esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Empirical Results  
### Excel Spreadsheet Simulation  
First, the authors pivoted the quantity of historical sale data to count monthly order frequency to generate the probability of demand. Next, the authors used the random function in excel to simulate customer demand and get the values response variables (TC and CSL). The experimental design was created using a central composite design and Box-Behnken design to find the possible independent variables settings for solving the order planning problem. The excel spreadsheet simulation template for the experimental design is described in Figure 4.  
In this paper, a case study of a distributor is presented to demonstrate the effectiveness of the developed computational framework to solve the current order planning problem in the company.  
The model's parameters setup is described as follows.  
* Unit holding cost, \(C_{h}\) = 0.075 USD/unit/month;
* Unit shortage cost, \(C_{s}\) = 0.553 USD/unit;
* Unit over-storage cost, \(C_{o}\) = 0.162 USD/unit/month;
* Warehouse capacity = 5000 units;
* Weight (CW100) = 22 kg/unit;
* Maximum capacity of container type 20 ft = 21,000 kg;
* Maximum capacity of container type 40 ft = 42,000 kg;
* Freight charge of container type 2 0 ft = 1500 USD/trip;
* Freight charge of container type 40 ft = 2700 USD/trip.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Variables** & **Notation** & **Interval Range** & **Unit** \\ \hline
1 & Review period (categorical factor) & T & \((3,4)\) & Month \\
2 & Order quantity (continuous factor) & Q & \((3500,4000)\) & Unit \\
3 & Safety stock (continuous factor) & SS & \((1000,1300)\) & Unit \\
4 & Total cost (response) & TC & TC (\(C_{h}\), \(C_{s}\), \(C_{o}\), \(C_{t}\)) & USD \\
5 & Customer service level (response) & CSL & CSL (90\% and 95\%) & \% \\ \hline \hline \end{tabular} Note: calculated by the researchers.  
\end{table}
Table 4: The interval ranges of the variables.  
In Figure 5, the analysis of variance of factorial regression, the results suggested that the model had significant curvature because the \(p\)-value (\(p\)-value = 0.000) was less than the confidence level at 95%. The presence of curvature usually indicates that the factor settings were near a feasible response value. Therefore, two response surface designs, central composite design, and Box-Behnken design were considered afterward to find the possible parameter settings for this case. In the step of full factorial design, for the starting center point, T = 3, Q = 3500, and SS = 1000, were considered. Figure 6 shows the Pareto chart of the standardized effects from the results and factors. It can be seen that their interaction significantly affected the model's results.
## 4 An Efficient Korean Embedding Model
In these experiments, we used the three popular Korean embedding models as comparators:  
* The public model snunlp/KR-SBERT-V40K-kblueNLI-augSTS ([[https://github.com/s/numlp/KR-SBERT](https://github.com/s/numlp/KR-SBERT)]([https://github.com/s/numlp/KR-SBERT](https://github.com/s/numlp/KR-SBERT)), accessed on 2 May 2023) is based on the KR-BERT-V40K pre-trained model. It was fine-tuned using the KLUE-NLI and KorSTS datasets. The KorSTS dataset was augmented by Augmented SBERT [19]:
* The public model jhgan/ko-sroberta-multitask ([[https://github.com/jhgan00/ko-se](https://github.com/jhgan00/ko-se)]([https://github.com/jhgan00/ko-se](https://github.com/jhgan00/ko-se)) ntence-transformers, accessed on 2 May 2023) is based on the KLUE-RoBERTa-base pre-trained model. It was fine-tuned using the KorNLI and KorSTS datasets. This model is considered the best among the models introduced on the GitHub page;
* The public model Hufton/sentence-klue-roberta-base ([[https://huggingface.co/H](https://huggingface.co/H)]([https://huggingface.co/H](https://huggingface.co/H)) ufton/sentence-klue-roberta-base, accessed on 2 May 2023) is based on the KLUE-RoBERTa-base pre-trained model. It was fine-tuned using the KLUE-STS dataset. For a fair comparison, we did not use any development sets or other datasets related to the test sets. We trained each of our approaches for only one epoch. The experimental results indicate that the highest average accuracy is achieved when using KLUE-RoBERTa-base as a pre-trained model and fine-tuning with three datasets: KLUE, KSTS, and KorSTS. Additionally, the best results for Korean test sets are obtained when only the KLUE and KSTS datasets are used. Based on these findings, we have identified three important factors. Firstly, using an appropriate pre-training model is crucial. The accuracy difference between the KR-SBERT-V40K based model and those based on KLUE-RoBERTa-base is significant. Secondly, dataset quality is more important than the dataset's quantity. Unfortunately, some popular datasets, especially KSICKR, do not lead to improved performance and can even degrade performance. Finally, although high-accuracy results are achieved on Korean test sets, the results on the English test sets are relatively lower. In the following sections, we describe novel ways to address these limitations.
# Approximate Sensory Data Collection: A Survey
## 3 Compressive Sensing Based Approximate Data Collection Algorithms
### Summary  
In conclusion, the idea of compressive sensing based data collection algorithms is to decrease the dimension of sensory data matrix as much as possible according to the users' requirements. These methods are quite efficient and effective when the sensory data matrixes are sparse in certain subspaces. However, such a fact may not be always true and seems a little strict for WSNs and IoT systems. Even if the above fact is true, it is also quite hard to get the sparsity (i.e., which subspace makes sensory data to be sparse) of sensory data, so that the compression rate of such algorithms cannot be derived theoretically. Second, all of the algorithms have a fixed global information loss rate, which cannot meet an arbitrary user-specified precision requirement. Moreover, for the centralized algorithms of generating compression bases, the communication cost is huge since these bases need to be broadcasted in a network. For the distributed algorithms of generating compression bases, they cannot guarantee that the bases are orthogonal with each other since each basis is generated independently, so that there still exist some redundant information after compression and the compression rate cannot reach the optimal one either. The hybrid algorithms provide a valuable idea for approximate data collection. However, the existing works seem a little simple and have their own drawbacks.
## 3 Results
### Antibacterial Activity  
The antibacterial effect of the extracts of seeds and stems was also tested against a Gram-positive bacterium, _S. aureus_, and a Gram-negative bacterium, _E. coli_ (Table 4).  
The extracts obtained from the stems of the different vine varieties showed no antibacterial activity, even at the maximum concentration tested of 200 \(\upmu\)g/mL.  
In the case of the E:W extracts obtained from the seeds, they proved to be active against _S. aureus_ but had no effect on the growth of _E. coli_ even at 200 \(\upmu\)g/mL.  
In terms of mechanism of action, and when tested at 200 \(\upmu\)g/mL, the E:W extract of Touriga Nacional, Touriga Franca, and Viosinho seeds is bactericidal for _S. aureus_. For Tinta Roriz, at 200 \(\upmu\)g/mL, the effect of the E:W extract varied between bacteriostatic and bactericidal.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variety** & **Yeast** & **Extract** & **MIC (\(\upmu\)g/mL)** \\ \hline \multirow{4}{*}{Touriga Franca} & & W & \(>\)200.0 \\  & _C. albicans_ & E & \(>\)200.0 \\  & & E:W (50:50 \(v/v\)) & \(>\)200.0 \\ \cline{2-4}  & & W & \(>\)200.0 \\  & _C. krusei_ & E & \(>\)200.0 \\  & & E:W (50:50 \(v/v\)) & 17.5 \(\pm\) 6.9 \\ \hline \multirow{4}{*}{Touriga Nacional} & & W & \(>\)200.0 \\  & _C. albicans_ & E & \(>\)200.0 \\  & & E:W (50:50 \(v/v\)) & 100.0 \(\pm\) 0.0 \\ \cline{2-4}  & & W & \(>\)200.0 \\  & _C. krusei_ & E & \(>\)200.0 \\  & & E:W & 10.0 \(\pm\) 3.4 \\ \hline \multirow{4}{*}{Viosinho} & & W & \(>\)200.0 \\  & _C. albicans_ & E & \(>\)200.0 \\  & & E:W (50:50 \(v/v\)) & 200.0 \(\pm\) 0.0 \\ \cline{2-4}  & _C. krusei_ & E & \(>\)200.0 \\  & & E:W (50:50 \(v/v\)) & 8.8 \(\pm\) 3.4 \\ \hline \multirow{4}{*}{Tinta Roriz} & & W & \(>\)200.0 \\  & _C. albicans_ & E & \(>\)200.0 \\  & & E:W (50:50 \(v/v\)) & \(>\)200.0 \\ \cline{1-1} \cline{2-4}  & & W & \(>\)200.0 \\ \cline{1-1}  & _C. krusei_ & E & \(>\)200.0 \\ \cline{1-1}  & & E:W (50:50 \(v/v\)) & 35.0 \(\pm\) 13.7 \\ \hline \end{tabular}
\end{table}
Table 3: Antifungal activity of the extracts prepared from the seeds of the different varieties tested.
## Materials and Methods
### UPLC-Orbitrap HRMS Analysis  
The negative and positive ion high-resolution ESI and collision-induced dissociation (CID) MSn spectra were obtained from an Orbitrap Elite mass spectrometer (Thermo Fisher Scientific, Darmstadt, Germany) equipped with a heated electrospray ion source (negative spray voltage of 3 kV, positive 4 kV, capillary temperature of 300 \({}^{\circ}\)C, source heater temperature of 250 \({}^{\circ}\)C, FTMS resolution of 30.000). Nitrogen was used as sheath and auxiliary gas. The MS system was coupled to an UHPLC system (Dionex UltiMate 3000, Thermo Fisher Scientific), equipped with a RP-18 column (particle size 1.8 um, pore size 100 A, 150 mm \(\times\) 1 mm ID, Acquity HSS T3, Waters; column temperature of 40 \({}^{\circ}\)C) and a photodiode array detector (220-600 nm, Thermo Fisher Scientific). The mobile phases were H\({}_{2}\)O (A; Fluka Analytical, LC-MS Chromasolv) and CH\({}_{3}\)CN (B; Fluka Analytical, LC-MS Chromasolv) with 0.1% formic acid, using the following binary gradient at a flow rate of 150 uL/min: 0 to 1 min, isocratic 95% A, 5% B; 1 to 11 min, linear from 5% to 100% B; 11 to 19 min, isocratic 100% B; and 19 to 30 min, isocratic 5% B. The injection volume was 2 uL. The CID mass spectra (buffer gas: helium) were recorded using a normalized collision energy (NCE) of 35%. The instrument was externally calibrated by the Pierce ESI negative ion calibration solution (product No. 88324) and Pierce ESI positive ion calibration solution (product No. 88323) from Thermo Fisher Scientific. The data were evaluated using the software Xcalibur 2.2 SP1. Metabolites were characterized by their UV-VIS spectra (220-600 nm), retention times relative to external standards, mass spectra and comparison to phytochemical dictionary of natural products database (CRC) and reference literature.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 6 Experimental Results
### Setup and Datasets
#### 6.1.2 Multi-View Antenna Dataset  
We collected two datasets of multi-view antenna data, each serving a different purpose. AntennaL consists of 65 groups of data captured on campus using an antenna whose downtilt angle ranged from approximately 1.5 degrees to approximately 11.5 degrees and whose azimuth angle ranged from \(-180\) to 180 degrees, and each group of data consists of three viewpoints. AntennaS is similar to AntennaL, but was collected based on working antennas; however, because of restrictions imposed by the local telecom company, our access was limited to only two antennas, from which we captured six groups of data consisting of three to six viewpoints for each group. A checkerboard was placed in the scene in both AntennaL and AntennaS to enable the determination of the extrinsic camera parameters. We evaluated the accuracy of our method on AntennaL, which contains many more groups of data and antenna poses, and verified the performance of our method in a real environment using AntennaS.
# Development of Fire Consequence Prediction Model
## 3 Configuration of the Prediction Model
### Training Result of the Hole Size Prediction Model  
The TensorFlow library of TensorFlow Python was used in this study. For the hole-size prediction model, a total of 115 data points were configured with three input parameters (LNG mass flow, velocity, and pressure), one output parameter (leak hole size), 100 training data points, and 15 test data points. The training was performed for 300 epochs by setting the learning rate to 0.0001. The number of hidden layers was set to three, and the number of neurons in these layers was reduced for each layer in the order of 6, 6, and 3 so that the nodes of the output layer finally converge to one.  
Figure 7 shows the resulting graph of the loss (MSE: mean square error) of the accident consequence prediction model. No additional training was performed because the MSE values of the training and test sets converged to \(10^{-4}\) or lower.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Constraints** \\ \hline \multirow{3}{*}{Input} & LNG mass flow & 56.28–58.99 kg/s \\  & LNG velocity & 6.21–6.50 m/s \\  & LNG pressure & 3.78 \(\times\)\(10^{-6}\)–9.86 \(\times\)\(10^{-5}\) bar \\ Output & Leak hole size & 10–120 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input and output data (hole size prediction data).  
Figure 6: Result of pipe modeling analysis.  
The mean errors of the training and test sets are presented in Table 2. The error rate of the training set is 5.299%, whereas the test set has a lower error rate of 2.874%.
###### Abstract  
To address the challenging issues of large deformation, control difficulties, and susceptibility to failure in the support structure of soft coal roadways, this study utilizes the CVISC block creep model in UDEC software. The model incorporates Coulomb slip without cohesive contact to simulate the characteristics of soft coal, such as its loose, fragile, and small-block nature. Additionally, a soft coal nonlinear discrete element creep model is developed to investigate the creep characteristics of soft coal under triaxial compression, with the aim of revealing the underlying creep destabilization mechanism in soft coal tunnels. Based on the research findings, a primary, strong active support technology is proposed. This approach involves the use of high-preload, high-strength anchor rods and anchor cables, as well as the implementation of steel mesh and plastic woven mesh to enhance surface protection. The study highlights that: (1) The shear cracks inside the coal body of the soft coal specimen transform into tensile cracks under external force, leading to an increase in the number of tensile cracks. This is an important symbol of accelerated creep in soft coal. Improvement in peripheral pressure helps inhibit the generation of tensile cracks inside the specimen. (2) The rapid development of creep and inter-particle tensile fissures within the coal body particles themselves, along with the change in stress state after the excavation of the roadway, are the main reasons for the overall creep damage of the roadway. (3) The support force in the early stage of shed cable support is small, which cannot inhibit the accelerated development of tensile fissures. This leads to continuous deformation of the roadway, resulting in the failure of the support structure in the later stage and aggravated roadway damage. (4) The new support technology helps control surface deformation by enhancing the strength of the roadway protection surface. This suppresses the development speed and number of tensile fissures during roadway deformation, improves the starting strength of the roadway for accelerated creep, and enables effective control of the overall deformation of the soft coal roadway. Thus, the effectiveness of roadway support is remarkable.  
CVISC model; soft coal roadways; creep test; numerical simulation; creep failure mechanism; primary strong active support +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Stability Study
#### 3.2.4 Structural Analysis by XRPD  
XRPD enables monitoring of structural changes in spray-dried samples during storage, provided the XRPD patterns of KTP and the excipients are known. The solid-state form of the active ingredient is crucial, as differences between crystalline and amorphous states can affect particle morphology, interparticle interactions, and aerodynamic performance. The XRPD diffractograms (Figure 8A) reveal characteristic peaks of raw KTP and the physical mixture (PM), with raw KTP displaying intense 2-theta (20) peaks at 13.5\({}^{\circ}\), 14.53\({}^{\circ}\), 17.23\({}^{\circ}\), 18.48\({}^{\circ}\), and 22.84\({}^{\circ}\), indicative of its crystalline structure. After formulation, the spray-dried sample (F1) retained these characteristic peaks at reduced intensity (Figure 8B), suggesting partial amorphization with a crystallinity index (Xc) of 44.54%. During storage, KTP's characteristic peaks persisted, supporting prior studies indicating that formulations stored  
Figure 7: DSC thermal analysis of (**A**) raw ketoprofen (KTP) and the physical mixture (PM), and (**B**) sample F1, analyzed as a freshly prepared sample (F1—0m) and after 1 (F1—1m) and 3 (F1—3m) months of storage.  
in HPMC capsules maintain structural integrity under stress [29]. However, rougher diffractograms observed in stored samples suggest a partial loss of crystalline order, reflected in reduced Xc values (40.17% for F1_1m and 33.81% for F1_3m). This partial transition to an amorphous state may impact both aerodynamic performance and drug release profiles.
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 2 Materials and Methods
### System Architecture  
Figure 4 depicts a block diagram of an ultrasonic through-metal communication system assisted with two alternative approaches of echo cancellation. The upper part of this figure is the system architecture similar to a general one, comprised of a signal transmitting side, a 50 mm long metal channel, and a signal receiving side. Signals undergo several processing steps upon entering the transmitting side, including source coding, ASK digital modulation, DA conversion, and signal amplification. Following that, the modulated ultrasound is transmitted into the metal channel via electroacoustic conversion afforded by the transmitting transducer. On the signal receiving side, ultrasonic signals are subjected to the reverse process including acoustoelectric conversion, signal amplification, AD conversion, synchronization, channel equalization, digital demodulation, and source decoding.  
The lower part of Figure 4 illustrates that two alternative routes, LMS adaptive filtering and DPRNN processing, are utilized to replace the equalization module in the above system. LMS adaptive filtering as a typical equalization approach has been frequently used in traditional communication systems, here being conducted just for making a comparison of equalization efficiency with DPRNN. As for the DPRNN route, it simultaneously accomplishes equalization and synchronization tasks, which are mainly composed of segmentation, block processing and overlap-add. The introduction of LMS adaptive filtering or DPRNN algorithm into the channel mitigates the intersymbol interference that resulted from acoustic echoing.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 4 Experimental Result  
In the experimental design and performance evaluation section, this study employs a three-phase progressive validation framework: first, establishing a benchmark experimental system based on standardized datasets; second, validating the effectiveness of the model components through ablation experiments; and finally, conducting horizontal comparative experiments to demonstrate methodological superiority. To ensure experimental reproducibility, this paper strictly adheres to international machine-learning research standards and implements interpretable experimental design principles: (1) selecting authoritative public datasets to avoid data bias; (2) ensuring feature engineering consistency through modular preprocessing pipelines; (3) balancing model capacity with computational efficiency via adaptive hyperparameter configurations; and (4) conducting comprehensive performance analysis using confusion matrix analysis, metric analysis, computational resource analysis, and ablation experiments. Notably, the experimental environment configuration fully considers industrial application requirements, implementing production-environment transferability verification on consumer-grade GPU hardware. This systematic experimental design not only guarantees the statistical significance of the research conclusions but also provides engineering practice references for deploying real-world network traffic analysis systems.
## 4 Unified Model of Permanent Deformation
### Model Parameter Analysis  
Based on the test data of the DT02 specimen, i.e., \(\sigma_{3}^{\prime}=100\) kPa, \(D_{f}=2.37\), and \(D_{c}=93\%\), the physical meaning of the proposed model (Equation (11)) parameters is analyzed by changing the model parameter values, as shown in Figure 13 (the model parameters of fitting curve in Figure 13 are \(p=4.8175\), \(q=135.5844\)).  
It can be seen from Figure 13 that, when \(q\) is constant, the \(N\) required before the permanent deformation curve tends to be stable is basically unchanged with the increase of \(p\), whereas the stable value of the permanent deformation curve increases accordingly. Therefore, \(p\) reflects the permanent deformation in the stable stage. The greater \(p\) occurs with the bigger permanent deformation. Similarly, when \(p\) is constant, with the increasing \(q\), the permanent deformation at the stable stage remains unchanged, whereas the \(N\) required before the curve tends to be stable increases. Hence, \(q\) indicates the \(N\) required before the permanent deformation curve tends to be stable, and the greater \(N\) required before the permanent deformation curve reaches the stable stage occurs with a larger \(q\).  
Figure 12: Comparison between the proposed model and the test curve: (**a**) under different confining pressure conditions, (**b**) under diverse compaction degree conditions, and (**c**) under various grading conditions.  
The above analysis studies the physical meaning of the model parameters by changing the parameter values. Then, the correctness of the above model parameter analysis will be further verified from a mathematical point of view.  
For the unified model of permanent deformation, i.e., Equation (11), when \(N\) tends to be 0:  
\[\lim_{N\to 0}\epsilon=\lim_{N\to 0}\frac{pN}{q+N}=0 \tag{13}\]  
Before test loading, the permanent deformation of the sample is 0, and the model is consistent with the actual situation. When the coal gauge sample experiences a large number of cyclic loadings:  
\[\lim_{N\to\infty}\epsilon=\lim_{N\to\infty}\frac{pN}{q+N}=\lim_{N\to\infty} \frac{p}{\frac{q}{N}+1}=p \tag{14}\]  
According to Equation (14), when \(N\) is large enough, the denominator approaches 1, and the maximum value of permanent deformation is equal to \(p\). Therefore, \(p\) reflects the permanent deformation in the stable stage. For Equation (14), the smaller \(N\) required before the permanent deformation curve reaches the stable stage occurs with the lesser \(q\). It can be seen that the analysis of model parameters from the perspective of mathematics and test curve is consistent.