esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### Development of Benchmarking Tool
#### 3.1.1 Requirement Analysis  
The initial step focused on a comprehensive assessment of the necessary requirements and goals essential for developing the benchmarking tool. In this phase, we determined the specific characteristics, input parameters, and output evaluation metrics needed for the benchmarking tool. Based on these requirements, we created a detailed design for the benchmarking tool, which is illustrated in Figure 1. This design shows the structure and workflow of the tool, including modules for input processing, basic cryptographic functions, and key evaluation metrics.
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
## 1 Introduction
[15] carried out numerical studies focused on the numerical simulation model of the fluid flow through the valve needs, considering a simplified geometry of an axial hole plus a radial diffuser. In addition, numerical studies were conducted using CFD to analyze the turbulence model effect. [PERSON] et al. [16] conducted the studies for the two phases flow field numerical simulation inside the export ball valve of new rotor oil-gas mixture pump in different open height and different volume gas rate. With different conditions of the volume of gas rate, the velocity field and stress field in the ball valve were investigated. [PERSON] and [PERSON] [17] performed the numerical study for a compressible fluid flow through a butterfly valve, and the accuracy of different turbulence models was compared for the pressure profile. [PERSON] [18] investigated turbulence model effects on the accuracy of solutions for valve disc angles and implementation between experimental and numerical results. Most numerical studies on flow behavior through valves have been conducted without considering the effects of turbulence model, and the validity of the numerical results of the turbulence models remain uncertain. Therefore, in the present numerical study, the three turbulence models most frequently used were selected: two-equation \(k-\varepsilon\) model of [PERSON] and [PERSON], two-equation \(k-\omega\) model of [PERSON], and two-equation \(k-\omega\) SST model of [PERSON]. Limited studies on numerical approaches with turbulence models have been conducted, especially for the butterfly valve, although the range of their applications of the butterfly valve are increasing for various fluid dynamic systems and processes. The present paper examined flow behavior and turbulence model effects on the flow behavior for different-sized butterfly valves with different disc opening percentage. The sensitivity analysis for the different two-equation turbulence models, \(k-\varepsilon\) and \(k-\omega\) model was conducted. The first part discusses a numerical study of flow behavior through different-sized butterfly valves under different disc openings of the valve. The effects of turbulence models of \(k-\varepsilon\), \(k-\omega\), and SST model on the flow characteristics were investigated. Sensitivity analysis of flow properties for the \(k-\varepsilon\), \(k-\omega\) turbulence models was performed in the second part to examine the effect of the turbulence model constant.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 2 Background and Literature Review
### Lower-Power IoT Technologies  
In order to elongate the lifetime of IoT devices and thus to reduce the maintenance and replacement cost of an IoT network, low power consumption is a crucial requirement for most of the envisioned IoT applications. Many terrestrial LPWAN technologies were developed in specific response to applications that do not require large bandwidth and are primarily located in hard-to-reach areas. Some practical IoT-over-Satellite products had been used, as shown in References [1; 2; 4]. The impact of the future SpaceX IoT potential is also studied in Reference [12]. The leading IoT technologies, at the time of writing this paper, are compared in Table 1.  
As can be seen from the table, current terrestrial IoT access systems can differ quite significantly in terms of their modulation schemes, radio bandwidth, carrier frequencies, and protocols. These differences lead to a significant variation in terms of interference, data throughput, battery lifespan, and the maximum number of devices per gateway.  
The extension of terrestrial LPWAN technologies for satellite access is taking a new momentum, for example, the studies in References [1; 2; 13; 14] address this possibility, with special focus on Narrowband IoT (NB-IoT) and Long Range Wireless Access Network (LoRaWAN) as these technologies have been widely used and these devices are publicly available. Some of these studies have found the suitability of such technologies for IoT-over-Satellite without taking terrestrial interference into account. On the other hand, there are a plethora of field trials for these technologies under realistic interference scenarios for terrestrial applications [15; 16]. The performance in terms of block error rate (BLER) and the relation between BLER and the number of users is shown in Reference [15], where NB-IoT outperforms other technologies due to the controlled spectrum access, having a 95 percentile uplink failure probability of less than 4% when there are less than 10 devices. However, the drawback of NB-IoT is that it has a long time on-air (when compared with other IoT technologies), causing a  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **NB-IoT** & **SigFox** & **LoRaWAN** & **Wi-Fi 802.11 ah** \\ \hline Carrier Frequency & LTE bands & 915 MHz & 915 MHz & 2.4 or 5 GHz \\ Channel bandwidth & 200 kHz & 100 Hz & \(\geq\)125 kHz & 1/2/4/8/16 MHz \\ Modulation Scheme & QPSK & BPSK & CSS & N-QAM/NPSK \\ Transmit power & 23 dBm & 20 dBm & 30 dBm & 30 dBm \\ Access Scheme & SR + ALOHA & ALOHA & ALOHA & CSMA/CA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Internet of Things (IoT) technologies comparison.  
reduction in the longevity of the field IoT devices. A comparison of some IoT-over-Satellite systems is listed in Table 2.
# Construction Industrialization in China: Current Profile and the Prediction
## 3 Construction Industrialization Profiles in China  
### Industrialized Construction Profiles by Province and Municipality  
#### 3.1.1 Gross Floor Area  
Construction industrialization is quantified as the total gross floor area of industrialized construction projects by province in this research. The total gross floor area of industrialized construction projects in 2014 within the 19 provinces and municipalities surveyed is found to be 16.76 million m\({}^{2}\). The regional distribution of construction industrialization is displayed in Table 1: (1) among the 19 provinces and municipalities, Hunan is found to be the leading province, accounting for 3.75 million m\({}^{2}\) of gross floor area (22.4% of the total); (2) Liaoning Province is ranked second with a gross floor area of 2.24 million m\({}^{2}\) (13.4% of the total); and (3) Shanghai is ranked third with a gross floor area of 2.04 million m\({}^{2}\) (12.2% of the total). Among the remaining provinces and municipalities under study, Anhui Province and Shandong Province have also shown the momentum of construction industrialization, each with a gross floor area of more than 1 million m\({}^{2}\). The industrialized construction profile by province and municipality is illustrated using a map of China (Figure 1). In Figure 1, the colours ranging from dark blue to light blue show the decreasing floor area of industrialized construction, and the white area represents the non-surveyed regions. Overall, east China and central China have developed a large scale of construction industrialization, and Hunan Province, Liaoning Province, Shanghai Municipality, Anhui Province, and Shandong Province are the leaders in terms of construction industrialization in China.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Province/Municipality** &
\begin{tabular}{c} **Industrialized Construction** \\ **Projects (million m\({}^{2}\))** \\ \end{tabular} & **Percentage (\%)** \\ \hline
1 & Hunan & 3.75 & 22.4\% \\
2 & Liaoning & 2.24 & 13.4\% \\
3 & Shanghai & 2.04 & 12.2\% \\
4 & Anhui & 1.89 & 11.3\% \\
5 & Shandong & 1.41 & 8.4\% \\
6 & Henan & 0.81 & 4.8\% \\
7 & Ningxia & 0.80 & 4.8\% \\
8 & Zhejiang & 0.72 & 4.3\% \\
9 & Fujian & 0.58 & 3.5\% \\
10 & Sichuan & 0.56 & 3.4\% \\
11 & Hebei & 0.47 & 2.8\% \\
12 & Hubei & 0.28 & 1.7\% \\
13 & Shaanxi & 0.25 & 1.5\% \\
14 & Qinghai & 0.23 & 1.4\% \\
15 & Tianjin & 0.21 & 1.3\% \\
16 & Chongqing & 0.20 & 1.2\% \\
17 & Beijing & 0.13 & 0.8\% \\
18 & Jilin & 0.11 & 0.6\% \\
19 & Xinjiang & 0.07 & 0.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Industrialized construction by province and municipality (gross floor area).
###### Abstract  
This paper proposes the design of a mixed-mode universal biquad configuration, which realizes generic filter functions in all four possible modes, namely voltage mode (VM), current mode (CM), transmittance mode (TAM), and transimpedance mode (TIM). The filter architecture employs two voltage differencing buffered amplifiers (VDBAs), two resistors and two capacitors, and can provide lowpass (LP), bandpass (BP), highpass (HP), bandstop (BS), and allpass (AP) biquadratic filtering responses without any circuit alteration. All passive elements used are grounded, except VM. The circuit not only allows for the electronic tuning of the natural angular frequency (\(\omega_{o}\)), but also achieves orthogonal tunability of the quality factor (\(Q\)). It also provides the feature of availability of output voltage at the low-output impedance terminal in VM and TIM, and does not require inverting-type or double-type input signals to realize all the responses. Moreover, in all modes of operation, the high-\(Q\) filter can be easily obtained by adjusting a single resistance value. Influences of the VDBA nonidealities and parasitic elements are also discussed in detail. PSPICE simulations with TSMC 0.18-\(\mu\)m CMOS process parameters and experimental testing results with commercially available IC LT1228s have been used to validate the theoretical predictions.  
active filter; mixed mode; universal biquadratic filter; voltage differencing buffered amplifier (VDBA) 2021  
13 September 2021  
12 October 2021  
15 October 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**1 School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL), Bangkok 10520, Thailand; [EMAIL_ADDRESS]**  
2 Faculty of Engineering, Symbiosis Institute of Technology (SIT), Symbiosis International University (SIU), Lavale, Mulshi, Pune 421215, Maharashtra, India; [EMAIL_ADDRESS]  
3 Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600 Brno, Czech Republic; [EMAIL_ADDRESS]  
4 Correspondence: [EMAIL_ADDRESS]
## 3 Modeling Uncertainty
### GARCH Price Model  
The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is widely used to model time-varying volatility in econometrics [24]. The process is defined as  
\[r_{t}=\mu_{t}+\epsilon_{t} \tag{6}\]  
where \(r_{t}\) is the asset return \(r_{t}=\frac{x_{t}}{x_{t-1}}\), \(\mu_{t}\) is a term that captures the mean of the return, and \(\epsilon_{t}\) is drawn from a normal distribution with zero mean and variance of \(\sigma_{t}^{2}\), i.e., \(\epsilon_{t}\sim N(0,\sigma_{t}^{2})\). The conditional variance of the return \(\sigma_{t}^{2}\) is modeled as  
\[\sigma_{t}^{2}=\omega+\sum_{p=1}^{p}\alpha_{p}\epsilon_{t-p}^{2}+\sum_{q=1}^{ Q}\beta_{q}\sigma_{t-q}^{2} \tag{7}\]  
A GARCH(1,1) model has \(P=1\) and \(Q=1\) and is thus defined as  
\[r_{t}=\mu+\epsilon_{t} \tag{8}\] \[\epsilon_{t}\sim N(0,\sigma_{t}^{2})\] (9) \[\sigma_{t}=\omega+\alpha\epsilon_{t-1}^{2}+\beta\sigma_{t-1}^{2} \tag{10}\]  
For using the GARCH(1,1) model, the four parameters \(\mu\), \(\omega\), \(\alpha\), and \(\beta\) need to be estimated. In this work, the Maximum Likelihood (ML) method is employed to estimate these parameters based on historical data. The process of how to use ML for parameter estimation in this work has been discussed in Appendix A.
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## 4 Proposed Imaging Method
### Optimization Modeling  
From (21), it can be seen that the target focusing can be accomplished via compensating the phase modulation in the 2-D frequency domain and a 2-D inverse Fourier transform. Based on (21) and (22), the filter for compensating the phase modulation is constructed as follows:  
\[H(f_{r},f_{a};v_{e},\alpha,\beta)=\exp\left\{\frac{j\pi f_{r}^{2}}{k_{r}}+ \frac{j2\pi\sqrt{4v_{e}^{2}R_{c}^{2}-\alpha^{2}}}{\lambda v_{e}}\sqrt{\begin{array} []{c}1-\Big{(}\frac{f_{a}}{2\lambda v_{e}}+\frac{\beta}{v_{e}}\Big{)}^{2}+ \Big{[}1-\frac{\beta}{v_{e}}\Big{(}\frac{f_{a}}{2\lambda v_{e}}+\frac{\beta}{v _{e}}\Big{)}\Big{]}\Big{(}\frac{2f_{r}}{f_{c}}\Big{)}\\ +\Big{[}1-\Big{(}\frac{\beta}{v_{e}}\Big{)}^{2}\Big{]}\Big{(}\frac{f_{r}}{f_{c }}\BigWith  
\[\text{Contrast}[s(t_{r},t_{a};v_{e,t},\alpha_{t},\beta_{t})]=\frac{ \sqrt{E\bigg{\{}\Big{[}|s(t_{r},t_{a};v_{e,t},\alpha_{t},\beta_{t})|^{2}-E\Big{(}| s(t_{r},t_{a};v_{e,t},\alpha_{t},\beta_{t})|^{2}\Big{)}\Big{]}^{2}\bigg{\}}}}{E \Big{\{}|s(t_{r},t_{a};v_{e,t},\alpha_{t},\beta_{t})|^{2}\Big{\}}} \tag{25}\] \[s(t_{r},t_{a};v_{e,t},\alpha_{t},\beta_{t})=\text{IDFT}_{2}\{S(f_{ r},f_{a})\cdot H(f_{r},f_{a};v_{e,t},\alpha_{t},\beta_{t})\} \tag{26}\]  
where \(\text{IDFT}_{2}\left(\cdot\right)\) indicates the 2-D inverse Fourier transform, \(E\left(\cdot\right)\) presents the spatial average, Contrast \(\left(\cdot\right)\) is the image contrast, \(\hat{v}_{e},\ \hat{\alpha},\ \text{and}\ \hat{\beta}\) are the estimates of the corresponding variables, \(v_{e,\text{min}}\) and \(v_{e,\text{max}}\) are the minimum and maximum values of \(v_{e}\), respectively, \(\alpha_{\text{min}}\) and \(\alpha_{\text{max}}\) are the minimum and maximum values of \(\alpha\), respectively, and \(\beta_{\text{min}}\) and \(\beta_{\text{max}}\) are the minimum and maximum values of \(\beta\), respectively.  
From the above analysis it can be seen that the solution for the optimization problem is the optimal estimation of the three parameters. Thus, the focused SAR image of the target can be given by  
\[I(t_{r},t_{a})=\text{IDFT}_{2}\big{\{}S(f_{r},f_{a})\cdot H(f_{r},f_{a};\hat{ \alpha}_{e},\hat{\beta})\big{\}} \tag{27}\]  
If the traditional ergodic search method is utilized to solve the above optimization problem, the computation load will be very high since it is a three-dimensional search. To decrease the computation load, in the following subsection, the GA is used to solve this optimization problem.
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 2 Literature Review
### Routing Protocols
#### 2.3.1 Routing Information Protocol Version 2 (RIPv2)  
Security and efficiency using a lightweight authentication technique to safeguard RIPv2 from malicious threats [15]. Optimisations make RIPv2 extra effective in IoT networks [13; 24] but RIPv2 declined over the years due to advance protocols. A route with the fewest number of hops can be adopted regardless of whether there are other faster paths. The way to update neighbouring nodes is to send the entire routing table to directly connected nodes.
###### Abstract  
Childhood obesity increases chronic disease risk, but no comprehensive synthesis has evaluated the impact of school-based combined nutrition education and physical activity interventions on cardiometabolic biomarkers in children aged 3 to 12 years. This systematic review was conducted in accordance with PRISMA guidelines and registered in PROS-PERO (CRD420251085194). Five databases were systematically searched through June 2025. Twelve randomized controlled trials involving 18,231 children were included and assessed using the PEDro scale. Ten trials demonstrated significant improvements in at least one cardiometabolic biomarker. Blood pressure (8 studies) outcomes showed systolic reductions of 1.41-6.0 mmHg in six studies. Glucose metabolism (5 studies) improved in two studies with reductions of 0.20-0.22 mmol/L. Lipid profiles (7 studies) improved in three studies, including total cholesterol (\(-\)0.32 mmol/L). Insulin levels (5 studies) decreased significantly in two investigations. Anthropometric improvements included BMI and body fat. Physical activity increased by >45 min/week and dietary habits improved significantly. Programs with daily implementation (90-min sessions 4x/week), longer duration (\(\geq\)12 months), family involvement (parent education), and curriculum integration (classroom lessons) showed superior effectiveness. Interventions targeting children with overweight/obesity demonstrated higher changes compared to the general population. However, methodological limitations included a lack of assessor blinding, absence of subject/therapist blinding, and inadequate retention rates. School-based interventions combining nutrition and physical activity can produce significant improvements in cardiometabolic biomarkers, supporting comprehensive, sustained multicomponent programs for early chronic disease prevention.  
biomarkers; glucose metabolism; lipid profile; cardiometabolic risk; childhood obesity prevention; health promotion; randomized controlled trials; school-based interventions
## 3 Bioactive Compounds of _Citrus_ Seeds
### Essential Oils, Sterols, and Fatty Acids  
The essential oils of _Citrus_ fruits are volatile aromatic compounds formed by the combination of secondary metabolites [30]. _Citrus_ seeds are considered reliable sources of essential oils based on their variety. It is estimated that _Citrus_ seeds contain approximately 20-40% oil [13]. Oils produced from _Citrus_ seeds have been identified by their composition of saturated fatty acids (32%) and unsaturated fatty acids (67%). In the seed oils of sweet orange and grapefruit, as well as those of mandarin, most of the acids are palmitic, oleic, and linoleic, with comparatively little stearic and linolenic acids [12; 13; 31; 32]. The highest oil quantity among _Citrus_ seed varieties was in lemon (78.9%), followed by sweet orange (51.8%), blood orange, bergamot, and bitter orange (26%). They also noted that linoleic acid was a remarkable fatty acid in all analyzed oil seeds [32]. The fatty acid profile of acid lime and sweet orange seed oils was evaluated [33]. Unsaturated fatty acids are abundant in these oils. Linoleic acid and oleic acids, with 36.69% and 22.54%, were the highest concentrations in acid lime and sweet orange, respectively. In another study, a high content of unsaturated acids in the 63.3-66.9% range was reported [34]. Also, the seed oils of four _Citrus_ species, including grapefruit (_CitCitrus_\(\times\)_paradisi_), mitha lemon, musami lime, and kinnow mandarin, were demonstrated as a potential source of unsaturated fatty acids, and their antioxidant activities were higher than sunflower, cottonseed, and groundnut [35]. Sterol (C\({}_{17}\)H\({}_{28}\)O) is composed of four rings that are steroid units. This compound is present in many fruit seeds and is much more stable than fatty acids. \(\beta\)-sitosterol is the most dominant sterol in grapefruit seed oil (81%), followed by campesterol, stigmasterol, and \(\Delta\)-5-avenasterol [36; 37]. The literature revealed that _Citrus_ seeds, which are regarded as waste materials, can serve as a benefcent and innovative source of edible oils. Moreover, a wide range of antioxidant, anti-diabetic, insecticidal, antifungal, and antibacterial properties are produced by _Citrus_ EOs, along with their pleasant aromas and flavors [38].
## 3 Multi-Adversarial Joint Distribution Adaptation Network
### Parameter Settings  
Hyperparameters can profoundly influence the model's performance. To enhance accuracy and the model's generalizability while curtailing training time, this study conducts experiments, taking the T0-T1 single operating condition transfer task as a case study, to examine the effects of hyperparameters on the model. The experiments were conducted by varying four different hyperparameters: batch size, initial learning rate, \(\lambda\), and \(\mu\). The model was trained for 50 epochs for each experiment. Figure 7 shows the comparison results with different hyperparameter settings. The results indicate that the values of \(\lambda\) and \(\mu\) have a significant impact on diagnostic accuracy, while batch size and initial learning rate have a relatively minor effect on accuracy. However, batch size and initial learning rate do affect the duration of the training process. After considering all factors, the final hyperparameter settings were chosen as follows: batch size of 64, initial learning rate of 0.03, \(\lambda\) set to 0.5, and \(\mu\) set to 0.5.  
After determining the optimal range of hyperparameters, carry out transfer task experiments on T0, T1, T2, and T3 working conditions. All labeled samples from the source domain and 80% of the unlabeled samples from the target domain are selected for domain adaptation training to reduce the distribution difference between the two domains. The remaining 20% of the samples from the target domain are used to test the classification performance in the target domain after unsupervised domain adaptation.  
Figure 8 illustrates the variation trend of accuracy in the source domain and target domain during the training of the MAJDAN model for the T0-T1 single-working-condition transfer task. The results indicate that the accuracy of the source domain samples reaches a stable state after five epochs of training, achieving a classification accuracy of approximately 100%. This is because the training of the source domain belongs to supervised learning, and the feature extractor used in this paper can extract data features well to classify labeled data. After 30 epochs of training in the target domain, the accuracy can reach about 96%, indicating that the MAJDAN model is capable of extracting domain-invariant features from unlabeled target domain data through domain adaptation techniques and accurately performing fault classification.  
Figure 6: Overlap sampling method.  
Figure 8: Variation trend of diagnostic accuracy for different training times of T0–T1 transfer task.  
Figure 7: Accuracy with different hyperparameters. (**a**) Batch size. (**b**) Initial learning rate. (**c**) Hyperparameter \(\lambda\). (**d**) Hyperparameter \(\mu\).