esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
## 2 Materials and Method
### Work Plan  
The NHATS contains more than 400 parameters that give us information including but not limited to social, medical, and daily living aspects of the subjects under study. This data provides us an opportunity to identify the parameters of our day-to-day life that have an impact on the onset and severity of dementia. The NHATS takes place each year with previous participants as well as new ones. Each yearly iteration of this survey is called a round. In total, data for 11 rounds is available. We used data from Round 9 to establish initial framework and then used temporal analysis to see if the findings match across all rounds. The parameter types are divided into the faculties as listed in Table 3  
In the given dataset, the parameters follow the naming convention as descried in Figure 1  
The parameter is prefixed by parameter type and interview round. We have total data from around 5000 individuals with around 1288 parameters. As the first step towards finding out vital parameters, we first need to clean the available data and identify which data is to be termed labelled data. We remove parameters that have more than 30% blank or null values and parameters that have same value for all patients.  
Each parameter's possible values and type are elaborately explained in the documentation provided along with the data. The parameter values are numeric. While most are categorical parameters, some are numerically ranged parameters. Table 4 lists meta data of two sample parameters.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Type** & **Possible Values** \\ \hline \multirow{2}{*}{hc9 disescn1} & \multirow{2}{*}{Categorical} & 1—dementia reported in this interview \\  & & 7—dementia reported in the previous interview \\  & & \(-\)1—dementia not present \\ \hline cs9 dnumchild & Ranged & An integer that indicates the number of children \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter Meta-data.  
Figure 1: Naming convention. _hc: (health condition)_; 9: round number; Disescn1: disease condition 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline Residence (RE) & Self-care activities (SC) & Cognitive assessment—proxy (CP) \\ \hline Health conditions (HC) & Smoking (SD) & Cognitive assessments—self (CG) \\ \hline Housing type (HT) & Well-being (WB) & Stroop assessment (STROOP) \\ \hline Service environment (SE) & Technological environment (TE) & Household activities (HA) \\ \hline Household members (HH) & Physical capacity (PC) & Social network (SN) \\ \hline Community (CM) & & \\ \hline \end{tabular}
\end{table}
Table 3: General Classification of parameters.  
For example, the parameter 'hc9 disescn1' is a categorical variable that may have values 1 (dementia reported in this interview), 7 (dementia reported in the previous interview), or \(-1\) (indicates the absence of dementia).
###### Abstract  
Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (_m,k_)-firm model. The proposed model processes captured information by employing a multimedia stream in the (_m,k_)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (_m,k_)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (_m,k_)-firm model regardless of the amount of traffic.  
(m,k)-firm model; adaptive real-time routing; traffic management; industrial wireless multimedia sensor network +
Footnote †: This paper is an extended version of our paper published in 7 th International Symposium on Sensor Science (I3S 2019), Naples, Italy, 9–11 May 2019.
## Appendix A Transfer Function Linearization  
The transfer matrices introduced in Section 2.3 describe the frequency-dependent transfer behavior from hub motion to hub loads. Due to unsteady aerodynamics, these transfer functions become nonlinear with respect to frequency (see Figure 12, blue curve). For comparison with the method by [PERSON] and [PERSON] or compatibility with legacy whirl flutter workflows, it is beneficial to linearize transfer functions \(\underline{H}_{prop}\) into a form similar to Equation (4). This is achieved by using two transfer samples for \(\underline{H}_{prop}(0)\) and \(\underline{H}_{prop}(i\omega_{1})\) and applying the linearization as shown in Equation (A1).  
\[\underline{H}_{prop,lin}(i\omega)\ =\ \underbrace{\underline{H}_{prop}(0)}_{ \underline{K}_{prop}}+\underbrace{\frac{\underline{H}_{prop}(i\omega_{1})- \underline{H}_{prop}(0)}{i\omega_{1}}}_{\underline{D}_{prop}}\ i\omega \tag{12}\]  
Because \(\underline{H}_{prop}(i\omega)\) is complex and nonlinear with frequency, the resulting damping matrix (\(\underline{D}_{prop}\)), the slope of the linearization, can be complex (see Figures 9 and 12). This would lead to complex derivatives, which are not compatible with the Houbolt/Reed derivatives. To achieve a real-valued damping matrix, only the real part of the damping matrix is considered. This leads to a constant real part of the final, linearized transfer function, \(\underline{H}_{prop,lin}(i\omega)\), while with the full, complex linearization of the real part of \(\underline{H}_{prop,lin}(i\omega)\) can be (linearly) frequency-dependent.  
Figure 12 compares the full, frequency-dependent transfer spectrum for the \(F_{y\theta}\) term (calculated with UPM) with the complex linearization (dotted green line) and the corresponding real linearization (dashed red line). The left plot shows the real part of the transfer function, and the right one, the imaginary part. For this example, 10 Hz was chosen for \(\omega_{1}\). It can be seen for the real linearization that only the transfer function for zero frequency is correctly approximated in the real part. For the complex linearization, the sample at 10 Hz also matches exactly, and the linearization gives a better fit in general. The imaginary part is captured very well, because \(\underline{H}_{prop}(0)\) is usually real, so no additional (constant) contribution to the imaginary part is to be expected.  
In this work, only the real linearization was used to obtain derivatives similar to the Houbolt/Reed method (see Figure 10) for better comparison.
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
###### Abstract  
This study studies the problem of on-orbit maintenance task planning for space-robot clusters. Aiming at the problem of low maintenance efficiency of space-robot cluster task-planning, this study proposes a cluster-task-planning method based on energy and path optimization. First, by introducing the penalty-function method, the task planning problem of the space-robot cluster under limited energy is analyzed, and the optimal-path model for task planning with comprehensive optimization of revenue and energy consumption are constructed; then, the maintenance task points are clustered to reduce the scale of the problem, thus reducing the difficulty of solving the problem; finally, a modified differential evolution algorithm is proposed to solve the problem of space-robot cluster task-planning, improve the performance of space-robot cluster task-assignment and path planning. Simulation results show that the proposed optimal-path model of space-robot cluster and the modified differential evolution algorithm can effectively solve the task-planning problem of spatial robot clusters.  
Keywords:spatial robot clusters; task planning; task clustering; modified differential evolution algorithm +
Footnote †: journal: Applied
## 4 Key Technology
### Status Prediction  
Status prediction technology is based on model synchronization and takes current state parameters as the starting point. Driven by the subsequent planned flight program, combined with the orbital and environmental parameters provided by the orbital computing module, over-real-time simulation is carried out to predict the status changes of the constellation in a certain period of time, as shown in Figure 10. The status prediction function has great value in the PHM system. The prediction data can not only be used as the input of the fault detection module to give advance warning of potential faults but can also be input to the fault diagnosis module for fault location to ensure that the O&M personnel have sufficient time to deal with possible faults.
## 2 Experiments
### Electroluminescence Efficiency Measurement  
For the electroluminescence measurements, the LED sample was placed inside an integrated sphere. The temperature of the sample was maintained at 25 \({}^{\circ}\)C by the TEC system. Pulsed currents with a pulse width of 0.5 ms and duty cycle of 1% were applied to the LED sample to reduce self-heating effects, and the device characteristics of the LEDs were measured. From the spectral measurements, the peak wavelength was observed to be \(\sim\)445 nm at 350 mA for both Sample A, and Sample B. Figure 2 presents the EQE versus current relation (EQE curve) for the measured LED samples. The peak EQEs of Sample A and Sample B were 0.582 and 0.524, respectively. The higher peak EQE for Sample A resulted from its better crystal quality, as one can see from the TDD and X-ray measurement results. For relatively low currents \(<\)250 mA, the EQE of Sample A was higher than that of Sample B. By contrast, for currents \(>\)250 mA, the EQE of Sample B was higher than that of Sample A. Consequently, the degree of efficiency droop of Sample B was considerably lower than that of Sample A.  
The internal electric field of InGaN/GaN QWs was investigated based on the reverse-biased electro-reflectance (ER) measurements [21; 22; 23]. The internal electric field was determined to be 1.50 MV/cm for Sample A and 0.92 MW/cm for Sample B [12]. The lower field strength for the QWs on silicon could be attributed to the reduced mismatch of the lattice constant between InGaN and GaN layers grown on silicon substrates compared with those grown on sapphire substrates. This interpretation is consistent with the previous strain measurement results of MQWs on sapphire and silicon by micro-Raman spectroscopy and high-resolution transmission electron microscopy [24], which showed that the lattice mismatch between InGaN and GaN layers could be lower due to the larger lattice constant of GaN on silicon substrates. This difference in the internal electric field was regarded as a major reason for the difference in the efficiency droop of EQE between Sample A and Sample B [12].  
Figure 1: Schematic cross-sectional diagram of the layer structures grown on (**a**) \(c\)-plane sapphire substrates (Sample A) and (**b**) silicon(111) substrates (Sample B).
## 3 Results and Discussion
### Shelf Life Analysis of Produced Cookies
#### 3.4.1 Color Measurement
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Temperature** (\({}^{\circ}\)C) & \(\Delta E_{max}\) & **k (Days\({}^{-1}\))** & \(t_{o}\) (Days) & \(\mathbf{R^{2}}\) \\ \hline \multirow{3}{*}{Control} & 25 \({}^{\circ}\)C & - & - & - & - \\  & 35 \({}^{\circ}\)C & - & - & - & - \\  & 45 \({}^{\circ}\)C & - & - & - & - \\  & 25 \({}^{\circ}\)C & 6.0 \(\pm\) 0.5 a  & 13 \(\pm\) 5 a  & 64 \(\pm\) 8 a  & 0.9729 \\  & 35 \({}^{\circ}\)C & 6.0 \(\pm\) 0.2 a  & 16 \(\pm\) 2 a  & 48 \(\pm\) 2 b  & 0.9966 \\  & 45 \({}^{\circ}\)C & 6.4 \(\pm\) 1.0 a  & 11 \(\pm\) 9 a  & 53 \(\pm\) 15 a  & 0.9979 \\  & 45 \({}^{\circ}\)C & 7.6 \(\pm\) 0.1 b  & 12 \(\pm\) 4 b  & 57 \(\pm\) 7 a  & 0.9996 \\  & 45 \({}^{\circ}\)C & 6.0 \(\pm\) 0.2 a  & 13 \(\pm\) 1 b  & 60 \(\pm\) 1 a  & 0.99729 \\  & 45 \({}^{\circ}\)C & 6.0 \(\pm\) 1.0 a  & 11 \(\pm\) 9 a  & 48 \(\pm\) 2 b  & 0.9966 \\  & 45 \({}^{\circ}\)C & 6.4 \(\pm\) 1.0 a  & 11 \(\pm\) 9 a  & 58 \(\pm\) 5 a  & 0.99975 \\  & 45 \({}^{\circ}\)C & 7.0 \(\pm\) 0.5 b  & 12 \(\pm\) 4 b  & 0.9833 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model parameters for the color change (\(\Delta E\)) of cookies with and without added SCG. Figure 3: Color change (\(\Delta E\)) of cookies vs. storage time for (**a**) the control cookie, (**b**) the 4% SCG-added cookie, and (**c**) the 7% SCG-added cookie at 25 \({}^{\circ}\)C, 35 \({}^{\circ}\)
# Parallel Crossed Chaotic Encryption for Hyperspectral Images
## 2 Chaotic Systems and Chaotic Encryption
### Piecewise Linear Chaotic Map  
The algorithm proposed in this paper, is based on the Piecewise Linear Chaotic Map (PLCM), but it can be implemented with any other chaotic map. The PLCM is defined in (5), where \(x_{n}\) is the state of the system and \(q\) is a parameter. The PLCM shows a chaotic behaviour when the parameter \(q\in(0,0.5)\) and the state \(x_{n}\) is bounded inside the interval \((0,1)\).  
\[x_{n+1}=c(x_{n},q)=\left\{\begin{array}{ll}\frac{x_{n}}{q}&\text{if}\quad x \in[0,q)\\ \frac{x_{n}-q}{0.5-q}&\text{if}\quad x\in[q,0.5)\\ c(1-x_{n},q)&\text{if}\quad x\in[0.5,1)\end{array}\right. \tag{5}\]  
The authors in [30] propose an improved version of the PLCM, called Modified Piecewise Chaotic Map (MPLCM). They report an information entropy of 7.9972-7.9976. This result overcomes the 7.8472-7.9413 reported in [29]. The MLPCM is defined in (6) and a simulation is shown in Figure 2.  
\[x_{n+1}=c(x_{n},q)=\frac{x_{n}-\lfloor\frac{x_{n}}{q}\rfloor q}{q} \tag{6}\]
# Sensitivity Analysis for Gaussian-Associated Features
## 5 Sensitivity Matrix Associated with a Least-Squares Sphere Fit to Data
### Points on a Longitudinal Segment of a Sphere  
The calculations in Section 5.1 can also be used to estimate sensitivities associated with measurements distributed in a longitudinal segment of a sphere defined by \(-\pi\leq-\alpha\leq\theta\leq\alpha\leq\pi\) and \(-\pi/2\leq\phi\leq\pi/2\) ( the curved surface of a segment of an orange). The area over which the integration is performed is \(|A|=4\alpha\). For points approximately uniformly distributed over the segment, if the point-cloud data are associated with variance matrix \(\sigma_{R}^{2}I\), then the variance matrix \(V_{A}\) associated with the fitted sphere parameters \(\mathbf{a}\) is approximated by  
\[V_{A}\approx\frac{\sigma_{R}^{2}}{m}V_{\alpha} \tag{20}\]  
where the nonzero elements of \(V_{\alpha}\) are specified by  
\[V_{\alpha}(1,1) =1/D_{14},\] \[V_{\alpha}(1,4) =-\pi\sin\alpha/(4\alpha D_{14}),\] \[V_{\alpha}(2,2) =3\alpha/(\alpha-(\sin 2\alpha)/2),\] \[V_{\alpha}(3,3) =3,\] \[V_{\alpha}(4,4) =3\alpha/(D_{14}(\alpha+(\sin 2\alpha)/2)),\]  
with  
\[D_{14}=\frac{\alpha+(\sin 2\alpha)/2}{3\alpha}-\bigg{(}\frac{\pi\sin\alpha}{4 \alpha}\bigg{)}^{2}.\]  
the determinant of the \(2\times 2\) submatrix of \(H_{\alpha}=V_{\alpha}^{-1}\) comprising of rows and columns 1 and 4. For \(\alpha\) near zero, \(D_{14}\approx 2/3-(\pi/4)^{2}\approx 0.05\),  
\[V_{\alpha}\approx\left[\begin{array}{cccc}20&0&0&-5\pi\\ 0&9/(2\alpha^{2})&0&0\\ 0&0&3&0\\ -5\pi&0&0&40/3\end{array}\right]. \tag{21}\]  
The quantities \(s(a_{j})=\sqrt{V_{\alpha}(j,j)}\) for selected values of \(\alpha\) are given in Table 4. The uncertainty associated with the \(y\)-coordinate of the sphere center scales with \(1/\alpha\), while allother parameters remain well defined. These calculations depend on measuring over the complete segment, including the two poles. Corresponding calculations for measurements reduced to an equatorial band of the segment can be made using the results in Section 5.1.
## 2 Literature Review
### Bibliometric Review
### Data Analysis Methods

All these variables are combined and then analyzed, as represented in Algorithm 1. Firstly, it is necessary to select the parameters for defining the time series models. These models require the specification of autoregression and moving average parameters, which are obtained with statistical tools like autocorrelation and partial autocorrelation functions. Additionally, the time series must be evaluated in relation to the presence of a unit root,which is achieved with the Augmented Dickey-Fuller test. Ultimately, the seasonality of the energy consumption is also assessed, both with the autocorrelation function and with a Kruskal-Wallis test. ```
1:time_series
2:exogenous_variables[]
3:procedureis_seasonal(data)
4:detrended_data[data]
5:if(autocorrelation_confirms(detrended_data) AND kruskall-wallis_confirms(detrended_data))then
6:return True
7:else
8:return False
9:endif
10:endprocedure
11:proceduregramger_variables(time_series,variables[])
12:\(results:=[]\)
13:for each var in variables[]do
14:if granger_causality(var,time_series)then
15:\(results.append(var)\)
16:endif
17:return results
18:endfor
19:endprocedure
20:ifis_seasonal(time_series)then
21:components \(\leftarrow\) STL_decomposition(time_series)
22:endif
23:\(diff\_order\gets n\_diff(time\_series)\)
24:autoregressive_order \(\leftarrow\)\(PACF(time\_series)\)
25:\(moving\_average\_order\gets ACF(time\_series)\)
26:\(granger\_var\gets granger\_variables(time\_series,\)\( exogenous\_variables+components\)
27:\(correlation\_matrix(granger\_var)\)
```

**Algorithm 1** Data analysis

Then, the persistence or long-term memory of the time energy consumption time series is examined. This analysis informs how the time series behaves in relation to previous values and whether it presents random walk patterns or not. Finally, statistical analyses such as Granger causality tests and correlation studies were executed further to examine the relationship between external factors and energy consumption. These statistical methods aid in pinpointing significant variables and quantifying their effect on energy demand fluctuations, thereby boosting the forecasting accuracy of the models. ### Models Evaluation and Forecasting

The forecasting models have undergone training and testing phases to evaluate the methodology. These models were trained on some historical data, with a specific time frame set aside for testing purposes, as depicted in Algorithm 2. The training process involves fine-tuning model parameters and assessing performance metrics through techniques like cross-validation. Performance metrics like Mean Absolute Percentage Error (MAPE), Mean Percentage Error (MPE), Mean Squared Error (MSE), and the Normalized Root Mean Square Error by the range between the minimum and the maximum values (NRMSE_minmax) are utilized to assess the precision and efficiency of the forecasting models.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 4 Proposed Deep-Learning Model
### Model Architecture
#### 4.2.3 Offline Training and Online Testing Operation of the Model  
Based on the generated data and the proposed model, the training process is carried out in the offline stage, as shown in Figure 4. The input of the model training system is combined with the received NOMA-OFDM signal as an input layer, and corresponding labels are used as supervised data to assist the DNN in optimizing the settings. The training process of the proposed model is summarized in Algorithm 1. Table 1 shows the training and optimized parameters. Results for training and validation accuracy versus its loss progress during learning of the model are illustrated in Figure 5, where the validation accuracy is 99.90% with the setting of training parameters of minibatch size 2000, epoch 100, and learning rate 0.01.  
```
1:Load the training and validation data samples.
2:Initialize model parameters such as minibatch size, maximum epochs, learning rate.
3:Train the model network accordingly and calculate the accuracy error by (14).
4:Adam optimization algorithm is used to compute the corrective parameter and to search for the optimal solution with update of the parameters.
5:Result: Trained model.
6: Save the model.
```  
**Algorithm 1** BiLTSM Training Process  
Figure 4: Training and testing process of the proposed model.  
Figure 5: Training and validation progress of the proposed model.
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 3 Proposed FRL-SR Algorithm
### Online Training
Therefore, we designed t to be inversely proportional to the node traffic density; the higher the traffic density, the smaller the t, and the faster agents perceive the network. This ensures faster awareness of the state of the network without increasing the network overhead too much. Algorithm 2 is the pseudocode of the online learning process of the algorithm. It differs from the traditional reinforcement learning algorithm in that this paper proposes an empty packet convergence method, which accelerates the obtaining of network status and is more suitable for the state-variant satellite network environment. ```
Input: Trained routing strategy model; user data request; \(l_{r}\); \(\gamma\) Output: Real time routing decision
1 whilenot donedo
2 iftime mod \(t==0\)then
3 Neighborhood discovery;
4 Update the Q-table according to Equation (2);
5
6 end if
7 whileuser data request receiveddo
8 Choose the next hop according to Q-table;
9 if\(q_{r}\) of the next hop is not fullthen
10 Transmit data packets;
11 Calculate the reward;
12 Update the Q-table according to Equation (2);
13
14 else
15 Wait a round;
16
17 end if
18
19 end while
20
21 end while
```  
**Algorithm 2**FRL-SR online training algorithm.
# Efficient and Robust Image Communication Techniques for 5G Applications in Smart Cities
## 4 Proposed System Model and Parameters  
In this work MRC diversity combining scheme is being used along with OFDM system to investigate the quality of image reception along with the use of DCT in place of FFT for OFDM system as presented in Figure 3. MRC scheme is being used in conjunction with OFDM system to investigate the image quality enhancement (PSNR and BER) for both the FFT and DCT transform. SNR vs. BER and SNR vs. PSNR are two parameters in which the system's performance is investigated by varying the number of receiving antennas and transforms.  
PSNR between the original image and the demodulated image is utilized to evaluate the quality of the received image, which is defined in Equation (7).  
\[\text{PSNR}=10\text{ }\log_{10}\left(\frac{255^{2}}{\text{MSE}}\right) \tag{7}\]  
where the mean square error (MSE) is defined as:  
\[\text{MSE}=\frac{1}{\text{N}^{2}}\sum_{\text{i}=1}^{\text{N}}\sum_{\text{j}= 1}^{\text{N}}\left[\text{f}(\text{i},\text{j})-\text{ }\text{f}(\text{i},\text{j})\right]^{2} \tag{8}\]  
Figure 3: Block Diagram of OFDM with MRC.  
where f(i,j) is the original image of dimensions N \(\times\) N and \(\hat{\text{f}}\)(i,j) is the demodulated image. MSE, as depicted in Equation (8), tells us about the quality of reconstructions of the image at the receiver side. If MSE is higher, than it is clear that the image is not reconstructed at the receiver side correctly, and PSNR will be less. Similarly, if the MSE is lower, it implies that the image reconstruction is near the optimum level, and the PSNR value will be high.