esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The bottom ash of a municipal solid waste incinerator (MSWI) and water filter silt (WFS) were applied to a controlled low-strength material (CLSM) in the present study. The CLSM of the control group was composed of cement, water, and fine aggregates. WFS was first used as a fill material to replace 10% of the volume of natural fine aggregates in the CLSM. MSWI bottom ash was used to replace 0%, 25%, 50%, 75%, and 100% of the volume of the remaining natural fine aggregates with a water-cement ratio of 1.6. The engineering properties of freshness, hardening, and durability were examined. The results revealed that the slump flows of all of the mixture proportions ranged between 50 and 70 cm. The tube flow ranged between 20 and 30 cm, conforming to ASTM D6103 and construction regulations regarding CLSMs stipulated by the Water Resources Agency of the Ministry of Economic Affairs in Taiwan. Increases in the replacement amount of MSWI bottom ash prolonged the time required to achieve a resistance to penetration of 2.74 MPa. The diameter of the drop test ball was less than 7.6 cm, indicating that the mixture proportions had sufficient bearing capacity for successive construction. At an age of 28 d, the compressive strength did not exceed the 8.4 MPa prescribed in ASTM D4832. The ultrasonic pulse velocity and water absorption exhibited identical growth tendencies. In summary, using MSWI bottom ash to create CLSMs is feasible on the condition that the appropriate amount of WFS should be added.  
municipal solid waste incineration (MSWI) bottom ash; water filter silt (WFS); controlled low-strength material (CLSM) +
Footnote †: journal: Applied Sciences
# Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images
## 2 Proposed Binocular Eye Gaze Tracking System  
### Proposed Gaze Tracking System  
In our study, we designed an eye tracker system which consists of a camera link interface eye capture camera [35], FPGA-frame grabber card [36], an IR illuminator group and eye tracker/stimulus PCs as shown in Figure 1.  
The camera is colorful and operates up to 340 fps with camera link interface. We adopted the camera with a lens which has a focal length of 16 mm, and vertical 23.7\({}^{\circ}\)-horizontal 30.7\({}^{\circ}\) field of view angles. IR illuminators are located around the eye capture NIR camera to ease the detection of pupil area in two ways. The first way is through dark pupil eye tracking, where an illuminator is placed away from the camera's optical axis causing the pupil to appear darker than the iris as seen in Figure 2a. The second way is through bright pupil eye tracking, where an illuminator is placed close to the optical axis of the camera causing the pupil to appear brighter than the iris as shown in Figure 2b.  
During remote eye tracking different factors can affect the pupil detection. Bright pupil eye tracking can be effected by environment illumination more than dark pupil tracking. Age and environmental illumination changes the size of the pupil in this method. Ethnicity is also another factor. Bright pupil eye tracking works very well for Hispanics and Caucasians but worse for Asians.  
We preferred first way of IR illuminator location and our design is based on dark pupil eye tracking. To obtain IR images from a colorful camera, there is something which is performed on the camera to change the spectral response. The spectral response gives information about the characteristics of lens, light source, and IR cut filter. Our camera has an IR cut filter that passes only visible light wavelengths. The filter transmits in a range from 400 nm to 720 nm, and it cuts off from 720 nm to 1100 nm [35].  
IR illumination effect can be obtained by modifying the camera filter. We removed the IR cut filter and placed a positive film in front of the camera sensor as shown in Figure 3. The camera spectral response is changed after the modification. The modified spectral response prevents undesired corneal reflections in our design.  
Figure 1: Proposed eye gaze tracking system. IR: infrared.  
Figure 2: infrared (IR) illuminator location around eye capture camera. (**a**) Dark pupil effect; (**b**) Bright pupil effect.  
Before and after the modification of the camera, we measured the spectral responses of the camera utilizing Shimadzu UV-3600 UV-VIS-NIR spectrophotometer (Shimadzu Corporation, Kyoto, Japan) and results are depicted in Figure 4.  
Our design contains two IR illuminators. Each illuminator includes 48 near infrared light emitting diodes (NIR LEDs) with wavelengths of 850 nm. The radiant intensity of each NIR LED is 40 mW/sr and the illuminators are harmless to human eyes [37]. These two illuminators generate one corneal reflection.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 5 An Algorithmic Metatheorem for Constant-Depth Circuits and Its Applications
### Back to the Original Subset Sum Problem  
Considering the unary version of subset-sum instead of the original one may seem a bit like cheating, and, at first sight, the complexity of unary-subset-sum is quite unrelated to the complexity of the \"real\" subset sum problem. However, it turns out that Theorem 4 actually _does_ tell us a lot about the complexity of the original version:  
**Corollary 13**: subset-sum _can be solved by a Turing machine running in pseudopolynomial time and polynomial space._  
**Proof Idea.** Already, our first-year student might know that upon input \((a_{1},\ldots,a_{n},s)\) the subset sum problem can solved using dynamic programming in time \(O(\sum_{i}a_{i})\), that is, in \"pseudopolynomial\" time. Unfortunately, the dynamic table needed for this algorithm also has size \(\sum_{i}a_{i}\), that is, we also need pseudopolynomial _space_. To reduce the space, we do the following: upon inputting \((a_{1},\ldots,a_{n},s)\), coded in binary, we run the algorithm from Theorem 4 on the same input, but _coded in unary_. Naturally, that does not really help since we still need space \(O(\sum_{i}a_{i})\) to write down this input, but let us ignore this for the moment.  
How much time and space does the run of the algorithm from Theorem 4 need? All logspace algorithms need time polynomial and space logarithmic in the input length. In our case, we need time polynomial in \(\sum_{i}a_{i}\) and space logarithmic in \(\sum_{i}a_{i}\). (We can ignore \(s\) since the answer is always \"no\" when it is larger that this sum.) Now, a runtime that is polynomial in \(\sum_{i}a_{i}\) is exactly a pseudopolynomial runtime with respect to the input coded in binary, and a space requirement of \(O(\log\sum_{i}a_{i})\subseteq O(\sum_{i}\log a_{i})\) is a polynomial space requirement in terms of the input coded in binary.  
Thus, running the algorithm from Theorem 4 on the input coded in unary has exactly the time and space requirements we are looking for--except that our having to write down the huge unary input spoils the idea. Thus, let us not write down the huge unary input! We turn the unary input into a \"virtual tape\": Since the machine we run is a logspace machine, it cannot modify this tape, and whenever the machine tries to read a symbol from this tape, we simply recompute what \"would be there if the unary input tape were real\" by doing a simple computation on our original binary input.  
The above corollary is another example of how algorithm metatheorems can help in simplifying proofs: a direct proof of the above corollary takes up most of a stoc 2010 paper [25].
## 2 Materials and Methods
### Non-Destructive Evaluation
#### 2.3.1 Lock-In Thermography  
The test setup includes the impacted specimen, the infrared camera, and two halogen lamps (1 kW each) for thermal stimulation of the specimen [8]. The used infrared camera is the SC6000 (Flir systems), which is equipped with a QWIP detector, working in the 8-9 \(\upmu\)m infrared band, NEDT \(<\)35 mK, spatial resolution 640 \(\times\) 512 pixels full-frame, pixel size 25 \(\upmu\)m \(\times\) 25 \(\upmu\)m and with a windowing option linked to frequency frame rate and temperature range. The camera is equipped with the lock-in module that drives the halogen lamps to generate a sinusoidal thermal wave of selectable frequency \(f\) and with the IRLock-In(c) software for data analysis.  
Lock-in thermography is a well-known technology and will not be herein described in detail; only a sketch of the test setup (Figure 2) and some basics are recalled for easy reading. The thermal wave, delivered to the specimen surface, propagates inside the material and gets reflected when it reaches parts where the heat propagation parameters change (in-homogeneities). The reflected wave interacts with the surface wave producing an oscillating interference pattern, which can be measured in terms of either temperature amplitude, or phase angle \(\varphi\), and represented as an amplitude, or phase, image, respectively. The basic link of the thermal diffusion length \(\mu\) to the heating frequency \(f\) and to the mean material thermal diffusivity coefficient \(\alpha\) is via the relationship:  
\[\mu=\sqrt{\frac{\alpha}{\pi f}}. \tag{1}\]  
The depth range for the amplitude image is given by \(\mu\), while the maximum depth \(p\), which can be reached for the phase image, corresponds to 1.8 \(\mu\)[9; 10; 11; 12]. In general, it is preferable to reduce data in terms of phase image because of its insensitivity to either non-uniform heating or local variations of emissivity over the monitored surface. The material thickness, which can be inspected, depends on the wave period (the longer the period, the deeper the penetration) and on the material thermal diffusivity.  
As stated in Equation (1), to check the material conditions at a given depth, both the knowledge of the thermal diffusivity and the correct choice of the heating frequency are fundamental parameters. The thermal diffusivity can be evaluated with either the lock-in technique itself or with flash thermography.  
\begin{table}
\begin{tabular}{c c c} \hline
**Code** & **Matrix** & **Thickness (mm)** \\ \hline PA6B & Polyamide & 3.0 \\ PPB & Pure polypropylene & 3.0 \\ PC2B & Polypropylene added with 2\% maleic anhydride & 3.0 \\ \hline \end{tabular}
\end{table}
Table 1: Some specimens details.
# Offline-Online Change Detection for Sentinel-1 InSAR Time Series
## 2 Methods
### Changes in Displacement Time Series
#### 2.2.3 Spatial Filtering  
A unique strength and challenge of InSAR time series analysis is that not only do we have a relatively dense sampling in time, we also have a dense pixel sampling in space. At full resolution (\(\sim\)5 m in range and \(\sim\)15 m in azimuth) a 250 km by 200 km Sentinel-1 radar scene may contain on the order of tens to hundreds of millions of pixels, each with its own time series that can be treated independently in terms of the change detection method described above.  
Such large quantities of data presents a challenge in terms of efficient data processing. But it is also an opportunity. Most geophysical signals of interest have a spatial pattern across multiple pixels. For example, volcanic inflation signal or vertical displacement due to subsurface fluid injection/extraction may occur over several kilometres, which could encompass several thousand to tens of thousands of pixels. Even smaller signals from active landslides will generally include several tens or hundreds of pixels.  
If a change point is detected at time \(t\) at pixel \(p_{ij}\), then there is a strong likelihood that if the deformation is real then it will be detected at neighbouring pixels too. If there is no such change point at neighbouring pixels then it is likely that the detected change is a false positive.  
Figure 4: Stationarity tests on the first and second order difference series. The top row is a synthetic time series with strong seasonal displacements and an offset at \(t=100\). We test for stationarity using the Augmented Dickey-Fuller (ADF) test and reject the null hypothesis (the series is not stationary) if the ADF p-value is less than 0.05. We show that the synthetic displacement series is not stationary but the first difference is. The second and third difference fail the stationarity test so we calculate the second order ‘difference of difference’ series for these, after which they pass the stationarity test.  
Therefore, to reduce the occurrence of false positives in the detections we apply a 2-dimensional Gaussian filter to each time step in our time series. Practically this involves the convolution of a Gaussian kernel of size of \(\Delta x\) and \(\Delta y\) with each binary detection image (0 for no detection and 1 for detection) at every time step. After smoothing, detection pixels with a value less than 0.5 are discarded. The result of this filtering is the reduction of spatially isolated detections, which we assume are false positives.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 5 Simulation Setup  
### Simulation Platform  
In order to quantitatively evaluate the impact of node selfishness caused by energy consumption in MANETs, we designed the simulation procedure using a proper platform and frameworks.  
For the purposes of our investigation, the integration of OMNeT++ (v5.5.1) [36] and INET Framework (v4.1.1) [8] is used as the simulation platform in this paper. OMNeT++ is an open source computer simulation platform written in C++ that is suitable for wireless and discrete network event simulations. INET Framework is an open source framework for the OMNeT++ platform. It provides adequate implementations of communication network protocols.
## 2 Literature Review  
This literature review is divided into two sub-sections. The first section examines studies on passenger satisfaction based on surveys and questionnaires. This survey-based method collects data through direct interactions with passengers, such as interviews. It is generally more reliable but time-consuming and expensive [22; 23; 24]. The second section explores studies employing web scraping to extract online reviews from platforms such as Skytrax, Twitter, Google Maps, and TripAdvisor, and utilizes various analytical tools including topic modelling, machine learning, and Deep Learning for ASQ analysis.
## 2 Materials and Methods
### Oscillators Tolerance  
The boards used in tests mount an external 8 MHz crystal oscillator. Due to temperature, voltage, capacitor tolerance and long term state of wear, frequency drift between units is unavoidable. As a consequence, CAN controllers on different units show frequency drift as well, which brings different peripherals to sample the same bit at different times. One of the goals of this work is to prove that synchronizing units on a common bus event leads to a residual PWM starting delay that depends on system frequency drift and not on the CAN bus resynchronization process.  
Oscillator tolerance is obtained by an indirect measurement of alignment time of PWM signals, nominally identical, generated by the nodes. Due to different periods (clock frequency drift), a progressive time shift is found between corresponding rising/falling edges, so, by measuring the interval between two consecutive alignments of the rising edge (\(T_{SY}\)), the frequency drift can be derived as follows. Considering two PWM signals generated by different oscillators with periods \(T_{1}\) and \(T_{2}\), respectively, during an interval \(T_{SY}\), a number of \(N_{1}\) periods are counted for the first PWM generator and \(N_{2}\) are counted for the second, with \(T_{SY}=N_{1}T_{1}=N_{2}T_{2}\). Considering \(T_{1}>T_{2}\) leads to:  
\[N_{1}=N,N_{2}=N_{1}+1=N+1 \tag{1}\]  
\[T_{1}=\frac{T_{SY}}{N},T_{2}=\frac{T_{SY}}{N+1} \tag{2}\]  
Normalizing periods difference to nominal switching period \(T_{sw}\) and using Equations (1) and (2):  
\[\Delta T_{norm}=\frac{T_{1}-T_{2}}{T_{sw}}=\frac{1}{N(N+1)}\frac{T_{SY}}{T_{ sw}} \tag{3}\]  
Considering that \(T_{sw}\) is approximately equal to the average of the periods of the two units (\(T_{sw}\approx\frac{T_{1}+T_{2}}{2}\)) and using (2):  
\[\Delta T_{norm}\approx\frac{1}{N(N+1)}\frac{T_{SY}}{T_{1}+T_{2}}=\frac{2}{2N +1} \tag{4}\]  
Since \(T_{SY}\gg T_{sw}\):  
\[N\approx\frac{T_{SY}}{T_{sw}} \tag{5}\]  
A PWM frequency of 10 kHz nominal was generated for the experimental measurement of realignment times. As an example, the realignment of converter 2 and converter 1 took an average of 20.61 s over 303 measurements (Table 1). This value of \(T_{SY}\) translates into a number of cycles \(N\) before realignment of about 206,100.
## 2 Related Works
By incorporating trust metrics, the proposed algorithm also enhances security, ensuring more reliable communication in scenarios vulnerable to malicious activity. Thus, our work effectively fills the gaps related to network stability, security, and efficiency in high-mobility MANET environments.
## 3 Literature Review  
Smart cities provide \"state-of-the-art approaches for urbanization... The notion of smart cities can be extended to smart societies... digitally enabled, knowledge-based societies, aware of and working towards social, environmental, and economic sustainability\" [1,31]. The foundations of these smart cities and societies are laid out with smart systems and applications. A range of technologies is contributing to the development of these smart systems. These include the Internet of Things (IoT) [32, 33, 34, 35, 36], social media [21, 22, 23, 37, 38], big data [39, 40, 41, 42, 43, 44], high performance computing (HPC) [45, 46, 47, 48], cloud, fog, and edge computing [49, 50, 51, 52], and machine learning [36, 53]. The applications include healthcare [34, 39, 54, 55, 56], transportation [57, 58], and others [59, 60]. Social media and IoT provide the pulse for sensing and engaging with the environments. Sentiment analysis, or opinion mining, is a vital tool in natural language processing (NLP), defined as \"the field of study that analyzes people's opinions, sentiments, evaluations, appraisals, attitudes, and emotions toward entities such as products, services, organizations, individuals, issues, events, topics, and their attributes\" [61]. Many of the notable works on sentiment analysis rely on machine learning and social media, including Twitter.  
In this section, we review the literature related to the use of Twitter sentiment analysis in healthcare. Section 3.1 focuses on healthcare-related analytics in English, while Section 3.2 focuses on the same but for the Arabic language. Section 3.3 describes the research gap.
## 6 Conclusions  
We developed for the first time a simple, fast and efficient antenna-independent PL model for in-out body communication for multilayered lossy tissues and heterogeneous human medium at 2.45 GHz, eliminating the need for time-consuming simulations. A maximum deviation of only 1.9 dB and an average deviation of 1.7 dB is observed between the PL model and the simulated PL in the heterogeneous medium, and the time taken to carry out the simulations is 86,400-times greater than that required for calculation for a sample size of 30 antenna separations. The simplified PL model can be used to design any in-out body communication systems and also to determine the link budget for such systems. As an application, the proposed model is used to calculate the range of distances between the Tx and the Rx in the case of various scenarios, such as in a hospital environment where the data transfer takes place from in-, on-, or off-body devices to a receiving unit. For the future extension of this work, we would like to develop the PL model for the MICS (medical implant communication service) band at a frequency of  
\begin{table}
\begin{tabular}{l c|l c} \hline
**Tx** & & **Rx** \\ \hline Frequency (MHz) & 2450 & Rx antenna gain Gr (dBi) & 11.04 \\ Tx power \(P_{t}\) (\(\mu\)W) & 25 & Feeding Loss \(L_{rx\ feed}\) (dB) & 0 \\ Tx power \(P_{t}\)(dBm) & \(-16\) & Ambient Temperature T (K) & 310.0 \\ Feeding Loss \(L_{tx\ feed}\) (dB) & 0 & Receiver NF (dB) & 3.5 \\ Tx antenna gain \(G_{t}\) & 2.14 & Boltzmann constant \(k\) & \(1.38\times 10^{-23}\) \\  & & Noise power density \(N_{0}\)(dBm/Hz) & \(-199.70\) \\ \hline
**Signal Quality** & & **Propagation** \\ \hline Bit error rate & \(1.0\times 10^{-5}\) & Free space loss \(L_{f}\)(dB) & 52.02 \\ Bit rate \(B_{r}\) (\(Mb/s\)) & 2 & \(PL_{Model}\) (dB) & 62 \\ Eb/N0 (ideal PSK) (dB) & 9.6 & \(PL_{Sim}\) (dB) & 63 \\ Coding gain \(G_{c}\) & 0 & Total propagation loss \(L_{t}\)(dB) = PL & \\ Fixing deterioration \(G_{d}\) (dB) & 2.5 & where PL = \(PL_{Model}\) for Model & \\  & & and = \(PL_{Sim}\) for Simulation & \\ \hline Distance = 2 cm in the Tissue Layer & & Total distance = 102 cm & \\ (Tx placed at 1 m from tissue) & & & \\ Link (PL Model) \(C/N_{0}\) (dB/Hz) & 123.84 & Link (PL Simulation) \(C/N_{0}\) (dB/Hz) & 122.84 \\ Required \(C/N_{0}\) (dB/Hz) & 75.11 & & \\ \hline \end{tabular}
\end{table}
Table 4: Parameter values: Link budget calculation for a distance of 102 cm of separation between the Tx and the Rx.  
402-405 MHz for a different set of antenna designs. We would also like to use this PL model for the prediction of SAR (specific absorption rate) in the layers.  
[PERSON] is supported by a grant from FWO (Research Foundation-Flanders). [PERSON] is a Post-Doctoral Fellow of the FWO-V (Research Foundation-Flanders).  
[PERSON] simulated and modeled the simulated data. [PERSON] and [PERSON] discussed the model. [PERSON], [PERSON], [PERSON] and [PERSON] discussed the results. [PERSON] and [PERSON] supervised the project direction. [PERSON] wrote the manuscript based on discussion with all the authors.
## 3 Results
### Results of Statistical Analyses
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Coefficient** & **Age** & **Ostell 0** & **Ostell 1** & **MBL** \\ \hline Age & Pearson’s r & \(1\) & \(-0.28\) & 0.22 & \(-0.06\) \\  & \(P\) & \(<\)0.001 & 0.082 & 0.201 & 0.735 \\ Ostell 0 & Pearson’s r & \(-0.28\) & \(1\) & 0.58 & \(-0.11\) \\ \(P\) & 0.082 & \(<\)0.001 & \(<\)0.001 & 0.531 \\ Ostell 1 & Pearson’s r & \(0.22\) & 0.58 & \(1\) & \(-0.06\) \\ \(P\) & 0.264 & 0.447 & 0.536 & 0.623 \\ MBL & Pearson’s r & \(-0.06\) & \(-0.11\) & \(-0.06\) & \(1\) \\ \(P\) & 0.735 & 0.531 & 0.736 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Linear correlation coefficient for groups one and two and their significance for the selected continuous variables. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Coefficient** & **Age** & **Ostell 0** & **Ostell 1** & **MBL** \\ \hline Age & Pearson’s r & \(1\) & \(-0.24\) & 0.47 & \(-0.37\) \\  & \(P\) & \(<\)0.001 & 0.325 & 0.047 & 0.128 \\ Ostell 0 & Pearson’s r & \(-0.24\) & \(1\) & 0.42 & 0.09 \\  & \(P\) & 0.325 & \(<\)0.001 & 0.084 & 0.730 \\ Ostell 1 & Pearson’s r & \(0.47\) & 0.42 & \(1\) & \(-0.05\) \\  & \(P\) & 0.584 & 0.222 & 0.430 & 0.874 \\ MBL & Pearson’s r & \(-0.37\) & 0.09 & \(-0.05\) & 1 \\  & \(P\) & 0.105 & \(<\)0.001 & 0.001 & 0.422 \\ Ostell 1 & Pearson’s r & \(-0.11\) & 0.69 & \(1\) & 0.000 \\ MBL & Pearson’s r & \(0.055\) & 0.320 & 0.733 & 0.814 \\ MBL & Pearson’s r & \(0.47\) & \(-0.20\) & 0.00 & 1 \\  & \(P\) & 0.047 & 0.422 & 0.992 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Linear correlation coefficients for group one and their significance for the selected continuous variables. The statistically important differences are highlighted in red. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Variable** & \multicolumn{3}{c}{**4.0 diameter**} & \multicolumn{3}{c}{**4.0 diameter**} & \multicolumn{3}{c}{\(T\)} & \multicolumn{3}{c}{\(P\)} & \multicolumn{3}{c}{**Cohen’s \(d\)**} \\ \cline{2-10}  & \(M\) & \(SD\) & \(M\) & \(SD\) & & & & \\ \hline MBL & 0.26 & 0.31 & 0.14 & 0.24 & 1.30 & 0.203 & 0.43 \\ Ostell 0 & 70.84 & 3.39 & 72.30 & 4.56 & \(-1.13\) & 0.266 & \(-0.36\) \\ Ostell 1 & 69.50 & 3.31 & 70.39 & 3.29 & \(-0.81\) & 0.425 & \(-0.27\) \\ Age & 42.95 & 11.30 & 40.15 & 5.38 & 1.00 & 0.326 & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Kruskal–Wallis test results for Ostell and MBL variables.