esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Uncertainty  
In this study, we used the vegetation phenology and productivity datasets from MODIS products to investigate the dynamics of vegetation phenology and GPP. It should be noted that uncertainty existed in remote sensing products due to the flaws of the model structures and input parameters. For example, MCD12Q2 was derived by using the threshold method based on the vegetation index data series, and the given threshold was set to extract the vegetation phenology. However, the selection of the threshold is often arbitrarily determined and could induce uncertainty [27; 76; 22]. In addition, the sparse  
Figure 9: Comparison between annual cumulative gross primary productivities in the SRYR during Period 1 (2001–2009) and during Period 2 (2010–2019) (**a**). Conceptual figure indicating the influences of the changes of vegetation phenology and GPPmax on annual accumulative GPP (**b**). SOS1, EOS1, LOS1, LOS1, POS1 and GPPmax1 are the average values of phenology indicators and maximum GPP during Period 1, and SOS2, EOS2, LOS2, POS2 and GPPmax2 are those during Period 2.  
temporal resolutions (e.g., 8 days for MODIS EVI) of satellite data might lead to limitations in capturing the accurate dynamics of vegetation phenology [78]. In the estimation of GPP, uncertainties were from climate input and the determination of maximum light use efficiency (LUE) for vegetation, which was influenced by land cover types, phenophases and environmental stresses [79; 80]. Due to the lack of ground observation, the performance of these remote sensing products on vegetation phenology and gross primary productivity was not evaluated in this study.  
This study analyzed the correlation between seasonal GPP and phenological factors, but the separate contributions of the changes of phenology factors and GPP\({}_{\text{max}}\) to seasonal and annual GPP were not quantified. In recent years, global scale solar-induced chlorophyll fluorescence (SIF) provides a better surrogate for studies of large-scale vegetation phenology and GPP [81; 82]. The study of phenology monitoring by SIF remote sensing may lead to further discoveries on the influence of phenology on vegetation photosynthesis and carbon sequestration in the source region of the Yellow River. Moreover, vegetation phenology was affected not only by precipitation and air temperature, but also by radiation, grazing, snowmelt and permafrost degradation and phenology and productivity in the preceding year [29; 83; 84; 85; 86]. We only considered the impacts of precipitation and air temperature on vegetation phenology in this paper. Besides vegetation phenology and GPP\({}_{\text{max}}\), other environmental factors such as atmospheric CO\({}_{2}\) concentrations, grazing, vapor pressure deficits and radiation also affected carbon assimilation. In further studies quantifying the impacts of vegetation phenology on productivity, these uncertainties should be taken into accounted, and the underlying mechanisms need to be further explored.
# A High-Resolution Texture Mapping Technique for 3D Textured Model
## The Proposed Texture Mapping Method
### Texture Transferring
#### 4.3.2 Extraction of Pixels from the Object Images  
The purpose of this step is to extract pixels from the front image with respect to a 3D mesh. A prospective projection is performed to project 3D meshes back to the image domain. As Figure 5 depicts, the triangle \(\Delta p_{1}p_{2}p_{3}\) denotes the projection of a 3D mesh \(\Delta v_{1}v_{2}v_{3}\) onto the image domain. All pixels and color information on and inside this triangle represent the corresponding texture for the 3D mesh. The extraction of a pixel inside a triangle is explained below. The image is made up of pixels in a grid plane containing horizontal and vertical lines, which gives each pixel a unique coordinate. A scanline method is implemented to compute all pixels inside a triangle. The scanline shown in Figure 7 intersects two triangle edges, which yields the two endpoints of the line segment inside the triangle. All pixels on this line segment can then be evaluated in sequence. An endpoint of the line segment can be evaluated by using the following equation:  
\[\delta_{x}=X_{2}-[\frac{(Y_{2}-Y)\cdot(X_{2}-X_{1})}{(Y_{2}-Y_{1})}], \tag{5}\]  
where \((X_{1},\,Y_{1})\) and \((X_{2},
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 5 Discussion
### Asymptotic Complexity  
Asymptotic complexity is dominated by the calculation of the weighted histogram array. Calculating weights directly between pixel pairs has a complexity of \(\mathcal{O}(n^{4})\).  
Creating the indicator array is proportional to the number of pixels: \(\mathcal{O}(n^{2})\). Fast Fourier Transform of a \(n\cdot n\) array requires two one-dimensional FFTs for every line (\(n\)), and the cost of one line's transform is \(\mathcal{O}(n\log n)\). The number of 2D arrays is proportional to the number of different pixel-value levels (\(m\)). Taking all of these into account, the asymptotic complexity is:  
\[\mathcal{O}(2m\cdot n^{2}\log n). \tag{18}\]  
As long as \(2m<n^{2}/\log n\), this is a faster approach than weights taken directly from pixel pairs. This condition is easily met for traditional photographs which usually have \(n>1000\) pixels for image width and height while the number of discrete levels is usually between 256 and 1024. In some medical applications, the image resolution is lower, while the number of levels is higher. For instance, computed tomography usually has images with \(512\times 512\) pixels with 4096 different quantization levels.  
Color images are usually processed in an 8- or 10-bit discretized brightness, lightness or value channel, but conversion from another color space with a large dynamic range might yield a huge number of levels, e.g., directly processing RAW data from digital cameras. A huge number of discrete levels might render the approach non-beneficial, or would require approximations, as is explained later in Section 6.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 5 Physical Layer Proposal
### Study of HF Standards  
Before the definition of the new physical layer, several HF standards were studied. None of them could be applied to the long-haul link due to the SNR requirements of all the standards.  
The hostile channel communication standards MIL-STD-188-110A [4] and STANAG 4415 [5] have been studied in order to be applied in the 12,760 km ionospheric channel; the robust mode of both have a standardized physical layer that reaches a throughput of 75 bps with a DS Walsh modulation. This technique can be used with poor HF channel conditions in a 3 kHz bandwidth. However, these standards define a BER of \(10^{-5}\) with a SNR higher than 0 dB, with a multipath delay spread lower than 10 ms and a Doppler spread between 2 and 20 Hz. The usual SNR observed in the 12,760 km transequatorial channel are substantially lower than the defined previously.  
After the conducted analysis, including sounding [6,11], modulation tests [8, 9, 10] and synchronization analysis, we conclude that a new physical layer design is needed, beyond the standards typically used in hostile channels.
## 2 Research Area and Data Selection
### Selection of Seismic Examples  
In this research, we selected Ms \(>\) 5.0 earthquakes with depths \(\leq\)40 km in the research area from December 2018 to January 2023 (CSES operation time). The seismogenic area of the lithosphere was estimated according to the seismogenic model proposed by [PERSON] et al. [29].  
\[\rho=10^{0.43\mathrm{M}} \tag{1}\]  
where M is the earthquake magnitude, and \(\rho\) is the straight-line distance from the epicenter (the unit is km). The investigated phenomena are Ms \(>\) 5.0 earthquakes, so the straight-line distance from the epicenter in the seismogenic area is more than \(\sim\)142 km (equal to \(\sim\)1\({}^{\circ}\)) according to Equation (1). Seismo-ionospheric perturbation signals can spread from the Earth's surface to the ionosphere, causing ionosphere perturbations based on the LAIC mechanism. Moreover, seismo-ionospheric perturbation signals may shift in the ionosphere during propagation, and the maximum offset can reach more than 10\({}^{\circ}\). To ensure the credibility of the considered seismic anomalies, we considered that no other Ms \(>\) 5.0 earthquakes occurred within the longitude 10\({}^{\circ}\)\(\times\) latitude 10\({}^{\circ}\) range around the epicenter from 3 months before to 1 month after the occurrence of the selected earthquakes. A total of 12 typical earthquakes were selected, as listed in Table 1.
## 5 Simulation and Analysis  
To test the performance of the proposed schemes, we constructed a multi-band signal \(s(t)\), \(s(t)=x(t)+w(t)\), where \(w(t)\) is Gaussian white noise. QPSK is a quadratic phase modulation with excellent noise immunity characteristics and band utilization, which is widely used in wireless communication systems. Therefore, we chose to use the QPSK signal to simulate \(x(t)\).  
\[x(t)=\sum_{i=1}^{N/2}\sqrt{2E_{i}B}x_{i}(t) \tag{33}\]  
\[\begin{split} x_{i}(t)&=\sum_{n}I_{i}[n]\text{sinc} \big{(}t-nT_{sym}\big{)}\cos(2\pi f_{i}t)\\ &+\sum_{n}Q_{i}[n]\text{sinc}\big{(}t-nT_{sym}\big{)}\sin(2\pi f_ {i}t)\end{split} \tag{34}\]  
where \(N\) is the sparsity (\(N=6\)). \(E_{i}\) is the energy coefficient that is randomly generated and conforms to i.i.d, \(E_{i}\in[1,3]\). \(B\) is the bandwidth of each sub-band (\(B=50\) MHz). \(f_{i}\in\left[-\frac{f_{\text{NYQ}}}{2},\frac{f_{\text{NYQ}}}{2}\right]\) is the carrier frequency of each signal. \(I_{i}[n]\) and \(Q_{i}[n]\) represent the randomly generated I-channel and Q-channel signals, respectively, with a range of values \(\{-1,+1\}\).  
The addition of Gaussian white noise \(w(t)\) is used to simulate the situation under a different average SNR (ASNR).  
\[ASNR=10\log\Bigl{(}\|x\|^{2}\Big{/}\|w\|^{2}\Bigr{)} \tag{35}\]  
We used Monte Carlo methods to validate the performance of schemes. Each of the data are a statistical average obtained by running the program independently 1000 times. The parameters related to the MWC system are set as shown in Table 2.  
In the subsequent simulations we will concentrate on the support set recovery accuracy and the reconstructed signals for the samples. The support set recovery accuracy is shown by the false alarm rate. The reconstructed signal accuracy will be analyzed in terms of the correlation coefficient, BER, and other dimensions.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 5 Concluding Remarks and Future Work  
In this paper, we introduced a new framework for applying multiclass classification to an antenna allocation system with multiple antennas in multiuser downlink communications, under the assumption of channel amplitude information at the transmitter. The proposed antenna allocation system based on an SVM multiclass classifier was numerically evaluated and verified based on sum-rate performance and computational complexity. The following main results were obtained: (i) If the number of classes \(L^{\prime}\) is suitably established, then the sum-rate performance of SVM is comparable to that of the optimization-driven method and significantly reduces computational complexity in the online antenna section; (ii) the classification performance of \(k\)-NN is inferior but still comparable to that of SVM; and (iii) for a given \(L^{\prime}\), the runtime complexity of the SVM classifier increases linearly with the number of antennas, which implies that the designed learning-based approach using SVM is appropriate, especially for large-scale antenna systems. Suggestions for future research in this area include (i) developing a variety of learning systems by precisely designing training data along  
Figure 11: Runtime over \(L^{\prime}\) when \(N_{t}=5\) and \(U=3\).  
Figure 12: Runtime over \(N_{t}\) when \(U=3\) and \(L^{\prime}=32\).  
with more channel information (e.g., channel phase information) in interference-limited multiuser communications, (ii) supporting multi-antenna users, and iii) developing an online learning algorithm to track channels with time-varying statistics.  
Conceptualization, J.J. and W.-Y.S.; methodology, J.J.; software, H.L.; validation, H.L.; investigation, H.L., W.-Y.S. and J.J.; writing-original draft preparation, H.L.; writing-review and editing, W.-Y.S. and J.J.; visualization, J.J.; supervision, J.J.; project administration, J.J.; funding acquisition, J.J.  
This research was supported by the National Research Foundation of Korea (NRF) grant (2018R1A4A1023826) funded by the Korean government (MSIT).  
The authors declare no conflict of interest.
# Review of Works Combining GNSS and InSAR in Europe
###### Abstract  
The Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) can be combined to achieve different goals, owing to their main principles. Both enable the collection of information about ground deformation due to the differences of two consequent acquisitions. Their variable applications, even if strictly related to ground deformation and water vapor determination, have encouraged the scientific community to combine GNSS and InSAR data and their derivable products. In this work, more than 190 scientific contributions were collected spanning the whole European continent. The spatial and temporal distribution of such studies, as well as the distinction in different fields of application, were analyzed. Research in Italy, as the most represented nation, with 47 scientific contributions, has been dedicated to the spatial and temporal distribution of its studied phenomena. The state-of-the-art of the various applications of these two combined techniques can improve the knowledge of the scientific community and help in the further development of new approaches or additional applications in different fields. The demonstrated usefulness and versatility of the combination of GNSS and InSAR remote sensing techniques for different purposes, as well as the availability of free data, EUREF and GMS (Ground Motion Service), and the possibility of overcoming some limitations of these techniques through their combination suggest an increasingly widespread approach.  
GNSS; GPS; InSAR; Europe; Italy; PS 2021  
13 1684. [[https://doi.org/10.3390/rs13091684](https://doi.org/10.3390/rs13091684)]([https://doi.org/10.3390/rs13091684](https://doi.org/10.3390/rs13091684))  
1
Footnote 1: [[https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing)]([https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing))
# Mechanism and Application of Soilbags Filled with Excavated Soil in Soft Soil Subgrade Treatment
## 3 Application of Soft Soil Subgrade Treated with Soilbags
### Monitoring Instrumentation  
The performance of the subgrades was facilitated by observing both the resilient modulus and the surface settlement. A schematic for the measurements is presented in Figure 8, which includes twelve designated locations for the measurement of the resilient modulus, marked as R1-R9 in the soilbag-treated subgrade and as r1-r3 in the original design of the subgrade, accompanied by eight predefined points for monitoring the surface settlement, identified as S1-S5 and s6-s8.  
The resilient modulus of the soilbags, the lime soil, the cement-gravel stabilized, cushion, and the asphalt pavement layer within the subgrade were tested using the portable  
Figure 8: Effect of length \(l\) on load–settlement curves of reinforced foundations.  
Figure 7: Construction of soilbag-treated subgrade: (**a**) pouring excavated clayey soil into large-sized soilbags: (**b**) completed large-sized soilbags: (**c**) common-sized soilbags: (**d**) roller compaction.  
D50-2017) stipulates a clear delineation of the relationship between the resilient modulus \(E\) and the deflection value \(l\), expressed as:  
\[E=\frac{176 pr}{l} \tag{2}\]  
where \(p\) is the loading stress (MPa) applied to the loading plate of the falling hammer, \(r\) is the radius of the loading plate type we used in this test, \(p\) equals 0.1 MPa, and \(r\) equals 150 mm.  
The surface settlements were monitored after the completion of the asphalt pavement. Settlement monitoring lasted for eighteen months. In the first six months, the settlement monitoring interval was one month, and after that, the interval was three to six months.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 6 Results  
### Model Training  
Each model is trained using the generated dataset. First, we examined the LSTM model with the generated dataset under different hyperparameter setups. Table 2 lists the optimal hyperparameters for the best training results. Figure 9a shows the training progress of the LSTM with single and double layers. Both models exhibit a similar trend in reducing loss; however, the single-layer LSTM model reaches stable accuracy sooner than the double-layer model. For the BiLSTM model, the trend of the loss and accuracy curves during the training phase is shown in Figure 9b. As the number of layers increases, the training accuracy remains almost unchanged. The accuracy and loss of GRU-based models are depicted in Figure 9c, where the double-layer GRU achieves slightly higher accuracy than the single-layer GRU model.  
Figure 8: (**a**) LSTM and BiLSTM combination model, (**b**) LSTM and GRU combination model, (**c**) BiLSTM and GRU combination model.  
Figure 10 shows the training and loss curves for the hybrid models. The training curves for the LSTM-BiLSTM model are presented in Figure 10. The validation accuracy for the LSTM-BiLSTM model is lower than that of the BiLSTM-LSTM model, indicating that the BiLSTM-LSTM model can extract more features from the training data and is expected to yield better results compared to the LSTM-BiLSTM model. Figure 10 displays the training curves for the LSTM-GRU and GRU-LSTM models. At the beginning of the training process, the LSTM-GRU model shows instability, but by the end of the training, it becomes more stable and comparable to the GRU-LSTM model. The BiLSTM-GRU and GRU-BiLSTM models are shown in Figure 10. Both models exhibit similar trends and are expected to have comparable prediction results.
## 1 Introduction
Some researchers have demonstrated that WUE is higher in wet regions with limited energy resources than in arid and semi-arid areas facing water constraints [20]; this is because arid and semi-arid regions have limited water availability for photosynthesis [21]. These studies indicate that the diversity of findings is partly due to differences in natural regions. Further, meteorological factors such as precipitation, temperature, and solar radiation play a key role in the variation in WUE [22]. Over the past two decades, Gansu Province has exerted a profound influence on local ecosystems through the implementation of two significant programs: Returning Farmland to Forestry (RFF) and Returning Farmland to Grassland (RFG). However, there is a paucity of empirical evidence regarding the regional characteristics of vegetation WUE in Gansu and its evolution over the past two decades. Especially at the watershed scale, as in the case of the Heihe and Shiyang River Basins [23; 24], although the spatial and temporal variations in WUE and its relationship with climatic factors have been the focus of some studies, the systematic analysis of vegetation WUE in the whole region is still lacking. Therefore, it is necessary to more extensively explore the dynamics of vegetation WUE and its response to the regional climate in Gansu Province, which is of great significance for promoting regional sustainable development. Gansu Province is situated in the upper reaches of the Yellow River, characterized by significant altitude variations, and the majority of the area is classified as arid and semi-arid. This has resulted in some challenges, particularly where there is a lack of vegetation cover, soil erosion, and land desertification. These issues are further exacerbated by the ongoing decline in water resources, resulting in an increasingly fragile ecological environment in the province. Nevertheless, Gansu Province plays a pivotal role in maintaining the ecological security barrier in the west and fulfills important functions in wind erosion control and sand stabilization, biodiversity conservation, and water recharge and nourishment [25]. In this study, we selected Gansu Province as the study area and estimated its vegetation WUE from 2000 to 2020 by using the MODIS series vegetation GPP and ET products. The spatial distribution characteristics and inter-annual variation dynamics of vegetation GPP, ET, and WUE in this region, as well as the spatial distribution of the WUE of different vegetation types and its response to climatic and environmental factor changes, were analyzed. This study thus provides a scientific foundation for ecological protection, the optimization of water resources, and adaptation to climate change in arid and semi-arid regions.
# Algorithmic Advances for 1.5-Dimensional Two-Stage Cutting Stock Problem
## 5 Computational Results
### Experimental Setting  
Test instances based on two types of randomly generated datasets, I1 and I2, are generated (All input data used for these test instances can be downloaded from the following GitHub repository: [[https://github.com/paolomarg-unisalento/1.5d-tscsp-dataset](https://github.com/paolomarg-unisalento/1.5d-tscsp-dataset)]([https://github.com/paolomarg-unisalento/1.5d-tscsp-dataset](https://github.com/paolomarg-unisalento/1.5d-tscsp-dataset)), accessed on 21 December 2024). Each instance type comprises ten subtypes, corresponding to varying numbers of second-stage orders (\(m_{2}\)), ranging from 10 to 50. For the sake of simplicity, there are no final orders at the first stage. For each subtype, ten instances were generated, resulting in a total of 100 instances. I1 is based on the test problems used by [17]; these instances feature single length ratios and a single length for finished rolls. In contrast, I2 includes multiple lengths for finished rolls and multiple length ratios and is designed to mimic practical industrial cases, as illustrated in Figure 3. Since I2 instances enforce a minimum trim loss at the first stage, indirectly, they reduce the number of intermediate orders produced, aligning the model with realistic operational constraints.  
The parameters utilized for generating these instances are presented in Table 1. Finished roll widths and order demands were generated using integer uniform distributions over a specified range of integer values (UR), while lengths were selected from specified discrete sets with uniform probability (US). The length ratios represent the admissible ratios between the lengths of intermediate rolls and finished rolls, ensuring compatibility during the cutting process. The minimum trims at each stage are determined by practical considerations, including machine limitations and quality requirements.  
All computational experiments were conducted on a computer equipped with an Intel Core i7-6700 CPU running at 3.40 GHz and 16 GB of RAM. The algorithms were implemented in C# using the.NET 8 framework. IBM ILOG CPLEX Optimizer version 22.1.1, along with CPLEX Concert Technology, was used to solve the linear and integer programming models.  
For both the G&S and R&CG algorithms, an execution time limit of 600 s was imposed on the generation of patterns and intermediate orders. A separate time limit of 600 s was  
Figure 3: Comparison between the setting of instance type I1 and I2. In I2 the model optimizes orders’ allocation to reduce trim loss and use all the input length. (**a**) I1: all orders have the same length. (**b**) I2: all orders have varying lengths.  
imposed for resolving the SRMP model. The best solution discovered up to that point was recorded.