esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reliability Prediction for New Prefabricated Track Structures
### Reliability Prediction Model Based on a Similar Product Method  
The basic principle of the similar product method [23] is as follows: assuming that a new model product is similar to an old model product, it can be assumed that they have the same ratio between their failure rates and the number of defects. If the number of defects in the old model product is \(d_{0}\) and the failure rate is \(\lambda_{0}\), then we have  
\[\lambda_{0}=\mathbf{k}\mathbf{d}_{0} \tag{10}\]  
where \(k\) is the ratio between the failure rate and the number of defects.  
If the number of defects in the new model product is \(d_{r}\) and the failure rate is \(\lambda_{r}\), then we have  
\[\lambda_{r}=\mathbf{k}\mathbf{d_{r}} \tag{11}\]  
Furthermore, considering the reliability function  
\[\mathrm{R(t)}=e^{-\lambda t} \tag{12}\]  
Based on Equations (10)-(12), the reliability \(R_{r}\) of the new model product and the reliability \(R_{0}\) of the old model product satisfy  
\[\mathbf{R_{r}}=\mathbf{R}^{\frac{d_{r}}{d_{0}}} \tag{13}\]  
In FMECA work, failure modes and severity are important indicators for assessing and predicting product reliability. Failure mode refers to the manifestation of product failures, while severity represents the comprehensive importance of failure modes. The similar product method is a commonly used reliability prediction method, but the concept of the number of defects is difficult to apply in practice. Therefore, severity can be used as a substitute for the number of defects. The new reliability prediction model becomes  
\[\mathbf{R_{r}}=\mathbf{R}^{\frac{C_{r}}{C_{0}}} \tag{14}\]
# Spectrum Based Power Management for Congested IoT Networks
###### Abstract  
With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.  
energy consumption; spectrum management; freescale launchpads; congested IoT +
Footnote †: journal: _Sensors_
## 3 Mathematical Model of Geolocation and Tracking Scenarios
### Conventional Geolocation Measurements
#### 3.1.2 Fdoa  
The FOA measurement at the \(i\)th sensors is given by  
\[\begin{cases}f_{i,k}^{o}=f_{o}-f_{o}\frac{d_{i,k}^{o}}{c},\\ \dot{d}_{i,k}^{o}=\frac{\left(d_{i,k}^{o}\right)^{\prime}\left(d_{i}^{o}-g_{i }^{o}\right)}{\left\|\dot{d}_{i,k}^{o}\right\|},\end{cases}\qquad\{i=1,2,\ldots,M\}, \tag{8}\]  
where \(f_{o}\) is assumed carrier frequency received by the sensor in (Hz), \(\dot{d}_{i,k}^{o}\) is the range rate, also called the Doppler or radial velocity between emitter, and \(i\)th sensors in (m/s), and \(\dot{g}_{i}^{o}=\left[\dot{x}_{i}^{o},y_{i}^{o},z_{i}^{o}\right]^{\prime}\) is initial velocity vector of \(i\)th sensor (m/s).  
The FDOA between \(i\)th sensors when use sensor #1 as reference is calculated as  
\[f_{i,1,k}=f_{i,k}^{o}-f_{1,k}^{o}+\dot{n}_{i,1,k}\,\ \ \ \ \ \{i=2,3,\ldots,M;\ \ k=1,2,\ldots,K\}, \tag{9}\]  
where \(\dot{n}_{i,k}\sim N(0,\sigma_{f}^{2})\). The vector form of FDOA can be represented as  
\[\mathbf{f}_{k}=\mathbf{f}_{k}^{o}+\mathbf{\dot{n}}_{k}\, \tag{10}\]  
where  
\[\begin{cases}\mathbf{f}_{k}=\left[f_{2,1,k},f_{3,1,k},\cdots f_{M,1,k}\right]^{ \prime}\,\\ \mathbf{f}_{k}^{o}=\left[f_{2,1,k}^{o},f_{3,1,k}^{o},\cdots f_{M,1,k}^{o}\right]^{ \prime}\,\\ \mathbf{\dot{n}}_{k}=\left[\dot{n}_{2,1,k},\dot{n}_{3,1,k},\cdots\dot{n}_{M,1,k} \right]^{\prime}\,\\ \end{cases} \tag{11}\]  
Covariance matrix \((\mathbf{R}_{\dot{n}_{k}})\) of FDOA noise is determined as  
\[\mathbf{R}_{\dot{n}_{k}}=\mathrm{E}[\mathbf{\dot{n}}_{k}\ \ \mathbf{\dot{n}}_{k}^{\prime}]= \left[\underbrace{\sigma_{f}^{2}\cdots\sigma_{f}^{2}}_{(M-1)(K+1)}\right], \tag{12}\]  
where \(\sigma_{f}^{2}\) is the variance of FDOA measurement noise [20].  
\[\sigma_{f}=\frac{\sqrt{3}}{\pi T}\frac{1}{\sqrt{B_{N}T}}\frac{1}{\sqrt{S/N}}. \tag{13}\]
## 5 Data Simulation  
A numerical simulation was performed to investigate under what conditions we can estimate the TTL error without any specific calibration maneuvers for it. Five types of data (GN1B, SCA1B, ACC1B, LRI1B, LSM1B) were generated:  
* Simulated GN1B--GNSS navigation data in terms of trajectory states consisting of satellite positions and velocities in an inertial frame (IF);
* Simulated SCA1B--star camera assembly (SCA) measurements in terms of quaternions, describing the relationship between the inertial frame and the satellite frame;
* Simulated ACC1B--linear non-gravitational acceleration measurements in the satellite frame;
* Simulated LRI1B--biased range, range rate, and range acceleration data;
* Simulated LSM1B--LRI steering mirror measurements in terms of the pitch and yaw pointing angles, which were used in the TTL estimation process.  
We chose the International Celestial Reference Frame (ICRF) as the inertial frame. A flowchart of the data simulation procedure is shown in Figure 5. Similarly to [27], after the error-free observations were calculated, the error terms were added up to generate the simulated measurements.
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 2 Environmental Vibration Model of Metro Depot  
Environmental vibrations caused by metro train operations are complex and involve the generation of train-induced vibrations and their propagation through the soil and buildings. In this study, numerical simulations are conducted to investigate this issue. The numerical model is divided into two substructure models: (a) the train-track model and (b) the track-soil-building model. First, a train-track dynamic model is established using SIMPACK software ([https://www.3 ds.com/zh-hans/products/simulia/simpack](https://www.3 ds.com/zh-hans/products/simulia/simpack)) to simulate the wheel-rail forces generated by the train. Then, a three-dimensional finite element model of the metro depot, including the track, soil, and building, is developed using the finite element software GFE ([[https://gfe.com.tr/en/downloads.html](https://gfe.com.tr/en/downloads.html)]([https://gfe.com.tr/en/downloads.html](https://gfe.com.tr/en/downloads.html))). The simulated wheel-rail forces are applied to the finite element model. Through calculations, the vibration response characteristics and propagation laws of the over-track building in the metro depot are analyzed. Based on these results, the effectiveness of vibration isolation measures, including open trenches and infilled trenches, is further studied.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## Abbreviations
#### Notations  
\(G_{i}^{t}\) HAP transmit antenna gain for user \(i\)  
\(G_{i}^{t}\) Receive antenna gain  
\(L_{i,h}\) Path loss between user \(i\) and HAP \(h\)  
\(\rho\) Angle subtended at the HAP by a cell centre and edge  
\(\beta_{i}\) Elevation angle of cell \(i\) centre to the HAP  
\(\Phi\) Angle of overlap between neighbouring cells  
\(\Gamma_{i}\) CNR of user \(i\) in cell \(i\)  
\(\gamma_{i}\) CINR of user \(i\) in cell \(i\)  
\(C_{pu}^{\gamma}\) Average per-user capacity in cell \(i\)  
\(\eta_{f}^{t}\) Average spectrum efficiency in cell \(i\)  
\(S_{\eta_{f}}^{1}\) Average area spectral efficiency in cell \(i\)  
\(d_{i}\) Distance of cell \(i\) centre from the sub-platform point  
\(x_{i},y_{i}\) Semi-major and semi-minor axes of cell \(i\), respectively  
\(B_{i}\) Bandwidth allocated to user \(i\) in cell \(i\)  
\(A_{i}\) Area of cell \(i\)  
\(h_{i}\) Slant distance of cell \(i\) centre from HAP  
\(h_{p}\) HAP height  
\(D_{i}\) Slant distance of user \(i\) in cell \(i\) from HAP
## 5 Conclusions  
Long-wave transmitting antennas have been widely used all over the world. Single-tower or four-tower structures are usually adopted. According to the development status of long-wave, high-power antenna technology at home and abroad, and referring to the practical experience of major Loran transmitting antennas in the world, the performance parameters of various Loran transmitting systems are compared. In addition, there are many innovations in the research work of this paper: firstly, this project will design and construct the Loran transmitting antenna with an altitude of more than 4500 m for the first time in the world. In addition, according to the geographical environment of the Naqu area, the propagation model of the Loran signal is established. Compared with previous papers, the innovations of this paper are shown in the content of this paper to have been rarely carried out before. Compared with the previous similar literature, this paper analyzes and compares the radiation characteristics of the transmitting antenna in detail, and simulates the radiation field strength and signal propagation of the four basic compass directions in the complex geographical environment of Tibet, China, in order to more intuitively design and enhance the signal propagation ability in this area. These simulation results not only show the advantages of the performance of the single-tower antenna in Tibet, but also design the structure and electrical parameters of the transmitting antenna according to the simulation results so that the enhanced Loran signal transmitted by the transmitting antenna can be extended to 1000 km or even further. Therefore, the single-tower umbrella antenna should be selected as the preferred form of enhanced Loran transmitting antenna [19; 20; 21].  
Conceptualization, Z.H. and S.L.; methodology, M.D.; software, C.Y.; validation, Z.H., S.L. and C.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Discussion
### Limitations and Perspectives of the Study  
The dynamic nature of LUEmax is posited to enhance the model's alignment with the adaptive responses of foliage to transient environmental conditions, thereby facilitating more precise estimations of GPP. However, the LUEmax parameter is intricately linked to the physiological conditions of vegetation, influenced not only by temperature, LAI, and water availability but also by factors such as nutrient constraints, forest maturity, and the ratio of diffuse radiation. Nonetheless, many single-leaf light energy utilization models lack the corresponding parameterization for these influential factors. Consequently, incorporating parameters that account for these determinants of LUEmax into the existing model's framework could significantly enhance the accuracy of VPM model predictions.  
Light-response parameters characterize the photosynthetic light-response curve (LRC), which is essential in terrestrial carbon models [87]. The continuous expansion of the network of flux observation towers has provided valuable opportunities for research on light-response parameters at the ecosystem scale [88]. However, flux data are frequently affected by measurement errors. These errors stem from multiple factors, including meteorological conditions during data collection, sensor accuracy, and environmental fluctuations [89]. The estimation of LUEmax typically depends on light-response curves (LRC). Errors in flux data can, thus, compromise the accuracy of the LRC [90]. Moreover, the estimation of LUEmax is based on certain assumptions regarding the photosynthetic mechanisms in plants. These assumptions may deviate from the actual situation under different ecosystems and climate conditions. Additionally, uncertainties can be introduced by various aspects, such as the method of fitting the LRC, the selection of light intensity, and the interaction of other environmental variables [91].  
The study above illustrates the dynamic variations in LUEmax across diverse vegetation types within different climatic regimes. However, because of the inadequacy of flux data across all climate classifications, the behavioral patterns in LUEmax for certain vegetation types in data-sparse climatic areas remain to be elucidated, along with the influence of other geographical environmental variables on LUEmax. Additionally, the methodologies employed to compute LUEmax are heterogeneous, leading to disparate estimations from various approaches. Future investigations should consider the seasonal fluctuations of LUEmax and the constraints imposed by climatic and environmental factors when modeling GPP with the LUE framework. This study predominantly draws upon data from the FUENET 2015 flux sites; however, the distribution and quantity of these sites are not uniformly representative across vegetation types. Therefore, further research is warranted, emphasizing the expansion of flux observations and integrating novel methodologies or tools, such as unmanned aerial remote sensing, the Chlorophyll Fluorescence Index (SIF), and the Photochemical Reflectance Index (PRI).
## 5 Simulation Experiment
### Disturbance Suppression without Loading Force Command
#### 5.1.1 Suppression Performance
Figure 17: Relationship diagrams of \(F_{\rm L}\)–\(\dot{x}_{\rm M}\) with disturbance 7 sin14\(t\) (mm/s), (**a**) comparative results under different cases; (**b**) enlarged graph of results of constant compensator with DOB case and hybrid controller case. is a relationship between the generation of sharp force disturbance and the changes in the direction of motion. From the characteristic of friction force _versus_ velocity \(\dot{x}_{\mathrm{L}}\) of the hydraulic cylinder, as depicted in Figure 18a, the friction force is changed sharply when the direction of \(\dot{x}_{\mathrm{L}}\) changes. Moreover, from the corresponding graph of the friction force and \(F_{\mathrm{L}}\) in the time domain, as depicted in Figure 18b, the start time of the peak of the force output is just corresponding to the time when the friction force is changing. Thus, this sharp force disturbance is mainly caused by the impact friction force of the hydraulic cylinder that is generated when the motion is reversing. It can be verified that the curves of \(F_{\mathrm{L}}\) to \(x_{\mathrm{M}}\) and \(F_{\mathrm{L}}\) to \(\dot{x}_{\mathrm{M}}\) are close to ellipses in the absence of friction force. Without the impact of friction force, the maximum output force will be 20 N in Figure 16c and 5 N in Figure 16d, and that means the extraneous force resulting from the disturbance motion is successfully suppressed. Simultaneously, it is known from Figures 17b and 18b that the designed hybrid controller has a certain restraining ability on friction force (reduced from 150 to 57 N), but the restraining effect is worse than that of the extraneous force. In order to verify our design of the hybrid controller, the assumed friction force is larger than the actual low-friction servo hydraulic cylinders. Therefore, in practical application, the impact of the friction force will be relatively small.
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 5 Experimental Design and Analysis
### Experimental Analysis  
#### 5.1.1 Performance of Cross-Domain Gesture Recognition  
Cross-domain refers to different environments. To verify the cross-domain performance of Wi-GC, we conducted experiments in conference rooms and office environments. We asked the experimenters to perform six gestures in the two environments. The experimental results are shown in Figure 12a. We set the equipment distance to 1 m, 2 m, 3 m, 4 m, 5 m and 6 m for experiments to find the optimal equipment distance. The results are shown in Figure 12b.  
As shown in Figure 12a, the accuracy rate of the conference room environment is higher than that of the office environment because the office environment is more complex and has more interference. From the final results, the average gesture recognition rate of both environmental domains is higher than 92%, which shows that Wi-GC has good recognition performance for cross-domain gestures. As can be seen from Figure 12b, no matter how far apart the two devices are, the recognition rate of LOS is always higher than that of NLOS. This is because the information received at the receiving end has not only the data affected by the action but also the influence of the items in the environment, resulting in lower recognition rates. When the distance between the transmitter and receiver is about 3m, the recognition rate is the highest, whether in a LOS environment or an NLOS environment. The signal propagation distance is short, the signal attenuation is small, and the perception range is extensive. When the device distance is equal to or greater than 5m, the gesture recognition rate decreases because the signal propagation distance is long, the signal attenuation increases and the perceived range is small.
# A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers
## 2 Related Work  
This section summarizes the research on experienced delays and workers' utilization based on their processing rates in different queueing system architectures. The first part presents work dealing with global queue systems only. It compares waiting times when its workers have a homogenous average speed rate vs. a heterogenous speed rate (GH vs. GHT). The second part presents work dealing with local queue systems only and analyzes the waiting times regarding different routing policies and the heterogeneity of workers (LH vs. LHT). The last part compares waiting times in global and local queue systems, mainly when workers' speed rates are heterogeneous. A single queue architecture with K homogenous workers is signed M/M/K when the K workers' task interarrival times and service times follow an exponential distribution. When there are K heterogeneous workers, the system is signed M/Mi/K, where each worker has a different mean service time following the exponential distribution.  
Parallel queues architecture with K homogenous multiple servers conforms to the M/M/K/JSQ/PS mode; when the task arrivals are Poisson and processing times of the K workers are exponentially distributed, the tasks are allocated using the Join-the-Shortest-Queue (JSQ) model. The worker handles tasks in their local queue in parallel. The M/Mi/K/JSQ/FIFO notation states that each worker can have a different processing rate while tasks are handled according to the FIFO order. Part of the following papers treats waiting time as synonymous with response time. Focusing on waiting time can simplify the analysis of theoretical models of the queue's dynamics. When the servers are identical, the distinction between waiting and processing times becomes less critical for the analysis.
# Large Thermal Expansion LTCC System for Cofiring with Integrated Functional Ceramics Layers
## 3 Results and Discussion
The shrinkage behaviors of CT708 and ScM green tapes are compared in Figure 7. The shrinkage of CT708 sets in at about 790 \({}^{\circ}\)C and the shrinkage rate has its maximum at 836 \({}^{\circ}\)C. The shrinkage curves document that both materials exhibit similar shrinkage behavior, although no perfect match is observed. The temperatures of maximum shrinkage rate of the ferrite and CT708 tapes fit well. However, the ferrite tape has a much broader shrinkage temperature window, starting at about 750 \({}^{\circ}\)C and continuing in the dwell at the sintering temperature of 900 \({}^{\circ}\)C. After 4 h at 900 \({}^{\circ}\)C, the shrinkage of the ferrite tape seems to saturate. Both tapes exhibit a similar total shrinkage. Integration of microwave ferrite functionality into a LTCC multilayer system was attempted through cofiring of ScM ferrite and commercial low-k dielectric and large-CTE CT708 tapes. A multilayer assembly with two ferrite layers embedded into CT708 layers was cofired at 900 \({}^{\circ}\)C. No delamination and camber were observed. A cross-sectional SEM micrograph reveals a well cofired multilayer architecture without cracks (Figure 8). This demonstrates that the LTCC CT708 multilayer package allows cofiring with embedded ferrite layers without additional applied pressure, and multilayer modules with good shrinkage and cofiring behavior were obtained  
Figure 5: Permittivity \(\varepsilon_{x}\) and loss tangent tan \(\delta\) vs. frequency for CT708 sintered at 900 \({}^{\circ}\)C, heating rate 2 K/min (**a**), and vs. heating rate (**b**). Figure 6: Thermal expansion of CT708 and Sc-substituted ferrite (ScM) vs. temperature (heating and cooling runs, heating rate 4 K/min). We have shown that cofiring of LTCC laminates (CT708) with integrated hexagonal ferrite layers is possible. Dense multilayer ceramics without microstructural defects and good functional performance are accessible, since the shrinkage mismatch between the LTCC- (CT708) and the functional ceramics layers is small. Moreover, due to the large CTE of the quartz based CT708 LTCC system, almost no thermal expansion mismatch with the ferrite layers arises, allowing for cofiring without cracks, delamination, or other macroscopic defects. The Sc-substituted ferrite layers, embedded in a glass ceramic LTCC architecture and cofired at 900 \({}^{\circ}\)C, represent an integral part of microwave components (self-biased ferrites for circulators) which will be used in future satellite communication systems.