esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Qualitative Data Analysis
#### 4.3.5 Satisfaction with the Degree of Precision Achieved During Implemented Interactions  
The results of the evaluation of the first level (Figure 10a) show that the participants were generally satisfied with the precision achieved at this level, with satisfaction being particularly high at the sublevels with shrunken objects and reduced contrast. Challenging tasks, such as retrieving smaller 3D objects in this case, are often associated with a greater sense of achievement when they are successfully mastered, which may contribute to higher subject satisfaction.  
As shown in Figure 10b, participants were satisfied with the precision they achieved when performing the tasks on both sublevels of the second level. At the sublevel without distractors, 56.60% of participants gave ratings of 4 and 5. The result indicates a generally high level of satisfaction with the precision achieved when enlarging the 3D object. A smaller number of participants rated the subjective perception of the precision achieved lower, which confirms the positive perception of the precision required for the successful change of size of the 3D object. At the sublevel with distractors, the number of ratings of 5 is slightly lower at 24.53%, but the rating of 4 was 30.19%. Participants were still relatively satisfied with their accuracy despite the greater challenge posed on this sublevel. Ratings of 2 and 3 were also slightly more common on this sublevel, suggesting that some participants struggled with distractors.  
Figure 10c shows how participants rated their satisfaction with the precision they achieved when rotating the 3D object. Most participants were generally very satisfied with the precision they achieved at this level, and no participant rated the precision as the lowest. In the direct interaction sublevel, most participants gave the two highest scores (39.62% gave a score of 5, and 32.07% gave a score of 4). Significantly more participants (73.58%) were very satisfied with the indirect interaction, which confirms the high rating of precision when using the slider to rotate the 3D object.  
The data in Figure 10d show that most participants gave high ratings in all sublevels. This confirms the ability of participants to achieve precision despite the changes to interface design and button interaction efficiency shown in the previous tasks.
## 5 Experimental Results
### Results for the Proposed SMC Controller  
Similar to the previous section, Figure 20 presents the results when the set point of the DC voltage is changed. Initially, it was set in 400 V and a change of 20 V was made, reducing the DC voltage from 400 to 380 V, as shown in Figure 20a. Then, the set point was increased from 380 to 400 V (Figure 20b). In both cases, the DC voltage presents a stabilization time of 1.55. In this case, it does not have any overvoltage and the \(PF\) is ensured to be close to 1. In addition, the AC current amplitude is reached in only half a cycle after the set point change; in contrast, with the PI-SMC control, the AC current amplitude is reached after several cycles.  
The violet waveform in Figure 20 corresponds to \(v_{o}\). This result confirms that the SM controller effectively works and quickly reaches the new steady-state conditions in the presence of a significant change in operating conditions. This measure confirms the stability and robustness of the control technique proposed in this paper.  
Figure 21 depicts the results for load perturbations; particularly, Figure 21a presents the results when load decreases 50%, while Figure 21b corresponds to a load increment of  
Figure 19: Experimental results with PI-SMC control for load changes: (**a**) load decreases 25%; and (**b**) load increases 25%.  
Figure 20: Experimental results with SMC for voltage set point changes: (**a**) DC voltage increases 20 V; and (**b**) DC voltage decreases 20 V.  
50%. In this case, the DC voltage does not present any overvoltage or oscillations around its set point. In addition, the SBBC instantaneously responds to load changes and the current amplitude is reached in the next half-cycle. The proposed SMC control presents the fastest response among the control strategies implemented, even when the load disturbance is twice as large as the load disturbance of PI-SMC control.
## 3 HAFNet and HAFNetE
Moreover, one objective of the research is to assess whether the previously proposed HAFNet three-streams network can be generalized and effectively being employed using different base models, such as U-Net. For these reasons, U-Net was selected as the single-stream subnetwork. To summarize, HAFNetE is a complete overhaul of the original HAFNet model. VGG16 encoders are substituted with EfficientNet encoders, and the SegNet architecture at the individual stream level is replaced with a U-Net. The only aspects retained from the previous version are the idea of combining features extracted in the HRI-RGB and LiDAR-derived DSM streams into a new cross-modal stream and the method used to fuse the encoded information. The substituted encoders and the restructured network architecture provide a completely new and, most importantly, efficient way of extracting and processing information from data. As it will be discussed thoroughly in Section 5, even though the HAFNetE model provides an improvement at an application level in terms of segmentation capability, the most remarkable and actionable result with respect to the previously proposed HAFNet is the advanced and carefully designed, efficient architecture, that translates into a massive enhancement of computational efficiency. A part from a few models, most of the newly proposed networks are designed to score highest in segmentation performances largely disregarding the associated computational cost. This latter can make the model impossible to use in most of real-world scenarios, where end users do not have enough computational resources, or, even if they do, the final application does not permit the use of related technologies (e.g., on-board spaceborne systems). Memory footprint, training time, and inference time are aspects that cannot be overlooked when deploying a system in production. HAFNetE is engineered taking all these details into account and with the explicit goal of making the network deployable in a on-board spaceborne system.
## 3 Controller Design and Stability Analysis
### Stability Analysis  
We present the i-MET and CAV controllers using stability analysis based on [PERSON]'s theory [40; 41; 42; 43]. We use the damping coefficient of the generator \(b_{g}\) as a control input. This value must be positive since it is assumed that the electric generator can only convert kinetic energy to electric energy, while the reverse process is not possible. Thus, our analysis assumes that this controller only performs with a positive \(b_{g}\) value. As mentioned above, both controllers share the objective of tracking \(\Omega_{d}\). However, \(\Omega_{d}\) for the i-MET strategy is time varying, whilst \(\Omega_{d}\) for the CAV strategy remains constant.  
Consider the dynamic equation of the system obtained from (12) and (13) as:  
\[\dot{\Omega}=\frac{1}{\gamma}\tau_{t}-\frac{b_{g}}{J}\Omega. \tag{19}\]  
With tracking error \(\dot{\Omega}=\Omega-\Omega_{d}\), the control law can be designed as:  
\[b_{g}=\frac{1}{\Omega}\left\{k_{p}\dot{\Omega}+\gamma\tanh\left(\frac{\gamma \dot{\Omega}}{\varepsilon}\right)\right\}, \tag{20}\]  
where \(k_{p}>0\), \(\varepsilon>0\) and \(\gamma=J\Omega_{\max}+\tau_{t,max}\) is a switching variable with \(\left|\Omega_{d}\right|<\Omega_{\max}\) and \(\left|\tau_{t}\right|<\tau_{t,max}\).  
In order to derive the control law, let us define the Lyapunov candidate as:  
\[V=\frac{1}{2}J\dot{\Omega}^{2}. \tag{21}\]  
The time derivative of (21) with substituting (20) yields:  
\[\dot{V} =\dot{\Omega}\left\{-k_{p}\dot{\Omega}-J\Omega_{d}+\tau_{t}- \gamma\tanh\left(\frac{\gamma\dot{\Omega}}{\varepsilon}\right)\right\}\] \[\leq-k_{p}\dot{\Omega}^{2}+\left|\gamma\dot{\Omega}\right|-\gamma \dot{\Omega}\tanh\left(\frac{\gamma\dot{\Omega}}{\varepsilon}\right). \tag{22}\]  
Using the following the hyperbolic tangent function property (\(\zeta\): real number):  
\[0\leq\left|\zeta\right|-\zeta\tanh\left(\frac{\zeta}{\varepsilon}\right)\leq 0.2785\varepsilon. \tag{23}\]  
\(\dot{V}\) yields:  
\[\dot{V} \leq-k_{p}\dot{\Omega}^{2}+0.2785\varepsilon\] \[=\ -\frac{2k_{p}}{J}V+c, \tag{24}\]  
where \(c=0.2785\varepsilon\).  
Multiplying (24) by \(\exp(2k_{p}t/J)\) and integrating over \(\left[0,t\right]\) provides:  
\[0\leq V\left(t\right)\leq V\left(0\right)\exp\left(-\frac{2k_{p}}{J}t\right)+ \frac{J}{2k_{p}}c. \tag{25}\]  
Therefore, \(V(0)\) disappears in \(V(t)\) as time increases and \(V(t)\) converges with a boundary of \(\frac{J}{2k_{p}}c\). \(c\) is the settable variable and defined as an arbitrarily small constant. As a result, the tracking error \(\dot{\Omega}\) becomes arbitrarily small. In the process of the stability proof for the controller, \(\Omega\) becomes \(\Omega_{d}\) with small margin of error.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 5 The ALPR System
### Detection
#### 5.1.3 Vertical-Edge Analysis and Size of Candidate Regions  
The Jordanian license plates are rectangular shaped with Arabic numerals that have vertical edges more than horizontal edges. These vertical edges can therefore be used to find the most candidate horizontal edges. This involves finding the candidate of the horizontal lines on the plate. A Sobel filter is used for edge detection, and the result is an image with white edges on black background. The vertical-edge density is then obtained by computing the sum of the edges in each row of the images. Next, a projection histogram of vertical edges is generated as shown in Figure 6.  
From the projection histogram, the image row with the maximum-edge density is obtained. Next, each of the candidate regions is assessed whether it passes through this row or not; if yes, the region is passed to the classification stage, otherwise, it is discarded. The size of each candidate region is also obtained and compared to the original image size. If the size of the candidate plate equals the original binary image, it is considered a non-plate; otherwise, it is a plate, thus it passes to the classification stage.  
Figure 5: Examples of candidate regions, (**a–d**) are samples of candidate regions.  
Figure 6: An example of a vertical-edge image and projection histogram of its vertical edges. (**a**) A vertical-edge image. (**b**) Projection histogram of the vertical edges.
## 3 Results
### Accuracy Assessment  
#### 3.4.1 Comparison with Field Plot Data  
To validate the classification accuracy of our proposed rice paddy mapping method, the classified rice paddy results were first compared against the field plot data. The accuracy validation was conducted on the basis of rice paddy and other land use/land cover types, where all non-rice paddy  
Figure 8: Spatial distribution of rice paddies in the Mun River basin: (**a**,**c**) the overall and local detail rice extent, respectively, from our calculation in this study and (**b**,**d**) the overall and local detail rice extent respectively in 2017 declared by the International Rice Research Institute (IRRI). Identification results are superimposed on grey scale images at VH polarization.  
Figure 7: Distributions of the rice phenological characteristics. (**a**) Date of the beginning of season (DBS); (**b**) date of maximum backscatter during the peak growing season (DMP); and (**c**) length of the vegetative stage (LVS).  
types were uniformly grouped into the other land use/land cover class. The classification accuracies shown in Table 2 demonstrate that only 63 plots out of all 722 rice plots were misclassified as other land use/land cover types, and 67 other land use/land cover plots were misclassified as rice paddy. The confusion matrix shows that the identified rice paddies results using our proposed approach owned a high accuracy with the UA, PA, OA, and F1 score of 90.77%, 91.27%, 89.52%, and 0.91, respectively.
## 2 State-of-the-Art  
Resulting from the historical development of PLC controllers, they have been developed as proprietary device systems that are operated locally under real-time conditions. If a networking of these controllers is necessary from a user viewpoint, proprietary protocols that operate on top of TCP/IP or standardized protocols, such as Modbus TCP, Profinet, etc., are used for this. The standard technologies widespread from the Internet and Web have so far hardly played any role for PLC controllers.  
For a number of years, however, a transformation has been underway, with PLC manufacturers increasingly integrating Information and Communication (IC) technologies from the web in their systems, such as web server and HTML pages for diagnosis and configuration, in order to adapt the controllers incrementally to the new requirements.  
Four different approaches to make PLC controllers I40 compatible can essentially be revealed from state-of-the-art technologies. These include the introduction of basic web technologies, the global networking of process data, the introduction of service principles and the virtualization of PLCs.
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 1 Introduction
For the second problem, since the duration of a group of pulses with the same CF and BW is generally longer than a second and the coherent processing interval (CPI) for coherent integration will not exceed one second, we perform coherent integration for signals with the same CF and BW, while the PRI is random and PW is staggered. In recent years, many methods have been proposed for coherent integration, such as Keystone transform [18], Radon Fourier transform (RFT) [19], axis rotation moving target detection [20], and scaled inverse Fourier transform [21], which are only applicable for conventional radar. As for radar signals with random PRI and staggered PW, it can be concluded that the random PRI will introduce the problems of irregular range cell migration (RCM) and nonuniform phase fluctuations among different pulses and that the staggered PW will introduce the problem of the irregular range-Doppler coupling effect. The problem of nonuniform phase fluctuations among different pulses can be converted to the problem of spectral analysis of nonuniformly sampled complex-valued data. As for spectral analysis of nonuniformly sampled data, a method based on interpolation [22; 23] cannot be applied when the Doppler frequency of the moving target is higher than the pulse repetition frequency of the radar. [PERSON] proposed a method based on nonuniform Fast Fourier Transform (NUFFT) for random PRI PD radar [24], but the compensation of the irregular RCM is not covered in this paper. [PERSON] proposed a method based on Radon nonuniform Fractional Fourier Transform (Radon-NUFrFT) for a random PRI radar [25], but it is only applicable for periodic nonuniformly sampled signals, which cannot solve the non-periodic, nonuniform phase fluctuations and the irregular range-Doppler coupling effect. [PERSON] proposed a method based on the Radon-iterative adaptive approach (Radon-IAA) [26] to solve the problems introduced by the random PRI, but it requires a lot of iterative calculations, which is difficult to apply directly to engineering practice. Hence, we propose a method based on Radon-NUFFT to deal with the problems in the coherent integration for a radar signal with random PRI and staggered PW. The remainder of this paper is organized as follows. Section 2 introduces the PBR system in general, including the geometry, hardware, and signal processing flow of the PBR system. Section 3 establishes the mathematical model of direct wave signal and describes the parameter estimation method based on template matching. Section 4 establishes the mathematical model of echo signal of a moving target firstly; then, the coherent integration method based on Radon-NUFFT is described, and simulation results are performed. Section 5 presents the experimental results to verify the performance of the PBR system. Section 6 concludes the paper.
# Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks
## 5 Mathematical Analysis  
The criteria for path selection are quite straightforward. A node will prefer to form the routing path with the node that has the minimum distance, least number of hops, and least number of neighbors.  
The combined values of these parameters are calculated based on the weight given to each parameter. If transmitting power is to be conserved, then signal strength will be given the maximum weight, whereas if the higher throughput is the objective then the least number of hops will be given the maximum weight. Hence, when a node receives the RR packet from multiple nodes \(n\), it stores the signal strength of the received signal (ss), the number of hops between the gateway and the node which sent the RR packet (\(n\)_th_), and the number of neighbors (_gh_). We propose to use a new score function \(F(n)\) evaluated for every node \(n\) to make the forwarding node selection. The expression of \(F(n)\) is given as follows:  
\[F(n)\ =\ (1\ +\ RS(n))\ \times\ (1\ -\ HP(n))\ \times\ (1\ -\ NG(n)), \tag{2}\]  
where  
\[RS(n\ )\ =\ NS(n)\ \times\ sswt,\ \{sswt|sswt\ \in\ R,\ 0\ \leq\ sswt\ \leq\ 1\}, \tag{3}\]  
\[HP(n)\ =\ NH(n)\ \times\ hpvwt,\ \{hpvwt|hpvt\ \in\ R,\ 0\ \leq\ hpvt\ \leq\ 1\}\, \tag{4}\]  
\[NG(n)\ =\ NB(n)\ \times\ glwt,\ \{glwt|glwt\ \in\ R,\ 0\ \leq\ glwt\ \leq\ 1\}\, \tag{5}\]  
_sswt_, _hpvt_, and _glwt_ are the weights for RSS, hops, and neighbors, respectively. The sets of normalized values of RSS, number of hops, and number of neighbors are given by the following expressions, respectively:  
\[NS\ =\ \{nss_{1},nss_{2},\ldots,\ nss_{n}\}, \tag{6}\]  
\[NH\ =\ \{nhp_{1},nhp_{2},\ldots,\ nhp_{n}\}, \tag{7}\]  
\[NB\ =\ \{ngh_{1},ngh_{2},\ldots,\ ngh_{n}\} \tag{8}\]  
A node \(i\) with a score \(F(i)\) will be selected as the forwarding node when it fulfills the next rule:  
\[F(i)=max\ \{F(1),\ F(2),\ \ldots F(n)\}. \tag{9}\]  
Received signal strength (ss) is calculated by:  
\[ss\ =\ Txywr-TL, \tag{10}\]  
Figure 10: Metrics in the RR packets sent by node B.  
Figure 9: Metrics in the RR packet sent by node A.  
where _Txpwr_ is the transmitted signal power in logarithmic units, and _TL_ is the transmission losses, also in dB. The _TL_ parameter is calculated by:  
\[TL\ =\ \alpha R\ +\ 20 log_{10}(R), \tag{11}\]  
where \(R\) is the distance between the nodes. The absorption coefficient \(\alpha\) (in dB/km) is calculated by [21]:  
\[\alpha=\left(\frac{0.11f^{2}}{1+f^{2}}\ +\ \frac{44f^{2}}{4100f^{2}}\right)\ +\ 2.75\times 10^{-4}f^{2}\ +\ 0.003, \tag{12}\]  
where \(f\) is the frequency (kHz).
## 4 Simulation Results and Discussions
This is due to the fact that the proposed system has a great improvement in time duration for information transmission with respect to the other counterpart. Here, it is assumed that \(E_{T}\) represents the total transmission energy. In Figure 12, the comparison of the BER performance between the proposed system and the CIM-SR-DCSK-CC system over the AWGN channel is shown. The number of replications is \(J=4\). Obviously, the BER performance of the proposed system is better than the CIM-SR-DCSK-CC system when some parameter settings are the same. For example, when \(U=50\), the gain of the proposed MCIM-SR-DCSK-CC system is about 4 dB at a BER of \(10^{-3}\) over the AWGN channel. Figure 8: BER performance of the proposed system versus the length of the chaotic signal over the AWGN channel and multipath Rayleigh fading channel with \(P=16,M=4,J=4,N=10\). Figure 10: The comparison of the BER performance with different \(M\) over the AWGN channel and multipath Rayleigh fading channel with \(U=100,P=64,J=4,N=10\). Figure 9: BER performance of the proposed system versus the number of the parallel groups over the AWGN channel and multipath Rayleigh fading channel with \(U=100,P=16,M=4,J=4\). Figure 11: The comparison of the normalized throughputs of the proposed system. The proposed system is primarily targeted at high-data-rate communication systems that typically operate in moderate-to-high SNR regions. For instance, in satellite communication systems and certain urban wireless environments, the SNR often exceeds 12 dB due to the use of advanced signal processing techniques and favorable propagation conditions [39; 40]. In these scenarios, our proposed system demonstrates significant performance improvements in terms of BER and throughput, making it highly relevant for applications that require high reliability and efficiency. The chosen SNR range (above 12 dB) was selected to highlight the superior performance of our proposed method in environments where high SNR is achievable. This range is particularly relevant for systems that benefit from advanced techniques such as beamforming, adaptive modulation, and error correction coding, which can effectively increase the SNR. By focusing on this range, we aim to demonstrate the potential of our method in scenarios where it can provide the most significant advantages.
# Impact of Cryogenic Condition and Drill Diameter on Drilling Performance of CFRP
## 3 Results and Discussion  
Drilling experiments were performed under dry and dipped cryogenic conditions using solid carbide drills with the size of 4 and 6 mm and varying feed rates. The machinability of CFRP was evaluated by the thrust force, delamination factor, hole surface quality of the CFRP and tool wear.
## 2 Materials and Methods
### System Configuration
#### 2.3.3 Filter  
As shown in Figure 5, a clear observation is impossible owing to the strong emitted radiation images acquired with conventional vision systems without an optical design during GMAW. Thus, an appropriate filter design is required to eliminate the emitted radiation. First, an imaging band suitable for image acquisition was selected by analyzing the relative intensity and characteristics of the wavelength band in the spectrum measured during GMAW. Because the spectral radiance was strong, owing to the high temperature of the arc plasma and molten pool, a wavelength band with a low relative intensity was selected from the measured spectrum. A wavelength band that allows clear observation of the molten pool with little interference from the arc plasma must be selected. In this case, a bandpass filter that allowed only the selected wavelength band to pass through and cut off other wavelength bands was selected. The full width at half maximum (FWHM) is important for bandpass filter selection.  
Next, the overall transmittance of the radiation must be reduced, because the spectral radiance of the radiation emitted from the arc plasma is strong across the input wavelength band of the image sensor. The bandpass filter cuts off the transmittance using a predetermined optical density (OD) value in wavelength bands other than the selected wavelength band. This further reduces the transmittance when an ND filter is used. The ND filter reduces the amount of light that passes through (light energy) based on the OD value. When the bandpass and ND filters are utilized together in the proposed vision system, the overall OD value is selected based on the score of the image quality assessment described in Section 2.4. A multi-coating UV filter was used to protect the lens and filters and reduce image noise.