esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Optimization of a Toothed-Plate Single-Roller Crushing Device for Waste Plastic Film
## 3 Design and Theoretical Analysis of the Crushing Component  
### Design of the Crushing Toothed Plate  
Considering the mechanical properties, structural features, and failure conditions of waste plastic film and crop stalks, the triangular-sectioned toothed plate has been designed. The structures of the moving toothed plate and the fixed toothed plate are illustrated in Figure 2a,b. The two work in coordination, exerting a compressing, cutting, and tearing effect on waste plastic film.  
Figure 1: The toothed-plate single-roller crushing device for waste plastic film. Components: 1. electrical control box; 2. programmable controller; 3. touchscreen; 4. frequency inverter; 5. feeding hopper; 6. transmission components; 7. discharge hopper; 8. crushing roller; 9. crushing box; 10. motor; 11. frame; 12. conveyor belt. The dashed yellow lines represent the electronic wires between each component and the controller.  
The cutting-edge angles, \(\theta_{1}\) and \(\theta_{2}\), have a major impact on the toothed-plate strength and crushing performance. When \(\theta_{1}\) and \(\theta_{2}\) are too large, the toothed plate has high strength and a long service life, but the crushing performance is poor. When \(\theta_{1}\) and \(\theta_{2}\) are too small, the crushing performance is good, but the strength of the toothed plate is poor. Referring to the crushing of soybean stalks [24], \(45^{\circ}\) is considered the optimal process parameter for the cutting edges (\(\theta_{1}\) and \(\theta_{2}\)). Considering the cleaning process of waste plastic film, referring to the existing washing and suspension separation equipment [22; 23; 25], the tooth heights, \(H^{\prime}1\) and \(H^{\prime}2\), were designed to be 45 mm. According to the mechanical design standard, the hole diameter, R, was selected as 13 mm, and the fixed hole margins, \(a_{1}\), \(\tau_{1}\), \(a_{2}\), and \(\tau_{2}\), should not be less than 10.3 mm. Thus, the design of \(a_{1}\) and \(a_{2}\) was 20 mm, and \(\tau_{1}\) and \(\tau_{2}\) were 15 mm. The length of the moving and fixed toothed plates was 400 mm and the number of fixed holes was 4, and a calculation showed that the hole spacings, \(b_{1}\) and \(b_{2}\), were 120 mm.
# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 3 Methodologies
### Equipment Activities for Analysis  
From the excavator's existing operations [6], we identified and defined six distinct areas to ascertain whether the activity identification ability of the developed DCNN could be based on piston displacement (movement) (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Activity** & **Description** & **Start** & **End** & **Image** \\ \hline
1 & Waiting & Standby state, without performing any operations & No movement & No movement & \\ \hline
2 & Scraping & Gathering or pushing surface soil with a constant height & No soil in the bucket, and the blade touches the ground & The bucket moves in the opposite direction after dragging & \\ \hline
3 & Excavating (under) & Digging below the ground to excavate soil & The blade touches the soil & The bucket is filled with soil and is fixed & \\ \hline
4 & Excavating (front) & Excavating soil from the surface at a specific location & The blade moves down to the ground & The blade comes up from the ground, and the boom is fixed & \\ \hline
5 & Dumping & Disposing of the collected soil from the bucket & The bucket piston contracts with soil in the bucket, and the bucket piston is fixed & The soil is emptied from the bucket, and the bucket piston is fixed & \\ \hline
6 & Repositioning & Adjusting its position by supporting the body with the boom on the ground & Place the bucket in a stable position on the ground & After the body is raised and lowered, lift the bucket & \\ \hline \end{tabular}
\end{table}
Table 1: Criteria for commencement and completion of activities (visual assessment).
## 3 Proposed Solution
### Compression and Privacy Protection  
The two-round secure process is applied within the compression stage of a H.264/AVC encoder. Entropy coding is the last compression stage of the H.264/AVC hybrid encoder. There are multiple residual parameters output from a CABAC entropy coder [51] that are suitable for encryption without compromising its compression efficiency. These residuals are Transform Coefficients (TCs), Motion Vector Differences (MVDs), delta Quantization Parameters (dQPs), and the arithmetic signs of TCs and MVDs. For selective encryption, two-round operations are applied to the signs bits of MVDs and the TCs levels (color components) of the compressed bitstreams. The selective encryption on MVDs and TCs keeps the video format compliant (playable) and without escalating bit rates. As previously discussed in the research of [24], H.264/AVC's CAVLC is more susceptible to channel errors as compared to CABAC. Thus, this paper devised a protection scheme for the H.264-CABAC entropy coder, which can be applied to HEVC compression, with a few comparatively minor modifications.  
For computationally efficient encryption rounds, a simple XOR cipher is used, which, however, can be easily compromised. Therefore, a further layer of data diffusion is added with a permutation round on selected sign bits of Motion Vector Differences (MVDs) and Transform Coefficients (TCs) levels. It is important to notice that in the software implementation of CABAC encoder, sign bits are allocated a byte, so that within the software code, actually bits within sign bytes are permuted. In each byte of chosen syntax elements, the bits are right-shifted circularly by three bit positions, as shown in Figure 1. The number of all permutations obtained with eight-bit elements is \"8!\" so, it is equivalent to 40,320 permutations per byte operation are performed.  
After applying a permutation round on selected bytes, XOR encryption is applied on permuted data bytes. An \(n\) bit encryption key is used, with at least 128 bits in length. Lower values of \(n\) increase the risk of a brute-force attack being applied, and some may prefer a 256-bit key for additional protection. Let, \(C_{i}\), \(K_{e}\), and \(M_{g}\) denote the encrypted data, the encryption key, and the original message, respectively.  
\[C_{i}=\mathrm{Enc}\big{(}K_{e},M_{g}\big{)}=K_{e}\oplus M_{g} \tag{1}\]  
On the receiver side, the reverse process takes place, i.e.,  
\[M_{g}=\mathrm{Dec}(K_{e},C_{i})=K_{e}\oplus C_{i} \tag{2}\]The ciphered data are then transmitted through a channel to the receiver, where it is again XORed with the key to decipher the original data before reverse permutation.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 2 WSN Management
### Security Management  
Management of security is difficult to provide as WSNs are mainly made up of ad-hoc wireless networks with intermittent connectivity and resource limitations [25; 45]. This leads to vulnerability to threats that could be internal, external, malicious or even accidental. Data and resources can thus be stolen or modified and a denial of service attack is also possible. Security management for example has been approached through the use of key management schemes [26]. [PERSON] et al. [26] mention various requirements in managing security such as authentication, confidentiality, integrity, availability and authorisation. A network with high security features should be developed to meet these requirements however, the challenge here is the limited resource in bandwidth and energy inherent to WSNs [46; 47]. Management of security therefore has to be based on effective key distribution [26] taking into account features such as bandwidth, sensor memory, transmission range and the necessary know-how [48]. Several key management schemes have also been discussed by [PERSON] et al. [26]. Much more recently Key management based on the dynamic clustering and optimal routing choice of the Mobile Sink has been proposed [49]. The scheme extends static key management to dynamic key management with an improved flexibility while satisfying storage efficiency and connectivity. A further contribution has been made by proposing a hybrid key management scheme which takes advantage of both the Polynomial Pool and Basic Random (PPBR) key pre-distribution techniques to improve the difficulty in cracking the key system [50]. To tackle issues related with link connectivity in key management, the tree base path key establishment method is used.
## 1 Introduction
### Motivations  
In this paper, PB-assisted, hardware impairments, underlay cognitive radio, and cooperative relaying networks are combined into a novel cooperative spectrum sharing relaying system. Our proposed protocols not only improve the energy efficiency, but also the spectrum efficiency for the dual-hop decode-and-forward relaying WSNs. Different from multi-hop PB-assisted relaying schemes [19; 20; 21; 22; 45; 46], this paper considers dual-hop PB-assisted cooperative networks with new relay selection methods. Firstly, we propose a hybrid PRS (H-PRS) protocol that combines the conventional PRS one in [13; 36] and the modified one in [37]. Particularly, the scheme in [13; 36] is used to select the cooperative relay if it obtains the lower value of OP; otherwise, the scheme in [37] is used. Secondly, to optimize the system performance, we propose a best ORS (B-ORS) protocol that outperforms the conventional ORS (C-ORS) one [34]. Finally, we attempt to evaluate the performance of the H-PRS, B-ORS and C-ORS protocols by providing closed-form expressions of the e2e OP and throughput (TP). The derived expressions are easy-to-compute, and hence they can be used to optimize the system performance.
# Dynamics Analysis of Space Netted Pocket System Capturing Non-Cooperative Target
## 1 Introduction
Meanwhile, the deployment dynamics for the inflatable rods mainly include a nonlinear finite element model [29], a finite volume inflation model [30] and an energy model [31]. Contact and collision are important features for the space netted pocket system in capturing the target. The nonlinear spring-damper method has been widely used to derive the dynamics for contact and collision force analysis of flexible multibody systems [32; 33; 34]. [PERSON] et al. [35] analyzed the deployment dynamics based on the contact behavior, and three preferable releasing conditions were given. [PERSON] et al. [36] utilized a fully implicit method to analyze the nonlinear dynamics of a slender rod net, involving interactions. Thereafter, [PERSON] et al. [37] derived the model of thread-ring sliding joints based on the mass-spring-damper method, which can be used for simulating the net closure process. [PERSON] et al. [38] developed a method based on the Cosserat rod model, which can be used in space nets to simulate full contact dynamics. [PERSON] et al. [39] studied the influence of the stiffness of the rope on the capture motion of the net and presented a contact dynamic model. In [40], the contact model is derived from the penalty-based method and the impulse-based method. Consisting of inflatable rods and flexible nets, the space netted pocket system is a flexible mechanical system which can be used to capture a non-cooperative target flexibly due to the advantages of having a stable structure, strong adaptability and a large capture range [17; 40]. Therefore, the space netted pocket system presents a wide application prospect in space debris active cleaning technology. The objective of this paper to study the dynamics characteristics of the space netted pocket mechanism capturing non-cooperative targets. A mathematic modeling and simulation method is proposed for the space netted pocket mechanism. Different capturing strategies are presented to analyze the capturing process. First, the dynamics model of the space netted pocket system is established based on ANCF. Then, the contact collision force model is presented to describe the contact collision characteristics during the space netted pocket system capturing the target. Finally, the dynamics characteristics of the space netted pocket mechanism capturing the target are analyzed and discussed for different capturing strategies. This paper is organized as follows. In Section 2, the structure of the space netted pocket system is presented. In Section 3, the dynamics modeling of space netted pocket is established. In Section 4, the dynamic simulation for different cases are presented,and the simulation results are analyzed and discussed. Finally, Section 5 concludes the presented work.
## 3 Results
### Case Study in Slovakia
#### 3.1.9 Mutual Comparison of the Resulting Practical Throughput of Individual Proposals  
Full control by the ETCS L3 system is considered in this variant. The occupancy time analysis of the model train sequence is shown in Figure 11. The calculated occupancy time for this train sequence is 19.5 min.  
When comparing the data obtained from the analysis of occupancy times for the model train sequence, we found the following results for configuration of the trackside signaling equipment:  
1. ETCS L3--19.5 min;
2. ETCS L2 with optimized section lengths--29.5 min;
3. LS system--29.5 min;
4. ETCS L2--32 min;
5. ETCS L1--41.5 min.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 3 Proposed Method
### Multi-Scale Degradation-Representation-Guided Deblurring (Reblurring) Subnetwork
```
Data: Real Blurred Image \(y\) and the corresponding Real Sharp Image \(x\) Result: Reblurred image \(y_{re}\) and deblurred image \(x_{de}\) Initialization: Set learning rate, batch size and hyperparameters of the Adam solver; Cropping images from datasets; whileTrainingdo Expand and Crop the real blurred image \(y\) and corresponding sharp image \(x\) from the training dataset----Gopro; Obtain the implicit degradation representations \(E\) using the degradation estimation network in Figure 2; Input \(x\) and \(E\) into multi-scale reblurring subnetwork in Figure 7 to obtain reblurred image \(y_{re}\); Calculate \(\mathcal{L}_{re}\) using Equation (7); Input \(y\) and \(E\) into multi-scale deblurring subnetwork in Figure 7 to obtain deblurred image \(x_{de}\); Calculate \(\mathcal{L}_{de}\) using Equation (9); Evaluate total loss using Equation (11); Back propagation and update the network parameters;
1 end while Obtain the reblurred image \(y_{re}\) and deblurred image \(x_{de}\); Obtain the test image pairs from the test dataset----RWBI or RealBlur;
2 whileTestingdo Extract the real blurred image \(y\) from the testing dataset; Obtain the implicit degradation representations \(E\) using the degradation estimation network in Figure 2; Input \(y\) and \(E\) into multi-scale deblurring subnetwork in Figure 7 to obtain deblurred image \(x_{de}\);
3 end while
```  
**Algorithm 1**The Overall Process of IDR\({}^{2}\)ENet
## 3 Tests and Results
### 3U Backplane Verification
When all four CPLDs were active, the measured current increased from 4.3 mA to 5.6 mA. As a result, the maximum power consumption was determined as approximately 18.5 mW. This result confirmed two important points. First, the power drawn by the backplane would have negligible impact on the overall power consumption of a satellite. For instance, the backplane would only need 28.0 mW of  
Figure 11: Bit error check test setup. energy per cycle in ISS orbit. Second, the four CPLDs consumed significantly low power compared to the voltage regulator. According to datasheets, the voltage regulator quiescent current was 4.0 mA, whereas the CPLD quiescent current was only 13 uA. Therefore, if it is necessary to reduce the power consumption of the backplane even further, the focus should be on the voltage regulator rather than the CPLD.
## 4 Security Analysis and Simulation Verification
### Formal Analysis of Protocol Security
#### 4.1.2 Interpretation and Result Evaluation  
Clarification of BAN Logic and Probabilistic Analysis  
Section 4.1.1 employs BAN logic to conduct a formal security analysis of the SE-UAVAKA protocol. This approach ensures the logical correctness of two-way authentication and session-key negotiation by modeling trust relationships and belief propagation among protocol entities. The analysis directly references key components from the original paper (such as \(H_{a}\) and \(H_{b}\) derived from fresh timestamps and shared secrets), applying specific rules to verify message authenticity and timeliness, ultimately enabling both parties to establish mutual trust in the session key \(SSK_{AB}\).  
Furthermore, the paper quantitatively assesses the protocol's security by evaluating the success probability of attackers in various scenarios through probabilistic analysis. It demonstrates that the protocol's security relies on well-established cryptographic problems, as evidenced by defined events (such as compromising hash functions or solving elliptic-curve discrete logarithm problems). Crucially, even with temporary session data leakage, the inclusion of long-term secrets (e.g., \(H(ID_{A}\|ID_{B})\)) in the key derivation process maintains the ongoing security of \(SSK_{AB}\). Thus, the paper effectively combines logical and quantitative analysis to comprehensively demonstrate the protocol's security.
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 2 VLC System Model Applied to Underground Mining Environments
### Optical Transmitters and Receivers
The gain of the optical concentrator can be written as \(g(\theta_{ij})=\eta^{2}/sin^{2}(\Theta)\), in which \(\eta\) is the internal refractive index of the concentrator.
## 4 DCB Voltage Factor and Ratio  
### DCB Voltage Factor  
The DCB voltage factor \((k)\) essentially defines the area of the space vector diagram, the inverter's output level characteristics, and the distribution of switching vectors. The area of the space vector diagram limits the maximum utilization of the DC bus voltage, while the level characteristics determine the output quality. It is calculated by dividing the maximum output voltage of a DTLI \(V_{ref,max}\) by the DC bus voltage \(V_{dc}/3^{1/2}\) in the linear modulation region, as expressed in Equation (12) [48].  
\[k=\frac{\sqrt{3}V_{ref,max}}{V_{dc}} \tag{12}\]
# The Comparison of Electron Density between CSES In Situ and Ground-Based Observations in China
## 5 Discussion
The region of the Ne peak around EIA occupies a wider area with stronger strength in March, June and September, and the values at mid to low latitude are relatively high in June. Compared to the pattern of CSES Ne distribution, it can be seen that the high value of the correlation coefficient may have a relationship with the EIA phenomenon. Affected by the fountain effect during the daytime, the phenomenon of EIA can be found at the low latitude for both the CSES and ionosonde; thus, the correlation coefficient is high around the peak of EIA. Compared to other locations, [PERSON] et al. [11] also found that the initial Ne observed by Swarm agrees relatively well with COSMIC data near the magnetic equator in the daytime. [PERSON] et al. [6] reported that the phenomena of EIA and longitudinal wave number around the magnetic equator can be observed by both the CSES and the Swarm B satellite. In the comparison between the CSES Ne and the IRI model, [PERSON] et al. [7] detected that the correlation coefficient in the daytime is higher around the peak of EIA than that in other regions. Compared with the QJISR data and ionosonde observations, the average values of the CSES Ne are relatively lower, with the relative change about 80-95% in the daytime corresponding to the ground-based data. Although the IRI model can exactly exhibit the ionospheric characteristics, some researchers found that the value of Ne at the topside ionosphere are overestimated by the IRI model [32; 40]. The systematic biases are also found in the comparison of electron density between the CSES and Swarm constellation [5; 6], DEMETER satellite and the IRI model [7], which may be caused by the different designs of the payload, different data processing and the overestimation of the IRI model at the topside ionosphere. Furthermore, similar to the above illustrations, there may be some biases in the interpretation of ionograms when the phenomena of spread F, stratification of F2 layer and Es occur in the ionosphere, which may induce the discrepancies of Ne between the CSES and ionosonde observations. Furthermore, the reliability of ISR data is  
Figure 10: Spatial distribution of CSES Ne in different months for 2019.  
widely accepted, although it is somewhat model-dependent. Different from ground-based observations for the topside ionosphere, the in situ datum of the satellite is the direct measurement of ionospheric parameters. The payload of LAP operates as an absolute instrument on the platform of the CSES, and is able to identify the plasma potential, thus retrieving the actual electron density, which is a valuable measurement for ionospheric in situ observations. Through the comparison, scientists found that the accuracy of LAP installed on a satellite could be different from mission to mission [21; 41]. Although there are differences in the Ne values between ground-based remote sensing and satellite in situ measurements, similar variations and relatively good correlation between the two datasets can certify the authenticity of in situ Ne observed by the CSES.
## 3 Results and Discussion
### Field Tests
This section performs the simulation and field tests. In the simulation, the proposed method is compared with the existing methods for coverage sampling design and path planning, including LMP and HGC. Simulation results show that our method has better coverage performance and can satisfy a wider range of measurements under the same budget. In field experiments, we use a UAV carrying gas sensors to follow a planned path  
Figure 10: PM2.5 data interpolation results. (**a**) Horizontal interpolation at 50 m height; (**b**) vertical interpolation at latitude 39.240467, longitude from 117.053912 to 117.054761. Figure 9: Comparison between the experimental measurement value and the national control station value. The yellow solid line is the station value, and the blue dotted line is the measured value. and collect gas data. The results show that the proposed method can help the UAV to collect the polluted gas in the detection space, and the data can be used for gas data spatial distribution reconstruction, pollution source tracking and pollutant diffusion trend analysis.