esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlation Technologies for Emerging Wireless Applications
## 3 Correlation-Based Power and Energy-Density Metrics for OTA-Testing of Mobile Devices  
An antenna array module for mobile phones compliant with the 5G NR FR2 band in Europe is used as DUT. The unitary antenna element composing the array is based on the aperture-coupled structure introduced in [16]. A 4 \(\times\) 4 mm\({}^{2}\) patch is placed 2 mm above the upper slot introducing an additional resonance, which allows the single-element bandwidth and gains to be increased. The antenna module is connectorized (see Figure 2a)for a 4-Port measurement configuration using both frequency and time-domain instruments with and without down-conversion to sub-6 GHz frequency bands. Figure 2d depicts the area of the DUT used for near-field scanning of magnetic (H) and electric (E) fields with the associated power-density as a function of power levels. Figure 2d,f shows the experimental setup for 3D near-field and far-field OTA scanning systems. The single-element S11 \(\leq-10\) dB impedance bandwidth is from 24.25 to 27.5 GHz, as shown in Figure 3 comparing measurement and simulations using 3D full-wave solvers. Figure 2: Experimental 26 GHz DUT with connectorized 1 \(\times\) 4 antenna array in user equipment (**a**) and associated 3D model including enclosure (**b**) and feeding lines (**c**) built using full-wave solvers in time and frequency domains. DUT near-field scanning area for power and energy density measurement (**e**). Three-dimensional near-field (**d**) and far-field OTA scanning (**f**) solutions. In Figure 4, the longitudinal, transverse, and total correlation functions are shown as a function of the \(SinC(k\rho)\) law approximation order. It is observed that only a few numbers of terms are required to reach very accurate estimations (below 1% uncertainties for near-field OTA sensing). Figure 4: Transverse, longitudinal and total antenna spatial correlation as a function of the separation distance r between array elements. Figure 3: Broadband measurement of return-losses S11 (**a**), S22 (**b**) and near-coupling S12 (**c**) of the antenna elements compared to 3D full-wave simulations. Measured correlation functions (**d**) versus 3D full-wave simulations as function of frequency. Figure 5 shows the simulated (_using 3D EM solvers: IMST-EMPIRE, HFSS, CST_) three components of the magnetic and electric fields at 2 mm from the DUT for 15 dBm input power. The power density measured using EO probing is presented in Figure 6a at 10 mm from the DUT. Ten millimeters is about a wavelength (\(\lambda\)), which corresponds to a distance where the extracted wave-impedance is very close (\(\sim\)99%) to 120 \(\Omega\) (Figure 6b). Figure 5: Magnetic field (**a–c**) and electric field (**d–f**) x, y and z components in near-field (2 mm from the DUT) at 26 GHz for 15 dBm input power. Figure 6: Measured power density at 26 GHz using electro-optical (EO) probing (**a**) and field-based extraction of wave-impedance (**b**) as function of distance between the DUT and the electro-optical probing.
## 3 Results and Discussion
### Characterization of BCs
The FT-IR spectra of BCs are shown in Figure 4. CPP200-CPP350 produced at low temperatures preserved most of carbohydrate components of the raw biomass. In the pyrolysis temperature range of 200-350 \({}^{\circ}\)C, no obvious changes could be observed in the FT-IR spectra. The peaks at 3337 cm\({}^{-1}\) correspond to the stretching vibration of O-H [30]; the absorption bands at 2925 and 2846 cm\({}^{-1}\) correspond to the C-H stretching vibrations of alkyl groups [31]; the stretching vibrations of -C=O of aldehydes or esters appear at various wave numbers in the range of 1511-1712 cm\({}^{-1}\)[32]; the skeletal vibrations of the O-H plane could be observed at 1456 cm\({}^{-1}\); the peaks appearing at 1317\(\sim\)1368 cm\({}^{-1}\) and 1030-1225 cm\({}^{-1}\) could be attributed to the C-O and C-O-C stretching vibrations, respectively [33]. The FT-IR results suggest that BCs retain a large number of original components, such as fatty acids, sugars, lignin and polar cellulose-containing molecules [34]. As the temperature increased, thermal pyrolysis occurred in the absence of oxygen, and volatile ingredients would be produced; the content of aromatic structures would appear and gradually increase. Other functional groups such as hydroxyl, alkyl and C-O-C groups were gradually pyrolyzed, and the peaks' heights decreased [35]. When the pyrolysis temperature reached above 400 \({}^{\circ}\)C, the aromatic structures became the dominant functional groups [36]. The bands in the wave number range of 1570-1688 cm\({}^{-1}\) and also at 1433 cm\({}^{-1}\) could be assigned to aromatic skeletal vibrations, and the bands in the wave number range of 619-873 cm\({}^{-1}\) correspond to in-plane bending vibrations of aromatic hydrocarbons, indicating that BCs obtained at higher pyrolysis temperatures were highly carbonized [37]. Obviously, C content in BCs increased while O content decreased with an increase in pyrolysis temperature, and this was consistent with the results of elemental analyses, again confirming that the higher carbonization of BCs occurred at higher temperatures. The elemental compositions and surface groups of CPP2000 were characterized by XPS (Figure 5). Obviously, CPP200 mainly contains C (51.57 wt.%), O (45.67 wt.%) and a small amount of S (0.12 wt.%) (Figure 5A), a finding that is consistent with the results of elemental analysis (Table 1). XPS-peak-differentiation-imitating analyses of C1s and O1s were also simulated. As shown in Figure 5B,C, the peak for the C1s can be deconvoluted into three components at 284.8 eV, 286.4 eV and 287.6 eV, which may be attributed to sp\({}^{3}\) C-C (58.40%), C-O (24.89%) and C=O (16.72%) bonds, respectively [30]. The deconvolution of the peak for the O1s confirmed the existence of C=O (28.91%), C-O (39.57%) and -OH (31.52%) species at binding energies of 533. 6 eV, 532.7 eV and 531.9 eV, respectively [38]. The XPS results again indicate that there are a considerable number of oxygen-containing functional groups on the surface of CPP200. Figure 4: FT\(-\)IR spectra of BCs prepared using different pyrolysis temperatures.
## 4 Experiments and Results
### Comparison with Other Methods  
We compared twelve advanced models to evaluate the advantages of the proposed model. In the evaluation process, four indicators of whole tumor (WT), tumor core (TC), enhancing tumor (ET), and average dice value (AVG) are used to evaluate the level of results. The numbers of compared networks were 2, 2, 3, and 5 for 2024, 2023, 2022, and classic networks, respectively. We compared five classic networks, which are 3D UNet, Att-UNet, UNETR, TransBTS, and VT-UNet. There are eight architecture variants based on basic UNet, and four are structures based on Transformers. Table 2 and Figure 5 show the online validation results on BraTS2021. The WT, TC, ET, and AVG are 91.67%, 87.70%, 83.35%, and 87.57%, respectively.  
The HD values, which are shown in Table 2 and Figure 6, are 5.34, 8.03, and 19.41 for the three tumor subregions (WT, TC, and ET), respectively. We compared the 3D U-Net as the baseline, and our WT, TC, ET, and average dice results increased by 3.65%, 11.53%, 7.15%, and 7.44%, respectively. The VT-UNet was compared with a pure Transformer, WT, TC, ET, and average dice results increased by 0.01%, 3.29%, 2.6%, and 1.96%, respectively. We compared UNETR, whose CNN + Transformer, our WT, TC, ET and average dice values increased by 0.78%, 3.97%, 2.42%, and 2.39%, respectively. The results show that our network dramatically improved for all targets. Although not all indicators are optimal, the average dice of the network is indeed the highest among all comparison  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Basic Configuration** & **Value** \\ \hline \(\bigcirc\) PyTorch Version & 1.11.0 \\ \(\bigcirc\) Python & 3.8.10 \\ \(\bigcirc\) GPU & NVIDIA RTX A5000 (24G) \\ \(\bigcirc\) Cuda & cu113 \\ Learning Rate & \(1\times 10^{4}\) \\ Optimizer & Ranger \\ Batch Size & 1 \\ Input Size & \(128\times 128\times 128\) \\ Output Size & \(128\times 128\times 128\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameter configuration.  
Figure 7 illustrates the visualization outcomes of the SCENet model when applied to the BraTS 2021 dataset, showcasing five randomly selected cases. The medical cases with identifiers 00631, 00446, 00586, 00618, and 00625 of Figure 7 were segmented by SCENet, respectively. The combined green, yellow, and red regions, the intersection of red and yellow, and the yellow regions correspond to whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. Generally speaking, SCENet's segmentation results closely match the labeled ground truth. When compared to networks based on 3D UNet or Transformer, as presented in Table 2, our model's performance is superior. Additionally, our network outperforms other UNet-based architectures. Collectively, our architecture and its constituent modules have achieved outstanding results on the BraTS 2021 dataset, laying a solid foundation for future research endeavors.
## 4 Case Study
### Technical Condition Assessment
#### 4.2.3 Operation of BECIA Software  
Simultaneously, the BECIA software is conducted on technical condition assessment of the BECI of Guo Bridge, where only the inspection index scales are required to be input, and the technical condition index and scale of overall BECI will be acquired automatically, as Figure 8 illustrates.  
Consequently, the results gained through BECIA are completely consistent with that of traditional manual calculation, which indicates that on-site inspectors are capable of participating in decision making independently via BECIA.  
In conclusion, the technical condition scale of overall BECI of Guo Bridge is defined as 4 according to Table 11, which implies that the BECI are seriously damaged, and the replacement should be carried out immediately, thus leading to the selection of a replacement plan.
# Design General Cam Profiles Based on Finite Element Method
## 3 Synthetical Kinematics  
In this section, the established approach to describe the deformation of membranes (see, e.g., [29]) is employed in the context of synthetical kinematics for designing cam profiles. To obtain the desired cam profile, a given initial configuration is selected. The initial curve is then \"deformed\" to the desired configuration. The differences between the initial (reference) configuration \(S_{0}\) and the current configuration \(S\) are shown in Figure 1. Both configurations are described by the geometrical quantities discussed in Section 2. For the reference configuration \(S_{0}\) we use the upper-case symbols \(\mathbf{X}\), \(\mathbf{A}_{\alpha}\), \(\mathbf{A}_{\alpha\beta}\), \(\mathbf{A}^{\alpha}\), and \(\mathbf{N}\). The corresponding lower-case symbols \(\mathbf{x}\), \(\mathbf{a}_{\alpha}\), \(\mathbf{a}_{\alpha\beta}\), \(\mathbf{a}^{\alpha}\), and \(\mathbf{n}\) are used in the current configuration \(S\). In order to characterize the deformation between the reference configuration \(S_{0}\) and the current configuration \(S\), a line element in \(S\) is considered as  
\[\text{d}\mathbf{x}=\mathbf{a}_{\alpha}\ \text{d}\xi^{,\alpha}, \tag{9}\]  
and its corresponding line \(\text{d}\mathbf{X}=\mathbf{A}_{\alpha}\text{d}\xi^{,\alpha}\) in \(S_{0}\). So that we can write \(\text{d}\xi^{,\alpha}=\mathbf{A}^{\alpha}\text{d}\mathbf{X}\). With this, \(\text{d}\mathbf{x}\) in the current configuration can be mapped to \(\text{d}\mathbf{X}\) in \(S_{0}\) by  
\[\text{d}\mathbf{x}=(\mathbf{a}_{\alpha}\otimes\mathbf{A}^{\alpha})\text{d}\mathbf{X}. \tag{10}\]  
Figure 1: Mapping between parameter domain P, reference configuration \(S_{0}\), and current configuration S.  
Here the tensor  
\[\mathbf{F}=\mathbf{a}_{\alpha}\otimes\mathbf{A}^{\alpha} \tag{11}\]  
denotes the deformation gradient. Vice versa  
\[\mathbf{F}^{-1}=\mathbf{A}_{\alpha}\otimes\mathbf{a}^{\alpha} \tag{12}\]  
maps the \(\mathrm{d}\mathrm{X}\) in \(S_{0}\) to \(\mathrm{d}\mathrm{x}\) in \(S\). Through \(\mathbf{F}\) we thus have the following transformation  
\[\begin{array}{l}\mathbf{a}_{\alpha}=\mathbf{F}\,\mathbf{A}_{\alpha},\ \ \ \ \ \mathbf{A}_{\alpha}=\mathbf{F}^{-1}\mathbf{a}_{\alpha}\,\\ \mathbf{a}^{\alpha}=\mathbf{F}^{-1}\mathbf{A}^{\alpha},\ \ \ \ \ \mathbf{A}^{\alpha}= \mathbf{F}^{\Gamma}\,\mathbf{a}^{\alpha}.\end{array} \tag{13}\]  
Finally, the stretch between configurations \(S_{0}\) and \(S\) is defined by  
\[\begin{array}{l}\mathrm{J}:=\frac{\mathrm{J}_{\mathrm{A}}}{\mathrm{J}_{ \mathrm{A}}}\,\\ \mathrm{J}_{\mathrm{A}}:=\sqrt{\det\,\mathrm{A}_{\alpha\beta}}\,\\ \mathrm{J}_{\mathrm{a}}:=\sqrt{\det\,\mathrm{a}_{\alpha\beta}}\.\end{array} \tag{14}\]  
Here, \"\(\mathrm{det}\)\" denotes the surface determinant.
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### Behavior of LoRaMESH Modules in Testbed  
To carry out the LoRaMESH network study, only the packages sent between the EEC and the SM were analyzed in a bidirectional way. In the case of the mesh, the ToA and delay metrics were not possible to be collected yet in this work due to implementation challenges for the collection of this information.  
Metrics like ToA and delay were not possible to be collected yet in this work. In the Figure 21 are the results obtained from the testbed using the LoRaMESH network in order to understand how the RSSI and SNR are behaving in relation to the different SF configurations.  
As seen in the Figure 21a it is noticeable that for the distance of 2 m in this testbed, the RSSI was better for the SF 7 to 9 has a worse RSSI than the larger SFs. In the case of SNR Figure 21b, all the nodes obtained the same value, considering that no matter the SF configuration, what will really affect the most will be the extent to which the nodes really move away from the coordinator as well as the barriers available in the real scenario.
# Inertial-Navigation-Aided Single-Satellite Highly Dynamic Positioning Algorithm
## 5 Conclusions  
This article outlined an inertial-navigation-aided single-satellite highly dynamic positioning algorithm. Aiming at the problem of divergence and poor positioning accuracy of existing single-satellite systems for dynamic targets, a Doppler pseudorange and altitude information were adopted. First, the least-squares method combined with UKF was utilized to achieve fast convergence and the initial filter parameters. Then, the UKF filter was directly utilized to obtain stable final positioning results in a smooth transition. In this paper, the single error and comprehensive error that have a great influence on single-satellite positioning were simulated and compared with the Doppler single-satellite positioning algorithm based on least-squares Newton iteration and the Doppler single-satellite positioning method combined with EKF. The simulation and testing results proved that our proposed algorithm's position accuracy was much better than that of the other algorithms. Under GNSS system-rejection conditions, it has strong practical value.  
Figure 11: Real-environment testing of different algorithms.  
Figure 10: Real-environment testing map.  
**Author Contributions:** Conceptualization, L.Z.; methodology, C.T.; software, H.S.; validation, C.T. and L.Z.; formal analysis, Y.Z.; investigation, L.Z. and Y.Z.; resources, L.Z.; data curation, C.T.; writing-original draft preparation, C.T.; writing-review and editing, L.Z.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 61803310, 61801394) and the Open Fund of the Shaanxi Key Laboratory of Integrated and Intelligent Navigation (SKLIIN-20180203).  
**Acknowledgments:** The authors would like to thank editors for rigorous work and the anonymous reviewers for their comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 5 Conclusions  
This paper proposes a lightweight hybrid architecture model named CMDDNet to address the defocus deblurring problem. By combining the strengths of CNN and Mamba, the model provides an efficient solution for image deblurring. The convolutional branch in CMDDNet alleviates the vanishing gradient problem by introducing residual connections, allowing the network to capture finer details in the image more effectively. The Mamba branch, on the other hand, leverages a cross-scanning mechanism to perform comprehensive global feature extraction of the feature maps. This mechanism enables the Mamba branch to capture long-range dependencies and global structural information in the image, compensating for the convolutional branch's limitations in modeling global dependencies. CMDDNet uses parallel feature extraction modules that combine the ability of CNN to extract local features with Mamba's capacity to capture global features, enabling the model to extract fine-grained and coarse-grained fused features simultaneously, thereby improving the deblurring performance. In current popular benchmark tests, CMDDNet demonstrates outstanding deblurring performance while maintaining a relatively low parameter count.  
However, the restoration performance of the proposed method still exhibits a certain gap compared to full-resolution high-definition images, particularly in the clarity and detail recovery of complex texture regions. To address this issue, we plan to improve the method from the following aspects: First, we will optimize the network architecture and training strategies to enhance the model's feature extraction capability and detail recovery performance. Second, based on global feature extraction and local detail capture, we will introduce a classification mechanism for blurry and clear regions, enabling more targeted restoration of details in blurry areas. Furthermore, we will incorporate more challenging data augmentation techniques during training, such as Gaussian noise and random texture occlusion, to further improve the model's robustness and generalization ability in complex environments.  
Conceptualization, W.Z.; Methodology, W.Z.; Validation, J.L.; Resources, C.W.; Data curation, R.L.; Writing--original draft, W.Z.; Writing--review & editing, W.Z.; Visualization, W.Z. and R.L.; Supervision, C.W. and J.L.; Funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript. This work is supported by the grants from the Natural Science Foundation of Shandong Province (ZR2024 MF145, ZR2020 MF136) and the Qingdao Natural Science Foundation (23-2-1-162-zyyd-jch). Not applicable. Not applicable. Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
# GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs
## Materials and Methods
### In-Field Images
To ensure homogeneity among training and test data used as input for GBCNet, we resized images at 800 px height. In addition, since the first part of the model consists of the first ten VGG-16 layers, it is important to normalize images with the same preprocessing techniques. To this end we employed channel normalization with the same parameters used by VGG-16 on CR1 and CR2. Finally, to increase the number of images available for training, we applied data augmentation techniques. At training time we randomly select patches in which the size is \(\frac{1}{4}\) of the original image size, and then we randomly flip images in the horizontal direction with 0.5 probability.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 2 Precoding Schemes  
In this section, the precoding schemes are grouped into three categories, i.e., linear, non-linear, and hybrid.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 2 The Considered Class of Flexible Job Shops and Their Representation
### Basic Assumptions
#### 2.1.3 The Operations  
The common working period starts at time 0, and its maximum time span is noted as \(T\). All considered jobs have a known release time and a preferred due time. Machines are available for operations since time 0; in the first stage, it is assumed that no breakdowns and maintenance operations occur until time \(T\). Non-dummy operations need only one machine to be performed, machines can only execute one operation at a given time, and pre-emption of machines by other operations is not allowed. A machine can be used more than once by a given job but in different operations. The set-up times of the machines are integrated into the processing times, which, with the transportation times between machines, are considered in this study. Some jobs may have a declared due time that induces a time constraint, which, depending on the context, may be considered hard or soft; the other jobs use \(T\) as the due time.
# An Automatic Transformer from Sequential to Parallel Java Code
## 5 Control Flow Graph Analysis
### Handling Conditional Branches and Loops  
To evaluate the workload that will be assigned to each thread, it is necessary that the execution paths are clearly defined and there is no ambiguity. Hence, given a pair of nodes in the graph, just one path should exist connecting them. This assumption must be satisfied by all nodes in the CFG. To ensure that, we need to properly handle conditional branches and loops. For conditional statements, having two alternative paths, it would be necessary to prune one branch from the graph. We follow the worst-case execution time (WCET) approach [41], i.e., we select the branch with the highest number of instructions, as this is likely the one with the lengthiest execution time, while the other branch is disconnected from the graph. Therefore, pruned nodes are not connected to any other node; however, we keep these nodes in our representation because they could be reconnected to the graph when needed for further analysis.  
Listing 5 shows an example where a conditional statement has a method call in each branch. When the analysis decides to parallelize the method call within the _then_ branch (i.e., line 79) because of the WCET, the _else_ branch will be removed from the graph; the resulting CFG is shown on the left side of Figure 2. Otherwise, the _then_ branch will be removed; see the right side of Figure 2. Using the example in Listing 4, despite there being no _else_ branch, the corresponding CFG will be modified by removing the edge connecting node 95 to node 98, because branch 95\(\rightarrow\)96\(\rightarrow\)CFG\({}_{96}\rightarrow\)98 is longer (i.e., with more instructions, and higher WCET value) than branch 95\(\rightarrow\)98.  
Regarding loop management, several approaches statically estimate the impact of the loops in the program execution [42; 43]. In our approach, to keep the analysis fast and effective, we consider loops as simple block statements, and we analyze them without estimating how many cycles could be run (such an analysis is outside the scope of the proposal, although loop estimation results could be readily included, without affecting the generality of our proposed approach). However, we keep track of these occurrences when evaluating the workload of the branch.  
Finally, another case should be considered: a chain of method calls that presents cycles, which means a method could call a previous method in the chain, creating a loop that, when inserting the corresponding CFGs in the main one, can break the assumption of one path for every pair of nodes. We observe that these occurrences are very rare, and if found, we simply remove the edge that creates the loop in the graph.