esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
## 2 Establishment of Dataset
### Input Parameters for Models  
During the preliminary steps of the analysis, using polynomial and power functions, a series of single-variable regressions were conducted to obtain more details about the relationship between TC and the independent rock properties. Table 2 summarizes the results of the single-variable regressions. As seen, the P-wave velocity and density had the highest and the lowest effects on the TC, respectively.  
To avoid redundancy in the future model generation, the relationships between the independent variables were investigated. As shown in Table 3, there were no redundancies between the independent parameters, and the relationships between porosity and density as well as between UCS and P-wave velocity were significant.
# Educational 5G Edge Computing: Framework and Experimental Study
## 4 Measurements and Experiments
### Evaluation of our Proposal
#### 4.2.3 The Proposed System Data Rate Testing  
To verify the performance of the proposed system architecture, a series of tests were carried out according to the following methods:  
1. We prepared test devices (5G UE with 5G SA sim card). Test locations were the information center of a university, simulation laboratory of a university, and an  
Figure 8: The system upload data rate performance.  
Figure 10: The system end-to-end latency performance.  
Figure 9: The system download data rate performance.  
office outside the university. The network access point was 10 m near the 5G sub-base station's antenna; the test time was 10:00 and 13:00 on weekdays and 17:00 on weekends. We tested 9 rounds at 10:00, 13:00 and 17:00 in 3 different places;
2. We performed the FTP download service to the 5G UE on eMEC;
3. After the rate became stable, we recorded the average rate, maximum rate, minimum rate, and air interface channel quality, e.g., reference signal receiving power (RSRP), received signal strength indicator (RSSI), signal-to-interference plus poise ratio (SINR) within 1 min;
4. We stopped the downloading;
5. We performed an FTP upload service to the 5G UE on eMEC;
6. After the rate is stable, we recorded the average rate, maximum rate, minimum rate, and air interface channel quality within 1 min;
7. We stopped uploading;
8. We exited the test;
9. We adjusted the test point position from the base station and repeated the above test 8 times.  
In the proposed system data rate testing, there is a certain rate difference in the test results at three places. Meanwhile, the change of time has little impact on the test results. From the analysis, 5G private network users have not reached the design capacity, rate difference caused by 5G network environment at different test points. Therefore, representative results are shown in Figures 8 and 9. It is close to the mean value. This test value was obtained at 17:00 pm in the information center of university.  
The results show that our architecture provides better coverage of the 5G campus private network and the channel quality of the air interface (SINR = 31.75; RSRP = \(-\)58.83 dBm). In the architecture, ordinary test users can obtain a stable 126 Mbps rate in the uplink and a higher stable rate, 800 Mbps, in the downlink. It is not difficult to find that the architecture can meet the uplink and downlink rate requirements of various educational applications defined by the standard [27].
## 4 System Design
### Physical Layer: Data Collection
#### 4.1.1 Stationary Node Design (Environment Parameter Monitoring)  
The basic sensing unit of the proposed IoT platform is Arduino [30], a microcontroller for digital and analog input/output (I/O). The Arduino MEGA 2560 microcontroller (ATmega1280, Atmel, San Jose, CA, USA) (Figure 4a) with MQ-9, MQ-4, MQ135 and DTH11 (D-Robotics, London, UK) (Figure 4c) sensor modules were used for monitoring the underground environment. Figure 4d shows the circuit diagram of sensors attached to the different pins of the Arduino MGEA. Arduino MEGA is an open-source microcontroller chip for reading and writing OUTPUT data in real-time. Arduino was used because of its fast and easy prototyping, low-cost, support of existing libraries, and the fact that it is used by a large community as a common hardware for IoT [31]. Besides this, MQ-9, MQ-4 and MQ-135 sensors are highly responsive to flammable gases regardless of the environmental effects (especially dust and water vapor), and the electrical signals of these sensors are stable. The DTH11 sensor has a temperature range of between \(-40\)\({}^{\circ}\)C and \(120\)\({}^{\circ}\)C. Therefore, it provides a wide range of temperature measurements with efficient monitoring of RH between 0 and 100 RH, making it highly appropriate for mines. These sensors are suitable for this application because they provide a high performance, low-cost, a simple architecture, and have been used in a wide range of applications. Each SN unit runs its own algorithm for local event detection, which allows for distributed processing. Thus, if SNs are disconnected from the network, they can analyze gathered data for the local identification of events. Table 2 summarizes the specifications of each sensor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specification**} & \multicolumn{4}{c}{**Sensor Model**} \\ \cline{2-5}  & **MQ-9** & **MQ-4** & **MQ-135** & **DTH11** \\ \hline Voltage & 5.0 V & 5.0 V & 5.0 V & 3–5 V \\ \hline Detection & CO and CG * & Methane & NH\({}_{3}\), smoke, CO\({}_{2}\) & Temperature \\  & & & & and Humidity \\ \hline Measurement Range & 10–1000 ppm (CO), 100–10,000 ppm (CG) & 200–1000 ppm & 10–300 ppm NH\({}_{3}\) & 20–90\% RH **, 0–50 \({}^{\circ}\)C \\ \hline Accuracy & \(\pm\)5\% & \(\pm\)5\% & \(\pm\)5\% & \(\pm\)5\% RH, \(\pm\)2 \({}^{\circ}\)C \\  & & & Temperature \\ \hline Sensitive Material & SnO\({}_{2}\) & SnO\({}_{2}\) & SnO\({}_{2}\) & – \\ \hline Configuration & 3-pin & 4-pin & 4-pin & 4-pin \\ \hline Digital/Analog & Analog & Both & Both & Digital \\ \hline \multicolumn{5}{c}{*} \\ \multicolumn{5}{c}{*} \\ \multicolumn{5}{c}{*} \\ \end{tabular}
\end{table}
Table 2: Hardware used for experiments and their specifications.
## 3 Results  
### Refractive Index of GaSe:S in IR and THz Region  
The results of measuring the refractive index in the THz spectral range and at \(\lambda=\) 1547 nm are presented in Figures 1 and 2, respectively. It can be seen that as sulfur elements are incorporated into the structure of the GaSe crystal, the value of the refractive index decreases in both ranges.  
Figure 1: Dispersion of the terahertz refractive index for an ordinary wave of GaSe:S crystal samples. The \(x\) value is labeled for each curve. Symbols—experimental data, solid curves—approximation by Equation (1).  
Figure 2: Ordinary refractive index of GaSe:S crystals measured at a wavelength of 1547 nm.
## 2 Materials and Methods
### Synthesis of TiO\({}_{2}\)/LAC Photocatalysts  
Luffa activated carbon (LAC) was prepared as follows: first, the collected fibers were washed several times with hot distilled water to remove dust and organic impurities and dried in an oven overnight at 90 \({}^{\circ}\)C. Then, the dried fibers were cut and milled using a grinder. Afterwards, they were sieved to collect samples of the desired size, 800 \(\mu\)m. These samples were put in contact with an 85 wt. % H\({}_{3}\)PO\({}_{4}\) solution in wt. ratio 3:1 (H\({}_{3}\)PO\({}_{4}\):luffa fibers) at 80 \({}^{\circ}\)C, 3 h under continuous stirring. In this step, H\({}_{3}\)PO\({}_{4}\) is the activating agent and the main responsible for developing the porous structure of the activated carbon. The obtained mixture was next treated at 500 \({}^{\circ}\)C for 1 hour in a tubular furnace with a heating rate of 5 \({}^{\circ}\)C/min under 60 ml/min N\({}_{2}\) flow [33]. The TiO\({}_{2}\)/LAC composites were prepared by a simple mixing method in which Degussa P25 TiO\({}_{2}\) was well blended in a mortar with a certain amount of LAC to obtain mixtures containing 70 and 90 wt.% of P25. The carbon content must be limited to allow the proper light absorption by the active TiO\({}_{2}\). The samples were referred to as TiLAC-x/y, where x/y refer to Ti/LAC weight proportion.  
The composite TiLAC-9/1 has been treated at 250, 300, and 350 \({}^{\circ}\)C, in air flow, to evaluate any possible influence of the oxidation state of titania and/or the generation of oxygen surface groups on the photocatalytic removal of MB.
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 3 Methods
### Vehicle Counting Based on Multiple Loops
#### 3.2.1 Design of Multiple Loops
When a secondary loop matches only one primary loop, its width is half the width of the primary loop plus the width of the shoulder on the side on which it sits, as shown in Figure 12b. * _Length of Loops_  
For safety reasons, the front and back vehicles driving on the same lane always keep a certain distance between them. Therefore, if the length of each loop is lower than the safety spacing of vehicles, it is guaranteed that only one vehicle exists in a loop at the same time. The safety spacing of vehicles consists of two parts, namely reaction distance and braking distance [31]. Therefore, the length is designed to be less than the reaction distance in this study. The reaction distance is the product of reaction time and driving speed [31]. For reaction time, the American Association of State Highway and Transportation Officials (AASHTO) mandated the use of 2.5 s for the reaction time [32]. While for the driving speed, we set it as 10 km/h--which is a very low speed--to ensure a long enough distance to  
Figure 12: Setting of loops. (**a**) Primary loops. (**b**) Secondary loops. (**c**) The overlap between primary loops and secondary loops. react. In this case, the length is designed to be less than seven meters, which can ensure that there is at most one vehicle in \(PLoop^{i}\) or \(SLoop^{i}\).
# GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides
## 3 Results and Discussion
### Evaluation of the PWVs Derived from BE-PPP
It is noteworthy that the stability of the RT-PWV surpasses that of  
Figure 8: Mean value (**a**) and STD (**b**) of the PWV differences for BE-PPP and RT-PPP with respect to ERA5 at 20 EPN stations during DOY 335–364 of 2022.  
the BE-PWV, as indicated by the lower STD values across all the stations examined. The average biases across all 20 stations were found to be 0.43 mm for the RT-PWV and 2.55 mm for the BE-PWV, while the average STD values were determined to be 1.39 mm for the RT-PWV and 3.09 mm for the BE-PWV, which satisfy the accuracy requirements. This is consistent with the mean bias of 2 mm and STD of 2.5 mm [39].
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 3 DM-CPDP Approach  
Comparing with the traditional methods of building prediction models in the feature space, an alternative way is to construct prediction models in the dissimilarity space, in which each sample is described by pairwise dissimilarity relations between original data and the prototype set. In this paper, the kernel tool is used to measure the pairwise dissimilarity between candidate training data and prototype set, and then the data are mapped into the dissimilarity space. In the space, we achieve thetransferring of multi-source datasets and assign labels to unlabeled target samples by a classification method, so that the process of model construction can use the standard supervised learning method. In order to offset the influence of useless features on the dissimilarity representation of samples, features need to be selected before the dissimilarity transformation. This paper chooses the FECAR [23] method for feature selection.  
The overall process is shown in Figure 1. The process is divided into three phases. The first phase focus on constructing dissimilarity space. The density-based clustering method is used to calculate the cluster center of samples in the target set, and the prototype set is formed by those cluster center samples. Then, the arc-cosine kernel is used to calculate the sample dissimilarities between the prototype set and the source domain or the target set to form the dissimilarity space. In the second phase, the earth mover's distance (EMD) method is used to calculate the cost, which is required to convert the data of the source domain, and in the dissimilarity space we select the corresponding samples with a small cost as the training set. In the third phase, the KNN method is used to assign labels to unlabeled target samples before building the prediction model, and then the TrAdaBoost method is used to construct the prediction model. In addition, the test set is also mapped into the dissimilarity space to obtain the predicted results.
# A Laser Irradiation Method for Controlling _Pieris rapae_ Larvae
## 3 Results
#### 3.1.4 Irradiation Position  
As shown in Figure 2d,h, the antifeedant percentages and mortality rates of the irradiation position were significant at each 24 h interval (Antifeedant percentage: 24 h, \(F\) = 26.91, df = 3, 8, \(p\)\(<\) 0.001; 48 h, \(F\) = 24.56, df = 3, 8, \(p\)\(<\) 0.001; 72 h, \(F\) = 26.66, df = 3, 8, \(p\)\(<\) 0.001; mortality rate: 24 h, \(F\) = 15.58, df = 3, 8, \(p\) = 0.001; 48 h, \(F\) = 11.33, df = 3, 8, \(p\) = 0.003; 72 h, Same, all 100%). Moreover, the larvae exhibited different reaction times when different body parts were irradiated. The head was the most sensitive, followed by the mesothorax and the middle and end of the abdomen (data not shown). However, the lowest killing effect was found with an irradiation position at the end of the abdomen (antifeedant percentages of 53.24%, 65.92% and 79.00% at 24 h, 48 h and 72 h; mortality rate of 33.33% at 24 h), and this effect was extremely significantly different from that obtained with the other irradiation positions (Figure 2d,h). To ensure the inclusion of three levels of each test factor for the response surface, the irradiation position was set as the head, mesothorax and middle of the abdomen.
## 4 Conclusions  
In this study, the chloride resistance of early-strength concrete prepared from a blended binder and PC-based chemical admixture was evaluated. The results are summarized as follows.  
When EPC and GGBS were mixed and used as concrete binders and a PC-based superplasticizer was used, the early and long-term strength were found to improve, while the total amount of binders and the unit water content significantly decreased. When EPC and GGBS were used to replace part of the OPC binder, a strength increase of 40% was observed after one day, and of an average of 20% by day 91. Therefore, it is confirmed that not only the increase in early strength, but also the improvement in long-term strength is effective. These findings could be attributed to EPC having more C3S crystals and a higher SO3 content than OPC.  
In addition, the chloride penetration depth and chloride ion diffusion coefficient confirmed that using EPC and GGBS rapidly developed the strength at early ages, thereby improving chloride resistance through the watertightness of the surface and the immobilization of chloride ions. The chloride ion diffusion coefficient was found to be about 50% lower than when using only OPC, and the chloride penetration depth test reduced by 40%.  
The internal porosity of concrete was evaluated, revealing that EPC and GGBS could reduce the overall porosity, which could increase the watertightness of the concrete. The relationship between the overall porosity and chloride ion diffusion coefficient confirmed that using EPC and GGBS reduced the chloride ion diffusion coefficient. It appears, however, that substitution with EPC and GGBS could not reduce the number of micropores, even though they decreased the overall porosity.  
The blended binder composition derived in this study and the application of the PC-based admixture are both expected to be used as basic data for the performance design of concrete mixes. They will also be useful for process control to increase the economic efficiency of concrete mixing and to avoid early form removal at actual sites. In this study, the concrete mixture using EPC, GGBS, and PC-based high-performance water reducing agent was examined as a commercialization study. Before applying this concrete on site, it is necessary to fully examine the concrete raw materials, workability in the field, and reproducibility of the early strength development according to the production in a batch plant.  
Conceptualization, T.L. and J.L.; investigation, T.L. and J.L.; resources, T.L.; writing--original draft, T.L.; writing--review and editing, T.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 12: Pore structure according to mercury intrusion porosimetry: (**a**) 7 days; (**b**) 28 days; (**c**) 91 days.
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 3 Beam Prediction Access Scheme
### BRNN-LSTM Deep Learning Model
A data structure is created with 60 time steps, each of 10 min, and a single output is created, since LSTM cells store long-term memory state. Hence, in each training stage, there are 60 previous training set elements for each taken sample. Consequently, in the testing stage, the first 60 samples are needed for an accurate estimate of the subsequent best beam index. Overall, the training objective is to compute weight matrices, and bias vectors that minimize the loss function for all training time steps, as shown next. See Table 2 for the parametric settings chosen for the layers in the BRNN-LSTM model. _Dataset_: The dataset used in this paper is part of the _BigData Challenge_ in [23] recorded over the period of two weeks. Namely, this dataset revealed MSs traffic volumes and used beam indices in sectorized geographical grids.
## 3 Methodology  
### Analysis Methods and Procedures  
The methods and procedures followed in this paper to estimate the effect of accidental torsion on building response are in accordance to ASCE 7-16. The commentary of ASCE 7-16, Section C12.8.4, suggests that inherent torsional moment be accounted for in structures with rigid diaphragms. Although inherent torsion is dominant in irregular structures, torsional response in general has also been observed in structures that are symmetric in floor plan as well as in layout of seismic force-resisting systems [10,11]. The fundamental reason torsional response exists in torsionally regular structures is that eccentricity between the center of stiffness and center of mass might exist due to uncertainties or unintended errors in determining the mass of the structure and stiffness values/distribution of structural elements during the modelling/design phase. In addition, earthquake ground motions might have torsional components, depending on building orientation, that are not included explicitly in code-based designs [12]. Therefore, ASCE 7-16 justifiably requires that the effects of accidental torsion  
Figure 1: Typical single eccentric system showing center of mass (\(CM\)) and eccentric center of rigidity (\(CR\)).  
must be considered for every structure, in addition to the amplification of torsion effects that occurs with torsional irregular structures.  
To account for accidental torsion, ASCE 7-16 prescribes the consideration of 5% the structure dimension perpendicular to the direction of ground motion, to any static eccentricity computed using idealized locations of the centers of mass and rigidity. Either static or dynamic analyses are recognized by this standard for consideration of torsional effects.
## 1 Introduction
### Previous Work  
#### 1.1.1 Traditional Flood Inundation Mapping  
Numerous techniques have been employed to map flood inundation from diverse satellite images. In the case of SAR images, the most common practice involves backscatter thresholding. Backscatter thresholding classifies a pixel as containing water if its backscatter value falls below a specified threshold [12]. In the context of optical imagery, flood inundation mapping involves techniques such as thresholding bands sensitive to water, like near-infrared (NIR) and shortwave-infrared (SWIR), or computing normalized difference indices like NDWI [13], MNDWI [14], and NDVI [15], where the normalized difference is found between NIR and SWIR; NIR and green; and NIR and red bands, respectively.
# Sentiment Analysis of Online New Energy Vehicle Reviews
## 5 Experimental Results
### Comparison of F1 Score Experimental Results  
The F1 score is a comprehensive metric that plays a vital role in evaluating the performance and effectiveness of a model in binary classification tasks. It combines precision and recall, providing a balanced measure of the model's ability to correctly predict positive and negative instances. Precision measures the proportion of correctly predicted positive samples among all the samples predicted as positive, while recall measures the proportion of correctly predicted positive samples among all the true-positive samples. The F1 score calculates the weighted average of precision and recall, offering a holistic evaluation of the model's performance.  
Figure 12 illustrates a comparative analysis of F1 scores between the EDC model and models based on the word2 vec and BERT pretraining methods.  
Upon analyzing the graph of F1 scores, it is evident that the EDC model outperforms the other models based on word2 vec. The EDC model consistently achieves the highest F1 scores across all epochs, with a peak value of 97.08, significantly surpassing the performances of the other models. In comparison to the other models, the EDC model demonstrates the superior performance in text classification tasks. It consistently outperforms models such as FastText, TextCNN, TextRCNN, TextRNN, TextRNN_Att, Transformer, and DPCNN in all epochs, particularly ex  
Figure 12: Comparison of F1 scores between EDC and different models. (**a**) Comparison with word2 vec word embedding model. (**b**) Comparison with BERT word embedding model.  
When compared to models based on BERT pretraining, the EDC model exhibits the superior performance in terms of the F1 score. Throughout the training process, the EDC model consistently maintains a stable and outstanding performance, with the F1 scores consistently at a high level. While models such as BERT_CNN, BERT_RNN, BERT_RCNN, and BERT_DPCNN show some fluctuations in their performances across different epochs, occasionally approaching the performance of the EDC model, the EDC model overall demonstrates greater stability and proximity to the optimal level. Notably, the BERT_DPCNN model shows a significant performance improvement between Epoch 3 and Epoch 8, while the other BERT-based models exhibit relatively stable performances across different epochs. This indicates that the EDC model exhibits good adaptability in text classification tasks and maintains excellent performance throughout the entire training process.