esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Downhole Seismic Method
#### 2.5.2 Body Waves  
Body waves refer to movements that travel with high velocity and frequency through the inner layers of the Earth. Such waves are categorized into two main types: P-wave (i.e., the primary wave) and S-wave (i.e., the secondary wave). Amongst all types of seismic waves, the P-wave occurs with the highest speed. It has the capacity of propagating through liquid or solid; it is the first type of wave that seismic detectors receive (Equation (1)). The wave causes the particles to be vibrating parallel to direction of the wave propagation. It makes some dilations and compressions of rock particles (see Figure 4).  
\[\mathrm{Vp}=\left[\frac{K+\ 4/3\mu}{\rho}\right]^{1/2} \tag{1}\]  
where, \(K\), \(\mu\), and \(\rho\) represent Bulk modulus, shear modulus, and density, respectively.  
On the other hand, the S-wave, which is indicated by particle vibration, is perpendicular to the wave propagation direction. In comparison with the P-wave, it has fewer levels of frequency and velocity (see Figure 5). This type of wave is separated into two components: a component parallel to surface of the ground (Sh) and another one that is vertical to surface of the ground (Sv).  
Normally, Vs is slower than Vp, and also the S-waves are not capable of passing through liquid (Equation (2)).  
\[\mathrm{Vs}=\left[\frac{\mu}{\rho}\right]^{1/2} \tag{2}\]  
where, \(\mu\) and \(\rho\) represent shear modulus and density, respectively.
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 2 Related Works
Upon association with a publicly available access point, the context-leashing engine would collect a list of context \(C_{i}=\{(c_{1},r_{1}),\ldots,\ (c_{n},r_{n})\}\), which contains the list of all visible SSIDs (denoted by \(c_{j},\ j=1,\ldots,n\)) that is reachable at the time of association with a particular SSID in the environment. For any future reassociation with a given SSID, a new context list is constructed and compared to the previously stored one. If the context-list of available neighboring SSIDs and their average RSSI values does not have a significant (empirically defined) overlap with the historical context-list, then the associated SSID is deemed an Evil Twin and the connection should be terminated. The main drawback of their method is the assumption that the list of SSIDs in a given geolocation remains relatively unchanged over time. However, with today's tethering capabilities of cellphones, this assumption is far from the truth.
## 3 Results and Analysis
### Contingency Statistics  
Contingency statistics uncover the performance of the probability of detection, the rate of successful hits and the false alarm rate. Figure 6 shows that 3B42 RT (3B42V7) has a zero POD and CSI when the rainfall rate is greater than 25 mm/h (23 mm/h). Similarly, this result implies that 3B42 RT and 3B42V7 have poor performances in detecting intensive rainfall in this case. Consistent results are also revealed by the probability distributions shown in Figure 5.
###### Abstract  
Research on brain-computer interfaces (BCIs) advances the way scientists understand how the human brain functions. The BCI system, which is based on the use of electroencephalography (EEG) signals to detect motor imagery (MI) tasks, enables opportunities for various applications in stroke rehabilitation, neuroprosthetic devices, and communication tools. BCIs can also be used in emotion recognition (ER) research to depict the sophistication of human emotions by improving mental health monitoring, human-computer interactions, and neuromarketing. To address the low accuracy of MI-BCI, which is a key issue faced by researchers, this study employs a new approach that has been proven to have the potential to enhance motor imagery classification accuracy. The basic idea behind the approach is to apply feature extraction methods from the field of emotion recognition to the field of motor imagery. Six feature sets and four classifiers were explored using four MI classes (left and right hands, both feet, and tongue) from the BCI Competition IV 2a dataset. Statistical, wavelet analysis, Hjorth parameters, higher-order spectra, fractal dimensions (Katz, Higuchi, and Petrosian), and a five-dimensional combination of all five feature sets were implemented. GSVM, CART, LinearSVM, and SVM with polynomial kernel classifiers were considered. Our findings show that 3D fractal dimensions predominantly outperform all other feature sets, specifically during LinearSVM classification, accomplishing nearly 79.1% mean accuracy, superior to the state-of-the-art results obtained from the referenced MI paper, where CSP reached 73.7% and Riemannian methods reached 75.5%. It even performs as well as the latest TWSB method, which also reached approximately 79.1%. These outcomes emphasize that the new hybrid approach in the motor imagery/emotion recognition field improves classification accuracy when applied to motor imagery EEG signals, thus enhancing MI-BCI performance.  
Electroencephalogram; emotion recognition; motor imagery; EEG feature extraction; brain-computer interface
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 3 Proposed Approach
### Computer Vision Model
#### 3.1.2 Model  
We used the train dataset to train the Faster R-CNN, Scaled-YOLOv4, YOLOv5 (Small), and YOLOv5 (XLarge) models. The Faster R-CNN was published in 2017 [28], the YOLOv5 models were made public in June 2020 [29], and the Scaled-YOLOv4 (Large) in November 2020 [30]. YOLO models allow testing of different depths of the same architecture, from Tiny to XLarge (changing only the number of layers and neurons). This allows testing the state of the art of object detection and evaluating the trade-off between performance and depth of the network. The R-CNN allows the testing of a two-stage model and compares performance with one stage models.  
To evaluate those object detection models, we considered three metrics. Mean Average Precision (mAP) is one of the most used metrics to assess the precision of object detection models [31]. We also considered the metrics memory consumption and average detection time for an image (using a NVIDIA Tesla T4 GPU).  
As described in Table 1, YOLOv5 (XLarge) presented the best performance (best mAP) in the test dataset and therefore was the model deployed in the IoT network. The memory consumption was not an issue because we used the cloud, and average time for each detection was not relevant because we did not have the goal of performing real-time detection.  
Figure 5: Part of a dataset image with WFs.
## 5 Results and Discussion
### Results from Integrated Consideration and Footfall Model
#### 5.2.4 Error in Realized Footfall Estimation by Neglecting Consideration and Non-Linearity  
The practical implication of accounting for consideration effects on ridership predictions is illustrated in Figure 9. The plot shows the effect of out-of-vehicle travel time (OVTT) and metro fare per km on ridership across three models: a linear model without consideration, a non-linear model without consideration, and a non-linear model with consideration. The plots show that the linear and non-linear models that ignore consideration tend to overestimate the effect of out-of-vehicle travel time (sum of access and egress times) as they have a larger slope (Figure 9a). The intercepts are also biased with overestimation in the linear model at low OVTT and underestimation in the case of the non-linear model (without consideration).  
Figure 9b illustrates the effect of price changes on metro ridership. It shows that neglecting consideration effects systematically distorts ridership estimates under varying levels of fares in both linear and non-linear footfall models. At higher fares (above INR 4.5/km), conventional models overestimate ridership, leading to an optimistic projection. On the other hand, when fares are lower, these models underestimate ridership. The non-linear model also shows that ridership decreases at an increasing rate as fares rise, especially up to INR 4.5/km, which is not accounted for by other models. Thus, the failure to account for non-linearity or consideration of propensity in the footfall model can result in bias and misleading estimates of policy changes in either direction. These findings directly reflect the economic definition of demand by showing how fare levels influence ridership through both behavioral responses and policy context.
## 1 Introduction
Another method to achieve a low-order envelope model based  
Figure 2: RLC network current response to sinusoidal voltage excitation at \(t=0\). on the energetic coupling between the system inductance and capacitance was proposed in [22]. However, it is only valid in the close vicinity of the resonance. Consequently, a nonlinear reduced-order model capable of accurately capturing the system behavior under large signal variations is necessary. It must be emphasized that due to the extremely high current flow required for relatively short periods, pulsed-power applications in general, and fusion inverters in particular, are typically powered by pre-charged capacitor banks rather than constant-voltage sources [16]. Consequently, the inverter input voltage should be considered as an additional non-controllable state variable, increasing the overall system order while imposing the operational frequency to serve as the only controllable input to the system. When frequency control is adopted, operation in the inductive region is usually preferable as it allows attaining zero voltage switching, alleviating converter switch stresses and losses, thus enhancing the longevity and increasing the efficiency of the system [23]. Table 1 summarizes the modeling methods presented above, indicating their validity range and applicability. Taking all the above into account, the nonlinear envelope-modeling process of a capacitor-powered resonant inverter driving series RLC load with time-varying component values is presented in this paper. First, a full-order envelope model is derived. Then, model order reduction is carried out under the assumption of a slow-varying amplitude derivative and phase (SVADP), as proposed in [21], yet without linearization, resulting in a novel nonlinear reduced-order envelope model, valid both at the resonance frequency and off-resonance. In order to demonstrate the validity of the proposed approach, the device-level switched model of the capacitor-powered resonant inverter driving series RLC load with time-varying component values and the corresponding reduced-order envelope model are simulated concurrently, and close matching between the outcomes is demonstrated under different operating conditions. The rest of this paper is organized as follows. The system under consideration and basic assumptions are introduced in Section 2. The derivation of the full-order nonlinear system envelope model is carried out in Section 3. The proposed model reduction process is revealed in Section 4 and verified by simulations in Section 5. This paper is concluded in Section 6.
###### Abstract  
Ground-based multichannel microwave radiometers can observe the atmospheric microwave radiation brightness temperature and continuously provide temperature and humidity profiles of the troposphere. At present, microwave radiometers are operated in many countries for monitoring climate and meteorological phenomena, and there have been many microwave radiometers of this kind presently implemented in China, but they lack a unified monitor for their operational condition, which is necessary if they are taken as a network. For this reason, a real-time monitoring receiving system of radiometer is fundamental and important. In order to check the system stability and the antenna performances, this paper studied the feasibility of applying the solar signals to monitor the antenna alignment, antenna pattern and stability of a radiometer system in working for operational field applications. An experiment was performed and the results from the analysis of the annual variation features with long-term solar observation data at four frequencies, 22.235, 26.235, 30.000 and 51.250 GHz, show that an antenna pattern retrieved from solar observations agrees well with that retrieved from the traditional method. In addition, a daily analysis of the solar signals in online data of a radiometer can be used for monitoring the alignment of the antenna and the stability of the ground-based microwave radiometer system.  
microwave radiometer; antenna pattern; solar observation +
Footnote †: journal: 31 March 2021
## 3 Analysis and Results of CGAN-IFCM
### Comparison Between CGAN-IFCM and Existing Works
The same feature vector is utilized to ensure a fair comparison. CGAN-IFCM achieved a higher TNR and TPR compared to RF and SVM for three key reasons: (i) CGAN reduces the issue of imbalanced datasets; (ii) CGAN increases the amount of training data; (iii) IFCM includes the relationship between \(X_{p}\) and its neighbor \(X_{p}\). Satisfactory performance (only a small deterioration of the TNR and TPN compared with the binary detection model) was achieved for CGAN-IFCM.
###### Abstract  
This paper presents the synthesis of silver nanoparticles (AgNPs) functionalized with fluorescent molecules, in particular with xanthene-based dyes, i.e., fluorescein isothiocyanate (FITC, \(\lambda_{\text{max}}=485\) nm) and rhodamine B isothiocyanate (RITC, \(\lambda_{\text{max}}=555\) nm). An in-depth characterization of the particle-dye systems, i.e., AgNPs-RITC and AgNPs-FITC, is presented to eValuate their chemical structure and optical properties due to the interaction between their plasmonic and absorption properties. UV-Vis spectroscopy and the dynamic light scattering (DLS) measurements confirmed the nanosize of the AgNPs-RITC and AgNPs-FITC. Synchrotron radiation X-ray photoelectron spectroscopy (SR-XPS) was used to study the chemical surface functionalization by structural characterization, confirming/examining the isothiamate-metal interaction. For AgNPs-RITC, in which the plasmonic and fluorescence peak are not superimposed, the transient dynamics of the dye fluorescence were also studied. Transient absorption measurements showed that by exciting the AgNPs-RITC sample at a wavelength corresponding to the AgNP plasmon resonance, it was possible to preferentially excite the RITC dye molecules attached to the surface of the NPs with respect to the free dye molecules in the solution. These results demonstrate how, by combining plasmonics and fluorescence, these AgNPs can be used as promising systems in biosensing and imaging applications.  
silver nanoparticles; rhodamine B isothiamate; fluorescein isothiocyanate; fluorescence; surface plasmon resonance; plasmonic materials; optical nanomaterials +
Footnote †: journal: Applied Sciences
## 8 Discussion  
### Summary of the Results  
Two technical issues relating to projection stability and placement accuracy were resolved through the adoption of novel projection techniques. Projection stability was resolved using an edge reduction algorithm to simplify the triangle meshes used for model representation. Systematic testing revealed that simplification rates of 40% and 20% were optimal for rectilinear and curvilinear components, respectively. Applying these rates to the bridge BIM model resulted in reducing its size from 2195 to 1686 meshes, and its projection remained stable without lags and remained stationary for over an hour.  
Such findings provide a more detailed approach to mesh simplification techniques. Existing approaches have devised ways to simplify mesh representations with the goal of improving the battery time of AR mobile devices [40; 41] or reducing the computation load for model rendering [42]. These studies focus on indiscriminately compressing large mesh models so that AR-related operations may function as normal. In comparison, this study differentiated the simplification ratios according to component shape types to ensure that AR-based on-site management operations can be correctly performed, while accounting for hardware performance restrictions.  
Placement accuracy was improved using point clouds and ARWorldMap functions to identify and leverage additional anchor points. The validation revealed that the mean errors of displacement along the longitudinal (x-y plane) and transverse (x-z plane) were improved by \(-0.070\) and \(-0.096\) m, respectively. As a result, the mean errors in AROS_\(\beta\) were 0.066 m for the longitudinal (x-z plane) axes and 0.013 m for the transverse (x-y plane)  
axes, which satisfied the required tolerance of 0.1 m. The experiments also revealed that a projection distance of 9 m was most suitable for on-site inspections.  
The optimal projection distance of civil structures is an issue that has not been addressed in depth in the existing research. Previous approaches to securing placement accuracy were mainly investigated for indoor work [43; 44], so projection distance was not considered as a significant factor in improving placement accuracy. On the other hand, the inspection of civil structures is focused on outdoor work, and due to the massive size of the target, it is necessary to secure adequate placement accuracy in long-distance projection compared to indoor work.  
Thus, the results derived from the empirical tests performed in this study provide concrete findings that can be exclusively utilized for AR deployment on large-scale, outdoor civil engineering structures.
## 2 Materials and Methods
### Air Sampling  
Indoor air pollutants including CO, CO\({}_{2}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), PM\({}_{2.5}\), temperature, and relative humidity were measured by real-time monitoring equipment for 24 h in the living rooms. We also measured the atmospheric CO, CO\({}_{2}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), and PM\({}_{2.5}\) on the balcony as outdoor concentrations. All instruments were placed on the bench at a height of approximately 1 m above the ground. The PM was measured by a real-time optical scattering instrument (DUSTTRAK(tm) DRX Aerosol Monitor Models 8533, TSI Incorporated, Shoreview, MN, USA) and the measurements were taken every 1 s by the flow rate of 3.0 L/min with detectable concentration from 0.001 to 150 mg/m\({}^{3}\). The CO, CO\({}_{2}\), NO\({}_{2}\), SO\({}_{2}\), indoor temperature, and relative humidity were also recorded (KD-airboxx, KD Engineering, Blaine, WA, USA) every 15 s with the measuring range of 0 to 500 ppm, 0 to 10,000 ppm, 0 to 20 ppm, 0 to 20 ppm, 0 to 50 \({}^{\circ}\)C, and 5% to 95%, respectively. The accuracy of CO, CO\({}_{2}\), NO\({}_{2}\), and SO\({}_{2}\) were \(\pm\)3% of reading or 2 ppm (whichever was greater), \(\pm\)5% of reading or 60 ppm (whichever was greater), 0.25 ppm, and 0.25 ppm, respectively. The resolution of CO, CO\({}_{2}\), NO\({}_{2}\), and SO\({}_{2}\) were 0.1 ppm, 1 ppm, 0.01 ppm, and 0.01 ppm, respectively. In terms of O\({}_{3}\), it was detected by a real-time monitoring (Model 202 Ozone monitor(tm), 2B Technologies Inc, Boulder, CO, USA) every 5 min with the measuring range of 0 to 250 ppm.  
All real-time monitors were manufacturer-calibrated for the study in the beginning of this study and every six months. Before every field sampling, the DUSTTRAK(tm) DRX Aerosol Monitor Models 8533 was calibrated using emery oil aerosol and nominally adjusted to the respirable mass of standard ISO 12103-1, A1 test dust, (Arizona Dust); and the KD-airboxx, the Model 202 Ozone monitor(tm) were calibrated using zero gas and span gas. In addition, the zero calibrators of instruments were carried out, and the flow rate of sampling pump also was adjusted by Gilian Gilibrator-2 NIOSH Primary Standard Air Flow Calibrator (Sensidyne, St. Petersburg, FL, USA) before every household sampling.
## 1 Introduction
Similarly, to address the conflict between static roads and dynamic moving features, we also convert the moving attributes on roads (i.e., velocity, acceleration and turning angle) into three kinds of static textual corpora from cycling trajectories. On top of the different corpora, latent Dirichlet allocation (LDA) is used to automatically extract various topics of cycling semantics based on the word probability, thus further reducing the effect of the uncertainty problem or missing GPS records to a certain extent. Moreover, due to the nature and intuitive connection between human beings and computers through interactive visual interfaces, visual analytics is frequently utilized to explore hidden facts/patterns behind raw data [10, 11]. Thus, we further implement multiple interactive user interfaces for analysts to facilitate the understanding of cycling semantics in a visualization manner. The main contributions of this paper can be summarized as follows. * We propose a new visualization system called VizCycSemantics for the exploration of underlying urban cycling semantics from both the cyclist and road segment's perspectives, based on large-scale cycling GPS trajectories and road network data. The system could help to improve cycling services in cities. * We textually convert the cycling trajectories into street name and moving feature corpora, then use a topic model to automatically extract the cyclists' behavior semantics (i.e., cycling topics of cyclists) and moving semantics on roads (i.e., moving topics on roads), respectively. We further employ a clustering algorithm to capture the groups of similar cyclists and road segments in the city. * We implement multiple interfaces to facilitate the understanding of cycling semantics for pervasive computing, including a cycling map, cycling groups and topics, the size of cycling groups, the street cloud of cycling topics, the temporal evolution of cycling topics, moving topics and moving topic distribution. A group of case studies in Beijing demonstrates the effectiveness of our system and also obtains various insightful findings and cycling advice. The rest of this paper is organized as follows. We review the related work in Section 2. Section 3 describes the system tasks and framework. In Section 4, we elaborate on the details of the backend algorithms. Section 5 provides the data source and visualization designs. We use two cases to demonstrate the effectiveness of our system in Section 6. Finally, Section 7 concludes this paper and discusses future directions.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 4 Simulation Results
### UAV Trajectories  
Figure 2 illustrates the optimal UAV trajectories obtained under various speed constraints, with the objective of maximizing the secrecy rate. During the initial phase of flight, the UAV rapidly approaches the communication destination, as closer proximity enhances the secrecy rate. However, after passing the node, the UAV exhibits different trajectory behaviors depending on the speed constraints. To enhance secrecy performance, the UAV dynamically adjusts its trajectory to reduce its exposure to eavesdroppers while maintaining effective communication with the intended destination. Ultimately, within the constraints of the total mission duration, the UAV completes its transmission task and proceeds to the designated endpoint.  
As shown in Figure 3, the UAV initially flies toward the communication destination at its maximum speed. Then, the speed decreases significantly, as proximity to the communication destination facilitates more secure transmission. Subsequently, the UAV undergoes two acceleration phases to reach positions favorable for secure communication, interspersed with brief periods of deceleration. After reaching the farthest location that still supports secure transmission, the UAV proceeds to the endpoint at a relatively high speed.  
Figure 2: The UAV trajectory maximizes the secrecy rate.