esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Description of Experimental Data  
[PERSON] et al. [19] conducted detailed experiments for fully developed laminar flow and measured the corresponding pressure drop in concentric and eccentric annulus for fluids with various concentration of Xanthan Gum (XCD) and Polyanionic Cellulose (PAC). The data from their study was utilized in current investigation (details of the fluid - Appendix B). The uncertainty in the data and its reliability for scientific analysis is crucial for algorithmic realization of a physical process, and numerous methods were proposed recently [60; 61; 62] which deal with analyzing the unreliability of the data. In the presented study water test were conducted to evaluate and verify the reliability of instrumentation and data acquisition system, followed by Yield Power Law (YPL) fluids after establishing strong confidence on measurement. Zero readings of differential pressure and flow rate was verified by [PERSON] [19] to minimize the uncertainty in the experimental setup. However, the exact values of uncertainty and inconsistency in data is not reported in the reference [19]. The flow rate was maintained until the steady-state condition was achieved. The data acquisition system recorded measurements at the rate of 4 Hz (four data points per second) for one to two-minute duration. The dimensions of the testing facility used in the prediction is tabulated in Table 1. The input parameters used to predict the pressure drop are outlined in Table 2, while the temperature is maintained from the range of 27.8-45 \({}^{\circ}\)C and the pressure from 144.37-1332 kPa. Table 3 shows the range of the data used in this study, where Di/Do is the inner and the outer cylinder diameter ratio, e is the eccentricity, n is the flow behavior index, flow consistency index is given by K, \(\tau_{y}\) is shear stress, Q is the fluid flow rate, \(\Delta\)P is measured pressure drop. Fluids of varying concentrations of XCD and PAC (Appendix B) are circulated through different di/do ratio and eccentricity. The properties of all the fluids used in the study were measured with pipe viscometer. Total of 903 data points are collected, where 70% of them are used for training the algorithm and 30% for testing the proposed models.
## 4 Key Agreement, Distribution, and Security Bootstrapping  
Key distribution and management is a crucial issue that needs to be addressed when security mechanisms have to be adopted. Key agreement protocols have been around for years: the Diffie-Hellman key exchange protocol is an example of a key agreement protocol that two parties perform in order to setup a shared key to be used in a session [57]. Other mechanisms have been defined and implemented. The Internet Key Exchange (IKE) [58] protocol is a the protocol defined to setup a Secure Association to be used in IPSec.
# A Portable Optical Sensor for Microplastic Detection: Development and Calibration
## 2 Materials and Methods
### Raspberry Pi 4 (8 GB RAM) and A4988 Driver  
A Raspberry Pi is a series of single-board computers developed by the Raspberry Pi Foundation [34].  
This study employed a Raspberry Pi 4 with 8 GB of RAM to control the prototype. The Raspberry Pi 4's features of low cost and power efficiency make it ideal for IoT devices and prototypes, and its GPIO capabilities enhance functionality. The Raspberry Pi functions as the central processor. The system incorporates a demultiplexer and a digital-to-analog converter, enabling it to interpret external signals from the LED.  
The GPIO pins were utilized to control the driver, which allows for axis movement via NEMA 17 stepper motors (Applied Motion Products, Morgan Hill, CA, USA) and an A4988 driver. This configuration ensures precise step motor control and accurate surface mapping. The schematic connections and the A4988 control library are illustrated in Figure 3 and Table 1, respectively. Figure 4 presents a flow chart of the programming code.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Argument** & **Name** & **Type** & **Description** \\ \hline
1 & Direction & Int & Connects the general-purpose input/output (GPIO) pin that sets the direction of rotation. \\ \hline
2 & Step\_Pin & Int & Step-pin Int connects to the GPIO pin that sets the number of steps. \\ \hline
3 & Mode\_Pins & Tuple 3 Int & Mode-pin tuple connects the GPIO that determines the micro-stepping mode. \\ \hline
4 & Motor\_type & String & Motor-type string controller type, for this project, the. A4988” will be used. \\ \hline \end{tabular}
\end{table}
Table 1: A4988 control library reference.  
Figure 2: The figure illustrates a 4-bar linkage system with bidirectional movement along the X and Y axes, resembling a CNC machine setup. The two stepper motors enable precise positioning and maneuvering for detailed surface mapping.  
Wiring Connections  
The A4988 driver was connected to the GPIO pins of the Raspberry Pi in the following manner:  
1. The A4988's step pin was connected to a Raspberry Pi's GPIO pin to control the stepping;
2. The A4988's direction pin was connected to a Raspberry Pi's GPIO pin to manage the rotation direction;
3. The A4988's MS1, MS2, and MS3 pins were attached to the Raspberry Pi's GPIO pins to select the micro-stepping mode, if necessary;
4. The A4988's VMOT pin was connected to an external power supply, which should be between 8 V and 35 V, to power the motor;
5. The ground pin of the A4988 was connected to the ground on the Raspberry Pi;
6. The A4988's 1A, 1B, 2A, and 2B pins were connected to the motor's corresponding coils (NEMA17 motor wires).  
Figure 3: The figure shows the wiring diagram detailing the connections between the Raspberry Pi, the A4988 stepper motor driver, and the stepper motor. This schematic illustrates how the system enables stepper motor control through GPIO pins.
# Research on Characteristics of Flow Noise and Flow-Induced Noise
## 1 Introduction
In the experiment, we need to consider the array size of the hydrophone in combination with the size of the carrier. As the hydrophone is approximated as a straight line in the array direction, we used the cylindrical shell as the model for simulation and experiment. Firstly, we validated the computational methods using a teardrop submarine model. Subsequently, we established a cylindrical shell model to conduct numerical simulations of flow noise and flow-induced noise characteristics. We investigated the differences in noise at various detection points along the X, Y, and Z axes. Lastly, we designed a towing experiment to verify the accuracy of the simulations. This provides a reference for analyzing the noise characteristics of military vessels and optimizing the arrangement of the flank array sonar. In the next step, a more realistic ship model can be established to calculate the noise distribution in key areas and for verification with the experimental results. Additionally, adjustments to the installation angles and positions of the sonar could be made specifically for noise-prone regions, thereby enhancing the signal-to-noise ratio of the sonar system.
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 4 Design of the DBREMM
### Direct Trust  
When a specific vehicle \(v_{i}\) observes a set of events, it forms \(W_{i}\) and uploads it to the RSU, the RSU then broadcasts \(R_{i}\) and invites nodes near the event location to cooperate for authentication. Vehicle node groups \(\{v_{j},v_{k1},...,v_{kn}\}\) make many comparisons with the \(m\) observation results \(W_{j}^{m}\) formed according to the event, and conduct quantitative fusion of the \(m\) results through the Bayesian inference algorithm to form direct trust from \(v_{n}\) to vehicle \(v_{i}\), which is denoted as \(T_{ni}^{di}\).  
Bayesian inference is a kind of decision logic. It uses Bayesian rules to evaluate the probability of the authenticity of specific assumptions based on evidence and information (called priori knowledge). As vehicles have high mobility and are equipped with high precision sensing equipment, vehicles will obtain a series of observation results formed according to the target event.These results can be seen as the priori knowledge and then the correct probability of the report can be calculated and the reliability of the target vehicle can be measured through the probability.  
Meanwhile, due to the dynamic complexity of VANET, there are disadvantages of using traditional BI directly. In this section, an improved Bayesian inference algorithm based on a multi-factor measurement is proposed. As for \(v_{j}\), multidimensional state information, such as position difference, velocity difference, acceleration difference, and time difference between \(v_{i}\) and \(v_{j}\) itself when generating reports should be taken into account when quantifying the trust value. The closer these statuses are, the more likely \(v_{j}\) is to obtain accurate information about the event. On the basis of traditional BI, multi-dimensional status  
According to Bayesian inference, after enough interactions, the posterior distribution of \(\pi_{ji}\) is proportional to the product of the prior distribution and the likelihood function, that is:  
\[g(\pi_{ji}\mid F)\propto g(\pi_{ji}:d\mid s^{2})\times f(F\mid\pi_{ji}) \tag{6}\]  
According to (4)-(6), the posterior density function of \(\pi_{ji}\) is:  
\[g(\pi_{ji}\mid F)\propto e^{\frac{1}{2\beta^{2}z^{2}/(2^{2}+m^{2})}[\pi_{ji} -\frac{e^{2}d+m^{2}z^{2}}{\beta^{2}z+m^{2}}]^{2}} \tag{7}\]  
We can see that the posterior probability function is also a normal distribution, and the posterior mean is:  
\[d_{ji}=\frac{1/s^{2}}{m/\os^{2}+1/s^{2}}d+\frac{m/\os^{2}}{m/\os^{2}+1/s^{2}}F \tag{8}\]  
The derivation of Bayesian inference under normal distribution can be referred to [46]. The posterior mean \(d_{ji}\) is the value of direct trust \(\pi_{ji}\). Here, the formula derivation of Bayesian inference under normal distribution is not developed. Once \(v_{j}\) forms \(m\) observations, \(W_{j}^{m}\) according to event \(E_{i}\), the prior distribution of direct trust can be obtained by (2) and (3). Then, direct trust \(T_{ji}^{di}\) of \(v_{j}\) to \(v_{i}\) can be obtained by (8).
## 2 System Model  
For the sake of simplicity, each independent transmitter, including the macro-cell site and micro-cell sites, is hereby designated as a transmit point (TP) in this paper. We consider a downlink system in a CoMP-based HetNet with \(M\) TPs and \(K\) user elements (UEs), as shown in Figure 2. \(N_{\text{T}}\) and \(N_{\text{R}}\) represent the number of antennas on each TP and each UE, respectively. A control unit (CU) is assumed to be located at the center of the network, which is responsible for managing data information, as well as collecting all CSI in this network. TPs are connected to the CU by backhaul links, whereby control information and data packets are delivered to TPs from the CU. Since JT CoMP is employed in our work, there are a large number of data packets that need to be transmitted via backhaul links.  
The unit of the radio resource in both time and frequency dimensions is referred to as the resource block (RB). As defined in the LTE standard, an RB consists of 12 consecutive subcarriers for a duration of a transmit time interval (TTI) [12]. In this paper, we assume all TPs share the same spectrum bandwidth, which is divided into \(N_{\text{RB}}\) RBs in total. Major notations used in this paper are listed in Table 1. Several other important assumptions are considered in our work: channel fading is considered to be quasi-static, so that channel coefficients remain constant per TTI; perfect CSI acknowledgment is assumed at both receivers and transmitters; TPs are synchronized in terms of time, frequency and phase, which is reasonable in the considered system thanks to backhaul connections.  
\begin{table}
\begin{tabular}{c|c||c|c} \hline
**Name** & **Meaning** & **Name** & **Meaning** \\ \hline \(N\) / \(K\) & number of TPs/UEs & \(\mathcal{U}_{m}\) & set of UEs that attaching to TP \(m\) \\ \(N_{\text{T}}\) / \(N_{\text{R}}\) & number of antennas at a TP/UE & \(R_{k}^{n}\) & data rate of UE \(k\) on RB \(n\) \\ \(\mathcal{M}\) & set of all of the TPs & \(\hat{R}_{k}\) & accumulated average data rate of UE \(k\) \\ \(\mathcal{M}_{k}\) & CoMP set of UE \(k\) & \(p_{m}^{n}\) & transmit power used at TP \(m\) on RB \(n\) \\ \(\beta_{m,k}^{n}\) & index implying the scheduling result & \(N_{\text{RB}}\) & number of RBs \\ \hline \end{tabular}
\end{table}
Table 1: Major notations. TP, transmit point; UE, user element; RB, resource block.  
Figure 2: System model.
## 3 Automatic Language Identification Using Speech Rhythm Features
### Automatic Language Identification Using Rhythm Metrics
#### 3.4.2 R-Vector-Based LID with i-Vector  
As the i-vector-based classification also requires an amount of training data, using only speech rhythm features may be insufficient for i-vector training. However, we attempted to combine R-vector with the i-vector obtained from the GMM-UBM and TV-matrix, considering that the two types of vectors have a strong similarity in terms of language-discriminative features. Figure 4 describes the proposed way of combining i-vector-based classification with R-vector.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rhythm Metrics** & **Formula** \\ \hline \(\%\)V & \(\sqrt{\sum\limits_{k=1}^{m_{c}}{d_{v,k}}/{\left(\sum\limits_{k=1}^{m_{c}}{d_{v,k}}+\sum\limits_{k=1}^{m_{c}}{d_{c,k}}\right)}}\) \\ \(\Delta\)V & \(\sqrt{\sum\limits_{k=1}^{m_{c}}{\left({d_{v,k}}-\mu_{v}\right)^{2}}}\) (\(\mu_{v}=\sum\limits_{k=1}^{m_{c}}{d_{v,k}}/{m_{v}}\)) \\ \(\Delta\)C & \(\sqrt{\sum\limits_{k=1}^{m_{c}}{\left({d_{c,k}}-\mu_{c}\right)^{2}}}\) (\(\mu_{c}=\sum\limits_{k=1}^{m_{c}}{d_{c,k}}/{m_{c}}\)) \\ \(\text{Varco-V}\) & \(\Delta\)V/\(\mu_{v}\) \\ \(\text{Varco-C}\) & \(\Delta\)C/\(\mu_{c}\) \\ nPVI-V & \(\left(\sum\limits_{k=1}^{m_{c}}{\left|{\frac{{d_{v,k}}-{d_{v,k+1}}}{{{({d_{v,k }}+{d_{v,k+1}})}/{2}}}}\right|}\right)/(m_{v}-1)\) \\ rPVI-C & \(\left(\sum\limits_{k=1}^{m_{c}}{\left|{d_{c,k}}-{d_{c,k+1}}\right|}\right)/(m_{ c}-1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Formulas of rhythm metrics. nPVI: normalized Pairwise Variability Index, rPVI-C: Raw Pairwise Variability Index.  
Figure 4: Procedure of Language IDentification (LID) based on a combination of i-vector and R-vector.  
As input features for i-vector training, 13 dimensional MFCCs are used with the same configuration of inputs for the detection of consonantal and vocalic intervals. The results of VAD are also incorporated to exclude silence frames while training GMM-UBM. After training GMM-UBM, a TV-Matrix is trained as a hyperparameter. Once the i-vector is extracted from the GMM-UBM and TV-Matrix, the R-vector obtained from the interval detection results is concatenated with the i-vector, generating an (i+R)-vector. Finally, a cosine similarity is used to determine a language.  
To quantify the cosine similarity, a mean vector should be firstly calculated for each language from the i-vectors and R-vectors estimated from training data. In a determination process, a cosine similarity value between the (i+R)-vector extracted from test data and a mean vector of each language are calculated as follows:  
\[\textit{Cosine Similarity}(X,Y_{i})\!=\frac{X\!\cdot\!Y_{i}}{\|X\|_{2}\!\cdot\! \|Y\|_{2}} \tag{2}\]  
where \(X\) denotes the (i+R)-vector of a given piece of test data and \(Y_{i}\) refers to a mean vector of the \(i\)-th language. A language \(i\) providing the highest cosine similarity is determined as the result of LID.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Data and Setup
This adjustment allowed us to strike a balance between computational efficiency and the evaluation of the relative importance of early samples from the crossband components and late samples from the band-to-band component. By discarding the two latest samples from the band-to-band component, we aimed to explore the tradeoff between the temporal characteristics of the crossband and band-to-band components. This experimental design enabled us to assess the respective significance of early samples from the crossband components and late samples from the band-to-band component in the dereverberation process.
## 2 Materials and Methods
### Methods
#### 2.3.3 Accuracy Validation  
To evaluate the accuracy of the NTL_LandScan and NTL_Heatmap data in identifying the spatial range of the PRD urban agglomeration, we conduct verification and analysis of the urban spatial identification results by randomly selecting validation points. The spatial identification results are assessed using a confusion matrix, which allows for an accuracy evaluation by comparing the actual values of the random verification points with the identification results [72]. The evaluation methods include overall accuracy, confusion matrix, and Kappa coefficient. The confusion matrix lists classification results for each category, assisting in analyzing model performance and identifying categories that are easily confused. The results of the confusion matrix can be displayed in a two-dimensional table to show classification accuracy and error distribution across categories. The Kappa coefficient is a measure of classification consistency, excluding consistency due to random factors to more scientifically reflect the accuracy of the identification results. Based on previous studies, a typical validation sample size for urban agglomerations is 3000 random pixels; we create 3000 random validation points within the PRD area using ArcGIS. This sample size design covers different areas of the Pearl River Delta urban agglomeration, including core cities (such as Guangzhou and Shenzhen), secondary cities (such as Dongguan and Foshan), and peripheral cities (such as Jiangmen and Zhaoqing). In particular, it reflects varying intensities of economic activities and population mobility. Additionally, the design of 3000 random points follows validation methods used in existing studies on urban agglomerations of similar scales. This approach ensures statistical significance while avoiding increased computational costs caused by excessive sample sizes. Finally, this sample size aligns with mainstream practices in accuracy validation at the regional scale in existing research, providing both practicality and reliability. The formula is as follows:  
\[k=\frac{p_{o}-p_{e}}{1-p_{e}} \tag{7}\]  
\[p_{e}=\frac{a_{1}\times b_{1}+a_{2}\times b_{2}+\ldots\ldots a_{i}\times b_{i}} {n\times n} \tag{8}\]  
where: \(p_{o}\) is the overall accuracy, \(a\) is the real sample number of each category, \(b\) is the predicted sample number of each category, and \(n\) is the total sample number. By comparing the actual values of random validation points with the predicted results, the method's reliability in identifying urban boundaries can be scientifically evaluated.
## 3 Results and Discussion
### Oxygen Concentration  
Although oxygen concentration cannot be directly used as an indicator of the spontaneous combustion of coal, it is an important indicator in this process, since the series of chemical reactions that occur during this process are related to oxygen consumption [32]. In this experiment, dry air flowed in from the bottom of the sample reactor, passed through the coal chamber, and then flowed out from the top of the sample reactor, while the gas components were analyzed at the outlet by gas chromatography. Figure 3 shows the oxygen concentration of the different coal types during the temperature-programmed process. Figure 3 shows how the oxygen concentration gradually decreases as the temperature increases. Under the same conditions, LN (low-rank coal) consumes the most oxygen and GZ consumes the least. This is due to the high content of oxygen-containing active functional groups in low-rank coal, which are more easily oxidized, consuming more oxygen under the same conditions. The oxygen-containing active functional groups of anthracite are gradually reduced after metamorphism, so the degree of oxygen consumption is less. LN has little change in terms of its oxygen content until 80 \({}^{\circ}\)C, after which it decreases rapidly. The oxygen concentration of the GZ coal and the SX coal drops rapidly at 110 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, respectively. This is also related to the metamorphic degree of coal. The oxygen concentration in the second oxidation process changes compared with the oxidation process of raw coal, but the behavior is similar. The oxygen concentration of SX-70 is lower than SX-raw because pre-oxidation causes the water to evaporate in the coal, which causes the reaction rate and the degree of oxygen consumption to increase [33]. The oxygen concentration of the other coal types in the secondary oxidation process at the same temperature is higher than that during primary oxidation. This is because a large number of active functional groups, which are easily reactive, are consumed during the pre-oxidation process. Therefore, oxidized coal does not react as easily with oxygen compared to raw coal, resulting in a reduction in oxygen consumption.  
Figure 2: The division of the stages of the spontaneous combustion of coal.
# Joint Maintenance Strategy Optimization for Railway
## 2 Problem Statement
\begin{table}
\begin{tabular}{c c} \hline
**The Current Maintenance Strategy** & **The Proposed Joint Maintenance Strategy** \\ \hline Reprofiling the wheel periodically & Reprofiling depending on the wheel condition \\ Reprofiling when the thickness of the flange is less than & Reprofiling when the thickness of the flange is less than \(Sd_{L}\) \\ the threshold & Reprofiling recovers the flange thickness to \(Sd_{H}\) \\ Reprofiling recovers the flange thickness to \(Sd_{H}\) \\ \hline Both of them need daily inspection, and reprofiling is carried out in time when the tread surface is a failure. & \\ The wheel is replaced when the diameter is below the usage limit. & \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of maintenance strategies. When the joint maintenance strategy is implemented, the degradation process of the wheel is discontinuous. Wear on the wheel flange and diameter is cumulative during vehicle operation. Reprofiling eliminates the tread surface damage and increases the wheel flange thickness but reduces the wheel diameter. Figures 5 and 6 show the wheel flange thickness and diameter deterioration processes, respectively. As shown in Figure 5, \(SD\) is the initial thickness of the wheel, and \(SD_{L}\) represents the floor of the thickness of the wheel flange. Once it is lower than \(SD_{L}\), the wheelset cannot be put into service and must be reprorified. \(Sd_{L}\) and \(Sd_{H}\) are the reprofiling strategy threshold. In Figure 6, \(D_{N}\) represents the diameter value of a new wheelset. The wheelset is replaced as soon as the diameter is smaller than \(D_{L}\). As can be seen from the two figures, daily inspection and periodic inspection cannot eliminate wheel deterioration. If the damages on the tread surface do not exceed the limit during daily inspection, it is kept as it is. Otherwise, corrective maintenance is performed. During periodic inspection, if the flange thickness does not reach the lower limit \(Sd_{L}\), only cleaning and maintenance are performed. The reduction in the wheel diameter is the wear caused during running. If the thickness of the flange is at or below \(Sd_{L}\), preventive reprofiling is carried out to restore the flange thickness to \(Sd_{H}\), and the wheel diameter decreases correspondingly. The effect of preventive maintenance is imperfect. With the reprofiling number increasing, the maintenance effect decreases, and the failure probability increases gradually.