esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SRPC Control Algorithm  
### Establishing the Compensation Current References  
The compensation currents in (10) can be implemented by using the instantaneous load section currents as in Equations (27) and (28), where, \(I_{Lxu}\), and \(I_{Lyu}\), are the RMS active currents of the load sections. The current components of \(I_{Lxr}\), and \(I_{Lyr}\), are the  
Multiplying Equation (27) by \(\sin(\omega t-\pi/6)\) gives a DC current component of \(\sqrt{2}/2\,I_{Lxu}\), as presented in Equation (29).  
\[\begin{array}{l}i_{Lx}\sin\!\left(\omega t-\frac{\pi}{6}\right)=\frac{\sqrt{2} }{2}I_{Lxu}-\frac{\sqrt{2}}{2}I_{Lxu}\cos\!\left(2\omega t-\frac{\pi}{3}\right) -\frac{\sqrt{2}}{2}I_{Lxv}\cos\!\left(2\omega t-\frac{5\pi}{6}\right)+\\ \sum\limits_{h=2}^{\infty}\sqrt{2}\,I_{Lxh}\sin\!\left(h\omega t+\varnothing_{ xh}\right)\sin\!\left(\omega t-\frac{\pi}{6}\right)\end{array} \tag{29}\]  
In a similar way, by multiplying Equation (28) by \(\sin(\omega t-\pi/2)\), this results in a DC current component of \(\sqrt{2}/2\,I_{Lyu}\). Summing the DC components of \(\sqrt{2}/2\,I_{Lxu}\), and \(\sqrt{2}/2\,I_{Lyu}\), then multiplying the result with the value of \(2/\sqrt{3}\) gives the peak value of phase \(x\) current after compensation \(I_{x2m}=\sqrt{2/3}\,\left(I_{Lx}+I_{Ly}\right)\) (taking into consideration that load power factor is close to unitary). Extracting the DC current component of \(\sqrt{2/3}\,\left(I_{Lx}+I_{Ly}\right)\) is possible after using a digital moving average low pass filter (LPF).  
On the other hand, using two single-phase enhanced phase-locked loop (E-PLL) is essential to acquire the phase angles for both catenary voltages of \(u_{x}\), and \(u_{y}\). The E-PLL generates an output waveform whose phase is related to the phase of an input waveform. It also generates an output waveform that lags the input waveform by 90 degrees [33]. In this case, the instantaneous currents of phase \(x\) and phase \(y\) after compensation are generated. Then, using Equation (10) gives the compensation current references. The next step is to use a controller to track these reference currents, as explained in the next paragraph Figure 5 shows the block diagram for establishing the references of compensation currents.
## 4 Results and Discussion
### Automatic Landslide Detection for Validation  
To best identify landslides using change detection, we removed changed areas of one-pixel value (900 m\({}^{2}\)) and generated buffer circles of 300 m diameter around the original inventory locations. Figure 9 shows the results of change detection overlain on the original landslide inventory map. The results show that many active changes occurred around the unlabeled landslide data inventory (Figure 9).  
Figure 9: Results of 7/5 band ratio change detection.  
Figure 10 summarizes the landslide location data obtained by automatic change detection for each landslide inventory polygon as follows.  
1. \"A\" represents the original inventory items count.  
2. \"B\" represents the total locations generated by the automatic classification change detection technique within all the inventory polygons (300 m diameter).  
3. \"C\" represents the maximum of one changes detected location per polygon in the study area.  
4. \"C/A\" represents is the ratio of polygons with a detected change to the number of original inventory items.  
Figure 10 clearly shows that unlabeled and landslide have the largest numbers of locations (B). Any changes within a circle of 30 m diameter were considered as a single event, and that is shown as the value of C. Thus, C/A values are normalized detection values suggesting a valid relationship between landslide and unlabeled data locations, with the latter probably representing the missing (undocumented) landslides.
# Classification of Defective Fabrics Using Capsule Networks
## 2 Literature Review
Markov random fields (MRF) use both statistical and structural information to recognize patterns [24]. The main principle in this method is to focus on pixel intensity and its neighborhood pixel values. Learning-based approaches are used to detect defects utilizing labeled data. This learning mechanism can be supervised and unsupervised. The main goal in these approaches is to create a model that has been trained with labeled data and then uses this model as a test for unseen images. These approaches have good defect tolerance, generalization capabilities, and self-learning capabilities. The most commonly used methods are Elo-rating and machine learning-based methods such as Artificial Neural Network (ANN), Support Vector Machines (SVM), Regression, and Nearest Neighbor (NN). Neural network models are flexible defect detection methods utilizing layer-by-layer structure to transform information forward and update the information backward to minimize network error [25]. The Elo Rating is performed to detect fabric defects and this method has high accuracy in the detection of defects on dot-and-star patterned fabrics [26]. The deep learning methods used for fabric defect detection, specific to the subject of this study, are summarized in three groups: CNN-based; Long Short Term Memory (LSTM) based; Autoencoder based publications. Table 1 gives details about discussed papers in terms of the used dataset, number of classes, and performance. The first seven rows show details of CNN-basedpublications. The eighth row shows details of the LSTM-based paper. The remaining rows are stands for fabric defect detection studies involving the autoencoder method. For example, [PERSON] and [PERSON] performed Multi-scale CNN to categorize and localize six fabric defects by using TILDA dataset. Their study has a higher performance rate with 96.55% accuracy [27]. [PERSON] et al. proposed a visual long short term memory method to classify three datasets (DHU-FD-500, DHU-FD-1000, Aliyun-FD-10500) into ten and seven classes, respectively. [PERSON] et al. used autoencoders to classify fabric defects into two categories [28]. The purpose of the work is not only to detect fabric defects, but also to try to extract features of fabrics with unique textures correctly, to create the optimum working parameters for the autoencoder. The autoencoder network was first trained and then tested. The training phase was carried out with 153 defective and 847 defect-free fabric samples. Data were separated as 70% training set, 15% validation set, and 15% test set. The overall performance rate for the study is 88%.
# Prediction of the Absorption Characteristics of Non-Uniform Acoustic Absorbers with Grazing Flow
## 3 Impedance Boundary Navier-Stokes Equations (IBNSE) Method
### Comparisons with Published Experimental Results  
In order to further verify the accuracy of the IBNSE method in this paper, the absorber given in Reference [15] was used, and the transmission loss and acoustic absorption coefficient of the absorber were calculated by FNSE method and IBNSE method, respectively.  
Figure 8: Transmission loss predicted using different perforated plate parameters. The solid lines represent the results computed by the IBNSE method with Goodrich model, while the circles represent the results computed by the FNSE method. (**a**) Different hole diameters; (**b**) different perforation rates.  
Figure 9: Transmission loss of the RDBRE system with different grazing flow Mach numbers. The solid lines represent the results computed by the IBNSE method with Goodrich model, while the circles represent the results computed by the FNSE method.  
The acoustic absorber is composed of a number of honeycomb cavities connected with a micro-perforforated plate. As shown in Figure 10, the height of the cavities is \(h=25\) mm, the side length of the cavity section is \(a=2.8\) mm and the hole diameter and the thickness of MPP are \(d=t=0.5\) mm. The cross section of the duct is rectangular, and corresponding the length and width are \(A=B=100\) mm. The total length of the duct is \(950\) mm and the length of the acoustic liner part is \(L=500\) mm. The Mach number of the grazing flow is \(0.035\). The geometric and FE models of the structure suitable for FNSE and IBNSE methods are shown in Figures 11 and 12, respectively.  
Figure 13 reveals that under different Mach numbers, the transmission loss and absorption coefficient obtained by the IBNSE method agreed very well with those obtained by the FNSE method. In addition, we can see that these calculated results are in good agreement with the experimental measurements, which once again proves the effectiveness of the developed IBNSE method.  
Figure 11: Structure in Reference [15] (suitable for the FNSE method). (**a**) Geometric model and (**b**) FE model.  
Figure 12: Structure in Reference [15] (suitable for the IBNSE method). (**a**) Geometric model and (**b**) FE model.  
Figure 10: Schematic view of the structure in Reference [15].  
Tables 2 and 3 show the errors of the predicted transmission loss and absorption coefficient obtained by the IBNSE method and the FNSE method compared to the experimental results given by Reference [15]. As demonstrated in Tables 2 and 3, the IBNSE method proposed in this paper had satisfactory computational accuracy.
## 2 Project Overview
### Strategy of Construction Support  
#### 2.3.1 Interlayer Soil Grouting  
Interlayer soil grouting reinforcement was performed to improve the strength of the surrounding soils. Several high-strength, flexible grouting pipes were employed to perform high-pressure grouting into the interlayer soils between the shield tunnel and existing tunnels. This process created a reinforced curtain, further enhancing the strength of the interlayer soil. After the shield tunneling machine cleared the existing tunnel, the soil within the outer channel area was subjected to additional grouting reinforcement through grouting holes in the tunnel segments. This helped to increase the segment's strength and stability, reducing the subsequent settling of the existing tunnel structure. Each tunnel segment was equipped with three grouting holes, and the grout mixture consisted of cement and sodium silicate mixed in an equal volume ratio. The grout formed a curtain with a thickness of no less than 2 m. Grouting pressure was maintained at 0.3 to 0.6 MPa, and both grouting pressure and volume were closely monitored and controlled. Grouting commenced once the tunnel machine had advanced by five rings. After grouting was complete, the grouting head was removed, and the holes were sealed. In this process, ordinary Portland cement with a grade of PC32.5 was used, the grouting pipes had a DN32 diameter, a 2.75 mm wall thickness (t), and a length (L) of 3 m. DN32 signifies a grouting pipe with an outer diameter of 32 mm, t represents the wall thickness of the grouting pipe (2.75 mm), and L indicates the length of the grouting pipe (3 m). The grouting reinforcement process is depicted in Figure 3.  
Figure 3: Grouting reinforcement diagram. (**a**) Interlayer soil grouting; (**b**) segment grouting.  
Figure 2: Geological profile diagram (unit: mm).
## 2 Study Area and Datasets  
### Overview of the Study Area  
The study area of this paper is located in Laibin City, Guangxi Zhuang Autonomous Region, China. Laibin City is situated in the central zone of Guangxi, with geographic coordinates ranging from \(23^{\circ}16^{\prime}\) N and \(24^{\circ}29^{\prime}\) N latitude and between \(108^{\circ}24^{\prime}\) E and \(110^{\circ}28^{\prime}\) E longitude, covering a total land area of about 13,411 square kilometers [24]. The terrain of the region is complex, characterized by a mountainous and hilly landscape with continuous mountain ranges. Mountains and hills occupy a significant portion of the area, including 38.4% of mountains, 26.2% of hills, 22.5% of plains, 8.8% of tablelands, and a small portion of waters such as swamps and wetland lakes. The city is rich in agricultural resources, with soil thickness averaging about 50 cm. Laibin City is located in the transition region of a subtropical monsoon climate, experiencing a warm and humid climate influenced by the monsoon circulation in summer, which is dominated by sea-phase air masses. In winter, the city is more influenced by cold and dry continental air masses. The summer season is long, while winter is short, and rainfall and temperature exhibit contemporaney. The region receives plentiful precipitation, which is favorable for crop growth and development. The climate and soil conditions of the region are conducive to sugarcane growth and sugar accumulation. Vegetation types are predominantly tropical and subtropical, including tropical rainforests, coniferous forests, and deciduous broadleaved forests. The lush vegetation cover supports soil conservation and water retention. The city's arable land covers approximately 400,000 hectares, with rice being a crucial component of grain cultivation and a significant rice-producing area in Guangxi Zhuang Autonomous Region. The forest area in the city spans 687,800 hectares, with a forest coverage rate of over 51.4%. In addition to this, the area of sugarcane cultivation area of around 117,000 hectares, making it one of the largest sugarcane production regions in Guangxi. As of 2018, the total population in Laibin City is 2.78 million, with the agricultural population constituting about 80% of the city's total population. Therefore, drought monitoring is essential for local agricultural development and ecological management in Laibin City. The geographic location of the study area is illustrated in Figure 1. In addition, this article presents the changes in temperature and precipitation in Laibin City throughout 2021, sourced from the Xihe Energy Meteorological Big Data Platform (xihe-energy.com (accessed on 10 September 2024)), as shown in Figures 2 and 3, for comparison and verification with the results obtained in the following sections.
## 5 Evaluation
### Evaluation Metrics
#### 5.2.2 Different Threshold  
The appropriate flow statistics threshold is extremely important for obtaining the flow statistics information from the switches. There are mainly two key functionalities about this value: (1) it can determine the starting point of the flow statistics collection; and (2) it can indirectly affect the time interval between the adjacent received flow statistics messages. As shown in Figure 11, if the flow statistics threshold is too low, the collection of the flow statistics information will be too frequent, which may be similar to the low-frequent periodic polling. Using this way may cause high network overhead during the flow statistics collection. As shown in Figure 12, if the flow statistics threshold is too high, the starting point of flow statistics collection may be delayed, which leads to the small flows (the flow statistics of them is less than the threshold) to be not monitored. In addition, it may not capture the traffic burst due to the possible long time interval between the adjacent received flow statistics messages.  
Figure 11: The change of P\({}_{2}\) and P\({}_{3}\) for the proposed approach when threshold =5 KB and T = 2 s.  
Figure 12: The change of P\({}_{2}\) and P\({}_{3}\) for the proposed approach when threshold =20 KB and T = 2 s.  
Figure 10: The change of P\({}_{2}\) and P\({}_{3}\) for the baseline approach when T = 1 s.
# Solubility/Insolubility: A Possible Option for Recycling Polystyrene
## 2 Materials and Methods
### Modeling of Continuous Stirred Tank (CST)  
Figure 1 shows the intended application of a continuous stirred tank as a means of separation. Enough time is supposed to be allocated to achieve equilibrium in the solution.  
The literature states that D-limonene can dissolve up to 55% (\(w/w\)) of PS at 323.15 K [35] and 47.1% (\(w/w\)) at room temperature [36]. We can estimate the maximum PS processing capacity by averaging these values, given a polymer concentration of 52% (\(w/w\)) for a feed mass flow of 100 kg/s. The ethanol supply is intended to be fresh, and the quantity of alcohol required to extract a minimum of 99% of the PS in the raffinate stream is computed. According to the information presented in Figure 1, the mass balances are as follows:  
\[E\cdot x_{3,E}=D \tag{1}\]  
\[E\cdot x_{2,E}=F\cdot x_{2,F} \tag{2}\]  
\[R=F\cdot x_{1,F}-E\cdot x_{1,E} \tag{3}\]  
\[E\cdot x_{1,E}=(D+F\cdot x_{2,F})\cdot S \tag{4}\]  
where \(F\) represents the feed containing a mixture of PS and limonene, \(D\) is the incoming ethanol stream, \(E\) corresponds to the extract, \(R\) indicates the raffinate, and \(x\) is the mass composition. The number in the subindex corresponds to the following substances: (1) polystyrene, (2) D-limonene, and (3) ethanol. The symbol \(S\) in Equation (4) denotes the solubility limit, which is dependent on the ethanol concentration in the solvent and represents a ratio between the mass of PS and the weight of the solvent mixture of ethanol and D-limonene.  
Figure 1: The suggested solubility/insolubility process’s modeling scheme. F is the feed stream containing a mixture of D-limonene + expanded polystyrene; D is the ethanol stream to precipitate the polystyrene; E is the extract current that contains D-limonene + ethanol + polystyrene; R is the raffinate current containing the recovered polystyrene.
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
## 4 Materials and Methods
### Algorithm  
Algorithm 1 shows the pseudo-code processes of the proposed defense. We assume cloud servers and IoT devices run our program when sending packets.  
Figure 1: Two communication scenarios between a cloud server and a client IoT device. The top shows the original traffic without any defense, and the bottom depicts the obfuscated traffic after utilizing our proposed defense.  
The program stores the received message from the application layer in a byte array \(Data\). Provided that the message is long enough to perform random segmentation (such that the length of \(Data\geq\) the minimum segment size \(Min\)), our algorithm randomly decides whether to split the message but with a specific probability threshold \(Prob\), such that \(0\leq Prob\leq 1\). If yes, the main loop loads a random chunk of \(Data\) into an individual segment \(Seg\) for transmission and loads another random chunk from \(Data\) in the next iteration process until the array becomes empty. The size of each segment is determined randomly by \(RandLen\), but does not surpass MTU to avoid fragmentation. However, the upper and lower bound of \(RandLen\) (i.e., \(Min\) and \(Max\)) is adjustable to suit the device traffic pattern. For example, for a device that sends light traffic with a maximum of 300 bytes in length, the upper bound of \(RandLen\) should be less than 300 to achieve randomness. Otherwise, the program will send the whole payload in \(Data\) if \(RandLen\) exceeds the message's size.
## 4 Results and Discussions  
### Transducer Calibration  
Synthesizing the sound field requires accordingly modulating the amplitudes and phases of incident waves. Due to the fabrication inconsistency, the generated acoustic pressure of each transducer is calibrated at the pedestal center by using a needle hydrophone (1376, Precision Acoustics, Ltd., Dorchester, UK). To obtain the phase differences, a synchronous signal that is triggered by the transducer driving voltage is used as the reference. Both the pressure and reference signals are sampled simultaneously by a multichannel data acquisition card with 100 MHz sampling rate. Figure 6 shows the measured pressure and the phase of each transducer. In Figure 6a, the pressure variation under the same excitation voltage is clear and the value changes along with the increase of driving voltages. The measurements in Figure 6b are actually the phase difference between the measured pressure and the reference signal. The constant phase difference at various excitation voltages indicates that the performances of employed PZT transducers are stable and time-invariant. For individual transducer, the relation between the excitation voltage and pressure amplitude shows good linearity within the measuring range 0 V-57 V. In the following experiments, the maximum excitation voltage is selected to be 40 V and the calibrated voltages will be applied to the transducer array to obtain the required output pressures.
## 1 Introduction
### Research Questions  
The limitations identified in Table 1 are addressed through this SLR, which systematically investigates five core research questions.  
**Research Question 1 (RQ1):** How do ML and DL techniques improve channel estimation in UWA communication, and what are the key system characteristics and performance metrics of these methods?  
**Research Question 2 (RQ2):** How do ML and DL techniques improve adaptive modulation in UWA communication, and what are the key system characteristics and performance metrics of these methods?  
**Research Question 3 (RQ3):** How effective are ML/DL-driven modulation recognition approaches in identifying modulation schemes under complex underwater conditions, and what are their strengths and limitations?  
**Research Question 4 (RQ4):** What innovative approaches and emerging trends in machine/deep learning can be employed to address unresolved challenges in underwater acoustic communication, and how can these advancements shape the future of intelligent, efficient, and scalable UWA systems?
## 4 Experimental Result Analysis
Three different machine learning algorithms are used to measure the classification performance of the proposed system. Figure 18 shows the comparison of classification results of different algorithms that are used in the proposed system for different groups of persons. We have compared the classification performance without feature selection and with the feature selection (FS) technique. SVM provides the best accuracy outcome than logistic regression and random forest. The accuracy values are 98%, 92%, and 94% in case of SVM, in case of LR, and in case of RF respectively with feature selection process. Figure 16: ROC curve of classification of 5 persons. Figure 15: Confusion matrix of the proposed model for the twenty-five persons. In [20], the researcher proposed a writer identification system using image-based handwriting feature analysis using K-NN and neural networks. Here, the overall recognition rate is 74.2%, where all feature vectors in the training set are used as prototypes. They varied the number of neighbor's k from 1 up to 25. In our case (pen-tablet sensor data-based), initially, we considered 25 persons, and the proposed system has 98% accuracy with the feature selection approach. Figure 19 depicts the intuitive comparison between the state-of-art (image-based) system [20] and our proposed system. Table 8 presents a comparison summary of our proposed system with some existing studies. From Table 8, we can see that our self-collected pen-tablet data are more robust in terms of noise effect compare to image-based. And our model outperforms with low computational costs. Figure 17: Classification accuracy for different persons with and without feature selection using SVM. Figure 18: Comparison of accuracy for different classification algorithms with and without FS.
## 3 Results and Discussion
### Stiffening Effects  
#### 3.5.1 Stiffening Effects Based on Laboratory Testing  
The limestone (basic) and granite (acidic) fillers were selected to measure and predict the stiffening effect of fillers. In Figure 8, constructed using the data of research, mastic stiffness modulus increases with the increase in filler content. The filler content is defined as \(\varphi_{2}=\frac{V_{1}}{V_{1}+V_{2}}\), where \(V_{1}\) is filler volume while \(V_{2}\) is bitumen volume. A discussion on the effect of variables on stiffening potential in the models can be found elsewhere [8].