esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
###### Abstract  
Absorbing aerosols can absorb solar radiation, affect the atmospheric radiation balance, and further have a profound influence on the global and regional climates. The absorption aerosol optical depth (AAOD) as well as the absorption Angstrom exponent (AAE) across China over 2005-2018 were systematically studied through the Ozone Monitoring Instrument (OMI) dataset. The monthly AAOD samples from the OMI generally showed a good correlation (-0.55) compared to the monthly data from AERONET at four typical sites (North: Xianghe, East: Taiwan, South: Hongkong Polytechnic Univ; Northwest: Sacol) across China. The ensemble annual average of the OMI AAOD at 388 and 500 nm is 0.046 and 0.022, with minor changes during 2005-2015, and a relatively fast increase after that. The winter and spring seasons depict the maximum mean AAODs, followed by autumn, whereas summer shows minimum levels. On the contrary, the high AAE values appear in summer and low values in winter. The order of the annual average AAOD\({}_{500}\) from 2005 to 2018 is the Tarim Basin (TB, 0.041) > the Yellow River Basin (YRB, 0.023) > Beijing and Tianjin (BT, 0.026) > the Sichuan Basin (SB, 0.023) > Nanjing and Shanghai (NS, 0.021) > the Pearl River Delta (PRD, 0.017), whereas the AAE\({}_{388-500}\) exhibits the opposite trend except for the TB (3.058). From 2005 to 2018, the AAOD rises by nearly 1.5-2.0 fold in the six typical regions, implying a severe situation of dust and/or BC aerosol pollution in the last several years. The monthly mean AAOD\({}_{388}\) over the TB, the SB, the YRB, BT, the PRD, and NS is estimated to be smallest at 0.072, 0.024, 0.026, and 0.027 in July, 0.024 in June, and 0.025 in September, respectively, whilst largest in January for NS, the YRB and BT, April for the TB, February for the SB, and March for the PRD with 0.055, 0.077 and 0.067, 0.123, and 0.073 and 0.075, respectively. The monthly averaged AAOD\({}_{500}\) in each region is consistently about half of the AAOD\({}_{388}\). The highest AAE appears in June while the lowest values are in December and January, and the daily AAE values in episode days slightly decrease as compared to non-episode days. Our study indicates that northwestern China plays an important role in the overall AAOD as a result of dust aerosols stemming from desert areas. Moreover, the meteorological conditions in winter and early spring are associated with more energy consumption conducive to the accumulation of high black carbon (BC) aerosol pollution, causing high alert levels of AAOD from November to the following March.  
a 2024 15 1099 1
## 3 Results and Discussion
### Factors Influencing Bone Tissues around Prama Implant according to Literature  
Peri-implant mucosal connective tissue attachment has clinical and histological features similar to those of teeth. However, the main difference lies in the cellular composition and fiber orientation. The connective tissue surrounding the dental implant is in direct contact with the titanium dioxide surface and contains a dense network of collagen fibers. These fibers, primarily originating from the periosteum of the alveolar bone crest, extend to the mucosal margin. Notably, the fibers are oriented parallel to the implant/abutment surface [33].  
The gingival biotype is a critical factor influencing MBL. Studies have shown that a thicker biotype (equal to or more than 2 mm) is associated with greater MBL stability, whereas a thin biotype (less than 2.0 mm) results in more pronounced marginal bone loss [34; 35]. Thicker gingival tissue around implants with a convergent neck provides a more stable marginal bone tissue, as previously demonstrated by various investigations [8; 17; 24].  
The positioning of the implant neck, whether supragingival (above the soft tissues) or subgingival (below the soft tissues), may affect the MBL. Implants with a supragingival neck positioning tend to have better maintenance of the marginal bone levels due to less mechanical and bacterial stress compared to subgingival placements [8; 17; 21; 30; 32].  
The timing of the implant placement is also a significant factor associated with early bone loss. The initial \"pre-loading\" bone/remodeling can be dependent on the preoperative status of the bone tissue. Histological and micromorphological scanning electron microscope studies demonstrated very different bone mineralization areas in relation to the initial bone density and structure independent of the type of implant surface proposed [36; 37; 38]. Different bone remodeling patterns can also be observed after loading procedures [39; 40; 41], which can result in a more dense and mineralized bone structure after several years of implant loading [42].
# Injury Patterns and Frequency in Swimming: A Systematic Review
## 4 Discussion
### Limitations and Future Directions  
This review has several limitations. One major challenge is the variability in injury prevalence reporting due to differences in study designs, injury definitions, and data collection protocols, which complicate direct comparisons. Additionally, the limited number of epidemiological studies on swimming injuries restricts the ability to estimate incidence and risk factors, with most research focusing on elite swimmers, reducing the generalizability of the findings to other populations.  
To improve comparability, future studies should adopt standardized injury surveillance protocols, defining clear criteria for injury diagnosis, severity classification, and exposure metrics (e.g., injuries per 1000 training hours). The use of validated injury-specific questionnaires, real-time monitoring tools (e.g., SIMS-Med Sport Systems), and international guidelines such as the Oslo Sports Trauma Research Center (OSTRC) questionnaire would enhance data reliability. Additionally, integrating wearable sensors could facilitate long-term injury tracking and improve cross-study comparability.  
Future research should also expand to include recreational-, youth-, and master-level swimmers to improve generalizability. Longitudinal studies tracking swimmers over time would provide better insights into training load, injury progression, and rehabilitation effectiveness. Lastly, investigating how technological advancements in swimmer and training equipment impact injury prevention could offer practical applications for reducing overuse injuries.  
By addressing these gaps and standardizing reporting methods, future research can improve injury surveillance, prevention strategies, and long-term athlete health.
## 2 Signal and System Model
The term \(\tilde{c}_{i}(t-\tau_{i})\) represents the spreading sequence after filtering of the front-end, and here, the simplifying condition:  
\[\tilde{c}_{i}(t)\approx c_{i}(t) \tag{8}\]  
is assumed and the impact of the front-end filter is neglected. \(\eta(t)\) is the down-converted and filtered disturbing component, \(\eta[t]=j[t]+w[t]\). Considering the interference term \(j[t]\), the mean power of the sweep interference can be defined as:  
\[J=\text{Var}\{j[t]\} \tag{9}\]  
The jammer-to-noise ratio (JNR) is defined as follows:  
\[\frac{J}{N}=\frac{J}{\sigma_{IF}^{2}}=\frac{J}{N_{0}B_{IF}} \tag{10}\]In order to avoid the cross-terms resulting from the interaction between the positive and negative frequency parts of the spectrum, the analytic representation of the received signal is adopted, provided as follows:  
\[y_{a}[t]=y[t]+j\hat{y}[t] \tag{11}\]  
where the analytic signal \(y_{a}[t]\) has a real part \(y[t]\) and an imaginary part \(\hat{y}[t]\), which contains the Hilbert transform of \(y[t]\). The imaginary part is a version of the original real part with a \(90^{\circ}\) phase shift. The use of the analytic signal has two advantages: first, interference between positive and negative frequencies can be eliminated, as the analytic signal \(y_{a}[t]\) has components belonging only to the half plane of positive frequencies; second, even though this filtering suppresses negative frequencies and the sampling rate is reduced, it does not introduce any loss of information.
## 1 Introduction
* In response to the complex frequency offset variations in Low Earth Orbit satellite links, we propose a Doppler frequency offset compensation method based on the coordinate system transformation. The method predominantly leverages the ephemeris information of Low Earth Orbit satellites for frequency offset estimation. By investigating the rapid parsing of ephemeris data and frequency offset calculation and compensation methods across different coordinate systems, it achieves a reduction in computational complexity. This method effectively addresses the high complexity associated with Doppler frequency offset estimation in dynamic channels. * In response to the challenges posed by the complex environment and lack of prior knowledge of non-cooperative signals in LEO satellite scenarios, we propose a modulation identification method based on the intrinsic features of OFDM. This method performs a multi-domain joint analysis and extraction of endogenous features of OFDM signals lacking prior knowledge, including time-frequency domain, higher-order domain, and entropy domain characteristics, and effectively enhances the accuracy of OFDM signal identification under weak prior knowledge. The subsequent sections provide a brief introduction to the contents of this paper. Section 2 delineates the generation process of OFDM signals and the blind detection and reception model in detail and conducts an in-depth analysis of the cyclic correlation properties of CP-OFDM signals. In Section 3, we propose a flexible monitoring algorithm based on resampling filtering and elucidate the design principles of the resampling filter comprehensively. Section 4 introduces a Doppler frequency offset estimation algorithm predicated on satellite ephemeris and coordinate system transformation and elaborates on the implementation of the algorithm. In Section 5, we present a identification methodology for OFDM signals that leverages intrinsic features, including detection, structure identification, and subcarrier modulation identification, with thorough descriptions of these approaches. Simulation outcomes are presented in Section 6, and Section 7 provides a summary and conclusion of the paper.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 3 Results
### Point Target Evaluations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polarization** & **S-1A RCS [dB]** & **S-1B RCS [dB]** & **S-1A/S-1B** \\
**Channel** & **(\(\mathbf{\mu\pm\sigma}\))** & **(\(\mathbf{\mu\pm\sigma}\))** & **Difference [dB]** \\ \hline VV & \(-\)0.19 dB \(\pm\) 0.21 dB & \(-\)0.11 dB \(\pm\) 0.17 dB & \(-\)0.08 dB \\ VH & \(-\)0.03 dB \(\pm\) 0.33 dB & \(-\)0.24 dB \(\pm\) 0.26 dB & 0.21 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RCS deviations derived from reference point targets reported as mean values and standard deviations (\(\mathbf{\mu\pm\sigma}\)) for both SAR instruments and polarization channels. Figure 2: Mean RCS deviation (symbol) and corresponding standard deviation (error bars) for corner reflectors (CR) and transponders (TR). Each DLR reference target is depicted by a specific symbol and evaluated for VV (red) and VH (blue) polarized products for S-1A (**left**) and S-1B (**right**). Note that three different satellite tracks are evaluated so that each target is observed several times at different look angles. A radiometric variation over look angle is visible for S-1A compared to S-1B within Figure 2. Furthermore, the spatial variation over look angle is even slightly higher compared to the temporal variation expressed by the standard deviation (error bar) for each target at a constant elevation angle which is below 0.15 dB. In particular, for the cross-polarization channel, the mean values differ up to 0.5 dB at different look angles even for the same reference target. Such radiometric variations over the elevation angle may arise from remaining uncertainties of the antenna pattern compensation executed during SAR data processing, as already analyzed [12; 13; 14].
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 3 Feature-selective and Spatial Receptive Fields (FSRF) Blocks  
In order to implement adaptive receptive field (RF) sizes of neurons through the additive effects of feature-selective and spatial attention, we propose a novel architectural unit called a \"Feature-selective and Spatial Receptive Fields\" (FSRF) block. The structure of the FSRF block is depicted in Figure 1. We implement the FSRF block via three operations: Multi-branch Convolution, Fuse, and Interactions between Feature-selective and Spatial Attention.
## 4 Discussion
### Older Landslides/Hazard  
The mountainous area of Karditsa, Thessaly is known to be significantly affected by numerous landslides, many affecting communities and infrastructure [44]. The Agrafa mountain area, where the majority of landslides triggered by Medicare Iancos occurred, is one of the most hazardous areas in Greece for landslides. Landslide occurrence is mostly due to the factors of highly susceptible lithology (flysch and molassic sediments) and high precipitation rates of the Pindos mountain range. We compare the landslide inventory of Medicare Iancos with historically reported landslide locations in Figure 18. Historical locations were retrieved from [49, 50] and official reports of the Institute of Geological and Mineral Exploration. A visual examination shows that Iancos landslides occurred along roughly the same locations of historical landslides, revealing a relation with long-term climatic and lithological/geomorphological conditions.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 4 Complex Prediction Method with Backward Adaptation
### Alcm+
#### 4.1.2 The Principle of Adaptation in Our Proposition of the Improved Version of ALCM+  
In the new version of ALCM+ presented in this paper, it was proposed to introduce five parallel predictive models with orders \(r_{j}\) of \(\mathbf{r}=\{10,22,30,56,110\}\), respectively, where the orders of predictions have been chose as a result of many experiments to optimise compression efficiency for the learning base.  
The predicted value is determined as the arithmetic mean of these five prediction models:  
\[\hat{x}_{Ch}(n)=x_{Ch}(n-1)+\frac{1}{5}\cdot\sum_{j=1}^{5}\sum_{i=1}^{r_{j}}w _{Ch}^{(j)}(i)\cdot g_{Ch}(i), \tag{7}\]  
where \(Ch\) is the indicator of \(L\) (left) or \(R\) (right) channel and \(j\) is the number of one of the five prediction models.  
In each of these models, after coding the next sample, six prediction coefficients are adapted. For this purpose, the three highest and lowest values of \(r_{j}\) from the \(g_{Ch}(i)\) elements from the vector \(\mathbf{g}_{Ch}(n)\) are determined, where \(i\in\overline{1;r_{j}}\).. Denoting these three elements, respectively, as the smallest value:  
\[g_{Ch}(q_{Ch(1)}^{(j)})\leq g_{Ch}(q_{Ch(2)}^{(j)})\leq g_{Ch}(q_{Ch(3)}^{(j)}) \tag{8}\]  
and three largest values:  
\[g_{Ch}(p_{Ch(3)}^{(j)})\leq g_{Ch}(p_{Ch(2)}^{(j)})\leq g_{Ch}(p_{Ch(1)}^{(j)}). \tag{9}\]  
Then, for everyone \(j\)-th \((j\in\overline{1;5})\) predictive models (if assuming that the condition \(g_{Ch}(q_{Ch(1)}^{(j)})<g_{Ch}(p_{Ch(1)}^{(j)})\) is true), the following adaptation prediction coefficients are used (see Algorithm 1).  
```
if\(\hat{x}_{Ch}(n)<x_{Ch}(n)\)then{ \(w^{(j)}_{C_{(\mathrm{Gk})}}}(n+1)=w^{(j)}_{P^{(j)}_{C_{(\mathrm{Gk})}}}(n)+\mu^{(j )}_{Ch(k)}\),for\(k=\{1,\;2,\;3\}\) \(w^{(j)}_{Q^{(j)}_{\mathrm{Gk})}}(n)=w^{(j)}_{Q^{(j)}_{\mathrm{Gk})}}(n)-\mu^{(j )}_{Ch(k)}\), for\(k=\{1,\;2,\;3\}\) else if\(\hat{x}_{Ch}(n)>x_{Ch}(n)\) \(w^{(j)}_{P^{(j)}_{C_{(\mathrm{Gk})}}}(n+1)=w^{(j)}_{P^{(j)}_{C_{(\mathrm{Gk})}}}( n)-\mu^{(j)}_{Ch(k)}\),for\(k=\{1,\;2,\;3\}\) \(w^{(j)}_{Q^{(j)}_{\mathrm{Gk})}}(n)+\mu^{(j)}_{Q^{(j)}_{\mathrm{Gk})}}(n)+\mu^{( j)}_{Ch(k)}\),for\(k=\{1,\;2,\;3\}\) }
```  
**Algorithm 1**Adaptation of prediction coefficients by predictive models.  
The modifying step-size \(\mu^{(j)}_{Ch(k)}\) value is determined as follows,  
\[\mu^{(j)}_{Ch(k)}=\min\biggl{\{}2^{-6};\overline{\mu}^{(j)}_{Ch(k)}\biggr{\}}, \tag{10}\]  
where,  
\[\overline{\mu}^{(j)}_{Ch(k)}=\frac{\left|x_{Ch}(n)-\hat{x}_{Ch}(n)\right|}{2^{3 }\cdot\sum\limits_{k=1}^{3}\alpha_{k}\cdot\biggl{(}g_{Ch}(p^{(j)}_{Ch(k)})-g_{ Ch}(q^{(j)}_{Ch(k)})\biggr{)}} \tag{11}\]  
and \(\alpha_{k}=\{1;0.5;0.5\}\).
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 4 Methodology  
In this section, we introduce the RRM methodology proposed in this paper to improve the spectral efficiency of users' communications. The main problem it has to resolve is to optimize the spectral efficiency being capable of mitigating the interferences and ensuring the different QoS requirements of ITS applications.  
Therefore, with the intention of solving the overall problem, we divide the main one into three subproblems, which are shown in Figure 2.  
1. Pairing Problem: The first one is the pairing problem, in which in order to determine if a C-UE and a V-UE can share the same RB to transmit their messages, we calculate the minimum distance there should be between a C-UE and a V-UE transmitter to mitigate the interference. Knowing this distance, the eNodeB can select which are all the possible pairs.
2. Transmission Power Allocation: After solving the pairing problem, we continue with the second subproblem which is the transmission power assignation for each possible pair of C-UE and V-UE transmissions. Thereby, we have a bipartite graph with all the possible pairs with their respective transmission powers. With this transmission powers, it is easy to calculate the achievable throughput of the pair of communications which will be considered the weight of each pair in the bipartite graph.  
Figure 2: Resource Allocation steps.  
RB Assignation: Having reached this point, we move to the third subproblem, where we end up with the RB assignation. To solve this combinatorial optimization subproblem we have design a specific parallel meta-heuristic.This subproblems are explained in the following subsections.
## 4 Results  
### Selection of Polarization  
Figure 5 shows the contrast of the backscatter (in dB) between land and water for the HH and HV polarizations. Standard deviation results are represented in bars as a measure of errors of the mean values. For the 2014 image (FQ15), histograms of water/land are slightly separated in HH with thresholds of -16/-11 (dB) (relative contrast of \(\sim\)4 dB) respectively, compared to the HV polarization where the thresholds reach -30/-18 (dB) and relative contrast of \(\sim\)12 dB. This is expected as the image was acquired in windy conditions, increasing the water surface roughness, and therefore its backscatter coefficient. As the 2016 (FQ25) image was acquired in calm conditions, one can expect well separated histograms of the water and land in both polarizations, with thresholds of -26/-11(dB) for the HH and -30/-16 (dB) for the HV polarizations, respectively (the relative contrast is \(\sim\)14 dB for both HH and HV).  
Figure 6 shows sections of the FQ15 and FQ25 HH and HV images. As shown in Figure 6a,c, surface water in HH is brighter and the contrast between land and water is not as clear as in the HV polarization images shown in Figure 6b,d. Furthermore, the mudflats show a low backscatter return in both HH and HV in calm conditions as shown in areas indicated with red arrows compared in Figure 6c,d. However, in windy conditions, the HH polarization is more adequate for discriminating these features.  
When evaluating simulated RCM data, the results demonstrate that, for the considered nominal NESZ and spatial resolution, the CP variables RH/RR produce similar separability between land and water as HH/HV (Figure 7). The relative contrast (land-water) is significant in the compact RR compared to RH in windy conditions and is almost similar in calm wind conditions (Figure 8). Similar to HH and HV, mudflat features appear darker in RH and RR polarization in calm conditions as indicated by red arrows in Figure 8.  
Figure 5: Land-water contrast in HH and HV polarizations.  
Figure 6: RADARSAT-2 FQ15 HH (**a**), HV (**b**) and FQ25 HH (**c**), HV (**d**) images acquired over the study area on 8 October 2014 and 7 September 2016 respectively. The red arrows point to mud-flat areas that can be clearly distinguished in HH but are barely visible in HV. RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2014/2016) – All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency.
## 2 Materials and Methods
### Calibration and Sample Preparation  
A three-point calibration using a blank consisting of 3% nitric acid and Urine Level 1 (REF 210605, Sero AS, Billingstad, Norway) and 2 (REF 210705, Sero AS, Billingstad, Norway) was prepared daily for the comparative analysis of trace element concentrations of Al, V, Cr, Mn, Fe, Co, Cu, Zn, As, Se, Mo, Cd, Sn, Ba, TI and Pb in urine. For assessment of linearity, slope, limit of detection (LOD) and limit of quantification (LOQ), a five-point calibration was used. For method validation reference materials, ClinCheck(r) Trace Elements Urine L-1 (REF 8847, Recipe(r), Munich, Germany) and ClinCheck(r) Trace Elements Urine L-2 (REF 8848, Recipe(r), Munich, Germany) were analyzed. A serial dilution in 3% nitric acid (made from suprapurn nitric acid 65%, Merck, Darmstadt, Germany) of the reference samples was prepared in decreasing amounts. For sample preparation, only 100 uL of the corresponding urine sample was acidified with 3% nitric acid filled up to 3 mL in a 15 mL polypropylene tube. After that, the samples were homogenized on a vortex for 5 s and were then ready for measurement. Devices used for sample preparation were pipettes (volumes 50-1000 mL) and a dispenser with adjustable volumes from 1-5 mL (Eppendorf, Hamburg, Germany). This is an easy, quick, and accurate dilute and shoot sample preparation method for preparing many samples in a short time. A similar amount of Rh and Ir were added as an internal standard to all samples throughout the measurement.
## 2 Materials and Methodology
### Data Description
#### 2.2.2 Actual and Potential Evaporation  
To quantify the evapotranspiration deficit index (SEDI) as well as the ecosystem's GPP resilience and ability to tolerate droughts, the yearly evaporation estimates of the Global Land Evaporation Amsterdam Model (GLEAM, version 3.3a) were used [64; 65]. In this product, the potential evaporation (PET) is estimated based on the Priestley-Taylor method, which considers the near-surface air temperature and surface net radiation as inputs. In this study, the PET and AET datasets were used on an annual basis at a spatial resolution of 0.25\({}^{\circ}\) for the period of 1982-2018 (www.gleam.eu (accessed on 7 May 2019)). It is well established that the actual evaporation (AET) is generally lower than the estimated PET. As such, GLEAM converted PET estimates into AET, or evaporation of bare soil (based on the land cover), by considering a multiplicative stress factor S (-) with values that ranged between 0 and 1. The AET data were calculated based on the GLEAM algorithm and validated against FLUXNET stations (eddy covariance towers) worldwide, which demonstrated a high agreement between these two independent datasets [64; 65; 66; 67; 68]. In this context, [PERSON] et al. [65] reported an average Pearson's correlation of 0.81-0.86 based on data from 91 FLUXNET stations. More recently, [PERSON] et al. [66] used five FLUXNET stations and noted an average correlation that ranged from 0.68 to 0.94 with a mean absolute error (MAE) of 0.4-0.8 mm d\({}^{-1}\). In addition, according to [67; 68], the GLEAM AET dataset outperformed other evaporation datasets and had better agreement with the Budyko, LSA-SAF, and Makkink estimates.