esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 4 Performance Analysis of the TT&C Signal and Data Transmission Signal  
In this section, we will investigate the performance of the integrated signal in terms of the acquisition time, tracking performance, bit error and measurement error. In addition, we will discuss the methods of reducing measurement error. Considering that data transmission services require more power than TT&C services, we set the power ratio of APSK and BOCcos to 10:1.
## 3 Numerical Experiments and Discussions
### Performance Index  
The performances index is defined with respect to the level of service that the system is designed to provide. The performance requirements for typical approach operations are shown in Table 1, including HAL/VAL, horizon/vertical accuracy (HA/VA (95%)) and time to alert (TTA) [32,33].
## 2 Development of the Controlled Cutting Fluid Impinging Supply System (Cut-List)
### Theoretical Work  
#### 2.2.1 Computation of Cutting Energy and Accurate Flow Rate  
Step shoulder-down milling was chosen as a cutting strategy for evaluating the new system, as shown in Figure 3. Equations (1)-(3) were used to calculate metal removal rate (MRR), table speed and total cutting energy, respectively [47; 48]. In step shoulder or side milling, where the cutter diameter (Dc) is larger than the radial depth of cut (\(a_{e}\)), the table speed should be adjusted to avoid chip thinning effects. The final equation of table speed was then expressed as in Equation (2). The total cutting energy was then determined as per Equation (3).  
\[MRR=V_{f}*a_{p}*a_{e} \tag{1}\]  
\[V_{f}=\ f\ *\ Z\ *\ N\ *\ K_{1} \tag{2}\]  
\[P_{c}=\frac{U\ *\ MRR}{60} \tag{3}\]  
During metal cutting, energy is used to deform the chip and to overcome friction in the tool and workpiece and tool-chip interfaces. A great proportion of this energy (i.e., 90-98%) is typically converted into heat, whereas the remaining energy is retained as elastic energy in the chip [49; 50; 51; 52; 53]. In the present work, 90% of the total cutting energy is assumed to be converted to heat (i.e., total heat generated in the primary, secondary and tertiary deformation zones). The accurate flow rate of the cutting fluid required to cool the machining zone was then computed according to Equation (4) [54].  
\[Q_{acc}=\frac{60\ *\ P_{c}}{4.148\ *\ C\ *\ \rho\ *\ \eta_{nozzle}\ *\ \Delta\theta} \tag{4}\]  
The inputs to calculate an accurate flow rate at different working conditions are presented in Table 2. Additionally, from a practical standpoint, the conventional flood calculations were based on 13 L/min per (kW) for cutting titanium as recommended by KENNAMETAL [55]. The calculation outcomes show that the fluid can be supplied at a flow rate of 8 L/min per (kW) using the Cut-list with a reduction in cutting fluid consumption by up to 42% compared to the conventional system (see Table 3).  
Figure 3: Schematic of step shoulder milling of Ti-6 Al-4V.
# Hazard Assessment for a Glacier Lake Outburst Flood in the Mo Chu River Basin, Bhutan
## 4 Results
### Hazard Potential  
Overall, the hazard potential for inhabited areas is comparably small. This is mainly due to the fact that the valley is sparsely populated and that most buildings are built on an  
Figure 8: Maximum flood depth for the worst case scenario (see text for explanations) in Punakha, based on SRTM elevation data. Letters a–c (marked by yellow circles) indicate the location of affected buildings. Background map: World Imagery (Source: Esri, Maxar, Earthstar Geographics, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community).  
elevated location above the river. The river itself is well incised into the valley floor over large parts of its course; this also helps to limit inundation. When EM3D is used, a total of around ten buildings is affected in the worst case scenario, only two of which seem to be built for residential purposes.  
Aside from this, infrastructure, such as roads and bridges, are most probably endangered along the flood route. Moreover, economic harm can be expected from the damage of farm and forestry land as well as from the loss of livestock. Our study, however, did not lead to definitive findings regarding affect to the Punatsangchu I hydropower plant. The worst case flood peak of 365 m\({}^{3}\)/s would most likely not threaten the site, but it may be required to implement some measures to ensure the continued safety of operation. If a lot of sediment load is involved, the flood wave could be a challenge for the desilting chamber.  
For the worst case scenario based on SRTM, the hazard potential is significantly higher. In the Gasa Hot Springs, all buildings of the bath, more than a few hundred meters upstream, would be affected too. In Punakha, the flood would hit ca. 20 additional buildings, including a small temple and the Dzong Downstream of Punakha, the damage would be even higher with up to 40 buildings in Khurutang, including a temple, a technical institute, a holiday resort, and a dairy farm that lies within the inundation area. Around 30 buildings in Jagathang and Wangdue Phodrang, respectively, would suffer from a worst case GLOF. In total, more than 130 buildings could be damaged or otherwise affected by flooding. However, we are convinced that SRTM overestimates inundation areas and depths and that the GLOF simulates much more realistic with the 5-m DEM.
# A Review of Thermal Error Modeling Methods for Machine Tools
## 2 Thermal Error Modeling Method for Machine Tools
### Neural Network (NN)
[67] established a MIMO artificial neural network model of the axial thermal elongation and radial thermal drift of the coordinate boring machine spindle, and the model prediction accuracy reached 86%. [PERSON] et al. [68] from Chongqing University used fuzzy C-mean clustering to select temperature variable, and used probabilistic neural networks to establish a compensation model for the relationship between the radial thermal deformation error M and temperature of a worm wheel gear grinding machine, then proposed a thermal error compensation method based on a probabilistic neural network. In l most previous studies, the correlation between the historical accumulated temperatures and the thermal error of machine tool were ignored. Based on a long short-term memory (LSTM) cyclic neural network, [PERSON] et al. [69] proposed a method that effectively used the temperature rise data of the current and historical moments of the machine tool. By using this method, the thermal error could be characterized more in line with the actual situation. However, this method had poor predictive performance for the thermal error under unknown conditions. [PERSON] et al. [70] also considered the actual working conditions, but they focused the relationship between the radial thermal error of the spindle and the inner diameter of the work piece. The measurement data of the inner diameter of the work piece on the production line was used as input, and the multi-layer perceptron neural network (MLP) was used to build the thermal error models of the machine tool under actual processing conditions. [PERSON] et al. [71] introduced the genetic algorithm and backpropagation algorithm in the model training process to train the fuzzy neural network parameters. It improved the situation that the robustness of the thermal error model was reduced due to the randomness of the membership function value, and improved modeling and prediction capabilities for complex systems. [PERSON] et al. [72] proposed a new thermal error modeling method based on a Bayesian neural network. By applying it into the feed drive system compensation, the maximum thermal error was reduced from 18.2 \(\upmu\)m to 5.14 \(\upmu\)m. [PERSON] et al. [73] constructed a thermal evaluation model of a machine tool spindle based on particle swarm optimization weighted naive Bayesian to obtain the global optimal solution of the weights, and the model prediction accuracy was 94.1%. In 2021, in the research of [PERSON] et al. [74], an improved adaptive genetic algorithm (IAGA) incorporated with a backpropagation neural network (BPNN) was presented. Compared with the experimental results, accuracy of the improved model forward went beyond that of the traditional modeling methods such as MRA, genetic algorithm with BPNN (GA-BPNN), and PSO with BPNN (PSO-BPNN) models.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 4 Optimal Path Discovery Phase
### Optimal Path Discovery
#### 4.2.2 Scenario 2  
In this scenario, we take into consideration Figure 4 with the source and destination nodes specified as follows:  
* Source node C3: G is the front node.
* Destination node C1: A is the rear node.
* Source and destination nodes are moving in the same direction.  
Assume that there are two paths to reach the target Node C1: A in this scenario. The first path is the other side of a road to the source node, i.e., C3: G >> CH >> C6: CH >> C5: CH >> C4: CH >> C1: CH >> A, and the second path on the same side of the road is C3:G >> CH >> C2: CH >> C1: CH >> A. Here, the existing greedy protocols may send the WM to Node C2: \(CH\) rather than Node C6: \(CH\). However, our suggested OPRP prefers Node C6: \(CH\) as the next hop. On successful message delivery, the ultimate path is C3: G >> CH >> C6: \(CH\) >> C1: \(CH\) >> A, since Node C6: CH travels in the opposite direction to the target node. This is due to the fact that by the time a WM is received by Node C6: \(CH\), the destination node C1: \(CH\) >> A has entered its communication range, causing the message to be forwarded to Node C1: \(CH\) >> A immediately rather than using the originally intended path. We suggest Equation (8) for the selection of \(O_{f}\) in this case, where \(H\) (\(S\), \(D\)) = 1, and the source node stays in front of a destination node.  
\[\delta_{i}=\Delta\ H(S,\ NG_{s_{i}}) \tag{8}\]  
where \(\delta_{i}\), \(\Delta\), \(S\), \(NG_{Si}\), and \(H\)(.), as described in Equation (1).
## 4 Performance Evaluation and Comparison
### Evaluation Metrics
#### 4.1.2 Precision  
As a prominent measure termed the positive predictive value, precision signifies the accuracy of positive predictions made by our model. It is ascertained by evaluating the ratio of true positive predictions to the overall instances classified as positive. Mathematically, precision is expressed as follows [27]:  
\[Precision=TP/(TP+FP)\]
## 4 Used Platform, Tools, and Research Methodology
### Used Tools  
For the purpose of this study, the following tools are used:  
* Network analyzer.  
The used network protocol analyzer is Wireshark [32]. Its packet capturing and unpacking capabilities are ideal for the process of testing whether a data exchange between a PED and a control center is secure. This includes testing how information is transmitted (encrypted or not), how user data (username and password) is exchanged, and many other capabilities.  
The used network analyzer is Capsa Free [33]. For this study, the capabilities of this tool will be used in the monitoring of IP networks. Due to the capabilities of this tool for monitoring certain parameters such as generated traffic, traffic generated by protocols, and protocols used, among others, it will be possible to characterize the traffic generated by a PED.  
Mathematical approximations can be used as additional tools that can be used to characterize the generated traffic by a PED [34; 35]. The most appropriate parameter that can be represented by approximations is the distribution of the size of the generated packets. This will give a very good visual representation of what the packet size distribution constitutes, and it will show what kind of packets the studied PED generates.
## 4 Results
### Explainable AI (XAI)  
The SHAP table (Table 5) provides valuable insights into the relative importance of various meteorological features in predicting energy production. The features with the highest impact include relative humidity in Kozani (0.0546), maximum 3-h wind speed in Kozani (0.0441), and temperature in Kozani (0.0390). These features stand out as the most significant contributors to the model's predictions, indicating that Kozani's local climate conditions, particularly humidity and wind speed, play a critical role in energy production. The prominence of these features suggests that changes in humidity and wind conditions in this region could lead to substantial variability in energy output, making them key factors to monitor for optimizing renewable energy sources. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Total improvement & 145.57 MWh \\ Mean absolute improvement & 61.60 MWh \\ Mean absolute percentage improvement & 13.50\% \\ Positive improvements & 14 \\ Improvement ratio & 46.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimization phase quantitative analysis. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **SHAP Value** \\ \hline Temperature (°C)—Kalamata & 0.06462843 \\ Relative humidity (\%)—Kozani & 0.05171667 \\ Temperature (°C)—Kozani & 0.04919851 \\ Maximum 3-h wind speed (knots)—Kozani & 0.04499661 \\ Relative humidity (\%)—Kalamata & 0.03563375 \\ Temperature (°C)—Argos & 0.03070799 \\ Temperature (°C)—Florina & 0.03042352 \\ Temperature (°C)—Iomanina & 0.02760436 \\ Temperature (°C)—Kavala & 0.02454198 \\ Relative humidity (\%)—Florina & 0.02201662 \\ Wind direction (°)—Kavala & 0.02028739 \\ Relative humidity (\%)—Ioannina & 0.02000038 \\ Wind speed (knots)—Kalamata & 0.01840336 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SHAP values for the input features of the dataset. The SHAP analysis provided insights into which meteorological features have the most significant impact on energy production. Below is a detailed explanation of the top features based on their SHAP values:  
* Temperature (\({}^{\circ}\)C)--Kalamata: With the highest SHAP value, the temperature in Kalamata is the most influential feature in the energy production model. This suggests that fluctuations in temperature in this region greatly affect the amount of energy produced. * Relative Humidity (\(\%\))--Kozani: Relative humidity in Kozani is the second most impactful feature. High or low humidity levels in this area are key indicators of changes in energy production. * Temperature (\({}^{\circ}\)C)--Kozani: Similar to Kalamata, the temperature in Kozani also plays a crucial role in predicting energy output. This indicates the importance of temperature variations in this region. * Maximum 3-h Wind Speed (knots)--Kozani: This feature represents the maximum wind speed recorded over a 3-h period in Kozani. Its high SHAP value highlights wind speed's critical role in driving energy production, particularly in windy regions like Kozani.
# Design and Analysis of Systematic Batched Network Codes
## 6 Concluding Remarks  
In this paper, we propose a design for systematic batched network codes, where the outer code is systematic and the inner code can protect the systematic property during network coding. Our design of the systematic code preserves the most salient features of the BATS code. The diagonal embedding approach is proposed to improve the design efficiency of the systematic outer code, and it can also be used for non-systematic outer coding to reduce the coding overhead and computation cost.  
The discussion in this paper can help to evaluate when and how to adopt systematic batched network codes. When the computation cost and the encoding latency are the major concerns, the use of systematic outer codes is preferred due to the lower computation cost compared to the ordinary outer code. The decision regarding whether to use message protection recoding depends on both the computation constraints and the application scenario. When the decoding computation is sensitive and the intermediate nodes have an additional computation capability, it is beneficial to use message protection recoding. Message protection recoding is also preferred for some application scenarios, e.g., for communications where part of the content can be consumed when ready, a systematic code is better. Another useful scenario for systematic codes is a network with dynamic network link qualities: the communication is reliable most of the time and serious packet loss occurs only in a small fraction of the time.  
There are still many refinements to be applied for the systematic batched network codes. This paper focused on the inner code design for unicast communications. The current inner codes designed to protect the message packets may not be suitable to achieve the multicast gain of network coding. Further study of the inner code design for multicast communication is desired.
## 3 Envelope Tracking Transmitter Experimental System
### Measurement Setup  
Figure 9 presents the ET experimental system diagram. The testing signals are 5 MHz, 10 MHz, and 20 MHz bandwidths 16 QAM long term evolution (LTE) signals with 6.7 dB PAPR at 2.1 GHz. The baseband modulated signals and envelope signals were generated and pre-processed in MATLAB in PC, and they were streamed to the R&S SMJ100A (R&S Inc., Munich, Bayern, Germany) and Tektronix AWG7122C (Tektronix Inc., Beaverton, OR, USA), respectively.  
The ET system includes an RF signal path and an envelope path. In the RF path, the up-converted RF signals from VSG are amplified by the driver, and then the output of the driver is provided to the input of the PA. The PA output comes into the spectrum analyzer R&S FSW50 (R&S Inc., Munich, Bayern, Germany), which can capture and record the baseband data of the transmitter system. In the envelope path, the wideband and high-efficiency EA amplifies the generated envelop signals from the AWG, and modulates the drain supply of the PA. To minimize the distortion caused by the time misalignment between the envelope path and the RF path, synchronization needs to be performed. The EA output envelope and the down-converted RF signal are monitored by a high speed oscilloscope Tektronix MSO5104B (Tektronix Inc., Beaverton, OR, USA). In the digital signal processing, an envelope-enhanced memory polynomial DPD model [7] is extracted by using the time-aligned input and output data of the ET system. Finally, the pre-distorted input is downloaded again to rectify the nonlinearity of the ET transmitter.  
Figure 8: (**a**) Simplified hybrid envelope amplifier (EA) block diagram; (**b**) EA efficiency versus output power of the envelope signals.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 2 State-of-the-Art
### Noise Figure of EDFAs
This procedure aims to exclude the contribution of pre-existing signal noise, which is also amplified. Figure 2 illustrates this process. The final expression for the noise figure is:  
\[F\left(\
u,f\right)=\frac{SNR_{IN}}{SNR_{OUT}\left(\
u,f\right)} \tag{2}\]0  
It should be noted that the formula explicitly depends on the ASE noise power and therefore on the input signal power, the amplifier gain, and the channel frequency.
# Therapeutic Potential of Seaweed-Derived Bioactive Compounds for Cardiovascular Disease Treatment
## 5 Seaweed-Derived Carotenoids and Other Components with Therapeutic Potential against CVD
Two plastoquinones (sargachromenol and sargaquinoic acid) isolated from the active n-hexane fraction of _Sargassum serratifolium_ produced concentration-dependent inhibition against \(\alpha\)-glucosidase [164]. These physiologically active dietary components derived from seaweeds have been actively studied at the in vitro and in vivo levels to inhibit or alleviate the symptoms of diabetes, obesity, hypertension, and hyperglycemia, which are closely related to the pathogenesis of CVD. However, research on the clinical trial stage using the seaweeds and bioactive dietary substances is insufficient. Bioactive dietary components derived from food are known to affect epigenetic gene expression in humans through various mechanisms, such as alteration of the chromatin structure or non-coding RNA,activation of transcription factors by signaling cascades, and direct ligand binding to nuclear receptors. Studies on the influence of polyphenols, PUFAs, and vitamins, which are bioactive dietary components contained in terrestrial plant resources, on epigenetic gene expression in humans through epigenetic change in the genome, miRNA level change, lncRNA level change, and transcription factor activity change are continuously being conducted [165]. By clearly analyzing the mechanisms of interaction between these dietary components and human genome structure and gene activity, a better understanding of these relationships will help design dietary guidelines that can help maintain good health by providing more effective dietary recommendations for individuals. Therefore, it is considered necessary to study not only the various bioactive dietary substances contained in seaweeds but also their influence on epigenetic changes in parallel.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 1 Introduction
### Mathematical Model of Magnetic Field Distribution around Downhole Drill String
Equation (20) can be simplified to Equation (21):  
\[e_{n}\cdot\left(\stackrel{{\rightarrow}}{{B}}_{1}^{\rightarrow}- \stackrel{{\rightarrow}}{{B}}_{2}^{\rightarrow}\right)=0\ \mbox{or}\ H_{1t}=H_{2t}. \tag{13}\]1  
The corresponding magnetic induction intensity is shown in Equation (22):  
\[e_{n}\cdot\left(\stackrel{{\rightarrow}}{{B}}_{1}^{\rightarrow}- \stackrel{{\rightarrow}}{{B}}_{2}^{\rightarrow}\right)=0\ \mbox{or}\ H_{1t}=H_{2t}. \tag{13}\]2  
According to Equation (9), the magnetic induction intensity corresponding to drill string at depth \(a\) can be expressed by Equation (23):  
\[e_{n}\cdot\left(\stackrel{{\rightarrow}}{{B}}_{1}^{\rightarrow}- \stackrel{{\rightarrow}}{{B}}_{2}^{\rightarrow}\right)=0\ \mbox{or}\ H_{1t}=H_{2t}. \tag{13}\]3  
In order to intuitively observe the corresponding magnetic induction intensity and current at any depth \(a\) on the drill string, the magnitude of magnetic induction intensity at any position within 1000 m of drill string depth and the changing trend of current size with depth are drawn, as shown in \[e_{n}\cdot\left(\stackrel{{\rightarrow}}{{B}}_{1}^{\rightarrow}- \stackrel{{\rightarrow}}{{B}}_{2}^{\rightarrow}\right)=0\ \mbox{or}\ H_{1t}=H_{2t}. \tag{13}\]4 It can be seen from Figure 6 that when the current is constant, the closer the anisotropic magnetoresistance sensor is to the drill string, the greater the measured magnetic induction intensity \(B\) is.