esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles and Methods
### Equation Solving  
Substituting Equations (8) and (9) into Equation (10) and comparing the coefficients of the corresponding \(e^{ik\theta}\) yields the following equation:  
\[\left\{\begin{array}{l}h_{1}-2R_{1}^{2}e_{2}-f_{1}=0\\ \\ 4R_{1}^{2}g_{1}-3h_{2}-4R_{1}^{2}e_{1}+3f_{2}-R_{1}^{4}f_{3}=0\end{array}\right. \tag{14}\]  
Substituting Equations (6)-(9) into Equation (11) and comparing the coefficients for the corresponding \(e^{ik\theta}\) yields the following equation:\[\left\{\begin{array}{l}\frac{1}{C_{1}}(\kappa_{1}+1)R_{1}^{-1}g_{1}-\frac{1}{C_{1 }}R_{1}^{-3}h_{2}-\frac{1}{C_{2}}(\kappa_{2}+1)R_{1}^{-1}e_{1}+\frac{1}{C_{2}}( 3-\kappa_{2})R_{1}^{3}e_{3}\\ +\frac{1}{C_{2}}R_{1}^{-3}f_{2}+\frac{1}{C_{2}}R_{1}f_{3}=-\frac{(1-\eta)\varphi _{x}}{G_{1}}\frac{k_{1}(1-\lambda)R_{1}}{2}\\ -\frac{1}{C_{1}}R_{1}^{-1}h_{1}+\frac{1}{C_{2}}(1-\kappa_{2})R_{1}e_{2}+\frac{1 }{C_{2}}R_{1}^{-1}f_{1}=\frac{(1-\eta)\varphi_{x}}{2C_{1}}(1+\lambda)R_{1}+ \frac{1}{C_{2}}\Big{[}(\kappa_{2}-1)b_{1}R_{1}-c_{1}R_{1}^{-1}\Big{]}\end{array}\right. \tag{15}\]  
Substituting Equations (8) and (9) into Equation (12) and comparing the coefficients of \(e^{ik\theta}\) on both sides of the equation gives:  
\[\left\{\begin{array}{l}2R_{1}^{2}g_{1}-3h_{2}=0\\ -2R_{1}^{2}e_{1}+6R_{1}^{6}e_{3}+3f_{2}+R_{1}^{4}f_{3}=0\end{array}\right. \tag{16}\]  
Substituting Equation (8) into Equation (13) and comparing the coefficients of \(e^{ik\theta}\) on both sides of the equation gives:  
\[\left\{\begin{array}{l}2e_{2}+R_{2}^{-2}f_{1}=0\\ e_{1}+3R_{2}^{3}e_{3}+R_{2}^{2}f_{3}=0\\ R_{0}^{2}e_{1}-R_{2}^{6}e_{3}-f_{2}=0\end{array}\right. \tag{17}\]  
Equations (14)-(17) can be solved with \(e_{1}\)-\(e_{3}\),\(f_{1}\)\(f_{3}\), \(g_{1}\), \(h_{1}\), and \(f_{3}\), and the analytical expression of the interaction between the surrounding rock and the lining can be obtained.
## 3 Results and Discussion
### Characterization of nZVISLP
#### 3.1.4 Particle Size and Zeta Potential Analysis  
The particle size of the sweet lime synthesized adsorbent was determined using a Zeta nanosizer (Nano-ZS90, Malvern, UK), after homogenization in C\({}_{2}\)H\({}_{5}\)OH at room temperature. The results obtained are presented in Figure 4a. This profile indicates a broad peak between 200 and 500 nm. The zeta potential of nZVISLP was also determined using the same equipment. The result of the zeta potential analysis is shown in Figure 4b. The magnitude of the zeta potential defines the stability of the adsorbent in a dispersion medium. It signifies the electrostatic repulsion between the adsorbent and the dispersion medium [32]. The result indicates that the magnitude of zeta potential increased at higher pH, depicting that the synthesized adsorbent was stable at higher pH.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Adaptive Technique
#### 5.2.2 Multi-Hop Network
In [93], [PERSON] and [PERSON] introduce the use of a soft-decision decoder called adaptive chase-2 decoding for a BCH code in multi-hop networks. The paper presents decoding energy consumption for BCH codes with standard chase and adaptive chase. The paper claims that adaptive chase consumes significantly higher energy compared with others. The throughput result of the proposed code is better than the others in most cases. However, there is a lack of comparison with other error correction codes. In [94], [PERSON] introduces adaptive hybrid-I BCH code (assuming that CSI is available) for process industry setting. The thesis investigates energy efficiency and latency of ARQ and the proposedadaptive code. The numerical analysis is based on AWGN and Raylaigh fading channel. The paper concludes that the proposed adaptive hybrid BCH code is energy efficient compared with ARQ. Table 4 shows a summary of adaptive ECS for WSN. As discussed in Section 5.1.2, a modified multi-hop WSN model is a more realistic network model. However, there is very limited work on design and energy analysis of ECS on this model. Therefore, researchers need to focus on design and energy analysis of adaptive ECSs on a modified multi-hop WSN model to cover a realistic network situation. As we discussed in Section 5.2.1, the researchers need to focus on the cost of extra energy consumption due to adaptive techniques, and it should be incorporated in energy analysis. Finally, the researcher should do more work on the design and implementation of re-configurable transmitter and receiver at the energy and computationally constrained WSN node.
# Movement-in-a-Video Detection Scheme for Sign Language Gesture Recognition Using Neural Network
## 2 Sign Language Recognition Method
### Sign Language Dataset  
To demonstrate the effectiveness of the proposed movement-detection scheme in detecting the movements necessary in a sign language gesture, we first used a dataset of gestures of short lengths: Argentinian sign language (LSA64) [20]. It consists of only two to three hand poses and positions per gesture. We then considered sign language gestures of medium and long lengths: DEVISIGN-D [21] and sign language recognition (SLR) dataset [22], respectively.
## 2 Materials and Methods
### Simulation Setup  
The three PID controllers discussed here were implemented on SOPDT process models with different gains and time constants. For the different process models simulated, the dead-time-to-time-constant ratio, \(\frac{\theta}{\tau}\), was varied over the range \(\begin{bmatrix}0.01&0.1&1&3&5&7&10\end{bmatrix}\) and the damping ratio, \(\xi\), was varied over \(\begin{bmatrix}0.2&0.4&0.6&0.8&1&2&3&4&5\end{bmatrix}\). The process gain \(K_{p}\) and time constant were kept constant. The IAE for each of the tuned PIDs and MPC were obtained for both setpoint tracking and disturbance rejection. The IAE for the best performing PID controller amongst the three is then used to compare with MPC by computing the normalized IAE \((IAE_{N})\) according to Equation (15), and the results are tabulated. To further evaluate the performance of the controllers, the total variation (TV) was computed, and the normalized TV \((TV_{N})\), given in Equation (17), was also used to compare the controllers. Both setpoint response and disturbance rejection response were investigated using a unit step reference and unit step disturbance, respectively. The disturbance model is assumed to be the same as the process model. The process model is assumed to be perfect in all controller designs for both MPC and PID.  
\[IAE_{N}=\frac{IAE}{IAE_{min}} \tag{15}\]  
\[IAE=\int_{\theta}^{T_{ss}}|e(t)|dt=\sum_{i=\theta/T_{s}}^{T_{ss}}e(i)T_{s} \tag{16}\]  
\[TV_{N}=\frac{TV}{TV_{min}} \tag{17}\]\[TV=\sum_{i=1}^{T_{\rm ss}}\lvert u_{i}-u_{i-1}\rvert=\sum_{i=1}^{T_{\rm ss}}\lvert \Delta u(i)\rvert \tag{18}\]  
where \(x_{min}\) is the value of \(x\) for the controller that has the minimum \(x\); \(T_{\rm ss}\) is the settling time; and \(\theta\) is the dead time.
# _Sci_
## 4 Discussion
### Modifying Satellite ICDS and KPPs for Laser Communications
#### 4.3.3 Key Performance Parameter (KPP) #3: The Laser Source Parameters  
The third key performance parameter sequences with the first and second by adding the laser source. The first decision will be determining the desired wavelength, assuming a selectable/tunable system is not easily developed. Laser \"color\" must account for ease of amplification (e.g., 1.55-micrometer distributed-feedback semiconductor laser can be boosted by thirty decibels [51]),desired operating areas beyond in-atmosphere (e.g., certain blue-green wavelengths can penetrate sea water and facilitate undersea communications [36; 37]), and type of laser--typically a driver of \"color\" (wavelength), efficiency, and operating life and reliability.  
Additionally, the laser and associated modulators must support the desired--both current and future--waveforms for encoding data onto the carrier wave. With the right waveform, high-order modulation and spatial multiplexing techniques may achieve transmission rates reaching one-hundred giga-bits per second [133]. Furthermore, the desire to support multi-beam communications and directional beamforming will require select characteristics regarding \"color\" coherence.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 3 Multiple Target Tracking
### Prediction and Filter Gain  
The state and covariance predictions are iteratively computed as  
\[\hat{\mathbf{x}}_{t}(k|k-1)=F\hat{\mathbf{x}}_{t}(k-1|k-1), \tag{10}\]  
\[P_{t}(k\Big{|}k-1)=FP_{t}(k-1\Big{|}k-1)F^{T}+Q, \tag{11}\]  
\[Q=q(\Delta)Q_{v}q(\Delta)^{T}, \tag{12}\]  
where \(\hat{\mathbf{x}}_{t}(k|k-1)\) and \(P_{t}(k|k-1)\), respectively, are the state and the covariance prediction of target \(t\) at frame \(k\); \(T\) denotes the matrix transpose. The residual covariance \(S_{t}(k)\) and the filter gain \(W_{t}(k)\), respectively, are obtained as  
\[S_{t}(k)=HP_{t}(k\Big{|}k-1)H^{T}+R, \tag{13}\]  
\[W_{t}(k)=P_{t}(k\Big{|}k-1)H^{T}S_{t}(k)^{-1}. \tag{14}\]
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 3 Proposed Method
### Threat Models and Security Evaluation  
In this paper, we use EtC images as compressible encrypted images. EtC images are not only compressible, but also robust against various ciphertext-only attacks (COAs) [40; 41]. As we focus on the privacy of datasets in an image classification scenario, the goal of an adversary is to recover visual information on encrypted images. We assumed that an adversary had access to encrypted images and knew the encryption algorithm, but not the secret key. In other words, we assumed that the adversary could carry out a COA only from encrypted images. In addition, we assumed that the adversary knew the distribution of the dataset; thus, the adversary may prepare synthetic examples in conducting an attack.  
EtC images were evaluated under COAs. The robustness of EtC images against BF and JPS attacks as COAs was evaluated in [40]. An EtC image had almost the same correlation among pixels in each block as that of the original image, and this property allows for it to efficiently compress images. Therefore, an attacker can utilize this correlation to decrypt the image in some way, so the security of the encryption against JPS attacks was discussed in [40], in addition to BF attacks. In contrast, recently, novel attack methods for restoring visual information have been proposed that use deep neural networks [42; 43]. The feature reconstruction attack (FR-Attack) [42] exploits the local properties of an encrypted image to reconstruct visual information from encrypted images. Furthermore, with a synthetic dataset and encrypted images, the adversary may carry out a GAN-based attack (GAN-Attack) [43]. EtC images were confirmed to be robust against these attacks [41].  
In addition, regarding the security for the content of a payload, we assume that the payload is encrypted before embedding. Thus, if the encryption scheme is secure, the encrypted payload should be robust against unauthorized disclosure.
## 1 Introduction
Such preprocessing techniques are crucial for reducing noise and normalizing the data, thereby providing a solid foundation for deploying sophisticated ML algorithms that can detect and interpret patterns within the corpus more effectively. Also, our novel contribution involved systematically implementing Synthetic Minority Over-sampling Technique (SMOTE) to balance class distributions across the dataset. This process was conducted to address class imbalances, particularly between the minority and majority classes. Topic identification using ML, establishes a foundation for Aspect-Based Sentiment Analysis, aiming at uncovering the diverse emotional responses of parents for each topic and enhancing the significance and value of the research efforts. This study aims to address a substantial gap in the predominantly English-focused research by proposing an innovative method tailored specifically for the Modern Greek language. The present work is an extended version of the prior study [7]. Compared to the previous research approach, the following were performed:  
1. topic-specific annotation by manual labeling of data to one of four predefined topics,
2. identification of Modern Greek idiosyncrasies and unique linguistic features,* feature-value text vectorization based on unigram models,
* text normalization techniques, lemmatization, words replacement and stemming to refine text preprocessing,
* implementation of SMOTE to address class imbalance and
* employment of ML techniques for topic identification
* support for data-driven decision-making for inclusive ICT integration in education. These findings have the potential to support inclusive education by providing valuable insights for educators and policymakers on improving digital learning strategies, communication, and the educational and psychological aspects of remote learning. They may also contribute to the refinement of instructional approaches, ensuring that digital tools are better aligned with the diverse needs of students. Furthermore, these insights could inform data-driven decision-making processes, facilitating the development of policies that promote equal access to quality education.
## 2 System Design and Principle
### Principle  
#### 2.2.1 Principle of Recovering Time Slot Synchronous Clock Based on Deep Learning  
After detecting the optical signal carrying information, SPAD outputs a discrete single photon pulse signal. The phase of time slot synchronous clock is hidden in the discrete single photon pulse signal; we designed a neural network and let it learn to extract the hidden phase. As shown in Figure 2, the initial phases of the time slot synchronous clocks are set between \(0\) and \(2\pi\), and the initial phases of these time slot synchronous clocks are evenly divided into \(M\) types. The underwater photon-counting wireless optical communication is simulated by the Monte Carlo method, and single photon pulse signals with different initial phases carrying the synchronization head information are obtained. These single photon pulse signals are sampled and converted into binary sequences as the data part of the training set. When single photon pulse signals with different initial phases are obtained, the phases of the corresponding time slot synchronous clocks are also different. In this paper, the phase with the smallest phase error from the ideal time slot synchronous clock phase among the \(M\) kinds of time slot synchronous clock phases uniformly classified is used as the class label of the current single photon pulse signal, and the class label is processed by one-hot encoding. After the training data are obtained through the above method, the training data are fed into the built neural network, and the weights and biases in the network are continuously trained and optimized. After the network converges, the trained network can be used to extract features from discrete single photon pulses, and then to identify and reconstruct the phase of the time slot synchronous clock from the extracted features, as shown in Figure 3.  
Figure 1: Photon-counting UOWC system based on deep learning.
## 1 Introduction
Figure 1 presents a simplified diagram of a kraft dissolving pulp mill with an approach to valorizing the pre-hydrolysate. [PERSON] et al. [13] published a study on the development of a new process by combining acidification and treatment with polyethylene oxide (PEO) to improve the extraction of lignin. The idea was to form lignin/PEO complexes which are easy to separate from the solution. With this technique, it was possible to extract more than 22% of lignin compared to only 3.8% when acid precipitation was used (at pH = 2). Figure 1: Pre-hydrolysate valorization options at a kraft dissolving pulp mill. [PERSON] et al. (2015) also studied the extraction of lignin from pre-hydrolysate using over-liming treatment (CaO) and/or surfactant molecules. The results showed that the highest extraction of lignin (17%) was achieved by using dodecyltrimethylammonium chloride (DTAC) with an optimal ratio DTAC/pre-hydrolysate of 0.3%. In addition, over-liming resulted in the extraction of 36% lignin and 23% hemicelluloses. The effect of the sequence/order of the two treatments on the lignin extraction rate was also studied. Over-liming followed by treatment with DTAC was more effective than treatment with DTAC followed by over-liming. In the first case, a yield of 44% lignin removal and 27% hemicellulose loss was obtained. In another study, [PERSON] et al. (2015) combined PEO and aluminum polychloride (PAC); 46% of lignin was extracted. [PERSON] et al. (2016) tested several methods of lignin extraction from the pre-hydrolysate. Extractions of 27%, 52%, 36%, and 25% were obtained using (1) precipitation at pH = 2, (2) ethanol, (3) polyDADMAC, and (4) PAC (aluminum polychloride), respectively. The corresponding sugar losses were 38%, 38%, 39%, and 1.7%, respectively. The results obtained by [PERSON] and [PERSON] (2014) were different since they were able to extract a higher amount of lignin (70%) using polyDADMAC. However, polyDADMAC was not selective for lignin, since it resulted in 37% and 38% losses of sugars and acetic acid, respectively. As previously reported by [PERSON] et al. (2018), these techniques are still far from being optimal for recovering lignin from the pre-hydrolysate. In fact, they do not take into consideration a subsequent step that is needed for the recovery and the valorization of lignin. The only objective of these studies was to remove (and not to recover) lignin because it was considered a fermentation inhibitor. Therefore, it had to be separated before utilization of hemicelluloses (these techniques can be considered as detoxification steps). In addition, significant losses of hemicelluloses were observed in most of these techniques. The objective of this study is to maximize the quantity of recovered lignin and to enhance its quality by determining the best conditions for the precipitation, coagulation, and filtration steps. To the best of our knowledge, this is the first study conducted to investigate the optimization of lignin recovery from a DP pulp mill pre-hydrolysate using the acidification method.
## 3 Results and Discussion
### Velocity Distributions of Evaporating Atoms  
Although the thermostat region boundary position at the right-side interface affects the liquid surface temperature and, in turn, the spontaneous evaporation rate, it has little to no effect on the average kinetic energy components of evaporating argon atoms in \(x\) and \(y\) directions, as shown in Figure 10. A slight increase of the average kinetic energy component in the \(z\) direction can be seen when the thermostat region boundary position changes in the investigated range; however, the increase is less than 3%. The velocity distribution functions given in Figure 11 also show that the thermostat region boundary position has little to no noticeable impact on the velocity distributions of evaporating argon atoms in all three spatial directions. The simulation velocity distribution functions in the \(z\) direction are slightly shifted to the right side in all thermostat boundary position cases with the mean velocity component values greater by 3.74 m/s on average than the Maxwellian distribution average \(\overline{v}_{z,Maxw}=171.5\) m/s at 90 K. Furthermore, the unchanged velocity distribution functions demonstrate that although the thermostat region boundary position is placed beyond the liquid surface position, the thermostat itself does not alter the dynamics of evaporating atoms at the interface in any meaningful way. This is because the last bin of the thermostat region contains not only the evaporating argon atoms at the interface but also a considerable amount of liquid atoms near the liquid surface. Therefore, there is no need for the thermostat algorithm to do significant alterations in the individual atom dynamics to maintain a constant temperature within the bin ensemble.  
Figure 10: The average kinetic energy components in \(x\), \(y\), and \(z\) axis of evaporating argon atoms as a functions of normalized thermostat boundary position at the right-side interface.  
Figure 9: (**a**) The spontaneous evaporation rate, and (**b**) the corresponding evaporation coefficient as functions of liquid surface temperature in steady-state vacuum simulations.
###### Abstract
The results indicated that the surface mesh structure of rubber powder particles could effectively adsorb asphalt and maintain stability at \(-20~{}^{\circ}\)C. As the temperature decreases, there is a moderate change in the trend of dynamic modulus and phase angle. The modified asphalt always maintains a certain degree of fluidity, which enhances flexibility and significantly improves resistance to low-temperature cracking. Therefore, it is suitable for use in northern areas of China [15]. In 2017, [PERSON] found that increasing the mass fraction of rubber powder can effectively enhance the low-temperature plastic deformation capacity and rheological properties of asphalt [16]. In 2021, [PERSON] found that compared with the basic asphalt, TB rubber power modified asphalt had excellent low-temperature performance, and the low-temperature crack resistance of the mixes was enhanced with the increase in TB rubber power [17]. In 2016, [PERSON] et al. found that the high-modulus asphalt mixture compound modified by Qingchuan rock asphalt and rubber powder had excellent high-temperature stability, water stability, and fatigue durability. Additionally, the best blending ratio was recommended to be 10% Qingchuan rock asphalt + 18% rubber powder [18]. In 2017, [PERSON] found that the addition of Qingchuan rock asphalt and SBS significantly improved the high-temperature performance of TB rubber powder-modified asphalt and its mixes and retained the low-temperature characteristics to some extent [19]. In 2016, [PERSON] et al. found that the pavement performance of asphalt mixtures was significantly improved with rubber powder dosed at 8-10%. The incorporation of rock asphalt allows asphalt to reach a more desirable high-temperature and service condition [20]. Recently, there has been insufficient research on the fundamental mechanical and viscoelastic properties of hard asphalt and rubber power composite-modified asphalt, as well as their impact on HMAM [21; 22]. Therefore, further investigation into the performance of these materials in asphalt mixtures is highly worthwhile. Based on the properties of hard asphalt and rubber powder, this study presents a comprehensive discussion on the preparation and performance of composite-modified asphalt and its mixes. The aim is to enhance and improve their modulus and low-temperature performance, which is crucial for promoting the application of high-modulus composite-modified asphalt mixes while providing technical support.