esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Prediction with Model Switching +
## 4 Accurate Early Warning System Design
### Prediction Horizon Tuning  
In the re-training flow, the error estimation results are utilized further to tune the prediction horizon. The advantages of a variable-length prediction horizon (PH) are as follows:  
* The occurrence of landslides can be predicted earlier; and
* The number of false predictions can be reduced.  
The prediction horizon tuning flow is shown in Figure 9. Prediction errors are non-linear, and the prediction model is applied (as described in Section 3.1) in order to learn the inherent pattern for predicting future errors, \(ERR_{predict}\). In this way, the size of the PH is set to predict the occurrence of landslides earlier. Then, the future target ranges, \(P_{future}\), can be determined by Equation (21). If the range of \(P_{future}<1\), this system can send alerts in advance.  
\[FS_{predict}-|ERR_{predict}|\leq P_{future}\leq FS_{predict}+|ERR_{predict}|. \tag{21}\]  
To decide whether the size of the PH is tuned, error boundary estimation is needed. As the program already has the predicted error, \(ERR_{predict}\), and the predicted FS, \(FS_{predict}\), then the predicted lower bound, \(Bound_{low}\), can be estimated by Equation (22). According to the estimated results, the tuning is performed based on the following rules:  
* If there is no \(Bound_{low}\) lower than the lower bound of Stable Class (i.e., 1.3), for every time point in the prediction horizon, the size of the prediction horizon is increased by 1; and
* If there exists one \(Bound_{low}\) lower than the lower bound of Stable Class (i.e., 1.3), for every time point in the prediction horizon, the size of prediction horizon is reset to the default value. \[Bound_{low}=FS_{predict}-|ERR_{predict}|.\] (22) Based on the above rules, we make our method a little more flexible for the landslide prediction scenario with variable-length prediction horizon.  
Figure 8: Learning-based re-training flow.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Data and Pre-Processing  
#### 2.2.1 Statistical Data  
The county-level total yield and total planting area data for winter wheat in Henan Province were sourced from the Statistical Yearbook of Henan Province published by the Henan Provincial Bureau of Statistics [39]. Unit yield data for each county-level entity were obtained by dividing the total yield by the total planting area. Considering factors such as administrative changes and occasional missing statistical data, this study selected counties with complete records from 2012 to 2021 (Figure 3) as the modeling and analysis samples, resulting in an effective sample size of 1020 records.
## 3 Results and Discussion
### FTIR Spectroscopy of PWPs  
FTIR spectrum of PWPs is shown in Figure 2. The analysis indicated a broad absorption peak at 3285 cm\({}^{-1}\) corresponding to the hydroxyl (O-H) stretching vibration of polysaccharide as well as water absorption [42]. The displayed weak absorption band at 2936 cm\({}^{-1}\) suggested the asymmetric vibration of (C-H) group [43]. The strong characteristic absorption peak at 1550 cm\({}^{-1}\) was attributed to the vibration of COO\({}^{-}\) group of the galacturonic acid and those at 1399 cm\({}^{-1}\) could correspond to ester carbonyl groups of the carboxylic function of GaA [42]. The signal obtained at 1017 cm\({}^{-1}\) resulted from the presence of carbohydrate (C-O) functions [44]. Consequently, the results from FTIR assumed a rich polygalacturonic acid pectin type structure of PWPs.
## 2 Background and Literature Study
### Smart Healthcare Facilities  
The recent turmoil of the COVID-19 pandemic has pushed society towards using smart hospitals as a solution to the NHS's vulnerabilities. It is important to highlight that this transformation is not to replace individuals such as healthcare professionals, but rather to improve their abilities and resources. This, in turn, will enhance patient care, and smart hospitals use these integrated technologies to conduct real-time monitoring and automated processes to create an interconnected healthcare ecosystem that enhances patient care, improves operational efficiency, and promotes innovation in the healthcare industries [19; 20]. The ratio of nurses to patients within hospital settings is lower than that in any other healthcare setting, and such observations have been reported and studied in different healthcare settings [21; 22; 23; 24; 25; 26] across different countries. Hence, there is a real need for a solution to relieve the strain these healthcare professionals experience. Real-time monitoring provides an opportunity for a solution to this strain. Real-time data monitoring enables us to translate factors from the environment, resources, and patients into usable data that can be used and acted upon. Indeed, advanced wearable health monitoring is transforming remote population health monitoring and changing the dynamics of the methods used for monitoring health in society [27] and for communication in hospital management using IoMT [28]. It is also important to make such medical devices portable and monitorable, and such a system is highlighted in [29]. Moreover, during the monitoring process, understanding the activity context recognition during ambient sensing is important [30]. So, developing smart innovative solutions for such a system is necessary to make it effective and efficient. Some incorporate AI into such systems to infuse intelligence into the medical device system [31]. However, there are many challenges in developing and designing such solutions; among these, one of the key challenges that need to be addressedto make it acceptable is the security and privacy issue [32, 33]. So, an analysis of medical devices and software to detect and identify security vulnerabilities is necessary to make such systems safe [34].
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
###### Abstract  
Automatic clothes pattern recognition is important to assist visually impaired people and for real-world applications such as e-commerce or personal fashion recommendation systems, and it has attracted increased interest from researchers. It is a challenging texture classification problem in that even images of the same texture class expose a high degree of intraclass variations. Moreover, images of clothes patterns may be taken in an unconstrained illumination environment. Machine learning methods proposed for this problem mostly rely on handcrafted features and traditional classification methods. The research works that utilize the deep learning approach result in poor recognition performance. We propose a deep learning method based on an ensemble of convolutional neural networks where feature engineering is not required while extracting robust local and global features of clothes patterns. The ensemble classifier employs a pre-trained ResNet50 with a non-local (NL) block, a squeeze-and-excitation (SE) block, and a coordinate attention (CA) block as base learners. To fuse the individual decisions of the base learners, we introduce a simple and effective fusing technique based on entropy voting, which incorporates the uncertainties in the decisions of base learners. We validate the proposed method on benchmark datasets for clothes patterns that have six categories: solid, striped, checkered, dotted, zigzag, and floral. The proposed method achieves promising results for limited computational and data resources. In terms of accuracy, it achieves 98.18% for the GoogleClothingDataset and 96.03% for the CCYN dataset.  
texture recognition; clothes pattern; deep learning; CNN; ensemble learning; entropy
# Reality Check: Experimental Studies on Microplastics Lack Realism
## 6 Leached Chemicals
### Human Effects  
Although many of the leached chemicals described above have been studied in the context of mammalian models, human cells, and human health, investigations of the dynamics of MPs and these chemical as MPs enter the body are less well known. When mice were fed MPs coated with phthalate esters (PAEs), this induced the accumulation of PAEs in the gut. As compared to virgin MPs, this resulted in inflammation, and altered intestinal gene expression [90]. Of all the PAEs tested, DEHP had the greatest adsorption capacity to MPs, and showed the strongest impacts on animal health [90]. In a different study, co-exposure to MPs and organophosphorus flame retardants worsened the impact of these chemicals, indicating that MPs act as more than just a vector for entry into the body [91]. These results aside, many more recent studies investigating how MPs impact mammalian models and human cells have used fresh, \"virgin\" MPs. Similar to aquatic organisms, this likely underestimates the effects observed after exposure to MPs.  
MPs coated with chemical additives can degrade in the environment, or after entering the body. A recent study found that the associated release of chemical additives is not equivalent. By simulating the ingestion of MPs coated with bisphenols, researchers found that intestinal fluids intensified their release from PVC MPs. Interestingly, bisphenols released under intestinal conditions caused greater cytotoxicity to human MCF-7 cells as compared to bisphenols released from MPs in water [92]. These studies indicate that not only the type of chemical additive is important, but also the dynamics of the exposure.
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 4 CS-FL Method
### Training Phase: Hybrid Encryption and SCI Incentive Method  
#### 4.3.1 Hybrid Encryption Algorithm  
After the selection phase of Zone-Chain, the ZS informs ENs within its service zone of the election results. CS-FL introduces a hybrid encryption mechanism to protect the privacy of the model parameters uploaded by selected ENs. The hybrid encryption combines symmetric encryption (AES) and asymmetric encryption (RSA) [33], which can enhance encryption strength and address the low computational efficiency issue of using asymmetric encryption alone at the same time.  
Specifically, upon achieving consensus on the selection phase in \(i\)-th round, each ZS generates its own pair of RSA public key \(PubKey_{m}\) and private key \(PriKey_{m}\). ZS packages \(PubKey_{m}\) along with \(S_{i}\) into a response notification \(R_{1}\leftarrow\langle m\ |\ S_{i}\), \(PubKey_{m}\rangle\) and sends it to the nodes within \(S_{i}\), while sending \(R_{2}\leftarrow\langle m\ |\ S_{i}\rangle\) to the nodes not selected. Furthermore, the ZS keeps the private key locally. After completing local training, ENs encrypt the latest gradient parameters using an AES key \(LocalKey_{n}\) to obtain encrypted data \(EncrypParam_{n}\), and encrypt \(LocalKey_{n}\) with \(PubKey_{m}\), generating \(EncrypKey_{m,n}\):  
\[EncrypParam_{n}=\text{AES}(LocalKey_{n},\bigtriangledown g_{n,i}) \tag{10}\]  
\[EncrypKey_{m,n}=\text{RSA}(PubKey_{m},LocalKey_{n}) \tag{11}\]  
Each EN packages \(EncrypKey_{m,n}\) and the encrypted message \(EncrypParam_{n}\), and sends them to the corresponding ZS. The ZS decrypts \(EncrypKey_{m,n}\) using the RSA private key \(PriKey_{m}\) stored in the blockchain ledger to obtain the AES key \(LocalKey_{n}\), and then decrypts the received Encryparam using \(LocalKey_{n}\) to retrieve the plaintext data \(\bigtriangledown g_{n,i}\):  
\[LocalKey_{n}=\text{RSA}(PriKey_{m},EncrypKey_{m,n}) \tag{12}\]  
\[\bigtriangledown g_{n,i}=\text{AES}(LocalKey_{n},EncrypParam_{n}) \tag{13}\]  
Since this paper only considers threats from the edge of FL system, it is assumed that data interactions between ZSs are secure and trustworthy. The detailed encryption process is illustrated in Figure 4.
## 3 The PSOTSC Algorithm
### Results of Segmentation Accuracy
#### 4.2.2 Maneuvering Target Simulation Data  
The profile of hurricane trajectory is relatively simple, and the trajectory generated by the maneuvering target tracking simulation model has more obvious motion changes, including sharp turning, moving along the arc, etc. The segmentation and compression experiments were carried out for typical maneuvering trajectories. As shown in Figure 10, the segmented trajectory generated by PSOTSC can fit the original trajectory profile well.
# The Survey of Lava Tube Distribution in Jeju Island by Multi-Source Data Fusion
## 5 Discussions
\[H\text{-}migration=0.694d_{h}-0.719d_{v}\]  
\[V\text{-}migration=-0.694d_{h}-0.719d_{v} \tag{4}\]  
Equation (4) explains the relatively small H-migrations compared to the V-migrations even on the same cavities as well as mixed V/H migrations over a potential cave as stated. In the end, we identified a group of potential LTDPs using InSAR observation and spatial analyses. To prove the reliability of this approach, we employed the road-crossing survey by [18] to measure the known collapses of lava tubes. Among 27 ongoing collapses of lava tubes in this survey, 14 caves have H or V migrations within the buffer zones built by their length, and seven caves have both H-V migrations (refer to Figure 10a). However, adequate validation of detected H-V migrations as LTDPs should be conducted based on the ground truth if possible. Reliable ground truth was provided by GPR and the electricity resistivity survey conducted by the [68] around Manjang cave, assigned as '\(E\)' in Figure 8). A total of 23 GPR and electricity resistivity survey tracks together with detected anomalies that were presumably part of undetected lava tube trails were presented compared to the H-V migration points by PS analysis (Figure 10b). The coincidence of H-V migration points and geophysical anomalies is not strong, but there are a few detections of geophysical anomalies within H-V migrations by PS analysis. Considering that spatial overlap between PS coverage hindered by vegetation and the small number of geophysical tracks are intrinsically limited, such detection of geophysical anomalies by spaceborne data demonstrated the potential of the InSAR approach for LTDP detections. Even though some deep instabilities would not be revealed as the surface topographic deformations, the estimated PS measurement in this study is capable of discriminating LTDP with considerable accuracy. However, the concern that remained unresolved in this study is eradicating the false/pseudo-LTDPs, which can lead to incorrect conclusions regarding the spatial distribution of lava tube networks. In future studies, a detailed validation is necessary using a planned GPS/electricity resistivity survey together with InSAR analysis or perhaps employing a corner reflector for synchronized observation. Near future SAR/InSAR missions, such as the L-band NASA-ISRO Synthetic Aperture Radar (NiSAR) [74] and P-band BIOMASS missions [75], will be particularly interested, in terms of coverage and penetrating powers to replace or enhance existing approaches, such as gravitation data sets [76], ground-penetrating radar [26], morphological analyses and spotting of skylight [13; 77]. Figure 10: (**a**) The distribution of detected deformations and buffer zones around collapsing lava tubes. Buffer zone was given by the cave length. (**b**) H-V migration points around Manjang cave compared to the GPR and electricity resistivity survey results.
## 3 Proposed System Technology, Architecture, and Operation
### Architecture of WIAS
An overview of some cloud web services' APIs is shown in Table 1. The description specific to UAI includes the four categories of connect, compute, search, and storage [33], which makes it easy to communicate with cloud databases, and the user can easily add and update related cloud information functions, and timely reflect them in the corresponding agent programs, thus achieving the research purpose of a cloud information agent system-based on web service technology. \begin{table}
\begin{tabular}{c c c} \hline \hline
**SERVICE** & **SERVICE** & **TO WHOM** \\
**NAME** & **DESCRIPTION** & **Data Mining Agent** \\ \hline CBR\_InsTmpCaseData & Adds temporary data for solutions & CBR Agent \\ CBR\_Solutions & Solution to case library & CBR Agent \\ DB\_InsCaseBase & Adds case base rule & Sharing \\ DB\_InsPediction & Adds a prediction rule & Sharing \\ DB\_UptCaseBase & Records whether it been converted & Sharing \\ DB\_UptRawData & Records whether it been converted & Sharing \\ DM\_Solutions & Solution to prediction rules & Data Mining Agent \\ DM\_TransCaseToPred & Converts from a semantic case library to a prediction rule library & Data Mining Agent \\ DM\_ViewCaseBase & Views case base & Data Mining Agent \\ IA\_InsNData & Adds original data & Interface Agent \\ IA\_Solutions & Solution to redefined rules & Interface Agent \\ Share\_DelUniqueTables & Deletes the data in each independent data table & Sharing \\ Share\_IsNumeric & Determines whether sur NUMBER is a number & Sharing \\ Share\_ViewCRR & Views case library summary & Sharing \\ Share\_ViewDSDT & Views system time & Sharing \\ UAI\_ConnectCrossDomain & Connects cross-domain Open Data Base & UAI\_Connect \\ UAI\_ConnectAlerReg & Links registration tips & UAI\_Connect \\ UAI\_ConnectRuleSetting & Connects rule settings & UAI\_Connect \\ UAI\_ComputeFileAccess & File access computation & UAI\_Compute \\ UAI\_ComputeTimeSeriesAccess & Time series computation & UAI\_Compute \\ UAI\_ComputeTriggerConfig & Triggers configuration computation & UAI\_Compute \\ UAI\_SearchTimeSeries & Time series search & UAI\_Search \\ UAI\_SearchBuildIndexFields & Creates an index field & UAI\_Search \\ UAI\_SearchStatistics & Searches statistics & UAI\_Search \\ UAI\_StorageFFStatus & Folder and file status & UAI\_Storage \\ UAI\_StorageFFManipulation & Folder and file operation & UAI\_Storage \\ UAI\_StorageFFTransmission & Folder and file transmission and status & UAI\_Storage \\ UAI\_StorageFFSharing & Folder and file sharing and integration & UAI\_Storage \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of some parts of APIs’ (application programming interface) functions. Case-Based Reasoning (CBR). Figure 3: WIAS (web service-based information agent system) architecture. Ubi-IA: ubiquitous interface agent, SQL IC: structured query language integrated circuits, OntoDMA: ontological data mining agent, OntoCBRA: ontological Case-Based Reasoning (CBR) agent, and WWW: World Wide Web.
# Adaptive Relay Selection Scheme by Using Compound Channel
## 2 System Model  
Figure 1 shows two-hop MIMO relay system model in a wireless network. The system consists of one source node (\(S\)) node and each destination node \(D_{u}\) (\(u=1,\ldots,U\)). The number of antennas on \(S\) and \(D_{u}\) is the same as \(N_{l}\). Furthermore, multiple relays exist in the system model. Each relay node is expressed as \(R_{k}\) (\(k=1,\ldots,K\)). \(R_{k}\) has \(N_{r}\) receiving antennas and \(N_{t}\) transmitting antennas. A set of \(R_{k}\) uses the decode and forward (DF) protocol. Since DF protocol demodulates received signal and re-encodes the signal before retransmission of the signal, effect of noise can be reduced. Direct links (\(S\to D_{u}\)) are assumed to be too weak support high quality transmission [31]. Therefore, direct links (\(S\to D_{u}\)) are not considered. Non-direct links (\(S\to R_{k}\)) and (\(R_{s}\to D_{u}\)) are only available. In addition, it is assumed that each node operating as a transmitter knows CSI through feedback according to transmission of a pilot signal to the receiver node. System model uses two time slots for signal transmission. \(S\) transmits signal to \(R_{k}\) during the first time slot. The received signal at \(R_{k}\) is as follows,  
\[y_{k}=\mathbf{H}_{k}\mathbf{x}+\mathbf{n}_{r}, \tag{1}\]where \(\mathbf{x}\in\mathbb{C}^{N_{l}}\) is the transmitted signal vector from \(S\). \(\mathbf{H}_{k}\) is the \(N_{r}\times N_{l}\) channel between S and \(R_{k}\). Furthermore, \(\mathbf{H}_{k}\) is modeled as Rayleigh fading. \(\mathbf{x}\) is a signal transmitted from S. And \(\mathbf{n}_{\mathbf{r}}\in\mathbb{C}^{N_{r}}\) is an additive white Gaussian noise (AWGN) vector with zero mean and variance \(N_{0}\) at \(R_{k}\).  
During the second time slots, \(R_{s}\) transmits a signal to \(D_{u}\). \(R_{s}\)\((s=1,\ldots,S)\) is selected as a relay among a set of \(R_{k}\). The received signals at \(D_{u}\) are as follows,  
\[\mathbf{y}_{u}=\mathbf{G}_{u}^{s}\mathbf{\hat{x}}^{s}+\mathbf{n}_{d}, \tag{2}\]  
where transmitted signal from a set of \(R_{s}\) is \(\mathbf{y}_{u}=[\mathbf{y}_{1},\ldots,\mathbf{y}_{U}]^{T}\). \(\mathbf{G}_{u}^{s}\) is the \(N_{l}\times N_{l}\) compound channel between \(D_{u}\) and a set of \(R_{s}\). A compound channel is a combination of channels between relays that can be used to transmit to the same destination. Furthermore, \(\mathbf{G}_{u}^{s}=[\mathbf{g}_{u}^{1},\ldots,\mathbf{g}_{u}^{S}]^{T}\) and each \(\mathbf{G}_{u}^{s}\) is modeled as Rayleigh fading. \(\mathbf{\hat{x}}^{s}=[\mathbf{\hat{x}}^{1},\ldots,\mathbf{\hat{x}}^{S}]\) is retransmitted signal from \(R_{s}\). \(N_{l}\) of \(D_{u}\) receives different signals. \(\mathbf{n}_{d}\in\mathbb{C}^{N_{l}}\) is an AWGN vector.
# An Efficient Finite-Difference Stencil with High-Order Temporal Accuracy for Scalar Wave Modeling
## 2 Method
### Determining FD Coefficients of the New Stencil through Taylor-Series Expansion
The function \(\varphi_{n,\beta,\theta}\) can be defined as  
\[\varphi_{n,\beta,\theta}=4[\cos(n\beta\cos(\theta))\cos(n\beta\sin(\theta))]. \tag{17}\]  
Then, we can extend \(\psi_{m,\beta,\theta}\) to matrix \(\mathbf{A}(\theta)\) involving a series of \(\beta\) and a fixed angle \(\theta\). Matrix \(\mathbf{A}(\theta)\) is  
\[\mathbf{A}(\theta)=\left[ \begin{array}{cccc}1&\psi_{1,\theta_{1},\theta}& \psi_{2,\beta_{1},\theta}&\cdots&\psi_{M,\beta_{1},\theta}\\ 1&\psi_{1,\theta_{2},\theta}&\psi_{2,\theta_{2},\theta}&\cdots&\psi_{M, \beta_{2},\theta}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ 1&\psi_{1,\beta_{2},\theta}&\psi_{2,\beta_{2},\theta}&\cdots&\psi_{M,\beta_{ 2},\theta}\end{array} \right], \tag{18}\]  
where \(\beta_{i}=\beta_{max}/\xi*i\) and \(\beta_{max}\) can be obtained by the optimization algorithm [23]. We also extend the function \(\varphi_{n,\beta,\theta}\) to the matrix  
\[\mathbf{B}(\theta)=\left[ \begin{array}{cccc}\varphi_{1,\beta_{1},\theta}& \varphi_{2,\beta_{1},\theta}&\cdots&\varphi_{N-1,\beta_{1},\theta}\\ \varphi_{1,\theta_{2},\theta}&\varphi_{2,\theta_{2},\theta}&\cdots&\varphi_{ N-1,\beta_{2},\theta}\\ \vdots&\vdots&\vdots&\vdots\\ \varphi_{1,\beta_{2},\theta}&\varphi_{2,\beta_{2},\theta}&\cdots&\varphi_{ N-1,\beta_{2},\theta}\end{array} \right]. \tag{19}\]  
Additionally, the right-hand side of Equation (6) is expanded to  
\[\mathbf{D}(\theta)=\left[ \begin{array}{c}r^{-2}[-2+2\cos(\beta_{1}r)]\\ r^{-2}[-2+2\cos(\beta_{2}r)]\\ \vdots\\ r^{-2}[-2+2\cos(\beta_{2}r)]\end{array} \right]. \tag{20}\]  
Thus, the time-space domain dispersion relation of our new method involving \(\xi\) wavenumbers and \(\xi\) angles can be expressed as follows:  
\[\left[ \begin{array}{cc}\mathbf{A}(\theta_{1})&\mathbf{B}(\theta_{1})\\ \mathbf{A}(\theta_{2})&\mathbf{B}(\theta_{2})\\ \vdots&\vdots\\ \mathbf{A}(\theta_{\zeta})&\mathbf{B}(\theta_{\zeta})\end{array} \right] \left[ \begin{array}{c}a_{0}\\ a_{1}\\ \vdots\\ b_{N-1}\end{array} \right]=\left[ \begin{array}{c}\mathbf{D}(\theta_{1})\\ \mathbf{D}(\theta_{2})\\ \vdots\\ \mathbf{D}(\theta_{\zeta})\end{array} \right]. \tag{21}\]  
Then, numerically solving this over-determined system can yield the DRP-based FD coefficients.