esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Daily Personal PM2.5 Exposure Level According to Four Major Activities among Children
## 2 Materials and Methods
### Study Design and Study Population  
We conducted this study with a convenient sampling design. Groups of study participants were recruited from those child patients who had joined our ESCORT (environmental health smart study with connectivity and remote sensing technologies) study [17]. A total of 25 participants aged 6 to 11 years and their parents agreed to carrying personal PM2.5 samplers (please refer to details below) and GPS monitors (Travel Recorder, QSTARZ) and writing their activity and place visited every 30 min on a diary provided (Figure 1). Participants and/or parents recorded the starting time and duration of each specific activity pattern and whether it occurred during a monitoring period.  
Over all various activities in their daily activity dairy, we selected 4 major activity pattern types (1) resting inside home, (2) Attending an educational institute, i.e., spending time inside elementary school or kinder garden, (3) spending time inside of car or bus for commuting and (4) spending time inside of other commercial shops including restaurants) which were the most experienced by our study participants. For validation purpose (i.e., outdoor locations), we compared GPS records (one second interval) and daily self-reported activities.  
This approach offers the opportunity to track spatiotemporal exposure patterns for a group of people sharing common activities in microenvironments from which their exposure profiles spread out in various directions from their residences, education institutes and other possible exposure places (private cars or commercial buildings). A set of a data (PM2.5 concentrations, spatiotemporal GPS information and contextual data) was supposed to be collected by each participant over a period of time, designed to capture everyday activities.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
###### Abstract  
The underwater acoustic (UWA) communication system is characterized by limited bandwidth, while continuous phase modulation (CPM) offers a constant envelope, improving power and spectrum utilization efficiency. However, severe inter-symbol interference (ISI) poses a significant challenge in CPM-based UWA communication. Traditional CPM frequency domain equalization (FDE) combined with simple phase detection neglects the inherent coding gain from CPM, leading to performance degradation. Although Viterbi detection provides high performance, its complexity makes it unsuitable for computationally constrained UWA systems. This paper proposes a symbol detection algorithm based on posterior probabilities combined with FDE (PS-FDE). PS-FDE improves CPM signal detection performance by effectively separating information, applying delay, and performing multiple rounds of information merging. Simulations using minimum shift keying (MSK) and Gaussian MSK signals demonstrate significant performance improvement in just a few iterations over UWA channels. A sea trial further validates the algorithm, showing a 15.83% reduction in bit error rate after three information mergings.  
Keywords:underwater acoustic communication; continuous phase modulation; frequency domain equalization; posterior probability-based symbol detection +
Footnote †: journal: Marine Science and Engineering
## 4 Analysis of the Impact of Air Quality on Residents' Health in China
### Empirical Analysis of Air Quality on Resident Health
#### 4.3.4 Analysis of Air Quality Equation Result  
From Model 2, the average number of residents' visits to the doctor in the current period increases by 1 unit, and the air quality index in the next period decreases by 1.0922 units. When the health level of residents declines, government departments will strengthen environmental regulations and control air pollution, and the air quality will gradually improve in the next period. When the air quality index of the surrounding provinces increases by 1 unit, the local air quality index will rise by 0.1117 units. The air is fluid. When the AQI of the surrounding provinces is high, the local AQI will be higher due to the influence of the surrounding provinces. The air quality of different provinces is spatially correlated, which is consistent with the spatial correlation network of the air quality index above.  
An increase of 1 unit in energy consumption intensity increases the AQI by 10.4944 units. Energy consumption remains a major culprit of air quality. An increase of 1 unit of per capita GDP will reduce the AQI by 0.0002 units. With an improvement in the quality of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Peh**} & **Resident Health Equation** & **AQI** & **Air Quality Equation** \\ \cline{2-4}  & **Model 1** & & **Model 2** \\ \hline AQI & 0.0426 *** & Peh\_01 & \(-\)1.0922 * \\ \hline  & (5.69) & & (\(-\)1.95) \\ \hline wAQI & \(-\)0.0083 *** & & 0.1117 *** \\ \cline{2-4}  & (\(-\)5.58) & wAQI & (7.10) \\ \hline wPeh & 0.1115 *** & & 10.4944 *** \\ \cline{2-4}  & (5.43) & Eci & (3.08) \\ \hline Pup & 0.0943 *** & & \(-\)0.0002 * \\ \cline{2-4}  & (9.81) & Pgdp & (\(-\)1.96) \\ \hline Dgv & 0.0006 *** & & 0.5188 *** \\ \cline{2-4}  & (3.62) & Psi & (3.10) \\ \hline \multirow{2}{*}{Erg} & \(2.26\times 10^{-7}\) & & \(-\)0.0213 ** \\ \cline{2-4}  & (1.09) & & (\(-\)2.50) \\ \hline \multirow{2}{*}{cons} & \(-\)3.6376 *** & & \(-\)2.5853 \\ \cline{2-4}  & (\(-\)5.28) & & (\(-\)0.28) \\ \hline obs & 180 & obs & 180 \\ \hline AIC & 612.260 & AIC & 1521.043 \\ \hline BIC & 634.611 & BIC & 1549.780 \\ \hline \hline \end{tabular} Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, and the parentheses are the t statistics.  
\end{table}
Table 1: Results of spatial simultaneous equations.  
life, people are more capable of improving air quality. An increase in the proportion of secondary industry by 1 unit will increase the AQI by 0.5188 units. Polluting enterprises in the secondary industry are still the target of national governance. Investment in science and technology by 1 unit will reduce the AQI by 0.0213 units. China's scientific and technological innovation has transformed from red innovation with the goal of making profits to green innovation with the goal of managing the environment. Technological innovation has begun to play an active role. All the above control variables have a significant effect on residents' health.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 5 Numerical Results
### Accuracy of the Theoretical Model  
Table 3 indicates the percentage difference between the mean number of RRs required in the typical cell derived by using our statistical dimensioning tools and the empirical network simulation results. The mean number of required RRs using former method is denoted as \(m_{N}\), the simulation one is denoted as \(m_{N,s}\). Consequently, the percentage difference is \(\Delta m_{N}/m_{N,s}=|m_{N}-m_{N,s}|/m_{N,s}\). We consider different collector intensities (from 0.1 to 0.9 nodes/km\({}^{2}\)) and different antenna configurations as well as transmission modes. As presented in the Table 3, the comparison shows that the difference between the results obtained by our statistical tools and the simulated values is very small, which verifies the accuracy of our model and approach.
## 1 Introduction  
Liver cirrhosis is the major risk factor for mortality in chronic hepatitis B carriers [1; 2]. The annual rate of progression from chronic hepatitis to cirrhosis is 2-5% in hepatitis B e antigen-positive and 3-10% in e antigen-negative patients [3; 4]. Patients with absent or low-grade fibrosis at diagnosis are thought to have a relatively lower risk of progression to cirrhosis in 20 years [5]. The diagnosis and further therapeutic decision usually relies on the fibrosis severity, so an accurate assessment is crucial to treatment outcomes. It is easy to diagnose cirrhosis by liver ultrasound [1; 2]--what is more difficult is assessing more subtle degrees of fibrosis. Liver biopsy is considered the gold standard for liver fibrosis assessment; however, it is an invasive procedure with rare, but serious complications(mortality rate \(<\) 0.01%) [6]. Sampling error and intra- or inter-observer variability also affect the diagnostic consistency [7]. Elastography, either by transient elastography or acoustic radiation force impulse (ARFI), is a promising, non-invasive modality for the diagnosis of liver fibrosis [8; 9; 10; 11]. It can be performed in a periodic follow-up and is generally superior to other serological modalities [12; 13; 14]. In a meta-analysis study, ARFI demonstrated a satisfactory ability to predict higher-stage liver fibrosis (F = 3) and liver cirrhosis (F = 4) [15]. However, inflammation, variation, and other factors have significant impacts on the interpretation [16; 17; 18; 19; 20; 21; 22]. In our previous study as well as in others, ARFI had a poorer performance in the measurement of liver fibrosis in chronic hepatitis B than in chronic hepatitis C [16]. How to make interpretation of liver fibrosis during anti-HBV therapy is unclear. To improve the interpretation of ARFI in chronic hepatitis B patients with or without anti-HBV therapy, we conducted a replication study using two-location measurements and limited the operator to one well-trained technician.
# Scheduling for Media Function Virtualization
## 6 Conclusions  
The transition from specialized hardware to COTS IT infrastructure is taking place in broadcast studios, i.e., IP networking for media transport and general-purpose compute for media processing. MFV proposes to utilize COTS compute hardware to run media processing as VMFs that can be chained together in the form of VMF-FGs. To ensure that broadcast-quality guarantees in an MFV environment are met, it is important to not only map VMFs and virtual links to the underlying MFVi but also to schedule them in a timely manner. To this end, we first formulate an instance of the VMF-FG scheduling problem as an ILP and prove its NP-completeness. We have then proposed a greedy-algorithm, based on BFS and DFS graph traversal, to find its solution. The evaluation of the algorithm shows an increase in the end-to-end delay with increasing cycle time. We also highlighted the improvement in the end-to-end delay with VMF-FG decomposition, particularly for high-quality formats.  
For future work, we plan to investigate the implementation of a media service in an MFV environment with VMF-FG scheduling.  
**Author Contributions:** Conceptualization, G.P.S., W.T., D.C. and M.P.; funding acquisition, M.P.; investigation, G.P.S., W.T. and D.C.; methodology, G.P.S., W.T. and D.C.; resources, M.P.; supervision, W.T., D.C. and M.P.; writing--original draft, G.P.S.; writing--review and editing, G.P.S. and W.T. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Impact of media formats on resource utilization for different \(M\).  
Funding: This research was (partially) funded by the Flemish FWO SBO S003921N VERI-END.com (Verifiable and elastic end-to-end communication infrastructures for private professional environments) project.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Investigating the Influence of Feature Sources for Malicious Website Detection
## 4 Dataset GAWAIN
### Feature List  
The dataset includes all of the features that are part of expanded dataset used in Section 3, as well as several new features as part of host-based and content-based categories. For a full list refer to Table 1. It is also worth mentioning that there was no obfuscated JavaScript (JS) code in the samples. This was verified through the usage of \"JS-Auto-DeObfuscator\" tool [37] and the methodology from [38, 39]. Therefore, this feature was dropped from the dataset.  
Here, we highlighted the features that are extracted to supplement features utilized in Section 3:  
* JavaScript average and max array length.
* HTML content length.
* Number of JavaScript tag references.
* Number of semicolons, zeros, spaces, hyphens, @ characters, queries, ampersands and equals.
* Domain and domain length.
* Dominant Colors Description (DCD).
* Hex length in content.
* Contains Hex.
* Unique number of characters, numbers, and letters.
* Ratio of letters to characters, numbers to characters.
* Is in Alexa's top 1 million.
* URL, content and image embeddings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Variation 1 (with JS)** & **Variation 2 (w/o JS)** & **Variation 3 (with JS)** & **Variation 4 (w/o JS)** \\ \hline N. Bayes & 93.09 & 83.72 & 85.57 & 55.26 \\ \hline SVM & 96.03 & 85.36 & 98.13 & 86.47 \\ \hline KNN & 94.54 & 82.82 & 96.70 & 81.86 \\ \hline XGBoost & 97.05 & 86.43 & 99.02 & 88.25 \\ \hline AdaBoost & 95.88 & 85.63 & 97.69 & 86.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification accuracy (%) with and without JavaScript features for original and expanded [PERSON] dataset.  
* Mean summary statistic for URL, content and image embeddings.  
In terms of the JavaScript bias, after conducting another investigation into the features, and extracting them using scripts from the original paper [21], it was evident that this bias vanished. Figure 3 shows a distribution plot of those JavaScript features by class label for GAWAIN samples. There is almost no separation in this data, which we believe is more realistic scenario to be encountered where attackers try incorporate as many techniques to avoid detection.  
Figure 2: Examples of two website snapshots.
## 4 Experimental Results and Discussion
### Datasets
#### 4.1.2 Lung Tumor Images (VIP-CUP18)  
The lung tumor image dataset is provided by the 2018 IEEE Video and Image Processing Cup (VIP-CUP18) [30]. It includes computed tomography (CT) scans of 300 patients with a varying number of slices for each patient, where each slice is manually annotated by a radiation oncologist. Because the data are provided in the DICOM format, we have to convert the DICOM file from voxels to world-coordinates and then normalize the data to generate \(512\times 512\) pixel images. Only slices containing tumors have been examined with a total of 5204 images. This dataset was resized to \(256\times 256\) pixels due to limitations in the computational resources.
## 2 Experimental Methods
### Test Method and Data Processing  
In the tests, the amplitude (\(A\)) was collected in the form of a voltage signal by a magnetic induction displacement transducer with a sensitivity of 0.1%, error range of \(\pm\)0.05%, and testing range of 0-800 mm. The oscillation frequency \(f_{osc}\) was calculated by the fast Fourier transform (FFT) method of the displacement time-history curves of the prism under continuous oscillation for 60 s. The instantaneous voltage of the system was measured by a data acquisition system in the form of a voltage signal, as shown in Figure 7.  
Active power (\(P_{harm}\)) and energy conversion efficiency (\(\eta_{harm}\)) were two significant parameters to estimate the energy conversion capacity of the system and were calculated by the following equations:  
The instantaneous power expression is calculated as:  
\[P(t)\ =\ \frac{u^{2}(t)}{R_{L}}, \tag{3}\]  
Figure 7: Data acquisition system.  
where \(P(t)\) is instantaneous power; \(u(t)\) is instantaneous voltage, \(R_{L}\) is the load resistance value.  
The active power is written as:  
\[P_{harm}\ =\ \frac{1}{T}\int_{0}^{T}P(t)dt\ =\ \frac{1}{T}\int_{0}^{T}\frac{u^{2}(t)} {R_{L}}dt, \tag{4}\]  
where \(P_{harm}\) is active power, \(T\) is a period of oscillation.  
The energy conversion efficiency is derived as:  
\[\eta_{harm}\ =\ \frac{P_{harm}}{P_{w}}, \tag{5}\]  
where \(\eta_{harm}\) is energy conversion efficiency [41]; \(P_{w}\) is the total power in the fluid which is written as:  
\[P_{w}\ =\ \frac{1}{2}\rho U^{3}(2A_{max}+D)l, \tag{6}\]  
where \(\rho\) is water density, \(U\) is incoming flow velocity, \(D\) is the projection width of the triangular prism in the direction of incoming flow, \(l\) is prism length.
# Design and Calibration of a Sensory System of an Adaptive Gripper
## 2 Materials and Methods  
### Logical Architecture of the Sensory System to Achieve Self-Adaptation  
The logical architecture (Figure 1) for each gripping module is designed to perform gripping independently from a mechanical, electrical, and software perspective. Self-adaptive gripping is achieved in 3 steps:  
Step 1--The sensor states are declared in the retracted position of the grasping module. If the module is in the forward position or in another intermediate position, then the controller makes the decision to reach the forward position and waits for the command to perform a new grasp.  
Step 2--The module advance command has been received, and the sensory system is in the \"ON\" state permanently waiting for the collection of new states. This stage can be completed if the sensory system has fulfilled its software condition; namely, the module jaw has come into contact with the object to be grasped. After entering into contact, the clamping is performed until the force declared in the software is reached. The object does not move after the clamping has been performed.  
Step 3--After step 2 has been completed, the sensory system detects that condition \"3\" in step 2 has not been met, which means that the slipping of the grasped object has been detected, and it enters the withdrawal phase. If, after the subsequent tightening, the sensory system detects movement, the tightening will be performed again; this will be repeated until the sensory system reaches the maximum declared thresholds, namely, the following:  
- The force has exceeded the maximum limits;  
- The slip has reached its maximum point.  
When one of the maximum limits, among the above conditions, is reached, then the module will stop gripping.  
According to the above logical architecture, the necessary steps were taken to design the slip sensor and force sensor on each gripping module and then to calibrate them, after which testing was carried out in real gripping situations with the designed and physically realized adaptive gripping; the details are presented below.
## 6 Results and Evaluation
### Blockchain Network Bandwidth Usage Analysis
The network with fifteen Monero blockchain nodes has a mean bandwidth usage of 113.5 Kbps [42]. Figure 8: Monero blockchain bandwidth usage. Figure 9: Duino coin blockchain bandwidth usage. As per the results in Figure 10, the network bandwidth of seven Duino coin blockchain nodes varies from 43.5 Kbps to 179 Kbps. The maximum bandwidth usage was recorded as 179 Kbps at the network with twenty Duino coin blockchain nodes, and the minimum was recorded as 43.5 Kbps at the network with seven blockchain nodes. The network with fifteen blockchain nodes has a mean bandwidth usage of 153.7 Kbps. Figure 10 summarizes all three blockchain algorithms' overall mean bandwidth usage [43]. As Figure 11 shows, the Hydrachain blockchain network has the highest bandwidth usage, and comparatively, the Monero blockchain algorithm has the lowest bandwidth usage. In summary, we can emphasize that the number of blockchain nodes or users affects bandwidth usage. The type of blockchain algorithm also causes bandwidth usage variations that can limit the scalability of blockchain networks. Table 2 indicates a bandwidth usage data sample of Hydrachain, Moner, and Duino coin blockchain networks. As per the results, we can emphasize that the bandwidth usage of blockchain networks increases with the increase in the number of nodes. We discuss the variance of each blockchain network's block transaction rate in the next section. Figure 11: Overall mean blockchain network bandwidth usages. Figure 10: Mean blockchain network bandwidth usage.
## 5 Conclusions  
This paper developed a CFD simulation model to verify experimental data and investigate the impact of sensor location on CCF flow estimation. In the experimental loop, only a limited number of sensor position options were available. The CFD model offers greater flexibility in selecting the optimal locations for a sensor pair. The results show that sensors should be placed where the local temperature exhibits the greatest perturbation induced by the injected water; however, the location of this region of greatest perturbation is highly dependent on the bulk flow rate and the injection pressure used to introduce process variations. Another finding is that the optimal distance between the sensors depends on the flow velocity. When the flow velocity is higher, the distance between the two sensors should be larger so that the transport time between the two sensors is long enough to be accurately inferred by cross-correlation analysis.  
Further study may be able to establish and demonstrate a relationship between the expected flow rate, sensor location, and injection pressure. Because the optimal injection pressure and sensor location are strongly dependent on the bulk flow rate, it would be challenging to employ this technique with a simple set of two temperature sensors. An array of sensors with variable injection pressure capability may provide the flexibility necessary to accurately estimate the flow rate under a variety of conditions. The design of such a sensor array system is an area for future work.  
Conceptualization, J.W.H. and J.B.C.; methodology, X.G. and L.B.C.; software, X.G.; validation, L.B.C.; formal analysis, X.G. and J.B.C.; investigation, X.G.; writing--original draft preparation, X.G.; writing--review and editing, J.B.C. and L.B.C.; visualization, X.G.; supervision, J.W.H., J.B.C. and L.B.C.; project administration, J.W.H.; funding acquisition, J.W.H. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the Lloyd's Register Foundation and the International Joint Research Center for the Safety of Nuclear Energy for funding this research. Lloyd's Register Foundation helps to protect life and property by supporting engineering-related education, public engagement, and the application of research.  
Not applicable.  
The raw data supporting the conclusions of this article may be made available by the authors on request.  
The authors gratefully acknowledge [PERSON] for the use of his experimental flow loop for initial demonstration and testing of the periodic injection cross correlation function-based flow estimation.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.