esa-sceva/satcom-chunk-collection huggingface dataset:
# Rough IPFCM Clustering Algorithm and Its Application on Smart Phones with Euclidean Distance
###### Abstract
[16] proposed a Hausdorff distance measure based an interval fuzzy possibilistic C-means (IFPCM) clustering algorithm for clustering SID. [PERSON] et al. [17] proposed an interval possibilistic FCM (IPFCM) clustering method for clustering SID. Because traditional IFPCM and IPFCM methods still have data overlapping problems for these SID, a novel IFCM clustering method that is roughly based on an interval possibilistic FCM (RIPFCM) clustering algorithm with Euclidean distance was proposed. The proposed method can outperform the IFCM clustering algorithm for SID clustering in noisy environments and for data overlapping problems. That is, we combined the rough set with the IPFCM algorithm under Euclidean distance for SID. We also extended the application of smart phones using machine learning and fuzzy clustering for SID. Finally, the contribution of the proposed RIPFCM clustering algorithm used the derived fuzzy membership degree (FMD) and possibilistic membership degree (PMD) formulas to enhance the proposed clustering method in SID that has noisy and data overlapping problems. An additional contribution is the successful implementation of the proposed clustering method on smart phones for new applications in mobile computing and data analysis. The organization of the rest of the paper is as follows. In Section 2, a rough interval possibilistic fuzzy C-means clustering algorithm is proposed and discussed. The experimental results are shown in Section 3. Finally, the conclusions are summarized in Section 4.
## 2 Materials and Methods
### Performance Evaluation  
It is important to use different evaluation parameters to comprehensively analyze the performance of the trained models and the proposed method, and to identify their strengths and weaknesses [32; 34]. Therefore, recall (R), precision (P), and F1 score metrics were used to evaluate the performance of the trained models. DSC and IoU metrics were used to select the best weight combination in the grid search algorithm. DSC, PPV (Positive Predictive Value), Specificity, accuracy, and Sensitivity metrics were used to evaluate the image segmentation performance. DSC and PPV measure how accurately the model predicts positive pixels, Sensitivity measures how well true positives are captured, and Specificity measures how effectively false positives are excluded. Accuracy generally refers to the model's correct prediction rate. Furthermore, Cohen's Kappa, Jaccard Index, and Matthews Correlation Coefficient (MCC) metrics were used to analyze the consistency between external and internal validation and to assess the balance between classes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Description** \\ \hline workers & 8 \\ \hline batch & 16 \\ \hline device & 0 \\ \hline epochs & 100 \\ \hline lr0 & 0.005 \\ \hline lrf & 0.1 \\ \hline momentum & 0.9 \\ \hline weight\_decay & 0.0005 \\ \hline warmup\_epochs & 5 \\ \hline warmup\_momentum & 0.8 \\ \hline warmup\_bias\_lr & 0.05 \\ \hline optimizer & SGD \\ \hline lr0: initial learning, lrf: final learning rate. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of the experiments.  
During this evaluation process, additional analyses were performed using an external dataset to measure the generalizability of the method and to determine how it performs in real-world scenarios. Equations (5)-(13) shows the metrics used in this study.  
\[R=\frac{TP}{TP+FN} \tag{5}\]  
\[P=\frac{TP}{TP+FP} \tag{6}\]  
\[F1\ score=2\frac{P\ x\ R}{P+R} \tag{7}\]  
\[DSC=\frac{2\ \times\ TP}{2\ \times\ TP+FP+FN} \tag{8}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{9}\]  
\[PPV=\frac{TP}{TP+FP} \tag{10}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{11}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{12}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{13}\]  
In these equations, TP (True Positive) is the number of pixels correctly predicted as positive, TN (True Negative) is the number of pixels correctly predicted as negative, FP (False Positive) is the number of pixels incorrectly predicted as positive when they are negative, and FN (False Negative) is the number of pixels incorrectly predicted as negative when they are positive.
## 4 Results of Content Analysis
### Types of ANN Applied in CM Research  
Figure 4 provides the statistical result of the types of ANN used in the reviewed articles. The results show that a total of 13 types of ANN were employed in the CM research. BPNN has captured the most attention and accounts for 45.1% of the total. MLPNN and CNN both account for 13.8%. In addition, RBFNN, GRNN, and PNN have also received some attention.  
BPNN is a multilayer feed-forward neural network trained according to the error back-propagation algorithm. It has the general advantages of all neural networks such as self-learning and adaptive capabilities, nonlinear mapping capabilities, and high fault tolerance [46], and its own advantages, such as simplicity and good generalization capability [47]. A detailed review found that BPNN as a representative of ANN in CM has been applied to all application fields, demonstrating its absolute domination.  
MLPNN is typically designed with a fully connected feed-forward-based architecture and trained by static back-propagation, LM, Gauss-Newton, or other algorithms. Its input layer contains a set of sensory nodes, and both hidden layers and the output layer contain computing nodes [48]. It has been applied to complete a wide range of classification and prediction tasks in the CM field. The CNN model achieves feature extraction through a stack of layers (such as convolution, activation, and polarization) on the input image [49]. It is one of the deep learning algorithms that has been widely used in the image field [50]. The characteristics and main research fields of BPNN, MLPNN, and CNN are listed in Table 3.  
Figure 4: Types of ANNs applied in selected articles.  
Figure 3: Publication trend of literatures on ANN in CM (2000–2020).
# Image Mosaicing Applied on UAVs Survey
## 4 Aerial Panorama Applications
### Feature-Based: Global Single Transformation  
#### 4.1.1 Harris Corner  
Harris Corner is still a widely used method for its low computational cost. New improvements have been proposed, such as applying a prefilter of the characteristic points detected using Harris Corner, to reduce the ghosting and luminance problems [77]. Another proposal is an improvement replacing the Gaussian Window function for a B-spline function; then, the corner points are preselected to obtain candidate corners, and an autoadaptive threshold method improves the adaptability of the algorithm. Another approach of Harris corner improvement is applying an adaptive nonmaximal corner suppression algorithm to reduce the pixels that cannot be corners. The local representative corners are retained, which reduces the corner detection time by 30.2%, improving the stitching speed [78]. The use of distinct algorithms on the matching process enhances the methodology: as an example, by applying Harris corner in a correlation on the registration, the accuracy and robustness of aerial panoramas are improved [79]. Another proposal is to combine it with another feature algorithm, such as SURF, in one process, making it possible to achieve a more robust algorithm than a simple Harris corner, as proposed in [80]. In Table 2, a resume of Harris Corner applied on UAV examples is presented.
# Analysis of Technologies for Carbon Dioxide Capture from the Air
## 2 DAC Technologies
### Comparison between the Absorption and Adsorption Processes  
A comparison between adsorption and absorption can be considered. Adsorption technology is characterized by a high modularity and small capture facilities, cyclic steps based on the variation of temperature or pressure or their combination, problems due to the sorbent degradation impacting on costs. However, low energy inputs with low regeneration temperatures can be provided by a low-grade heat, allowing an integration with waste and/or renewable heat. On the other hand, absorption has a higher complexity for the regeneration system, although it works continuously with the absorption cycle and requires a high temperature for regeneration, which is supplied by a high-grade heat source. In the absorption process, packed columns or spray towers are used as air contactors, while, in the adsorption systems, contactors of monolithic, fluidized bed, fixed bed and moving bed designs have been proposed [39]. In addition, while in the adsorption process, water is a by-product, in the absorption system, water is removed and then lost before the calcination step. Another point of comparison for the two technologies is the number of interactions between the solvent/sorbent and CO\({}_{2}\). For the absorption, the solvent may be present up to 30 %wt, limiting the number of interactions with CO\({}_{2}\). On the other hand, for the adsorption, the base is chemically bound on the sorbent such that it is more contained and can therefore be loaded at a much higher weight percent, thereby increasing the number of interactions with CO\({}_{2}\). Another difference between absorption and adsorption is how the material (solvent/sorbent) is packed in the air contactor. In the first case, the solvent flows inside a structured packing material with channel sizes large enough to allow the flow of the liquid but small enough to create sufficient surface area to maximize the number of interactions between CO\({}_{2}\) and the solvent. On the other hand, in the second case, solid sorbents have micro- and mesopores that allow the maximization of surface area and facilitate transport, respectively. As an additional point of comparison, the solvent has a CO\({}_{2}\) loading of about one order of magnitude lower than the sorbent because this last has a higher contact area.
# A Stackelberg Game Approach for Price Response Coordination of Thermostatically Controlled Loads
## 3 Stackelberg Game Coordination
### Existence and Uniqueness of Stackelberg Equilibrium
1: By (22), EECC determines the optimal broadcast price \(p^{*}\) w.r.t the best strategy trajectory \(u_{i}^{*}(p)\) in (21);
2: Each TCL user \(i\in\mathcal{N}\) determines the optimal strategy \(u_{i}^{*}\) w.r.t. \(p^{*}\) by (21);
3: Each TCL user \(i\in\mathcal{N}\) computes their optimal set-point temperature \(\widehat{\theta}_{i}^{*}\) by \(\widehat{\theta}_{i}^{*}=f_{i}^{(-1)}\left(u_{i}^{*}\right)\). ```  
**Algorithm 1** DR algorithm by Backward Induction. As stated in [18; 19], compared with other methods which usually involve interactive iteration processes between the leader and the followers, which are EECC and the TCL users respectively in the underlying games, the DR algorithm based on Backward Induction proposed in our work can significantly reduce the computational time in implementing the equilibrium of the underlying Stackelberg games.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 8 Conclusions  
The cellular-based NB-IoT technology connects a plethora of devices to the IoT. Consequently, there is an urgent need to localize such devices in an energy-efficient way. While OTDoA is introduced in 3 GPP Release 14, few operators deployed this localization feature in their networks, given the cost and complexity to synchronize eNBs. Therefore, we investigated three RSS-based localization approaches, providing an energy-efficient solution at low cost and low complexity. Experiments carried out in a large-scale urban environment led to mean location estimation errors ranging from 340 m in a basic proximity algorithm to 204 m in an optimized fingerprinting algorithm. Traditional multilateration approaches could not be applied, since with current hardware and software only the serving cell antenna can be used to locate a mobile UE. In order to address this issue, we studied the mobility of NB-IoT in a localization context. Due to unpredictable antenna patterns and environmental influences, a UE might connect to a cell while not located inside the theoretical sector of that cell. Finally, this poor cell sector reliability and the long cell reselection times indicate the need for proper handover support and observation of multiple cells in NB-IoT networks.  
Conceptualization, [PERSON]; Data curation, [PERSON]; Funding acquisition, [PERSON]; Methodology, [PERSON]; Project administration, [PERSON]; Software, [PERSON]; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
[PERSON] is funded by the Fund For Scientific Research (FWO) Flanders under grant number I503821N.  
The authors declare no conflict of interest.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Ice (SI)
#### 2.2.1 Optical  
Although the primary focus of SI remote sensing has been on microwave RS systems, especially active sensors, optical imagery, which measures the solar radiation reflectance from the earth, has also provided valuable information for SI studies [66, 73]. SI usually appears brighter than the surrounding water in the visible bands of the optical satellite images due to the high surface albedo. Many researchers considered this feature and applied a global or local threshold to distinguish SI from ocean water in optical imagery [66, 69]. Additionally, histogram analysis based on the higher reflectance of the SI has been used for SI classification in optical images [73]. Moreover, texture analysis and image segmentation algorithms, considering differences in statistical texture features between ice and water, have been utilized through various texture analysis methods (e.g., Gray Level Co-occurrence Matrix (GLCM)) for the SI extent and outer edge detection [67].  
Many multispectral satellites have been used for SI studies. Some of the most frequently used spaceborne optical systems for SI studies are the Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High-Resolution Radiometer (AVHRR), Visible/Infrared Imager Radiometer Suite (VIIRS), Landsat, and Sentinel-2. For instance, Ref. [67] utilized the GLCM texture analysis for SI detection using MODIS multispectral images over the Bohai Sea. The prominent differences between the SI and water statistical texture features were used in this study, along with a texture segmentation method for mapping SI extent and its outer edge. Textural analysis resolved the spectral confusion and SI misassignment due to the suspended sediment presence, which is problematic in Bohai SI detection through conventional thresholding methods. The 30 m spatial resolution imagery of HJ1B-CCD was also used for visual validation and statistical accuracy assessment by calculating the confusion matrix. It was reported that the difficulty of cloud separation from SI due to their similar textural features was the main limitation of the proposed method.  
Despite the feasibility of SI monitoring through optical imagery, multiple limitations restrict the practical application of visible, NIR, and Shortwave Infrared (SWIR) spectral bands for SI monitoring. For example, since visible and infrared radiations can be reflected and emitted from clouds, optical systems cannot collect data under clouds, which is very common in polar regions. Additionally, since the reflection of the sunlight is an essential prerequisite for imagery in the visible, NIR, and SWIR bands, these sensors can only collect daytime data, which is problematic in the dark seasons of the polar regions. Finally, other natural phenomena, such as the suspended sediment or the clouds, have similar spectral characteristics with SI making it difficult to distinguish them in optical imagery [58, 67].
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 4 The Framework and Construction for Privacy-Preserving Image Classifier
### Description of Proposed Scheme
#### 4.4.1 System Initialization  
The \(\mathcal{TPA}\) takes a security parameter \(\lambda\) as input and generates a group \((G,p,g)\) and a master secret key \(msl^{FEIP}=\mathbf{s}=(s_{1},...,s_{l})\leftarrow\mathbb{Z}_{p}^{l}\), where \(p\) is the order of group \(G\) generated by \(g\in G\), \(p\) is a \(\lambda\)-bit prime number and must be chosen, such that \(p-1\) has at least one large prime factor [36], \(l=|S|\) is the size of a mini-batch. The master key generation algorithm creates a public key \(pk^{FEIP}=(h_{i}=g^{s_{i}})_{i\in[l]}\) corresponding to \(\mathbf{s}\). Then, \(\mathcal{TPA}\) distributes the public keys \(pk^{FEIP}\) to the users, and provides private key service to the server \(\mathcal{S}_{\mathcal{NN}}\) during the training phase.
# Union Models for Model Families: Efficient Reasoning over Space and Time
## 1 Introduction
1**: Models in a family shall be captured (in both dimensions of variability) in a _complete_ and _exact_ way such that all and only individual members of a family are included in one union model. **Req. 2**: The resulting union model shall be as _compact_ as possible, in the sense that it should not contain redundant elements, especially when there are many elements in common between models. **Req. 3**: The union model shall be _self-explanatory_, i.e., it should be supported with a mechanism that distinguishes which elements belong to which models. There are other challenges at the _metamodel level_ associated with the use of union models. In particular, a union model may _not_ be a valid instance of the language's metamodel, and the latter might need to have its constraints relaxed accordingly. The metamodel-level challenges have been already discussed in our previous work [11], and are outside the scope of this paper. This paper extends previous work in [12] with a complete formalization of union models, including annotations, and enhanced experiments with a deeper analysis, and empirically demonstrates the usefulness of union models for analyzing a family of models _all at once_ compared to individual models _one model at a time_. The main contributions of this paper are:  
1. A _language-independent_ graph-based formalization of model families and union models;
2. A generic language-independent _algorithm_ to produce a union model from a set of models (in a compact and exact manner) in a given language (satisfying _Req. 1_ and _Req. 2_);A _Spatio-Temporal Annotation Language_ (STAL) to support the representation of variability in model families in the space and time dimensions and to facilitate reasoning about union models (satisfying _Req._ 3); and
4. Improved _efficiency_ of analysis and reasoning over a set of models all at once using union models, compared to reasoning on single models one model at a time. As our approach starts with existing models as input, how such models are constructed in general is beyond the scope of this paper. From a methodological perspective, this paper first presents the motivation behind this work in Section 2. Then, the theoretical and algorithmic foundations of our work are presented in the next three chapters, specifically, a graph-based formalization of model families (Section 3), a complementary Spatio-Temporal Annotation Language (Section 4), and the propositional encoding of models (Section 5). To determine how efficient and beneficial the reasoning and analysis with union models are, reasoning tasks and the experimental setup are first presented in Section 6, whereas empirical results and threats to validity are reported in Section 7. A discussion of closely related works is covered in Section 8. Finally, Section 9 concludes the paper.
## 4 SeKeQ: Secure Key Exchange with QR Code Protocol
Companies use the QR Code for their banners, to make clients scan it to get more information about the publicity, or even it can be used for sending an email, setting a professional appointment and nowadays, stores uses the QR code for allowing their clients to know about a product by scanning a QR and getting more information. In our protocol, it is used to store the hash value of the shared secret key calculated on the P2P GO device. Automatic key comparison process works as follows, since \(H(S_{1})\) and \(H(S_{2})\) should be equal, device A that is the Group Owner will fetch the value of its calculated result \(H(S_{1})\) into a new generated QR Code. By the following, device B, acting as a P2P client--with the help of the back camera--can scan the generated QR Code that contains the device A result \(H(S_{1})\) and save it as a new string called \(N\). Now, a system comparison will be in device B, comparing the scanned value \(N\) with the value of its result \(H(S_{2})\). If it matches, the communication between A and B is performed automatically without any verbal or visual comparison and both devices can start sharing their data now. The scenario of key exchange between users is shown in Figure 2.  
Providing a shared key as an authentication proposal among devices is securely efficient. However, while we generate the key exchange, we use it to guarantee data confidentiality. Making sure that data is not sent in clear over public channels and avoiding the possibility of a Man in the middle or Eavesdropping attack. Since the devices are mutually authenticated, End-to-end encryption is used based on the generated shared secret key. Thereafter, we convert the key in an AES key format as a symmetric encryption key from the shared key. If the communication between users is a chat scenario, the messages are encrypted before getting ready to be sent. A user who wants to send a message, uses its shared secret key to provide an AES symmetric key for message encryption. After receiving the message in an encoded format, and while we are using symmetric encryption, which means that the encryption and decryption key are the same. The receiver uses the shared key to generate the AES key and decrypts the message.
## 3 Definition of Main Design Parameters for Three-Phase Six-Switch Converter Example
### Pre-Design of 125 kW Grid-Connected Three-Phase Six-Switch Converter Stage
Based on the specifications defined in Table 1 the nominal current rating for each phase results in:  
\[I_{\text{N,rms}}=\frac{S_{\text{N}}}{3\cdot V_{\text{N,rms}}}=181\text{ A}. \tag{6}\]  
According to a rule of thumb derived from on [15], the current rating of the two-level IGBT power module at a case temperature of 100 \({}^{\circ}\)C should be 3-5 times higher than the nominal current rating per respective phase (depending on maximum power rating). Therefore, an Infineon FF600R12 KE4 [16] 1200 V/600 A IGBT module was chosen for this investigation. The module comprises of a high-side and low-side IGBT and a reverse conducting diodes. Very general applications of such modules are motor drives, wind turbine related power electronics, UPS systems and high-power converters. Considering previously described assumptions for control and reference setup the current ripple of the two-level topology with sinusoidal excitation results in  
\[\Delta i_{\text{L}}(\varphi)=\frac{V_{\text{DC}}}{4 Lf_{\text{sw}}}\Big{[}M^{ 2}\sin(\varphi)^{2}-1\Big{]} \tag{7}\]  
for \(0\leq\varphi\leq 2\pi\), a maximum current ripple value of:  
\[\big{|}\Delta I_{\text{L,max}}\big{|}=\frac{V_{\text{DC}}}{4 Lf_{\text{sw}}} \tag{8}\]  
and a minimum ripple of:  
\[\big{|}\Delta I_{\text{L,min}}\big{|}=\frac{V_{\text{DC}}}{4 Lf_{\text{sw}}} \Big{[}1-M^{2}\Big{]} \tag{9}\]The averaged and rms values of the current ripple for each phase compute to:  
\[\left|\Delta I_{\text{L,avg}}\right|=\Delta I_{\text{L,max}}\left[1-\frac{M^{2}}{ 2}\right] \tag{10}\]  
and  
\[S_{\text{N}}=\sum_{i}V_{\text{N,rms,i}}\cdot I_{\text{N,rms,i}}=3\cdot V_{ \text{N,rms}}\cdot I_{\text{N,rms}} \tag{2}\]0  
respectively. Thus, an inductor \(L_{\text{inv}}\) of 250 \(\mu\)H was chosen to allow a maximum current ripple of approximately 30% of the nominal input current \(I_{\text{N,rms}}\).