esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Test Verification
### Test Results and Comparative Analysis from ARP
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Center Point Coordinates (m)** & **Path Length (m)** \\ \hline IDMMFO-GM & (1.56, 2.39), (1.57, 4.63), (1.92, 6.21) & 9.435 \\ IIMPO-GC & (1.44, 2.38), (1.47, 4.64), (1.81, 6.22) & 9.483 \\ IISSO & (1.18, 2.41), (1.43, 4.65), (1.72, 6.23) & 9.693 \\ IPSO & (1.10, 2.40), (1.22, 4.60), (1.56, 6.20) & 9.768 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fixed-point regions’ positions and path lengths for ARP trajectory tracking control. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Symbol** & **Value** \\ \hline \hline \multirow{2}{*}{ \begin{tabular}{c} Multi population size \\ Iteration number \\ Logarithmic helical waveform adjustment coefficient \\ Minimum weight coefficient \\ Maximum weight coefficient \\ Non-linear decreasing optimization factor \\ Directional mutation adjustment coefficient \\ Maximum vertical axis offset for collision-correction garage \\ Abscissa moving to the right empirical coefficient \\ Maximum vertical axis offset of modification berthing tilt \\ \end{tabular}  } & \(n\) & 30 \\  & \(t_{\max}\) & 80 \\  & \(t_{\max}\) & 80 \\  & \(t_{\max}\) & 10.3 \\  & \(t_{\max}\) & 0.9 \\  & \(t_{\max}\) & 0.85 \\  & \(t_{\max}  
Tables 4 and 5 indicate that IDMMFO-GM surpasses the three comparative algorithms in both reference and actual trajectories. Specifically, it results in a reduced reference trajectory length. Moreover, when the fuzzy PID tracking algorithm is employed, it achieves a shorter actual trajectory length in tracking control. As presented in Table 6, compared with the three optimization algorithms used for comparison, IDMMFO-GM demonstrates a notably smaller expected berthing tilt angle in its reference trajectory. Furthermore, when employing the same tracking control algorithm (fuzzy PID), it achieves a significantly smaller actual berthing tilt angle in its actual trajectory. As illustrated in Table 7, in comparison with the other three algorithms, IDMMFO-GM exhibits a markedly shorter optimization time requirement. Moreover, when utilizing the same fuzzy PID control algorithm, it further attains a significantly reduced tracking control time. Figure 8: Optimization curves for ARP trajectories obtained by monitoring upper computer. Figure 9: Tracking control curves for ARP trajectories obtained by monitoring upper computer.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## System Model
### Outage Probability (OP)  
This is an important performance metric for measuring the network reliability. Detailed information concerning OP can be found in [27; 28]. The expression of OP under the single-relay condition can be expressed as  
\[\mathcal{R}=\frac{1}{2}\log_{2}\left(1+|h|^{2}\frac{P_{t}}{N_{0}}\right) \tag{5}\]  
where \(P_{t}\) is the transmitting power and \(N_{0}\) denotes the noise power; \(h\) represents the channel gain of the intra-body fading channel; and \(\mathcal{R}_{SR}\) and \(\mathcal{R}_{RD}\) denote the mutual information of the SR and RD communication links. The maximum mutual information of the SD communication path should be followed by \(\mathcal{R}_{SD}=min\{\mathcal{R}_{SR},\mathcal{R}_{RD}\}\). The OP of the communication link between the in-body sensor to an on-body relay node for a given data rate \(\gamma\) can be written as \(P_{SR_{i}}=P\Big{\{}min\Big{\{}\mathcal{R}_{SR_{i}},\mathcal{R}_{R_{i}D}\Big{\}} <\gamma\Big{\}}\).  
The intra-body PL model follows a lognormal distribution at stated in Section 3.2; thus, \(\left|h\right|^{2}\) follows a lognormal distribution and the maximum mutual information can be written as  
\[P\{\mathcal{R}<\gamma\}=\frac{1}{2}erfc\left[-\frac{\ln\left(\left(2^{2\gamma}- 1\right)N_{0}\right)/P_{t}\right)-\mu}{\sqrt{2}\sigma}\right] \tag{6}\]  
where erfc(.) denotes the complementary error function [27]. According to [10], the in-to-out body PL model parameters are such that \(\mu\) = 0 and \(\sigma\) = 2.93. Equation (6) can be rewritten as \(P\{\mathcal{R}<\gamma\}=\frac{1}{2}erfc\left[-\frac{\ln\left(\left(2^{2\gamma }-1\right)N_{0}\right)/P_{t}\right)}{4.14}\right]\). Considering a wireless biomedical implant network with two on-body relay nodes, each of the relay nodes performs an AF operation on the information packets it receives from the implanted sensors and sends to the coordinator. The maximum mutual information \(\mathcal{R}_{SD}\) can be expressed as  
\[\mathcal{R}_{SD}=\prod_{j=1}^{2}\left[1-\left(1-P\Big{\{}\mathcal{R}_{SR_{j}} <\gamma\Big{\}}\right)\left(1-P\Big{\{}\mathcal{R}_{R_{j}D}<\gamma\Big{\}} \right)\right] \tag{7}\]  
where \(\mathcal{R}_{SD}\) should also be subject to \(\mathcal{R}_{SD}=min\Big{\{}\mathcal{R}_{SR_{j}},\mathcal{R}_{R_{j}D}\Big{\}}\), \(\forall j\in\{1,2\}\).
###### Abstract  
Assessment of muscle tone in a clinical setting is important for the physiotherapist to better analyse and establish appropriate treatments for CNS disorders. This study aims to review and summarise how to assess changes in tone in the context of adult rehabilitation. Secondarily, this study aimed to identify the central nervous system disorders, the respective variable/concept under study, and the testing procedures employed. PRISMA-ScR guidelines were followed using the \"population\", \"concept\", and \"context\" to define the eligibility criteria and to delineate the research question. PubMed[9], Science Direct[10], Web of Science[11], and Google Scholar[12] databases were used to search the literature. The search included studies published between 2011 and March 2023 in Portuguese, English, French, and Spanish that assessed an adult population (>19 years) with CNS injury. Review articles, qualitative studies, conference proceedings, letters to the editor, and editorials were excluded. Initially, 1519 references were identified, of which eight met the eligibility criteria. The measurement instruments included the Modified Ashworth Scale (n = 5), the Modified Modified Ashworth Scale (n = 3), the BioTone[13] system (n = 2), the Montreal Spasticity Measurement (n = 1), and the Tone Evaluation Scale (n = 1). The health conditions considered included stroke sequelae (n = 7), multiple sclerosis (n = 4), spinal cord injuries (n = 4), cerebral palsy (n = 2), brain tumour (n = 2), and traumatic brain injuries (n = 3). The concepts of spasticity (n = 7) and muscle tone (n = 2) were explored. Considering the variables spasticity and muscle tone in different CNS disorders, mainly stroke, subjective instruments were preferred compared to objective ones, with the Modified Ashworth Scale being highlighted.  
tone disorders; spasticity; evaluation; rehabilitation; adults +
Footnote †: journal: Medical Sciences
## 4 Experimentation and Results
### NNDSS Dataset  
In this section, we study applying our proposed algorithms to the NNDSS Dataset. Figure 6 shows the RIS of each column of the dataset, accompanying with the corresponding histogram of NNDSS dataset. The results of applying Algorithm 1 on this dataset are depicted below: Then, by applying Algorithm 2, we can obtain the best fit as follows, where the top three distributions are listed in Table 2. The result is that logistic distribution is the best fit for the NNDSS dataset. The probability density function for logistic distribution is:  
\[f(x)=\frac{exp(-x)}{(1+exp(-x))^{2}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distribution** & **Statistics** & \(p\)**-Value** \\ \hline logistic & 0.168 & 0.306 \\ hypsecant & 0.184 & 0.214 \\ nct & 0.185 & 0.208 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top three distributions to fit the NNDSS Dataset.  
Figure 5: quantile–quantile plot for BCG Strains Dataset.  
Figure 6: RIS of each column, histogram of the NNDSS dataset.  
best fit as follows, where the top three distributions are listed in Table 3. The result is that beta distribution is the best fit for Provisional Counts of Death Dataset. The probability density function for beta distribution is:  
\[f(x,a,b)=\frac{\Gamma(a+b)x^{a-1}(1-x)^{b-1}}{\Gamma(a)\Gamma(b)} \tag{5}\]  
for \(0\leq x\leq 1\), \(a>0,b>0\) where \(\Gamma\) is the gamma function. beta takes \(
## 3 Materials and Methods
### Data Persistence
#### 3.4.1 Session-Level Data Persistence  
The term \"session\" refers to the finite period of active microcontroller operation that may involve the creation of virtual machine instances [29]. In its standard implementation, the use of a single hash map for global variables is envisioned, which is beneficial for centralized access to not only these variables, but also the available native classes and functions. However, the values within this data structure are erased when the VM's execution ends, as the garbage collector returns the execution environment to its initial state. To address this, an additional hash map named \"cache\" is added to the global instance of the VM, mapping the names and values of persisted records. Access to this map is facilitated through the methods \"Persistence.load(name)\" and \"Persistence.save(name, value)\", as defined with code snippets in Figures 12 and 13. If a record does not exist when loaded, the method returns null, whereas saving an existing key will replace the old value with the new one.  
A potential issue with executing any method from the \"Persistence\" class is related to saving external references, such as closed values in functions and methods within classes. Since functions are statically compiled, their access to variables is also static and index-based, which may not align in subsequent executions, potentially destabilizing the virtual machine. To resolve this, the function \"checkContainsExternalReferences\", a code snippet  
Figure 11: Default file browser layout with placeholder dates used for illustrative purposes.  
Figure 12: Implementation of the “Persistence.load” method via the native interface.  
of which is presented in Figure 14, verifies whether a value with external references is being saved, accommodating complex types like lists or instances by enabling a deep inspection of object structures. Technically, it would be feasible to save function values as long as they lack closures or access to global variables, which would require checking the set of instructions for \"OP_GLOBAL\" bytecodes, a feature that has not been implemented.  
Ordinarily, invoking the garbage collector would delete all values from the \"cache\" map if they are not referenced in the code. To prevent this, all saved values in the map are marked before the garbage collection cycle, ensuring they are excluded from the cleaning phase. This approach is also used to reset the virtual machine state, which should retain the map's contents while deallocating all other values created during program execution. The map keys must also be interned and ignored to allow consistent access, as each VM reset clears the string interning map. Given that the equality check for strings relies on this mechanism, failing to intern keys would result in the user being unable to find records, as the key and the string in the code would reference different objects.
## 4 Numerical Simulation Considering the Stress Ratio \(R\) Based on the XFEM  
### FEM of the CT Specimen  
Figure 7 shows the FEM of the CT specimen and the model size that is consistent with the test. The mesh is fine near the crack and coarser away from the crack. Two reference points are defined such that they are coupled to the inner surface of the circular hole. This is done so that the boundary conditions and loads can be applied directly to the reference points. Reference point (RP)-1 constrains translation in the x, y, and z directions and rotation in the x and z directions. Translation and rotation in the x, y and z directions of RP-2 are constrained. The cyclic load P is applied on RP-1 according to the loading scheme in Table 2. The elastic modulus and Poisson's ratio of steel Q345 qD are 2.06 \(\times\) 10\({}^{5}\) MPa and 0.3, respectively [34]. As shown in Figure 7, the cyclic load P is only in the XY plane. Therefore, the CT specimen under the cyclic load P experiences plane stress because the stress is only in the XY plane and the stress perpendicular to the XY plane can be ignored. Under these conditions, \(E^{\prime}\) in Equations (3) and (7) equals to \(E\), that is 2.06 \(\times\) 10\({}^{5}\) MPa.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**FCP Equation**} & \multirow{2}{*}{**Guarantee Rate**} & \multicolumn{4}{c}{**Material Parameters**} \\ \cline{3-5}  & & **log(\(C_{0}\)) or log(\(\medapprox\))** & \(m_{0}\) or \(m\) & \(\gamma\) \\ \hline \multirow{2}{*}{Paris} & Mean & \(-\)10.80 & 2.687 & \multirow{2}{*}{} \\  & With 95\% guarantee & \(-\)10.54 & 2.847 & \\ \hline \multirow{2}{*}{Walker, da/dN-\(\Delta\)G-R} & Mean & \(-\)10.85 & 2.696 & 0.947 \\  & With 95\% guarantee & \(-\)10.71 & 2.780 & 0.957 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters of steel Q345 qD.  
Figure 6: Experimental data fitting of steel Q345 qD. (**a**) Paris; (**b**) Walker and da/dN\(-\Delta\)G\(-\)R.
# NB-IoT for D2D-Enhanced Content Uploading with Social Trustworthiness in 5G Systems
###### Abstract  
Future fifth-generation (5G) cellular systems are set to give a strong boost to the large-scale deployment of Internet of things (IoT). In the view of a future converged 5G-IoT infrastructure, cellular IoT solutions such as narrowband IoT (NB-IoT) and device-to-device (D2D) communications are key technologies for supporting IoT scenarios and applications. However, some open issues still need careful investigation. An example is the risk of threats to privacy and security when IoT mobile services rely on D2D communications. To guarantee efficient and secure connections to IoT services involving exchange of sensitive data, reputation-based mechanisms to identify and avoid _malicious_ devices are fast gaining ground. In order to tackle the presence of malicious nodes in the network, this paper introduces _reliability_ and _reputation_ notions to model the level of _trust_ among devices engaged in an opportunistic hop-by-hop D2D-based content uploading scheme. To this end, _social awareness_ of devices is considered as a means to enhance the identification of trustworthy nodes. A performance evaluation study shows that the negative effects due to malicious nodes can be drastically reduced by adopting the proposed solution. The performance metrics that proved to benefit from the proposed solution are data loss, energy consumption, and content uploading time.  
trustworthiness; D2D communications; 5G systems; Internet of things; NB-IoT +
Footnote †: journal: _Future Internet_  
0000-0001
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 3 Engineering Background and Database Construction
### Data Processing and Dataset Construction
#### 3.3.4 Sample Equalization (Class Balancing)  
Landslide prediction inherently presents a severe class imbalance problem, with significantly fewer landslide occurrences (positive samples) compared to non-occurrences (negative samples). To address this, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm was applied exclusively to the training set. SMOTE generates synthetic examples for the minority landslide class by interpolating between existing real minority samples.  
This process structures the training dataset to have an approximately equal proportion of landslide and non-landslide samples. Severe class imbalance biases models towards predicting the majority class (non-landslide), as simply predicting \"no landslide\" yields high accuracy but fails the core prediction task. By balancing the classes within the training set using SMOTE, the model is exposed to sufficient examples of landslides during learning, enabling it to learn the characteristics of both classes effectively and reducing prediction bias towards the majority class.  
After the above steps, the finally constructed database contains multi-dimensional and multi-temporal landslide prediction data, covering a variety of factors such as topography, meteorology, soil, etc., which provides high-quality data support for model training and prediction. The reasonable construction and preprocessing steps of this database lay the foundation for improving the performance of the CNN-LSTM-SAM-Attention model in landslide prediction.
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
The preliminary processing of data and plotting of the physicochemical parameters were performed using Microsoft Excel 2016 software. The one-way analysis of variance (ANOVA) and unary linear regression analysis (U-LRA) were performed with IBM SPSS Statistics 19 software. The ANOVA was based on the Duncan test at a significance level of \(p<0.05\). Hierarchical clustering heatmaps were obtained from MetaboAnalyst 5.0 ([[http://www.metaboanalyst.ca](http://www.metaboanalyst.ca)]([http://www.metaboanalyst.ca](http://www.metaboanalyst.ca)) (accessed on 1 June 2022)) using the 'Statistical Analysis'module. The 'Autoscaling' method was used as the normalization procedure for data before proceeding. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were performed using SIMCA 14.1 (Umetrics, Umea Sweden).
## 2 Open-Loop Tracking and Inversion Algorithm  
### Open-Loop Tracking Algorithm  
OL tracking uses the predicted Doppler and pseudo-range models to track the signal [25; 26]. The difference between the OL and the traditional PLL is that there is no feedback between the observation signal and the control model, but only the model control signal is used, so it will not beaffected by the change of the signal [27; 28]. It can track full information of phases (the effects of motion, atmospheric multipath, etc.), and can detect rising occultation events more conveniently [29; 30].  
The mountain-based OL tracking algorithm is different from the spaceborne OL tracking algorithm in terms of the model Doppler and pseudo range. In the satellite OL tracking module, we employ the international reference atmospheric model CIRA86-Q for pseudo-range/Doppler prediction. The mountain-based OL tracking algorithm is mainly used to verify the correctness of OL tracking, not to verify the correctness of the Doppler and pseudo range model. In the verification process of the mountain-based OL tracking algorithm, we assume that the Doppler and pseudo-range are mainly changed by the movement of GNSS satellites, while the effects of the atmosphere and the ionosphere are small. In the mountain-based test, the position and speed of the navigation satellite can be obtained through the ephemeris. The geometric distance of between the station and the satellite at an occultation and the Doppler of motion obtained based on the velocity of that satellite are used as the output of the pseudo-range/Doppler model to narrow the phase search range in full OL tracking. The chip array of the mountain-based occultation receiver is expanded to ensure that the phase delay caused by the atmosphere is within the chips' range.
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 4 Results and Discussion
### Simulation of Scratch Width Recognition  
#### 4.3.1 Training Result of DRT  
Since the simulation light intensity distribution revised by PSF is almost consistent with the experiment gray distribution of the scratch, we take 600 simulation light intensity distribution curves from the simulation model to train and test the DRT in scratch width recognition. These curves are divided into two parts. Five hundred curves are randomly chosen as the training data set of DRT, and the remaining 100 curves are taken as the test data set to test the recognition accuracy of DRT. The root mean square error (RMSE) is taken as the parameter to represent the recognition accuracy. As mentioned in Section 3.2, the overfitting (i.e., width recognition result is too good in the training data set, while it is very poor in the testing data set) easily happens during the training of DRT. Thus, pruning optimization is carried out to avoid overfitting during the training of DRT. Table 2 is the width recognition result of the scratch by DRT under pruning and without pruning. As for the width recognition in the training data set, although the RMSE of unpruned DRT is 0.008 \(\upmu\)m, which is smaller than the RMSE of 0.041 \(\upmu\)m of pruned DRT, the width recognition in the test data set is totally opposite. The RMSE of unpruned DRT is 0.153 \(\upmu\)m in the test data set; it is not only larger than the RMSE of 0.008 \(\upmu\)m of unpruned DRT in the training data set but also larger than the RMSE of 0.045 \(\upmu\)m of pruned DRT in the test data set. It indicates that the overfitting of the unpruned DRT will lead to a poor result in actual width recognition. Hence, the pruning optimization of the DRT is necessary. The RMSE of the pruned DRT in the training data set and test data set is 0.041 \(\upmu\)m and 0.045 \(\upmu\)m, respectively, which is better than the unpruned DRT.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **RMSE of Unpruned DRT (\(\upmu\)m)** & **RMSE of Pruned DRT (\(\upmu\)m)** \\ \hline Training data set & 0.008 & 0.041 \\ Test data set & 0.153 & 0.045 \\ \hline \hline \end{tabular} RMSE: root mean square error; DRT: decision regression tree.  
\end{table}
Table 2: Scratch width recognition results by DRT in simulation data.
# Model Updating of Bridges Using Measured Influence Lines
## 3 Results
### Updated FE Model
#### 3.2.2 MAC-Based EDMF  
Figure 20 shows the MAC-based EDMF results. For the well-correlated modes, the MAC values should be greater than 0.90 [31], and for 'acceptable', 0.80 [35]. For MACs, the upper value of the thresholds was set to 1.00 for all modes, representing the scenario of perfectly similar modelled compared with the measured mode shape. The lower thresholds were set to 0.80 for all modes except for B-Z-3, which was, as already mentioned, removed from all analyses.  
The MAC-based EDMF results fell into ranges [1.50, 1.60] for \(\alpha_{\mathrm{MG1}}\), [1.10, 1.50] for \(\alpha_{\mathrm{MG2}}\), and [0.80, 1.10] for \(\alpha_{\mathrm{OTHER}}\).
## 2 Material and Methods
### Detection and Quantification of Nitric Oxide by ELISA  
The Total Nitric Oxide and Nitrate/Nitrite Parameter Assay Kit (R&D Systems Inc., Minneapolis, MN, USA) was used for an accurate quantitative determination of nitric oxide in the collected saliva. The determination of nitric oxide concentration is based on the enzymatic conversion of nitrate to nitrite by nitrate reductase, followed by the colorimetric detection of nitrite secondary to the Griess reaction. Treatment of the nitrate-containing saliva with the Griess reagent forms a pink-red azo dye through a two-step diazotization reaction--the acidic NO\({}_{2}\)\({}^{-}\) produced by a diazotizing agent reacts with sulfanilic acid to produce the diazonium ion, which reacts with N-(1-naphthyl)ethylenediamine in an azo coupling reaction to form the pink-red azo dye which absorbs light at 540-570 nm [8; 9; 10].  
The kit requires two testing stages because NO- has a very short half-life (less than 0.1 s) and cannot be identified as such. The first step involves the quantification of endogenous nitrite. In the second step, nitrate is converted to nitrite using nitrate reductase, thus, quantifying total nitrite. The difference between total nitrite and endogenous nitrite matches the nitrate concentration representing the NO- level.  
Prior to analysis, saliva was prepared according to the manufacturer's instructions, requiring double dilution with reagent diluent (1\(\times\)). All reagents were brought to room temperature prior to use. Double distilled water was used for the reconstruction and dilution of the reagents to avoid contamination with nitrates or nitrites. After completing all the steps according to the kit instructions, the samples were analysed by spectrophotometry (Tecan SunriseTM, Mannedorf, Switzerland) to quantify NO.