esa-sceva/satcom-chunk-collection huggingface dataset:
# Control Strategy of In-Port U-Turn for Ships Based on Arctangent Function Nonlinear Feedback
## 5 Matlab Simulation and Result Analysis
After the nonlinear feedback link is added, the turning range of the ship is reduced by 5.40%, the average rudder angle is reduced by 3.24%, and the MIA, MAE, and MTV are reduced by 1.25%, 2.83%, and 33.33%, respectively. It can be seen that, after the introduction of nonlinear feedback, the energy output of the whole system is reduced, and the rudder angle output is smoother. \begin{table}
\begin{tabular}{l c c c c c} \hline  & **Turning Range** & \(\bar{\delta}\) & **MIA** & **MAE** & **MTV** \\ \hline NF+CGSA & 2.10\(L\) & 9.23 & 7.07 & 37.38 & 0.14 \\ [PERSON] et al. [28] & 2.22\(L\) & 9.54 & 7.16 & 38.47 & 0.21 \\ PID & 2.41\(L\) & 9.53 & 7.53 & 40.23 & 0.14 \\ Decline ratio & 5.40\% & 3.24\% & 1.25\% & 2.83\% & 33.33\% \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of parameters of automatic U-turn in port at a water depth of 15 m.  
Figure 4: Rudder angle under a Beaufort wind scale of No. 4, \(\psi_{T}\) = 225\({}^{\circ}\)[28]. Figure 5: Ship trajectory of U-turn under a Beaufort wind scale of No. 4, \(\psi_{T}\) = 225\({}^{\circ}\)[28]. (3) Beaufort wind scale of No. 5, \(\psi_{T}=45^{\circ}\)  
It can be seen from Figures 9-11 and Table 5 that, under the condition of a Beaufort wind scale of No. 5, a wind direction of \(45^{\circ}\), a water depth of 15 m, and moving with slow speed (4.5 kn), the ship can complete an automatic U-turn in a restricted water area. After adding a nonlinear feedback link, the control output curve has no overshoot, the turning range is reduced by 1.58%, the average rudder angle is reduced by 18.26%, and the three parameters of MIA, MAE, and MTV are reduced by 13.55%, 1.89%, and 25.00%, respectively. It can be seen that the turning range of the ship is reduced, which  
Figure 8: Ship trajectory of U-turn under a Beaufort wind scale of No. 4, \(\psi_{T}=315^{\circ}\)[28]. Figure 6: Heading angle under a Beaufort wind scale of No.4, \(\psi_{T}=315^{\circ}\)[28]. Figure 7: Rudder angle under a Beaufort wind scale of No. 4, \(\psi_{T}=315^{\circ}\)[28]. (4) Beaufort wind scale of No. 5, \(\psi_{T}=135^{\circ}\)  
It can be seen from Figures 12-14 and Table 6 that, when the wind direction angle is \(135^{\circ}\), the ship can automatically complete U-turn in a restricted water area. With or without nonlinear feedback link, the time to reach the given course is roughly the same; however, with a nonlinear feedback link, the turning range of the ship is reduced by 5.11%, the average rudder angle is reduced by 3.88%, and the three indexes of MIA, MAE, and MTV are reduced by 2.43%, 3.60%, and 22.22%, respectively; this shows that the deviation between the course and the actual course is reduced, the energy consumption is reduced, and the rudder angle output curve is smoother.
## 4 Computational Results
### Test Instances  
The instances used to conduct the experimentation were adapted from the ones used in the literature to evaluate the multi depot VRP with heterogeneous fleet: [PERSON] et al. [41] (Kou15), [PERSON] and [PERSON] [42] (CaX10), [PERSON] and [PERSON] [43] (GaJ76-7-Gal76-12), and [PERSON] et al. [44] (Pn16k8 and Pn23k8). Even though there are some instances proposed by [PERSON] [45] and [PERSON] et al. [46] for the classical Heterogeneous Fleet Vehicle Routing Problem, we decided to use these instances since some of them provide a reasonable size in the number of customers for assessing the model.  
The size of the instances ranges from 12 to 100 nodes and from 2 to 10 vehicles. The generated problem instances are characterized by the following criteria: (i) number of customers; (ii) number of vehicles; (iii) coordinates (x,y) for all locations (including the depot); (iv) demand of each node; and (v) priority index for each node. Since the original instances consider multiple depots and do not consider any preference index between the customers to be served, we selected the first depot as the single-origin, and we included a priority parameter by assigning a numerical index within \(\{1,\lceil\sqrt{n}\rceil\}\), based on a uniform distribution. The customers having the highest value of the index represent the ones that should be first served (highest level of priority).  
Since the modified instances are set to deal with a different problem, and to facilitate the report of the results, we decided to rename them using the nomenclature \"FNO-x\", where \(x\) denotes a consecutive number assigned according to the rank assigned to the instance (in terms of the number of nodes, following a non-increasing order). For example, the instance Kou15 is the one with the lowest number of customers (12); therefore, it was renamed as FNO1. The remaining instances based on Pn16k8, CaX10, Pn23k8, GaJ76-7, GaJ76-8, GaJ76-9, GaJ76-11, and GaJ76-12 were renamed as FNO2, FNO3, FNO4, FNO5, FNO6, FNO7, FNO8, and FNO9 respectively. In the case of the instance GaJ76-10, it was named FNO10 because it has the largest size in the number of customers. These new instances are available by request.  
All of the experiments were conducted using a PC Intel Core i7 @2.30 GHz with 16 GB of RAM Memory under Windows 10. The formulation was modeled using AMPL and solved using Gurobi 9.0. For each instance, we established a time limit of 7200 s (2 h). In the case of the MA-RK, both versions were coded using the C++ language. In the next subsection, the parameter tuning is presented.
## 2 Theory and Simulation of Five-Frame Variable Phase-Shifting Method for SD-OCT
### Simulation
#### 2.2.2 Polychromatic Error Analysis  
In reality, polychromatic error and phase-shifting error of PZT have a great influence on the OCT imaging quality; also, the bandwidth of the light source will influence the polychromatic error. Generally, the wider the bandwidth, the bigger the polychromatic error. We changed the bandwidth of the light source to obtain Figure 5 by MATLAB. In Figure 5, the effect of light source bandwidth on CCA suppression was analyzed. The solid line is for FIP method and the dashed line is for the FVP method. We noticed that when the light source bandwidth is narrow, the CCA suppression ratio of FIP and FVP methods are almost the same. For example, if the light source bandwidth is 30 nm, the CCA suppression ratio for FIP and FVP methods are 88 dB. However, if it is a broadband light source, the difference of CCA suppression between the FIP and FVP methods will be large. For example, if the light source bandwidth is 290 nm, the CCA suppression ratio for FIP method is 45 dB whilst the one for FVP method is \(\sim\)87 dB. It could be found that the SD-OCT system's CCA suppression ratio decreases exponentially with the increasing of bandwidth of light source if the FIP method is employed.  
Figure 4: The correlation of polychromatic error and bandwidth of light source. The phase shift is 180\({}^{\circ}\), 90\({}^{\circ}\), 0\({}^{\circ}\), \(-\)90\({}^{\circ}\), \(-\)180\({}^{\circ}\).  
Figure 3: Fourier transform results. The solid line is for the five-frame invariant phase-shifting (FIP) method and the dashed line is for the five-frame variable phase-shifting (FVP) method.  
However, the CCA suppression ratio for the FVP method is nearly unaffected by the bandwidth of the light source.  
The influence of phase-shifting error due to PZT movement also takes an important role in CCA suppression ratio. In Figure 6, the effect of phase-shifting error on CCA suppression was also analyzed. The error of phase-shifting displacement and expected displacement is affected by many factors, including random noise of voltage loaded on PZT, random disturbance of the surrounding environment, and so on. This error distribution is generally a normal distribution model. Therefore, we added a random phase-shifting displacement error with a mean of 0 nm and a variance of 0 to 7 nm during the simulation by MATLAB. The CCA suppression ratio of the FIP and FVP methods will decrease exponentially with the increasing of phase-shifting displacement error. As could be seen from Figure 6, the CCA suppression of our novel method is better than FIP method when the phase-shifting displacement error is less than 5 nm. In summary, it is proved that the FVP method can get better CCA suppression for polychromatic errors and phase-shifting errors.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results
### Results of Decolorization Methods
are recorded for both the DL (method mean = 0.3284) and GD (method mean = 0.3260) colorization methods, as shown in Table 6. Analyzing the results on an image-by-image basis, the poorest outcomes are again linked to artificially generated images, specifically Images 9, 13, and 18 (0.1492, 0.1216, and 0.1371, respectively). In contrast, natural images yield very similar results with Image 25 (image mean = 0.3550), also a natural image, achieving the highest score in this category. In terms of FSIM, the results are highest for the Rec. 601 (method mean = 0.8949) and Rec. 2020 (method mean = 0.8775) methods, while the DL method results in the lowest performance (method mean = 0.6448). This method consistently struggles with artificially generated images. Interestingly, Image 6, which performs exceptionally well in the RMS (1073.39), yields very low results with FSIM (0.1678), highlighting a significant inconsistency between the evaluation metrics. Overall, FSIM significantly diverges from the results of other measures in evaluating colorization methods. A common challenge faced by most decolorization methods is encountered in images where colors exhibit approximately similar luminance. In such cases, the luminance of the colors does not adequately stand out against the luminance of the background, complicating the decolorization process. Additionally, it is clear that decolorization is generally more successful on natural images compared to artificially generated ones, underscoring the importance of image content and characteristics in achieving optimal results. If we consider methods based on the characteristics of the human visual system, such as Rec. 601, Rec. 709, Rec. 2020, and LAB, we can conclude that all these methods yield similar decolorization results. These methods are derived from features of the human visual system and are not well-suited to tasks like image segmentation and object detection. Instead, they are more appropriate for tasks where human observers evaluate the image, such as image compression.
## 3 Analysis of Cracked Plates  
The present HSDTPEs are also applied to the calculation of Stress Intensity Factors (SIFs) of cracked thick-thin plates. As illustrated in Figure 7, the mid plane of a cracked plate is taken as the \(x\)-\(y\) plane and is divided into arbitrary triangular elements. Accurate computation of SIFs remains challenging in the field of fracture mechanics. For plates loaded by a combination of bending and tension, the SIFs can also be computed by the Virtual Crack Closure Technique (VCCT) [61; 62; 63], the path-independent \(J\)-integral technique or interaction integral [64; 65], and the stiffness derivative method [66]. In this paper, the Virtual Crack Closure Technique (VCCT) [61; 62; 63] is employed to calculate the SIFs of the cracked plate. For the convenience of implementing the VCCT, point \(T_{2}\) shown in Figures 7 and 8 is temporarily moved to \(T_{3}\) along the extended line direction of \(T_{1}-T\). In the local coordinates \((s,n,z)\) shown in Figure 9, the relative displacements \(\left[\Delta\overline{u}(s,z),\Delta\overline{v}(s,z),\Delta\overline{w}(s,z)\right]\) of \(T_{1}-T\) and the stresses \(\left[\tau_{ns}(s,z),\sigma_{n}(s,z),\tau_{nz}(s,z)\right]\) of \(T\ -\ T_{1}\) can be easily calculated using Equations (18) and (22) for the fictitious thin layer \(e_{l}\). For example, assuming that the \(T_{1}-T\) is simulated by a fictitious thin layer with width \(d\) shown in Figure 3, the relative displacements can be evaluated by \(\Delta\overline{u}(s,z)=\gamma_{ns}d\), \(\Delta\overline{v}(s,z)=\ \varepsilon_{n}d\) and \(\Delta\overline{w}(s,z)=\gamma_{nz}d\). Then, the energy release rate at crack tip \(T\) is obtained by the VCCT as:  
\[\left\{\begin{array}{l}G_{\mathrm{I}}\cong\frac{1}{2h\tau_{0}}\int_{-h/2}^{h /2}\int_{0}^{r_{0}}\sigma_{n}(s,z)\Delta\overline{v}(s-r_{0},z)\mathrm{ds}\; \mathrm{dz}\\ G_{\mathrm{II}}\cong\frac{1}{2h\tau_{0}}\int_{-h/2}^{h/2}\int_{0}^{r_{0}}\tau_{ ns}(s,z)\Delta\overline{u}(s-r_{0},z)\mathrm{ds}\;\mathrm{dz}\\ G_{\mathrm{III}}\cong\frac{1}{2h\tau_{0}}\int_{-h/2}^{h/2}\int_{0}^{r_{0}}\tau_ {nz}(s,z)\Delta\overline{w}(s-r_{0},z)\mathrm{ds}\;\mathrm{dz}\end{array}\right. \tag{43}\]  
where \(G_{\mathrm{I}}\) is the energy release rate of crack mode I, \(G_{\mathrm{II}}\) is that of crack mode II, and \(G_{\mathrm{III}}\) is that of crack mode III. Then, the SIFs of the crack tip can be computed by means of the relations between the energy release rate and SIFs for the plate theory, for instance, \(K_{1}=\sqrt{3 EG_{\mathrm{I}}}\)[63].
## 3 Evaluation and Results
### Experimental Setup
#### 3.2.2 Setup: Variant 2  
This variation is customized for high-resolution (and thus slow) measurements of the optical spectrum using the OSA and the radio-frequency spectrum using a vector network analyzer (VNA). We replaced the DFB laser with a tunable laser source (TLS), featuring a resolution of 0.001 nm (\(\sim\)123 MHz), to enable accurate adjustment of the wavelength. The CLC system is excluded from this setup, as we are looking into the effect of the wavelength dither for several discrete points, one at the time. We will refer to these as misalignments of the carrier wavelength and the MRRs resonance. A photodiode terminating the RoF link is a high-performance avalanche photodiode (APD) with a bandwidth exceeding 40 GHz.  
Figure 11 shows a block diagram of setup Variant 2 including the following components starting from the left: a TLS, an optical polarization controller (PC), a MZM, an EDFA, another PC, an MRR on a PIC, a 2.512 km SMP, a 50:50 fiber coupler to provide an optical signal to both the OSA and the APD terminated by a VNA.  
Figure 11: Setup Var. 2.  
Figure 10: Setup Var. 1.  
The particular length of the fiber is chosen in such a way that the first dip in the power-penalty function is encountered, given the intended mmW frequency \(f_{0}\). This should demonstrate that even at relatively short distances the chromatic-dispersion-related effects might need to be considered and addressed.  
In Figure 12 there is a photograph of our experimental environment in the laboratory, accommodating both of the above-depicted setup variations.
# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
## 3 Experimental Design and Results Analysis
### Experimental Design  
Middlebury datasets [31] provide a standard for evaluating stereo matching algorithms, which contain original images of different sizes in various scenes and ground truth maps generated by structured light. Four datasets, Cones, Teddy, Tsukuba and Venus, are selected to conduct experiments, which have been widely used to evaluate stereo matching algorithms in Middlebury 2001 and Middlebury 2003 datasets [5; 32]. Figure 5 shows the left images and their corresponding ground truth maps of the four datasets.  
The related algorithms are implemented in Ubuntu 18.04 by C++ and OpenCV library. The experimental hardware configuration is Intel (R) Core (TM) i5-8250U CPU @ 1.60 GHz, 8 GB RAM. In the initial matching cost computation, the neighborhood window with size of \(9\times 9\) is selected for census transform. Table 1 shows some important parameter settings that have a significant impact on the experimental results.  
Figure 4: Schematic diagram of cost space conversion.  
To reflect the improvement of the proposed algorithm in cost aggregation and disparity optimization, SGM-MSIF algorithm is compared with SGM [11], MGM [12], OmniSGM [15], CSCA + BOX (the fastest matching speed under CSCA [16]) and CSCA + GF (the best matching accuracy under CSCA [16]). Three evaluation indicators, the visual effect of the disparity maps, the error matching rate (EMR) in three regions (Non-occ, All and Disc regions) and the running time of these algorithms, are used for comparison. The threshold is set to 1 when calculating EMR; this means it is judged as a mismatched pixel when the difference between the matching result and the real disparity value (Ground Truth) provided by the dataset is greater than 1. To verify the processing efficiency of these algorithms, all codes are tested without any acceleration technology.
## 2 Background for Location-Aware Beamforming in 5G Ultra-Dense Networks
Monte Carlo simulation validate analytical results and prove significant network performance improvement with deployment of mmWave gNB with directional beamforming. However, on the link level authors in [74] consider directional transmission with model of sectored antenna pattern. According to such model antenna gain can take only two values: \(g_{s}\), if the UE is within gNB main beamwidth and \(g^{\prime}s\), if the UE is outside gNB main beamwidth, where \(g_{s}\) and \(g^{\prime}s\) denote the gains of the main lobe and side lobe respectively. Interference characterization would be more accurate if we account complex radiation pattern for antenna array types, depending on the number of array elements. More realistic approach for interference evaluation in 5G mmWave UDN is reported in [32; 33; 34; 35; 36; 37; 38], however, for stationary UE case. To the best of our knowledge, the problem of interference evaluation for the case of mobile UE had not yet been thoroughly investigated. In this work we consider the problem of interference evaluation for the case of two gNB-UE links with mobile UE, as a basis for further evaluation on the level of a set of links inside single UDN microcell and finally, on the network level of a set of UDN microcells. Despite the fact, that considered case of interference evaluation includes only two gNB-UE links, it is rather challenging. First challenge consists in the instantaneous dependency of signal to interference ratio on UE angular and spatial separation, which changes during their mutual motion. Second challenge includes strong SIR dependence on the nonlinear ARP, defined by the AA type and the number of array elements. Third challenge is due to the need to take into account UE positioning accuracy for gNB, utilizing location-aware beamforming. Oversimplification of these factors can lead to the network SIR overestimation or underestimation, which can take values of several tens of dB. The novelty of current work is in instantaneous SIR evaluation model for cases of two links between two mobile UE and two stationary gNB, equipped with smart antennas,which perform location-aware beamforming during UE motion in predefined scenarios, accounting for their SOI and SNOI roles. Developed and publicly released open-source simulator includes thorough account of UE mobility and complex models for ARP for various AA types and the number of array elements. Combination of various primary measurements of heterogeneous radio access network for UE location estimation and further beamforming in the simulated scenarios can be accounted with positioning inaccuracy, defined by standard deviation of UE coordinates calculation. Further simulation results are performed for the case of perfect positioning accuracy, and even for such scenario it demonstrates considerable SIR fluctuation, that needs to be accounted for, when assessing UE terrestrial and angular separation. In the further subsection we will consider simulation model of two gNB-UE radio links.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 2 AI for Propagation Modeling
### Comparison between DL and ML Approaches
#### 2.3.2 Dataset Origin and Integrity  
The second limitation of DL-based models is data source mismatch, where data is rather simulated instead of being empirically collected. Tuning tens of millions of parameters requires enormous datasets, which has consequently led the researchers to resort to simulated data, as a large-scale measurement campaign would be time-consuming and expensive, defeating the concept of creating a predictive model. For instance, it took the authors of DeepLoRa about 4 months to collect 30,000 data samples [35]. Lowering the resolution of the images to reduce the model parameters and, in turn, the data volume should not be considered as a solution, as it has been reported to have an adverse effect on the prediction performance. [PERSON] et al. [32] documented an increase in mean absolute error (MAE) as the image resolution was reduced. In contrast, ML-based models have fewer parameters and do not require enormous data for training, which can be collected empirically.  
Consequently, DL-based models could be misrepresenting the propagation. As these models are trained by ray tracing simulation data, at best, they are mimicking the utilized algorithm, assuming they achieve perfect accuracy. They may have discrepancies with real-world propagation, do not capture the characteristics of novel technologies such as LoRa's CSS modulation, and vary between different ray tracing simulations. Furthermore, the grinding process reduces the granularity and the accuracy of the prediction, as only one RSSI value can be associated with a single pixel. For instance, [PERSON] et al. [35] collected 30,000 data points; however, as part of the griding process for landcover prediction, authors had to take an average of all the measurements within an area of 100 square meters, which reduced the samples to 4000, effectively not utilizing about 85% of the data for training. The use of simulation and griding degrades the originality of prediction. The reports do not provide further statistical analysis, such as examining the mean and standard deviation of the data within each grid, to justify whether averaging was the most accurate method to represent data in areas of the grid.
## Materials and Methods  
### Testing Field  
The tests were carried out in the campus area of the University of Palermo (13\({}^{\circ}\)20'58.21\" E; 38\({}^{\circ}\)6'25.99\" N; 31 m a.s.l.), along the edge of a flat field, with a perimeter of 287 m and an area of 4350 m\({}^{2}\), divided into different experimental parcels (_Lolium peremne L_. and _Poa prtensis L_. turf; official plants