esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Stage II
#### 5.3.1 Less Demanding LiDAR Configuration
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{**Additional Attenuation (dB)**} & \multirow{2}{*}{**Packet Size (B)**} & \multicolumn{2}{c}{**Average RTT Latency (ms/SD)**} \\ \cline{3-4}  & & 16 & 29.73/3.86 \\  & & 512 & 30.18/3.99 \\  & & 1436 & 29.40/2.90 \\ \cline{2-4}  & & 16 & 31.87/10.42 \\ \cline{2-4}  & 10 & 512 & 31.54/5.43 \\  & & 1436 & 44.35/30.97 \\ \cline{2-4}  & & 16 & 102.20/20.71 \\ \cline{2-4}  & 20 & 512 & 106.38/38.76 \\ \cline{2-4}  & & 1436 & 154.51/121.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average RTT latency (ms) and standard deviation for Configuration 3, grouped by packet size and attenuation level, in the less demanding LiDAR scenario. The results in Table 8 show that under low attenuation (0 and 10 dB), the packet size has a negligible effect on average RTT latency, which remains in the 29-32 ms range for all sizes. However, as attenuation increases to 20 dB, both the latency and its variability rise sharply, especially for larger packets. The average RTT latency for the largest packet (1436 B) at 20 dB exceeds 150 ms, with a much higher standard deviation. This indicates that while the configuration is robust under good radio conditions, both packet size and link quality start to significantly impact latency performance as the environment deteriorates. These findings highlight the importance of selecting both configuration and sensor parameters according to the expected channel conditions in practical deployments.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 2 System Architecture and Mathematical Modeling
### Resource Scheduling Algorithm
```
1: Initialize: \(\tau_{D}\), \(\tau_{S}\), \(\tau_{L}\), \(\tau_{st}\), \(\eta_{EDRX}\), \(\tau_{i}\), \(\tau_{i}^{\prime}\), \(\tau_{DRX}\), \(\tau_{DRX}^{\prime}\), \(\chi_{iUL/DL}\), and \(\chi_{i_{UL}}\)
2:for\(i=1:k\) //\(k\) is the number of total users
3:while\(\chi_{i_{UL/DL}}>0\) //data transfer request is present between \(i\) user and BS
4: Start data transfer (\(\tau_{D}\))
5: update \(\chi_{i_{UL/DL}}\)
6: enable inactivity timer (\(\tau_{i}\))
7:if\(\chi_{i_{UL/DL}}>0\) && \(\tau_{i}<\tau_{i}^{\prime}\) //more data to transmit within the inactivity timer
8: Go to Step 3
9:else
10: enable \(\tau_{DRX}\)
11:for\(l=\tau_{DRX}:\tau_{L}-\tau_{S}:\tau_{DRX}^{\prime}\)
12:for\(m=\tau_{S}:\tau_{L}\)
13:if\(\chi_{i_{UL/DL}}>0\) && \(\tau_{DRX}<\tau_{DRX}^{\prime}\)
14: Go to Step 3
15:else
16:\(m=m+\tau_{st}\)
17:for\(n=\tau_{L}:\tau_{S}\)
18:if\(\chi_{i_{UL/DL}}>0\) && \(\tau_{DRX}<\tau_{DRX}^{\prime}\)
19: Go to Step 3
20:else
21:\(n=n+\tau_{st}\)
22:\(i=i+1\)
```  
**Algorithm 1** Algorithm for the communication between the \(i\)th NRT application-based device and BS\[P_{Save}(\%)=\left[1-\left[\frac{E[T_{Data}]}{E[T_{Total}]}+\frac{P_{ Wait}\times E[T_{Wait}]}{P_{Data}\times E[T_{Total}]}\right.\right.\] \[\left.\left.+\frac{P_{OFF}\times E[T_{OFF}]}{P_{Data}\times E[T_{ Total}]}\right]\right]\times 100\]  
In our proposed model, an algorithm has also been presented based on PSM and eDRX, which are two solutions to optimize the device power consumption for the NB-IoT technologies for delay insensitive but energy sensitive applications. As soon as an end device is powered on, it goes for the frequency and time synchronization with some necessary configurations from the network. This Radio Resource Control (RRC) connection setup is an important step, after which the end device can exchange data with the network. When the RRC connection is established, the device is said to be in the RRC Connected state. In NB-IoT, there are two RRC states for devices, namely, RRC Connected and RRC Idle, as shown in Figure 6. When the device releases its active RRC connection, it moves to the RRC Idle state. The RRC connected state consists of the mobile originated (MO) or mobile terminated (MT) data transfer, tracking area update (TAU), and an inactivity timer. In this state, the device consumes more energy, as it obtains dedicated bearers established to begin the data transmission and needs to monitor the DL channel. The expiration of the inactivity timer makes the device switch from the RRC Connected to RRC Idle state. In the RRC Idle state, NB-IoT defines two power-saving schemes, i.e., eDRX and PSM. A summary of PSM and eDRX are described below, and the details of the RRC states are illustrated in Figure 6.
## 4 Conclusions  
In this study, we identified the main variables affecting the effective properties of the RVEs of nanocomposites consisting of unidirectionally aligned CNTs and determined the mechanisms through which these properties are affected.  
1. Finite element models of nanocomposite RVEs inside which one CNT filament was arranged inside the RVE were created, and their effective properties were calculated.
2. An equation for analyzing the interaction between the CNT length fraction and RVE length was presented, using which the effective properties of unidirectionally aligned nanocomposite RVEs were calculated.
3. The interaction between the CNT length fraction and RVE length was analyzed via Monte Carlo simulations, and the CNT length fraction that yielded the greatest influence on the effective properties of the RVE was obtained.
4. Using the geometrical relationship between the RVE and CNTs, a simplified two-parameter equation that can calculate the effective properties of the RVE was derived.
5. As the RVE increased, it was confirmed that the effective properties converge to a certain value, but only up to 10 \(\upmu\)m RVE due to the limitation of H/W. Therefore, there is a need for further studies by expanding the size of the RVE.
6. In this study, the orientation tensors of CNTs were placed only along the \(x\)-axis. In future studies, analysis of the various orientation tensors of CNTs is also required.  
Data curation, J.-W.J. and K.-W.K.; software, C.-W.C.; writing--review and editing, C.-W.C. and K.-W.K. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (Nos. 20213030020120 and 20203020020020).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy of this research.  
The authors declare no conflict of interest.  
Figure 15: Changes in the effective properties according to changes in the COV: (**a**) rate of increase in the effective properties based on a 3 \(\upmu\)m RVE according to an increasing RVE length; (**b**) difference in the increasing rate of the effective properties according to the increasing RVE length.
## 2 Materials and Methods
### System Development  
#### 2.3.1 Video Planning  
Initially, a pilot study was carried out with four classes using the synchronous modality. However, the method did not prove to be productive, for the following reasons: (1) difficulty for older adults to follow the exercises due to vision and hearing problems, (2) difficulty accessing classes at scheduled times, (3) common during classes because too many older adults were talking at the same time, and (4) Internet problems. Finally, we chose to adhere to the \"asynchronous\" method, recording videos and sending them to older adults twice a week via WhatsApp, as well as posting the material on YouTube, Instagram, and the U3A website.  
The planning of the activities of the \"U3A in Motion\" video program originated from the set of field experiences carried out between 2015 and 2019 by the U3A teams (i.e., Physical Education, Medicine, Psychology, Nursing, Pharmacy, Languages, Law for older adults) with the local older population. These experiences have been documented [48; 52]. Initially, the executing team contacted 15 U3A older students by telephone to learn about their daily lives during the COVID-19 confinement. Thus, based on this information, the contents that were worked on before the pandemic were adapted for video classes.
# Feature-Weighted Sampling for Proper Evaluation of Classification Models
## 2 Materials and Methods  
As mentioned earlier, the ideal training and test sets should have the same distribution as the original dataset. To achieve this goal, we propose a method called feature-weighted sampling (FWS). Our main idea is as follows:  
1. Generate numerous candidate cases of train/test sets using modified RBS.
2. Evaluate the similarity between the original dataset and candidate cases. The similarity is measured by the distance.
3. Choose the case that has smallest distance to original dataset.  
Figure 2 summarizes the proposed method in detail. The first phase generates \(n\) train/test set candidates with stratified random sampling. Stratified sampling uses the modified RBS method, which reflects the amorphic property of the data, called class overlap. The second step is to select one of the candidates with the distribution that is most similar to the original dataset. To evaluate the similarity of distribution, we measured the distance between the train/test sets and the original dataset in terms of distance. To calculate the distance between the original dataset and train/test sets, we tested Bhattacharyya distance [10], histogram intersection [11], and Earth Mover's Distance [12]. Finally, we adopted the Earth Mover's Distance. Feature importance was applied to the weighting feature during the distance calculation. As a result, the train/test sets that had the smallest distance from the original dataset were selected. For the evaluation of the sampling method, we devised a metric named the mean accuracy index (_MAI_). Using the _MAI_, we compared the proposed FWS and RBS. Twenty benchmark datasets and four classifiers, including \(k\)-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and C50 were used for the comparison.
# Safety of Nuclear Power Plants with Respect to the Fault Displacement Hazard
## 5 Sensitivity/Vulnerability of the Fundamental Safety Functions to the PGD
### Shut Down of the Reactor  
The shutdown and control of the reactivity of the reactor is ensured by the insertion of the neutron absorbers into the reactor core that use of gravity, or stored energy or driving force (compressed air) that does not need external power. The PGD can cause tilting of reactor building that can inhibit the solid neutron absorbers falling to the reactor core. Tilting due to uneven consolidation settlement is limited for both reasons, for avoiding structural consequences and providing free-fall for the absorbers. For example, according to the Russian standard PiNAE-5.10-87 \"Construction Design Code for NPP with Different Types of Reactors\" [43], under normal conditions, the strain in the walls of the reactor building and in the foundation is controlled by limiting the settlement and the tilt. For normal operational conditions, a tilt \(\leq\)0.001 is allowed. In case of specific effects, e.g., the seismic settlement, the tilt should be \(\leq\)0.003. The latter value corresponds to a drop of an edge of the base mat relative to the opposite one, approximately equal to 0.2 m and it is qualified as acceptable by the standard. The drop of absorbers into the core is not hindered by the allowed tilt. Practice shows that the rather old VVER-440/213 type reactors, like at Paks, Hungary, equipped by heavy absorber assemblies are even less sensitive to tilting. Even less sensitive to tilting should be the forced injection of absorbers in case of Boiling Water Reactors.  
The shutdown shall be accomplished even if the solid neutron absorbers fail to be inserted. Boron as neutron poison can be injected by the chemical control system into the core for diverse control of the reactivity. The reactor can be brought into a controlled state by this system and can be kept in subcritical condition for a prolonged period of time. These systems are located in the containment and safety buildings. The systems are composed by piping, vessels and pumps that are not sensitive to the tilt of supporting structures.
# Effect of Initial Granular Structure on the Evolution of Contact Force Chains
## 2 Materials and Methods  
### Digital Photoelasticity  
In order to microscopically study the dynamics of granular assemblies, discrete element simulations, X-ray and CT-based methods, experimental observations of displacement fields using model particles, and photoelastic-based contact force chain confirmation methods can be employed. Among these, the photoelastic technique is an experimental method based on photo-physical properties that exhibit fringe patterns when polarized light passes through a photoelastic material subjected to an external force. This method has attracted much attention in the field of granular physics because it can intuitively visualize stress and strain distributions.  
In the past, quasi-assemblies were evaluated qualitatively because it was nearly impossible to evaluate the stress of a granular assemblies composed of thousands of particles using traditional photoelastic theory [22; 23; 24; 25]. More recently, a method for evaluating the stress of such assemblies has been proposed, thereby enabling quantitative analysis [26; 27]. Monochromatic light sources have been popularly used in previous experimental studies [26; 27; 28], whereas nowadays, using digital images with true color has become prevalent thanks to significant progress in computer vision technology. To use the full-color digital images for the photoelastic analysis of the model soil particles, a new technique using the white light source that is cheaper than the monochromatic light source is indispensable. RGB photoelasticity was firstly introduced by [PERSON] and [PERSON] [29], which allows the determination of the photoelastic responses, usually using a single acquisition of the isochromatic fringes in white light by a color camera [30; 31; 32]. [PERSON] and [PERSON] [21] also proposed a method of RGB photoelastic analysis to quantitatively evaluate the scalar components of the force of reflective photoelastic particles under white light. This method is especially suitable when the fringe order cannot be confirmed or when the contact points are unclear, and allows for the quantitative evaluation of the main force transmission structure to be performed easily and quickly.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 2 General Setting and Definitions  
In this section, a clear mathematical definition of an HRTF along with the reference system used for measurements is provided. In more detail, Sections 2.1 and 2.2 show the HRTF definition and some metrics to evaluate the distance between a measured and a generated HRTF, respectively. Finally, the mathematical framework behind the definition of the HRTF is described in Section 2.3.
# Position Detection of Doors and Windows Based on DSPP-YOLO
## 3 Dspp-Yolo  
### The Algorithm Design  
Applying YOLOV3 algorithm to door and window position detection, the precision of door and window position detection is much higher than that of window position detection. There are three reasons for this: (1) compared with the door, the window is a transparent object, there is no obvious large area, fixed color and other characteristics. Therefore, in the process of network learning, the features learned by CNN in the window will change with the change of the background, and it is difficult to find a universal feature as a symbol to identify the type and location of the window. (2) certain types of windows and doors have some common features, such as glass doors and French windows, doors with windows. (3) some high-rise building sample image window size is too small, resolution is too low, with small target features, less than \(32\times 32\) pixels. Because of the size difference, the detection network should consider not only the recognition of door semantic information, but also the determination of remote window position information. If the network depth is only increased, the feature information of the small-size remote window will disappear in the deep network due to the down sampled, and the detection accuracy will decrease.  
In order to solve these problems, based on the YOLOV3 network structure, this paper optimizes the network structure of Darknet53 by combining DenseNet [23] and SPP module, as shown in Figure 2. Firstly, add the Res-4 module between Res-2 and Res-8 models in Figure 1 to deepen the network hierarchy for more semantic features. Secondly, combining DenseNet's idea of dense block and residual block, dense block is used to replace Res-1, Res-2, Res-4 and Res-8 models. Thirdly, based on the original 3 rd output scale \(52\times 52\) feature map, the 4 th output scale \(104\times 104\) is generated by connecting the 3 rd output scale with the 24 th layer feature map by up sampling. As mentioned above, large-scale feature maps have smaller receptive fields and are suitable for detecting smaller targets; four-scale output feature mapping can consider the position detection between the door, the near window and the far window. Finally, the SPP module is added after the feature extraction network.
# Safety Assessment of Gas Pipelines Crossing River through Hydrodynamic Analysis
## 2 Materials and Methods
### Study Area Description
The formula is based on dimensional analysis and can be expressed as follows:  
\[X=\frac{G_{gr}sd}{D\left(\frac{u_{*}}{\psi}\right)^{n}} \tag{1}\]  
where  
\(X=\) Sediment flux;  
\(G_{gr}=\) Transport parameter;  
\(s=\) Sediment specific gravity;  
\(d=\) Median particle size;  
\(D=\) Effective depth;  
\(u_{*}=\) Shear velocity;  
\(V=\) Average channel velocity;  
\(n=\) Transition exponent (based on sediment size)  
which is simple, dimensionless, power function of excess mobility with transport potential \(G_{gr}\) as follows:  
\[G_{gr}=C\left[\frac{F_{gr}-A}{A}\right]^{m} \tag{2}\]  
where  
\(C=\) Empirical coefficient;  
\(F_{gr}=\) Sediment mobility;  
\(A=\) Threshold mobility;  
\(m=\) Empirical power coefficient. In Equation (2), the empirical coefficients \(C\) and \(m\) play important roles in the characterization of sediment transport. The coefficient \(C\) serves as an empirical scaling factor that adjusts the magnitude of the transport parameter \(G_{gr}\). It is derived from experimental data and calibrated according to specific site conditions. This coefficient reflects the overall sediment transport capacity of the system and is necessary to ensure the model accurately represents the observed transport behavior under varying hydrodynamic conditions. The coefficient \(m\) is an empirical exponent that governs the sensitivity of the transport parameter \(G_{gr}\) to changes in excess sediment mobility, defined by \((\frac{F_{gr}-A}{A})\). A higher \(m\) value indicates a more pronounced response to increases in sediment mobility, while a lower \(m\) value suggests a more gradual relationship. The value of \(m\) is similarly determined through calibration and reflects the specific sediment characteristics and flow dynamics of the river being modeled. Both coefficients are essential for fine-tuning the sediment transport model to the specific hydrodynamic and sedimentary conditions of the river, thereby enhancing the accuracy of the predictions. By adjusting \(C\) and \(m\), the model can account for a wide range of transport scenarios from minimal sediment movement to significant riverbed alterations.
###### Abstract  
Ultra-wide-band (UWB) positioning is a satisfying indoor positioning technology with high accuracy, low transmission cost, high speed, and strong penetration capacity. However, there remains a lack of systematic study on inevitable and stochastic errors caused by factors originating from the multipath effect (ME), non-line-of-sight interference (NLOSI), and atmospheric interference (AI) in UWB indoor positioning systems. To address this technical issue, this study establishes a dynamic error-propagation model (DEPM) by mainly considering the ME, NLOSI, and AI. First, we analyze the UWB-signal generation principle and spread characteristics used in indoor positioning scenarios. Second, quantization models of the ME, NLOSI, and AI error factors are proposed based on data from related studies. Third, to adapt to various environments, we present a variable-weighted DEPM based on the quantization models above. Finally, to validate the proposed dynamic error-propagation model, UWB-based positioning experiments in an intelligent manufacturing lab were designed and conducted in the form of static and dynamic longitude-tag position measurements. The experimental results showed that the main influencing factors were ME and NLOSI, with a weight coefficient of 0.975, and AI, with a weight coefficient of 0.00025. This study proposes a quantization approach to main error factors to enhance the accuracy and precision of indoor UWB-positioning systems used in intelligent manufacturing areas.  
UWB positioning; multipath effect; non-line-of-sight interference; atmospheric interference; error-propagation model +
Footnote †: journal:  
Article
## 3 Results and Discussion
### Performance Evaluation Results
#### 3.2.8 Temperature Safety  
The test of temperature safety showed that, as shown in Table 7, samples A and B were not permeated with water under the repeated heat and cold resistance conditions, but sample C was permeated for all three specimens. In the case of C, it flowed down under high-temperature conditions due to the relatively low viscosity compared to samples A and B. It was identified that the viscosity could secure a performance under high-temperature conditions where it exceeded the viscosity value of sample C.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 4 Neural Networks  
Neural Networks (NNs) are algorithms used in machine learning to solve problems via data processing. Input data move through a series of interconnected nodes, where each node is weighted initially and re-calculated as the information passes through the network. Some everyday tasks include regression and classification. A general NN consists of an input, a hidden, and an output layer. The hidden layer may have multiple layers and use different algorithms in which data are processed [16]. Figure 4 shows the general layout of a Neural Network with two hidden layers.  
Neural Networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are potent tools  
Figure 4: General layout of a Neural Network with two hidden layers.  
Figure 3: Wide Area Network diagram.  
in Computer Science and Artificial Intelligence, allowing us to classify and cluster data at a high velocity. One of the possible applications of NNs in communication networks is traffic prediction. In the following subsection, we discuss the relevant papers that use NNs.