esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
As the applications and demands of modern radar systems grow, current and future frequency spectrum coexistence needs to be considered. The multimode radar design and approach to implementing digitally reconfigurable PMCW radar signals described in this article demonstrate how a benchtop PMCW radar can be easily designed and reconfigured to test various CW modulation schemes. The experimental results show that while there is an increased load on the baseband processing, vital sign and simple gesture detection is achievable with both PSK and PMCW radar systems. Four gestures were measured that have identifying characteristics between one another, and respiration measurements strongly correlate with those measured with a respiration belt. Future works will focus on identifying and classifying a wider array of subjects' gestures and movements, as well as an in-depth comparison to CW and FMCW responses. Further exploration of PMCW respiration responses will investigate scenarios with more subjects at various distances, as well as multiradar and multipatient scenarios, heart rate detection, and supporting passive radar systems.  
Conceptualization and validation, M.C.B. and C.L.; methodology, software, formal analysis, investigation, original draft preparation, and software, M.C.B.; review and editing, supervision, resources, and funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge funding support from the National Science Foundation (NSF) under grants NSF CIS-2112003 and ECCS-2028863.  
Ethical review and approval were waived for this study as the research team investigated the validity and reliability of the radar system and not for generalizability of human responses or activities.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are openly available in FigShare at DOI: 10.6084/m9.figshare.24082266 and available on request from the corresponding author.  
The authors declare no conflict of interest.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 4 Photocatalytic Oxidation  
Among the various methods available to reduce pollutant concentrations in indoor environments, photocatalytic oxidation has emerged as a particularly promising approach in recent years [75,76]. This technique is a type of advanced oxidation process (AOP) that utilizes the high reactivity of oxygen species in oxidation/reduction reactions. These reactions are facilitated through the interaction of the target pollutant with a photocatalyst and light irradiation, offering the advantage of not requiring the addition of external compounds like H\({}_{2}\)O\({}_{2}\). Heterogeneous photocatalysis, in particular, has been extensively studied for its application in purifying indoor air, as it typically involves the removal of gaseous pollutants using the surface of a semiconductor, such as transition metal oxides [77]. In semiconductors, the valence band and the conduction band are separated by a specific band gap width. When the semiconductor surface is irradiated with light of sufficient energy, electrons are excited from the valence band to the conduction band, initiating photocatalytic reactions. The effectiveness of photocatalytic oxidation is influenced by the properties of the semiconductor used, such as its adsorption capacity and band gap width. Additionally, external factors like humidity, light intensity, pollutant concentration, and airflow significantly affect the process's efficiency [78].
## 4 Experiments and Results
### Implementation Details  
We trained our deployed CNN model separately based on the age and gender classification task. We handle both tasks as a classification approach. The models are trained using the deep learning framework PyTorch [75] developed by Facebook's artificial research lab. The training was performed on Nvidia GeForce GTX 1080 Ti GPU that consists of 3584 CUDA cores with 11 GB of video memory. Training on the large IMDB-WIKI datasets took almost one day whereas the fine-tuning on the smaller dataset only required a couple of hours.  
In the case of age classification, we calculate the expected value from the Softmax probabilities belonged to output neurons, similar to what was considered in [24]. On the contrary to [24], we train the model with a balanced IMDB-WIKI dataset and introduce the batch normalization layer to reduce the training time. We consider every age as an individual class that ranges from 0 to 100. For all experiments regarding age estimation, the CNN is initialized with the weights trained on ImageNet [76]. This pre-trained model is then further trained on the IMDB-WIKI image dataset for classification with 101 output neurons. Finally, the CNN is fine-tuned on the test dataset.  
For the gender classification, we report the gender class of the neuron carries the highest probability. We first deployed the pre-trained model trained on ImageNet. In the next step, we fine-tuned the pre-trained model with the real-world face image dataset Adience [9] with two output neurons and the performance is reported from the test split of the Adience dataset.  
The training set consists of 80% of the images from the dataset and 20% is reserved for the testing. Further 90% of images from the training set are used for learning the weights and the rest of the images are used as validation set during the training phase. Every experiment begins in conjunction with pre-trained ImageNet weights from [25]. When fine-tuning the pre-trained network with a smaller dataset, the learning rate of 0.001 remains fixed except for the last layer. The last layer weights are initialized randomly as the number of output neurons are changing. We used the Adam optimizer with the setting of momentum 0.9 and a weight decay rate of \(5\times 10^{-4}\). We adjust the learning rate by a factor of 10 after every 30 epochs.
## 4 Experiment and Result Analysis
### Visual Comparison
#### 4.7.1 Strong Light, Close Range, and Occlusion  
As shown in Figure 11b,c, in strong light conditions, YOLOv8n and YOLOv9c struggle to detect small blueberry targets at long distances, under intense lighting, and when heavily obstructed by branches and leaves, resulting in missed and false detections. While YOLOv9c demonstrates higher confidence levels, it still produces some missed and false detections. In contrast, the proposed model exhibits lower missed and false detection rates, achieving the best detection performance and higher confidence levels.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone** & **Precision/\%** & **Recall/\%** & **[EMAIL_ADDRESS]/\%** & **Parameters/M** & **FLOPs/G** & **ModelSize/MB** \\ \hline MobileNetV3 & 87.2 & 75.5 & 84.7 & 5.6 & 10.6 & 11.1 \\ ShuffleNetV1 & 86.3 & 70.5 & 80.4 & 3.5 & 6.5 & 7.0 \\ ShuffleNetV2 & 85.3 & 73.0 & 82.3 & 2.8 & 7.4 & 5.6 \\ GhostNetV2 & 86.1 & 72.6 & 82.2 & 6.3 & 8.7 & 12.6 \\ C2f-RFAonv & 88.5 & 74.6 & 84.7 & 3.4 & 8.4 & 6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison experiment of different feature extraction backbone networks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **Precision/\%** & **Recall/\%** & **AP/\%** & **AP/\%** & **AP/\%** & **[EMAIL_ADDRESS]/\%** & **Parameters/M** & **FLOPs/G** \\ \hline YOLOv3-tiny & 84.9 & 66.5 & 79.2 & 79.9 & 65.1 & 74.7 & 12.3 & 18.9 \\ YOLOv5n & 87.0 & 73.7 & 85.4 & 85.7 & 78.6 & 83.2 & 2.5 & 7.1 \\ YOLOv6n & 84.6 & 72.2 & 83.3 & 83.1 & 76.6 & 81.0 & 4.2 & 11.8 \\ YOLOv6s & 88.7 & 77.4 & 88.3 & 87.7 & 82.7 & 86.2 & 16.2 & 44 \\ YOLOv7-tiny & 85.9 & 75.1 & 85.7 & 85.4 & 76.5 & 82.5 & 6.0 & 13.2 \\ YOLOv9c & 88.9 & 79.4 & 89.4 & 89.8 & 84.9 & 88.0 & 25.3 & 102.3 \\ Ours & 89.1 & 79.2 & 89.1 & 91 & 85.5 & 88.5 & 4.3 & 15.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results on different object detection models.  
4.7.2. Weak Light, Long Distance, and Occlusion  
As shown in Figure 12b,c, under conditions of insufficient light, long distances, and severe occlusion, YOLOv8n and YOLOv9c frequently misidentify darker branches and leaves as ripe or unripe blueberries, resulting in high missed and false detection rates. In contrast, the proposed model demonstrates superior detection performance in challenging environments, with overall higher confidence levels.  
Figure 11. Comparison of results under strong lighting.  
Figure 12. Comparison of results under insufficient light.
## 2 Field Monitoring Program and Data Collection
### ESDD and NSDD Measurements  
In this study, the pollution severity at the test station and the pollution performance of testing insulators were generally determined by measuring the equivalent salt deposit density (ESDD) and non-soluble material deposit density (NSDD) from a pilot insulator every month from February 2018 to March 2019. As shown in Figure 4, ESDD and NSDD increased significantly from September to March due to strong, occasional monsoons bringing plenty of salt contamination in the winter. During the monitoring period, the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Leakage Current Measuring Device: Measure Leakage Current from the Insulator** \\ \hline A. Range & 20 \(\mu\)A to 100.0 mA. \\ \hline B. Accuracy & \(\leq\)1.5\% \(\pm\) 1 dig. \\ \hline C. Communication & RS-232 or RS-485 or IC2 or TCP/IP, etc., analog to digital signal to prevent electromagnetic interference. \\ \hline D. Protection & Metal shell and surge protection device. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of leakage current measuring devices.  
Figure 2: Insulator strings installed in the outdoor test station.  
maximum ESDD was around 0.3 mg/cm\({}^{2}\). Figure 5 shows the high constituents of sea salt, including Cl\({}^{-}\) (32%), SO\({}_{4}\)\({}^{2-}\) (24%), Na\({}^{+}\) (19%), NO\({}_{3}\)- (10%), and Ca\({}^{2+}\) (9%), with the rest being less than 4%. Figure 6 displays the ratio of the proportion of insoluble non-soluble components, with SiO\({}_{2}\) at 65%, Fe\({}_{2}\)O\({}_{3}\) at 15%, Al\({}_{2}\)O\({}_{3}\) at 14%, and the remaining components being less than 3%. Namely, this indicates that the main components, such as sand-like SiO\({}_{2}\) and high constituents of Al\({}_{2}\)O\({}_{3}\), representing pollution in this area, include sea salt, sand, dust, and industrial pollution. The results correspond to salt contamination and industrial pollution, aligning with the location of the test station.  
Figure 4: The measurement of ESDD and NSDD every month from February 2018 to March 2019.  
Figure 3: The weather data collection system.
# Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm
## 5 Battery Model Parameter Identification Based on Improved Sparrow Search Optimization Algorithm
### Experimental Results
#### 5.2.2 Model Verification under PULSE Test  
The experimental data in the pulsed discharge test are used to verify the model accuracy. Furthermore, the terminal voltages are obtained using the identified parameters given in Table 5. Figure 7 presents the comparison of the estimated and measured terminal voltages. Moreover, the errors of terminal voltage with six different groups of FOM parameters under the PULSE test are displayed in Table 7. It is clear that the MAE, RMSE and MaAE with the battery parameters identified by QSSA are 3.3242 mV, 4.3880 mV and 17.6197 mV, respectively, which are all the smallest among the six algorithms. Moreover, although the FOM parameters obtained by PSO are not far behind QSSA in MAE and RMSE, they have the worst performance in terms of MaAE. These findings reveal the superior performance of the proposed algorithm in the total error.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **PSO** & **GA** & **GWO** & **DOA** & **SSA** & **CQSSA** \\ \hline MAE/mV & 3.5833 & 4.1330 & 4.2167 & 3.9922 & 4.0579 & **3.3242** \\ RMSE/mV & 4.7421 & 5.5249 & 5.1398 & 5.1015 & 4.9662 & **4.3880** \\ MaAE/mV & 22.9509 & 19.9808 & 17.9134 & 20.8257 & 18.8050 & **17.6197** \\ \hline \end{tabular}
\end{table}
Table 7: Terminal voltage errors with six different groups of FOM parameters under PULSE test.  
Figure 6: The convergence characteristics of six algorithms under HPPC test.  
Figure 7: The comparison of estimated and measured voltages under PULSE test. (**a**) The comparison of measured and estimated terminal voltages. (**b**) The comparison of terminal voltage errors of six algorithms.
## 2 Materials and Methods
### Methodology
#### 2.4.4 Machine Learning Model Selection  
After training samples are collected, many classifiers can be employed to implement land cover classification, such as maximum likelihood classifier (MLC) [41; 42], Support Vector Machine (SVM) [43] classifier, and Random Forest (RF) classifier [44; 45; 46]. The advantages of MLC include simplicity, computational efficiency, and robustness, but its accuracy is usually limited by its simplicity. The SVM classifier has been widely used for its outstanding performance in remote sensing classification, especially for instances with fewer samples [47]. Much research has reported that the RF classifier performs well [48]; in addition, the RF classifier is robust and accurate while dealing with high-dimensional data, such as multi-spectral and multi-temporal remote sensing images [44; 49; 50], which is more suitable for the multi-feature dimensions constructed in this paper. Therefore, the RF classifier was employed as the training model and the parameters were set as follows: the number of trees was 100 because it proved it can achieve a better result when considering the classification accuracy and efficiency; the number of variables per split was set to 0 (default), which means the square root of the number of variables; the min leaf population is 1 (default) and the bag fraction was 0.5 (default).
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 1 Introduction
### Problem Statement  
Most DNN-based SE methods involve many labeled samples to train the SE model parameters. In this case, the trained model learns the characteristics of the labeled samples. However, in some cases, real speech recordings are captured in diverse and noisy conditions, where the same microphone captures simultaneously and under the same acoustic conditions both speech and noise. Therefore, simulating such conditions when using synthetic training data is challenging, as the clean speech and noise signals are captured independently or under different acoustic conditions. As a result, a potential mismatch between the synthetic training sets and real test sets [9] imposes a substantial performance drop.  
The optimum way to ensure that the trained SE models are suitable for real noisy speech tasks is to collect the training sets from the same noisy conditions. However, this strategy has the following significant challenges:When focused upon specific tasks such as speech information acquisition, it is difficult to collect a large enough number of real noisy speech samples.
2. For most SE tasks, the training set comprises noisy and clean speech pairs. However, the captured noisy speech commonly has no corresponding pure speech usable for real applications.
3. It is hard to match and pair the noisy speech with clean speech when the Signal-to-Noise Ratio (SNR) of the collected noisy speech is very low or there is continuous interference or interruptions in the recording process.  
In practical applications, especially in voice detection related to intelligence acquisition and national security, we can only obtain limited noisy voice samples, as we cannot enter the detection scene in advance, and the time of voice detection is limited. Hence, only limited noisy speech can be collected, and the lack of adequate noisy speech combined with the unavailability of clean speech signals seriously affect current DNN-based SE tasks.
# Modeling of Traffic Flows Sustainability on Highway Network Stretches
## 3 Results
An increase in crosswalks, \(k\), and traffic lights, \(s\), on the controlled highway stretch leads to decreasing sustainability, promoting the emergence of traffic jams. It should be noted that the robustness range at \(k\) and \(s\) changing does not exceed \(RR\) = 6 values. The results of the RR robustness test calculation for all values of the evaluation factors are displayed in the resulting Figure 4:2  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter for Modeling**} & \multicolumn{10}{c}{**Grad p \(\cdot\) Grad v \(\cdot\) 10\({}^{-9}\), 1/m\({}^{3}\)s**} \\ \cline{2-13}  & & **2.5** & **5** & **7.5** & **10** & **12.5** & **15** & **17.5** & **20** & **22.5** & **25** & **27.5** \\ \hline \multirow{3}{*}{Vehicle length, m} & 4 & 10 & 9.3 & 8.6 & 8 & 7.3 & 6.6 & 6 & 5.3 & 4.6 & 3.3 & 2.6 \\  & 12 & 24 & 22.3 & 20.6 & 19 & 17.3 & 15.6 & 14 & 12.3 & 10.6 & 7.3 & 5.6 \\  & 20 & 50 & 49 & 48 & 47 & 46 & 45 & 44 & 43 & 42 & 40 & 39 \\ \hline \multirow{3}{*}{Vehicle weight, kg} & 2000 & 3.84 & 3.2 & 2.64 & 2.15 & 1.74 & 1.4 & 1.12 & 0.91 & 0.78 & 0.72 & 0.7 \\  & 10,000 & 6 & 4.15 & 3.17 & 2.7 & 2.36 & 2.12 & 1.94 & 1.8 & 1.68 & 1.58 & 1.5 \\  & 18,000 & 19.7 & 14.07 & 11.55 & 10 & 9 & 8.25 & 7.65 & 7.17 & 6.77 & 6.43 & 6.1 \\ \hline \multirow{3}{*}{Vehicle engine power, kW} & 100 & 5.13 & 4.65 & 4.18 & 3.73 & 3.29 & 2.88 & 2.48 & 2.1 & 1.73 & 1.38 & 1.05 \\  & 150 & 5.65 & 5.13 & 4.63 & 4.15 & 3.7 & 3.24 & 2.8 & 2.38 & 1.98 & 1.6 & 1.22 \\  & 200 & 6.3
# Developing Machine Learning-Based Models for Railway Inspection
## 2 Overview of Railway Defect Detection Techniques and Acceleration Data  
### Techniques of Rail Defect Detection  
There have been many methods and techniques developed in order to reduce the number of derailments. One way is to improve the quality of rail steel. For example, harder steels will lead to longer life and reduce the probability of rail defects. However, with the increasing of traffic on the railroad, the failure of rail still remains and seems inevitable. Early detection of rail defects is still necessary in order to repair the rail track before it causes any serious problems such as derailments. Rail defects are mainly detected by using an instrumented train car [11], a railway-monitoring vehicle or an instrumented in-service train car [12]. Such instrumented vehicles contain various sensors or devices, including video cameras, accelerometers on the car body, acoustic sensors, and ultrasonic wave sensors, GPS, linear potentiometers, strain gauges on the top chord, and so on. A rail defect detection system consists of not only an instrumented vehicle, a sensor suite,but also a data acquisition system, data analytics tool, and knowledge extraction algorithms. For rail inspection, the methods can be grouped into vision-based methods such as laser scanner [14; 17; 18; 19] and video image processing [17; 18; 19], and motion-based methods such as acceleration [20], which measures the vertical motion of the vehicles to detect rail defects and other track components.
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 5 Results of the Numerical Computations and Discussion
### Effect of the Microstructure Change  
To examine if the microstructure determines the performance of the cell, the different microstructures taken from the literature ([39; 41]) serve as an input for the simulation. The summary of both reference and literature microstructure parameters is shown in Table 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Reference** & **Microstructure from the Literature** & **Literature** \\  &  
For the computations, the wall and inlet temperatures were 750 \({}^{\circ}\)C. The fuel composition was \(\chi_{\mathrm{H_{2}}}=0.6\) and \(\chi_{\mathrm{N_{2}}}=0.4\). The total pressure equals 101.325 Pa. The flow rates were 0.28 L min\({}^{-1}\) at the anode channel inlet and 3.2 L min\({}^{-1}\) at the cathode channel inlet.  
The results of the computations are shown in Figure 15. The results are compared with the experimental data from Figure 2. As can be observed, when random microstructure is adopted, the model cannot predict the experiment properly. The cell with the microstructure taken from the literature has worse performance compared to the microstructure examined in this study. These results show that the microstructure plays a key role in the SOFC modeling and the multiscale model is necessary to analyze the impact of the microstructure on the cell performance. The inclusion of the microstructure data may increase the quality of the SOFC simulations and open the possibility to optimize the anode and cathode microstructural design.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 4 Experiment
### Datasets and Evaluation Indicators  
#### 4.1.1 Dataset  
S3 IDS dataset covers different types of indoor areas, making it a representative indoor point cloud dataset. The dataset contains over 200 million points, each categorized into 13 different semantic categories, providing large-scale data for training and testing. In this dataset, we use Area 5 as a test set, while the rest of the area is used for training in order to perform an objective evaluation of the model performance. This dataset provides an important resource for the task of semantic segmentation of point clouds, which can be used to develop and evaluate various algorithms and models, thus advancing research and applications in the field of indoor environment understanding.  
ShapeNet Part dataset is a richly annotated and large-scale 3D image dataset designed to be used to evaluate the performance of 3D object part segmentation networks. The dataset consists of 16 different classes and a total of 50 segments, containing a total of 16,846 samples that exhibit a variety of inhomogeneous features. The dataset is divided into 12,137 samples for training, 1870 samples for validation, and 2874 samples for testing, reaching a total of 16,881 samples. Notably, it contains 35 duplicate samples. Using this dataset for in-depth research work could advance the field of computer vision in 3D image analysis.