esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
###### Abstract  
Video deblurring faces a fundamental challenge, as blur degradation comprehensively affects frames by not only causing detail loss but also severely distorting structural information. This dual degradation across low- and high-frequency domains makes it challenging for existing methods to simultaneously restore both structural and detailed information through a unified approach. To address this issue, we propose a wavelet-based, blur-aware decoupled network (WBDNet) that innovatively decouples structure reconstruction from detail enhancement. Our method decomposes features into multiple frequency bands and employs specialized restoration strategies for different frequency domains. In the low-frequency domain, we construct a multi-scale feature pyramid with optical flow alignment. This enables accurate structure reconstruction through bottom-up progressive feature fusion. For high-frequency components, we combine deformable convolution with a blur-aware attention mechanism. This allows us to precisely extract and merge sharp details from multiple frames. Extensive experiments on benchmark datasets demonstrate the superior performance of our method, particularly in preserving structural integrity and detail fidelity.  
video deblurring; wavelet transform; deformable convolution; attention mechanism Article
# A Secure and Efficient Group Key Management Scheme for Clusters of String Inverters
## 5 Performance Evaluation
### Memory Overhead  
As to memory overhead we intended the memory occupancy of the executable image of the prototype. Since smart string inverters are constrained devices, the lower the memory footprint the better it is.  
In Figure 8 we present the memory footprint of the prototype implementation. We still considered the case of a leave operation and the following cryptosystem: ECDHE-RSA-AES128-GCM-SHA256 (i.e., RSA keys). The security level considered was 128-bit whereas the group size was equal to the maximum cluster size (i.e., 50 inverters).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cryptosystem** & \begin{tabular}{c} **RSA-Keys** \\ **Length** \\ \end{tabular} & \begin{tabular}{c} **Security** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **Group** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Latency for** \\ **Key** \\ **Distribution** \\ \end{tabular} & \
\begin{tabular}{c} **Deadline for** \\ **Key** \\ **Distribution** \\ \end{tabular} & **Ratio** & **Efficiency** \\ \hline ECDHE-RSA-AES128- & 3072-bit & 128-bit &
\begin{tabular}{c} 50 \\ inverters \\ \end{tabular} & 5.7 s & 60 s & 5.7s/60 s = 9.5\% & 90.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Latency for group key distribution: main results.  
Memory usage was measured by means of the Unix-like process pseudo-file system, namely the _proc_ (virtual) file system. Particularly, we gathered information from _/proc/pid/status_ where _pid_ was the process id of the prototype.  
Figure 8 shows that the Virtual Memory Size (VmSize) of the prototype process was less than 15 MB (i.e., green plus brown rectangles). Of this, less than 5 MB is the maximum Resident Set Size (VMRSS), i.e., the amount actually resident in the main memory. Thus, as far as the VMRSS is concerned, we can state that it accounts (only) for less than 1% of the overall inverter main memory.  
With respect to memory overhead, we defined efficiency according to this other equation:  
\[efficiency_{memory-overhead}=1-\frac{runtime\ memory\ requirements}{overall\ inverter^{\prime}s\ main\ memory} \tag{2}\]  
Thus, the efficiency of the scheme in terms of memory overhead is: \(efficiency_{memory-overhead}=1-\frac{5\times 1024\times 1024}{512\times 1024 \times 1024}=99\%\). Since smart inverters are constrained devices this is quite an important result. Finally, in Table 5 we summarized the main results of this subsection. Due to the lack of existing related research works, results could not be used to make comparisons.
## 5 Experiments
### Localization Using the Compact Maps
#### 5.3.3 Localization When Several Maps Are Available  
In some applications, several maps of some different environments are initially available. If the robot has no information about the environment it is located in, first, it has to use the visual information to select the correct environment. After that, the localization can be solved in the selected environment, as presented in Section 4. Considering this, in this section, the ability to select the right environment  
Figure 18: Results of the localization process with HOG and _gist_ used to describe the representatives of the clusters and the test images: average localization error (cm) vs. number of clusters. Freiburg environment.  
is studied. In order to check the goodness of the descriptors for this purpose, the two COLD maps built in Section 5.2.2 are considered. Additionally, a test dataset is created as a combination of images from the Freiburg and Saarbruken environments. A total of 60 test images compose the test dataset (34 from Freiburg and 26 from Saarbrucken). In this experiment, only HOG and _gist_ are tested again. Furthermore, since the cosine distance presented the best solutions for COLD, only this kind of distance is applied. Figure 19 shows the percentage of success in selecting the right environment for the two descriptors.  
By and large, the correct environment selection is almost always done. Many cases are given in which 100% success is reached, whereas the worst cases do not present a success rate under 75%. If the environment selection is carried out with HOG, results depend substantially on the chosen \(k_{2}\) value. For instance, the worst cases are presented for \(k_{2}=2,4\). However, for \(k_{2}=32-128\), 100% success is reached. Through the use of _gist_ descriptor, 100% success is given independently of the number of clusters or the \(k_{3}\) value.
## 4 Discussion
### Impact of the Choice of Explanatory Variables on the Model  
In general, machine learning models are often criticized for their lack of interpretability compared to physical models. However, the XGBoost algorithm supports SHAP analysis, which enables a quantitative assessment of the impact of each feature variable on the model's predictions. Previous studies have varied significantly in their selection of input features--some used a wide range of variable types, while others relied solely on surface reflectance [42]. The choice of input parameters typically depends on the target variable to be downscaled and the specific model. For instance, [PERSON] et al. [52] reported that land cover played only a minor role in their SIF prediction model. In this study, we conducted a SHAP-based interpretability analysis to quantitatively evaluate the contribution of each input feature to the model's performance. As land cover type is a categorical variable, it cannot be ranked in terms of numerical value and is therefore displayed in gray inAs shown in Figure 9, land cover data had a relatively smaller impact on the model's output compared to variables such as NDVI and LST. However, during model training, we initially included MODIS land cover data as one of the input features and compared the model performance with and without this variable. We found that including land cover data improved the model's performance across all evaluation metrics. This indicates that although land cover ranks lower in feature importance, its inclusion still has a positive effect on model training. Furthermore, as demonstrated in Section 3.4, our HRSIF product shows a stronger correlation with GPP compared to the SIF\({}_{\text{Dweiller}}\) product, which does not incorporate land cover information. This is because the land cover type can indirectly reflect vegetation structure and growth characteristics; removing this variable could reduce the model's ability to distinguish between different ecosystems. This highlights a key advantage of machine learning models over empirical physical models: the flexibility to incorporate diverse types of input variables, including categorical data.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Smart Cities and Automotive Applications
#### 3.3.3 Plant Microbial Fuel Cell-Based Wireless Sensor Node  
The self-powered plant microbial fuel cell (PMFC)-based wireless sensor node [32] is targeted for monitoring the environment of smart cities. Concerning the processor and memory specifications, the 16-bit RISC ultra-low-power MCU (MSP430 FR5969) developed by Texas Instruments contains 64 kB ferroelectric random-access memory (FRAM) and can attain a clock speed of 16 MHz was utilized.  
The LoRa radio module (RFM95W), which utilizes the SX1276 LoRa transceiver with serial peripheral interface (SPI), was adopted by the authors. The transceiver operates in the 868.1 MHz band with a coverage range of 2 km in line-of-sight situations or approximately 20 km using directional antennas. The transmit power is adjustable ranging from around +5 to +20 dBm. Furthermore, the MQ-131 and MQ-135 sensors are exploited for detecting ozone and carbon dioxide levels, respectively.  
The node is powered by energy harvesting using PMFCs. The PMFCs are capable of supplying 1.25 mW/cm\({}^{2}\) to the energy-scavenging circuit (BQ25570), which supplies a voltage of around 3.3 V. A supercapacitor is used for energy storage. The MCU (MSP430 FR5969) can operate in the standby low-power mode (LPM3) and real-time clock (LPM3.5) mode, having typical current consumptions of approximately 400 nA and 250 nA, respectively. The peak current at maximum power output (20 dBm) is approximately 30 mA when the transceiver is in the active-listening state. The average power consumption of the mode is 2.92 mW.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 2 Methods
### HRNet  
A high-resolution network (HRNet) is a convolutional neural network that was introduced in 2019 for high-resolution image classification and segmentation. It enhances the quality of feature representation through multi-resolution feature extraction and fusion while bolstering computational efficiency via a cross-branch network structure [32]. HRNet employs a parallel branch configuration, directing input images to multiple sub-networks for feature extraction. Subsequently, it interacts with and fuses different resolution features, performing classification or segmentation through global pooling and fully connected layers [33]. HRNet's exceptional performance stems from the application of techniques, such as multi-resolution feature extraction and fusion, and cross-branch network structures, which effectively elevate the quality of feature representation and computational efficiency.
## 6 Conclusions and Discussion  
In this paper, a new method of topic identification has been proposed. First, a word embedding algorithm was adopted that was based on a pre-trained model, which generates a word representation that can capture the context of a document. After that, we used a spherical k-means clustering algorithm to construct topic clusters. Finally, a topic assignment method was used to assign documents to different topics. The assignment process was in order to calculate the similarity between documents and topics.  
The method proposed in this paper was applied to the literature abstracts related to geospatial data. First, it shows the characteristics of geospatial data technology and application development trends in related research in several leading countries. Second, the topic coherence of this method was evaluated by using U-Mass and _NPMI_, and its performance was compared with that of the existing method, PLSA. The results show that the proposed method can produce highly coherent topics. The research in this paper provides new ideas for the trend analysis of technologies and applications related to geospatial data, and helps professionals engaged in research related to geospatial data to identify their future research directions at any time. In addition, this method captures the development trends of related technical fields through text, which can be used as an information tool for anyone who is responsible for strategic decision-making in sectors related to geospatial data, to determine the prospect and market of the fields. This paper also has some shortcomings, for example, it is unable to successfully identify the topic when the number of texts is extremely large. In the future, we will work hard on topic modeling for a large number of texts.  
Conceptualization, methodology, validation, formal analysis, Q.C. and Y.Z.; Software, Q.C.; Supervision, J.S., H.Z., J.Z., K.S. and S.W.; Funding Acquisition, Y.Z.; Writing--original draft preparation, Q.C. and Y.Z.; Writing--review and editing, Q.C. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (grant numbers: 42050101, 41771430, 41631177) and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number: XDA23100100).  
Not applicable.  
The data presented in this study are available in Section 2.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Electrical Features of LS-APP  
The waveforms of the discharge voltage and current of the BPPJ, PPPJ, and NPPJ are shown in Figure 2. The waveforms of the three discharges are all measured under the conditions of a 24 kV pulse peak voltage, 150 Hz pulse repetition rate, and 3.5 L/min gas flow rate. It can be seen that both the bipolar and the unipolar pulses have a rising time of about 25 ns, and the current peak value of the BPPJ is higher than that of both the PPPJ and NPPJ. Specifically, the peak current of the positive pulse in the BPPJ is 1.7 times that of the PPPJ, while the peak current of the negative pulse in the BPPJ is 2.1 times that of the NPPJ.  
The discharge energy of each pulse of the BPPJ and NPPJ can also be calculated from the waveforms of the voltage and current, as in the following equation:  
\[E=\int P_{\text{P,g}}(t)dt==\int U_{\text{g}}(t)\times I_{\text{P,g}}(t)dt, \tag{1}\]  
where \(P_{\text{P,g}}(t)\), \(U_{\text{g}}(t)\), and \(I_{\text{P,g}}(t)\) are the instantaneous input power, the voltage, and the discharge current of the plasma jet, respectively. Then, the discharge power of the plasma jet can be determined by multiplying the energy per pulse by the pulse repetition rate. The average powers of the BPPJ, PPPJ, and NPPJ are 2.42 W, 1.59 W, and 1.93 W, respectively.
## 5 Factors Affecting the Anaerobic Digestion Process and Biogas Production
### Effect of Inoculation on AD Process Parameters  
The use of inocula positively reinforces sustainability through the recovery of material and reduced energy consumption [101; 102]. Research has been found that the use of inocula is more significant than alkaline pre-treatment of raw material substrates since inocula have sufficient bacterial content and increase active microorganisms [103]. Since inoculum is highly cellulosic, it is unable to be digested by itself; accordingly, it is suitable to be reused in AD with other substrates. Types of inocula used in biogas production include sludge from wastewater treatment plants, digested silage, paper mill wastewater, digested sewage sludge, etc. [104; 105]. Depending on the composition of each inoculum, the influence on biogas production will vary. For example, palm oil mill effluent has been used as an inoculum with cow manure biogas production, resulting in higher biogas production [106]. Activated digestate from an anaerobic digestion plant that treats crop and agriculture waste was used as an inoculum by [PERSON], et al. [107], where the best biogas production was obtained with an inoculum/substrate ratio of 2:1. Some studies have investigated the effects of different inocula types on specific substrates while others have looked at the effects of mixed inoculation and data that show a positive influence of inoculation are available in literature [108; 109].
## 3 Results
### Comparison between Pre- and Post-LST  
Table 4 and Figure S1 show a comparison of the pre-LST (10:00-10:40 a.m.) and post-LST (10:40-11:40 a.m.) physiological measurements, which are the primary endpoints. In the LST group, the mean glucose levels decreased and the AUC increased 1 h after the intervention. Furthermore, the SD significantly decreased 1 h after the intervention. Conversely, in the control group, the mean glucose levels decreased, and the AUC increased over the time same period; however, the M-value significantly increased at the same time as the LST intervention.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **(Index)** & \multicolumn{3}{c}{**LST Group**} & \multicolumn{3}{c}{**Control Group**} \\ \cline{3-8}  & **Pre** & **Post** & **\(p\)-Value** & **Pre** & **Post** & **\(p\)-Value** \\ \hline Average glucose levels & (mg/dL) & 1353 \(\pm\) 41.9 & 97.0 \(\pm\) 19.7 & 0.007 & 1241 \(\pm\) 16.2 & 91.8 \(\pm\) 10.4 & 0.005 \\ Glucose SD & (mg/dL) & 11.8 \(\pm\) 7.0 & 6.0 \(\pm\) 4.4 & 0.022 & 9.0 \(\pm\) 4.9 & 66.6 \(\pm\) 4.7 & 0.114 \\ In target range (\(\gamma\)0-140 mg/dL) & (\%) & 69.2 \(\pm\) 47.8 & 93.3 \(\pm\) 21.1 & 0.066 & 76.7 \(\pm\) 37.6 & 99.2 \(\pm\) 2.6 & 0.068 \\ High blood sugar frequency (-7140 mg/dL) & (times/day) & 4.1 \(\pm\) 6.2 & 9.2 \(\pm\) 28 & 0.066 & 1.8 \(\pm\) 3.6 & 0 \(\pm\) 0 & 0.109 \\ Low blood sugar frequency (-70 mg/dL) & (times/day) & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.1 \(\pm\) 0.3 & 0.317 \\ Total area under the curve & 81498 \(\pm\) 2594.4 & 85648 \(\pm\) 1183.9 & 0.007 & 74448 \(\pm\) 9969.8 & 54090 \(\pm\) 629.8 & 0.005 \\ Total area under the curve \(>\)140 mg/dL & 946.8 \(\pm\) 1775.7 & 44.8 \(\pm\) 141.5 & 0.068 & 175.8 \(\pm\) 397.5 & 0 \(\pm\) 0 & 0.109 \\ Total area under the curve \(<\)70 mg/dL & 0.0 \(\pm\) 0.0 & 1.000 & 0.0 \(\pm\) 0 & 0.3 \(\pm\) 0.8 & 0.317 \\ M-value 120 & 3.5 \(\pm\) 6.2 & 2.7 \(\pm\) 2.7 & 0.575 & 0.4 \(\pm\) 0.7 & 2.7 \(\pm\) 2.0 & 0.017 \\ M-value 100 & 7.9 \(\pm\) 14.1 & 0.9 \(\pm\) 1.3 & 0.508 & 1.9 \(\pm\) 2.9 & 0.5 \(\pm\) 0.4 & 0.114 \\ \hline \end{tabular}  
* Data are displayed as mean \(+\) standard deviation (SD). LST, low-intensity resistance corrections with slow movements and ionic force generation.  
\end{table}
Table 4: A comparison of pre-LST (10:00-10:40 a.m) and post-LST (10:40-11:40 a.m) physiological measurements.
# Explainable Security Requirements Classification Through Transformer Models
## 3 The Method
### Dataset  
In order to conduct our experiments, we used the publicly available dataset SecReq introduced by [PERSON] et al. (2017). This work aimed to assist all steps in eliciting security requirements and provide mechanisms to trace security requirements from high-level security statements (security objectives) instead of secure design. This dataset contains 623 requirements, 415 security-related, and 208 non-security-related requirements (Table 1 shows an example per class). It is composed of three industrial specifications: (1) Common Electronic Purse (ePurse), (2) Customer Premises Network (CPN), and (3) Global Platform Specification (GPS). During the dataset analysis, we calculated the length distribution of the requirements, finding (as shown in Figure 2) that, on average, the non-security-related requirements are more extended (with an average number of characters of 183.52) than security-related requirements with an average length of 154.05. Additionally, we conduct a word frequency analysis showing the 20 most frequent words in the requirements. Figure 3 shows that the most used word is the verb \"shall\", which, also seeing the example shown in Table 1, indicates a requirement or obligation; it is often used in legal, technical, or formal documents to express that something is mandatory or must be conducted, and for this reason in this context, this word is the most used. The latter verb is followed by \"cng\", which is the acronym for \"Common Name Group\". It categorizes and groups similar security requirements based on common characteristics or themes. The last word in the figure is \"ngn\", which refers to \"Next Generation Network\". These advanced telecommunications networks provide a wide range of services, including voice, data, and multimedia.  
Figure 1: The workflow of the proposed method to classify security and non-security requirements that rely on transformers fine-tuning and Few-Shot learning with transfer learning models leveraging pre-trained models using data augmentation.  
Figure 3: The most 20 frequent words in the dataset.  
Figure 2: Average sentence length distribution about its category.  
In order to fine-tune the models, we split the dataset into training, testing, and validation sets using the 80, 10, and 10 criteria. Table 2 shows the number of elements obtained from this split and the category distribution in each set.
# Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm
## 2 Lithium-Ion Battery Model
### Objective Function of Parameter Identification  
For an FOM model, the traditional online identification algorithms such as RLS are not suitable owing to the complexity brought by fractional order calculus. Intelligent offline optimization algorithms are usually effective for this problem. The FOM model parameters include ohmic internal resistance (\(R_{0}\)), polarized internal resistance (\(R_{1}\), \(R_{2}\)), a constant phase element (\(C_{1}\), \(C_{2}\)) and two orders of fractional calculus (\(m\), \(n\)) in this study. They can be determined by minimizing the sum of squared errors (SSE) between the actual measured terminal voltage \(V_{r}\) and the estimated terminal voltage \(V_{e}\) of the battery model. Therefore, the objective function of optimization is established as:  
\[F(x)=\text{min}{\sum\limits_{i=1}^{T}{\left({V_{e}}^{i}-{V_{r}^{i}}\right)^{2}}} \tag{9}\]  
where \(T\) indicates the total number of voltage sampling points.