esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
## 2 Prerequisite Knowledge
### Second-Order TGV Regularization  
The standard TGV regularization is expressed by Equation (3) [25].  
\[\text{TGV}_{2}(\mathbf{F})=\alpha_{0}(\|\mathbf{K}_{\mathbf{h}}\ast\mathbf{F}-\mathbf{V}_{\mathbf{h}} \|_{1}+\|\mathbf{K}_{\mathbf{v}}\ast\mathbf{F}-\mathbf{V}_{\mathbf{v}}\|_{1})+\alpha_{1}(\|\mathbf{K} _{\mathbf{h}}\ast\mathbf{V}_{\mathbf{h}}\|_{1}+\|\mathbf{K}_{\mathbf{v}}\ast\mathbf{V}_{\mathbf{v}}\|_{1}+ \|\mathbf{K}_{\mathbf{v}}\ast\mathbf{V}_{\mathbf{h}}+\mathbf{K}_{\mathbf{h}}\ast\mathbf{V}_{\mathbf{v}}\|_{1}), \tag{3}\]  
where \(\alpha_{0}\) and \(\alpha_{1}\) serve as the balance coefficients between the first-order total variation \(\|\mathbf{K}_{\mathbf{h}}\ast\mathbf{F}-\mathbf{V}_{\mathbf{h}}\|_{1}+\|\mathbf{K}_{\mathbf{v}}\ast\mathbf{F}- \mathbf{V}_{\mathbf{v}}\|_{1}\) and the second-order total variation \(\|\mathbf{K}_{\mathbf{h}}\ast\mathbf{V}_{\mathbf{h}}\|_{1}+\|\mathbf{K}_{\mathbf{v}}\ast\mathbf{V}_{\mathbf{v}}\| _{1}+\|\mathbf{K}_{\mathbf{v}}\ast\mathbf{V}_{\mathbf{h}}+\mathbf{K}_{\mathbf{h}}\ast\mathbf{V}_{\mathbf{v}}\|_problem) challenging. In 2004, [PERSON] [26] discovered that, when the noise is an impulse noise, the use of an L1 norm for constructing a fidelity term model allows better removal of outliers. Later, many studies used an L1-norm fidelity term for image denoising related to impulse noise or outliers. Image deconvolution in the presence of impulse noise is often treated as solving a minimization problem involving an L1-norm fidelity term and a regularization function. In fact, the sparsity of the L1 norm, which is the convex relaxation of the L0 norm, is not as good as that of the L0 norm. In 2016, [PERSON] and [PERSON] [14] pointed out that the Lp norm is a better convex approximation of the L0 norm than the L1 norm is, and proved that Lp shrinkage has better sparsity than soft-threshold shrinkage. The Lp norm is defined as \(\|\mathbf{F}\|_{p}=(\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\left|\mathbf{F} _{ij}\right|^{p})^{1/p}\), and the Lp-pseudo-norm is defined as \(\|\mathbf{F}\|_{p}^{p}=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\left|\mathbf{ F}_{ij}\right|^{p}\).  
To better elaborate on this issue, the contours of the Lp-pseudo-norm are shown in Figure 1. Figure 2 presents a group of diagrams, where the contours represent the fidelity terms for image restoration, while the dashed lines represent the regularization terms. As shown in the figure, it is more likely for the regularization term to intersect with the fidelity term of \(p<1\) than that of \(p=1\) on the coordinate axis, thereby making the likelihood of obtaining a sparse solution greater. Therefore, by using the Lp-pseudo-norm as a constraint, it is possible to better extract the sparse characteristics of signals in the transform domain to more precisely restore the image in the optimization problem.
## 5 Discussion
### Embedded Deployability and Future Evaluation  
One of the core motivations behind the DRFAN is its deployability in resource-constrained platforms such as embedded vision systems, mobile devices, and industrial edge modules. With only 1057K parameters and 65.1G FLOPs, the DRFAN meets the general efficiency requirements of embedded hardware. Our current evaluations show that it runs smoothly on high-end GPUs, but deployment on mobile SoCs (e.g., ARM-based NPUs, NVIDIA Jetson, or Huawei Ascend Lite) has not yet been quantitatively benchmarked.  
In future work, we plan to evaluate the DRFAN's runtime latency, power consumption, and memory footprint on embedded platforms. We will then explore optimizations such as quantization, weight pruning, or TensorRT/NPU acceleration for low-precision inference. Finally, we will assess its real-time capability with practical image streams from industrial cameras. These steps will help further validate the DRFAN's real-world usability and pave the way for integration into automated inspection systems, portable grain analysis terminals, and low-bandwidth visual transmission modules.
# Optimization and Experimental Study of Penetration Depth of Pocket Penetrometer
## 1 Introduction
### Test Program  
As shown in Table 3, a pocket penetrometer test study was conducted. As described in Section 2.3, two kinds of red clay (red clay 1, red clay 21) were configured into three different states of red clay specimens; the indoor penetration test, moisture content test, direct shear test were carried out six times for each water content, and the standard value was taken as the test result after statistical analysis. Using the test results, empirical formulas for the penetration resistance and liquidity index, penetration resistance, and shear strength were established, and the effect of the plasticity index on the empirical formulas was investigated.  
Figure 10: Shear strength distribution of samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Soil Samples Name**} & \multicolumn{3}{c}{**Samples Moisture Value**} & \multirow{2}{*}{**Test Content (Number of Tests)**} \\ \cline{2-3} \cline{5-5}  & **Hard Plastic** & **Plastic** & **Soft Plastic** & \\ \hline Red Clay 1 & 32\%, 34\% & 36\%, 38\%, 40\%, 42\%, 44\% & 46\%, 48\% & Indoor penetration test (6), moisture content test (6), direct shear test (6) \\ \hline Red Clay 21 & 42\%, 45\%, 48\% & 52\%, 55\%, 58\%, 62\%, 65\%, 68\% & 72\%, 75\% & Indoor penetration test (6), moisture content test (6), direct shear test (6) \\ \hline Red Clay 22 & & & & In situ penetration test (200), water content test (6), direct shear test (6) \\ \hline \hline \end{tabular} Note: Red clay 21 and red clay 22 are soil in the same place. The difference is that one is a remolded soil, which needs to be configured with different moisture content specimens. The other is a natural in situ soil with only one standard value of water content. Penetration tests were conducted at 0.5 cm/s the undrained shear was used in all the direct shear tests, and the normal stress was 50 kPa.  
\end{table}
Table 3: Research scheme of penetration test.  
Figure 9: Moisture content distribution of samples.  
In order to study the applicability of the empirical formulas for field test results, an in situ penetration test was carried out 200 times, as described in Section 2.2. The moisture content test and direct shear test were also carried out 6 times each using the in situ ring cutter specimen (red clay 2), and the standard values were taken as the test results after the same statistical analyses. A set of correspondences between the penetration resistance and liquidity index and the penetration resistance and shear strength was finally obtained to judge the accuracy of the empirical formula.
# Numerical and Experimental Investigation of Recycled Brick Coarse Aggregate Concrete
## 3 Results and Discussion
### Parameters Investigation  
The above analysis shows that the finite element model results are in good agreement with the experimental test results, which verifies the accuracy of the numerical simulation results. Therefore, more research on the content of RBCA was carried out using the finite element model. Recycled concrete models with five RBCA contents (0%, 20%, 40%, 60%, 80%, and 100%) were established, and the stress-strain relationships for specimens with RB-0 to RB-100 were obtained.  
Figure 16 shows the stress-strain curve of recycled concrete predicted by the finite element method. Compared with RB-0, the compressive strengths of RB-20, RB-40, RB-60, RB-80, and RB-100 concrete were reduced by 4.5%, 8.9%, 17.9%, 20.9%, and 29.8%, respectively. This is due to the generally low strength of RBCA. The elastic modulus decreased by 9.1%, 20.8%, 27.3%, 36.6%, and 39.4%, respectively, due to the more significant elastic deformation of RBCA. The peak strain increased by 2.6%, 5.2%, 10.5%, 13.1%, and 15.7%, respectively, due to the more excellent ductility of recycled concrete. It was observed that the compressive strength and elastic modulus of recycled concrete decreased with increased recycled aggregate content, and the peak strain increased with increased recycled aggregate content. Compressive strength, elastic modulus, and maximum strain are the indexes that control a material's strength, deformation, and ductility. Through these three indexes, a detailed and comprehensive understanding of the mechanical properties of concrete can be obtained. Our results show that adding RBCA with low mechanical  
Figure 14: Stress–strain relationship of RBCA concrete with different interface thickness.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on MARS Dataset
#### 5.2.4 Comparative Analysis and Discussion  
On the iLIDS-VID dataset, GAF-Net, building upon the PiT architecture for appearance features, achieved state-of-the-art performance, with a rank-1 accuracy of 93.20% and a rank-5 accuracy of 99.34%, surpassing contemporary methods such as DenseIL [8], DCCT [11], and PSTA [9] by margins of 1.20%, 1.50% and 1.70%, respectively, in rank-1 accuracy. This performance demonstrates GAF-Net's effectiveness in using full gait cycles, which are more consistently available in iLIDS-VID's long sequences.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{5}{c}{**Results**} \\  & **mAP (\%)** & **Rank-1 (\%)** & **Rank-5 (\%)** & **Rank-10 (\%)** & **Rank-20 (\%)** \\ \hline OSNet [16] & 81.64 & 86.20 & 95.71 & 96.96 & 97.99 \\ MGH [7] & 85.52 & 89.40 & 96.63 & 97.72 & 98.42 \\ PSTA [9] & 85.80 & 91.50 & - & - & - \\ AA-RGTCN [49] & 85.90 & 91.00 & - & - & - \\ STRF [50] & 86.10 & 90.30 & - & - & - \\ PiT [10] & 86.80 & 90.22 & **97.23** & **98.04** & - \\ DenseIL [8] & 87.00 & 90.80 & 97.10 & - & **98.80** \\ DCCT [11] & **87.50** & **92.30** & - & - & - \\ \hline GAF-Net (MGH-PCA & & & & & \\ concatenation) & 86.09 & 89.78 & 96.90 & **98.04** & 98.59 \\ \hline \hline \end{tabular} _Note: Bold values indicate the best performance for each metric._  
\end{table}
Table 10: Comparison with state-of-the-art methods on the MARS dataset [6].  
A key innovation of GAF-Net is its use of skeleton-based gait information, which differs from traditional silhouette-based approaches. This method improves the accuracy of re-identification by capturing more precise movement patterns over time. Skeleton-based approaches work better than silhouette-based methods because they are less affected by changes in clothing and can detect small details in movement. This allows for better identification of individuals, especially in situations where people look similar or backgrounds are complex, which can make silhouette extraction difficult.
## 3 Methodology
### Derivation and Upscaling of Landsat 8-Based Indices  
#### 3.1.1 Landsat 8 VIs Calculation  
Surface albedo and vegetation indices were derived from Landsat 8 OLI surface reflectance. Although surface reflectance data for the Landsat 8 OLI sensor are provided from USGS, the internal atmospheric correction algorithm has not been published and validated [37]. Research has found that different atmospheric correction methods, such as Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6 SV), radiative transfer algorithm, Dark Object Subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) algorithm do not produce significant differences in vegetation index calculation from Landsat 8 OLI data [27]. Therefore, in this study we adopted the FLAASH algorithm in ENVI software to perform atmospheric correction for the Landsat 8 images. Surface reflectance of bands 1-7 were used to calculate albedo and vegetation indices, and a brightness temperature of band 10 was used to generate LST. Cloud and cloud shadows were detected using the \"Fmask\" algorithm [38] in order to create a mask layer. The mask layer, in combination with Landsat 8 OLI LIT-provided Quality Assurance (QA) data were used to remove contamination by cloud/cirrus/shadow. Eight vegetation indices showing vegetation greenness and water content including NDVI, EVI, SAVI, MSAVI, NDMI, NDWI, NDMIb7, and D\({}_{1609}\) were calculated from surface reflectance for each cloud-clear pixel (see Table 2[39; 40; 41; 42; 43; 44; 45; 46] for equations).  
Figure 3: Flow diagram of the machine learning-based ET downscaling model.
## 3 Results
### Greenhouse Experiment, Gaseous Emissions  
Ammonia emissions, measured using acid traps, showed no differences between AS and CAN. Emissions of NH\({}_{3}\) remained low in the first 10 days after fertilization: 0.02-0.3% of added N (Figure A1). There was a significant effect of soil type (under maize), sandy soil had higher emissions than clay soil. Emissions were much lower than what would have been estimated using the corresponding emission factor from the Dutch National Emission Model Agriculture (NEMA), which is 2.5% of applied N for CAN and 1.8% of applied N for air scrubber effluent [25]. While there are questions about the applicability and accuracy of these emission factors based on small scale experiments, they give an indication of the range that might be expected and relative differences between fertilizing products. The measured ammonia was a factor of seven or more lower compared to the NEMA values. Since the experiment was conducted with closed chambers, the effect of wind was missing from the experiment. Several examples in the literature show that ammonia emissions are consistently underestimated when no wind is allowed to blow over the measuring area [26; 27]. To make more reliable ammonia emissions, an additional sheltered flux chamber experiment was conducted (Section 3.4).  
The N\({}_{2}\)O emissions measured in the greenhouse experiment were very low (<0.04% of the applied N) (Table A8) and contained many values below the detection limit: 50 ug of N-N\({}_{2}\)O m\({}^{-2}\) h\({}^{-1}\). Therefore, they could not be tested with a linear model approach and were tested with a Kruskal-Wallis non-parametric test. This showed no significant effects of soil, dosage, or fertilizer type. CH\({}_{4}\) emissions in the greenhouse experiment were also very low (values were below the detection limit: 100 ug of CH\({}_{4}\) m\({}^{-2}\) h\({}^{-1}\)), and showed no significant effects for maize or grass (Kruskal-Wallis test).
## 3 New Data Structure
### Table Lookup
#### 3.2.2 Expected Table Size  
In the rank/select structure as described in Section 2.3, all _possible_ small blocks are listed. This implies that \(b_{s}\) has to be small, which increases the number \(N:=\frac{n}{b_{s}}\) of small blocks. In our structure the lookup table only contains blocks that do actually occur in \(T\). (This is possible since both of the codes above--Canonical Code and Exception Code--imply an implicit enumeration of the occurring blocks _without gaps_; see, for example, the first paragraph of Section 3.1.1 for how to compute this numbering.) Thus the table size is limited by \(O(n)\), without restricting the choice of \(b_{s}\). For predictable texts (\(P_{k}(T)\gg\frac{1}{2}\)), we intuitively expect most blocks to occur very often within \(T\) (and thus not increasing the lookup table size). This intuition is confirmed in the following, by calculating the expected number of entries in the lookup table.  
To this end, look at a fixed block \(B\in\{0,1\}^{b}\) of \(b:=b_{s}\) bits. Its probability of occurrence depends mainly on the number \(i\) of bits that are not as predicted. Assuming the first \(k\) bits are distributed uniformly at random, the probability of \(B\) (with \(i\) mispredictions) to occur is:  
\[P_{b,i}=\frac{1}{2^{k}}\cdot P_{k}^{b-k-i}(1-P_{k})^{i}\.\]  
There are  
\[C_{b,i}=2^{k}\cdot\binom{b-k}{i}\]  
blocks with \(i\) mispredictions, so we expect  
\[E_{b,i}(N)=C_{b,i}\cdot(1-(1-P_{b,i})^{N})\]  
of them to appear within \(T\). This is the well known formula of bounded exponential growth, a monotonic concave function bounded from above by \(C_{b,i}\). Its derivative \(E^{\prime}_{b,i}(N)\) is proportional to the satiation \(C_{b,i}-E_{b,i}(N)\), and \(E^{\prime}_{b,i}(0)=P_{b,i}\). For small \(i\) the upper bound \(C_{b,i}\) is quite small. For larger \(i\) the growthrate \(E^{\prime}_{b,i}\) quickly falls behind the linear growth of other data structures used. Summing over all possible \(i\), we get the expected number of blocks occurring in the lookup table:  
\[E_{k,b}(N)=\sum_{i=0}^{b-k}E_{b,i}(N)=\sum_{i=0}^{b-k}\left(1-(1-P_{b,i})^{N} \right)\cdot\left(2^{k}\cdot\binom{b-k}{i}\right).\]  
In Figure 2 we plot the expected table size, which is \(E_{k,b}(N)\cdot b\), using \(P_{k}=0.99\) and \(b=64\) as an example. For comparison we also plot \(N\cdot\log b\), which is a lower bound for the mid-level data structure. We can see that despite the large block size the table's memory consumption is less than that of the mid-level data structure. As the lookup table is the only data structure that grows with increasing block sizes and the mid-level structure significantly profits from large block sizes, this shows that \(b=b_{s}\) has to be chosen quite high.
# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
## 2 Channel Modeling and Indoor Positioning System
### Indoor Positioning Framework  
The proposed indoor positioning framework, as illustrated in Figure 1, is based on a terahertz wireless sensor framework, which operates in both offline and online phases. The localization system consists of six blocks: signal and interference input, channel, positioning device, fingerprint generator, localization server, and position estimation output. The positioning device is typically a mobile device such as a smartphone or an IoT sensor. This device collects the 6G signal and sends the gathered data to the server for location processing.  
Figure 1: Indoor positioning framework.  
The fourth block is the fingerprint generator block. A database of pre-collected signals is created across the environment to generate the fingerprint data. The same set of CIR features described in Section 2.2 is used for training and evaluation. Cartesian coordinates transformation is applied to enhance positioning accuracy.  
The localization server is the most important block of the system. In this research, we use ResNet to process the fingerprint and estimate the positions based on the processed data. In other words, the processed data are used to train the ResNet, enabling it to learn spatial patterns and associate them with physical locations. Given the computationally intensive nature of training, adequate data and processing resources are required to ensure the system's accuracy and robustness. The CIR features are shuffled and split into the training and testing datasets. The training process utilizes ResNet to map the CIR features to spatial coordinates, improving indoor positioning performance by addressing the associated challenges posed by multipath effects and environmental variations. The offline phase is dedicated to data preprocessing and training ResNet, while the online phase estimates positions in real time based on the processed data.
###### Abstract  
Orthogonal frequency division multiplexing (OFDM) has become an indispensable part of waveform generation in wideband digital communication since its first appearance in digital audio broadcasting (DAB) in Europe in 1980s, and it is indeed in use. As has been seen, the OFDM based waveforms work well with time division duplex operation in new radio (NR) systems in 5G systems, supporting delay-sensitive applications, high spectral efficiency, massive multiple input multiple output (MIMO) compatibility, and ever-larger bandwidth signals, which has demonstrated successful commercial implementation for 5G downlinks and uplinks up to 256-QAM modulation schemes. However, the OFDM waveforms suffer from high peak to average power ratio (PAPR), which is not desired by system designers as they want RF power amplifiers (PAs) to operate with high efficiency. Although NR offers some options for maintaining the efficiency and spectral demand, such as cyclic prefix based (CP-OFDM), and discrete Fourier transform spread based (DFT-S-OFDM) schemes, which have limiting effects on PAPR, the PAPR is still as high as 13 dB. This value increases when the bandwidth is increased. Moreover, in LTE-Advance and 5G systems, in order to increase the bandwidth, and data-rate, carrier aggregation technology is used which increases the PAPR the same way that bandwidth increment does; therefore, it is essential to employ PAPR reduction in signal processing stage before passing the signal to PA. In this paper, we investigate the performance of an innovative peak shrinking and interpolation (PSI) technique for reducing peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) based signals at waveform generation stage. The main idea behind the PSI technique is to extract high peaks, scale them down, and interpolate them back into the signal. It is shown that PSI technique is a possible candidate for reducing PAPR without compromising on computational complexity, compatible for existing and future telecommunication systems such as 4G, 5G, and beyond. In this paper, the PSI technique is tested with variety of signals in terms of inverse fast Fourier transform (IFFT) length, type of the signal modulation, and applications. Additional work has been carried out to compare the proposed technique with other promising PAPR reduction techniques. This paper further validates the PSI technique through experimental measurement with a power amplifier (PA) test bench and achieves an adjacent channel power ratio (ACPR) of less than -55 dBc. Results showed improvement in output power of PA versus given input power, and furthermore, the error vector magnitude (EVM) of less than 1% was achieved when comparing of the signal after and before modification by the PSI technique.  
Keywords:EVM; FPGA; LTE; OFDM; PAPR; power amplifier; PSI; 5G NR 2020