esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Self-Organizing Map  
A self-organizing map (SOM) is a neural network used for dimensionality reduction that typically reduces higher dimensional data into a two-dimensional representation. Unlike many other artificial neural networks, SOM uses competitive learning, rather than error-correction learning, during training [21].  
The following formula shows the weight calculation for each SOM neuron [22]:  
\[W_{v}(s+1)=W_{v}(s)+\theta(u,v,s)*\alpha(s)*(D(t)-W_{v}(s))\]  
where:  
* \(W_{v}\) is the current weight vector of node \(v\)
* \(s\) is the current iteration
* \(u\) is the index of the best matching unit (BMU) in the map
* \(v\) is the index of the node in the map
* \(\theta(u,v,s)\) is the neighboring function used to restrain learning based on the distance
* \(\alpha(s)\) is a learning decline due to iteration progress
* \(t\) is the index of the target input data vector in the input data set \(D\)
* \(D(t)\) is a target input data vector The quantization error is used to measure the distance between the final SOM value and the original input value. It is calculated with the following formula [23; 24]:  
\[QE=1/N\Sigma_{i=1}^{N}||\phi(x_{i})-x_{i}||\]  
where \(N\) is the number of input vectors and \(\phi:D\mapsto M\) is the mapping from the input space \(D\) to the SOM \(M\).  
The topographic error measures how well individual data points are mapped to the SOM's nodes following the formula [23; 24]:  
\[TE=1/N\Sigma_{i=1}^{N}t(x_{i})\]  
\[t(x)=\begin{cases}0&\text{if $\mu(x)$ and $\mu^{\prime}(x)$ are neighbors}\\ 1&\text{otherwise}\end{cases}\]  
\(\mu(x)\) and \(\mu^{\prime}(x)\) are the best-matching unit and the second-best matching unit, respectively. The process of training a SOM, presented in Figure 1, consists of these four steps:  
1. Initialization--a vector of the same dimensionality as the input vector is assigned to each neuron.
2. Competition--SOM nodes compete, and the best matching unit is considered a winner. The winner node's vector values are updated to learn from the input vector and get closer to it.
3. Cooperation--The winner node cooperates with its neighbours to propagate the change it underwent.
4. Adaptation--Neighboring nodes adapt to the change on the winner node, but the magnitude of this change decreases with time and grid distance from the winner node.  
The benefit of using a SOM is that it keeps the structural information of the training data set (programming code) intact. The main drawback is that a value for each dimension of the input vector is needed for each member of the input data set in order to generate a SOM. Since we provide either 0 or 1 for each dimension, this drawback does not affect our research. Another problem is that every SOM is different and finds different similarities among the sample vectors.  
Thus far, we have not found any references in research on this type of source code analysis with a focus on the extraction of the programming concepts from the content. SOMs are typically used for visualization and exploratory data analysis [25; 26; 27], but they are rarely, if ever, used on the source code.
## 3 Results and Discussion
### Thermal Performance of the Insulating Envelope and Energy Simulation  
#### 3.4.1 Thermal Inertia Results  
Table 3 shows the findings from the thermal inertia analysis, indicating that the rice husk fiber used in the test samples possesses an energy storage of 98 (KJ/m\({}^{2}\)K). This means that, when incorporated into a building's component, this building will exhibit over 30% of the energy storage capacity compared to the use of recycled PUR, and similar behavior was observed in a previous study with sawdust material [29].  
The thermal transmittance results indicate that rice husk fiber with a transmittance value of 0.17 W/m\({}^{2}\)K in the wall component outperforms recycled PUR, which has a thermal transmittance of 0.201 W/m\({}^{2}\)K. Both materials exhibit transmittance values well below the maximum limit of 0.45 W/m\({}^{2}\)K established by the atmospheric decontamination plan (PDA) for walls in Temuco [55]. The superior performance of rice husk fiber suggests a greater ability to reduce heat transfer, enhancing the thermal efficiency of the building envelope. This lower transmittance contributes to a more stable and comfortable environment, minimizing temperature fluctuations regardless of the external weather conditions.  
Figure 16 shows the surface temperature behavior when the insulation material is changed from recycled PUR to rice husk fiber. Notably, replacing the insulation significantly increases the temperature wave delay from 8.5 h to 14.5 h, enhancing thermal storage capacity, reducing energy consumption, and improving indoor thermal comfort. This demonstrates the potential to enhance the energy efficiency of buildings [56].  
Figure 17 shows the temperature behavior when recycled PUR insulation materials are replaced with rice husk fiber. A notable reduction in temperature fluctuation amplitude occurs, demonstrating its effectiveness in mitigating the impact of external temperature peaks. Such thermal performance improvements are typically not observed with conventional insulation materials like recycled polyurethane, which tend to closely follow external temperature fluctuations [29].  
Figure 15: Smoldering propagation velocities are calculated as the slope of the obtained linear regression.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Property** & **Unit** & **Wall M Rice Husk** & **Wall M Recycled PUR** \\ \hline U-value & W/m\({}^{2}\)K & 0.17 & 0.201 \\ Time lag & h & 14.50 & 8.50 \\ Decrement factor & – & 40.30 & 11 \\ Heat storage capacity & KJ/m\({}^{2}\)K & 98 & 69 \\ Thermal capacity of inner layers & KJ/m\({}^{2}\)K & 49 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of thermal performance.
# Machine Learning for Evaluating Hospital Mobility: An Italian Case Study
## 3 Materials and Methods
### Prediction Model  
The predictive model will employ the logistic regression algorithm to estimate the mobility gradient of hospitals in 2021, categorizing them into three classes: LOW, MEDIUM, and HIGH mobility, based on the distance patients travel to reach the hospital. This choice is supported by studies demonstrating the effectiveness of logistic regression in predicting mobility behaviors and health risks, such as the analysis of travel behavior during the COVID-19 pandemic [28] and the assessment of cardiovascular risk [29], highlighting its applicability in complex healthcare contexts.  
To optimize the model's performance, we configured the parameters with Ridge (L2) regularization, C=1, and without differentially weighting the classes, reflecting a standardized approach to maximize computational efficiency and predictive performance. Logistic regression, known for its ability to handle multinomial classifications and quick training times, is ideal for analyzing large datasets in the healthcare sector, providing a solid foundation for interpreting the influence of various features on hospital mobility.  
In the logistic regression process, feature values are combined into a weighted sum and transformed through the logistic function, producing probability values that determine the mobility classification of each hospital. This method not only provides clear predictions but also insights into the features that most significantly influence hospital mobility, supporting informed decisions to improve the accessibility and efficiency of healthcare services. This choice is based on the demonstrated effectiveness of such configurations in analyzing complex data, as highlighted in recent studies that have explored the application of logistic regression in healthcare contexts. To address the challenge of training and validation in a context of limited data, we adopted an integrated approach, dividing the dataset consisting of 127 instances (hospitals) into a training set (70%, equivalent to 89 hospitals) and a validation set (30%, corresponding to 38 hospitals) through a data sampling process. This was accomplished using the Data Sampler widget, as illustrated in the blue box in Figure 2. This strategy allowed us to mitigate the risk of overfitting and ensure the model's generalizability using cross-validation techniques, which facilitated a complete and iterative use of the dataset.  
We paid particular attention to data quality, committing to rigorous data preparation and cleaning to provide the model with accurate and significant inputs. This approach maximized the effectiveness of the available information, strengthening the robustness and reliability of the model in a scenario characterized by a limited number of observations. The model training was designed to be replicable, using a method of stratified cross-validation sampling with 10 folds.
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 2 Materials and Methods
### The TC RTK/INS Integration Architecture  
This section describes the TC RTK/INS integration architecture comprehensively. In addition to what we have described above, the INS mechanization and the initial alignment are also important parts of the TC RTK/INS integration. The INS mechanization is a procedure that converts the measurements from the IMU into the position, velocity, and attitude information in the navigation frame [20]. The EKF measurements in the TC RTK/INS integration are calculated based on the INS mechanization results. The states' errors estimated by the TC RTK/INS integration will be fed back to the INS mechanization to suppress the long-term drift. In our implementation, the INS mechanization operates at the same frequency as the prediction step, whereas the update step operates lower. The initial alignment, which refers to the attitude initialization, aims to obtain the rotation matrix between the body frame and the navigation frame [78]. The TC RTK/INS integration should operate when the initial alignment has been completed. Readers can refer to the documents of PSINS for more details on the INS mechanization and the initial alignment. Notably, when it comes to the outputs, there are three cases. The INS mechanization results will be directly output when there are no valid GNSS measurements. Otherwise, the states' errors will be estimated by EKF and fed back to the INS mechanization, which generates the float solution. The corresponding fixed INS solution will be output if the ambiguity can be correctly resolved. Table 3 shows these three cases.  
Figure 2 illustrates the overview of the TC RTK/INS integration architecture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Outputs**Situation & **Valid GNSS Measurements** & **AR** & **Generation** \\ \hline Fixed Solution & Yes & Yes & Feedback to the float solution \\ \hline Float Solution & Yes & No &
\begin{tabular}{c} EKF estimation and \\ feedback to the INS solution \\ \end{tabular} \\ \hline INS Solution & No & No & The INS mechanization \\ \hline \hline \end{tabular}
\end{table}
Table 3: The outputs cases.  
Figure 2: The overview of the TC RTK/INS integration architecture.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### Samza
#### 4.4.4 Fault Tolerance  
To ensure fault tolerance, each task in Samza runs a changelog-capturing service in the background that records incremental changes at a known place in the native file system. A failed task can be restarted by replaying the changelog. When a container restarts after a failure, it looks for the latest checkpoints and begins accepting messages from the latest checkpoint offset, this ensures at least once processing guarantees. This changelog mechanism is more efficient in comparison with full state check pointing, where a snapshot of the entire state is stored [52]. For further efficiency gains, the change log updates do not use the main computation hot path. Updates are stored in batches and sent to Kafka periodically in the background using spare network bandwidth. Re-scheduling a task after failure improves efficiency by Host Affinity mechanism that reduces the overhead of accessing changelog remotely, however, this overhead is unavoidable in case of permanent/long term machine failures.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 1 Introduction
In this paper, therefore, we present a dynamic spectrum access strategy for cognitive radio-enabled LPWAN operating in both licensed bands and unlicensed bands. We also derive the blocking probability for the licensed users and carried traffic for the unlicensed users. In Section 2, related works are investigated and CR-LPWAN architecture is introduced in Section 3. A dynamic spectrum access model is presented and numerical analysis is given in Sections 4 and 5, respectively. Results and discussions are presented in Section 6. Conclusions are given in Section 7.
## Materials and Methods
### Methodology  
There are 3 independent variables (structure, rules, and control), and one dependent variable is the outcome. We added another transformed binary dependent variable for which control is an independent variable. We established a threshold of 4 to convert outcome into a binary dependent variable (Agree/Disagree) of 1 (general agreement) and 0 (overall disagreement). The response data are ordered on a 5-point Likert scale with 5 for strongly agreed, 4--agreed, 3--neutral, 2--disagreed, and 1 for strongly disagreed; for response 4, we assigned a value of 1 to this additional variable and for all responses < 4, we set them as 0. In this way, we adopted a conservative approach towards our analysis in converting qualitative judgements into quantitative.  
Further, we calculated the paired correlations of the variables. As they are survey data with ordinal characteristics, we also calculated Spearman and Kendall correlations to allow for the ordinal nature of the data. [PERSON]'s \(\tau\) and [PERSON]'s \(\rho\) have been computed as a particular case of generally paired correlations.  
Additionally, we produced multivariate ordered logistic regression results to deepen our understanding of the nature of the relationships that exist among various variables used in the analysis. As the response categories in our data are limited to 5, using a nonparametric ordered logistic regression model seems most appropriate for our analytical purpose [22; 23; 24]. We can describe our model as:  
\[logit[P(Y\leq j)]=\left[\frac{P(Y\leq j)}{P(Y>J)}\right]=\alpha_{j}-\beta X,\ j\in[1,J-1] \tag{1}\]  
where \(j\in[1,J-1]\) are the levels of the ordinal outcome variable \(Y\); the proportional odds model assumes there is a common set of slope parameters, \(\beta\), for the predictors. The ordinal outcomes are distinguished by the \(J-1\) intercepts \(\alpha_{j}\). The benchmark level is \(J\). For ordinal regression, we make certain assumptions about the underlying data, i.e., the response variable is ordinal, and that the explanatory variables are continuous or categorical (though they are ordinal too). Still, we treat them either as continuous or categorical. We also assume that there is no multicollinearity and the odds are proportional where each independent variable has an identical effect at each cumulative split of the ordinal dependent variable [25; 26; 27].  
For the statistical analysis based on the methodology stated here, we used STATA 16.0 version to generate the results, which have been described in detail in the next section.
###### Abstract  
Background: The aquaculture sector is vital for food supply and marine species conservation. However, vibriosis poses significant threats, affecting fish and invertebrates. _Vibrio harveyi_ outbreaks are increasing due to global warming-driven expansion of virulent strains. Methods: This study investigates the bactericidal potential of in situ-generated electrolyzed water (EW) as a complementary strategy to antibiotic therapy. Strains of _V. harveyi_ isolated from diseased fish were exposed to EW under varying conditions of salinity (1.5 and 3%), pH (5, 6.5, and 7.5), and free available chlorine (FAC) (5, 20, 25, and 125 ppm) to reduce bacterial concentrations. Results: EW demonstrated high bactericidal potential at 20-25 ppm FAC and non-acidic pH, reducing bacterial populations by over four logarithmic units within 15 min. Efficacy diminished when EW was stored for days. Conclusions: EW shows a promising disinfection method during vibriosis outbreaks and as a preventive measure during stress in aquaculture. Its advantages include on-site production and avoidance of concentrated chemicals, which minimize health risks.  
electhroized water; bacterial pathogens; aquaculture; free available chlorine; pH; _Vibrio harveyi_; on-site production; sustainable disinfection
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 2 Materials and Methods
### Methods
#### 2.3.1. Top-of-Atmosphere (TOA) Reflectance Calculation
For the record, the calibration coefficients are not always the same because the vicarious calibration is routinely carried out annually, and updated through the China National Satellite Meteorological Center (NSMC) website ([[http://www.nsmc.cma.gov.cn/](http://www.nsmc.cma.gov.cn/)]([http://www.nsmc.cma.gov.cn/](http://www.nsmc.cma.gov.cn/))). The calibration coefficients of FY-3A/MERSI published by NSMC have experienced three updates since the launch. That is, four stages of calibration can be divided from launch until 2015. All the calibration coefficients are listed in Table 3.  
The radiometric calibration of the FY-3B/MERSI has experienced two stages. In the first stage, the TOA reflectance of the FY-3B/MERSI data from 18 October 2010 to 6 March 2013 can be calculated using Equation (4) using coefficients in Table 4. After that, the organization structure of the FY-3B/MERSI data source file has been updated and calibration equation is changed. The TOA reflectance of the FY-3B/MERSI data from 6 March 2013 until now can be calculated using Equation (4)  
\[\rho_{\lambda}=\frac{RefFactor\cdot d^{2}}{100\cdot\cos  
Both FY-3A/VIRR and FY-3B/VIRR data can be calibrated using Equation (5)  
\[\rho_{\lambda}=\frac{(S\cdot C_{E}+I)\cdot d^{2}}{100\cdot\cos(\theta_{SZ})} \tag{5}\]  
where \(S\) is the calibration slope, \(I\) is the calibration intercept, and \(C_{E}\) is the channel count value. \(S\), \(I\) and \(C_{E}\) are calibration coefficients and can be obtained from the data source file. Both of them have two stages of calibration coefficients, which are listed in Table 5.  
Unlike the CCDs, MERSIs and VIRRs, whose calibrations are performed relying on the site calibration annually, the calibration of MODIS in VNIR bands are based on the regular measurements from the onboard calibrators, so the calibration coefficients of MODIS data are different according to the data acquisition time and satellite attitude, etc. The TOA reflectance of the MODIS can be calculated using Equation (6). \[\rho_{\lambda}=reflectance\_scale\cdot(DN-reflectance\_offsets)/\cos(\theta_{SZ}) \tag{6}\]  
where \(\rho_{\lambda}\) is the TOA reflectance; \(\theta_{SZ}\) is the solar zenith angle; \(reflectance\_scale\) and \(reflectance\_offsets\) are the gain and offset of the reflectance, respectively; \(DN\) is the channel count value.
## 4 System Model  
Since MPTCP is applied with dual or multiconnectivity, where more than one wireless interface is used in data transfer, energy consumption remains a significant issue, especially for devices with limited energy supply (e.g., battery-operated devices).  
Consider a scenario where several stationary devices are receiving data traffic from some sending nodes via a communication backbone using MPTCP. The receiving nodes can connect to this backbone through dual connectivity via the simultaneous usage of different radio access technologies (RATs), such as WiFi and LTE/5G. This implies that the receiving nodes are under the overlapped coverage of a WiFi access point (AP) and an eNB or a gNB as shown in Figure 1. The wireless channel of each interface of each node is assumed to be different and affected by the location and the surroundings of the receiving node. This scenario can represent human-based traffic, where users with dual-interface user equipment (UEs) download files or stream data from some server over the Internet. It may also represent dual-interface IoT gateways receiving data from their cloud management entities or servers. This implies that only the receiving nodes are energy-constrained (battery-powered devices), while the sending nodes (e.g., servers) are assumed to be connected to the power grid. Moreover, we assume that all traffic from the sending nodes is routed through SDN-enabled network switches managed by an SDN controller that can steer the traffic of MPTCP subflows before it arrives at the wireless interfaces of each receiving node, and hence it can send the MPTCP traffic over a receiving node's WiFi interface only, its cellular interface only, or both interfaces. The SDN controller is assumed to be capable of running deep learning artificial intelligence models such as DNNs [41].
# Augmenting Environmental Interaction in Audio Feedback Systems+
## 3 System Design and Validation
### Ambient Noise Level with Amplitude Control  
As mentioned in the previous Section 3.1, the adaptive gain control determines amplification or de-amplification by the high/low threshold values. In the previous tempo control, the threshold values were fixed to 0.3 and 0.7. However, use of different thresholds generates different output levels: a large threshold value tends to generate loud feedback sounds (Figure 6). The range of the thresholds are constrained between 0.2 and 1.0 to ensure the Larsen tone generation is possible.  
In order to control the system's output level depending on the volume level of ambient noise, the threshold values themselves are determined by measuring the amplitude of ambient noise. The threshold is updated when the amplitude of the output signal, which is measured as the maximum  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Room Types** & **RT60 (s)** & **Xcorr** & \(L_{1}\) & \(L_{2}\) \\ \hline Livingroom & 0.28 & 0.0407 & 23496 & 46991 \\ Bathroom & 0.58 & 0.1365 & 30674 & 42633 \\ Church & 0.97 & 0.4205 & 46311 & 30691 \\ Long Echo Hall & 3.07 & 0.4904 & 59604 & 25302 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the reverberant characteristics measured by RT60 (the reverberation time over a 60 dB decay range), the cross-correlation values when the delay line is set to 28,000 samples, and average delay line lengths (in sample) using the proportional (\(L_{1}\)) and reflected (\(L_{2}\)) mappings.  
Figure 5: Delay line length curves simulated using impulse response data of several locations with different reverberant characteristics (measured in Table 2), using the (**a**) proportional (\(L_{1}\)) and (**b**) reflected (\(L_{2}\)) mappings.  
Figure 6: Simulated amplitude control of feedback sounds when the low and high threshold values are (**a**) small (0.3 and 0.6) and (**b**) large (0.5 and 1.0). The length of the delay line is set to 30,000 samples (1.47 Hz).  
amplitude value in the audio frame, is below the current low threshold, in order to exclude the feedback itself from the adaptation: if the amplitude is below threshold, it is assumed that the ambient sound dominates in the input, and the threshold value is updated according to the peak amplitude of the input signal. Figure 7 compares the average threshold and peak amplitude values of the input signals according to the volume level of ambient noise, which is simulated using white noises with different amplitudes.