esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Definition and Distance Measures for Symbolic Interval Data  
In this paper, the definition of an interval was outlined as the lower and upper bounds. If it was on the line, it was expressed as \([a,\,b]\), where \(b>a\). When we represent data in multiple intervals, it can be expressed as the following: let \(A=\big{(}A^{1},\,\ldots,\,A^{p}\big{)}\) and \(B=\big{(}B^{1},\,\,\ldots,\,B^{p}\big{)}\) be two interval datasets, where \(A^{j}=\big{[}a^{j},\,b^{j}\big{]}\) and \(B^{j}=\big{[}\alpha^{j},\,\beta^{j}\big{]}\) indicate the values of the interval for the \(j\in 1,\,\,\ldots,\,\,p\)[30].  
The first distance measure is termed the L1 norm, or city block distance [31]. The representation in the interval is the sum of the absolute values of the differences in all intervals:  
\[\text{D}_{\text{L1}}(A,\,B)=\sum_{j=1}^{p}\text{D}_{\text{L1}}\Big{(}A^{j},\, B^{j}\Big{)}=\sum_{j=1}^{p}\Big{(}\Big{|}a^{j}-\alpha^{j}\Big{|}+\Big{|}b^{j}- \beta^{j}\Big{|}\Big{)},\,\,A=[a^{j},\,b^{j}],\,\,B=[a^{j},\,\,\beta^{j}],\, \,j\in 1,\,\ldots,\,p. \tag{1}\]  
The second is called the L2 norm, or Euclidean distance [32]; the representation in the interval is the sum of the square roots of the differences in all intervals:  
\[\text{D}_{\text{L2}}(A,\,B)=\sum_{j=1}^{p}\text{D}_{\text{L2}}\Big{(}A^{j},\, B^{j}\Big{)}=\!\!\sum_{j=1}^{p}\!\!\left(\Big{(}a^{j}-\alpha^{j}\Big{)}^{2}+ \Big{(}b^{j}-\beta^{j}\Big{)}^{2}\right),\,\,A=[a^{j},\,b^{j}],\,\,B=[a^{j},\, \,\beta^{j}],\,\,j\in 1,\,\,\ldots,\,p. \tag{2}\]  
Figure 3 shows the relationship between city block distance and Euclidean distance under the center point in 2-dimensional space. The equidistant distance between the city block distance and the center point is similar to the rhombus in Figure 3 with the black line. Moreover, the Euclidean distance was equidistant from the center point according to the Pythagorean theorem, which is similar to a circle shape with a red line [37].  
It can be seen from Figure 3 that different distance measures have different characteristics for different sets of data, and we discussed these differences in the experimental results section.  
Figure 3: The relationship between city block distance and Euclidean distance and the center point in 2-dimensional space.
## 3 Materials and Methods
### Decision Support System
#### 3.2.2 Calculating the Environmental Impact Value  
As mentioned above, three categories of parameters were used to calculate the environmental impact. These were determined using mathematical expressions, except for gaseous emissions, which is a directly measured value. Thus, Equation (1) was used to calculate the effluent [23], Equation (2) was used to obtain the electricity efficiency value [24], and Equation (3) was used for water efficiency [24].  
\[\text{Effluent}=5\times\text{(COD+P+TKN+HM)+100\times\text{AOX}} \tag{1}\]  
where  
* COD: Chemical oxygen demand;
* P: Pesticides;
* TKN: Total Kjeldahl nitrogen;
* HM: Heavy metals;
* AOX: Adsorbable halogenated organic compounds.  
Figure 5: Results page.  
Figure 6: Representing environmental impact levels.  
Electrical energy efficiency = (electrical energy consumption)/(quantity of fabric manufactured) (2) Water efficiency = (water consumption)/(quantity of fabric manufactured) (3) As the categories reflect different quantities, they cannot be added together; thus, they are calculated as proportions using Equation (4).  
(measured value)/(reference value) \(\times\) 100  
The maximum value allowed by law for each parameter was taken as the reference value, except for electricity and water consumption, where the average value for the sector was taken. They are all organized in Table 1.  
The maximum value for effluents was obtained by substituting the maximum values for each parameter into the formula for calculating effluents, Equation (1). In contrast, the value for resource efficiency was obtained from the average consumption of electricity and water. This means that  
Environmental impact (%) = (gaseous emissions + effluents + resource efficiency)/3 (5)  
Based on the percentage of environmental impact, five levels were created, the limits of which are shown in Table 2. It was noted that the last level exceeded the values permitted by law, which is why it exceeded 100%.
## 2 Results and Discussion  
### Preparation of Protein Hydrolysate from Spotless Smoothhound Cartilage (MGCH)  
The type of bioactive peptides generated from a particular protein is dependent on the primary sequence of the protein and the specificity of proteases used to generate peptides [3; 20]. Proteins and peptides from cartilages of Chondrichthyes have been investigated as a good source for searching antiangiogenesis, antioxidant, and antihyperuricemic agents [21; 22; 23]. Therefore, in the experiment, crude proteins of spotless smoothhound cartilage were extracted using HCl-Guanidine buffer with yield of 1.69% \(\pm\) 0.03% (on dry cartilage), and hydrolyzed for 4 h using trypsin at pH 8.0, 40 \({}^{\circ}\)C with atotal enzyme dose of 2.5%. The resulted hydrolysate, named as MGCH, showed good antioxidant activity, and its radical scavenging activities on DPPH\(\bullet\) and HO\(\bullet\) were 63.4% \(\pm\) 1.86% and 73.15% \(\pm\) 2.57%, respectively, at the concentration of 20 mg protein/mL.
## Respiratory and Cardiovascular Parameters
###### Abstract  
Obstructive sleep apnea (OSA) is associated with a number of cardiovascular comorbidities, including hypertension, heart rate (HR) alterations, cardiac arrhythmias, endothelial dysfunction and atherosclerosis. HR, in particular, is an important sign correlated with cardiac stress and survival. Previous investigations on the effects of mandibular advancement device (MAD) therapy on HR resulted in contradictory findings. The aim of the present retrospective cohort study was to evaluate the effects of MAD therapy with a fully customizable appliance on respiratory and cardiovascular parameters. Fifty adult consecutive cases with mild to severe OSA underwent split-night polysomnography (SN-PSG) at baseline (T0) and after three months of MAD treatment (T1), after appropriate titration. The apnea/hypopnea index (AHI), 4% oxygen desaturation index (ODI) and HR (average, minimum and maximum) values are recorded at T0 and at T1 and statistically compared. The AHI and ODI values improved after three months of MAD treatment. The minimum HR increased after treatment, while the maximum HR decreased after treatment. MAD treatment with a fully customizable appliance was effective in improving breathing and cardiovascular parameters in adult patients with mild to severe OSA.  
**Keywords:** obstructive sleep apnea; OSA; heart rate; mandibular advancement device
# Electrophysiological Hyperscanning of Negotiation During Group-Oriented Decision-Making
## 3 Results
### EEG Results  
#### 3.2.1 Single-Brain Results  
The results relating to the EEG frequency bands will be reported in the following sections (Figure 2).  
Delta Band  
A significant main effect was found for ROI (F[2,50] = 10.010, \(p\) < 0.001, \(\eta^{2}\) = 0.093), with higher delta activity in the frontal region compared to the parieto-occipital region (\(p\) = 0.002) and in the temporo-central region compared to the parieto-occipital region (\(p\) = 0.022).  
Theta Band  
A significant main effect was found for ROI (F[2,50] = 9.170, \(p\) < 0.001, \(\eta^{2}\) = 0.098), with higher theta activity in the frontal region compared to the temporo-central region (\(p\) = 0.003) and the parieto-occipital region (\(p\) = 0.020).  
Figure 2: Bar graphs of EEG bands (delta, theta, alpha, beta, and gamma) for each ROI (F = frontal, TC = temporo-central, PO = parieto-occipital). The second row shows topographic maps of the average power in the five EEG frequency bands, highlighting the spatial distribution of EEG activity across the ROIs. The color scale represents the relative intensity of EEG power (blue = low power, red = high power). Bars represent \(\pm 1\) standard error, and stars (*) mark statistically significant effects.  
Alpha Band  
A significant main effect was found for ROI (F[2,50] = 9.593, \(p\) < 0.001, \(\eta^{2}\) = 0.062), with higher alpha activity in the frontal region compared to the temporo-central region (\(p\) < 0.001).  
Beta Band  
A significant main effect was found for ROI (F[2,50] = 10.916, \(p\) < 0.001, \(\eta^{2}\) = 0.092), with higher beta activity in the parieto-occipital region compared to the frontal region (\(p\) = 0.002), as well as in the parieto-occipital compared to the temporo-central region (\(p\) = 0.01).  
Gamma Band  
A significant main effect was found for ROI (F[2,50] = 4.439, \(p\) = 0.017, \(\eta^{2}\) = 0.058), with higher gamma activity in the parieto-occipital region compared to the frontal region (\(p\) = 0.008).
## 2 The 0.25 um GaN/SiCProcess  
GaN process is particularly suitable for the implementation of a high-efficiency MMIC PA due to the characteristics of high power, wideband capability, high performance, and high efficiency. In this paper, a GaN/SiC HEMT process with 0.25 um gate length on a 100-um-thick of SiC substrates is utilized for implementation of this PA. In this process, the maximum drain biases rating of 40 volts. Cutoff frequency (\(f_{t}\)) and maximum frequency (\(f_{max}\)) of the transistors are 25 and 75 GHz, respectively. This process utilizes a source-coupled field-plate design to provide the breakdown voltage required for reliable operation at a high drain bias. This process offers two interconnected metal layers, high-reliability metal-insulator-metal (MIM) capacitors with 215 pF/mm\({}^{2}\) density, and precision tantalum nitride (TaN) resistors with 50 \(\Omega\)/square sheet, resistivity. This process provides integrated source via (ISV) transistor device model, the structure features good electrical ground from front to backside. This process also provides air bridge, back-vias and MIM capacitors with back-vias. According to the handbook of the process, the 0.25 um gate length GaN HEMT is well-suited for high-power applications from the C-band to the Ku-band and exhibits a high degree of process maturity [8].
## 2 Methods
### The Proposed Hybrid Early Warning Method
#### 2.1.1 Conventional Early Warning
In conventional early warning, the velocity and acceleration of the landslide displacement must be calculated, which is usually done using the differential calculation method of the following equation [12]:  
\[v_{i}=\frac{S_{i}-S_{i-1}}{t_{i}-t_{i-1}} \tag{2}\]  
\[a_{i}=\frac{v_{i}-v_{i-1}}{t_{i}-t_{i-1}} \tag{3}\]  
However, due to the characteristics of automated monitoring data, such as large volume, periodic fluctuations, outliers, and no equal intervals, this differential calculation method encounters several new problems, such as large oscillations, resulting in many false warnings reported by the early warning system. In order to tackle this challenge, velocity and acceleration are recommended to be estimated using a time window and regression computation. The 3\(\sigma\) criterion is first used for coarse rejection of outliers for data within a certain time period (called a time window) before the present, followed by linear and quadratic polynomial regression using the least-squares method, and finally, velocity and acceleration are calculated based on the regression coefficients. Supposing the time window is \(w\), the data used for calculating the speed and acceleration at \(t_{N}\) are  
\[S_{w}=\left\{(t_{N-w},S_{N-w}),(t_{N-w+1},S_{N-w+1}),\cdots,(t_{N},S_{N})\right\} \tag{4}\]  
For the above data, first use the 3\(\sigma\) criterion to eliminate obvious outliers, and then use the following equation to perform regression analysis:  
\[S(t)=a_{0}+a_{1}t+a_{2}t^{2}+\cdots+a_{n}t^{n}=\sum_{j=0}^{n}a_{j}t^{j} \tag{5}\]  
Substitute the sequence \(S_{w}\) into Equation (5) and write Equation (5) in the matrix form as follows:  
\[ \begin{bmatrix}t_{N-w}^{0}&t_{N-w}^{1}&\cdots&t_{N-w}^{n}\\ t_{N-w+1}^{0}&t_{N-w+1}^{1}&\cdots&t_{N-w+1}^{n}\\ \vdots&\vdots&\ddots&\vdots\\ t_{N}^{0}&t_{N}^{1}&\cdots&t_{N}^{n}\end{bmatrix}\begin{bmatrix}a_{0}\\ a_{1}\\ \vdots\\ a_{n}\end{bmatrix} = \begin{bmatrix}S_{N-w}\\ S_{N-w+1}\\ \vdots\\ S_{N}\end{bmatrix} \Rightarrow\textbf{AX=Y} \tag{6}\]  
Then, the least-squares method can be used to obtain the optimal value of each coefficient:  
\[\textbf{X=}(\textbf{A}^{\textbf{T}}\textbf{A})^{\text{-1}}\textbf{A}^{\textbf {T}}\textbf{Y} \tag{7}\]  
Finally, let \(n=1\), Equation (5) is a linear regression:  
\[S(t)=a_{0}+a_{1}t \tag{8}\]  
Then, the deformation velocity in the current time window is \(a_{1}\). Similarly, let \(n=2\), Equation (5) is a quadratic polynomial regression:  
\[S(t)=a_{0}+a_{1}t+a_{2}t^{2} \tag{9}\]  
Then, the deformation acceleration in the current time window is \(2a_{2}\). This calculation method utilizes all of the data in the monitoring data series, and the calculated acceleration and velocity have more consistency and less oscillation, which can be utilized to make early warning judgments and reduce false alarms.
## 3 Results and Discussion
### Characterization of CNCs-Coated Plastics
#### 3.2.3 Oxygen Permeability of Coated Polymers (P-CNC)  
When comparing the oxygen permeability of coated polymers with the three types of cellulose nanocrystals, the difference is evident at both RH (50% and 80%). The improvements of the oxygen barrier properties are much higher on polymers coated CNC\({}_{\text{COOR}}\), following by those coated with CNC\({}_{\text{COOH}}\) and CNC\({}_{\text{SOGH}}\) (Figure 8). This demonstrates the relevance of the esterification of cellulose nanocrystals, which allowed creating structures less sensitive to water, without modifying the crystallinity or compromising the integrity of the crystals. Although the oxygen barrier was enhanced in the three cases, the oxygen permeability of the coated polymers showed a significant loss of barrier properties at high relative humidity (80%), due to a strong affinity between the CNCs coatings and water.
## 4 Discussion
### Grid-Friendliness Evaluation Based on Power Ramp Rate  
To further evaluate the grid-interactive characteristics of the proposed wind/storage dispatch model, this section investigates the smoothness of the system's net power output by analyzing its power ramp rate. The power ramp rate, defined as the change in net power output to the grid over consecutive time intervals, serves as a critical indicator of grid-friendliness. Excessive fluctuations in output power can lead to frequency instability, increase the burden on grid regulation resources, and hinder the reliable integration of renewable energy. In this study, the net power output is defined as the sum of the scheduled wind power and the difference between discharging and charging power of the battery system. The power ramp rate is calculated as the variation in net output between two consecutive 10 min intervals. The variation of net power ramp rate over 10 min intervals is shown in Figure 9.  
As shown in Figure 9, the net power ramp rate remains relatively stable over the scheduling horizon, with limited short-term fluctuations. This reflects that the optimized dispatch strategy avoids large power swings between consecutive intervals. Combined with the scenario-based modeling and the inclusion of time-of-use pricing and CVaR constraints, the dispatch results demonstrate a smooth power output trajectory that is compatible with grid operational requirements.  
To quantitatively assess the output smoothness of the wind/storage system, Table 10 presents the maximum and average power ramp rates calculated over consecutive 10 min intervals.  
As shown in Table 10, the maximum and average ramp rates are 1083.26 kW/10 min and 179.36 kW/10 min, corresponding to 27% and 4.5% of the turbine's rated power, respectively. This falls within the acceptable range based on domestic and international grid standards, confirming the proposed strategy's capability to deliver grid-friendly power fluctuations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value (kW/10 min)** \\ \hline Maximum Ramp Rate & 1083.26 \\ Average Ramp Rate & 179.36 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ramp rate metrics of net power over 10 min intervals.  
Figure 9: Net power ramp rate over 10 min intervals.
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 3 Results
### Data Description: PQube Live World Map of Power Quality  
The PQube Live World Map of Power Quality [22] is a publicly available database that includes data and variables of many PQ events across the world. The database is maintained by a manufacturer of PQ measurement systems, with the support and availability of facilities and academic institutions that host the measurement systems and distribute the data on the cloud platform.  
For the purpose of this paper, the voltage data coming from PQube3 measurement systems are picked; these systems are indeed the ones able to monitor supraharmonics according to the IEC 61000-4-30 standard in the considered PQube database; therefore, the analysis reported below considers only locations at which a PQube3 measurement system is installed. To provide diversity, four locations--Austin (TX), Delray Beach (FL), Murphys (CA), Alameda (CA)--are selected from the USA, and four locations--Wohlen (Switzerland), Rochford (UK), Amsterdam (The Netherlands), Skelleftea (Sweden)--are selected from Europe.  
The supraharmonic data is collected at each location for 14 days at a 1-minute time resolution. The data is directly provided by the form of voltage supraharmonic components grouped at 2 kHz; thus, 75 individual components are available. For each voltage supraharmonic component, the average (avg) and the peak value are provided for each minute, according to the specifications of the IEC 61000-4-30 standard. The case studies presented below will include both the avg and the peak data, either through the TSHDavg and TSHDpeak or the individual avg and peak components. Note that for the Austin and the Delray Beach dataset, only the avg components are provided.
## 3 RE Process as \"Game Changer\": Clinical Applications  
### Diagnosis  
#### 3.1.1 3D Analysis from CBCT  
Modern orthodontics is massively digitally oriented. The possibility to acquire 3D information regarding skeletal and dento-alveolar anatomy and transpose this data into a virtual environment which allows for the performing of analyses and elaborations has dramatically changed diagnosis in orthodontics and many other fields. Modern low-dose CBCT allows the 3D rendering of the investigated anatomical structures, and it is possible to export this information in a DICOM file. Several packages allow for the creation of an.STL file, starting with DICOMs and performing different operations such as segmentation and editing. Segmentation means virtually isolating the structure of interest by removing all the neighboring anatomical regions for better visualization and analysis. With CBCT technology, the structure of interest is reconstructed by selecting the corresponding group of voxels characterized by specific image intensity and density signals. Segmentation can be performed manually or by a semi-automatic computer-aided approach with the most recent novelty being represented by artificial intelligence (AI) [7] (Figure 6).  
Figure 6 shows an example of the segmentation of structures with opposite imaging density characteristics (hard tissues and soft tissues): the segmentation of dental anatomy (Figure 6A), valid for the planning of complex and multidisciplinary rehabilitation procedures, and the segmentation of upper airways (Figure 6B) which is critical for the  
Figure 5: The model is uploaded into 3D printer PreForm (Formalab, Somerville, MA, USA) for setting 3D printing parameters and slicing the digital model for final prototyping process.  
Figure 6: Example of digital segmentation of anatomical structures obtained from CBCT scans. (**A**), Tooth; (**B**), upper airways.  
identification of specific areas of obstruction for example in subjects affected by obstructive sleep apnea.  
However, CBCT scans involve X-ray exposure for the patient, which is usually unjustified in routine orthodontic clinical practice. According to the ALADA principle [8], the clinician can use a CBCT only when 2D radiology is inadequate to make an appropriate diagnosis and therapeutic planning. For routine orthodontics diagnosis, clinicians should refer to conventional 2D imaging packages including panorex, cephalospor, intra-oral and extra-oral photographs, or radiation-free 3D imaging systems, such as IOS and facial scanner (FS).
## 2 Study Area and Materials
### Reference Data  
Approximately 3290 sampling points (SP) were randomly collected within the Chi River Basin and used as reference data in order to train and validate the RF classifier approach. Subsequently, SP was randomly spilt in two different datasets in order to obtain the training and validation sets involved in the RF classification algorithm. In particular, 2303 (70%) sampling points were used for the training phase and 987 (30%) for the validation phase (Figure 2). Within SP, eight different major classes were collected, as reported in Table 1, including rice, sugarcane, cassava, para rubber, forest, built-up, water and bare soil. It is worth highlighting that SP field campaigns were performed between May and July 2021. Consequently, different methods aimed at collecting reference data were involved in this work in order to construct and validate the five classifications corresponding to the 2001, 2006, 2011, 2016 and 2021 years. Details on how the reference data were acquired for the different years considered in this work are given below.  
* 2021 year: A total of 3290 sampling points were collected as reference data from a field survey performed between 15 May 2021 and 30 July 2021. Point position was then georeferenced with high accuracy by a handheld Global Positioning System (GPS) version Garmin 62 s [30].
* 2006, 2011 and 2016 years: The same 3290 sampling points considered in the 2021 year were photo-interpreted and digitized using very high-resolution data available within the Google Earth pro imagery database.
* 2001 year: In addition, the same reference points were photo-interpreted, digitized and finally acquired through very high-resolution color orthophoto images provided by the Land Development Department (LDD) [11]. LDD color orthophoto images were used to search and record different agricultural land fields/LC within the Chi River Basin. LDD is characterized by having spatial resolution equal to 1 m. Moreover, to improve sampling point dataset reliability, authors interviewed 20 local farmers who have been farming and living for over 20 years in the area.  
Figure 2: Area of Interest (Chi River Basin, Thailand): background shows elevation (height from 0 to 1324 m). The red boxes are nine Landsat tiles across the study area. Yellow points are the 2303 training points, red points are the 987 validating points. Both sampling datasets are taken from field campaigns performed in 2021.