esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Design
### Ftn-Dcsk
\begin{split} G^{p}_{k,n}=&\langle g^{k}_{\mathcal{ P}_{(p)}}(t),g^{n}_{\mathcal{P}_{(d)}}(t)\rangle\\ =&\int_{-\infty}^{\infty}g^{k}_{\mathcal{P}_{(p)}}( t).\overline{\delta^{n}_{\mathcal{P}_{(d)}}(t)},\end{split}  \tag{6}\]  
by applying The Plancherel transform for integral substitution, we obtain  
\[ \begin{split} G^{p}_{k,n}=&\int_{-\infty}^{\infty}e^ {-j\omega k\rho_{(p)}.T}G_{T}(\omega).\overline{e^{-j\omega n\rho_{(d)}.T}G_{T} (\omega)}d\omega\\ =&\int_{-\infty}^{\infty}e^{-j\omega(k\rho_{(p)}-n \rho_{(d)}).T}|G_{T}(\omega)|^{2}d\omega,\end{split}  \tag{7}\]  
where \(G_{T}(\omega)\) is the Fourier transform of the regular sinc filter. The Fourier transform is applied again with a variable change \(\theta=\omega T\), which gives  
\[ \begin{split} G^{p}_{k,n}=&\frac{1}{2\pi}\int_{- \pi}^{\pi}e^{-j\theta(k\rho_{(p)}-n\rho_{(d)})}d\theta\\ =&\sqrt{\rho_{(p)}\rho_{(d)}}sinc(k\rho_{(p)}-n \rho_{(d)}).\end{split}  \tag{8}\]  
For the same sampling parameter \(\rho_{(d)}\) in transmission and reception, the dot product of the two vectors obtained at the output of the matched filters, as shown in [28], is expressed as,  
\[\langle g^{k}_{\mathcal{P}_{(d)}}(t),g^{n}_{\mathcal{P}_{(d)}}(t)\rangle=\rho_{ (d)}sinc(\rho_{(d)}(k-n)). \tag{9}\]We consider a dissimilar multi-path Rayleigh channel model with \(L\) paths and a PDF expressed as  
\[f(\alpha_{i,l}^{p})=\frac{\alpha_{i,l}^{p}e^{-{\alpha_{i,l}^{p}}^{2}/\sigma^{2}}} {\sigma^{2}},\qquad\alpha_{i,l}^{p}>0, \tag{10}\]  
where \(\alpha_{i,l}^{p}\) is the channel gain and \(\sigma\) is its standard deviation. The overall energy over a symbol duration is  
\[\sum_{l=1}^{L}E(\alpha_{i,l}^{p})=1,\]  
here \(E(\alpha_{i,l}^{p})\) is the energy level for the \(l\)th path for both cases of similar and dissimilar power distribution over the channel paths. The following assumptions are considered for transmission: (1) the coefficients of the channel \(\alpha_{i,l}^{p}\) are independent and identically distributed (iid); (2) the bit energy is constant over the symbol duration of length \(2\beta\); (3) the delay spread of the channel \(D_{s}\) is neglected for the same reason, ensuring that the channel introduces no ISI, i.e., \(D_{s}<<\beta\).
## Materials and Methods
### Data Analysis  
Odor-active compounds collected from wine headspace was tentatively identified by matching mass spectra to the NIST11 library, Wiley 6N library. All compounds with 80% spectral match or higher and above the 1000 peak area count threshold were considered for the analyses. Aroma descriptors from the panelist were compared to known aroma descriptors for additional verification. The matching of retention time indices was not appropriate in this case due to the GC columns of different polarity in-series configurations. The identification of compounds by the analysis of the pure standard was not performed, but the specific ions of a compound are provided in Table 11, when present in the chromatogram above the threshold.  
Aroma extract dilution analysis (AEDA) was used to identify the most important compounds. From the aromagram, the odor dilution (OD) of each aroma event was multiplied by the measured intensity value resulting in the weighted intensity. This data was plotted with intensity (% full-scale) vs. time. Compounds with a higher OD were considered to be major contributors to the aroma profile of the wine.  
Aroma descriptor intensity and OD were analyzed by principal components analysis (PCA) and cluster summary analysis using JMP Pro 12.0.1 (SAS, Cary, NC, USA). PCA is useful in summarizing all the odor-active compounds, detected by the human nose, in the wines among all conceivable linear combinations. A cluster summary analysis was also performed to determine the most representative aroma compound (i.e., the cluster variable with the largest squared correlation with its cluster component).
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
###### Abstract  
With the fast development of giant LEO constellations, the effective spectrum utilization has been regarded as one of the key orientations for satellite communications. This paper focuses on improving the spectrum utilization efficiency of satellite communications by proposing a non-continuous orthogonal frequency division multiplexing (NC-OFDM) method. Based on the models of NC-OFDM system, we first propose a sub-carrier allocation method by using spectrum sensing to efficiently perceive and utilize the spectrum holes in the satellite communication system. Then, a hybrid genetic particle swarm optimization method is adopted to allocate the channel resources effectively. Finally, simulation results verify that the proposed algorithm can significantly improve the spectrum efficiency of satellite communications.  
NC-OFDM; satellite communications; compressed spectrum; hybrid genetic algorithm particle swarm optimization
###### Abstract
Therefore we focus on a hypothesis of using data from smart meters already widespread in Europe, with the idea of verifying the applicability of disaggregation methodologies even in the absence of dedicated measurement instrumentation. In the lack of specific data for this application we will use a literature dataset limiting the sampling rate to 1 min. Starting from the observation that a reasonably experienced user is potentially able to realize a realistic disaggregation of the load starting from the aggregate load graph, in this work we propose an approach based on deep convolutional networks, using techniques developed in the field of image recognition. The choice of a deep neural network allows the automatic learning of the features, which are derived from the raw signal of the overall active power through convolutive filters and sampling at different time scales. In detail in this paper we will:  
* Introduce a Temporal Pooling module to add context information in the recognition of activation states;
* Show that this approach allows to achieve high performances recognising the on/off state of the appliances;
* Show that this approach has good generalization properties;
* Improve the state of the art performance in a reference dataset. The paper is structured as follows: the problem is formulated in Section 2, the proposed methodology for its solution is described in Section 3, Section 4 describes the numerical experimentsconducted on a reference dataset, whose results are presented in Section 5 and discussed in Section 6. Conclusions are drawn in Section 7, where also some possible further developments are presented.
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 3 Proposed Scheme
### Extraction of watermark  
For this, following information must be known to get the watermark extracted from the watermarked image: (i) Image which is watermarked; (ii) Size of watermark; (iii) Watermark Key; and (iv) Pair-wise numbers of co-prime \(P_{1}\) and \(P_{2}\).  
With knowledge of the watermark key, the DCT coefficient that is embedded with watermark information \(\hat{\theta}\) is extracted. Thus, with values of \(P_{1}\), \(P_{2}\) and \(\hat{\theta}\), the \(E_{1}\), \(E_{2}\) are determined. Next, using a CRT scheme, a comparison is made between the \(z\) and D. If \(z\geq D/u\), the bit 1 would be extracted, otherwise the bit 0 would be get extracted. The scale factor \(u\) is then set to 2 for the coefficient of DC, otherwise it is get set to 3. Thus, the above steps were then repeated until every block consecutively to get all watermark bits extracted.  
In order to improve robustness, the preferred choice is to embed the watermark into the most significant coefficients. However, it will degrade the quality of the watermark. If insignificant coefficients are used for embedding, the watermark may have been lost during lossy compression. Since the proposed scheme use block-size 8 \(\times\) 8 pixels, the resulting DCT block is also of same size. This gives a total of 64 DCT coefficients.
## 2 Recent Evolution in Actor-Critic Frameworks
### Recurrent Neural Network (RNN)-Based Frameworks  
In RNN-based actor-critic frameworks, actor-critic networks are mainly constructed based on either LSTM or GRU cells [46]. In some cases, an LSTM or GRU-based state encoder-decoder is formed to predict the dynamic topology of UAVSNs. In [4], a mean field game (MFG) hybrid PPO-based actor-critic framework is proposed to minimize the average AoI by jointly optimizing UAV trajectories and data collection scheduling of GUs in a hybrid action space. To predict dynamic topology and stabilize the training process, this framework utilizes a learnable LSTM-based state encoder network. Then, LSTM-based state encoder output is fed into the two isolated actor networks to generate continuous and discrete actions related to the UAV's trajectory and transmission scheduling of GUs, respectively. In [24], LSTM is utilized in both actor-critic networks to control the trajectory and maintain the adaptive formation of UAVSNs in continuous action space (i.e., acceleration) in the presence of dense obstacles, with the help of an improved potential field-based reward function. The model is trained using twin-delayed MA-DDPG. This model performs well owing to the advantages of capturing long-term dependencies in sequential trajectory data by using LSTM.  
In [47], the authors utilize an FCN-based DDPG actor-critic framework to maximize energy efficiency and throughput for UAV-aided data collection with the help of energy harvesting at UAV using a rectangular array-based terrestrial base station with power beamforming toward UAV. In [48], an MA-DRL-based actor-critic framework is employed to make cooperative packet routing decisions in a hierarchical topology-based UAVSN. The objective of this framework is to minimize packet routing latency along the end-to-end path by selecting the least congested shortest path. It incorporates a weighted reward function based on the queue backlog difference and distance progress toward the destination. Additionally, the framework trains an MLP-based binary classifier to determine whether to flood information within the local zone of UAVs based on their observations. Furthermore, a GRU-based communication channel is trained using the actor network's policy function and the critic network's Q-value function to significantly compress communication data.
## 4 Simulation Analysis
### Analysis of Actual Measurement Data  
To validate the practical performance of the SP-Rife algorithm, actual data are collected using FMCW radar at five different distances. Subsequently, the SP-Rife algorithm is employed to estimate the frequency of the received echo signals containing distance information. The estimated frequency values are then used in Equation (16) to obtain the distance estimates.  
\[R=\frac{cTf_{0}}{2B}, \tag{19}\]  
where \(T=N/f_{s}\) is the sampling period, \(c=299,792,458\) m/s is the speed of electromagnetic waves, and \(B\) is the signal bandwidth.  
The experimental data acquisition environment is shown in Figure 7. The metallic panel on the wall serves as the target for measurement. The radar-to-target distance is set using the launching platform, and then the equipment is fixed on the track. The FMCW radar transmits the signal, which reflects off the metallic panel in front and is received by the device's receiving panel, generating beat signals. Before the measurement, the sampling frequency of the FMCW radar is set to \(f_{s}=92.7835\) KHz, the signal bandwidth \(B=999.47\) MHz, and the sampling number \(N=1024\). The actual distances measured by the radar are 2378 mm, 2420 mm, 2561 mm, and 2653 mm.  
The beat frequency of the FMCW radar's beat signal is estimated using various frequency estimation algorithms, and the corresponding distances are calculated. Table 3 displays the distance results obtained with different beat frequency estimation algorithms. For a more intuitive comparison, measurement errors from these algorithms are plotted in Figure 8. The analysis reveals that the Rife algorithm provides the least accurate estimates across all datasets. The PAI-Rife algorithm reduces errors by optimizing the signal's estimation range. Conversely, the SP-Rife algorithm achieves the most precise beat frequency estimates through spectrum refinement and phase interpolation, resulting in the smallest distance measurement errors.  
Figure 7: Experimental data collection environment.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
###### Abstract  
Space-borne infrared remote sensing specifically for the detection and characterization of fires has a long history in the DLR Institute of Optical Sensor Systems. In the year 2001, the first DLR experimental satellite, Bi-spectral Infrared Detection (BIRD), was launched after an intensive test period with cooled IR sensor systems on airborne systems. The main basis for the development of the FireBIRD mission with the two satellites, Technology Erprobungstrager No 1 (TET-1) and Bi-spectral-Infrared Optical System (BIROS), was the already space-proven sensor and satellite technology with successfully tested algorithms for fire detection and quantification in the form of the so-called fire radiation power (FRP). This paper summarizes the development principles for the IR sensor system of FireBIRD and the most critical design elements of the TET-1 and BIROS satellites, especially concerning the attitude control system--all very essential tools for high-resolution infrared fire monitoring. Key innovative tools necessary to increase the agility of small IR satellites are discussed.  
high temperature event; fire detection; infrared sensor system; small satellite; FireBIRD
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 2 Materials and Methods  
### The Process of Obtaining Pruning Points  
The process of obtaining pruning points proposed in this study is shown in Figure 1. The process of localizing the pruning points on the grape branches is divided into the following steps. (1) Input the original RGB image into the PSP-net network model, segment the branches and trunks and obtain the ROI image and image mask with the background removed. (2) The ROI image is input into the YOLOv5 model to identify the buds and determine the center coordinate points of the buds. (3) The image mask is input into the Zhang-Suen thinning algorithm to extract the centerline and obtain the skeleton of the image. (4) Whether the bud is a key point is determined, that is, the second bud described in Section 2.3, the depth information of the image is combined, the location of the pruning point on the skeleton line is determined and the coordinates are made to correspond to the pixel coordinates in the original image.
## 3 Results and Discussion
### Cured Physical Properties  
Figure 3 shows the length plastic shrinkage results of the different combinations tested after the curing period.  
The pure resin results for all of the following tests are depicted as a continuous line as a reference. In this case, the resin plastic shrinkage was 2.263%. The sample plastic shrinkages were lower than the control resin value, and showed a direct relationship with the resin content. The lowest length plastic  
Figure 3: Length retraction test results.  
Figure 2: Resin dosage intervals and consistency test results.  
shrinkage values were obtained in the sand combinations within the 0.067%-0.604% range, probably due to the lower resin dosages needed for this target. Sand also showed the lowest result variability, with standard deviations (\(\sigma\)) between 0.026 and 0.044. The highest length plastic shrinkages and variability values were obtained by the LS combinations with ranges of 0.917%-1.235% and 0.118-0.191, respectively. Finally, the AF samples reached plastic shrinkages between 0.640% and 0.898% with \(\sigma\) values between 0.031 and 0.088. This test shows the importance of the use of target materials for the PPC length plastic shrinkage decrease, because of the reduction of resin content in the PPC. The length plastic shrinkage differences and the results' variability highlights the influence of the target granulocyte in this PPC characteristic.  
The cured PPC densities are shown in Figure 4. An increase in the sample densities was observed from 1.220 g/cm\({}^{3}\), corresponding to the resin, until 1.870-1.912 g/cm\({}^{3}\) for the sand, until 1.749-1.902 g/cm\({}^{3}\) for the LS, and until 1.589-1.749 g/cm\({}^{3}\) in the case of the AF combinations. The sand samples showed very low-density differences for the different resin dosages, with a maximum observed density of 1.912 g/cm\({}^{3}\) for the 25% resin dosage. On the other hand, AF and LS showed an indirect relationship between the samples' cured densities and the resin content. Thus, the LS densities varied between 1.749 g/cm\({}^{3}\) for the LS-45 combination and 1.902 g/cm\({}^{3}\) for LS-35, and the AF samples varied between 1.589 g/cm\({}^{3}\) for AF-45 and 1.749 g/cm\({}^{3}\) for AF-35. The sample densities showed, in general, low standard deviations (\(\sigma\)) in the range of 0.007-0.016 for the sand, 0.004-0.013 for the LS, and 0.008-0.030 for the AF. Different density relationship trends were observed between the sand, as well as between the LS and AF resin content combinations. This suggests that the resin does not only fill the interparticle pores, but also produces a partial substitution of target particles, thus decreasing the sample density. This highlights the complexity of the relationship between the bulk target material densities, their particle sizes, the optimum mixes resin dosage ranges, and the PPC densities.
## 6 Field Application
### Micro-Seismic Monitoring  
The mining period before cutting roof was from 26 September 2023 to 17 October 2023, with a cumulative total of 22 days of mining. The mining time after cutting roof was from 18 October 2023 to 8 November 2023, with a cumulative total of 22 days of mining. Two areas (I and II) in a rock mass were selected for seismic monitoring to control the effect of different blasting solutions by micro-seismic analysis. A comparison comprised the total daily energy, maximum daily energy and total daily number of seismic events. In light of these findings, an in-depth examination was conducted to elucidate the distribution pattern of micro-seismic frequency before and after roof cutting. This entailed the calculation of  
Figure 16: Total drilling powder weight from the section of 20–57.5 m to the stopping line before and after blasting (**a**) 70 m; (**b**) 82.5 m; (**c**) 95 m; (**d**) 107.5 m.  
Figure 15: Total drilling powder weight from the section of 20–57.5 m to the stopping line before and after blasting (**a**) 20 m; (**b**) 37.5 m; (**c**) 45 m; (**d**) 57.5 m.  
was 887, comprising 685 events of energies 10\({}^{2}\) J, 201 events of energies 10\({}^{3}\) J and 1 event of energy 10\({}^{4}\) J, with a total energy of 693,955 J and an average energy of 782 J. The cumulative number of micro-seismic events after cutting roof was 1182, of which the numbers of seismic events of energies 10\({}^{2}\) J, 10\({}^{3}\) J and 10\({}^{4}\) J were 998, 183 and 1, respectively. The total emitted energy was 707,412 J, with an average energy of 598 J. The total energy released during the mining period increase by 33.3%, but the energy of a single event decreased by 23.5%. This suggested that micro-seismic progresses towards a trend of \"high-frequency and low-energy\" after the implementation of blasting, thereby reducing the risk of rock burst.
## 3 Results
### Scaling Relation between Lost Irrigated Area and the Landscape Shape Index  
In Section 2.2, we hypothesized a relationship between the Landscape Shape Index and the negative areal change of the irrigation mapping with spatial resolution. When applying the LSI to the 36 sub-tiles in each of the three selected Sentinel-2 tiles, the results in Figure 7 show a strong linear relationship between the LSI and the negative change of the mapped irrigation area at 300 m compared to at 10 m (Sudan: \(r=-0.92\), USA: \(r=-0.95\), China: \(r=-0.96\)). Figure 7 shows the result in the three regions and the different characterization of the areal change and the LSI.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** &
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Sudan** & **USA** & **China** \\ \hline PROBA-V & \(\sim\)300 m & 2671 km\({}^{2}\) & 1035 km\({}^{2}\) & 2940 km\({}^{2}\) \\ Sentinel-2\({}_{300}\) & 300 m & 2599 km\({}^{2}\) & 1021 km\({}^{2}\) & 2908 km\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the irrigation mapping results using PROBA-V and the degraded Sentinel-2 data at 300 m.  
Figure 6: Irrigated area of 2016 derived from PROBA-V data at 10 arc seconds (approx. 300 m) for the regions of China (**left**), Sudan (**middle**) and USA (**right**).  
At small values, the relationship in all three regions behave similarly. Higher LSI values are observed in Sudan and changes the linear equation compared to the equations in USA and China. This shows that the mapping result depends on the spatial formation and arrangement and the complexity of the shape of the irrigation network. These relations seem to be independent from the region and are based solely on the spatial arrangement and the complexity of the shapes of the mapped irrigated area.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 6 Conclusions  
This paper has proposed a loop closure detection algorithm based on multi-scale deep feature fusion. Our proposed algorithm has a higher accuracy and recall rate than the traditional BoW method, and it has better robustness to illumination changes. The key ideas that allowed us to achieve this efficiency are as follows. First, we used CNN to extract features. The features extracted in this way are more advanced and abstract, and have better robustness to illumination changes. Second, we used SPP to fuse the extracted features. By setting the multi-layer SPP with different receptive field sizes to fuse the different scale features in the image, it is more conducive to detecting loop closure. Last but not least, in the similarity calculation process of the decision layer, we added a weight to each feature node according to the distinguishability of the feature nodes to the scene. We have verified the benefits of the above points with experiments. In fact, the neural network used only plays the role of extracting more abstract features. Later, we will consider introducing the difference between the two image features in the middle layer of the deep network, and using the difference feature to detect loop closure.  
Conceptualization, B.C. and C.L.; Data curation, D.Y.; Formal analysis, B.C. and D.Y.; Investigation, Q.W.; Methodology, B.C. and C.L.; Project administration, B.C.; Resources, Q.W.; Validation, D.Y.; Writing--original draft, D.Y.; Writing--review & editing, B.C. and D.Y.  
This research was supported by the National Natural Science Foundation of China under Grant No. 61403423, the Natural Science Foundation of Hunan Province of China under Grant No. 2018J3689, National  
Figure 16: Average precision of spp1+, spp12+, spp124+, and FabMap on four image sequences.  
Key Research and Development Plan (2018 YFB1201602) and Science and Technology Major Project of Hunan Province of China under Grant No. 2017 GK1010.  
The authors declare no conflict of interest.