esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Retry and Threshold Multirate Loss Models for Impatient Calls
## 4 Performance Evaluation
In Figure 5, we present the analytical together with the simulation CBP of the first service-class calls assuming the probabilistic threshold loss model (p-TM in Figure 5) for \(p_{2c}=0.2\) and \(p_{2c}=0.6\) and the corresponding analytical CBP for the threshold loss model of [40] (TM in Figure 5) and the EMLM. On the same hand, in Figure 6, we present the corresponding CBP results of \(B_{2c}\). In both figures, we see that the simulation results and the analytical results obtained via the probabilistic threshold loss model are quite close. Regarding Figure 5, we observe that decreasing \(p_{2c}\) results in a CBP decrease for the first service-class calls compared to the values of [40] or those obtained via the EMLM. In addition, the CBP of the first service-class are lower to the corresponding CBP results obtained via the probabilistic retry loss model (compare Figure 3 with Figure 5). This can be explained by the fact that in the probabilistic threshold loss model, calls from the second service-class may decide earlier (due to the value of \(J_{0}=4\)) not to enter the system, a fact that increases the available b.u. for calls of the first service-class. As far as Figure 6 is concerned, we observe that a decrease of \(p_{2c}\) significantly increases the CBP of calls of the second service-class in the case of the probabilistic threshold loss model, compared to the model of [40] or the probabilistic retry loss model (compare Figure 4 with Figure 6). Regarding the EMLM, it is obvious that it cannot capture the behavior of the proposed model or the model of [40]. As a general comment for both the probabilistic retry and the probabilistic threshold loss models, note that particular attention is necessary when choosing the values of \(p_{kc}\) and \(p_{kr}\) for calls of service-class \(k\).
## 3 Materials and Methods
### Application of PSO and DE to the Postman Delivery Routing Problem
#### 3.3.1 Particle Swarm Optimization (PSO)  
Particle swarm optimization (PSO), proposed by [PERSON] and [PERSON] in 1995, is a population-based random search technique that imitates the behavior of fish schooling or birds flocking. In PSO, a solution is represented as a particle, and the population of solutions is called a swarm of _NP_ particles. Each particle consists of two main attributes: The position and the velocity. The evolutionary concept of PSO is that each particle learns from the cognitive knowledge of its experiences (personal best, or _pbest_) and the social knowledge of the swarm (global best, or _gbest_) to guide itself to the better position. A particle moves to a new position using the updated velocity, which is adjusted according to the experiences of a particle. In particular, the updated velocity of a particle depends on three main control parameters: The inertia weight, personal best coefficient (_cp_) and global best coefficient (_cg_). Once a new position is obtained, the best position of each particle and the best position of the swarm are then updated as needed. These processes are repeated until a stopping criterion is met. Figure 1 shows the evolutionary procedure of the PSO.
# A Performance Benchmark for the PostgreSQL and MySQL Databases
## 3 Materials and Methods
### Comparative Analysis and Benchmarking Process Flow  
The activity diagram defines the dynamic behavior of the modeled system and assists in understanding program flow at a high level. Figure 4 shows the behavior of the database benchmarking framework.  
Initially, configuration variables are set based on the nature of the benchmarking test. Along with configuration variables, environment variables are required for the database. Once this information is provided, it is fetched and stored until the end of the test and used at different stages. Next, it creates a unique test ID to be used to identify the test.  
Later, the configuration variables are scanned to determine if both PostgreSQL and MySQL tests are required or not. If they are required, then the following steps must be followed to run the test:  
Figure 4: Database benchmarking activity diagram.  
First, a database connection is made and verified; failure to connect to the database would terminate the test.
* If the connections succeed for both databases, the configuration variables are used to build MySQL and PostgreSQL queries for benchmarking.
* Queries are executed on the respective databases.  
The number of times a query should be executed depends on the configuration variables 'runs' and 'test_cycles'. For example, if 'runs' is 100 and 'test_cycles' is 5, then each query will be executed 100 times across 5 cycles (a total of 500 executions). Adding cycles helps determine performance patterns after executing queries. Simultaneously, the following steps are followed:  
* Query time/latency (time between query trigger and completion) is calculated.
* Results are saved in CSV files named according to experimental file naming conventions (explained in Section 3.3).  
These results undergo various analyses:  
1. A statistical analysis is conducted to find the median, min, max, and percentiles
2. A graphical analysis is conducted to plot query time vs. runs for the complete test/cycles.  
The analyses are saved in JSON (statistical) and PDF (graphical) files per the naming conventions. These analyses help identify patterns in the MySQL/PostgreSQL results; comparing both reveals overall patterns throughout tests. The subsequent steps include creating comparison PDFs showing the MySQL/PostgreSQL query times/moving averages for smoother patterns before completing the tests. If neither PostgreSQL nor MySQL tests are required, we check if a PostgreSQL test is needed:  
* Create/test connection; failure terminates test.
* Build/execute queries using configuration variables; calculate/save query time/results in CSV.  
The results undergo statistical/graphical analyses and are saved as JSON/PDF files, marking the end of the test. Similarly, MySQL tests are conducted if needed; otherwise, we terminate the test if neither is required.
# Towards a New Digital Era: Observing Local E-Government Services Adoption in a Chinese Municipality
## 4 Methodology
### Regression Analysis  
The results of the regression analysis testing the hypotheses regarding four types of local e-services are presented in Table 2 below. All 605 valid cases were included in the process, which have been divided into four sections representing different e-services applications. The results explain the impacts of demographic factors and awareness, intention to use and satisfaction on adoption of the four aforementioned applications: \"Government website portal\", \"Government official app\", \"Weibo Government official account\", and \"WeChat public services\" (including government official accounts and city service platforms), marked as \"Web\" \"App\" \"Weibo\" and \"WeChat\" for short. Firstly, in response to H1b, the results show that men adopt Weibo services more readily than women. However, gender differences seem to be non-existent in the other three service applications. The increased willingness to use Weibo has been explained in Figure 4: government accounts on Weibo provide politics-dominated information and functions that may appeal more to men. Local residency is introduced as a special element of Chinese demographics that shows extremely positive correlations with adoption of e-services in all the four types of services. As for the impact of the age issue, it is clear that there are significantly positive relationships between age and adoption of government services on social networking platforms: the Weibo government official account and WeChat public services. This is strong evidence suggesting that mobile-oriented social networking applications have their own strengths in acquiring older users. The results, however, do not provide support for either educational background or IT literacy related to adoption of any type of e-service. In response to H2, the table shows that with the higher level of awareness of services themselves, people are indeed tending to adopt local e-government services regardless of the distinctions between channels. The same result suggests that as more people intend to use these services, the real usage will increase in all kinds of local e-services. Finally, it reflects the evidence to support H4 that higher satisfaction will positively influence e-services adoption in all types. The results have revealed that the cognitive determinants of the adoption of traditional e-government services apply to new social networking applications as well. Multicollinearity was not a concern with this data set as confirmed by the results of variance inflation factors (VIF range from 1.02 to 1.59) and the level of tolerance (tolerance range from 0.63 to 0.98) in Table 3.
## 3 Results and Discussion
### Pharmacokinetic and Toxicity Profiles Prediction  
The pharmacokinetic profile of the PBD compounds were evaluated using ADME (absorption, distribution, metabolism, and excretion) prediction. The key parameters analyzed included human intestinal absorption (HIA), Caco-2 cell permeability, and protein plasma binding (PPB). HIA indicate the extend of which compounds are absorbed in the human intestinal tract with value classified as poorly absorbed (0-20%), moderately absorbed (20-70%), and well absorbed (70-100%). Caco-2 cell permeability reflects a com  
Figure 2: In silico prediction of cell line cytotoxicity for PBD compounds by CLC-Pred 2.0.  
pound's ability to penetrate human intestinal epithelial cells, classified as high permeability (P\({}_{\text{Caco-2}}\) = more than 70 nm/s), middle permeability (P\({}_{\text{Caco-2}}\) = 4-70 nm/s), and low permeability (P\({}_{\text{Caco-2}}\) = less than 4 nm/s) [67]. Plasma protein binding (PPB) estimates the fraction of a drug that binds to plasma proteins, particularly albumin serum, \(\alpha\)-1-glycoprotein acid, and lipoproteins, influencing drug distribution and bioavailability [68]. Compound with >90% PBB are considered strongly bound, while those with <90% are weakly bound [69]. Drugs that are strongly and irreversibly bound, such as those forming covalent bonds, may pose long-term drug toxicity risks. In contrast, drugs that exhibit weak binding through hydrogen bonds or van der Waals forces demonstrate better distribution and reduced toxicity risks [70]. Toxicity was also predicted using parameters including carcinogenicity in mouse and rats, hepatotoxicity, immunotoxicity, mutagenicity, cytotoxicity, and toxicity classification [71]. These toxicity assessment are crucial in identifying safe compounds early in the drug development pipeline, helping to minimize late-stage failures [72,73]. The pharmacokinetic screening results (Table 3) showed that all PBD compounds and the positive control (4-OHT, ralo caffeine, and lasofoxifene) were classified as well absorbed based on the %HIA. Based on Caco-2 permeability, the positive control and twenty-four PBD compounds were classified as moderate permeability, while twenty-one PBD compounds showed low permeability. In terms of PBB, the positive control and twenty-two PBD compounds were classified as strongly bound, and twenty-three PBD compounds were classified as weakly bound. Compounds with weak PPB (>90%) are expected to distribute more efficiently throughout the body compared to those with high PPB, which remain largely bound to plasma proteins and may have limited bioavailability [74,75].
## 3 Design and Preliminary Verification  
The theoretical analysis in Section 2 shows that the proposed LC/S compensated IPT system can realize CCO and CVO characteristics with ZPA operation at fixed operating frequencies. This section describes the structural design of the LCT, providing a strict system parameter design process. In addition, by building an equivalent model, the above theoretical analysis is preliminarily verified.
## 6 LncRNAs and Cardiovascular Disease
### Roles of LncRNAs in Atherosclerosis  
Atherosclerosis causes the narrowing of blood vessels due to immunological responses, which produce plaques in subendothelial spaces [63]. The formation of plaques is stimulated by monocytes that differentiate into macrophages [64]. The obstruction of blood vessels is considered as a high risk factor for atherosclerosis because it may cause high blood pressure and stroke [65,66].  
Recently, some lncRNAs have been implicated in the etiology of plaque formation leading to atherosclerosis. In a study conducted by [PERSON] _et al._, a lncRNA called RP5-833A20.1 was found to regulate a gene called _nuclear factor IA_ (_NFIA_) [40]. NFIA is a member of the nuclear factor I family which regulates gliogenesis [67]. NFIA regulates transcription by directly binding to DNA containing a TTGGC motif [68]. It was suggested that NFIA is a primary regulator in adipocyte formation and lipid development. Moreover, a study has revealed that aberrant expression of NFIA caused an increase in lipid droplet formation [69]. Another study showed that the lncRNA RP5-833A20.1 downregulates NFIA expression by inducing miR-382-5p expression, which leads to the formation of THP-1 macrophage-derived foam cells. The study noted that normal levels of NFIA suppress atherosclerotic plaque formation, while aberrant levels of NFIA expression leads to higher levels of plaque formation [40].  
Reverse cholesterol transport is a physiological process to remove and mobilize lipids and cholesterols that might contribute to the plaque formation. Within this pathway, a transmembrane transport protein called ABCA1 functions to transport cellular cholesterols to its corresponding apolipoproteins [39,70]. In a mouse study, oxidized low-density lipoproteins (LDLs) stimulated an lncRNA called DYN-LRB2-2 (Table 1 and Figure 3), which promoted ABCA1-mediated cholesterol efflux and up-regulated G protein-coupled receptor 119 (GPR119). GPR119 was shown to be a key player in glucose and lipidmetabolism, which regulates atherosclerotic plaque formation by decreasing cellular cholesterol levels and inflammation [39].
## 3 Results: Numerical Analysis
### Numerical Simulation Results for a Cubic Spring Design  
In this section, the bilinear spring is replaced with a cubic spring with the following constitutive model: \(F_{spring}=K_{1}x+K_{2}x^{3}\). In the cubic spring, the change in stiffness is gradual and not abrupt like the bilinear spring. To create a cubic spring, an extensive literature review was conducted. The best nonlinear spring solution for our application appears to be using either beams, air springs, or rubber mounts. Since the bottom piston moves in both the up and down directions, the cubic spring needs to be active in both directions. With air springs, the stiffness increases when they are compressed; therefore, two air springs will be required, or one air spring with a special design that can provide cubic stiffness in both directions. The same is true for rubber mounts.  
Another approach to achieve cubic stiffness is to use beams. Using two cantilever beams or one clamped-clamped beam supported on two blocks and attached to the bottom side of the floating piston, as shown in Figure 15, can provide cubic stiffness. The two cantilever beams are shown in the grey color in Figure 15. The cantilever beams need to be attached to the piston shaft, either through welding or using bolts.  
Reference [20] states that the nonlinear stiffness of just one clamped-guided beam is found using the following expressions:  
\[F_{k}=K_{0}x+\frac{K_{0}}{2\hbar^{2}}x^{3} \tag{11}\]  
Figure 14: Bottom piston motion at different engine displacement inputs, linear vs. bi-linear springs.  
Figure 15: Bottom piston attached to two cantilever beams.  
When two cantilever beams are attached to a piston or one clamped-clamped beam is attached to a piston, the stiffness felt by the floating piston will be as follows:  
\[F_{k}=2K_{0}x+\frac{K_{0}}{h^{2}}x^{3} \tag{12}\]  
where \(K_{0}=\frac{12 EI}{L^{3}}\), and \(E\), \(I\), and \(L\) are beam's Young's modulus, area moment of inertia, and length, respectively. Using a beam with a rectangular cross-section of height \(h\) and width \(w\), will have an area moment of inertia equal to \(I=\frac{1}{12}h^{3}w\). From Figure 15, we can clearly see that when the bottom piston moves up or down, the clamped-clamped beam provides nonlinear stiffness in both directions. This design is now investigated in the next section.
# Online Determination of Boron Isotope Ratio in Boron Trifluoride by Infrared Spectroscopy
## 3 Results and Discussion
### Calculation and Correction of the Isotope Ratio
\(A_{10}\) or \(A_{11}\) could be regarded as the addition of several fixed number of absorptions roughly:  
\[ \begin{array}{ll}A_{10}/A_{11}&=\sum\limits_{i=1}^{n}\lg(I_{0}/I_{10})_{i}/ \sum\limits_{i=1}^{n}\lg(I_{0}/I_{11})_{i}\\ &=\sum\limits_{i=1}^{n}\varepsilon_{10i}b_{10i}c_{10i}/\sum\limits_{i=1}^{n} \varepsilon_{11i}b_{11i}c_{11i}\end{array}  \tag{2}\]  
For \(b_{10}=b_{11}\), \(c_{10i}/c_{11i}\) is a constant for the same gas (fixed concentration). Then:  
\[A_{10}/A_{11}=(\sum\limits_{i=1}^{n}\varepsilon_{10i}/\sum\limits_{i=1}^{n} \varepsilon_{11i})\times(c_{10}/c_{11}) \tag{3}\]  
Let \(\sum\limits_{i=1}^{n}\varepsilon_{10i}/\sum\limits_{i=1}^{n}\varepsilon_{11i}\) = \(K\). \(K\), which is the correction factor, then:  
\[K=A_{11}/A_{10}\times c_{10}/c_{11} \tag{4}\]  
and  
\[c_{10}/c_{11}=A_{11}/A_{10}\times K \tag{5}\]  
Figure 5: Schematic of the calculation method. The correction factor, \(K\), is the ratio of the addition of a series of molar absorption coefficients in essence according to Equations (1)-(5). So, boron isotope ratio can be calculated by the peak areas of \(A_{10}\) and \(A_{11}\). The correction factor can be obtained by testing the BF\({}_{3}\) gas of a known isotope ratio. Table 3 shows the continuous determination results of the \(A_{10}/A_{11}\) ratio for the normal, natural abundance BF\({}_{3}\) sample. The ICP-MS result of \({}^{10}\)B/\({}^{11}\)B for this sample is 0.2473. While, the isotope ratio of \({}^{10}\)B/\({}^{11}\)B is equal to \(c_{10}/c_{11}\), based on the ICP-MS result and Equation (5), the value of correction factor \(K\) is 0.5431.
# Comparison of Measurement Possibilities by Non-Invasive Reflectometric Sensors and Invasive Probes
## 2 Materials and Methods
### Samples and Preparations
#### 2.3.2 Samples for Non-Invasive Measurements  
The dimensions of the samples used in non-invasive research were 220 mm \(\times\) 120 mm \(\times\) 40 mm. Similarly to the samples for invasive measurements, they were dried to a constant mass and gradually moistened to achieve 69% and 55% of moisture. The samples were weighed to check the moisture status. Then, the samples were examined with the non-invasive S1 sensor and subsequently with the S2 sensor in order to obtain the measurement results.
## 3 Results
### Subjective Evaluation
#### 3.2.3 Results of Word Error Perception Rate  
The word error perception rate (WER, or maybe syllable or pinyin error perception rate) is another important metric to evaluate the enhancement effect of Mandarin EL speech. WER can be used to evaluate the enhancement effect of the consonants and vowels of Mandarin EL speech. The WER in Table 7 is calculated based on the comparison of the results written by the listeners and the real contents. Please note that here WER means the pinyin/syllable error rate and does not include tonal errors.  
Table 7 shows the WER of unenhanced/enhanced continuous Mandarin EL speech. Interestingly, tone variation does not significantly influence the WER of Mandarin EL speech. In addition, for EL-FT, the WER of CLDNN-FT decreases by 3.07%, the WER of VC3-FT by 6.41% and that of our approach by 8.59%. For EL-VT, the WER of CLDNN-VT decreases by 5.26%, the WER of VC3-VT by 6.53% and that of our approach by 9.15%. The results show that tone variation has substantial influence on the enhancement effect of the CLDNN-based approach but little influence on the VC3-based and our approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Speech Types** & **Chinese Characters** & **Chinese Words** & **Chinese Idioms** & **Short Sentences** & **Long Sentences** \\ \hline CLDNN-FT & 40.00/0.49 & 45.00/0.38 & 48.75/0.40 & 53.28/0.24 & 26.22/0.14 \\ VC3-FT & 35.00/0.48 & 47.50/0.40 & 63.75/0.27 & 64.45/0.15 & 63.78/0.12 \\
2C1T2C-FT & 30.00/0.46 & 42.50/0.33 & 68.75/0.25 & 71.74/0.16 & 73.69/0.16 \\ CLDNN-VT & - & - & 87.5/0.15 & 71.56/0.15 & 73.98/0.09 \\ VC3-VT & - & - & 87.5/0.15 & 80.46/0.10 & 78.13/0.11 \\
2C1T2C-VT & - & - & 88.75/0.15 & 83.05/0.08 & 85.93/0.12 \\ \hline \end{tabular}  
* Please note that in Table 6 every type of testing data has 20 items, such as 20 characters, 20 words, 20 idioms and 20 sentences. “-” means no results.  
\end{table}
Table 6: Tone accuracy of Mandarin EL speech with different amount of semantic information (average accuracy %/STD) 1.
## 2 Short-Term Load Forecasting Method Based on Feature Reconstruction  
### The Process of the Method in This Paper  
The short-term load forecasting process proposed in this paper based on feature reconstruction is shown in Figure 1, which mainly includes the model training and prediction process. The steps are as follows:  
1. Load type analysis: Perform GMM clustering on historical load data to analyze the characteristics of different load classes.
2. Input feature reconstruction based on MIC: Rearrange the load variables and feature variables in order of the sampling points, calculate the maximum information coefficient between each variable and the load, select the features based on the MIC values, and obtain the reconstructed feature matrix according to the proposed method.
3. Training of the prediction model: Use the reconstructed feature matrix \(I\) as the input and the load values as the output to train a BI-LSTM neural network.
4. Short-term load forecasting: After obtaining the feature data for the prediction day, first determine the type of the prediction day, then determine the reconstructed input feature matrix based on the different types, and then input it into the trained model to complete the prediction.  
Figure 1: Pre-test of the process of this method.
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Equations of motion for subsystem B:
#### 3.2.3 Validation Approach Using the Modal Method  
As previously discussed in the earlier case study, the modal approach offers a systematic way to validate the dynamic behavior of the system by analyzing its natural frequencies and mode shapes. For the second case study, this approach is employed to ensure consistency and accuracy across different validation methods. To apply the modal method to the entire system AB, the natural frequencies and mode shapes can be determined by solving the eigenvalue problem. By obtaining these natural frequencies and mode shapes, it becomes possible to compute the components of the receptance matrices based on Equation (4). This approach provides a comprehensive validation framework, ensuring that the receptance matrix components derived through the modal method align with those obtained from other methods, thereby reinforcing the robustness of the results.