esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Factor 1**} & \multicolumn{1}{c}{**Factor 2**} & \multicolumn{1}{c}{**Factor 3**} & \multicolumn{1}{c}{**Factor 4**} & \multicolumn{1}{c}{**Response 1**} \\ \cline{2-5} \cline{7-7} \multicolumn{1}{c}{**Std**} & \multicolumn{1}{c}{**A:Coeff. of**} & \multicolumn{1}{c}{**B:Process.**} & \multicolumn{1}{c}{**C:Bottleneck**} & \multicolumn{1}{c}{**D:Num. of**} & \multicolumn{1}{c}{**Productivity**} \\ \cline{1-1}  & **var.** & **Time** & **C:Bottleneck** & \multicolumn{1}{c}{**D:Num. of**} & \multicolumn{1}{c}{**Productivity**} \\ \hline
19 & 0.25 & 60 & NO & 2 & 0.959 \\  
O & 2 & 11.334 \\
77 & 0.25 & 5 & YES & 3 & 8.927 \\
39 & 0.25 & 60 & YES & 2 & 0.7262 \\
14 & 0.86 & 5 & YES & 3 & 8.468 \\
80 & 0.86 & 60 & YES & 3 & 0.580475 \\
42 & 0.86 & 5 & NO & 3 & 11.48 \\
2 & 0.86 & 5 & NO & 2 & 11.364 \\
62 & 0.86 & 5 & YES & 3 & 8.487 \\
38 & 0.86 & 5 & YES & 2 & 8.51 \\
65 & 0.25 & 5 & NO & 2 & 11.937 \\
23 & 0.25 & 60 & YES & 2 & 0.7303 \\
8 & 0.86 & 60 & YES & 2 & 0.5154 \\
69 & 0.25 & 5 & YES & 2 & 8.945 \\
66 & 0.86 & 5 & NO & 2 & 11.328 \\
71 & 0.25 & 60 & YES & 2 & 0.7215 \\
20 & 0.86 & 60 & NO & 2 & 0.5646 \\
28 & 0.86 & 60 & NO & 3 & 0.651168 \\
45 & 0.25 & 5 & YES & 3 & 8.925 \\
59 & 0.25 & 60 & NO & 3 & 0.96143 \\
7 & 0.25 & 60 & YES & 2 & 0.7277 \\
32 & 0.86 & 60 & YES & 3 & 0.53714 \\
13 & 0.25 & 5 & YES & 3 & 8.932 \\
46 & 0.86 & 5 & YES & 3 & 8.547 \\
70 & 0.86 & 5 & YES & 2 & 8.59 \\
9 & 0.25 & 5 & NO & 3 & 11.95 \\
72 & 0.86 & 60 & YES & 2 & 0.521 \\
15 & 0.25 & 60 & YES & 3 & 0.7277 \\
37 & 0.25 & 5 & YES & 2 & 8.959 \\
58 & 0.86 & 5 & NO & 3 & 11.47 \\ \hline \hline \end{tabular}   
* Std--standard order. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Factor 1** & **Factor 2** & **Factor 3** & **Factor 4** & **Response 1** \\ \cline{2-5} \cline{5-6} \multirow{2}{*}{**Std**} & **A:Coeff. of var.** & **B:Process. Time Time** & **C:Bottleneck** & **D:Num. of Cards** & **Productivity** \\ \hline
64 & 0.86 & 60 & YES & 3 & 0.5568 \\
26 & 0.86 & 5 & NO & 3 & 11.4524 \\
74 & 0.86 & 5 & NO & 3 & 11.4715 \\
2 & 0.86 & 5 & NO & 2 & 11.3676 \\
22 & 0.86 & 5 & YES & 2 & 8.593 \\
32 & 0.86 & 60 & YES & 3 & 0.5319 \\
48 & 0.86 & 60 & YES & 3 & 0.4924 \\
71 & 0.25 & 60 & YES & 2 & 0.7276 \\
17 & 0.25 & 5 & NO & 2 & 11.9389 \\
5 & 0.25 & 5 & YES & 2 & 8.9441 \\
46 & 0.86 & 5 & YES & 3 & 8.552 \\
30 & 0.86 & 5 & YES & 3 & 8.568 \\
66 & 0.86 & 5 & NO & 2 & 11.3415 \\
53 & 0.25 & 5 & YES & 2 & 8.9558 \\
78 & 0.86 & 5 & YES & 3 & 8.587 \\
25 & 0.25 & 5 & NO & 3 & 0.7328 \\
14 & 0.86 & 5 & YES & 3 & 8.594 \\
44 & 0.86 & 60 & NO & 3 & 0.647 \\
70 & 0.86 & 5 & YES & 2 & 8.626 \\
11 & 0.25 & 60 & NO & 3 & 11.977 \\  
O & 3 & 0.6554 \\
61 & 0.25 & 5 & YES & 3 & 8.934 \\
39 & 0.25 & 60 & YES & 2 & 0.722 \\
60 & 0.86 & 60 & NO & 3 & 0.6111 \\
62 & 0.86 & 5 & YES & 3 & 8.594 \\
6 & 0.86 & 5 & YES & 2 & 8.5958 \\
1 & 0.25 & 5 & NO & 2 & 11.923 \\
23 & 0.25 & 60 & YES & 2 & 0.7256 \\
36 & 0.86 & 60 & NO & 2 & 0.5331 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiment design table—Hybrid Kanban/Conwip.
# Expectation vs. Reality: Attitudes Towards a Socially Assistive Robot in Cardiac Rehabilitation
## 3 Materials and Methods
### Patients of the Study
#### 3.2.1 Intervention Group  
The long-term study included patients that carried out therapies with the social assistive interface described before, during a period of time of 18 weeks (36 sessions of phase II). Once they finished the program, a perception questionnaire was applied to evaluate their attitudes towards the robot after a long-term interaction (Table A1). Additionally, two open questions, stated below, were implemented to have more detailed information about their experience and recommendations. As the open questions were formulated only in a positive manner, the results and discussion regarding these questions are subject to bias.  
Question 1:  
Would you recommend the use of the robotic system to the patients that are starting the rehabilitation therapy?  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Study** & **Participants** \\ \hline \multirow{2}{*}{Patients} & Intervention: long-term study (N = 8) & Patients attending Phase II \\  & Control: interviews (N = 20) & Patients attending Phase II–III \\ \hline \multirow{3}{*}{Clinicians} & \multirow{3}{*}{Focus Group (N = 15)} & 3 Nurses \\  & & 4 Outpatient Clinic/CR \\ \cline{1-1}  & & 6 Physiatrists \\ \cline{1-1}  & & 2 Occupational Therapists \\ \hline \hline \end{tabular}
\end{table}
Table 3: Participants in the perception study.  
Question 2:  
According to your experience, what would you recommend to improve the robot-based therapy?
## 2 Materials and Methods
### Statistical Analysis  
Data are presented as mean \(\pm\) standard deviation (SD). Normality was assessed by the Kolmogorov-Smirnov test. For variables exhibiting a normal distribution, comparisons between groups (controls vs. PaCa, or groups stratified by median ccf-mtDNA concentration) were performed using the unpaired Student's \(t\)-test. For non-normally distributed variables, the non-parametric Mann-Whitney U test was applied.  
To explore the relationship between ccf-mtDNA concentrations and levels of inflammatory biomolecules, [PERSON]'s rank correlation analysis was conducted. Statistical significance was set at an alpha level of 5% (\(p<0.05\)). All analyses were performed using GraphPad Prism version 6.0 (GraphPad Software, San Diego, CA, USA).  
\begin{table}
\begin{tabular}{c c c} \hline
**Primer** & **Name** & **Sequence** \\ \hline Forward & mtDNA-85F & 5\({}^{\prime}\)-CTCACTCCTTGGCGCCTGC-3\({}^{\prime}\) \\ Reverse & mtDNA-85R & 5\({}^{\prime}\)-GGCGGTTGAGGCTCTG-3\({}^{\prime}\) \\ Probe & FAM-mtDNA-85P & FAM-5\({}^{\prime}\)-CCTCCAAATCACCACAGGACTATTCCTAGCCTAGCA-3\({}^{\prime}\)-BHQ-1 \\ \hline \end{tabular} Abbreviations: BHQ1, Black Hole Quencher-1; F, forward; FAM, 6-carboxyfluorescein; mtDNA, mitochondrial DNA; R, Reverse.  
\end{table}
Table 1: Primer and probe sequences for the droplet digital PCR assay.  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **Mediator** \\ \hline Cytokines & IFN-\(\gamma\), IL-1\(\upbeta\), IL-1 ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, \\  & IL-9, IL-10, IL-12, IL-13, IL-15, IL-17A, TNF-\(\upalpha\) \\ Chemokines & CCL11, IP-10, MCP-1, MIP-1\(\upalpha\), MIP-1\(\upbeta\), RANTES \\ Growth factors & FGF-basic, G-CSF, GM-CSF, PDGF-BB, VEGF \\ \hline \end{tabular} Abbreviations: CCL, C-C motif chemokine ligand; FGF, fibroblast growth factor; G-CSF, granulocyte colony-stimulating factor; GM-CSF, granulocyte macrophage macrophage colony-stimulating factor; IFN, interferon; IL, interleukin; IL-1 ra, interleukin 1 receptor agonist; IP, interferon-induced protein; MCP-1, monocyte chemoattractant protein 1; MIP, macrophage inflammatory protein; PDGF-BB, platelet derived growth factor BB; RANTES, regulated upon activation, normal T cell expressed and secreted; TNF, tumor necrosis factor; VEGF, vascular endothelial growth factor.  
\end{table}
Table 2: List of inflammatory biomolecules assayed by multiplex immunoassay.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 4 Technology Trends to Achieve the Requirements in the 5G Network
### Massive MIMO (Multiple-Input Multiple-Output) and 3D MIMO  
According to [PERSON]'s theorem for the capacity of a network, the upper limit to the capacity of a communication link in bps is fixed, and this rule might be used for investigation on achieving superior data rates [65].  
A setup that is working on MIMO, the channel capacity is approximated as  
\[C=min(M,N).Blog_{2}(1+SNR)\]  
\(M\) and \(N\) are number of antennas at the transmitter and receiver sides for MIMO system, B is the bandwidth of the channel, while _SNR_ is Signal-to-Noise Ratio in the link. From the above theorem, we can obtain that by increasing numbers of antennas in the system, the data rate can be improved. Accordingly, we can use the MIMO system to achieve a high data rate, which can solve the first trend for the next generation network 5G. In massive MIMO, a considerable collection of antennas are utilised in the base station to assist multiple terminals simultaneously [66]. Additionally, by using the active antennas, the control of the antenna beam in both direction horizontal and vertical is possible (called 3D MIMO), this can maximise the cell partitioning [67]. Additionally, SNR can be improved to control antenna beam, which leads to minimising the transmission power or maximising the capacity of the link. Figure 6 shows the idea of MIMO and 3D MIMO. In [68], it was shown that, by using massive MIMO, the capacity can be maximised up to 10 times and the efficiency of radiated energy can be maximised up to 100 times approximately.
###### Abstract  
Vegetation top-of-canopy reflectance contains valuable information for estimating vegetation biochemical and structural properties, and canopy photosynthesis (gross primary production (GPP)). Satellite images allow studying temporal variations in vegetation properties and photosynthesis. The National Aeronautics and Space Administration (NASA) has produced a harmonized Landsat-8 and Sentinel-2 (HLS) data set to improve temporal coverage. In this study, we aimed to explore the potential and investigate the information content of the HLS data set using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model to retrieve the temporal variations in vegetation properties, followed by the GPP simulations during the 2016 growing season of an evergreen Norway spruce dominated forest stand. We optimized the optical radiative transfer routine of the SCOPE model to retrieve vegetation properties such as leaf area index and leaf chlorophyll, water, and dry matter contents. The results indicated percentage differences less than 30% between the retrieved and measured vegetation properties. Additionally, we compared the retrievals from HLS data with those from hyperspectral airborne data for the same site, showing that HLS data preserve a considerable amount of information about the vegetation properties. Time series of vegetation properties, retrieved from HLS data, served as the SCOPE inputs for the time series of GPP simulations. The SCOPE model reproduced the temporal cycle of local flux tower measurements of GPP, as indicated by the high Nash-Sutcliffe efficiency value (>0.5). However, GPP simulations did not significantly change when we ran the SCOPE model with constant vegetation properties during the growing season. This might be attributed to the low variability in the vegetation properties of the evergreen forest stand within a vegetation season. We further observed that the temporal variation in maximum carboxylation capacity had a pronounced effect on GPP simulations. We focused on an evergreen forest stand. Further studies should investigate the potential of HLS data across different forest types, such as deciduous stand.  
Keywords:top-of-canopy reflectance; harmonized landsat-8 and sentinel-2; radiative transfer model; vegetation properties; SCOPE model; gross primary production +
Footnote †: journal: Remote Sensing, 2020, 12, 3773; doi:10.3390/rs12223773
## 3 Proposed LAO-3D Block Cipher
### Algorithm Specification  
LAO-3D is a 20-round symmetric lightweight block cipher with a 64-bit block and a 128-bit key length. The block cipher realizes the substitution-permutation network (SPN) structure. SPN is chosen over the Feistel network because its execution time is shorter, the energy consumption is lower, and it has fewer round functions.  
Each round of LAO-3D contains three main characteristics. First, the linear layer ensures multiple diffusion rounds. Second, the non-linear layer has parallel applications of S-boxes with sufficient confusion characteristics. Third, the key addition layer applies the XOR operation on the RoundKey and the cipher State.  
Different transformations on the cipher State are represented as four rows of rectangular bit arrays. Meanwhile, the total number of cipher State columns is computed by dividing the block size (64 bits) by the number of rows. Identically, the secret key is depicted as four rows of rectangular arrays. The total number of secret key columns is equal to the key size (128 bits) divided by four rows. These representations of the cipher State and the key are shown in Figure 4.  
Figure 4: Cipher State and Key State.  
Figure 3: The 3 DBitRotation Formation.
# Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
## 3 LiMha
### Description of Algorithm 1  
For better understanding, Algorithm 1 has been elaborated briefly step wise in this section. Input shows that control parameters need to be defined before the initialization of an algorithm. Transmission range and network radius along with \(NN\) and \(SN\) deployment. After the initialization of network parameters in step 1, which is deployment of nodes, sink and network configuration then step 2 is initiated known as loop of rounds. At step 3, each node computes its distance from every other node in order to find out minimal route to forward its data packets towards the destination. Also \(NN\) calculate distance from \(NN\) and \(SN\) too. In case, distance between \(NN\) and \(SN\) is in multi-path environment then \(NN\) looks for another \(NN\) to multi-hop its packets towards the \(SN\). The reason behind avoiding long distance route is that chances of fading and attenuation of data signal are very high, hence the probability of packet loss is maximum. In order to minimize the aforementioned factors, shorter path is selected to relay the data packet towards the destination. The distance is checked via step 4 that ensures that data packets are transmitted over the path have receiver with in the communication range. After the computation of distance and finding forwarder node in transmission range, one of the most crucial factors of our proposed work is data traffic estimation at each hop to avoid overburden of data on the intermediate super nodes. The traffic load is estimated via using Equation (9) and average load is computed with the help of Equation (10). Also energy dissipation is considered along with the data traffic in order to pick optimal routing path that has less load and minimal energy consumption. Using Equations (7) and (8) both receiving and transmitting energies are computed before delivering data to next \(SN\). At step 7, if the traffic is very high then \(SN\) is selected in near vicinity of \(NN\) for compensating high energy consumption with its heterogeneity nature. Then data traffic is transmitted to the \(SN\) until it reaches the sink. Step 15, at the completion of each round, overall energy consumption is computed in order to measure the network lifetime. All steps are repeated till the time no node in the network remains alive or is unable to deliver data at the destination due to the unavailability of intermediate nodes.
## 4 Advanced Persistent Threat Life Cycle Analysis
### Three Stage Attack  
The authors in [68] proposed a life cycle described in three-stages, based on the analysis of different methods and techniques of 22 APT campaigns. Each stage contemplates at least three characteristics or techniques that are used to carry out the attack. The stages contemplated are:  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors** & **Algorithm** & **Approach** & **Approach Detail** & **APT Life Cycle Used** & **Detection Accuracy** \\ \hline [PERSON] et al. [6] & DT, SVM, \(k\)-NN and Ensemble learning & MLAPT & Phases: Threat detection & 6 phases & 81,8\% \\  & & & Alert correlation & & \\ [PERSON] et al. [65] & Genetic programming, classification and regression tree, dynamic bayesian game model and SVM. & DFA-AD & Phases: Threat detection & & \\  & & & Attack prediction Phases: Network traffic Correlation event Voting service & & \\ [PERSON] et al. [66] & \(k\)-NN and Correlation fractal dimension. & Fractal-based anomaly. & Steps: Combined packet capture (pcap files) Feature vector Notice removal Anomaly classification with ML algorithms & Non-specified Non-specified reuse reuse & 93.58\% \\ [PERSON] et al. [67] & \(k\)-NN & Detection based on Big Data & Priese & Non-specified Non-specified reuse & 92.83\% \\  & & & & reuse & \\ [PERSON] et al. [5] & LR, GNB, DT, RF and LB & RDP-based LM detection & Steps: Preprocessing of dataset Defining metrics Apply ML techniques Compare results Steps: Non-specified & 99.99\% (LB) \\ [PERSON] et al. [33] & PCA, SVM, NB, DT and MLP & Early discovery of APT attack of APT attack & Steps: Progress & \multirow{2}{*}{Non-specified (SVM)} & \multirow{2}{*}{97.22\% (SVM)} \\ [PERSON] et al. [7] & Fuzzy clustering & APT attack scenarios & Steps: Data preprocessing event classification Fuzzy clustering & & \\  & & & Attack scenario mining & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of machine learning (ML)-based APT detection approaches.  
Initial compromise (IC): In this stage, attackers attempt to access to the target system. The most commonly used techniques in this phase are spear-phishing (e.g., attaching an email or a link to a compromised server), watering-hole (malicious code on a regularly visited website), server-side attacks (exploiting vulnerabilities on servers or stealing brute-force credentials), and infected storage media (compromised USB, CD, or DVD).  
Lateral movement (LM): Attackers attempt to compromise other services on the target system or network. The objective is to try legitimate credentials that will allow them to persist in the system. Some of the LM techniques used are standard operating system tools (e.g., RDP, PsExec, and Powershell), and exploit a vulnerability (zero-day exploit).  
Command and control (C&C): When the system has been compromised, it is necessary to establish an external connection to exfiltrate data. Attackers use services such as HTTP, HTTPS, or FTP. Also, they can use tools such as remote connection tools like VNC (Virtual Network Computing) or RDP.
## 2 Theoretical Foundation
### Signal Detection Techniques
#### 2.1.1 Maximum Likelihood Detection (MLD)  
MLD offers optimal performance but is computationally expensive, as it evaluates the conditional probability of the received signal, given transmitted symbols and channel conditions. Researchers employ simulation and theoretical analyses to assess MLD performance across various scenarios, considering factors like SNR, channel conditions, modulation schemes, and coding techniques. Equation (5) represents the log-likelihood function (\(logL\)), a statistical inference tool used to assess how well a model fits observed data. The summation (\(\Sigma\)) iterates over \(n\) observations (\(i=1\) to \(n\)), \(L\) denotes the likelihood function, and \(f(x_{i}|\theta)\) represents the conditional probability density function of observing \(x_{i}\), given parameter \(\theta\). Let \(x_{1}\), \(x_{2}\), \(x_{3}\), \(\ldots\), \(x_{n}\) be observations for n independent and identically distributed random variables.  
\[logL=\Sigma_{i=1}^{n}logf(x_{i}\mid\theta) \tag{5}\]  
Equation (5) represents the log likelihood function, represented as \(logL\). This function assesses how well a statistical model fits observed data in statistical inference. The conditional probability density function of observing signal \(x_{i}\), given a parameter, \(\theta\), is represented with the function \(f(x_{i}|\theta)\). Given a certain set of parameters, \(\theta\), the likelihood function, \(L\), is effectively the joint probability density function (PDF) of the observed data. It calculates the likelihood of obtaining the provided data using the presumptive statistical model. Equation (5) makes computations simpler and frequently results in easier interpretation and numerical stability by taking the logarithm of the likelihood function.
## 4 Proposed Learning-Based Optimization Scheme
### Training the Neural Network  
The training of a neural network is a crucial part of deep learning. A well-trained neural network can learn the mapping between the input and output. In supervised learning, to train a neural network, we need a large number of data samples from the input and output, from which the DNN learns the relationship between the input and output. We trained the neural network with training data that consisted of samples containing \((|hi|,P^{*},\tau^{*})\) generated using the SPCA-based iterative method discussed in the previous section. The training of neural networks is a process to find the network parameter \(\mathbf{w}\) by minimizing the loss function. The loss function is basically the difference between the actual \((P^{*},\tau^{*})\) from trained data and \((P,\tau)\), the output of the DNN. For this, we consider mean squared error (MSE) as the loss function that should be minimized, so that the output of the DNN, \((P,\tau)\), can approach optimal solution \((P^{*},\tau^{*})\). Thus, a well-trained DNN should perfectly minimize the following loss function:  
\[J(\mathbf{w})=E(|(P,\tau)-(P^{*},\tau^{*})|^{2}) \tag{12}\]  
There are many optimization algorithms which can be used to minimize the loss function. Optimization algorithms compute and update the weights of a network while minimizing the loss function. In our proposed scheme, we used the Adam optimizer to minimize the MSE loss function. The Adam optimizer [21] is a first-order gradient-based optimization algorithm that solves stochastic objective functions with adaptive estimates of lower-order moments. The method is easy to implement, computationally efficient and requires little memory.  
There were total \(10,000\) data samples available, generated by the SPCA method. We divided all the training samples into three data-sets. We used \(60\%\) of the data for training the DNN, \(20\%\) for validation and the other \(20\%\) for testing. Validation is a process of unbiased evaluation of the network while training the model. The validation of the performance of the DNN model on the training dataset results in a biased score; thus, for unbiased evaluation, we utilized the \(20\%\) of dataset for validation that was unknown to the trained model. This was used to determine how well our model learns while training. Testing is a final, unbiased evaluation of the trained model of the neural network. We used data normalization to avoid training and validation errors due to the overfitting problem. After training the DNN, we can utilize it to find the optimal solution for \((P^{*},\tau^{*})\) for any new value of channel \(|h_{k}|\).