esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Unbalanced Disc in Off-Center Position (Rotor025 un)
Each subplot on Figure 25 shows the relationship between quantiles (2.5%, 25%, 50%, 75%, and 97.5%) on the x-axis and the corresponding displacement in millimeters on the y-axis. Both configurations show similar trends across all positions, indicating consistent behavior. Rotor05 un generally exhibits larger displacements compared to Rotor025 un, particularly at higher quantiles. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rotor Configuration**} & \multirow{2}{*}{**Cross-Section and Position**} & \multicolumn{5}{c}{**Quantile**} \\ \cline{3-6}  & & **2.5 [\%]** & **25 [\%]** & **50 [\%]** & **75 [\%]** & **97.5 [\%]** \\ \hline \multirow{4}{*}{Rotor05 un} & A–A (horizontal) Left & \(-\)2.5781 & \(-\)2.4677 & \(-\)2.4152 & \(-\)2.3597 & \(-\)2.2495 \\  & A–A (horizontal) Right & 0.7926 & 0.9022 & 0.9581 & 1.01222 & 1.1244 \\  & B–B (vertical) Lower & \(-\)2.6144 & \(-\)2.5105 & \(-\)2.4553 & \(-\)2.4007 & \(-\)2.2904 \\  & B–B (vertical) Upper & 0.7576 & 0.8663 & 0.9219 & 0.9766 & 1.0806 \\ \hline \multirow{4}{*}{Rotor025 un} & A–A (horizontal) Left & \(-\)2.5200 & \(-\)2.4010 & \(-\)2.3285 & \(-\)2.2818 & \(-\)2.1843 \\  & A–A (horizontal) Right & 0.7321 & 0.8291 & 0.8770 & 0.9504 & 1.0725 \\ \cline{1-1}  & B–B (vertical) Lower & \(-\)2.5637 & \(-\)2.4415 & \(-\)2.3699 & \(-\)2.3212 & \(-\)2.2217 \\ \cline{1-1}  & B–B (vertical) Upper & 0.6837 & 0.7849 & 0.8330 & 0.9065 & 1.0273 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantiles of shaft orbital positions [mm] for Rotor05 un and Rotor025 un configurations across horizontal (A–A) and vertical (B–B) cross-sections. Figure 23: Boxplots of shaft orbital positions for Rotor05 un configuration: (**a**) Horizontal cross-section (A–A); (**b**) Vertical cross-section (B–B). Figure 24: Boxplots of shaft orbital positions for Rotor025 un configuration: (**a**) horizontal cross-section (A–A); (**b**) vertical cross-section (B–B). Quantile analysis of orbital behavior helps establish more accurate operational limits. For example, the 97.5% quantile values could be used to set vibration alarm thresholds, ensuring safe operation while minimizing false alarms.
# Evolution towards Smart and Software-Defined Internet of Things
## 2 IoT Classification and Applications
### IoT for Smart Waste Management  
Waste management models based on the IoT play a significant role in improving the quality of life and improving human well-being by increasing energy efficiency, improving governance, and lowering costs. Due to unprecedented population increase, increasing waste generation has become a significant challenge in developing countries.  
Many issues have been investigated in the literature and found to have a strong correlation with the high waste material and hardships associated with managing it in a smart city, according to the findings. These problems are the result of insufficient collection and disposal mechanisms for waste material, an increasing trend in the urban population, and a lack of smart technology to assist in the operation of the municipal solid waste management system. As a result, due to the large amount of waste that has accumulated everywhere, waste management has become a difficult task.  
[PERSON] et al. in [49] devise an IoT architecture for smart garbage waste management system aiming at increasing mobility of endpoints and enhancing communication all the while optimizing cost and energy by using resource-friendly protocols and sensors. The architecture addresses the IoT requirements of reliability, mobility, service continuity, user convenience, and energy efficiency. As a result, the operational costs are reduced as well as the overall food wastage. However, this model is faced with challenges. As the smart bins are mobile and battery-powered, enhancing the battery life while keeping it economical is a challenge for the system. Additionally, even though operational costs have been reduced by this model, unless the maintenance costs are minimized, the cost of the overall project is not going to be optimized.
## 5 Methodology, Experiment Design, and Data Analysis
### Data Analysis
For matching the ridge-based features, we used correlation-based matching, where the alignment of the stored template and the new fingerprint are fed into the system and the correlation is computed. For the creation of the micro-movement behavior template, all micro-movement behavior, during and after a fingerprint is provided, is fed into a One Class Support Vector Machine (OCSVM) to create the class. For matching the micro-movement behavior, we used the OCSVM to evaluate if the new behavior matches the class of the stored behavior template. If the ridge-based features match the submitted fingerprint, the matching of the micro-movement behavior of the fingerprint is accomplished if it passes a pre-defined threshold, thus authenticating the user's identity and providing access. We concluded with four datasets:  
1. A legitimate user providing their fingerprint. 2. A legitimate user providing their Play-Doh fingerprint. 3. An attacker providing the legitimate user's Play-Doh fingerprint. 4. A legitimate user unwillingly providing their fingerprint. All of the datasets are in four different conditions:  
1. Dry surface, dry skin. 2. Dry surface, wet skin. 3. Wet surface, dry skin. 4. Wet surface, wet skin. A total of 16 datasets were created, after being analyzed by the MFAS, to achieve the objectives of this research work and support the hypotheses. The next section provides the results obtained. In summary, the MFAS system enhances all temporal images as they are collected and then extracts the six features in all images. The MFAS then matches the newly acquired micro-behavioral features with those already stored and returns the results. Figure 12 depicts the overall MFAS data analysis as a fully automated fingerprint-based biometric system from the start of fingerprint placement until a decision of access is granted or denied. Figure 11: Fingerprint contour detection over time.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 6 Results
### Mptcp Rtt
It is worth noting that when wVegas is used with the simulator, the RTT difference of the subflows measured is nearly similar to the testbed, as observed in Figure 14. This is the reason behind the close match observed in wVegas throughput performance in the simulator and testbed, as shown in Figure 8. The other algorithms, especially Cubic, demonstrate a relatively larger RTT difference between the LTE and Wi-Fi subflows when compared to the testbed. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline
**RTT (ms)** & \multicolumn{4}{c}{**OLIA**} & \multicolumn{4}{c}{**LIA**} & \multicolumn{4}{c}{**Cubic**} & \multicolumn{4}{c}{**Wvegas**} \\ \hline  & **TB** & **Sim** & **TB** & **Sim** & **TB** & **Sim** & **TB** & **Sim** & **TB** & **Sim** & **TB** & **Sim** & **TB** & **Sim** & **TB** & **Sim** \\  & **LTE** & **LTE** & **Wi-Fi** & **Wi-Fi** & **LTE** & **LTE** & **Wi-Fi** & **Wi-Fi** & **LTE** & **LTE** & **Wi-Fi** & **Wi-Fi** & **LTE** & **LTE** & **Wi-Fi** & **Wi-Fi** & **Wi-Fi** \\
**Average** & 2.94 & 1.35 & 2.50 & 1.87 & 3.07 & 1.316 & 1.741 & 2.72 & 2.93 & 1.240 & 2.86 & 1.35 & 2.86 & 3.50 & 4.31 & 1.96 \\
**Std** & 0.36 & 0.29 & 0.24 & 0.15 & 0.51 & 0.10 & 0.29 & 0.51 & 0.26 & 0.04 & 0.47 & 0.09 & 0.49 & 0.63 & 0.27 & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Setup 1 uplink RTT for 75 MB flows. Figure 13: Setup 2 uplink MPTCP RTT for 20 MB flows. In brief, the difference between subflows' RTT values measured by the simulator and testbed with long-lived flows (75 MB) is significantly smaller than their counterparts with short flows (20 MB).
###### Abstract  
An efficient direction of arrival estimation method is proposed. The proposed algorithm accurately estimates the instantaneous frequency of signals received by multiple sensors (array of sensors/antennas). The estimated instantaneous frequency is then used to separate sources and estimate their direction of arrivals. Experimental results indicate that the proposed method achieves better performance than the existing methods both in terms of computational requirements and localization accuracy. It is also shown that the proposed method can work in under-determined situations.  
amplitude modulation-frequency modulation (AM-FM); crossing components; direction of arrival estimation; instantaneous frequency; time-frequency +
Footnote †: journal: Applied sciences  
1
## 5 Dynamic Pressure Control Technology for a Roadway's Surrounding Rock
### Support Mechanism of a Combined Bracket  
The overall active and passive support technology, comprising an anchor cable and channel steel beam-CFST column combined bracket, brings together anchor bolts and cables as the active support technology and a combined bracket as the passive support technology. When implementing this technology, the coordination between the anchoring stiffness of the anchor cables in the deep surrounding rock and the surface support stiffness of the adjacent rock enhances the overall stability of the supported surrounding rock. The combined bracket consisting of a channel steel beam and CFST columns is rectangular in shape, as depicted in Figure 12. The bottom of the CFST column is inserted into the roadway floor to a depth of about 300 mm and fixed with quick-drying cement.  
Rationally arranging anchor cables in key support areas, such as the roof and sides of the roadway, can enhance the structural stability and load-bearing capacity of the combined bracket, enabling it to effectively withstand substantial deformation caused by the surrounding rock. In turn, the combined bracket can provide better surface protection components for anchor cable supports and improve the anchoring efficacy of the anchor cables. Simultaneously, due to the pressure-giving performance of the combined support system, it can allow a certain degree of deformation in the surrounding rock during the initial stage of mining impact. In this way, the rock mass releases deformation energy, making the contact between the surrounding rock and the support more uniform.  
Figure 11: Distribution of rock surrounding the roadway.  
Figure 12: Schematic diagram of the combined bracket.  
Research indicates that enhancing the confining pressure exerted on the fractured rock mass surrounding the roadway can lead to a substantial enhancement in the residual strength of the surrounding rock and an increase in the load-bearing capacity of the superficial rock mass within the roadway [31]. As such, the overall active and passive support technology, comprising an anchor cable and channel steel beam-CFST column combined bracket, can enhance the stress conditions of the surrounding rock in roadways, boost its residual strength, and work together with the rock to withstand mining impacts.
# Assessing Voided Reinforced Concrete by Numerical Modelling of Impact-Generated Rayleigh Waves
## 2 Simulation Model
### Material Properties and Damping Coefficients  
The material properties needed to define a medium in wave propagation problems are the density, Young's modulus, Poisson's ratio and damping-related coefficients. The material properties are shown in Table 2.  
For RC with small stress intensity (no void), the damping ratio for concrete, \(\xi\),concrete, is between 0.007 and 0.010. For steel reinforcement, the damping ratio, \(\xi\)steel, is between 0.001 and 0.002 ([PERSON], 1995) [23]. Based on the values suggested, the initial damping ratios were assumed. Through further calibration with the experimental data, the final damping ratios used by the model were 0.010 for the concrete and 0.002 for the reinforcing steel. The mass and stiffness damping coefficients were calculated using Equations (4) and (5). The presence of angular frequency, \(\omega\), in the equations suggests that the damping depends on the wave frequency. The variations in the damping coefficients for concrete and steel are shown in Figure 5.  
\[\xi=1/2\omega\ \alpha\rightarrow\alpha=2\omega\xi, \tag{4}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Concrete Properties** & \\ \hline Density (kg m\({}^{-3}\)) & 2313 \\ Young’s modulus (Pa) & \(28\times 10^{9}\) \\ Poisson’s ratio & 0.2 \\ \hline
**Steel properties** & \\ \hline Density (kg m\({}^{-3}\)) & 7850 \\ Young’s modulus (Pa) & 200 \(\times\) 10\({}^{9}\) \\ Poisson’s ratio & 0.3 \\ \hline
**Air properties** & \\ \hline Density (kg m\({}^{-3}\)) & 1.225 \\ Bulk modulus (Pa) & 142,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties.  
Figure 4: The meshing of distributed voids.  
\[\xi=1/2\;\omega\beta\rightarrow\beta=2\xi/\omega \tag{5}\]  
where \(\alpha\) and \(\beta\) are the mass and stiffness damping coefficients, respectively.
# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 3 Our Method
### Problem Description  
The difficulty of RGB-IR Re-ID is related to the spatial misalignment associated with differences in person posture, lighting, and viewing angle conversion; additionally, image modality misalignment can be associated with variations in imaging principles. In this paper, \(V=\{v_{i}|i=1,2,\ldots,N_{1}\}\) represents the RGB image data set, \(T=\{t_{i}|i=1,2,\ldots,N_{2}\}\) represents the IR image data set, and \(Y=\{y_{i}\}\) represents the identity labels. \(D=\{D_{train},D_{test}\}\) represents a multimodality dataset, where \(D_{train}=\{V_{train},T_{train}\}\) represents a training set and \(D_{test}=\{V_{test},T_{test}\}\) represents a test set. RGB-IR Re-IDaims to automatically learn a feature extraction function \(f(.)\) that can convert the input RGB image into a feature vector \(f_{ij}^{v}\). Similarly, the input IR image can be converted into a feature vector \(f_{i}^{t}\). The superscripts \(v\) and \({}^{t}\) represent different modalities, and the subscript \({}_{i}\) represents the index of the current image. The core step in solving RGB-IR Re-ID problem is to obtain feature representations that are as similar as possible for similar features corresponding to different modalities.
## 4 Software Defined Networking (SDN)
### SDN Controllers Design  
SDN controllers can be implemented in different deployment models, such as in a centralized or distributed manner, to address different performance requirements. A centralized SDN controller, unlike a distributed one in which each device has a limited knowledge of the network, has comprehensive information of the network topology, traffic flows and the switches load. In other words, it can monitor and manage the entire network. The centralized SDN controller remains connected either in-band or out-of-band to the forwarding elements and defines optimal paths for traffic flows in the network.  
In a distributed deployment model, the controllers can be placed vertically, horizontally or as a hybrid (i.e., as peers vertically and horizontally). The design gets more complex when the controllers support both IP and SDN switches. These different designs provide various levels of scalability, consistency, reliability and security.
## 2 Description of the Experiment
### Description of LIF/Mie Droplet Sizing  
The SMD of an ensemble of the spherical droplets in sprays can be calculated from the ratio of their LIF/Mie optical signals simultaneously measured from the same probe location [11; 24; 31]. The method assumes that the fluorescence from the liquid phase, \(I_{\rm LIF}\), is proportional to the droplet volume, \(d^{3}\), and the Mie scattered light intensity, \(I_{Mie}\), from the same droplet is proportional to its surface, \(d^{2}\), [32; 33; 34]. Apart from this assumption, three important conditions must be respected. First, the technique is only valid for spherical droplets and it does not withstand for non-spherical liquid structures and ligaments [24; 31]. Second, the fluorescence from the gas phase must be excluded [35]. Third, all photons reaching the camera should have experienced only one scattering event prior to detection [16]. If those conditions are respected, the resulting intensity ratio, \(R_{LIF/Mie}\), is then proportional to SMD of droplets as:  
\[R_{LIF/Mie}\ =\ \frac{I_{LIF}}{I_{Mie}}\ =\ \frac{K_{LIF}}{K_{Mie}}\frac{ \sum_{i=\ 0}^{n}N_{i}d_{i}^{3}}{\sum_{i=\ 0}^{n}N_{i}d_{i}^{2}}\ \ =\ K \cdot SMD \tag{3}\]  
where \(K_{LIF}\) and \(K_{Mie}\) are coefficients related to dye concentration, laser power, signal collection angle, detector response, and scattering efficiency, etc. Here \(K\) defines the calibration curve which is equal to the ratio \(K_{LIF}/K_{Mie}\). The calibration curve can be either deduced by using the calibration of LIF/Mie ratio with the absolute SMD measured by the PDA system [21; 22] or calibrating the ratio by means of monodisperse droplet generated by a droplet generator [24; 36].
## 1 Introduction
### LEO Satellites for IoT  
In recent few years, Low Earth Orbit (LEO) satellite communication for IoT became viewed as a realizable and powerful supplement to terrestrial LPWAN networks [5]. Today, in Europe, two operators offer IoT satellite connectivity in sub-GHz bands--Kineis [6], Lacuna Space [7]. Both have deployed their first satellites in orbit, and offer customers demo modes for testing and development.  
Kineis Company, created in 2019, has eight ARGOS satellite systems [8] in orbit and 20 ground stations. The company plans to launch 25 nanosatellites by 2023. The predicted average revisit time, by achieving a complete constellation, is less than 15 min [9]. Kineis devices use the Argos Receiver Transmitter with Integrated Control (ARTIC) chipset. To communicate with the actual constellation, two private frequency bands 401.5 and 466 MHz are used for uplink and downlink, respectively [10; 11]. Upcoming satellites will use 401.5 MHz for both uplink and downlink. The ARTIC chipset is able to perform many modulation types compatible with ARGOS satellite systems [10]. With the Kineis solution, we can send up to 30 bytes per message; geolocation feature is also available with 150 m accuracy. Kineis launched an ARGOS nanosatellite (ANGELS [12]) at the end of last year, which uses a VLD-A4 modulation. This modulation type, compared with the other modulation techniques, reduces the effective radiated power needed so that the transmitted signals can be detected by Kineis satellites down to 100 mW [9].  
Lacuna Space Company, founded in 2017, has four satellites in orbit [13], with plans to launch two satellites by the end of 2021. Lacuna Space developed two new satellite series LS-2/3 with double-performance and two new features compared to the LS-1 series. The added new features are geo-localization and downlink. The Lacuna Space satellites use the same modulation techniques used for LoRaWAN; they can be considered an extension of the terrestrial LoRaWAN network. Thus, the actual LoRaWAN modules available on the market can be used to send messages to Lacuna satellites, by adding an extra code and using an antenna with good performance [14; 15; 16].
# An Investigation on Base Metal Block Shear Strength of Ferritic Stainless Steel Welded Connection
## 4 Strength Comparison with Design Prediction and Recommendation
### Stress Analysis for Extended Variables
#### 4.2.3 Strength Equation Proposal  
In Table 5, block shear fracture strength equation (Equation (4)) by [PERSON] provided the closest estimates, and the values were overestimated as the weld length (\(L\)) increased in the direction perpendicular to the load. Based on the results obtained from the finite element analysis, the tensile stress factor of 1.25 was modified to 1.36, in reference to [PERSON]'s equation (4), as a tensile stress 1.36 times the tensile strength (\(\sigma u\)) of ferritic stainless steel material in the tensile section of the block shear fracture was generated by the stress triaxiality effect of the welded connection from Table 6 and Figure 10b. It can be noted that suggested tensile stress, 1.36, is similar to that (=1.35) of austenitic stainless steel welded connection by [PERSON] et al. [13], and the block shear fracture critical section in this study is identical to that of Equations (4) and (6) by [PERSON] [14] and [PERSON] et al. [13]. Furthermore, the shear stress factor (\(S\!f\)) was modified to the average shear stress (\(\sigma_{u}+\sigma_{y})/2\sqrt{3}\)) of the material in the shear section based on the rightmost mean value (=0.97) of Table 7. Consequently, the following block shear fracture strength equation for the base metal block shear fracture ferritic stainless steel (STS 430) welded connection was proposed:  
\[P_{utp}=1.36A_{gt}\sigma_{u}+\frac{\sigma_{u}+\sigma_{y}}{2\sqrt{3}}A_{gv} \tag{7}\]  
In order to verify the validity of the proposed block shear fracture strength equation (Equation (7)) with modified tensile stress factor considering the stress triaxiality effect and stress distribution of the critical shear section at the ferritic stainless steel welded connection, Table 8 summarizes the predicted strength (\(P_{utp}\)) by Equation (7), test ultimate strength (\(P_{ue}\)) and the analytical ultimate strength (\(P_{uu}\)). The average strength ratios (\(P_{utp}/P_{ue}\), \(P_{utp}/P_{uu}\)) to the predicted strength (\(P_{utp}\)) by proposed equation for the test strength (\(P_{utp}\)) and analytical strength (\(P_{uu}\)) was 0.96 and 0.94, respectively, and the proposed equation provided improved (more conservative) evaluations than Equations (4) and (6) by [PERSON] and [PERSON] el al., which are the block shear fracture equations for the carbon steel welded connection and austenitic stainless steel welded connection, respectively.  
Figure 11: Shear stress distributions along the shear plane (Path 4).