esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### MSD Algorithm  
The MSD algorithm utilizes the maximum likelihood criterion to decide the received signal within the observation time, with the aim of minimizing the error rate in detecting the transmitted symbol.  
The sequence of transmitted symbol can be expressed as  
\[\mathbf{s}=[s_{1},s_{2},s_{3},\cdots,s_{n}] \tag{1}\]  
Taking into account the impact of noise, a potential symbol sequence obtained from demodulation can be expressed as  
\[\bar{\mathbf{r}}=[\tilde{r_{1}},\tilde{r_{2}},\tilde{r_{3}},\cdots,\tilde{r_{n}}] \tag{2}\]  
Therefore, the BER equation can be expressed as  
\[P_{e}=P(\bar{\mathbf{r}}\
eq\mathbf{s}\mid r(t))=1-P(\bar{\mathbf{r}}=\mathbf{ s}\mid r(t)) \tag{3}\]  
where \(r(t)\) is the received signal.  
According to Equation (3), in order to minimize the BER in the demodulator, the receiver must maximize the value of \(P(\mathbf{s}\mid r(t))\). Based on Bayes' Rule, \(P(\mathbf{s}\mid r(t))\) can be rewritten as  
\[P(\mathbf{s}\mid r(t))=\frac{P(\mathbf{s}r(t))}{P(r(t))}=\frac{P(r(t)\mid \mathbf{s})P(\mathbf{s})}{P(r(t))} \tag{4}\]  
In Equation (4), \(P(r(t))\) remains invariant regardless of the decision outcome. It is commonly presumed that the transmitted symbols adhere to uniform probability distribution, whereby the value of \(P(\mathbf{s})\) remains constant with variations in the decision outcome. To determine the \(\mathbf{s}\) that maximizes \(P(\mathbf{s}\mid r(t))\), it suffices to identify the \(\mathbf{s}\) that maximizes the \(P(r(t)\mid\mathbf{s})\). As the noise is additive white Gaussian noise, \(P(r(t)\mid\mathbf{s})\) can be expressed as  
\[P(r(t)\mid\mathbf{s})=\frac{1}{(2\pi N_{0})^{n/2}}\exp\left[-\frac{1}{2N_{0}} \sum_{k=1}^{n}(r_{k}-s_{k})^{2}\right] \tag{5}\]  
Taking the logarithm of Equation (5), we obtain  
\[ln(P(r(t)\mid\mathbf{s}))=-\frac{n}{2}ln(2\pi N_{0})-\frac{1}{2N_{0}}\sum_{k =1}^{n}(r_{k}-s_{k})^{2} \tag{6}\]  
In order to maximize the \(P(r(t)\mid\mathbf{s})\), it is necessary to minimize \(\sum_{k=1}^{n}(r_{k}-s_{k})^{2}\), which can be rewritten as  
\[\sum_{k=1}^{n}(r_{k}-s_{k})^{2}=\sum_{k=1}^{n}r_{k}^{2}-\sum_{k=1}^{n}(2r_{k} s_{k})^{2}+\sum_{k=1}^{n}s_{k}^{2} \tag{7}\]  
where \(\sum_{k=1}^{n}r_{k}^{2}\) and \(\sum_{k=1}^{n}s_{k}^{2}\) can be considered to be constant values. Consequently, the fundamental principle of the MSD algorithm is minimizing the BER of the demodulation at the receiver by identifying the symbol sequence \(\bar{\mathbf{r}}\) that corresponds to the maximum of \(\sum_{k=1}^{n}(2r_{k}s_{k})^{2}\).
## 4 Results
### Seismic Analysis  
1. The internal forces on an underground structure, as calculated by FBM, are small, which is related to the fact that the friction and shear stresses caused by soil deformation are not considered in the FBM. The DBM is simple to establish, and the principle is to simulate the interaction between soil and structure by establishing springs and applying horizontal displacement to the key stratum.
2. The DBM simulates the interaction between soil and structure by establishing springs and applying horizontal displacement to a stratum, and the model is simple. The correct values of spring stiffness and ground horizontal displacement make the output of structural shear force and bending moment approach those calculated using the time-history method of analysis. The output accuracy of shear force and bending moment values is higher than that of DESANM, but the accuracy of axial force output is poorer than that of DESANM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Observation Point**} & \multicolumn{2}{c}{**Bending Moment/kN-m**} & \multicolumn{2}{c}{**Shear Force/kN**} & \multicolumn{2}{c}{**Axial Force/kN**} \\ \cline{2-7}  & **Normal Use** & **Under Lateral** & **Normal Use** & **Under Lateral** & **Normal Use** & **Under Lateral** \\  & **Stage** & **Seismic Action** & **Stage** & **Seismic Action** & **Stage** & **Seismic Action** \\ \hline a & \(-0.014\) & \(215.6\) & \(0.005\) & \(-201\) & \(-1180\) & \(-1031.7\) \\ b & \(78.2\) & \(-153.9\) & \(-132.000\) & \(-284\) & \(-1260\) & \(-1154.5\) \\ c & \(0.005\) & \(131.6\) & \(0.003\) & \(-419.6\) & \(-1560\) & \(-1421.9\) \\ d & \(90.5\) & \(-197.5\) & \(-134\) & \(-517.6\) & \(-428\) & \(-1558.6\) \\ \hline e & \(-248\) & \(-658.2\) & \(-976\) & \(-118.2\) & \(-547\) & \(-506.9\) \\ f & \(-550\) & \(49.2\) & \(1050\) & \(-180.9\) & \(-727\) & \(-794.8\) \\ g & \(-347\) & \(-224.9\) & \(-183\) & \(-272.5\) & \(-610\) & \(-854.3\) \\ h & \(-418\) & \(-1352.4\) & \(568\) & \(-358.2\) & \(-1080\) & \(-1395\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of structural internal forces in normal use.  
3. The loess stratum is established in the calculation model of the DESANM, so the calculation relies on fewer parameters. When the value of horizontal relative displacement is accurate, the results of structural internal force of DESANM are consistent with that of the time-history method of analysis. However, the calculation is more onerous, ranking second only in complexity compared to that in the time-history analysis.
# BoxRF: A New Machine Learning Algorithm for Grade Estimation
## 2 Materials and Methods
### Nuances of the Method  
Since boxes (or leaves) are formed randomly, two leaves in the same tree may represent overlapping regions. This is shown in Figure 4. Assuming Leaf 1 is formed first, all the samples within it are unavailable when Leaf 2 forms. Thus, Leaf 2 only has samples at one end as it does not have access to the blue samples that fall within its region, making Leaf 2 not very representative of its space. The lack of representativeness creates an issue when the dark red point, which falls within both leaves, is to be predicted. Both leaves claim the sample location, but clearly, the prediction from Leaf 2 is problematic for the red location, as it contains no samples that span the red location.  
This situation is dealt with using the hyperparameter number of times to reuse samples (NR). When NR = 1, a sample is deleted from the database once it is inside a leaf. When NR = 2, the sample is allowed to be claimed by two leaves before it is deleted from the database. This minimizes the occurrence of the situations described previously. The deletion of samples is essential, as otherwise, leaf formation will not stop. Also, if samples are not deleted after being claimed by a leaf, too many leaves represent a region, and a legitimate trend contained within a leaf becomes diluted by the presence of other leaves. Therefore, NR was only tested with values of 1 and 2.  
The BoxRF method contains another nuance. What is the prediction from the tree when multiple leaves within it are allowed to make predictions for a given location? In this paper, this issue was tested through a hyperparameter called predict from first hit only (FH). When a location is to be predicted, the tree scrolls through the leaves to determine leaves that can claim the location and make the prediction. When FH = 1, the prediction of the tree comes from the first leaf that is found to contain the prediction location (Figure 5). When FH = 2, the tree prediction is the median of predictions from the various leaves that claim the location.
## 1 Introduction
ICN, with its novel routing protocols, which allow flexible definition and use of fields, can achieve flexible routing of interest packets based on user preferences [21; 22], demonstrating the potential to implement a preference-based resource scheduling mechanism. Therefore, this paper proposes an ICN-based on-path computing resource scheduling architecture with the following main contributions:  
* Designing a hierarchical scheduling architecture that supports on-path resource scheduling: by carrying resource demands and preference parameters in the computing resource request packets, the scheduling nodes can adjust their scheduling destinations promptly during the packet routing process based on demand parameters and current resource status information, thus improving scheduling success rates. * Developing computing resource scheduling strategies tailored to different resource request preferences: scheduling nodes read the preference field in the packet header, make decisions, and forward the packet based on the corresponding scheduling strategy, meeting diverse user requirements. The remaining sections of this paper are organized as follows: Section 2 analyzes relevant research on the Computing Network, existing resource scheduling mechanisms,ICN, and constructing a Computing Network based on ICN. Section 3 describes the overall structure of the proposed distributed computing resource scheduling architecture IPCRSA, presenting the design of computing resource request packets and the typical process of resource scheduling within this architecture. Section 4 expounds on the new mechanisms and optimizations of IPCRSA, encompassing the preference-based computing resource scheduling strategies and the processing and forwarding mechanisms for computing resource request packets. In Section 5, a performance analysis of IPCRSA is conducted through a comparative simulation experiment. Finally, Section 6 summarizes the work presented in this paper.
## 3 Results
### Osmotic Dehydration of PEF Pre-Treated Tomatoes
#### 3.2.4 Sensory Evaluation of PEF Treated Tomatoes during OD  
The results for total sensory evaluation acceptance of untreated and PEF-treated tomatoes during OD are presented in Figure 3e. It was observed that the perceived intensity of glossiness and deformation (Supplementary Figure S1) exhibit an inverse trend: as OD time increases, deformation increases and glossiness decreases due to the shriveling of tomatoes, due in turn to dehydration. Glossiness did not differ between PEF treatment conditions for all OD times, while deformation was more intensely perceived at a treatment of 300 pulses, attributed to the extensive degree of cell disintegration. A slight increase in the perceived peel detachment was observed as OD time increases, for all PEF treatment conditions studied. No treatment condition led to unacceptable levels of  
Figure 3: Evolution of (**a**) water activity (\(a_{w}\)), (**b**) pH value, (**c**) relative firmness (**d**) total color difference and (**e**) overall acceptance during osmotic dehydration of untreated and PEF-treated tomato samples, at 1.8 kV/cm with 20, 50, 100 and 300 pulses (average values \(\pm\) standard deviations). Different superscript letters indicate significant differences between means of overall acceptance \(\pm\) standard deviation as calculated by [PERSON]’s multiple range test for a significance level of \(p=0.05\).  
peel detachment. Color intensity remained relatively constant during OD for all treatment conditions studied. This is in contrast to the determination of the objective color, where color difference was determined to be noticeable. It is possible that shrinkage and glossiness caused by the osmotic medium masks the perception of this color difference. In terms of tomato flavor and other flavor intensity, a slight loss of tomato flavor was observed as OD progresses, accompanied by an increase in the intensity of other flavors perceived. The panel reported an increase in vinegar aroma with the increase of OD time, which is consistent with the decrease of pH, as discussed above. A marked improvement was observed for bite hardness and crunchiness of the samples. Immediately after processing (OD time 0), both characteristics deteriorate with increasing PEF processing intensity. However, as OD progresses, this deterioration is reversed. This is attributed to the increase of sample cohesiveness caused by the interaction of endogenous pectins with the calcium ions present in the OD medium. Although this is not reflected in the measurements of objective firmness discussed in Section 3.1, it seems that perceived texture cannot be fully represented by firmness alone, as the complex physicochemical interactions taking place within the tissue leads to the improvement of other textural characteristics not detectable by the measurement protocol used. All samples exhibited a high degree of overall acceptance (7-8), regardless of OD time or PEF processing conditions, leading to the conclusion that PEF as a pre-treatment to OD does not lead to quality deterioration.
## 2 Background
A standard choice for signal normalization is the rectified linear unit (ReLU) that converts all negative values to 0's [30; 31; 32]. A layer of normalization units is called a _normalization layer_. Some ConvNet architectures (e.g., [26]) consider normalization to be part of convolution layers. The size of the output signal from a layer can be downsampled with a _maxpooling layer_, where a window is shifted in steps, called _strides_, across the input signal retaining the maximum value from each window. The fourth type of layer in a ConvNet is a _fully connected_ (FC) layer, where the stack of processed signals is treated as a 1D vector so that each value in the output from a previous layer is connected via a _synapse_ (a weighted connection that transmits a value from one unit to another) to each node in the next layer. In addition to the four standard layers, ConvNets can have custom layers that implement arbitrary functions to transform signals between consecutive layers. Since standard and custom layers can be stacked arbitrarily deeply and in various permutations, ConvNets can model highly non-linear manifolds. The architectural features of a ConvNet that cannot be dynamically changed through backpropagation are called _hyperparameters_ and include the number and size of filters in convolution layers, the window size and stride in maxpooling layers, and the number of neurons in FC layers. These permutations and hyperparameters distinguish one ConvNet architecture from another. In audio processing, ConvNets have been trained to classify audio samples either by classifying raw audio or vectors of various features extracted from raw audio. For example, [PERSON] [26] evaluated the potential of ConvNets to classify short audio samples of environmental sounds by developing a ConvNet that consisted of two convolution layers with maxpooling and two FC layers. The ConvNet was trained on the segmented spectrograms of audio data. When evaluated on three public datasets of environmental and urban recordings, the ConvNet outperformed a baseline obtained from random forests with mel frequency cepstral coefficients (MFCCs) and zero crossing rates and performed on par with some state-of-the-art audio classification approaches. [PERSON] et al. [33] developed a deep ConvNet to learn directly from raw audio waveforms. The ConvNet was trained by transferring knowledge from computer vision to classify events from unlabeled videos. The representation learned by the ConvNet obtained state-of-the-art accuracy on three standard acoustic datasets. In [34], [PERSON] et al. showed that generative networks can be trained to predict the next sample in an audio sequence. The proposed network consisted of 60 layers and sampled raw audio at a rate of 16 kHz to 48 kHz. [PERSON] and [PERSON] [35] designed a ConvNet to classify 5-s tiles of pitch spectra to produce an optimized chord recognition system. The ConvNet yielded state-of-the-art performance across a variety of benchmarks.
# Recent Progress in Pressure Sensors for Wearable Electronics: From Design to Applications
## 2 Pressure Transduction Mechanism and Design Principles
### Resistive Type  
The sensing mechanism of a resistive pressure sensor is based on the variation of resistance under applied pressure (Figure 1a). Resistive pressure sensors have been widely studied due to their simple structure, high sensitivity, and wide measurement range. In resistive pressure sensors, the contact resistance between two conductive layers under applied pressure is usually measured. In these sensors, structured conductive layers are typically fabricated. The contact resistance between the electrodes decreases under external pressure because more parts of the active layer come into contact with each other. Coating an as-prepared porous structure with a conductive material is one of the simple ways to obtain a conductive porous structured active layer. Various conductive porous structures have been simply formed using reduced graphene oxide (rGO) with polyurethane (PU) sponge [41], Au NWs with tissue paper [42], rGO, fabric coated with graphene or CNTs [43; 44; 45], and single-walled carbon nanotubes (SWCNTs) have been coated on an M13 bacteriophage [46]. The sensitivity of a pressure sensor could also be enhanced with micro-patterned PDMS which is coated by conductive materials [47; 48; 49]. In addition, combining conductive materials such as CNTs [50], multi-walled carbon nanotubes (MWCNTs) [51], rGO [52], and Cu NWs [53] with other highly-deformable materials can help modify the performance of pressure sensors. Randomly-oriented electrospun nanofibers (NFs) have also been used as porous structures to enhance the sensitivity, detection limit, and response time of sensors [54; 55; 56]. Electrospinning technique will be briefly discussed in Section 3.  
In order to fabricate the sensing layer of the resistive pressure sensors, not only chemical vapor deposition but also simple methods such as dip-coating, spray-coating, and drop-casting have been widely used. However, since metals such as silver and gold are generally used, and nano-powders with a size of tens to hundreds of nanometer are used, the material is generally expensive. In addition, for sufficient repeatability, measures to mitigate permanent deformation of the sensing layer are also required.  
Figure 1: Picture and schematic diagrams of the pressure transduction mechanism. (**a**) Resistive type. Reproduced with permission from [38], Copyright (2018), American Chemical Society. (**b**) Capacitive type. Reproduced with permission from [39], Copyright (2019), American Chemical Society. (**c**) Piezoelectric type. Reproduced with permission from [40], Copyright (2018), Royal Society of Chemistry.
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Projects** & **Description** & **Contribution** & **Structure** \\ \hline \multirow{4}{*}{Hybrid Energy Efficient Distributed Cluster (HEEDR) [18]} & The cluster head is selected based on the remaining energy of the node and the cost of its communication & The residual energy-based strategy is used in both intra-cluster communication and inter-cluster head communication, improving network lifetime & Mesh \\ \cline{1-1} \cline{2-4}  & An energy-efficient distributed scheduling algorithm (CLU-DDAS) [19] & Reduced data transfer delays in the network while singing network longevity & Tree \\ \cline{1-1} \cline{2-4}  & Reducing the energy consumption in network by reducing the number of data forwarding hops & Transmission of data with the minimum number of hops reduces forwarding energy consumption and transmission delay & Linear \\ \cline{1-1} \cline{2-4}  & Minimize the energy consumption & Minimizing the variance of energy consumption balances the energy consumption of each node in the network and increases the network lifetime & \\ \cline{1-1} \cline{2-4}  & A routing protocol for heterogeneous networks is proposed, which selects cluster heads based on the ratio of remaining energy to the average energy level of the network & The strategy of selecting cluster heads by residual energy and average energy level successfully improves the energy efficiency of the network & Mesh \\ \cline{1-1} \cline{2-4}  & Control the transmission power of each node and minimize the energy consumption of node to achieve the purpose of improving the life of the network & Constrains the maximum energy consumption of nodes in the network so that the energy of nodes is preserved and the life of the network is improved & \\ \hline \multirow{4}{*}{Energy Efficient Unequal Clustering Routing Protocol (EEUCR) [24]} & Inhomogeneous clustering and dual-cluster head techniques are used to solve hotspot problems, and a hybrid rotation strategy based on node time and energy is also proposed to reduce energy consumption & Mitigates hotspot issues in the network with Rotational Forwarding & Mesh \\ \cline{1-1} \cline{2-4}  & Elect two cluster heads within a cluster and have them responsible, respectively, for data transmission and data fusion tasks, thereby extending the network lifespan & The strategy of selecting dual cluster heads and conducting data fusion reduces the amount of network data transmission and reduces network energy consumption & Mesh \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing methods and their characteristics. the efficiency of the current high-speed railway linear monitoring network. These issues are addressed in this article.
# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 4 Experimental Results and Analysis
### Analysis of Experimental Results on Synthesized Dataset  
We compared the proposed method with several other image-restoration methods, including DAD [19], DeepOtsu [11], Uformer [31], MPR-Net [32], VDIR [33], S-CycleGAN [24], and YTMT [34]. For these methods, we retrained the network using the implementations provided by the respective authors and then evaluated them. Table 1 presents the quantitative analysis of two synthetic datasets, while Figures 11 and 12 show examples of sample restoration.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Index**} & \multicolumn{8}{c}{**Methods**} \\ \cline{3-10}  & & **DAD** & **DeepOtsu** & **Uformer** & **MPRNet** & **VDIR** & **S-Cycle** & **YTMT** & **Ours** \\ \hline \multirow{2}{*}{S-color0.5} & PSNR & 28.65 & 21.49 & 24.69 & 32.86 & 27.81 & 32.92 & 25.17 & **33.79** \\  & SSIM & 0.977 & 0.861 & 0.902 & 0.972 & 0.956 & 0.979 & 0.912 & **0.984** \\  & PSNR & 28.87 & 21.48 & 24.68 & 32.98 & 27.80 & 32.99 & 25.40 & **33.91** \\ \multirow{2}{*}{S-color1.0} & SSIM & 0.980 & 0.861 & 0.914 & 0.981 & 0.958 & 0.983 & 0.948 & **0.989** \\  & PSNR & 28.76 & 21.49 & 24.69 & 32.92 & 27.81 & 32.96 & 25.29 & **33.85** \\ \multirow{2}{*}{Average} & SSIM & 0.979 & 0.861 & 0.908 & 0.977 & 0.957 & 0.981 & 0.930 & **0.987** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation of synthesized dataset.  
As shown in Table 1, on the synthetic datasets, the proposed method achieved higher average SSIM and PSNR (dB) values than the compared methods. Figures 11 and 12 respectively present the restoration results of a sample image from the S-color0.5 and S-color1.0 synthetic datasets. It is observed that both the DAD and DeepOtsu methods removed the show-through effects, but the result of the DeepOtsu method suffered from color distortion, while the DAD method resulted in color deviation. The results of the Uformer and YMT methods still contained a significant number of show-through components. The MPR-Net, VDIR, and S-CycleGAN methods all removed the show-through effects to some extent, but some residual show-through components remained. In contrast, the proposed method completely removed the show-through effects and achieved better restoration results.