esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Atmospheric Deposition
#### 3.2.2 PCDD/Fs and DL-PCBs  
Table 3 summarizes the PCDD/Fs + DL-PCBs values in WHO-TE\({}_{1998}\), for each location and season. The estimated annual value is also reported on the basis of seasonal data (Table S3 of the Supplementary Material). The comparison of the deposition rates for the PCDD/Fs + DL-PCBs shows a substantial homogeneity among the sampling sites with the exception of Roccaccia where the mean deposition rate is from 4 to 11 times higher, with a maximum in summer. These average deposition rates are significantly lower than the limit ones envisaged in the legislation and guidelines of various European countries [32]. In particular, in Germany the guide values for PCDD/F and DL-PCBs in deposition define a target value (annual average) of 4 pg WHO-TE\({}_{1998}\) m\({}^{-2}\)d\({}^{-1}\), and 9 pg WHO-TE\({}_{1998}\) m\({}^{-2}\)d\({}^{-1}\) is the reference for some industrial areas. In the Roccaccia and Allumiere stations, the highest concentrations of PCDD/Fs are recorded in winter and autumn seasons with much lower concentrations in summer and \begin{table}
\begin{tabular}{c c c c c} \hline
**Season** & **Roccaccia** & **S. Agostino** & **Allumiere** & **Rocca Resp.** \\ \hline Annual & 21.5 & 35.8 & 18.8 & 27.9 \\ \hline \end{tabular}
\end{table} 
Table 2: Deposition rate (expressed as mg m\({}^{-2}\)d\({}^{-1}\)) of the total sedimentable dusts in each location. \begin{table}
\begin{tabular}{c c c c c} \hline
**Season** & **Roccaccia** & **S. Agostino** & **Allumiere** & **Rocca Resp.** \\ \hline Summer & 14.1 & 0.33 & 0.39 & 1.04 \\ Autumn & 0.90 & 2.70 & 0.29 & 0.82 \\ Winter & 0.53 & 0.33 & 0.59 & 0.27 \\ Spring & 0.19 & 0.21 & 0.14 & 0.16 \\
**Annual average** & **3.93** & **0.89** & **0.35** & **0.57** \\ \hline \end{tabular}
\end{table} 
Table 3: Mean atmospheric deposition rate (expressed as pg WHO-TE\({}_{1998}\) m\({}^{-2}\)d\({}^{-1}\)) of PCDD/Fs + DL-PCBs by season and location. spring. S. Agostino shows the highest total PCDD/F concentrations in spring and autumn. Rocca Respampani shows the highest concentrations in winter, but the values are still more comparable between the different seasons. The Roccaccia station is the one that shows a significantly higher value (in WHO-TE). Table 4 shows the average rates profile of such micropollutants, expressed as \(\mathrm{fg\ m^{-2}\ d^{-1}}\) and in equivalent toxicity (WHO-TE\({}_{1998}\)). Tables S4-S7 of the Supplementary Material report the data of the PCDD/F and DL-PCB rates profiles in each season and location, expressed both in \(\mathrm{fg\ m^{-2}\ d^{-1}}\) and in equivalent toxicity with respect to the International Toxicity Equivalent (I-TE) factors, WHO-TE\({}_{1998}\) and WHO-TE\({}_{2005}\).
# Light Transmission in Fog: The Influence of Wavelength on the Extinction Coefficient
## 3 Materials and Methods  
### Pavin BP: A Fog and Rain Facility  
This study was made possible by using the PAVIN BP\"Fog and Rain\" platform for intelligent vehicles. This platform was developed to investigate all transport systems that could be affected by adverse weather such as fog and rain. The PAVIN BP platform, shown in Figure 5, is an infrastructure allowing fog and rain to be generated in controlled conditions. This 31 m-long platform includes a 15-m fixed section (tunnel) and a 16-m greenhouse with a transparent envelope for daytime conditions and an opaque cover for night time conditions. This platform is 5.5 m wide and 2.3 m high. An observation post is located at the end of the platform, in order to install the equipment outside of the wet area during testing. Fog is produced by nozzles spraying water under high pressure. It is therefore possible to produce fog of different densities by modifying the quantity of water injected, in all temperature and humidity conditions. Visibility (MOR) is measured by a transmissometer and can be kept constant by 10-m increments between 10 m and 100 m. Different DSDs can be produced by changing the nature of the water injected (normal tap water or demineralized water). These are similar to those obtained for some natural fogs as measured by a PSA [17].  
Figure 4: Spectrum of the 400-W halogen lamp used as a light source.  
Figure 5: The PAVIN BP platform. (**a**) Exploded diagram of the infrastructure, presenting the 3 parts: the observation post, the tunnel section for night conditions, and the greenhouse section for day conditions; (**b**) day view with cars, work zone, and various signaling equipment.  
In this infrastructure, methodologies to compare artificial and human vision systems can be developed, and the performance of dedicated sensors for driving assistance systems and vision sensors (cameras, LiDARs, and radars) used for the development of perception systems for future autonomous vehicles can be measured. Various driving situations can be reproduced with realistic targets and also reference reflectance-calibrated targets.
## 3 Results
### Total Fatty Acid and TAG Profiles
_tricornutum_ cultures (\(N=5\)) subjected to the three considered light treatments at the end of the 96 h exposure period (average \(\pm\) standard error, letters denote significant differences between light treatments at \(p<0.05\)). Regarding the TAG fatty acid profile, the different light qualities to which the cultures were subjected also induced some significant differences (Figure 9a). The TAG fatty acid profile of the cultures grown under monochromatic Red LED illumination showed significantly lower values of 14:0 (myristic acid), 16:0, and 16:1 fatty acid relative concentration and significantly higher concentrations of 16:3 (hexadecadienoic acid) and EPA fatty acids. Cultures grown under Blue LED illumination on the other hand showed a significant increase in the TAG concentration of 18:2 (linoleic acid), 18:3 (\(\gamma\)-linolenic acid, GLA) and 18:4. This had inevitable results in the saturation classes of the fatty acid profile of the TAG of the cells cultured at different light conditions (Figure 9b). Significant differences could only be observed in the cells cultured under Red LED illumination, with higher concentration of _PUFA_ and lower contents of TAG SFA and _MUFA_ in TAG, when compared to the cells cultured under RGB LED illumination. Regarding the TAG cellular content (Figure 9c), this was found to be significantly reduced by the cultivation of the diatom cells under Red LED illumination. In addition, considering the changes above reported regarding the total fatty acids content, the TAG relative content when compared to the total fatty acid in the cells cultivated under red and blue LED illumination decreased to 0.7% and 2.6% respectively versus 5.2% observed in the cells cultured under RGB LED illumination. Nevertheless, the slight differences observed, the TAG fatty acid profile also proved to efficiently discriminate (100% classification accuracy) the cultures cultivated under different light qualities, indicating that this TAG fatty acid profile is specific of the light used for diatom cultivation and thus can be modified by the illumination wavelength range (Figure 10). Figure 8: Canonical Analysis of Principal (CAP) components of the _P. tricornutum_ cultures (\(N=5\)) subjected to the three light treatments, having as basis the total fatty acid profile (relative concentrations) at the end of the 96 h exposure period (93.3% classification efficiency).
# Multi-Module Combination for Underwater Image Enhancement
## 3 Proposed Method
### Color and White Balance Correction Module  
Correcting color distortion caused by lighting conditions can be achieved by selecting an appropriate color balancing algorithm, thereby mitigating the impact of the lighting environment on color performance. The grayscale world algorithm [20] is the most commonly employed method for white balance.  
A color-biased image is defined as \(I(x)\). \(\hat{Z}(x)\) is defined as the output image. The 3D vector \(\gamma=\left[\gamma(R),\gamma(G),\gamma(B)\right]^{\top}\) is used to represent the color of the scene light source and is known as the color bias vector. \(\ell\) denotes a simple modification of \(\gamma\), \(\ell=\left[\gamma(G)/\gamma(G),1,\gamma(G)/\gamma(B)\right]^{\top}\). [PERSON] and [PERSON] [12] proposed a polynomial color mapping function to deal with the nonlinearity in \(I(x)\). The expression is given in Equation (8).  
\[\hat{Z}(x)=r(M)\varphi(I) \tag{8}\]  
Figure 3: Color deviation detection result of underwater image.  
The above formulation is based on the polynomial kernel function proposed by [PERSON] et al. [13]. \(\varphi\): \(\varphi\) ([R, G, B]\({}^{\top}\))\(\rightarrow\)[R, G, B, RG, RB, GB, R\({}^{2}\), G\({}^{2}\), B\({}^{2}\), RGB, 1]\({}^{\top}\) and M is denoted as a \(3\times 11\) matrix.  
\[M=H\mathcal{E} \tag{9}\]  
where H is the correction function [21]. The mapping of the input color clustering to H is derived from the mapping model [22].
# Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension
## 3 Methodology
### Feature Interaction Part
#### 3.4.1 Graph with Entity Module  
The first step in initializing the graph structure is referred to as entity extraction. Named entities are vital for MRC. Considering the SQuAD as an example, we found that at least 95% of the questions or answers contained at least one entity through statistics on the dataset. Most of the remaining parts are time, number type entities, that is why we expand the scope of the entity. According to the characteristics of the dataset, we have extended the scope of named entities to include numeric entities and temporal entities. The range of named entities was extended to ensure the accuracy of the reading comprehension answers. Further, the keywords of a text are generally referred to as entities. As presented in Figure 6, the words typed in red-colored font are answer-related entities. It is clear to see that not only is the central idea of a passage usually the one that contains named entities but also these entities play a significant role in the answers. The fact that these entities appear several times at different locations, some of them at a distance from each other, provides us with ideas for using the graph structure to obtain semantics.  
Closely related fields in theoretical computer science are analysis of algorithms and computability theory. A key distinction between analysis of algorithms and computational complexity theory is that the former is devoted to analyzing the amount of resources needed by a particular algorithm to solve a problem, whereas the latter asks a more general question about all possible algorithms that could be used to solve the same problem. In turn, imposing restrictions on the available resources is what distinguishes computational complexity from computability theory: the latter theory asks what kind of problems can, in principle, be solved algorithmically.  
In particular, we use the named entities obtained by extraction as nodes of the undirected graph, the embedding representation M output by the fusion layer mentioned in Section 3.3 as the feature vector of the named entities, and the relationship of association between the named entities as the edge weights. Here, we use the sliding window approach to obtain the edge weights: first, we define a sliding window \(w\) and sliding step \(s\), where \(s<w\). After each text segment is counted, the sliding window will move backwards. If two or more named entities appear in a window \(w\), an edge is attached to the nodes and initialized with weight 1. In the process of sliding the window, the content of the text may appear repeatedly, and we still use the same approach to calculate the weights. According to the characteristics of the dataset, we have extended the scope of named entities to include numeric entities and temporal entities. The range of named entities was extended to ensure the generality of the reading comprehension answers. Finally, we obtain an updated graph node representation G = (\(g_{1}\), \(g_{2}\),..., \(g_{n}\)).
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 4 Proof-of-Concept Implementation  
The proof-of-concept implementation described in this section is designed to operate in an environment closely resembling a real maritime environment. Key aspects of the implementation are:  
* The mIBC infrastructure (see Section 4.1) is implemented using the IBC model and the cryptographic values specified in the \"Appendix A: Test Data\" of RFC6507 and RFC6508;
* We use conventional ID6/8 AIS messages as the carriers of the mIBC cryptographic parameters (see Section 4.2);
* We simulate conventional AIS devices by using a third-party online AIS VDM/VDO decoder (see Section 4.3);
* The SecAIS-App is coded in Java using third-party code (see Section 4.4).
# Machine Learning Techniques for Non-Terrestrial Networks
## 6 Machine Learning Techniques in NTN
### ML for Protocol Stack Layers  
In the next subsections, we will analyze some benefits of adopting ML in different layers.
## 1 Introduction
### Related Works
ALOHA-based protocols that exploit capture effect and interference cancellation have also been proposed in the context of uncoordinated NOMA cellular systems [32]. In [33], Coded Slotted Aloha (CSA) performance is analyzed in the presence of capture effect, whereas [34] considers an energy-constrained NOMA scheme based on power and packet diversity and implementing interference cancellation. Motivated by the renewed visibility of backscattering communications in the context of beyond 5G and massive IoT arenas, many recent studies explore the possibility of considering NOMA solution in the backscattering communication frame and extend the applicability of traditional RFID paradigms. In [35], the authors proposed the adoption of the power-domain NOMA (i.e., multiplexing the backscattering nodes in different regions or with different backscattered power levels) to enhance the spectrum efficiency of the backscattering communication system. For an ALOHA-type random access, the work in [36] introduced a framework for backscattering-based intelligent sensing with machine learning. In [37], the authors derive a closed-form BER expressions for a bi-static backscattering communication system employing NOMA with imperfect successive interference cancellation under a Nakagami-m fading channel. Finally, multi-antenna solutions, that are widely used in traditional one-way wireless channels, have been considered to improve performance and increase capacity of backscattering communication systems. Recent studies in this direction propose Multiple Input Multiple Output (MIMO) architectures considering multiple antennas at either node side or illuminator/receiver side. Specifically, traditional space-time coding (STC), beamforming-like techniques, receive and transmit diversity for the forward link, have been adapted for backscattering MIMO systems [38; 39; 40; 41; 42; 43]. The rest of this paper is organized as follows. In Section 2 we introduce the system model and describe the considered multi-static architecture, the propagation channel abstraction and the channel access contention protocol. In Section 3 we propose a methodology to characterize the statistics of multiple access interference via moment matching approximation. Moreover, we introduce and describe the concept of capture diversity. In Section 4 we provide numerical results, analyze the performance of the considered multi-static system and validate the proposed cross-layer approach. Finally, Section 5 concludes the paper.
# A Miniaturized RHCP Slot Antenna for Wideband Applications Including Sub-6 GHz 5G
## 5 Parametric Study
Future work could involve the substrate optimization by replacing FR4 with low-loss dielectric materials such as Rogers RO4003C, RO4350B, or RT/Duroid 5880, which offer significantly lower loss and more stable dielectric constants to improve the bandwidth and gain. Additionally, adaptive metamaterials could be used to adjust the antenna characteristics, offering enhanced reconfigurability and performance. To evaluate the robustness of the antenna performance to manufacturing variations in the dielectric constant of the FR4 substrate, a parametric sweep of 4.1, 4.3, and 4.5 was performed. The results indicate that there is a minor impact on the impedance bandwidth and axial ratio bandwidth. This analysis confirms that the design is tolerant of typical material variations, and FR4 remains a practical choice considering the cost, availability, and fabrication constraints. It is worth noting that this analysis was included for completeness, although the dielectric constant of FR4 is generally well characterized and its impact on performance is well understood in the literature. Figure 12 shows the sensitivity analysis of the \(\varepsilon_{r}\) variations on the reflection coefficient and axial ratio. The simulated normalized LHCP and RHCP power patterns of the proposed antenna are displayed in Figure 13 for the YZ-plane and XZ-plane at three resonant frequencies: 2.2 GHz, 3.1 GHz, and 4.2 GHz. It can be seen that the antenna operates as an RHCP antenna for both planes in the broadside direction (\(\theta=0^{\circ}\)) in the positive \(z\)-axis direction. By mirroring the shape of the presented antenna with respect to the \(y\)-axis, LHCP can be generated in the broadside direction. The simulated 3D normalized power pattern of the proposed antenna is demonstrated in Figure 14 at the resonant frequency of 2.2 GHz. This pattern provides a comprehensive view of the antenna's radiation behavior in space and is essential for evaluating its directional characteristics and overall radiation performance. Figure 11: Simulation results of the proposed antenna with \(W_{S1}=11\) mm, \(L_{p}=6\) mm, t = 8.14 mm, \(W_{S2}=13\) mm, and different values of S: (**a**) reflection coefficient and (**b**) AR. Figure 12: Sensitivity analysis of the \(\epsilon_{r}\) variations on the (**a**) reflection coefficient and (**b**) AR. Figure 13: Simulated LHCP and RHCP patterns of the proposed antenna at (**a**) f = 2.2 GHz, (**b**) f = 3.1 GHz, and (**c**) f = 4.2 GHz.
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 2 Data and Methods
### Multi-Frequency LEO-Augmented PPP AR Method
With the long wavelength, the extra-wide-lane and wide-lane ambiguity can be fixed to an integer very efficiently, and the integer extra-wide-lane and wide-lane ambiguities will then formulate an ambiguity-fixed ionosphere-free wide-lane measurement (\(P_{r,AFIF}^{s}\), [14]), which can be expressed as Equation (6). \[P_{r,AFIF}^{s}=\frac{f_{1}}{f_{1}-f_{3}}L_{r,w}^{s}-\frac{f_{3}}{f_{1}-f_{3}} L_{r,e}^{s}-\lambda_{wl}\frac{f_{1}}{f_{1}-f_{3}}(N_{wl}^{s}+d_{r,val}-d_{wl}^{s}) \tag{6}\]  
with  
\[L_{r,w}^{s}=\frac{f_{1}}{f_{1}-f_{2}}L_{r,1}^{s}-\frac{f_{2}}{f_{1}-f_{2}  
it also makes it difficult for the users to fix all ambiguities. Therefore, the partially fixed integer solution was introduced for rapid ambiguity resolution [39]. To conduct the PPP test, observations of 37 globally distributed MGEX stations were selected. Twenty-nine stations denoted by blue dots in Figure 2 were used for UPD estimation and the other eight stations denoted by red dots were selected as user stations. At the user end, daily observables were separated into 24 1-hour-long observable sessions for experiments. The sampling interval was 5 s and the cutoff elevation was set as 7\({}^{\circ}\). An elevation-dependent weighting strategy was applied for ambiguity-fixed PPP, with the carrier noise set to 3 mm and the pseudorange noise set to 0.3 m for each system.
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 2 Scheduling Operations Based on Priority/Dispatching Rules
### Scheduling in the Flexible Job Shop Systems
According to related literature, the frequently studied performance objectives in this area were, e.g., mean flow time, the maximum makespan (completion time), the mean weighted completion time, the mean weighted tardiness, the maximum tardiness, the mean weighted earliness, the maximum earliness, maximum lateness, the ratio of tardy jobs, or mean WIP (Work In Progress) [20]. In real practice, the consideration of several performance goals is challenging from the control aspect because it demands either an a priori decision about the hierarchy of objectives or a responsive adjustment to varying conditions on the shop floor [23].
# Robust Hessian Locally Linear Embedding Techniques for High-Dimensional Data
## 4 Robust Hessian Locally Linear Embedding  
### Robust PCA  
In the original HLLE algorithm, the local tangent coordinates are obtained by performing PCA on each local patch. However, as is well known, PCA is quite sensitive to outliers. An iteratively-reweighted PCA algorithm, called Robust PCA (RPCA) [31], has been proposed to make PCA less sensitive to outliers. The optimization objective for the weighted PCA on the neighborhoods of \(x_{i}\) can be expressed as:  
\[min\sum_{j=1}^{k}w_{j}^{i}\|\dot{e}_{j}^{i}\|^{2}=\underset{c^{i},(U^{i})^{T}U ^{i}=t_{d}}{\operatorname{argmin}}\sum_{j=1}^{k}w_{j}^{i}\|x_{j}^{i}-c^{i}-U^{i }(U^{i})^{T}(x_{j}^{i}-c^{i})\|^{2} \tag{13}\]  
where \(U^{i}\in\mathbb{R}^{D\times d}\) forms the orthonormal basis of the local tangent space of the point \(x_{i}\), \(c^{i}\in\mathbb{R}^{D}\) gives the displacement of the local tangent space, and the weights \(w_{j}^{i}(j=1,2,\dots,k)\) are fixed. The least squares solution of the problem is given by:  
\[U^{i}=[u_{1}^{i},\dots,u_{k}^{i}],c^{i}=\frac{\sum_{j=1}^{k}w_{j}^{i}x_{j}^{i}} {\sum_{j=1}^{k}w_{j}^{i}}=\bar{x}_{w} \tag{14}\]  
where \(\{u_{1}^{i},\dots,u_{k}^{i}\}\) are the eigenvectors of the weighted sample covariance matrix:  
\[S_{w}=\frac{1}{k}\sum_{j=1}^{k}w_{j}^{i}(x_{j}^{i}-\bar{x}_{w})(x_{j}^{i}-\bar {x}_{w})^{T} \tag{15}\]  
Following the basic ideas from robust statistics, Robust PCA determines the weights \(w_{j}^{i}\) by the corresponding projection error \(\varepsilon_{j}^{i}\):  
\[w_{j}^{i}=\begin{cases}1,&\varepsilon_{j}^{i}\leq\frac{1}{2}\bar{\varepsilon} ^{i}\\ \frac{\bar{c}^{i}}{2\varepsilon_{j}^{i}},&\varepsilon_{j}^{i}>\frac{1}{2}\bar{ \varepsilon}^{i}\end{cases} \tag{16}\]  
where \(\bar{c}^{i}=\frac{1}{k}\sum_{k=1}^{1}\varepsilon_{j}^{i}\) is the mean value of the projection error of the k nearest neighbors of \(x_{i}\). Then RPCA uses an iterative procedure to find the solution by starting from some initial estimates.  
The iterative procedure of RPCA can be summarized as follows [32]:  
1. Perform PCA to compute the orthonormal basis matrix \(U^{i}\) and displacement \(c^{i}\) of the local tangent space. Set \(l=0\).
2. Repeat until convergence: 1. Compute \((\varepsilon_{j}^{i})^{l}=x_{j}^{i}-(c^{i})^{l}-(U^{i})^{l}{(U^{i})^{l}}^{T} (x_{j}^{i}-(c^{i})^{l})\); 2. Compute \((w_{j}^{i})^{l}(j=1,\dots,k)\) according to (16), where \(\varepsilon_{j}^{i}=(\varepsilon_{j}^{i})^{l}\) 3. Compute the weighted least squares estimates \((U^{i})^{(l+1)}\) and \((c^{i})^{(l+1)}\) according to (14.15), where \(w_{j}^{i}=(w_{j}^{i})^{l}\) 4. if \(\|(U^{i})^{\binom{l+1}{l}}-(U^{i})^{l}\|_{F}<\epsilon\), return; else \(l=l+1\).
# From GPS Receiver to GNSS Reflectometry Payload Development for the Triton Satellite Mission
## 3 GNSS Reflectometry (GNSS-R) Mission Payload
### GNSS-R Payload Design
The kernel of the GNSS-R receiver, its science unit, is responsible for the reception and processing of reflected GNSS signals to generate DDMs in real time. The science unit of the GNSS-R receiver is implemented by using a FPGA co-processor (Zynq-7045) to meet the demands of real-time processing. Compared with the SGR-ReSI used in the TechDemoSat-1 and CYGNSS missions [20], both reflectometry receiver designs are similar in terms of major functionality. However, there are still some upgraded/distinguishable features of the NSPO GNSS-R payload, which are listed as follows:  
* A higher DDM resolution (CYGNSS: one-fourth chip/500 Hz, Triton: one-sixteenth chip/125 Hz)
* A real-time QZSS reflected signal processing capability
* The capability to generate eight DDM generations per second
* On-orbit software/firmware modification capability