esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parameter Analysis
### Effect of Pile Diameter  
The pile diameter \(D_{\text{p}}\) is varied as follows: 0.6 m, 0.8 m, 1.0 m, 1.2 m, and 1.4 m, to investigate the effect on the lateral displacement and bending moment of the pile. It should be noted in Figures 13 and 14 that the maximum lateral displacement and bending moment are 5.8 mm and 143 kN\(\cdot\)m, respectively, when the pile diameter is 0.6 m. Additionally, the maximum lateral displacement and bending moment of the pile with a diameter of 0.6 m are 1.6 times and 0.16 times that of a pile with a diameter of 1.4 m. Therefore, with the increase in the pile diameter, the horizonal displacement of the pile gradually decreases, while the bending moment increases due to the increased bending stiffness of the pile with a larger diameter. Additionally, Figure 13 illustrates that with the increase in the pile diameter, the displacement at the pile head initially increases and then decreases. This is due to the fact that as the pile diameter increases, the lateral displacement decreases, leading to a reduction in the rotation angle of the pile. Conversely, a smaller pile diameter exhibits larger flexural deformation at the pile head. The jointed effect of these two aspects results in a variation in the lateral displacement and bending moment of the pile. It is notable from Figure 13 that the lateral displacement of the pile head is \(-0.9\) mm when the pile diameter is 1.0 m, which is the largest among the calculated results.
# Sustainable Urban Water Management in China: A Case Study from Guangzhou and Kunming
## 6 Water Sensitive Solutions for Guangzhou and Kunming
### Solutions for Guangzhou
#### 6.1.3 Updating the Laws and Strengthening the Supervision  
New regulations and environmental governance related legislation should be introduced. For instance, individuals or enterprises who release untreated water into rivers should be penalized, as should excessively fishing and illegal aquaculture farms. The Environmental Protection Law, which went into effect in China in 2015, added some new penalties for enterprises that violate environmental laws and increased the responsibility for illegal pollutant discharge, effectively resolving the problem of \"low cost of law violation\" [53]. According to a recent statistic, there were 503 consecutive daily punishment cases nationwide in the first half of 2017, with a fine of 610 million yuan, up 131% from the same period in 2016, indicating that ecological and environmental protection laws are becoming more useful and playing a role [53]. From 2016 to 2017, strict environmental enforcement directly contributed to the resolution of over 80,000 environmental issues, including garbage, stench, oil fumes, noise, black and smelly water bodies, and pollution from \"scattered and dirty\" enterprises [53].  
To ensure that stakeholders have access to the most up-to-date water information, the Combined Water Information Sharing (CWIS) Platform is beneficial. CWIS is a new web-based information-sharing platform for water management that is linked to a collection of simulation models. CWIS simulates systems that affect urban water management interactions, providing insight into how certain activities affect other parts of the system. It can be used as a communication tool for data sharing and to assist in strategic planning for urban water management across multiple catchments [54]. This platform comprises of a database and a variety of data visualization tools that enable users to visualize the physical space of a city in the same way as GIS does, as well as its logical spatial and non-spatial component elements and their relationships via a system viewer.
## 3 The Proposed Control System
### VSG-Based Control of Grid-Connected MMC
Conversely, the reactive power \(Q\) is controlled through the voltage magnitude \(E^{*}\). The VSG adjusts the output voltage according to the reactive power demand, ensuring that the system remains stable and responsive to changes in grid conditions.
## 3 Classification Based on Function
### Seismic Monitoring Sensors
The collected sensor data are first sent to the monitoring node based on ZigBee technology and then transmitted to the monitoring center together with the monitoring images. The results of physical experiments show that using these wireless sensors, the monitoring center can display the monitoring image of the monitoring area in real time and visualize the collected sensor data [29]. The ongoing research has been using intelligent monitoring algorithms (such as object recognition or intrusion detection) on monitoring nodes to achieve better monitoring performance [51]. Other advancements include the optimization of the mechanical design of the monitoring nodes (e.g., miniaturization or lightweight) and the positioning algorithms for the sensor nodes. Figure 1: The operating wavelength profile of the different sensors. The co-seismic dislocation and optical data are the main parts of seismic monitoring via remote satellites [52], where GNSS and InSAR measure the co-seismic dislocation. Ground-based receivers using satellite signals from global navigation satellite systems (GNSS) such as the Global Positioning System (GPS) have served as primary sensors for over a decade to measure co-seismic ground deformation [53; 54; 55]. The combination of ground-based GPS and remote satellite information is very useful to improve earthquake deformation [56]. Synthetic Aperture Radar (SAR) is an imaging radar that uses a small antenna that moves at a constant speed along a trajectory of a long array and radiates coherent signals to process the echoes received at different locations coherently for a higher resolution [57]. Similarly, InSAR (Interferometric Synthetic Aperture Radar) is an advanced technique that combines synthetic aperture radar imaging technology with interferometry to measure the phase difference of two or more SAR images [58]. InSAR accurately measures the three-dimensional position and small changes in any points on the Earth's surface and has been demonstrated to be a reliable tool for measurements [59]. The use of optical satellite data to detect various anomalies before a strong earthquake is the key to predict seismic activity because it can identify phenomena related to thermal radiation in the initial stage of an earthquake. Therefore, satellite observations are powerful tools for monitoring earthquake preparedness areas in near real time on a large scale [60].
# On Pseudorandomness and Deep Learning: A Case Study
## 4 DL-Based Distinguisher
### Loss Functions  
Loss functions are used, during the training process, for evaluating the model instances and selecting the most suitable one. Precisely, the loss functions quantify how well the siamese network made the correct decision. The lower the loss value, the better decisions were. Several loss functions have been proposed in the literature. In this work, both the _binary cross-entropy_ loss function and the _contrastive_ loss function have been exploited to train the network showed in Figure 2. The results obtained are provided in the next section.  
A cross-entropy loss function is usually used in classification problems. It measures the difference between the desired probability distribution and the predicted probability distribution. In our specific context, _binary cross-entropy_ is a valid choice because what we are essentially doing is _2-class classification_: _(i)_ either the two sequences given as input to the network are of the same type, random or cipher output (i.e., class 1), _(ii)_ or the two sequences are of different types (i.e, class 2). Binary cross-entropy loss is mathematically defined as:  
\[\textit{binary cross-entropy loss}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}\log p_{i}+(1-y_{i}) \log(1-p_{i}), \tag{2}\]  
where \(y_{i}\) is the _label_ of the \(i\)-th training sample (pair of sequences), i.e., \(y_{i}=1\) if the \(i\)-th training sample, given as input to the network, belongs to class 1; otherwise, \(y_{i}=0\). Furthermore, \(p_{i}\) is the probability that the \(i\)-th training sample belongs to class 1, while \(N\) denotes the number of training samples.  
However, state-of-the-art Siamese networks exploit contrastive loss functions when training. Usually, these functions are better suited for such a kind of networks, and tend to improve accuracy. The idea is that the goal of a Siamese network is not to classify a set of sequence pairs but, instead, to differentiate them, by computing a similarity measure. Essentially, a contrastive loss function evaluates how good the Siamese network is in distinguishing between the sequence pairs. Contrastive loss is mathematically defined as:  
\[\textit{contrastive loss}=\frac{1}{N}\sum_{i=1}^{N}(1-y_{i})\times\frac{1} {2}d_{i}^{2}+y_{i}\times\frac{1}{2}\{max(0,m-d_{i})\}^{2}, \tag{3}\]  
where \(y_{i}\) is the _label_ of the \(i\)-th training sample, that is, as in the previous case, \(y_{i}=1\) if the \(i\)-th training sample belongs to class 1; otherwise, \(y_{i}=0\). The value \(d_{i}\) represents the Euclidean distance between the outputs of the two twin sub-networks, when given in input the \(i\)-th training sample, for \(i=1\ldots N\). Furthermore, \(m>0\) is a value called the _margin_. When \(y_{i}\) equals 0, the amount of loss caused by similar pairs is quantified only by the first term and is minimized. Conversely, if \(y_{i}\) equals 1, the loss is quantified only by the second term and is maximized by \(m\). Thus, there is no loss when input pairs are non-similar and their distance exceeds \(m\).
## 4 Discussion
### Impact of Population and Economy on GPP Changes  
Maintaining the stability of cropland area, quality, and structure is vital for ensuring regional grain productivity [43]. The quantity and structure of cropland directly influence grain crops, while the spatial transfer of cropland alters natural production conditions, including temperature, water, light, and soil, thereby affecting production potential. This study primarily investigates the impact of the cropland use transition on GPP, including abandonment, non-agriculturalization, and cropland compensation. Non-agriculturalization and abandonment are largely driven by population and economic development [44]. Due to policy support and an advantageous location adjacent to Hong Kong and Macau, Guangdong province has experienced rapid economic development, promoting urbanization and industrialization processes. This economic growth has created numerous non-agricultural employment opportunities. It has attracted rural populations within Guangdong to migrate to cities and drawn labor from other provinces, such as Hunan and Jiangxi [45]. This has formed a strong talent market and labor advantage, which contributes to economic growth. This manifests as a spatial pattern of population and economic agglomeration, mutually promoting each other, and this trend is anticipated to persist. However, the rapid increase in population and economic development has driven soaring demand for construction land, leading to the non-agriculturalization of high-quality cropland near cities and significantly threatening food production [46]. Additionally, the reduction in rural population and agricultural labor in Guangdong province, coupled with higher income and opportunities brought by non-agricultural employment, has increased the opportunity cost of agricultural production. This has resulted in a swift increase in agricultural labor costs and overall agricultural expenses, causing large-scale cropland abandonment, which negatively impacts food production.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 2 Background and Related Work
### Edge Computing in IoT Applications
[41] focused on edge computing for the IoT in the context of mobile gaming as it tests the edge computing paradigm. Their research performed an evaluation on an open-source 3D arcade game whereby the mobile phone sent the game input to the server, which rendered the content of the game and returned it to the end-device. Their research concluded that hosting computational resources extremely close to the end-user was the only suitable route to achieve an acceptable quality of experience and to enable fast-paced real-time interaction. The work we present in this paper is different as we do not rely on extra external equipment such as staff gauges or light systems. It focuses on implementing a stereoscopic depth camera for hydrological data extraction purposes, while running on a compact edge computing device.
## 6 Result and Discuss
### Transmission Efficiency Analysis
#### 6.3.2 Transmission Delay Analysis  
RCAR [5] is a state-of-the-art reinforcement learning routing algorithm for underwater wireless sensor networks with the goal of avoiding congestion. RL-ADTA (0.1, 0.2, 0.01) and RCAR algorithms conduct a performance comparison in terms of three aspects: transmission delay, energy consumption, and packet-sending success rate.  
The number of nodes in the simulation environment was set to 100, 200, and 300, respectively. RL-ADTA (0.1, 0.2, 0.01) and RCAR were used for the comparison of transmission delay. The average end-to-end delays of data packets from generation and transmission to reception are shown in Figure 13.  
Figure 13: RCAR and RL-ADTA (0.1, 0.2, 0.01) delay comparison. The end-to-end delay of RCAR and RL-ADTA includes transmission delay, propagation delay, and processing delay.  
Figure 13 shows that the trend in the delay change for the four algorithms is that the end-to-end delay decreases with an increase in the number of nodes. As the number of nodes increases, more and better routes are generated, and the delay is further reduced. As the amount of transmitted data in the RL-ADTA is reduced due to edge prediction, even considering the certain calculation delay, the transmission delay of RL-ADTA (0.1, 0.2) is still better than that of RCAR. RL-ADTA (0.01) does not compress a sufficient amount of the data sent, and adds a fixed calculation delay, which makes the delay larger than that of RCAR. Compared with RCAR, when the number of nodes is 100, RL-ADTA (0.1) reduces the delay by 15.8%; when the number of nodes is 200, RL-ADTA (0.1) reduces the delay by 9.58%; and when the number of nodes is 300, RL-ADTA (0.1) reduces the delay by 0.21%.  
In the experiment, in each period when data is sent, it is mandatory to undertake an automatic traversal process to select the best (p, d, q), and then carry out edge prediction. According to the results of 'prediction parameter analysis', (p, d, q) changes slowly. The previous set of (p, d, q) parameters can be first used for edge prediction; it is then decided whether to update (p, d, q). By reducing the update frequency of (p, d, q), the end-to-end delay of RL-ADTA can be further compressed.
# Helmholtz Coils Based WPT Coupling Analysis of Temporal Interference Electrical Stimulation System
## 2 Principles and Methods Analysis
### Wireless Power Transfer System Based on Helmholtz Coils
#### 2.2.2 Circuit Analysis of a WPT System with Helmholtz Coils
The mutual inductances between the transmitting and receiving coils are:  
\[M_{13}=\frac{\mu_{0}N_{1}N_{3}}{4\pi}\int\limits_{\tilde{C}_{1}}\int\limits_{ \tilde{C}_{3}}\frac{\dj^{\rightarrow}_{1}\times\dj^{\rightarrow}_{3}}{r}= \frac{\mu_{0}N_{1}N_{3}}{4\pi}\int\limits_{0}^{2\pi}\int\limits_{0}^{2\pi} \frac{r_{1}r_{3}\cos(\theta-\varphi)\d\theta\d\varphi}{R_{QN}} \tag{9}\]  
\[M_{23}=\frac{\mu_{0}N_{2}N_{3}}{4\pi}\int\limits_{\tilde{C}_{2}}\int\limits_{ \tilde{C}_{2}}\frac{\dj^{\rightarrow}_{2}\times\dj^{\rightarrow}_{3}}{r}= \frac{\mu_{0}N_{2}N_{3}}{4\pi}\int\limits_{0}^{2\pi}\int\limits_{0}^{2\pi} \frac{r_{2}r_{3}\cos(\theta-\varphi)\d\theta\d\varphi}{R_{QN}} \tag{10}\]  
Among them:  
\[R_{QN}=\sqrt{\left(r_{1}\cos\theta-r_{3}\cos\varphi\right)^{2}+\left(r_{1}\sin \theta-r_{3}\sin\varphi\right)^{2}+h^{2}} \tag{11}\]  
Figure 2: Equivalent circuit model for Helmholtz coils WPT.
# Verification of Convolutional Neural Network Cephalometric Landmark Identification
###### Abstract  
**Introduction**: The mass-harvesting of digitized medical data has prompted their use as a clinical and research tool. The purpose of this study was to compare the accuracy and reliability of artificial intelligence derived cephalometric landmark identification with that of human observers.
**Methods**: Ten pre-treatment digital lateral cephalometric radiographs were randomly selected from a university post-graduate clinic. The x- and y-coordinates of 21 (i.e., 42 points) hard and soft tissue landmarks were identified by 6 specialists, 19 residents, 4 imaging technicians, and a commercially available convolutional neural network artificial intelligence platform (CephX, Orca Dental, Hertzylia, Israel). Wilcoxon, Spearman and Bartlett tests were performed to compare agreement of human and AI landmark identification. **Results**: Six x- or y-coordinates (14.28%) were found to be statistically different, with only one being outside the 2 mm range of acceptable error, and with 97.6% of coordinates found to be within this range. **Conclusions**: The use of convolutional neural network artificial intelligence as a tool for cephalometric landmark identification was found to be highly accurate and can serve as an aid in orthodontic diagnosis.  
**Keywords**: artificial intelligence; convolutional neural networks; lateral cephalometric radiographs; diagnostics +
Footnote †: journal: _Sciences_
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Data Inputs
#### 3.5.5 Grid Data  
The last major category of data used in this field was grid-related data. These datasets include emissions, operations, and generation. The authors of [8; 11; 16; 17; 29; 40; 44; 55; 59; 68] considered emissions or other generation characteristics in their analyses, which included the emission intensity of the grid, the availability of a renewable resource, and general characteristics of generation, such as the ramp rate. Moreover, [8; 13; 16; 59; 64; 68] included grid infrastructure data or models that illustrated the overall topology and operational constraints of the system. These inputs were commonly used to illustrate the effects of demand flexibility on system performance, for instance, node voltages, line flows, and the operating conditions and health of assets.  
Related to this category, some studies included temporal electricity prices, including tariffs and locational marginal prices (LMPs) [6; 9; 15; 17; 22; 35; 39; 44; 62; 65] to inform savings calculations and optimal scheduling of end-uses.  
Although the analysis above summarizes common data inputs, many studies included less common data types. Some examples are indoor air temperature [37] or occupant comfort measurements [29], socioeconomic or demographic data [12; 45; 54], and industrial production data [18; 21; 67]. Although directly related to flexibility estimations, thesedatasets are not as readily available publicly or require direct measurements during testing, which makes them harder to incorporate into analysis.
## 4 Analysis and Findings  
### Descriptive Statistics  
Figure 1 presents the number of followers for each account (see also Table 1). The numbers range from nine to 56,808. The Prime Minister's account has the most followers. Next, the police account is followed by 31,423 and, interestingly, in the third place, the VisitGreecegr account is followed by 22,707 followers. The Ministry of Foreign Affairs account, the Fire Brigade account, and open government accounts come next.  
This section also reports on the number of followers with regards to the number of e-government accounts they follow. The distribution is reported in Figure 2. Figure 2 presents the medians of the percentages of followers that follow just one account, two accounts, three accounts, _etc._ The medians are computed after calculating for each account how many of its followers also follow other accounts.  
Figure 1: Followers of the Greek e-government Twitter accounts.  
We chose to use the median and not the total percentage for the total pool of the followers, because there is a great diversity, dispersion and skewness of such percentages among the accounts. Therefore, accounts with more followers could affect the total percentage, and if these accounts have a much different distribution of followers, the whole picture would be biased. For example, the Prime Minister's account has 56,804 followers, and 55% of them do not follow any other account; see also Table 1. The VisitGreece account has 22,707 followers, and 60% of them do not follow any other account (Table 1). On the other hand, these percentages range from zero to 60%. Therefore, a total percentage or a mean would not represent the data accurately.  
Figure 2 presents the median percentages of the followers that follow one, two or more accounts. The median percentage of the followers that follow only one account is 22%. The median percentage for those who follow three accounts is 11%; for those who follow three accounts, it is 9.3%. Next, medians drop down more smoothly as the number of accounts increase. The last column of Table 1 presents what percentage of the followers of each account also follows other accounts. The average and the median percentage is nearly 80%. It is very common for accounts to share followers. The Prime Minister's account and VisitGreecegr account are the two accounts that share relatively fewer followers that the other accounts.  
Regarding the number of citizens, who follow just one account, 45% of them follow the Prime Minister's account, 20% follow the VisitGreece account, 14% follow the Police account, 6.3% follow the Ministry of Foreign Affairs account, 4% follow the Fire Brigade account, 3.2% follow the Startup Greece account, 2.6% follow the account of Greek government and 1% follow the open government account. Percentages are smaller than 1% for the rest of the accounts.
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Effect of Piperine to the Gastrointestinal Tract  
During investigation of whether the bioavailability enhancement potential of piperine is related to the delay in transit time of solids and liquids, and therefore to a prolonged exposure of drugs to the absorptive surface area of GI tract, piperine was found to inhibit gastric emptying and gastrointestinal transit in rats and mice [38]. In the study, in which piperine (20 and 30 mg/kg, i.p.) and indomethacin (10 mg/kg, orally) were administered to mice, there was a dose-dependent decrease in the ulcer index in the mice treated with both doses of piperine [39]. In another experimental model of gastric ulcer, piperine in doses of 25, 50 and 100 mg/kg protected against gastric ulceration induced by stress (inhibitory rates from 16.9-48.3%), indomethacin (4.4-64.4%), HCl (19.2-59.6%), and pyloric ligation (4.8-26.2%) in rats or mice [40].