esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Validation of Crawler Tools and Prediction Models  
Firstly, with the theme of \"film and television\" as the main topic, the Wikipedia film and television and Baidu encyclopedia film and television were extracted as the seed links in a manual way, and the quality of crawlers in this study was evaluated. A total of 5000 web pages were downloaded by this method, and the recall and accuracy of downloaded text were calculated for every 5000 downloads. The results were compared with best first search and Naive Bayes, respectively, as shown in Figure 6. As concluded from Figure 6A, the recall rate of the study construction method was significantly higher than that of the Best first search and Naive Bayes method; as the number of pages crawled increased, the recall rate of the Best first search method was gradually higher than that of the Naive Bayes method. As concluded from Figure 6B, the accuracy of Best first search and Naive Bayes is lower than the method used in this study, and with the increase of page grasping, the accuracy of the algorithm decreases, but Best first search is the lowest.  
Figure 5: The framework of a VR virtual interactive system.  
The influence of the increase of the number of weak learning machines under the AdaBoost-BP algorithm constructed in this study on the classification accuracy was evaluated. The results are shown in Figure 7. As the number of weak learning machines increased, the classification accuracy of the algorithm constructed in this study also increased. However, when the number of weak learning machines increased to a certain number, the classification accuracy of the algorithm constructed in this study also gradually became stable. Therefore, the number of six weak learning machines was selected as the structure of the subsequent AdaBoost-BP algorithm for the experiment.  
The existing data were used to analyze the prediction accuracy of the AdaBoost-BP algorithm. The results are shown in Tables 2 and 3. The error values of root mean squared for different weak learning machines were all lower than 0.5, while the accuracy error of the AdaBoost-BP algorithm constructed in this study was all lower than 20%.  
Figure 6: (**A,B**) The recall rate and accuracy rate comparison of crawling network text.  
Figure 7: Effects of different numbers of weak learning machines on the classification accuracy of the AdaBoost-BP algorithm.
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 3 Depth Map Estimation
### Semi-Supervised Learning Network for Depth Map Estimation  
Figure 2 shows the training processing of a semi-supervised learning network for depth map estimation. During the training process, the inputs are the left and right views, and the corresponding sparse depth labels that have been matched with the left and right views, respectively. The outputs of the deep network are two disparity maps corresponding to the left and right views, respectively. The loss functions used for training this network contain appearance matching loss, disparity smoothness loss, left-right disparity consistency loss, and supervised loss.  
Figure 1: The depth estimation principle based on the left and right views.  
Figure 2: Depth estimation network and its loss functions.  
As shown in Figure 2, the semi-supervised depth estimation network in this paper is based on the encoding-decoding network structure. It uses ResNet-50 as the feature extraction model in the encoding stage [28], and the ResNet-50 network in the opposite direction in the decoding stage.
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 4 Simulation Studies: Effects of V2V Communication
### Active Safety Control  
Due to the fact that the V2V communication allows the host vehicle to use more enriched information of the remote vehicle's state compared to radar only, V2V/Radar fusion algorithm shows more reliable and accurate target detection performance of the remote vehicle when it applies sudden brake as seen in Figure 8b. The simulation results demonstrate the active safety control intervention moment can be detected earlier by 0.5 s because of the V2V communication. The radar only case in Figure 8a has underestimate issue when it compares to V2V/Radar fusion.
## 1 Introduction
Although some of the control methods apply the PMV model, they give priority to the indoor thermal environment, thus simply treating all occupants without discrimination and not thinking about outdoor environment conditions. Outdoor climatic factors have different influences on interior environments through different ways and means. For example, strong solar radiation in summer can affect the interior temperature of buildings so as to increase the cooling energy consumption. Our contributions in this work are as follows: first, we propose the architecture of the enhanced MPC-based thermal control system for controlling and optimizing the thermal comfort in the building. A PMV regression model has been designed and a simplified conventional PMV equation has been proposed. Secondly, a fuzzy controller is designed, aimed at being under real-time control and optimization of the thermal comfort level in the building. The most relevant part of this work, which makes it a step forward for building HVAC systems, is that the fuzzy controller taking account of the outdoor weather conditions such as season and outside temperature. As a result, the fuzzy controller is capable of automatically regulating the indoor comfort conditions according to the seasons. Lastly, an IoT smart space prototype is implemented as the proof of concept for the designed system to evaluate the performance of the designed system. Intel Edison boards, which support the wireless communication and computing capabilities, have been used to integrate with these physical sensors and actuators. The performance of the prototype is experimentally tested in summer in a lab of a building on Jeju Island, Korea. The remainder of this paper is organized as follows: Section 2 gives a brief introduction on HVAC systems for thermal comfort and discusses some of the similarly related research. Section 3 provides a brief introduction of PMV index along with its equations. Section 4 details the proposed MPC comfort control system by describing the multiple linear regression (MLR) based predictive model and the fuzzy logic controller, respectively. Section 5 provides some insight into the implementation of the case study. Section 6 overviews the experimental setup and reports the results of the proposed thermal control system. Section 7 outlines the significance of the proposed work by a comparative analysis with the existing approach. Section 8 discusses a specific application that could benefit from the significance of this work. Finally, Section 9 summarizes the main conclusions and discusses the future direction of this paper.
## 2 Adaptive Filtering Systems for Active Control
### Filtered-X Least Mean Squares Method  
In most practical applications, there are usually effects from secondary path dynamics due to AD/DA converters, filters, actuators, sensors, amplifiers, and circuit noise, among others. However, we assume that the whole control system includes no such dynamics and that the algorithm will not behave properly with such effects. The most common types of secondary path dynamics are phase shifts or time delays. The whole control structure become unstable or converges to an inappropriate solution under these effects, which changes the filter output \(f[k]\) and the signal \(y[k]\) subtracted from the desired signal \(d[k]\).  
The filtered-X LMS (FX-LMS) algorithm is a modified version of the traditional LMS algorithm that manages the secondary path dynamics, which are identified in advance [36]. This algorithm requires a filtered reference signal \(\widetilde{\mathbf{u}}[k]\) with a numerical model of the secondary path dynamics \(\widehat{P}_{s}(z)\). The signal is applied in the original LMS algorithm to obtain the adaptive filter output \(y[k]\). FX-LMS is more stable than the LMS method when the secondary path dynamics cannot be disregarded. Figure 2 shows a schematic of the FX-LMS algorithm.  
The formulation for the FX-LMS algorithm is almost the same as that of the LMS method except that the filtered reference input \(\widetilde{\mathbf{u}}[k]\) is used in the updating equation of the filter weights:  
Figure 2: Filtered-X least mean square algorithm.  
\[\mathbf{\hat{w}}[k+1]=\mathbf{\hat{w}}[k]+\mu\mathbf{\widetilde{u}}[k]\cdot e[k] \tag{15}\]  
The most important property of the filter \(\hat{P}_{s}(z)\) is that its impulse response contains the same amount of time lag as the secondary path dynamics \(P_{s}(z)\). However, a significant defect of FX-LMS is that an offline system identification procedure is essential to obtain \(\hat{P}_{s}(z)\) beforehand. Although online system identification might be possible, it makes the overall controller complex and difficult to implement.
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## 5 Two Approaches Using Neural Networks
### Application of Graph Neural Networks
The error in each epoch is computed by averaging the mean squared error over a randomly selected set \(\mathcal{T}_{i}\subset\mathcal{T}_{\text{train}}\). To complement this approach, we have also included an alternative that replaces the computation of \(h\) for each node with a Graph Attention Network (GAT) [20]. In this type of model, the weight for each neighbor is not fixed and will be learned in the training process. In contrast with the previous model, the number of learnable parameters in the network does not depend on the number of nodes in the graph but on the dimension of the MLP, determined by \(\mathbf{\Theta_{1}}\) and \(\mathbf{\Theta_{2}}\). The number of parameters for \(\mathbf{\Theta_{1}}\) is \((|f|+1)\times|h|\), where \(|f|\) is the number of input features and \(|h|\) is the dimensionality of \(h_{i}\), which has been set to 8 in our experiments. The number of parameters for \(\mathbf{\Theta_{2}}\) is \((|f|+|h|)\times(M^{2})\times(l-1)+M+(l-1)\times M+1\), where \(M\) is the number of neurons at every hidden layer, and \(l\) is the number of hidden layers. In this paper, we have chosen to limit our model to two hidden layers, of the same dimensionality as the \(h\) vector. Time complexity at prediction time is thus \(\mathcal{O}(E+V\times((|h|+|f|)l\times(M^{2})))\). In addition to the learned parameters, the model needs to have access to the graph to generate predictions. Using a sparse representation for the adjacency matrix, the memory requirement is \(\mathcal{O}(|E|)\)[18]
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 3 Results
### Irradiance Transposition Model  
The conversion of GHI a GTI stands out as a crucial element for the accuracy of the power forecast [21]. This evidence is also supported by the results of Table 11, where the estimation of power is presented with the variables \(\mathrm{LMD_{GHI}}\) and \(\mathrm{LMD_{GTI}}\). This underlines  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Predictors** & **Metric** & \multicolumn{6}{c}{**Station**} & \multicolumn{6}{c}{**Mean**} \\  & & **0** & **1** & **2** & **3** & **4** & **5** & **8** & \\ \hline \(\mathrm{NWP_{DHI}}\) & MAE & 25 & 25 & 44 & 25 & 43 & 35 & 21 & 31 \\  & RMSE & 49 & 43 & 85 & 53 & 89 & 56 & 40 & 59 \\  & \(R^{2}\) & 0.86 & 0.87 & 0.86 & 0.83 & 0.93 & 0.92 & 0.87 & 0.88 \\ \hline \(\mathrm{NWP_{DHI}}\) & MAE & 25 & 24 & 45 & 25 & 43 & 34 & 21 & 31 \\ Block GHI & RMSE & 48 & 43 & 86 & 53 & 88 & 55 & 40 & 59 \\  & \(R^{2}\) & 0.93 & 0.87 & 0.82 & 0.92 & 0.86 & 0.88 & 0.86 & 0.88 \\ \hline \(\mathrm{NWP_{DHI}}\) & MAE & 25 & 23 & 43 & 24 & 43 & 34 & 21 & 30 \\ Skartveit & RMSE & 49 & 42 & 81 & 52 & 87 & 54 & 39 & 58 \\ Block GHI & \(R^{2}\) & 0.93 & 0.88 & 0.85 & 0.93 & 0.86 & 0.88 & 0.86 & 0.88 \\ \hline Reference & MAE & 104 & 99 & 125 & 142 & 155 & 110 & 90 & 118 \\ \(\mathrm{NWP_{DHI}}\) & RMSE & 174 & 138 & 198 & 204 & 244 & 157 & 115 & 176 \\  & \(R^{2}\) & 0.18 & 0.03 & 0.21 & 0.01 & 0.16 & 0.17 & 0.07 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of Deep Decomposition Model block. RMSE and MAE are measured in [\(\mathrm{W/m^{2}}\)], and \(R^{2}\) is unitless.  
the need to implement and optimize this specific phase of the physical chain. Particularly for this block, the inclination angle of the photovoltaic module is added in all the tests carried out due to its importance.  
The metrics in Table 12 demonstrate that the chain model trained with the sets Best\({}_{\text{Ipoa}}\) and NWP\({}_{\text{Ipoa}}\) improves precision, but there is still room for optimization, especially considering that irradiance measurements are not available in the plane of the modules.
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
###### Abstract  
Quasi-cyclic low-density parity-check (QC-LDPC) codes are introduced as a physical channel coding solution for data channels in 5G new radio (5G NR). Depending on the use case scenario, this standard proposes the usage of a wide variety of codes, which imposes the need for high encoder flexibility. LDPC codes from 5G NR have a convenient structure and can be efficiently encoded using forward substitution and without computationally intensive multiplications with dense matrices. However, the state-of-the-art solutions for encoder hardware implementation can be inefficient since many hardware processing units stay idle during the encoding process. This paper proposes a novel partially parallel architecture that can provide high hardware usage efficiency (HUE) while achieving encoder flexibility and support for all 5G NR codes. The proposed architecture includes a flexible circular shifting network, which is capable of shifting a single large bit vector or multiple smaller bit vectors depending on the code. The encoder architecture was built around the shifter in a way that multiple parity check matrix elements can be processed in parallel for short codes, thus providing almost the same level of parallelism as for long codes. The processing schedule was optimized for minimal encoding time using the genetic algorithm. The optimized encoder provided high throughputs, low latency, and up-to-date the best HUE.  
channel coding; 5G new radio; low-density parity-check (LDPC) codes; encoder architecture; hardware usage efficiency; circular shifter; genetic algorithm optimization
# Anticancer Effects of Seaweed-Derived Bioactive Compounds
###### Abstract
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Searseed** & **Active Ingredient(s)** & **Activity** \\ \hline _Sagassum pallidum_ & Highly sulfated polysaccharide SP1, SP2, and SP3 & Cytotoxicity against A549, HepG2, and MGC-803 cells \\ \hline _Dictyopteris delicatula, Dictyopteris polyodoides_ & Sulfated polysaccharides & Anticancer activity against HeLa and RPMI-7951 cells \\ \hline _Sargassum hornery, Ecolonia cava, and Costaria costa_ & Fucoidan (**3**) & Inhibiting colony formation of melanoma (SK-MEL-28 cells) and colon cancer cells (DLD-1 cells) \\ \hline _Cococophora langsdorffis_ & Fucoidan (**3**), laminarin (**1**), and alginate (**2**) & Anticancer activity against SK-ML-5 and SK-ML-28 melanoma cells \\ \hline _Laminaria difitata_ & Laminarin (**1**) & Induction of apoptosis via the ErbB signal pathway in colon cancer cells \\ \hline _Ecklonia cava_ & Dieckol (**14**) & Inducing apoptosis and inhibited the growth of human pancreatic cancer cells PANC-1, inhibition of non-small-cell lung cancer \\ \hline _Laminaria japonica_ & Phlorotannin-rich fraction & Inhibits BEL-7402 human hepatocellular carcinoma cells (30\%) and P388 murine leukemic cells (43\%) at 100 \(\mu\)g/mL concentration \\ \hline _Ecklonia cava_ & Phloroglucinol (**7**) & Induction of apoptosis in HT-29 cells, decreasing CD44 (+) cancer cells and cancer stem cells, inhibition of KRAS and its downstream regulators in breast cancer stem like cells \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cytotoxic marine natural products isolated from seaweeds.
## 1 Introduction
Moreover, WSNs cannot be regarded just as stand-alone systems intended for ITS; on the contrary, they should be considered in the ITS context as additional components of a heterogeneous system, where they cooperate with other technologies such as VANET. Figure 1 illustrates a possible WSN-based application example in which a WSN is employed to detect wildlife on the road and interacts with VANET (or other related technology) equipped vehicles to enhance the driver's and passengers' safety and at the same time to avoid, for instance, endangered species fatalities. Therefore, this survey paper details the fundamental aspects of the design of WSNs for ITS, considering not only WSN independent applications, but also their position in heterogeneous systems. There are other works surveying some specific issues about the application of WSNs to ITS. However, we differentiate from them by adopting a broader approach. In this respect, [PERSON] _et al._[6] introduced an interesting work about the application of WSNs to ITS, but it readily focuses on estimation algorithms for traffic congestion avoidance, and it does not consider safety or applications that combine different technologies, among others. [PERSON] _et al._[7], in turn, focused their attention on architecture, providing their vision of the architecture of ITS. Our work, conversely, has a more general scope and makes an effort to cover a diversity of closely related technological issues in order to offer the readers and developers a complete state-of-the-art of the actual role and challenges of applying WSNs to ITS. On the other hand, there are other surveys covering the development of heterogeneous ITS systems such as the works presented by [PERSON] _et al._[8] and [PERSON] _et al._[9], however WSN contributions are not tackled in any of them. The former focuses on the challenges of using multiple wireless technologies in a collaborative manner, providing the AHVN (Advanced Heterogeneous Vehicular Network) architecture for the development of applications. The latter is devoted to the so-called Vehicular Sensor Networks (VSN) which are built on top of a VANET by equipping vehicles with onboard sensing devices [10] and, unlike WSN, are not subject to strict resource limitations. Figure 1: WSN-based ITS application example. The rest of the paper is organized as follows. In Section 2, the possible applications of WSNs to different traffic domains are presented. Section 3 gives a vision of the network architecture of WSN-based ITS systems which stresses the importance of several factors such as the layout of nodes or the use of heterogeneous devices. Section 4 goes into detail about how vehicle and road state detection is performed by WSN nodes. Section 5 reviews and remarks several crucial design issues which govern the performance of the ITS application. Section 6 deals with the issues related to communication protocols. Finally, Section 7 presents the concluding remarks of this work and introduces relevant open issues which have been identified.
## 2 Methods
### Scalability and Reliability Test Setup  
To evaluate the performance and scalability of JAVIS, we conducted two distinct experiments: an efficiency test on a standard consumer personal computer (PC) and a high-volume stress test using a multi-GPU setup. These experiments were designed to assess JAVIS's ability to handle varying computational demands and user loads, ensuring its robustness and reliability in clinical environments.  
For all the experiments, the following models and quantizations were used in order to represent different size deployment, as well as LLM and VLM testing.  
Models and Formats
* **LLMs:** We deployed Llama 3.2 with 1 billion (1b) and 3 billion (3b) parameters, as well as Llama 3.1 with 8 billion (8b) parameters.
* **VLMs:** The multimodal models included Qwen2 2b and Qwen2 7b parameters, along with Llava 1.5 with 13 billion (13b) parameters.
* **Quantization formats:** LLMs were deployed in a generalized post-training quantization (GPTQ) quantized [16] format to facilitate fast inference while maintaining high accuracy. VLMs utilized the activation-aware weight quantization (AWQ) [17] format, chosen for its availability and demonstrated ability to retain high accuracy.
# Examination of Viscosity Effect on Cavitating Flow inside Poppet Valves Based on a Numerical Study
## 2 Materials and Methods
According to previous studies on oil cavitating jet through small-size orifices, employment of turbulence model might eliminate the small-size vortex structure, causing inappropriate representation of vortex-cavitation inter-relationship [19]. The current simulation assumes the direct simulation strategy, for the sake of a correct representation of the cavitation phenomenon, even though the spatial resolution is insufficient to capture \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Definition** \\ \hline \(A\) & Volume content \\ \(p\) & Density \\ \(\Psi\) & Compressibility \\ \(rR_{b}\) & Cavitation model constant \\ \(c_{a}\) & Artificial compression coefficient \\ \(\Sigma\) & Surface tension coefficient \\ \(K\) & Interface curvature \\ \(p_{v}\) & Cavitation threshold pressure \\ \(\dot{m}\) & Total mass transfer \\ \(\dot{m}^{+}\) & Mass transfer due to vaporization \\ \(\dot{m}^{-}\) & Mass transfer due to condensation \\ \(rR_{b}\) & Cavitation model constant \\ \(U_{r}\) & Interphase relative velocity \\ \(T\) & Local stress tensor \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of variables. accurately the smallest turbulent structures. Therefore, the current turbulence treatment is a compromise between disadvantages of current turbulence models and deficient computation resource. The geometric structure of the poppet valve is offered by Figure 1, which also includes the grid model. The computational domain covers a sector of 90 or 45 degrees depending on used fluid medium and valve seat structure. In the current investigation, the openness is set as 0.4 mm only in Section 3.2, which offers a validation study for the numerical method by means of time-average pressure distribution. And in the other cases, the openness is 0.6 mm. The total amount of calculation grid is approximately 5 million and 4.8 million for the water-flow case in poppet valves with sharp and chamfered valve seat, respectively. To resolve the critical vortex dynamics on the wall shearing layer, the region neighboring to poppet wall is given specific treatment with a minimum thickness of 0.2 \(\upmu\)m, which also ensures \(\mathrm{ylpus}<1\). The experimental data used for validation of the simulation results comes from a previous study with a half-cut prototype [4]. The fluid properties are listed in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Liquid Type** & **Mineral Oil \#46** & **Water** \\ \hline liquid density & 872 kg/m\({}^{3}\) & 1000 kg/m\({}^{3}\) \\ liquid dynamic viscosity & 0.042 N s/m\({}^{2}\) & 0.001 N s/m\({}^{2}\) \\ vapor pressure & 3000 pa & 2000 pa \\ vapor density & 0.13 kg/m\({}^{3}\) & 0.02558 kg/m\({}^{3}\) \\ vapor dynamic viscosity & 0.00572 N s/m\({}^{2}\) & 0.00126 N s/m\({}^{2}\) \\ temperature & 313 K & 313 K \\ surface tension & 0.03 N/m & 0.0782 N/m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fluid property. Figure 1: Geometric structure of poppet valve and calculation grid for water-flow case (dimensions in mm).