esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Verification
### One-Dimensional Parametric Euler Equation
In contrast, MulSPIDeepONet makes full use of the information fusion between the trunk network and the branch network and enhances the feature fusion between the final output of the network and the independent variables of the solution function. Thus, it captures the occurrence of discontinuities more accurately, and the density curve in the discontinuous area exhibits better fitting performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Name** & **Subnetwork Categories** & **Network Setting** \\ \hline PIDEepONet & Trunk Net & [2,200,200,200,200,200,100] \\  & Branch Net & [1,200,200,200,200,200,200,300] \\ \hline  & Trunk Net & [2,200,200,200,200,100] \\ MulSPIDeepONet & Branch Net & [1,200,200,200,200,200,300] \\  & Transition SubNet & [3,100,100,300] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Network settings. Figure 12: Solving a one-dimensional parametric Euler equation: training loss of a physics-informed DeepONet and multi-step physics-informed DeepONet. Figure 14 shows the changes in the density field with time when \(a=4.4\). Panels (c) and (d) indicate that the proposed method is closer to the analytical solution at the discontinuity, demonstrating that MulSPIDeepONet has better processing ability in the face of strong discontinuities. Figure 15 shows the predicted density field given by PIDeepONet and MulSPIDeepONet when \(a=5.0\) without training. The average absolute error between the PIDeepONet results and the analytical solution at the discontinuity (area framed by the red rectangle) is 0.7726. It is obvious that PIDeepONet dissipates significantly at the discontinuity. However, the average absolute error between the MulSPIDeepONet results and the analytical solution in this region is just 0.1918, which is very impressive. For more test error results, see Table 5.  
Figure 13: Prediction results of velocity (\(u\)) and density (rho) when \(a=4.4\) and \(t=2\): (**a**) baseline model, (**b**) proposed method. Figure 14: Density field prediction results when \(a=4.4\). (**a,c**) Prediction results and absolute error of the baseline model (PIDeepONet), respectively; (**b,d**) prediction results and absolute error of the proposed method (MuSPIDeepONet), respectively. Figure 15: Comparison of density field prediction when \(a=5.0\). (**a**) Prediction of baseline model (PIDeepONet): (**b**) prediction of the proposed method (MuSPIDeepONet). The experimental results show that PIDeepONet produces obvious errors at the discontinuity. MuSPIDeepONet provides better compensation for the dissipation at the discontinuity, resulting in predictions that are about three times better than PIDeepONet and improved generalization ability. This experimental result confirms that MuSPIDeepONet can effectively capture and reveal the processes behind complex physical phenomena with moving contact discontinuities. This is an important achievement for theoretical research.
## 4 Numerical Evaluations
### Numerical Results
#### 4.3.3 Ground Reflection Experiment  
The earth-reflected waves will be investigated in this section. The throughput between two UAVs with downward-facing antennas, each of which plays the role of transmitter and receiver, respectively, is indirectly used to investigate the propagation characteristics of the earth-reflected radio waves. The two UAVs are kept at the same altitude. In contrast, this altitude is gradually increased, knowing that the relative locations of the two UAVs on the horizontal plane are fixed similarly to the experiment. Since there is no terminal on the ground at this time, the UAVs communicate with each other via ground-reflected waves, which means that the larger the throughput, the greater the interference effect of ground-reflected waves. The two antennas on the UAVs are considered to have the same antenna gain. Numerical evaluations were performed at different horizontal-plane separation distances of 5 m, 10 m, and 30 m, and the results are shown in Figure 18, separately. When the altitude is low, the antenna directivity deteriorates due to a broad incident angle, resulting in almost no throughput. The antenna's directivity at higher altitudes improves owing to a steeper incident angle, resulting in higher throughput. On the other hand, throughput is decreased again at further higher altitudes due to the effect of distance attenuation. The attenuation is even higher than that in the direct experiment in Section 4.3.1 because the two UAVs are simultaneously flying at the same altitude, which doubles the traveling distance of the communication link. Also, when increasing the horizontal distance between the two UAVs and the antenna directivity deteriorates, the throughput performance decreases when evaluating it at the same UAV altitude. Therefore, the ground-reflected wave interference might be ignored in realistic scenarios with sufficient horizontal separation between the two UAVs.  
Figure 17: Numerical results of coverage measurement: (**a**) UAV 10 m altitude, (**b**) UAV 30 m altitude, and (**c**) UAV 50 m altitude.  
Figure 18: Numerical results of ground reflection with different horizontal separation distance: (**a**) 5 m, (**b**) 10 m, and (**c**) 30 m.
# Unilateral Condylar Hyperplasia in Surgeons' Perspective--A Narrative Review
## 3 Classifications  
Some types of mandibular asymmetry (MA), named mandibular condyle hyperplasia (CH) or unilateral condy hyperplasia (UCH), are related to one-sided pathological growth in one of the affected condy of an unknown origin. The presence of MA might start early in puberty and last even until the patient is fully grown, or the pathological growth might become self-limiting over time, in either way causing visible mandible asymmetry and lack of facial balance with the presence of various dentoalveolar discrepancies [2]. In 1986, [PERSON] and [PERSON] used the first classification to distinguish hemimandibular hyperplasia (HH), an abnormal self-limiting condylar growth, resulting in pathologic one-sided bone overgrowth of the entire mandible on the affected side (Figure 1), as well as other forms such as hemimandibular elongation (HE) and a mixed, rare form HH-HE [1]. Currently, a different classification presented by [PERSON] et al., which includes some temporomandibular joint manifestations, is also known [6]. Types IA (the most common) and IB have excessive growth in the horizontal vector, while IIA and IIB have excessive growth in the vertical vector. Type III is caused by benign tumors (e.g., chondroma, osteoma), while type IV (the rarest) is caused by malignant tumors (e.g., chondrosarcoma, osteosarcoma) in the TMJ area [6].  
After the MA etiology and scope of changes within the mandibular body are evaluated, it is quite easy to identify UCH and schedule the necessary proceedings (Table 1).  
Figure 1: Typical facial appearance of UCH. A notable elongation and overgrowth of the right side of the face with chin asymmetry is visible. Changes in proportions of mouth angles are clearly visible along with the different positions of mandibular gonial diameters.
# The Duality of Similarity and Metric Spaces
## 3 Partial Metric Space  
The concept of a partial metric space is a generalization of metric space. We think that it should be implemented as a superset of the axioms for a metric space, as well as the definition of a similarity space. We can see both as special cases of a partial metric space.  
**Definition 2** (Partial Metric Space).: _Let \(\mathbf{X}\) be a non-empty set. A partial metric or \(p\)-metric function \(p\colon\mathbf{X}\times\mathbf{X}\to\mathbb{R}\) is a function such that_  
1. \(p(x,y)=p(y,x)\) (symmetry),
2. \(p(x,z)+p(y,y)\leq p(x,y)+p(y,z)\) (triangle inequality),
3. \(p(x,x)=p(x,y)=p(y,y)\Longleftrightarrow x=y\) (identity of indiscernibles),
4. \(p(x,y)\geq 0\) (non-negativity),
5. \(p(x,y)\geq p(x,x)\) (small self-distance).  
_A partial metric space is an ordered pair \((\mathbf{X},p)\)._  
Our introduced partial metric space comes from the original definition [10,11]. Note that it is possible to have \(p(x,x)\
eq p(y,y)\). The definition of a partial metric allows for the possibility that the self-distance is non-zero. Thus, a metric space can be defined as a partial metric space in which each self-distance is zero, so \(p(x,x)=p(y,y)=0\), and hence the term \(p(y,y)\) will disappear in P2. The reason for allowing non-zero self-distances first came with the definition of a similarity metric.
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 3 Selection Criteria of BCI Frequencies
User satisfaction, as mentioned earlier, is also an important component of UX. In the current study, we employ the theta-to-beta ratio. This approach has been used withreasonable success in previous work, reflecting the stress in VR/AR users [49]. We used the theta-to-beta ratio as the _discomfort index_. Indeed, existing literature closely associates the theta-to-beta ratio with manifestations of stress and anxiety [50; 51], motivation [52], and the influence of emotional factors on cognitive processes, such as attention [24]. All of these are essential components for successful human interactions with BCIs. To our knowledge, this is the first explicit attempt that takes into account emotional and motivational factors while training or calibrating BCIs. The theta-to-beta ratio was calculated based on the averaged powers of EEG theta (4-8 Hz) and beta (13-29 Hz) bands, derived from the two frontal leads, namely, F3 and F4.
# Privacy and Security in Federated Learning: A Survey
## 3 Security in Federated Learning  
Clients participating in a federated learning model can be numerous. This opens doors to a variety of attacks on the client, server, and communication sides. Thus, the development of models via this technology must follow and take into account the main concepts of information security confidentiality, integrity, and availability.  
Current studies that explore vulnerabilities and provide existing defensive techniques for security attacks of FL are very limited. Thus, we define the following research questions on the security aspect of the FL:  
* RQ1: What are the major vulnerabilities in the FL domain?
* RQ2: What security threats and attacks do FL models face?
* RQ3: What are the defensive techniques against security attacks in the FL ecosystem?  
In the rest of this section, we answer each research question based on the studied publications in the FL domain.
## 3 Performance Attributes and Scenarios
### MCDM Approach for Evaluating Thermal Gasification Techniques for MSW
#### 3.4.3 Normalization of the Decision Matrix
The alternative thermal gasification technique i with a maximum positive value of \(\mathrm{C_{i}}\) ranked as number one. Thus, the decision matrix for the two MSW scenarios and the corresponding six thermal gasification techniques and 12 evaluation criteria is established according to the data in Table 6. The weighted decision matrix is estimated after the normalized decision matrix is established as given in Table 8, and the best and the worst solutions are obtained. For the non-recycling MSW scenario, as shown in Table 11 above, the evaluation ranks for the six thermal gasification techniques are downdraft > circulating fluidized bed > bubbling fluidized bed > dual fluidized bed > updraft > cross-draft, when equal weights are assigned to each criterion. Since equally weighted criteria are an exaggeration, this is used as a reference. On the other hand, when the need is to focus on minimizing cost (Scenario 4), the preference order for the six thermal gasification techniques is circulating fluidized bed > dual fluidized bed > downdraft> bubbling fluidized bed > cross-draft > updraft. Similarly, when the objective is to minimize the environmental impact (Scenario 3) for the six thermal gasification techniques, the ranking is downdraft > cross-draft > updraft > circulating fluidized bed > bubbling fluidized bed > dual fluidized bed, and the downdraft strategy is the best option. Similarly, for the recycling MSW scenario, as shown in Table 11 above, the evaluation ranks for the six thermal gasification techniques are downdraft > cross-draft > updraft > circulating fluidized bed > dual fluidized bed > bubbling fluidized bed when equal weights are assigned to each criterion. However, equally weighted criteria (Scenario 5) are an exaggeration, and this scenario is used as a reference. On the other hand, when the need is to focus on minimizing cost (Scenario 4), the preference order for the six thermal gasification techniques is circulating fluidized bed > bubbling fluidized bed > dual fluidized bed > downdraft > updraft. Similarly, when the objective is to minimize the environmental impact (Scenario 3) of the six thermal gasification techniques, the ranking is downdraft > cross-draft > updraft > circulating fluidized bed > dual fluidized bed > bubbling fluidized bed, in which case the downdraft gasifier is the best option. A comparison of the different scenarios indicates that the downdraft gasifier ranks well. This paper mainly focuses on the selection of the best thermal gasification technique for meeting MSW requirements using the MCDM. The entropy weight and the MCDM, which have a high resolution and a simple calculation process, could objectively be used to evaluate the thermal gasification technique. This approach is different compared to other known approaches.
## 3 Characterization of the Compounds Presented in Balsamic Vinegas
### Organic Acids  
One of the most abundant groups of VOCs are organic acids, which are formed by the oxidation of alcohols by acetic acid bacteria. Volatile acids with the highest concentration levels typically include acetic acid, benzoic acid, phenylacetic acid, isovaleric acid, isobutyric acid, and octanoic acid [10; 38; 39]. [PERSON] et al. [28] in their study focused on the determination of organic acids by GC using derivatization procedures. The authors identified malic acid as the most abundant organic acid. During the maturation of vinegar, the concentration of organic acids increases significantly, especially for vinegas aged in wooden barrels [90]. Acid content also increases during the storage of vinegar in glass bottles [35].  
[PERSON] et al. [61] applied the method of ion exchange chromatography to samples of different vinegas, including one BVM. The authors focused on the main organic acids in vinegas, namely citric, tartaric, malic, lactic, and acetic acid. The method was successfully validated. Of the selected acids, acetic acid had the highest concentration in BVM, followed by malic acid.  
[PERSON] et al. [13] used the HPLC method for the characterization of organic acids, sugars, and furans of balsamic vinegar. The highest concentration of acetic acid was detected, which is in agreement with the study by [PERSON] et al. [61]. Succinic acid, malic acid, and gluconic acid were detected as other major organic acids in this study as well.
# Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites
## Materials and Methods
### Characterization and Testing
#### 2.3.5 Breakdown Strength  
A voltage breakdown test equipment (Huabo Technology Industry Co., Ltd., HJC-100 KV, Changchun, China) was used to determine the breakdown strength of the composites with different mass fractions at different temperatures. A test ball electrode with a diameter of 25 mm was used, and the speed was uniformly increased by 1 V/s by pressing until the sample broke down. The ratio of the breakdown voltage of the test sample to the average thickness of the sample is the breakdown strength, expressed in kV/mm. Each sample was measured 10 times.
###### Abstract  
Previous studies have reported the effect of transcranial pulsed current stimulation (tPCS) on eliminating cognitive fatigue, but there is little research on optimizing the intervention program of tPCS. The purpose of this study was to explore the effect of different tPCS intervention programs on the elimination of physical fatigue in college athletes. Accordingly, 40 healthy college athletes were randomly divided into two groups of 20, denoted as A and B. Both groups exercised on treadmills. There were 15 subjects in group A who met the criteria of moderate physical fatigue, and 15 subjects in group B who met the criteria of severe physical fatigue. The subjects in each group were intervened with five different intervention programs of tPCS (intervention programs I, II, III, IV and V). The heart rate variability (HRV) and concentrations of oxygenated hemoglobin (HbO\({}_{2}\)) were measured before and after each intervention to judge the elimination effects of different intervention programs on different degrees of physical fatigue; the measurement indicators of the HRV include RMSSD, SDNN, HF and LF. The results indicated that tPCS intervention can eliminate both moderate and severe physical fatigue. Programs II, III, and IV had a significant effect on eliminating the moderate physical fatigue of athletes (\(p<0.05\)), among which program II, with a stimulation time of 30 min and a stimulation intensity of sensory intensity, had the best effect. Programs I, II, III, and IV all had significant effects on eliminating the severe physical fatigue of athletes (\(p<0.05\)), among which program I, with a stimulation time of 30 min and a stimulation intensity of sensory intensity + 0.2 mA, had the best effect. We conclude that different tPCS intervention programs can have different effects on the elimination of physical fatigue. The effects of the five intervention programs on the elimination of physical fatigue in athletes are as follows: program II is most suitable for moderate physical fatigue, and program I is most suitable for severe physical fatigue.  
physical fatigue; transcranial pulsed current stimulation; fatigue degree; intervention program; intervention effects +
Footnote †: journal: Applied Physics Letters
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing
#### 3.3.8 Integration with Other Models  
DDPM's generative capabilities and denoising properties make it suitable for integration with other models. Combining DDPM with deep learning architectures or probabilistic graphical models can lead to hybrid models that benefit from the strengths of both approaches [13; 35].  
The application of DDPM in image processing spans a wide range of tasks, from enhancing medical imaging to generating artistic styles. Its ability to denoise images, generate realistic samples, and estimate uncertainties makes it a versatile tool in machine learning and computer vision. The cited references showcase the diverse applications and the ongoing research efforts to explore the full potential of DDPM in addressing complex challenges in image processing. As the field continues to evolve, DDPM stands as a promising model contributing to advancements in image synthesis, denoising, and other related tasks.
## 5 Discussion
### Soil Salinity Prediction Using Interpolation Method  
Salts are highly soluble in surface and groundwater and can be transported with water movement. There are two types of salinity: primary and secondary. Primary salinity occurs naturally in soils and waters, such as salt lakes, pans, marshes, and salt flats. Meanwhile, secondary salinity is salting from human activities, usually land development and agriculture [72] ([[https://omexcanada.com](https://omexcanada.com)]([https://omexcanada.com](https://omexcanada.com)), accessed on 10 August 2022). Generally, soil salinity can vary temporally and spatially due to the dynamic nature of the soluble salts [73; 74; 75].  
The primary and secondary types of salinity are found in the Non Thai district since it is situated over deep-seated rock salt [6; 8] and has human activities. Based on the modeling dataset, the soil salinity prediction of two measuring modes was successfully interpolated using the IDW, OK, OCK, and RK methods. The spatial pattern of soil salinity distribution from two measuring modes with four different methods at the sub-district level was slightly different, see Figures 9-12. The mean values of ECe using the four methods of the two measuring modes at the district level varied from about 2156 to 2293 mS/m for HH and from about 2377 to 2401 mS/m for VV. This result indicates a significant difference among soil spatial salinity variances from two measured soil depths (topsoil and subsoil). When comparing CV values between topsoil (HH node) and subsoil (VV mode) of four interpolation methods, the topsoil delivered a higher CV than that of the subsoil. See Tables 8, 10, 12 and 14. This result indicates a variation of soil salinity at the topsoil level that is higher than that at the subsoil level since most topsoil is utilized for agricultural uses, i.e., paddy fields and field crops. According to the 2015 Land Development Department report, paddy fields and field crops cover about 465.80 sq. km or about 88.22% of the study area [68]. The comprehensive effects of human activities are critical factors for the salt distribution process [76].
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 2 Related Work
### Air Quality Monitoring System  
With the rise of IoT and the combination of miniaturized sensor devices and wireless technologies, nowadays, many of the AQ monitoring solutions are based on the traditional IoT architecture to build a remote monitoring system for AQ [9]. Composed of various sensor devices, the perception layer identifies various air parameters and collects the data. The network layer, composed of wireless technology, network management system and cloud computing platform, transmits and processes data information collected from the perception layer, then makes corresponding decisions according to current AQ conditions. The application layer presents relevant information back to the user.  
[PERSON] et al. [20], designed and used an IoT-based, multi-purpose architecture for monitoring environmental variables in urban areas. In their four-tier architecture, customer service interface handles requests from clients via the HTTP protocol, when the management layer receives the data and stores it in the database. [PERSON] and [PERSON] [21] proposed a large city pollution monitoring system based on wireless sensor network. The system uses AVR (Atmel AVR) ATmega-32 microcontroller to transfer the values from ADC (Analog to Digital Converters) to the server, and uses a Bluetooth microcontroller to build a communication channel between gas delivery to the server. In the agricultural sector, [PERSON] et al. [22] established an IoT-based monitoring and control system for AQ in greenhouses. [PERSON] et al. [23] proposed a system which can reduce hardware costs to the previous 1/10 by replacing monitoring devices that use traditional empirical analysis with sensor networks in IoC (Inversion of Control) technology. [PERSON] and [PERSON] [24] used the Raspberry Pi to build an IoT-based low-cost air quality monitoring system. As shown in Figure 1, the RPi [25; 26; 27] just as a sensor network node, only collects monitored data and pushes data to the gateway layer. The gateway layer filters and predicts data, then uploads the results to the cloud layer through the ESP 8266 wireless module. The cloud layer analyzes the received data and responds to various requests from the client. Similarly, [PERSON] et al. [28] also used the RPi as a bridge between the sensor network and the web server. The reasons that the above solutions adopt RPi are due to its low cost and card-like features.  
Although the existing studies have proposed a very mature and extensive AQ monitoring program, in China, most agriculture areas are in remote locations and in harsh environments [10]. Data analysis and processing through the cloud computing solution at the network layer cannot meet the requirements of low latency [7]. In addition, the accuracy of the sensors selected in the existing studies will be different, which will have different degrees of influence on the accuracy of the monitoring data, thus failing to guarantee the scientific nature of the prediction results.