esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 1 Introduction  
Hypersonic vehicles in near space fly over the speed of Mach five, which is five times the speed of sound [1]. A bow shock is generated by friction between vehicle and atmosphere, which can result in a huge amount of heat. In such cases, the temperature of the area near the vehicle nose can be up to thousands of degrees Kelvins and air molecules can ionize and dissociate. The plasma sheath is formed as a result of the generation of numerous free electrons. The plasma sheath can shield communication signals and cause 'blackout' problems which are serious threat to vehicle safety [2]. The problem remains unsolved despite the enormous amount of research efforts have been made. Methods of minimizing communications blackout also are being explored. The most famous program is called Project RAM (radio attenuation measurements) and consisted of eight rocket flights (seven successful) from 1961 to 1970 [3, 4]. The project was conducted at the NASA Langley Research Center to study, both theoretically and experimentally, the effects of the reentry plasma sheath upon reentry vehicle communications systems [5, 6].  
With the rapid development of terahertz (THz) technology and instruments, THz communication has been considered as a potential approach to communication blackout for its high frequency, which leads to better penetration [7, 8]. The performances of antennae in idealized plasma layers have been researched. Electron density and the electron collision frequency have been revealed as the two most important plasma parameters thatsignificantly affect antenna performance [9; 10; 11]. On the other hand, some previous studies reveal that the parameters of the plasma sheaths depend on many factors, e.g., atmospheric conditions [12], flight speed [8], angle of attack [13], and the shape of the vehicle [14], in which case antenna performance is affected.  
Vehicle shape is one of the most important parameters for the flight condition and directly determines the distribution of the plasma sheath. However, the impacts of vehicle shape on the radiation performance of antenna have only been roughly discussed in previous studies. Moreover, the antenna in the previous studies was a single antenna working in the HF-VHF band [11]. EHF array antenna performance in plasma sheaths corresponding to different vehicle shapes are rarely investigated. A method of replacing an on-board single antenna with an array antenna and using beamforming technology has been proposed by researchers to mitigate the problem [15]. Array antennae can provide a certain amount of gain to the communication link [11]. In the present study, a 0.14 THz array antenna and plasma coupling models were established to obtain the detailed influence of vehicle shape on the antenna performance. The corresponding vehicle shapes are blunt-coned and sharp-coned. The antenna involved in the study operates at 0.14 THz, which is an 'atmospheric window'. Based on the results, suggestions of vehicle shape design to guarantee communication quality in hypersonic flight mission are proposed.
## Appendix A X-Ray Experimental Procedures and Data
\begin{table}
\begin{tabular}{l l} \hline Formula & C\({}_{18}\)H\({}_{10}\)O\({}_{3}\) \\ Formula Weight (g/\(mol\)) & 274.26 \\ Crystal Dimensions (\(mm\) ) & \(0.30\times 0.24\times 0.20\) \\ Crystal Color and Habit & clear prism \\ Crystal System & orthorhombic \\ Space Group & F d d 2 \\ Temperature, K & 173 \\ \(a\), Å & 18.0996(6) \\ \(b\), Å & 20.9271(7) \\ \(c\), Å & 26.0789(8) \\ \(\alpha\),\({}^{\circ}\) & 90.00 \\ \(\beta\),\({}^{\circ}\) & 90.00 \\ \(\gamma\),\({}^{\circ}\) & 90.00 \\ V, Å\({}^{3}\) & 9878.0(6) \\ Number of reflections to determine final unit cell & 9975 \\ Min and Max 2\(\theta\) for cell determination, \({}^{\circ}\) & 5.0, 56.84 \\ Z & 32 \\ F(000) & 4544 \\ \(\rho\) (g/\(cm\)) & 1.475 \\ \(\lambda\), Å, (MoK\(\alpha\)) & 0.71070 \\ \(\mu\), (\(cm^{-1}\)) & 0.101 \\ Diffractometer Type & Bruker-Nonius X8 Apex2 \\ Scan Type(s) & omega and phi scans \\ Max 2\(\theta\) for data collection, \({}^{\circ}\) & 58.24 \\ Measured fraction of data & 1.000 \\ Number of reflections measured & 103146 \\ Unique reflections measured & 6360 \\ R\({}_{\text{merge}}\) & 0.0403 \\ Number of reflections included in refinement & 6360 \\ Cut off Threshold Expression & \(>\)2 sigma(I) \\ Structure refined using & full matrix least-squares using F\({}^{2}\) \\ Weighting Scheme & calc w = 1/[sigma\({}^{2}\)(Fo\({}^{2}\)) + (0.0555P)\({}^{2}\) + 3.0465P] where P=(Fo\({}^{2}\) + 2 FC\({}^{2}\))/3 \\ Number of parameters in least-squares & 458 \\ R\({}_{1}\) & 0.0342 \\ wR\({}_{2}\) & 0.0846 \\ R\({}_{1}\) (all data) & 0.0400 \\ wR\({}_{2}\) (all data) & 0.0880 \\ GOF & 1.038 \\ Maximum shift/error & 0.000 \\ Min \& Max peak heights on final \(\Delta\)F Map (\(e^{\prime}\)/Å) & \(-\)0.219, 0.216 \\ \hline Where: & \\ R\({}_{1}\) = \(\Delta\) (\(|\) F\({}_{0}|-|\)F\({}_{\text{C}}|\)) /\(\Sigma\)F\({}_{0}\) & \\ wR\({}_{2}\) = [ \(\Delta\) (\(w\) (Fo\({}^{2}\) – F\({}_{\text{C}}\)\({}^{2}\)) /\(\Delta\)(\(w\) F\({}_{\text{O}}\)\({}^{4}\)) ] \({}^{\prime}\)\({}^{\prime}\)\({}^{\prime}\) & \\ GOF = [ \(\Delta\) (\(w\) (Fo\({}^{2}\) – F\({}_{\text{C}}\)\({}^{2}\)) \({}^{2}\)) /(No. of refrls. – No. of params. ) ]\({}^{\prime}\)\({}^{\prime}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of Crystal Data for _Id_.
# Multi-Level Site Selection of Mobile Emergency Logistics Considering Safety Stocks
## 4 Analysis of Examples  
### Description of the Example  
A total of 148 alternative points were selected for mobile emergency logistics multilevel site selection in the Chongqing-Sichuan region. The center of mass of each administrative county and district is regarded as an alternative point in the region. The population distribution of each county and district is used to determine the demand for each alternative point. The fixed site selection cost is divided by 10 for the average house price in Sichuan and Chongqing. The normalization of alternative site coordinates and population distribution is shown in Figure 3. The population distribution data for Chongqing and Sichuan were obtained from the seventh population census. Population density distribution data is obtained from the World Pop Hub. The coordinates of the demand points are obtained by calling the Gaode map to find the center of mass of the region through the Data Map. The fixed cost of the alternative point location is equal to that shown in the parameter setting in Table 2. The alternative sites in Sichuan and Chongqing are summarized in Table 3.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Symbols** & **Charges** & **Value** \\ \hline \(F_{i,jk}\) & alternative site selection fixed costs & 700 \\ \hline \(h\) & unit storage cost of logistics center and distribution warehouse & 0.2 \\ \hline \(L\) & lead time for distribution of storage silos to mobile emergency facilities & 1 \\ \hline \(P\) & lead time from logistics center to distribution and storage warehouse & 1 \\ \hline \(\alpha\) & service level factor & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic parameter settings.  
Figure 3: Population distribution in Sichuan and Chongqing regions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Administrative City** & **Political Districts and Counties** & **Coordinates** & **Population (10,000)** \\ \hline
1 & Deyang & Jingyang District & 104.411173, 31.128261 & 66.32 \\
2 & Deyang & Luojiang District & 104.516205, 31.320187 & 31 \\
3 & Deyang & Zhongjiang County & 104.672767, 31.04837 & 25 \\
4 & Deyang & Shifang city & 104.172259, 31.133931 & 37 \\
5 & Deyang & Mianzhu & 104.22251, 31.343409 & 38 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
161 & Chongqing & Youyanggan & 108.77212, 28.8412 & 23.85 \\
162 & Chongqing & Xiushankian & 108.97297, 28.4526 & 13.09 \\
163 & Chongqing & Qianjiang District & 108.77067, 29.5333 & 103.05 \\
164 & Chongqing & Jiangjin District & 106.25928, 29.2837 & 63.66 \\
165 & Chongqing & Hechuan District & 106.27328, 29.9909 & 49.48 \\
166 & Chongqing & Nanchuan District & 107.09896, 29.1566 & 46.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Coordinates and population size of Sichuan and Chongqing regions.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 4 Failure Prediction and Container Migration Strategies
### Problem Formulation
#### 4.1.1 LSTM  
The LSTM algorithm, first proposed by [PERSON] et al. in 1997 [30], is a temporal recurrent neural network (RNN) suitable for processing and predicting important events with relatively long intervals and delays in a time series. We applied it to failure prediction as described in Algorithm 1. LSTM assumes that RNNs can have a long memory of weights and that the weights change slowly during training. The LSTM model introduces an intermediate key with a memory function, called a memory cell. In contrast to an RNN, each neuron in the hidden layer of an LSTM is replaced by a memory cell. As shown in Figure 1, each storage cell has a composite structure with simple nodes. A standard LSTM memory cell should contain the following components:  
* Input node \(g_{c}\): The state of the input data point \(X_{1}\) of the current time step and the output \(h_{t-1}\) of the previous time step are weighted using the _tanh_ activation function, which is primarily used to update the current state of the LSTM cells and add new information for the current state.
* Input gate \(i_{c}\): The input gate \(i_{c}\) weighs \(X_{t}\) and \(h_{t-1}\) using a sigmoid activation function. It is called a \"gate\" because the value produced by passing through it is multiplied by the value of another node. If its value is zero, the traffic from another node is eliminated. If its value is 1, all information from the other node passes.
* The cell state \(S_{c}\): The \(S_{c}\) is the central part of the LSTM memory cell, which has a self-connecting circular edge that functions as a cellular memory. When the cell state is transferred in adjacent time steps, this edge has its own fixed weight so that the gradient does not disappear or explode.  
```
Input: Trained LSTM model, a set of time-series data from edge devices Output: prediction result for the next time slot Procedure:
1: while (there exists new measurement data x)
2: do
3: result = predict (LSTM model, data x) # predict the next step;
4: if the result shows failure
5: output the result and break;
6: end
```  
**Algorithm 1** Device failure prediction based on LSTM.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 3 Preliminaries and Background
### Blockchain  
Blockchain is a distributed, tamper-resistant ledger technology. In this paper, it serves as a decentralized coordinator, trust anchor, and audit log, leveraging its transparency, immutability, and traceability to provide a highly trusted environment for multi-party collaboration without relying on traditional trusted third parties. The blockchain is primarily responsible for securely storing critical metadata, such as content identifiers (CIDs) for data on IPFS, access rules, task allocation indices, and reputation records of computational nodes [19].
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 4 Laboratory Experiment of the Roadheader Digital Twin Monitoring System  
To test the performance of the digital twin monitoring system for the roadheader, an experimental prototype of the roadheader was established with the EBZ55 roadheader as the prototype and a size design at a ratio of 1:11, as shown in Figure 14. The control module of the roadheader experimental prototype is implemented using a Raspberry Pi 4B microcomputer. The Raspberry Pi 4B features an advanced ARM architecture and utilizes SD/MicroSD cards for storage. It is capable of running Linux-based operating systems and supports the expansion of various hardware components, making it a versatile control platform.  
The angle variables, including the movements of the shovel plate, cutting arm, rear support, and slewing platform, are simulated using MG996R small DC servo motors. A total of four servo motors are deployed in the experimental prototype to replicate the four key actions: the up-and-down pitching motion of the cutting arm, the left-and-right swinging motion of the slewing platform, the up-and-down swinging motion of the shovel plate, and the up-and-down movement of the rear support. These servo motors offer two primary modes: adjustable full-angle speed and adjustable partial-angle position. They convert input voltage into motor speed and torque to drive the rotor, achieving precise position adjustments. Their notable features include controllable operating speed, high positional accuracy, a small electromechanical time constant, and excellent linearity.  
In addition, the state variables--representing the movement information of the traveling part, star wheel, transfer part, and cutting head--are simulated using DC motors. Together, these components provide a comprehensive representation of the roadheader's key functional movements in the experimental prototype.  
The angle sensor utilized in this study is the JYME01-TTL high-precision angle sensor, which offers an angular accuracy of up to 0.1\({}^{\circ}\). This level of precision aligns with the performance of most high-precision angle sensors commonly employed in roadheaders available on the market. The JYME01-TTL sensor ensures reliable and accurate angle measurements, supporting the precise control and monitoring requirements of the digital twin-based system in complex operational environments.  
Based on this experimental prototype, the simulation experiment mainly examines the accuracy and timeliness of the virtual mapping of the digital twin monitoring system for the roadheader, simulates the behavioral actions such as walking, raising and lowering the front shovel plate and rear support, starting the cutting head and star wheel, and cutting the coal wall along the trajectory during the tunneling operation of the roadheader, and records the angles and corresponding moments of the actions of the experimental prototype.
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 4 Discussion
### Practical Applications  
This study introduces a novel spectral classification framework for sediment differentiation, demonstrating that acoustic reflection coefficients provide a more refined alternative to backscatter intensity-based methods. This methodology sets the foundation for high-resolution, non-invasive seabed classification, which has potential applications in environmental monitoring, geotechnical engineering, and marine habitat assessment.  
For example, seabed composition is critical for subsea construction, pipeline installations, and offshore wind farms in offshore engineering and infrastructure. The proposed method can aid in geotechnical assessments for these applications. In environmental monitoring, spectral acoustic analysis can be used to track sediment transport, erosion patterns, and pollution impacts in marine environments. For hydrographic surveys and geophysical exploration, the method offers a cost-effective and scalable seafloor geological and geophysical mapping solution.
## 2 Methodological Background
### Fuzzy Theory
#### 2.5.1 Mamdani Fuzzy Inference Method  
Professor [PERSON] at the University of London and his research group designed in 1974 a controller composed of fuzzy parameters and fuzzy rules and applied it to the control of boilers and steam engines. It successfully simulated human reasoning and obtained good control results [20]. The fuzzy logic control directly adopts linguistic control rules, and thus it is very suitable for systems where mathematical models are difficult to obtain, their dynamic characteristics are difficult to handle, or the situation changes are very significant.  
The general fuzzy control system basically includes four parts: fuzzification, fuzzy rule base, fuzzy inference mechanism, and defuzzification. In general, the monitor in the system measures a concrete value, but to match the verbalized conditional rules of the fuzzy logic controller, the variable ranges of the input and output variables are divided into several fuzzy sets. Hence, the value is first fuzzified and appointed to the fuzzy set where it belongs. The fuzzy rule base is the judgmentcriterion of the entire controlled system, which is assembled by experience and human wisdom. It is expressed by the conditional expression of \"IF-THEN\" and various possible situations of the controlled system are described in logical sentences. The fuzzy inference mechanism is based on the theory set by the fuzzy rule base and obtains a fuzzified output. The final step is to defuzzify the output of the fuzzification to transfer an exact value to the plant. A small example is used to illustrate the procedure of the Mamdani fuzzy inference method [31; 32; 33].
## 2 The Proposed Method  
### Walsh Series and Transform  
The Walsh series is a family of non-sinusoidal orthogonal rectangular functions, originally defined by [PERSON] in 1923 [24; 25]. Figure 1 shows the first eight Walsh series. Each function in the Walsh series, of different orders, jumps from 1 to \(-\)1 within the interval [0, 1] [26]. They are orthogonal, symmetrical and linear, thus extremely suitable for signal reconstruction [27], but only a few studies in literature have addressed their applicability to the recovery of random series.  
In digital processing, the discrete Walsh transform (DWT) and the inverse DWT (IDWT) of a signal \(x(t)\) in terms of the Walsh series \(wal(k,n)\) are defined as [28]:  
\[X(k)=\frac{1}{N}\sum_{n=0}^{N-1}x(n)wal(k,n);k=0,1,2,\ldots,N-1 \tag{1}\]  
\[x(n)=\sum_{k=0}^{N-1}X(k)wal(k,n);n=0,1,2,\ldots,N-1 \tag{2}\]
###### Abstract  
This paper presents an echo suppression system that combines a linear acoustic echo canceller (AEC) with a deep complex convolutional recurrent network (DCCRN) for residual echo suppression. The filter taps of the AEC are adjusted in subbands by using the normalized sign-error least mean squares (NSLMS) algorithm. The NSLMS is compared with the commonly-used normalized least mean squares (NLMS), and the combination of each with the proposed deep residual echo suppression model is studied. The utilization of a pre-trained deep-learning speech denoising model as an alternative to a residual echo suppressor (RES) is also studied. The results showed that the performance of the NSLMS is superior to that of the NLMS in all settings. With the NSLMS output, the proposed RES achieved better performance than the larger pre-trained speech denoiser model. More notably, the denoiser performed considerably better on the NSLMS output than on the NLMS output, and the performance gap was greater than the respective gap when employing the RES, indicating that the residual echo in the NSLMS output was more akin to noise than speech. Therefore, when little data is available to train an RES, a pre-trained speech denoiser is a viable alternative when employing the NSLMS for the preceding linear AEC.  
residual echo suppression; acoustic echo cancellation; deep-learning; speech enhancement +
Footnote †: journal: _arxiv.org/abs/1208.00022_  
1
## 3 Materials and Methods  
Dataset: This work was carried out at SRM Institute of Science and Technology, Chennai, India. The implementation was carried out with MATLAB with the proposed ACF-FRCNN method and later compared the proposed method's performance with existing methods for ship and iceberg detection and classification. The SAR dataset was collected, and it had images of a ship and iceberg with a resolution of 75 \(\times\) 75 pixel values, each image with two bands [3]. A total of 1604 images, of both ships and icebergs, were taken for validation and verification with training and testing. Among dataset sizes, 80% were trained, and 20% were tested. Table 1 describes the dataset information and implementation details of the detection of classification of icebergs and ships with the SAR dataset.  
Recent trending methodologies such as GAN, ACF Detector, and FRCNN were used for better object detection performance in the proposed system. The first step of the proposed method is generating multi-transformed images of a single image using GAN architecture from the SAR image dataset [3]. The GAN architecture augments different images with another direction at a distant object location. Then, all generated images undergo an ACF detector to generate ROI in each image. Then, FRCNN generates a feature vector of all  
\begin{table}
\begin{tabular}{l l} \hline
**Content** & **Description** \\ \hline Method used & ACF-FRCNN \\ Dataset & SAR images of ships and icebergs \\ Volume of dataset & 1604 \\ Implementation & MATLAB \\ Image resolution in pixels & \(75\times 75\) \\ Training dataset size & 1283 \\ Testing dataset size & 321 \\ \hline \end{tabular}
\end{table}
Table 1: Accuracy comparison for ship detection over the SAR dataset with different methods.  
Figure 1: (**a**) Top-down pathway, (**b**) bottom-up pathway.  
region proposals nominated by ACF, followed by object detection and object classification. The proposed research's methods are explained in detail in the following segments.
## 2 Starch Modification Processes
### Applications of Modified Starch
#### 2.5.2 Applications of Physically Modified Starch  
Physically modified starch is also utilized in food and non-food applications. [PERSON] et al. [108] and [PERSON] et al. [109] prepared slowly digestible starch using HMT and dry heating treatment (DHT), respectively. Out starch was modified using an ultrasound treatment by [PERSON] and coworkers [110]. The authors reported the improved amylose content, swelling power, solubility, water and oil holding capacity, retrogradation tendency of starch upon ultrasound treatment, beneficial for food applications. [PERSON] et al. [111] studied the effect of DHT on the properties of maize starch. Their results showed a decrease in the gelatinization enthalpy and increased shear resistance as the DHT increases. Moreover, the authors also reported that modification of starch using the DHT would benefit the industry.  
Apart from food applications, physically modified starch is extensively used in non-food applications such as packaging and 3D printing. For example, [PERSON] and coworkers [112] prepared modified mango kernel starch using HMT, which showed potential applications for producing biodegradable films. [PERSON] et al. [113] investigated the use of DHT modified cassava starch for 3D printing. Increased oxidation and granular size and decreased water absorption index, crystallinity, and apparent viscosity peak of starch were observed upon DHT. Interestingly, DHT could also enhance the pasting properties, gel texture, and printability of cassava starch.