esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiments and Results
### Simulation of Fruit Picking  
This experiment mimicked picking fruit from a tree and placing it in a basket, as an example of one of the robot's applications (see Figure 16). In (A), the arm is at the zero reference position in the initial condition. In (B), the MA rotates the first joint (\(x\)-axis rotation joint) by \(35\) degrees. The MA then moves toward the third and fourth joints (\(z\)-axis rotation joint) and rotates them to angles of \(28\) and \(22\) degrees, respectively, as seen in parts (C) through (E). In (F), the MA moves toward the end of the fifth link and closes the gripper on the hanging ball. In parts (G) and (H), while still gripping the ball, the MA moves backward to rotate the fourth and third joints to angles of \(-24\) and \(-20\) degrees, respectively. Finally, in (I), the MA reaches its target and releases the ball into the basket (see Video S1). The interest of this fruit-picking task lies in the fact that fruits usually grow in clusters, and thus require fewer joint actuations than conventional arms, and hence lower energy consumption.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model II**} & \multicolumn{2}{c}{**Newton–Raphson**} \\  & **NN** & **NN + Jacobian** & **\(m=1000\)** & **\(m=10,000\)** \\ \hline Mean error \(\overline{z}\) (mm) & \(5.5\) & \(0.29\) & \(2.2\) & \(2.4\) \\ Mean time \(\overline{\tau}\) (s) & \(7.3\) & \(7.91\) & \(8.5\) & \(8.2\) \\ \(\overline{\varepsilon}<1\) mm & \(73\) & \(95\) & \(92\) & \(91\) \\ Computer runtime (s) & \(6\times 10^{-4}\) & \(1\times 10^{-3}\) & \(0.75\) & \(3.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the NN IK model to the Newton–Raphson solver.  
Figure 16: The 3D MASR picks a ball hanging above it, changes its configuration, and drops it into a basket. Starting in (**A**), the MA rotates the first joint (**B**), then advances along the arm (**C**), rotates the first joint (**D**), continues to advance along the arm (**E**), until it reaches its tip (**F**) and grabs a flexible ball, the MA returns (**G**), rotates the arm (**H**) and drops the ball in a bin (**I**).  
Figure 15: Stills of the joint operation of the 3D MASR. Starting in (**A**), the MA rotates the first joint (**B**), then advances to the center of the arm (**C**), rotates the joint (**D**), rotates it back (**E**), advances further (**F**), elongates the prismatic joint (**G**) and finally advances along the prismatic joint (**H**).
## 2 Materials and Methods  
### Sample Preparation  
Tomato pomace was obtained from the industrial processing of tomato products, the Colibri F1 variety and was dried in a cabinet dryer at 40 \({}^{\circ}\)C until the moisture content reached a constant value. Dried tomato pomace with a moisture content of 6% was ground in a domestic grinder (Bosch, Stuttgart, Germany) and sieved through a Retsch Vibratory Sieve Shaker AS 200 basic (Retsch, Haan, Germany). The tomato pomace powders (TP) with a particle size \(<\) 120 \(\upmu\)m and proximate composition consisting of moisture 6%, protein 20.36%, fat 0.35% and ash 8.97% were used in this study. Tomato pomace suspension was obtained by the rehydration of TP using distilled water in order to obtain three different concentrations, 15%, 20% and 25%. These chosen concentrations have a similar consistency with tomato juice (15%), tomato sauce (20%) and tomato paste (25%). Before heating, the samples were rested for 12 h at an ambient temperature for complete hydration. Tomato pomace suspension samples were treated in closed tubes for 30 min at two different temperatures (60 \({}^{\circ}\)C and 95 \({}^{\circ}\)C) which correspond to a pretreatment called \"cold-break\" and \"hot-break\", respectively [32], that can be applied for tomato by-products processing.  
The analyses of the suspensions (total soluble solid content, titratable acidity, pH, water activity, pectin soluble in water, hydration properties, color and rheological properties) formulated with 15, 20 and 25% TP were performed before heat treatment, at an ambient temperature of 20 \({}^{\circ}\)C (MT1, MT2 and MT3) and after heat treatment at 60 \({}^{\circ}\)C (CT1, CT2 and CT3) and 95 \({}^{\circ}\)C (HT1, HT2 and HT3), respectively.
# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 5 Conclusions  
In this paper, we first proposed an MHMM personalized driving behavior analysis model for drivers in a car-following scene. The results of the semantic segmentation of the driving behaviors show that the MHMM can not only divide the car-following data into different behavioral semantic segments but also keep each segment within a reasonable duration. After that, similarity evaluation was performed based on the analysis results of the MHMM, and homogenous drivers were divided into a group. A controlled experiment was used to predict the drivers' acceleration using both GRU and LSTM, and the experimental results verified the usefulness of the MHMM in personalized driving behavior analysis, and also showed that the performance of GRU is better than that of  
Figure 7: Prediction results of 10 drivers. (**a**) Distribution of _MSEs_; (**b**) distribution of _MAEs_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Group** & **Model** & _MSE_ & _RMSE_ & _MAE_ \\ \hline \multirow{2}{*}{Group 1} & \multirow{2}{*}{10 drivers} & LSTM & 0.0077 & 0.0876 & 0.0593 \\  & & GRU & 0.0075 & 0.0711 & 0.0511 \\ \hline \multirow{2}{*}{Group 2} & \multirow{2}{*}{20 drivers} & LSTM & 0.0307 & 0.1752 & 0.1067 \\  & & GRU & 0.0163 & 0.1373 & 0.0836 \\ \hline \multirow{2}{*}{Group 3 (Control group)} & \multirow{2}{*}{30 drivers} & LSTM & 0.0232 & 0.1524 & 0.0928 \\  & & GRU & 0.0147 & 0.1302 & 0.0804 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction performance of LSTM and GRU for different driver subgroups.  
LSTM. For future work, the proposed car-following prediction approach can be applied to traffic data collected from other sites in order to further validate the findings from this study.  
Conceptualization, L.D. and H.Z.; methodology, L.D.; software, T.Z.; validation, Y.Z., L.D. and H.Z.; formal analysis, L.W.; investigation, Y.Z.; resources, Y.Z.; data curation, H.Z.; writing--original draft preparation, L.D. and H.Z.; writing--review and editing, Y.Z.; visualization, L.D.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data and models used during the study appear in this article.  
This research was funded by the National Natural Science Foundation of China (Grant No. 71971160) and the Fundamental Research Funds for the Central Universities (Grant no. 22120220013).  
The authors declare no conflict of interest.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 3 Numerical Simulation Analysis and Verification
### Numerical Simulation Model and Input Parameters
#### 3.4.2. Diaphrag Wall and Support Parameters  
Within the unit length cross-sectional area of the diaphragm wall, the elastic modulus E\({}_{\text{c}}\) was determined using the formula \(E_{c}=1471\times\sqrt{f_{c}^{r}}\,\text{MPa}\), where \(f_{c}^{r}\) is the 28-day compressive strength of the concrete. For this study, the design specified a compressive strength of 4000 psi, equivalent to 27.46 MPa (280 kgf/cm\({}^{2}\)). Plugging this value into the formula, we get \(E_{c}=914\) MPa. According to [PERSON] [32], the wall's stiffness (EI) is generally reduced by 60% to 80% for typical analyses. Here, a reduction factor of 70% was applied, resulting in an effective elastic modulus of 640 MPa for the diaphragm wall.  
The design drawings of the retaining support system specified the elevation of each support layer, the applied pre-stress, the type of H-beam used, and its EA value, all detailed in Appendix A, Table 10. Node-to-node anchors simulate the supports for each layer, while beam elements represent the surrounding parts. According to [PERSON] [32], the stiffness of the supports is typically reduced by 50% to 70% for general analysis. In this study, a reduction factor of 70% was used, meaning 70% of the EA value was applied as the input for the support stiffness.
## 4 Machine Learning to Identify Bat Calls
### Real-Time Identification and Prediction of Bats  
The first stage of the identification consists on the use of a triangular filter bank. The filter bank used in this parameterization is the same as the one used to extract the features of the t-SNE plot to study species separability (Figure 8). The filters that comprise the filter bank are created using a linear scale, which means that all the centers are equally separated. They are triangular in shape, with equal base widths and heights, so their area is unitary. These filters start at the frequency of 38 kHz and end at 80 kHz, which are the start and end frequencies of the calls of the two bat species plus a margin. That way, the frequencies below are not taken into consideration when parameterizing the signal. This is performed to avoid irrelevant noise interference in our analysis. The length of the samples to parameterize has been studied, according to the accuracy of the results obtained in training and testing the classifier. From the study undertaken, presented in Section 4.3, we have selected the 7 ms window. An example of this kind of parameterization is shown in Figure 10. In this figure, a plot of the FFT of the signal and the filters that compose the filter bank are shown. The signal has energy on the frequencies of interest, but also on the lower frequencies. The methodology used only takes into account the higher frequencies.  
Figure 10: Frequency representation of a sample bat call being filtered with the linear scale triangular filter bank for the coefficients extraction.  
Figure 9: Block diagram of the process used for the automatic classification of the calls contained in an audio file.  
Both the call pulses and the ambient noise of the audio files are identifiable by their power value in the spectrogram matrices, which reveals the energy of the calls at each time interval and frequency value. The second parameterization technique has followed an image processing approach, using the spectrogram matrices obtained from the analysis of the call fragments starting from 38 kHz frequency and ending at 90 kHz. Their values have been normalized. The Y axis of the matrices corresponds to frequency and its X corresponds to time. As all the sizes are equal, the temporal length used has also been studied to achieve the best performance in the machine learning algorithms. In this case, we have used 20 ms length matrices, after the study described in Section 4.3. An example of a 400 ms length audio file parameterization is shown in Figure 11.  
The example of Figure 11, which has five call intervals, has generated four matrices. The fact that one matrix has not been generated is because the distance between the last call and its predecessor was not enough to create two separated matrices that contain only a call. This last call is encircled in purple colour. Both calls would have appeared in the same matrix, so the specifications of the model we wanted to create would have not been accomplished. The last call is discarded and it is not used to train the model.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 7 Conclusions  
In this paper, we achieve fast and reliable bulk burst data transmission by optimizing goodput based on burst transmission when there are critical and emergency data to be transmitted. First, we optimize the EPC C1G2 protocol by introducing burst transmission mechanism and erasure codes and control the optimal number of transmission frames and sleep time of CRFID according to the current energy-harvesting and channel conditions. Then, we fragment large data packets into blocks and design an online adjustment strategy to adjust the frame length and coding redundancy dynamically by the feedback of the reader at runtime. Our results show that our proposed scheme significantly outperforms the current fixed frame length approach and the DFCA scheme, and the goodput is close to the theoretically optimal value under different energy-harvesting and channel conditions.  
Our proposed scheme in this paper enables passive sensing communication to adapt to dynamic energy-harvesting and channel conditions and to facilitate their application in the field of mobile sensing and pervasive computing. Future research work will consider the issue of data frame retransmission and how to deal with collisions and to ensure data transmission priority to further improve system performance in multi-tag scenario. In addition, this method can be combined with ambient back-scattering technology to make use of all the energy available in the environment such as temperature difference energy supply, mechanical vibration energy, etc. to improve the intelligence of smart devices and to establish a true smart system. That is to say, in the near future, data communication will not be restricted by the outside world, and it can flexibly realize the conversion between different protocols, thereby achieving barrier-free communication between various IoT devices.  
Conceptualization, J.Z. and X.L.; methodology, X.L.; software, J.Z.; validation, J.Z. and X.L.; formal analysis, X.L.; investigation, X.L.; resources, D.L.; data curation, X.J.; writing--original draft preparation, X.L.; writing--review and editing, J.Z.; visualization, J.Z. and X.L.; supervision, J.Z. and D.L.; project administration, D.L.; funding acquisition, J.Z. and D.L.  
This research was funded by The General Object of National Natural Science Foundation under grant number 61972273; by The General Object of National Natural Science Foundation under grant number 61572346; by The General Object of National Natural Science Foundation under grant number 61772358; by International Cooperation Project of Shanxi Province under grant number 201603D421012; and by The General Object of National Natural Science Foundation under grant number 61572347.  
We are grateful for the experimental equipment and platform provided by the Communication Signal Processing Institute of Taiyuan University of Technology in Shanxi Province.  
The authors declare no conflict of interest.
# A Watermarking Protocol Based on Blockchain
## 5 Watermarking Protocol
### Protection Protocol  
The protocol, whose scheme is reported in Table 2, starts when \(\mathcal{B}\) visits the \(\mathcal{CP}\)'s web site, chooses the content \(X\), and sends the purchase request to \(\mathcal{CP}\) in the message \(m_{1}\). Upon receiving the purchase request, \(\mathcal{CP}\) contacts \(\mathcal{RA}\), by sending the message \(m_{2}\), in order to obtain the security tokens to complete the purchase transaction. In fact, \(\mathcal{RA}\) is a TTP that publishes a list of pairs, each including a public key \(pk^{X}_{\mathcal{RA}}\) and an encrypted token \(\mathbb{E}_{pk^{X}_{\mathcal{RA}}}(N)\). In particular, \(pk^{X}_{\mathcal{RA}}\) corresponds to the secret key \(sk^{X}_{\mathcal{RA}}\). They represent a one-time key pair that can be used only in the current transaction [52]. \(N\) is a \"nonce\" represented by a binary string. It is encrypted by employing the public key \(pk^{X}_{\mathcal{RA}}\) and a cryptosystem that is \"privacy homomorphic\" [49] with respect to the subsequent watermark insertion. In fact, the resulting token \(\mathbb{E}_{pk^{X}_{\mathcal{RA}}}(N)\) will be then used to generate the watermark to be inserted into the content \(X\). The chosen pair \((pk^{X}_{\mathcal{RA}},\mathbb{E}_{pk^{X}_{\mathcal{RA}}}(N))\) is returned by \(\mathcal{RA}\) in the message \(m_{3}\) together with the signature \(\mathbb{S}_{\mathcal{RA}}(pk^{X}_{\mathcal{RA}},\mathbb{E}_{pk^{X}_{\mathcal{ RA}}}(N))\). Upon receiving \(m_{3}\), \(\mathcal{CP}\) can confirm the purchase request made by \(\mathcal{B}\). In fact, \(\mathcal{CP}\) generates two tokens, \(X_{d}\) and \(T_{X}\). The former is a string that identifies the requested content \(X\). It includes the name of the content and further data that can unambiguously describe it. The latter is
###### Abstract  
A Non-Terrestrial Network (NTN) is a network system that enables service for areas where terrestrial networks cannot cover. An NTN provides communication services using flying objects such as UAVs, HAPs, and satellites. In the case of satellites, they move in Earth's orbit at a constant speed. Ground services from continuously moving satellites cause frequent handovers. In addition, frequent handovers may come as a load between User Equipment (UE) and the communication system, which leads to degradation of service quality. Unlike Terrestrial Networks (TN), communication services are provided to UEs at altitudes ranging from 20 km to 35,584 km, rather than from base stations close to the ground. Service at high altitudes is unreliable due to the measurement values that were previously used as quality indicators to operate terrestrial networks. Moreover, service at high altitudes demands long-distance communication, and propagation delay occurs from the long-distance communication. In the 3 GPP Rel. 17 document, it is suggested that the above problems should be solved. This paper tries to solve the problem by proposing the two-step XGBoOST, a CART-based Gradient Boosting Model. Handover in TN uses measurement-based conditional handover (CHO), but the measured values in the NTN environment are not valid. Using this, the distance between the UE and the center of the cell and the elevation angle are used to construct a model that predicts the HO triggering time point. In order to overcome the propagation delay caused by communication at a high altitude, a model that predicts the distance and elevation angle between the UE and the center of the cell considering the propagation delay is proposed. The model is composed of two-step XGBoOST. The one-step model is a model in which the UE predicts the distance and elevation angle between cell centers after propagation delay at the time when satellite position information is transmitted to the UE. The two-step model predicts handover triggering occurrence based on the data predicted by the one-step result. As a result of the experiment, the model considering the propagation delay showed about 8% better performance on average than the model not considering the propagation delay, and the XGBoOST model achieved an average F1-score of 0.9891 in the propagation delay experiments.  
n
# DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid Network
As advancements in science and technology unfold, researchers are actively exploring more sophisticated methods to improve the pixel accuracy and detection efficiency in crack detection [25; 26]. In this study, we conducted extensive experiments on crack datasets, specifically Structure [27]. Compared with typical segmentation networks, the experiments validate the superior pixel accuracy of our work. The research presented in this paper primarily addresses two main challenges:  
(i) Timely crack monitoring in a concrete building is the foundation of aging buildings' safety. Object detection methods require limited effort because they can only locate crack regions but not shapes. Hence, it is essential to integrate the crack monitor with semantic segmentation based on deep learning to precisely locate cracks pixel by pixel. (ii) Considering the significant variations in the width and length of cracks, with some cracks occupying only a few pixels in minimum area, accurate identification of cracks is challenging when the extracted information is insufficiently detailed. Another challenging issue is the need to increase the attention to detail of information to enhance segmentation accuracy. The subsequent sections are outlined as follows: Section 2 provides a comprehensive review of crack localization methods. Section 3 details the DFP-Net, encompassing the network structure, the image cascade mechanism, the cascade feature fusion unit, and the loss function. In Section 4, we present the experiments and results conducted using the STRUCTURE dataset. Finally, the advantages and limitations of the proposed method are discussed in Section 5.
# Recent Advances in Fiber-Shaped Electronic Devices for Wearable Applications
## 3 Device Applications  
In this section, we focus on recent research about directly integrating fibriform electronic devices into textiles, apart from attaching electronic components. Monolithic integration could potentially simplify the manufacturing process and improve user comfort. Integrating analog and digital microelectronics into textiles has attracted significant attention, but the integrating of electronic functions into a textile structure while retaining the mechanical properties of the textile still requires a great deal of effort.
## 4 Experiment and Analysis of Experimental Results
### Data Set and Evaluation Indicators
#### 4.1.2 Evaluation Indicators  
The performance of dim object detection methods is generally evaluated using three criteria: background suppression, target enhancement, and detection accuracy.  
(1) Background suppression factor (_BSF_) [9]:  
The _BSF_ is defined as follows:  
\[BSF=\frac{\delta_{out}}{\delta_{in}} \tag{30}\]  
where \(\delta_{out}\) and \(\delta_{in}\) represent the local background standard deviation around the target of the output image and the original image.  
(2) Local contrast gain (LCG)  
The _SCRG_ represents the signal and noise ratios (_SCR_) before and after processing:  
\[SCRG=\frac{SCR_{out}}{SCR_{in}} \tag{31}\]  
In which the _SCR_ uses the same expression as in reference [38]:  
\[SCR=\frac{|\mu_{t}-\mu_{b}|}{\delta_{b}} \tag{32}\]  
where \(\mu_{t}\), \(\mu_{b}\) and \(\delta_{b}\) represent the average gray values of the targets in the image.  
In this paper, both _BSF_ and _SCRG_ need the determination of the background range around the target. Figure 4 shows the background around the target calculated in this paper, where d takes 20.  
Figure 3: Six Infrared Image Sequences Selected.  
For the reason the \(\delta_{b}\) is close to zero in the Formula (32), it is difficult to evaluate the performance as the \(SCR\) approaches infinity. Therefore, we evaluate the performance of the target augmentation using \(LCG\):  
\[LCG=\frac{LC_{out}}{LC_{in}} \tag{33}\]  
\[LC=\frac{|\mu_{t}-\mu_{b}|}{\mu_{t}+\mu_{b}} \tag{34}\]  
where \(LC_{out}\) and \(LC_{in}\) represent the local contrast (\(LC\)) of the output image and the input image, the \(\mu_{t}\) and \(\mu_{b}\) the are consistent with those in the Formula (32).  
(3) Receiver operating characteristic curve (ROC)  
In order to further compare the methods, the ROC curve is used to evaluate the methods which can be used to select the best category judgment model and abandon the sub-optimal model. When judging the category, the ROC curve can give a correct evaluation without being limited by cost or benefit.  
All the samples, which is actually the target but is wrongly judged. It is defined as follows:  
\[P_{d}=\frac{N_{true}}{N_{act}} \tag{35}\]  
\[P_{f}=\frac{N_{false}}{N_{img}} \tag{36}\]  
where \(N_{true}\), \(N_{act}\), \(N_{false}\) and \(N_{img}\) represent the number of really detected targets, the actual targets, the falsely detected targets and the frames, respectively.