esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
It was shown that all tested complex agents work as preservatives for As(III)/As(V) concentration distribution for four days or more. Citric acid in combination with HAc is a suitable agent for stabilization during the storage of samples containing high concentrations (up to 100 mg L\({}^{-1}\)) of iron, and for the preservation of arsenic species for up to 7 days. Longer storage times are not advisable. Fe coprecipitates, formed in the aqueous phase, may adsorb arsenic, which results in a significant difference in the total arsenic concentration. Acidification with acetic acid can assist in avoiding their formation and in stabilising the inorganic arsenic species in their original state. It also clearly indicates that the initial distribution of As(III)/As(V) in untreated samples can undergo a larger change over the study period. This unpredictable change in distribution, coupled with the initial loss of arsenic to the formation of Fe-As coprecipitates, makes the use of no preservation agents an unsuitable experimental protocol and would result in questionable data. The proposed treatments were demonstrated to stabilize native As(III)/As(V) concentrations in four well waters, held at room temperature for 7 days. This data indicates the potential of utilizing citric acid and sodium citrate in combination with HAc as a means of preserving As(III) and As(V) in drinking waters with high iron levels. Further research is needed, in terms of testing different concentrations of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \multicolumn{2}{c}{**C**} & \multicolumn{2}{c}{**D**} \\ \hline  & \begin{tabular}{c} **Initial As(III)** \\ **Concentration** \\ **(ug L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **As(III)** \\ **Concentration** \\ **(ug L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Initial As(III)** \\ **Concentration** \\ **(ug L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **As(III)** \\ **Concentration** \\ **(ug L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **As(III)** \\ **Concentration** \\ **After 6 Days** \\ **(ug L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **As(III)** \\ **Concentration** \\ **After 6 Days** \\ **(ug L\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **\% Deviation** \\ **(ug L\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **\% Deviation** \\ **(ug L\({}^{-1}\))** \\ \end{tabular} \\ \hline Unpreserved & 6.87 & 4.50 & 34.39 & 8.30 & 5.50 & 33.73 \\ CA & 9.00 & 4.80 & 46.67 & 8.35 & 7.43 & 11.02 \\ CA + HAc & 9.02 & 9.60 & 6.43 & 8.70 & 8.50 & 2.30 \\ NaC & 8.50 & 5.15 & 39.41 & 8.65 & 6.20 & 28.32 \\ NaC + HAc & 9.30 & 9.86 & 6.02 & 6.40 & 6.00 & 6.25 \\ TAR & 8.20 & 5.40 & 34.15 & 7.70 & 5.40 & 29.87 \\ TAR + HAc & 9.25 & 7.80 & 15.68 & 8.05 & 5.20 & 35.40 \\ OX & 9.20 & 8.40 & 8.70 & 8.80 & 7.41 & 15.80 \\ OX + HAc & 9.21 & 1.97 & 78.61 & 8.15 & 5.02 & 38.40 \\ HAc & 8.90 & 4.90 & 44.94 & 9.54 & 1.00 & 89.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the results for arsenic preservation, using nine preservatives in natural groundwater samples C and D, in the presence of iron.
###### Abstract  
Accurate weather data are very important for the navigation of ships. However, due to the insufficient coverage of the maritime network, the high cost of satellite communication, and the limited bandwidth, it is difficult for ships to obtain high-resolution weather data during route planning. This challenge greatly limits the accuracy and effectiveness of ship navigation. To solve this problem, this paper proposes a marine weather data reconstruction model based on deep super-resolution. Firstly, the model uses a convolutional neural network to extract features from wind speed and wave height data. Secondly, the model uses SRResNet as the reconstruction framework and effectively captures the complex nonlinear feature relationship in weather data through the residual block structure to realize the fine reconstruction of low-resolution weather data. In addition, the attention mechanism is integrated into the model to dynamically adjust the weights of different weather features, which further enhances the attention to key features. The results show that the model has a good effect on the super-resolution reconstruction of weather data. The PSNR, SSIM, GMSD, and FSIM of wave height reconstruction are 49.73 dB, 0.9949, 0.0082, and 0.9999, respectively, and the PSNR, SSIM, GMSD, and FSIM of wind speed reconstruction are 41.52 dB, 0.9797, 0.0400, and 0.9997, respectively. Based on the reconstructed data, route planning can effectively reduce the navigation distance of the ship and avoid unnecessary detours, thus saving fuel consumption and reducing operating costs.  
weather data; route plan; SRResNet; attention mechanism; super-resolution +
Footnote †: journal: Marine Science and Engineering  
1  
J. Mar. Sci. Eng.2025  
19 May 2025  
21 May 2025  
2
# Implications of GNSS-Inferred Tropopause Altitude Associated with Terrestrial Gamma-ray Flashes
## 3 Analyses and Results
### TGF-Related Tropopause Altitude
The tropopause altitude anomaly is defined as the monthly mean tropopause altitude associated with the TGF flashes on a 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) longitude-latitude grid minus the long  
Figure 2: Locations of the GNSS-RO primary tropopause altitudes co-located with the TGF in a time window of 3 h and space window of 300 km for four seasons from April 2006 to December 2019. Circles denote single tropopause events and squares denote double tropopause events. The African continent, the Asian Monsoon, and the Caribbean Sea regions are depicted by rectangles. The average tropopause altitudes for the TGF events and climatology are given close to the rectangles. Red (blue) texts indicate a lower (higher) TGF tropopause than climatology (black). Gray texts indicate unexceptional values with respect to the climatology. term monthly tropopause at the same grid. We require the monthly mean TGF-related tropopause altitudes containing more than three samples. Figure 3 illustrates the seasonal cycle of the tropopause altitude anomaly for the NH and SH subtropics, as well as the tropics. Most of the median values are slightly higher than zero, which confirms the preference of TGFs for high tropopause altitudes [22]. In the tropics, some of the 5% percentiles are lower than \(-1\) km, while most of the 95% percentiles are lower than 1 km. In the subtropics, the variability of the tropopause anomaly enlarges. In the SH subtropics, the 5% percentiles for 9 out of 12 months are lower than \(-2\) km, whereas only 5 months have a 5%-percentile lower than \(-2\) km in the NH. Most of the 95% percentiles for TGFs in the SH subtropics are lower than 1 km, whereas the 95% percentiles can range from 1 to 3 km in the NH subtropics. The TGF-related tropopause has a greater variability over the ocean. There is a significant seasonal cycle in the subtropics, with a smaller variability in summer and early fall and a larger one in winter and spring. In summary, most of the tropopause altitudes associated with the TGFs are slightly higher than the climatology, while some TGF-related tropopause altitudes are found to have a slightly negative bias.
## 4 Inertia Identification of Wind Farms Based on Analytical Optimization  
### Sparse Relaxed Regularization Algorithm  
In the identification model described above, \(\Xi=[\xi_{1},\xi_{2},\ldots\xi_{n}]\) is the parameter matrix to be determined, which requires the selection of an appropriate identification algorithm for estimation. The most common method employed in this context is the LASSO (Least Absolute Shrinkage and Selection Operator) sparse regularization algorithm [42], which incorporates a regularization term to mitigate the risk of overfitting; however, it faces challenges in effectively selecting sparsity. Alternatively, the Sequential Thresholded Least Squares (STLS) [43] method integrates least squares with stepwise thresholding to iteratively enhance sparsity optimization. Nonetheless, this approach is associated with significant computational complexity, as it necessitates the repeated resolution of the least squares problem.  
To enhance robustness and efficiency, the SR3 algorithm [44] has been employed to determine the parameters, resulting in the derivation of an analytical iterative expression. The optimization problem is restructured by the introduction of an auxiliary variable \(\mathbf{W}\), leading to the following formulation:  
\[\begin{array}{l}\mathbf{min}_{\Xi,\mathbf{W}}\mathbf{\frac{1}{2}}\left\| \dot{\mathbf{X}}-\mathbf{\Theta}(\mathbf{X})\Xi\right\|^{2}+\lambda\mathbf{R} (\mathbf{W})+\mathbf{\frac{1}{2\
u}}\left\|\Xi-\mathbf{W}\right\|^{2}\\ \mathbf{s.t.}\mathbf{C}\xi=\mathbf{d},\,\xi=\Xi(:)\end{array} \tag{26}\]  
where \(\mathbf{\frac{1}{2\
u}}\left\|\Xi-\mathbf{W}\right\|^{2}\) is the regularization penalty term that governs the disparity between the sparse solution \(\xi\) and the relaxation variable \(\mathbf{W}\).  
In addressing this regression issue, the \"divide-relax\" strategy decomposes the original problem into subproblems, optimizing \(\xi\), and \(\mathbf{W}\) alternately. A vectorization equation is presented to reformulate the SR3 optimization as a linear system, as follows:  
\[\begin{bmatrix}\mathbf{\Theta_{X}^{T}}\otimes\mathbf{\Theta_{X}}+\mathbf{\frac {1}{\
u}}\mathbf{I}&\mathbf{C}^{T}\\ \mathbf{C}&\mathbf{0}\end{bmatrix}\begin{bmatrix}\xi\\ \phi\end{bmatrix}=\begin{bmatrix}\mathbf{\Theta_{X}^{T}}\otimes\dot{\mathbf{X}}+ \mathbf{\frac{1}{\
u}}\mathbf{W^{k-1}}\\ \mathbf{d}\end{bmatrix} \tag{27}\]  
where \(\phi\) is the Lagrange multiplier, \(\mathbf{\Theta_{X}^{T}}=\mathbf{I}\otimes\mathbf{\Theta}\), and \(\otimes\) is tensor operators.  
Employing the identity of the Kronecker product along with associated equations,  
\[\begin{array}{l}\left(\mathbf{\Theta_{X}^{T}}\otimes\mathbf{\Theta_{X}}+ \mathbf{\frac{1}{\
u}}\mathbf{I}\right)^{-1}=\left(\mathbf{\Theta}(\mathbf{X })^{T}\mathbf{\Theta}(\mathbf{X})+\mathbf{\frac{1}{\
u}}\mathbf{I}\right)^{- 1}\end{array} \tag{28}\]  
Consequently, by addressing the aforementioned matrix equation,  
\[\phi=\left(\mathbf{C}\bigg
## 2 Materials and Methods
### Batch Sorption Experiments  
Equilibrium adsorption of PTSA onto the resin was conducted in a conical flask containing a 100 mL solution shaken with 0.1 g resin, and the initial concentration range of PTSA was set to 200-1200 mg/L. 1 M H\({}_{2}\)SO\({}_{4}\) or NaOH was used to adjust the solution pH, and Na\({}_{2}\)SO\({}_{4}\) was added into the solution to investigate the effect of coexisting anions. The flasks were then sealed and placed in a constant temperature shaker at a pre-settled temperature and 140 rpm for 24 h to ensure the adsorption process had reached equilibrium. The equilibrium concentration of PTSA was measured spectrophotometrically at 261 nm by using a UV-vis spectrophotometer (Shanghai Jinghua Technology Instrument, China). The equilibrium adsorption capacity was calculated by conducting a mass balance on the PTSA before and after adsorption.  
For the kinetic study, 0.3 g of each resin and 300 mL of PTSA solution with an initial content of 1000 mg/L were introduced into a 1000 mL flask quickly and shaken at 140 rpm and 303 K continuously. Moreover, a 0.2 mL solution sample was taken out from the flask at different time intervals to determine adsorption kinetics.
## 4 Current Limitations  
Some factors limited the performance of the current algorithms and could be avoided in future research. Firstly, there were too few participants to allow making general statements about all individuals. A priori, a total of twenty participants would have been enough to get statistically significant results. However, there were too few emotions labelled per participant. Often participants forgot to label their emotional experience. Therefore, it is recommended for future research to use an ecological momentary assessment (EMA) system, which works according to the same principles as EMI, but does not have an interventional aspect. These EMAs could remind participants to label their feelings. In this way, more labels can be collected within the same time, which does not increase the burden for the participants. It was also hard for the participants to remind thirteen different emotions, so we recommend decreasing the number of emotions that needs to be labelled. Moreover, shorter questionnaires lead to more reliable answers in EMA or EMI, so the quality of the labels will be enhanced [50].  
Besides that, it is important to search for methods that could get rid of the class imbalance in the data set. Methods to reduce the class imbalance already exist, but they mostly rely on under-sampling, which would result in a too low number of training points, or on the generation of simulated data. As emotion recognition based on the mental heart rate component is still in its infancy, not much data are available. The best solution for future research is to collect more balanced data of a lower number of emotions.  
Thirdly, the used wearables did not feature thermometers. Therefore, the thermal HR component was estimated by continuous monitoring of the participants. Most big market players are implementing thermometers in their new wearables. The algorithm will improve when working with those wristbands.  
Lastly, the sampling frequency of the mental HR needs to be increased. Due to restrictions in the data transfer between Fitbit Inc. and Mindstretch, mental HR could only be calculated every minute. When a higher sampling frequency can be used, the dynamics will be better modelled. Besides, it would probably also be possible to detect certain events in which the emotion was elicited suddenly and subtly, which in the current study might be lost.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
###### Abstract  
The utilisation of 3D models in low-cost devices, such as the internet of things, virtual reality, and augmented reality, is expanding. The challenge lies in the lack of lightweight solutions for verifying the authenticity of models in the graphics library transmission format (gITF) on devices with limited resources. The gITF standard, which allows storage in glb format, is the leading standard for representing 3D assets. Despite its popularity, research on watermarking glTF models remains limited. This paper proposes a novel method for authenticating 3D models in glb format based on fragile watermarking. Additionally, an analysis was conducted to determine the impact of embedding the watermark in vertex attributes other than position on the integrity and visual quality of the model. The methodology is as follows: (1) embedding the watermark, (2) applying model modification or omitting it, and (3) verifying authenticity based on the recovered watermark. The proposed algorithm attaches a 512-bit hash-based message authentication code (HMAC) to a 3D model using the least significant bits (LSBs) modification method. The use of HMAC and LSBs has resulted in a computationally efficient algorithm that can be implemented in low-cost devices.  
3D model; cybersecurity; integrity protection; fragile watermarking; hash-based message authentication code +
Footnote †: journal: Applied sciences  
Article
# Combinatorial Generation Algorithms for Some Lattice Paths Using the Method Based on AND/OR Trees +
## 3 Results
### Combinatorial Generation Algorithms for North-East Lattice Paths  
#### 3.1.1 Combinatorial Set  
A North-East lattice path is a lattice path in the plane which begins at \((0,0)\), ends at \((n,m)\), and consists of steps \((0,1)\) and \((1,0)\)[19]. The step \((0,1)\) is called the North-step and is denoted by \(N\); the \((1,0)\) step is called the East-step and is denoted by \(E\).  
Figure 1 shows all possible 20 variants of the considered North-East lattice paths for \(n=3\) and \(m=3\).  
The total number of North-East lattice paths is defined by the following binomial coefficient (the sequence \(A007318\) in OEIS [20]):  
\[L_{n}^{m}=\binom{n+m}{m}=\binom{n+m}{n}. \tag{1}\]  
The value of \(L_{n}^{m}\) can also be calculated using the following recurrence that belongs to the required algebra \(\{\mathbb{N},+,\times,R\}\):  
\[L_{n}^{m}=L_{n}^{m-1}+L_{n-1}^{m},\quad L_{n}^{0}=L_{0}^{m}=1. \tag{2}\]  
In addition, the sequence of values of \(L_{n}^{m}\) is defined by the bivariate generating function  
\[\sum_{n\geq 0}\sum_{m\geq 0}L_{n}^{m}x^{n}y^{m}=\frac{1}{1-x-y}.\]  
Figure 1: All North-East lattice paths beginning at \((0,0)\) and ending at \((3,3)\).
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 7 Implementation Results  
A prototype partial LDPC decoder, designed to decode the (607, 60, 6) array LDPC code that protects 4 KB messages, is implemented in a 65 nm CMOS process. Including the dedicated SYNCHK unit, the 4 KB LDPC decoder is realized with 1018 k equivalent gates and consumes 279 mW. Table 2 compares the proposed decoder to recently reported decoders for NAND flash storage systems. For quantitative comparisons, the energy efficiency normalized to 65 nm CMOS technology is compared with assuming an operating voltage of 1 V. More precisely,  
\[\text{Energy efficiency (J/bit)}=\frac{\text{Power consumption (W)}}{\text{Decoding throughput (bit/s)}}. \tag{23}\]  
Compared to a previously reported LDPC decoder [18], the decoder adopting the proposed algorithm saves 93% of the energy consumed in decoding. The hard-decision based ECC decoders, such as concatenated-BCH (CBCH) or BCH decoders, may consume smaller amounts of energy in the decoding process; however, these are difficult to apply to recent NAND flash memories due to their weak error-correcting performance. The proposed decoder even consumes less energy than the BCH decoder in [21], which is not tightly optimized. The comparison with the implementation results shows that the proposed decoder using the proposed energy-efficient LDPC decoding algorithm has outstanding performances compared to recent implementations.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **This Work** & **DASIP [22]** & **TVLSI [18]** & **ISCAS [23]** & **JSSC [24]** & **ISCAS [21]** \\ \hline ECC type & LDPC & LDPC & LDPC & LDPC & CBCH & BCH \\ Technology & 65 nm & 45 nm & 130 nm & 45 nm & 65 nm & 45 nm \\ Code rate & 0.96 & 0.96 & 0.96 & 0.9 & 0.93 & 0.9 \\ User-message size & 4 KB & 8 KB & 8 KB & 1 KB & 8 KB & 1 KB \\ Quantization level & 4 bits & 7 bits & 4 bits & 1 bit & 1 bit & 1 bit \\ Internal precision & 8 bits & 7 bits & 4 bits & N. A. a  & 1 bit & 1 bit \\ Operating voltage (V) & 1.0 & N. A. & 1.2 & N. A. & 1.2 & 1.05 \\ Operating frequency (MHz) & 200 & 179 & 131 & 200 & 250 & 400 \\ Decoding throughput (Gb/s) & 2.96–29.6 & 0.9 & 5.4 & 3.2 & 17.7 & 6.4 \\ Area (Equation gate count) & 1018 k & N. A. & N. A. & 700 k & 335 k & 230k \\ Power consumption (mW) & 279 & N. A. & 2090 & N. A. & 48.5 b  & 88.4 \\ Energy efficiency (pJ/bit) & 9.43–94.26 & N. A. & 387 & N. A. & 2.74 b  & 13.8 \\ Normalized energy efficiency (pJ/bit) & 9.43–94.26 & N. A. & 134 & N. A. & 1.9 b  \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Various ECC Decoders for NAND Flash Controllers  
Figure 8: The detailed architecture of the decoding function unit.
## 6 Conclusions  
In this paper, we have proposed a cooperative spectrum sensing scheme, while considering the sea environment. The optimal number of samples for entropy-based detection for MCRN has been calculated. A cooperative spectrum sensing scheme has been proposed while considering the sea environment. In addition, throughput optimization of the \(m\)-out-of-\(n\) rule for MCRN has also been considered. Simulation results showed that the performance of the energy detector and entropy-based detector is almost same for the land environment. However, for sea states \(4\), \(6\) and \(7\), the entropy-based  
Figure 9: Probability of detection _vs_. Probability of detection _vs_. the probability of a false alarm with 15 SUs participating in cooperative spectrum sensing at; (a) the land network; (b) sea state 4; and (c) sea state 6.  
detector with the optimal number of samples, \(N^{*}\), performs better than the energy detector. In the case of cooperative spectrum sensing with the optimized \(m\)-out-of-\(n\) rule, the entropy-based detector and energy detector are used as local detection schemes. We showed further that for the desired range of sea states, the majority fusion rule is either optimal or near optimal in terms of the throughput of the MCRN.  
For sea state \(7\) and higher, although the entropy-based detector could detect PUs with a high probability in comparison with the energy detector, still, the constraints of the probability of detection and false alarms are not satisfied. One way to improve the sensing performance at higher sea states is to design a new advanced signal processing algorithm in order to detect a distorted signal. An advanced algorithm, like cyclostationary feature detection, needs a relatively long time for sensing in comparison with the energy detector and entropy-based detector.  
The advanced sensing algorithms should be investigated in the future for efficient spectrum sensing in the MCRN. Moreover, the multi-path effect, due to the reflections of the signal on the sea surface, is also a good direction to be considered in the future.
## 2 Queue Scheduling and Optimization Mechanism of Large-Scale LEO Satellite Communication Networks
### Queue Analysis and Optimization Mechanism Based on PSO Algorithm
The sending queue is numbered 0, while the remaining receiving queues are sequentially numbered as \(N=1,2,\cdots,n-1\). When the time slot switches, the sending queue from the previous time slot, having been emptied, switches its number to \(n-1\) and its state to Close; the numbers of the other receiving queues change to \(N^{{}^{\prime}}=N-1\). Subsequently, the queue with \(N^{{}^{\prime}}=0\) switches its state to Open, becoming the new sending queue for the upcoming time slot. This process repeats in a cyclical manner, resulting in periodic changes in queue status. Therefore, when a packet arrives during the \(i\)th time slot and enters a queue numbered N, it must wait until the \((i+N)\)th time slot to have a chance for transmission. ```
Input: Number of particles, \(NP\); Inertia weight, \(\omega\); Learning factors, \(c_{1}\&c_{2}\);  Maximum/minimum particle velocity, \(Vmax/Vmin\); Maximum/minimum number of queues, \(n_{max}/n_{min}\); Maximum/minimum number of maximum transmission units each queue contains, \(u_{max}/u_{min}\); Maximum load, \(L_{peak}\);  Maximum transmission unit, \(MTU\); Iteration times, \(T\); Output: Optimized queue length, \(L_{b}\); Optimized queue number, \(N_{b}\);
1\(X_{i}=(u_{i},n_{i})\), \(1\leqslant i\leqslant 20\leftarrow\) Init \(u\) and \(n\) selected by each individual particle;
2\(V_{i}\leftarrow\) Init velocity for all individual particles;
3\(p_{i}=[MTU\cdot u_{i}\cdot(n_{i}-1)-L_{peak}]^{2}\leftarrow\) Init the result of fitness function for each individual particle;
4\(\textit{{pbest}}_{i}\leftarrow\) Init the optimal combination of queue length and number for each individual particle;
5\(\textit{{gbest}}\leftarrow\) Init global optimal combination of queue length and number;
6\(\textit{Flag}\) = True;
7 whileFlagdo
8 for\(t\gets 1\)to\(T\)do
9 for\(i\gets 1\)to\(NP\)do
10 Update the velocity of each particle: \(V^{\prime}_{i}=\omega\cdot V_{i}+c_{1}\cdot rand()\cdot(\textit{{pbest}}_{i}- X_{i})+c_{2}\cdot rand()\cdot(\textit{{gbest}}_{i}-X_{i})\);
11 Update \(u\) and \(n\) selected by each individual particle: \(X^{\prime}_{i}=(u^{\prime}_{i},n^{\prime}_{i})=X_{i}+V^{\prime}_{i}\);
12 Calculate the result of fitness function for each individual particle: \(p^{{}^{\prime}}_{i}=[MTU\cdot u^{{}^{\prime}}_{i}\cdot(n^{\prime}_{i}-1)-L_{ peak}]^{2}\);
13 Update \(\textit{{pbest}}_{i}\textit{{gbest}}\);
14\(\textit{{gbest}}=(u_{b},n_{b})\);
15\(U_{b}=\lceil n_{b},N_{b}=\lceil n_{b}\);
16 if\(MTU\cdot U_{b}\cdot(N_{b}-1)\geqslant L_{peak}\)then
17\(\textit{Flag}\) = False;
18\(L_{b}=MTU\cdot U_{b}\);
19 return\(L_{b},N_{b}\);
```  
**Algorithm 1**PSO-QSO mechanism.