esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Novel Technologies of 6G for Smart Grid Applications
### Intelligent Reflecting Surface (IRS)  
In the wireless communications sector, intelligent reflecting surface (IRS) is a revolutionary emerging technology for the manipulation and utilization of radio waves. Alsoknown as reconfigurable intelligent surfaces or smart reflecting surfaces, IRS is a passive array of electromagnetic elements, such as specially designed meta-materials or low-cost antennas, which can intelligently reflect, refract, and manipulate incoming wireless signals. IRS manipulates the transmission between sender and receiver in the case of a non-line-of-sight (NLOS) scenario. IRS controls the communication environment, as well as regulates the communication channels to improve performance [95].  
IRS improves spectral efficiency, energy efficiency, and area capacity as well [96]. Moreover, IRS is also a promising and cost-effective solution for 6G.
## 7 Research Directions
### Secure Service Discovery
The processing of the malicious discovery messages and malformed requests can result in resource exhaustion, such as shorter battery life, and service unavailability. A service authentication scheme can be designed to prevent service devices from offering fabricated services and client devices from discovering unauthorized services. Figure 21 shows an overview of such a secure service discovery model. A trusted entity, such as a certificate authority, issues service capability certificates (SCC) and client capability certificates (CCC) to service and client devices, respectively. The SCC and CCC contain the signature of the trusted party; therefore, they cannot be forged. The SCC contains device and service specifications of a service device. The device specification provides information on sensors and actuators embedded with the service device. The service specification includes a list of services the device is authorized to provide as well as a list of web interfaces, such as APIs, the device is authorized to expose for exchanging information. Similarly, a CCC issued to a client device contains information on the types of services the client is allowed to interact with. A service device must attach its SCC to its service announcement message. A client device does not respond to a service announcement message that contains service information not included in the service device's SCC. Thus, the client device avoids allocating resources, such as memory, storage, and CPU cycles, for storing and processing service information. Likewise, a client device has to attach its CCC with a service discovery message. A service device does not process a discovery request made by a client device if it finds that the client device is not capable of consuming the requested service as mentioned in its CCC. Hence, the verification of service and client devices' capabilities can enable IoT devices to prevent service and client impersonation attacks. Figure 21: _Cont._
## 2 Materials and Methods
### Identification of Phenolic Compounds by LC-ESI-QTOF-MS/MS  
Flavonoids are a very diverse group of polyphenolic compounds. The identification of these compounds was performed by liquid chromatography in UPLC Acquity class H, coupled to a mass analyzer G2-XS QTof (Waters Corporation, Santa Clara, CA, USA), and following the methodology previously described [29]: the separation was performed with an Acquity UPLC BEH C18 1.7 \(\upmu\)m 2.1 \(\times\) 100 mm column at 40 \({}^{\circ}\)C. The mobile phase consisted of phase A (acidified water with 0.1% formic acid) and phase B (acetonitrile), with a flow rate of 0.2 mL/min and 2 \(\upmu\)L injection volume. The gradient elution procedure was as follows: 0 min, 90% (A); 3 min, 70% (A); 9 min, 60% (A); 11 min, 50% (A); 12 min, 0% (A), 13 min, 0% (A); 15 min, 90% (A); and 17 min, 90% (A). The ionization of the compounds was performed by electrospray (ESI). The parameters were set as the capillary voltage of 1.5 kV, sampling cone 30, desolvation gas 800 (L/h), and temperature of 500 \({}^{\circ}\)C. Collision energies of 10, 20, and 30 V were used. The North America Massbank database (MoNA)was used to identify compounds. Compounds were quantified using a standard curve of quercetin, naringenin, and luteolin.
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Light Intensity  
In the case of the reference measurement we strive to eliminate environmental factors, mainly reflections. However, it is interesting and also crucial to know what kinds of deviations are produced in the measurements in non-ideal conditions. In this chapter the effect of reflective materials and the tilt of the PCPA are be analyzed on the recorded light intensities.
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 2 Numerical Approach
### Model Description  
The finite element model was developed using data obtained from the experimental setup carried out by [PERSON] [26]. Only one-quarter of the silo is modelled for the reason of symmetry, as shown in Figure 4, in order to represent both the stored granular materials and the silo structure. The model consists of three parts: the bulk solids, the silo walls, and the base. An elasto-plastic model using the Drucker-Prager criterion is used to simulate the behavior of the bulk materials during filling, while the behavior of steel walls and the base is modelled using [PERSON]'s law in the elastic zone and plasticity criterion to define the yield and hardening zone of the behavior.  
Figure 3: Dimensions of the model silo. (**a**) Elevation view of the model showing pressure cell locations; (**b**) plan view of the model. (All dimensions in meters).  
The filling phase is modelled by applying the bulk weight over several numerical steps. At the end of each step of loading, equilibrium is attained before increasing the weight. Previous research [27] showed an insignificant difference in lateral wall pressure in the model between the one-time or progressive filling process, when using the static equilibrium approach. The granular materials, silo walls, and base are discretized using hexahedron finite elements with 8 nodes, as shown in Figure 5 (cubic brick element C3D8R).
# Variance-Triggered Two-Step GPS Acquisition
## 1 Introduction
However, even for the average case complexity, when the correct satellite pair is midway through the list of pairs to be tested, it might not be necessary to use all available samples if the quality of the signal is sufficiently high. We argue that it is possible to detect higher quality signals faster by testing all pairs incrementally for a group of samples. This paper extends two partial results [12; 13] to propose a folded Variance-Triggered Two-Step (VTTS) acquisition method in the time domain that presents a reduced acquisition time while keeping the same hardware requirements and slightly better success and false-positive rates when compared to the conventional serial acquisition. Using the same arithmetic operations, the new method implements an adaptive folding that allows the complexity to vary according to the C/N0 quality of the signal at the receiver. The higher the quality of the signal, the shortest the time required for acquisition. The variable complexity arises from the transition between the two steps of the approach, that alternate between a coarse search and a fine search until the specified satellite is acquired or defined as not present. To enable this transition to happen more efficiently, we have proposed a detection criterion to trigger the fine search step from the coarse search step. The proposed strategy allows a reduction of about 80% of processing time in comparison to the conventional strategy. In addition, the use of the proposed variance-based detection threshold provided faster acquisition and lower false-positive rate when compared to the use of ratio detection [14]. The proposed method also uses less memory than the conventional acquisition at a reduction factor that is proportional to the number of samples per chip or, equivalently, proportional to the sampling frequency. This paper is organized as follows. In the next section, we provide background about the conventional acquisition. Then, we present the proposed two-step acquisition technique. The experiments we carried out to analyze and validate the proposed approach are presented in the sequel together with the collected results. Finally, after a brief presentation of the related works, we draw our conclusions and discuss future work.
# In-Silico Characterization of von Willebrand Factor Bound to FVIII
###### Abstract  
Factor VIII belongs to the coagulation cascade and is expressed as a long pre-protein (mature form, 2351 amino acids long). FVIII is deficient or defective in hemophilic A patients, who need to be treated with hemoderivatives or recombinant FVIII substitutes, i.e., biologic drugs. The interaction between FVIII and von Willebrand factor (VWF) influences the pharmacokinetics of FVIII medications. In vivo, full-length FVIII (FL-FVIII) is secreted in a plasma-inactive form, which includes the B domain, which is then proteolyzed by thrombin protease activity, leading to an inactive plasma intermediate. In this work, we analyzed through a computational approach the binding of VWF with two structure models of FVIII (secreted full-length with B domain, and B domain-deleted FVIII). We included in our analysis the atomic model of efanesococogo alfa, a novel and investigational recombinant FVIII medication, in which the VWF is covalently linked to FVIII. We carried out a structural analysis of VWF/FVIII interfaces by means of protein-protein docking, PISA (Proteins, Interfaces, Structures and Assemblies), and protein contact networks (PCN) analyses. Accordingly, our computational approaches to previously published experimental data demonstrated that the domains A3-C1 of B domain-deleted FVIII (BDD-FVIII) is the preferential binding site for VWF. Overall, our computational approach applied to topological analysis of protein-protein interface can be aimed at the rational design of biologic drugs other than FVIII medications.  
hemophilia A; FVIII; von Willebrand Factor; protein contact networks; bioinformatics; biologic drugs +
Footnote †: journal: Applied Sciences
# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
## 3 Data and Methods
### Automated Georeferencing and Pixel Coordinate Conversion  
Reducing the manual intervention of pixel recalibration is important to develop an automated system. Since the experiment allows for the free movement of UAVs, it is vital to utilize tools that can enable an automated workflow. This was achieved by creating an automated georeferencing system based on the scale-invariant feature transform (SIFT) algorithm developed by [PERSON] (2016). SIFT works by detecting the most consistent features between two images that are resilient to rotation, scaling, and lighting variations. After identifying the most stable key points, a dominant orientation is assigned. Then, it creates a 128-dimensional descriptor for each key point, capturing detailed information about local image gradient magnitudes and orientations. A descriptor is formed by dividing the region around the key point into smaller subregions and creating histograms of gradient orientations. Using these descriptors, SIFT can identify key points across various images, facilitating functionalities, such as object detection, merging images, and creating 3D models. In contrast to the other feature-matching algorithms, like SURF and ORB, SIFT is slower but has superior resilience to variation in pixel intensities, making it ideal for applications with temporal variations, like georeferencing [23].  
An orthomosaic template image was created to project geographical space onto the input footage using a DJI mini 3 pro drone flying at 90 m, equipped with a 48-megapixel camera set at an angle of 90 degrees. A visual line of sight was maintained throughout the flight, and several images were taken to generate orthomosaics over a larger area. The image was then georeferenced using the WebODM application of OpenDroneMap, and an orthomosaic was constructed with a UTM projection system. This georeferenced image acted as a template to automatically transfer geographical coordinates onto each frame. The process involved identifying the matching features between a template image and the input frame and then georeferencing the input image based on the matching key points (as illustrated in Figure 2). For the quality assurance of descriptors, [PERSON]'s ratio test was employed, and only the matches passing the criteria were used for the homography calculation [24]. The root mean square error (RMSE) threshold was also enforced as an additional check, and frames with a higher RMSE were discarded [25].  
Figure 1: Showcases the methodological framework of the study.  
Figure 2: Feature-matching algorithm SIFT applied to input and template image. The highlighted markers depict the key points matched between the two images.
# Joint Transmit Waveform and Receive Mismatched Filter Design to Suppress Range Sidelobe
## 2 Design of NLFM-CPM Integrated Waveform and Signal Processing Methods
### EWMF Design Based on the ADMM Algorithm
If its level is higher than that of the adjacent cells, it is considered a peak level. The smallest of all the peak levels is identified as the lowest peak level \(\mathrm{Pr}^{k}\). The algorithm steps for the joint design of NLFM-CPM waveform and EWMF based on ADMM are shown in Table 2.
# COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?
## 4 Results
### Evaluations  
The quantitative results are reported in Tables 1 and 2 for the COVIDCT and COVIDx datasets, respectively. The accuracy of the COVIDCT dataset varied from 70% to 81%. Moreover, GoogleNet achieved the highest average recall of 94.29%, and DenseNet169 had the highest precision of 100%. The highest performance for the area under the curve was 88.80%, achieved via ResNet101. The accuracy of the deep learning models on the COVIDx dataset was higher than that for the COVIDCT dataset, ranging from 78.23% to 87.1%. On average, the accuracy was more than 82%. On the other hand, the deep models yielded lower recall results while having similar results for precision on the COVIDx dataset. The highest recall achieved was 47.62% by MNASNet1.0. Similarly, the best precision was from GoogleNet and EfficientNet-b3, which was 83.33%. In addition, the models struggled to produce comparable results for the AUC metric, where DenseNet201 gave the top performance, achieving 78.59%.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Methods** & **Average Recall** & **Average Precision** & **Average F1** & **Average Accuracy** & **Average AUC** \\ \hline DenseNet121 & 0.7238 & 0.8444 & 0.7795 & 0.7882 & 0.8700 \\ DenseNet161 & 0.7905 & 0.8218 & 0.8058 & 0.8030 & 0.8729 \\ DenseNet169 & 0.7524 & 0.8495 & 0.7980 & 0.8030 & 0.8586 \\ DenseNet201 & 0.8476 & 0.7739 & 0.8091 & 0.7931 & 0.8698 \\ \hline GoogleNet & 0.9429 & 0.6306 & 0.7557 & 0.6847 & 0.7815 \\ \hline Efficient-b0 & 0.7333 & 0.7700 & 0.7512 & 0.7488 & 0.8518 \\ Efficient-b1 & 0.8095 & 0.7589 & 0.7834 & 0.7665 & 0.8588 \\ Efficient-b2 & 0.7143 & 0.7895 & 0.7500 & 0.7537 & 0.8325 \\ Efficient-b3 & 0.8190 & 0.8037 & 0.8113 & 0.8030 & 0.8862 \\ Efficient-b4 & 0.8190 & 0.8037 & 0.8113 & 0.8030 & 0.8862 \\ Efficient-b5 & 0.6952 & 0.7300 & 0.7122 & 0.7094 & 0.7903 \\ Efficient-b6 & 0.7524 & 0.7900 & 0.7707 & 0.7685 & 0.8552 \\ Efficient-b7 & 0.7905 & 0.7905 & 0.7905 & 0.7833 & 0.8566 \\ \hline SqueezeNet1.0 & 0.9048 & 0.7090 & 0.7950 & 0.7586 & 0.8722 \\ SqueezeNet1.1 & 0.9333 & 0.7259 & 0.8167 & 0.7833 & 0.8725 \\ \hline MNASNet0.5 & 0.5238 & 0.7857 & 0.6286 & 0.6798 & 0.7938 \\ MNASNet1.0 & 0.8952 & 0.7833 & 0.8356 & 0.8177 & 0.8845 \\ \hline ResNeXt50-32x4d & 0.8000 & 0.8235 & 0.8116 & 0.8079 & 0.8726 \\ ResNeXt101-32x8d & 0.8000 & 0.7368 & 0.7671 & 0.
## 3 Proposed Method
### Acoustic Summary Statistics Extraction for ASC
#### 3.3.2 Textures-Enhancing Weights Generation  
To enhance sound textures for ASC, we develop weights for feature frames based on membership probabilities to the acoustic scene. Laplace model, due to its robustness to outliers, is introduced to derive such probabilities [22]. By fitting \(h\) to the model, \(\mu_{h}\), \(b_{h}\) and likelihood can be estimated:  
\[\text{Lap}(h|\mu_{h},b_{h})=\frac{1}{2b_{h}}\text{exp}\left(-\frac{|h-\mu_{h}| }{b_{h}}\right) \tag{7}\]  
Laplace model puts much less probability density to events which are unrelated to scene category. To further enhance sound textures, we introduce soft thresholding:  
\[\text{w}=\left\{\begin{array}{ll}\text{Lap}(h|\mu_{h},b_{h})&h>\tau\\ 1&h\leq\tau\end{array}\right. \tag{8}\]  
Figure 6: Example of acoustic texture enhancing weights generation.  
where \(\tau=\mu_{h}+\sqrt{2\beta_{h}}\). Such setting assures textures will obtain highest weights (1, from probability aspect) while scene-irrelevant events would be suppressed by smaller weights. In Figure 6, we present an example of textures-enhancing weight generation using proposed method.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 2 Control Design
### PD Sliding Mode Control (PDSMC)
#### 2.3.1 Proposed Scheme Characteristics  
The proposed logic outperformed conventional SMC as:  
Less ChatteringIn our proposed scheme during the reaching phase, the gain \(k_{d}\) was multiplied with perturbation \(\psi\) and reduced the effect of perturbation more than conventional SMC. That's why in PDSMC, a small switching gain \(K^{\prime}\) was required as compared with conventional SMC gain K. Due to the large gain K in SMC, the chattering was observed in the output of SMC, whereas there was small chattering in PDSMC.  
Insensitive to High Frequency PerturbationWhen the system reached the sliding surface, or was inside the boundary layer \(|s|\leq\varepsilon_{c}\), a transfer function which was a low pass filter between the sliding surface and perturbation was obtained in PDSMC. The breaking frequency of this filter \(\frac{K^{\prime}}{\varepsilon_{c}}\), was smaller than the breaking frequency \(\frac{K}{\varepsilon_{c}}\) of conventional SMC (\(K^{\prime}<K\)). This meant that during the sliding phase, our proposed scheme was insensitive to the high-frequency perturbation signal.  
The two points mentioned above are the advantages of PDSMC. In the next section, PDSMC is integrated with SPO, and its characteristics will be compared with conventional SMCSPO.
# Quality Index of Supervised Data for Convolutional Neural Network-Based Localization
## 3 Approach
### Quality Index Based on Important Pixels for CNN-Based Localization
#### 3.2.2 Step 2: Quality Index  
We define the quality index of supervised data using the co-efficiency of consecutive frames of the visualization result, which shows important pixels for localization. This is based on the assumption that if the supervised training data are of good quality, then the important pixels for CNN-based localization would change gradually in a factory situation. The quality index is defined by Equation (2).  
\[C(u,v)=\frac{\sum_{\hat{u},\hat{v}}(\hat{T}(\hat{u},\hat{v})\cdot\hat{I}(u+\hat {u},v+\hat{v}))}{\sqrt{\sum_{\hat{u},\hat{v}}\hat{T}(\hat{u},\hat{v})^{2}\cdot \sum_{\hat{u},\hat{v}}\hat{I}(u+\hat{u},v+\hat{v})^{2}}}, \tag{2}\]  
where  
\[\hat{T}(\hat{u},\hat{v})=T(\hat{u},\hat{v})-\frac{1}{(w\cdot h)\cdot\sum_{\hat {u},\hat{v}}T(\hat{u},\hat{v})} \tag{3}\]  
Figure 6: Example of augmented data. Randomized square patch, \(p\) is augmented to the original input image \(x\).  
Figure 7: Examples of raw input (**top**) and importance image (**bottom**). In this example, pixels around a pallet that the AGV approaches are important for convolutional neural network (CNN)-based localization.  
\[\hat{I}(u+\hat{u},v+\hat{v})=I(u+\hat{u},v+\hat{v})-\frac{1}{(w\cdot h)\cdot\sum_{ \hat{u},\hat{v}}I(u+\hat{u},v+\hat{v})}. \tag{4}\]  
These equations are used to calculate the correlation of consecutive visualized images. Here, \(w\) and \(h\) are the width and height of the image and \(I\) and \(T\) represent an image at the times \(t\) and \(t+1\), respectively. The other variables are indexes.
###### Abstract  
In relation to power converter design, power density is increasing while the form factor is decreasing. This trend generally reduces the rate of the cooling process, which increases the mutual thermal coupling among the surrounding power components. Most of the traditional models usually ignore the mutual effects or just focus on the conduction coupling. To deal with these factors, the thermal modeling for a boost converter system has been built to compare the junction temperatures (\(T_{j}\)) and the increments under different working conditions in order to consider the conduction coupling. A multi-variable thermal resistances model is proposed in this paper to incorporate the convection thermal coupling into the mutual thermal effects. The coupling resistances, MOSFET to the diode \(\binom{R}{(\sigma-M\overline{D})}\), and the diode to MOSFET \(\binom{R}{(\sigma-M\overline{D})}\) have been calculated and the relationships between coupling resistances and their impact factors (separation distances and working currents) have been discussed. New case temperatures (\(T_{c}\)) obtained by calculation and additional measurements at other separation distances serve to verify the efficacy of the proposed model. This model enhances the current thermal models and provides an effective method to calculate the thermal coupling resistances which can be used to estimate the \(T_{j}\). As the coupling resistances are distance dependent, the model also helps to optimize and fine-tune the placements of components in high-power density converters.  
Keywords:thermal modeling; mutual thermal coupling; thermal resistances; power converter; case temperature +
Footnote †: journal: Applied
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 3 Details of Selection of Parameters  
The proposed method will be tested on standard images with size of \(512\times 512\times 8\). The maximum embedding rate and the peak signal-to-noise ratio (PSNR) of decrypted image are employed to evaluate the performance of the proposed method. To achieve the highest ER and PSNR, a few implement details of selecting parameters need to be introduced in advance.  
Figure 6: Flow chart of the steps.