esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
The signals characteristic of each class create the reference databases capable of identifying a destructive process, e.g., \"cracking at the concrete-reinforcement interface\" corresponds to the database that groups selected signal parameters assigned to the specific process. Databases for individual processes (or their groups) are determined on the samples of materials, on models used in special laboratory tests (where a given destructive process or a group of processes predominates) and on full-size structural elements during strength tests, attenuation tests, and during normal operation of the facility. The database of AE reference signals makes it possible to identify active destructive processes over the entire element under test. Long-term measurements can be used to determine the damage under true loading conditions, with the external factors such as rain, frost, or wind taken into account. Appropriate installation of AE sensors allows the measurement of the entire volume of the examined element and the location of the emission source (the point of damage). To build the reference signal database with the Recognition of Destructive Processes (RPD) method, the NOESIS 5.8 program was used with hierarchical and non-hierarchical statistical grouping methods and neural networks. For the RPD database, two versions of the pattern recognition method were used: with arbitrary division into classes--unsupervised pattern recognition (USPR) and self-learning, in which the division into classes was carried out using the reference signals--supervised pattern recognition (SPR). In the first case, analysis of arbitrary patterns is mainly used for creating a reference signal database if the number of classes is unknown. The second method is applied when model signals characterizing the destructive processes are available. The reference signals are the signals previously collected in databases generated during independent experiments. While the USPR method classifies AE sources based on the similarity of signals without assigning appropriate mechanisms to the groups, the SPR method assigns specific processes to the groups, provided they have a base of reference signals. Creating a reference signal database is organized in several stages. These are:  
* generating signals in the laboratory while destroying specially designed specimens (bars, cubes, rolls) in a specific way,
* comparison of signals received from the specimens with the signals generated during the destruction of model beams (reinforced concrete and pre-tensioned concrete),
* verification of reference signals based on the monitoring results obtained for various types and lengths of prestressed concrete girders loaded to failure,
* final verification of selected elements of the bridge during its normal operation.
## 2 Materials and Methods
### Statistics  
IBM SPSS Statistics v.26 software (IBM Corporation, Armonk, NY, USA) was used to analyze the data. A statistical power analysis (software package GPower 3.0) prior to the study showed that a total number of 60 participants (30 participants per sex group) would yield adequate power (>0.85) and a level of significance (<0.05). Additionally, the Kolmogorov-Smirnov test was used to examine the normal distribution of the data (all variables followed the normal distribution). Independent samples \(t\)-tests were used to investigate possible differences in cervical range of motion, maximal strength, and musculoskeletal pains between males and females, while paired \(t\)-tests were used to examine possible differences in range of motion and strength values between right and left sides. Furthermore, bivariate correlation analyses were used to examine the associations among neck pain, cervical strength, and range of motion as well as among cervical strength, range of motion, and anthropometric characteristics (body mass and body height). [PERSON]'s effect sizes were also calculated using the following equation: d = difference between means/pooled SD. The level of significance for all statistical analyses was set at \(p\) < 0.05 and the results are presented as mean \(\pm\) standard deviation.  
Figure 3: Cervical maximal isometric strength measurements: (**A**) forward flexion, (**B**) extension, and (**C**) lateral flexion.
# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 4 UAV-Enabled MEC and Assisted MEC Based on AI  
MEC is an effective method for overcoming time-constrained computing challenges in resource-constrained mobile IoT devices. The objective is to bring CC services closer to customers to minimize latency and backhaul network congestion. UAVs with storage units, processing, and communication capabilities can function as flying MEC servers at the IoT system's edge. In this context, resource-constrained IoT devices may offload their
## 2 Carbon Nanostructures as Templates for Titania Nanomorphologies
### Single-Walled Carbon Nanotubes (SWCNTs)  
The use of SWCNTs is less common than MWCNTs due to the latter being easier to handle and disperse and presenting lower costs of production. In one study, SWCNTs successfully templated the formation of titania through a sol-gel method, and subsequent calcination at 600 \({}^{\circ}\)C allowed for the crystallization into anatase/rutile phases onto the tubes and the formation of an aerogel, which was tested for photocatalysis. The addition of platinum nanoparticles as co-catalysts onto the tubes, prior to titania nucleation and growth, was also studied to enhance the activity thanks to a more efficient charge carrier separation [106].  
In another work, SWCNTs were first coated with a dendrofullerene (Figure 6) to provide suitable anchoring points for the nucleation and growth of titania while leading to good performance with regard to photo-catalytic H\({}_{2}\) evolution under visible-light irradiation. Co-axial inorganic-organic nanowires were thus obtained with a high photo-activity that was rationalized in terms of the electron-extracting TiO\({}_{2}\) layer accelerating the electron forward-transfer and the concomitant deceleration of the undesired back-transfer [107].
## 2 Derivation of 3D Array Factor
### Improvement of Pattern Degradation of Conformal Arrays through Phase Compensation
Figure 3: Comparison of 2D radiation patterns of \(12\times 24\) planar and conformal arrays: (**a**) \(\phi\) = 0\({}^{\circ}\) plane and (**b**) \(\phi\) = 90\({}^{\circ}\) plane. Assuming that the interval between elements of conformal arrays is 0.5\(\lambda\), \(p_{k}^{{}^{\prime}}\), \(P_{k}^{{}^{\prime\prime}}\) is expressed as follows:  
\[P_{k}^{{}^{\prime}}=(x_{k},0,z_{k}) \tag{3}\]  
Figure 2.0  
This is based on the corresponding coordinates \(P_{k}^{{}^{\prime\prime}}\) considering the phase difference, which is substituted into Equation (1) and calculated as follows:  
Figure 2.1  
Substituting \(\textit{phasedelay}_{k}\) into Equation (5) and rearranging \(z\), we obtain Equation (6). Figure 2.2  
As expressed in Equation (6), the array factor considering \(\textit{phasedelay}_{k}\) in the uniform amplitude feeding example for cylindrical conformal arrays is converted into a universal coordinate equation including the z-coordinate. In a case where y-axis coordinates do not exist, the variable for \(\phi\) is excluded because the value of \(\phi\) cannot be adjusted. For 3D expansion, we consider the y-axis coordinates and \(\phi\) in the array factor. Since the coordinates on the z-axis do not change with a change in \(\phi\), it maintains the form \((\text{zcos}(\theta))\), which changes only under the change of \(\theta\). The xy plane changes to a shape displayed on the UV plane \((\text{xsin}(\theta)\text{cos}(\phi)\), \(\text{ysin}(\theta)\text{sin}(\phi))\). If this is expanded with respect to point \(A_{i}\) on the 3D coordinate axis of the elements constituting an arbitrary array shape including the y-axis coordinate, \(A_{i}\) is expressed as follows:  
Figure 2.3  
\[ \begin{split} AF_{uniform}=\sum_{i=1}^{I}e^{|t|\frac{2\pi}{ \lambda}(x_{i}\sin(\theta)\cos(\phi))-\frac{2\pi}{\lambda}(x_{i}\sin(\theta_{ 0})\cos(\phi_{0}))]}\\ \times e^{|t|\frac{2\pi}{\lambda}(y_{i}\sin(\theta)\sin(\phi))- \frac{2\pi}{\lambda}(y_{i}\sin(\theta_{0})\sin(\phi_{0}))]}\times e^{|t|\frac {2\pi}{\lambda}(z_{i}\cos(\theta))-\frac{2\pi}{\lambda}(z_{i}\cos(\theta_{0} ))]}\end{split}  \tag{8}\]  
We propose an equation that can calculate the phase regardless of the grid format, curvature, and nonlinearity if the positions of all possible elements that can be specified on the 3D coordinate axis are located through the array factor for uniform amplitude feeding in Equation (8). Figure 5: Detailed locations of conformal array elements.
## 3 Discussion
### Monitoring and Displacements
The multidisciplinary approach resulted in the quantification of the relative GNSS velocity field, a new morphotectonic map, a morphotectonic evolution model of the study area, the slip rate of a segment of the westernmost of the two detected faults, a new active tectonic deformation in this region, and a new 3D modelling of two NNW-trending active faults. In the research [116], by employing static positioning and precise point positioning (PPP), 5-year-long time series of _UNIPA GNSS CORS_ were analysed, and a linear trend for all CORS time series was shown with its geodynamic implications. The last multidisciplinary research resulted in the integration of GNSS and InSAR (interferometric synthetic aperture radar) data [117]. Combined investigations discovered the potential seismogenic sources of destructive earthquakes documented in south-western Sicily. The approach was aimed at estimating the current deformation rate in south-western Sicily and enhancing knowledge about the seismic potential of this area. Besides proving the versatility of CORS on a wide range of topics, several tests on the accuracy reachable by GNSS were carried out.
## 1 Introduction
Indeed, the readiness potential (RP) is observed only before voluntary movements, not involuntary ones, and can therefore be used as a discriminator. As disadvantage, a daily use of EEGs can become tiresome for users. Other methods involve exploiting signal processing techniques, such as filters [12]. In individuals with motion disorders, including those with spinal cord injuries, the presence of involuntary movements can also be verified. One of the most common involuntary movements in SCI subjects is associated with sleep-related periodic leg movements [13]. Involuntary contractions are characterized by brief episodes of high intensity activity (e.g., muscle spasms) or by long-lasting motor unit activity at low firing rates. The spasms can affect both legs and hands [14]. Even in severe cases, the residual movements often exceed the number of variables needed to control devices such as powered wheelchairs or computers [15]. By leveragingthese movements to control assistive devices, users can regain functional independence, also offering several advantages in assistive technology, as users rely on existing motor skills rather than learning entirely new control paradigms. This approach reduces cognitive load and accelerates the adoption of assistive devices, leading to lower abandonment rates. Additionally, residual motions can also be used in a rehabilitative context to fasten physical rehabilitation using the preserved muscles, promoting neuromuscular recovery, supporting muscle tone, and preventing atrophy [15]. Despite their potential, using residual movements presents challenges, particularly the variability in motor function among individuals. This necessitates personalized calibration and adaptive algorithms for optimal control. Additionally, muscle fatigue and signal degradation over time can affect the long-term usability of these systems [16]. This study investigates a specific control approach for assistive robotic devices, outlined in [17], which leverages the user's residual motions. Although this control strategy has only recently been introduced and shows promise for individuals with various motor impairments, our focus is on adapting it for people with spinal cord injuries (SCIs). To achieve this, we first revisited the key aspects of the control algorithm and outline the modifications implemented to tailor it specifically to SCI users. The primary objective of our study is to conduct a statistical analysis involving 12 unimpaired subjects performing two tasks, in order to evaluate the system's accuracy, usability, and time performance before and after targeted training sessions. As a secondary goal, we aim to test the same experimental protocol in a pilot study with two SCI subjects. This will provide a broader perspective on the system's functionality with the intended population and assess whether they can complete the tasks within the desired maximum time. This final pilot study will assist us in refining the control strategy and training sessions for future investigations, specifically targeting the SCI population.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
###### Abstract  
Recent advancements in power electronic switches provide effective control and operational stability of power grid systems. Junction temperature is a crucial parameter of power-switching semiconductor devices, which needs monitoring to facilitate reliable operation and thermal control of power electronics circuits and ensure reliable performance. Over the years, various junction temperature measurement techniques have been developed, engaging both non-optical and optical-based methods, highlighting their advancements and challenges. This review focuses on several optical sensing-based junction temperature measuring techniques used for power-switching devices such as metal-oxide-semiconductor field-effect transistors (MOSFETs) and insulated-gate bipolar transistors (IGBTs). A comprehensive summary of recent developments in infrared camera (IRC), thermal sensitive optical parameter (TSOP), and fiber Bragg grating (FBG) temperature sensing techniques is provided, shedding light on their merits and challenges while providing a few possible future solutions. In addition, calibration methods and remedies for obtaining accurate measurements are discussed, thus providing better insight and directions for future research.  
junction temperature; electroluminescence; FBG; IGBT; SiC; MOSFET
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 4 Challenges and the Road Ahead
### AI/ML Techniques for FSO-Based NTNs
Comprehensive future research efforts are needed to address all of the above points and provide empirical evidence, simulation results, and case studies demonstrating the practical benefits and efficiency gains that AI/ML could bring in real-world scenarios. Conceptualization, M.E. and M.U. ; methodology, M.E. and M.U. ; software, M.E. and M.U. ; validation, M.E. and M.U. ; formal analysis, M.E. and M.U. ; investigation, M.E. and M.U. ; resources, M.E. and M.U. ; data curation, M.E. and M.U. ; writing--original draft preparation, M.E. and M.U. ; writing--review and editing, M.E. and M.U. ; visualization, M.E. and M.U. ; supervision, M.E. and M.U. ; project administration, M.E. and M.U. ; funding acquisition, M.U. All authors have read and agreed to the published version of the manuscript. This work was supported by the Turkish Scientific and Research Council under Grant 120E312. The statements made herein are solely the responsibility of the authors. Not applicable. **Data Availability Statement:** Research data from this study will be made available upon request by contacting the authors. The authors declare that this paper is completely original and free from any plagiarism or improper citation. This paper was carried out during the course of my study, research or work The authors declare no conflict of interest.
###### Abstract  
The Belt and Road Initiative (BRI) is the first and currently the most expansive global infrastructure initiative, notably for its scale and emphasis on connectivity. In response, alternative initiatives such as the Partnership for Global Infrastructure and Investment (PGII) and Free and Open Indo-Pacific Strategy (FOIP), including their components the Blue Dot Network (BDN) and Partnership for Quality Infrastructure (PQI), as well as Global Gateway (GG) and the Three Seas Initiative (3 SI), have emerged to counterbalance the BRI's influence and promote more transparent, sustainable, and rules-based infrastructure frameworks. This review investigates how global and regional infrastructure initiatives--namely PGII/BDN, GG, FOIP/PQI, and 3 SI--compare with the BRI in terms of development objectives, implementation models, institutional structures, and implications for developing economies. Adopting an inductive approach, this review identifies key themes from the literature to evaluate these initiatives across seven dimensions: (1) infrastructure objectives, (2) the quality and transparency of investments, (3) investment policy orientation, (4) trade policy orientation, (5) inclusivity and regional integration, (6) coordination mechanisms, and (7) environmental sustainability. While PGII/BDN, GG, FOIP/PQI, and 3 SI appear well-positioned to address some of BRI's shortcomings, the evidence does not clearly favour one model over another in terms of achieving welfare-enhancing outcomes and bridging development gaps. Nonetheless, strategic competition and complementarities among the connectivity policies of multiple initiatives can ultimately contribute to more accountable, multidimensionally sustainable, and socially inclusive infrastructure development. We also illustrate how stated preference methods, i.e., willingness to pay (WTP) and willingness to accept (WTA), can be used to quantify the value of soft infrastructure, particularly public preferences for sustainable investment and norm diffusion, which are central to evaluating the social welfare gains from participating in these initiatives.  
quality infrastructure; hard and soft infrastructure; economic development; official development assistance; private sector investment; public-private partnership; sustainability standards; governance; transparency; connectivity; commerciality; complementarity; bilateralism; multilateralism  
**JEL Classification:** F51; F52; P51; P52
## 3 The ALIEN Architecture
### Problem Formulation  
We formulate the problem of CTR prediction. Let \(\mathcal{U}\) denote a set of users and \(\mathcal{V}\) denote a set of items, where \(|\mathcal{U}|\) and \(|\mathcal{V}|\) are the total numbers of users and items, respectively. The user-item interaction matrix \(\Upsilon\in\mathbb{R}^{|\mathcal{U}|\times|\mathcal{V}|}\) is defined according to users' implicit feedback, where binary \(y_{uv}=1\) indicates that there is an implicit interaction between user \(u\) and item \(v\), e.g., behaviors of clicking, watching and browsing. Otherwise, there is no interaction between \(u\) and \(v\) when \(y_{uv}=0\). Take the example of user \(u\in\mathcal{U}\) and item \(v\in\mathcal{V}\). There are \(m\) and \(n\) fields of features in \(u\)'s and \(v\)'s attributes, respectively. \(u\)'s and \(v\)'s \(k\)-dimensional embeddings \(\mathbf{u}=[\mathbf{u}^{(1)}\ \mathbf{u}^{(2)}\...\ \mathbf{u}^{(m)}]\in\mathbb{R}^{m\times k}\) and \(\mathbf{v}=[\mathbf{v}^{(1)}\ \mathbf{v}^{(2)}\...\ \mathbf{v}^{(n)}]\in\mathbb{R}^{n\times k}\) denote the concatenations of \(u\)'s and \(v\)'s feature embeddings, where \(k\)-dimensional \(\mathbf{u}^{(i)}\) and \(\mathbf{v}^{(j)}\) are encoded from the i-th and j-th fields in \(u\)'s and \(v\)'s attributes, respectively. The procedure of embedding will be discussed in Section 3.3. The problem of click-through rate prediction is to predict the probability of \(u\) clicking the candidate item \(v_{c}\). Our goal is to make precise the CTR prediction by modeling \(u\) and \(v_{c}\)'s low and high-order feature interactions and \(u\)'s history interactions. \(u\)'s history interactions consist of \(u\)'s history items before he or she interacts with \(v_{c}\), and the embedding set of \(u\)'s history items is denoted as \(\mathbf{S}_{u}^{v_{c}}=\{\mathbf{v}_{1},\mathbf{v}_{2},...,\mathbf{v}_{|\mathbf{S}_{u}^{v_{c}}|}\}\).
## 1 Introduction
### Problem Setting and Initial Data  
Toward the goal of efficient analysis of roads, Reach-U Ltd., a company specializing in geographic information systems, location based solutions, and cartography, has developed a fast speed mobile mapping system employing six high resolution cameras for recording panoramic images of roads (Figure 1). The data, panoramic images and orthophotos assembled from the very same panoramic images using essentially a ground plane projection technique, collected by the system are further visually inspected, and detected deformations are localized and digitized. All this is carried out manually by experienced operators. The resulting information is used by the Estonian Road Administration via a web application called EyeVi.  
Compared to our previous work on the same topic [2], several changes were introduced. First of all, Reach-U Ltd. has recently upgraded from the Ladybug 5 360\({}^{\circ}\) Spherical Camera Imaging System to Ladybug 5+, which is equipped with Sony Preigius global shutter CMOS sensors that provide more consistent overall quality of acquired images across a wide range of lighting conditions. Secondly, we decided to focus on individual 4096 \(\times\) 4096 pixel orthoframes, the building blocks of the orthophotos that we employed in [2] to avoid some side-effects of the assembly process (e.g., inherent blurring of interpolated regions in the orthophotos). Finally, the orthoframes were manually redigitized for the defects by the authors of this paper because the digitization of defects carried out by experienced operators occasionally suffers from spatial inaccuracy and thus does not necessarily meet the demands of machine learning. The existing defect layer, however, was used as a template in the process of redigitization.  
The initial selection pool consisted of over 30,000 orthoframes from 14 defect-ridden roads covering about 100 kilometers that, incidentally, contained over 25,000 pavement defects. There are two companion files for each orthoframe, a mask setting the boundaries of the orthoframe and a.vrt file that supplements geographical data to the image. The defect information provided by operators appears in the form of.shp files. This is the initial data that must be processed in an appropriate manner so that information about road distress is most efficiently extracted from the images.
## 4 Detailed Proposed Technology and Test Results
### Automation Technology for Maintaining the Performance of AI Algorithms
#### 4.4.2 Automatic Data Acquisition Method for Re-Learning AI Algorithms  
It is difficult to implement the AI vision inspection system in the industrial field because the performance of the algorithm deteriorates due to changes in the inspection environment. When the algorithm performance degrades, it is essential to manage performance through algorithm re-learning immediately. However, it is difficult to maintain the performance of the algorithm owing to the high time and cost requirements incurred during the labeling task to transform the acquired data into learning data. Essentially, an automatic data labeling method is required to maintain algorithm performance for the implementation of AI technology in industrial sites. In this study, a classification score was used to automatically label the acquired data. To automatically secure the re-learning data, the cross entropy score value was set as high as possible in the Softmax step of the image classification process to ensure that it was OK, as shown in Figure 1. The OK data is automatically classified without operator intervention, and NG and NA, which require operator correction, can be labeled as data by clicking through a GUI that enables the operator to determine whether it is OK, NG, or NA.