esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Edge Artificial Intelligence
### Federated Learning (FL)  
An innovative framework conceived by Google researchers, called Federated Learning (FL), enables data holders to collaboratively train an ML model without the need to share their local datasets. This approach effectively mitigates key challenges related to communication overhead, data privacy, and legal compliance. The FL training process involves five stages, beginning with a selection of an appropriate ML model to be hosted on an edge-based centralized FL server. Next, the server deploys client selection algorithms, such as Federated Client Selection (FedCS), to randomly choose a subset of available clients [121]. Subsequently, the current global model is distributed among the selected clients for local training. These clients train the model on their local datasets and retransmit the updated model parameters back to the edge servers. The centrally located edge servers then aggregate the updated parameters using techniques like Federated Averaging (FedAvg) to yield an improved global model [122].  
FL is typically classified into three categories: horizontal FL, which involves clients with similar feature spaces but different data samples; vertical FL, which includes clients with common data samples but different feature sets; and federated transfer learning, which involves clients with datasets that differ in both sample space and feature space [48]. Let \(\text{N}=\{1,2,\ldots,K\}\) denote a set of K clients having dataset \(D_{k\in K}\) each. The aggregate weight of the models at the client site is determined by the ratio of the size of the data set used for training the models to the size of the entire training data set and is given by Equation (17) [123]:  
\[w_{t}^{G}=\sum_{k=1}^{K}\frac{|D_{k}|}{|D|}w_{t}^{k} \tag{17}\]Here, \(w_{t}^{k}\) and \(w_{t}^{G}\) are the local models of client k and the global model, respectively, in the \(t^{th}\) training round. \(D_{k}\) is the dataset at client k, and D is the entire set of training data on all K clients given by \(D_{1}=\cup_{k=1}^{K}D_{k}\).  
FL intends to learn the model under the edge devices' storage and limited processing capability constraints, periodically update the model parameters, and communicate them to edge or cloud servers. Thus, the goal of FL is to minimize the objective function or average training loss given by Equation (18) [124],  
\[w^{\min}F(w),F(w)=\sum k=1^{m}p_{k}F_{k}(w) \tag{18}\]  
where \(m\) is the total number of devices participating in training, \(p_{k}\) specifies the relative weight of influence attributed to each device, and \(F_{k}(w)\) in Equation (19) is the local objective function of the \(k\)th device.  
\[F_{k}(w)=\frac{1}{n_{k}}\sum_{i=1}^{n_{k}}f_{i}(w,x_{i},y_{i}) \tag{19}\]  
where \(n_{k}\) is the data volume of the \(k\)th device, and \(f_{i}(w,x_{i},y_{i})\) is the loss function of the model with the parameter \(w\) on the instance (xi, yi) in the \(k\)th device-local dataset. The optimization process within FL focuses on minimizing the value associated with the local loss function.
# R-PreNet: Deraining Network Based on Image Background Prior
## 3 Proposed Work
### Residue Channel Prior (RCP)  
The appearance of rain streaks is commonly modeled as a linear combination of the background and rain streak layers [14; 20; 22; 34]. Based on this model, [PERSON] et al. [8] demonstrated that subtracting the minimum color channel from the maximum color channel produces a rain-free image. Rain streaks are colorless (white or grey) and appear at the same location in different RGB color channels. As such, subtracting the minimum color channel from the maximum one nullifies the presence of rain streaks, as in Figure 3.  
The colored-image intensity of a rainy image is defined [8] as:  
\[\overset{\sim}{I}(x)=\tau\,\rho_{rs}(x)L\sigma+(T-\tau)B\pi \tag{2}\]  
where \(\mathbf{L}=\left(L_{r},L_{g},\,L_{b}\right)^{T}\) is the color vector of luminance and \(\mathbf{B}=\left(B_{r},\,B_{g},\,B_{b}\right)^{T}\) is the color vector of background reflection.  
\[L=L_{r}+L_{g}+L_{b},\quad B=B_{r}+B_{g}+B_{b} \tag{3}\]  
In the model (Equation (2)), the first term represents the rain streak component, while the second term denotes the background component. \(\sigma=\mathbf{L}/\,L\) and \(\pi=\mathbf{B}/\,B\) define the chromaticities of \(\mathbf{L}\) and \(\mathbf{B}\). \(T\) represents the exposure time, while \(\tau\) denotes the time taken by a raindrop to pass through pixel \(x\). \(\rho_{rs}\) consists of the refraction coefficients of the raindrop, the specular reflection coefficients, and the internal reflection coefficients. The assumption is made that \(\rho_{rs}\) is wavelength-independent, implying that raindrops are colorless.  
As a consequence, it becomes necessary to cancel the light chromaticity \(\sigma\) in the rain-streak term in Equation (2) to generate a residual channel without rain streaks. To achieve this, any existing color constancy algorithm [35] is employed to estimate \(\sigma\), and then apply the following normalization step to the input image:  
\[I(x)=\frac{\overset{\sim}{I}(x)}{\sigma}=I_{rs}(x)\mathbf{i}+\mathbf{I}_{bg}(x) \tag{4}\]  
Figure 3: RCP extraction module.  
where \(\mathbf{i}=(1\,,1,\,1)^{T}\), \(I_{rs}=\tau\rho_{rs}L,\mathbf{I}_{bg}=(T-\tau)\mathbf{B}/\sigma\).  
Vector division is done element-wise. It should be noted that upon normalizing the image, not only is the luminance of light eliminated, but the color effects of spectral sensitivity are also removed. Hence, according to the previous equation and a rainy image \(\mathbf{I}\), the residual channel is defined as:  
\[I_{res}(x)=I^{M}(x)-I^{m}(x) \tag{5}\]  
where:  
\[I^{M}(x)=\max\bigl{\{}I_{r}(x),I_{g}(x),I_{b}(x)\,\bigr{\}} \tag{6}\]  
\[I^{m}(x)=\min\bigl{\{}I_{r}(x),I_{g}(x),I_{b}(x)\,\bigr{\}} \tag{7}\]  
\(I_{res}\) is the residual channel of the image \(\mathbf{I}\), which has no rain streaks.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
###### Abstract  
The field of indoor localization is fast developing and has important ramifications for a number of areas, such as smart infrastructure development, healthcare settings, industrial automation, and military operations. Advances in a range of technologies, each suited to certain use cases and objectives, have been fueled by the capacity to precisely locate objects or people inside places. Prominent indoor localization technologies like Bluetooth, Wi-Fi, ultra-wideband (UWB), ZigBee, and RFID-based systems are examined in this review, along with hybrid solutions that combine several technologies to get around their individual drawbacks and enhance system performance. The field still faces several obstacles in spite of these developments. Widespread acceptance is hampered by persistent problems such as signal interference, high energy consumption, and restricted scalability. The deployment of these systems is further complicated by elements like cost-effectiveness, privacy issues, and compatibility in a variety of situations. This study also examines potential avenues for future research to improve the precision, dependability, and versatility of indoor localization technology in order to overcome these obstacles. Designing systems with increased resilience to environmental changes, utilizing edge computing for real-time processing, and integrating artificial intelligence for predictive modeling are all promising areas of emphasis. This study attempts to help academics and practitioners navigate the changing terrain of indoor localization by offering a comprehensive picture of the field's present status and future directions.  
indoor localization; hybrid localization techniques; real-time locating systems; wireless positioning; short-range IoT 202515154  
[PERSON], [PERSON], [PERSON] A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors. _Appl. Sci._**2025**, 15, 1544. [[https://doi.org/10.3390/app15031544](https://doi.org/10.3390/app15031544)]([https://doi.org/10.3390/app15031544](https://doi.org/10.3390/app15031544))  
1
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## Materials and Methods
### Long-Term Climate Data from NOAA  
In order to study the sensitivity of the phenological processes to changes in air temperature (\({}^{\circ}\)C) and precipitation (mm) at monthly and inter-annual scale, we obtained long-term air temperature and precipitation data (1939-2000) from NOAA's National Centers for Environmental Information (NCEI) (station ID: CAMILLA 3 SE, GA US; [47]), using it as a baseline for the calculation of air temperature and precipitation anomalies for the study landscape (Appendix A, Figure A1).
## 2 K-Nearest Neighbor Entropy Estimation  
The K-NN estimate of the differential entropy of a \(d\)-dimensional random variable \(Y\) with unspecified density function \(f(y)\) is defined as [45; 46]:  
\[H(Y)=\phi(N)-\phi(K)+\log(C_{d})+\frac{d}{N}\sum_{i=1}^{N}\log(e_{i}^{K}) \tag{1}\]  
where \(N\) is the number of samples of the random variable \(Y\) and \(\phi(x)\) is the digamma function expressed as follows [46]:  
\[\phi(x)=\frac{1}{\Gamma(x)}\frac{d\Gamma(x)}{dx} \tag{2}\]  
where \(\Gamma(x)\) is mathematically defined as \(\Gamma(x)=\int_{0}^{\infty}e^{-t}tx^{x-1}dt\).  
Here, \(e_{i}^{K}\) is the distance between the sample \(y_{i}\) of random variable \(Y\) and its \(K\) nearest neighbors in \(d\)-dimensional space.  
Figure 1: Block diagram of the proposed epileptic EEG classification method.  
\(C_{d}\) denotes the volume of a \(d\)-dimensional unit ball. For the Euclidean norm, \(C_{d}\) is defined as [45]:  
\[C_{d}=\frac{\pi^{\frac{d}{2}}}{\Gamma(1+\frac{d}{2})} \tag{3}\]  
In order to study the estimation of K-NN entropy for a highly random signal in the presence of a dominant low-frequency component with different weights, we have studied the similar example used to estimate the sample entropy in [37]. It has been found that K-NN entropy estimation is highly affected by the low-frequency signal component of the analyzed signal. Even though the analyzed signal is highly random, the K-NN entropy estimates the value closer to the K-NN entropy estimate of the dominant low-frequency component present in that analyzed signal.  
Figure 2 shows the K-NN entropy estimates with varying nearest neighbors for dominant low-frequency component signal \(x_{1}(t)\), highly random signal \(x_{2}(t)\) (Gaussian noise) and their weighted sums expressed as \(x_{3}(t;w)=wx_{1}(t)+x_{2}(t)\). It is clear from the figure that though \(x_{3}(t;w)\) is highly random in nature, the K-NN entropy estimate of \(x_{3}(t;w)\) (with increased value of the weight parameter \(w\)) is closer to the K-NN entropy estimate of \(x_{1}(t)\). Therefore, the conventional measure of K-NN entropy may not be well suitable for physiological signals where the dominance of low-frequency trends is high. Therefore, a multilevel filtering approach prior to the computation of entropy is necessary for the discriminant analysis of biomedical signals. In the next section, we propose the multilevel filtering concept using TQWT for the computation of multi-scale K-NN entropy.
## 4 Software System
### Three-Dimensional Modeling of the Lasted Upper
#### 4.1.1 Image Acquisition  
The scanning process begins when the main control system instructs the scanning module to initiate image acquisition. The shoe last transfer platform then clamps and moves the lasted upper, during which a trigger generation module synchronizes the camera captures with the platform's motion. Upon reaching the end position, the system signals the scanning module to complete the process.  
Figure 9: Both robotic arms operate in parallel to spray the left and right lasted uppers.  
To accurately define the adhesive application areas--located on the bottom and the curved sidewalls of the lasted upper--three scanning setups are employed, as shown in Figure 5, to capture views from the left, right, and bottom. This configuration enables the system to generate a complete 2.5D model of the lasted upper in a single scanning cycle, covering both the side and bottom surfaces. A sample image captured during this process is shown in Figure 10a.  
The innovation of our work lies in the integration of three laser-line triangulation modules mounted at different angles, which enables the precise and robust 3D reconstruction of complex curved lasted uppers under challenging industrial conditions. This multi-angle configuration minimizes occlusions and blind spots and allows us to capture the full bonding profile required for high-accuracy robotic adhesive spraying.  
When constructing a 3D model using the three laser triangulation systems, a common reference plane must be established to unify the height measurements. As shown in Figure 10b, the first reference plane--defined as the X-Z plane with a height value of 0 mm--is located between the left and right laser setups. Height values from the left and right scans are assigned negative and positive signs according to the direction of the \(Y\)-axis in the figure. Additionally, a second reference plane, the X-Y plane, is defined perpendicular to the left-right baseline to serve as the 0 mm reference for the top-view laser. This dual-plane calibration allows all three systems to perform height measurements within a unified coordinate framework.  
After establishing the reference planes, the laser-line image data must be converted into height information using geometric triangulation principles. This process involves addressing two key challenges. The first is that the laser-line images lack sufficient resolution due to the thickness of the laser stripe. The second is that the displacement of the laser line is not linearly proportional to the actual height values. The following sections detail the methods employed to overcome these issues.
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 5 Inversion Analysis of Characteristic Parameter
When the number of parameters to invert in the structural model is larger than the number of observations, the stability of the inversion analysis solution will be low, and it will be difficult to meet the requirements of construction applications. Meanwhile, the arrangement of observation points and the number of observation data also affect the accuracy and precision of parameter identification.
# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
## 3 Pre-Data Analysis
### Two Types of Discontinuation  
In this section, we discuss the differences between the two types of discontinuation based on the analysis in Section 3.2. We also describe how the differences can be handled effectively using our framework.  
The short-term discontinuation is labeled _cut-out_. In digital advertising, a best practice is to create various patterns of ad creatives and look for effective ones by serving them. Clearly, it is not easy to find an effective pattern, and thus, many ad creatives are discontinued in the short-term. As Figure 1 shows, many ad creatives are discontinued early on, but this selection process is conducted manually. Thus, it is a burden on the operation loads. In fact, automation of the process has significant cost advantages (i.e., both time and money) from a business perspective.  
In contrast, the discontinuation of an ad creative after it has been served for a long-term period is called _wear-out_[16]. Wear-out occurs when the ad creatives are overexposed to customers and response drops. The discontinuation of long-running ad creatives damages sales; however, the wear-out of ads is inevitable. Thus, ad operators are tasked with creating new compelling ad creatives for their replacement. Ad operators prioritize this task by estimating wear-out, which heavily depends on the operator's experience. Thus, by predicting wear-out, we expect that the sales loss could be minimized. In terms of ad operations, predicting wear-out has a different role from predicting cut-out.  
In short, cut-out occurs when the user is not interested, and wear-out occurs when the user becomes fatigued. The operations involved in each type of discontinuation also differ. In the case of a cut-out, the operator discontinues many inefficient ad creatives rapidly, whereas in the case of a wear-out, the operator estimates when an ad creative's performance degrades. It is important that our framework predicts both types of discontinuation. Predicting cut-out (for the short term) can save human effort and enable efficient advertising operations. Predicting wear-out (for the long term) can have a significant impact on business revenue.  
The properties of these two types of discontinuation are so different that they should be considered as two different tasks. Therefore, we employ a two-term estimation technique  
Figure 2: Histogram of ad creative sales in descending order. The \(y\)-axis is log-scaled. Most ad creatives are included in the first bin. Because the sales data are confidential, the actual values cannot be disclosed. Thus, relative values are used here.  
(as described in Section 4.3). Additionally, we analyze two real-world cases to evaluate the availability of each type of discontinuation (as described in Section 6).
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 2 Unmanned Aerial and Surface Vehicle Networks: Applications and Advancement in Their Optimisation
### Architectures for Unmanned Vehicles Networks (UVNs)  
The communication between UAVs may be classified as follows:  
1. UV communication based on a shore control centre. This is a centralised architecture where direct communication between vehicles is not possible. Moreover, due to the position of the surface vehicles and due to the specific characteristics of the regions where they are deployed, i.e., rich vegetation, as in deltas, this configuration may not be recommended because the entire architecture is dependent on the ground control centre. If the ground control centre communication fails or is not in Line-of-Sight (LoS) conditions, the entire network will be compromised.
2. UV communication based on cellular networks. It eliminates the constraints related to the coverage area and UVs' mobility. Unfortunately, there are areas where cellularbase stations are not deployed. Additionally, there are high costs associated with the deployment of new base stations, and operators are discouraged from installing new equipment. Moreover, customers must subscribe for all the UVs connected to the cellular network.
3. UV communication based on satellite constellations. This solution benefits from global coverage, especially in hard-to-reach regions where a cellular network cannot be deployed. Due to disadvantages such as high latency, high energy consumption, the antenna's dimensions, subscriptions and contract issues or because both the UV and the shore control centre must be in the LoS of a satellite, this solution may not be a good candidate for UV networks.
4. UV communication based on ad hoc networks. This solution allows for the cooperation between UVs, especially by using mesh topology. It may also allow communications with the devices on the shore, thus resulting in the monitoring missions having a higher success rate. In addition, this topology enables cooperation between nodes, as each node can act as a relay [42]. Unlike the solutions mentioned above, the implementation of this network ensures a large coverage area. In addition, it is decentralised, thus eliminating the dependence of a central node. Yet, decentralised communication may result in challenges such as limited communication bandwidth, delays and failures [43].
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 4 Experiment and Results
### Passenger Number Monitoring Results  
We tested the passenger counting algorithm by installing it on a vehicle. We used smartphone 1 in this experiment because it had the highest performance. The infrared sensors were installed at all doors of the vehicle to monitor the passengers entering through all doors. We allowed passengers to enter and leave the vehicle through all doors to monitor the change in the number of passengers. The experiment was repeated 50 times, in which the number of passengers within the legal limit was repeated 25 times, and the number of violating cases was repeated 25 times. The proposed systems (i.e., edge and fog layers) were compared with the system in [17] (i.e., edge layer alone), and the results are reported in Table 5.  
At the traffic monitoring station, the accuracy was also the same, which means that the edge, fog, and cloud layers communicate well and no errors arose in the algorithms in these layers.
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 1 Introduction
CRT provides additional security, for example, in selecting a set of relatively prime numbers \(\mathcal{E}=\{P_{1},\,P_{2},\,\ldots\,Pr\}\), a large integer K can be represented by a set of smaller integers \(\{k_{1},\,k_{2}\,\ldots\,k_{n}\}\). It is extremely difficult to get back the K value without the knowledge of \(\mathcal{E}\). This fact provides additional security. Further, CRT is based on simultaneous congruence of modular arithmetic and computationally efficient. This motivates the authors to combine listless SPIHT with CRT techniques in a blind watermarking scheme to provide added security and robustness against compression attacks. To the best of the authors' knowledge there is no such work proposed in the literature that combines listless SPIHT with CRT in blind watermarking schemes. The proposed method exhibits superior performance in terms of PSNR (peak signal to noise ratio)/structural similarity (SSIM) and tamper assessment function (TAF) over most of the recently proposed schemes in literature. Good TAF (in %) and Normalized Correlation (NC) values are also obtained for most of the common attacks such as cropping, tampering, noise addition, brightening, sharpening and compression. Attacks such as scaling, rotation, filtering and geometric transformation are not considered in this paper. The rest of the paper is organized as follows: Related works are presented in Section 2. Section 3 discusses the implementation of embedding and the extraction aspect of the proposed scheme. Comparison results are presented in Section 4. Finally, in Section 5 the conclusion and future research direction have been made.
## 4 Methods
### SAR Data and Interferometric Processing
To restrict the analysis over high-quality measurements points, interferometric processing was performed, setting the amplitude dispersion for PS detection to a value of 0.35. We avoided the definition of a local reference area for the PSI processing. This is a preferable solution to minimize potential influence by unknown deformation signal, especially when no relevant information on the stability of the areas is available a priori. Thus, each solution was referenced to the average motion of the entire area (mean velocity is set to zero). The above decision does not affect the relative motion between neighboring points or the observed temporal patterns in the time series; rather, it only affects the absolute velocity of the point measurements. SNAPPING PSI measurements are provided in the satellite Line-of-Sight (LoS) direction. Positive values correspond to uplift or motion towards the satellite, whereas negative ones correspond to subsidence or motion away from the satellite platform. A detailed description of the SNAPPING service and the processing steps involved are described elsewhere [122]. Finally, a decomposition scheme was applied [123] to retrieve vertical and horizontal E-W motion components combining ascending and descending SNAPPING measurements. Initial steps involved the rasterization of point measurements into a common 100 m grid, assigning the median displacement rate value when multiple points are identified within the raster resolution cell.