esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Influence of MICP on Properties of Unsaturated Soil
### Permeability of MICP Unsaturated Soil  
The CaCO\({}_{3}\) crystals deposited in the soil reduced the porosity (or porosity) of the soil. [PERSON] et al. [105] showed that the permeability of the rock cracks filled with bio-cementation decreased by about four orders of magnitude. The water pressure of the repaired rock cracks was about three times higher than that of original rock cracks. In addition, the field results showed that the permeability of the rock fractures decreased significantly within a few square meters around the injection well [106]. [PERSON] et al. [107] suggested that the CaCO\({}_{3}\) precipitation generated divides the large voids in the soil sample into many small voids, which makes the internal distribution of the sample more uniform and reduces the seepage channel in the soil sample. [PERSON] et al. [108] found that the permeability of the surface decreased by 50% after 45 h, while the soil pores were almost completely blocked after 120 h. They concluded that the permeability coefficient of the soil sample was determined by the content of CaCO\({}_{3}\) in the soil sample. Figure 7 shows the schematic diagram of the MICP soil structure during water infiltration. There were three aspects resulting in the permeability decrease in soil: (1) the generated CaCO\({}_{3}\) crystals occupied the void space, which was caused by the reduction in porosity; (2) the interspace or pore throat was blocked by crystals; (3) the bioblockage caused by the production amount or related metabolites. [PERSON] and [PERSON] [15] found that some CaCO\({}_{3}\) precipitations do not contribute to contact bio-cementation, since these carbonates were either randomly crystallized on the soil particles or precipitated on the formed carbonate bridges. In conclusion, the permeability of soil samples was improved by MICP, since pores within soil were blocked by the formation of CaCO\({}_{3}\) crystals and the metabolism of microorganisms.  
Figure 6: Micromorphology of sand soil after treatment: (**a**) filling pore; (**b**) cementation.  
Figure 7: MICP soil infiltration diagram: (**a**) MICP soil diagram before wetting, (**b**) water infiltration into the pores, (**c**) saturated state.
# An Adaptive Linear Programming Algorithm with Parameter Learning
## 2 How Does the ALP Work? ### The Adaptive Linear Programming (ALP) Algorithm  
The ALP algorithm is implemented in DSIDES. DSIDES is used to formulate and solve engineering design problems, and it is especially efficient in dealing with nonlinear problems [6]. In DSIDES, the nonlinear problem is formulated as a compromise decision support problem (cDSP). Then, the ALP is invoked to solve the nonlinear problem. The nonlinear problem is linearized in a synthesis cycle. The resulting linear problem is solved using the revised dual simplex algorithm. The synthesis cycle is repeated until the solution satisfies a set of stopping criteria or is terminated after a fixed number of iterations. The ALP incorporates a local approximation algorithm [6; 31], in which a secant plane of the paraboloid (with the second-order derivatives at the starting point as the coefficients) replaces the original nonlinear function. In Figure 4, we show two dimensions of a problem being approximated in two iterations (synthesis cycle). The weighted sum of the goals is \(\sum_{\text{k}\in\text{K}}\text{W}_{\text{k}}\cdot\text{G}_{\text{k}}\). The starting point \(\X_{0}^{0}\) may not be in the feasible region. A random search or a Hooke-Jeeves pattern search can be invoked to identify a point \(X_{1}^{0}\) in the feasible area. In the i\({}^{\text{th}}\) first iteration, the problem is linearized at \(X_{1}^{0}\). In iteration i, Figure 4a, a projection of a nonlinear constraint NF\({}_{\text{j}}\) onto a two-dimensional plane, \(X_{1}\)-\(X_{2}\), is approximated at \(X_{1}^{0}\), so an approximated constraint LF\({}_{\text{ij}}\) is obtained. Doing this for all nonlinear functions and framing a linear model, the revised simplex dual algorithm is used to obtain solution \(X_{i}^{*}\). Using the RMC heuristic, we find the starting point of the next iteration \(X_{i+1}^{0}\). In iteration i + 1, Figure 4b, the approximated linear constraints of both iterations LF\({}_{\text{ij}}\) and LF\({}_{\text{i+1}}\) are accumulated, and a solution \(X_{i+1}^{*}\) is returned and the starting point of iteration i + 2 is again defined using the RMC, \(X_{i+1}^{0}\) and \(X_{i+1}^{*}\). In Figure 5a, we illustrate the two-step linear approximation method. First, NF\({}_{\text{j}}\) (Paraboloid ABC) is approximated to NF\({}_{\text{ij}}\)\({}^{\prime\prime}\) (Paraboloid AB\({}^{\circ}\)C\({}^{\prime}\)) with the diagonal terms of its Hessian matrix at \(X_{i}^{0}\) as coefficients. Then, NF\({}_{\text{j}}\)\({}^{\prime\prime}\) is approximated to a secant Plane LF\({}_{\text{ij}}\) (Plane AB\({}^{\circ}\)C\({}^{\prime}\)). NF\({}_{\text{j}}\)\({}^{\prime\prime}\) and LF\({}_{\text{ij}}\) are computed as follows. NF\({}_{\text{i}}\)\({}_{\text{j}}\)\({}^{\prime\prime}\) is obtained using the second-order full derivatives at \(X_{1}^{0}\), Equation (10), because the second-order partial derivatives have limited impact on the gradient.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 2 Overview of the Technologies and Comparative Discussion
### Sidelink LTE-V2X
The algorithm presents several parameters, although it has been shown in [22; 23] that its performance is mostly determined by the setting of \(p_{\text{k}}\). The choice of \(p_{\text{k}}\) in most scenarios leads to a trade-off between reliability and latency: on the one hand, a low value makes reservations changing often and thus the sensing procedure fail to correctly estimate the occupancy in the next future; on the other, a high value makes possible incorrect selections to last for long intervals, causing longer bursts of errors.
## 3 Proposal
### Noise Reduction  
The first step is to identify benign domains via comparison with whitelists, which consist of domains owned by trustworthy organizations and those used by specific services that cause massive NXDOMAIN messages. Examples of these domains include kyutech.ac.jp and kyutech.jp, which are owned by our institution; uribl.com and dnswl.org, which are queried for DNS-based lists (DNSBL) [40]; and trendmicro.com, which is associated with endpoint antivirus software. Next, domain names that violate DNS specifications are deemed to be the result of misconfiguration and are discarded [41].  
Figure 2: Overview of our proposed approach for identifying malicious domain names by analyzing their character strings at the word level.  
Notably, any domains prefixed with \"xn-\(-\)\" are excluded here because our approach does not support internationalized domain names (IDNs) [42]. We expect IDNs to be not often used by dict-DGA malware that generates domain names from word dictionaries because in name resolution IDNs are replaced by seemingly random character strings, such as xn--sjqw@xtwbgyd9 ay881 and xn--djry4 lk1 bj@u, which are distinctly different from human-generated domain names. Incidentally, one of the most common abuses of IDNs is the registration of domain names that exhibit a high visual and semantic similarity to those of reputable brands [43; 44]. The remaining domain names are considered possible DGA-generated strings and are passed as training datasets \(X_{i}^{*},i\in 0\cdots n\) to subsequent functional units for further analysis.
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 3 Results and Discussion
### Robot Control and Planning
They compared their teleoperation interface to the one presented in [39], that directly tracks an operator's movements and therefore requires operators to simultaneously control both the position and orientation of the robot, and found that their system had a 93.75% success rate compared to a 25% rate in completing a stacking task, which they attribute to their decoupling and force regulation in their controllers. Similar results were found in [40], where they showed that people preferred to control the movement of a robot by clicking-and-dragging a virtual sphere rather than have the robot directly follow the movements of the VR controller. Improvements in virtual reality robot control and planning are also being looked at for specific applications. Ref. [41] presents an architecture that estimates human intent in VR to operate a welding robot. Their results show an increase in performance with their human intent recognizer. Instead of recognizing human intent, ref. [42] develops an optimization based planner to control a painting drone in VR. Qualitative results, where an operator controls the painting drone to trace a previously painted contoured line, show a high accuracy in system. Similarly, ref. [43] designs a teleoperation system for aerial manipulation that includes tactile feedback. Ref. [13] defines a control architecture that utilizes a VR headset, VR controllers, and an omni-directional treadmill to create a fully immersive teleoperation interface to operator a humanoid robot.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 3 Methods
### Problem Analysis and Overview  
In Equation (1), when \(\mathbf{x_{T}}\) represents the stock data, we hope to capture the inter correlation between \(\mathbf{x_{t_{k}}}\) and \(\mathbf{x_{t_{l}}}\). Hence, it is highly recommended to explicitly model the relationship between \(\mathbf{x_{t_{k}}}\) and \(\mathbf{x_{t_{l}}}\). Another problem of the stock data aforementioned is the subtle change problem. As such, ideally it is preferred that the same features at different time steps are comparatively more stable. To address this, we hope to do some data transformation to the original scattered features.  
In detail, let \(x_{tj}\) denote the \(j_{th}\) feature at time step t, and assume that the \(p_{th}\) column is where the mid price located. We propose frequency decomposition based GRU Transformer (FDG-Trans) framework, consisting of a CEEMD module and a hybrid GRU Transformer. First, we use the CEEMD to do the decomposition for \(\mathbf{x_{p}}=\{x_{1p},x_{2p},\cdots,x_{Tp}\}\) and get \(\mathbf{x_{p1}},\mathbf{x_{p2}},\mathbf{x_{p3}},\mathbf{x_{p4}}\). Then, we put the new data matrix into 1 LSTM and 1 GRU, followed by 1 multi-head transformer, finally concatenated with timely invariant features and 1 linear layer. The sketch of our method can be shown in Figure 2.  
Figure 2: The overview of architecture of FDG-Trans. The series of prices is first decomposed to 4 more stable series, then concatenated with other futures as the input of the LSTM layer, followed by the GRU layer and transformer layer. Finally, the output of transformer concatenated with timely invariant features is passed by a linear layer to get the predicted rate of return.
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 3 Nutritional and Biochemical Composition of Date Fruit and Date By-Products
### Bioactive Compounds--Phenolic Compounds and Antioxidant Activity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound Name** & **Classification** & **Structure** & **Molecular Formula** & \begin{tabular}{c} **Extraction and** \\ **Identification Method** \\ \end{tabular} & **References** \\ \hline \(p\)-coumaric acid & Hydroxycinnamic acid & C\({}_{6}\)H\({}_{4}\)(OH)COOH & \
\begin{tabular}{c} Solvent extraction; \\ Ultrasonic bath \\ extraction; HPLC \\ \end{tabular} & [65; 66] \\ \hline Caffeic acid & Hydroxycinnamic acid & C\({}_{9}\)H\({}_{8}\)O\({}_{4}\) & Solvent extraction; HPLC & [65] \\ \hline Rutin & Flavonoid glycoside & C\({}_{27}\)H\({}_{30}\)O\({}_{16}\) & Solvent extraction; HPLC; LC-MS & [65; 67] \\ \hline Gallic acid & Phenolic acid & C\({}_{6}\)H\({}_{2}\)(OH)\({}_{3}\)COOH & Solvent extraction; HPLC & [65] \\ \hline Catechin/epiccatechin & Flavan-3-ol & C\({}_{15}\)H\({}_{14}\)O\({}_{6}\) &
\begin{tabular}{c} Ultrasonic-assisted \\ extraction; HPLC \\ \end{tabular} & [68] \\ \hline Ferulic acid & Hydroxycinnamic acid & C\({}_{6}\)H\({}_{8}\)O\({}_{4}\) & Solvent extraction; HPLC; LC-MS & [65; 67] \\ \hline Vanillic acid & Benzoic acid derivative & C\({}_{8}\)H\({}_{8}\)O\({}_{4}\) & Solvent extraction; HPLC & [65] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Important phenolic compounds isolated from date pits. of phenolic compounds, including gallic acid, protocatechuic acid, \(p\)-hydroxybenzoic acid, vanillic acid, caffeic acid, \(p\)-coumaric acid, ferulic acid, \(m\)-coumaric acid, and \(o\)-coumaric acid. These compounds have been identified in this particular type of date seed [70]. [PERSON] [67] analyzed three varieties of dates, Ajwa, Khalas Alkharj, and Al-Qasim, using LC-MS and LC-MS/MS, and a large group of unique phenolic compounds was identified, including caffeic acid and ferulic acid. [PERSON] and [PERSON] [68] reported that the total soluble phenolic content of the date seeds ranged from 68.7 to 82.6 mg GAE/g, and several phenolic compounds were also identified in their date seed extracts, including proanthocyanidin dimers, catechin, epicatechin, and others. The extract of soluble phenolic from date seeds is effective in preventing DNA strand scission by 74.2% and can efficiently inhibit the mRNA level of COX-2 at concentrations as low as 5 \(\upmu\)g/mL, which represents the potency of date seed samples in the inhibition of radical-induced DNA scission.
## 3 Results and Discussion
### Color Characteristics of Pasteurized Orange Juice
#### 3.3.2 Redness (a*)  
Regarding the redness of the juice, a linear model presented the best fit (AICc = 96.97, R\({}^{2}\) = 0.64):  
\[Y=a-b*day \tag{4}\]  
The redness of the pasteurized orange juice declines during storage, as manifested in Figure 4 and statistically described in Table 6. In other words, the rate of the decrease is greatly affected by the storage temperature level. Although at 4 and 15 \({}^{\circ}\)C, the reduction was relatively mild, with a change from 6.7 to 6.5, at the higher temperature levels of 20 and 25 \({}^{\circ}\)C, the reduction was steeper, and dropped from approximately 7 to 5 at the end of the storage time (Figure 5). This observation was also noticed in the study performed by [PERSON] et al. (2018), where the a* parameter of blood orange juice decreased from 17.5 to 15.4 during storage for 7 weeks, and this was associated with both the degradation of anthocyanins, which are the main color agents of orange juice, and ascorbic acid. Anthocyanins, which are polyphenols, present high antioxidant activity and have numerous beneficial effects on human health. Thus, maintaining high levels of anthocyanins after the production of orange juice improves the nutritional value of the product ([PERSON] et al., 2018). This fact enhances the relationship between the optical and the nutritional value of orange juice, enabling the establishment of new indirect nutritional indicators.  
Figure 4: Curve fitting plot of experimental data in the logistic 3P model of L* value vs. storage days. (a) 4 \({}^{\circ}\)C; (b) 15 \({}^{\circ}\)C; (c) 20 \({}^{\circ}\)C; (d) 25 \({}^{\circ}\)C.
# Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication
###### Abstract  
The currently deployed terrestrial wireless networks experience difficulties while coping with the massive connectivity demands of coexisting users and devices. The addition of satellite segments has been proposed as a viable way of providing improved coverage and capacity, leading to the formation of integrated satellite-terrestrial networks. In such topologies, non-orthogonal multiple access (NOMA) can further enhance the efficient use of wireless resources by simultaneously serving multiple users. In this paper, an integrated satellite-terrestrial NOMA network is studied where cooperation between ground users is allowed, following the device-to-device (D2D) paradigm. More specifically, the proposed satellite NOMA cooperative (SANOCO) D2D scheme optimally selects pairs of users, by considering the channel conditions of the satellite and the terrestrial D2D links. In SANOCO-D2D users are served through NOMA in the satellite link, and then, if the weak user fails to decode its signal, terrestrial D2D communication is activated to maintain the total sum rate of the system. Comparisons with conventional orthogonal multiple access (OMA) and an alternative NOMA optimal user pairing scheme show that significant sum rate and spectral efficiency gains can be harvested through SANOCO-D2D under varying channel conditions and terrestrial D2D bandwidth.  
Keywords:Integrated satellite-terrestrial; NOMA; D2D; cooperative communications +
Footnote †: journal:
# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Deblur Problem
However, it is not effective if the blurred text image has low contrast. In the same year, [PERSON] et al. proposed an unsupervised method for domain-specific single-image deblurring based mainly on disentangled representation [63] by untangling content and blurred features in blurred images and adding KL divergence loss to prevent blurred features from encoding content information. To preserve the content structure of the original image, a blurring branch and cyclic-consistency loss are added to the framework, while the added perceptual loss helps to remove unrealistic artifacts from the blurred image. The innovation is precisely the attempt to separate content from ambiguous information. In 2020, a method using the Local Maximum Difference Prior (LMD) was proposed in Ref. [64] by introducing a linear operator to compute the LMD and using the L1 norm to constrain the terms involved in the LMD. The LMD method decomposes a complex multicomponent signal into several components. This method is more general. However, when the image contains non-Gaussian noise, the processing is not very effective. In 2021, Blur2 sharp was proposed by [PERSON] et al. as an end-to-end model based on cycleGan [65], as shown in Figure 13. This model can perform deblurring without knowledge of the blurring kernel and generates clear images. However, the model processing results are similar to those of other methods and are not particularly advantageous. In 2022, [PERSON] et al. proposed DeblurGAN-CNN [66], as shown in Figure 14, a combination of DeblurGAN and CNN models which was upgraded from the effect of the original DeblurGAN to solve many noise problems and enhance the recognition performance of handwritten character images. Figure 14: DeblurGAN-CNN model structure constructed by [PERSON] and [PERSON] [66]. Published under a Creative Commons Attribution 4.0 license. Figure 13: Training process of blur2 sharp model structure constructed by [PERSON] et al. [65]. Published under a Creative Commons Attribution 4.0 license. DocImgCN  
A batch of PDFs were downloaded from the China National Knowledge Infrastructure (CNKI) [80] to prepare document images with Chinese characters. DocImgCN includes 230,000 training samples, 10,000 validation samples, and 10,000 test document images. Table 1 shows a summary of the relevant contents of the dataset.
# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 6 Discussion
This is accompanied by the problem that the continuous improvement of the shelter facility network is to serve the residents, and people are simultaneously flowing and changing; these changes should also be reflected in the spatial optimization of shelters in the future. For urban science, it is difficult to accurately measure the size and spatial distribution of people, although the development of big data provides the possibility for this, and the use of POI data to estimate the spatial distribution of the population in this study is a novel attempt at doing so. In the future, to continuously optimize the spatial distribution of emergency shelters, continuous and real-time mobile phone signaling data can be considered as the data basis for shelter demand assessment. The evacuation simulation and spatial optimization of emergency shelters involve complex conditions and numerous influencing factors. This study analyzed the supply and demand status of emergency shelters using an urban-scale evacuation model. The results provide a quantitative basis for the spatial optimization of shelters; however, there remains a gap between evacuation organization in real and simulated disaster environments. Due to the numerous and complex variables of evacuation activities after disasters (such as evacuation efficiency, physical obstacles, secondary disasters, human behavior, psychology, etc. ), this has always been a difficult subject in the related practice and research fields. Therefore, to realize an evacuation simulation at the urban scale, the model parameters and assumptions in this study were simplified to varying degrees, which had a certain impact on the results. However, from the perspective of providing a quantitative analysis basis for urban disaster prevention planning, this research provides a practical reference.
## 3 Methods--Model Architecture
### Attention Mechanism  
The outputs of the encoding line-based models are handed over to the decoder, using a user-defined number \(n\) of outputs \(y_{t\cdot n}\) from the last \(n\) historic network states. These historical network states are used as encoding \(e\) to be used by the attention mechanism Figure 6. The first step of the attention mechanism is to derive _Queries (Q)_, _Keys (K)_, and _Values (V)_. To obtain these values, a matrix product is calculated between \(e\) and the previously randomly initialised corresponding weights as shown below:  
\[Q=e\times W_{Q}\] \[K=e\times W_{K}\] \[V=e\times W_{V}\]  
where:  
\(e=\) embedding of user-defined \(n\) network states.  
\(W_{Q},W_{K},W_{V}=\) randomly initialised weights for \(Q\), \(K\) and \(V\) respectively.  
The decoder employs a scaled dot product attention as described by [34]. The authors used the following scaling method:  
\[Attention(Q,K,V)=softmax\Big{(}\frac{QK^{T}}{\sqrt{d_{k}}}\Big{)}V\]  
where:  
\(d_{k}=\) dimension of queries and keys.  
\(Q\), \(K\), \(V=\) Queries, Keys and Values respectively.  
The authors of [34] hypothesised that the reasons this scaling is necessary are issues caused by vanishing gradients of the softmax layer if the input data of the encoder had high dimensions. We found in our experiments that scaling did worsen the overall performance of the prediction model, and therefore the scaling was abandoned, and a simple dot product attention was modified by upscaling the attention by a constant of 1.5 which was empirically chosen by testing the performance of small subsets of data.  
\[Attention(Q,K,V)=softmax\Big{(}QK^{T}c\Big{)}V\]  
Figure 6: Schema of attention without fully connected layer or sigmoid. This illustrates the attention mechanism itself.  
where:  
_Q, K, V, c_ = Queries, Keys, Values and upscaling constant (1.5) respectively.  
The output of this attention decoder was fed through a fully connected layer followed by a sigmoid layer to give the final prediction.