esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In a cloud environment, user authentication and data security are the challenging issues. Therefore, an efficient and scalable access control scheme has been proposed in this paper. Further, this scheme employs a blowfish hybridized weight attribute-based encryption mechanism not only to provide data security against the semi-trusted cloud service provider, but also the weight attribute authority and the central authority provides lightweight key management in large scale-consumers. Besides, the partial signing construction implemented in this scheme can reduce the computation overhead of user to the cloud server, which is efficient and appropriate for resource-constrained devices. Here, blowfish encryption and decryption algorithms are used to transmit data securely. When the authenticated user makes a request to the cloud, the corresponding files are sent to the consumer in an encrypted format based on its weight. Subsequently, the data consumer can decrypt the data using the key generated by the blowfish algorithm. The result shows that the proposed method BH-WABE is efficient in terms of security, reliability, and efficiency, as well as performing well when it is juxtaposed to the conventional HABE scheme by means of data confidentiality, flexible access control, data collaboration, full delegation, partial decryption, verification, and partial signing.  
The future extent of the proposed work can be accessible, quality-based encryption and protection-saving property-based information-sharing with re-encryption. These are territories in which we can look into going ahead to use diverse methods to accomplish information-sharing.  
Both the authors have carried out the entire research work jointly.  
The funding was provided by CSIR-Central Scientific Instruments Organization; Sector 30-C, Chandigarh-160030.  
Authors are thankful to the Director of Thapar Institute of Engineering and Technology, Patiala, Punjab, India and Director, CSIR-Central Scientific Instruments Organization; Sector 30-C, Chandigarh-160030, India for providing the environment to carry out this work.  
The authors declare that they have no conflict of interest.
## 3 Preliminaries
### Cryptographic Primitives
#### 3.4.2 Symmetric Key Cryptography  
Symmetric key cryptography involves the use of a single secret key \(K\) for both encryption \(E_{K}(\cdot)\) and decryption \(D_{K}(\cdot)\) operations [8]. The chosen algorithm (e.g., AES-128--Advanced Encryption Standard with 128-bit key, specified in FIPS PUB 197 [8]) should be resistant to known cryptanalytic attacks and provide semantic security (IND-CPA: Indistinguishability under Chosen-Plaintext Attack). Our protocol uses AES-128 in a mode like CBC (cipher block chaining) for OTAC encryption.  
* Encryption: \(C=E_{K}(M)\), where \(M\) is the plaintext and \(C\) is the ciphertext.
* Decryption: \(M=D_{K}(C)\). The security of symmetric encryption relies entirely on the secrecy of the key \(K\).
# Use of Image Correlation to Measure Macroscopic Strains by Hygric Swelling in Sandstone Rocks
## 1 Introduction
Argillaceous rocks are inhomogeneous materials with different hygric properties that lead to incompatibilities of free-swelling deformations for all its different particles. Additionally, the moisture gradient in the transient state of moisture transport makes some parts swell before others, which confers deformation additional incompatibility. These incompatibilities result in mechanical internal stresses that affect macroscopic local movement during swelling. In summary, previous works have demonstrated that the argillaceous swelling process is substantially affected by non-controllable factors such as particle distribution in the sample or moisture transport distribution, which mean that any swelling experiment is hardly reproducible. From the marked uncertainty point of view that comes with measuring rock swelling, our approach involved simplifying the procedure to obtain a comparable measure of the macroscopic strain to those obtained by LVDT, but without using complex setups and with the possibility of obtaining strains in both directions. This paper analyzes the swelling process of an argillaceous rock, with a sample of one centimeter in size and a similar image resolution to the papers herein cited, but by using a simpler setup and image processing methods. Rock expansion was analyzed by changes in the rock surface texture. The movement of texture imposed by swelling was tracked by DIC, applied to six different regions of interest (ROIs) located on the edge of the rock surface. The errors of both the experimental setup and numerical methods were carefully analyzed. Finally, it was possible to obtain the relative deformation in the vertical and horizontal directions, and to observe non-uniform stone deformation depending on the proximity of the ROIs to the wet surface. Some preliminary results obtained with this study have been presented in [11]. Based on these results, the setup and the calculation method very much improved. Mathematical methods have been discussed in [12], where full access to the code is available. This manuscript is structured as follows. First, we describe the experimental setup and numerical methods, including the error analysis. The results of the dry and wet experiments, and their discussion, are included in Section 3. Finally, the main results are summarized in the last section.
# Extended Evolutionary Algorithms with Stagnation-Based Extinction Protocol
## 4 Experimental Design and Result
### Experimental Design
#### 4.2.6 Evaluation Methodology  
The performance of SDEP-GA is compared against Simple GA on all three classical test functions. We adopt some parameters used in [23] to evaluate the performance of these two algorithms: mean generation number (\(Mgn\)) and post-extinction number (\(Pen\)). The definitions of these two parameters are as follows:  
* \(Mgn\): the average number of generations for the best result to be obtained in one complete run
* \(Pen\): the average number of generations needed to achieve a new best found solution in an entire run  
To assist in understanding the way these parameters function, an example will be given below for each of the parameters described above. Suppose we run the experiment for 5 runs, with each run having obtained the best result at 10 th, 11 th, 20 th, 15 th, 21 st generation, respectively. The \(Mgn\) will be the average of these five values: \(Mgn=(10+11+20+15+21)/5=15.4\). This value shows that on average the best result is found at the 15.4 th generation. Suppose we are working on an optimization problem where we are trying to find the global minimum of a function and these are the best results obtained after each generation for 7 generations: 80, 92, 90, 78, 81, 83, and 74. To calculate the value of _Pen_, we first need to see how many times the best result is updated for one run, which in this case over the duration of 7 generations. The best result is updated at the 1 st, 4 th, and 7 th generation, for a total of 3 times. The value of _Pen_ will then be \(Pen=7/3=2.33\). This value indicates that on average the algorithm will obtain a better result after every 2.33 generations.  
Besides that, we will compare the performance of the two algorithms in terms of accuracy. The average result of 30 runs will be used, where each run will have a total number of 400,000 evaluations. Namely, we used a population size of 400 chromosomes in the population and a maximum number of generations of 1000.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 2 M2D2 System Framework
### Drift-Aware Federated Learning  
(1) Motivation and Mechanism:  
Large enterprises or multi-site manufacturing deployments often require a shared global view of production behavior without centralizing proprietary data. FL naturally emerges as a solution, wherein local nodes train models on their respective data, only transmitting model parameters to a central coordinator. However, naive averaging approaches such as FedAvg frequently fail when certain machines undergo severe distributional shifts that skew the global model. M2D2 addresses this shortcoming by infusing a drift-aware component into FL: each site computes a drift score--incorporating short-term reconstruction errors and long-term distributional deviations--and passes this score alongside its model parameters. If the drift score is significantly high for a given site, the central aggregator can deploy additional local micro-rounds to correct local drift or temporarily exclude the site from aggregation, thereby safeguarding the global model's robustness.  
(2) Multi-Head/Multi-Task and Deployment Strategies  
Given the heterogeneous nature of many industrial processes, M2D2 can adopt a \"shared-backbone-plus-local-heads\" or multi-task architecture. The central server mergesthe shared backbone (e.g., front-end layers of a neural network) across all sites, while each site independently fine-tunes local task-specific heads for more nuanced adaptation. In cases of radical \"concept drift\", such as hardware upgrades or major process overhauls, M2D2 can dynamically create new heads and preserve older ones, thus enabling continual learning while retaining previously acquired knowledge.
## 2 Materials and Methods  
The developed procedure is based on the classic stereo-pair of pictures taken for a test site using a popular camera. The control network consisting of linear values has been marked directly on the object, the points of which represent its characteristic elements. The control points gain Cartesian coordinates expressed in the local system encompassing the pictured entity as well as the pixel coordinates determined for the left and right photograph. Then, the measured linear elements of the control network related to the local coordinates of the selected points are adjusted by the least-squares method employing the Gauss-Markov model [21]. The adjusted positions of the control points are then used to set out the orientation and distortion parameters both for the left and right image, respectively. For the resulting center projections, the accuracy assessment is carried out considering the estimated errors in determining their parameters. As a result, the photogrammetrically derived linear elements of the control network are compared with the actual measures derived from the direct object survey.  
To verify the developed method, we took control measurements in the university classroom, assuming an office desk with some auxiliary elements. The control network oriented in the local \(XYZ\) coordinate encompassed all its characteristic points (object vertices). All linear components were measured three times with a surveying tape, thus determining their values with \(\pm\) 1 mm precision. The 3D linear network established on the investigated object consisted of 12 points joined by 40 distances. Then, using a professional mirror-reflex camera, a Canon EOS 500D [22], two photos were taken covering the item from its left and right side (Figures 1 and 2).  
The key technical parameters of the camera used are presented in Table 1[23].  
It should be added that the camera is commonly available on the market, emphasizing the low-cost feature of the presented method. For the experiment, the photos were taken using the classic indoor shooting program at short distances to the subject (portrait function at a fixed focal length, \(f/2\) aperture, shutter speed 1: 200 s, ISO 400, no flash).  
All mathematical operations were performed using Mathcad v. 15.0 software (PTC--[[https://www.mathcad.com--accessed](https://www.mathcad.com--accessed)]([https://www.mathcad.com--accessed](https://www.mathcad.com--accessed)) on 28 April 2022; previously known as Mathsoft) by programming appropriate calculation worksheets.  
Figure 1: The left stereo picture with the 3D precise linear network on the object (the points 1,..., 12, A, B, C, D represent characteristic vertices of the object; \(X\), \(Y\), \(Z\) define a local 3D coordinate system; \(x\), \(y\) represent pixel image coordinates, while \(h\) is the object’s approximate height).
## 3 Results and Discussion  
### Ageing Based on the Actual Elapsed Time  
In this section, measurement data of thermal resistance, while actual time passed for 50 mm thick XPS under 23 and 50% RH with and without solar radiation exposure conditions, are described. The thermal resistance profile graph of 50 mm thick extruded polystyrene (XPS) material under two experimental conditions was quoted as a function of time in log scale based on data in our previous study are as shown in Figure 4 [15]. The thermal performance of XPS exhibited a primary stage that showed rapid degradation in performance for approximately 1000 days and slow degradation in a second stage. The initial thermal resistance was measured in the range of 2.485\(\sim\)2.498 m\({}^{2}\cdot\)K/W, and performance degradation of approximately 41% was observed compared to the initial value after approximately 5300 days, where the thermal resistance was measured in the range of 1.424\(\sim\)1.452 m\({}^{2}\cdot\)K/W. As compared with the criteria value of 1.8519 m\({}^{2}\cdot\)K/W in the Korean Standard (KS) M 3808 [16], which is positioned as a vertical dot line, thermal resistance values of two samples satisfied the value until 185 days as thermal resistance of 1.8173 m\({}^{2}\cdot\)K/W for sample 3 and 188 days as of 1.82562 m\({}^{2}\cdot\)K/W for sample 4, respectively. Consequently, it was concluded that thermal resistances of samples were over the Korean Standard at first installation, but deteriorated down 3 months later below the Korean Standard.  
Figure 3: Relative thermal resistance of cellular plastic foams with three different thicknesses after scaling factor was applied. Redrawn by importing the points from image under the permission by ISO member in author’s country [6].
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 3 Interactive Model and Architecture of IoV
### Interactive Model  
One of the key highlights of the IoV is its interactive model (Figure 2) that includes V2V [29; 30; 31], V2R [32], V2S, V2M, and V2I. The IoV implementation requires different devices such as vehicles, portable gadgets, RSUs, sensors, and actuators, to serve as fundamental necessity for ITS applications. For these communication systems, Data Acquisition System (DAS) is required where the vehicular data is transferred on the network through on road diagnosis interface. It helps in avoiding accidents, renders safety driving and improves driving experience [33]. Figure 2 shows the layout of an immediate connection between these gadgets and the IoV server. The registration and authentication processes are conducted through a secure link such as Secure Sockets Layer (SSL)/Transport Layer Security (TLS) convention [12; 18; 34].
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 4 Discussion
This may indicate that there is room for improvement in data collection methods and grouping structures during the experiment, warranting further exploration. 2. Choice of training data: This study only used the peak value of impact acceleration for training. Whether including additional data, such as bandwidth and frequency could lead to better prediction results still needs to be investigated. 3. Dimensionality issues: The data used in this experiment had 10 dimensions. Under higher-impact conditions, the data length may exceed this dimension, leading to longer training times and potentially impacting prediction results. To address this, dimensionality reduction techniques, such as Principal Component Analysis (PCA), could be considered. However, the specific impact of these techniques on prediction results is difficult to estimate and warrants further research.
## 4 Discussion
### Isolated vs. Progressive LBNP Applications  
Most LBNP studies applied different pressure levels by progressively increasing the negative pressure [18; 19; 20; 21], in some cases until reaching a pre-syncope state [2; 3; 22]. However, this approach does not allow a comparison of the effects obtained at the different pressure levels since a cumulative effect takes place with the progression of the stimulus, i.e., the effects observed at a certain pressure level are affected by the exposure to preceding pressure levels. To account for this effect and to improve comparison between studies that may have followed different pressure steps or different step durations, the cumulative stress index (CSI), defined as the pressure time integral [1], is often adopted. In the present study, isolated and randomized stimuli at the different pressure levels were delivered, thus minimizing the CSI. This approach allowed us to detect the true dependence of the different variables on the LBNP.
## 3 Simulation Methodology
Figure 4: Power amplifier IC layout in HFSS: (**a**) 3D isometric view where black circles represent input ports, (**b**) side-view with dielectric layers information about material, (**c**) zoomed in view of layout with substrate, and (**d**) zoomed in view of layout without substrate (blue color represents via). GaN, gallium nitride; SiC, silicon carbide; SiN, silicon nitride. of the magnetic field between the two methods. As solved Maxwell equations in FEM (3D method) will be more accurate, MOM (2.5D method) should be used with caution. The impact of the H-field is more significant than electric field emissions in vicinity of the device, as wave impedance for the H-field is small and increases with the distance from the source [36]. After some distance from the source, wave impedances of the H-field and electric field become same, which is found to be 318 mm for the present case, as calculated using the Equation in the work of [36]. However, near field measurements in the present scenario are conducted at 0.2 mm, and thus magnetic field analysis becomes important. For the effect of EMI on 3D-IC circuits, the distance is even smaller, and hence magnetic field computation is the focus of this work. Dielectric layers used in ANSYS HFSS simulation methodology are silicon carbide (SiC), GaN, and silicon nitride (SiN) to resemble the actual IC. Perfect electric conductor (PEC) boundary condition is used for ground plane and lumped port excitation is provided [34]. The overall electromagnetic simulation takes around 20 min. Figure 6 shows the simulation result for 3 GHz operating frequency, respectively. Such a simulation method has been verified experimentally in our other works [34]. Figure 5: Comparison of method of moments (MOM) and finite element method (FEM) simulation: (**a**) side and top view of test structure used for simulation, (**b**) magnetic field distribution in HFSS (3D method) and Slwave (2.5D method) software, respectively.
## 3 Results
### Aroma Recombination  
To construct the aroma recombination model, we prepared a reconstituted sample (Rec A) by precisely blending 70 quantified volatile compounds based on their individual quantitative results. An additional 55 non-volatile compounds were incorporated into Rec A to form the full recombination sample (Rec B). Rec A, Rec B and the original baijiu samples were subjected to QDA analysis.  
During the QDA training period, the reviewers finally selected 10 aroma descriptors (alcoholic, roasted, pit, fruity, flower, grain, acidity, sweet, grass, and sauce) to characterize the aroma characteristics of strong-aroma baijiu samples. The results of QDA (Figure 3) showed that the aroma profile of the recombination samples was similar to the original baijiu samples, indicating that the aroma-active compounds selected in this study were qualified and quantified reliably. Compared with Rec A, the aroma profile of Rec B (added non-volatile compounds) were closer to the original baijiu samples, specifically in the aspects of sweet, fruity, acidity and grass notes. However, there were still differences in roasted and sauce aroma between the reconstructed samples and the original samples.  
Figure 2: Concentration of non-volatile compounds in strong-aroma baijiu. (**A**) Total concentration by category; (**B**) heatmap of non-volatile compounds.  
T test was used to determine the difference between the average score of each descriptor in Rec A and Rec B, so as to further explore the effect of non-volatile compounds on the aroma characteristics of baijiu. As shown in Figure 4, six descriptors (fruity, sweet, sauce, acidity, alcoholic and pit aroma) had significant differences between Rec A and Rec B (\(p<0.05\)), while there was no significant difference in flower, roasted, grass and grain aroma. The aroma intensity of fruity, sweet, sauce and acidity in Rec B was significantly higher than that in Rec A (\(p<0.01\)), especially the fruity aroma, while alcoholic and pit aroma were stronger in Rec A.
# Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm
## 5 Conclusions and Future Work  
This study aims to build a regression model to predict the value of creatinine, then combine the predicted value of creatine with the common 23 health factors to evaluate the risk of CKD. As the creatinine value, which is the target variable, is extremely unbalanced, we used an undersampling method and proposed a cost-sensitive mean squared error (MSE) loss function to deal with the problem. Regrading model selection, we used three machine learning models: a bagging tree model named Random Forest, a boosting tree model named XGBoost, and a neural network based model named ResNet. To improve the result of creatinine predictor, we averaged results from eight predictors and ensembled their results. The ensembled model showed the best performance of R2 0.5590. The top six factors that influence creatinine are sex, age, hemoglobin, the level of urine protein, waist, and habit of smoking. With the predicted value of creatinine, an area under Receiver Operating Characteristic curve (AUC) of 0.76 is achieved when classifying samples for CKD.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Threshold** & **TP** & **FN** & **TN** & **FP** & **TPR** & **TNR** \\ \hline
0.0 & 3248 & 0 & 0 & 86,864 & 100\% & 0\% \\ \hline
0.1 & 2648 & 600 & 42,096 & 44,768 & 82\% & 48\% \\ \hline
0.2 & 2171 & 1077 & 60,526 & 26,338 & 67\% & 70\% \\ \hline
0.3 & 1805 & 1443 & 69,360 & 17,504 & 56\% & 80\% \\ \hline
0.4 & 1598 & 1650 & 74,922 & 11,942 & 49\% & 86\% \\ \hline
0.5 & 1245 & 2003 & 79,564 & 7300 & 38\% & 92\% \\ \hline
0.6 & 971 & 2277 & 82,275 & 4589 & 30\% & 95\% \\ \hline
0.7 & 720 & 2528 & 84,422 & 2422 & 22\% & 97\% \\ \hline
0.8 & 476 & 2772 & 85,770 & 1094 & 15\% & 99\% \\ \hline
0.9 & 138 & 3110 & 86,730 & 134 & 4\% & 99\% \\ \hline
1.0 & 0 & 3248 & 86,864 & 0 & 0\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance with varying threshold value of 90,112 testing samples.  
Figure 11: The ROC curve of 90,112 testing samples.  
**Author Contributions:** Formal analysis, W.W. and G.C.; Software, W.W.; Supervision, G.C. and B.C.; Writing--original draft, W.W.; Writing--review and editing, G.C. and B.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data available in a publicly accessible repository that does not issue DOIs Publicly available datasets were analyzed in this study. This data can be found here: [[[https://nhiss.nhis.or.kr/](https://nhiss.nhis.or.kr/)]([https://nhiss.nhis.or.kr/](https://nhiss.nhis.or.kr/))].  
**Conflicts of Interest:** The authors declare no conflicts of interest.