esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation of the Temperature-Drift Effect on Strain Measurement of Concrete Beams
## 5 Conclusions  
This study used a wireless resistance strain gauge measurement system to measure the strain response of beams. The research performed long-term load tests for a steel beam and a concrete beam, and nonelastic response tests for a concrete beam, to examine the effects of temperature changes on the strain responses obtained by the tests.  
Based on the test results, conclusions were obtained as follows:  
1. When the load-generated mechanical response was smaller than the thermal strain response induced by temperature changes, the measured apparent strain response would be influenced by temperature changes, and the load-generated mechanical strain could not be obtained through analyzing apparent strain.
2. Mechanical strain could be obtained by eliminating the temperature-induced strain from the apparent strain. The mechanical strain of abrupt changes generated by load application and removal could be observed in the mechanical strain time-history curve. However, because of the presence of temperature-induced drift, the mechanical strain response could not return to the  
Figure 14: The concrete beam nonelastic response test: (**a**) strain response after deleting short-term loads; (**b**) beam bottom temperature changes; (**c**) mechanical strain; (**d**) change in mechanical strain per unit time.  
0 position even after the removal of all loads. Moreover, as time passed, the temperature drift effect caused the mechanical strain to move further away from 0. This temperature drift made analyzing mechanical strain difficult.
3. Given that temperature changes are slow and mechanical strain changes are momentary, this study proposed a simple data-processing method, namely, to use adjacent data subtraction on the mechanical strain time-history curve to eliminate the temperature drift effect. The obtained mechanical strain change per unit time can be used to accurately determine the mechanical strain generated by load application and removal.  
In addition to precise analysis of load-generated mechanical strain, the adjacent data subtraction of mechanical strain time-history data could also be used to analyze the residual strain of the nonelastic response of a concrete beam subjected to substantial short-term loads.  
Conceptualization and methodology, Y.L.; measurements, Y.-W.C.; development sensors, Y.-T.L.; data analysis, Y.L., Y.-T.L. and Y.-W.C.; writing--original draft preparation, Y.L., Y.-T.L. and Y.-W.C.  
This work was sponsored by the Ministry of Science and Technology, Taiwan, ROC under Grant No. MOST 106-2221-E-005 -036 -MY3.  
The authors declare no conflict of interest.
###### Abstract  
Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and a challenge is how to accurately separate the inner and outer race fault features from noisy compound faults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Q-factors and improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, the compound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonance components of the signal (compound fault impact component and small amount of noise) are obtained, but it can only highlight the impact of compound faults, and failed to separate the inner and outer race compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection of parameters (the shift order M and the filter length L) based on the iterative calculation method with the Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filtered and de-noised by the proposed method, the inner and outer race fault signals are obtained respectively. The fault characteristic frequency is consistent with the theoretical calculation value. The results show that the proposed method can efficiently separate the mixed fault information and avoid the mutual interference between the components of the compound fault.  
rolling bearings; compound faults; fault features; parallel dual-Q-factor; MCKD +
Footnote †: journal: Applied Sciences
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 3 Results
### Comparison with Hydrological Models and GRACE  
Taking into account that a large part of the solar annual cycle in GNSS time series in Europe is likely due to hydrological loading, we compared it with loading estimates computed from hydrological models and GRACE/GRACE Follow-On-derived continental water storage variations. The site displacements that we computed with the GLDAS2.1, MERRA2, and GRACE models are expressed in the centre of figure (CF) reference frame for SNREI Earth. The time series of loading models were adjusted according to Equation (1), excluding offsets and draconitic harmonics but adding the interannual polynomials. Since the hydrological models and EOST loading service do not provide uncertainties, we adjusted the models with the nonweighted least squares method (LS). We show in Figure 9 the differences between the amplitude and phase of the _cats_d_ annual solar cycle with those of each loading model. Important differences compared with centred Gaussian distribution are shown by the histograms, especially in the phase shift, where we see that the centre of the distribution is systematically shifted towards negative phase shifts. In other words, hydrological loading models are systematically in advance of phase compared to GNSS. Concerning the amplitudes, the GLDAS2.1 and MERRA2 amplitudes are slightly larger than those for GNSS, even if the distribution is well centred on zero. For GRACE, the amplitude seems to be quite underestimated compared to GNSS. Figure 9 highlights the presence of a coherent spatial pattern, suggesting the existence of disparate common modes between the GNSS solution and the models. Nevertheless, some stations with high signal intensity have the same values for every model so that they simply indicate the difference existing locally between the GNSS solution and the models.
## 3 Case Study and Results  
The aim of this study is to obtain a model to predict, with high accuracy, the electrical energy demand, by using SNN to manage the energy storage system in order to reduce the electricity expenses of a building. The validation of the methods proposed in the previous section is implemented in an office building divided into seven floors and located in Bangkok, Thailand. The monitored data have a period of 1 min and are collected in different zones on each floor. These data correspond to indoor temperature, ambient illumination and lighting and loads consumption [54]. Furthermore, the external monitored data have a period of 30 min and correspond to outdoor temperature.  
The study is performed in zone 2 of the second floor due to data quality. The monitoring period goes from 1 July at 12 a.m. to 12 September at 4 p.m., both in 2018, and from 3 March 2019, at 2 p.m. to 1 January 2020, at 12 a.m. The percentage of errors of each variable monitored in the collection period are shown in Table 1. As can be seen, the errors of the building's interior sensors are 1.56%, the exterior ones, 0.66%, and those of the energy meters, 0.15%.  
The tariff considered has hourly discrimination with three periods, peak hours, flat hours, and valley hours. On working days, peak hours go from 10 a.m. to 2 p.m. and from 6 p.m. to 10 p.m., flat hours, from 8 a.m. to 10 a.m. and from 10 p.m. to 12 a.m., and valley hours, from 12 a.m. to 8 a.m. On the other hand, nonworking days are always considered as valley hours. The prices of energy and power prices in each period are presented in Table 2.  
The energy storage system must have values according to the demand in the selected zone. The capacity must be adjusted to the consumption, avoiding oversizing or undersizing. The charging and discharging powers must also be high enough in order to be able to supply the energy in each instant. Therefore, from the study of the electricity demand of the building zone, the data used to simulate the battery are shown in Table 3.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 6 Conclusions  
In this paper, we survey network coding based P2P file sharing systems. This survey paper summarizes, assesses, and compares the most recently used techniques to improve P2P content distribution systems performance using network coding. Furthermore, we present a classification of the main network coding based P2P file sharing systems which include full coding, generation coding, sparse coding, combined coding, and overlapping coding. The extensive survey shows that applying network coding on P2P file sharing systems is a trade-off process. The challenges of network coding can be alleviated, but not completely vanished. These challenges are still a hot research topic.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Robustness to Churn** & **Decoding Speed** & **Network Overhead** \\ \hline Full Coding & \(\star\star\star\star\star\) & Constant: very slow (infeasible) & \(\uparrow\) \\ \hline Sparse Coding & \(\star\star\) & Variant: slow at the beginning to quick at the end. & \(\uparrow\) \\ \hline Generation Coding & \(\star\star\star\) & Constant: based on generation size (moderate usually). & \(\uparrow\uparrow\) \\ \hline Combined Coding & \(\star\star\star\) & Variant: moderate at the beginning to quick at the end. & \(\uparrow\uparrow\) \\ \hline Overlapping Coding & \(\star\star\star\star\) & Variant: moderate at the beginning to very quick at the end. & \(\uparrow\uparrow\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of network coding approaches.  
**Author Contributions**: Conceptualization, A.A.A. and A.M.; Funding acquisition, A.M., M.A.-A., and T.S.; Methodology A.A.A. and A.M.; Supervision A.M. and T.S.; Writing--Original draft, A.A.A.; Writing--Review and editing, A.M. and M.A.-A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was funded by King Fahd University of Petroleum and Minerals (KFUPM).  
**Acknowledgments**: The authors would like to acknowledge the support provided by King Fahd University of Petroleum and Minerals (KFUPM) and the Department of Computer Engineering.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 2 Methods  
### Lamb Wave Dispersion Compensation  
The Lamb wave dispersion can be expressed as the function of the phase velocity changing with the frequency. The dispersion function is described as different frequency components of a certain excitation signal passing through the transformation system. Let the excitation signal be \(k(t)\). The received Lamb wave \(y(t)\) propagating a certain distance can be expressed as  
\[y(t)=\frac{1}{2\pi}\int_{-\infty}^{+\infty}K(\omega)e^{i\omega(t-\frac{d}{c( \omega)})}d\omega \tag{1}\]  
where \(t\) is the time, \(K(\omega)\) is the spectrum of \(k(t)\), \(\omega\) is the angular frequency, \(d\) is the propagation distance, \(c(\omega)\) is the phase velocity of the Lamb wave [19], and \(j\) is the imaginary unit.  
According to the Lamb wave dispersion compensation theory, the dispersion compensation is the inverse process of the dispersion transformation. The theoretical dispersion transformation function is known to perform dispersion compensation on the received Lamb wave signal. The dispersion compensation function is  
\[H^{-1}(\omega)=\frac{1}{H(\omega)}=e^{j\frac{\omega d}{c(\omega)}} \tag{2}\]Therefore, the original signal can be expressed as  
\[k(t)=\frac{1}{2\pi}\int_{-\infty}^{+\infty}Y(\omega)H^{-1}(\omega)e^{i\omega t}d\omega \tag{3}\]  
where \(Y(\omega)\) is the spectrum of \(y(t)\).
## 5 Proposed Hybrid V2X Technology Selection Algorithm
### Intelligent Hybrid V2X Technology Selection Algorithm
In instances of nearly stationary conditions, such as a traffic jam, the decision frequency can be reduced and multiple CAMs can be sent via the previously chosen technology(ies), as traffic dynamics are anticipated to exhibit prolonged stability. Conversely, in scenarios where the average vehicle speed surpasses a specified threshold, indicating rapidly changing traffic dynamics, the decision frequency for technology selection can be increased. If the network traffic is dense via a specific technology, the frequency of CAM transmissions via that technology is reduced (while satisfying the minimum and maximum frequency bounds based on ETSI standard i.e., 1 Hz to 10 Hz) and vice versa. From the decision tree in the Appendix A, based on the specific decision tree branch, a single or a set of technologies is selected to be used for the next CAM transmission along with a higher/lower CAM frequency. After this intelligent decision making, the process is continued for transmitting subsequent CAMs. For space constraints and to avoid redundancy, the decision tree for joint CAM and DENM transmission scenario is not shown separately. When DENMs are also enabled in any V2X node, both CAMs and DENMs are sent via the chosen technology, as in the detailed decision tree in Appendix A. However, the transmission frequencies of CAMs and DENMs can be adapted to avoid network overload in case of joint CAM and DENM transmission. A small part of the decision tree is shown in Figure 5 as an example to showcase the selection between the three technologies. DSRC, C-V2X PC5 and 5G are referred to as D, P and Uu, respectively. Based on the received CAMs, the reported latency values via all three technologies satisfy the latency threshold, \(L_{thr}\). After this, the 95 th percentile of the distance is monitored. If both \(D_{P}\) and \(D_{Uu}\) are less than \(D_{thr1}\) (following blue dashed box) and the speed is below the threshold, \(S_{thr}\), only DSRC is selected for the next CAM transmission. If the speed is higher than \(S_{thr}\), implying highly varying traffic, both DSRC and C-V2X PC5 are selected to maximize CAM reachability even in cases where \(D_{thr1}\) is not satisfied. An extra check on traffic density ensures that the frequency of CAM transmission can be set to a minimum if traffic is dense, in turn adapting the technology selection in a better way without overloading a particular technology. The red dashed box is followed if both \(D_{P}\) and/or \(D_{Uu}\) are not less than \(D_{thr1}\). In this scenario, firstly, if \(D_{P}\) is greater than \(D_{thr1}\) and/or \(D_{Uu}\) is between \(D_{
## 5 Simulation Framework and Experimental Scheme
### Generation of Traffic Scenarios to Analyze Human Uncertainty Effects  
To thoroughly capture the effects of human uncertainty on the network, 30 unique traffic scenarios were generated for each VFH load (5% and 10%). Each simulation was repeated 15 times to account for variability in driving behaviors and network conditions. Random route deviations were introduced through a parameter \(\rho_{i}\), quantifying the probability of a driver deviating from the optimal route. By varying this parameter, the simulation tested a range of decision-making scenarios and their corresponding impact on network performance.  
The maximum CDR was set at 1%, aligning with 5G network performance standards under high traffic conditions. This ensured that the results conformed to realistic network expectations, providing a robust dataset for evaluating the effects of human uncertainty on 5G resource management.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Interval** & **Peak Load** & **5\% VFH Load** & **10\% VFH Load** \\ \hline HC1 & 7:55-8:25 & 2500 & 125 & 250 \\ HC2 & 12:10-12:40 & 2386 & 119 & 238 \\ HC3 & 16:30-17:00 & 2965 & 148 & 296 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters during hours of congestion.
## 4 Experiment Settings
### Evaluation of Calibration Approaches  
We use the following metrics to quantify calibration quality:  
_Expected Calibration Error (ECE)._ Partition the interval \([0,1]\) into \(K\) bins of equal width: \([b_{0},b_{1}),\ldots,[b_{K-1},b_{K}]\), where \(b_{0}=0\) and \(b_{K}=1\). Let \(B_{k}\) be the set of indices whose predicted probability falls into bin \(k\). Then define the mean predicted probability \(\overline{p}_{k}\) and the empirical frequency of positive outcomes \(\overline{y}_{k}\) in bin \(k\) as:  
\[\overline{p}_{k}=\frac{1}{|B_{k}|}\sum_{i\in B_{k}}\hat{p}_{i},\quad\overline{ y}_{k}=\frac{1}{|B_{k}|}\sum_{i\in B_{k}}y_{i}. \tag{30}\]  
The ECE is given by:  
\[\text{ECE}=\sum_{k=1}^{K}\frac{|B_{k}|}{N}\,\big{|}\,\overline{p}_{k}-\overline {y}_{k}\big{|}. \tag{31}\]  
A smaller ECE indicates that predicted probabilities align more closely with observed outcomes across bins.  
_Maximum Calibration Error (MCE)._ While ECE is an average measure, MCE shows the largest single-bin deviation:  
\[\text{MCE}=\underset{1\leq k\leq K}{\max}|\overline{p}_{k}-\overline{y}_{k}|. \tag{32}\]  
A high MCE reveals at least one region where predictions are significantly over- or underconfident.  
_Logarithmic Loss (LogLoss)._ LogLoss (or cross-entropy) penalizes overconfident but incorrect predictions:  
\[\text{LogLoss}=-\frac{1}{N}\sum_{i=1}^{N}\Big{[}\,y_{i}\ln(\hat{p}_{i})+(1-y_{ i})\ln(1-\hat{p}_{i})\Big{]}. \tag{33}\]  
Lower LogLoss values indicate that the model's predicted probabilities better match actual labels, accounting for both correctness and confidence.  
These calibration metrics offer more than a statistical summary; each provides a distinct insight into the model's clinical utility and trustworthiness. A low ECE signifies that the model's probabilities are reliable on average, making them suitable for strategic decisions like resource planning. The MCE, in contrast, serves as a stress test for protocol safety by identifying the single risk bracket where the model is most untrustworthy, thus preventing systematic errors for specific patient subgroups. Finally, a low LogLoss is crucial for individual patient safety, as it penalizes a model severely for high-confidence mistakes, thereby discouraging the kind of \"false reassurance\" that can lead to delayed care. A comprehensive assessment of a model's practical value requires evaluating all three aspects.
## 7 Networks with Multiple Data Flows
The data flow \(K\)\(\rightarrow\)D\(\rightarrow\)\(E\) noted in Figure 11 becomes in Figure 11\(K\)\(\rightarrow\)\(K^{\prime}\)\(\rightarrow\)\(B^{\prime}\)\(\rightarrow\)\(D\)\(\rightarrow\)\(E^{\prime}\)\(\rightarrow\)\(E\) since \(B^{\prime}\) is a Network layer database shared by \(D\) and \(B\). The main difference with respect to the one-flow example is that the controller will have two forwarding tables, one for each data flow. In the case of _Consultation_ data flow, the labeling tables for the two routers will be the same as the one for the one-flow example. The new implementation configuration is as described earlier; we have two routers that connect the network entities: one to connect the storage entities and one to connect the workstations. Each router will have a forwarding table for each flow; each incoming packet will contain its flow identifier together with its label; and the controller will use the flow identifier to switch to the appropriate forwarding table for each packet. The partial order for the new _Diagnostic_ flow is shown in Figure 13. We clearly see in this figure that the diagnostics start at entities \(K\) and \(K^{\prime}\), which are the _ChiefMedicWkstn_ and its database, and end at the patients and their databases. We also see that the dataflows do not allow sending one patient's data to other patients. The labeling table is still obtained automatically by the rule of Section 4.2, as shown in Figure 14.  
Figure 11: Two-flow network for the hospital example. Figure 14: Labeling table for Figure 9.  
Figure 12: Cloud configuration of Figure 11.  
Figure 13: Partial order for the Diagnostic Flow. The data flow \(K{\rightarrow}K^{\prime}{\rightarrow}B^{\prime}{\rightarrow}D{\rightarrow}E^{ \prime}{\rightarrow}E\), discussed above, can be traced in the labeling table by checking that the name of each entity in the sequence is included in the Holds of the following entity. It can also be checked that unwanted flows are impossible, e.g., [PERSON]'s workstation \(L\) cannot receive [PERSON]'s diagnostics because it does not have \(E\) in its Holds. The routing tables, derived from the labeling table, will not enable this forwarding.
## 7 Open Research Areas
### In-Network Data Aggregation for Control Operations  
Two types of aggregation, data aggregation and packet aggregation, are supported by WIA-PA in order to reduce the number of packet transmissions. WirelessHART, WISA, and ZigBee Pro do not support this function. In certain industrial closed-loop control applications involving multiple sensors and an actuator, raw sensor readings are streamed from the sensors to the actuator. The actuator subsequently performs computations using the readings to carry out the relevant control operations. This traditional approach, however, is not suitable for multihop wireless sensor networks, since they have highly limited bandwidths. The idea would then be to allow an intermediate node to carry out the computations and only send the final control output to the actuator, thus saving network bandwidth. However, as every actuation operation may be dependent on a different set of sensors, the nodes need to autonomously decide which node should act as the intermediate aggregation node that will be responsible for computing the control output. This technique will also contribute towards improving real-time operation.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 2 Proposed Approach
### Low-Power Hardware Development  
#### 2.1.1 EDS-Based Operation  
The challenge in long-term monitoring systems, particularly for infrastructures like bridges, is managing power consumption to ensure continuous operation over extended periods. To address this, the EDS was designed to activate the power supply only when monitoring is necessary. This design significantly reduces power usage by placing sensors in a completely inactive mode when not in use. The EDS is composed of manual switch, a Real-Time Clock (RTC), a D-type latch IC, and a 5 V regulator, which, together, controls the power to the MCU, specifically a Raspberry Pi (RPI) Zero 2W (as shown in Figure 2) [27]. The ICs utilized for the manual switch, RTC, latch, and 5 V regulator include the DS1339U from Analog Devices, along with the SN74 LVC1G373, TPL5110, and TPS61032 PWP from Texas Instruments [28; 29; 30; 31]. The manual switch is initially used to supply power and has been programmed to automatically turn off after 1 min by configuring the resistance. The RTC is programmed by the MCU via I2C communication, setting the alarm and interrupt times. At the scheduled time, the RTC sends an interrupt signal through the interrupt pin to wake up the system. When the MCU begins operation, it first sets the latch to hold the current power status, clears the alarm interrupt, and schedules the next alarm time. After all required processes are completed, the MCU powers down and releases the latch, allowing the system to enter a complete sleep mode. This design ensures that the system only consumes power, when necessary, thereby extending battery life and operational periods.