esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 2 The Theory of Particle Damping Energy Dissipation
### Analysis of Normal Particle-Particle Collisions  
The normal particle collision deformation diagram and contact model are shown in Figures 1 and 2, respectively. In these figures, \(r_{a}\), \(R\), \(F_{n}\), \(\delta_{n}\), \(k_{n}\), \(c_{n}\), and \(m\) are the contact surface radius, the particle radius, normal force, relative displacement, normal stiffness, normal damping coefficient, and particle mass, respectively.  
The normal overlap after contact deformation between particles is expressed as  
\[\delta_{n}=R_{i}+R_{j}-\left|c_{i}-c_{j}\right|>0 \tag{1}\]  
where \(c_{i}\) and \(c_{j}\) are the displacement vectors of the particle centers.  
The normal stiffness is expressed as  
\[k_{n}=2E^{*}\sqrt{R^{*}\delta_{n}} \tag{2}\]  
where \(E^{*}\) and \(R^{*}\) are the effective elastic modulus and effective radius of the particle, respectively. They can be expressed as  
\[\left\{\begin{array}{ll}\frac{1}{E^{*}}=\frac{1-\mu_{1}^{2}}{E_{i}}+\frac{1 -\mu_{j}^{2}}{E_{j}}\\ R^{*}=\frac{1}{K_{i}}+\frac{1}{K_{j}}\end{array}\right. \tag{3}\]  
The normal damping coefficient \(c_{n}\) is expressed as  
\[c_{n}=2\sqrt{\frac{5}{6}}\gamma\sqrt{k_{n}m^{*}} \tag{4}\]  
Figure 1: Schematic diagram of normal collision deformation of particles.  
Figure 2: Particle normal collision contact model.  
where \(m^{*}\) is the effective mass of the particle, and \(\gamma\) is the critical damping coefficient. \(m^{*}\) and \(\gamma\) are expressed as  
\[\left\{\begin{array}{l}m^{*}=\frac{m_{i}m_{i}}{m_{i}+m_{j}}\\ \gamma=\frac{\ln\epsilon}{\sqrt{\ln^{2}\epsilon+\pi^{2}}}\end{array}\right. \tag{5}\]  
where \(e\) is the coefficient of restitution of the particle.
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 5 Experimental Validation
### Experimental Results
#### 5.3.2 Manual/Automatic Mixed Control Strategy Based on Cartesian Impedance Control
By careful observation, it can be seen that the motion trend of the operator remained consistent with that of the commanded position and the actual running position. As can be seen in Figure 11, the maximum contact force was about 7 N. The contact force was short-lived because the impedance algorithm kept it away from the collision.
## 2 Materials and Methods
### Sample Characterization
#### 2.2.3 XRD  
X-ray diffraction analysis was carried out to determine the crystalline structure of CNTs before and after modification. XRD measurements were made using Cu-K\(\alpha\) radiation at an accelerating voltage of 30 kV via a Bruker D2 Phaser X-ray diffractometer (Bruker Corporation, Billerica, MA, USA). The X-ray patterns were recorded in the range 2\(\theta\) = 10-80\({}^{\circ}\) with a step of 0.02\({}^{\circ}\) at the research park of St. Petersburg State University (Centre for X-ray Diffraction Studies, St. Petersburg, Russia).
# Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
## 5 Implementation
This value is similar to the average retransmission time in a TelosB node running TinyOS, which is 3.63 ms [55]. To measure the frame processing time, we used a RCB256 RFR2 node, which includes an IEEE 802.15.4 transceiver. The MAC layer was implemented using Atmel Software Framework (ASF), which provides direct access to the payload and the header of the frame, without requiring an operating system in the node. This feature allowed us to measure delays using iACK and eACK with IDnode = IDPAN = 2 bytes, BO = 3 and with a frame processing time equal to LIFS 0.640 microseconds. Tables 6 and 7 show how there is also a small reduction in the processing time when using iACK both in the node working as TXN or the node working as RXN. This also helps saving energy. The difference in the frame processing time is related to the low-level operation of the software stack of the node. Other protocols, such as AODV, HTR [56] COLBA, ABORT [57], RPL, or 6 LowPAN [58] require higher times. For example, we can compare our proposal with 6 LowPAN, a protocol that also provides address autoconfiguration, is robust against link failure or device unavailability, and that is designed for large multi-hop networks. Table 8 shows the end-to-end delay measured in [58] using 6 LowPAN and using our proposal. 6 LowPAN is a protocol designed for multi-hop networks, but it is not specifically adapted to large-scale multi-hop linear topologies, causing high delays because of retransmissions in higher transport layers. It is also possible to place nodes closer than the maximum communication range to reduce the transmission power and reduce the probability to require retransmissions. For example, nodes can be placed in each one of the segments that are used to build the pipeline. Typically, those segments have a maximum length of 15 m. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Processing Time at Node RXN**} \\ \hline  & **Measured [ms]** & **Calculated** & **\% error** \\ \hline eACK & 3.78 & 3.32 & 12\% \\ \hline iACK & 3.43 & 3.27 & 4.6\% \\ \hline Difference & 0.35 & 0.05 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Frame processing time (RXN node). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Processing Time at Node TXN**} \\ \hline  & **Measured [ms]** & **Calculated** & **\% error** \\ \hline eACK & 6.98 & 6.33 & 9.31\% \\ \hline iACK & 6.66 & 5.82 & 12\% \\ \hline Difference & 0.32 & 0.51 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Frame processing time (TXN node).
# A Low-Order Series Approximation of Thin-Bed PP-Wave Reflections
## 5 Application Example
The intersection of the four contour curves, including those of amplitude and phase of \(A_{0}\) and \(A_{2}\), indicates thin-bed \(R_{1}\), \(R_{2}\) and thickness. The P-wave impedance ratios at the top- and bottom-interfaces of the thin bed can be obtained from \(R_{1}\), \(R_{2}\) as follows,  
\[\frac{z_{P2}}{z_{P1}}=\frac{1+R_{1}}{1-R_{1}} \tag{12}\]  
and  
\[\frac{z_{P3}}{z_{P2}}=\frac{1+R_{2}}{1-R_{2}} \tag{13}\]  
In order to illustrate the estimation approach clearly, we build up a testing thin-bed model, marked as Model 5, whose parameters are listed in Table 2. For Model 5, amplitude and phase of \(A_{0}\) are \(0.1006\) and \(0.9355\) respectively, amplitude and phase of \(A_{2}\) are \(0.2364\) and \(-2.2545\) respectively. We obtain the corresponding contour lines in the amplitude and phase templates at different thicknesses. Taking the cases of \(\lambda/10\), \(\lambda/20\), \(\lambda/40\), and \(\lambda/60\) for examples, we exhibit the contours lines of amplitude and phase of \(A_{0}\) and \(A_{2}\) at fixed thicknesses, which are shown in Figure 14. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Layer No.** & \(\mathbf{v_{\mathrm{P}}}\) & \(\mathbf{v_{\mathrm{S}}}\) & \(\mathbf{\rho}\) & \(\mathbf{h}\) & \(\mathbf{R_{1}}\) & \(\mathbf{R_{2}}\) \\ \hline \multirow{4}{*}{Model 5} & 1 & 3000 & 1414 & 2.29 & / & / & / \\  & 2 & 3440 & 1793 & 2.37 & \(\lambda/10\) & 0.0854 & \(-0.0854\) \\ \cline{1-1}  & 3 & 3000 & 1414 & 2.29 & / & / & / \\ \cline{1-1}  & \(A_{0}\) & Amplitude & 0.1006 & & \multirow{2}{*}{\(A_{2}\)} & Amplitude & 0.2364 \\ \cline{1-1}  & Phase & 0.9355 & & & & Phase & \(-2.2545\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Rock properties, \(A_{0}\) and \(A_{2}\) of the testing thin-bed model, units of velocities and densities are m/s and g/cm\({}^{3}\), respectively. Figure 13: \(A_{2}\)’s phase of 120 thin-bed models with different thickness, \(R_{1}\) or \(R_{2}\). In Figure 14, the four contour curves intersect at a point \"p\" approximately when the thin-bed thickness is equal to \(\lambda/10\). While there is no common intersect of the four contour curves for the other thickness cases. Therefore, we estimate the thin-bed thickness is equal to \(\lambda/10\), which is consistent with the true value (\(\lambda/10\)). \(R_{1}\) and \(R_{2}\) are estimated through the coordinates of the intersect point \"p\", as \(R_{1}=0.087\) and \(R_{2}=-0.085\). The estimated \(R_{1}\) and \(R_{2}\) are very close to those of Model 5 (\(R_{1}=0.085\), \(R_{2}=-0.085\)) respectively. Meanwhile, the estimated P-wave impedance ratios at the top- and bottom-interfaces are 1.190 and 0.843, of which the relative errors are 0.32% and 0.08%, respectively. The good estimated results verify the availability of the estimation approach for thin-bed properties based on the approximate formula.
# A Lightweight Forest Test Image Recognition Model Based on Improved YOLOv8
## 2 YOLOv8 Object Detection Algorithm
### Head  
YOLOv8's detection module adopts the prevalent decoupled head structure, employing two independent branches for target classification and bounding box regression predictions, each utilizing distinct loss functions. Binary cross-entropy loss (BCE loss) is employed for the classification task. For bounding box regression, we have chosen to utilize DFL [24] and CIoU [25] as the loss functions. This detection structure aims to enhance detection accuracy and accelerate the model's convergence speed. Notably, YOLOv8 departs from the anchor-based approach in its design and embraces the anchor-free [26] philosophy. This design decision is aimed at further improving detection performance, enabling the model to be more flexible in adapting to various scales and shapes of targets. Simultaneously, it enhances bounding box localization accuracy and overall detection precision in object detection tasks.
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
###### Abstract  
We have considered the problem of secure communication by means of chaotic carrier communication channels. Data protection is provided if the signal in the communication channel looks like a stochastic or noisy one, i.e., by the steganography property, and also by using advanced and secured approaches of mixing a useful signal into a chaotic carrier. The problem is to find effective and efficient techniques for useful signal reconstruction at the receiver side. We firstly use a synergetic nonlinear mathematical observer to recover the two useful signals transmitted simultaneously over a single communication channel. Compared to a known observer, the synergetic one operates with initial nonlinear models, i.e., it may be applied directly to chaotic systems. In that system structure, we consider the useful signals as unobservable variables and estimate them by the given model of a chaotic generator. A chaotic data transmission system with two channels is demonstrated; the presented mathematical procedure explains the steps of synergetic two-channel observer design. Computer simulation results prove the viability of our approach. The proposed data transmission scheme and the observer design procedure provide effective transmission and reconstruction of informational signals. Reconstruction error is up to approximately 25% but has a very short pulses shape and could be eliminated with further analog or digital filtering algorithms. As an example application, simulation of image transmission and recovery is demonstrated. The study's results may be used as a basis for further research into secure data transmission system design.  
s 2020, 9, 497; doi:10.3390/electronics9030497
# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
## 5 Conclusions  
This paper presents a comprehensive performance analysis of 5G positioning based on UL-SRS and DL-PRS under LOS and NLOS conditions. The objective of this research is to evaluate the precision and robustness of UE localization within 5G networks. Through the analysis of positioning errors and computational efficiency, significant improvements are observed when both UL-SRS and DL-PRS signals are used for UE positioning. The findings indicate that, under LOS conditions, PRS exhibits superior positioning accuracy, with a positioning error of 1.94 m, as compared to 2.06 m for SRS. However, in NLOS environments, SRS demonstrates superior performance in terms of positioning accuracy, with an error margin of 8.48 m, in contrast to the 10.47-m error margin exhibited by PRS. These results emphasize the dynamic performance variations of SRS and PRS across different propagation conditions. The research highlights the critical role of signal configuration parameters in determining overall positioning accuracy, particularly in challenging NLOS environments. Future work will extend this analysis by optimizing signal configurations and leveraging emerging technologies, such as machine learning-based error correction, which could improve localization performance in urban environments. This research not only validates the effectiveness of UL-SRS and DL-PRS for 5G positioning but also provides critical insights into optimizing localization techniques for next-generation wireless communication systems.  
Conceptualization and methodology, S.S.S. and C.S.; software and validation, S.S.S. and C.S.; formal analysis and investigation, C.S.; resources and data curation, S.S.S.; writing original draft preparation, S.S.S.; review and editing, C.S., D.Y., M.W., Y.H., B.L. and Y.X.; visualization, S.S.S.; supervision, C.S.; project administration and funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Open Project Program of Shaanxi Key Laboratory of Integrated and Intelligent Navigation (No. SXKLIIN202403002) and the Open Project Program of State Key Laboratory of CNS/ATM (No. 2024A02). It was also supported by the Youth Program of the National Natural Science Foundation of China (NSFC) with No. 62001015. (Corresponding author: [PERSON]).  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
## 3 Results and Discussion
### Adsorption Tests
#### 3.1.2 Addition of Montmorillonite  
In Figure 4 and Table S2 in Supplementary Materials, the results of the adsorption of the heavy metals by the soil (reference, Figure 3 and Table S1), montmorillonite, and the soil additivated with montmorillonite are presented. As was observed when the additive was the MWCNTs, the adsorption by montmorillonite is characterized by an initial rapid adsorption, equilibrium being reached in the first 5 min.  
Examining the adsorption by montmorillonite alone, it is obvious that only lead was adsorbed in high quantity, while for the other HMs, adsorption was very low. Confronting these results with the ones obtained for MWCNTs alone (Figure 3 and Table S1), it is seen that montmorillonite has a lower adsorption capacity, which can be related to its lower specific surface area due to the larger size.  
The addition of montmorillonite to the soil increased the adsorption capacity of the soil tested for all HMs considered. In the case of Zn\({}^{2+}\), adsorption was always very poor for all the situations considered, which, as referred, is certainly related to the higher mobility of this metal. The results for the soil additivated with montmorillonite show again that the affinity order of the heavy metals studied is Pb \(>\) Cu \(>\) Ni \(>\) Zn, in-line with the one registered in the tests with soil and MWCNTs (Cu \(>\) Pb \(>\) Ni \(>\) Zn), since the adsorption results for Pb\({}^{2+}\) and Cu\({}^{2+}\) are very similar in the latter case. Moreover, as discussed previously for the case of the MWCNTs, the combination of soil with montmorillonite allowed increasing substantially the heavy metals adsorption efficiency, when compared to adsorption by montmorillonite alone and even to the tests where soil alone was used, especially for the HMs Cu\({}^{2+}\) and Ni\({}^{2+}\).  
If we compare the addition of montmorillonite (Figure 4 and Table S2) with the addition of MWCNTs to the soil (Figure 3 and Table S1), it may be verified that adding MWCNTs leads to a better performance, especially for Cu\({}^{2+}\) and Ni\({}^{2+}\), since in the case of Pb\({}^{2+}\) adsorption is already very high using the soil alone, as previously discussed. Still, even if montmorillonite presents a slightly lower adsorption capacity for Cu\({}^{2+}\) and Ni\({}^{2+}\), the cost can be substantially lower. Therefore, the final choice  
Figure 4: Results from the adsorption tests. Individual adsorption of lead (**a**), copper (**b**), nickel (**c**) and zinc (**d**) by the soil, Montmorillonite and suspension of soil with the addition of Montmorillonite.  
of the additive to remediate a soil contaminated with HMs should take into consideration these two factors (efficiency and cost), at least for the cases where montmorillonite presented also a high immobilization capacity.
## 2 Materials and Methods
### Measurement Chain
Since the magnitude of the stray magnetic field represents the input parameter necessary for the magnetic hysteresis curve reconstruction, its value is continuously sensed by the magnetometer probe at a rate of about 500 samples per second. The measured value is first processed by a magnetometer post-processing unit and then transmitted to the personal computer as a scalar quantity of the magnetic field flux density. From the received data, the magnetic polarization of the magnetized core is computed by the application software. Moreover, in addition to the magnetic polarization, the excitation field value for a given current level is also quantified using the coil configuration parameters. Subsequently, based on the calculated values, the polarization contribution is graphically represented in real-time as a dependence on the latest value of the excitation field. In conclusion, the application software requests the microcontroller unit to change the excitation current value. This measurement chain cycle is systematically repeated until the magnetization characteristic in the form of a hysteresis curve is reconstructed. The measurement chain of the used measuring workstation is in the form of a block diagram depicted in Figure 3.  
The above-described process is algorithmized by the application software into three successive phases, which control how the excitation current level changes. During the first phase, the excitation current value increases monotonically from zero to the maximum value, the magnitude of which is defined within the process of the workstation configuration. Therefore, an initial magnetization curve is created at the end of the first phase. The output of a second magnetization phase is a curve ranging from positive to negative saturation. Accordingly, the third phase creates a magnetization curve that reverses to positive saturation, giving the final form the hysteresis curve, interpreted as the sample magnetic polarization dependence on the excitation magnetic field. The output of each measurement phase as the reaction on the excitation signal is depicted in Figure 4.  
A view of the measuring workstation in a laboratory environment can be seen in Figure 5.  
Figure 3: Block diagram of the workstation measurement chain. Figure 4: Outputs of the individual integrated measurement phases of the presented measuring workstation. Figure 5: The measuring workstation in a laboratory environment: (**a**) The photo of the configured measuring workstation with all devices included in the measurement chain; (**b**) A detailed view of the measuring stand with attached probe and integrated excitation and compensation coils.
## 5 Experiments
### Frequent Approximate Subgraphs Mined by the Proposed Algorithm
#### 5.2.2 Comparison Regarding the Number of Frequent Approximate Subgraphs Mined and Runtime
In the first plot, when the dissimilarity threshold is 1, the runtime is less than 30 min; in the second plot, when the dissimilarity threshold is 2, the runtime is less than 3 h and 30 min; and in the third plot, when the dissimilarity threshold is 3, the runtime grows to more than 22 h. To compare the runtime of both algorithms, we need to focus on each plot, where it is observed that our algorithm's runtime is shorter in all cases. To observe how varying the dissimilarity threshold impacts the runtime, we compare the increase in the runtime across the three plots. The results show that as the dissimilarity threshold increases, the runtime increases for both algorithms, which is related to the increase in the number of frequent approximate subgraphs mined. However, the runtime increases significantly for REAFUM compared to our proposed algorithm. Figure 9 is similar to Figure 8, except that it uses a constant support threshold of 80%. Figure 9a contains three horizontal plots showing the number of frequent approximate subgraphs mined, one for each dissimilarity threshold value (1, 2, and 3). The colored bars represent the number of frequent approximate subgraphs mined for the different cases (both, only REAFUM, and only AGCM-SLV). It is important to analyze how the support threshold impacts the number of frequent approximate subgraphs that are mined. At an 80% support threshold, more frequent subgraphs are mined compared to a 90% support threshold, as fewer occurrences are required for a subgraph to be considered frequent, as shown across the plots. Figure 9b, on the other hand, contains three horizontal plots, one for each dissimilarity threshold value (1, 2, and 3), showing the corresponding runtime for both algorithms. The red line represents REAFUM, and the blue line represents AGCM-SLV. The results reveal an increase in the number of frequent approximate subgraphs mined as a result of a smaller support threshold, which also impacts the rise in runtime. As with the previous experiment, if the dissimilarity threshold increases, there is a consistent growth in the number of frequent subgraphs and runtime. However, the results show that our proposed algorithm's runtime increases at a slower rate than REAFUM's, which is consistent with the previous experiment. These experiments reveal that varying the dissimilarity or support thresholds impacts the number of frequent approximate subgraphs mined and the runtime for REAFUM and the proposed algorithm. However, our proposed algorithm mines more frequent subgraphs in a shorter runtime than REAFUM's.
## Dynamic Resource Allocation and QoS Control Capabilities of the Japanese Academic Backbone Network  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
National Institute of Informatics (NII), 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan;  
E-Mails: [EMAIL_ADDRESS] (K.F.); [EMAIL_ADDRESS] (M.K.); [EMAIL_ADDRESS] (M.N.);  
[EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (S.Y.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 22 July 2010 / Accepted: 5 August 2010 / Published: 9 August 2010_  
**Abstract:** Dynamic resource control capabilities have become increasingly important for academic networks that must support big scientific research projects at the same time as less data intensive research and educational activities. This paper describes the dynamic resource allocation and QoS control capabilities of the Japanese academic backbone network, called SINET3, which supports a variety of academic applications with a wide range of network services. The article describes the network architecture, networking technologies, resource allocation, QoS control, and layer-1 bandwidth on-demand services. It also details typical services developed for scientific research, including the user interface, resource control, and management functions, and includes performance evaluations.  
**Keywords:** QoS; bandwidth on demand; next-generation SDH; MPLS/GMPLS