esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Experimental Analysis
#### 5.2.1 Experiment 1: Comparing the Compression Values Achieved by the Different Enhancements  
In order to further improve the compression ratio that our algorithm, proposed in [21], achieved, we employed multiple enhancements. First, we used Huffman coding on the output of the original algorithm, which consisted of an ID and the parameters in string format, for example, \"1 CXP9029630_4 R9D3925\", where 1 stands for \"NPU Software <<>\". This approach is labeled \"Huff\". The second idea was that the template IDs should be assigned based on frequency, and parameters should also be encoded, since the same values appear multiple times, and the average length of a parameter ID is less than the average length of a parameter's string representation. If \"CXP9029630_4\" is represented by 1 and \"R9D3925\" is represented by 2, the output would be \"1 1 2\", which is 27 characters \begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Name** & **Number of Messages** & **Size in Kilobytes** & **Alphabet Size** & **Shannon Entropy** \\ \hline Small & 39,139 & 1152 KB & 76 & 5.08167 \\ Mid & 124,433 & 4607 KB & 76 & 5.08872 \\ Large & 280,002 & 10,198 KB & 74 & 5.05287 \\ Big & 637,369 & 22,840 KB & 71 & 5.00206 \\ A & 50,000,000 & 2,039,483 KB & 76 & 4.97789 \\ B & 130,000,000 & 5,303,394 KB & 76 & 4.97798 \\ C & 254,000,000 & 10,361,437 KB & 76 & 4.97781 \\ D & 1,264,000,000 & 51,562,601 KB & 76 & 4.89284 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Size of the datasets. less than the original entry. This approach is labeled \"Enh\". Finally, we combined the first two ideas. This approach is labeled \"Huff\". The compression rates achieved by these enhancements can be seen in Figures 7-10. Based on our experiments, it can be said that each enhancement improves the compression rate. The single use of Huffman coding provides the smallest improvement; the compression rate is around \(\approx\)75%, which is \(\approx\)17% more than the original algorithm (where the compression rate is 1 minus the ratio of the size of the compressed file to the uncompressed size). The reason behind this is that the lines to be encoded could contain various characters since the parameters are in a plain text format. Due to the large number of possible characters, some of them will have lengthy representations, which results in worse compression capability. The codec size also depends on the number of unique characters. Table 2 contains information about the size of the template dictionary, parameter dictionary, Huffman codec, and the overall compressed data size. WPE stands for \"Without Parameter Encoding\". This is used in our second approach, where the parameters are not encoded, only the Huffman coding is used. Figure 8: Compression rate of the different enhancement approaches of the Mid dataset. Figure 7: Compression rate of the different enhancement approaches of the Small dataset. Figure 10: Compression rate of the different enhancement approaches of the Big dataset.
## 3 Experimental Results and Analysis
### Explainable Analysis  
SHAP (SHapley Additive exPlanations) is a technique utilized to interpret model predictions that is grounded in the Shapley value from game theory, which quantifies the contribution of each feature to the model output. For deep learning models, Deep SHAP serves as an interpretation tool [34]. Deep SHAP is a SHAP explainer specifically tailored for deep learning models, integrating the principles of SHAP with the architecture of deep neural networks. By incorporating feature importance analysis into deep learning models, Deep SHAP effectively quantifies the contribution of each feature to the model's prediction outcomes. Based on the core concept of the SHAP algorithm, Deep SHAP measures feature contributions by occluding (or setting to zero) specific features for a given input and analyzing the activation process layer by layer within the neural network. Specifically, the shap. DeepExplainer module was employed for interpreting deep learning models.
## 3 Numerical Calculation Application and Experimental Setup
### Experiment Setup
#### 3.5.2 Vibration Measurement  
Vibration behaviors of the bearing end cover of the centrifugal pump was collected during the test, which can be used to approximately represent the vibration level of the rotor system according to Chinese standards of GB/T 29531-2013 [32] and GB/T 6075.1-2012 [33].  
It can be seen from Figure 10 three sensors with a common base were mounted at the center of the bearing end cover. With that test instrument, vibration accelerations on three orthogonal spatial directions (two radial directions and one axial direction) were measured with PTZ piezoelectric ceramic sensors, and the sample frequency was set as 10.24 kHz. The frequency response and measurement ranges of the sensors are 0.5 Hz-5 kHz and 0-50 g, respectively. The INV3018A data collection instrument with 24-bit resolution and 4 simultaneous-capture analog input channels was utilized for analog acquisition, digital conversion, signal filtering, and data storage, and the acquisition system accuracy was 0.02%.  
Figure 9: Hydraulic performance test rig: 1—Inlet valve; 2—Inlet pressure gauge; 3—Laser tachometer; 4—Electromotor; 5—Torque transducer; 6—Centrifugal pump; 7—Outlet pressure gauge; 8—Electromagnetic flowmeter; 9—Outlet valve; 10—Water drainage tank; 11—Base line; 12—Water supply tank.
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 4 Implementation and Experiments
### Neural Network Architectures  
Neural networks have been used for various sequence-labeling NLP tasks, such as Part of Speech (POS) tagging and Named-Entity Recognition (NER). Similarly, we investigated treating error detection for Arabic text as a sequence-labeling task by utilizing token-level labeled Arabic sentences. For the sequence-labeling model, we built a neural network, which takes a sequence of tokens (words) as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Original Corpus** & **Augmented Corpus** \\ \hline Number of Sentences & 494 & 620 \\ Number of Words & 1216 & 1380 \\ Number of Tokens & 2027 & 2705 \\ Number of Correct Words & 1438 & 2122 \\ Number of Incorrect Words & 576 & 592 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Corpus Statistics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Case** & **Explanation** \\ \hline Deletion & Incorrect sentence has a missing word & Augment by duplicating the sentence and adding the missing word \\ Syntax & Words in the incorrect sentence are & Augment by duplicating the sentence and reordering \\  & incorrectly ordered & the words correctly \\ From Rules & There were some example sentences & Extracting sentences from rules \\  & in “The Rules” of the book & Augment by duplicating the sentence as many times \\ Multi-error & Incorrect sentence has multiple errors & as there are errors, fix only one error per sentence \\ \hline \hline \end{tabular}
\end{table}
Table 3: Types of Augmentation.  
input, and outputs the probability of each word in the sentence. We explored a few types of neural networks, including a SimpleRNN, LSTM, and BiLSTMs.  
We used a similar sequence of layers for every experiment while varying the hyperparameters and RNN types (i.e., SimpleRNN, LSTM, or BiLSTM) for comparison. A SimpleRNN layer is just plain vanilla RNN in Keras where the output is to be fed back to input.  
Models are basically composed of an input layer, a single embedding layer, a dropout layer, a SimpleRNN layer (or 1-2 LSTM layers or 1-2 BiLSTM layers) with recurrent unit dropout, and TimeDistributed wrapped Dense output layer with sigmoid activation. The sigmoid function outputs a probability for each class as a value between 0 and 1.  
For our task, we trained word embeddings using the Embedding layer in Keras [34]. The Embedding layer receives the input sequence and encodes it into a sequence of vectors, thereby learning an embedding for all the words in the training dataset. The advantage of this approach in comparison to using pre-trained embeddings is that learned vectors are targeted to the specific corpus as well as the error detection task.
# Influence of Suction on Capacity of Pile Foundation
## 4 Results
### Change in GWT  
A temporary increase in the ground water table occurs in the process of rainwater infiltration of unsaturated soil layers. In order to investigate the effect of the increase in GWT on the pile capacity, the obtained values for Type 1 and Type 2 piles were subjected to an increase in the saturation level. Tables 19 and 20 show the decreasing trend of the pile capacity due to higher GWT.  
Figure 8: Pile bearing capacity in unsaturated soil Type 2 using modified \(\alpha\) method vs. depth with variating matric suction.  
Figure 9: Pile bearing capacity in unsaturated soil Type 2 using modified \(\beta\) method vs. depth with variating matric suction.  
Generally, the results show that an increase in the saturation level will lead to a decrease in pile total capacity. In the Type 1 pile, the change in the GWT by 3.2 m diminishes the capacity by 15%, 16.82%, and 6.98% for the modified \(\alpha\), \(\beta\), and \(\lambda\) methods, respectively. This is the same for the Type 2 pile, increasing GWT by 3 m results in a decrease of 16.33%, 17.71%, and 9.92%, respectively. In accordance with the obtained data, the modified \(\lambda\) method is found to have less effect on the variation in the GWT.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**GWT**} & \multicolumn{2}{c}{\(\alpha\)**Method**} & \multicolumn{2}{c}{\(\beta\)**Method**} & \multicolumn{2}{c}{\(\lambda\)**Method**} \\ \cline{2-7}  & **Qf (kPa)** & **\% Decrease** & **Qf (kPa)** & **\% Decrease** & **Qf (kPa)** & **\% Decrease** \\ \hline
7.5 & 285.76 & & 648.20 & & 821.80 & \\ \hline
6 & 270.21 & 5.44 & 609.94 & 5.90 & 794.63 & 3.31 \\ \hline
4.5 & 254.66 & 10.89 & 571.68 & 11.80 & 767.46 & 6.61 \\ \hline
3 & 239.11 & 16.33 & 533.42 & 17.71 & 740.28 & 9.92 \\ \hline
1.5 & 223.55 & 21.77 & 495.16 & 23.61 & 713.11 & 13.22 \\ \hline
0 & 208.00 & 27.21 & 456.90 & 29.51 & 685.94 & 16.53 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Capacity of Type 20 pile under changing GWT.  
Figure 10: Pile bearing capacity in unsaturated soil Type 2 using modified \(\lambda\) method vs. depth with variating matric suction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**GWT**} & \multicolumn{2}{c}{\(\alpha\)**Method**} & \multicolumn{2}{c}{\(\beta\)**Method**} & \multicolumn{2}{c}{\(\lambda\)**Method**} \\ \cline{2-7}  & **Qf (kPa)** & **\% Decrease** & **Qf (kPa)** & **\% Decrease** & **Qf (kPa)** & **\% Decrease** \\ \hline
6.4 & 164.51 & & 596.28 & & 603.20 & \\ \hline
4.8 & 152.16 & 7.51 & 546.12 & 8.41 & 582.14 & 3.49 \\ \hline
3.2 & 139.81 & 15.01 & 495.97 & 16.82 & 561.07 & 6.98 \\ \hline
1.6 & 127.46 & 22.52 & 445.81 & 25.24 & 540.00 & 10.48 \\ \hline
0 & 115.11 & 30.03 & 395.65 & 33.65 & 518.94 & 13.97 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Capacity of Type 1 pile under changing GWT.
## 3 Results
### Validation of the Self-Adjustment Technique  
For the validation, the field data were obtained between 1 March 2022 and 15 March 2022. In reference to the beginning of the experiment, the validation period corresponded to the interval between Days 568 and 582. Right after the validation period, the self-adjustment of the HFP01 SC sensor sensitivity was done to update the accuracy and validate the outputs from the HFP01 sensors. The self-adjustment of the sensitivity was done several times, while the interval between individual executions was at least 6 h as recommended by the manufacturer. The summary of experimental data obtained during the validation period is shown in Figure 13. The thermoelectric voltage in this figure refers to the output recorded on the HFP01 SC and will be further processed with the adjusted sensitivity determined through the adjusting procedure. The data from the sensor sensitivity self-adjustment procedure are summarized in Table 4.  
After performing the sensitivity adjustment, the thermoelectric voltage from Figure 13 was divided by the new sensitivity to obtain the time-resolved heat flux which served as the reference value in the validation procedure. Simultaneously, the heat fluxes \(q_{\text{IN}}(t)\) and \(q_{\text{OUT}}(t)\), obtained from Steps 2-4 as described in Section 2.2, were plotted in Figure 14. Prior to the calculation of \(q_{\text{IN}}(t)\) and \(q_{\text{OUT}}(t)\), the computational model was adjusted according to Step 1 of the proposed procedure to achieve the best performance of the semi-virtual environment. The heat transfer coefficient \(\alpha\) was optimized to 5.0 W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\) producing the least difference (\(RMSE=0.2627\)) between computationally and experimentally determined  
Figure 12: Regression analysis of the verification results.
## 3 Results and Discussion
### IDIVS and Information Security
We also identified the need for anomaly detection to be integrated into the IDIVS for real-time attack detection. This is important as cyberattacks, particularly those targeting the IoT, including zero-day attacks, are incessantly evolving. These security-enhancing mechanisms may be (partially) provided through the installation of a trusted operating system for enabling the connected devices. Another suggested mechanism that can help strengthen the security of data and prevent potential attacks, similar to attack (iii), is leveraging technologies like blockchain during incremental training. This mechanism can help keep track of any changes made to the system, including those that are potentially caused by an adversary. Nonetheless, a major challenge in the application of blockchain in such environments is the dynamic and heterogeneous nature of an IoT environment [35]. This makes the implementation of security frameworks based on blockchain more challenging to port to IDIVS. Moreover, it is essential to constantly monitor the incoming/outgoing data flows during the user feedback process to counter possible anomalies that may affect the current IDIVS output. Some examples of anomalies are the deliberate falsification of labels and the injection of malicious content. This will help to distinguish between the available content and what may or may not be malicious during continued learning. Further, machine learning techniques may be considered for the detection of attacks, as suggested and analysed by [PERSON] et al. [36] and in the more general review by [PERSON] et al. [37]. Based on the above-mentioned pointers, it is worth highlighting the need for enhancing information security techniques through continued research to protect aspects of the IDIVS in online learning.
## 1 Introduction
### Review  
The structural codes and articles regarding codes are divided into three accuracy levels: I, II and III [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12].  
The Eurocodes were previously based on level I, i.e., on deterministic, historic and empiric methods, where the code calibration was also deterministic, historic and empiric.  
The safety factors of the current Eurocodes are based on level II, i.e., on the first-order reliability method. The primary assumption of this method is the independent load combination where a load reduction occurs. It is implemented by sensitivity factors \(\alpha_{\rm E}\) and \(\alpha_{\rm R}\), which decrease the target reliability and cause a load reduction, as in this load combination, it is improbable that the highest permanent load and the highest variable load occur simultaneously. The load reduction is implemented by calculating the variable load reliability for 5-year loads [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12] and should be calculated for 50-year loads [13].  
The accuracy level III is a full probabilistic method. Such a method has not been implemented in any structural codes yet but was allowed for in [1]. The codes presented in this article are based on the full probabilistic model.  
The current safety factors are normally [14; 15; 16; 17] set by minimizing the sums of second powers of reliability index differences of the actual ones regarding to the target. In other words, the safety factors \(\gamma_{\rm G}\), \(\gamma_{\rm Q}\) and \(\gamma_{\rm M}\) are set by minimizing the expression (1) in all feasible design cases, i.e., in all materials and in permanent-variable load proportions.  
\[\sum_{\rm i}w_{\rm i}(\beta_{\rm i}-\beta_{\rm t})^{2} \tag{1}\]  
where \(w_{\rm i}\) denotes the weight factor of the design case \(\rm i\), \(\beta_{\rm i}\) is the reliability index in the design case \(\rm i\) and \(\beta_{\rm t}\) is the target reliability index. In this calculation, the overall outcome is correct for its mean reliability index only. There is roughly an equal number of unsafe and safe design cases, and the codes may include substantial safe and unsafe design cases. Further, Equation (1) is biased as explained in [18].  
This article proposes a code calibration process without using the reliability index, and the proposed codes have high accuracy in terms of the target reliability.  
In the current codes, the characteristic values are constant. This article proposes that the characteristic values are changeable.
# Information Relaying Methods in VANET: Algorithms, Standards and Tests
## 5 Real-World Test Summary  
It is a popular design in the C-V2X demonstration area for the RSU to be deployed at either the intersection or highway road side for information collection, information refinement and relay function. However, requiring substantial infrastructure costs, the vehicle-based relay may provide another route. Though such relay candidate selection area seems easy to find, the various real road conditions introduce a wide variety of scenario-specific difficulties in accurately identifying such area in detail. However, with the development of the network data analytics function (NWDAF) and other AI topics such as Federated Learning introduced into 3 GPP, the exposure of VANET communication characteristics from UE to gNodeB and core network may provide a platform for data analysis, relay on-request area and relay candidate selection area determination. Similar to how the base station manages the area of the cell, the high-definition map can also be divided into different VANET communication blocks with the intersection area, the bend area and semi open location close to obstacles as relay node selection areas. The mechanism to define transmission block and intersection should be based on the quality of service (QoS) of packet transmission; the QoS of the packet transmission within one VANET communication block shall remain aligned and stable, while Tx UE and Rx UE belonging to different communication blocks differ largely with location differences. The relay node selection area shall bridge multiple communication blocks, while the UEs inside the relay node selection area can establish a stable short range transmission link with the UE in those blocks within the surroundings. The base station may take the role of data collector to identify short range data transmission quality characteristics with the locations of both sender and receiver, reporting the statistics to the core network, enabling NWDAF to analyze the VANET transmission quality in relation to different areas with the help of the location management function (LMF), to decide VANET communication blocks and relay node selection areas. Vehicle type UE entering a relay node selection area can be validated by the base station or Core Network functions as relay nodes dynamically minimize the NLOS transmission from obstacle (e.g., buildings, vehicles) shadow effects.
## 3 Results
### Acoustic Modeling
#### 3.3.1 Identification of Relevant Acoustic Characteristics  
The evaluation of the most influential acoustic characteristics in the detection of sound events was carried out through a detailed analysis that involved the comparison of the detection of good events against environmental noises in different controlled environments and under various acoustic conditions. This methodology sought to discern which characteristics were most pertinent to identifying relevant events, separating them from general environmental noise. Table 4 presents the results of this evaluation, showing how each characteristic influences the identification of sound events.  
Different types of ambient noises were introduced in the test environments, and how each acoustic feature reacted to the relevant sound events in the presence of these noises was observed. For example, a sound amplitude and frequency spectrogram proved highly effective, even with significant noise, indicating its high detectability capability.  
While acoustic energy showed a moderate ability to discern sound events from background noise, event duration, and dominant frequency had more limited performance, being more effective in situations with specific sound patterns.  
These quantitative results, derived from the tests performed, guided the acoustic modeling and feature tuning process, allowing a focus on the most discriminative and relevant features. Thus, with this detailed evaluation, it was possible to develop an algorithm that capitalizes on these critical characteristics for early detection of emergencies and safety monitoring in public spaces.
## Materials and Methods
### Analyses  
#### 2.3.1 Flow Cell Coupled DLS for Application in Real-Time Measurements of Nanoparticle Dispersions  
For flow cell coupled real-time measurements of nanoparticle dispersions, we used a DLS set-up based on the 3D cross correlation geometry, as developed by LS Instruments. This set-up comprises a laser which emits light at a wavelength of 685 nm. This light is emitted and directed at the dispersion through a polarization-maintaining optical fiber. The light scattered by the nanoparticles at 90\({}^{\circ}\) with respect to the incident beam, and is collected by a second optical fiber. The latter is connected to a single-photon avalanche diode (SPAD) and the signal is analyzed by a LSI Correlator (LS Instruments). In combination with a flow cell, the DLS unit equipped with LsLab software version 1.0.4.0. was used for real-time particle size measurements. To perform measurements on moving liquid dispersions, a tailored analysis loop was prepared and applied to couple the DLS instrument to a stirred 2 L double-walled glass tank reactor. The DLS instrument was integrated in the analysis loop in direct connection with the stirred tank reactor, and adapted to accommodate a flow cell including tubing material to enable real-time measurements. A direct connection of the sample cell to the reactor via a tubing system, however, could easily transmit vibrations which would affect the particle motion in the liquid. For this reason, it was necessary to decouple and reduce the vibrations sufficiently, such that the Brownian motion of particles in the measured sample volume can be detected.  
Directly after the addition of precursor to the reaction mixture and start of the sol-gel reaction to form nanoparticles, the dispersion was pumped with a speed of 12.9 mL\(\cdot\)min\({}^{-1}\) through the DLS flow cell (type Starna, 71-F/Q/10) using a Watson pump with flexible tubing (type Inacom Tygon R3607, inner tubing diameter 2.79 mm), after which the measurement was started. The particle dispersion circulates through the flow cell back into the reaction mixture.