esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Construction 4.0. BIM and Information Technologies
### BIM and IoT  
Advances in data transmission networks (Wi-Fi, 5G, ZigBee or LoRa) have enabled a wide increase in published studies to successfully implement the integration of IoT devices in the construction industry [75], especially through wireless networks such as LoRa (Long Range). This research studied how to complement BIM models, in terms of geometry and space organisation, with data sent from IoT devices in real time [49, 69, 89, 90].  
However, the integration of BIM and IoT was found to be more complex than expected, and most studies focus on exploring it theoretically or conceptually [91, 92]. The most advanced research recreates spaces inside buildings equipped with sensors, mainly for temperature, humidity, and gases. IoT devices can send information that is updated in the Digital Twin [93, 94]. Users can always access information through a custom API. Depending on the complexity of the platform and the programming experience of the research team, the information can be viewed by commercial APIs [29, 95], or own APIs, optimised for more advanced models with a higher number of sensors [96, 97, 98]. These data can even be used for fire prevention and control fire [31, 99, 100] or the management of prefabricated house construction [101]. In addition, the data and collected information can be hosted in SQL DB [65] and processed to facilitate the implementation of predictive maintenance to achieve a dynamic and automated management strategy [102].  
Linear infrastructure projects are left out of these studies, since studies related to BIM application integrated with IoT are scarce. It is even more remarkable how advances in other related industries such as transport are tending towards increasingly automated and connected vehicles, which are themselves a multitude of moving sensors connected to each other. In this sense, there are many publications focused on smart cities and roads. Nevertheless, they are not integrated with BIM methodology. BIM and IoT in transport infrastructure stand out for issues related to O&M [103], bridge maintenance [41, 104], the study of vibrations in bridges [21], monitoring in tunnel construction [105], urban services galleries [106], or the airport pavements management [107].
## 3 Materials and Methods
### Key Features  
A sophisticated communication network was deployed on a ship to enable seamless tracking and monitoring of cargo containers. Central to this setup is a radio base station operating on Band 28 (700 MHz), complemented by an NB-IoT core network. These  
Figure 4: 5G Global Tracking System overview.  
Figure 5: Block diagram of the 5 GT System showing the contribution of each partner.  
components are connected via satellite backhauling, ensuring robust connectivity even in remote maritime environments.  
Each container is equipped with a CTD that acts as a gateway between NB-IoT and BLE Mesh. This dual functionality ensures reliable communication, even when containers are densely packed, stacked in tight spaces, or placed in areas where cellular signals struggle to penetrate.  
A key innovation is the deployment of a 5G-specialized Satellite Platform designed with satellite communication protocols optimized for NB-IoT. This platform ensures that data transmission between the ship and the satellite network is both efficient and reliable. To further enhance this system, Ku-band frequencies are employed for transmitting and receiving data. This frequency band is particularly advantageous in mobile scenarios, as it offers a more cost-effective alternative to traditional L-band frequencies. The reduced cost of Ku-band bandwidth makes it attractive to shipping lines, helping lower operational expenses. Moreover, this shift is beneficial for satellite operators without L-band licenses, enabling them to enter the IoT market, which has historically been dominated by L-band users.  
The system leverages Eutelsat's GEO satellite constellations, known for their high service reliability. Compared to emerging LEO constellations, GEO satellites currently offer superior stability, making them an ideal choice for uninterrupted communication in maritime settings.  
To ensure seamless integration between the satellite and cellular network components, SDNs are implemented. This approach enhances flexibility, allowing satellite resources to be dynamically integrated into 5G networks, thus improving overall network efficiency.  
Finally, the system supports a range of tracking and monitoring applications for both onboard crew and shore-based stakeholders through a OneMZM-compliant platform. This standardized framework ensures interoperability between various IoT devices and networks, offering a cohesive user experience and enabling efficient cargo management across different environments.  
This comprehensive solution not only improves operational efficiency for shipping companies but also reduces costs and opens new business opportunities for satellite operators, making it a valuable technological advancement in maritime logistics.
## 4 Conclusions  
This paper has dealt with the state estimation problem for the target tracking scheme in wireless communication environments. Firstly, the unified algorithm of FMS filtering and smoothing has been proposed. The unified algorithm has been derived by solving one optimization problem directly with incorporation of the unbiasedness constraint using only finite measurements and inputs on the most recent window. The proposed unified algorithm provides simultaneously the current state estimate as well as the lagged state estimate. The proposed unified algorithm has shown that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness while the recursive IMS filter such as Kalman filter does not have these properties. The on-line computational complexity of the proposed unified algorithm has been compared with the IMS filter such as the fixed-lag Kalman smoothing filter. Secondly, the target tracking in wireless network environment has been considered via extensive computer simulations as an application of the proposed unified algorithm of FMS filtering and smoothing. Through simulation results for moving targets' accelerations of various shapes, the proposed unified algorithm-based target tracking scheme has been shown to provide estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. It has been shown that the performance of the proposed unified algorithm-based target tracking scheme can be better than the IMS filtering-based scheme for suddenly changing acceleration.  
Actually, research on the FMS filter and smoother in nonlinear systems is relatively inactive compared to research in linear systems. There is some research, such as an adoption of nonlinear measurement model, on the FMS filter for nonlinear systems, but little research is going on in the case of the FMS smoother for nonlinear systems. Thus, for nonlinear systems, research on the FMS smoother should be preceded and  
Figure 4: Simulation result for randomly changing acceleration.  
then a unified algorithm of FMS filter and smoother can be researched as a future work. In addition, the estimation of target's acceleration can be extended to identification and classification of moving targets, which could be another future work.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03033024).  
The authors declare no conflict of interest.
## 1 Introduction
Therefore, to contribute to the weight reduction, the idea of the architecture studied in this paper is to connect the permanent magnet synchronous machines through an AC bus without using power electronics. Indeed, taking as an example the specific power values mentioned in the framework of the European HASTECS project [21; 22], on an electric propulsive chain of 500 kW, a decrease in the weight of the chain by more than 30 kg for each power electronics can be foreseen by 2025 (with specific power of 15 kW/kg) and still 20 kg by 2035 (with specific power of 25 kW/kg). Considering the implementation of the single propeller powertrain studied in this paper in an aircraft with two power channels (one for each wing), the previous figures have to be multiplied by four, decreasing the propulsive system weight by 130 kg by 2025 and still by 80 kg by 2035. Currently, this subject appears to be quite new in the literature, as numerous patents were filed recently by many well-known industrial companies (Rolls-Royce [23; 24; 25], Siemens [26], Collins Aerospace [27] and Safran [28]), but these patented solutions only focus on architecture propositions without dealing with their operation. In fact, very few studies deal with the static and dynamic operation of AC propulsion electrical architectures in general and only a study led by NASA [29; 30] stands out. This study considers an AC electric power chain in a distributed propulsion case where the electrical machines are doubly fed induction machines with power electronics on a rotor circuit. Consequently, it appears that the subject of this paper is rather original, being part of the very broad topic of AC propulsive architectures which offers many possibilities but is for now poorly studied. In this context, in order to provide innovative additions to this literature, this paper proposes a study of the static and dynamic behavior with a special attention on stability issues of AC bus electric propulsion channels that are composed exclusively of permanent magnet synchronous machines and not using any power electronics. * _Main contribution_  
The main idea of this paper is therefore to analyze electromechanical behavior (static and dynamic) of this AC electric propulsion architecture composed of permanent magnet synchronous machines and in particular the stability of its operation in an aeronautical application context. To summarize, the main contributions of this paper are:  
* A transient modelling of an AC electric propulsion architecture composed of one surface mounted permanent magnet synchronous generator (PMSG) and one permanent magnet synchronous motor (PMSM). * An implementation of an analytical nonlinear then linearized state model in order to study the stability of the system based on the linearized model previously established. * A comparison of this analysis by means of a reduced power scale experimentation, in order to compare and validate the theoretical analysis results. * A characterization of the stability issues of the AC coupled powertrain and an assessment of the stability's sensitivity to parametric variations.
# Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning
## 2 Build Multi-Modal Feature Fusion Network
### Multi-Scale Feature Fusion Network  
The feature pyramid network (FPN), built upon the original multi-scale feature extraction framework, introduces a bottom-up fusion branch to enhance the fusion of deep feature maps with shallow feature matrices. This integration enables the shallow detection branch to access more abstract feature semantic information, thereby significantly boosting the network's multi-scale target detection performance. Ref. [9] Although FPN indirectly integrates shallow semantic features through P3, further enhancements are required as the multi-scale feature fusion effect of FPN can still be improved. This is due to the elongated information flow route and the presence of multiple convolution operations within it.  
In the YOLOv4 target detection algorithm proposed in this paper, a path aggregation network (PANet) is introduced to address these limitations. PANet facilitates faster information fusion by introducing bottom-up bypass connections between the FPN's P1 and P3 layers, thereby shortening the information path between low-level and high-level features. This enriches the network's feature representation capabilities and improves the accuracy of information storage. The PANet multi-scale feature fusion network structure, an enhancement built upon FPN, is depicted in Figure 5.  
The hybrid mode multi-scale pedestrian detection network, proposed in this paper and based on YOLOv4, enhances the spatial pyramid pooling network (SPPNet) utilized in YOLOv4 by integrating it with the CSPNet structure. The SPP module employs multiple pooling layers to generate features of varying abstraction levels, culminating in the fusion and output of a fixed-size matrix. This significantly enhances the perception of deep-level feature maps [10]. In conjunction with the CSPNet, the SPP module in this paper amplifies the gradient propagation path, thereby augmenting the network's learning capabilities. This combination leverages the strengths of both SPPNet and CSPNet, enabling the retention of SPPNet's capacity to process multi-scale features while addressing gradient disappearance issues through CSPNet, consequently enhancing the network's learning capabilities.  
Figure 4: Channel stack fusion module.  
The hybrid mode multi-scale pedestrian detection network presented herein is adept at tackling challenges inherent in pedestrian detection tasks, such as scale changes, variations in pose, and complex background scenarios. Consequently, it elevates the accuracy and robustness of detection outcomes. Figure 6 illustrates the CSP-SPPnet structure, showcasing the integration of CSPNet and SPPNet components.
## 3 Results and Discussion
### Influence of the Amount of Load F
Table 3 compares the obtained average crack expansion rates (\(da/dN\)), i.e., the change in crack length for the number of loading cycles (up to \(N\)/), for the cases investigated. They were 1.88 \(\times\) 10\({}^{-8}\), 1.39 \(\times\) 10\({}^{-8}\), and 0.69 \(\times\) 10\({}^{-8}\) mm/cycle for Cases 1-3, respectively. It was observed that when \(F_{\max}\) was decreased by 20% (4000 N to 3200 N), \(da/dN\) decreased by approx. 63%.
# Seismic Isolation Effect of Non-Water Reacted Two-Component Polymeric Material Coating on Tunnels
## 1 Introduction
As shown in Figure 1, the tensile strength and compressive strength of NRTCPM are one order ofmagnitude, and the tensile strength has a much better performance than cement [22]. In terms of the mechanical and tribological properties, scanning electron microscope (SEM) [23] and the atomic force microscopy method (AFM) [24] have been applied to measure the mechanical and tribological properties of polymer and copolymer. [PERSON] et al. (2017) applied SEM to test NRCPM specimens with different densities. The higher the density of the specimen is, the more stable the microstructure is. Specimens with a lower density are polyhedrons, and their contact area is large [24]. Based on the mechanical properties and microstructure of NRTCPM, it has many advantages.To be specific, NRTCPM has a better waterproof performance and durability than traditional expansive polymer. It can expand rapidly (up to 10 to 20 times its initial volume) and immediately after injection, therefore saving time in the construction process. Furthermore, it does not shrink or cause additional load on the structure, and it is environmentally-friendly. The equipment for injecting NRTCPM is movable, convenient, and affordable. [PERSON] et al. (2016) studied the effectiveness of the seismic response mitigation of NRTCPM for a dam via a centrifuge test [25]; however, to the best knowledge of the authors, this material has never been utilized for the seismic isolation of tunnels. Figure 1: Density and strength. Within this framework, this paper aims to apply this novel NRTCPM as the isolation layer material to cover the tunnel. The compressive and tensile strength of this polymeric material is investigated, and the damping ratios and dynamic elastic moduli are obtained by an Impact Resonance Test (IRT). Furthermore, a parametric study is conducted to evaluate the influence of material properties (e.g., densities, Poisson's ratio, shear modulus, elastic modulus, etc.) on the seismic response of the lining.
## 3 Results
### Outcomes and Estimation
#### 3.2.2. Test for Differences between Treatment Effects
The conducted analysis showed smaller differences between the measurements in the A/B group than in the B/A group, both in conditions without stabilization (Table 5) and with stabilization (Table 6). In order to compare the significance of differences in the measured parameters in the non-stabilized and stabilized position depending on the group (timstim(r) vs. placebo), the Wilcoxon test was performed. The analysis showed that in the tipstim(r) group, for the measurement in the stable position, higher values of parameters were obtained for Hz wrist and Hz fingers till 2 to 5, and handgrip strength. In the placebo group, higher values were obtained in the stabilized position for the measurement of Hz wrist, MaxROM wrist, Hz for fingers 2 to 5, and lower values for MaxROM and for fingers 4 and 3. For the remaining parameters, the differences between the stable and unstable positions turned out to be insignificant (Table 7). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**A/B (n = 15)**} & \multicolumn{4}{c}{**B/A (n = 14)**} \\ \cline{2-10}  & **Average Rank** & **Mdn** & **IQR** & **Average Rank** & **Mdn** & **IQR** & **Z** & **\(p\)** & **r** \\ \hline Hz wrist [cyc/s] & 11.63 & \(-\)0.10 & 0.70 & 18.61 & 0.20 & 0.55 & \(-\)2.22 & 0.026 & 0.41 \\ MaxROM wrist [mm] & 12.80 & \(-\)0.30 & 2.30 & 17.36 & 0.55 & 13.73 & \(-\)1.44 & 0.150 & 0.27 \\ HzF5 & 9.70 & \(-\)0.10 & 0.50 & 20.68 & 0.30 & 0.65 & \(-\)3.50 & \(<\)0.001 & 0.65 \\ MaxROM F5 & 12.13 & \(-\)0.50 & 1.60 & 18.07 & 0.15 & 3.10 & \(-\)1.88 & 0.060 & 0.35 \\ HzF4 & 9.20 & \(-\)0.10 & 0.50 & 21.21 & 0.30 & 0.85 & \(-\)3.82 & \(<\)0.001 & 0.71 \\ MaxROM F4 & 12.33 & \(-\)0.60 & 2.60 & 17.86 & 0.35 & 4.83 & \(-\)1.75 & 0.081 & 0.32 \\ HzF3 & 9.03 & \(-\)0.10 & 0.50 & 21.39 & 0.35 & 0.55 & \(-\)3.92 & \(<\)0.001 & 0.73 \\ MaxROM F3 & 11.37 & \(-\)0.40 & 2.00 & 18.89 & 0.60 & 5.98 & \(-\)2.38 & 0.017 & 0.44 \\ HzF2 & 9.13 & 0.00 & 0.60 & 21.29 & 0.35 & 0.55 & \(-\)3.86 & \(<\)0.001 & 1.00 \\ MaxROM F2 & 11.57 & \(-\)0.50 & 4.50 & 18.68 & 0.40 & 3.48 & \(-\)2.25 & 0.025 & 0.58 \\ HzF1 & 13.30 & \(-\)0.30 & 1.50 & 16.82 & \(-\)0.05 & 0.90 & \(-\)1.11 & 0.265 & 0.29 \\ MaxROM F1 & 10.13 & \(-\)0.40 & 1.50 & 20.21 & 0.80 & 4.38 & \(-\)3.19 & 0.001 & 0.82 \\ Grip strength [kg] & 10.43 & \(-\)0.50 & 3.80 & 19.89 & 0.80 & 0.88 & \(-\)2.99 & 0.003 & 0.77 \\ \hline \hline \end{tabular} A/B—A-tipstim® group (first measurement), B-Placebo (second measurement); B/A; B-placebo group (first measurement), A–tipstim® (second measurement); ROM—range of motion; one cycle = the movement from flexion to extension. Bolded \(p\)-values are statistically significant alt level \(p<0.05\).  
\end{table} 
Table 6: Comparison of groups in terms of differences in parameters from the first and second measurement with stabilization.
## 3 Inter-Turn Fault Diagnosis of Integrated Motor and Drive Systems
### Space Voltage Vector in Three-Phase Inverter  
There are six switching components in a three-phase inverter, as shown in Figure 4a. Each phase has two switching states (ON and OFF), resulting in three possible configurations: (1) upper switch ON, lower switch OFF: '1'; (2) upper switch OFFON: '0'; (3) both switches OFF: '-'. In a three-phase inverter, the possible voltage states are expressed as shown in Figure 4b. The space voltage vector is defined as follows:  
\[\mathbf{V}=V_{m}e^{i\theta_{m}} \tag{16}\]  
where \(\mathbf{V}\) is the space voltage vector, \(V_{m}\) is the magnitude of the space voltage vector, and \(\theta_{m}\) is the phase angle of the space voltage vector. The space voltage vector is determined by the switch states for each phase. Table 3 shows the phase angles of the space voltage vector corresponding to each switching state.
## 2 Description of the Systems Under Investigation and Experimental Setups
### Harmonic Vibration Testing
#### 4.3.1 Side Accelerometer  
The first investigation that will be discussed is the frequency response function (FRF) plots from the side accelerometer in the Y-direction. The goal is to determine the possible presence of nonlinear behaviors, including nonlinear softening and nonlinear damping, which can be scarcely seen here. It follows from the plotted frequency response curves in Figure 16 that nonlinear softening is observed for all BARC systems with non-zero central-cut widths under consideration. The strongest nonlinear softening can be seen in the 0.25\({}^{\prime\prime}\) central-cut system, since the dominant peaks shift to the left as excitation increases. This is further displayed in Table 8, showing a percentage change of 0.60% between the lowest excitation and the highest excitation. Interestingly, nonlinear hardening is shown in the smaller central-cut width configuration, depicted by the negative sign in Table 8. The 0.1\({}^{\prime\prime}\) cut system shows a nonlinear hardening of 0.30%, and the no-cut system has a nonlinear hardening of 0.84%. Further comparison between the other accelerometers and response directions will be investigated to determine whether similar trends are experimentally observed.  
Figure 16: Harmonic vibration results from the Y-direction of the side accelerometer: (**a**) 0.5\({}^{\prime\prime}\) cut, (**b**) 0.25\({}^{\prime\prime}\) cut, (**c**) 0.1\({}^{\prime\prime}\) cut, and (**d**) no-cut BARC systems.