esa-sceva/satcom-chunk-collection huggingface dataset:
# Face Swapping Consistency Transfer with Neural Identity Carrier
## 2 Related Work
### Uncertainty Modeling  
There are two main types of uncertainty: epistemic (uncertainty of model) and aleatoric (uncertainty of data) in deep learning fileds [16]. Thus the predictive uncertainty should consist of two parts, epistemic uncertainty and aleatoric uncertainty. As the face-swapping proxy performs severe inconsistency, the main kind of uncertainty for this issue is the aleatoric uncertainty. Further, aleatoric uncertainty also has two sub-types: homoscedastic and heteroscedastic [17].  
The homoscedastic regression assumes constant observation noise \(\sigma\) for all input point while the heteroscedastic regression, on the other hand, assumes that observation noise can vary with input [18; 19]. Especially, the heteroscedastic models are helpful when parts of the observation space might have higher noise levels than others. In previous face-swapping work, the observation noise parameter \(\sigma\) is often fixed as part of the model's weight decay.  
Previous methods point out that the observation noise parameter \(\sigma\) can be learned as a function when data is independent [17]. Given the output, we can perform MAP inference to find a single value for the model parameters \(\theta\):  
\[\mathcal{L}(\theta)=\frac{1}{N}\sum_{i=1}^{N}\frac{1}{2\sigma(x_{i})^{2}}||y _{i}-f(x_{i})||^{2}+\frac{1}{2}\log\sigma(x_{i})^{2} \tag{1}\]  
where \(y_{i}\) is the ground truth of the output data, \(f(\cdot)\) is the model's function, \(x_{i}\) is the input data point, \(N\) is the number of data points, \(\sigma\) is the model's observation noise parameter which captures how much noise we have in the outputs, and \(\theta\) is the distribution's parameters to be optimized.  
In our work, we realize the artifacts in face-swapping proxy always occur in facial outlines and local patches. The inconsistency of face-swapping results always performs like facial outline flicker, mouth area collapse, and eye shaking. We leverage aleatoric uncertainty to predict the output's difficult-to-generate area according to the input and reduce the weight of these areas.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 5 Experimental Results on the TEXBAT Dataset
### Comprehensive Performance Verification with TEXBAT Dataset
The analysis of Cases 1 to 8 shows that the EDDM-MSC-MMP can maintain an efficient detection rate with a false positive rate of less than or equal to 0.01 in scenarios with either a static or dynamic receiving platform and with different types of spoofing signals. In particular, in Case 5, even though the detection efficiency of the other methods was close to zero, the EDDM-MSC-MMP was able to achieve a detection rate of over 95%, which was significantly better. This consistently excellent performance highlights the reliability and efficiency of the EDDM-MSC-MMP in complex signal environments, which could make it the technology of choice for detecting spoofing signals.
## 3 Global Loss Model of PMSM  
As the PMSM is fed by a three-phase inverter, there must be fundamental current and harmonic current in the PMSM, which is caused by the PMW output voltage of the inverter. The fundamental current drives the motor with the output electromagnet torque and produces a fundamental copper loss and a fundamental iron loss. The harmonic current will impact the stability of the system and creates harmonic copper loss and harmonic iron loss. To optimize the losses of a PMSM drive system, it is important to build an accurate global loss model of the PMSM which consider about both the fundamental motor loss and the harmonic motor loss.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## The Model of Room Monitoring System
### Perspective Correction Technique  
Owing to the camera angle, some difficulties may occur in counting the number of people in the image, especially small-sized ones or a crowd. To solve this issue, we use a perspective correction technique to transform the input image into a 2D top-view image. Furthermore, many studies applied the perspective correction technique [35; 36; 37; 38] owing to its advantage of easy implementation. Figure 2 shows the process of this technique.  
The input images are consecutive frames of a video captured by the monitoring camera. To obtain a perspective transform, four pairs of corresponding points in the source and destination images must be determined. In this situation, the source image is the input image, and the destination image is the top-view image. These four points form a quadrangular area called the region of interest (ROI) of the input image, as shown in Figure 2b. The function calculates the \(3\times 3\) matrix of a perspective  
Figure 1: System overview.  
Figure 2: Perspective correction process.  
transform [39], as described in Equation (1). Here, \((x_{i},y_{i})\) are coordinates of the quadrangle vertices in the input image, and \((x_{i}^{{}^{\prime}},y_{i}^{{}^{\prime}})\) are coordinates of the corresponding quadrangle vertices in the top-view image. \(i\) ranges from 0 to 3. After we obtain the perspective transform matrix, we can quickly transform the input image to the top-view image, which is shown in Figure 2c. The transformation function is described in Equation (2). Next, we calculate the density of people and create a density heat map using a kernel density estimation (KDE) algorithm [40]. KDE is one of the most popular approaches to estimate the underlying probability density function of a dataset. To estimate a density at a location, we construct a probability density function. In this function, KDE shape is an essential factor. This study uses a quartic kernel shape and the heat map result is shown in Figure 2d. The yellow point denotes a person in this figure. Therefore, users can easily observe and analyze hot areas in the room.  
\[\begin{bmatrix}t_{i}x_{i}^{{}^{\prime}}\\ t_{i}y_{i}\\ t_{i}\end{bmatrix}=\begin{bmatrix}M_{11}&M_{12}&M_{13}\\ M_{21}&M_{22}&M_{23}\\ M_{31}&M_{32}&M_{33}\end{bmatrix}\times\begin{bmatrix}x_{i}\\ y_{i}\\ 1\end{bmatrix} \tag{1}\]  
\[\begin{pmatrix}x^{{}^{\prime}}\\ y^{{}^{\prime}}\end{pmatrix}=\left(\frac{M_{11}x+M_{12}y+M_{13}}{M_{31}x+M_{32 }y+M_{33}},\frac{M_{21}x+M_{22}y+M_{23}}{M_{31}x+M_{32}y+M_{33}}\right) \tag{2}\]
## 2 Materials and Methods
### Kinematics and Kinetics  
A 10-camera motion analysis system (Oqus700+, Qualisys AB, Goteborg, Sweden) was used to record kinematics. The sampling frequency was 200 Hz. Kinetic data were collected using two recessed force platforms (Kistler 9260 AA6, 0.4 m \(\times\) 0.6 m; Kistler Instruments Corp., Winterthur, Switzerland) at a sampling frequency of 1000 Hz. A modified, full-body [PERSON] marker system (Orthopedic Engineering and Research Center, Helen Hayes Hospital, New York, NY, USA) with marker clusters was used in addition to a custom-made model for the racket (57 reflective markers) [17; 18]. There were five markers on the racket. One was attached to the racket top, and the other four markers were symmetrically attached. The racket velocity was calculated from the data of the top marker on the racket [4] (Figure 2). The other markers on the racket's side were added to calculate the racket's spatial orientation, described as the center displacement and angles between the racket surface and axes planes [19]. Given a few markers lacking partial trajectory data, we used Qualisys Track Manager (QTM 2023.1, Qualisys AB, Sweden) to fill in the marker data gaps according to the actual conditions. Linear, Polynomial, Relational, and Kinematic were the main gap-fill types that interpolate the data in the software.  
Figure 1: Experimental setup of the study.
# About Rule-Based Systems: Single Database Queries for Decision Making
## 4 Stages in the Design of a Rule-Based Database System
### \(STEP\) 1 in Detail-Discussion of Selected Rule Generation Techniques
#### 4.1.1 Exhaustive Set of Rules  
Calculation of an exhaustive set of rules consists of browsing through combinations without repetition of attributes, starting from the shortest, and checking their consistency against the decision. The MDL (Minimal description length) concept is used in this method. MDL consists in not accepting longer rules, which contain the correct shorter ones. This method is from the Brute Force family of techniques. Despite the fact that it uses the greatest possible knowledge from decision systems, it is at risk of using rules calculated from data noise. The method is quantitative, not necessarily giving the best possible results for a specific subgroup of objects. The exponential computational complexity limits usability on large data. It is worth knowing that a tool useful in calculating exhaustive rules is the relative indiscernibility matrix-see [15].  
Considering the decision system from Table 2, the exhaustive set of rules is as follows:  
* \((\textit{Weather}=\textit{Overcast})=>(\textit{Play\_Tennis}=\textit{Yes}[4])\)
* \((\textit{Humidity}=\textit{Normal})\&(\textit{Wind}=\textit{Weak})=>(\textit{Play\_Tennis}=\textit{Yes}[4])\)
* \((\textit{Weather}=\textit{Sunny})\&(\textit{Humidity}=\textit{High})=>(\textit{ Play\_Tennis}=\textit{No}[3])\)
* \((\textit{Weather}=\textit{Rain})\&(\textit{Wind}=\textit{Weak})=>(\textit{ Play\_Tennis}=\textit{Yes}[3])\)
* \((\textit{Weather}=\textit{Sunny})\&(\textit{Temperature}=\textit{Hot})=>(\textit{ Play\_Tennis}=\textit{No}[2])\)
* \((\textit{Temperature}=\textit{Cold})\&(\textit{Wind}=\textit{Weak})=>(\textit{ Play\_Tennis}=\textit{Yes}[2])\)
* \((\textit{Weather}=\textit{Rain})\&(\textit{Wind}=\textit{Strong})=>(\textit{ Play\_Tennis}=\textit{No}[2])\)
* \((\textit{Weather}=\textit{Sunny})\&(\textit{Humidity}=\textit{Normal})=>(\textit{ Play\_Tennis}=\textit{Yes}[2])\)
* \((\textit{Temperature}=\textit{Hot})\&(\textit{Wind}=\textit{Strong})=>(\textit{ Play\_Tennis}=\textit{No}[1])\)
* \((\textit{Weather}=\textit{Sunny})\&(\textit{Temperature}=\textit{Mild})\&( \textit{Wind}=\textit{Weak})=>(\textit{Play\_Tennis}=\textit{No}[1])\)
* \((\textit{Weather}=\textit{Sunny})\&(\textit{Temperature}=\textit{Cold})=>(\textit{ Play\_Tennis}=\textit{Yes}[1])\)
* \((\textit{Weather}=\textit{Sunny})\&(\textit{Temperature}=\textit{Mild})\&( \textit{Wind}=\textit{Strong})=>(\textit{Play\_Tennis}=\textit{Yes}[1])\)
* \((\textit{Temperature}=\textit{Hot})\&(\textit{Humidity}=\textit{Normal})=>(\textit{ Play\_Tennis}=\textit{Yes}[1])\)  
Now allow us to present a toy example of a sequential covering type algorithm.
# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 3 Service Chains to Realize the Video Broadcasting Infrastructure Elements
Focusing on a specific flow, which can be either with the original quality or a quality \(q\) obtained for transcoding, it is first managed by a LB block and a DPI block with the same functionalities explained so far when we referred to Figure 5a. Then, the flow arrives to the edge streamer (ES) block, which is the block that is in charge of the communications with all the clients assigned to it. Finally, as in the acquirer nodes, the video flow enters the transcoding section if transcoding is required in this node, and then in the point-to-multipoint section to be replicated to all the clients that have to receive it. Moreover, if some client requires watching a stored video flow,  
Figure 5: Platform service chains. (**a**) acquirer service chain; (**b**) edge streamer service chain. its ES requests the component sub-flows to \(F\) storage drones decided by the orchestrator, in order to reassemble it, and passes the received parts to the flow rebuilder (FR) block, which creates again the flow with the original quality, applying an inverse FEC. Then the flow traverses a transcoding section if transcoding is needed, and finally is delivered to the requesting client.
# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 3 Materials and Methods  
### Overall Structure of CloudformerV3  
The structure of CloudformerV3 is composed of two main components: the encoder and the decoder, as depicted in Figure 2. Within the encoder, the backbone incorporates the Mix Transformer [29], optimizing the extraction of image features and striking a balance between accuracy and efficiency. During the fine-tuning process with cloud datasets, a multi-scale adapter is introduced to work in conjunction with the backbone for downsampling operations. Concurrently, the prior information from both dark and bright channels is combined and infused into the backbone through the adapter, fostering comprehensive interaction between the prior information and the input image. The encoder obtains the multi-level feature layer and channels it to the decoder. This enables distinct feature layers at varying levels to mutually focus on one another through a multi-level large window attention. Subsequently, during the resolution recovery phase, the decoder progressively upsamples and integrates the multi-level feature layer with higher-level feature layers. This culminates in the generation of the segmentation result.  
Figure 1: The overarching framework of related work, delineated into two categories: traditional approaches and modern approaches. Traditional approaches comprise spectral threshold-based methods and machine learning methods. On the other hand, modern approaches encompass convolutional neural networks, transformers, and specific remote sensing cloud detection models.
# Design and Implementation of a Polar Wind and Solar Hybrid Power Supply Controller
## 3 Controller Hardware Design
### Signal Acquisition and Conditioning Circuit
#### 3.2.2 Current acquisition and conditioning  
In this paper, the current detection chip ACS712-30A based on the Hall induction principle is used. The load current of one channel is shown in Figure 6a.  
The chip-level Hall current sensor is connected in series in the current loop, its peripheral circuit is simple; it has a milli-ohm path resistance built in; the primary current flows only from the inside of the chip, and there is no contact with the secondary circuit, so that the primary secondary side is isolated. The operating temperature is \(-40\)\({}^{\circ}\)C to 85 \({}^{\circ}\)C, the accuracy of the full temperature range is \(\pm\) 1.5%, The input-domain output in the range of measurement has a good linear relationship Vout = 0.5 Vcc + Ip*0.06. The Vcc power supply of the ACS712 is generally recommended to use 5 V, and the output voltage signal is between 0.5 V and 4.5 V. Figure 6b shows the signal conditioning section. The four-channel TLV2374 forms a voltage follower to increase the output impedance, and then filters out the interference through the low-pass filter circuit and outputs it to the ADC pin of the master.
# Efficient Parallel Implementation of CTR Mode of ARX-Based Block Ciphers on ARMv8 Microcontrollers
## 3 Related Works
proposed secure and fast implementation of HIGHT and CHAM by utilizing the NEON engine in ARMv8 platforms. In the HIGHT and revised CHAM' fast implementation, data and task parallelism was effectively applied to process multiple operations and data simultaneously. Through the proposed data parallelism of HIGHT and revised CHAM, 24 encryptions were performed simultaneously in HIGHT, and 16, 10, and 8 encryptions were performed simultaneously in revised CHAM-64/128, CHAM-128/128, and CHAM-128/256, respectively. However, the authors did not maximize the scheduling of vector registers, so the maximum number of encryptions was not processed simultaneously. In MDPI eletronic'20 [6], [PERSON] et al. proposed the first CHAM-CTR mode optimization in an 8-bit AVR processor. The authors utilized the property of CTR mode that nonce part is always the same during encryption. Through this CTR mode property, the authors optimized the some round operations up until round 7 in CHAM-CTR. The overall process of the proposed CHAM-CTR mode [6] is shown in Figure 5. In MDPI eletronic'20 [7], [PERSON] et al. proposed the first LEA-CTR and HIGHT-CTR mode optimization in an 8-bit AVR processor. Utilizing the same CTR mode property as above, the authors optimized the same round operations up until round 3 in LEA-CTR and round 5 in HIGHT-CTR. The overall process of HIGHT-CTR optimization is shown in Figure 6.  
When encryption in an actual application, massive amounts of data are encrypted, so the application of the mode of operation is essential. Currently, the modes of operation widely used industrially are CBC and CTR modes. However, the studies until now have tended to focus on optimization of the block cipher rather than optimization of mode of operation. Thus, in this paper, the objective of our work is to present the first parallel implementation of CTR mode optimization in ARMv8 architecture. For parallel implemen  
Figure 5: Optimization of revised CHAM-64/128-CTR mode through partial pre-computation for the initial seven rounds in previous work [6]. tation, we introduce efficient register scheduling and data parallelism techniques. For CTR mode optimization, LEA-CTR proposed the method to optimize one round more than the previous study [7]. HIGHT-CTR and revised CHAM-CTR utilized the same method as the previous CTR mode optimization [6; 7]. Proposed Parallel Implementation of LEA-CTR, HIGHT-CTR, and Revised CHAM-CTR on ARMv8 Microcontrollers
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 5 Experiment and Analysis
### Distributed Data Integration Experiment  
The experimental images of distributed integration test mainly include Landsat 8 OLI_TIRS, Landsat 7 ETM+, Landsat 5 TM, Landsat 1-5 MSS, Aster L1T, CEBERS-1/2 CCD, HJ-1A/B CCD, HJ-1A HSI, and FY-3A/B VIRC images, which were freely downloaded from the USGS ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))), NSMC ([[http://satellite.nsmc.org.cn/portalsite/default.aspx](http://satellite.nsmc.org.cn/portalsite/default.aspx)]([http://satellite.nsmc.org.cn/portalsite/default.aspx](http://satellite.nsmc.org.cn/portalsite/default.aspx))) and CCRSDA ([[http://www.cresda.com/CN](http://www.cresda.com/CN)]([http://www.cresda.com/CN](http://www.cresda.com/CN))) websites. A total of 3380 files were downloaded. These images were distributed in the eight sub-centers according to data type. The total number of our experimental images are shown in Table 3.  
The distributed data integration experiment mainly includes remote sensing data polling, metadata extraction, thumbnail generation, file transferring, thumbnail archiving, metadata index, and other processes. The experimental results are primarily with respect to the already-crawled data volume and total time consumption from the RemoteCrawler launch to metadata being indexed by SolrCloud/Lucene. Because no two push-pull daemons ever run concurrently, the distributed data integration experiment was carried out one sub-center at a time. The experiment procedures and results are shown in Table 4.  
Figure 8: The framework of the virtual multi-center data environment.  
As can be seen in Table 4, the number of main center-integrated remote sensing images is equal to the total number of each sub-center's stored images. That is to say, there is no information lost during the process of data integration. Moreover, our designed ISO 19115-2:2009-based uniform metadata model includes all fields of integration by participating remote sensing metadata, and the SolrCloud indexed metadata can also maintain the metadata information of each remote sensing image perfectly. As for the transfer rate, it mainly depends on the window size for OODT-push-pull component. In our experiment, the window size was set at 1024 bytes, and the average transfer rate is between 9.8 and 13.8 MB/s. This is enough to satisfy the demands of metadata and thumbnail transfer across a distributed data center spatial infrastructure. Therefore, the experimental results showed that our OODT-based distributed remote sensing data integration was feasible.
## 3 Results and Discussion
### Tensile Test  
The most researched mechanical properties of edible film are tensile strength and elongation break [50]. The upsides of these mechanical characteristics are introduced in Table 4. Figure 4 demonstrates strain behavior for the edible gelatin-consistent film with variable contents of papaya and soy protein under the impact of tensile tension. In the beginning, there was a quick increase in stress for all the edible films tested, and the elastic deformation was linearly proportional to the strain. After this step, the food material was reached and the plastic deformation began. The stress behavior-strain charts in the plastic phase of the edible film specimens vary. The stress-strain graphs in Figure 6 demonstrate that the tensile strength of the edible films decreases while ductility increases when the soy protein levels increase [51]. Elongation at break (%E) of investigated films went from 48.9 to 39.8 MPa and 6.2 to 50.5% individually and relied upon the kind of polymer utilized which is similar to LPDE [52]. Without defatted soy protein, the addition of papaya to the gelatin films essentially expanded TS and arrived at a limit of 48.9 MPa. Gelatin has a direct design and a restricted monomer component, which leads to good film-forming properties. When defatted soy protein was added to papaya films, within the high concentration of gelatin, the TS diminished while EAB additionally declined extraordinarily. Perhaps with the addition of papaya puree in gelatin films (without defatted soy protein), the TS of film expanded fundamentally, which shows that papaya adds to the increment in TS of the gelatin films. The increase in tensile strength was regulated, not only for the geometry and the hardness of film but also due to the formation of a rigid, continuous network of hydrogen bonding with papaya fruit [53]. When both the defatted soy protein and papaya were added to the gelatin films, a desperate pattern was noticed, i.e., TS decreased significantly. Unexpectedly, the gelatin films added to defatted soy protein and papaya puree showed diminished EAB, likely because of the decrease in protein sub-atomic versatility by cross-connecting [25].  
Elongation break measures the flexibility of a film composed of G, DSP, and PP. When the concentration of defatted soy protein was increased in the gelatin film, the elongation rate was reduced. The quantity of papaya in the gelatin film increased the elongation break value. G2 film also exhibited a strong elongation break. Gelatin had the greatest influence on film stretch, and EB increased with the G content in composite films [43].  
Figure 5: Hydrophilic nature of the edible film, showing water droplet shape as well as measured corresponding contact angles (CA) of G-2, G/PP-2, and G/PP/SP films.