esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The vine is a climbing, sarmentous plant that adapts to the most diverse training systems. Manipulating the vegetative canopy growth structure is a technique applied to define the plant's shape. The canopy structure objectives are to expose leaf areas to maximise light absorption, optimise the leaf area-to-fruit ratios, control diseases, and avoid competition for sunlight between vines [1]. Additionally, modifying microclimatic conditions inside the canopy affects plant temperature, photosynthesis, and metabolic activity [2, 3, 4] and promotes good insolation and aeration of the leaves and clusters [5, 6]. Some of these parameters and how they influence certain classes of volatile compounds were recently reported on by [PERSON] et al. [7].  
Sparkling wine aromas can be expressed by primary (varietal), secondary (ferrmentative), and tertiary (ageing on lees) compounds, which contribute to the _bouquet_. Regarding the discrimination of sparkling wines by the adopted training systems, the microclimate established in the vine due to the organisation of the sprouts and branches throughout the cycle influences the physiology of the plant and the composition of the berries, impacting wine composition and quality [8; 9].  
Several training systems are described in the literature and applied worldwide. In the Douro region, for the varieties Touriga Nacional and Touriga Franca, the influence of two systems on the composition of terpen, norisoprenoids, and their precursors was evaluated, and it was shown that the double-cordon LYS 2/3 system produced grapes and Porto wines with higher contents of terpen and norisoprenoid glycosylated fractions compared with vertical shoot positioning (VSP) [10]. In southern Italy, the interactions between soil management and training systems (bilateral and monolateral Guyot) were shown to be related significantly to aromatic compounds in Negroamaro wine [11] (and other studies). The most widely applied systems in Brazil are lyre (Y or V), VSP, GDC, and pergola systems. Papers in the literature have evaluated Y-trellis and VSP systems in the control of downy mildew and botrytis bunch rot diseases in a high-altitude region in Santa Catarina (south of Brazil) [12], the effects of VSP and Y systems on Cabernet Sauvignon grape berry ripening [13], and the agronomic performance and chemical composition differences associated with GDC, pergola, simple curtain, and VSP systems [14].  
In the southeast of Brazil, the physiological responses and productivities of Syrah grapevines were assessed in different training systems [15]. However, data are lacking regarding the volatile compositions of grapes or wines under different training systems.  
Serra da Maniqueira is a new winemaking frontier for producing sparkling wines [16; 17]; therefore, this study aimed to evaluate the aromatic profile of Chardonnay sparkling wines (made according to the _champenoise_ method) under two training systems, lyre and GDC.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 6 Conclusions  
In 802.11 WLANs, it is essential that the load balance among APs and fair service to clients are achieved simultaneously. In this paper, we first present the FBA-BP algorithm, which allocates the bandwidth of APs based on their business and priority. Therewith, the aggregated demand time of APs can be calculated according to all the associating clients' bandwidth demands, which is considered as the metric of AP load. Furthermore, we also propose the CAA-BD algorithm that makes a fine-grained association decision based on the categories of APs, as well as AP load whose metric is provided by FBA-BP algorithm. We evaluate the CAA-BD algorithm in terms of average AP utilization, system throughput, delay, and the fairness index under two kinds of deployments. The results show that the CAA-BD can solve the performance anomaly to some extent and obtains load balance simultaneously.  
[PERSON] contributes to proposing the network model and evaluating methodology; [PERSON] contributes to presenting the optimized FBA-BP algorithm and performing some evaluating experiments; [PERSON] contributes to presenting the CAA-BD algorithm and performing some evaluating experiments.  
This research was supported by the National Science Foundation of China (61602073), National Science Foundation of Chongqing (cstc2014 kjrc-qnrc40002) and Fundamental and Advanced Research Project of Chongqing Municipal (cstc2016 jcyjA2213).  
Figure 10: Fairness index in the hotspot distribution.  
Figure 9: Fairness index in the uniform distribution.  
**Acknowledgments:** This paper was presented in part at Springer CSOC2017. The authors would like to thank the anonymous CSOC2017 reviewers for their helpful comments on an earlier version of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 RF Feature Extractors
### I/Q Data Spectrograms and Other Short-Time-Short-Frequency (STSF) Transforms  
The short-time Fourier transform (STFT) \(\mathcal{T}_{STFT}\) is simply a Fourier transform within a window (i.e., short time); and the discrete STFT over a window of \(N_{w}\) samples of the received signal \(r(\cdot)\) is given by  
\[\mathcal{T}_{STFT}(f,m)=\sum_{n=1}^{N_{w}}r(n)w(n-m)e^{-j2\pi fn} \tag{20}\]  
where \(m\) is the time sample index, the \(r(n)\) is the complex sampled signal, containing the I and Q components (\(r(n)=I(n)+jQ(n)\)), \(f\) is the frequency, and \(w(\cdot)\) is a time window (e.g., Hamming, Hannig, etc.). The spectrogram \(\mathcal{T}_{Spectrogram}\) is squared absolute value of the STFT transform, namely:  
\[\mathcal{T}_{Spectrogram}(f,m)=|\mathcal{T}_{STFT}(f,m)|^{2} \tag{21}\]  
Clearly, \(\mathcal{T}_{Spectrogram}(f,m)\) and \(\mathcal{T}_{STFT}\) are two-dimensional frequency-time transforms and can be stored both as a matrix and in image form. We investigated both approaches and found that by storing the spectrogram into an image form, we obtained more accurate results than by operating with the matricial form.  
Figure 12 shows the comparisons of spectrogram-based results between a genuine Galileo E1 transmitter and a spoofer also based on Galileo E1 signal characteristics. The results are based on our in-house Matlab-based simulator, based on the block diagrams in Figure 7a,b and at a very high carrier-to-noise (CNR) ratio of 100 dB-Hz, in order  
Figure 11: Example of the Galileo E1 and spoofer histograms when kurtosis is applied as a feature extractor.  
to be able to also identify (for illustration purposes) the different HW features by the naked eye. The results are shown in the absence of channel and receiver effects. It can be seen in Figures 7a,b that there exist visible differences between these two images, e.g., the spectrogram of spoofer I/Q data has one extra line on the upper half of the image compared to the spectrogram of the genuine Galileo I/Q data. The underlying models of the HW features used in our simulator for the genuine and spoofer transmitters were based on the assumptions that phase noises and I/Q imbalances were weaker for a genuine signal than from the spoofer signal. The PA non-linearity models were based on [59], by picking two different PA non-linearity models from there to characterize the spoofer and the genuine GNSS transmitter.
## 4 Discussion  
Concluding, our numerical simulation showed that as a result of irradiation of thin (approximately a few microns thick) foils by relativistically intense laser pulses with a duration of several hundred femtoseconds, an expanding plasma sheath is formed on the rear side of the target, the conditions in which correspond to a cold magnetized plasma with parameters \(\beta\sim\) 0.01-0.1 and \(\sigma\sim\) 1-10. To create such conditions, an intensity higher than \(10^{20}\) W/cm\({}^{2}\) and a pulse duration higher than 100-400 fs are required; conditions that are already available in experiments. To organize magnetic reconnection, two pulses are required, which are focused at a distance of about 50-100 \(\upmu\)m from each other. This opens up new possibilities for the study of relativistic magnetic reconnection in the laboratory.  
Additionally, we note that it can be expected that next-generation laser facilities carrying hundreds of joules in sub-picosecond pulses will be able to create conditions in which the effects of radiation cooling and generation of electron-positron pairs become observable. Recently, it has been proposed that they can play an important role in relativistic magnetic reconnection [29, 30]. The study of such a possibility is an interesting possible subject of a future work.  
This research was supported by Center of Excellence \"Center of Photonics\" funded by The Ministry of Science and Higher Education of the Russian Federation, contract 075-15-2020-906. The simulations were performed on resources provided by the Joint Supercomputer Center of the Russian Academy of Sciences.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The author declares no conflict of interest.  
Figure 5: Distributions of (**a**) electron concentration, (**b**) electron temperature, (**c**) transverse magnetic field, (**d**) \(\beta\) parameter, (**e**) \(\sigma\) parameter, and (**f**) \(\delta\) parameter in the case of 100 fs, \(10^{21}\) W/cm\({}^{2}\) laser pulse at \(t=500\) fs after start of the simulation.
## 4 D2D Group Communication Scheme with Bidirectional and InCrentental A-Star (BICA*) Routing  
A path can be established if the SINR threshold constraint is satisfied. For a multi-hop path, the overall data rate is determined by the hop with the worst SINR. For example, we consider a two-hop path:  
\[\begin{array}{lcl}\gamma_{i,j}&=&\min\bigl{(}\gamma_{i,r},\gamma_{r,i} \bigr{)}\\ &=&\min\Bigl{(}\frac{P_{i}P_{i}L_{i,r}}{I_{i}+N_{0}},\frac{P_{i}\cdot P_{i}L_{ i,j}}{I_{i}+N_{0}}\Bigr{)}\end{array} \tag{25}\]  
where \(\gamma_{i,j}\) is the SINR from UE \(i\) to UE \(j\); \(\gamma_{i,r}\) is the SINR from UE \(i\) to relay \(r\); \(\gamma_{r,j}\) is the SINR from relay \(r\) to UE \(j\); \(P_{i}\) and \(P_{r}\) are transmission powers of UE \(i\) and relay \(r\); \(PL_{i,r}\) is the path loss from UE \(i\) to relay \(r\); \(PL_{r,j}\) is the path loss from relay r to UE \(j\); \(I_{i}\) and \(I_{r}\) are interference powers experienced by UE \(i\) and relay \(r\); \(N_{0}\) is the noise. \(\gamma_{i,j}\) should be larger than the SINR threshold, \(\gamma_{\mathrm{th}}\).  
The \(D_{delay}^{i,k,j}\) in Equation (4) consists of three parts: propagation delay, transmission delay, and processing delay. The \(j\) can be a UE or a base station. We consider first that the case \(j\) is a UE.  
\[D_{delay}^{i,k,j}=D_{prop}^{i,k,j}+D_{trans}^{i,k,j}+D_{pro}^{i,k,j} \tag{26}\]  
The propagation delay \(D_{prop}^{i,k,j}\) can be evaluated according to:  
\[D_{prop}^{i,k,j}=\frac{\sum_{m=1}^{hop_{i}}l_{m}}{c} \tag{27}\]  
where \(l_{m}\) is the length of an individual hop on the path from UE \(i\) to UE \(j\); \(c\) is the speed of the radio wave. \(l_{m}\) should be small than \(l_{\max}\), which is the maximal transmission range for D2D communication. \(T_{trans}^{i,k,j}\) can then be evaluated according to:  
\[D_{trans}^{i,k,j}=\frac{S_{k}\cdot N_{r}}{W\cdot\log(1+\gamma_{i,j})} \tag{28}\]  
where \(S_{k}\) is the packet size; \(N_{r}\) is the number of transmissions; \(W\) is the bandwidth.  
The processing delay, \(D_{\mathrm{proc}}^{i,k,j}\), depends on the computing power of a node. According to [32], the typical measurement of \(T_{\mathrm{proc}}^{i,k,j}\) is around 1 ms, and it can be neglected in most applications.  
We now consider the case UE \(i\) downloading content \(k\) from the base station. The reasoning and derivation are similar to the previous case.  
\[D_{delay}^{i,k,nB}=D_{prop}^{i,k,nB}+D_{trans}^{i,k,nB}+D_{proc}^{j,k,nB} \tag{29}\]  
\[D_{prop}^{i,k,nB}=\frac{L_{i,c,nB}}{c} \tag{30}\]\[D_{trans}^{i,k,\kappa NB}=\frac{S_{k}\cdot N_{r}}{W\cdot\log(1+\gamma_{i,\kappa NB})} \tag{31}\]  
where \(L_{i,\kappa NB}\) is the distance between UE \(i\) and the base station; \(\gamma_{i,\kappa NB}\) is the SINR from UE \(i\) to the base station.  
As for the evaluation of throughput, \(T_{thr}^{i,k,j}\), in Equation (5), we follow the [PERSON] capacity equation, as shown in Equation (16):  
\[T_{thr}^{i,k,j}=W\cdot\log_{2}\left(1+\gamma_{i,j}\right) \tag{32}\]
# Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Machine Learning method** & **r** & **R\({}^{2}\)** & **MSE [dBA]** & **Accuracy (\(\pm\)1.0 dBA)** \\ \hline Decision Trees & 0.798 & 0.637 & 1.781 & 40.0 \\ (DT) & 0.955 & 0.913 & 0.809 & 66.7 \\ Random Forests (RF) & 0.880 & 0.774 & 1.291 & 46.7 \\ Generalized Linear Model (GLM) & 0.880 & 0.774 & 1.291 & 46.7 \\ Neural Networks (ANN) & 0.880 & 0.774 & 1.291 & 46.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results for the prediction models (testing dataset) without considering honking. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Machine Learning Method** & **r** & **R\({}^{2}\)** & **MSE [dBA]** & **Accuracy (\(\pm\)1 dBA)** \\ \hline Decision Trees & 0.933 & 0.871 & 0.902 & 64 \\ (DT) & 0.978 & 0.957 & 0.526 & 82 \\ Generalized Linear Model (GLM) & 0.904 & 0.818 & 1.182 & 40 \\ Neural Networks (ANN) & 0.904 & 0.818 & 1.182 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results for the prediction models in the training phase (without considering honking).
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 4 Experimental Results and Analysis
### Comparison Experiments
In groups (e) to (h), other models' images exhibit blurred details, deformed ground features, twisted boundaries, and missing target areas. The RSVQ-Diffusion model's images show a natural texture distribution at the beach-sea boundary, orderly structured baseball fields and buildings, a consistent spatial distribution of residential areas and roads, and clear, reasonable surrounding ground features of swimming pools. These target areas are rich in detail, have realistic colors, and have different regional structures that match the distribution of real remote sensing images, clearly outperforming the other models. Based on the evaluation metrics and generated images from the comparative experiments, it is evident that the RSVQ-Diffusion model significantly outperforms the other three models. The images generated by the RSVQ-Diffusion model exhibit clearer ground features and boundaries, with minimal blurring and distortion. The spatial structure is more consistent, with reasonable positional relationships between ground features, avoiding overlapping and misalignment issues. Visually, the images more closely resemble real images with natural textures and materials. Additionally, the semantic information satisfies the scenes described in the input text.
## 3 Network Model
### General Assumptions of the WSN Model  
1. The network is composed of a sink node, \(\tilde{s}_{s}\) sensors, and \(\tilde{s}_{r}\) RNs, which can be linked following an ST approach if they are at a distance lower than the communication radius \(r_{c}\). All these devices are placed on a same outdoor unrestricted 2D-surface with size \(d_{x}\times d_{y}\), where there is not any relevant obstacle nor external interference.
2. Sensors are powered by batteries. The sink node and RNs are energy-harvesting devices, having enough energy capacity for operating over network lifetime.
3. Initially, at time \(t=0\), all sensors start with the same energy capacity _iec_ in the batteries. If a sensor is exhausted during operation (\(t>0\)), it cannot be linked again, i.e., lifetime after battery replacement is not considered.
4. Sensors capture information about the environment on a regular basis with a sensitivity radius \(r_{s}\), i.e., a sensor covers a circumference of radius \(r_{s}\). All information captured is immediately sent to the sink node.
5. The sink node is the only connection point of the network to the outside.
6. RNs are low-cost devices with a similar conception that sensors, but without capturing data. Thus, they only send all the information received to the sink node.
7. RNs have the necessary computational resources to manage network traffic while maintaining a low-power consumption to facilitate the harvesting design and reduce the cost of the solution. This fact is usually addressed by using a low-power microprocessor, which generally has a higher computing capacity than the one used in the sensors.
8. The routing protocol for all devices is the one provided by [PERSON]'s Algorithm Dijsktra (2008) for minimum path length among devices.
9. A perfect synchronization among devices and an efficient MAC protocol are supposed, reducing energy cost because of retransmissions and idle time.
## 2 Measurement and Roughening Methods
### Roughening Methods
#### 2.2.2 Laboratory Test Methods
Conversely, abrasion and etching methods adjust the surface roughness of particles without changing their shape and size, thereby eliminating the potential influence of particle morphology. However, these methods are primarily used for fundamental research rather than for practical production. Additional methods are also being explored, providing more options for modifying surface roughness.
# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
## 3 Radio Telescope Measurements with Active Transmitters
### Arecibo Observatory  
The Arecibo Observatory was once among the world's largest and most sensitive radio telescopes. During a debris radar experiment conducted from late June to early July 1989, the Arecibo Observatory collected approximately 19 h of data and detected 90 objects ranging in size from 5 mm to 20 mm in diameter [23]. The experiment utilized the Arecibo radar as the transmitting antenna, while a 3.5 m reflector auxiliary antenna, positioned near the main observatory, served as the receiving antenna. These antennas were separated by a 10 km baseline and were aligned to observe a common point at an altitude of 575 km, and to detect the smallest objects, ranging from 0.5 to 2 cm, by comparing the observed results with a predictive model. The comparison results from the experiment are shown in Figure 6, where the detected objects are plotted against their diameter. Unfortunately, the recent structural failure of the Arecibo dish has resulted in the loss of these significant capabilities. However, the proposal for a Next Generation Arecibo Telescope (NGAT) holds promise for improved radar performance compared with the already substantial legacy system. This enhanced sensitivity could be leveraged to address the knowledge gaps in the orbital debris environment within the size range of 1 mm to 1 cm in low Earth orbit, while simultaneously obtaining measurements across an underrepresented range of orbital inclinations [42].  
Figure 5: Radar cross-section (RCS) versus altitude of targets detected by the Goldstone bistatic radar during a 3-year period (1994–1997). The plot highlights the distribution of object sizes and altitudes, reflecting the radar’s ability to detect a range of space objects across different orbital regions [22]. Reprinted from Advances in Space Research, Copyright 1999, with permission from Elsevier.  
Figure 6: Comparison results from the Arecibo debris radar experiment (June–July 1989), showing detected objects plotted against diameter. Around 19 h of data collection led to the detection of 90 objects between 5 mm and 20 mm [23]. Reprinted from Geophysical Research Letters, Copyright 1992, with permission from John Wiley and Sons.
# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 6 ReID and Spatial-Temporal Multi Object ReID Methods
### Spatial-Temporal Constrained and Multi Object RelD Methods
#### 6.2.3 Feature Learning for Vehicle ReID
To address the challenges posed by a limited number of observations (e.g., sparse cameras), and change in pedestrian appearance cues across different cameras, a new descriptor is defined as social affinity maps (SAMs) to link broken, or unobserved trajectories of individuals in the crowd. To continuously track the pedestrians, a Markov-chain model is used to connect every intermediate track \(x_{i}^{i}\) in trajectory \(T\), to subsequent track \(x_{t}^{i+1}\) with a given probability encoded as priors over Origin and Destination (OD) preferences. In addition, the proposed work also introduces a dataset of 42 million trajectories collected in train stations. The conducted experiments were performed using SAM features, and results showed that the performance of OD forecasting with a different number of in-between cameras increased, and more accurate trajectories were predicted, obtaining an overall OD error rate of 0.672. The SAM enables the extraction of relevant features to maintain continuous track in occluded pedestrians over time. The fine-grained recognition of vehicles, mainly in traffic surveillance applications, is addressed in [101]. The approach is based on recent advancements in fine-grained recognition: automatic part discovery and bilinear pooling. In contrast to other methods that focus on fine-grained recognition of vehicles, viewpoints are not limited only to a frontal/rear viewpoint, but it allows the vehicles to be seen from any viewpoint. The approach is based on 3D bounding boxes built around the vehicles that are automatically constructed from traffic surveillance data. An CNN based on ResNet50 is used for the estimation of the directions towards the identified vanishing points by feeding the vehicle image into a ResNet50 with three separate outputs regarding the probabilities for directions of vanishing points in quantized angle space. A new annotated dataset BoxCars116k is proposed, focusing on images gathered from surveillance cameras. Several experiments were conducted, with the proposed method significantly improving the CNN classification accuracy, achieving a 12% increase (80.8%) on bounding box determination. A summary regarding the methods for vehicle ReID is presented in Table 9.