esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experimental Results on TRESS01 Dataset  
The training set of the TRESS01 datasets was used as the input of the model, which was trained for 200 epochs (Figure 7). The model basically reached a steady state after the 50 th epoch. Then, the classification ability of the trained model was evaluated on the test set. The median results of 11 replicate trials are selected for discussion. Figure 7 shows the accuracy and loss curves of the model on the training and testing datasets. Figure 8 shows the confusion matrix of the model on the test set. The overall correct recognition rate for these three categories is 98.49% (Figure 8). For the results of single target types, the accuracy values of tracked vehicles and noise are high, both exceeding 98%. For people, the classification accuracy is 97.66%. The false alarm rate is 0.43%, and the leakage rate is 1.08%. The experimental results show that the algorithm performs well. Among the misclassified samples are several of people misclassified as ambient noise. A few samples of tracked vehicles are classified as pedestrians and noise. Misclassification is mainly due to ambiguous identification of samples with low signal-to-noise ratios.  
\begin{table}
\begin{tabular}{c c c c} \hline  & _V1/dB_ & _SPL/dB_ & **Temperature** & **Date** \\ \hline First recording & 36.88 & 25.71 & 26 \({}^{\circ}\)C & 30 May 2022 \\ \hline Second recording & 38.82 & 26.32 & 25 \({}^{\circ}\)C & 31 May 2022 \\ \hline Third recording & 39.03 & 24.49 & 23 \({}^{\circ}\)C & 1 June 2022 \\ \hline Fourth recording & 41.99 & 28.44 & 23 \({}^{\circ}\)C & 11 June 2022 \\ \hline Fifth recording & 40.83 & 26.69 & 27 \({}^{\circ}\)C & 15 June 2022 \\ \hline \end{tabular}
\end{table}
Table 2: Recording environments.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Background** & **Person/Second** & **Vehicle/Second** \\  & **Noise/Second** & **Person/Second** & **Vehicle/Second** \\ \hline Train datasets & 2372 & 2483 & 1954 \\ \hline Test datasets & 403 & 371 & 452 \\ \hline \end{tabular}
\end{table}
Table 3: Size of datasets.
## 3 Proposed Algorithm
### Baseline Scheduler  
A comparison of the performance of the proposed scheduler versus that of a baseline scheduler design is now presented in order to demonstrate the advantages provided by the proposed mechanism of simultaneously transmitting a packet via multiple CCs.  
For the baseline scheduling algorithm, the key working principle is that the various CCs cannot be simultaneously used for the transmission of a user packet, although they can be used independently. More specifically, each RT user will at first be randomly assigned to only a single specific CC out of all the CCs for the transmission of his/her packets. At the same time, the random assignment of each NRT user to only a single specific CC among all the CCs designated for NRT users under the definition of \(\eta\) will also be made. For the case illustrated in Figure 3, for example, if the baseline algorithm is used, then only the 1 st CC can be used for transmissions by all the NRT users. Otherwise, the steps of the baseline algorithm are exactly the same as those listed above for the proposed algorithm.  
Figure 3: An example of downlink transmissions in a case involving six users and two CCs aggregated in a base station (BS). More specifically, both RT packets (for user-1, user-2, user-3, user-4 and user-6) and NRT packets (user-5) are included in the cell.
# Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model
## 2 Materials and Methods
### Inverse Problem Statement  
In this section, the inverse problem is established and a gradient-based solver is implemented to estimate the TRF.  
In the presence of noise \(\mathbf{b}\), which refers to modeling and acquisition noise, Equation (A3) becomes:  
\[\tilde{\mathbf{g}}=\mathbf{\
abla}_{\mathbf{\gamma}}+\mathbf{b}. \tag{26}\]  
A straightforward way to solve the problem of Equation (26) would be to minimize in a least squares sense: \(\|\mathbf{\
abla}_{\mathbf{\gamma}}-\tilde{\mathbf{g}}\|_{2}^{2}\), where \(\|\ \|_{2}\) represents the \(l_{2}\) norm. Under the condition that \(\mathbf{\
abla}\) is well-conditioned, the solution would be \(\hat{\mathbf{\gamma}}=\mathbf{\
abla}^{\intercal}\tilde{\mathbf{g}}\), where \(\mathbf{\
abla}^{\intercal}\) is the Moore-Penrose pseudo-inverse of \(\mathbf{\
abla}\).  
As shown in Appendix C, \(\mathbf{\
abla}\) is ill-conditioned. Thus, the use of the pseudo-inverse would provide a degraded TRF estimation due to the amplification of the noise \(\mathbf{b}\). This problem can be overcome by constraining the solution space through a regularization [44]. The TRF can then be estimated as:  
\[\hat{\mathbf{\gamma}}_{\text{SPW-FM-LP}}=\operatorname*{argmin}_{\mathbf{\gamma}}\{\| \mathbf{\
abla}_{\mathbf{\gamma}}-\tilde{\mathbf{g}}\|_{2}^{2}+\lambda\|\mathbf{\gamma}\|_{p}\}, \tag{27}\]  
where \(\|\ \|_{p}\) is the \(\ell_{p}\) norm, and the scalar \(\lambda\) allows for a trade-off between the data fitting and the regularization term.  
Finding a general regularization term in Equation (27) is an open problem in the ultrasound domain. The norm \(l_{p}\) and the weight \(\lambda\) are usually adapted as a function of the TRF [33; 35; 36]. In this article, the TRF is obtained using \(l_{p}=l_{1}\); thus, the weight \(\lambda\) always remains between 0 and 1. If the expected solution is sparse, \(\lambda\) is set high, otherwise smaller values are used [45]. Here, \(\lambda\) is adapted on a trial-and-error basis.  
The estimation of the TRF \(\mathbf{\gamma}\) is obtained using the fast iterative shrinkage-thresholding algorithm (FISTA) [46]. A detailed implementation of FISTA is presented in [37]. Similar to [37], the FISTA stopping criterion was set to be a drop in the relative difference (betweensuccessive estimations) of the TRF of under \(10^{-3}\). A final step of log-compression is applied to the TRF.
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 4 The K3P Algorithm
### Joint Reconfiguration  
In lines 9 and 16 of Algorithm 1, the new positions of the body body frame \(B\) and both tripods \(\mathcal{O}\) and \(E\) are obtained, using PD controllers on their target positions.  
Figure 4: The two situations that may occur when driving the subset of legs during the swing phase of the gait. (**a**) Gait forward, \(\rho\geq l_{\rho}\), reference frame \(E\) moves towards \(L\), which is located straight ahead from \(B\), along the \(x\) axis, \(d_{max}\) meters. (**b**) Turning gait, \(\rho<l_{\rho}\). Reference frame \(L\) is located \(d_{max}\) meters ahead over the momentaneous trajectory around the turning point \(\mathcal{C}\).  
After the corresponding location for the tripods in the support and swing phases have been updated to follow the movement of the CoM \(B\), their corresponding state vectors \({}^{B}\mathbf{X}_{W,k+1}\), \({}^{O}\mathbf{X}_{B,k+1}\) and \({}^{E}\mathbf{X}_{B,k+1}\) define the spatial relationships between reference frames \(W\), \(B\), \(O\), and \(E\). From here, it is possible to obtain the positions of each leg tip \(SP_{i}\) with respect to the robot's body and, using the inverse kinematics of the 3 DoF RRR limb (line 24 of Algorithm 1), we can compute the values for all articular joints \([\theta_{s},\theta_{l},\theta_{k}]_{1,2,\ldots,6}\)[6].  
At this moment, K3P tests the stability of the arrangement at time instant \(k+1\) by measuring the Euclidean distance from the projection of \(B\) on the support polygon to all edges; in case the minimum distance falls below a predefined threshold \(l_{\mathit{Ihd}}\), K3P will command the robot to come to a halt. This rarely occurs because the phase shift tests K3P1,2,3, which will be introduced in the following section, were designed to guarantee a stable gait.
## 3 The Signal Adaptive Iterative Optimization Resampling (SAIOR) Algorithm
### Estimation of Modulation Period (MP)  
After the previous step of signal adaptive iterative elimination, the input signal approximately retains only noise and PFM interference signals, expressed as:  
\[x(t)\doteq\sum_{m}^{M}j_{m}(t)+n(t) \tag{13}\]  
Since the interference and noise components in the received signal are statistically uncorrelated, their cross-correlation function theoretically approaches zero and can be neglected. Therefore, the period of periodic components in the received signal is estimated through autocorrelation processing below.  
From Equation (13), the autocorrelation function of \(x(t)\) can be expressed as:  
\[R_{x}(\tau)=\sum_{m=1}^{M}R_{j_{m}}(\tau)+R_{n}(\tau)=R_{j}(\tau)+R_{n}(\tau) \tag{14}\]  
where \(R_{j_{m}}(\tau)\) and \(R_{n}(\tau)\) are the autocorrelation functions of \(j_{m}(t)\) and \(n(t)\), respectively. Thus,  
\[\begin{split}&\left|R_{j}(\tau)\right|=\left|\sum_{m=1}^{M}R_{j_{m}}( \tau)\right|\\ &=\left|\sum_{m=1}^{M}\frac{A_{m}^{2}}{2}\exp(2\pi f_{0m}\tau) \lim_{T\rightarrow\infty}\frac{1}{T}\int\limits_{-T}^{T}\exp\left\{\frac{ \Delta f_{m}T_{m}}{2}\cos(\frac{2\pi}{T_{m}}t)-\frac{\Delta f_{m}T_{m}}{2} \cos\left[\frac{2\pi}{T_{m}}(t-\tau)\right]\right\}dt\right|\end{split} \tag{15}\]  
From Equation (15), it can be observed that:  
\[\left|R_{j}(\tau)\right|\leq\sum_{m=1}^{M}\frac{A_{m}^{2}}{2} \tag{16}\]  
\(\frac{\Delta f_{m}T_{m}}{2}\cdot\cos(\frac{2\pi t}{T_{m}})\) is a periodic function with \(T_{m}\) as its modulation period; so, Equation (14) holds true if and only if \(t=nT_{m}\). That is, \(\left|R_{j}(\tau)\right|\) reaches a maximum at \(nT_{m}\). Therefore, by detecting the peaks of \(\left|R_{x}(\tau)\right|\), the estimated value of the PFM interference modulation period \(T_{m}\) can be obtained.
# Damage Characteristics of Thermally Deteriorated Carbonate Rocks: A Review
## 2 Thermal Dilatancy and Alteration in Rock Fabric
### Thermal Cracking and Microstructural Variations
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature Range** & **Major Findings** & **Reference** \\ \hline
200–800 \({}^{\circ}\)C & They analyzed the thermally treated limestones using scanning electron microscopy. They showed orientation of thermal tension and shear cracks developed in limestone. The cracks were straight, curved, parallel, vertical, oblique, and crossed layers. & [64] \\ \hline
100–500 \({}^{\circ}\)C & They studied the monomineralic carbonate rocks subjected to various temperature ranges. They observed that, in these kinds of carbonate rocks, thermal damage was the function of anisotropic dilation of calcite and shrinkage of clay minerals. The mineral expansion was observed at a temperature range of 100–200 \({}^{\circ}\)C, whereas intergranular and intragranular cracking was noted at 300–500 \({}^{\circ}\)C. & [13] \\ \hline
25–600 \({}^{\circ}\)C & They demonstrated the thermal deterioration of the limestone in terms of spectral reflectance. They found that, at an initial level of temperature, mineral expansion under elastic constraints increased the spectral reflectance, and, at a temperature above 500 \({}^{\circ}\)C, the thermal degradation of minerals decreased their spectral reflectance. & [20] \\ \hline
20–1000 \({}^{\circ}\)C & He investigated the effect of mineral crystal structures on the thermal behavior of carbonate rocks. In the case of dolostone, he observed that larger crystals of dolomite minerals decomposed more than the dolostone containing the smaller size dolomite crystals. & [65] \\ \hline
25–800 \({}^{\circ}\)C. & XRF technique was used to investigate the microstructural changes in limestones. They noticed an appreciable alteration in the percentage of the mineral content at a temperature window of 400–700 \({}^{\circ}\)C. & [66] \\ \hline
20–800 \({}^{\circ}\)C & They studied microstructural variations in carbonate rocks and found no noticeable changes in the chemical composition at a temperature below 400 \({}^{\circ}\)C. Furthermore, they observed that calcite and dolostone were decomposed at 400–500 \({}^{\circ}\)C and clay minerals started to decay at a temperature above 500 \({}^{\circ}\)C. In the case of trace minerals and impurities, their concentration was decreased gradually up to 400 \({}^{\circ}\)C and then increased sharply above 600 \({}^{\circ}\)C. & [67] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of major findings regarding the microscopic evaluation of the thermally damaged carbonate rocks.
# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 2 Related Work
proposed a new approach to interconnect multispectral images via diffusive morphology techniques to improve aviation vision systems. Unlike traditional morphological methods, this approach does not require image segmenting to describe its shape and offers strong resistance to noise and distortions. They developed procedures for interconnecting two- and three-channel images using diffusive morphological filtrations and an image pyramid. A promising prototype of this improved vision system was tested with multispectral data collected during an aeronautical test [15]. [PERSON] et al. focused on the problem of filtering multispectral images by studying the effectiveness of filtering image sequences. They analyzed variations in error variance as a function of the number of images in the sequence and the inter-image correlation coefficient. To statistically model these images, they used doubly stochastic random fields [16]. Furthermore, [PERSON] et al. explored the restoration of images under conditions where a portion of the observations is subject to additive noise, or more precisely, the restoration of randomly sampled images with pixels affected by white Gaussian noise. They developed nonlinear filters based on deep doubly stochastic Gaussian models, which were more effective than linear methods and classical algorithms. These filters allow images to be restored using only 50% of the data with a relative error rate of 9% [17]. These studies show the importance of robust and innovative approaches to improve image quality, particularly in multispectral vision and image reconstruction, where precision and resistance to noise are essential. Most existing methods for approximating spectral responses must be adapted to our specific problem of 8-band MSFA one-shot cameras. Our article presents a new approach based on deep learning to address this challenge.
# Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection
## 6 Conclusions  
The proposed improved model's detection skills were thoroughly assessed in this study in comparison to the baseline YOLOv5s model. Extensive experiments were carried out using real road scenes and diverse traffic sign scenarios, demonstrating the effectiveness of the enhanced model.  
The outcomes demonstrate how well the upgraded network model detected small items. The introduced enhancements significantly improved the accuracy of detecting small traffic signs, as shown in Figure 10. Additionally, the proposed model outperformed the standard YOLOv5s model under challenging conditions such as dim lighting and interference from surrounding advertisements by accurately identifying traffic signs and exhibiting robustness against interference.  
Moreover, the expanded dataset helped the model become more universal. The proposed model successfully detected small traffic signs like pl80, indicating the effectiveness of the improvements made for detecting small objects. This trend continued in subsequent experiments, with the model accurately recognizing various traffic signs, even in complex scenarios.  
Moreover, the improved model showcased its robustness and superior detection performance in various situations. It accurately identified traffic signs during turns, detected distant and smaller objects, and avoided misclassifications even in the presence of interference. The model's ability to accurately detect tilted traffic signs and smaller distant signs  
Figure 10: Comparison of images collected from real vehicles. The left column represents the results of the standard YOLOv5s network model image, and the outcomes of the enhanced network model suggested in this article are shown in the right column.  
was especially impressive, demonstrating its advanced characteristics that outperformed those of the YOLOv5s model.  
In summary, the outcomes of comprehensive tests confirm that the suggested enhancements are successful. Not only does the improved model greatly increase the accuracy of small item identification, but it also demonstrates exceptional resilience in a range of difficult situations. This research builds a solid basis for future developments in practical applications, like autonomous driving and intelligent transportation systems, and adds significant insights to the field of traffic sign identification algorithms.  
Conceptualization, Y.C. and H.Y.; methodology, Y.C. and D.G.; software, Y.C.; validation, Y.C. and H.G.; formal analysis, Y.C.; investigation, H.Y.; data curation, Y.C. and H.Y.; writing--original draft preparation, Y.C. and H.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Grant 2020 YFB1313400.  
Not applicable.  
The data source used in this article is TT100K (China Traffic Sign Dataset).  
The authors declare no conflicts of interest.
## 4 Conclusions  
In this work, a sensing scheme for the simultaneous measurement of vibration and temperature by a single FBG sensor routed through an FSO link is demonstrated for the first time. The vibration measurement signals under different ambient temperatures can be deciphered by a DNN sequential model to obtain the vibration frequency and temperature information. The DNN sequential model recognizes 32 scenarios at various ambient temperatures, i.e., 30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C, and at different vibration frequencies, i.e., 0.1 Hz, 0.5 Hz, 1 Hz, 1.5 Hz, 20 Hz, 40 Hz, 60 Hz, and 80 Hz, with an accuracy of  
Figure 6: Results of using the DNN model to recognize the vibration frequency and temperature from vibration signals: (**a**) training accuracy, (**b**) training loss, (**c**) validation accuracy, and (**d**) validation loss.  
Figure 7: DNN model recognizing vibration measurements at dynamic temperature: (**a**) 0.1 Hz vibration frequency, (**b**) 80 Hz vibration frequency.  
up to 89%. In addition, at dynamic temperatures, i.e., 31 to 36 \({}^{\circ}\)C, 41 to 46 \({}^{\circ}\)C, and 51 to 56 \({}^{\circ}\)C, the accuracy of recognition reaches 95% for vibration frequencies of 0.1 Hz and 80 Hz. This solution integrates FSO technology and a single FBG sensor to simultaneously measure vibration and temperature, with the machine learning to recognize the signal data, which is expected to save the cost of optical cable laying and maintenance, the cost of the number of FBG sensors, and the cost of human identification of the signals, and promote the development of related surveillance applications.  
Conceptualization, C.-K.Y., P.K. and P.-C.P.; methodology, C.-K.Y., P.K. and B.-X.L.; software, P.K.; data curation, B.-X.L.; model validation, B.-X.L., C.-K.Y. and P.K.; formal analysis, C.-K.Y., A.M.D.; investigation, C.-K.Y., B.-X.L., P.K., A.M.D. and P.-C.P.; visualization: C.-K.Y.; writing, C.-K.Y.; supervision, P.-C.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science and Technology Council, Taiwan, under Grant NSTC 112-2221-E-027-076-MY2.  
Not applicable.  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
###### Abstract  
Vehicular Ad Hoc Networks (VANETs) are envisaged to be a critical building block of Smart Cities and Intelligent Transportation System (ITS) where applications for pollution, congestion reduction, vehicle mobility improvement, accident prevention and safer roads are some of the VANETs expected benefits towards Intelligent Vehicle Communications. Although there is a significant research effort in Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication radio channel characterization, the use of a deterministic approach as a complement of theoretical and empirical models is required to understand more accurately the propagation phenomena in urban environments. In this work, a deterministic computational tool based on an in-house 3D Ray-Launching algorithm is used to represent and analyze large-scale and small-scale urban radio propagation phenomena, including vehicle movement effects on each of the multipath components. In addition, network parameters such as throughput, packet loss and jitter, have been obtained by means of a set of experimental measurements for different V2I and V2V links. Results show the impact of factors such as distance, frequency, location of antenna transmitters (TX), obstacles and vehicle speed. These results are useful for radio-planning Wireless Sensor Networks (WSNs) designers and deployment of urban Road Side Units (RSUs).  
Smart Cities; Wireless Sensor Networks (WSN), 3D Ray-Launching; Vehicular Ad-Hoc Networks (VANET), Vehicle-to-Infrastructure communication (V2I) 20181821330/s180721330/18/07/2133
## 2 Background  
### Forecasting Models  
A forecasting model is a mathematical algorithm or statistical tool used to predict future trends, values, or events based on historical data and patterns. A forecasting model \(M\) that analyzes the historical values from the current time \(t\) to return the predicted future value at time \(t+1\) (denoted as \(\hat{y}_{t+1}\)) is built. The objective of the forecasting model is to minimize the discrepancy between the estimated value \(\hat{y}_{t+1}\) and the actual value \(y_{t+1}\) by seeking the closest approximation. To achieve this in a temporal context where data points are indexed in time order, a specific type of forecasting model is often used.  
A time series forecasting model is a predictive algorithm that utilizes historical time-series data to anticipate future trends or patterns in the data over time. Two techniques are available for building the model \(M\) to obtain this objective, i.e., _(i)_ univariate and _(ii)_ multivariate time-series (TS) [10; 11; 12]. In univariate TS, only a 1D sequence of energy consumption value \(\mathbf{y}_{t}=\{y_{t-(k-1)},y_{t-(k-2)},\ldots,y_{t-1},y_{t}\}\) is utilized to produce the estimated value \(\hat{y}_{t+1}\), where \(k\) is a period of time from the current time \(t\)[13; 14]. By contrast, for multivariate TS, we could employ one or more other historical features in addition to the energy consumption for training model \(M\)[15]. They can be time fields or other specific sources. Therefore, the input for multivariate TS is a multi-dimensional sequence \(\mathbf{X}_{t}=\{\mathbf{x}_{t-(k-1)},\mathbf{x}_{t-(k-2)},\ldots,\mathbf{x}_{ t-1},\mathbf{x}_{t}\}\), with \(\mathbf{x}_{i}\in\mathbb{R}^{n}\) is a vector of dimension \(n\). In forecasting new values, the model could be enhanced if related available features are taken into account. The additional information could help the model capture the dependencies or correlations between features and the target variable. Therefore, the model could better understand the context, mitigate the impact of missing values, and make more precise predictions. Therefore, multivariate TS has been frequently employed for building the forecasting model recently [16].