esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 2 Theoretical Background of the Models
### The Procedure of the Proposed Method  
The structural acceleration response signal measured by the sensor is nonlinear. It fluctuates significantly, and the response signal has complex components due to the influence of ambient temperatures, structural damping, and stiffness; thus, it is difficult to achieve good performance using a traditional time series prediction method to achieve missing data complementation alone. Therefore, we propose the construction of a hybrid prediction model. The core idea of this method is to decompose the original signal into a series of simple parts with the behavior of EEMD to obtain more patterns that can reflect the original signal and then use deep learning algorithms to predict them. The modeling process of the method is shown in Figure 3 and is described as follows.  
Figure 2: Internal memory block of an LSTM.  
Step 1: The EEMD method decomposes the original measured sensor signal, \(X_{e}\), into subseries IMF components and residual RES;  
Step 2: All subsequences, \(D\), are input into the EEMD-LSTM model from the decomposition;  
Step 3: The input data were divided into a training set, which was used to train the model, and a test set, which was fed into the prediction model to evaluate the prediction results.  
The inputs into the EEMD-LSTM model can be expressed as follows:  
\[D=\{imf_{1},imf_{2},\ldots,imf_{n-1},res\} \tag{7}\]  
where \(D\) represents all subsequences of the initially measured signal data decomposition, and \(imf_{i}\) is the intrinsic mode function (IMF) after decomposition.  
Above is the modeling process as shown using the EEMD-LSTM model as an example; other types of deep learning algorithms can be chosen above; in this paper, the LSTM deep learning model was used and compared with the deep neural network (DNN), gated recurrent unit (GRU), and bidirectional gated recurrent unit (BiGRU). The hybrid methods are denoted as EEMD-LSTM, EEMD-DNN, EEMD-GRU, and EEMD-BiGRU.  
The features and innovations of the method are:  
1. The use of the algorithm to decompose the original measured signal data can enhance the robustness of prediction;
2. The LSTM in the framework of the proposed method can make full use of the long-term correlation of the time series data;
3. The underlying EEMD-LSTM relies on a data-driven approach with no assumption restrictions on the input data.
# Analysis of the Sugarane Biomass Use to Produce Green Hydrogen: Brazilian Case Study
## 6 Discussion
### Strategic Opportunities and Challenges  
Despite its strong renewable energy foundation, Brazil faces challenges in reducing its reliance on hydropower, which remains susceptible to climate variability. While hydropower accounted for 60.2% of electricity generation in 2023, diversifying the energy mix through the expansion of wind, solar, and bioenergy sources is critical to ensuring energy security. Sugarcane bagasse, currently contributing 8.2% of electricity generation, exemplifies the untapped potential of bioenergy resources within Brazil's energy strategy.  
The global shift toward biohydrogen represents an opportunity for Brazil to leverage its extensive sugarcane infrastructure. However, realizing this potential requires addressing technological and logistical barriers, including the optimization of supply chains, development of advanced conversion technologies, and expansion of research collaborations. By investing in these areas, Brazil can strengthen its role in shaping the future of biohydrogen production.
## 3 Robot Error Prediction and Accuracy Compensation Method
### Anisotropy Analysis of Robot Positioning Error Distribution in Configuration Space
The semivariance distribution of the x-direction error component across the joint dimensions is shown in Figure 10b. In the A3 and A5 joint dimensions, the semivariance distribution of the y-direction error component across the joint dimensions is unfolded. The spatial similarity of the y-direction error component is more prominent, with higher precision and consistency at distances close to zero in the A2, A6, and A1 joint dimensions compared to the A3, A4, and A5 joint dimensions. As shown in Figure 10d, the semivariance distribution of the z-direction error component across the joint dimensions is also unfolded. Clearly, the spatial similarity of the z-direction error component is more significant in the A1, A3, A4, and A5 joint dimensions, while it is less evident in the A2 and A6 dimensions, and the nugget effect is also stronger. According to the analysis of the experimental results, although robot positioning errors exhibit significant and consistent spatial similarity in configuration space, this characteristic also shows markedly different nugget effects and sensitivities across various joint angle dimensions. Notably, the distribution of robot errors exhibits anisotropy across different joint dimensions in configuration space, which can directly impact the effectiveness of error prediction. Therefore, when constructing the error prediction model based on robot error spatial similarity, emphasis should be placed on enhancing the influence of joint dimen  
Figure 10: The semivariograms of robot errors in configuration space, where **(a)** shows the semivariogram of the x-, y-, and z-axis error components with respect to the distance of joint angles; **(b)** shows the semivariogram of the X-axis error component expanded across the six joint dimensions; **(c)** shows the semivariogram of the Y-axis error component expanded across the six joint dimensions; **(d)** shows the semivariogram of the Z-axis error component expanded across the six joint dimensions. sions with more significant spatial similarity while suppressing those with a pronounced nugget effect. This approach will improve the sensitivity of error prediction and reduce random errors.
## 4 Discussion  
### Temperature, pH and GI  
The temperature of all treatments increased sharply at the beginning of composting because the forced aeration provided abundant oxygen to promote the degradation of organic matter [19]. C1 treatment had the highest temperature among the three treatments. The oxygen consumption rate was fast in the thermophilic phase [23], while the intermittent aerated treatment stopped the ventilation for a long time, resulting in the hypoxia of the reactor, which affected the activity of microorganisms and further affected the increase in temperature. With the complete degradation of easily degradable organic matter, the activity of microorganisms decreased, and the temperature was close to the ambient temperature. Compared with C1 and I1, the temperature of I2 is lowest owing to the higher heat loss by a high instantaneous aeration rate. A similar result was reported by [24]. The thermophilic phase (temperature \(\geq\) 50 \({}^{\circ}\)C) lasted around 9 days, which met the Chinese Technical requirement for non-hazardous treatment of animal manure [25], which reach over 50 \({}^{\circ}\)C for at least 7 days to ensure elimination of pathogens.  
The pH value not only affected the activity of microorganisms, but also was the key parameter to control ammonia volatilization [26], which directly affected the forms of nitrogen in the composting system. With the increase in temperature, ammonization was serious at the beginning of composting, which promoted the transformation of organic nitrogen to NH\({}_{4}\)\({}^{+}\)-N, resulting in a rapid increase in pH value [27; 28; 29]. After the fourth  
Figure 6: Redundancy analysis (RDA) of gaseous emissions and physiochemical parameters during composting. (**a**) C1 treatment, (**b**) I1 treatment, (**c**) I2 treatment. Correlations between physiochemical characteristics and RDA axes were represented by the length and angle of arrows.  
day, due to ammonia volatilization and ammonium oxidation to nitrates of nitrogenous bacteria, pH began to decline and then stabilized [5].  
The GI is regarded as a sensitive indicator of compost maturity and phytotoxicity [30]. The GI decreased during the early phase, which may be attributed to the increase in NH4+-N content (Figure 2b) and the decomposition of organic matter to organic acids. At the end of composting, the GI of three treatments exceeded 80%, which indicated that the compost was phytotoxic-free and mature [31].
## 4 Results
### Key Technologies of the Platform
#### 4.4.2 Blockchain Technology: Platform Information Sharing  
Blockchain technology can establish a transparent data-sharing platform for community information. With its distributed and tamper-proof characteristics, the combination of IoT and blockchain technology allows for automatic data recording on the blockchain, reducing human intervention and ensuring data authenticity [45]. Blockchain is suitable for recording carbon emission data and carbon credit information, meeting the needs of participants in the Carbon Generalized System of Preferences to conveniently view and verify relevant data, thereby increasing the credibility of information sharing.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Resistance Capability of ECSSP-SC against Attacks
#### 6.2.8 Resistance to MITM (Man-in-the-Middle) Attacks  
In this attack, an _Attacker A_ sits between the _USR_ and the _SVR_. From there, it tries to mimic the behavior of either the _SVR_ or the _USR_, i.e., it modifies the messages in transit to fool the other party. Since the ECSSP-SC can successfully resist user impersonation and server impersonation, it can also counter MITM attacks.
## 4 Discussion  
Based on the experimental results and the analysis of the heat transfer characteristics in fractured granite presented in this study, it was observed that fractures significantly impede heat conduction within the granite, resulting in distinct regional characteristics in its internal temperature field distribution. To discuss the disparities in heat transfer between fractured granite and intact granite, heat transfer data for intact granite under identical experimental conditions [40] were referenced. A comparison of the stable temperature field distribution characteristics between fractured granite and intact granite under heating by a heat source ranging from 60 to 100 \({}^{\circ}\)C is presented in Figure 15.  
Figure 14: The relationship between the peak temperature gradient and the heat source temperature at monitoring points in the fracture.  
Figure 13: The peak response characteristics of temperature gradient: (**a**) time to reach the peak; (**b**) peak temperature gradient.  
Figure 15: Comparison of temperature field distributions between intact rock and fractured granite after heating at different heat source temperatures: (**a**-**e**) are fractured granite; (**f**-**j**) are intact granite.  
Figure 15 illustrates that the stable temperature field distribution in intact rock exhibits similar patterns under different heat source temperatures, namely, nearly horizontal isothermal contours. Minor undulations in the isothermal contours occur in localized regions due to variations in the thermal conductivity coefficients of different mineral constituents within the granite. For fractured granite, the stable temperature field distribution under various heat source temperatures consistently demonstrates isothermal contours concentrating around the fracture and diverging towards both sides. Furthermore, under the same heat source temperature, the temperature in the top region of the intact rock is higher than that in the fractured rock, and this trend becomes more pronounced with increasing heat source temperature.  
During geothermal exploitation, the injected low-temperature fluid in production wells exchanges heat with the rock mass, requiring continuous heat replenishment from the surrounding rock. However, the presence of fractures impedes this process to some extent, representing a significant factor contributing to reduced thermal extraction efficiency. Additionally, [PERSON] et al. [35] demonstrated through thermal resistance analysis and numerical calculations that the obstruction effect of fractures on heat replenishment diminishes as the angle between the fracture and the heat source increases. Therefore, in the implementation of geothermal development projects, the construction of thermal reservoirs should try to avoid the formation of large-scale unconnected fracture groups, which will greatly hinder the transfer of heat.
## 3 Results
### DEMs Evaluation
9\(\cdot\)280,657 points, 16.4\% bare-earth, 83.6\% vegetated} \\ \hline CRDIM2020 & 0.5 & 3.6 & \(-\)116/92 & 0.9 & \(-\)0.7 & 0.3 & 1.0 & \(-\)0.7 & 8.5 & 4.4 & \(-\)4.9 & \(-\)0.4 & 4.0 \\ MERIT & \(-\)0.7 & 3.9 & \(-\)143/95 & 0.9 & \(-\)2.2 & \(-\)1.3 & \(-\)0.4 & \(-\)3.0 & 11.0 & 5.7 & \(-\)8.3 & \(-\)1.9 & 3.3 \\ BEST & \(-\)7.9 & 6.2 & \(-\)150/86 & 2.3 & \(-\)12.0 & \(-\)9.9 & \(-\)4.6 & \(-\)5.4 & 11.6 & 6.7 & \(-\)11.7 & \(-\)5.0 & 1.6 \\ NASAADEM & \(-\)0.4 & 3.0 & \(-\)116/92 & 0.8 & \(-\)1.3 & 0.2 & 0.5 & \(-\)8.5 & 9.8 & 5.8 & \(-\)14.2 & \(-\)6.4 & \(-\)15.5 \\ SRTMCL1 & \(-\)3.5 & 3.0 & \(-\)121/80 & 0.7 & \(-\)4.5 & \(-\)2.8 & \(-\)2.5 & \(-\)11.0 & 9.6 & 5.6 & \(-\)16.6 & \(-\)8.9 & \(-\)4.3 \\ SRTM4 & \(-\)3.5 & 3.5 & \(-\)167/94 & 0.9 & \(-\)4.6 & \(-\)2.8 & \(-\)2.5 & \(-\)11.0 & 11.3 & 6.2 & \(-\)17.2 & \(-\)8.9 & \(-\)3.9 \\ SRTM3 & \(-\)3.5 & 3.5 & \(-\)149/94 & 1.0 & \(-\)4.6 & \(-\)2.8 & \(-\)2.5 & \(-\)11.0 & 11.3 & 6.2 & \(-\)17.2 & \(-\)8.9 & \(-\)3.9 \\ TanDEM-X & \(-\)2.3 & 13.0 & \(-\)201/115 & 4.2 & \(-\)6.0 & \(-\)1.0 & 2.2 & \(-\)8.6 & 15.5 & 8.4 & \(-\)17.7 & \(-\)7.1 & \(-\)0.3 \\ ASTER & 0.1 & 6.9 & \(-\)131/106 & 2.5 & \(-\)3.7 & 1.2 & 2.0 & \(-\)6.0 & 13.0 & 8.2 & \(-\)13.6 & \(-\)4.8 & 3.0 \\ AW3D30 & \(-\)3.3 & 3.1 & \(-\)112/89 & 0.7 & \(-\)4.1 & \(-\)2.8 & \(-\)2.5 & \(-\)11.3 & 9.8 & 5.9 & \(-\)17.2 & \(-\)9.2 & \(-\)4.3 \\ \hline  & \multicolumn{10}{c}{LVIS1998. 2570,730 points, 0.3\% bare-earth, 99.7\% vegetated} \\ \hline CRDIM2020 & 1.5 & 3.5 & \(-\)46/25 & 1.7 & \(-\)0.5 & 1.0 & 2.9 & \(-\)3.1 & 8.6 & 4.7 & \(-\)7.4 & \(-\)1.7 & 2.3 \\ MERIT & 1.3 & 3.7 & \(-\)67/31 & 2.2 & \(-\)1.1 & 0.7 & 3.5 & \(-\)6.1 & 10.8 & 6.2 & \(-\)11.6 & \(-\)3.9 & 1.5 \\ BEST & \(-\)2.3 & 4.6 & \(-\)67/28 & 2.3 & \(-\)4.7 & \(-\)2.6 & \(-\)0.2 &
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 7 Filtering Operation  
The deblocking filtering operations are performed on the edges of the luma and the chroma Cb and Cr pixel blocks based on the computed boundary strength value. Boundary strength is computed based on the coding information given by the codec.
# A Review of the Evolution Characteristics and Argillization of Clay Interbeds in Rockslides
## 3 Parameter Changes during the Argillization Process
### Microstructure and Characteristics
#### 3.4.2 Pore  
The porosity will show different phenomena at different pH values [37]. When the pH value was 1, a large number of honeycomb-like clay minerals, dissolution voids, micro cracks would appear. The voids were filled with a large number of iron and aluminum oxides, peroxides and aluminosilicate. When it was 3, local kaolinite enrichment would occur. When it was 5, sheet-like mica minerals would curl, peel off and accumulate at the entrance of dissolution holes and cavities. Sheet-like mica minerals would accumulate. When it was 7, flocculent clay minerals appeared, and micro-dissolution could be seen on the surface of albite.  
[PERSON], [PERSON] et al. [38] studied the argillization process through the response evolution law of pore distribution characteristics, load and dry-wet cycle times. Both expansibility and disintegration were observed in the mudstone under load. The load restrained the expansion of the mudstone and promoted its disintegration. The pore volumes of compacted samples decreased significantly after undergoing dry-wet cycles. It also exhibited bimodal characteristics, primarily distributed within the pores of 0.3 and 10.  
The underlying cause of the disintegration and eventual weakening was the microstructural changes in soil particles and pores. It was attributed to the change in the joining mode between particles [39]. Transition changed from surface-to-surface contact to surface-to-edge and surface-to-corner contact (Figure 6) [40]. The experiment led to an increase in the number of pores and a peak in the distribution of pore diameters. In addition, the rate of rock fracture growth would gradually decrease until reaching equilibrium as the number of dry-wet cycles increases, but the porosity disturbance would show a change from large to small [41].  
There are differences between the water absorption capacity and swelling force of the remolded samples of silty mudstone and sandstone-mudstone interlayers. Furthermore, it can be explained from the pore view [42]. The pores in the remodeled silty mudstone samples were intergranularly compacted residual pores, and the presence of intergranular pores allowed for the formation of reservoir spaces and channels for fluid migration. So, the samples were easy to absorb water. However, the pores in sandstone-mudstone interlayers consisted of intergranular pores and interlayer pores. Even though they had the same capacity, they could store and transport water, and the structure was different. Water can enter the sandstone-mudstone interlayer, remolding the sample quickly and uniformly. Eventually, it would lead to the swelling of the rock sample.
# Ion Beam Nanopatterning of Biomaterial Surfaces
## 2 Nanoscale Pattern Formation on Solid Surfaces during Ion Beam Irradiation
### Amorphous, Amorphizable, and Crystalline Surfaces  
An important factor that has to be considered when nanopatterning biomaterial surfaces by ion beam irradiation is the crystallinity and amorphizability of the target surface. The surfaces of many crystalline materials such as Si or quartz are amorphized within the first few seconds during heavy ion irradiation [28]. This amorphization may result in notable changes in the interaction of the irradiated surfaces with their environment, for instance, due to alterations in surface energy [40; 52] or reactivity [90; 91]. Metal surfaces, on the other hand, remain crystalline during ion irradiation, which typically results in a richer parameter and pattern space [77]. In particular, the crystallinity of the surface is often associated with Ehrlich-Schwoebel-type barriers to surface diffusion, which, thus, may become anisotropic. In this regime, the symmetry, shape, and orientation of the nanopatterns are no longer governed by the incident ion beam but rather by the crystal structure of the surface, resulting in faceted nanostructures that are oriented along certain crystallographic directions independent of the direction of the ion beam. Furthermore, the orientation and symmetry of these patterns often shows a pronounced dependence on temperature [92]. For polycrystalline surfaces, these effects may result in different orientations of the nanopatterns obtained on different grains [93]. Similar crystallinity-related effects may also be observed for certain amorphizable materials such as Si or Ge at high temperatures, where the ion-induced damage to the crystal lattice is dynamically annealed during sputtering [56]. Finally, it should be mentioned that some oxide surfaces such as MgO(001) [94] and \(\mathrm{A_{2}O_{3}(0001)}\)[89] were found to remain crystalline during irradiation even at room temperature, even though the resulting patterns were dictated by the ion beam and not the crystal structure of the surfaces.
This measure combines accuracy, precision, and recall into a single fuzzy function using the Unified And-Or (UAO) fuzzy operator. Several fuzzy decision rules are developed that can be used in the performance evaluation process. To the best of our knowledge, no such attempt has been reported in the literature. It is worth mentioning that while the proposed measure is evaluated in the context of spam email detection, the measure is generic and can be used in any problem domain concerned with performance evaluation. 2. As proof of concept, two deep learning models are used for comparative performance evaluation. These models are Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM). The use of BERT and LSTM is motivated by the fact that the models have seen a limited use for spam detection (as highlighted in the literature review in Section 2). This is a minor contribution, which supports the major contribution listed above. 3. As another minor contribution, a preliminary performance evaluation is carried out using several commonly used benchmark datasets. These datasets include Enron [32], Lingspam [38], and PU [39]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** \\ \hline A & 85\% & 80\% & 88\% \\ B & 76\% & 87\% & 72\% \\ C & 89\% & 78\% & 81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An example of different values of accuracy, precision, and recall for three hypothetical algorithms. The rest of the paper is organized as follows. Section 2 presents the literature review. Section 3 provides a brief description of two deep learning models, namely BERT and LSTM, as used in this study. Then in Section 4, the fuzzy-logic-based performance measure is discussed, with details on the Unified And-Or (UAO) aggregation operator (which serves as the basis of the new performance metric), the fuzzy membership functions, and the decision rules. Results and discussions are provided in Section 5. Finally, a conclusion and future research directions are given in Section 6.
## 4 Experiments and Results
### Benchmarking
#### 4.2.1 Multiple-Choice Benchmark  
The multiple-choice benchmark evaluated the ability to select GDPR-compliant data categories, testing knowledge and accuracy. The models were assessed based on three metrics: diversity, accuracy, and F1 score. All models showed diversity in their responses, reflecting their ability to generate varied yet contextually relevant responses. In terms of accuracy and F1 score (see Figure 8), Owen2-7B achieved high scores of 0.78. This highlights a high level of precision and recall in identifying GDPR-compliant data categories. This minimizes the risk of misclassifying sensitive data and reduces the likelihood of compliance violations. It also reduces the reliance on manual documentation, which is both time-intensive and prone to human error.  
The final results, summarized in Table 4, indicate that in the context of GDPR document drafting, open-source models can provide precise and varied responses without requiring proprietary resources. This finding supports the potential for open-source LLMs to be used in GDPR compliance tasks, offering accessible and adaptable solutions that meet regulatory requirements in a cost-effective way.