esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks
## 5 Discussion  
* Loss of Image Texture: The proposed method is primarily designed to enhance image fidelity in the context of QIS reconstruction, where input signals are extremely noisy due to photon-limited conditions and sparse bit-plane encoding. In such scenarios, suppressing heavy noise and faithfully recovering the underlying image structure become the main objectives. As a result, our model is trained to minimize pixel-wise reconstruction error, which is well-reflected by metrics such as PSNR. However, this emphasis on fidelity can sometimes lead to a perceptual compromise. Specifically, the network exhibits a mild tendency toward over-smoothing, which may result in the suppression of some fine details, even when such details remain partially visible in the noisy input. This phenomenon stems from the inherent trade-off between aggressive noise suppression and texture preservation.To address this, our network architecture integrates modules for capturing local and global pixel correlations, which help preserve detailed structures to a certain extent. Nevertheless, the current design is still biased toward achieving higher fidelity scores, rather than optimizing for perceptual quality. To further improve the balance between these two aspects, future extensions of this work will consider introducing perceptual-oriented loss functions and incorporating structural attention mechanisms. These improvements are expected to enhance the retention of fine details and visual textures, particularly in important regions, while maintaining strong denoising performance under extreme noise conditions.
* Image Degradation: While the proposed QIS-SPFT framework demonstrates strong performance for photon shot noise suppression in QIS image reconstruction, it is specifically designed for binary bit-plane data under low-light conditions. Its generalization to other degradation types--such as motion blur [32] or haze [33]--has not been explored in this work. The blur degradation is a convolution of the Poisson process of imaging and require additional pre-processing or architectural adaptations. Equation (5) now can be described as: \[P([\mathbf{Y}]_{l}=y)=\frac{[\gamma\cdot\mathbf{F}\mathbf{\Theta_{tot}}]_{l}^{ y}e^{-[\gamma\cdot\mathbf{F}\mathbf{\Theta_{tot}}]_{l}}}{y!},\] (18) where \(\mathbf{F}\) represents the blur kernel. The haze degradation follows the atmospheric scattering model. Future work may consider extending the framework to incorporate deblurring or dehazing modules to broaden its applicability.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 2 Related Work
According to [PERSON] et al., the number of reported cells is reduced to a minimum in order to minimize the signaling cost and support a large number of devices [22; 23]. On the other hand, from 3 GPP Release 16 onward, a UE may optionally report about strongest neighbors [1]. Many simulation studies use the LTE module in Network Simulator 3 (NS3) to simulate a real NB-IoT network [24]. However, by doing so, they do not face the aforementioned issue. Therefore, we chose to evaluate the performance of NB-IoT localization using real-world measurement data. Another mobility issue in the NB-IoT protocol is the lack of handover support. [PERSON] et al. explain the concepts of extended Discontinuous Reception (eDRX) and cell reselection of NB-IoT [25]. They acknowledge that cell reselection can only take place while in idle mode. Moreover, the cell selection process described in [23] suggests that only after failing to connect to the currently serving cell, the cell reselection process is initiated. Additionally, this issue introduces latency which can reach up to 10 s [26]. Furthermore, it is proven that when reselecting a cell more frequently, despite the increased energy consumption, the mobility performance increases significantly [25].
# Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China
## 3 Parametric Study of GSER Application Status
### Length of the Retained Entry  
The length of the retained entry is a key parameter in engineering applications for selecting GSER technology and controlling the retained entry surrounding rock. The influence of engineering geological conditions, such as the burial depth of the working face, thickness, and dip angle of coal seams, should be fully considered to determine a reasonable length of the retained entry. The longer the length of the retained entry, the longer the time for stable maintenance of the surrounding rock. Considering the strong ground pressure characteristics during the GSER implementation, achieving effective control over the surrounding rock of the retained entry becomes increasingly challenging. Therefore, the long-term stability requirements for the filling body and supporting structure of the retained entry surrounding rock are elevated. Among the counted 587 cases of GSER engineering projects, 66 cases clearly provide the length of the retained entry. The largest length of the retained entry is in the Xuehu Coal Mine of Henan Shenhuo Group, with a retained entry length of 2059 m. The high-water materials are used for the entry-side filling in the GSER engineering projects. Figure 11 illustrates the application of different lengths of the retained entry in GSER engineering projects with a statistical interval of 500 m.  
It can be seen that the number of GSER projects with a retained entry length of 0-500 m is the highest, followed by those with a retained entry length of 500-1000 m. The above cases account for 77.2% (51 cases) of the total GSER projects. There are 12 cases with a retained entry length of 1000-2000 m and only 3 cases with a retained entry length exceeding 2000 m. As shown in Figure 11, the number of GSER projects in practical engineering gradually decreases with the increase in retained entry length. Currently, the general length of retained entries does not exceed 1000 m. The main reason is that the current surrounding rock control technology and the construction technology of entry-side filling cannot meet the needs of long-distance GSER engineering applications.
## 2 Results and Discussion  
### Characterization of Materials  
#### 2.1.1 SEM and EDX Characterization  
Table S1 (see Supplementary Information, SI) shows the SEM-EDX analysis of the surface of the beads (APEI): (a) before and (b) after chemical modification (i.e., after nitrilation (CN-APEI), (c) after amidoximation (AO-APEI)), (d) after Sr(II) binding, (e) after metal desorption, (f) after five cycles of sorption and desorption, and (g) after exposure to NaCl solution. The surface morphologies of APEI and CN-APEI beads are roughly smooth, with some irregularities and large superficial holes. After functionalization with amidoxime groups (AO-APEI), the surface becomes more irregular with thin wrinkles and sharp edges. After Sr(II) sorption, the surface progressively loses its smooth aspect with the formation of wider wrinkles and small holes. The desorption of Sr(II) does not significantly change the surface aspect of sorbent beads; the surface hardly changes after five cycles of sorption/desorption (compared with the beads after metal desorption). The exposure of raw beads to NaCl solution causes some surface breakage; amidoximated beads are less sensitive to the ionic strengths (high salinity of the solution); however, the wrinkles are more marked (compared with reference AO-APEI beads in low-ionic strength solutions). The semi-quantitative EDX analyses confirm the progressive increase in the nitrogen content of the materials, from APEI to AO-PEI; this is consistent with the proposed mechanism of functionalization. On the other side, the O content substantially increases at the amidoximation step (from CN-APEI to AO-APEI).
## 4 Discussion  
As a reliable substitute for photosynthesis rate, SIF has been used in several research studies, with various spatio-temporal scales and ecosystems. Due to the low temporal resolution, most of the results of the current analyses of the relationships between SIF data based on satellite remote sensing and the environmental factors are not desired. Although the research that has been conducted into the leaf scale can explain some issues from a mechanistic perspective, it is doubtful whether the interpretation of individual leaves can represent the whole canopy. To clarify the influence process of the environmental factors on the canopy SIF of forest ecosystems, in this study, data on the SIF of a Chinese cork oak plantation during 2020 were continuously collected by a tower-based fluorescence automatic observation system. We analyzed these data collectively, using five environmental factors. The results showed that SIF was not only affected by PAR, but also by the VPD, SM, and other factors. This finding is crucial in properly understanding the relationship of SIF to other physiological processes, under different conditions.
# SqueezeNet: An Improved Lightweight Neural Network for Sheep Facial Recognition
## 3 Results
### Analysis of the Ablation Experiment Results  
The three proposed improvement methods in this study are SqueezeNet, ECA, and BalancedL1. On the self-constructed sheep face dataset, the following ablation experimental methods are designed: (1) Based on the original SSD network before improvements, each of the three enhancement methods is incorporated separately to assess the optimization effect of each improvement method on the original algorithm. (2) Building upon the enhanced SSD algorithm, each of the three improvement methods is individually removed to evaluate the impact of each improvement on the final algorithm, as shown in Table 6. From Table 6, it can be observed that compared to the original SSD algorithm, the ECA mechanism module exhibits the most pronounced improvement in accuracy. Its average precision increased by 1.91%. The model size remained largely unchanged. The detection speed increased by 0.56 frames per second. The introduction of the lightweight neural network SqueezeNet showed the most significant improvement in the detection speed. The detection speed increased by 5.14 frames per second, and the model size decreased by 96.4 MB, but the average precision decreased by 3.01%. Compared to the improved  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Model** & \
\begin{tabular}{c} **Mean Average** \\ **Precision mAP/\%** \\ \end{tabular} & **Model Volume/MB** &
\begin{tabular}{c} **Detection** \\ **Speed/(Frame-s\({}^{-1}\))** \\ \end{tabular} \\ \hline SSD & 80.22 & 132 & 58.98 \\ SSD + Sq & 77.21 & 35.6 & 64.12 \\ SSD + ECA & 82.13 & 132 & 59.54 \\ SSD + B & 81.69 & 132 & 62.13 \\ SSD + Sq + CA1 & 79.36 & 36.1 & 55.42 \\ SSD + Sq + CA2 & 76.43 & 36.1 & 57.66 \\ SSD + Sq + SE1 & 77.08 & 35.9 & 66.91 \\ SSD + Sq + SE2 & 76.99 & 35.9 & 63.52 \\ SSD + Sq + CBAM1 & 77.89 & 36.0 & 61.76 \\ SSD + Sq + CBAM2 & 77.57 & 36.0 & 65.06 \\ SSD + Sq + ECA1 & 79.72 & 35.8 & 67.15 \\ SSD + Sq + ECA2 & 79.11 & 35.8 & 65.31 \\ \hline \hline \end{tabular} Note. Sq = SqueezeNet; B = BalancedL1; attention mechanism \(1=1^{2}\times 1000\) of the avgpool layer backend, attention mechanism \(2=1^{2}\times 1000\) of the avgpool layer backend.  
\end{table}
Table 5: Comparison of experimental results after improvements in SSD.  
SSD-Sq-ECA1-B algorithm, removing the ECA1 attention mechanism module had the most significant impact on the accuracy, resulting in a decrease of 2.43% in the average precision. The removal of the lightweight neural network SqueezeNet had the most significant impact on the model size. The model size increased by 96.2 MB, and the detection speed decreased by 3.95 frames per second, indicating the most significant impact on speed. The SSD-Sq-ECA1-B algorithm proposed in this paper, compared to the original SSD algorithm, resulted in a 2.17% improvement in the average precision on the sheep face dataset. The model size decreased by 96.2 MB, and the detection speed increased by 7.13 frames per second. The SSD-Sq-ECA1-B algorithm can enhance the detection accuracy while ensuring detection efficiency.
# A Study on Identification of Development Status of MASS Technologies and Directions of Improvement
## 2 IMO MASS RSE and Human Element Issues
[11] identified design-based solutions and operation-based solutions in order to resolve human factor challenges such as situation awareness, problem recognition, and decision-making. [PERSON] et al. [12] suggested that important and relatively less important competences for deck officers for MASS degree 2 take into account the current requirement of the STCW 1978 convention. [PERSON] et al. [23] suggested human-technology aspects of new operating paradigms and emerging regulatory, liability, and security concerns. According to the results of the IMO MASS RSE and studies regarding MASS human elements, it can be seen that the design and operation of the MASS relating to human factors are still very important factors. Therefore, the IMO human element, human reliability assessment (HRA), and operational risk assessment take into account that the human element should be actively researched and developed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Degree of Autonomy** & \begin{tabular}{c} **The most appropriate way(s) of addressing MASS** \\ **operations (I, II, II, IV)** \\ \end{tabular} & \begin{tabular}{c} **Reasons for selecting the most appropriate way(s)** \\ **of addressing MASS operations** \\ \end{tabular} \\ \hline \multirow{7}{*}{Degree 3} & \begin{tabular}{c} Changes to the Convention and Code to establish \\ definitions and provisions to include the ”remote \\ operator” can be made through the existing \\ (Determination that ”remote operator is a seafare”) \\ \end{tabular} & \begin{tabular}{c} Changes to the Convention and Code to establish \\ definitions and provisions to include the ”remote \\ operator” can be made through the existing \\ (Coverning process and other flexibilities through \\ authorized equivalences or amendments to \\ the Codes or regulations. \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} The provisions will need to include the relationship \\ between seafarers on board and the ”remote \\ operator”. However, this relationship will also need to be established in the STCW Convention through \\ the existing processes and other flexibilities through \\ authorized equivalences or amendments to \\ Codes or regulations. \\ \end{tabular} \\ \hline \multirow{2}{*}{Degree 4} & \
\begin{tabular}{c} Categories IV \\ \end{tabular} &
\begin{tabular}{c} There are no trained and qualified seafarers serving \\ on board to perform the operational functions on \\ board the vessel. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Particularly, it is important that the development of a risk assessment tool for MASS operation takes into consideration the required competence for seafarers and remote operators.
## 3 Methodology
### Model Training by Machine Learning Classifiers
#### 3.6.3 Multinomial Naive Bayes (MNB)  
We trained our data set with MNB. As the name suggests, the MNB is an upgraded version of the Bayes theorem that works with probabilities.  
Equation (4) is derived from the naive Bayesian theorem to find the likelihood of class A with B.  
\[\text{P}(\text{A}|\text{B})=\text{P}(\text{A})*\text{P}(\text{B}|\text{A})/ \text{P}(\text{B}) \tag{4}\]  
where P(A) denotes the prior probability of class A. P(B) denotes the prior probability of class B. P(B\(\text{1}\)A) denotes the occurrence of predictor B given the class A probability.  
The mathematical expression for the MNB classifier is in Equation (5)  
\[\Pr(x|C_{k})=\frac{(\sum_{i=1}^{n}x_{i})!}{\prod_{i-1}^{n}x_{i}!}\prod_{1=1}^{n}PK _{i}x_{i}^{x} \tag{5}\]  
Equation (5) shows that prior \(\Pr(xs|C_{k})\) is the quotient. Where the numerator is estimated as the factorial of the sum of all features \(\forall x_{ki}\in x_{i}=1...n\). In turn, the denominator is obtained as a product of all feature \(x_{ki}\) factorials. The numerator is evaluated as a probability distribution, which is the likelihood of all possible outcomes where S occurred in document D from class \(C_{k}\).  
* MNB is very easy and simple to implement as well; probabilities are calculated on real-time applications.
* It works well with continuous and discrete data types, handles large data sets with ease, and is scalable.
* MNB only works with textual data; numeric data are not handled.
* MNB is unable to address the regression problems accurately.  
In our case, we obtained our data in real time from Twitter and in textual form; the probability and likelihood were calculated by the use of the MNB classifier; therefore, textual data were assigned to word classes for categorizing purposes. The frequency of words did not affect the classification of data.
## 6 Performance Evaluation
### Performance Comparison between DETO and Other Baselines  
In this section, the performance of the DETO method is compared to a GA solution and a greedy solution under various conditions, including different numbers of MDs, different numbers of task request rates, and different numbers of UAVs. The GA and greedy approaches are described as follows:  
* GA-based ellipsoidal trajectory optimization (GAETO): A rank-based genetic algorithm is employed to find a solution for ellipsoidal trajectory optimization, where each solution is a vector representation of real numbers. The initial population is generated randomly, and the fitness function is calculated using the reciprocal value of the system objective for each individual solution. Lower fitness values are given to higher ranks and have a higher chance of being selected as parents. Then, new solutions are generated by applying crossover and mutation, and the best solution is recorded. This procedure repeats for a given number of generations.
* Greedy: In this setup, each UAV's center position is chosen from the cluster center. The major radius of each UAV is equal to one half of the distance along the x-axis between its center and its farthest associated user, while the minor radius is equal to one half of the distance along the y-axis between its center and its farthest associated user. The rotation angle is set to 0\({}^{\circ}\).
# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 2 Materials and Methods
### Materials  
The dataset employed in this study is the Beijing Multi-Site Air-Quality dataset [12], a publicly available resource that contains environmental data collected from 12 monitoring stations across Beijing, China. These stations are equipped with reference-grade instruments that record hourly measurements, providing a detailed and structured dataset suitable for air quality analysis. The dataset spans multiple years, from 1 March 2013 to 28 February 2017, whilst offering a consistent temporal resolution with hourly observations. The recorded air pollutant measurements at each station include PM\({}_{2.5}\) (\(\mu\)g/m\({}^{3}\)), PM\({}_{10}\) (\(\mu\)g/m\({}^{3}\)), SO\({}_{2}\) (\(\mu\)g/m\({}^{3}\)), NO\({}_{2}\) (\(\mu\)g/m\({}^{3}\)), CO (mg/m\({}^{3}\)), and O\({}_{3}\) (\(\mu\)g/m\({}^{3}\)), capturing a range of airborne contaminants. Each station also collects meteorological data, including air temperature (\({}^{\circ}\)C), pressure (hPa), dew point temperature (\({}^{\circ}\)C), wind direction (degrees), wind speed (m/s), and precipitation (mm), which describe atmospheric conditions. Additionally, the dataset includes structured temporal information in the form of year, month, day, and hour, ensuring that each data point is accurately time-stamped.  
The combination of pollutant concentrations, meteorological variables, and detailed temporal information provides a comprehensive representation of air quality conditions across Beijing. This structured dataset was used in this study to support nowcasting tasks,  
Figure 1: The complete ML pipeline applied, consisting of data preparation, initial modeling, feature reduction, and explainability.  
where PM\({}_{2.5}\) concentrations were estimated for each hour using all available environmental and meteorological features except PM\({}_{2.5}\) itself.
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 3 Experimental Design
### Error Measures  
The performance of the reference methods and the different approaches were evaluated with five different error measures for ANNs. The equation shows the mean-square error (MSE) [50,59].  
\[\text{MSE}\big{(}x^{\prime},x\big{)}=\frac{1}{N}\sum\
olimits_{n=1}^{N}(x^{ \prime}_{n}-x_{n})^{2}\enspace, \tag{13}\]  
In the equations, \(x\) is the measured power time series, \(x^{2\prime}\) is the predicted power time series, and \(N\) denotes the number of samples of the time series [47,48,59].
# Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm
## 3 System Model
### The Optimization of the Target  
In the cluster head selection process of the edge-side WSN, in order to balance the node energy and distance in the cluster head selection process, improve the distribution uniformity between clusters, and reduce the distance between cluster head nodes, our final optimization objective problem is expressed as  
\[\min W=\theta_{1}\sum_{i\in H}E_{i}^{r}+\theta_{2}\sum_{i\in H}D_ {i}^{e}+\theta_{3}\sum_{j\in\text{C}_{q,q}\in M}D_{q,j}^{c}+\theta_{4}D^{h} \tag{10}\] \[s.t.\text{ C1}\ \theta_{1}+\theta_{2}+\theta_{3}+\theta_{4}=1\] (11) \[\text{ C2}\ 0<\theta_{1},\theta_{2},\theta_{3},\theta_{4}<1\] (12) \[\text{ C3}\ E_{i}\leq E_{i}^{s} \tag{13}\]  
where \(\theta_{1}\), \(\theta_{2}\), \(\theta_{3}\), and \(\theta_{4}\) are the weight factors, and C3 is the energy consumption constraint of the node.
# Removal of Heavy Metal Cd Element from Paddy Soil by Geo-Electrochemical Technology
## 3 Materials and Methods  
### Experiment Site  
The experiment site was at the Agricultural Science Institute in Quanzhou County, Guilin City, Guangxi Zhuang Autonomous Region. The experiment paddy field was consistent in fertility level and pollution degree. Before the experiment, soil sampling and related analyses were performed on the paddy soil. The soil's basic physical and chemical properties were determined: the soil type was red soil paddy field, its pH was approximately 6.4, and the total Cd content was approximately 0.58 mg/kg. The content of heavy metals in the soil is detailed in Table 1. The selected evaluation standard was the risk screening of soil contamination of agricultural land with a pH of 5.5 to \(\leq\)6.5 in the \"Soil Environmental Quality Risk Control Standard for Soil Contamination of Agricultural Land\" (GB 15618-2018) [23]. Table 1 shows that the Cd content of the experimental paddy soil exceeded the pollutant screening value for agricultural land (0.4 mg/kg for paddy fields), indicating ecological and environmental risk in that soil. Therefore, appropriate remediation should be performed to reduce the risk of Cd exceeding the limit in agricultural products. Apart from Cd, no other elements exceeded the standard, so Cd was the key element of concern for excessive heavy metals in that area.