esa-sceva/satcom-chunk-collection huggingface dataset:
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 7 Experimental Results
### Effectiveness of IoTBystander  
The effectiveness of IoTBystander is evaluated based on the recognition performance (precision, recall, and F1-score) of traffic (i.e., TER), audio (i.e., AER), and dual channels (i.e., DCA). The results are collectively shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event Type** & **Total Length (s)** & **Event Type** & **Total Length (s)** \\ \hline clock-alarm & 200 & door-ring & 497 \\ vacuum-cleaner & 200 & washing-on & 200 \\ faucet-on & 224 & gas-stove-on & 363 \\ kettle-heating & 451 & keyboard-typing & 200 \\ microwave-heating & 295 & mouse-clicking & 200 \\ range-hood-on & 430 & gas-alarm & 200 \\ toilet-flushing & 200 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Detailed information of the dataset _HomeSound-13_.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Event Type**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} \\ \cline{3-10}  & **(Device Model)** & **TER** & **AER** & **DCA** & **TER** & **AER** & **DCA** & **TER** & **AER** & **DCA** \\ \hline  & plug on/off (HP/XP) & 1.00 & – & 1.00 & 1.00 & – & 1.00 & 1.00 & – & 1.00 \\  & switch on/off (GP) & 0.50 & – & 0.50 & 0.50 & – & 0.50 & 0.50 & – & 0.50 \\  & camera on/off (X) & 1.00 & – & 1.00 & 1.00 & – & 1.00 & 1.00 & – & 1.00 \\ Smart & door on/off (XS) & 1.00 & – & 1.00 & 1.00 & – & 1.00 & 1.00 & – & 1.00 \\ Device & smoke-stop (XD) & 1.00 & – & 1.00 & 1.00 & – & 1.00 & 1.00 & – & 1.00 \\  & smoke-detected (XD) & 1.00 & – & 1.00 & 1.00 & – & 1.00 & 1.00 & – & 1.00 \\  & gas-alarm-stop (XG) & 1.00 & 0.97 & 1.00 & 1.00 & 0.97 & 1.00 & 1.00 & 0.97 & 1.00 \\  & gas-detected (XG) & 1.00 & 0.97 & 1.00 & 1.00 & 0.97 & 1.00 & 1.00 & 0.97 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental results of IoTBystander. See Table 6 for the abbreviations of device models. “-” denotes _cannot recognize_.
# Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
## 4 Experiments and Results  
### Dataset  
We performed a set of experiments on the BOU dataset taken for the tasks of anomaly detection of BOU on Korea metro vehicles. The train manufacturer is Hyundai Rotem, and the operating organization is KORAIL (Korea Railroad Corporation). Train Control and Management System (TCMS) data of KORAIL's 40 trains delivered in 2019 among a total of 128 trains were extracted and used. The data used in this study is the TCMS data of the electric control unit (ECU) and BOU devices operated between Seoul and Incheon between January and March 2020. The BOU dataset consists of two different BOU data (normal, abnormal) which were both extracted for an hour every 0.25 s. In abnormal BOU data, the anomaly points are manually labeled by experts. We extracted BC pressure data from the BOU data to build the anomaly detection model as shown in Figure 3. The normal BOU data is used for the training model to learn the normality of BC pressure data, and the abnormal BOU data is used for the testing model using our trained model to verify the effectiveness of our proposed model in terms of detecting an anomaly.
## 1 Introduction
The key idea is to use Answer Set Programming (ASP [1]) for conflict detection, routing, and serialization and to rely upon difference constraints for scheduling. Moreover, scheduling is used for ordering actions in view of collision avoidance. As a result, action variables need no longer be indexed by time steps and upper bounds on the length of plans become obsolete. This comes at the price of restricting ourselves to acyclic plans, since multiple occurrences of the same action cannot be distinguished any more. A similar approach was already used for train disposition [2; 3] in a joint project between Swiss Federal Railways, Switzerland, and Potassco Solutions, Germany. In multi-agent path finding (MAPF) a related idea was implemented using activity constraints from constraint-based scheduling [4]. The rest of this paper is organized as follows. The next section deals with practical facets of ASP, focusing on the language elements used later on. Section 3 introduces a formalization of the specific warehouse delivery problem addressed in our application. An interesting aspect of this formalization is that it reflects an industrial application and as such combines a multitude of features at once. Section 4 presents our ASP-based solution to the warehouse delivery problem. To this end, we provide two different approaches, which we refer to as _step-_ and _path-based_, respectively. The first approach directly reflects the concepts in our formalization, and bares similarities to traditional techniques for modeling action and change in ASP through the use of a step-based encoding [1]. The second approach pursues the aforementioned step-free approach that builds on more refined scheduling. We describe both approaches bottom-up from the respective source code while paying attention to grounding reductions and other pragmatic aspects relevant for scalability. We provide an informal argumentation of the correctness of both approaches with respect to the formalization developed in Section 3. Completeness is only obtained in the step-based setting,since the path-based one only yields acyclic plans. This lack in expressiveness is however largely compensated by a superior performance, as demonstrated in Section 5, where we empirically evaluate our approach and contrast various alternatives. Last but not least, we discuss related work and summarize our contributions in Sections 6 and 7, respectively.
## 7 New Technologies and Future Perspectives
### Positron Emission Tomography/Computed Tomography  
Positron emission tomography/computed tomography (PET/CT) with \({}^{18}\)F-sodium fluoride (NaF) is an innovative imaging technique that has been used to identify arterial wall micro-calcification before macro-calcification can be detected by ultrasound, CTA, or MRI. It is based on the accumulation of 18F-NaF at sites of active calcification within atherosclerotic plaques. NaF-PET/CT has been shown to be a promising imaging modality for the detection of vulnerable plaques in the carotid arteries, which are associated with a higher risk of stroke. The technical details of NaF-PET/CT involve the injection of a small amount of 18F-NaF into the patient's bloodstream. The NaF-PET/CT scan is then performed after a short waiting period to allow the 18F-NaF to accumulate in the atherosclerotic plaques. The PET scan measures the amount of 18F-NaF that has accumulated in the plaque, while the CT scan provides a detailed anatomical image of the carotid arteries.  
[PERSON] et al. [169] used CCN-based segmentation obtaining faster standardized uptake mean values with the same accuracy of human operators. The abovementioned new technologies represent some of the most modern tools for the recognition of symptomatic carotid atherosclerosis. Thus, the combination of AI with these technologies could provide new important information about correct risk plaque classifications.
# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
## 2 Automatic Reading Algorithm of Dial Gauge Based on Coordinate Positioning
### Center Positioning Based on Arc-Support Line Segment
#### 2.2.1 Circle Detection  
To solve these problems, this paper proposes a circle detection method based on arc-support line segments to extract the center and radius. First, obtain straight lines from the image by using the LSD straight line detection algorithm, and then extract the centers and radius of the circle that was determined by the arc-support line segment. Finally, verify the fitting of the circle through the least square method.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Constant** & \(N\) & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(b\) & \(\alpha\) & \(\beta\) & \(\omega_{n}\) \\ \hline Value & 3 & 15 & 80 & 250 & \(-\)30 & 125 & 46 & 1/3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameter table of MSRCR.  
Let us introduce the definition of the arc-support line segment first. Assuming an arc on a circle, the end points are A and B, respectively. Therefore, the straight-line segment AB is the arc supporting line segment. The arc-support line segment is shown in Figure 3.  
Due to the randomness of the line segment pairing, it is necessary to judge whether the line segment pair comes from different circles according to the directionality of the arc-support line segment [23]. Figure 4 shows the area range of a pair of arc-support line segments. It is obvious that [L1, L3] and [L1, L4] belong to different circles. As shown in Figure 4, although L3 is in the reasonable area of L1, L1 is not in the area of L3; therefore, it cannot be paired. Only L1 and L2 can be paired within a reasonable area of each other [24].  
Therefore, we have the following:  
\[\left\{\begin{array}{c}\overset{\rightarrow}{AC}\cdot A\overset{ \rightarrow}{RC}_{L1}>\rho_{d}\\ \overset{\rightarrow}{AD}\cdot A\overset{\rightarrow}{RC}_{L1}>\rho_{d}\\ \overset{\leftarrow}{CA}\cdot A\overset{\rightarrow}{RC}_{L2}>\rho_{d}\\ \overset{\rightarrow}{CB}\cdot A\overset{\rightarrow}{RC}_{L2}>\rho_{d} \end{array}\right. \tag{8}\]  
where the \(A\overset{\rightarrow}{RC}_{Li}\) is the normalized arc-support direction vector of \(Li\) (\(i=1\) or \(2\)). \(\rho_{d}\) is distance threshold.  
The intersection of the vertical bisector of the line segment \(L1\), \(L2\) is detected as the center of the circle \(O\). Assume that \(R1\) and \(R2\) are the distances from \(O\) to the pair of line  
Figure 4: The area range of a pair of arc-support line segments.  
Figure 3: Arc-support line segment.  
segments [\(L1\), \(L2\)], respectively. If the radial distance tolerance \(\varepsilon_{rd}\) is satisfied, the radius \(R\) is the average value of \(R1\), \(R2\). Therefore, we have the following:  
\[R=\frac{R_{1}+R_{2}}{2},|R_{1}-R_{2}|<\varepsilon_{rd} \tag{9}\]  
After the above calculations, a series of center \(O\) and radius \(R\) can be obtained, that is, the detection of the initial circle is complete.
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 3 AN Maximizing Relay's SER Performance
### Assigning \(|z|\) for a Given \(E_{z}\)
Here, we summarize the procedure to design and generate AN symbols as follows:  
1. compute the amplitudes of the AN symbols by numerically maximizing Equation (24),
2. determine the corresponding phase value for each amplitude using Equation (19),
3. generate the AN symbols by the obtained amplitudes and phases. From the procedure, we can see that the computational complexity of the AN generation mainly stems from the optimal amplitude computation in step (1). There exist numerous non-linear optimization algorithms to solve this optimization problem, such as the Newton-Raphson method, and the Nelder-Mead method [48], which is known as \"fminsearch\" implemented in MATLAB. For example, using \"fminsearch\" with the default termination tolerance, i.e., \(10^{-4}\), the iteration number to solve the problem is generally under 100 for the numerical examples in Section 5. Moreover, note that the computation in steps (1) and (2) is required after the change of the channel coefficients \(h\) or \(g\).
## 6 Experimental Verification  
### Introduction of the Experimental Setup  
In order to verify the effectiveness of the control algorithm, an experimental setup comprised of a ds1104 [17], a power supply equipment, a swing table, a gyroscope, and three transmission components is built, as shown in Figures 4 and 5. The swing table is controlled by the ds1104 to simulate the flight of aircraft. Meanwhile, the transmission components (i.e., a direct-driving component, a harmonic-driving component, and a RV-driving component) and the gyro are mounted on this swing table. Each component consists of a motor, an encoder or a tachometer, and a reducer. The main attributes of these components are listed in Table 1. Meanwhile, both the system and controller parameters of the components are listed in Table 2.  
Figure 4: The appearance of the experimental setup for motion performance validation of opto-electric servomechanisms (OESs).
## 3 Results
### The Light Path Receiving Verification  
To verify the actual sensing optical path, an experimental system is built as shown in Figure 4. The SMF-MMF-SHDCF structure connected with a light source and the SMF connected with a spectrometer are placed on the left and right motors of the fiber fusion splicer, respectively, and the distance between SHDCF and SMF is as small as possible. Move the SMF in the X-axis and Y-axis directions to receive the transmitted light from core 1, core 2, air hole, and cladding of the SHDCF, respectively. The position of core 1 can be determined by observing the transmission spectrum in the spectrometer, and the position of core 2 and cladding can be determined by geometric calculation of cross section and transmission spectrum.  
The SMF receives transmitted light from SHDCF with lengths of 3 cm, 5 cm, and 7 cm, as shown in Figure 5. The core in the air hole is exposed and the loss is greater than that in the cladding, so core 2 in Figure 5 is the core in the air hole. It can be seen from the results that the loss of core 2 is much less than that of the air holes and the cladding, and the longer the SHDCF, the greater the loss of transmitted light. According to Formula (5), the longer the length of SHDCF, the higher the sensitivity. As shown in Figure 5a, the light intensity in the cladding and air holes in the 3 cm SHDCF is too high, which will bring high-order mode interference. As shown in Figure 5c, the loss of core 2 in 7 cm SHDCF is too large, so the intensity of interference spectral will be very low. Referring to the above factors, the length of SHDCF is determined as 5 cm, and the light transmission intensity of each part of the SHDCF is shown in Figure 5b. The length of MMF is 1 mm, because of the beam can be expanded into two cores of SHDCF with the length, and the relevant theoretical analysis can refer to the published article [25].  
The splicing parameters are observed to fuse well. The automatic splicing procedure in the splicing machine is used to fuse SMF and MMF, and manual splicing mode is used to fuse SHDCF and MMF. The main parameters of splicing are discharge intensity and discharge time, and the collapse of the air hole (Figure 6a) and the over carbonization of the MF (Figure 6b) will be caused when the discharge intensity is too high, or the discharge time is too long. The SHDCF and MMF cannot be fused when the discharge time is short and the discharge intensity is small, and multiple discharges are needed, which will lead to the taper (Figure 6c). After a series of comparative experiments, the discharge time of 300 ms and the discharge intensity of 30 unit were finally selected; the effect drawing of no collapse splicing is shown in Figure 6d.  
Figure 4: The experiment of light receiving verification.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 3 Materials and Methods
### Evaluation Measures  
In this study, the proposed ensemble system for predicting customer churn is evaluated using accuracy, precision, recall and F1 score.  
Equation (3) calculates the accuracy metric. It is defined as the ratio of the number of samples correctly classified by the classifier to the total number of samples for a given test dataset.  
\[\text{Accuracy }=\frac{(\textit{TP}+\textit{TN})}{(\textit{TP}+\textit{TN}+ \textit{FP}+\textit{FN})} \tag{3}\]  
In Equation (3) '\(\textit{TN}\)' is True Negative, '\(\textit{TP}\)' is True Positive, '\(\textit{FN}\)' is False Negative and '\(\textit{FP}\)' is False Positive. Equation (4) is the formula for Precision. It identifies that the part of the prediction data is positive.  
\[\text{Precision }=\frac{\textit{TP}}{(\textit{TP}+\textit{FP})} \tag{4}\]  
The recall is another measure for completeness, i.e., the true hit of the algorithm. It is calculated by using Equation (5).  
\[\text{Recall }=\frac{\textit{TP}}{(\textit{TP}+\textit{FN})} \tag{5}\]  
The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contributions of precision and recall to the F1 score are equal. It is calculated by using Equation (6).  
\[\text{F1 score}=\frac{2*\text{Precision }*\text{Recall}}{(\text{Precision}+ \text{Recall})} \tag{6}\]
## 4 Routing Protocols for HCWSNs
The performance results were promising in experiments that dealt with scalability, latency, and mobility. However, the lack of reliable communication has been identified as a gap in the implementation of CodeBlue [13]. TinyADMR is an unreliable multicast routing protocol which allows the system to scale many patient sensor and receiving devices. One technique that can be investigated to improve reliability is redundant transmissions. Preliminary results from [PERSON] _et al._[13] have shown that retransmitting packets increase the PDR, but at the cost of increased bandwidth and end-to-end delay, plus a reduction in energy efficiency. Therefore, optimized techniques for redundant transmission should be further researched. The performance of CodeBlue has been compared with MEDiSN [19] and results show that MEDiSN outperforms CodeBlue both in terms of PDR for the same number of active sensors and also in terms of the maximum number of supported sensors. The authors of MEDiSN provide two lessons learned from their research that can improve the performance of a routing protocol employed in a HCWSN: (1) Congestive losses and end-to-end transmission delays can be minimized by dynamically adjusting the maximum number of retransmissions that relay nodes issue and by computing the optimal IPI, and (2) The division of functionality between acquiring (PM) and relaying data (RP) enables PMs to achieve consistent and predictable behavior. As a result, a well engineered wireless backbone with strategically deployed relay points can improve PDRs up to threefold [19]. \begin{table}
\begin{tabular}{|p{56.9 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline
**Criteria** & **RMCP [20]** & **TinyADMR [29]** & **Modified CTP [19]** \\ \hline Reliability (in terms of PDR) & (+) Dynamic adaptation of reliability based on the cluster member density and event proximity & (+) PDR is very high for data rates below 5 packets per second with 10 senders transmitting data over multiple hops & (+) Proposed RP selection scheme allows PMs to connect to a more reliable RP once initial connectivity has been lost \\ Scalability & (+) Supports a large number of nodes due to event-triggered and energy-aware cluster formation & (+) System can scale to a large number of devices each with a modest data generation rate. & (+) Can support at least five hundred physiological monitoring sensors (PM) depending on the amount of data each PM generates \\ Timeliness (Latency) & (–) Time required for data packet aggregation severely hinders end-to-end latency & (+) End-to-end message delay less than 200 ms for destinations up to 7 hops away & (+) Delay is minimized using dynamic adjustment of retransmissions and computing optimal inter-packet arrival time at RPs \\ Robust to Mobility & (–) Cannot achieve real-time data collection if user moves quickly & (+) Deals gracefully with node movement for mobility rates typical of walking or moving patients & (+) The dedicated wireless backbone architecture effectively masks the effects of mobility \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of routing protocols for HCWSN.
## 3 Results and Discussion
### Principal Components Analysis
Although PCA is useful to visualize regions with different chemical composition, a detailed interpretation of chemical information brought by the PCs is not always successful since the vibrational modes of the chemical compounds are not always represented in the  
Figure 4: **(A)** Explained variance by the first 10 PCs. (**B**) Loadings profile of PC1 (**blue**) and PC2 (**orange**) in the range between 666 and 3120 cm\({}^{-1}\) with corresponding chemical band assignment. Figure 3: **(A)** Confocal Microscope image of a replicating _E.coli_ cell spotted on a gold surface taken with a 100\(\times\) microscope objective obtained in dark field. (**B**) Raman chemical map of a replicating _E. coli_ cell colored on the basis of the Raman Intensity at 2940 cm\({}^{-1}\). (**C**) Scores’ intensity of PC1 distribution. The color scale is based on PC1 scores’ intensity, from lowest (**blue**) to highest (**red**). (**D**) Score intensity of PC2 distribution. The color scale is based on PC2 scores’ intensity, from lowest (**blue**) to highest (**red**). PCs loadings maintaining the same profile of the chemical Raman spectra, for example, negative peaks can appear since the shape of the bands are the results of the mathematical elaboration and do not reflect the exact vibrational spectral bands. For this reason, an exhaustive interpretation of PCA loadings in relation to respective Raman signals might lead to some difficulties. In PCA, the importance of a principal component is assessed by its eigenvalue or contribution. Eigenvalues represent the amount of information explained in each component. Components explaining large amounts of information are preferentially chosen over components with less information. Although it is well known that in biological systems, large changes may be triggered by small changes [25]. Thus, we analyzed data with MCR, which has no relation to the amount of information and can identify more components with low information than PCA can [26].