esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Chemical Activating Agent on the Properties of Activated Carbon from Sago Waste
## 3 Results
### Thermal Decomposition  
Figure 4a shows thermogravimetric curves in activated carbons from sago waste, indicating gradual mass loss during the thermal investigation. Meanwhile, Table 3 depicts the moisture, volatile matter, fixed carbon, and ash percentage in reference to the curves. The samples showed three mass loss steps, where the first step below 70 \({}^{\circ}\)C indicated mass losses up to 17% due to the physically adsorbed water on the material, during storage. The decomposition product of the second mass loss steps, in the period between 100 to 400 \({}^{\circ}\)C, are assigned to the surface groups that formed during the activation process. Furthermore, the third mass loss steps at high temperatures are assigned to carbon skeleton decomposition. Rapid weight loss at temperatures of 400 to 600 \({}^{\circ}\)C indicated the typical activated carbon behavior, due to the formation of ash as a result of devolatilization [49; 50]. However, the carbon prepared with KOH activating agent showed the lowest moisture,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{4}{c}{**Mass (\%)**} \\ \cline{2-5}  & **H\({}_{3}\)PO\({}_{4}\)** & **ZnCl\({}_{2}\)** & **KMnO\({}_{4}\)** & **KOH** \\ \hline C & 75.88 \(\pm\) 0.05 & 76.52 \(\pm\) 0.03 & 57.06 \(\pm\) 0.05 & 81.30 \(\pm\) 0.04 \\ O & 21.28 \(\pm\) 0.07 & 21.56 \(\pm\) 0.05 & 42.93 \(\pm\) 0.10 & 18.70 \(\pm\) 0.07 \\ P & 2.84 \(\pm\) 0.01 & - & - & - \\ Zn & - & 0.09 \(\pm\) 0.00 & - & - \\ Cl & - & 1.83 \(\pm\) 0.02 & - & - \\ K & - & - & 0.01 \(\pm\) 0.01 & not detected \\ Mn & - & - & not detected & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elemental composition of activated carbons from sago waste prepared using various chemical activating agents.  
Figure 3: Scanning electron micrographs (10,000\(\times\)) of activated carbons from sago waste prepared using (**a**) H\({}_{3}\)PO\({}_{4}\), (**b**) ZnCl\({}_{2}\), (**c**) KMnO\({}_{4}\), and (**d**) KOH. (Bar length = 1 \(\upmu\)m).  
ash, and the highest fixed carbon contents of 0.92 %, 10.90 %, and 43.78, respectively. The low moisture content may be the reason for the low total pore volume of the sample prepared with KOH [8]. In addition, the activated carbon samples prepared from KOH have unique decomposition profiles with intense exothermic peaks from the thermogravimetric derivative curves. This is related to organic matter thermal decomposition, which means the last mass loss step may be related to the carbon skeleton of the sample, leading to low ash content.
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 4 Identity-Based Full Homomorphic Encryption Scheme
### Security Reduction  
**Theorem 4**.: _If the \(\left(\mathbb{Z}_{q},n,\overline{\mathbf{v}}_{\alpha}\right)-\)LWE hardness assumption holds, the IBFHE scheme given in this section is IND-sID-CPA secure._  
**Proof.** The security of the IBFHE scheme proposed in this section can be proved based on the IBE scheme constructed in the previous section, because the homomorphic **IBFHE.Eval** algorithm in the IBFHE scheme is public and has no effect on the security of the scheme. Under the LWE assumption, let \(\mathbf{C}=A_{id}^{\prime T}\mathbf{Y}+\mu\mathbf{M}+\mathbf{E}\in\mathbb{Z}_{q}^{m^{\prime} \times N}\) be the ciphertext obtained by encrypting the plaintext message 0 in the IBFHE scheme, which can be regarded as the concatenation of the N ciphertexts of a bit 0 in the IBE scheme. It can be seen from theorem 2 that \(\mathbf{C}\) and any random uniform matrices in \(\mathbb{Z}_{q}^{m^{\prime}\times N}\) are indistinguishable. Therefore, according to the definition of the IND-sID-CPA security model, the IBFHE scheme proposed in this section is IND-sID-CPA security. \(\square\)
## 2 Preliminary Concepts
### Inner Product Modulo 2 Operation  
Inner product modulo 2 is a binary operation defined on bit vectors of equal length. This operation requires two bit vectors \(\mathbf{x},\mathbf{y}\in\mathbb{B}^{m}\) as inputs and returns their inner product, denoted by \(\mathbf{x}\bullet\mathbf{y}\). Assuming that \(\mathbf{x}=x_{m-1}\ldots x_{0}\) and \(\mathbf{y}=y_{m-1}\ldots y_{0}\), then \(\mathbf{x}\bullet\mathbf{y}\) is given by  
\[\mathbf{x}\bullet\mathbf{y}\coloneqq x_{m-1}y_{m-1}\oplus\cdots\oplus x_{0}y _{0}\;, \tag{3}\]  
where the symbol \(\coloneqq\) stands for \"is defined as\", and \(\oplus\) is addition modulo 2. This operation is employed in many important formulae of quantum computation and information. One such fundamental formula is given below; it concerns the application of the \(m\)-fold Hadamard on an arbitrary ket \(|\mathbf{x}\rangle\), and we shall use it during the detailed analysis of our quantum protocol. This formula is proved in many textbooks, such as [63; 67]:  
\[H^{\otimes m}|\mathbf{x}\rangle=\frac{1}{\sqrt{2^{m}}}\sum_{\mathbf{z}\in \mathbb{B}^{m}}(-1)^{\mathbf{z}\bullet\mathbf{x}}|\mathbf{z}\rangle\;. \tag{4}\]  
An important property of the inner product modulo 2 operation is a special type of symmetry that can be used to partition the set of bit vectors into two subsets of equal cardinality. Specifically, any constant bit vector \(\mathbf{c}\) of \(\mathbb{B}^{m}\), other than \(\mathbf{0}\), exhibits the following property: for exactly half of the bit vectors \(\mathbf{x}\in\mathbb{B}^{m}\), \(\mathbf{c}\bullet\mathbf{x}=0\), and for the remaining half, \(\mathbf{c}\bullet\mathbf{x}=1\). Clearly, \(\mathbf{0}\) is an exception to the above rule because if \(\mathbf{c}=\mathbf{0}\), then for all \(\mathbf{x}\in\mathbb{B}^{m}\), \(\mathbf{c}\bullet\mathbf{x}=0\). In accordance with [68], this fact is henceforth referred to as the Characteristic Inner Product (CIP) property:\[\mathbf{c}=\mathbf{0} \Rightarrow \text{for every }2^{m}\text{ bit vector }\mathbf{x}\in\mathbb{B}^{m},\;\mathbf{c}\bullet\mathbf{x}=0 \tag{5}\] \[\mathbf{c}\
eq\mathbf{0} \Rightarrow \left\{\begin{array}{l}\text{for }2^{m-1}\text{ bit vectors }\mathbf{x}\in\mathbb{B}^{m},\;\mathbf{c}\bullet\mathbf{x}=0\\ \text{for }2^{m-1}\text{ bit vectors }\mathbf{x}\in\mathbb{B}^{m},\;\mathbf{c} \bullet\mathbf{x}=1\end{array}\right\} \tag{6}\]  
For our protocol, and also the purpose of creating decoys, we utilize two other signature states, namely \(|+\rangle\) and \(|-\rangle\), defined as  
\[|+\rangle=H|0\rangle=\frac{|0\rangle+|1\rangle}{\sqrt{2}} \tag{7}\]  
\[|-\rangle=H|1\rangle=\frac{|0\rangle-|1\rangle}{\sqrt{2}} \tag{8}\]  
During the execution of the protocol itself, all measurements are carried out in relation to the computational basis \(\{|0\rangle,|1\rangle\}\). However, during the test for eavesdropping detection, decoys are also measured with respect to the Hadamard basis.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### CubeSat Platform Limitations and Intrinsic Challenges
Similarly, due to limited space again, a variety of CubeSats lack propulsive systems altogether or feature a very small propulsion system with limited \(\Delta v\) potential. In the case of LEO satellites this can be translated to reduced mission durations as the residual atmosphere drag effects lead to relatively quick orbital altitude degradation and eventual deorbit of the satellites. On the other hand, in the case of CubeSats used for interplanetary missions, this can be translated into a very limited \(\Delta v\) potential that is available to the satellite for the performance of trajectory correction, orbital insertion, and station keeping maneuvers. The low cost of CubeSat development can in some cases be offset by other factors. For example, the industry-wide push for extensive use of low-cost, standardized COTS equipment can lead to high failure rates in demanding and radiation-heavy operational environments such as the Van Allen radiation belts or the interplanetary space, leading to duplicate costs because of the need for the development and launch of replacement missions, or to high initial development costs because of the need to incorporate high-cost, custom designed and built radiation tolerant components in the design of the satellite for the execution of such missions. Furthermore, to fully realize their potential, CubeSat constellations still require the design and construction of ground-based infrastructure which includes assembly, integration and testing facilities with adequate capacity, as well as a large number of dedicated ground-stations and the formation of networks. The establishment of such infrastructure is of course characterized by significant development and operation costs, something that can significantly offset the per-unit savings of the CubeSat constellations in question. Methodology, K.-P.B. and G.M. ; investigation, K.-P.B. and G.M. ; writing--original draft preparation, K.-P.B. and G.M. ; writing--review and editing, V.K. and V.L. ; supervision, V.K. and V.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. During the preparation of this manuscript/study, the authors used ChatGPT by OpenAI (version GPT-4o) and DeepSeek (version DeepSeek-V3) for supplementing the bibliographical review procedure, text refinement, and reference formatting. The authors have reviewed and edited the output and take full responsibility for the content of this publication. The authors declare no conflict of interest. The following abbreviations are used in this manuscript. The list is limited only to acronyms of physical and engineering definitions, space science and engineering entities and other abbreviations commonly used in the field of astronautics. Acronyms such as mission and payload names are excluded as they may be found in the relevant sections of the main text.
# Automatic Identification of Children with ADHD from EEG Brain Waves
## 4 Preprocessing  
EEG signals contain different artifacts and noises that should be removed before the analysis. The sampling frequency of the EEG signal is 128 Hz. For the preprocessing method, we used a 4 th-order FIR Butterworth filter with the cutoff frequencies 0.5 Hz and 63 Hz. To remove the power-line noise, a 50 Hz notch filter was used. We designed the notch filter using a stop Butterworth filter with the cutoff frequencies 49 Hz and 51 Hz.  
After filtering, the time-series data were filtered in EEG frequency bands (delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and lower gamma (30-63 Hz)) using an FIR filter with zero phase distortion. As ADHD patients have a high theta-to-beta ratio, we worked with only four frequency bands: delta, theta, alpha, and beta.  
For each subject, the time-series EEG signal was divided into 2 s segments for each channel with 50% overlap. This means each EEG window contained 1 s of previous and 1 s of current windows. In the dataset, for the control group, the minimum task duration was 50 s for one subject, and the maximum task duration was 285 s for one subject with ADHD. As the task timing differed for each subject, the number of segments varied for every subject [24]. For the classification of the EEG signal, we followed the pipeline shown in Figure 2.
# Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions
## 2 Adaptive Ellipsoidal Clustering Technique
### Objective Function  
The calculation of fitness for a given state indicates the quality of the solution it represents. Initially, points are assigned to clusters based on the Euclidean distance, determining the minimum distance between centers and points. This step is crucial for applying the assignment of the ellipsoidal points from Equation (14), as discussed in Section 2.3  
In this work, we use \(SF\) as an objective function that is optimized by the newly proposed ellipsoidal clustering silhouette function (\(ESF\)). This involves applying \(SF\) (Equation (19)) to the transformed data. \(ESF\) demonstrated promising results for data with ellipsoidal clusters, as shown in Figure 3b,d. However, for spherical clusters, such as those in Dataset_3_2 (Figure 2b), ESF struggles to identify the optimal number of clusters (three classes) and instead identifies only two clusters, as depicted in Figure 5a. Efforts were focused on preventing such scenarios, as pure SF outperformed ESF in identifying the optimal number of clusters for spherical data. To address this, the nearest-neighbor algorithm is employed alongside the \(ESF\). The key difference is that the nearest-neighbor algorithm is applied to the original data using the same cluster points, as detailed in Section 2.4.  
The decline in the ESF's performance occurred because two clusters were incorrectly assigned as a single spherical cluster, as per Equation (14). As a result, the diameter of this spherical cluster was significantly smaller than the combined diameter of the two clusters in the original data. This discrepancy creates a scenario in which the objective function \(SF\) for \(K=2\) might produce a higher value than for \(K=3\) (the optimal number of clusters), as illustrated in Figure 5a.  
We introduce a new objective function, \(ESF\)-\(NN\), which combines the \(SF\) based on the ellipsoidal clustering technique with the nearest-neighbor distance. This function is calculated by adding the values of \(SF\) from Equation (19) and the value of the nearest-neighbor algorithm \(NN\) from Equation (18), as follows:  
\[ESF\text{-}NN(\text{K})=SF(K)+NN(K). \tag{21}\]  
Appropriate clusters are identified when the \(ESF\)-\(NN\) value is sufficiently large, ideally falling within the range of zero to two. Figure 5b illustrates the final result for the same dataset (Dataset_3_2) after applying \(ESF\)-\(NN\), where \(ESF\) alone previously failed to determine the optimal solution.
# Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems
## 2 Methodology
### Calculation Tools
#### 2.1.3 Cost Model  
Besides AEP, capital expenditure (CAPEX), operational expenditures (OPEX) and financing are needed as inputs to perform overall economic analysis. These are calculated using the models described in this section.  
The CAPEX herein represents the overall cost of a single wind turbine. It includes turbine manufacturing cost and the sharing portion of wind farm construction and maintenance costs. The manufacturing cost uses the individual component masses for the turbine to evaluate the costs of individual components. The blade mass is derived by a structural model using beam finite element theory and classical laminate theory to determine the effect of the design loads [2]. The blade structure is formed from a double web I-beam. The material used for the bulk of the blade is glass fiber with reinforced polyester and foam.  
A set of physics-based models are used to estimate the sizes and costs of a subset of the major load-bearing components (the main shaft, gear box, bedplate, gearbox, tower) and parametric formulations representative of current wind turbine technology are used to evaluate sizes and costs for the remaining components (the hub and yaw system). For example, the hub is treated as a thin-walled, ductile, cast iron cylinder with holes for blade root openings and main shaft flanges. The bedplate module is separated into two distinct front and rear components. The rear frame is modelled as two parallel steel I-beams and the front frame is modelled as two parallel ductile cast iron I-beams. The physics-based models have internal iteration schemes based on system constraints and design criteria, which are described in Reference [21].  
The balance-of-station costs vary with the rotor diameter and tower height, and the OPEX are accounted for by taking a small, fixed percentage of the capital cost as the annual maintenance cost [18]. Decommissioning costs are also considered in this model.  
Figure 1: Blade root flap-wise moments of SLC and full DLC calculations.
## 3 Results
### Relationships among Climate, Phenology and GPP  
.5.1 Responses of Phenology and GPP\({}_{\text{max}}\) to Seasonal Precipitation and Air Temperature  
Figure 7 illustrates the Pearson correlation coefficients between vegetation phenology and GPP\({}_{\text{max}}\) and their corresponding seasonal precipitation and air temperature in the SRYR from 2001 to 2019. We found that seasonal temperature had a stronger influence on SOS, EOS, POS and GPP\({}_{\text{max}}\) than seasonal precipitation did. Each phenological factor had a significant correlation with the air temperature in their corresponding season in the SRYR. For example, spring phenology (SOS) was significantly and negatively correlated to average spring air temperature (\(p<0.05\)). The increasing spring air temperature could stimulate photosynthetic activities and promote vegetation growth from the cold environment [13; 61]. The spring phenology in the northeastern Tibetan Plateau was strongly advanced by increasing temperature and slightly advanced by precipitation, which is consistent with our results [62].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pattern** & **GPP\({}_{\text{ann}}\)** & **GPP\({}_{\text{spr}}\)** & **GPP\({}_{\text{sum}}\)** & **GPP\({}_{\text{aut}}\)** & **Area Proportion** \\ \hline I & \(+\) & \(+\) & \(+\) & \(-\) & 51.79\% \\ II & \(+\) & \(+\) & \(+\) & \(+\) & 32.32\% \\ III & \(-\) & \(+\) & \(-\) & \(-\) & 7.75\% \\ IV & \(-\) & \(+\) & \(+\) & \(-\) & 3.48\% \\ V & \(+\) & \(-\) & \(+\) & \(-\) & 1.31\% \\ VI & \(+\) & \(-\) & \(+\) & \(+\) & 0.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Six main patterns of the trends of annual and seasonal GPP in the SRYR from 2001 to 2019. “+” indicates an increasing trend while “\(-\)” indicates a decreasing trend.  
Autumn phenology (EOS) was significantly and positively correlated with autumn temperature (\(p<0.05\)). The increasing autumn temperature enhanced the photosynthetic activities and decelerated chlorophyll degradation during leaf senescence processes [63; 64]. In addition, increasing autumn temperature delayed the occurrence time of first frost and reduced the impacts of it on EOS [35; 65]. However, EOS showed an insignificantly advancing trend, although autumn temperature showed a significantly increasing trend (\(p<0.05\)) (Figure 2h), which might result from the stronger impacts of other factors, such as spring and summer phenology, on soil water and nutrients in autumn.  
Summer phenology (POS) had a significantly negative correlation with summer temperature (\(p<0.05\)). The results also showed that GPPmax had a significantly positive correlation with summer temperature (\(p<0.01\)), consistent with the study conducted on the QTP [23], which found that GPPmax was dominated by temperature rather than precipitation in most regions of the QTP.
# Predicting Compressive Strength of
## 2 Related Work  
It was found that various machine learning techniques were used to predict the state of various materials' features based on SEM images of these materials. [PERSON] et al. used machine learning-based classifiers and statistical analyses to predict the compressive strength of metakaolin-based geopolymers. They applied random forests; Naive Bayes, and the k-nearest neighbors classifiers, while the statistical analysis was based on an analysis of variance (ANOVA) [19].  
The prediction of the compressive strength of concrete modified with glass cullet was conducted by [PERSON] and co-workers [20], who used genetic programming to build the prediction models of the compressive strength. Their sensitivity and parametric analyses confirmed that the compressive strength is very sensitive to curing time and temperature, as well as to the surface area of the particles.  
[PERSON] et. al. employed a deep learning technique to establish an end-to-end mapping of the nonlinear relationship between SEM images of the cemented paste backfill and its mechanical strengths. However, the average accuracy of the prediction of the mechanical strength was very low [21]. There more examples of this approach e.g. [22; 23].  
Perhaps the most related study to this paper is the work of [PERSON] and co-workers [24], which explores the application of computer vision and machine learning methods to predict the compressive strength of consolidated molecular solid (triaminotrinitrobenzene (TATB)) samples based on their SEM images. However, the TATB samples are not as complex as the CSRE ones, and therefore the reported results may not be able to be generalized to the CSRE, the core of this paper.  
The deep learning techniques allow for a wide range of their applications and better solutions for many problems in the computer vision domain, as shown by [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35], and others. All of these works recommend the use of deep learning techniques to find better solutions in the area of computer optics, in the computer vision domain, which is the main motivation for using the deep learning technique in this paper.  
To the best of the authors' knowledge, this work is the first attempt to the problem of predicting the compressive strength of CSRE based on SEM images using computer vision and deep learning. The main contribution of this paper is twofold. It proposes a new deep learning approach to solve the research problem; secondly, it describes a new SEM image database of the CSRE samples, which was created for this study.  
Figure 1: Wall made from compacted earth. The compacted layers of mixed soil are shown.