esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
## 4 System Model
### Initialization  
In this step, all existing nodes of the network are initialized. In the proposed model, RSA is used for securing data transmission. It is composed of three processes: Key generation, encryption and decryption. Each node generates the public and private keys for itself. The public key of every node is stored on the BS, where public blockchain is deployed, while the private key of each node is kept secret and is only known by the authorized node. Moreover, it is assumed in the proposed work that both BSs and ANs are the trusted nodes and ANs aggregate and forward the data. The SNs encrypt the collected information with the public keys of ANs and send it to the ANs. The ANs decrypt the data packets with their private keys. The same process is performed when sending the data packets from ANs to BSs.
# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
## 3 Methodology
### LSTM Model  
To solve the problem of gradient disappearance in recurrent neural networks (RNNs), [PERSON] et al. [38] proposed a long short-term memory neural network (LSTM) capable of automatically learning information over a period of time in the past. LSTM increases the storage capacity of the loop cell by introducing 'gates' into the repetition module, including input gates, forgetting gates, and output gates. As a modified version of an RNN, LSTM classifies repetitive and periodic patterns in the data into long-term or short-term cycles and uses them as the output. Figure 4 illustrates the structure of the repetition module in which the amount of forgetting and memory information is regulated by the gates.  
The LSTM model's iterative formula is displayed in Equations (13)-(18) with initial values of \(C_{0}=0\) and \(h_{0}=0\), where \(b\), \(W\), and \(U\) represent the bias vector, weight connection matrix to the input, and weight connection matrix to hidden layers, respectively. And \(i\), \(f\), and \(o\) denote the input, forgetting, and output. The cell output vector is calculated as \(h_{t}\).  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{13}\]  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)} \tag{14}\]  
\[o_{t}=\sigma(W_{0}x_{t}+U_{0}h_{t-1}+b_{O}) \tag{15}\]  
\[\widetilde{C}_{t}=\tan(W_{c}x_{t}+U_{0}h_{t-1}+b_{c}) \tag{16}\]  
\[C_{t}=f_{t}\otimes C_{t-1}+i_{t}\otimes\widetilde{C}_{t} \tag{17}\]  
\[h_{t}=o_{t}\otimes\tan(C_{t}) \tag{18}\]  
The cooling load of cold stores has significant periodicity, and thus the LSTM algorithm is well-suited for cooling load prediction. The environmental conditions (dry bulb temperature, relative humidity, wind speed, light intensity), the temperature of the warehouse, and the cooling load from the previous several days are used as model inputs in this paper. The scope of this research is the development of novel peak shifting strategies with the cooling load forecasts acting as the necessary pre-work. Therefore, in this research, the MATLAB toolbox is utilized directly to optimize the hyperparameters of the LSTM model rather than adopting new approaches.
## 3 Result and Discussion
### Calibration
#### 3.4.2 Calibration Correction Factor Calculation  
The calibration correction factor was calculated using a linear regression method to reduce the measurement result deviation between the sensor node and CO\({}_{2}\) probe (validator device). The calculation result is shown in Figure 11. The R\({}^{2}\) (coefficient of determination) value close to 1 indicates that the measurement results of the sensor node are equivalent to the measurement results of the validator device. The equation of calibration factor for CO\({}_{2}\) measurement by sensor node is y = 1.0336x\(-\)270.55; y is the value of CO\({}_{2}\) with a correction factor, and x is the CO\({}_{2}\) value without the correction factor. The calibration factor will include the sensor-node programming code to improve its measurement result.
# RCM: A Remote Cache Management Framework for Spark
###### Abstract  
With the rapid growth of Internet data, the performance of big data processing platforms is attracting more and more attention. In Spark, cache data are replaced by the Least Recently Used (LRU) Algorithm. LRU cannot identify the cost of cache data, which leads to replacing some important cache data. In addition, the placement of cache data is random, which lacks a measure to find efficient cache servers. Focusing on the above problems, a remote cache management framework (RCM) for the Spark platform was proposed, including a cache weight generation module (CWG), cache replacement module (CREP), and cache placement module (CPL). CWG establishes initial weights from three main factors: the response time of the query database, the number of queries, and the data size. Then, CWG reduces the old data weight through a time loss function. CREP promises that the sum of cache data weights is maximized by a greedy strategy. CPL allocates the best cache server for data based on the Kuhn-Munkres matching algorithm to improve cooperation efficiency. To verify the effectiveness of RCM, RCM is implemented on Redis and deployed on eight computing nodes and four cache servers. Three groups of benchmark jobs, PageRank, K-means and WordCount, is tested. The result of experiments confirmed that compared with MCM, SACM and DMAOM, the execution time of RCM is reduced by 42.1% at most.  
cache weight generation; cache replacement; cache placement; cache management framework; Spark +
Footnote †: journal: Applied Sciences  
11 October 2022  
Accepted: 8 November 2022  
12 November 2022  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright**: © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)]([https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)) 4.0/).
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
## 4 Equipment Operation Testing and Visual Scanning Simulation
### Analysis of Groove Working Conditions  
This section plans and analyzes the processing of the V-shaped butt groove of this plate. The specific groove sectional view and simulation diagram are shown in Figure 33a,b.  
In Figure 33a, P\({}_{\mathrm{t}}\), P\({}_{\mathrm{b}}\), and P\({}_{\mathrm{c}}\) are the vertex of the center line of the plate groove, the midpoint of the division, and the bottom endpoint, respectively. P1-P4 are the endpoints of each plane; P(A) and P(B) are the left and right endpoints of the corresponding slope line segments. L1, L2, L3, and L4 are the sectional widths of planes 1 to 4; \(\alpha\) is the angle of the butt groove; **w** is the distance range value between the left and right edge points at the bottom end of the groove; T is the overall thickness of the groove; the dashed line represents the division line between the butt groove and the ceramic backing. The groove parameters of the panel and web are shown in Table 11.  
Welding is performed in the plane where L1 is located at the midline distance between L2 and L3. Both the panel and the web adopt multi-layer and multi-pass welding. The welding procedures include root welding, fill welding, and cap welding. The panel's welding preparation process consists of five layers and seven passes, while the web's welding preparation process consists of three layers and five passes. The welding method  
Figure 33: Cutaway view of plate butt level.  
Figure 32: Small assembly plates working conditions.  
uses FCAW-GA, and CO2 is employed as the shielding gas. The start and end welding positions are set with run-on and run-off plates according to processing requirements.
# Adaptive Linearization for the Sub-Nyquist Photonic Receiver Based on Deep Learning
More importantly, due to the characteristics of adaptive intelligence, the deep learning method is more suitable for non-cooperative communication scenarios and applicable to the time-varying electromagnetic environment.
## 7 Validation and Results
### Negation and Speculation Results  
#### 7.3.1 Cue Detection Results  
Three experiments were performed to measure the impact of regular expressions (Regex) to detect both negation and speculation cues:  
1. Only the first and second Regex are used for cue detection (See Section 5.2). These Regex were adapted to Spanish from the popular rule-based Negex [56] proposal. The Negex adaptation to Spanish is used as baseline.
2. The third Regex is added to measure the contiguous cues behavior.
3. The fourth Regex was added, this experiment includes all Regex proposed in Section 5.2.  
Table 4 shows the results obtained in the cue detection task. The best performance using all the Regex proposed in Section 5.2, for both negation and speculation cue detection was obtained. In the case of negation detection, adding the third Regex has a significant improvement in the F-score, as evidenced in Table 4. This fact suggests that detecting contiguous cues improves negation detection in clinical notes written in Spanish. Adding the fourth Regex also improves performance but to a lesser improvement rate.  
On the other hand, for speculation detection, using the first two Regex an F-score of 89% is obtained. When adding the third and fourth Regex, the improvement rate is not as significant as in negation detection. This fact suggests that contiguous cues do not have an important impact on speculation detection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Negation**} & \multicolumn{3}{c}{**Speculation**} \\ \hline
**Regex** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Regex 1,2 & 0.84 & 0.81 & 0.82 & 0.91 & 0.88 & 0.89 \\ Regex 1,2,3 & 0.93 & 0.91 & 0.92 & 0.92 & 0.89 & 0.90 \\ Regex 1,2,3,4 & **0.96** & **0.94** & **0.95** & **0.92** & **0.89** & **0.90** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for the cue detection task.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **P** & **R** & **F1** \\ \hline BiLSTM-CRF & 0.83 & 0.78 & 0.80 \\ BiLSTM-CRF + General domain embeddings & 0.80 & 0.73 & 0.76 \\ BiLSTM-CRF + Medical embeddings & 0.87 & 0.84 & 0.85 \\ BiLSTM-CRF + Medical Embeddings + Char embeddings & 0.87 & 0.85 & 0.86 \\ BiLSTM-CRF + Medical Embeddings + Char embeddings + Lemmas + POS & **0.91** & **0.89** & **0.90** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for Named Entity Recognition.
## 2 Materials and Methods
### Custom PD-AFO Design and Production
#### 2.1.3 Additive Manufacturing  
The present PD-AFOs are produced via selective laser sintering (SLS) of a fiberglass-reinforced-polyamide-based powder (Windform(r) GT). This is a highly elastic and flexible material (tensile strength = 56 MPa; tensile modulus = 3.3 GPa; elongation at break = 14.8%; bending strength = 88 MPa; bending modulus 3.2 MPa), has been granted skin contact safety certification, and thus it appears to be potentially suitable for this novel, custom PD-AFO.  
According to the SLS process, microscopic particles of plastic polymer (Nylon) are melted by a high-power laser to form a solid three-dimensional object. The powder is initially released on a platform and laser sintered to form a 0.1 mm compact layer of material. The parts are built layer by layer, with the build platform lowering after each layer. The process takes place in a nitrogen-filled chamber to prevent oxidation (oxygen concentration < 1%). The printer preheats the powder to 10 \({}^{\circ}\)C below the melting point of the raw material (melting point of Windform(r) GT = 193 \({}^{\circ}\)C). The unfused powder supports the part during printing, avoiding the need for supporting structures. After printing, the build chamber needs first to slightly cool down inside the printer enclosure and subsequently outside the printer to ensure optimal mechanical properties and to reduce risks of warping. The printed parts are then removed from the build chamber, separated, and cleaned from the excess powder. A three-dimensional scan is performed to measure changes in the AFO dimensions over time. The average production time of the AFO, from the delivery of the 3D model file, is about 24 h.
# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
###### Abstract  
Blockchain technology is used to support digital assets such as cryptocurrencies and tokens. Commonly, smart contracts are used to generate tokens on top of the blockchain network. There are two fundamental types of tokens: fungible and non-fungible (NFTs). This paper focuses on NFTs and offers a technique to spot plagiarism in NFT images. NFTs are information that is appended to files to produce distinctive signatures. It can be found in image files, real artifacts, literature published online, and various other digital media. Plagiarism and fraudulent NFT images are becoming a big concern for artists and customers. This paper proposes an efficient deep learning-based approach for NFT image plagiarism detection using the EfficientNet-B0 architecture and the Triplet Semi-Hard Loss function. We trained our model using a dataset of NFT images and evaluated its performance using several metrics, including loss and accuracy. The results showed that the EfficientNet-B0-based deep neural network with triplet semi-hard loss outperformed other models such as Resnet50, DenseNet, and MobileNetV2 in detecting plagiarized NFTs. The experimental results demonstrate sufficient to be implemented in various NFT marketplaces.  
NFT; blockchain; plagiarism check; EfficientNet-B0; deep neural network; triplet semi-hard loss Article
## 2 National and Regional Indigenous Media Networks
The television channel airs nationally also on the Shaw Direct basic satellite channel 267, in Nunavut and NWT on Arctic Co-ops cable 240, in Nunaviv von FCNQ cable 308, and online. The third example presented here is Indigenous Television of Nepal (Figure 4), a community channel that reaches across this Himalayan country through fiber-optic cable networks, and the world beyond via a mobile app, website, and social media. Run by the Indigenous Media Foundation, a not-for-profit organization founded by Nepalese journalists belonging to the country's diverse Indigenous communities, the channel launched on the occasion of the World Indigenous Peoples Day on the 9 th of August 2016, as a 24 h source of news, views, educational content, entertainment, and other informative programs on issues of interest and importance, created for, by, and of, and in the language representing Nepal's Indigenous Peoples (known there as Adivasi Janajati). The Nepalese government classifies these into 59 distinct ethnic groups, each being the custodian of its distinct culture, language, spiritual practices, traditional knowledge, and biological diversity. Indigenous Television of Nepal is a unique case within South Asia, in the manner in which it provides representation for communities in their respective mother tongues, as well as being grounded in their traditional cultures and written and oral histories, while also recognizing and including the traditional homeland and geographical cluster of each. Adivasi Janajati comprise 36 percent of the country's total population of 26.8 million. Additionally, at least 25 more ethnic communities are engaged in the process of being recognized as Indigenous Peoples. Of the 123 different languages spoken by 125 ethnic and caste groups in Nepal, 95 percent are spoken by Indigenous Peoples. From its inception,  
Figure 3: Isuma TV, a project of Isuma Distribution International Inc. Source: www.isuma.tv/isuma, accessed on 11 September 2021. Figure 2: Indigenous Native Peoples International Festival of Cinematography and Communication. Source: www.clacpi.org, accessed on 11 September 2021. Indigenous Television of Nepal has produced and broadcast TV shows, news, music videos, feature films, and documentaries in fourteen of these Indigenous languages (Bantawa-Rai, Chamling-Rai, Kulung Rai, Tamang, Sunuwar, Magar, Newar, Limbu, Sherpa, Hyolmo, Thami, Chhantyal, Yakkha, Gurung), with the goal to educate and connect the country's Indigenous Peoples with one another, facilitating dialogue and promoting mutual understanding among Nepal's diverse ethnic groups.
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### CH Selection Algorithms Based on Si
#### 3.1.1 Cluster Classification Properties  
* Sensor coverage mode  
Divide sensor node coverage into two types: deterministic coverage and random uncertainty coverage. Random uncertainty coverage refers to the random distribution of sensor nodes. The distribution of sensors controls deterministic coverage.  
* Clustering method  
Classify the clustering methods into static clustering and dynamic clustering. In the static clustering network, the network is divided into many clusters. In dynamic clustering, a cluster is passively created near the CH node.  
* The number of clusters  
The number of clusters represents the number of clusters per turn. There are two types: fixed (F) and variable (V).  
* The size of the cluster  
The size of the cluster refers to the maximum path length from the CH to other nodes in the cluster. There are two types: fixed (F) and variable (V).  
* Intra-cluster communication method  
Inter-cluster communication means communication between sensor nodes and communication between sensors and CH. There are two types: multi-hop jumps and single-hop, depending on whether multi-hop communication is used.  
* CH selection factors  
The main factors that need to be considered when selecting CH: residual energy (E), distance to sink (D2S), distance to BS (D2B), cluster center (C), distance to other nodes (D2N).
# A Survey on Change Detection and Time Series Analysis with Applications
## 4 Change or Breakpoint Detection within Non-Stationary Time Series
### BFAST Monitor  
The BFAST Monitor is a near-real-time monitoring method that uses a stable historical variation to enable change detection within newly acquired data [71]. [PERSON] et al. suggest the use of the reversed-ordered-cumulative sum (CUSUM) of residuals or BFAST, slower than CUSUM, to select a stable history period [71]. Then they use the moving sums of residuals in the monitoring period to determine whether the stable season-trend model remains stable for new observations. Thus, if a disturbance occurs, the moving sums will deviate systematically from zero. A break is detected if the absolute value of moving sums exceeds some threshold that is asymptotically only crossed with 5% probability under structural stability.  
Unlike BFAST, BFAST Monitor can handle missing values and is considered a robust near-real-time monitoring method. The BFAST Monitor has been recently implemented on the Google Earth Engine to support large-area and sub-annual change monitoring using Earth observation data [72]. Since BFAST Monitor only needs a single observation to exceed a boundary for a change to be detected, it can potentially increase the false positive rate. Like BFAST, BFAST Monitor also has the over/under-fitting issue due to the use of sinusoids of fixed frequencies. Furthermore, the observational uncertainties are not considered in BFAST Monitor, meaning that the model is more sensitive to noisy observations. Using simulated data sets, [PERSON] et al. in [69] showed that BFAST Monitor performs poorly in estimating break magnitude.
## 4 Discussion  
Sleep, its quality and the disorders presented during sleep, have been the object of investigation in different populations [26; 27], but it is not strongly studied in DS individuals. To the best of our knowledge, this is the first study to report the prevalence of sleep problems in Brazilian DS children and adolescents. In the current study, the quality and sleep pattern of 41 DS children and adolescents was evaluated, determining the prevalence of them. Of the DS children and adolescents investigated, most males, 68.28% presented indications of sleepiness during the day, movements during night sleep and need to wake up during night sleep by the QRL questionnaire. We also found data related to the habit of peeing during sleep in this observational study. Across the SDSC applied in this current cross-sectional study, 15 children showed an increase in the score of at least one factor, while only three DS children and adolescents had above 69 points of the total score, suggesting that they had sleep disorders.  
In DS individuals, sleep disorders have shown a significant prevalence [9], and especially in children, sleep is an important and dynamic process, affecting numerous aspects of health and development such as physical, behavioral, and physiological development, and poses an additional risk for obesity, endocrine disorders, depression, immunological and heart diseases [28].  
As it was done in the current observational study, [PERSON] et al. [29] conducted semi-structured interviews with 34 parents, with open-ended questions about parents' experiences with sleep, family dynamics and health care. Parents normalized their experiences of having a DS child and sleep problems. Parents recognized that sleep disruption had adverse and pervasive impacts on their well-being and family dynamics, but they also found it difficult to identify this as a health issue.
###### Abstract  
This article proposes a displacement reducer based on distributed compliant mechanisms to improve the motion resolution of actuators commonly used in precision operation systems that require high-precision control and positioning, such as micro-grippers, biological manipulation, and micro-alignment mechanisms. Distributed compliance significantly diminishes its effective moving lumped mass, endowing the structure with advantages such as reduced stress concentration and an expansive range of motion. Additionally, the design incorporates an over-constraint structure through a dual-layer displacement reducer, ensuring that the reducer achieves a nearly constant reduction ratio. According to the compliance matrix method, the analytical model of the reducer is established to predict the input-output behaviors, which are verified by finite element simulations. On the basis of sensitivity analysis to structure parameters, including node positions and beam parameters, the Particle Swarm Optimization (PSO) algorithm is used to optimize the displacement reduction performance. Through finite element analysis and experimental results on the prototype, the proposed displacement reducer demonstrates a large reduction ratio of 11.03, an energy transfer efficiency of 39.6%, and a nearly constant reduction ratio with an input displacement range of 0 to 2000 \(\upmu\)m.  
displacement reducer; nearly constant reduction ratio; distributed compliance; kinetostatic model; structure optimization; over-constraint Article