esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Observing-System and Power-System Considerations
### Mathematical Modeling of the Power System
#### 2.4.2 Wind Turbine  
The wind turbine from TYUT was employed in the power system of this study. The key specifications of the TYUT wind turbine are presented in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Value** \\ \hline Open circuit voltage (\(V_{ac}\)) & 21.6 V \\ Optimum operating voltage (\(V_{mp}\)) & 18 V \\ Short circuit current (\(I_{sc}\)) & 0.916 A \\ Optimum operating current (\(I_{mp}\)) & 0.833 A \\ Maximum power at STC \({}^{1}\) (\(P_{max}\)) & 15 W \\ \hline \hline \end{tabular} \({}^{1}\) Standard test conditions.  
\end{table}
Table 2: Key specifications of the TYUT PV panel.  
Figure 5: Schematic of the power-supply system.  
Different wind-turbine types put out different power based on their power-curve characteristics. Through a comprehensive literature review, a model used to describe the performance is proposed as follows [21]:  
\[\left\{\begin{array}{l}E_{wind}=E_{a}+E_{b}(v_{c}\leq v\leq v_{F})\\ E_{a}=\sum P_{R}t\left(\frac{v}{v_{R}}\right)^{3}(v_{c}\leq v\leq v_{R})\\ E_{b}=P_{R}\sum t(v_{R}\leq v\leq v_{F})\end{array}\right. \tag{2}\]  
where \(E_{wind}\) is the output of wind power (Wh/day), which consists of \(E_{a}\) and \(E_{b}\); \(v_{c}\) is the cut-in wind speed (3 m/s); \(v_{R}\) is the rated wind speed (10 m/s); \(v_{F}\) is the cut-off wind speed (40 m/s); \(v\) is the wind speed; \(P_{R}\) is the rated electrical power (10 W), which is average energy at the wind speed of 10 m/s for one minute; and \(t\) is the time (hours).
## 5 Challenges and Future Research Directions  
As UDNs continue to evolve to support 5G and 6G technologies, several key challenges must be addressed to enhance network performance, energy efficiency, and security, as presented in Figure 4. The following areas require further research and innovation.
## 4 Qualitative and Quantitative Analyses
### Quantitative Analysis
#### 4.2.3 Slope Gradient  
Slope gradient STD serves as a critical metric for evaluating internal consistency within slope units. As analyzed in Figure 11, SUD-CGHP-derived units exhibit higher STD values (0-6%) compared to HPAM-derived units while showing lower values in the extreme range (>6%). Cumulative frequency analysis further reveals that SUD-CGHP achieves superior overall consistency, with cumulative percentages exceeding those of the HPAM across all gradient intervals. This demonstrates that SUD-CGHP produces more homogeneous slope units by effectively balancing gradient variability and maintaining natural terrain characteristics.  
Figure 11b,c compare the slope distribution patterns and their associated average STDs between the two methods. While both approaches yield comparable slope distributions (Figure 11b), SUD-CGHP demonstrates significantly lower average STDs across all slope intervals (Figure 11c). This superior homogeneity indicates that SUD-CGHP-derived slope units achieve higher internal consistency by minimizing artificial discretization errors commonly associated with HPAM segmentation. Such methodological advantages position SUD-CGHP as a more reliable tool for terrain analysis applications requiring both geometric coherence and hydrological representativeness.  
Figure 12 presents a comparative visualization of slope gradient STDs between SUD-CGHP-derived and HPAM-derived slope units, accompanied by slope unit examples obtained using two methods and background slope grids. While both methods show comparable spatial distribution patterns of low and high STD values (Figure 12a), a closer inspection of the slope unit boundaries and contained grid values reveals significant differences (Figure 12b,c). Notably, SUD-CGHP-derived units exhibit superior alignment  
Figure 10: (**a**) STD of the aspect of SUD-CGHP; (**b**) slope unit using SUD-CGHP; (**c**) STD of the aspect of HPAM; (**d**) slope unit using HPAM.
# Light-Controlled Microbots in Biomedical Application: A Review
## 5 Imaging
### Ultrasonic Imaging  
Ultrasound (US) imaging is commonly used in medical diagnostics, as well as in the examination of the ovaries during pregnancy and to monitor the health of the growing baby. Various US modes are used on the basis of their applicability. B-mode (brightness mode) US uses multiple transducers and simultaneously scans through the body and creates a 2D tomographic image. Other mentionable techniques are the C mode, the M mode (motion mode), the Doppler mode, the pulse inversion mode, and the harmonic mode. The harmonic mode provides better imaging capabilities. in harmonic mode,after passing through biological tissues, a transducer delivers fundamental ultrasonic pulses into the body, and a centered, narrow beam of harmonic overtone is reflected back. Ultrasonic imaging has been used successfully in the microrobotic operation of targeted drug delivery [159; 220]. This technique could be used to image light-actuated microbots, since it does not interfere with the basic control mechanism of light-actuated microbots. The disadvantage of using ultrasonic imaging is the finest feature that it can resolve. Until now, the US has only been able to achieve millimeter-scale resolution, but some promising research has been done to bring imaging down to the nanoscale level, which broadens the usefulness of technology to microrobots in biomedical applications [221; 222]. The most recent advances and challenges in imaging and tracking microrobots that use known medical ultrasound imaging technology are reviewed and summarized in this article [223].
# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 3 Speaker Identity Discrimination in Different Feature Space
### MFCC Discrimination  
MFCC is a conventional frequency-domain auditory perception cepstral coefficient method. MFCCs have been widely used in both the speech and speaker recognition applications [1]. We first extracted the 20-dimensional (c0-c19) MFCC features using the Kaldi Toolkit [25]. Then, to visualize the MFCC discrimination between different speakers more clear, we applied the Principal Component Analysis (PCA) algorithm to reduce the 20-dimensional MFCCs to a two-dimensional feature space. Finally, we performed the feature analysis in this low-dimensional space. Similar to the above NCCF information contour, Figure 3 demonstrates the two-dimensional PCA feature distribution of MFCCs of both the reading and singing speech between two different female speakers, given the same text.  
In Figure 3, we can easily observe that the overlap of orange dots and blue circles in the left subfigure is less than that in the right subfigure. This indicates that the speaker discrimination in MFCC feature space of singing speech is larger than the discrimination in reading speech feature space. By comparing the same color parts of the left and right subfigures, it can be seen that, even for the same speaker with the same text, there is also large difference between singing and reading speech in the MFCC feature space. Therefore, we guess that it may be better for us to use the singing speech instead of normal reading speech to characterize a speaker's identity. Actually, even if we see discriminate  
Figure 1: NCCF contour of the same speaker’s reading and singing speech with the same text.  
Figure 2: NCCF pitch contour of reading (**left part**) and singing (**right part**) speech between two females with the same text.  
information in the feature space, we cannot guarantee that the speaker model would be able to exploit the discriminations very well. Therefore, we provide detail validation experiments in next sections to see what would happen.
# Case Study of an Underpinning Pile Foundation for an Interval Tunnel Crossing an Existing Bridge
## 5 Numerical Simulation Result Analysis  
### Verification of the Numerical Model  
In order to verify that the established numerical model is reasonable and reliable, numerical simulation results and field monitoring data were compared for the settlement of the foundation pit enclosure (ground connection wall) and the settlement of bridge piers at different construction stages. In the bridge pile foundation underpinning construction site monitoring, the models Trimble-Diniol.3-748408 digital level (measurement accuracy of 1 mm) and LeicaTS09 plus-1890787 total station (measurement accuracy of 1 mm) were used to monitor the foundation pit enclosure structure and bridge pier settlement.  
It can be seen from Figure 8 that the measured data in the field of the foundation pit enclosure structure and the calculated results of numerical simulation are in the range of the settlement control requirements (<20 mm), so the numerical simulation results of this paper are reliable.  
The settlement variation curve of the bridge pier monitoring is shown in Figure 9b. From the figure, we can see that the maximum settlement of the bridge pier appears in pier 2, at 2.72 mm (<8 mm); the uplift of the bridge pier occurs at the foundation pit excavation, with a maximum value of 0.73 mm; and the settlement of the bridge pier meets the monitoring requirements. Overall, the numerically simulated bridge pier settlement is close to the monitoring results, which further indicates that the numerical simulation results in this paper are reliable.
###### Abstract

Table 1: Traditional approaches in AQI prediction. drawback of this type is that it often falls into local optimal stages. To overcome this drawback, many studies have been used, such as DNN [8; 23], hybrid and ensemble [17; 22; 28; 29]. The AQI forecast should assess the implications of air pollution on several sectors, such as health, agriculture, land transportation, aviation, and energy, among others, and issue warnings and recommendations based on various thresholds, risks, and cost functions [30]. Some works concentrate on RNN [31; 32; 33] and the Spatial-Temporal Network can deal with the complicated non-linear spatial and temporal correlations [34; 35]  
Some conclusions of previous studies are very significant, such as: Forecasting tasks often employ neural networks and support vector machine-based algorithms, pollution estimation is typically carried out using ensemble learning and linear regression-based methodologies [36]. Between 2010 and 2018, multi-method hybrid strategies significantly surpassed single-method approaches in terms of usage [37]. Land-use Regression (LUR) models are an important tool for integrating traffic and geographic information to characterize variability in exposures [38; 39]. Some reviews are limited in scope within a country or province, such as in India [40], and Hebei of China [41]  
Among these studies, DNN is the preferred application direction of many current researchers in the field of AQI prediction. DNNs are considered superior to traditional AI techniques because they are stable, give lower model errors, and respond well to data diversity. A collection of algorithms known as the \"ensemble learning technique\" trains several models to tackle a single issue by integrating the outcomes that each model produces. Strong and weak predictors are combined in ensemble learning approaches, which are more generalizable and less susceptible to overfitting. Overall, these methods outperform single-base learning methods such as ANNs and SVMs.
## 3 Ubiquitous Tourist System Based on MCDM and AR
### UTS-mAR Methodology
#### 3.2.1 Hotel Recommender Model Based on SF and MCDM
One of the applications of this modified model is in a scenario where the user selects a search radius too large to use all of the candidate hotels. 3.2 shows the different stages of creating such a model. The model is based on a combination of hierarchical clustering and TOPSIS [19; 40]. Here, the candidate hotel data are normalized, and the results of this operation are provided as inputs to the next step, wherein the candidate hotels are clustered using hierarchical clustering. Considering that in this method, the analysis is performed on all candidate hotels, and the number of candidate hotels in our experiment was 30, four clusters were used to perform the clustering operations. This cluster number is determined by checking dendrogram data and normal distribution of data for achieving a small number of candidates for comparison with TOPSIS. Subsequently, the agents of the clusters are determined using the clustering results. The agent of each cluster has a value for each criterion equal to the average of all members of that cluster for that criterion. In the next step, criteria values of the cluster agent are provided as inputs to the TOPSIS method to select the best cluster with the highest closeness index (CI). Finally, the candidate hotels within the best cluster are compared using the TOPSIS method to select the final hotel. * **Visualization** After this step, the location of the selected hotel is obtained from the environmental context and used for visualization. This method expresses the location of the hotel as geographic coordinates (longitude and latitude), which are subsequently displayed in Google Maps (Google LLC, Mountain View, CA, USA).
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 2 Materials and Methods
### Workflow and Data-Processing Algorithm
#### 2.3.3 Design of Wireless Communication Protocol  
1. Rescue Information Protocol for PIT: The frame format of the rescue information protocol of the PIT is shown in Table 2. This includes the header, message length, message body, check digit, and tail. The header and the tail are 0 xFF and 0 xFF, respectively. These are used to separate and identify the information. The message length helps the message content to be correctly parsed. The message body is the main object of transmission, and it includes the information required for emergency rescue. The CRC16 algorithm is employed for the check digit, which is used to detect whether the information has been tampered with, and the algorithm's polynomials are \[g(x)=x^{15}+x^{12}+x^{5}+1\] (11)  
The content of the message body is shown in Table 3. This includes number information, the current latest valid positioning data, latitude/longitude increment information of key positioning data, and functional information. The numbering information includes the base-station number and personnel number, which are each composed of eight characters and are used to associate the marine operators and rescue vessels. The current latest valid positioning data are the data stored at the time of the alarm of the PIT. These are stored as float type data. The key positioning information comprises the key data points within the drift trajectory of the person in distress during the corresponding cycle, and it is stored as short type data. The key positioning functional information is used to transmit the battery information of the PIT, and its value is 0x23 when the voltage is lower than 3.45 V, which warns the rescue team to give priority to rescue.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Message Head** & **Message Length** & **Message Body** & **Check Digit** & **Message Tail** \\ \hline
0 xFF & 4 \(\times\) i + 25 & \
\begin{tabular}{c} Basic rescue \\ information \\ \end{tabular} &
\begin{tabular}{c} Content \\ validation \\ \end{tabular} & 0 xFFE \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rescue information packet structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field Name** & **Content** & **Data Type** & **Length** \\ \hline Number information & \begin{tabular}{c} Base-station number \\ Personnel number \\ \end{tabular} & \begin{tabular}{c} Char \\ Char \\ \end{tabular} & 16 \\ \hline Latest valid positioning data & \begin{tabular}{c} Longitude data \\ Latitude data \\ \end{tabular} & \begin{tabular}{c} Float \\ Float \\ \end{tabular} & 8 \\ \hline \begin{tabular}{c} (\(m\))th key positioning \\ information \\ \end{tabular} & \
\begin{tabular}{c} Longitude increment \\ Latitude increment \\ \end{tabular} &
\begin{tabular}{c} Short \\ Short \\ \end{tabular} & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Message body contents.
## 3 Method
### Multiple Endmember Spectral Mixture Analysis
#### 3.3.2 Spectral Unmixing Modeling  
After the selection of the optimal spectra for each endmember, the combinations of all the possible endmember spectra were considered to run the MESMA procedure (Table 2). Specifically, we tested 35 two-endmember models, 446 three-endmember models and 2640 four-endmember models for each pixel. The process of running the MESMA models based on the established spectral library is as  
Figure 3: Spectral reflectance of endmembers including (**a**) water; (**b**) vegetation; (**c**) impervious surface; (**d**) soil.  
follows. Take the three-endmember models as an example. Table 2 shows that the three-endmember models consisted of six combinations, _i.e._, water + veg + shade, water + imp + shade, water + soil + shade, veg + imp + shale, imp + soil + shade. Because the water category had 12 spectral curves while the vegetation category had 10 spectral curves, the combination of water + veg + shade would have a total of 120 (12 \(\times\) 10 \(\times\) 1) SMA models. Similarly, we could calculate model numbers of the other five combinations. Hence, the three-endmember models would consist of 446 (120 + 96 + 60 + 80 + 50 + 40) SMA models. During the modeling process, several constrains must be satisfied according to previous studies [21, 22, 23]: (a) the non-shade fractions should be constrained between \(-\)0.05 and 1.05; (b) the maximum allowable shade fraction should be 0.8; (c) the maximum RMSE was set to be 0.025.  
How the variation of the main endmembers was handled in MESMA is as follows. As for LSMA, only a few SMA models can be established due to the limited combinations of different endmembers. However, MESMA can account for the variation by considering all the combinations of the endmember spectra in order to build the candidate SMA models [21]. Meanwhile, the SMA model with the lowest RMSE was chosen as the final output for each pixel in the MESMA procedure [21]. Pixels that cannot be modeled were left unmodeled in the output. From another perspective, the variation within each endmember was an objective existence; the MESMA method allows for the establishment of all the possible combinations of SMA models to account for the variation. The mechanism in that the SMA model with the lowest RMSE was chosen as the final output makes MESMA outperform LSMA. Besides, as shade fractions were added initially to account for illumination variation and they did not belong to the interested land cover types, we generated the shade normalized maps by dividing each land cover fraction by the sum of total non-shade fractions [22].  
After the implementation of MESMA, thematic land cover types should be extracted from the fraction images. However, considering that with the simple threshold method it is difficult to discriminate different land cover types from fraction maps [21, 22], a machine learning based classifier, Random Forest [28] was employed to identify the land cover types using the sub-pixel fractional information. This part of the work is documented in detail in the next section.
# Orthogonal Subblock Division Multiple Access for OFDM-IM-Based Multi-User VLC Systems
## 2 Osdma with DFT-Spreading and Interleaving for Bandlimited MU-VLC
### The Review of OFDM-IM
#### 2.1.1 OFDM-SM-IM  
For OFDM-SM-IM, the constellation symbols from the constellation set \(\mathcal{M}=[S_{1},S_{2},\ldots,S_{M}]\) is transmitted on the selected \(k\) subcarriers, while no data is transmitted on the non-selected \(N-k\) subcarriers within each subblock, where \(M\) is the size of \(\mathcal{M}\). The mapping table of OFDM-SM-IM for \(N=2\) and \(k=1\) is given in Table 1. At the receiver side, log-likelihood ratio (LLR) detection is adopted which has been shown to be able to achieve near optimal performance with relatively low computational complexity [16]. We assume that \(y_{\beta}^{\dagger}\) (\(\beta=1,\ldots,G;\eta=1,\ldots,N\)) denote the input signal of the LLR detector, the LLR value with \(\beta\)-th subblock for OFDM-SM-IM signal is shown by \[\kappa_{\beta}^{\eta}=\ln(k)-\ln(N-k)+\frac{\left|\left(y_{\beta}^{\eta}\right) \right|^{2}}{N_{0}}+\ln\left(\sum_{m=1}^{M}\exp\left(-\frac{1}{N_{0}}\left|y_{ \beta}^{\eta}-S_{m}\right|^{2}\right)\right), \tag{1}\]  
where \(N_{0}\) denotes the noise power. Hence, the spectral efficiency per subblock for OFDM-SM-IM with \(M\)-ary quadrature amplitude modulation (\(M\)-QAM) constellation is expressed by  
\[SE_{\text{OFDM-SM-IM}}=\frac{\left|\log_{2}(C(N,k))\right|+k\log_{2}(M)}{N}, \tag{2}\]  
where \(\left|\cdot\right|\) and \(C(\cdot,\cdot)\) denote the floor operator and the binomial coefficient, respectively.
## 5 Analysis of Overall Trends  
### General Motion Contours  
One global finding from the sound-tracing data is that of a clear arch shape when looking at the vertical motion component over time. Figure 4 shows the average contours calculated from the z-values of the motion capture data of all subjects for each of the melodic phrases. It is interesting to note a clear arch-like shape for all of the graphs. This fits with suggestions of a motor constraint  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Motion Features** & **Description** \\ \hline
1 & VerticalMotion & z-axis coordinates at each instant of each hand \\
2 & Range & (Min, Max) tuple for each hand \\
3 & HandDistance & The euclidean distance between the 2d coordinates of each hand \\
4 & Quantity/Motion & The sum of absolute velocities of all the markers \\
5 & DistanceTraveled & Cumulative euclidean distance traveled by each hand per sample \\
6 & AbsoluteVelocity & Uniform linear velocity of all dimensions \\
7 & AbsoluteAcceleration & The derivative of the absolute velocity \\
8 & Smoothness & The number of knots of a quadratic spline interpolation fitted to each tracing \\
9 & VerticalVelocity & The first derivative of the z-axis in each participant’s tracing \\
10 & CubicSpline10 Knots & 10 knots fitted to a quadratic spline for each tracing \\ \hline \hline \end{tabular}
\end{table}
Table 2: The features extracted from the motion capture data.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Melodic Features** & **Description** \\ \hline
1 & SignedIntervalDirection & Interval directions (up/down) calculated for each note \\
2 & InitialFinalHighestLowest & Four essential notes of a melody: initial, final, highest, lowest \\
3 & SignedRelativeDistances & Feature 1 combined with relative distances of each successive note \\  & from the next, only considering the number of semitones for each \\  & successive change. \\ \hline \hline \end{tabular}
\end{table}
Table 3: The features extracted from the melodic phrases.  
hypothesis suggesting that melodic phrases in general have arch-like shapes [24; 54]. In our study, however, the melodies have several different types of shapes (Figure 1), so this may not be the best explanation for the arch-like motion shapes. A better explanation may be that the subjects would start and end their tracing from a resting position in which the hands would be lower than during the tracing, thus leading to the arch shapes seen in Figure 4.