esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Previous work and definitions  
### Definitions and Notation  
We start by giving a few definitions and setting up the nomenclature that we use along the paper. A _string_\(s\) is a sequence of characters \(s_{1}\ldots s_{n}\), its length, \(|s|=n\). \(\epsilon\) denotes the empty string, and \(s[i:j]=s_{i}\ldots s_{j}\), \(s[i:j]=\epsilon\) if \(j<i\). We say that a word \(w\) occurs at position \(i\), if \(w=s[i:i+|w|-1]\). \(w\) is a repeat of \(s\) if it occurs more than once in \(s\) and \(|w|>1\). We denote by \(\textit{repeats}(s)\) the set of substrings of \(s\) that occur more than once and of length greater than one. For example \(\textit{repeats}(abaaab)=\{ab,aa\}\) and \(aa\) occurs at position 2 and 3.  
A _context-free grammar_ is a tuple \(\langle\Sigma,\mathcal{N},\mathcal{P},S\rangle\), where \(\Sigma\) is the finite set of _terminals_ and \(\mathcal{N}\) the finite set of _non-terminals_, \(\mathcal{N}\) and \(\Sigma\) disjoint. \(S\in\mathcal{N}\) is called the _start symbol_ and \(\mathcal{P}\) is the set of _productions_. Each production (also called _rule_) is of the form \(A\rightarrow\alpha\) where its _left-hand side_\(A\) is a non-terminal and its _right-hand side_\(\alpha\) belongs to \((\Sigma\cup\mathcal{N})^{*}\). We say \(\alpha\xrightarrow{\ \ }\beta\), if \(\alpha\) is of the form \(\delta C\delta^{\prime}\), \(\beta=\delta\gamma\delta^{\prime}\) and \(C\rightarrow\gamma\) is a production. A sequence \(\alpha\xrightarrow{\ \ }\alpha_{1}\xrightarrow{\ \ }\ldots\xrightarrow{\ \ \ } \beta\) is called a _derivation_ and in this case we say that \(\alpha\)_produces_\(\beta\) and that \(\beta\)_derives_ from \(\alpha\) (denoted by \(\alpha\Rightarrow\beta\)).  
Given a string \(\alpha\) over \((\mathcal{N}\cup\Sigma)^{*}\), its _constituents_ (\(\textit{cons}(\alpha)\)) are the possible strings of terminals that can be derived from \(\alpha\). Formally, \(\textit{cons}(\alpha)=\{w\in\Sigma^{*}:\alpha\Rightarrow w\}\). The constituents of a grammar are all the constituents of its non-terminals. The _language_ is the set of constituents of the start symbol \(S\), \(\textit{cons}(S)\).  
Because the Smallest Grammar framework seeks for a context-free grammar whose language contains one and only one string, the grammars we consider here neither branch (every non-terminal occurs at most once as a left-hand side of a rule) nor loop (if \(B\) occurs in any derivation starting with \(A\), then \(A\) will not occur in a derivation starting with \(B\)). In this type of grammars, any substring of the grammar has a unique constituent, in which case we will drop the set notation and define \(\textit{cons}(\alpha)\) as the only terminal string that can be derived from \(\alpha\). Note that if the grammar is in Chomsky Normal Form [19], it is equivalent to a straight-line program.  
Several definitions of the grammar size exist. Following [9], we define the _size of the grammar_\(G\), denoted by \(|G|\), to be the length of its encoding by concatenation of its right-hand sides separated by end-of-rule markers: \(|G|=\sum_{A\rightarrow\alpha\in\mathcal{P}}(|\alpha|+1)\).
## 4 Results
### Model Performances and Comparisons  
In this study, the landslide susceptibility models were evaluated by using the areas under the ROC curves (AUC), standard error, 95% confidence interval, and significance level \(p\)-value. The ROC curve can be used as a useful tool to indicate the quality of deterministic and probabilistic prediction system [93; 94; 95]. The sensitivity (true positive rate) is shown as y-axis and 1-specificity (false positive rate) as x-axis [94; 96]. The AUC values are in the range of 0.5 to 1 [97], and the excellent attributes of the model increase with the AUC values [98].  
Using the training dataset, the performance of the landslide susceptibility models was evaluated (Table 4). The BFT model has the highest AUC value (0.947), the lowest standard error (0.011), and the narrowest 95% confidence interval (0.925-0.969). It is followed by the RFFT model, the FT model, and the DFT model. For the validation data, the calculation results are shown in Table 5. The BFT model has the highest AUC value (0.804), the lowest standard error (0.035), and the narrowest 95% confidence interval (0.736-0.871). It comes before the DFT model, the FT model, and the RFFT model. These results show that all performance in the validation dataset is slightly worse than those of the training data. These results show that the BFT model is the best model among the four models, and the ensemble model is not necessarily superior to the single model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Average Merit** & **Standard Deviation** \\ \hline Distance to rivers & 0.382 & \(\pm\)0.015 \\ Slope & 0.224 & \(\pm\)0.008 \\ Lithology & 0.180 & \(\pm\)0.012 \\ Elevation & 0.185 & \(\pm\)0.018 \\ Distance to roads & 0.172 & \(\pm\)0.015 \\ TWI & 0.171 & \(\pm\)0.014 \\ SPI & 0.152 & \(\pm\)0.015 \\ Aspect & 0.147 & \(\pm\)0.011 \\ Soil & 0.141 & \(\pm\)0.013 \\ Profile curvature & 0.136 & \(\pm\)0.020 \\ NDVI & 0.105 & \(\pm\)0.024 \\ Landuse & 0.097 & \(\pm\)0.013 \\ Plan curvature & 0.038 & \(\pm\)0.011 \\ STI & 0.041 & \(\pm\)0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation attribute of landslide conditioning factors.  
Figure 4: Percentages of landslide susceptibility classes.  
Figure 3: Landslide susceptibility maps: (**a**) functional trees (FT) model; (**b**) bagging-functional trees (BFT) model; (**c**) rotation forest-functional trees (RFFT) model; (**d**) dagging-functional trees (DFT) model.  
A Chi-squared test was used to analyze the significance of the four models (Table 6). It can be seen that only the comparison of FT and RFFT exhibits lower Chi-squared value (0.044) and higher \(p\)-value (0.834), which indicate no significant difference between the two models. The other five groups all present larger Chi-squared values and lower \(p\)-values. The significant differences between the models indicate that the differences between the models are good, which is more conducive to the modeling work and enables this study to obtain the susceptibility results smoothly.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Replay Attack  
The replay attack is avoided because all entities involved in our protocol use different random values freshly calculated in each authentication process. Therefore, an attacker cannot force messages using old random values.
## 4 Discussion
Although there are many different mechanisms involved in memory improvement and this study had limitations in analyzing these mechanisms, the results of the NORT study suggest that HSCT was effective in improving short-term memory rather than long-term memory. In the Y-maze test, the spontaneous alternation value of the HSCT group was significantly higher than that of the scopolamine group (Figure 5); the spontaneous alternation value evaluates short-term memory of rats in the Y-maze test [50]. Animal behavioral experiments confirmed that HSCT consumption improved the memory of rats whose memory was impaired by scopolamine, and the mechanism of memory improvement was investigated by examining ACh content and AChE activity in the rat brain. As shown in Figures 6 and 7, the ACh content in the brains of HSCT-treated rats was significantly increased and AChE activity was significantly decreased compared to that in the brains of scopolamine-treated rats. ACh is a neurotransmitter that transmits chemical messages released by neurons and is synthesized under the action of acetyl CoA and cholineacetyltransferase enzymes, and plays an important role in the cholinergic system related to memory [10]. ACh is broken down into acetate and choline by AChE. Scopolamine administration increases the activity of AChE and accelerates the breakdown of ACh, resulting in decreased ACh concentrations in the brain [51]. Memory decline is thought to be directly or indirectly associated with cholinergic dysfunction in the hippocampus. For example, in the hippocampus of patients with AD, ACh synthesis is reduced, and AChE activity is increased [41]. This is because a decreased concentration of ACh, a neurotransmitter that plays an important role in memory and learning in the brain, can lead to memory impairment [37]. Therefore, to treat memory loss in AD, AChE inhibitors such as domepezil are administered to reduce AChE activity and increase the brain ACh content, thereby alleviating the symptoms of AD [52]. Based on these results, HSCT is expected to improve memory impairment induced by increased AChE activity and decreased brain ACh concentration following scopolamine administration. In addition, the memory improvement effect of HSCT in animal behavioral experiments may be due to an increase in brain ACh concentration caused by a decrease in AChE activity. Currently, drugs used clinically to reduce AChE activity have temporary effects, and their use is limited because they cause side effects such as cardiac bradycardia, gastrointestinal disorders, and liver toxicity when consumed over a long period [41]. Therefore, it is necessary to develop natural product-derived materials, which can be consumed over a long period of time and do not have any side effects, to improve memory decline caused by AD. HSCT has the potential to be utilized as a natural substance to improve memory.
# Optimization Technique for High-Gain CMOS Power Amplifier for 5G Applications
###### Abstract  
In this study, a differential power amplifier (PA) with a high gain of over 30 dB by configuring a three-stage common source unit amplifier was designed. To ensure the stability of the high-gain differential PA, the analysis to apply the capacitive neutralization method to the differential common source PA was conducted. From the analysis, the required neutralized capacitance was quantitatively calculated from the estimated parasitic components of a power cell used in the PA. To verify the feasibility of the proposed optimization technique, a Ka-band PA was designed with a 65 nm RFCMOS process. The measurement results showed a gain of 30.7 dB. The saturated output power was measured as 16.1 dBm, maximum power-added efficiency (PAE) was 29.7%, and P1 dB was 13.1 dBm.  
5G mobile communication; capacitive neutralization; CMOS; high gain; power amplifiers +
Footnote †: journal: Applied Sciences
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 10 cm Depth in Bangladesh  
We combined three types of lead times, five sizes of time windows, eight input scenarios, and five tree-based models to forecast soil temperature at the depth of 10 cm (ST10), and then revealed the optimal combination of the tree model, time window, and  
Figure 2: Average daily changes in soil temperature at depths of 10 cm (red), 30 cm (blue), and 50 cm (green).  
input scenario in forecasting ST10. The model performance was evaluated using the root mean squared error (RMSE) and Pearson correlation coefficient (R).
## 4 Discussion
This is in accordance with previous research showing that cold atmospheric plasma could support optimal pluripotent stem cell attachment by turning polystyrene cell culture dishes from hydrophobic to hydrophilic state [31]. It is also speculated that polystyrene surface had been changed in topography and elasticity at the nanoscale level, enhancing adhesion and spreading of cells through promoted focal adhesions [29]. In this study, image-based analysis of label-free live hDPSCs on NTAPP-treated dishes showed significantly higher lipid droplets than controls. It is worth emphasizing that this study demonstrated lipid droplets through live-cell imaging in real-time without labeling. Previously, intracellular lipid droplet formation was visualized by fluorescence microscopy using dyes, which are phototoxic, and require reduced acquisition length and frequency to limit perturbations. Lipid droplets play a key role in physiological processes involving gene expression and cellular signaling, which may influence metabolism and differentiation. Therefore, further lipidomic analyses of hDPSCs and related NTAPP applications are warranted [32]. There are limitations in this study. In addition to current findings focused on these initial hDPSCs behaviors, in depth investigations on cell cycle analysis and secretion profiles are necessary to investigate odontogenic, osteogenic, chondrogenic, neurogenic, or angiogenic differentiation of dentin-attached hDPSCs. Furthermore, elucidating the underlying mechanism of NTAPP-induced tissue-specific differentiation of stem cells including adipose-derived stem cells, bone marrow-derived mesenchymal stem cells, and hDPSCs will require reactive oxygen and nitrogen species analysis. For instance, NTAPP on human adipose tissue-derived stem cells generated nitric oxide (NO), which increased cytokine and growth factor expression, and downregulated intrinsic apoptotic pathways [5; 19]. NO at a low concentration increased pluripotent genes (Oct4, Sox2, Nanog) expression, and at high concentrations promoted human embryonic stem cell differentiation independently of its second messenger role [33]. Exogenous NO promoted DPSC differentiation into odontoblasts and induced tertiary dentin formation in rats [34]. Therefore, NTAPP's effects on differentiation could be expanded to guide endocrine/paracrine conditioned cells toward various osteogenic/chondrogenic lineages [35; 36]. Long term observation using HTM will be required to trace these subcellular changes during differentiation. As these NTAPP-induced cell- and dentin- changes may concurrently direct array of lineages in pulp-dentin regenerations, cell- and dentin-specific applications for NTAPP will need to be thoroughly characterized and standardized, so that they can be customized for clinically applications in regenerative endodontics.
# A Water Tank Level Control System with Time Lag Using CGSA and Nonlinear Switch Decoration
## 4 Stability Analysis
### Improved Non-Linear Switch Modification  
**Theorem 1.**_The closed-loop system of the controller designed using the third-order closed-loop gain-shaping algorithm is stable with amplitude margin \(h=9\) and phase angle margin \(\gamma=71.2528^{\lx@math@degree}\) under the condition \(T_{1}\
eq 0\)._  
**Proof:** The open-loop transfer function of the system is obtained from Equation (8):  
\[\begin{array}{l}H(s)=K(s)G(s)=\frac{1}{T_{1}s\left(T_{1}^{2}s^{2}+3T_{1}s+3 \right)}\\ =\frac{1}{T_{1}s\left(T_{1}s+\frac{3}{2}+\frac{\sqrt{3}}{2}j\right)\left(T_{1} s+\frac{3}{2}-\frac{\sqrt{3}}{2}j\right)}\end{array} \tag{14}\]  
Taking \(j\omega\) into the Equation (14).  
\[G(j\omega)=\frac{1}{T_{1}j\omega\left[j(T_{1}\omega+\frac{\sqrt{3}}{2})+\frac{ 3}{2}\right]\left[j(T_{1}\omega-\frac{\sqrt{3}}{2})+\frac{3}{2}\right]} \tag{15}\]  
Assume that the cut-off frequency is \(\omega_{c}\) and the phase angle junction frequency is \(\omega_{g}\).  
\[\left\{\begin{array}{l}|G(j\omega_{c})|=1\\ \angle G\left(j\omega_{g}\right)=-180^{\lx@math@degree}\end{array}\right. \tag{16}\]  
The solution is  
\[\left\{\begin{array}{l}\omega_{c}=\frac{0.3273}{T_{1}}\\ \omega_{g}=\frac{\sqrt{3}}{T_{1}}\end{array}\right. \tag{17}\]  
Further solving for the phase angle margin \(\gamma=71.2528^{\lx@math@degree}\), amplitude margin \(h=9\).  
When \(\omega=0^{+}\), the real part of \(G(j\omega)\) is  
\[\mathrm{Re}[G(j\omega)]=\frac{-3}{T_{1}^{4}\omega^{4}+3T_{1}^{2}\omega^{2}+9} =-\frac{1}{3} \tag{18}\]  
\(\square\)  
Apparently, \(A(\omega)=+\infty\), \(\varphi(\omega)=-\pi/2\). As \(\omega\) changes from \(0\) to infinity, the amplitude and phase angle decreases. When \(\omega\) tends to infinity, the phase angle of all three complex vectors is \(90^{\lx@math@degree}\), and the amplitude is infinity. Substituting into Equation (7) we obtain \(A(\omega)=0\), \(\varphi(\omega)=-3\pi/2\). We make the magnitude-phase characteristic curve of the system as shown in Figure 3.  
Figure 3: Amplitude phase characteristic curve of controller.  
From the Nyquist stability criterion, we obtain  
\[Z=P-2N=0-2\times 0=0 \tag{19}\]  
P is the number of poles in the right half-plane of the open-loop transfer function. N is the number of turns of the Nyquist curve enclosing \((-1,j0)\) and \(Z\) is the number of poles in the right half-plane of the closed-loop transfer function. In summary, this closed-loop system is stable. Its phase angle margin \(\gamma=71.2528^{\circ}\) and amplitude margin \(h=9\).
## 2 Novel Contributions of the Current Paper
### Problems Addressed  
Edge computing environments are highly heterogeneous, with varying levels of computational power, memory, and connectivity. Smart city applications may have reliable infrastructure, while smart village applications face limited resources and unpredictable connectivity. Conventional cybersecurity solutions are often too computationally expensive for low-power edge devices. The primary goal of this research is to study the security challenges of resource-constrained edge computing environments and present a novel secure authentication protocol that is based on concepts of hardware-assisted security and security-by-design. The key idea is to propose an integrated security model for secure authentication. The research explicitly addresses the challenge of securing authentication in resource-constrained edge environments using PUF, where direct transmission of binary CRP data increases the system's vulnerability to external attacks.  
* There is a lack of lightweight yet secure authentication mechanisms for resource-constrained edge. Traditional cryptographic authentication methods like Public Key Infrastructure (PKI) and symmetric encryption consume high computational resources and energy, making them unsuitable.
* Authentication protocols must be resilient against attacks while ensuring low power consumption and minimal latency.
* Edge devices are prone to physical and cyberattacks such as side-channel attacks, replay attacks, cloning, and key extraction. Existing authentication mechanisms lack uniqueness and resistance against cloning, making devices susceptible to unauthorized access.
* Authentication protocols require frequent communication between edge devices and central servers. In collaborative edge computing, there will be frequent communication between the participating clients, like the edge data centers, which increases bandwidth consumption. In remote areas with low network reliability, excessive bandwidth usage can lead to delays and authentication failures.
* Secure storage and transfer of data need to be ensured to prevent data breaches or authentication failures, especially in systems that use PUF CRP data for authentication purposes.
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 4 Design of Dual-Band Microstrip Antenna  
In this section, the design of the rectangular dual-band microstrip antenna for WLAN is studied. The influence of different slot styles on the antenna performance is discussed. A microstrip antenna with a stepped groove is designed and improved, and the dual-band operation is realized by opening a pair of symmetrically folded grooves. The antenna works in two frequency bands of 2.4 GHz-2.46 GHz and 5.16 GHz-5.4 GHz, and the antenna gain can achieve 4 dB in most of the working frequencies. The antenna patch is a square patch whose length of a side is 25 mm, which ensures the appropriate gain of the antenna while reducing the volume of the antenna.
## 2 Materials and Methods
For each of the estimated parameters, \(\beta\), a weighted variance was also calculated as follows:  
\[v\hat{w}r(\beta)\ =\ \frac{\sum_{i\ =\ 1}^{R}w_{i}(\hat{\beta}_{i}-\beta)^{2}}{ \sum_{r\ =\ 1}^{R}w_{i}}. \tag{5}\]  
Therefore, it was possible to calculate an asymptotic Z confidence interval for each estimated parameter. The significance level used was 5%. \[CI_{95\%}\ =\ \beta\pm z_{0.025}\times\sqrt{v\hat{w}r(\beta)}. \tag{6}\]  
**Models' performance metrics and comparison. ** We analyzed the training dataset, which consisted of 316 individuals (116 ICN; 200 IAD) and 518,257 SNPs. The proposedprocedure was applied for the two following scenarios: one considering only the SNPs (from here on referred to as \"Scenario SNPs\"), and another considering SNPs that was adjusted to also consider the covariates of age, sex, educational level, and _APOE4_ genotype (here on referred to as \"Scenario SNPs + Cov\"). Therefore, for each value of the \(\alpha\) parameter, two final models were proposed. The performance of the models was evaluated on the test dataset, focusing on the area under the curve (AUC), the accuracy, and the F1-measure. Traditional logistic regression (without shrinkage) is one of the most used methods with which to model binary outcomes; however, it can only be used when the number of variables is lower than the number of observations. To overcome this issue, a penalized regression technique can be applied before traditional logistic regression. We compared each final model with the corresponding traditional logistic regression model, built based on the same set of selected variables, in order to discuss the impact of shrinkage on the estimation of the coefficients (effect sizes). We also compared the two models using the above-mentioned performance metrics. **Technical details. ** The \(\overline{\text{R}}\) language was used to carry out the statistical analysis. In this work, prediction models were constructed using two penalized regression techniques, namely LASSO (\(\alpha\) = 1) and Elastic-Net (0 \(<\alpha<\) 1), for the \(\alpha\) values in the grid (0.75, 0.50, 0.25, 0.10, 0.05, 0.01). For each value of \(\alpha\), we obtained \(R~{}=~{}100\) models. For each model, the value of the penalization parameter was achieved via a 10-fold cross-validation, and the model with the lowest validation error (deviance) was chosen. The generalized linear models, made via the penalized maximum likelihood, were obtained using the _cv.glmnet_ R function, available in the _glmnet_ package, using the default parameters.