esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
\begin{tabular}{c c c} \hline \hline  & **(1)** & **(2)** \\ \hline  & **Prader-Willi** & **Control** \\ \hline U1-L1 & & \\ Prader-Willi & \(-0.906\)** & 1.139 *** \\  & (0.317) & (0.103) \\  \_cons & 4.938 *** & 4.839 *** \\  & (0.00609) & (0.00526) \\ \hline  / & & \\ var(e.Co-A) & 0.000430 ** & 0.0000532 ** \\  & (0.000151) & (0.0000185) \\ var(e.ANB) & 0.0282 ** & 0.00624 ** \\  & (0.00969) & (0.00217) \\ var(e.SArGo) & 0.000734 ** & 0.0000168 ** \\  & (0.000259) & (0.0000586) \\ var(e.U1-L1) & 0.000310 ** & 0.0000370 \\  & (0.000110) & (0.0000225) \\ var(e.ANS-PNS) & 0.0000504 & 0.0000495 ** \\  & (0.0000315) & (0.0000191) \\ var(e.Ar-Go) & 0.00314 ** & 0.000281 ** \\  & (0.00110) & (0.000102) \\ var(e.Go-Pog) & 0.000592 ** & 0.000448 ** \\  & (0.000211) & (0.000162) \\ var(e.Co-Gn) & 0.0000772 & 0.000478 ** \\  & (0.0000443) & (0.0000174) \\ var(e.SNA) & 0.0000418 ** & 0.0000390 ** \\  & (0.0000171) & (0.00000136) \\ var(e.ANPog) & 0.0288 ** & 0.0759 ** \\  & (0.0100) & (0.0261) \\ var(e.B\_Pog) & 0.00445 ** & 0.00210 ** \\  & (0.00168) & (0.000701) \\ var(e.ArGoGn) & 0.000422 ** & 0.0000629 ** \\  & (0.0000156) & (0.0000221) \\ var(e.SNB) & 0.0000605 ** & 0.00000918 ** \\  & (0.0000224) & (0.00000334) \\ var(Prader-Willi) & 0.000390 & 0.000381 ** \\  & (0.000242) & (0.000144) \\ \(N\) & 18 & 18 \\ \hline \end{tabular}  Note: Standard errors in parentheses, * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\). Source: Authors’ research in Stata 16. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**SEM 1 (Figure 6a)**} & \multicolumn{3}{c}{**SEM 2 (Figure 6b)**} \\ \hline
**Item** & **Obs** & **Sign** & \
\begin{tabular}{c} **Item-Test** \\ **Correlation** \\ \end{tabular} & **Alpha** & **Sign** &
\begin{tabular}{c} **Item-Test** \\ **Correlation** \\ \end{tabular} & **Alpha** \\ \hline SN & 18 & \(-\) & 0.4909 & 0.8009 & + & 0.8890 & 0.9198 \\ NSAr & 18 & \(+\) & 0.7462 & 0.7754 & \(-\) & 0.7974 & 0.9237 \\ NSBa & 18 & \(+\) & 0.8229 & 0.7668 & \(-\) & 0.9180 & 0.9186 \\ N-A-PG & 18 & \(-\) & 0.5083 & 0.7993 & \(-\) & 0.3205 & 0.9412 \\ ANS-N & 18 & \(+\) & 0.9083 & 0.7567 & \(+\) & 0.8920 & 0.9197 \\ ANS-Me & 18 & \(+\) & 0.2839 & 0.8188 & \(+\) & 0.9209 & 0.184 \\ N-Me & 18 & \(+\) & 0.7710 & 0.7727 & \(+\) & 0.9171 & 0.9186 \\ S-Go & 18 & \(-\) & 0.5724 & 0.7932 & \(+\) & 0.9399 & 0.9176 \\ AR-PTM & 18 & \(-\) & 0.2849 & 0.8187 & \(+\) & 0.2284 & 0.9442 \\ PTM-N & 18 & \(+\) & 0.2532 & 0.8212 & \(+\) & 0.6200 & 0.9307 \\ SN-MP & 18 & \(+\) & 0.4036 & 0.8087 & \(+\) & 0.8800 & 0.9202 \\ FH-MP & 18 & \(-\) & 0.4201 & 0.8073 & \(+\) & 0.8199 & 0.9227 \\ SAr & 18 & \(+\) & 0.6876 & 0.7817 & \(+\) & 0.4809 & 0.9357 \\ \hline
**Total** & & & & & & & \\
**scale** & & & & & & & \\ \hline \hline \end{tabular}  
* Source: Authors’ research in Stata 16.  
\end{table} 
Table 1: Alpha Cronbach calculations associated with SEM models presented in Figure 6a,b.
## 3 Materials and Methods
### Analysis of Volatile Organic Compounds
#### 3.4.1 HS-SPME-GC-MS Analysis  
The VOCs absorbed as described above were immediately desorbed at 280 \({}^{\circ}\)C in the injection port of a 7890A GC system (Agilent Technologies, Santa Clara, CA, USA), separated by a DB InnoWAX column (0.4 \(\upmu\)m d.f. \(\times\) 0.2 mm i.d., 50 m) and detected by a quadrupole Mass Spectrometer 5975c MSD (Agilent Technologies, Palo Alto, CA, USA) operating in EI mode at 70 eV. Initial oven temperature was 40 \({}^{\circ}\)C, held for 1 min, then raised to 220 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\), then raised to 260 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) and finally held at 260 \({}^{\circ}\)C for 4 min. Carrier gas, helium at a flow rate of 1.2 mL min\({}^{-1}\). Mass spectrometer worked in the mass range 29-350 \(m/z\) and with an electron ionization of 70 eV and the Total Ion Current chromatograms were recorded. Compounds were tentatively identified by comparing the mass spectra of each peak with those reported in mass spectral database as the standard NIST08/Wiley98 libraries, with a minimum matching factor of 80%. Peaks identification was then confirmed by comparing their retention index, calculated by the generalized equation [19] after injecting a mixture of linear alkanes (C9-C30) in hexane (Sigma Aldrich, St. Louis, MO, USA) in the same condition already described for sample analysis, in the literature [36].  
Relative concentration of each identified compound was calculated according to previous literature [12], using the following formula:  
\[[\text{VOC ($\mu$g/g)}]=(\text{A}_{\text{VOC}}/\text{A}_{\text{ISTD}})\times( \text{m}_{\text{ISTD}}\ (\mu\text{g})/\text{m}_{\text{sample}}\ (\text{g})),\]  
where \(\text{A}_{\text{VOC}}\) is the area of the VOC, \(\text{A}_{\text{ISTD}}\) is the area of the internal standard, \(\text{m}_{\text{ISTD}}\) and \(\text{m}_{\text{sample}}\) are the amount of internal standard and of sample added into the vial, respectively.
## 5 Applicability on Various Technologies  
This section first introduces a comprehensive description of the methodology used for demonstrating the range of applicability of the IPSI framework. Then, three case studies depicted in recent literature are presented with respect to the IPSI framework.
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 5 Performance Evaluation  
In this section, the performance of the proposed algorithm was evaluated by MATLAB simulations and compared with FML, ESRAD, and SPFL algorithms which are defined in Section 2. In the simulations, the network domain was a square grid network layout of the size 500 \(\times\) 500 m\({}^{2}\) with 300 sensor nodes distributed randomly. The BS was located outside the network field at (400, 600). The BS was positioned outside the network to set up a more realistic environment. The values of \(\mu\) and \(\delta\) were selected to be 0.9 and 0.2 respectively because better results were obtained with these values [24]. The simulation parameters are listed in Table 2. Simulations were run for 500 iterations, and average values were considered to support the conclusion in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Electronics Energy (\(E_{dec}\)) & 50 nJ/bit \\ Data Aggregation Energy (\(E_{DA}\)) & 5 nJ/bit/signal \\ Initial Energy of Node (\(E_{init}\)) & 1 J \\ Number of Nodes (\(N\)) & 300 \\ Position of BS (\(X\),\(Y\)) & (400, 600) \\ Network Area (\(F\)) & \(500\times 500\) m\({}^{2}\) \\ Packet Size (\(p\)) & 1000 bits \\ CH to CH: Amplification Energy (\(d\ <\ d_{th}\)) & \(\varepsilon_{fs}=10\) pJ/bit/m\({}^{2}\) \\ CH to BS: Amplification Energy (\(d\ \geq\ d_{th}\)) & \(\varepsilon_{amp}=0.0013\) pJ/bit/m\({}^{4}\) \\ \(\mu\), \(\delta\) & 0.9, 0.2 \\ Threshold Energy (\(E_{th}\)) & 0.2 J \\ Transmission Range of a Node (\(r_{t}\)) & 15 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 5: Flowchart of data transmission phase.
## 4 Proposed Strategies for NSR Deployment  
### PROMETHEE-II Strategy for NIRA Preparation  
The preparation of the NIRA was used to carry out the node allocation of the logical structure on the physical structure in this piece of work. The NIRA for physical infrastructure was developed before receiving the NSR, and the same for the logical structure was prepared upon receipt of the NSR, respectively. The value of each node was determined by a variety of elements engaged in the network connecting to the node, including the NCF, NTF, NBF, and NCCF. Consequently, it is critical to construct the ranking without compromising the specifics of the node's configuration. A famous multiple-criteria decision-making approach established by [PERSON] [35] was utilized for NIRA preparation, and PROMETHEE-II was one of the methods employed. More precisely, the preferences of and differences in the elements were taken into account by this technique. The following are the stages that are involved in the PROMETHEE-II approach:  
**Step 1:** Preparation of evaluation table. The evaluation table keeps the values of factors for each node. Therefore, the factors of each node are evaluated with their respective weight parameters for preparing Table 2. It is assumed that \(N=n_{1},n_{2},\ldots n_{i}\) is a set of nodes and \(F=f_{1},f_{2},f_{3},f_{4}\) is a set of factors. **Step 2:** Determination of preference function. This value is determined by the pairwise comparison between all of the nodes for each factor of a network. \[d_{k}(n_{i},n_{j})=f_{k}(n_{i})-f_{k}(n_{j}) \tag{23}\]  
where \(d_{k}(n_{i},n_{j})\) refers to the difference between the evaluations of two actions for factor \(f_{k}\). The preference function is introduced to convert the \(d_{k}(n_{i},n_{j})\) unified value to the following:  
\[P_{k}(a,b)=G_{k}[d_{k}(n_{i},n_{j})] \tag{24}\]  
**Step 3:** Determination of global preference index. \[\pi(n,m)=\sum_{k=1}^{i}P_{k}(a,b).w_{k} \tag{25}\]  
where \(w_{k}\) is a weight function of factor \(k\), which is assumed to be greater than 0, and the sum of the weights is equal to 1. **Step 4:** Determination of positive and negative outranking flows. The following two factors are calculated to locate each node with respect to all other nodes. \[\mathcal{O}^{+}(a)=\frac{1}{(i-1)}\sum_{x\in A}\pi(a,x) \tag{26}\]  
\[\mathcal{O}^{-}(a)=\frac{1}{(i-1)}\sum_{x\in A}\pi(x,a) \tag{27}\]  
where \(\mathcal{O}^{+}(a)\) is obtained by ranking the nodes according to the non-increasing values of their respective positive flow values. and \(\mathcal{O}^{-}(a)\) is obtained by ranking the nodes according to the non-decreasing values of their respective negative flow values. **Step 5:** Determination of net flow. \[\mathcal{O}(a)=\mathcal{O}^{+}(a)-\mathcal{O}^{-}(a) \tag{28}\]  
The highest value of \(\mathcal{O}(a)\) refers to the best node in NIRA. Algorithm 1 is used to explain the process of node allocation through NIRA.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 2 Related Work
### Markov Chain Application in Process Mining  
A Markov chain or Markov process is a stochastic model describing a series of events where the probability of each event is dependent on the state attained in the previous event [44]. And there are overall two types of Markov chains according to the interval type of the change between states in the chain, discrete-time Markov Chain (DTMC) and continuous-time Markov Chain (CTMC). They are appropriate for modelling data which contain a random process and the state of the system changing over time. Therefore, Markov chains have been widely applied to the exploratory analysis of time-series (i.e., sequential) data over a large range of domains.  
For instance, a kernel variable length Markov Model (KVLMM) is proposed in a study about GPS trajectory prediction [45]. Similarly, in the context of social robotics, there has been a study [46] using machine learning techniques as well as eye trackers, specifically incorporating Markov Models for the stochastic process of gaze movement. The authors have developed a probabilistic algorithm for gaze trajectory prediction. In other industries, such as communication, a study in Nigeria [5] applies Markov models to the telecommunication industry use case, helping to predict customers' churn and retention rates. For bank customer behaviour modelling, ref. [47] proposes Markov chains to describe customers' dynamically changing delinquent status and behavioural scores. In [48; 49], clickstream data are explored for studies on online purchasing processes, and a Markov Model is used to characterise page changes. Similarly, refs. [50; 51] utilise Markov Models to visualise website clickstream data. Another study [52] uses a Markov Model to construct an intelligent pre-loading system for online resources, wherein the navigation graph is considered a Markov model. In addition, studies on healthcare data [53] emphasise the use of non-homogeneous Markov Models for sequential pattern mining of records pertaining to the behaviours of elderly patients.  
Meanwhile, the trending application of Markov chains has also facilitated the development of handy packages for modelling. There are R packages (such as 'clickstream' [48] and'markovchain' [17]) based on Markov characteristics that facilitate study on applications of the Markov Model. The'mar-kovchain' package closes a gap in the R framework for discrete and continuous time Markov chain analysis as well as probabilistic statistical analysis [17].  
Above all, in this paper for our study, compared to previous studies in terms of the eye tracking application and HCI analysis techniques, here we focus more on the trajectory of gaze movement, which is about the gaze transition information. Also, to distinguish our method from traditional quantitative analysis of eye tracker data, we chose Markov chains to model the viewer's gaze movement when they're watching the BT Player.
# SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
###### Abstract
Second, SPOT monitors the program execution at the instruction level and delegates the part that is symbolically analyzed to a third-party symbolic execution engine. Third, we model the stream and measure its ordering with entropy. Based on this, we present algorithms to generate all possible reordering streams, and then take one stream for each reordering situation as potential test data. To evaluate SPOT, we take subject programs from previous work [25; 26]. We also build some programs based on the snippets and descriptions from some public websites (e.g., GitHub, Flink official website, et cetera). We conduct four case studies to show: (1) its applicable range (i.e., to what extent does it support to analyze the common operators); (2) its efficiency when compared with the most recent two DSP testing techniques (i.e., DiffStream and FlinkCheck) in terms of JDU path coverage; and (3) efficiency in triggering failure. Our case study also illustrates that the entropy of randomly generated data (i.e., degree of reordering) is highly skewed, while SPOT can generate various reordering streams evenly. In total, the main contributions of this work are:  
* We realize the symbolic execution idea for DSP applications by mimicking DSP APIs (i.e., application programming interfaces) and generate independent test data corresponding to each path. * We implement JPF-Flink, which coordinates the symbolic analysis specifically for Flink programs. * We present algorithms to compose time-series data covering different possible reordering situations. * SPOT is an alternative solution to current DSP testing approaches. It integrates symbolic execution and stream synthesizing. The former improves the random generator in PBT methods, leading to high coverage test suites. The latter covers different stream reordering situations that existing symbolic execution-based approaches have not provided. * We conduct four case studies to demonstrate SPOT's efficiency and applicability. The remainder of the paper is organized as follows. Section 2 contains DSP examples to demonstrate motivations, a brief overview of DSP and Flink, metrics to measure reordering, and a framework for how SPOT works. Symbolic analysis and JPF-Flink implementation are introduced in Section 3. In Section 4, we describe how SPOT composes independent test data into streams with various ordering. Four case studies are conducted in Section 5. Related works about testing big data programs are presented in Section 6. We conclude with a summary and discussion of future work in Section 7.
## 3 Experimental Setup and Results
### Dataset Description
#### 3.1.2 The Multifunctional Medical Center of Astana  
The Multifunctional Medical Center of Astana gathered thermograms of patients who consented to collect thermal breast images. For this project, forty thermal images with human features were selected. Breast thermograms for women aged 18 to 80 are currently available in the database. Figure 3 shows the clinical office and IR camera FLUKE TiS60+ used for collecting thermograms.  
A summary of the consolidated dataset is given in Table 1, and the sample grayscale thermal images are given in Figure 4.  
The combined dataset consisted of a total of 306 images; 123 thermal images of patients classified as \"sick\" (as indicated in the doctor's diagnosis report), and 183 thermal images classified as \"healthy\". Patients' thermographic images classified as \"healthy\" represented that the patient was not diagnosed with having tumors or any cancerous cells in the breast area. If the thermograms were labeled as \"sick,\" the patient had a tumor. It may have been either malignant or benign. The dataset was be divided into three sets dedicated to training,  
Figure 2: Depiction of fuzzy (**left**) and accepted (**right**) thermal images of the database.
# Reinforcement Learning-Based Algorithm to Avoid Obstacles by the Anthropomorphic Robotic Arm
## 4 Discussion  
Research conducted in the virtual environment and then validated on the physical robot in the laboratory environment showed that the proposed RL-based algorithm can be used to control a robotic arm to avoid obstacles that dynamically appear in its workspace. The simulation results have shown that the average error \(e\) is equal to 3.84 mm. In laboratory investigations, the average error \(e\) is almost the same, equal to 3.91 mm. The standard deviation \(\sigma_{e}\) of the error \(e\) is 1.83 mm for simulation and 2.51 mm for tests on a real robot. The results obtained in reality are very similar to those obtained during simulation tests. In order to achieve even better accuracy of the positioning, an approach that uses the embedded robot controller for the final positioning of the TCP of the robot can be used. The RL approach can be applied to perform just coarse positioning when the RL agent avoids the obstacle, and when the TCP of the robot is very close to the goal, the embedded robot controller can do fine positioning. In [29], the authors applied RL methods for the positioning task of a robotic arm and in laboratory investigation reached an accuracy of 8 mm, which is almost twice as high as the accuracy of positioning obtained in our research. Furthermore, the algorithm proposed in this article has the ability to avoid obstacles and not only position the TCP of the robot at a target position.  
Figure 16: The trajectory on which the TCP of the robot moved in experimental research. Human as an obstacle.  
During the tests carried out in the laboratory, the robot performed 100 collision-free obstacle avoidance maneuvers of various shapes dynamically placed in its workspace. Tests conducted on a real robot proved that the algorithm is reliable, which gives a good prognosis for the implementation of the proposed algorithm in the industry, because a large amount of research on obstacle avoidance by robotic arms is still performed only in simulation, e.g., [22; 24; 25]. Furthermore, the system can be applied to another type of robot due to the integrated obstacle detection system attached to the wrist of the robotic arm, in contrast to the approach in [9], in which the authors tested their algorithm with only one type of an obstacle and used an external camera mounted on a tripod, requiring a computer with better performance comparing to the head with laser sensors.  
The proposed approach proved that a domain agent trained in simulation can be implemented to control the real Mitsubishi robot. A trained agent and proposed algorithm can be used to control another type of robotic arm. There is no need for additional training in the case of transfer learning, but the same type of observation introduced in Section 2.3.2 must be provided. The robotic arm must be equipped with a sensor that detects an obstacle and returns the distance in meters in relation to the origin of the robot's coordinate system. Movements of the TCP of the robotic arm must be also normalized in relation to the origin of the robot's coordination system.
## 3 Communication Protocols in IoMT  
With the increase in the number of smart devices present in IoT environments, especially for IoMT systems, the need to allow them to communicate quickly emerged. Again, to meet resource constraints (network, processing, storage, energy), new technologies and communication protocols have been developed or adapted to fulfil certain needs, such as low consumption and a large range, but also ease of implementation. Among the most important of these protocols, we mention ZigBee, Bluetooth, 802.11 ac, Z-Wave, LoRaWAN, and Sigfox. Table 1 summarizes the main differences between these different communication technologies [11, 12, 13].  
Figure 1: IoMT architecture.  
The setting that we are discussing requires maintaining the maximum level of reliability for communications among the devices, as there could be thousands of Internet-connected devices of various types and resources. It is quite impossible to strictly regulate the types, models, and vendors of the users' and hospitals' used medical devices. Hence, there could often be critical differences among the devices. Therefore, an adaptable layered design is required. Various approaches for IoMT architectures and layers have been presented in the literature, including those that depend on the three aforementioned layers (if we project the IoMT system onto the Open Systems Interconnection, OSI reference model). We show, in Table 2, the primary protocols used in IoMT systems at the levels of each layer.