esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Framework (Fed-DTB)  
Building on the trust-based and adaptive concepts discussed in previous sections, we introduce Fed-DTB, a Federated, Dynamic Trust-Based framework designed to enhance the security, reliability, and efficiency of federated learning in IoV networks. The core idea behind Fed-DTB is to be a first line of defence by integrating multiple trust factors into the vehicular selection process and FL training, ensuring that only the most trustworthy and contextually suitable vehicles influence the global model. Fed-DTB consists of three main components:  
1. A trust management system that evaluates the trustworthiness of each vehicle by integrating a diverse range of factors, including network status, vehicle state, energy consumption, compliance with traffic rules, contributions to the federated learning process, user feedback, and IDS alarms.
2. Using computed trust scores, our framework implements a trust-based vehicle selection mechanism that identifies and selects the most reliable vehicles for global model updates. Vehicles that do not meet the trustworthiness criteria or trigger IDS alerts are excluded.
3. A federated learning algorithm that allows the selected set of trusted vehicles to participate in training the shared global model [44; 45].  
The architecture of the Fed-DTB is depicted in Figure 2. Each vehicle gathers data from its sensors and trains an LM locally without disclosing raw data, thus preserving privacy. The trust management system for each vehicle calculates the overall trust score by evaluating various factors that capture the IoV's complexity and dynamism. This overall trust score, along with LM, is transmitted to the central server. The central server aggregates all trust scores, selects the most trustworthy vehicles for federated learning, and then integrates their contributions into the GM. The optimised GM is then shared back with all vehicles for subsequent rounds of local training and trust assessment [3]. This process allows the global model to adapt regularly to the evolving IoV scenario.
## 2 Materials and Methods
### Radionic Features Extraction  
The radiomic analysis was performed on the lesion exhibiting the highest SUV value (SUV\({}_{\text{max}}\) lesion) and on the largest lesion (VOL\({}_{\text{max}}\) lesion). First, the 0-80 SUV interval was discretized into 256 bins resulting in a 0.31 SUV fixed bin size. Fifty-three radiomic features (RFs) were extracted from each lesion ROI, including 2 Shape features, 14 first-order features (HISTO category), and 37 second-order texture features. In particular, texture features were obtained from gray level co-occurrence matrix (GLCM) (calculated considering both 1 and 2 voxels offset, referred to as GLCM_1 and GLCM_2, respectively), gray level run length matrix (GLRM), neighboring gray level dependence matrix (NGLDM) and gray level zone length matrix (GLZLM). The full list of extracted RFs is reported in Table 1.  
Clinical and PET imaging data possibly affecting the PET image texture and the RFs value were collected: glucose level, injected activity/body weight, time between injection and acquisition, scanner model, frame duration. Then, after RFs log transformation, the reproducibility of RFs was assessed by fitting an ANOVA model for each RF and each clinical/imaging parameter. False discovery rate (FDR) correction was applied to account for multiple comparisons. RFs with FDR-adjusted \(p\)-values \(<\) 0.05 were excluded from the analysis.
## 2 Materials and Methods
### Transport Measurements  
Electrical transport measurements were carried out in a helium cryostat (Quantum Design PPMS, Quantum Design Inc., San Diego, CA, USA) with a minimum temperature of 2 K. IV characteristics were measured using a combined delta-mode setup consisting of a Keithley-6221 current source and Keithley-2182 Nano-voltmeter (Keithley Inst., Cleveland, OH, USA). The measurement configuration is illustrated in Figure 1c. While the current was passed through the junction, the voltage was measured simultaneously across it (VSC-O-SC) and on the top electrode (VSC), in order to probe the state of the SC during junction activation. To minimize the effect of Joule heating, devices were measured in a pulsed  
Figure 1: Device structure and measurement configuration. (**a**) Schematic of the SC-O-SC structure. Materials’ order and thicknesses are marked. (**b**) Cross-sectional TEM focusing on the interface of MEH-PPV, Al, and NbN layers (scale bar = 10 nm). (**c**) Configuration of the contacts for applying current and measuring voltage simultaneously along the top superconductor and through the organic layer of the device.  
delta-mode scheme [24]: current was supplied and voltage was measured during a 2 ms interval, followed by a 100 ms off-time before the next current-pulse.
###### Abstract  
As an integral part of a machine, gears are subject to wear, which is influenced by a number of factors. For polymer gears in particular, the uncertainties due to wear are high. These uncertainties outweigh the advantages of polymer gears, such as lower inertia. Improved condition monitoring, for example, with better data acquisition, could reduce these uncertainties and is therefore of great interest. This study addresses the challenges of condition monitoring in polymer gears by investigating the integration of sensors directly onto the gears for improved sensitivity. A compact sensor module mounted on a polymer gear is presented to demonstrate the benefits of integrated sensors. The research compares the effectiveness of integrated acceleration and temperature sensors with state of the art external methods. The results show that the in situ sensor module (ISM) provides reliable measurements for condition monitoring with integrated sensors. A comparative analysis with methods based on the current state of research highlights the increased sensitivity of condition monitoring based on the ISM acceleration sensors compared to traditional bearing block sensors. This increased sensitivity shows a clear advantage of integrated sensors over established methods. The temperature curve of the integrated sensors is sensitive to abrasive wear and gear failure, indicating the wider potential of integrated temperature sensors. In conclusion, this research lays the foundation for advanced condition monitoring using integrated sensors in polymer gears. The knowledge gained contributes to optimising gear applications, promoting cost-effectiveness and aligning with the principles of the Internet of Things and Industry 4.0.  
in situ; condition; vibration; analysis; smart gear; thermal behavior; damage +
Footnote †: journal: Applied Sciences
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
###### Abstract  
Although several years have passed since its first introduction, the significance of Delay-Tolerant Networking (DTN) remains evident, particularly in challenging environments where traditional networks face operational limitations such as disrupted communication or high latency. This survey paper aims to explore the diverse array of applications where DTN technologies have proven successful, with a focus on emerging and novel application paradigms. In particular, we focus on the contributions of DTN in the Future Internet, including its contribution to space applications, smart cities and the Internet of Things, but also to underwater communications. We also discuss its potential to be used jointly with information-centric networks to change the internet communication paradigm in the future.  
DTN; ICN/NDN; IoT/smart cities; space; underwater applications; protocols +
Footnote †: journal:  
_Future Internet_**2024**, 16, 129. [[https://doi.org/10.3390/616040129](https://doi.org/10.3390/616040129)]([https://doi.org/10.3390/616040129](https://doi.org/10.3390/616040129))  
Footnote †: journal:
# Effectiveness of LRB in Curved Bridge Isolation:
## 4 Effect of Incidence Angle  
The earthquake's incident angle (\(\alpha\)) is an important consideration because the bridge is of a curved configuration and the contribution of responses from both horizontal directions is vital for the bearing design. This study considered the key response parameters, such as deck acceleration, bearing displacement, force transmitted to the pier, and pier torsional moment.  
Figure 7a-l depict the bridge response to the uni-directional ground motion for the Imperial Valley, Kobe, and Turkey earthquakes, with incident angles ranging from 0\({}^{\circ}\) to 90\({}^{\circ}\). It was observed that the variations of response parameters with respect to the earthquake incidence angle were non-linear. For all the considered ground motions, the change in bearing displacement was small (up to 5%). The maximum deck acceleration is decreased for all the motions, especially between 45\({}^{\circ}\) and 67.5\({}^{\circ}\). The maximum reduction of deck acceleration was observed for Kobe ground motion (about 13.16%). The variation of force transmitted to the pier was small (0-4%) for the Imperial Valley and Kobe earthquakes. In the case of the Turkey earthquake, however, the force transmitted to the pier was reduced by about 8%. The variation of the pier torsional moment was almost negligible in the Imperial Valley earthquake, whereas the variations were different for other ground motions. In the case of the Kobe earthquake, the maximum reduction of pier torsional moment (about 33%) was observed for 45\({}^{\circ}\) incidence angle, while for 90\({}^{\circ}\) incidence angle, the variations were almost insignificant. In the case of Turkey's earthquake, the maximum reduction of pier torsional moment (about 14%) was observed for higher incidence angles of 67.5\({}^{\circ}\)-90\({}^{\circ}\).  
Figure 6: Response spectrum of Imperial Valley (IV), Kobe, and Turkey ground motions.  
Figure 8a-l depict the bridge response for bi-directional Imperial Valley, Kobe, and Turkey earthquakes at various incident angles ranging from 0\({}^{\circ}\) to 90\({}^{\circ}\).  
In the case of bi-directional motion, the variation of deck acceleration was almost insensitive (0-3%) for the Imperial Valley and Turkey earthquakes. In contrast, for the Kobe earthquake, the same was increased by about 11% at an incidence angle of 90\({}^{\circ}\). The variation of bearing displacement and force transmitted to the pier was minimal (0-3%) in all the earthquake motions.  
Pier torsional moment variation increases with the increase in incidence angle for all the considered ground motions, with the maximum value occurring at an incidence angle of 90\({}^{\circ}\). However, in all cases, the pier torsional moment values are insignificant.  
Figure 7: The bridge responses for uni-directional Imperial Valley (**a–d**), Kobe (**e–h**), and Turkey (**i–l**) Ground Motions with the variation of incidence angle.
# Remote-Controlled Method with Force and Visual Assists Based on Time to Collision for Mobile Robot
## 2 Modeling
### Camera Coordinate Transformation  
To draw trajectories on the monitor, the 3D environmental information is collected by a camera equipped on the mobile robot. The coordinates transformed from the camera coordinates to the monitor coordinates are used for the visual assist.  
Figure 4 shows an image of the coordinate transformation from the camera coordinates to the monitor coordinates. Superscripts \({}^{C}\)\(\bigcirc\) and \({}^{M}\bigcirc\) are the mean values in the camera and monitor coordinates. The origin of the camera coordinate system \(\Sigma_{C}\) is defined as the position of the visual sensor on the mobile robot. The Y- and Z-axis directions in the camera coordinate system are defined as vertically downward and the camera focal direction from the origin in \(\Sigma_{C}\). The origin of the monitor coordinate system \(\Sigma_{M}\) is defined as the upper left edge point of the monitor.  
The coordinate transformation from the camera coordinate system to the monitor coordinate system is achieved by multiplying the intrinsic parameters [40]. The intrinsic parameters \(K\) are expressed as follows.  
\[K=\begin{bmatrix}k_{x}&0&MU_{c}\\ 0&k_{y}&MV_{c}\\ 0&0&1\end{bmatrix}\begin{bmatrix}I_{f}&0&0\\ 0&I_{f}&0\\ 0&0&1\end{bmatrix} \tag{12}\]  
where \(k_{x}\) and \(k_{y}\) are the lens distortions for each axis. Therefore, this coordinate transformation is calculated by multiplying the intrinsic parameters as follows:  
\[\begin{bmatrix}M_{u}\\ M_{v}\\ 1\end{bmatrix}=K\begin{bmatrix}C_{x}/C_{z}\\ C_{y}/C_{z}\\ 1\end{bmatrix} \tag{13}\]
## 3 Results
### Technology Access and Use by Older Persons in the Arab Region  
The most prominent of the technologies identified in our review were smartphones [2; 6; 9; 32; 33; 34]. Three papers reported on smartwatches [4; 5; 7]. The other three papers studied ambient assistive living (AAL) [8; 34]. Older persons reported using smartphones for calling purposes, paying bills, and online banking applications and e-government services [2]. WhatsApp and Snapchat, two popular social media applications in the Arab region, were used to document the aging experiences of older persons [9; 32]. These studies also spotted an increased daily use of these social media applications for communication and information sharing among older persons [9; 32]. A range of smartphone functions and applications, including mHealth, were reported in five out of 11 studies [2; 6; 9; 32; 33]. mHealth technologies are mobile devices (e.g., smartphones and tablets) and software integrated with mobile medical services and healthcare systems via wireless devices or used for health self-care and management. As the focus of this paper is on ICT use by older persons and their family members and caregivers, only health self-care and management technologies were considered and analyzed. Six of the reviewed studies reported the use of mHealth applications with supportive functions [4; 5; 7; 32; 33], including medication management [32] and information recall, such as remembering personal details and familiar people the older persons meet in their daily living [4], supporting caregivers by tracking older persons' status (e.g., heart rate) at any time and place through communication between a smartwatch and the older person's smartphone [4; 7]. AAL involves technical systems that enable and support older persons in their activities of daily living to promote independence and a healthy lifestyle [35]. Our findings highlight that AAL was used to remotely monitor older persons who might have emergencies and alert caregivers when needed [5; 7]. Other remote technologies studied were smart homes features and live or recorded surveillance cameras [34]. More than half of the studies pertain to developing new technologies that are not yet in the market [2; 4; 5; 8; 32; 33]. Among these technologies, we used the technology readiness level (TRL) [36], to identify the different stages of development of the technologies studied in the papers mentioned above. According to TRL, there are nine technology readiness levels, with one being the least ready and nine being prepared for use in real-life conditions [36]. Three out of the 11 studies in our corpus [3; 7; 34] introduced technologies classified as 'developed technology.' Six out of 11 papers focused on technologies classified as 'technology under development' [2; 4; 5; 8; 32; 33]. Of the included studies, six studies introduced technologies that are either being validated in the lab with a small set of users (TRL 4) or at the prototype level, where the technology is being tested in the wild with real users in their authentic settings (TRL 7) [2; 4; 5; 7; 32; 33].
# A Numerical Approach for the Efficient Concept Design of Laser-Based Hybrid Joints
## 2 Method and Models
### Automated Generation of Microstructure Concepts
Various clustering algorithms such as k-means, agglomerative clustering, and DBSCAN were tested in terms of computational time, robustness, and ability to generate laser manufacturable microstructure concepts. The k-means algorithm showed the best and most robust results since large contiguous areas which can be well manufactured are usually identified. For k-means the number of clusters must be specified manually. The optimum number of clusters depends on the size of the joining zone, the possible microstructures, and the laser production parameters and is difficult to predict in advance. Preliminary investigations show that a cluster number of 2-5 delivers useful results for small components (\(\sim\)200-500 mm\({}^{2}\)). For each cluster mean values of theta, phi, and von Mises were calculated. To each cluster, a microstructure (distance, number of runs, etc.) was then assigned. The optimum choice, however, was realized via the optimization algorithm (see Section 3.2). Finally, the microstructures were drawn as lines in the clusters, with each line representing a cavity. The cavities were aligned perpendicular to the mean phi angle of each cluster and the areas were filled with line structures at the specified distance. The final result of the design method is comprehensive information about each element (phi, theta, and von Mises, coordinates), each cluster (mean values, microstructure), and the positioning of microstructures over the joining zone (position and length of lines on the joining zone). The cohesive zone model for the strength estimation was developed based on the cluster information. Based on the cavity lines, the production time and costs can be determined (see subsequent sections).
## Experimental Setup
### Performance Metrics  
In this paper, we target the recognition performance of mobile AR frameworks since recognizing objects is the most essential feature of the AR frameworks. The recognition performance can be affected by multiple factors, such as distance, viewing direction, and occlusion. Thus, we use the following four performance metrics to compare the performance of the selected AR frameworks: the maximum recognizable distance, the minimum recognizable viewing angle, the maximum recognizable occlusion, and the maximum number of targets for simultaneous recognition and tracking. First, the maximum recognizable distance refers to from how far the target object is recognized. The longer the distance, the better the performance. Second, the minimum recognizable viewing angle is used to assess the recognition performance when looking at the object obliquely from an angle other than the front of the object (90 degrees). The closer the minimum recognizable viewing angle is to 0 degrees, the better the recognition performance. Third, the maximum recognizable occlusion is the ratio of occluded area acceptable to recognize the object while some part of the object is not visible. The greater the ratio, the better the performance. Fourth, the maximum number of targets for simultaneous recognition and tracking refers to how many objects can be recognized and tracked at the same time. The larger the number, the better the performance.
## 3 Results
### Impact on Personal Evacuation  
#### 3.4.1 Impact on Available Safe Egress Time  
Based on the distribution of temperature, visibility, and CO concentration, the available safe egress time (ASET) was determined when any factor reached a hazardous level. In the case of a tunnel fire, personal safety is primarily threatened by the following three factors: the temperature rise in the fire source area, the decrease in visibility due to smoke diffusion, and the dispersion of toxic gases. These three factors combine to the hazardous range gradually. A comparative analysis reveals that temperature is the key hazardous factor contributing to disaster in the core area of a tunnel fire. At approximately 120 s, temperatures enter hazardous levels within the core area. When a fire occurs in a highway tunnel, the longitudinal wind velocity would be accelerated over critical velocity [21]. Therefore, the following analysis is based on the results of the critical velocity group.  
As discussed above, when a fire occurs in a curved tunnel, the distribution of high-temperature smoke on the inner and outer sides of the tunnel presents a certain difference. Figure 11 shows the differences between the ASET on the inside and outside of the tunnel. From left to right and from top to bottom, the corresponding curvature radii of each tunnel are 250 m, 400 m, 600 m, 800 m, 1000 m, 1200 m, 1500 m, and 2000 m.  
As shown in Figure 11, regardless of the radius of curvature, the inner side of the tunnel has more available safe egress time than the outer side within the core area of the fire and an area distance of 150 m from the fire source. Within a distance range of 75-150 m from the fire source, as previously mentioned, when high-temperature smoke impacts the outer tunnel wall and reflects towards the inner wall, it gives rise to a localized weak zone of high-temperature smoke through successive reflections. However, it is important to note that the weak region of the temperature distribution near the core zone of the fire source, as depicted in Section 3.2.1 when high-temperature smoke impacts the opposite side of the tunnel, will still transition into the hazardous range of human visibility at an early stage.
## 5 Results
### Computational Costs  
The computational costs of the antialiased Lockhart wavefolder model were measured by porting the algorithms into C code using the 128-bit _long double_ data type. Table 5 shows the computation times for a 1-s 100-Hz sinusoidal input processed using the proposed model for different peak amplitude values. These results were computed by averaging the processing times of one hundred implementations. All tests were performed under identical circumstances, using a fixed resistance value of \(R_{\mathrm{L}}=50\) k\(\Omega\), the highest recommended value. From these results we can observe that the complexity of the model does not depend on the input and that the overhead of implementing the antialiasing method is minimal. When operating at audio rate, the added computation time is approximately 1 ms for a 1-second simulation. Moreover, these time measurements show that the antialiased Lockhart model, implemented at a sample rate \(F_{\mathrm{s}}=88.2\) kHz, is approximately 3.5 times faster than oversampling by a factor of 8 (i.e., \(F_{\mathrm{s}}=352.8\) kHz) and nearly twice as fast as oversampling by a factor of 4. Changing the value of \(R_{\mathrm{L}}\) did not affect the execution times of the algorithms.  
Figure 15: Measured A-weighted noise-to-mask ratios (ANMRs) for a range of sinusoidal waveforms processed: (**a**) using the [PERSON] wavefolder model (\(R_{\mathrm{L}}=50\) k\(\Omega\)) under six different sampling rates; and (**b**) using the Serge wavefolder model under two different sampling rates, with and without the proposed antialiasing method. Values below the \(-10\) dB threshold indicate lack of perceivable aliasing.  
For high values of \(R_{\mathrm{L}}\), long double representation is necessary to account for the large values that will result at the argument of the Lambert-W function. For smaller values, 64-bit precision will be sufficient to accommodate most input levels of interest. For instance, when \(R_{\mathrm{L}}=7.5\,\mathrm{k}\Omega\) the signal at the input of the proposed Lockhart wavefolder model can have a peak amplitude of up to 9 V.  
The measurements in Table 5 were conducted by synthesizing all input signals at the target rates. In practical implementations, oversampling will require additional pre- and post-filtering stages that will further increase the computational costs of the system. The complexity and costs of these filtering stages will be directly proportional to the required oversampling factor. This constitutes another advantage of the proposed antialasing method.