esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation of Parameter Estimation
### Parameter Estimation Results  
By searching for the impulses after IFFT, we can easily obtain the time delay \(\tau_{i}\) of both LOS and the reflected signals. As the reflected signals are always later than the LOS signal to reach the receiver antenna, so it is easy to distinguish LOS from multipath signals. Also, when to estimate the parameters of multipath signals, we just ignore the first impulse and get the impulses corresponding to multipath signals.  
In this subsection, we tested the performance of estimating multipath delay using the averaging-FFT method, direct-FFT method and MEDLINE on BOC(1,1) signal. The setting of simulation parameters is concluded in Table 1. In this test, for convenience, we just retain one multipath signal with an attenuation of \(-3\) dB. The according time delay \(\tau_{1}\) is set to vary from 0.1 to 1 code chip. The window length \(L\) of the aFFT method is set to 128 points and the total signal length is 40 ms. C/N\({}_{0}\) is set to 36 dB-Hz, which is a relative low value for a GNSS receiver on the ground. For each value of \(\tau_{1}\), multipath estimation has been performed for 1000 times, and the estimation results are used to calculate the estimation deviation.  
Figure 5 shows the estimation deviation of the multipath delay using three methods. We can see that the averaging-FFT method keeps the smallest estimation deviation compared with the other two methods. The maximum value is slightly more than 0.05 code chip. MEDLINE method also has an acceptable estimation deviation curve. But when \(\tau_{1}\) is smaller than 0.3, its estimation error gets larger, which indicates that MEDLINE may not perform well when the reflected signal is too close to the LOS. Finally, the direct-FFT method shows the worst estimation performance, due to its high vulnerability to the noise.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Signal modulation type & BOC(1,1) \\ Signal length & 40 ms \\ Sampling rate & 20 MHz \\ Window length \(L\) & 128 points \\ Window function & Hamming \\ Multipath attenuation (power) & \(-3\) dB \\ C/N\({}_{0}\) & 36 dB-Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Setting of Multipath Parameter Estimation.  
Figure 4: (**a**) Impulse responses of the averaging-FFT method with C/N\({}_{0}\) of 38 dB-Hz; (**b**) impulse responses of the direct-FFT method with C/N\({}_{0}\) of 38 dB-Hz.
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Title of Study and Year** & **Light Shelf Parameters** & **Experimental Measurement** & **Daylighting Simulation** & **Integrated PV with Light Shelf** \\ \hline Maximizing the light shelf performance by interaction between light shelf geometries and a curved ceiling, Jordan, 2010 [11] & Geometries, curved ceiling & no ceiling & yes & no \\ \hline Power performance of photovoltaic-integrated lightshelf systems, South Korea, 2014, [17] & Full solar panel (one size) & yes & yes & yes \\ \hline Analysis of the Impacts of Light Shelves on the Useful Daylight Illuminance in Office Buildings in **Toronto**, 2015, [18] & Width, depth, and height & no & yes & no \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies conducted on light shelf performance on daylighting. Figure 1: Design solutions and variables associated with a light shelf that influence the daylight distribution.
## 5 Performance Analysis and Optimization
### Optimization Performances on Lifetime  
The death the time that the first node's death occurs in the network is the lifetime of the network, the energy consumption rate of an aggregator which die first is inversely proportional to the lifetime. In data aggregation and transmission process, the energy consumption is directly proportional to the length of data packets queue. In the simulation, the energy consumption of aggregating and transmitting a data packet is set as 0.01 Joule and the energy to start aggregation and transmission is 0.2 Joule.  
Figure 10: Average \(\rho\) in the network with different \(\eta\).  
Figure 9: The ratio of service guarantee rate under different cases.
## 2 Sample Preparation and Experimental Scheme  
### Preparation of Damaged Rock Samples with Different Liquid Contents  
The preparation of rock samples strictly follows the International Society for Rock Mechanics (ISRM) recommendations for rock characterization, testing, and monitoring methods [22]. The end face flatness of the prepared samples is controlled within 0.05 mm, and the diameter accuracy is maintained within 0.1 mm. The rock samples are cylinders with diameter \(\times\) height = 50 mm\(\times\) 100 mm.  
First, the rock samples were dried in an oven at a temperature of 105-110 \({}^{\circ}\)C for 24 h. After the drying process was completed, uniaxial compression tests were conducted to determine the average uniaxial compressive strength of the samples. Subsequently, uniaxial cyclic loading-unloading tests were performed to simulate the stress disturbances experienced by surrounding rock in underground mine roadways, thereby preparing the initially damaged coal samples. The preparation method was as follows: the loading rate of the rock mechanics testing machine was set to 0.5 kN/s, once the target pressure (60% of the peak strength) was reached, the load was unloaded at a rate of 0.5 kN/s; the loading-unloading cycle was repeated eight times (Figure 2).  
The preparation of damaged rock specimens with different liquid contents was conducted using a soaking method in direct contact with the liquid, which is more in line with the actual working conditions of underground oil storage caverns. However, during the soaking process, certain mineral components of the rock may dissolve into the liquid, which will affect the calculation of the rock's liquid content [23]. Therefore, the liquid content of the specimens was subsequently categorized based on soaking time. In the experiment, 0W-20 automotive engine oil was used as a substitute for petroleum to prepare the oil-watermixture. Due to the difference in density between oil and water, stratification occurs when the mixture is left undisturbed. To better simulate real conditions, the oil-water mixture was allowed to settle for a period before placing the dried damaged rock specimens into the liquid for complete soaking. The soaking times were set at 8 h, 16 h, and 24 h, respectively.
## 4 Experimental Set Up and Result Analysis
### Evaluation Parameters  
The proposed model performance was tested using the ML algorithms and evaluated and analyzed by the WEKA tool. For analyzing the WEKA outcomes, we used the evaluation parameters accuracy, precision, recall, F1-Score, MAE (mean absolute error), and RMSE (root mean square error) as defined below:  
(1) Accuracy illustrates the number of correctly classified anomalous behaviors in all predicted instances:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
(2) The precision factor shows anomalous behaviors with respect to the number of correctly classified positive instances:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
(3) The recall factor shows the percentage of all correctly classified anomalous behaviors:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Type** & **Train** & **Test** \\ \hline \multirow{3}{*}{NSL-KDD} & Anomaly & 58,630 & 12,833 \\ \cline{2-4}  & Normal & 67,343 & 9711 \\ \hline \multirow{6}{*}{UNSW\_2018\_IoT\_Botnet} & DDoS & 1,541,315 & 330,112 \\ \cline{2-4}  & DoS & 1,320,148 & 385,309 \\ \cline{1-1} \cline{2-4}  & Reconnaissance & 72,919 & 18,163 \\ \cline{1-1} \cline{2-4}  & Theft & 65 & 14 \\ \cline{1-1} \cline{2-4}  & Normal & 370 & 107 \\ \hline \end{tabular}
\end{table}
Table 2: Information of existing data sets based on number of instances for training and testing procedures.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Batch Size & 100 \\ \hline Number of Iterations & 100 \\ \hline Random Seed & 1 \\ \hline Size of Population & 20 \\ \hline \end{tabular}
\end{table}
Table 3: The specific parameters of the GA-RF algorithm in WEKA for prediction process.  
(4) The F1-score is calculated by a set of weighted factors from precision and recall:  
\[\text{F1}-\text{Score}=\frac{2\times\text{precision}\times\text{recall}}{\text{ precision}+\text{recall}} \tag{4}\]  
(5) MAE and RMSE measures the average volume of the errors and the implementation and performance of a forecast model in a set of predictions, without assuming their direction.
# Pro-Pre and Postbiotic in Celiac Disease
## 8 Clinical Trials Using Pro- and Pre-Biotic in CD
### Probiotics and celiac Disease
#### 8.1.1 Administration of Pro-Biotics in Patients with CD at GFD with or without Gluten Challenge  
(a) Effect of pro-biotics associated with a gluten-free diet  
In a study by [PERSON] et al. [51]_B. longum CECT 7347_ was given for three months to children with CD in combination with a GFD diet. The intervention resulted in; an increase in the height percentile compared to placebo treatment; significant reductions in mature T lymphocytes (CD3p), and; HLA-DRP T lymphocytes and reductions in TNF-a compared to placebo treatment. Moreover, in the study by [PERSON] et al. [52], the daily administration for three months of _B. short BR03_ and _B632_ in association with the GFD diet in subjects with CD led to a significant decrease in serum TNF\(\alpha\) levels compared to the placebo group. In the study by [PERSON] et al. [53] the administration of a mixture of 2 strains, _B. short BR03_ (DSM 16604) and _B. short B632_ (DSM 24706) (1:1) for three months in subjects with CD in association with GFD induced the restoration of the physiological _Firmicutes/Bacteroidetes_ relationship in the intestinal microbiota, and led to an increase in members of the _Actinobacteria_ phylum but this increase was not significant. In a multicenter study, the probiotic formulation VSL # 3 was administered for 12 weeks in 45 subjects with CD who reported only partial improvement in symptoms despite adhering to a strict GFD [54]. While, in the study of [PERSON] et al. [55], the 6-week intervention of a mixture of 5 strains of lactic acid bacteria and bifidobacteria: _Lactobacillus casei_ LMG 101/37 P-17504, _Lactobacillus plantarum_ CECT 4528, _Bifidobacterium animals subsp. lactis_ Bi1 LMG P-17502, _Bifidobacterium short_ Bbr8 LMG P-17501, _B. short_ Bi10 LMG P-17500, in patients suffering from CD at GFD with symptoms, led to a decrease in pain perception and a modification of the intestinal microbiota, characterized by an increase in _bifidobacteria_ which is still detectable 6 weeks after stopping probiotics.  
(b) Effect of pro-biotics on the gluten challenge in CD  
In the study by [PERSON] et al. [56] the administration, in subjects with CD, for 3 weeks and of _Bifidobacterium infants ntern_ life in conjunction with the daily con-sumption of 12 g of gluten led to a significant improvement in gastrointestinal symptoms compared to the placebo group. It is conceivable that the observed beneficial effect may be related to the modulation of innate immunity. In this regard, a second study was conducted in which the administration _of B. infants natren_ Life Start super strain in sub-jects with CD at GFD with challenge of 12 g of gluten resulted in reductions in \(\alpha\)-defensin and in the number of Paneth cells in duodenal biopsy samples [57].
## 3 Results and Discussion
### Convective Heat Transfer Coefficient Results  
The convective heat transfer coefficient, \(h_{c}\), was determined for the HPFPC using Equation (35). For the HPETC, the useful heat was first determined using Equation (21). Subsequently, the \(h_{c}\), in the condenser was determined by solving it for Equation (31). Figure 8 illustrates the heat transfer coefficient in relation to fluid temperature for both the HPETC and the HPFPC. The data reveal that the heat transfer coefficient for the HPETC increases at approximately 159 W/m\({}^{2}\) K for each degree Celsius. By contrast, the increase for the HPFPC using water is much lower, at 22 W/m\({}^{2}\) K per degree Celsius. Similar growth rates are observed with ethylene glycol and propylene glycol; however, the heat transfer coefficient for fluid mixtures is notably lower than that for pure water.  
Moreover, for the HPFPC, the heat transfer coefficient diminishes by 28% and 41% when antifreeze solutions are applied at concentrations of 30% and 50%, respectively. This was previously demonstrated by [14], where they show how collector efficiency decreases as the percentage of antifreeze increases. To counteract the decrease in the heat transfer coefficient, the mass flow rate could be increased, as can be seen in Figures 6c and 7c, which show that the collector efficiency increases with mass flow. This variation in growth rates of the heat transfer coefficients is likely attributed to the differing factors impacting  
Figure 7: Efficiency vs. mass flow for five different working fluids and for three different solar collectors: (**a**) flat plate solar collector, (**b**) evacuated tubes solar collector, (**c**) flat plate with heat pipes solar collector.  
each collector type. In the HPETC, the heat removal factor is influenced by all collector components, including the spacing of the tubes and the properties of the circulating fluid within the evaporator. By contrast, the heat transfer coefficient for the HPFPC reflects the conditions of the fluid receiving heat from the condenser of the heat pipes located inside the manifold [29; 30; 32].
## 2 Preliminaries  
### Kriging  
In this study, we use the Kriging model to approximate the objective function and constraint functions. As a typical surrogate model, Kriging can effectively fit complex functions and provide uncertainty estimates for the prediction results. The dynamic evaluation strategy relies on the prediction accuracy and uncertainty information of the Kriging model. By monitoring the model prediction errors (such as the mean square error and the feasibility misclassification rate), it dynamically adjusts the usage frequency of the surrogate model. When the model reliability is low, the number of surrogate evaluations is reduced to prevent it from misleading the search direction. When the reliability is high, the number of surrogate evaluations is increased to save computational resources. The mathematical model of the Kriging model can be expressed as:  
\[y(x)=f(x)+\epsilon \tag{3}\]  
where \(\epsilon\sim\mathcal{N}(\mu,\sigma_{n}^{2})\) is a uniformly distributed Gaussian noise and \(\mu\) and \(\sigma\) are two constants independent of \(x\).  
Given the collected data \(X=\{x_{1},x_{2},\ldots,x_{N}\}\) and its corresponding output \(Y=\{y(x_{1}),y(x_{2}),\ldots,y(x_{N})\}\), for an unevaluated point \(z^{*}\), the estimated output \(y(z^{*})\), the mean \(\overline{f}(z^{*})\), and the variance \(\sigma^{2}\) are calculated as follows:  
\[\overline{f}(z^{*})=\psi(z^{*})+k_{*}^{\top}(K+\sigma_{n}^{2}I)^{-1}(Y-\psi(X)) \tag{4}\]  
\[\sigma^{2}(z^{*})=k(z^{*},z^{*})-k_{*}^{\top}(K+\sigma_{n}^{2}I)^{-1}k_{*} \tag{5}\]  
Among others, \(X\) is the average vector of \(X\), the mean vector of \(k^{*}\) is the average vector of \(X\), and \(z^{*}\) is the vector of covariances between \(K\), where \(K\) is the covariance matrix of \(X\). The covariance matrix of two data points is measured using the squared exponential function as the covariance function of \(X\) and \(X^{\prime}\). The similarity between the two data points \(X\) and \(X^{\prime}\) is given by:  
\[k(x,x^{\prime})=\theta\exp\biggl{(}-\frac{1}{2\ell^{2}}(x-x^{\prime})^{\top} (x-x^{\prime})\biggr{)}. \tag{6}\]Predicting the mean \(\overline{f}(z^{*})\) is directly used as \(y(z^{*})\), the predicted value, and the predicted variance \(\sigma^{2}[f(z^{*})]\) represents the uncertainty. The hyperparameters in the covariance function can be obtained by maximizing the log-marginal likelihood function as follows:  
\[\log p(Y\mid X)=-\frac{1}{2}(Y-m(X))^{\top}(K+\delta_{n}^{2}I)^{-1}(Y-m(X))- \frac{1}{2}\log|K+\delta_{n}^{2}I|-\frac{N}{2}\log 2\pi. \tag{7}\]
# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 3 Theoretical Framework
### Spatial Optimization of Shelters Based on the Redistribution of Remaining Non-Evacuated Evacuees  
The basic principle of the spatial optimization model is to redistribute the remaining non-evacuated people from all the demand points to surrounding plots on which shelters have not yet been constructed or that do not contain available resources for shelters. As shown in Figure 3, the black points are the demand points (1, 2, 3..., 10) that have not been completely evacuated; the red plots indicate that there are already available resources for shelters (i.e., the plot has already participated in the calculation of the evacuation model, and no personnel will be assigned to it in the spatial optimization model); the green plots indicate that there are no resources available for shelters in the current situation (in the spatial optimization model, these are the target plots). These plots can be used to increase the shelter area in the future. In addition, in the interest of resource conservation, the economy, and the ease of implementation, three key points in the model should be explained: (1) the \"Code for Design of Disaster Mitigation Emergency Congregate Shelter\" (GB51143-2015) requires that the effective area of a short-term fixed shelter is no less than 2000 square meters and 2 square meters per capita. Therefore, priority is given to the plots with more than 1000 remaining non-evacuated people in the model; (2) overlapping target plots, such as A, B, C..., I in Figure 3, should be used as the main optimization target plots, and the targets should not be too numerous or too scattered; (3) by combining the relevant standards, the shelter supply, and the demand analysis, the size and type of shelter that should be provided on a target plot can be determined.  
Figure 2: A brief principle map for the evacuation simulation.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 3 Mapping and Localization
### Pit Survey Phase  
Once the SphereX bots arrive near the pit entrance, a survey of the pit entrance is conducted by hopping from one side of the pit to the other side. Multiple hops will be performed to map the pit entrance by using the ICP-based Pose-Graph SLAM algorithm, and the maps generated during each hop are registered together to estimate the depth and map of the pit floor. These hopping points will be manually selected and communicated to the robots from a ground station.  
Figure 7a shows the trajectories of SphereX for surveying the pit with the hopping points for four preselected hops. Figure 7b-e show the maps generated of the pit entrance during each hop. Figure 7f shows the final map of the pit entrance by registering each map generated during each hop. It can be seen that a fraction of the pit floor is mapped, which will then be used for planning the pit entrance phase.  
Figure 6: Framework for ICP-based Pose-graph SLAM algorithm for simultaneous localization and mapping.  
Figure 7: (**a**) Model of the pit entrance along with the trajectories of 4 hops used for mapping; (**b–e**) Local maps generated during Hop 1, Hop2, Hop3, and Hop 4; (**f**) Final map of the pit entrance.
## 2 Methodology
### Parameter Estimation with the SCEM-UA Algorithm  
This algorithm assumes an inherent uncertainty in parameter identification, partly due to measurement error, model output error, or the fact that the model does not perfectly represent the model. The SCEM-UA algorithm coordinates with a Bayesian scheme where parameters are represented as probabilities instead of as a single optimal set of values to derive the posterior pdf of the structural system parameters.  
The SCEM-UA algorithm starts with a population of \(s\) parameter sets. The parameter sets randomly distribute within the feasible parameter space, which is defined by the joint prior parameter distributions \(p(\boldsymbol{\uptheta})\) initially. The posterior density \(p(\boldsymbol{\uptheta}|\textbf{y})\) is computed by Bayesian inference scheme for each parameter set. A total of \(s\) samples from the parameter space \(\boldsymbol{\uptheta}\) are stored in order of decreasing \(p(\boldsymbol{\uptheta}|\textbf{y})\). The sample points are partitioned into \(q\) complexes with the expectation that \(q<<s\). Partitioning is done by striping the total population across the complexes. More precisely, complex \(c\) contains all samples numbered \(i\) such that \(i\) mod \(q=c\). After the samples have been divided into complexes, each complex is evolved independently employing the sequence evolution metropolis (SEM) algorithm.  
This SEM algorithm operates in each parallel sequence to generate new candidate points from an adaptive proposal distribution based on the information derived from the m samples. After the SEM algorithm is completed, all the updated complexes are combined and stored in order of decreasing posterior density. The evolution and shuffling will stop when the Gelman-Rubin convergence criteria is achieved, which demonstrates convergence to a stationary distribution [33].  
The operations including use of complexes during evolution and the shuffling of these complexes conduct an efficient and robust search for the optimal parameter set. The SCEM-UA algorithm collects information of each individual sequence and enhances the survivability of each sequence by globally sharing the information during the evolution process. The flowchart diagram shows the SCEM-UA algorithm implementation scheme as shown in Figure 1. Please refer to [PERSON] et al. and [PERSON] et al. for more information about the algorithm [17; 29].
## 4 Results and Discussion
### Movement Disorder Detection  
The initial phase of our proposed pipeline emphasizes the application of a binary classifier for categorizing input signal segments as normal or anomalous. The evaluation reveals that HARDenseRNN outperforms random forest in detecting anomalous events in patients with [PERSON]'s disease when focusing on recall. The optimal configuration for HARDenseRNN is a segment size of 250 and S1 labeling, achieving the highest recall of 93.03%, along with an accuracy of 88.58% and precision of 86.13%. For S2 labeling, HARDenseRNN's best recall is 87.92% with a segment size of 150. However, HARDenseRNN shows greater variability in performance depending on the segment size and  
Figure 6: Plot depicting accuracy and loss curves for movement disorder detection and classification of HARDenseRNN models utilizing GeneActiv smartwatch data, employing a window size of 250 and S1 labeling approach.  
labeling approach. In contrast, random forest consistently performs well across various configurations, with the highest recall of 89.10% at a segment size of 250 and S2 labeling, followed by 88.65% with the same segment size and S1 labeling. While random forest offers more consistent performance across various pre-processing configurations, the superior recall achieved by HARDenseRNN with larger segment sizes and S1 labeling makes it the most effective approach for this task.  
Confusion matrices for movement disorder detection with HARDenseRNN and optimal pre-processing configurations (segment size 250 and S1 labeling) are shown in Figure 7. In addition to achieving a higher recall, HARDenseRNN exhibits faster execution times and a smaller model size (approximately 9 MB only), even when compared with a random forest model trained only on the top 10 features, as shown in Figure 8, making it optimal for real-time applications and deployment on low-end devices. Detailed results of 12 models evaluated for movement disorder detection can be observed in Figure 9.