USA). This was the second such case in the state. As a result, the state is exploring the available economical, more sustainable, and secure alternatives. From a security standpoint, the VRFB technology has no risk of thermal runaway [55; 56]. The advantage of VRFB is the indefinite-depth discharge cycle, while the rate of physical degradation of the Li-ion battery varies from 2% to 18% after 2500 cycles compared to the new state. The disadvantage of the VRFB technology is the approximately 10%-20% lower round-trip efficiency compared to Li-ion technology [57; 58; 59; 60; 61]. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Battery Technology**} & **Reference Energy Installation** & **Expected Reference energy Installation Cost in** \\  & **Cost in 2020 [USD/kWh]** & **2030 [USD/kWh]** \\ \hline
**VRFB** & 282 & 119 \\
* **LTO** & 887 & 478 \\
* **LFP** & 477 & 224 \\
* **NMC** & 348 & 167 \\
* **NCA** & 279 & 145 \\ \hline \hline \end{tabular}  
* LTO = lithium titanate, LFP = lithium iron phosphate, NMC = nickel manganese cobalt oxide, NCA = nickel cobalt aluminum.  
\end{table} 
Table 1: Energy installation cost reference values of Li-ion and vanadium redox flow battery (VRFB) batteries for 2020 and 2030 based on [42].
# Seismic Response Study of L-Shaped Frame Structure with Magnetorheological Dampers
## 5 Analysis and Interpretation of Results
### Results Analysis on Multi-Dimensional Vibration Reduction of the Structure
According to the differential equation of motion of the MRD damping structure [32], a control force matrix will be added because of the setting of MRDs in the structure; it is  
Figure 10: Contrasts of acceleration response of Node 154 of the structure with and without MRDs. the Taft wave, the maximum horizontal displacement, acceleration, and story drift ratio of every floor of structure with MRDs are obviously lower than those without MRDs [44]. For Node 126 on the eighth floor, during the action time of the Taft wave, the horizontal bi-directional maximum displacements of the structure without MRDs are 66.05 and 45.07 mm, and those with MRDs are 38.62 and 35.77 mm, which are decreased by 41.53% and 20.64%, respectively. The horizontal bi-directional maximum accelerations of the structure without MRDs are 2.95 and 3.15 m/s\({}^{2}\), and those with MRDs are 2.54 and 2.74 m/s\({}^{2}\), which are decreased by 14.01% and 13.12%, respectively. The horizontal bi-directional story drift ratios of the structure without MRDs are 1.51 \(\times\) 10\({}^{-3}\) rad and 0.92 \(\times\) 10\({}^{-3}\) rad, and those with MRDs are 0.97 \(\times\) 10\({}^{-3}\) rad and 0.76 \(\times\) 10\({}^{-3}\) rad, which are decreased by 35.55% and 17.51%, respectively. As shown in Figure 11b, for Node 126 on the eighth floor, during the action time of the Kobe wave, the horizontal bi-directional maximum displacements of the structure without MRDs are 82.99 and 59.99 mm, and those with MRDs are 52.67 and 50.32 mm, which are decreased by 36.53% and 16.14%, respectively. The horizontal bi-directional maximum accelerations of the structure without MRDs are 3.15 and 3.38 m/s\({}^{2}\), and those with MRDs are 2.48 and 2.91 m/s\({}^{2}\), which are decreased by 21.08% and 13.89%, respectively. The horizontal bi-directional story drift ratios of the structure without MRDs are 1.74 \(\times\) 10\({}^{-3}\) rad and 1.17 \(\times\) 10\({}^{-3}\) rad, and those with MRDs are 1.25 \(\times\) 10\({}^{-3}\) rad and 1.01 \(\times\) 10\({}^{-3}\) rad, which are decreased by 28.41% and 14.44%, respectively. It can be seen from Figure 11 that the story drift ratios of the L-shaped frame structure with and without MRDs first increase and then decrease with the increase in the number of floors of the structure, and the maximum value appears on the third floor. This is because the bottom column of the structure is assumed to be consolidated, and the angle displacement of the bottom column is 0, resulting in a small story drift ratio between the bottom floors at not less than 1/2 of the second layer displacement angle; it shows that the envelope diagram is reasonable.
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 5 Discussion
### Strengths and Weaknesses of the Proposed Methodology
#### 5.2.2 Weaknesses  
1. **Computational Complexity.** * High Computational Demand: The application of differential geometry to seismic data processing and interpretation is computationally intensive. The algorithms involved often require significant computational resources and time, which can be a limitation in practical scenarios.
* Algorithm Complexity: Developing and implementing differential geometric algorithms can be complex, requiring specialised knowledge and expertise in both mathematics and geophysics.
2. **Data Quality and Availability.** * Dependence on High-Quality Data: The effectiveness of differential geometric methods is highly dependent on the quality and resolution of the input data. Poor data quality can lead to inaccurate models and interpretations.
* Data Integration Challenges: Integrating different types of geophysical data using differential geometry can be challenging, especially when dealing with disparate data sources and varying resolutions.
3. **Practical Implementation.** * Specialised Training: Geoscientists and engineers need specialised training to effectively use differential geometric methods. This requirement can be a barrier to widespread adoption in the industry.
* Cost Considerations: Implementing differential geometric techniques can be costly due to the need for high-performance computing resources and specialised software.
4. **Approximation and Simplification.**Model Simplifications: Despite its precision, differential geometry often involves approximations and simplifications to make the problem tractable. These simplifications can sometimes overlook finer details of the geological structures.
* Assumptions and Limitations: The models used in differential geometry may rely on certain assumptions that do not always hold true in all geological settings, potentially leading to errors in interpretation.
## 4 Boundary Treatments
### Repulsive Force  
Repulsive force boundary treatment is widely used in WCSPH because of its simplicity of implementation. Repulsive force is exerted on the fixed boundary particles to keep the adjacent inner fluid particles away and prevent them from penetrating the boundaries, as shown in Figure 1. The repulsive force is calculated by  
\[f(r)=D\bigg{(}\Big{(}\frac{r_{0}}{r}\Big{)}^{P_{1}}-\Big{(}\frac{r_{0}}{r} \Big{)}^{P_{2}}\bigg{)}\frac{\mathbf{r}}{r^{2}},\ r<r_{0} \tag{23}\]  
_f(r)_ is set to be zero when \(r>r_{0}\) so that the force is purely repulsive. Mostly, \(P_{1}=4\) and \(P_{2}=2\), D = 5 gH, and H is the total water height [3]. The length scale \(r_{0}\) is taken to be the initial spacing among the particles.  
The penetration of fluid particles could be avoided by applying a repulsive force. Yet the kernel domain is still truncated, and other modification of particle approximations is necessary to obtain a correct computation. Furthermore, this repulsive force is largely dependent on the distance between two particles. Thus, in the situation of violent fluid impact, this force can be extremely large when particles approach each other instantly. Consequently, the inner fluid particle might be forced to move fiercely away from the boundary with a large velocity. All the surrounding fluid particles will then be influenced, and unphysical particle explosion might happen.
## 3 Results and Discussion
### The Effect of Filter Material Properties on Filtration Efficiency
#### 3.1.4 Comparison of Filtration Efficiency of Filter Media with Different Formulations  
Under standard test conditions, a comparative study of the filtration efficiency of PPS fibers (common, sample 1), 50% PPS + 50% PTFE (blending, sample 7), 70% conventional PPS + 30% ultra-fine PPS (blended ultra-fine, sample 8), 70% conventional PPS + 30% ultra-fine PPS (gradient filter media, sample 9), PPS fibers + laminate (common + membrane, sample 10), and 50% PPS + 50% PTFE + membrane (blended + membrane, sample 11) was carried out, and the experimental results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Common Fiber** & **Blending (\%)** & **Blended** & **Gradient Filter** & **Common Fiber** & **Blended Fiber** \\  & **(\%)** & **Blending (\%)** & **Ultra-Fine (\%)** & **(\%)** & **Coating a** & **Coating a** \\  & & & & **Membrane (\%)** & **Membrane (\%)** \\ \hline Before aging & 99.9790 & 99.9776 & 99.9702 & 99.9831 & 99.9998 & 99.9996 \\ After aging & 99.9970 & 99.9969 & 99.9911 & 99.9988 & 99.9997 & 99.9995 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Filtration efficiency of different formula filters.  
Figure 4: Filter material surface after immersion treatment (**a**) and coating treating (**b**).  
From Table 6, it can be concluded that among the six experimental filter media, whether it was conventional laminated filter media or blended laminated filter media, the laminated film played a major role in the filtration, which realized the transformation of deep filtration to surface filtration in the filtration mechanism, and the micro-particles did not easily enter the inner part of the filter media, with a high-efficiency filtration performance and filtration efficiency close to 100%. The gradient filter material was similar to the membrane-covered filter material in the structure, and formed the characteristics of the \"surface filtration\" mechanism; its filtration performance was close to the membrane-covered filter material [35; 36]. Although the blended ultrafine fiber media was blended with ultra-fine ingredients, it did not form a gradient structure, so the filtration performance of blended microfiber filter media has no obvious advantages.
# Binaural Auralization of Room Acoustics with a Highly Scalable Wave-Based Acoustics Simulation
## 3 Scalability of Wave-Based Room-Acoustics Solver on an HPC Cloud Environment
### Computation Setting and Evaluation Index  
Both room models were spatially discretized with uniform cubic-shaped eight-node elements with 0.0125 m edge length. The resulting FE meshes have 34,832,384 elements and 35,179,305 DOFs for Problem 1, and 97,069,568 elements and 97,917,391 DOFs for Problem 2. Problem 2 has about 2.8 times larger problem size. For both models, RIRs were calculated up to 1 s with \(\Delta t=1/57,000\). As an excitation signal for the sound source, the impulse response of the FIR filter with an upper-limit frequency of 6 kHz, created using the Parks-McClellan algorithm, was employed. The following speed-up index \(S\) used to evaluate the parallel performance of the solver was defined as  
\[S=\frac{T_{b}}{T_{X}}, \tag{37}\]  
where \(T_{X}\) is the computational time consumed for the case using \(X\) computational nodes, and where \(T_{b}\) is the computational time consumed using \(b\) computational nodes as a baseline. The maximum number of computational node \(X\) is 64. Because one node has 96 CPU-cores, we use 6144 CPU-cores for \(X=64\). The measure \(S\) represents how the solver speeds up when using \(X\) nodes against the baseline. For Problem 1 and Problem 2, we set \(b\) to 1 and 2, respectively. The reason for \(b=2\) for Problem 2 is the memory limitation on a node, i.e., in Problem 2, single node parallelization consumes the memory that exceeds 384 GBytes because of storage of information of the whole model on a node in addition to the global boundary condition matrix assembling in each process. We evaluated the parallel performance of the wave-based solver in two computational parts. One is the parallel performance of the time-marching scheme part, i.e., Equations (10)-(19), which is the main operation of the solver. Another is that, of all computational parts, which includes matrix construction parts as well as the time-marching scheme part. Parallel performance was verified up to 64 nodes while doubling the number of computation nodes. Metis library [28] was used to create subdomain meshes that achieve an even workload among the subdomains. Regarding the workload balance, Figure 4 shows coefficients of variance in DOFs and communication data per communication via MPI among computational processes in each parallel condition. The deviations in DOFs are very small: less than 3%. The coefficients of variance in communication data are around 20%. This is not a severe deviation. We therefore infer that the workloads of all subdomain meshes are well balanced.  
Figure 3: Problem 2 for scalability test: classroom model. Blue and magenta spheres respectively represent the source point and the receiving point.
# Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering
## 6 Case of Study  
A prototype hotel recommender app was developed for the purpose of validating the presented proposal. We must emphasize that this app was developed and used only for research and evaluation purposes. This app allowed users to obtain hotel recommendations upon submitting a query. For example, if a user enters \"Hotel in London\", the app returns 10 London hotels and relevant excerpts from reviews, ranked by category of user experience. Figure 2 provides information about one of the recommended hotels.  
Hotels are recommended by applying the FC method described in Section 5.1. Opinions classified by user experience are obtained using the rules and filtering methods presented in Sections 4 and 5.3, respectively. Users must rate at least 10 hotels which they have visited the first time they enter the system. The system allows users to search for 10 or more previously visited hotels and give them a rating. The history of user ratings is built with this information, avoiding the cold start problem. Also, the system builds the rating matrix of the hotels in the system by taking the explicit ratings given by users on the TripAdvisor website. More specifically, ratings from 1 to 5 given by users are obtained for the construction of the rating matrix of the hotels in the system. The matrix of the hotels in the location chosen by the user and the historical ratings of that user are used to calculate the FC.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **MSE** \\ \hline SVM & 0.589 \\ Lexical Rules & 0. 364 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MSE values of the lexical rules and SVM.  
Opinions regarding recommended hotels were obtained from various websites, such as Tripadvisor.com and Booking.com. We did this only for research purposes and the development of a prototype in a controlled environment.  
Seventy people from countries such as Argentina, Brazil, Australia, Colombia, Belgium, Spain, France and Portugal participated in the validation. The app was used by 41 women and 29 men, aged from 26 to 58. Participants were required to enter a query about a location they intended to visit. Then, the system presented hotels in that location. A total of 320 queries were made in the system. After receiving the recommendations, users had to rate their level of satisfaction on a scale from1 (not at all satisfied) to 5 (very satisfied).
# A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection
## 3 Filter Feature Selection Algorithm
On the other hand, the proposed RPFMI algorithm is different from other algorithms in terms of penalty factors, which considers not only the relationship between features but also the relationship between selected features and classes. Moreover the RPFMI algorithm takes into account the relationship between candidate features and classes. Involving considerations into these three relationships makes the proposed algorithm more effective than other algorithms. To analyze the complexity of the proposed filter feature selection algorithm, the training data is arranged in the following manner: \(M\) is the number of each sample feature; N is the number of samples; and \(|S|\) is the number of the selected features. In the first three phases of the proposed algorithm, \(H(C)\) has time complexity \(O(N)\); \(H(f_{i})\) has time complexity \(O(NM)\); \(H(C,f_{i})\) has time complexity \(O(NM)\). In the fourth phase of the proposed algorithm, \(f^{+}\) has time complexity \(O\big{(}(N+|S|)NM^{2}\big{)}\). Therefore, the proposed filter feature selection algorithm has time complexity \(O\big{(}NM^{2}\big{)}\) and space complexity \(O\big{(}NM^{2}\big{)}\) in the training dataset. In addition, in the testing dataset, the proposed filter feature selection algorithm has time complexity \(O\big{(}N|S|^{2}\big{)}\) and space complexity \(O\big{(}N|S|^{2}\big{)}\).