esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Time Domain Semi Analytical Method for Horizontal Vibration of Pile in Saturated Clay
## 3 Equation Solving
### Solving the Control Equation of Saturated Clay Movement
\begin{array}{l}\widetilde{w}_{r}=\left[\varepsilon_{1}B_{1}\frac{\partial K _{1}(\beta_{1}r)}{\partial r}+\varepsilon_{2}B_{2}\frac{\partial K_{1}(\beta_ {2}r)}{\partial r}+\frac{\varepsilon_{3}}{r}B_{3}K_{1}(\beta_{3}r)\right] \cos\theta\text{ch}(\gamma_{k}z)\\ \widetilde{w}_{\theta}=\left[-\frac{\varepsilon_{1}B_{1}}{r}K_{1}(\beta_{1}r) -\frac{\varepsilon_{2}B_{2}}{r}K_{1}(\beta_{2}r)-\varepsilon_{3}B_{3}\frac{ \partial K_{1}(\beta_{3}r)}{\partial r}\right]\sin\theta\text{ch}(\gamma_{k} z)\end{array}  \tag{29}\]  
Using Equation (11), the pore pressure in the Laplace domain is obtained as:  
\[ \begin{array}{l}\dot{p}=-\alpha M\bigg{(}\frac{\partial\dot{u}_{r}}{ \partial r}+\frac{1}{r}\frac{\partial\dot{u}_{\theta}}{\partial\theta}\bigg{)} -M\bigg{(}\frac{\partial\dot{u}_{r}}{\partial r}+\frac{1}{r}\frac{\partial\dot {u}_{\theta}}{\partial\theta}\bigg{)}\end{array}  \tag{30}\]  
Substituting boundary condition Equations (16) and (20) into Equation (30), we obtain:  
\[ \begin{array}{l}B_{2}=\kappa_{1}B_{1},B_{3}=\kappa_{2}B_{1}\end{array}  \tag{31}\]  
where \(\kappa_{1}\), \(\kappa_{2}\) are known coefficients, and the specific expression is shown in Appendix A. According to the stress balance conditions at the pile-soil interface, there are:  
\[ \begin{array}{l}\dot{q}=\int_{0}^{2\pi}\bigg{\{}\mu\bigg{(}\frac{\partial \dot{u}_{\theta}}{\partial r}+\frac{1}{r}\frac{\partial\dot{u}_{r}}{\partial \theta}-\frac{\dot{u}_{\theta}}{r}\bigg{)}\sin\theta-\bigg{[}\lambda\bigg{(} \frac{\partial\dot{u}_{r}}{\partial r}+\frac{1}{r}\frac{\partial\dot{u}_{ \theta}}{\partial\theta}\bigg{)}+2\mu\frac{\partial\dot{u}_{r}}{\partial r}- \dot{p}\bigg{]}\cos\theta\bigg{\}}\cdot rd\theta\end{array}  \tag{32}\]  
Substituting Equations (28)-(30) into Equation (32) and taking \(r=r_{0}\), the load concentration of the pile-soil interaction is obtained as:  
\[ \begin{array}{l}\dot{q}=-\pi r_{0}B_{1}\text{ch}(\gamma_{k}z)\big{[}\big{(}L \alpha-\alpha^{2}+\varepsilon_{1}\big{)}M\beta_{1}^{2}K_{1}(\beta_{1}r_{0})+ \\ \big{(}L\alpha-\alpha^{2}+\varepsilon_{2}\big{)}M\kappa_{1}\beta_{2}^{2}K_{1}( \beta_{2}r_{0})+\mu\kappa_{2}\beta_{3}^{2}K_{1}(\beta_{3}r_{0})\big{]}\end{array}  \tag{33}\]
## 3 Results and Discussion
### Comparison of Low-Level and Mid-Level Data Fusion Models  
Data fusion of the raw data was performed using the method shown in Figure 5, and PLS-DA models were established separately (Figure 6). It could be seen that there was a clustering trend between the different samples from the score scatter plots of the three models. As shown in the red circle in the figure, the scatter plot of PLS-DA for low-level data fusion is roughly clustered into four parts, the scatter plot of PLS-DA for the mid-level data fusion (VIP \(>\) 1) is roughly clustered into five parts, while the scatter plot of PLS-DA for the mid-level data fusion (latent variable and PCs) has the best clustering effect, basically clustering thirteen spices. This indicates that the mid-level data fusion models had better clustering performance than the low-level data fusion model. The reason was that the mid-level data fusion performed feature selection, reducing the interference of redundant information, and thereby improving the classification performance of the model. Moreover, the results of the permutation testing (n = 200) of the three PLS-DA models show that all models had no risk of overfitting. The PLS-DA score plot of the mid-level data fusion model for latent variables clusters thirteen species of Zingiberaceae more intuitively and effectively, but its score plot only explained 10.13% of the variation (PC1:0.0508, PC2:0.0505), which indicates that the interpretation ability of the model may be reduced while improving the classification performance of the model by reducing redundant information. Comparing the score plots of these three models, it can be found that the mid-level data fusion model (VIP \(>\) 1) has the best performance and classification trend, with PC1 (54.3%) and PC2 (30.1%) explained by 84.4% of the variation.  
Although some species were not separated in the score plots, the accuracy of both the training and test sets of the models was 100% (Table 2). The goodness of fit of R\({}^{2}\)Y was more than 90.7%, and the goodness of prediction Q\({}^{2}\) was more than 83.3%, indicating that the performances of both low-level and mid-level data fusion models were excellent. The mid-level data fusion model based on latent variables has the best performance among them (R\({}^{2}\)Y = 0.952, Q\({}^{2}\) = 0.913, RMSEE = 0.0121, RMSECV = 0.0450, RMSEP = 0.0122). The results showed that NIR and MIR fusion spectra can effectively identify and classify fruits of different Zingiberaceae species as a non-destructive method.  
Figure 5: The flow chart of the data fusion processes: (**a**) low-level data fusion; (**b**) mid-level data fusion (VIP \(>\) 1); (**c**) mid-level data fusion (latent variable and PCs).
## 2 Related Work  
### Convolutional Neural Network  
Convolutional operations, akin to fully connected operations, are pivotal in neural networks and serve as the foundational elements of CNNs, extensively applied in deep learning tasks. At the heart of a convolution operation lies the convolution kernel, with each kernel capturing a specific feature [43]. The fundamental principle involves utilizing diverse convolution kernels to compute the dot product sum of overlapping regions between these kernels and input data on images or feature maps using a sliding window approach, thereby extracting features corresponding to the convolution kernels [44]. One significant property of convolution operations is parameter sharing, reducing parameter count and enhancing model efficiency [45]. Nonetheless, as each convolutional kernel convolves with a local input data region and faces constraints from a fixed-size receptive field, its capacity to capture distant features remains limited.  
To augment the receptive field of convolution operations without escalating computational demands, dilated convolution, also referred to as atrous convolution, comes into play. Dilated convolution, a specialized form of convolution operation prevalent in deep learning, introduces dilation between convolution kernel elements to expand the kernel's receptive field, thereby broadening the network's perceptual scope [46]. By integrating dilated convolution, the effective receptive field of convolutional kernels is enhanced, empowering the network to better grasp long-range correlations in input data, enhance feature abstraction, and acquire multi-scale contextual information [47].  
While traditional CNNs exhibit proficiency in deep learning applications, they encounter challenges related to high computational loads. To alleviate model computational complexity, depthwise separable convolution emerges as a viable solution. Consisting of deep convolution and pointwise convolution, deep separable convolution represents a common convolution operation [48]. Deep convolution executes separate convolution operations for each channel, yielding an equal number of feature map channels as input channels. Conversely, pointwise convolution focuses on extracting features at individual points through convolutions [49].
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 2 Related Works
### Proxy Re-Encryption for Medical Data Using Blockchain  
In addition to the aforementioned proposals, methods for implementing PRE using blockchain and personal medical data have been proposed to address security issues. [PERSON] et al. [35] proposed a blockchain-based scheme for sharing and protecting EHRs usingPRE. The participants of the system include a hospital, user, and system manager, where the hospital maintains its own private blockchain, and the system manager generates the master key and system parameters as a trusted third party. The user stores his/her medical data in the hospital's blockchain by joining the network, and the system manager re-encrypts the data when the data are requested by other doctors. [PERSON] et al. suggested a personal health record (PHR) system for secret data sharing that involved the blockchain and PRE [36]; furthermore, they developed an access-control model for PHR systems [37]. This model stores the encrypted PHR for cloud storage, and the PHR metadata are stored in the blockchain with access logs. The gateway server re-encrypts the data requests from the cloud storage and sends it to the user client. [PERSON] et al. proposed Ancile [38], which is an EHR access-control framework involving PRE and the blockchain. However, none of the previous works discussed possible security attacks due to the untrustworthy blockchain outsourcing companies.
## 3 The Proposed Routing Mechanism
### CCS-DTN Routing
#### 3.4.2 Inter-Cluster Routing  
A new forwarding mechanism has been proposed for inter-cluster routing. By the new mechanism, the parameter of centrality is used to determine the next hop node. When the source node and the destination node do not belong to the same cluster, the source node will first accumulate and encode a sufficient number of messages. Then the coded messages will be forwarded to the relay nodes with a higher centrality. With the movement of the relay nodes, messages will be gradually delivered to the destinations or the nodes in the same cluster with the destination node. When messages are forwarded to the node which belongs to the same cluster with the destination node, they can be delivered to the destination using intra-cluster routing. For example, in Figure 4 we assume \(CT_{s}<CT_{a}<CT_{b}<CT_{c}<CT_{e}<CT_{f}\), \(CT_{a}>CT_{g}\), \(CT_{b}<CT_{d}\), \(CT_{d}>CT_{e}\). From Figure 4, the source node \(S\) has messages to be transmitted to the destination node \(D\). Since node \(S\) and node \(D\) are not in a same cluster, the inter-clustering routing is used. Firstly, when messages are cached in \(S\), they will be coded using a random linear network coding scheme. Secondly, the coded messages will be forwarded according to the node's centrality. From Figure 4, messages will be forwarded to the destination by two ways. In the way of \(S\)-\(>\)\(a\)-\(>\)\(b\)-\(>\)\(d\)-\(>\)\(D\), messages are directly transmitted to the destination \(D\) while in the way of \(S\)-\(>\)\(a\)-\(>\)\(b\)-\(>\)\(c\)-\(>\)\(e\)-\(>\)\(f\)-\(>\)\(D\), messages are transmitted to node \(f\) first and then the messages are delivered from node \(f\) to node \(D\) using intra-cluster routing.  
By the forwarding mechanism messages are accumulated at the nodes with higher centrality. These nodes may suffer from buffer overflow, since the buffer size at each node is limited. In this way, the delivery ratio will be reduced. Therefore, a load balancing mechanism is required to handle it. First, a threshold \(CT_{\gamma}\) is pre-set. If node \(i\) with centrality less than \(CT_{\gamma}\) meets node \(j\) with a higher centrality, node \(i\) will send \(CT_{j}\)/(\(CT_{i}+CT_{j}\)) numbers of the copies to node \(j\). In this way, messages will be forwarded to multiple nodes with higher centrality, thus avoiding the risk of the messages to be lost at one node. And the node with a higher centrality will have more messages. If node \(i\) with a centrality greater than that of \(CT_{\gamma}\) meets node \(j\) with a greater centrality than that of \(CT_{\gamma}\), node \(i\) will send half number of the copies to node \(j\).  
Figure 4: The process of inter-cluster routing.
# IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
## 1 Introduction
Section 2 presents a critical analysis of the literature pertaining to the hip fracture rehabilitation process, activity recognition methods, and IoT-enabled system architectures within rehabilitation healthcare. Section 3 illustrates the proposed post-operative hip fracture rehabilitation model, leading to the clarification of the involved process and associated events as well as the main movements of interest. Section 4 discusses the architectural representation of the IoT-based rehabilitation movement monitoring system. Section 5 discusses the system performance in possible scenarios of architectural implementation while taking into consideration the available network functions, information transparency, and the wireless sensor's lifetime. Future directions and conclusions are presented in Section 6.
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 2 Results
 \\ \hline \multicolumn{5}{l}{_Alcohals_} \\ \hline
2-Methyl-1-butanol & 19 \(\pm\) 2 a & 25 \(\pm\) 3 ab & 19 \(\pm\) 2 a & 20 \(\pm\) 2 a & 34 \(\pm\) 2 b & **\\
3-Methylbutanol & 25 \(\pm\) 1 b & 14 \(\pm\) 2 a & 24 \(\pm\) 1 b & 21 \(\pm\) 1 b & 14 \(\pm\) 0 a & **\\
1-Hexanol & 1.0 \(\pm\) 0.1 & 1.0 \(\pm\) 0.1 & 1.1 \(\pm\) 0.1 & 1.2 \(\pm\) 0.1 & 1.0 \(\pm\) 0.1 & ns \\ Phenyl ethyl alcohol & 21 \(\pm\) 2 & 18 \(\pm\) 2 & 22 \(\pm\) 2 & 25 \(\pm\) 2 & 22 \(\pm\) 1 & ns \\ \hline \multicolumn{5}{l}{_Hydrocarbons_} \\ \hline Tridecane & 0.02 \(\pm\) 0.00 & 0.02 \(\pm\) 0.00 & 0.02 \(\pm\) 0.00 & 0.02 \(\pm\) 0.00 & 0.02 \(\pm\) 0.00 & ns \\ Tetradecane & 0.03 \(\pm\) 0.00 a & 0.05 \(\pm\) 0.00 ab & 0.04 \(\pm\) 0.00 a & 0.05 \(\pm\) 0.00 ab & 0.06 \(\pm\) 0.01 b & * \\ \hline \multicolumn{5}{l}{_Esters_} \\ \hline Acetic acid, 2-phenylethyl ester & 0.09 \(\pm\) 0.01 & 0.10 \(\pm\) 0.01 & 0.10 \(\pm\) 0.00 & 0.12 \(\pm\) 0.00 & 0.11 \(\pm\) 0.01 & ns \\ Diethyl succinate & 0.70 \(\pm\) 0.13 & 0.72 \(\pm\) 0.05 & 0.85 \(\pm\) 0.07 & 0.79 \(\pm\) 0.14 & 0.55 \(\pm\) 0.04 & ns \\ Ethyl acetate & 6.5 \(\pm\) 1.0 & 6.4 \(\pm\) 0.7 & 6.5 \(\pm\) 0.4 & 6.0 \(\pm\) 0.9 & 4.3 \(\pm\) 0.5 & ns \\ Ethyl butanoate & 0.29 \(\pm\) 0.04 & 0.43 \(\pm\) 0.05 & 0.43 \(\pm\) 0.07 & 0.35 \(\pm\) 0.03 & 0.34 \(\pm\) 0.04 & ns \\ Ethyl decanoate & 0.97 \(\pm\) 0.13 a & 2.1 \(\pm\) 0.0 c & 1.2 \(\pm\) 0.2 ab & 1.1 \(\pm\) 0.1 ab & 1.7 \(\pm\) 0.3 bc & **\\ Ethyl dodecanoate & 0.52 \(\pm\) 0.06 c & 0.30 \(\pm\) 0.02 ab & 0.43 \(\pm\) 0.02 bc & 0.26 \(\pm\) 0.01 a & 0.20 \(\pm\) 0.03 a & *** \\ Ethyl heptanoate & 0.11 \(\pm\) 0.01 & 0.06 \(\pm\) 0.01 & 0.09 \(\pm\) 0.01 & 0.09 \(\pm\) 0.01 & 0.06 \(\pm\) 0.01 & * \\ Ethyl hexanoate & 7.1 \(\pm\) 0.6 & 8.2 \(\pm\) 0.5 & 6.4 \(\pm\) 0.5 & 7.2 \(\pm\) 0.5 & 6.2 \(\pm\) 0.9 & ns \\ Ethyl hexyl salicylate & 0.10 \(\pm\) 0.01 & 0.06 \(\pm\) 0.00 & 0.10 \(\pm\) 0.03 & 0.07 \(\pm\) 0.01 & 0.07 \(\pm\) 0.01 & ns \\ Ethyl nonanoate & 1.2 \(\pm\) 0.1 b & 1.3 \(\pm\) 0.1 b & 1.1 \(\pm\) 0.1 ab & 1.3 \(\pm\) 0.2 b & 0.63 \(\pm\) 0.02 a & * \\ Ethyl octanoate & 8.4 \(\pm\) 1.4 a & 16 \(\pm\) 1 b & 8.9 \(\pm\) 1.0 a & 10 \(\pm\) 1 ab & 13 \(\pm\) 2 ab & * \\ Hexadecanoic acid, ethyl ester & 4.7 \(\pm\) 0.2 & 3.0 \(\pm\) 0.1 & 4.8 \(\pm\) 1.0 & 2.7 \(\pm\) 0.6 & 2.1 \(\pm\) 1.8 & ns \\
2-Methylbutyl acetate & 0.23 \(\pm\) 0.05 & 0.20 \(\pm\) 0.01 & 0.20 \(\pm\) 0.01 & 0.21 \(\pm\) 0.01 & 0.16 \(\pm\) 0.02 & ns \\
3-Methylbutyl acetate & 0.64 \(\pm\) 0.10 & 0.88 \(\pm\) 0.02 & 0.86 \(\pm\) 0.08 & 0.91 \(\pm\) 0.06 & 0.68 \(\pm\) 0.11 & ns \\ Methyl hexanoate & 0.03 \(\pm\) 0.00 & 0.03 \(\pm\) 0.00 & 0.03 \(\pm\) 0.00 & 0.04 \(\pm\) 0.00 & 0.02 \(\pm\) 0.01 & ns \\ Methyl octanoate & 0.08 \(\pm\) 0.01 a & 0.13 \(\pm\) 0.02 ab & 0.09 \(\pm\) 0.01 a & 0.11 \(\pm\) 0.02 ab & 0.18 \(\pm\) 0.01 b & * \\ Octanoic acid,
## 1 Introduction  
The Fengyun No.3 (FY-3) meteorological satellite is the second generation of China's polar-orbiting meteorological satellite series that realizes global, all-weather, multispectral, three-dimensional, and quantitative remote sensing, including three batches in total. Among them, the third batch aims at the advanced level of international development of meteorological satellites, realizes the operation of the multi-satellite networking of China's second-generation polar-orbiting meteorological satellites, and drive its application into a mature development stage [1, 2, 3, 4, 5].  
The HEPD is one of the remote sensing instruments on the FY-3E satellite, which is the first satellite in 03 batches, and was launched in July 2021, consisting of six space environment detection payloads: high-energy particle detector, intermediate-energy proton detector, intermediate-energy electron detector, radiation dosimeter, magnetic field detector, and potential detector. Monitoring and warnings of the space environment are some of the important measures to ensure the life of astronauts and the safety of spacecraft on-orbit operations [6, 7, 8, 9]. They also hold great significance for the normal operation of systems, such as wireless communication, navigation and positioning [10, 11, 12, 13], aircraft, and ground power grids in the mid-high latitudes. Space environment detection also provides important scientific data for climate and meteorological research [14, 15], solar-terrestrial physics research [16], and experiments.  
For the characteristics of the space particle environment in the orbit of the FY-3 satellite, this study reports the design and development of a three-directional high-energy particle detector on the FY-3E satellite, and conducts the energy spectrum and flux observations of space high-energy protons and electrons in multiple directions [17] in this orbit for the first time. Generally, for charged particle detectors on satellites, most of them are detected in one direction, or rarely two kinds of charged particles are measured simultaneously on the same load [18; 19].
## 1 Introduction
It was proven that the use of feature selection methods was very helpful for the establishment of calibration models with higher accuracy and fewer variables than a full spectrum model [29; 30]. But it is difficult in finding the appropriate hyperparameter combinations. It remains uncertain which approach is more effective for the sweetness estimation of kiwifruit. Moreover, modeling methods are also important for quality prediction. For instance, multiple linear regression (MLR) was used to analyze the contents of flavonoids and saponins in soybean powder based on hyperspectral reflectance at 900 -1700 nm [31]. [PERSON] et al. (2019) compared the use of partial least squares (PLS) and least squared support vector regression (LSSVR) to predict apple's SSC [32]. The results showed that both methods had high accuracy, and the accuracy of the LSSVR method was slightly higher than that of the PLS. The MLR and error back propagation (BP) network methods were compared in the determining of the SSC in plums [25]. The result indicated the advantages of the MLR method. The CARS combined with the MLR method could obtain the best SSC estimation model with the R\({}^{2}\) greater than 0.9. So far, MLR, PLS, LSSVM, BP, and other methods have been applied to predict the internal qualities of fruits. However, there is no conclusion which one is necessarily the best for internal quality predicting. For different fruit objects and internal quality characteristics, the applicability of these methods requires comparison and screening. In addition, different fruit surface characteristics cause complicated scattering effects on reflection spectrum. Which combination of spectral noise removing methods and modeling methods is best for sweetness estimation need to be discussed. Overall, there were less researches on predicting kiwifruit sweetness, especially the Cuixiang cultivar famous for its sweetness. Previous studies have focused on the spectra of 400-1000 nm in the selection of sensitive waveband for kiwifruit sweetness. With the development of hyperspectral technology, 1000-2500 nm has attracted wide attention. Therefore, based on the hyperspectral reflectance of kiwifruit spanning the range of 400-2500 nm, the objectives of this study are to (1) explore the optimal sensitive bands of sweetness in each harvest period and the whole harvest period. (2) analyze the responsiveness of spectral processing and feature selection methods to sweetness. (3) develop the best sweetness estimation model for kiwifruit. The research results will provide a technical basis for the potential application of Vis/NIR technology in the sweetness prediction of different fruits. The rest of this paper is organized as follows: Section 2 introduces the geographic location of the study area, measures the Vis/NIR spectra, and determines kiwifruit sweetness. Section 3 establishes the estimation model of spectrum and sweetness and verifies the accuracy of the model. Section 4 discusses the relationship between this study and other studies. Section 5 presents the conclusions.
## 3 Flightload Calculation Modeling
### Training and Test Data  
The data used in this study consisted of a total of 56 sets of flight load data. Using randomization, the data were divided into a training set of 46 flight load datapoints and a test set of 10 flight load datapoints.
# Removal of Metals and Dyes in Water Using Low-Cost Agro-Industrial Waste Materials
## 3 Results and Discussion
### Competitive Adsorption Tests  
Multicomponent assays (Figure 12) show that the adsorption of MB is not affected by the presence of Cr (VI), and no notable changes are observed when varying the concentration of the interfering metal. Nevertheless, Cr (VI) adsorption is favored by the presence of MB in the system (Figure 12A). It can be said that the MWBC has selectivity towards Cr (VI) when they coexist in an aqueous solution.  
The results of this study can be compared with other investigations that focus on the preparation of biochar for the removal of metals and dyes in solutions. In particular, the results of our study are presented and contrasted with those reported in the literature on marketable biochar in Table 6. This comparison allows us to evaluate the performance of our biochar concerning other materials on the market.  
These results indicate that the prepared MWBC is a promising adsorbent material for the remediation of water contaminated with cationic dyes and heavy metals. In addition, its selectivity towards MB and Cr (VI) adsorption is highlighted.