esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation
## 6 Use-Cases: Modeling the Emilia-Romagna and Lombardy Regions
### Social-Demographic Model  
We introduced family groups and occupations (work or student) for the individuals involved in the simulation. In light of that, we can define a new socio-demographic model. The municipalities, with their geographical coordinates and the agents representing the population, are created following the data and probability distributions provided by the census. In our model, there are nine different family types, characterized by a different composition and number of members:  
1. Single with children
2. Single without children
3. Single with children plus another adult
4. Couple without children
5. Couple without children plus another adult
6. Couple with children
7. Couple with children plus another adult
8. Adults that live together
9. Family groups (with at least one child)  
With \"other adult\" we intended a person who is not strictly inside the family group but lives in the same habitation. Each type of family has a frequency of appearance,  
Figure 5: Image representing the difference between the gravity model and the radial one. In this example, we consider the municipality of Albinea (departure) and the municipality of Reggio mell’Emilia (arrival).  
and the number of members may vary depending on a distribution probability shown in the Figure 6.  
We introduced three fundamental constraints for building a consistent family group:  
1. Any family group must contain at least one adult
2. The age of each child must be between 18 and 43 years less than the younger parent
3. The age difference of a couple is less than or equal to 15 years and they must be adult  
We assigned an occupation to each individual based on its age. Moreover, we exploited some probability distributions based on the attendance rate. Finally, we implemented six different school categories, each with a specific attendance rate:  
1. Kindergarten, attendance rate: 90%
2. Preschool, attendance rate: 90%
3. Elementary school, attendance rate: 100%
4. Middle school, attendance rate: 100%
5. High school, attendance rate: 92%
6. University, attendance rate: 31%  
We have divided the students into classes, with different sizes depending on their category:  
1. Kindergarten: 40 children
2. Preschool: 20 children
3. Elementary school: 19 children
4. Middle school: 21 lads
5. High school: 21 lads
6. University: 34 lads  
On the other hand, as for workers, we considered different employment rates based on their age:  
1. 15-19 years: 8%
2. 20-26 years: 30%
3. 27-34 years: 62.5%
4. 35-54 years: 73.5%
5. 55-70 years: 54.3%  
We grouped workers into job groups, which can be of seven different types that differ according to the number of employees:  
Figure 6: Family type distribution chart with relative number of members.
###### Abstract  
The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different types of pathological voice samples in the MEEI database. This study aimed to develop a VPD system that uses the fuzzy clustering synthetic minority oversampling technique algorithm (FC-SMOTE) to automatically detect and classify four types of pathological voices in a multi-class imbalanced database. The proposed FC-SMOTE algorithm processes the initial class-imbalanced dataset. A set of machine learning models was evaluated and validated using the resulting class-balanced dataset as an input. The effectiveness of the VPD system with FC-SMOTE was further verified by an external validation set and another pathological voice database (Saarbruecken Voice Database (SVD)). The experimental results show that, in the multi-classification of pathological voice for the class-imbalanced dataset, the method we propose can significantly improve the diagnostic accuracy. Meanwhile, FC-SMOTE outperforms the traditional imbalanced data oversampling algorithms, and it is preferred for imbalanced voice diagnosis in practical applications.  
imbalanced learning; voice pathology detection and classification; SMOTE; intelligence medical diagnosis system +
Footnote †: journal: Medical Imaging  
11  
class=MSC] [Primary: ]00C10,  
the insufficient number of samples in the pathological voice database, which also makes it difficult for the traditional VPD system to classify multiple pathological types. Given its importance, pathological voice diagnoses with imbalanced data have attracted the interest of researchers [10; 11].  
Due to the high class-imbalanced ratio in the MEEI database, is not suitable for researchers to use Acc as the primary model evaluation measure. Thus, it is necessary to consider imbalanced learning to model the VPD system based on the class-imbalanced pathological voice database.
## 5 A Case Study
### Solution Approach  
The model presented is an optimization framework designed to manage the interaction between EVs and the power grid. In real-world applications, this model can be utilized to minimize the cost of electricity consumption while ensuring efficient energy distribution and storage. This model supports optimal decision-making for charging (or discharging) EVs at the right times, maintaining energy levels across time periods, and initial state constraints. Furthermore, utility providers can use electric vehicles as distributed energy resources, which enhances grid stability, reduces peak load, and facilitates more effective integration of renewable energy sources.  
In order to solve the problem, a heuristic method has been developed. Figure 4 shows a flowchart of the algorithm. The heuristic acts as a decision-maker, determining whether each vehicle should charge, discharge, or wait until the next hour for each hour of the day. To make this decision, the algorithm requires information about the vehicles' arrival and departure times, as well as their state of charge upon arrival. The algorithm begins by generating a list of available vehicles for each hour, considering their arrival and departure times. If a vehicle's departure time is in the next hour, it is fully charged and removed from the list of available vehicles. For each remaining vehicle, a decision is made based on the current electricity price. If the price is low, the vehicle is fully charged. Conversely, if the price is high, the vehicle is discharged, and the energy is provided to the grid. When the electricity price is average, the algorithm considers the price for the next hour. If the next hour's price is high, the vehicle is charged, as the energy will be later supplied to the grid. If the next hour's price is low, the vehicle's energy is provided to the grid, as it will be charged at a lower price in the following hour. If the next hour's price is also average, the vehicle remains idle and waits for the next hour. Each time a vehicle is charged or discharged, the current cumulative cost is updated and at the end of the day, the algorithm returns the total cost.
## 2 Related Work in Aphasic Speech Recognition
In the work described in [39], an AM based on a MoE of DNN models was proposed, where each expert in the model was specialized on specific aphasia severity. Additionally, an Speech Intelligibility Detector (SID) composed of two hidden layers and a final softmax function was trained to detect the Aphasia Quotient (AQ) severity level of a given speech frame by using the acoustic features and utterance-level speaker embeddings. At inference time, the contribution of each expert was decided by theSID module. Once again, the train-validation-test partitions were randomly generated, and they achieved PER values ranging from 33.37 on mild test set to 61.41 on severe test set. Finally, the first ASR system for Cantonese continuous aphasic speech was described in the work presented in [40]. They used a Time Delay Neural Network (TDNN) combined with a BLSTM model as the main AM, which was trained with both in-domain and out-of-domain speech data and a syllable-based trigram LM. The performance of the system was evaluated at the syllable level by using the SER metric. In this work, any distinctions between aphasia severities, yielding an overall SER of 38.77 for aphasic speech and 15.07 of SER for the healthy speakers, were not reported. As it can be concluded, over the last years, the speech recognition of aphasic voices has benefited from the latest improvements in the ASR based on fully supervised learning methods, gradually enhancing its performance and, thus, allowing its application in real clinical and therapists tools. In this work, we show that semi-supervised learning methods have great potential in this particular domain, reporting interesting WER improvements for English and competitive results for Spanish considering the scarcity of annotated PWA data (less than 1 h) for this language.
# Comparison and Identification of the Aroma-Active Compounds in the Root of _Angelica dahurica_
## 2 Results and Discussion
### PCA and PLS-DA of the Aroma Compounds of A. dahurica  
PCA and PLS-DA were performed to determine the differences in the key aroma compounds in the three kinds of _A. dahurica_. The concentrations of the ten shared key aroma compounds were used for PCA and PLS-DA. PCA score plots provide useful information about clustering samples into groups [16]. As shown in Table 4, three principal components were extracted, and these components represented 99.1% of the variation. The first, second and third main factors explained 57.7%, 39.7%, and 1.72% of the total variance, respectively; PC1 and PC2 were sufficient to explain the maximum variation in all the original samples of _A. dahurica_ with a combined contribution of 97.4%. Figure 4 shows the PCA score plots of _A. dahurica_. The Hangbaizhi was located on the left while the Bobaizhi was located on the bright side, and the Qibaizhi was the upper middle portion of the correlation plot, indicating that they have the difference on aroma, three different kinds of _A. dahurica_ can be clearly divided into three groups.  
PLS-DA is a chemometric technique used to optimize the separation between different groups of samples; its loading plots reflect the relationships among important variables specific to the group of interest, and variable importance on projection (VIP) can be used to identify the most important variables [17; 18]. The resulting loading plot (PC1 vs PC2) is shown in Figure 5, for the investigation of relationships of the aroma compounds and classification of Baizhi, the aroma compounds \(\beta\)-pinene  
Figure 3: Odor sensory profiles of the three kinds of _A. dahurica_.  
(No.2), (\(-\))-\(\beta\)-elemene (No.9) and caryophyllene (No.10) were clustered with Qibaizhi (class 2); which were located at the same area, and it was indicated that these compounds were positive correlation with Qibiazhi. The seven aroma compounds such as the 2-methyl-2-butenal (No.1), preenol (No.5), etc., related to Bobaizhi, were in the lower right portion of the diagram.
## 4 Hardware Architecture
### Onboard Sensors and Memories  
For integrating environmental and process monitoring sensors, three onboard sensors were selected: one accelerometer, one internal temperature and humidity sensor, and one gyroscope.  
* The accelerometer is a _STMicroelectronics(tm) LIS2 DH12_. The role of this component is to measure the acceleration concerning the orthogonal axes on a scale of 2 g, 4 g, 8 g, or 16 g. The digital motion sensor has an acceleration range of [1 Hz \(\div\) 5.3 kHz] anda supply voltage range of \([1.71~{}\mathrm{V}\div 3.6~{}\mathrm{V}]\). The serial communication interface is the serial peripheral interface (SPI) [24].
* The internal temperature and humidity sensor is a _Texas Instruments(tm) HDC2022_. The role of this sensor is to monitor the overheating or entry of liquids into the device. This component has a very low power consumption, with an average current consumption of 50 nA. The serial communication interface is the inter-integrated circuit (I2C); the microprocessor behaves as a master, whereas the sensor acts as the slave. When the accelerometer is in one-shot mode, the measurement starts. Then, the default sleep mode is restored. The device has periodical wakeups from the default mode, in which it works in conversion mode. Finally, when a measurement is triggered, the device performs operations in measurement mode. To verify the state of data after measurement conversions, information related to the condition of the sensoris sent to the microprocessor [25].
* The gyroscope is a _STMicroelectronics(tm) A3G4250D_. The role of this component is to monitor the device's orientation in workpiece monitoring applications to prevent damage. A second-order low-pass filter is utilized to limit noise. Very low power consumption is one of the main characteristics of this device. The serial communication interface is the SPI [26]. The two main memories are EEPROM and FLASH.
* The EEPROM is a 4 kbit memory, used only for basic configurations, such as data type and production settings. It has a low power dissipation, with a typical average current of 5 mA and a maximum clock frequency of 10 MHz. The communication peripheral needed to link the memory with the microprocessor is the SPI.
* The FLASH is a 64 Mbit memory used to store system logs of the microprocessor. It has a low power dissipation, with an average current of 4 mA and a maximum clock frequency of 20 MHz. The communication peripheral needed to link the memory with the microprocessor is the SPI.
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
## 2 CAPS Structure and Mathematical Model of Observations
### Relative Positioning Model
Assuming the observation noise of code and carrier-phase measurements obeys Gaussian distribution with zero-mean, the standard deviation of the float ambiguity can be described by:  
\[\sigma_{\Delta Ni}=\frac{1}{\sqrt{\mathrm{L}}}\sqrt{\frac{\sigma_{\Delta\rho^ {i}}^{2}+\sigma_{\Delta\phi^{i}}^{2}+\Delta\tau_{mul}^{2}}{\lambda^{2}}} \tag{10}\]  
where \(\sigma_{\Delta\rho^{i}}^{2}\) and \(\sigma_{\Delta\phi^{i}}^{2}\) are the variance of the differential code and carrier-phase measurements, respectively; \(\Delta\tau_{mul}\) represents the total effect of the multipath on \(\Delta\rho^{i}-\Delta\phi^{i}\); and \(\mathrm{L}\) is the epoch number of averaging. From Equation (10), it can be seen that, if the observation conditions are good enough that the code and carrier-phase measurements do not have multipath errors, the correct \(N\) can be obtained over a proper period and the epoch number \(\mathrm{L}\) depends on the value of \(\sigma_{\Delta\rho^{i}}^{2}\) and \(\sigma_{\Delta\phi^{i}}^{2}\). In the presence of the multipath, the value of \(\Delta N^{i}\) will have an integer error, which depends on the size of the multipath. This error will eventually affect the positioning result; its impact on the positioning error will depend on the geometric factor corresponding to the satellite \(i\). Therefore, and open observation environment was selected while installing the antenna in our experiment in order to minimize the impact of the multipath. In addition, this equation is only a preliminary estimative approach for obtaining the integer value of the differenced ambiguity; however, further research is required on how to test and validate the ambiguity estimates with such a short wavelength.
## 3 Methodology
### Data Preparation  
This section details the data source selection, patent record extraction and selection, SME classification of distinct topic areas, and visualizations of the topic distributions and their dominant keywords. According to data from the World Intellectual PropertyOrganization (WIPO), the United States and China were the world's top patent holders in CV development [35]. Therefore, this research selected the United States Patent and Trademark Office (USPTO) as a representative baseline, focusing on the English language to classify patent topics in connected vehicle development [36]. A bigram search of the USPTO patent summary from 2018 to 2022 extracted 622 CV-related records from a total of 1,637,725. Selecting the last five full years (rather than partial data available for 2023) provided a more accurate representation of annual trends. The author, serving as the SME, subsequently identified 220 of these patents that were directly relevant to CV technology development. The SME classification resulted in the 11 distinct topic areas summarized in Table 1. The table lists each topic and describes the general types of problems addressed within that category. Figure 1 shows a distribution of these topic areas by year. The literature review revealed that safety and security are broad aims of CV technology, and these align with the SME classified topics. Traffic flow efficiency, another broad aim, includes topics such as traffic signaling, information management, vehicle monitoring, vehicle navigation, smart parking, and platooning. Topics focused on wireless communications and allocating vehicular computing resources enable the efficient deployment of CVs. The notable concentration in cybersecurity-related patents indicates a trend toward prioritizing security in CV development. The SME observed that every topic had overlapping objectives. For example, advancements in traffic signaling and multi-vehicle networking can improve both driving safety and the efficiency of traffic flows. Therefore, the SME assigned the dominant topic to each patent summary.
## 3 Results  
### Spatial and Temporal Pattern of NPP  
The NPP of grasslands on the TP generally decreased from east to west over the period 2001-2022, with NPP values exceeding 450 \(\mathrm{gC/m^{2}}\) on the eastern margins and less than 10 \(\mathrm{gC/m^{2}}\) in the western regions (Figure 2). \(\mathrm{NPP_{slope}}\) varied between \(-\)30.2 and 15.8 gC/ m\({}^{2}\)/yr over the 22 years, with a mean value of 1.01 gC/ m\({}^{2}\)/yr. For 91.91% of the TP grasslands, NPP\({}_{\text{slope}}\) was positive (Table 1). The eastern TP exhibited a predominantly positive slope in NPP, with the highest values at the eastern margin, while the southwestern TP exhibited a predominantly negative slope in NPP. Overall, NPP changed significantly in 51.84% of the grasslands. Among them, 51.47% exhibited a significantly increasing trend and 0.37% exhibited a significantly decreasing trend (Table 1).  
Figure 2: The average NPP of the Tibetan Plateau grassland during 2001 to 2022 (**a**) and the Sen’s slope value of NPP after the Mann–Kendall test and Sen’s slope assessment (**b**).  
In addition to horizontal patterns, NPP and NPP slope also exhibited vertical patterns across the TP grassland (Figure 3). NPP fluctuated and increased with rising elevation up to 3400 m (r\({}^{2}\) = 0.23, \(p\) < 0.05), and then decreased sharply with rising elevation above 3400 m (r\({}^{2}\) = 0.70, \(p\) < 0.05). NPP slope also varied with elevation in a generally unimodal curve. Below 2500 m, the slope increased rapidly with elevation (r\({}^{2}\) = 0.91, \(p\) < 0.05). Above 2500 m, the slope gently declined with elevation (r\({}^{2}\) = 0.89, \(p\) < 0.05), except for a slight fluctuation between 3000 and 3200 m. At all elevation gradients, the mean value of NPP slope was greater than 0, indicating an overall positive trend for the TP grassland.
## 4 Simulations
### Simulation Setting
#### 4.1.3 Methods  
In our simulations, the following routing schemes were used: (1) Enhanced community-based routing with ferry node (ECRF): Our proposed routing scheme. (2) Enhanced community-based routing (ECR): ECRF without ferry nodes. (3) Community-based routing (CR) [32]: In this scheme, an encounter node is forwarded the message if it is within the community of the destination node. (4) Space filling way-point (SFWP) [25]: The scheme divided the whole network i into grids, and deployed ferry nodes in the center of grids. In addition, it used the ECR algorithm to forward messages. (5) Spray-and-wait (S-W) [14]: In the first phase, the current node gives half of the message replicas to the encountered node; in the second phase, all nodes do not forward the message replicas until they meet the destination node. (6) SimBet [31]: The messages are forwarded based on the SimBet utility. (7) ESR [42]: An effective algorithm based on the relationship between nodes. (8) FCNS [43]: An effective algorithm based on the fuzzy routing-forwarding.
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 3 Proposed Point-Image Fusion Network (PIFNet)
### Two Stream Network Architecture  
Our two-stream network consists of a geometric stream and a semantic stream. As shown in Figure 1, the geometric stream and the semantic stream produce the point-based features and RGB image features, respectively. We developed a fusion module to effectively fuse the point-based features and image features. A multi-level fusion module is used to enhance the discriminative feature representations in multiple scales.  
As illustrated in Figure 1, PIFNet consists of two parts, a point cloud feature extraction stream and an image feature extraction stream. In the image feature extraction stream, camera images are fed into the Encoder-Decoder Fusion (EDF) module. In this module, the image is passed through convolution and deconvolution layers, and the four feature maps obtained from the decoder will be integrated with a corresponding point-wise feature by the fusion module. In the point cloud feature extraction stream, the LiDAR point cloud is processed by a series of Set Abstraction (SA) modules and Feature Propagation (FP) modules. Each output obtained by the SA module will be fused with the corresponding  
Figure 1: The overall architecture of PIFNet.  
camera image features. Considering the tradeoff between the performance and computation time, we use 4 pairs of SA layers and FP layers to extract point-wise features. The fused features are used to generate accurate 3D proposals.
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 5 Numerical Simulations  
In this section, we investigate the performance of the proposed user association and threshold scheme through simulations. In the system performance simulations, we evaluate numerically the energy efficiency proportional fairness of a two-tier downlink heterogeneous WPCNs. Unless otherwise specified, the system parameters are assumed as: path loss exponent \(\alpha=4\), system bandwidth \(B=10\) MHz, static power consumption of BS in each tier is assumed to be equal to \(P_{s}=0\) dBm, the transmit power of BSs in tier 1 and 2 are \(P_{t,1}=41\) dBm, \(P_{t,2}=33\) dBm, the density of BSs intier 1 and 2 is \(\lambda_{1}=4\times 10^{-5}/\text{m}^{2}\), \(\lambda_{2}=28\times 10^{-5}/\text{m}^{2}\), the bias factor of tier 1 is \(B_{1}=0\) dB, the density of UEs is \(\lambda_{u}=20\lambda_{1}\), the ratio of energy conversion for EH \(\eta=0.7\).