esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Summary of Field Test Results  
Field evaluations of the All Materials Locator (AML) system were conducted at a certified underground utility testbed designed to simulate standardized detection conditions. The facility includes both metallic and non-metallic pipelines, notably Reinforced Concrete Pipes (RCP) and polyethylene (PE) pipes, installed horizontally at controlled depths of 2 m and 3 m.  
This study focuses on the 3 m deep non-metallic targets. To evaluate detection accuracy, each pipeline was conceptually divided into 10 equal segments along its longitudinal axis.  
Figure 6: Experimental flowchart of AML-based subsurface utility detection and analysis.  
The vertical depth of each corresponding surface point, as well as its horizontal distance from a reference point (placed above ground), was recorded. The geometric layout of the subsurface pipelines and the vertical projection of measurement points onto the surface are visualized in Figure 7. This illustration helps clarify the spatial relationships between the pipelines and the surface reference points used during testing.  
Table 1 presents the measured results for both RCP and PE pipelines buried at 3 m depth. Each row shows the horizontal surface position and vertical depth of each target segment, along with the horizontal distance between the surface point and the reference point above ground.  
The AML system demonstrated consistent detection capabilities under these controlled conditions. All 10 points on each pipeline were successfully detected. The average depth of the detected signals for RCP and PE pipelines was approximately 2.630 m and 2.524 m, respectively, with minimal variation across repeated measurements. The horizontal distance from the reference point averaged approximately 2.18 m for RCP and 5.75 m for PE pipelines.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **RCP (3 m)** & **RCP (3 m)** & **PE Pipe (3 m)** & **PE Pipe (3 m)** \\  & **Position (m)** & **Depth (m)** & **Position (m)** & **Depth (m)** \\ \hline
1 & 2.154 & 2.630 & 5.651 & 2.407 \\
2 & 2.159 & 2.630 & 5.675 & 2.433 \\
3 & 2.165 & 2.630 & 5.698 & 2.459 \\
4 & 2.170 & 2.629 & 5.722 & 2.485 \\
5 & 2.175 & 2.629 & 5.746 & 2.511 \\
6 & 2.180 & 2.629 & 5.769 & 2.537 \\
7 & 2.816 & 2.629 & 5.793 & 2.563 \\
8 & 2.191 & 2.628 & 5.817 & 2.589 \\
9 & 2.196 & 2.628 & 5.840 & 2.615 \\
10 & 2.202 & 2.628 & 5.864 & 2.6115 \\ \hline \hline \end{tabular} RCP (Reinforced Concrete Pipe), PE (polyethylene).  
\end{table}
Table 1: Surface projection distances and depths of RCP and PE pipeline targets buried at 3 m.  
Figure 7: Spatial configuration of the underground pipelines and measurement reference points.  
These results confirm the device's ability to detect non-metallic utilities with high positional accuracy and repeatability in favorable soil conditions.
# A Novel Channel Coding Scheme for RFID Generation-2 Systems
## 2 System Model
### The Proposed Decoding Algorithm  
The proposed scheme allows an easy soft decoding detector that finds the maximum of the received codeword and outputs the decimal-to-binary conversion of the position index as decoded string.  
Figure 3 shows the proposed receiver architecture--it consists of a signal correlator block followed by the proposed decoder to obtain the estimated output \(\mathbf{\hat{y}}\).  
The received signal \(r(t)\) is affected by additive, white, and Gaussian noise (AWGN), slow-fading, and inter-tag interference. We consider--as usual for UHF RFID systems--no inter-symbol interference:  
\[r(t)=\alpha y(t)+n(t)+i(t), \tag{1}\]  
where \(y(t)\) is the transmitted signal, \(n(t)\) represents the AWGN, the attenuation \(\alpha\) represents the slow-fading effect, \(i(t)\) the inter-tag interference, while the estimate of the transmitted coded bits is:  
\[\hat{y}(iT_{b})=c_{0}(iT_{b})-c_{1}(iT_{b}), \tag{2}\]  
where \(c_{0}(iT_{b})\) and \(c_{1}(iT_{b})\) represent the output of the squared correlation between the received signal \(r(t)\) and, respectively, the FM0 bit waveform \(S_{ref0}(t)\) and \(S_{ref1}(t)\), sampled at the bit timing \(T_{b}\)[20]:  
\[c_{0}(iT_{b})=\left|\int_{(i-1)T_{b}}^{iT_{b}}S_{ref0}(t)r(t)dt\right|^{2} \tag{3}\]  
\[c_{1}(iT_{b})=\left|\int_{(i-1)T_{b}}^{iT_{b}}S_{ref1}(t)r(t)dt\right|^{2} \tag{4}\]  
We have considered a square-law non coherent detector, since it is most used in applications because of its simplicity. Anyway, the proposed decoding idea is independent of the particular detector, so it is possible to use, for instance, a more performant coherent detection scheme.  
After that, a serial-to-parallel (S/P) converter block outputs the estimate of the transmitted codeword, representing the decoder input as in the following:  
\[\hat{\mathbf{y}}=[\hat{y}(iT_{b}),\hat{y}((i-1)T_{b}),...,\hat{y}((i-N+1)T_{b} )]\doteq\{\hat{y}((i-j)T_{b})\}, \tag{5}\]  
with \(j=0,1,...,N-1\).  
Finally, the decoder output \(\mathbf{\hat{x}}=(x_{0},x_{1})\) provided by the detector is the decimal-to-binary conversion of \(\arg(\max_{j}(\mathbf{\hat{y}}))\).  
It is important to note that the proposed decoding algorithm only uses the outputs of the usual symbol detector, without adding further complexity. A possible hardware implementation could be realized by a comparator which chooses the maximum among \(N=4\) different input signals, represented by Equation (5). Moreover, it requires the same robustness against synchronization errors of the common detector.  
Figure 3: Proposed receiver architecture scheme.
## 4 Results and Discussion
### Model Variants  
To demonstrate the advantages of collaboratively using the labeled and unlabeled alarm sequences, two variants of the proposed S-ECNN model are given. They are the S-ECNN-rand model and the S-ECNN-static model. In the S-ECNN-rand model, the pretrained alarm vectors are not applied in the parameter initialization of the embedding layer. The parameters are randomly initialized. Therefore, the diagnosis results of the S-ECNN-rand model are only based on the labeled alarm sequences. In the S-ECNN-static model, the pretrained alarm vectors are applied in the parameter initialization of the embedding layer. When training the model, the parameters of the embedding layer are kept static. In other words, the vector representations of alarms are determined by the unlabeled alarm sequences and not updated in the embedding layer during the training process. The gradients are backpropagated to the first convolutional layer. Only the parameters of networks after the embedding layer are learned using the labeled alarm sequences. The training process can be regarded as a downstream classification task to evaluate the pretrained alarm vectors. Therefore, the diagnosis results of the S-ECNN-static model are mainly based on the unlabeled alarm sequences.
## 3 Results and Discussion
### Research on Wave Amplitudes  
Since different optical power fluctuation frequencies have little effect on the UWOC link, we further studied the influence of the optical power amplitude on the UWOC link. Over time, the waves will become increasingly smaller fluctuations, but the frequency variation of the waves is basically the same. Damped vibration can be used to explain this phenomenon. We finally observed three optical power variation amplitudes, 10 \(\upmu\)W, 20 \(\upmu\)W, and 40 \(\upmu\)W. Based on these three different optical power variation amplitudes, we verified the performance of the UWOC link with a total reflection angle of 7\({}^{\circ}\). The experimental results are shown in Figure 11.  
Figure 11a-c shows the optical power variation curve with the optical power frequency of 1 Hz and the amplitude variation of 10 \(\upmu\)W, 20 \(\upmu\)W, and 40 \(\upmu\)W, respectively. Figure 11d-f is their corresponding waveform diagram, respectively. Note that the data rate in Figure 11d is 290 Mb/s, the data rate in Figure 11e is 240 Mb/s, and the data rate in Figure 11f is 210 Mb/s.  
Figure 12 shows the relationship between the data rate and BER under experimental conditions where the optical power vibration is at the same frequency but the optical power amplitude vibration is different. Due to the different magnitudes of the optical power amplitude, the maximum data rate that the UWOC link with a total reflection angle of 7\({}^{\circ}\) can reach under different experimental conditions of amplitude vibration is also different. Under the experimental condition where the optical power frequency is 1 Hz, the maximum achievable data rates of up to 280 Mb/s, 230 Mb/s, and 200 Mb/s were obtained at the optical power amplitudes variation of 10 \(\upmu\)W, 20 \(\upmu\)W, and 40 \(\upmu\)W with the corresponding BERs of 2.63 \(\times\) 10\({}^{-3}\), 3.42 \(\times\) 10\({}^{-3}\), and 2.57 \(\times\) 10\({}^{-3}\), respectively.  
Figure 10: BER versus data rate at the different optical power frequencies and same amplitude.  
Figure 11: Optical power variation curve versus different waves amplitudes of **(a)** 10 \(\mu\)W, **(b)** 20 \(\mu\)W and **(c)** 40 \(\mu\)W; waveform diagram versus different optical power variation amplitudes of **(d)** 10 \(\mu\)W at data rate of 290 Mb/s, **(e)** 20 \(\mu\)W at data rate of 240 Mb/s and **(f)** 40 \(\mu\)W at a data rate of 210 Mb/s.  
Figure 12: BER versus data rate at the different optical power variation amplitudes and same frequency.
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 3 Proposed Relay Selection Scheme
### Proposed Relay Selection Scheme
In addition, since relay is used in cooperative communication, there is no line-of-sight (LOS) signal and the SNR value between source and destination node is also initialized to zero. Furthermore, when the learning process is finished at the destination node, the next episode begins and the reward is also reset to zero. Reward is defined on the basis of SNR and the relay is selected through the learning process. The greatest benefit of the proposed SNR based Q-learning scheme is less exchange of CSI and the optimal relay can be selected through the self-learning. Furthermore, it reduces the latency and the waste of resources when the target BER is satisfied. Therefore, it can mitigate the overhead occurred in a cooperative communication without exchanging the information and also can reduce the latency compared to the conventional BHM scheme.
# Initial Results from SQUID Sensor: Analysis and Modeling for the ELF/VLF Atmospheric Noise
###### Abstract  
In this paper, the amplitude probability density (APD) of the wideband extremely low frequency (ELF) and very low frequency (VLF) atmospheric noise is studied. The electromagnetic signals from the atmosphere, referred to herein as atmospheric noise, was recorded by a mobile low-temperature superconducting quantum interference device (SQUID) receiver under magnetically unshielded conditions. In order to eliminate the adverse effect brought by the geomagnetic activities and powerline, the measured field data was preprocessed to suppress the baseline wandering and harmonics by symmetric wavelet transform and least square methods firstly. Then statistical analysis was performed for the atmospheric noise on different time and frequency scales. Finally, the wideband ELF/VLF atmospheric noise was analyzed and modeled separately. Experimental results show that, Gaussian model is appropriate to depict preprocessed ELF atmospheric noise by a hole puncher operator. While for VLF atmospheric noise, symmetric \(\alpha\)-stable (S\(\alpha\)S) distribution is more accurate to fit the heavy-tail of the envelope probability density function (pdf).  
amplitudes probability density; atmospheric noise; superconducting quantum interference device; S\(\alpha\)S distribution +
Footnote †: journal: Sensors
## 2 Materials and Methods
### ROM Assessment  
IR and ER were measured using a digital inclinometer (ACUR001 Lafayette Instrument, Lafayette, IN, USA). Three measurements of each shoulder joint were recorded, and the average of the three trials was calculated.  
The tROM (sum of IR and ER), and the GIRD (difference between IR RS and IR non-TS as a comparable reduction in ROM described in negative value) were calculated after the measurements [13; 14].  
IR and ER ROM were measured with the patient in supine position with the arm at 90\({}^{\circ}\) of abduction and the elbow at 90\({}^{\circ}\) of flexion with a towel under the arm to ensure the correct alignment of the upper limb in the frontal plane. The examiner manually stabilized the scapula in the anterior part of the shoulder applying pressure with one hand. The other hand was used to perform passive IR and ER. The inclinometer was placed by the second examiner on the dorsal part of the distal part of the forearm [13; 14].  
\begin{table}
\begin{tabular}{l c c c} \hline  & **ICC (95\% CI)** & **SEM** & **MDC** \\ \hline ER strength (Kg) & 0.97 (0.96, 0.98) & 0.52 & 1.44 \\ IR strength (Kg) & 0.98 (0.97, 0.99) & 0.64 & 1.79 \\ Extensibility (\({}^{\circ}\)) & 0.92 (0.9, 0.94) & 2.23 & 6.19 \\ Tone (Hz) & 0.98 (0.96, 0.99) & 0.29 & 0.80 \\ Stiffness (N/m) & 0.97 (0.95, 0.98) & 8.99 & 24.91 \\ Elasticity & 0.95 (0.9, 0.97) & 0.08 & 0.23 \\ Relaxation time (ms) & 0.98 (0.97, 0.99) & 0.67 & 1.88 \\ Creep (D) & 0.97 (0.94, 0.98) & 0.04 & 0.12 \\ \hline \end{tabular} ICC: intraclass correlation coefficient; CI: confidence interval; SEM: standard error of measurement; MDC: minimal detectable change; ER: external rotation; IR: internal rotation.  
\end{table}
Table 1: Test–retest reliability of the study variables.
# Calculation Method for Electricity Price and Rebate Level in Demand Response Programs
## 6 Conclusions  
The authors proposed a theoretical approach that calculates electricity prices and rebate levels in DRs, based on the framework of SWM. In the authors' proposal, first, the utility functions of the power suppliers and the consumers were set, and then, the power supply-demand-balancing operation was represented under the SWM framework. Next, the authors derived the acceptable conditions for price-based DRs as Equations (18) and (19) and the acceptable conditions for incentive-based DRs as Equation (25). Besides, the optimal values of electricity prices and rebate levels were defined as Equations (20) and (26), respectively, in consideration of burden on the society. The distinctive feature of the proposed approach was to make influences of the DRs measurable as an increment/decrement in the utility functions. Finally, to verify the validity of the authors' proposal, the numerical simulations were carried out with the model, which was constructed using the approximated fuel cost function of power generation units and the record of smart power meters.  
Figure 14: Results on the typical time slots in scenario 2 (at 4:00). In this scenario, the power suppliers could not request the DR cooperation from 18:00 to 22:00.  
Figure 13: Results on the typical time slots in scenario 1 (at 22:00).  
As shown in Section 5, the calculated electricity prices and rebate levels became smaller than the values applied in the demonstrative field tests. This is because the calculation results, as defined in Equations (18)-(20) as well as Equations (25) and (26), strongly depended on the utility functions of the power suppliers and the consumers. In other words, the assumed utility functions have room for discussion on their appropriateness. However, the results of the numerical simulations reflected the controllability in electricity consumption, and we can conclude that the authors' proposal functioned appropriately.  
In future works, the appropriateness of the assumed utility functions will be discussed in more detail. Furthermore, the authors will analyze influences of the replacement of the actual electricity consumption, \(d_{t}^{*}\), with the estimated one, and the proposed framework will be expanded.  
Conceptualization, H.T. and N.Y.; methodology, H.T. and N.Y.; software, N.Y.; validation, H.T., N.Y., H.A., A.H. and N.D.T.; writing--original draft preparation, H.T., H.A., A.H. and N.D.T.; writing--review and editing, H.T.; supervision, H.T. and H.A.; project administration, H.T. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the Japan Society for the Promotion of Science (JSPS; grant number: 19K04325).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
The article uses numerical and modeling studies to evaluate the temperature stresses (in the depth direction) caused by heating pavement with carbon fiber. The following are the primary conclusions:  
1. The trends in the thermal strain field and temperature field from the model tests are typically similar, and the fitted correlation between the two implies that temperature changes cause thermal strains to occur.
2. The temperature gradient of carbon fiber-heated pavement is significantly greater than the value stated in the concrete pavement design standard. The horizontal temperature gradient is greater than the vertical temperature gradient. This demonstrates the importance of studying the temperature stress of carbon fiber-heated pavement.
3. The temperature stresses within carbon fiber-heated pavement were investigated along the depth direction in the numerical model testing. Root mean square error (RMSE) analysis of the model test results and numerical test results proved the accuracy of the numerical model of carbon fiber-heated pavement.
4. It was shown that the temperature gradient causes the temperature stress by fitting the relationship between the temperature gradient and temperature stress for pavement at various depths. Furthermore, to offer recommendations for real-world engineering applications, a linear equation for the temperature gradient and temperature stress in the depth direction is offered.  
The study's findings may be utilized to guide and optimize the structural design and construction of carbon fiber-heated concrete pavement, as well as to review the safety performance and structural integrity of this pavement.  
Conceptualization, N.Z., Z.C. and H.X.; methodology, Z.C.; software, N.Z.; validation, N.Z., Z.C. and H.X.; formal analysis, N.Z.; investigation, Z.C.; resources, H.X.; data curation, N.Z.; writing--original draft preparation, N.Z.; writing--review and editing, Z.C. and L.Z.; visualization, N.Z.; supervision, Z.C., H.X. and L.Z.; project administration, Z.C., H.X., L.Z. and Q.M.; funding acquisition, Z.C., H.X., L.Z. and Q.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Research and Development Program of Hubei Province (No. 2021 BGD015) and the Technology Innovation Project of Hubei Province (No. 2023 BEB010).  
Figure 13: Pavement temperature gradient and temperature stress fitting.
## 4 Materials and Methods
### Methods
#### 4.2.2 Predictive Mode  
Different statistical and machine learning methods were used to select the predictive model. The process for inferring data (in this case, speed) for both regression and machine learning models is similar. The process starts by separating the data (into training and validation sets) in both cases. This is usually done on a 70% (training)-30% (validation) basis. The training set is used to train (tune the model hyperparameters) the predictive model.  
Figure 11: An invalid sample.  
Figure 9: Vehicle identified and tracked.  
Once the model is trained, the validation set is used to perform the speed estimations. These estimations (accuracy) are used to validate the trained model. The model process is depicted in Figure 12.  
The validation process takes the trained model and receives input data from the validation set. The output of this model (speed estimation) is compared against the existing velocity captured. We call this difference the error, and the smaller the error, the better the learning model is. The evaluation metric (error) used for the vehicle's speed predictive model was Mean Absolute Error Equation (7), expressed meters/second.  
\[MAE=(\frac{1}{n})\sum_{i=0}^{n}\mid y_{i}-x_{i}\mid \tag{7}\]  
In general, to obtain the output speed estimation, with the methods used in this work, we started operating as input data all the features described in Table 2, such as the output angle, distance traveled, input area, output area, time, and lane. Nevertheless, we obtained an average error of 2.16 km/h, representing a significant difference between the real and the estimated speed. Hence, we decided to reduce the number of input features and consider the distance (such as [PERSON] et al. [38] and [PERSON] et al. [33]) and time since with only these two features we obtained the best results.
## 2 Materials and Methods
### Satellite Data  
The LAI data set used in this study was generated using an Artificial Neural Network (ANN) from the latest version (third generation) of the GIMMS AVHRR NDVI data for the period July 1981 to December 2010 at a 15-day frequency ([PERSON] _et al._ this issue [36]). The ANN was trained with best-quality Collection 5 MODIS LAI product and corresponding GIMMS NDVI data for an overlapping period of 5 years (2000 to 2004) and then tested for its predictive capability over another five year period (2005-2009). The average uncertainty of the MODIS LAI product is estimated to be 0.66 LAI units [24], though it varies depending on the mean LAI, and the data is for 1-sided LAI; further details are provided in [PERSON] _et al._[36]. The 10 years of MODIS LAI/FPAR (2000-2009) was further processed to generate climatology. The ANN was further trained on the climatology fields. The NDVI3g data have now a 30-year history of development. The data was further regridded to the same 1 \(\times\)1 grid, using a linear interpolation; all missing values were filtered when average over a coarser resolution.
## 3 Experimental Results and Analysis
### Deductions from Proposed Model
#### 3.3.1 The Relationship between Robustness \(R\) and Repeat Times \(N\)  
Intuitively, it may be felt that the more times watermark is embedded, the more likely the watermarking algorithmic robustness get enhanced. The computation experiment based on the proposed model has been conducted to verify this intuition. In the experiment, the binary watermark information length \(L\) is set to 100, 200, 300 and \(N\) is set to 2, 4, 6, 8, 10, 20, and 40 respectively. Then robustness index is calculated by the proposed model. Results of the relationship are shown in Figure 17.  
Figure 17: _Cont._As shown in Figure 17, two new results have been achieved as follows: with an increase in the watermark repeat times \(N\) and fixed \(L\): (1) the image cropping robustness is enhanced; and (2) the rate of robustness improvement becomes slower. As shown by the curves of each \(N\) value, in those particular sections where \(R\) begins to drop, a larger \(N\) appears on the right side of those curves with a smaller \(N\). For example: for each figure the curve in which \(N=20\) begins to drop near \(S=0.9\), while the curve in which \(N=4\) is near the line \(S=0.6\). Thus this phenomenon indicates that the robustness improvement is in accordance with the repeat of \(N\). Additionally, the changed rate of the curves reveals a further interesting result. It can be deduced that when \(N\) becomes sufficiently large, the curves will then be reliably and sufficiently close to \(R=1\) and \(S=1\).  
Based on the above results, new knowledge has been gained. Increasing watermark repeat times will definitely enhance the watermark robustness against image cropping attacks, however, embedding too much watermark information will cause significant distortions in remote sensing images. Hence, it is easily concluded that the robustness against image cropping can be enhanced by increasing the watermark repeat times to an appropriate value, or in other words, achieve the balance between robustness and imperceptibly.