esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Notations and Preliminaries
: _Let \(0\leq n-1<\alpha\leq n\) and assume \(u(x,t):I\times[t_{0},t_{0}+R^{1/\alpha})\to\mathbb{R}\) can be expressed as the following multiple fractional PS about \(t=t_{0}\):_  
\[u(x,t)=\sum\limits_{k=0}^{\infty}f_{k}(x)\big{(}t-t_{0}\big{)}^{k\alpha},\ x\in I,\ t_{0}\leq t<t_{0}+R^{\frac{1}{\alpha}},\ R>0. \tag{8}\]  
_Let \(u(x,t)\) be continuous on \(I\times[t_{0},t_{0}+R^{1/\alpha})\) and \(\frac{\partial^{k\alpha}}{\partial t^{\alpha}}u(x,t)=T_{t}^{k\alpha}u(x,t)\in C \big{(}t_{0},t_{0}+R^{1/\alpha}\big{)}\), for \(k=1,2,\ldots\), then the coefficients \(f_{k}(x)\) are given by \(f_{k}(x)=\frac{T_{t}^{k\alpha}u(x,t_{0})}{\alpha^{k}(x)! }\), where \(T_{t}^{k\alpha}\) stands for sequential conformable_  
\[T^{k\alpha}u(x,t)=\frac{T^{\alpha}\cdot T^{\alpha}\cdots T^{\alpha}u(x,t)}{k- \mathrm{times}}\.\]  
_time-fractional derivative of order \(k\) that is defined by_  
_The nth-partial sum of the multiple fractional PS (Equation (8)) is given by_  
\[u_{n}(x,t)=\sum\limits_{k=0}^{n}f_{k}(x)\big{(}t-t_{0}\big{)}^{k\alpha},\ x\in I,\ t_{0}\leq t<t_{0}+R^{\frac{1}{\alpha}},\ R>0. \tag{9}\]  
**Corollary 1**. : _Let \(\alpha\in(n-1,n]\), \(T_{t}^{ka}u(x,t)\) exist at a neighborhood of a point \(t_{0}\) for \(k=0,1,2,\ldots,n+1,\) and \(u(x,t)\) can be expressed by the multiple fractional PS (Equation (8)) about \(t=t_{0}\) such that \(\left|T_{t}^{(n+1)a}u(x,t)\right|\leq M(x)\), for some \(n\in\mathbb{N}\). Then, for all \(\left(\tau_{0},\tau_{0}+R^{1/a}\right)\), the reminder \(\mathcal{R}_{n}(t)\) of the multiple fractional PS satisfies_  
\[\left|\mathcal{R}_{n}(x,t)\right|\leq\frac{M(x)}{\alpha^{n+1}(n+1)!}\big{(}t- t_{0}\big{)}^{(n+1)a}, \tag{10}\]  
_where \(\mathcal{R}_{n}(x,t)=\sum_{k=n+1}^{\infty}\frac{T_{t}^{ka}u(x,t_{0})}{a^{k}( k)! }\big{(}t-t_{0}\big{)}^{ka}=u(x,t)-\sum_{k=0}^{n}\frac{T_{t}^{ka}u(x,t_{0})}{a^{k}( k)! }\big{(}t-t_{0}\big{)}^{ka}.\)_
## 3 Results and Discussion
### Characterization of Gypsum/RT18 HC@AC Composite Boards  
#### 3.2.1 Density and Microtomography Results for GB/RT18 HC@AC Composite Boards  
Figure 5 displays the images of the reference gypsum board (GB) and the GB/RT18 HC@AC composite boards. The results of the density measurements are presented in Table S1, and the plotted versus % w.t. content of 3 RT18 HC@AC are shown in Figure 6. The density results reveal a systematic reduction in the density of gypsum boards with an increase in the RT18 HC@AC additive content. The reduction in density is attributed to the incorporation of the RT18 HC@AC additive, which introduces lightweight particles into the matrix of the gypsum. Despite the reduction in composite densities with an increasing additive content, all composites still meet the minimum density requirement of \(0.60\) g cm\({}^{-3}\) set by the European regulation UNE-EN 13279-1 for gypsum binders and gypsum plasters [57, 58].  
Figure 5: (**a**) Reference gypsum board (GB) and GB/RT18 HC@AC composite boards (\(200\times 200\times 12.5\) mm) with (**b**) 5, (**c**) 10, (**d**) 15, (**e**) 20, and (**f**) 30% w.t. RT18 HC@AC.  
Figure 6: Graphical plot of GB/RT18 HC@AC density versus % w.t. content of RT18 HC@AC additive.  
To validate the findings and examine the structure of the gypsum, X-ray computed microtomography (uCT) was performed. These results provide critical insights into the relationship between the additive concentration and the microstructural characteristics of the composites. The microscale images (Figure 7) reveal distinctive features corresponding to the different levels of additive incorporation. The colored scale bar in the images refers to the different X-ray attenuation values, and as a result, the different physical density values of the gypsum board components, beginning from the lower index (black) to the higher index (white). The black spots in all images correspond to the lower intensity areas (air voids), which are the porous areas of the boards. In the case of the GB reference board, the images mainly consist of green- and yellow-colored areas. At a 10% additive concentration, the microtomography images show initial alterations in the material's density, with obvious differences in the RT18 HC@AC particle distribution. The images exhibit less green and yellow areas, while new red and purple areas (lower index components) appear. The last probably corresponds to RT18 HC@AC particles, which are observed to be homogeneously integrated into the gypsum matrix, marking the beginning of structural modifications. When increasing the additive concentration to 20% and 30%, these lower index areas (red and purple coloured) become more intense, resulting in a more pronounced impact on the internal architecture. The microtomography images demonstrated an enhanced homogeneity in RT18 HC@AC dispersion, even at the board with 30% additive, indicating a uniform integration within the gypsum matrix.  
Figure 7: X-ray computed microtomography (micro CT) results from GB (**a**) and GB/RT18 HC@AC boards with 10% (**b**), 20% (**c**), and 30% (**d**) additive content.
# Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient
## 4 Analysis of Measurement Results
### Calculation of the Scale Factor \(k\)  
The reference resistor \(R\) is selected to be 100 k\(\Omega\), the sampling time interval \(\Delta\) is 1.78571 \(\upmu\)s, and the period of the square wave excitation signal is 16 ms. The fitted images of k values for capacitance 800 pF, 1200 pF, 2000 pF, 3500 pF, 15,000 pF, and 25,000 pF are shown in Figure 10. From the fitting curves in the figure, it is obvious that the slope of the fitted image, i.e., the scale factor \(k\), becomes gradually smaller as the capacitance increases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Nominal Value of Capacitance/pF**} & **Commercial Instrument** & **Excitation Signal** & **Sampling** & **Average Result of 100** \\  & **Measurements/pF** & **Period/ms** & **Frequency/Hz** & **Measurements/pF** \\ \hline \hline \multirow{3}{*}{18,000} & \multirow{3}{*}{18,402.7} & 6 & 168,000 & 18,397.6 \\  & & 20 & 42,000 & 18,421.9 \\ \cline{1-1}  & & 40 & 28,000 & 18,371.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of capacitance measurements with different cycle effects.  
The different capacitances are selected for direct fitting to obtain the proportional factor method of measurement; the difference of the response curve is proportional to the difference between the input and the output of the scale factor \(k\) and the results of the measurements are shown in Table 4.
## 5 Epigenetic Regulation of ALS
### Studies Using Human Cell and Mouse Models
#### 5.1.2 Noncoding RNAs  
Differential expression of various miRNAs was reported in the motor neurons of ALS patients [49; 50]. For example, miR-155 and miR-142 targeting the _UBQLN2_ gene and other genes associated with neurodegeneration are upregulated [51]. Moreover, genes involved in immune responses are dysregulated in ALS patients accompanying abnormal patterns of DNA methylation [49]. Other studies reported that miR-206 was upregulated in both skeletal tissues from ALS model mice targeting _SOD1_ and the plasma of ALS patients [52]. In addition, other miRNAs, such as has-miR-46469-5p and has-miR4299, are upregulated and downregulated, respectively, in ALS patients [53]. Both of these miRNAs target the _EPH receptor A4_ (_EPHA4_) gene that is associated with ALS in mouse and rat models [54]. Furthermore, a number of miRNAs, including miR-338 and miR-638, were reported to be dysregulated in the leukocytes of ALS patients [55]. Both miR-338 and miR-638 are involved in FTD and hereditary spastic paraplegia [56; 57]. In addition, FUS is involved in the biogenesis of miRNAs, including miR-409 and miR-495 [58]. Thus, many miRNAs play crucial roles in the pathogenesis of ALS (Table 2).  
Expansion of the hexanucleotide GGGGCC repeat in the _C9 orf7_ gene was identified as one of the causes of ALS/FTD [59; 60; 61; 62]. The _C9 orf7_ gene with repeat expansion is transcribed in both the sense and antisense orientations to form nuclear and cytoplasmic sense and antisense RNA foci containing TDP-43 and FUS. This suggests that the sequestration of TDP43 and FUS in this cellular compartment plays a role in the pathogenesis of ALS/FTD associated with _C9 orf7_[60; 63; 64]. Furthermore, TDP-43 and FUS may be involved in regulating the expression of lncRNAs such as _NEAT1_ and _MALAT1_. The depletion of TDP-43 or FUS upregulated both _NEAT1_ and _MALAT1_ lncRNAs in mouse ALS/FTD models (Table 2) [65; 66]. It was also reported that in the early stage of ALS pathogenesis, the formation of _NEAT1_ RNA foci increased post-mortem as a result of the abnormal subcellular localization of TDP-43 (Table 2) [67].
## 4 Photon Diagnostics Systems
### Online Photon Diagnostic Systems
#### 4.1.2 Photoelectron Spectrometer  
The photo-electron spectrometer (PES) measures the spectrum and polarization of the photon pulse based on an angular resolved time-of-flight measurement of photo-electrons [55; 56]. This device is integrated initially only in the SASE3 beam transport, because for soft X-rays one cannot employ crystal-based schemes to measure the spectrum, and instead the energy distribution of XFEL-generated photo-electrons can be used to deduce the center and width of the photon energy spectrum. In addition, it is planned to employ variable polarization schemes at the SASE3 FEL source, hence requiring measuring and monitoring of the polarization state. The PES has a spectral resolving power of \(\Delta\)E/E \(\leq 10^{-4}\) and the polarization direction and degree can be measured with an accuracy of 1%.
# Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study
## 4 Tele-Assessment of Executive Functions
Neither version involves the use of letters or numbers between stimuli. This is important because people with ADHD frequently have learning disabilities (e.g., dyslexia and dyscalculia) that may interfere with their performance on CPTs employing numbers and/or letters [78]. The MOXO d-CPT assesses the individual's attentional skills through four different indices: Attention, Timeliness, Impulsivity, and Hyperactivity. These indices assess different aspects of ADHD: an attention deficit, problems with time performance, impulsivity, and hyperactivity [52]. The measurement of these four indices enables the clinician to obtain a specific profile of attentional skills for everyone, allowing them to identify the subject's strengths and weaknesses [79]. The description of this tool, the only example of a standardized tele-assessment test of attention and hyperactivity/impulsivity in adults, demonstrates the utility of proposing and standardizing additional tools for the in-depth study of executive functions in adults. Considering the impact of EF problems on the quality of life of people with ADHD, it is important to better understand executive dysfunctions in young adults with this disorder to establish neuropsychological profiles and enable the development of more effective interventions. The present study aimed to provide an overall picture of the EF profile of young adults with ADHD through the analysis of EF components. In particular, the objectives were to analyze differences in the performance of tele-assessment EF tasks between adults with and without ADHD and consider these differences according to the type of conditions and stimuli. It was expected that young adults with ADHD would show a more compromised EF profile than young adults without ADHD.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 2 Data and Methods
### Data
#### 2.2.1 SIF Products
GRSIF001 effectively captures vegetation photosynthetic dynamics, including seasonal phenology shifts and stress responses, and is suitable for studies on drought monitoring, carbon cycle modeling, and fine-scale ecosystem productivity assessment. The summary of three SIF products with different spatial resolutions is shown in Table 1.
## 4 Simulation
### Simulation Platform and Parameter
\tag{45}\]  
where \(x_{1}\) and \(x_{2}\) are, respectively, the displacement error and velocity error of the flexible manipulator; \(x_{3}\) and \(x_{4}\) are, respectively, the displacement error and velocity error of motor shaft; \(l_{L}\) is the moment of inertia of the flexible manipulator; \(l_{m}\) is the mass of the flexible manipulator. \(m_{m}\) is the mass of the motor shaft; \begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Physical Meaning** & **Unit and Size** \\ \hline \(B\) & Bearing viscous friction coefficient & 0.015 \\ \(L\) & Reactance & 0.0008 \(\Omega\) \\ \(D\) & Load diameter & 0.05 m \\ \(R\) & Resistance & 0.075 \(\Omega\) \\ \(m\) & Connecting rod mass & 0.5 kg \\ \(J\) & Actuator torque & 0.05 N-m \\ \(l\) & Connecting rod length & 0.6 m \\ \(K_{b}\) & Back electromotive force coefficient & 0.085 \\ \(M\) & Load mass & 0.05 kg \\ \(K_{t}\) & Torque constant & 1 \\ \(g\) & Acceleration of gravity & 9.8 m/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The physical meaning of parameters in dynamic equations. and \(g\) is the gravitational acceleration. The variable parameters of the linear space state equation are shown in Table 3.  
The second set of the backstepping control method based on nonlinear state space equations based on an RBF neural network is designed for comparison. The dynamic equations established are as follows:  
\[\left\{ \begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=-\frac{EI_{k}}{I_{k}}x_{1}+\frac{EI_{k}^{2}EI_{k}^{2}}{R_{m}I_{k}}x _{4}-\frac{EI_{m}EI_{k}}{R_{m}I_{k}}u-\frac{m_{m}EI_{k}}{I_{k}}\sin(x_{1}+x_{3} )\\ \dot{x}_{3}=x_{4}\\ \dot{x}_{4}=\frac{EI_{k}}{I_{k}}x_{1}-\frac{EI_{k}^{2}EI_{k}^{2}}{R_{m}I_{k}}x _{4}+\frac{EI_{m}EI_{k}}{R_{m}I_{k}}u\end{array} \right. \tag{46}\]  
The parameters of the nonlinear state equations are shown in Table 4:  
In above equation, \(u\) is the input of the controller, \(u=k_{1}x_{1}+k_{2}x_{2}+k_{3}x_{3}+k_{4}x_{4}\). In this equation, \(k_{1}=1\), \(k_{2}=0.02\), \(k_{3}=10\) and \(k_{4}=0.01\) are the gain coefficients of each item. The simulation time is set to 1.3 s. Since the flexible manipulator is the end of the whole rigid-flexible manipulator system, the whole system is not completely flexible, so the natural frequency of the flexible manipulator is not considered. The natural frequency of the whole system is set to 0.
## 4 Results
### Spatial-Temporal Characteristics
#### 4.3.2 Analysis of Bus Headway Stability  
The coefficient of variation of headway, which is generally used to evaluate the stability of headway, is calculated as follows [5]:  
\[Cov=\frac{standard\;devition\;of\;headway}{mean\;scheduled\;headway} \tag{10}\]  
1. Daily variation Figure 7 shows the daily variation of \(Cov\) at each bus stop based on the AVL data of route 113. The \(Cov\) generally had a local peak from 7:00 to 9:00 and 17:00 to 20:00 when commuter trips significantly increase and traffic conditions worsen; thus, the stability of headway is easily affected by more random factors. In addition, there was a peak time that appeared from 20:00 to 21:00 after the regular evening peak, probably caused by overtime commuter trips. Furthermore, the \(Cov\) at stops 2 and 17 were more stable than at following stops. 2. Variation on a work day and non-work day The AVL data of route 113 from 15 April to 21 April 2019 are divided by bus stops and dates, of which the 20 th and 21 st are non-working days and others are working days. Figure 8 shows that the date with the maximum \(Cov\) is Wednesday, 17 April 2019, while the minimum occurred on Saturday, 20 April 2019. The \(Cov\) on a non-work day was obviously lower than on a work day.  
Figure 8: Variation of \(Cov\) on a work day and non-work day.  
Figure 7: Daily variation of \(Cov\).  
Variation at different bus stops  
To clearly show the _Cov_ at different bus stops on a work day and non-work day, several bus stops were selected, as shown in Figure 9. The _Cov_ increased severely from 7:00 to 9:00, probably because of unexpected travel demands and traffic congestion, which is more likely to cause bus bunching. The _Cov_ on a non-work day was about 1.0, and the local peak value mainly appeared from 11:00 to 13:00 and 15:00 to 17:00. In general, the trend was relatively stable throughout the non-work day.
# Fracturing Fluids and Their Application in the Republic of Croatia
## 5 Fracturing Fluids--Composition and Additives
### Seatawater Fracturing Technology
They concluded that base gel viscosity was not affected whether HPG or CMHPG polymers were used to make seawater-based fluid, that seawater-based fluid could meet the stability criteria but that it was more stable with CMHPG than with HPG, and full hydration is slightly delayed when using both polymers with seawater. The crosslinking mechanism is affected when seawater is used, and the fluid is less stable than fresh water with HPG and CMHPG. [PERSON] et al. (2016) developed a low-residue fracturing fluid that uses seawater as a base fluid and a low-residue polymer, a polysaccharide gelling agent (PGA), crosslinked with zirconium that has a pH \(<\) 10 to minimize damage and residue encountered using other fluids. This fluid exhibits good thermal stability within a temperature range of 65 to 163 \({}^{\circ}\)C (3.6 kg/m\({}^{3}\) PGA, 500 mPa\(\cdot\)s after 100 min at 93 \({}^{\circ}\)C). The thermal stability of PGA seawater-based fluid was improved when compared to the alternative hydroxypropyyl guar (HPG) seawater-based fluid. This fluid system has excellent proppant transport (2.5 h at 93 \({}^{\circ}\)C) and generates low residue content upon breaking (1%). A regained permeability of 92% was obtained from a sandstone core, demonstrating the low-damage nature of the fluid [66]. A new PGA crosslinked with zirconium seawater-based fracturing fluid was used in the three horizontal wells: A (a pure sandstone formation with natural fractures), B and C (both wells were identified as naturally fractured sandstone formations with a high amount of carbonates) in the offshore Romanian Lebada field located in the Black Sea. This new seawater-based fracturing fluid was used in the pad and subsequent proppant-laden stages because of moderate permeabilities in the range of 0.1 to 2\(\cdot\)10\({}^{-3}\) um\({}^{2}\). Operations were performed during a short time frame using multistage fracturing technology. A total of 20 hydraulic fracturing stage operations were designed and executed successfully in 2015 and 2016. In well A, six zones were treated with 515 metric tons of proppant carried by 1651 m\({}^{3}\) of crosslinked fluid. In well B, seven zones were treated with of 508 metric tons of proppant carried by 2055 m\({}^{3}\) of crosslinked fluid. In well C, seven zones were treated with total of 510 metric tons of proppant carried by 2209 m\({}^{3}\) of crosslinked fluid. A total of 1533 metric tons of 16/20-mesh resin-coated proppant (RCP) was pumped in 5915 m\({}^{3}\) of crosslinked gel [66]. Properly designed seawater-based fracturing fluids offer the oil industry an acceptable solution to the economic and environmental challenges associated with the large amounts of fresh water required for the multistage hydraulic fracturing of unconventional deposits.
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 3 Results
### Ablation Experiments  
#### 3.3.1 Effectiveness of DarkNet Extracted Features  
In order to evaluate the efficacy of the chosen DarkNet network structure for feature extraction, we employed DeepLab V3+ and compared it with two other network structures, namely Xception [32] and MobileNetV2 [33], which were used as the control group. The experiment involved performing road semantic segmentation on remote-sensing images. The results of the experiment are presented in Table 1.  
As DANet is primarily utilized in the problem of semantic segmentation binary classification, the CHN-CUG Roads Dataset of remote-sensing images has been chosen as the dataset for evaluation purposes. The evaluation index is calculated based on the predicted segmentation results of this dataset.  
Figure 8: The provided figure displays the original map and labeling results for the Macau region in the dataset. The **left** side of the image shows the map, while the **right** side displays the corresponding labels.
# Personalized Context-Aware Authentication Protocols in IoT
## 4 Discussion  
The study so far has established a few points that could serve as a basis for further research:  
* As has been seen during the review phase, there are many methods of varying usefulness available to authenticate a person only by their individual characteristics rather than their knowledge. A lot of these are not context-sensitive and only use a singular biometric scan to assure the identification. Thus, they remain susceptible to mimicking or session hijacking.
* At the same time, most people using any kind of networked personal device during their daily routine produce a tremendous amount of personal data from various sources. Some of these data are publicly available, and some have to be protected due to privacy reasons.
* IoT devices used by any individual have a wide array of sensors that are mainly dedicated to the primary function of the device. In more significant numbers and when used simultaneously, they are able to build a digital representation of the owner's behaviors and routines.
* Given all of the above, our research shows proof-of-concept methods and algorithms that could utilize the IoT's strengths and downplay its weaknesses, relying on distributed and already available data.  
As such, the proposed methods seem to fill a market niche, enforcing IoT devices' connectivity and functionality while downplaying their limited computational power by distributing the load and authentication tasks between many devices. Care is being given not to use or send personalized data out of the device that collected it (with the owner's consent). Further study of the method's effectiveness is still ongoing, and care is being givenfor it to remain ethical, safeguard privacy laws, and at the same time, secure individuals' data and communication by embracing their humanity and individuality.  
R.B.: Conceptualization, methodology, validation, formal analysis, investigation, writing--original draft, and visualization. M.R.O.: Conceptualization, methodology, validation, writing--review and editing, and supervision. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the funds of the Polish Ministry of Education and Science assigned to AGH University of Science and Technology. This research project was supported by the program \"Excellence initiative--research university\" of AGH University of Science and Technology.  
Not applicable.  
Not new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.