esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### InSAR for Remote Ground Motion Monitoring
Although for urban classes, the density of measurements can be very high at around 1800 points per km\({}^{2}\) using C-band SAR data, this rapidly falls to 300 or less for bare land, and 100 or less over the sparse mountain vegetation typical of this study area. In comparison, the ISBAS method can achieve 4-26 times more coverage than PSInSAR [44], routinely achieving more than 2200 pixels per km\({}^{2}\) over the entire rural landscape when using Sentinel-1 and a survey resolution of 20 m. Moreover, as an intermittent coherent pixel technique, ISBAS does not rely on pixels being of permanently high coherence throughout the full period of observations, but instead merges observations of opportunity in each interferogram as they occur. Accordingly, this characteristic could help to readily mitigate the impact of seasonal snow, provided that the cover is neither ubiquitous nor sustained. Another advantage of the ISBAS algorithm, and a consequence of the high density of measurements that can be achieved, is the ability to map ground motion without the use of ground control, such as GPS receivers and artificial corner reflectors. This has been demonstrated for a variety of disaster risk reduction applications associated with slope failures, such as landslide inventory mapping [45; 46] and tailings dam monitoring [47]. The ability to monitor ground motion without any requirements for ground control or repeated field visits is particularly advantageous in inaccessible mountainous terrain. Although the Lake Sarez area is equipped with various sensors, including GPS stations, as part of a remote monitoring and early-warning system [35], these only provide a sparse set of discrete point measurements. Therefore, the detailed and widespread coverage provided by satellite InSAR has the potential to greatly complement the monitoring of the area.
# Editorial Board Members' Collection Series: Functional Foods
## 3 Future Research
### Encapsulation Technologies  
Encapsulation is increasingly recognized as a cornerstone in the development of functional foods, primarily because it guards fragile bioactive ingredients--such as polyphenols, carotenoids, vitamins, and probiotics--against degradative factors like heat, oxidation, and moisture. By employing advanced techniques (e.g., nanoencapsulation, extrusion, and co-extrusion), food technologists ensure that these compounds are released in a controlled manner, maintaining their therapeutic efficacy and organoleptic properties during processing, storage, and eventual digestion [21; 22; 23; 24]. This is particularly crucial in products targeting metabolic benefits, such as anti-diabetic foods, where effective delivery of nutraceuticalals helps regulate blood glucose levels. Meanwhile, wall materials like proteins, polysaccharides, and lipids not only stabilize the encapsulated bioactives but also mask undesirable flavors, improving consumer acceptance [25; 26].  
Beyond boosting stability and delivery, encapsulation methods have proven vital for safeguarding probiotics under harsh gastrointestinal conditions and preserving the bioactive peptides found in fish protein hydrolyses [27]. These peptides exhibit antioxidant, antihypertensive, anticancer, and antimicrobial properties but are prone to degradation under varying processing environments. By employing additional pre-treatment methods such as high-pressure processing, microwave, and ultrasound, food manufacturers can further enhance peptide yields, while film and encapsulation technologies increase their bioavailability and controlled release. Such strategies extend shelf life. They also ensure that functional foods provide real health benefits. As a result, consumers get products that are nutritious, safe, and pleasing to the senses [21; 26].
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 6 Performance Evaluation
### Performance Evaluation of Adaptive FakeBob Attacks against the Noise-Adding Defense
#### 6.4.1 Bypassing the Plugin Function  
An adaptive attack can bypass the plugin function and generate adversarial audios using an SV system without the defense. The generated adversarial audios were then fed into the SV system with noise-adding during the test time to obtain the ASR. Figure 6 shows the performance of generated adversarial audios with different values of the perturbation threshold, i.e., \(\epsilon=0.0005\), 0.001, 0.002, and 0.005, against the proposed noise-adding defense with different values of the standard deviation of noise (i.e., \(\sigma\)). It can be observed that when \(\sigma\) increases from 0 to 0.005, the EER of an SV system slowly increases from 6.20% to 11.57%. When \(\sigma=0.002\), \(\text{EER}=7.14\%\). Moreover, it can be observed that when \(\epsilon\) is not large (e.g., \(\epsilon\leq 0.002\)), the noise-adding plugin is very effective against adversarial audios. For example, when \(\sigma=0.002\), the noise-adding defense can reduce the ASR to 9.28%, 14.17%, and 33.45% for adversarial audios with \(\epsilon=0.0005\), 0.001, and 0.002, respectively. On the other hand, when \(\epsilon\) is large (e.g., \(\epsilon=0.005\)), the defense with \(\sigma=0.002\) can only reduce the ASR to 72.72%. However, it is noted that an adversarial audio with a higher value of \(\epsilon\) is more perceptible to humans. Therefore, our proposed noise-adding method is still effective against the bypassing-the-plugin strategy by adaptive FakeBob attacks, especially when \(\epsilon\) is not large.
## 3 Results and Discussion
### Preparation and Structures of CCNF
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample a** & **Zeta Potential (mV)** & **Crystallinity (\%)** \\ \hline Cellulose & & - & 68.04 \(\pm\) 3.40 \\ \hline CC & & - & 30.81 \(\pm\) 1.23 \\ \hline CC groundat & 7 & 3.90 \(\pm\) 0.04 & 10.77 \(\pm\) 0.22 \\ pH & 3 & 8.97 \(\pm\) 0.09 & 8.78 \(\pm\) 0.26 \\  & 1 & 16.49 \(\pm\) 0.10 & 2.27 \(\pm\) 0.11 \\ \hline \hline \end{tabular} a Values are mean \(\pm\) standard deviation (n = 5).  
\end{table} 
Table 2: The crystallinity of cellulose before and after modifications and zeta potential in supernatant of the CC (DS = 0.123) after grinding at different pH values. Figure 4: SEM images of original cellulose (**a**) and nanocellulose carbamates fibers (CCNF) from CC with DS = 0.123 ground at pH = 7 (**b**), pH = 3 (**c**), pH = 1 (**d**). As can be seen from Figure 5, after the reaction of eucalyptus cellulose with urea, the degree of crystallinity was lowered by 42.71%. This means that at high temperatures the molten urea can enter part of the crystal regions of the cellulose [40]. The data in Table 2 also present a significant reduction in crystallinity of the cellulose carbamate after colloid milling, which suggests that the hydration enhanced by the cationization of CC in an acidic milling media, combined with the high grinding mechanical force, greatly reduced the crystallization of cellulose.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.6 Air Abrasion with Bioactive Glass Particles  
Bioactive glasses have demonstrated a significant role in promoting dentin remineralization [110; 121]. Their principal constituent, silicon (Si), functions as a nucleation site for calcium and phosphorus ions. Scanning electron microscopy (SEM) studies have revealed that the application of this bioactive substance leads to the formation of an apatite layer, ultimately occluding dentin tubules [122; 123].  
Bioactive glasses represent specialized biomaterials comprised of glass that are notable for their tendency to react on their surfaces. When these glasses come into contact with the surface of teeth and interact with saliva in acidic conditions, they can dissolve. This dissolution releases calcium and phosphate ions while also raising the pH levels [124]. This series of actions set off a chain of chemical responses that result in the formation of a layer made up of hydroxycarbonate apatite [125]. This apatite layer shares similarities with the mineral hydroxyapatite, which is commonly found in dentin. Both substances can create a chemical bond, promoting a stronger attachment of the glass particles to the dentin surface [126]. It is important to note that the complete development of this apatite layer takes some time, often spanning multiple hours. This gradual formation of reparative dentin, characterized by its low permeability, hinders the diffusion of harmful substances from dentin tubules toward the dental pulp. As a result, it restricts the movement of dentin fluid, offering a long-lasting solution for tooth sensitivity [127].  
A commonly used variety of bioactive glass in dentistry is known as Bioglass 45S5, which is a calcium sodium-phospho-silicate compound sold under the trade name NovaMin(r). In general, glasses are amorphous solids lacking a defined crystal structure, primarily composed of silica-based elements with minor additives. However, Bioglass 4555 diverges from this standard by featuring lower levels of silica and higher concentrations of calcium and phosphorus compared to typical glasses [128]. A previous study demonstrated that using a powder of Bioglass 4555 for air abrasion can effectively block the dentin tubules (>99%) [76].
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 1 Introduction  
In modern industrial environments, edge computing has emerged as a crucial technology for processing data closer to the source while reducing delays and bandwidth usage to enable real-time decision-making. This capability is particularly valuable in tasks such as predictive maintenance and job scheduling, where timely and accurate insights are essential to maintain operational efficiency and minimize costs. With the increase of IoT (Internet of Things) devices and the growing complexity of industrial processes, efficient job scheduling has become essential for optimizing resource use and ensuring that tasks are completed on time. In these dynamic settings, predictive maintenance has gained significant attention because early detection of potential machine faults can prevent unexpected downtimes and expensive repairs [1, 2]. However, the challenge lies not only in predicting when a machine is likely to fail but also in how to optimally schedule jobs across multiple machines so as to minimize these fault risks. Traditional scheduling methods often fail to adapt to the real-time nature of production environments, where machine conditions and job requirementsare constantly changing. To tackle this problem, recent research has explored combined approaches that integrate machine learning techniques with optimization algorithms to provide more adaptive and intelligent scheduling solutions.  
To address this challenge, we propose a novel approach that integrates deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling optimization. Our system uses real-time sensor data, including tool wear, temperature, vibration, and operating hours, to predict the fault probabilities of machines. These predictions are then input into an SMT solver to optimize job assignments across the production line.  
By combining the predictive abilities of deep learning with the decision-making strengths of SMT solvers, the proposed system improves both the efficiency and reliability of production processes. The primary contributions of this research are threefold: (1) the development of a hybrid system that integrates predictive fault models with job scheduling optimization to enhance real-time decision-making, (2) the introduction of a proactive approach to fault prevention in dynamic and edge-based production environments, and (3) the implementation of a scalable framework adaptable to various industrial contexts requiring real-time predictive maintenance and scheduling optimization. The remainder of this paper is organized as follows: Section 2 reviews related works on job scheduling and fault prevention in edge computing; Section 3 defines the system and formulates the problem; Section 4 presents the deep learning-based fault prediction model, while Section 5 details the SMT-based scheduling optimization; Section 6 describes the integration of both approaches, and Section 7 discusses the results of our case study and experiments; finally, Section 8 concludes the paper with insights and future research directions.
# Analytical Investigation of Vertical Force Control in In-Wheel Motors for Enhanced Ride Comfort
## 2 Dynamics Models and Methods
### Ride Comfort (ISO 2631)
#### 2.4.1 Power Spectral Density (PSD)  
PSD is a measure of the power distribution of a signal over different frequencies. It shows how the power (or variance) of a time series is distributed with frequency, providing insights into the dominant frequencies present in the signal. To compute the PSD of the sprung mass vertical acceleration, \(\tilde{z}_{b}\), the road profile input is supplied to the vehicle system or the transfer function as described in Equations (18) and (34). The system will generate an output, which will then be used to calculate the PSD of the vertical acceleration of the sprung mass. The PSD of the vertical acceleration of the sprung mass at frequency \(i\) can be computed by Equation (41). An example of calculating the PSD of the vertical acceleration of the sprung mass is shown in Figure 8. The PSD of acceleration at frequency, \(f\), is calculated from the PSD of road profile and the frequency response of the vehicle as shown by the red line in Figure 8.  
\[P_{arg,i_{out}}=\int_{-\frac{1}{2}}^{\frac{T}{2}}\left[A_{i}|G(\omega_{i}i)| sin(\omega_{i}i+\phi_{i}+\angle G(\omega_{i}i))\right]^{2}dt=\frac{(A_{i}|G( \omega_{i}i)|)^{2}}{2} \tag{41}\]  
Figure 7: The calculation of \(P_{i}\).  
where \(i\) is frequency, \(G(s)\) is transfer function as described in Equations (18) and (34), and the road input at frequency \(i\) is  
\[z_{i}(t)=A_{i}sin(2\pi f_{i}t+\phi_{i}) \tag{42}\]  
The instantaneous power of road profile, \(P_{avg}\), is  
\[P_{avg}=\int_{-\frac{T}{2}}^{\frac{T}{2}}P(t)dt=\int_{-\frac{T}{2}}^{\frac{T}{2} }z_{i}^{2}(t)dt=\frac{A_{i}^{2}}{2} \tag{43}\]  
Equation (43) shows that \(P_{avg}\) depends on the amplitude of the road profile as described in Equation (42).
## 3 System Model
### Link Quality
#### 3.2.5 Link Quality Evaluation  
Therefore, by integrating the expected transmission count and the SNR, the link quality between node \(i\) and its neighboring node \(j\) could be defined as follows:  
\[LQ_{i,j}=\mu SNR_{i,j}^{*}-\
u ETX_{i,j}^{*} \tag{8}\]  
where \(LQ_{i,j}^{*}\) and \(ETX_{i,j}^{*}\) are the normalized values of the link quality and the expected transmission count of link \(L_{i,j}\), respectively. \(\mu\) and\(\
u\) are the weights of the expected transmission count and SNR, respectively, and \(0<\mu,\
u<1\), because the ETX was as small as possible, so the negative sign was added before the weight.
# A Novel Fractional Fourier Transform-Based ASK-OFDM System for Underwater Acoustic Communications
One method to improve the performance of OFDM systems is to replace the inverse fast Fourier transform (IFFT) and FFT by the FrFT; this enhances the BER performance, and thus makes it suitable for longer range communications in UWA channels. FFT-based OFDM is limited to the rotation of the time-frequency plane by multiples of (\(\pi/2\)) only. On the other hand, FrFT-based OFDM allows for a rotation of the signal by any fractional angle (\(\alpha\)), and thus gives it one more degrees of freedom. Therefore, it can reduce the BER to a large extent. The FrFT is similar to Fourier transform when \(\alpha=\pi/2\). The FrFT-OFDM system has been recently proposed for wireless communications, and was investigated in Rayleigh fading channels assuming binary phase shift keying (BPSK) modulation [20]. Studies of FrFT-OFDM for UWA communications are still in their early stages. In [21,22], channel estimation was investigated, and in [23] a more complicated form of FrFT was considered for channel equalization. In [24,25], the authors proposed pulse position modulation (PPM) based FrFT-OFDM for UWA channels. The suggested technique has a low BER performance and does not utilize the scarce bandwidth, thus resulting in a poor bandwidth efficiency when compared to the ASK FrFT-OFDM system proposed in this paper. Also, even though the ra to eliminate the multipath effect was complex, the system suffered from inter-symbol-interference (ISI). In [26], we investigated the performance of the FrFT-OFDM system for UWA communications with QAM modulation. This article proposes a novel FrFT-ASK-OFDM system for frequency selective underwater acoustic (UWA) channels. The proposed system has the potential to greatly enhance the bit error rate (BER) performance as compared to conventional OFDM systems. This enhancement is traded for higher peak-average-power-ratio (PAPR). It also compares the BER performance of the proposed system to the conventional OFDM systems with \(M\)-ary QAM modulation in UWA channels. Moreover, it studies the effect of CFO on the BER performance in UWA channels. The remainder of this article is organized as follows. An overview of conventional OFDM systems with ASK modulation is given in Section 2. The FrFT is briefly introduced in Section 3. A summary of the UWA channel is given in Section 4. Section 5 introduces the proposed system model. Then, Section 6 provides the simulation results and performance evaluation of the \(M\)-ary ASK FrFT-OFDM system under the effect of CFO over the UWA channel. Finally, conclusions and future work are given in Section 7.
## 5 Proof of Concept  
A system prototype was developed and tested on an emulated satellite network in CloudLab [25]. The prototype uses Open vSwitch (OVS) to emulate the gateway handover with the intelligent agent-based routing methods implemented as a VNF. The implementation is based on the Ryu SDN framework [26]. For testing purposes, the user requests are modeled as REpresentational State Transfer (REST).
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 3 Proposed Method
### Piecewise Soft-Margin Triplet Loss Function
If the cosine similarity is adopted to measure the similarity of cross-view images, the formula of loss function presented in Equation (7) will be modified as follows:  
\[L=\left\{ \begin{array}{ll}\log(1+e^{\kappa(d_{neg}-d_{pos})})&(d_{neg}-d_{pos})< M\\ \log(1+e^{\kappa(d_{neg}-d_{pos})}+e^{-\beta d_{pos}})&\text{otherwise}\end{array} \right. \tag{8}\]  
where \(\alpha\), \(\beta\) and threshold \(M\) are the hyper-parameters.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 3 Data Preparation and Models Setup
### Evaluation Metrics  
This subsection presents the performance metrics used to evaluate and compare the performance of the proposed ML-based path loss models. The performance metrics adopted are (1) R-squared (or \(R^{2}\)). (2) Root mean squared error (RMSE). (3) Mean absolute percentage error (MAPE). (4) Mean square error (MSE). (5) Correlation (Corr) coefficient. The main reason for choosing these five well-known metrics is to compare our results withthe values obtained by other researchers for similar and different environments since most works in the literature adopt these metrics. The performance metrics are expressed as [7]:  
\[R^{2}=1-\frac{\sum_{i=1}^{Q}(PL_{i}-P\hat{L}_{i})^{2}}{\sum_{i=1}^{Q}(PL_{i}- \overline{PL})^{2}}, \tag{1}\]  
\[\text{RMSE}=\sqrt{\frac{1}{Q}\sum_{i=1}^{Q}(PL_{i}-P\hat{L}_{i})^{2}}, \tag{2}\]  
\[\text{MAPE}=\frac{1}{Q}\sum_{i=1}^{Q}\left|\frac{PL_{i}-P\hat{L}_{i}}{PL_{i}} \right|, \tag{3}\]  
\[\text{MSE}=\frac{1}{Q}\sum_{i=1}^{Q}(PL_{i}-P\hat{L}_{i})^{2}, \tag{4}\]  
\[\text{Corr}=\frac{\sum_{i=1}^{Q}(PL_{i}-\overline{PL})(P\hat{L}_{i}-\overline{ PL})}{\sqrt{\sum_{i=1}^{Q}(PL_{i}-\overline{PL})^{2}}\sqrt{\sum_{i=1}^{Q}(P \hat{L}_{i}-\overline{PL})^{2}}}, \tag{5}\]  
where \(Q\) is the total number of samples used for the calculation of the performance metrics, \(PL\) is the empirical path loss value, \(\hat{PL}\) is the predicted path loss value, and \(\overline{PL}\) and \(\overline{PL}\) are the mean values of \(PL\) and \(\hat{PL}\), respectively.
# A Low-Cost Contactless Overhead Micrometer Surface Scanner
## 3 Method
### Image Registration  
We call a _map_ the imaged surface, in pixel coordinates, in the coordinate frame of the mosaic to be created. The input to image registration is the proprioceptive estimates of the camera centers and the established point correspondences across laterally or medially adjacent images. The output is a set of projective homography transforms \(H_{i}\), estimated for each image \(I_{i}\), where \(i\) enumerates the images across all pyramid layers. These homographies associate image locations in each \(I_{i}\) to the corresponding locations in the mosaic.  
World points \(\mathbf{C}_{i}\) are the proprioceptively obtained coordinates for these locations in 3D space. Image points \(\mathbf{c}_{i}\) are the image centers of images \(I_{i}\). Initially, projective homography \(H_{g}\) is estimated across this map and the 3D grid locations \(\mathbf{C}_{i}\), using least-squares.  
For each pair of adjacent images \(I_{i}\) and \(I_{j}\), we enumerate the correspondences between them using \(k\) and denote their locations in \(I_{i}\) and \(I_{j}\) as \(\mathbf{f}_{ki}\), \(\mathbf{f}_{kj}\), respectively. The computation estimates the homographies by optimizing the following objective function:  
\[\sum_{i}\sum_{j}(H_{i}\mathbf{f}_{ki}-H_{j}\mathbf{f}_{kj})^{2}+\sum_{i}(H_{g} \mathbf{C}_{i}-H_{i}\mathbf{c}_{i})^{2}. \tag{1}\]  
The first term is the conventional reprojection error metric for point correspondences. In that term, \(j\) enumerates the neighbors of \(I_{i}\). The second term promotes compliance with the scanner coordinates. In Figure 5, the notation is illustrated.  
The projective homography has 8 free variables and, thus, the optimized variables are 8 times the number of images. The optimization capitalizes on the adjacency information  
Figure 5: Illustration of objective function notation.  
contained in the pyramid data structure, to create a topological graph, as the one in Figure 1 (right). This graph has points \(\mathbf{C}_{i}\) as nodes and, as vertices, their adjacency relations. These relations constrain the search space of the optimization. We employed the work in [21], which is a framework for least-squares optimization of an error function that can be represented by a graph and has been specifically designed for SLAM or bundle adjustment problems.  
An additional benefit of using the aforementioned graph-based method is the robustness to \"missing estimates\". Such a case was encountered in Section 3.2, where we discarded unreliable homography estimates. Another is the case where the apex of the pyramid cannot be reached by the hardware. The latter case is encountered when covering wider areas, using multiple, laterally overlapping pyramids. There is no special treatment for running the method in this way. The difference is the lack of the cues that would have been provided by images acquired from a larger distance.