esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes
###### Abstract  
The identification of trolley codes poses a challenge in engineering, as there are often situations where the accuracy requirements for their detection cannot be met. YOLOv7, being the state-of-the-art target detection method, demonstrates significant efficacy in addressing the challenge of trolley coding recognition. Due to the substantial dimensions of the model and the presence of numerous redundant parameters, the deployment of small terminals in practical applications is constrained. This paper presents a real-time approach for identifying trolley codes using a YOLOv7 deep learning algorithm that incorporates channel pruning. Initially, a YOLOv7 model is constructed, followed by the application of a channel pruning algorithm to streamline its complexity. Subsequently, the model undergoes fine-tuning to optimize its performance in terms of both speed and accuracy. The experimental findings demonstrated that the proposed model exhibited a reduction of 32.92% in the number of parameters compared to the pre-pruned model. Additionally, it was observed that the proposed model was 24.82 MB smaller in size. Despite these reductions, the mean average precision (mAP) of the proposed model was only 0.03% lower, reaching an impressive value of 99.24%. We conducted a comparative analysis of the proposed method against five deep learning algorithms, namely YOLOv5x, YOLOv4, YOLOv5m, YOLOv5s, and YOLOv5n, in order to assess its effectiveness. In contrast, the proposed method considers the speed of detection while simultaneously ensuring a high mean average precision (mAP) value in the detection of trolley codes. The obtained results provide confirmation that the suggested approach is viable for the real-time detection of trolley codes.  
channel pruning algorithm; YOLOv7; image identification +
Footnote †: journal: Medical and Power Engineering  
**Clication:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes. _Appl. Sci._**2023**, 13, 10202. [[https://doi.org/10.3390/app131810202](https://doi.org/10.3390/app131810202)]([https://doi.org/10.3390/app131810202](https://doi.org/10.3390/app131810202))
## 3 Results and Discussion
### Adsorption Energy Analysis  
The adsorption energies at the interfaces (\(E_{ads}\)) of DGEBA, the DGEDDS, and the AEOR were determined to be \(-56.97\), \(-76.54\), and \(-74.70\) kcal \(\cdot\) mol\({}^{-1}\), respectively, as shown in Figure 7. A higher adsorption energy indicated stronger adsorption strength. The adsorption energy data aligned with those of the previous analysis based on the number of hydrogen bonds and the number of hydrogen bonds per unit molecular weight. DGEDDS exhibited the strongest adsorption strength. While AEOR formed fewer hydrogen bonds, its adsorption energy remained relatively high, indicating that hydrogen bonding was still the primary force governing its interaction with sand. DGEBA, due to its lack of hydrogen bond formation, exhibited the lowest adsorption energy and weakest adsorption strength.
# Efficient and Robust Image Communication Techniques for 5G Applications in Smart Cities
## 2 Related Work
### Our Contribution  
Based on the study of the research work reported in the literature survey, the following research gaps are addressed to realize the goal of efficient multimedia telecommunication methodology in a sustainable smart city:  
* DCT transform is being utilized to improve the BER performance in place of Fourier transform in the OFDM system for efficient transmission of the image.
* A hybrid combination of MIMO-OFDM system is presented as a valid alternative to the conventional OFDM system for image transmission.
* The numbers of antennas are varied at the receiver side to access the MIMO-OFDM system's performance and incorporate diverse transforms for multimedia applications.
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 3 Investigation of the Relationship Between Vertical Curves and Track Irregularities  
With the accuracy of the proposed model and method validated, these tools were employed to analyze the factors influencing detection accuracy for tracking vertical irregularities in vertical curve segments. By varying key parameters--including train speed \(V\), vertical curve length \(L\), gradient \(i\), gradient difference \(\Delta i\), and radius \(R\)--we obtained virtual irregularities through the virtual track inspection method based on the acceleration integration approach. The virtual irregularities under different conditions were then compared with the excitation irregularities in both the time and frequency domains, identifying the primary factors affecting detection accuracy in vertical curve segments.
## 3 Results
### Stress Behaviour of Guide Platform  
#### 3.4.1 Influence of Shield Body Weight on Guide Platform Stress  
The weight of the shield per metre has an impact on the load applied on the guide platform, which consequently affects the force exerted on the guide platform. In order to analyse this, the stress of the segments at different burial depths was calculated, by varying the weight of the shield per metre to 350, 400, 450, 500, and 550 kN/m. The results,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lining Thickness (m)** & **M\({}_{\text{max}}\) (kN\(\cdot\)m)** & **-M\({}_{\text{max}}\) (kN\(\cdot\)m)** & **N\({}_{\text{max}}\) (kN)** \\ \hline
0.30 & 64.73 & 68.96 & 620.42 \\
0.32 & 79.77 & 84.83 & 640.10 \\
0.34 & 93.35 & 99.13 & 660.40 \\
0.36 & 113.64 & 120.52 & 679.40 \\
0.38 & 131.71 & 139.53 & 698.14 \\
0.40 & 149.85 & 158.58 & 712.65 \\
0.42 & 176.07 & 186.15 & 728.21 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Segment calculation results under different lining thickness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mu\) & **M\({}_{\text{max}}\) (kN\(\cdot\)m)** & **-M\({}_{\text{max}}\) (kN\(\cdot\)m)** & **N\({}_{\text{max}}\) (kN)** \\ \hline
0.15 & 187.59 & 196.31 & 664.88 \\
0.20 & 168.27 & 176.41 & 662.01 \\
0.25 & 146.28 & 153.74 & 660.35 \\
0.30 & 121.59 & 128.29 & 658.39 \\
0.35 & 94.29 & 100.13 & 660.81 \\
0.40 & 64.63 & 69.49 & 664.68 \\
0.45 & 33.85 & 36.83 & 673.29 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Segment calculation results under different soil poisson’s ratio.  
displayed in Table 12 and Figure 8a, demonstrate that the internal forces acting on the guide platform were altered. The maximum bending moment of the guide platform exhibited a linear and positive correlation with the weight of the shield, with its value increasing as weight increased. Since the weight has a greater influence on the maximum positive bending moment compared to the maximum negative bending moment of the guide platform, the disparity between the maximum positive and negative bending moments increases with the shield weight. Consequently, this leads to an uneven distribution of the internal bending moments within the guide platform.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Backfill Layer Thickness (m)** & **Mmax (kN\(\cdot\)m)** & **-Mmax (kN\(\cdot\)m)** & **Nmax (kN)** \\ \hline
0.26 & 91.80 & 96.65 & 648.34 \\
0.28 & 92.40 & 97.57 & 650.24 \\
0.30 & 93.14 & 98.69 & 653.12 \\
0.32 & 94.80 & 99.55 & 656.12 \\
0.34 & 96.36 & 100.64 & 660.75 \\
0.36 & 97.40 & 102.60 & 666.14 \\
0.38 & 98.67 & 104.58 & 674.46 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Segment calculation results under different backfill layer thickness.  
Figure 8: Guide platform stress variation curve. (**a**) Influence curve of shield body weight on guide platform stress; (**b**) influence curve of guide thickness on guide platform stress; and (**c**) influence curve of subgrade reaction coefficient on guide stress.
## 4 Performance Analysis and Discussion
### Packet Delivery Ratio  
The Packet Delivery Ratio falls with the rise in the number of sensor nodes in a wireless sensor computing, because the high congestion of routing overhead around sensor nodes causes packets to be dropped. Figure 15 plots the Packet Delivery Ratio of DSR and AODV, showing a big fall when the number of nodes in the network rises above 100. This big descent occurs because DSR and AODV broadcast RREQ throughout the network to discover a new route. In the proposed RRM mechanism, if one node of a routing path fails, then the source or border node immediately selects a cached routing path, thus improving the Packet Delivery Ratio.
## 5 The Non-Ideal Case in Practical Application
To illustrate this constraint more visually, we suppose that \(|\psi_{0}\rangle_{\text{X}}=|0\rangle\) and \(|\psi_{1}\rangle_{\text{X}}=\cos\theta|0\rangle+\sin\theta|1\rangle\) are two qubits in the qubit set and \(\varepsilon=3\%\) in a practical application, hence we have \(\!\times\!\psi_{1}|\psi_{0}\!\succ_{\text{X}}\!\!\!<\!\!\psi_{0}|\psi_{1}\!\! \succ_{\text{X}}=cos2\theta\). In this case, the constraint \(1-_{\text{X}}<\!\psi_{1}|\psi_{0}\!\succ_{\text{X}}\!\!\!<\!\!\psi_{0}|\psi_{1 }\!\!\succ_{\text{X}}\geq 2\varepsilon\) requires \(\cos^{2}\theta\leq 94\%\), i.e., the angle \(\theta\) is not smaller than \(\arccos\left(\sqrt{0.94}\right)=14.1788^{\circ}\).
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 4 Algorithm Design  
Based on the modelling of Section 3, this section aims to solve the anti-jamming problem of UAV radar networking by introducing information representation theory and a modified Q-Learning algorithm. The objective of system optimization is to generate a reasonable multi-parameter policy of a radar system through the algorithm, so as to achieve the maximum information acquisition capability of a radar in any given jamming form. In the design of reinforcement learning algorithm, the rationality of the reward model is critical to the evaluation of generated policy.  
Taking the following radar angle measurement case as an example, the target has multiple scattering points distributed in the azimuth direction, while the radar needs to measure the receiving angle of a strong scattering point from the target. In order to improve the angle measurement accuracy, a narrow beam should be formed, but this may result in a low SIR as the beam may not cover the target area. Therefore, using SIR as the reward function may not capture the information acquisition capability of the radar system. Therefore, we introduce the theory of radar information representation to set the reward properly.
# Measurements of Acoustical Parameters in the Ancient Open-Air Theatre of Tymdaris (Sicily, Italy) +
## 1 Introduction  
In recent years, there has been a growing interest in the acoustical characterization of ancient open-air theatres, which led to the development of several research projects (see, e.g., in [1; 2; 3; 4]) and a prolific production of scientific literature on the topic (see, e.g., in [5; 6; 7; 8; 9; 10; 11; 12; 13; 14]). This reflects a general trend observable in the broader field of archaeoacoustics [15]. There is indeed a renewed attention to the \"acoustics of past\" [16; 17; 18], which is driven by both a pure scholarly interest and a more practical need to adapt historical facilities to contemporary use [9; 19; 20; 21] and historical relevance, which could help to better understand the design and evolution of other performance spaces [22; 23]. However, performing acoustical measurements poses a number of challenges because room acoustics parameters and standards are conceived for enclosed spaces and might not be straightforwardly applicable in outdoor contexts [24; 25]. Above all, the ISO 3382-1:2009 standard [26] deals with \"performance spaces\" but mostly refers to indoor environments (e.g., auditoria, concert halls, etc.). The topic of acoustical characterisation has already been examined in detail for indoor spaces through statistical analysis, in order to investigate the reproducibility of the measures, the accuracy of the parameter calculation [27], the influence of source-receiver position displacement, the measurement chains of different systems [28; 29; 30] and the sensitivity to materials characteristics variation [31; 32; 33]. The aim ofthis work was to compare two measurement techniques for ancient theatres, using the archaeological site of Tymdaris as case study [34; 35]. Particularly, traditional room acoustics measurements have been carried out according to the reference standard ISO 3382-1:2009, with both a dodecahedron source and with firecrackers. The behaviours of the reverberation time, early-to-late energy and sound energy parameters were assessed. The results of the measurements were compared with the findings of other measurement campaigns available in literature and considerations about the applicability of ISO 3382-1 for open-air ancient theatres are presented.
## 5 Discussion  
### Experimental Results  
The petrographic study of the two samples, with SEM-EDS, did not reveal any carbonate phase precipitation. However, an increase in textural roughness in the samples was detected with the number of days of exposure into brine SC CO\({}_{2}\), as also mentioned in [61]. The diffractograms on global powdered fractions for the two experimental specimens are not considerably different between 0 and 64 days. Nevertheless, they reflect trace amounts of talc and vermiculite, as alteration products, after most experimental runs of mafic silicates. Also present is halite. The grazing incidence diffraction of CG and GD specimens, which helps to detect the mineralogy overlaid on the face specimens, additionally indicated the presence of gypsum and smectite (Table 9). Considering that talc and vermiculite were not detected by this technique within 64 days, those mineral phases should already be present before the experiments, resulting from the replacement of iron-magnesium igneous phases. Their trace amounts may justify the fact that they are not systematically detected in powder XRD. The absence of significant differences in the global mineralogical and chemical compositions of samples from the two experiments allows us to conclude that the reactions between minerals and fluids were not significant. On the other hand, the mineralogical and physical changes are limited to external areas of the CG and GD specimens exposed to CO\({}_{2}\)-rich brine. The changes could be due to local precipitations/dissolutions at the specimen surface and could represent the early effects of influence of the CO\({}_{2}\)-rich brine on the rock. The measure of pH values in the brine is conditioned by the release of CO\({}_{2}\) during depressurization after chamber opening, and thus the acidity during the experiments should be even lower than that measured when the chamber is decompressed (Tables 6 and 7). However, the increase in pH correlates positively with the time of rock-brine interaction (Tables 6 and 7). The trends of the pH measurements correspond to those found in previous experimental works carried out in an autoclave [25; 37; 38; 39]. The variability of the concentration of elements in the brine (Tables 6 and 7) reflects the reaction dynamics imposed by the composition of the high-salinity brine, the rock composition (Table 5) and the experimental conditions. In general, the concentrations of the experimental solutions, with higher Na and lower Ca and Mg concentrations for GD, agree with the less mafic composition of this lithotype when compared to the CG. The correlation of the measurements of Ca\({}^{2+}\), Mg\({}^{2+}\) and total Fe in the brine and CaO, MgO and Fe\({}_{2}\)O\({}_{3}\) in the rock is as follows:  
* In the case of CG, the Ca\({}^{2+}\) concentration measured in the brine shows a decrease after each run. This decrease in Ca\({}^{2+}\) is not described elsewhere [14; 25]. On the contrary, and in line with the
###### Abstract  
A communication system based on unmanned aerial vehicles (UAVs) is a viable alternative for meeting the coverage and capacity needs of future wireless networks. However, because of the limitations of UAV-enabled communications in terms of coverage, energy consumption, and flying laws, the number of studies focused on the sustainability element of UAV-assisted networking in the literature was limited thus far. We present a solution to this problem in this study; specifically, we design a \(Q\)-learning-based UAV placement strategy for long-term wireless connectivity while taking into account major constraints such as altitude regulations, nonflight zones, and transmit power. The goal is to determine the best location for the UAV base station (BS) while reducing energy consumption and increasing the number of users covered. Furthermore, a weighting method is devised, allowing energy usage and the number of users served to be prioritized based on network/battery circumstances. The suggested \(Q\)-learning-based solution is contrasted to the standard \(k\)-means clustering method, in which the UAV BS is positioned at the centroid location with the shortest cumulative distance between it and the users. The results demonstrate that the proposed solution outperforms the baseline \(k\)-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.  
s +
Footnote †: journal: 2021  
ulti-Objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones  
1 Faculty of Engineering and Natural Sciences, Ankara Yildirim Beyzat University, Ankara 06760, Turkey; [EMAIL_ADDRESS]  
20 October 2021  
Subtainable wireless connectivity; energy saving; UAV; communication system; 5G; positioning; reinforcement learning
## 3 Materials and Methods
### Quantitative Analysis of Phenolic Compounds
#### 3.5.2 Analysis of Phenolic Compounds  
The analysis of phenolic compounds in grape skins was conducted using high-performance liquid chromatography coupled with triple quadrupole mass spectrometry (HPLC-QQQ-MS/MS, Agilent 1200 series HPLC system, Agilent 6410 QqQ-MS). HPLC separation was performed on a Poroshell 120 EC-C18 column (2.1 \(\times\) 150 mm, 2.7 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA), with the mobile phase consisting of (A) water containing 0.1% formic acid (FA) and (B) acetonitrile/methanol (50:50, \(v/v\)) containing 0.1% FA. Electrospray ionization (ESI) was used as the ionization source, with positive ion mode for the analysis of anthocyanins and flavonoids and negative ion mode for flavan-3-ols. The specific chromatographic and mass spectrometric conditions have been described in previous articles [36; 37].
## 4 Hardware Estimation and Comparison  
### Hardware Estimation  
On the assumption that the input image is \(N\times N\) with 8-bit depth, the hardware consumption of the entire 3-level 2-D DWT architecture is listed in Table 1, where 1:1, 2:1 and4:1 represent the structures with 1:1, 2:1 and 4:1 clock-to-data ratio respectively, the clock-to-data ratio represents the number of clock cycles it takes to get a valid LL component of data, and S represents the parallelism. Totally, the architecture has 6N words temporal buffer, 53S/4 multipliers, 21S adders and 229S/4 registers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Architecture** & **Multiplier** & **Adder** & **Register** & **Temporal RAM (in Word)** & **Parallelism** \\ \hline
1:1 & 10 & 16 & 28 & 3N & \(S\) \\
2:1 & 5 & 8 & 33 & 2N & _S_/2 \\
4:1 & 3 & 4 & 51 & \(N\) & _S_/4 \\
3-level & 53S/4 & 21S & 229S/4 & 6N & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware consumption of 3-level 2-D DWT architecture for 9/7 filter with \(N\times N\) image size.  
Figure 8: Structure of the transposing module and the orders of the input and output. (**a**) For the condition of 1:1 clock-to-data ratio. (**b**) For the condition of 2:1 clock-to-data ratio. (**c**) For the condition of 4:1 clock-to-data ratio.
## 3 Experiments and Analysis
### Selection of Hyperparameters  
Parameters and hyperparameters are two important concepts in the deep learning network model. In the process of convolutional neural network iteration, parameters (weights and bias) will automatically be retrieved and updated, they often do not need manual settings, and hyperparameters as external variables usually need to be artificially set according to experience. Different hyperparameter combinations will have different effects on the update of network parameters and the convergence of the model.  
As for the hyperparameter search algorithm, we chose the grid search algorithm which takes a long time but can usually achieve the global optimal solution and evaluate the accuracy on the verification image set. Table 7 shows the hyperparameter evaluation results for the model in this paper. It can be seen that the influence of learning rate on accuracy was higher than the minimum batch. The optimal hyperparameter settings obtained were as follows: the learning rate was 0.0001, the minimum batch size was 16, and the backpropagation algorithm was Adam. In the same way, optimal hyperparameter evaluation of different network models is performed, and the optimal hyperparameter combination obtained is shown in Table 8. All subsequent experimental network models were carried out under the above optimal hyperparameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{5}{c}{**True Number**} \\ \cline{2-6}  & **Stain** & **Breakage** & **Scratch** & **Circle** & **Total Number** \\ \hline Stain & 72 & 1 & 1 & 2 & 76 \\ Breakage & 0 & 74 & 0 & 0 & 74 \\ Scratch & 0 & 0 & 74 & 0 & 74 \\ Circle & 3 & 0 & 0 & 73 & 76 \\ Total number & 75 & 75 & 75 & 75 & 300 \\ Correct number & 72 & 74 & 74 & 73 & 293 \\ Accuracy & 96.00\% & 98.67\% & 98.67\% & 97.33\% & 97.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy analysis of images generated through DCGAN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **All** & **Stain** & **Breakage** & **Scratch** & **Circle** \\ \hline Generated by & 3.850 \(\pm\) 0.027 & 1.951 \(\pm\) 0.014 & 1.982 \(\pm\) 0.009 & 1.965 \(\pm\) 0.004 & 1.956 \(\pm\) 0.002 \\ DCGAN & 3.947 \(\pm\) 0.040 & 1.999 \(\pm\) 0.001 & 1.992 \(\pm\) 0.012 & 1.997 \(\pm\) 0.002 & 1.995 \(\pm\) 0.004 \\ Real images & 3.947 \(\pm\) 0.040 & 1.999 \(\pm\) 0.001 & 1.992 \(\pm\) 0.012 & 1.997 \(\pm\) 0.002 & 1.995 \(\pm\) 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Inception score of the real and generated images (IS \(\pm\) std).