esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The experiments were carried on spruce wood (_Picea abies_ L.). Experimental equipment LCS 400 (VEB Feinmechanische Werke, Halle, Germany) was used for cutting. This system consists of a CO\({}_{2}\) laser (wavelength 10.6 \(\upmu\)m and maximum power output 400 W), a positioning table system (laser head positioning in the plane formed by the \(x\) and \(y\) axes) and a special PC control system. The sample was placed in lens focus. The focused laser beam stroked perpendicularly on the surface of the sample and the laser head carriage moved along the width (axis \(x\)) at a certain scanning speed until the wood sample was cut off. Cutting kerf was obtained from the wood specimens with dimensions T \(\times\) R \(\times\) L (8 \(\times\) 100 \(\times\) 1000) mm (Figure 1) with average density \(\rho\) = 428.4 \(\pm\) 27.9 kg\(\cdot\)m\({}^{-3}\). Samples were cut tangentially by continuous laser powers 100 and 150 W, with cutting speeds of (3, 6, 9)\(\cdot\)10\({}^{-3}\) m\(\cdot\)s\({}^{-1}\). The focal length was 1.27\(\cdot\)10\({}^{-1}\) m, beam diameter was 10\({}^{-2}\) m, and spot diameter was 3\(\cdot\)10\({}^{-4}\) m. The focal point position of laser beam was set up to 1/2 of the sample thickness (measured from the upper surface of the board). The process gas was supplied through a Laval contour nozzle with compressed air of 0.25 MPa. The parameters of the cut were determined using digital microscopy through \(K\)-cluster analysis. The total number of measurements was 108 in one block. The ratio of kerf width on the upper and lower surface was calculated subsequently.  
Abbreviations are used in the text, the meaning of which is as follows: cutting kerf width on upper surface (WKU), cutting kerf width on lower surface (WKL), ratio of WKU and WKL (WKR), width of heat affected zone on upper surface (WHAZU), width of heat affected zone on lower surface (WHAZL), laser power (\(P\)) and number of annual rings (AR).  
Definitions of investigated parameters are described in Figure 1.  
Figure 1: Cutting scheme.  
In the research, we define 5 cut characteristics whose meaning are as follows: cutting kerf width on upper surface (WKU), cutting kerf width on lower surface (WKL), ratio of WKU and WKL (WKR), width of heat affected zone on upper surface (WHAZU), width of heat affected zone on lower surface (WHAZL), laser power (P) and number of annual rings (AR).
# An Analysis of Bubble Migration in Horizontal Thermo-Capillarity Using the VOF Modeling
## 4 Results and Discussion
### Bubble Sizes from \(R=0.01\) m to \(0.05\) m
#### 4.2.2 Effect of Bubble sizes  
Figure 4a-e show the centroid positions of the bubbles with different radii. When the temperature of the right wall decreases, the bubble tends to move toward the left wall, as shown in Figure 4a-d. With a radius \(R=0.01\) m or 0.015 m, the bubble tends to move faster to the left wall, except for no temperature gradient applied. The medium-sized bubble with a radius \(R=0.025\) m or 0.035 m tends to move gradually towards the left wall, and the biggest bubble, \(R=0.05\) m, prefers to stay near the center, as shown in Figure 4e. Therefore, the bubble size plays a significant role in the migration dynamics. Furthermore, a smaller bubble does not change its shape, so its migration velocity is faster. However, a larger bubble changes its shape from sphere to spheroid or spherical cap, reducing its flow velocity and moving slowly.  
Figure 2: Transient bubble position with a radius \(R=0.005\) m and \(\
abla T=8.33\times 10^{2}\cdot\) K/m.  
Figure 3: Schematic representation of the movement of the bubbles with different bubble radii in the \(x\)-direction under different longitudinal temperature gradients (**a**) \(\Delta T=0\) K, (**b**) \(\Delta T=20\) K, (**c**) \(\Delta T=40\) K, (**d**) \(\Delta T=60\) K, (**e**) and (**f**) \(\Delta T=100\) K.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 3 Properties of Video Watermarking
### Watermark Capacity, BIR, and Real-Time Performance  
Watermark capacity refers to the number of watermark bits embedded in unit time or a single video. The watermark capacity, imperceptibility, and robustness are mutually restricted [22], and their relationship is shown in Figure 7.  
For a specific watermarking algorithm, if it needs good imperceptibility in practical applications, it is necessary to avoid too much modification to the original video. As a result, the watermark capacity  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Processing Operations** & **Geometric Attacks** & **Temporal Synchronization** \\ \hline Gaussian filter (GF), Median filter (MF) & Scaling (Scl) & Frame dropping (FD) \\ Average filter (AF), Wiener filter (WF) & Cropping (Crp) & Frame swapping (FS) \\ Circular filter (CF), High-pass filter (HFP) & Rotation (Rnt) & Frame insertion (EI) \\ Gaussian noise (GN), Impulsive noise (IN) & Frame averaging (FA) \\ Salt \& pepper noise (SPN), Speckle noise (SN) & Frame cropping (FC) \\ JPEG, MPEG-2, MPEG-4, H.264, H.265 & & \\ Gamma correction (GC), sharpening (Sbp) & & \\ Histogram equalization (HE), Blurring (Blu) & & \\ Luminance modification (I.M), Recompression (Rec) & & \\ Contrast enhancement (CE), Transcoding (Tts) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Several typical attacks and their corresponding abbreviations.  
Figure 7: Relationship among watermark capacity, imperceptibility, and robustness.  
will be reduced. Meanwhile, too little modification will also lead to a decrease in the robustness of the algorithm. If the algorithm needs to have good robustness in practical applications, it will definitely need to make more modifications to the video, which will increase the watermark capacity and reduce the imperceptibility of the watermark. Theoretically speaking, it is impossible to design a watermarking algorithm that can achieve the three optimally at the same time. It is necessary to realize a compromise among the three according to the actual situation. When designing robust video watermarking algorithms, the robustness is improved as much as possible on the premise that watermark capacity and imperceptibility can meet certain conditions. Watermark capacity is usually quantified by the number of watermark bits embedded in the video.  
BIR is used to measure the increase of the video bit rate, which is often measured in watermarking algorithms based on compressed videos.  
Real-time performance refers to the low complexity of watermark embedding and extracting, which is usually evaluated by the length of time. Only when the watermarking algorithm meets the real-time requirement, can the smoothness of video data stream be ensured. The better the real-time performance of the watermarking algorithm is, the wider its application scope will be.
# Numerical Simulation of Seismic-Wave Propagation in Specific Layered Geological Structures
## 4 Methodological Extension
### Explosion Response of Water and Layered Geological Structures Models
#### 4.1.2 Numerical Results for Water  
Figure 9 visualizes the pressure distributions resulting from underwater explosions, showcasing how stress propagates in water. A review of the distributions reveals that, except for compressive stress, all other forms of stress are insignificant during the propagation of the stress wave. This is a characteristic behavior of water, an inviscid fluid, which can only bear compressive stress and is incapable of withstanding tensile or shear stress.  
Figure 8: Schematic representations of numerical explosion models for (**a**) water and (**b**) combined underwater granite and basalt.  
Figure 10a illustrates the temporal stress variations at varying distances from the source in water. The peak compressive stress values recorded at distances of 1, 2.5, 4.0, 5.5, 7.0, and 8.5 km are 68.7, 22.0, 12.2, 8.2, 5.95, and 4.65 MPa, respectively. The corresponding arrival times for these stress values are 0.64 s, 1.68 s, 2.71 s, 3.75 s, 4.76 s, and 5.81 s. Notably, at a distance of 9 km (which represents the upper water surface), stress unloading occurs. This effectively means a zero-stress zone is present due to the free surface conditions. Figure 10b presents a fitted exploration into the attenuation properties of seismic waves across water, based on data points extracted from numerical simulations. As illustrated in Figure 10b, the attenuation pattern of stress waves in water exhibits exponential decay. This also demonstrates that the model is capable of responding to the laws of seismic-wave propagation within geological structures, thereby validating the numerical simulation model.
# Analysis of Sequential Pretreatments to Enhance the Early-Stage Biorefinery Designs
## 1 Introduction
Therefore, combinations or sequences could be an alternative to improve the pretreatment stage by finding more suitable designs for better biomass fractionation, which will decrease the loss of valuable fractions. For example, a second pretreatment stage is proposed to maximize lignin removal after a recycled aqueous ammonia expansion process since the post-hydrolysis solid will have almost 30% of the initial lignin [12]. It has been shown that the sequencing of liquid hot water (LHW) and dilute acid (DA) substantially increases hemicellulose and lignin removal compared to single pretreatment, or the DA and LHW sequence maximizes glucose recovery [13]. Other authors concluded that the organosolv and alkali sequence achieves a cellulose purity of 78%, as they were able to remove high hemicellulose, lignin, and silica content from rice straw [14]. [PERSON] and co-authors demonstrated that presoaking with Fe\({}^{2+}\) helps to considerably decrease the acid buffering of rice straw ash on LHW pretreatments [15]. Consequently, sequential pretreatments could not only improve feedstock fractionation but also involve overcoming operational barriers. To date, there is no pretreatment systematization to maximize biomass fractionation, which involves a techno-economical description of process scale-up. This assessment is crucial to finding suitable production methods and overcoming possible technical and economic hurdles. Although pretreatments are selective to the type of process and feedstock,this work gives an overview of the best pretreatment schemes to maximize lignocellulosic components upgrading. Different sequential pretreatments were analyzed through a heuristic analysis involving the assessment of operational and techno-economic indicators. It was proposed to evaluate indicators through compositions and removals reported in the literature, as well as simulation results for process scale-up. The objective was to analyze the removal or isolation of lignocellulosic fractions to improve biorefinery design proposals. It was concluded that to maximize the integral valorization of lignocellulosic, hemicellulose fractionation must first be followed by lignin solubilization, obtaining the three lignocellulosic fractions as platform products.
## 4 Case Study and Economic Analysis  
### Parameters Setting of Case Study  
The simulation is conducted on a 24-hour day-ahead dispatch with 10 coal-fired units and wind power plants. The capacity of the thermal and wind power plant is, respectively, 1600 MW and 900 MW; namely, the wind power penetration rate is 36%. In China, thermal generator outputs are mainly determined by the day-ahead dispatch schedule with an hourly resolution, therefore the proposed peak-shave economic analysis of thermal units is also conducted accordingly on an hourly temporal interval. The coal-fired units data are given in Tables 1 and 2 of Appendix A[32], while the daily load level and wind power output are displayed in Figure 3. Two cases are studied: In case 1, all coal-fired units are assumed UPI units, while in case 2, all are assumed UOI units. According to the typical oil and coal price, \(S_{oil}\) is 0.96 $/kg and \(S_{coal}\) is 0.1 $/kg in the following cases; \(\tau_{1}\) is 1.2 and \(\tau_{2}\) is 1.5 [33]; \(P_{i,t}^{b}\) and \(W_{i,t}^{a-c}\) are, respectively, 105 kW and 22 kW [34]; \(S_{e}\) is 0.03 $/(kWh); the type of coal in cases is coking coal with \(\delta_{s}\) and \(\delta_{N}\) are 0.53% and 0.58%, respectively;\(\alpha\) is 1.4571 and \(\beta\) is 0.011 kg/$, and \(W_{s}\) and \(W_{N}\) are, respectively, 100 $/(mg/m\({}^{3}\)) and 125 $/(mg/m\({}^{3}\)). Referring to the peak-shaving requirements in China, \(P_{a}\), \(P_{b}\), and \(P_{c}\) are, respectively, set to 50%, 45%, and 30% of rated power \(P_{n}\) for defining RPS, DPSW, and DPSC in Figure 1.
## 4 Discussion
### Ethical Considerations  
Considering the wider moral ramifications of machine learning algorithm application for predicting aggressive behaviors in vulnerable populations, we must address related ethical issues such as privacy and misclassification risk [50; 51].  
**Privacy:** Sensitive information is often included in predictive models such as video recordings, images, audio biometric data and behavioral logs. In order to avoid misuse or illegal use, we must therefore adhere to strict privacy laws and ethical norms. The anonymity of all datasets and participants is a critical part, and the individuals must be fully informed about the intended use of their data and provide their consent. Strict data protection is also a very important role of the researchers especially for vulnerable groups.  
**Risk of False Positives and False Negatives:** Various consequences could result from false positives, such as unnecessary treatment or stigmatization due to inaccurate aggressive behavior prognostications. These may negatively affect the health of vulnerable patients or result in erroneous measures. Predictive models are employed as tools for supporting clinical decision making. The combination of machine learning with the expertise of trained staff can lead to the mitigation of such risks.  
**Bias and Fairness:** Algorithms can apply biases in training data and cause the production of wrong results. This is especially problematic when working with communities or vulnerable populations. Mitigation strategies are included during the development of such models. Steps to ensure the bias reduction, fairness and transparency of such systems must be carried out regularly.  
**Ethical Deployment and Oversight:** Engaging mental health and interdisciplinary teams of experts is crucial before implementing such models in community settings such as psychiatric hospitals, schools or prisons. The morality of these models must be guaranteed in order to avoid undue hazards to those who are already at risk.  
**Accountability and Human Agency:** Machine learning models are meant to support human judgment. Experts able to comprehend the model's behavior and predictions in the larger context should be responsible for case-by-case utilization. In order to assure such functionality, explicit accountability frameworks should be put in place when working with vulnerable populations.
## 3 The Static Form of the Problem
### Solution to WCCP
#### 3.2.2 Approximation  
So far, we have discussed the pruning rule. When the pruning rule is carried out at a node, the number of branches at this node in the search tree is significantly reduced. However, what happens when no pruning rule is applicable? We use quantization to remove the scale effect of the weights on vertices.  
We first determine a parameter \(Q\) in the algorithm. \(Q\) denotes the level of quantization of weights used in the search algorithm. Consider a vertex with \(W\) weights and \(D\) participating cliques. In the brute-force search algorithm, we will have \(\binom{W+D-1}{D-1}\) branches at this node, because there are a total of \(\binom{W+D-1}{D-1}\) ways to allocate weights to the participating cliques. In our approximation algorithm, the total number of weights \(W(=Q\times m)\) is split into \(Q\) equal shares, and these \(Q\) shares are allocated to participating cliques instead. Therefore, the number of branches at this node is reduced to \(\binom{Q+D-1}{D-1}\).  
We then analyze the amount of error introduced in the approximation step. For a single vertex, its weight can be arbitrarily allocated to its participating cliques. However, due to quantization, each participating clique can only be assigned an integer multiple of \(m\) as its weight. Suppose that a real optimal allocation exists and is denoted by \(\Sigma^{*}\), and the \"optimal\" allocation found by our search algorithm is \(\Sigma\). We derive \(\Sigma-\Sigma^{*}\) by comparing the allocated weights for each participating clique. Consider the transformation from \(\Sigma^{*}\) to \(\Sigma\). Any negative value in \(\Sigma-\Sigma^{*}\) implies a revocation of weights for the clique in allocation \(\Sigma^{*}\); any positive value in \(\Sigma-\Sigma^{*}\) implies a reallocation to the corresponding clique. Note that any revocation or reallocation involves at most one share of quantized weight, and this is exactly \(1/Q\) of the number of weights in the original problem. The size of the problem is reduced to \(1/Q\) of the original problem, and we can derive that the error involved is limited to \(W/Q\).
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 3 Methods
### Optimization Idea  
Based on the traditional combination forecasting model, this paper proposes the EALL (EEMD-ARIMA-LSTM-LSTM) algorithm based on the decompose-predict-combine approach, combined with various techniques introduced earlier to optimize the data components in the traditional combination forecasting model. In order to solve the problem of neural network parameters being randomly initialized leading to local optima or insufficient training, an improved gray wolf optimization algorithm was introduced to optimize LSTM in EALL, and the dam deformation prediction model IEALL was proposed. Then, based on the single-point prediction model IEALL, the spatial correlation between deformation sequence multiple points is considered, and the dam deformation prediction model STAGCN is used to extract the spatial features between multiple measurement points, resulting in the STAGCN-IEALL dam deformation prediction model. The optimization process for the combined model is illustrated in Figure 1.
## 4. Degradable Sensor Materials
### Degradable Polymeric Materials
To address these issues, researchers are actively exploring coating techniques, interface treatments, and additional modification methods [163]. The aim is to overcome these limitations and expand the performance and application scope of natural fibers in medical implant devices. The [PERSON] team proposed leaf skeleton-reinforced electronics as a brand-new and environmentally friendly platform for electronic devices and circuits and successfully developed a coated cellulose ethanol film that can be used for commercial reflow soldering circuits [164]. This film exhibits excellent flexibility, with a transparency of over 80% and a surface roughness of less than 5.5 nm. The advanced organic photodiodes (OPDs) and organic electrochemical transistors (OECTs) fabricated based on this film have performance similar to that of their counterparts fabricated on glass substrates. When the circuit board reaches the end of its service life, the soldered components can be conveniently separated and extracted from the substrate. The developed method can not only achieve the non-destructive recovery of electronic components, but also recycle the metals used for printed circuit interconnections without damaging the substrate itself. This research has opened up new possibilities for the application of biodegradable polymers. As a kind of metal-organic hybrid material, the discovery of molecular ferroelectric materials has brought the piezoelectric performance of implantable piezoelectric materials to a new level. The adjacent molecules of this compound interact with each other to form a two-dimensional hydrogen bond network. This characteristic enables the HFPD crystal to exhibit excellent solubility in various solvents (especially body fluids). The compound also has good biosafety, biocompatibility, and biodegradability. Considering the brittleness and rigidity of the crystal, the [PERSON] research team prepared a flexible piezoelectric composite film through the solution evaporation method [129]. A controllable transient electromechanical device was assembled using this piezoelectric composite film, and it was confirmed to have good biosensing performance. This research provides a promising candidate material for degradable implantable electronic medical devices and also offers an essential application scenario closely related to human health for molecular piezoelectric materials. There are also some materials suitable for implantable devices. The types and characteristics are detailed in Table 6.
# Cellulose Nanofibril (CNF) Films and Xylan from Hot Water Extracted Birch Kraft Pulps
## 1 Introduction
Due to their hydrophilic nature, the presence of hemicelluloses in the pulp and in the resulting CNF may also be detrimental for the production of films in which moisture barrier properties are required [18]. In unbleached chemical pulps, the presence of residual lignin (up to 15%) has also been reported to enhance the deconstruction of the cell wall during mechanical fibrillation [19; 20]. Previous studies have investigated the effects of extracting hemicelluloses from the pulp on the preparation of CNF and on the properties of the films made thereof [14; 15; 16; 17; 18; 21; 22]. From a biorefinery point of view, the extraction of hemicelluloses should also be accompanied by their recovery and subsequent utilization in high-value applications. Hemicelluloses can be utilized in the manufacture of films, fuels, chemicals, food additives, hydrocolloids, nutraceuticalals, etc [23; 24]. Their utilization potential comes mainly determined by the purity, molar mass and degree of substitution. In recent years, several strategies to recover the hemicelluloses from pulp have been investigated using alkali [25; 26], ionic liquids [27], and hydrothermal treatments [28; 29]. Among those, the hydrothermal treatment offers the obvious advantage of being an environmentally friendly process, as water is the only solvent used. Up to 80% of the xylan in unbleached birch kraft pulp can be extracted during a hot water treatment in a flow-through reactor, with an average molar mass between 5-10 kDa depending on the flow rate [29]. The cellulose yield after the treatment remains largely unaffected, but the degree of polymerization (DP) of the cellulose fraction is considerably reduced. This decrease in cellulose DP should not be an impediment for the effective fibrillation of the water-extracted pulp, as cellulosic materials with low DP values have been successfully converted into homogeneous CNF networks by mechanical processing [8]. Moreover, chemical pre-treatment of pulp with TEMPO is also known to promote significant depolymerisation of cellulose [13]. In this study, hot water extractions of bleached and unbleached birch kraft pulp were conducted in a flow-through system to partially remove and recover the xylan, and to produce pulps with varying chemical composition. The xylan fractions in the aqueous hydrolysates were characterized in terms of purity and molar mass to assess potential applications. The water-extracted pulps were then subjected to TEMPO-mediated oxidation for the preparation of CNF, and the resulting CNF dispersions were used to make films. The effects of xylan removal on the preparation and quality of the CNF as well as on the properties of the CNF films are discussed.