esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Image Quality Analysis
### Image Quality Metrics Calculation  
For each image quality parameter, we used a mathematical estimation of it. For the pixel error of the 2D projections, we calculated the MSE, which gives the error interpretation of the approximated image [44]. The formula of MSE is the following:  
Figure 12: CTP401 slice geometry and sensitometry module.  
Figure 13: I0-image: 2D projection without object.  
\[MSE=\frac{1}{V}\sum_{j=1}^{V}\left(A_{j}-S_{j}\right)^{2} \tag{8}\]  
Here, \(A_{j}\) is the pixel value of the main image and \(S_{j}\) is the pixel value of the estimated image [43]. As the main image, we selected the pre-processed image with _double_ format.  
For calculating the values of the _noise_, _uniformity_ and _low contrast_ from the reconstructed volume, we considered a different ROI per module, as suggested by the CATPHAN(r) [500] Manual [15]. For selecting the ROI, we used the reconstructed images shown with red and blue circles in Figure 14, where the pre-processing was done with the double format. For the _noise_ analysis, we calculated the standard deviation of the CT number for each of the ROIs, placed on the uniformity module and shown in Figure 14.  
For the uniformity analysis, five ROIs with 40 pixels in diameter are placed on the module, four peripheral ROIs and one central ROI. The average CT number, in HU, is obtained for each of these ROIs, and the uniformity is measured as the maximum difference between the mean value of the center ROI and one of the peripheral ROIs.  
For the _low contrast_ analysis, we calculated the contrast noise ratio (CNR) by placing a ROI of 20 pixels in diameter in the larger targets of both the supra-slice 1.0% and supra-slice 0.5%, and in the background area right beside it. The CNR was calculated using Formula (9) and averaged over 32 reconstructed slices.  
\[CNR=\frac{\left|S_{T}-S_{B}\right|}{\sigma_{B}} \tag{9}\]  
In Formula (9), \(S_{A}\) and \(S_{B}\) are the signal intensity of the supra-slice target and the background region, respectively, and \(\sigma_{B}\) is the standard deviation of the background.
# LPCP: An efficient Privacy-Preserving Protocol for Polynomial Calculation Based on CRT
## 9 Conclusions  
In this paper, an efficient CRT-based secure computation protocol LPCP for polynomial calculation is proposed. Based on LPCP, we propose a privacy-preserving distance measurement computation protocol that is affordable for mobile devices.  
In the end, we implement LPCP in the ARM architecture environment to show its compatibility with mobile devices. We also implement LPCP in the X86 architecture environment and compare the experimental results with fully homomorphic encryption schemes and different kinds of MPC schemes. In both 2 PC and MPC comparisons, our LPCP scheme has a great advantage of both running time and communication overhead, which makes it possible to implement secure computation on mobile devices. For future works, we aim to perform a formal security analysis of LPCP protocol and strengthen the security model against the malicious adversary.  
Conceptualization and methodology, J.T., Z.C. and J.S.; writing--original draft, formal analysis, software and visualization, J.T.; writing--review and editing, Z.C., J.S., X.D.; project administration, funding acquisition, Z.C., J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China (Grant No. 2020 YFA0712300), in part by the National Natural Science Foundation of China (Grant No. 62132005, 61632012, 62172162).  
Figure 4: The running time comparison of multiplicative operation between LPCP and Emp-sh2 pc.  
Figure 5: Running time of Semi, Hemi and LPCP in different party numbers conditions.  
Figure 6: Total communication overhead of Semi, Hemi and LPCP in different party numbers conditions.
# XOR Multiplexing Technique for Nanocomputers
## 2 Error Distributions in the XOR Multiplexing System
### Error Distributions in the XOR Multiplexing System and the NAND Multiplexing System  
Considering the multiple stages of error distribution of this XOR multiplexing and [PERSON]'s NAND multiplexing with different restorative stages, i.e., \(n=5\), \(7\), \(9\), and specifying \(x=y=0.8\) (given \(80\%\) of the inputs are stimulated), \(N=100\) and \(\varepsilon=0.01\). For von [PERSON] error, the probability distributions against the number of fault outputs are plotted in Figure 4.  
Figure 4 shows that for both XOR multiplexing and NAND multiplexing, the output error distributions move to the lower end as the number of multiplexing stages increases. Namely, the reliability can be improved by using more restorative units. When XOR multiplexing consists of seven stages, the probability that less than \(10\%\) of the output is faulty is approximately \(0.9473\), while \(0.7739\) for seven stages NAND multiplexing. Since \(N=100\), the probability that less than \(10\%\) of the output is faulty refers to the probability that the number of faulty outputs below \(10\) is a cumulative probability, which is the sum of the probability of the number of faulty outputs from \(0\) to \(9\)  
Figure 3: The cumulative error distributions of the XOR multiplexing unit and the NAND multiplexing unit: (**a**) \(N=100\); (**b**) \(N=1000\).  
Figure 2: The error distributions of the XOR multiplexing unit and the NAND multiplexing unit: (**a**) \(N=100\); (**b**) \(N=1000\).  
When the stages increase to nine stages, the probability that less than 10% of the output is faulty is approximately 0.9971 for XOR multiplexing and 0.9927 for NAND multiplexing. Based on the above analysis, Figure 4 demonstrates the conclusion that the XOR multiplexing unit is more reliable than the NAND multiplexing unit.
# Augmented Reality in Heritage Apps:Current Trends in Europe
## 4 Results
### AR Usability Dimension  
With respect to the final dimension, which addresses the usability of AR in heritage apps, V12 (Table 4) showed that 65.7% of the apps wholly or partially included some type of reconstruction of heritage spaces, monuments or other places of interest (\(n=23\); 65.7%), followed by apps that used AR to view objects (\(n=14\); 40.0%) or to add written information about a physical object that the visitor can see on-site (\(n=10\); 28.6%).  
The V13 relates to how apps activate AR. In most cases, users activated AR by scanning a code (\(n=19\); 54.3%). This was followed by apps in which AR could be used in conjunction with GPS (\(n=14\); 40.0%). There was only a minimal use of QR codes (\(n=1\); 2.9%); this was also the case with apps that only needed to be downloaded and the device's camera activated to view the heritage item (\(n=1\); 2.9%).  
The requirements for using the AR tool (V14) were, in most cases, in situ (\(n=29\); 82.9%) compared to 17.1% (\(n=6\)) of apps for which this was not a requirement. A small percentage of apps allowed the tool to be used anywhere (\(n=5\); 14.3%) by using codes provided by the developer, while only one of the apps (2.9%) allowed for AR to be used by simply activating the mobile device's camera.  
With respect to the prominence or importance given to AR in the app (V15), the highest percentage corresponded to apps in which AR was the main and only tool (\(n=15\); 45.7%), followed by a high prominence in proportion to other elements offered in the app (\(n=10\); 28.6%). The percentage of apps that afforded medium prominence to AR (\(n=4\), 11.4%) was the same as for apps in which AR was barely given any prominence at all and was not much more than a mere add-on (\(n=4\), 11.4%).
## 3 Results and Discussion
### Effect of HP on the Physicochemical Characteristics of Sea Bass Fillets
Although fish samples treated at 100-300 MPa obtained similar rates of lipid oxidation, sea bass fillets treated at 300 MPa exhibited a significantly (_p_ < 0.001) longer shelf life (based on oxidation data) due to the observed lag phase in the oxidation development. Overall, based on the obtained results, it seems that the lipid oxidation cannot be considered a suitable quality index for the estimation of the shelf life of HP-treated sea bass fillets under storage at 0.0 \(\pm\) 0.2 \({}^{\circ}\)C since it leads to a significant overestimation of the shelf life when compared to the estimation based on microbiological data (shelf life estimation: 10 vs. 20 d, 14 vs. 23 d, 18 vs. 27 d, and 25 vs. 34 d, based on microbiological data vs. oxidation data, for samples at 100, 200, 300, and 400 MPa, respectively). Another chemical index that has been considered in relevant studies is the trimethyl amine oxide (TMAO) to trimethyl amine (TMA) conversion. As the application of HP in sea food can lead to lower values of TMA after processing and in cold storage ([PERSON] et al. 2021 [10]), TMA was not quantified in this study.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
###### Abstract  
This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. One hundred twenty-nine online exam data were analyzed by the researcher with three different scenarios to reveal the best model performance in regression and classification. For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. In the regression analysis conducted within the scope of Scenario-1, the model we proposed to detect \"cheating\" behavior, which is one of the unethical learner behaviors, was found to be a 5-layer DNN model with a test performance success of 80.9%. In the binary classification analysis for Scenario-2, students who \"copied\" from unethical behaviors were obtained with an accuracy rate of 96.9% by the model established by the 10-layer DNN algorithm we proposed. In the triple classification analysis for Scenario-3 defined in the study, the XGBoost model was found to have the highest accuracy rate of 97.7% for students who \"cheated\" due to unethical behaviors and the highest performance in all other metric values. In addition, SHAP and LIME methods, which are explanatory methods for the XGBoost model, which is one of the best-performing models, were applied, and the attributes and percentages affecting the model were shared. As a result of this study, it has been shown that the application of the most appropriate layer functions and parameter selection that will increase performance can be effective in estimating complex problems and target values that cannot be solved using classical mathematical models. The proposed models can provide educational institutions with a roadmap and insight in evaluating online examination practices and ensuring academic integrity. Future researchers may need more data sets and different analyses for better performance of the established models.  
deep learning; machine learning; cheating; academic integrity; online examination
## 4 Discussion  
This work presents a spatial carrier phase-shift DSPI for the simultaneous measurement of in-plane displacement, in two orthogonal directions, by using a single camera. The carrier frequency is generated by aperture misalignment. Two pairs of observation beams are used to make the DSPI sensitive to in-plane displacement in two directions. Considering that the beams, whose polarization directions are perpendicular, do not interfere with each other, cross-interference between the two pairs of beams is avoided. Using the Fourier transformation procedure, the phase can be evaluated from a single interferogram by the spatial carrier phase-shift technique. Only two interferograms are required to simultaneously measure the displacement in two directions: The first image is acquired before the load and the second image is acquired after the load. The displacements in two directions are  
Figure 9: Synthetic in-plane displacement vectors and contours of the absolute in-plane displacement.  
measured independently, dynamically, and simultaneously, making the DSPI suitable for dynamic and high-accuracy testing.  
Conceptualization, P.Y.; data curation, S.Z.; funding acquisition, Y.W.; investigation, P.Y.; methodology, X.L.; software, F.S.; supervision, Y.W.; validation, X.L.; visualization, F.S. and Q.Z.; writing--original draft, P.Y.; writing--review and editing, P.Y.  
This research was funded by National Natural Science Foundation of China (No. 51805137); Natural Science Foundation of Anhui Province (No. 1808085 QE129); the Fundamental Research Funds for the Central Universities of China (JZ2019 HGTB0076); the open project of Anhui Province Key Laboratory of Non-Destructive Evaluation, Hefei ZC Optoelectronic Technologies Ltd. (No. CGHBMWSJC04); and the open project of Key Laboratory of Micro Opto-electro Mechanical System Technology, Tianjin University, Ministry of Education (No. MOMST2015-6).  
The authors would like to express their sincere thanks to [PERSON] from University of Science and Technology of China, who made careful correction of the English for the manuscript.  
The authors declare no conflict of interest.
# Effect of Controlled Expiratory Pressures on Cerebrovascular Changes During Valsalva Maneuver
## 3 Results  
### Comparison of PWV and Stiffness Index  
PWV significantly decreased after performing at both 30 and 40 mmHg VM, indicating that as the PWV decreases, the stiffness index of the blood vessels also decreases while the elasticity improves. In this study, PWV significantly decreased by 0.69 m/s (T = 4.05, \(p<0.001\)) in POST compared to PRE at 30 mmHg VM, and by 0.34 m/s (T = 2.43, \(p=0.022\)) at 40 mmHg. The stiffness index significantly decreased by 1.76 in POST compared to PRE in 30 mmHg VM (T = 4.24, \(p<0.001\)). After 40 mmHg VM, it decreased by 0.79; however, the change did not reach significance (T = 2.01, \(p=0.056\)) (Table 2).
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 4 Ergodic Capacity  
Ergodic capacity can be defined as the maximum achievable mutual information from \(S\) to \(D\), and it can be expressed as:  
\[\mathcal{C}_{erg}=\frac{1}{K}\mathbb{E}\left[\log_{2}(1+\gamma_{e2e})\right], \tag{19}\]  
where \(\mathbb{E}[\cdot]\) denotes the expectation operator. By substituting \(\gamma_{e2e}=\min(\Gamma_{S\to R_{1}},\Gamma_{R_{1}\to R_{2}},\ldots,\Gamma_{ R_{K-1}\to D})\) into (19), the ergodic capacity can be expressed as:  
\[\mathcal{C}_{erg}=\frac{1}{K}\mathbb{E}\left[\log_{2}(1+\min(\Gamma_{S\to R_{ 1}},\Gamma_{R_{1}\to R_{2}},\ldots,\Gamma_{R_{K-1}\to D}))\right]. \tag{20}\]  
With the aid of [PERSON]'s inequality, ergodic capacity can be upper bounded as:  
\[\mathcal{C}^{\text{up}}_{erg}\leq\frac{1}{K}\log_{2}\big{(}1+\min\big{(} \mathbb{E}[\Gamma_{S\to R_{1}}],\mathbb{E}[\Gamma_{R_{1}\to R_{2}}],\ldots, \mathbb{E}[\Gamma_{R_{K-1}\to D}]\big{)}\big{)}, \tag{21}\]  
and \(\mathbb{E}[\Gamma_{R_{i}\to R_{i+1}}]\) can be obtained by using the following formula:  
\[\mathbb{E}[\Gamma_{R_{i}\to R_{i+1}}]=\int_{0}^{\infty}\big{(}1-F_{\Gamma_{R_ {i}\to R_{i+1}}}(\gamma)\big{)}d\gamma. \tag{22}\]
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 3 Data Source Search, Analysis, Purge, and Selection  
To comprehensively evaluate the different AI techniques for fault prediction in reconfiguration scenarios for SGs, a high-quality dataset is essential. Both the quality and quantity of data are crucial for defining a consistent model. In the energy context, our experience proves there is a lower degree of availability of datasets. The protection of user privacy in the measurement and analysis of their energy behavior reduces the number of publicly available datasets, as they primarily depend on energy companies [27]. Nevertheless, the pursuit of optimizing energy distribution and reducing user consumption, along with other environmental motivations, has led to the creation of public datasets in recent years. Table 1 represents several analyzed residential datasets, along with details about their implementation, such as location, number of residences, deployment period, sampling frequency, and measured electrical parameters. As part of our research analysis, to evaluate and select the most appropriate dataset from the ones available in Table 1, the following requirements were considered:  
* _Quantity:_ it is crucial to review the quantity of data available in each dataset, ensuring that they include measurements from a large number of residential buildings and cover extended periods to analyze energy consumption patterns effectively.
* _Quality:_ The quality of the data depends on the resolution of the measurements. Datasets such as _REDD_ and _BLUED_ offer high sampling frequencies, allowing for better energy disaggregation and a more representative analysis of energy behavior.
* _Location:_ The location of the data is important because it affects voltage differences between countries. For example, datasets from the U.S., such as _BLUED_, operate at less than 120 V, whereas European datasets like _ECO_ handle up to 230 V.  
* _Parameterization:_ The samples include voltage (V), current (I), and power (P, Q, S), each associated with a timestamp and an identifier corresponding to the respective residence. Some datasets, such as _HUE_ and _SustDataED_, also include environmental data, which is important for understanding the impact of climatic conditions.
* _Photovoltaic generation:_ in the context of SGs, it is essential to choose datasets that include information on renewable energy generation, such as _Smart_* and _SustDataED_.  
Among the analyzed datasets, _SustDataED_ was particularly selected due to the large number of multiple users, both in terms of consumption and production, over a long period. Likewise, it is important that the samples have a good resolution and provide real-time information on the climatic conditions of the location where the residences are situated. In the following sections, we examine this dataset in detail and outline the main design criteria applied to generate the final dataset, curated from _SustDataED_ and complemented with the PVWatts tool, which was eventually leveraged for our evaluation.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 2 UAVs Overview
### UAV Networks and Communications
#### 2.3.2 Communications and Networks Systems  
When discussing UAV networking and communications, a few definitions are needed. Three different types of aerial entities can be used as terrestrial networking: Low Altitude UAVs (LAUs), High Altitude UAVs (HAUs), and satellites. Through these entities, there is a sort of communication network. The LAUs and HAUs can communicate to each other, or other UAVs of that class, on an Air-to-Air (A2A) channel. HAUs communicate with the satellites on a Satellite-to-Air (S2A) channel. LAUs can communicate with ground objects on an Air-to-Ground (A2G) channel. This set of communication channels help develop the UAV assistance paradigm.  
LAUs have many disadvantages as they are typically quadcopters, and so they have lower flight endurance, little geographical data, and lower payload capacity. Disregarding these disadvantages, LAUs are much cheaper and faster to deploy [21]. LAUs are commonly connected via a local area network (LAN) [28]. HAUs, as they are primarily found in fixed-wing, last longer in the air and are more energy-efficient to provide more extended coverage. The major drawback to this type of UAV is its cost and the possibility  
Figure 3: Satellite systems of various countries in GNSS [25].  
of interference in the networks. The satellites in this paradigm are often used to manage the HAUs' and LAUs' networks [21].
# Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions
###### Abstract  
Fuzzy clustering is a form of unsupervised learning that assigns the elements of a dataset into multiple clusters with varying degrees of membership rather than assigning them to a single cluster. The classical Fuzzy C-Means algorithm operates as an iterative procedure that minimizes an objective function defined based on the weighted distance between each point and the cluster centers. The algorithm operates decently in many datasets but struggles with datasets that exhibit irregularities in overlapping shapes, densities, and sizes of clusters. Meanwhile, there is a growing demand for accurate and scalable clustering techniques, especially in high-dimensional data analysis. This research work aims to address these infirmities of the classical fuzzy clustering algorithm by applying several modification approaches on the objective function of this algorithm. These modifications include several regularization terms aiming to make the algorithm more robust in specific types of datasets. The optimization of the modified objective functions is handled based on several numerical methods: gradient descent, root mean square propagation (RMSprop), and adaptive mean estimation (Adam). These methods are implemented in a Python environment, and extensive experimental studies are conducted, following carefully the steps of dataset selection, algorithm implementation, hyper-parameter tuning, picking the evaluation metrics, and analyzing the results. A comparison of the features of these algorithms on various datasets is carefully summarized.  
Keywords:fuzzy clustering; objective function; regularization terms; numerical methods; gradient descent; root mean square propagation; adaptive mean estimation _Article_
# Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation
## 2 Knowledge-Building Process
### Ontology Matching  
Also known as _ontology alignment_, ontology matching [19; 20; 21; 22; 23; 24; 25; 26; 27] is the process of finding semantic correspondences (mainly similarities) among entities from different ontologies. Each type of entity (_classes, object properties, datatype properties_, and _instances_) is normally matched in isolation, so _class-to-property_, _class-to-individual_, or _property-to-individual_ correspondences are not addressed. Entity pairs that have the same name and meaning or have different names but the same meaning should be matched. Ontology matching is a critical step for ontology integration. Current ontology matching tools have become proficient in identifying equivalence correspondences between two ontologies. Ontology matching systems may use different matching algorithms.