esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation and Parameters
### Downlink and Depth-Variant Horizontal Link  
Although mobile UOWC links are oblique in nature, results for a worst-case scenario (receiver looking directly above) and typical scenario (horizontal) can establish insights for the intermediate configurations.  
In the vertical scenario, the transmitter depth \(z_{0}\) is varied by distinct intervals of unit depths as seen in Figure 8a, and the corresponding O-SNR is modeled for vertical depth, wavelength, transmit power, and beam divergence. In the horizontal scenario, the transmitter depth \(z\) is varied by distinct intervals as seen in Figure 8b, and the corresponding O-SNR is modeled for horizontal distance, wavelength, transmit power, and beam divergence.  
Figure 8: (**a**) A vertical UOWC link where the receiver receives both signal and sunlight; the O-SNR is estimated for increasing link distance. The link is established for a vertical distance \(d\) from a transmitter depth of \(z_{0}\) to the receiver at a depth of \(z\); (**b**) a horizontal UOWC link where the receiver receives both the signal and horizontally diffused sunlight; the O-SNR is estimated for increasing link distance. The link is established for a horizontal distance \(d\) at a transmitter and receiver depth of \(z\).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Typical Value** & **Unit** \\ \hline Active area (\(A_{r}\)) & 13 & mm\({}^{2}\) \\ Dark current (\(I_{DC}\)) & 0.25 & nA \\ Rise time (\(t_{R}\)) & 70 & ns \\ FOV (\(\Omega\)) & tan\({}^{-1}\) (6.1/4.84) & rad. \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of physical parameters of the PIN photodiode used in the model [61].
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## 3 System Model
### Interference Cancellation Model  
In [41], it is shown that canceling just the strongest interferer shows huge performance improvement. The aggregate interference from the randomly located concurrent transmitters reduces the SIR of the desired signal at the receiver, and can cause the transmission failure. By applying SIC [12], the perished signal can be resuscitated. Among the interfering signals, the strongest interference would be decoded by the receiver if the SIR of the strongest interference is greater than a target threshold, and strong interference signal is extracted from the aggregate interference. Using the remaining signals, the receiver recalculates the SIR of the desired signal, and if the SIR of the desired signal steps over the target SIR \(\beta\), the signal can be finally decoded.  
Figure 2 illustrates an example of the cancelable interference. There are one communication pair with communication distance \(r_{t}\) and the target SIR is \(\beta\). There are three interferers. We define that a cancelable distance \(r_{c}\) is the maximum distance that the transmission of the interferer within \(r_{c}\) can be canceled by the receiver. If the only one interferer is at a distance \(r\) from the receiver, in order to decode the signal from the interferer, it should satisfy following condition.  
\[\begin{split}&\frac{r^{-\alpha}P}{r_{t}^{-\alpha}P}\geq\beta,\\ & r\leq\beta^{-\frac{1}{\alpha}}r_{t}.\end{split} \tag{2}\]  
Figure 1: A snapshot of wireless ad hoc network, where transmitters are randomly located according to a 2-dimensional homogeneous Poisson point process (PPP) with intensity \(\lambda\). Each transmitter \(i\) has a dedicated receiver \(k(i)\), who is situated at a distance \(r_{t}\) from the transmitter with random direction.  
Thus, the interfering signal within the distance \(r_{c}=\beta^{-\frac{1}{\pi}}r_{t}\) would be cancelable by the rx. On the other hand, the other two interfering signals cannot be canceled. If the target SIR increases, the cancelable region shrinks.  
In the fading environments, due to the randomness of the channel gain, not all interference within \(r_{c}\) can be canceled, and not all interference farther than \(r_{c}\) cannot be canceled. We denote the values \(g_{c}\) and \(g_{t}\) as the channel gains of channel from the interferer at a distance \(r\) and the desired transmitter at a distance \(r_{t}\), respectively. To cancel the interference signal traveled distance \(r\), the following condition should be satisfied,  
\[\frac{g_{c}r^{-\alpha}P}{g_{t}r_{t}^{-\alpha}P+I^{\prime}}\geq\beta, \tag{3}\]  
where \(I^{\prime}\) denotes the aggregate received power excluding the desired signal and the interfering signal from a distance of \(r\). In the next section, we explain a new random access scheme using IC and quantify the performance improvement.
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 5 Influence of Linear Movement within the Signal Bandwidth due to Doppler  
This section deals with the particular implication (c) of the Doppler's bandwidth-dependent contribution as split in Equation (17) in the range-Doppler domain. If a violation of the 'narrowband' stationary boundary (Section 2.5) cannot be avoided, certain coherent processing degradations will be a direct consequence of relatively fast and range migrating targets. The arising spread will cause a strong decorrelation and it restrains the maximal achievable integration gain \(G_{\mathrm{int}}\) by pulse compression for a range-Doppler analysis. Further, the impact of linear motion on the achievable resolutions (Section 5.2), the decrease of the coherent integration gain (Section 5.3) and arising limitations to multi-frequency channel  
Figure 8: Illustration of the fold-over effect in unambiguous Doppler limited processing.  
Figure 9: Range-Doppler gate straddling loss of a confined point scatterer return.  
processing (Section 5.4) will be quantified. Interestingly, the appearing effect can even resolve the response coherency of moving target returns if it is wrongly processed by a classical range-Doppler analysis (Section 5.5). The principal implication of non-linear motion will also be shown (Section 5.6). All identified impacts have to be considered for the development of appropriate compensation and adaptive processing approaches that will be discussed in Section 6.
## 2 Materials and Methods  
### Materials  
Tetraethoxysilane (TEOS, 98%), (3-Aminopropyl)trimethoxysilane (APTMS, 97%), \(\alpha\)-Bromoisobutyryl bromide (BiBB, 98%), anisole (anhydrous, 99.7%), trifluoroacetic acid (TFA, 99%), CuBr (99.0%) and 2,2\({}^{\prime}\)-Bipyridine (bpy, \(\geq\)99%) were purchased from Macklin Chemical Reagents Co., Ltd, Shanghai, China. Tetrahydrofuran (THF, \(\geq\)99.0%), ammonium hydroxide solution (NH\({}_{4}\)OH, 25 wt%), ethanol (C\({}_{2}\)H\({}_{5}\)OH, 99.5%), toluene (C\({}_{6}\)H\({}_{5}\)CH\({}_{3}\), AR), hydrochloric acid (HCI), sodium hydroxide (NaOH), dichloromethane (CH\({}_{2}\)Cl\({}_{2}\)), triethylamine (Et\({}_{3}\)N), 2-Propanol (IPA, 99.5%) were purchased from Sinopharm Chemical Reagent Co., Ltd, Xi'an, China. Ethyl \(\alpha\)-bromoisobutryate (EBiB, 98%), tert-Butyl acrylate (tBA, 98%), oligo (ethylene glycol) methacrylate (OEGMA, \(M_{n}=300\) g/mol, 99%) were purchased from Sigma-Aldrich, Shanghai, China. The inhibitors in tBA and OEGMA were removed by passing through a basic alumina column. THF and toluene were refluxed with sodium under nitrogen and distilled prior to use. Other chemicals and reagents were used as received without further purification. Milli-Q water was used for the sample preparation.
## 3 Proposed System Model
### Proposed Methodology and Q-Value Updates
#### 3.4.1 Q-Value Update  
The agent minimizes the loss function to update the Q-value (where Q is an action-value that the agent receives by taking an action at state). It does so by estimating MSE (Mean Squared Error) of predicted Q-value and target Q-value. A similar way to express the update rule is as the one in Equation (20):  
\[L(\theta)=\mathbb{E}\Big{[}(y_{t}-Q(s_{t},a_{t};\theta))^{2}\Big{]}. \tag{20}\]  
where:  
* \(y_{t}=r_{t+1}+\gamma\max_{a^{\prime}}Q_{\theta^{\prime}}(s_{t+1},a^{\prime})\) is the target Q-value (derived from the Bellman equation),
* \(Q(s_{t},a_{t};\theta)\) is the predicted Q-value for the state-action pair \((s_{t},a_{t})\) given the current Q-network with parameters \(\theta\),
* \(\theta\) are the parameters of the current Q-network.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
###### Abstract  
Cellular vehicle-to-everything (C-V2X) is one of the enabling vehicular communication technologies gaining momentum from the standardization bodies, industry, and researchers aiming to realize fully autonomous driving and intelligent transportation systems. The 3 rd Generation Partnership Project (3 GPP) standardization body has actively been developing the standards evolving from 4G-V2X to 5G-V2X providing ultra-reliable low-latency communications and higher throughput to deliver the solutions for advanced C-V2X services. In this survey, we analyze the 3 GPP standard documents relevant to V2X communication to present the complete vision of 3 GPP-enabled C-V2X. To better equip the readers with knowledge of the topic, we describe the underlying concepts and an overview of the evolution of 3 GPP C-V2X standardization. Furthermore, we provide the details of the enabling concepts for V2X support by 3 GPP. In this connection, we carry out an exhaustive study of the 3 GPP standard documents and provide a logical taxonomy of C-V2X related 3 GPP standard documents divided into three categories: 4G, 4G & 5G, and 5G based V2X services. We provide a detailed analysis of these categories discussing the system architecture, network support, key issues, and potential solution approaches supported by the 3 GPP. We also highlight the gap and the need for intelligence in the execution of different operations to enable the use-case scenarios of Level-5 autonomous driving. We believe, the paper will equip readers to comprehend the technological standards for the delivery of different ITS services of the higher level of autonomous driving.  
4G; 5G; V2X; 3 GPP; standards +
Footnote †: journal: Accepted: 8 December 2022
###### Abstract  
Currently, there is a growing demand for ready-to-eat, prepared, high-quality, pressure-free products. However, the shelf-life of these products is often so short that a relatively high percentage of these products cannot be sold and end up as food waste. In this study, pork loin with different marinades (paprika and herbs) was treated with different high hydrostatic pressures (0 MPa, 300 MPa, 450 MPa and 600 MPa) and a bioactive component (piperine) and then the quality of the meat was examined after 0, 4, 8, 12 and 14 days of cold storage. Changes were monitored using color, pH, texture and microbiological analyses. Both pressure, piperine enrichment, storage time and the interactions of different factors had a significant effect on the quality of the loin samples with different marinades. Due to the denaturation of myoglobin, meat slices were less red and lighter after HHP treatment. The addition of piperine reduced this lightness. The pH increased with increasing pressure and decreased with storage time. HHP treatment significantly increased meat hardness, with samples treated at 600 MPa being 19% harder than those treated at 450 MPa. Microbiological results indicated that HHP at 450 MPa and 600 MPa effectively reduced anaerobic total live plate counts, ensuring satisfactory sensory and microbiological quality throughout storage. Piperine fortification also resulted in a more favorable microbiological status during storage without any perceptible change in quality properties during storage. These findings underscore the effectiveness of HHP and piperine enrichment in enhancing the safety and quality of marinated meats.  
pork; natural bioactive component; piperine; preservative-free; color; texture; high hydrostatic pressure +
Footnote †: journal: Applied Sciences
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)  
In this section, we present our approach for applying supervised learning to address our research problem. Our approach encompasses seven key steps, outlined as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Focus Area** & **Methodology** & **Applicability to Urban VANETs** \\ \hline (19) & V2V Path Loss Prediction & Random Forest & V2V Communication \\ (20) & Signal Strength Prediction & ANNs & VHF broadcast stations \\ (21) & Signal Strength Prediction & Deep Learning with & \\  & & Geographical Imagery & Mobile networks \\ (22) & Reliability Estimation in CR-VANETs & Analytical Framework, Markov & \\  & & Model, ANN Model & \\ This work & Signal Degradation in & Supervised Learning with ANNs & Urban VANETs \\  & Urban VANETs & & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Components of an artificial neural network.
# Polarization Differential Visible Light Communication: Theory and Experimental Evaluation +
## 4 Measurement Results
### Setup B  
The same procedure as for setup A is repeated with setup B to assess the \(SNR\) for the quadrature receiver. The resulting graphs are plotted in Figure 12, also including an angular error of \(\pm 2^{\circ}\) considering the deviation on the polarization axis for the used polarizers. The measured function for the \(QD_{B}-QD_{D}\) receiver pair progresses similarly to the receiver pair used in setup A. The LSE fit results in \(SNR_{0}=46.54\) dB and \(SNR_{0}=46.20\) dB for transmitted '1'- and '0'-bits respectively. At \(\theta=45^{\circ}\), the measured \(SNR\) severely drops to 17.79 dB and 18.02 dB respectively. In theory, no communication would be possible for this angle as Equation (11) drops to zero, yet a small deviation in angle from the intended \(\theta=45^{\circ}\) has an immense impact on the measured SNR. Nevertheless, a severe decrease in \(SNR\) occurs for this angle, which can be resolved by switching to the second differential receiver pair \(QD_{A}-QD_{C}\) for signal demodulation. For this receiver pair, the LSE fit on the measured data points results in \(SNR_{0}=45.59\) dB and \(SNR_{0}=45.32\) dB for transmitted '1'- and '0'-bits respectively. Please note that the \(SNR_{0}\) derived by LSE fit is about 1 dB lower for this receiver pair, this is most probably caused by a combination of inequalities in transimpedance gain, quadrant responsitivity and small differences in aperture shadowing. Similar to the \(QD_{B}-QD_{D}\) receiver pair at \(\theta=45^{\circ}\), the \(SNR\) for receiver pair \(QD_{A}-QD_{C}\) greatly decreases at \(0^{\circ}\) and \(90^{\circ}\). This is however not an issue, as receiver pair \(QD_{B}-QD_{D}\) can be used for demodulation at these angles. Figure 13 illustrates the determined \(SNR\) for all data points measured. It is clear that robustness to receiver orientation can be achieved by selecting the pair with the highest \(SNR\) for demodulation. The theoretical BER is again determined. For pair \(QD_{A}-QD_{C}\), this results in a BER of 0.40 and 0.56 at \(\theta=0^{\circ}\) and \(\theta=90^{\circ}\) respectively. For pair \(QD_{B}-QD_{D}\), this leads to a BER of \(2.5\times 10^{-15}\) at \(\theta=45^{\circ}\). Please note that for differential receiver pair \(QD_{B}-QD_{D}\), the measured BER is still considerably low, again indicating that the angle deviates slightly from the intended \(45^{\circ}\).  
Figure 11: Measured \(SNR\) and theoretical fit as a function of receiver rotation \(\theta\) at \(h=1.35\,\mathrm{m}\) for transmitted '1’-bits (top) and '0’-bits (bottom), for setup A [17].  
Figure 12: Cont.  
Figure 12: Measured \(SNR\) and theoretical fit as a function of receiver rotation \(\theta\) at \(h=1.35\,\mathrm{m}\) for both transmitted \({}^{\prime}1^{\prime}\)-bits and ‘\(0^{\prime}\)-bits for both receiver pairs used in setup B.
## 2 Related Works  
### Tree-Crown Detection  
Existing tree detection methods based on remote sensing images can be divided into three categories: traditional image-processing-based methods, classical machine-learning-based methods, and deep-learning-based methods.  
The traditional image processing methods basically include image binarization, local maximum filtering, and image segmentation [21; 22; 23]. These methods require sophisticated image processing procedures and intricate scenarios, e.g., crown overlap, may lead to a deterioration of the detection performance. With the development of classical machine learning, many machine learning methods, such as random forest, support vector machine (SVM), and artificial neural network (ANN) [1; 2; 3; 4] have been used to detect tree crown in remote sensing images. For example, [PERSON] et al. [2] optimize an SVM with the features extracted from a histogram of oriented gradient (HOG) to realize the detection of the oil palm in UAV images from Malaysia, obtaining a precision of over 94%. Nevertheless, most of these methods require high-resolution and high-quality images, which greatly limits their application.  
Following Alex-Net [24], many deep learning methods have been developed and are becoming the mainstream methods for many remote sensing tasks. [PERSON] et al. [25] first proposed a deep-learning-based method to detect the tree crown in high-resolution remote sensing images. Later, [PERSON] et al. [26] and [PERSON] et al. [27] combined the sliding-window algorithm and a convolutional neural network (CNN) classifier to detect oil palm and banana trees and achieved good results. However, the sliding-window-based method not only involves an avoidable computational cost but also restricts the complexity of the classifier to meet the speed requirements, both of which bring a negative impact on the model performance. In contrast, deep-learning-based object detection methods [9; 10; 11; 12] discard the traditional sliding-window algorithm and further optimize the network structure, improving the accuracy and speed of detection. Nowadays, deep-learning-based object detection methods are used to detect tree crowns in remote sensing images taken by satellite [28; 29], UAV [30; 31], and other aircraft [32], realizing an accuracy of up to 90%. Nevertheless, the above deep-learning-based methods work effectively based on two premises: First, the quantity of training data is sufficient and the data are fully labeled. Second, the training and testing images are obtained under the same condition. The model performance will drop significantly once either of the above conditions are not satisfied, which brings challenges to the large-scale cross-regional tree-crown detection.
## 2 Method
### Weakly Supervised Loss Construction
#### 2.4.1 Projection Loss  
Projection loss [32] restricts the foreground and background probabilities of pixels in the image through the projection of the real bounding box so that the foreground prediction is distributed in the real bounding box as much as possible and the background prediction is distributed outside the real bounding box as much as possible.  
The calculation process of projection loss is shown in Figure 5 where the left side is the true value of the bounding box. Assuming that the image pixel is a matrix of the form \(\{0,1\}^{H\times W}\), the row matrix XG and the column matrix YG are established, respectively. The form is the \(\{0,1\}^{W}\) and \(\{0,1\}^{H}\) vector, respectively. The right side of Figure 5 is the prediction mask, and its image pixel matrix is in the form of \((0,1)^{H\times W}\). The maximum probability of the prediction mask pixel matrix being the foreground object is calculated in the row and column directions, respectively. The calculation result is value of the two vector matrices \((S_{1},S_{w})\) and \((S_{1},S_{h})\). The two vector matrices are row matrix XM and column matrix YM, respectively; XM and YM are of the form \((0,1)^{W}\) and \((0,1)^{H}\), respectively.  
Dice Loss is used to calculate projection loss, and the foreground probability distribution of image pixels is constrained to narrow the gap between XM, YM, XG, and YG so as to solve the problem of poor segmentation accuracy caused by the small proportion of  
Figure 4: Diagram of the GWM structure.  
small objects in the image. \(L_{X}\) and \(L_{Y}\) are defined as orthogonal losses for row and column matrices, respectively, as follows:  
\[L_{X}=1-\sum_{n=1}^{W}\frac{2x_{n}s_{n}}{x_{n}^{2}+s_{n}^{2}} \tag{7}\]  
\[L_{Y}=1-\sum_{n=1}^{H}\frac{2y_{n}s_{n}}{y_{n}^{2}+s_{n}^{2}} \tag{8}\]  
Among them, \(x_{n}\) and \(y_{n}\) are XG and YG, respectively; \(s_{n}\) represents the corresponding XM and YM; W and H are the width and height of the image.  
The final projection loss \(L_{XY}\) can be obtained by adding the losses computed in the row and column directions:  
\[L_{XY}=L_{X}+L_{Y} \tag{9}\]
# A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN-NER +
## 2 Origin of the Work: A Cloud-Based Services for Cultural Heritage Science
#### Thespian-Ner  
To navigate the scientific database created by THESPIAN-Mask, and to ease the burden of metadata generation, the THESPIAN-NER web service has been designed and implemented. Based on the natural language processing (NLP) model for automatic named entity recognition (NER), (for some references about the application of named entity recognition in cultural heritage applications, see [13; 14; 15; 16], and references therein), THESPIAN-NER allows users to (semi)automatically  
1. generate custom THESPIAN-Mask queries; and
2. fill in THESPIAN-Mask metadata fields.  
Both tasks are performed by using named entities inferred by the NLP model within written documents or reports (only available in the Italian language at present).  
The NLP and NER models are capable of automatically annotating either archaeological documents and/or scientific reports by identifying and labelling relevant semantic entities. Those labels are in correspondence with CRMhs ontology (classes, properties), as well as metadata fields. For the time being, THESPIAN-NER is only available in the Italian language, as data mainly come from Italian collaborations and institutions working in the filed of cultural heritage. The adoption of English as well as other languages is foreseen in the near future.  
The machine learning models implemented and used in the THESPIAN-NER application are trained neural networks: ArcheoNER, for processing archaeological texts, and hsNER, for scientific reports of analysis on cultural heritage. These models were built by using the open-source Python package _spaCy_[17; 18; 19; 20] and fine tuned by using transfer learning to classify named entities with the aforementioned custom labels.  
THESPIAN-NER offers the possibility of either generating queries on the CHNet database in a (semi)automatic way, or fill-in metadata fields. At the time of writing, only Italian written documents and reports, related to archaeological and heritage scientific topics, can be used for the analysis. This limitation is due to the fact that, at the moment, THESPIAN-NER is adopted by Italian researchers who use the tool to compose queries and metadata forms starting from their Italian written documents and reports. Future work is planned to extend NER models to other languages and, in this way, to engage the service adoption also to other scientific communities.  
Furthermore, the semiautomatic query and metadata generation process, made compliant with FAIR principles, is performed though a user-friendly web graphical interface.