esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation Metrics  
In accordance with the principle of minimizing the risk of missed detection, classification accuracy is employed as an evaluation metric during the training and validation phases of the network, as presented in Equation (15). A higher classification accuracy indicates a more effective network training outcome:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{15}\]  
where \(TP\) denotes the true-positive cases, \(TN\) denotes the true-negative cases, \(FP\) denotes the false-positive case, and \(FN\) denotes the false-negative case.  
The recall rate is utilized as an evaluation metric during the testing phase of the network, as shown in Equation (16).  
\[Recall=\frac{TP}{TP+FN} \tag{16}\]  
A higher recall rate signifies a greater proportion of correctly classified samples for a specific type of data. Additionally, the confusion matrix is employed to assess the classification performance of the trained network model across all types of test samples.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 16 Conclusions  
Photocatalytic nanomaterials have demonstrated significant potential for VOC removal, offering promising solutions to improve air quality across a wide range of applications. Materials like TiO\({}_{2}\), carbon-based nanocomposites, MOFs, and emerging semiconductors such as perovskites and TMDs provide various advantages, from stability to responsiveness under visible light. Research in this field has advanced the efficiency of these materials, allowing for targeted applications, including air purification systems, surface coatings, and industrial air treatment. However, several challenges remain, especially around the stability of certain photocatalysts (such as perovskites and CdS), the limited visible-light responsiveness of conventional catalysts, and the need to balance performance with environmental sustainability. Despite these issues, the progress in doping, hybridization, and surface modifications suggests a promising future where these materials could be further optimized for both effectiveness and durability.  
Looking forward, the development of multifunctional photocatalysts capable of degrading VOCs while simultaneously capturing CO\({}_{2}\), neutralizing pathogens, or even providing self-cleaning functions, presents an exciting frontier for the field. Advanced composite materials combining, for instance, TiO\({}_{2}\) with silver for antibacterial effects, or MOFs with engineered porosity for VOC selectivity, could transform indoor and outdoor air purification approaches. Further work on real-time responsive systems will also enhance the applicability of photocatalytic nanomaterials in smart cities, where sensor-linked adaptive air purification could respond dynamically to changing pollution levels. Additionally, implementing scalable fabrication methods and environmentally friendly synthesis will be crucial to bringing these materials from the laboratory to real-world applications at scale.  
In summary, photocatalytic nanomaterials represent a crucial advancement in air purification technology, addressing pressing environmental needs in both urban and industrial settings. As research continues to push the boundaries of these materials' capabilities, we move closer to a future where air quality can be continuously monitored and improved through advanced, sustainable technologies. Addressing current limitations in stability, efficiency under visible light, and environmental impact will be essential to realize the full potential of these systems, making them integral to next-generation pollution control and environmental health solutions.
## 3 Results
### Electron Microscopy Analysis  
The morphology of the prepared pure HAp and HZ1 composite powder was analyzed using TEM and FESEM (Figure 2). The TEM images illustrate the distribution of nanoparticles with a nonuniform morphology for pure HAp (Figure 2a). Figure 2c shows the FESEM image of the as-prepared HAp-Zirconia composite (HZ1); the formation of bulk agglomerates can be observed. The image at higher magnification revealed the presence of minute freely dispersed particles attached to the sample's surface. Figure 2b,e shows the HRTEM images of the pure HAp and the HAp-Zirconia composite (HZ1), respectively. The obtained d-spacing value of 0.28 nm corresponds to the (211) hkl plane of pure HAp, and 0.241 nm is associated with the (111) hkl plane of zirconia present in the composite, which are in good agreement with the XRD results.  
Figure 1: (**a**) XRD patterns of HAp, zirconia, and the HAp-Zirconia (HZ1–HZ4) composites (**b**) XRD patterns in the region of the major intensity peaks.  
The elemental distribution of the prepared composite (HZ1) powder was determined using elemental mapping, as shown in Figure 2g-k. This confirmed the major distribution of Ca, P, and O in the middle region, which corresponded to HAp, and the sparse distribution of Zr in between Ca, P, and O. This illustrated the formation of a HAp-zirconia composite with a major HAp component that acts as a matrix and trace zirconia as a reinforcement material. The EDS spectrum (Figure 2l) revealed a <10% addition of zirconia to the HAp matrix. The atomic percentages of Ca, P, Zr, and O were 6.49, 3.54, 0.31, and 89.66%, respectively.
## 2 Related Works
Since the instances in the FSD method require clustering, which involves manually setting thresholds and is prone to inductive bias, the improved FSD v2 [22] employs virtual voxels to replace these instances, then utilizes a Sparse Virtual Voxel Mixer (VVM) to aggregate the features of virtual voxels belonging to the same object, further enhancing the recognition ability for distant objects. **Data augmentation for point cloud object recognition. **  
Data augmentation can effectively enhance the generalization ability of networks, thus playing a crucial role in point cloud object recognition. Commonly used point cloud data augmentation techniques include random rotations around coordinate axes, random global scaling, the application of random rotations and translations to each ground truth and its corresponding point clouds, etc. These methods are ubiquitous in point cloud object detectors. [PERSON]. [9] introduced an efficient data augmentation technique known as Sample Ground Truths from the Database (GT-Aug). This approach initially creates a database encompassing all ground truths from the training dataset and subsequently selects several ground truths randomly to place in current training samples during training. GT-Aug significantly increases the number of ground truths in the training samples, greatly aiding network training and boosting generalization ability. GT-Aug has been extensively applied in subsequent studies, such as [5; 6; 7; 10; 11; 12; 13; 14; 15; 16; 18; 19; 20; 21; 22]. Some of these methods [14; 18; 21; 22] discontinued the use of GT-Aug in the final epochs to facilitate training convergence. However, GT-Aug may occasionally introduce ground truths into unreasonable areas, leading the network to learn erroneous contextual information and preventing further performance enhancements. Addressing this issue, [PERSON]. [24] proposed Context-Aware augmentation (CA-aug), which initially segments training samples into ground points and obstacle points. These are then projected onto a range view (RV) to identify a \"ValidSpace\" (an area suitable for placement). Subsequently, ground truths are randomly selected from the database and inserted into the training samples after rotating them around the \(Z\)-axis to find an appropriate position. [PERSON]. [12] introduced Distance-Aware Data Augmentation (DA-aug), which initially applies random offsets to the bounding boxes of ground truths and their internal points. These points are then voxelized using spherical voxels, and sampling is conducted based on the distance from the voxel centers, resulting in a series of sampled points. These sampled points exhibit a similar distribution pattern to LiDAR scanning points, thus better simulating realistic point clouds. While these methods have produced positive effects on training, the assistance of semantic information from the scene in finding suitable placement areas has been overlooked. Therefore, this paper proposes a point cloud data augmentation method that considers scene semantic information.
## 2 System Model and Complexity Analysis
### Maximum Likelihood (ML) Decoding  
At terminal node \(j\), a ML decoder for DBPSK employs the decision rule [12]:  
\[\hat{\mathbf{c}}=\underset{\mathbf{c}}{\operatorname{argmax}}\wp(\mathbf{y}_{ j}[k]|\mathbf{c},\mathbf{y}_{j}[k-1]) \tag{10}\]  
where \(\mathbf{y}_{j}[k]=(y_{1j}[k],y_{2j}[k],\ldots,y_{Nj}[k])\), \(\mathbf{y}_{j}[k-1]=(y_{1j}[k-1],y_{2j}[k-1  
where \(y_{ij}[k-1]\) does not depend on \(a_{i}[k]\).  
Using (10) to (12), we have the following:  
\[\begin{split}\hat{\mathbf{c}}&=\operatorname*{argmax}_ {\mathbf{c}}p(\mathbf{y}_{j}[k]|\mathbf{c},\mathbf{y}_{j}[k-1])=\operatorname*{ argmax}_{\mathbf{c}}\prod_{i=1}^{N}\frac{p(y_{ij}[k],y_{ij}[k-1]|a_{i}[k])}{p(y_{ ij}[k-1])}\\ &=\operatorname*{argmax}_{\mathbf{c}}\prod_{i=1}^{N}p(y_{ij}[k], y_{ij}[k-1]|a_{i}[k])\end{split} \tag{13}\]  
where \(p(y_{ij}[k-1])\) does not depend on the choice of \(\mathbf{c}\). Using similar derivation steps to those in [12], and given the fact that in our system, \(a_{i}[k]\) are real, we obtain the final ML decoding metric for DBPSK:  
\[\hat{\mathbf{c}}=\operatorname*{argmax}_{\mathbf{c}}\sum_{i=1}^{N}a_{i}[k](y_ {ij}^{*}[k]y_{ij}[k-1]+y_{ij}[k]y_{ij}^{*}[k-1]) \tag{14}\]
# Finding Optimal Stations Using Euclidean Distance and Adjustable Surrounding Sphere
## 3 Study Areas  
This study used three cities--Sejong: South Korea, Bangkok: Thailand, and Bonn: Germany--as the primary research areas. The three cities are developed smart city prototypes facing urban expansion shortly. Another reason for our choices is that the three regions have different sizes and shapes. The sizes of the cities in ascending order are Bonn, Sejong, and Bangkok, respectively. Because the size of Sejong is in the middle, we chose Sejong city as the primary implementation, and the other two cities are used as test cases. Our study areas are described in this section.  
Sejong city is located almost the middle of Korea on a long, stretching mainly north to south. The population was about 350,000 in October 2020, and the size of Sejong city is 465.23 km\({}^{2}\). Sejong was specifically designed to be a smart city, so it serves as an example of the standard for the other cities experimenting with developing smart city infrastructure. Sejong city has four air quality monitoring stations, as shown with triangle symbols in Figure 1a. For this study, the whole area of the city is divided into 2024 grid cells of 500 \(\times\) 500 m, as shown in Figure 1d.  
Bangkok is the capital city of Thailand. The city is located in almost the middle of Thailand and occupies 1568.7 km\({}^{2}\). Because this city has the highest population density in Thailand, Bangkok city has an air pollution problem from traffic and energy consumption. There are 12 air quality monitoring stations from the pollution control department, which  
Figure 1: Study areas (**a–c**) show administrative boundaries, and triangle symbols show current stations. The map regions (**d**) divided into 2024 grid cells in Sejong city.  
are depicted with the triangle symbols, as shown in Figure 1b. Our experiment divided the area of Bangkok into 7010 grid cells of the same size as we used for Sejong city.  
Bonn city is located in western Germany and occupies 141.06 km2. This city is one of eight major smart cities studied and one of two cities that will now access their brand new 5G network. Bonn has only one air quality monitoring station, which is located in the north of the city. Figure 1c shows the map and location of the air quality monitoring station in Bonn. Furthermore, for our experiment, the whole area of the city is divided into 653 grid cells.
# Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
## 3 Preliminary Design and Simulation Verification
### HFSS Modeling and Parameterization  
Boundary condition settings: In reality, a helix is a three-dimensional structure. In order to facilitate the simulation calculation with a flat structure of the equivalent helix, the helix was set to the metal surface in HFSS in order to achieve the ideal simulation effect. Four spirals were selected and set to perfect E to achieve the desired effect of the metal structure.  
Radiation surface settings: In principle, the radiation boundary size cannot be smaller than the distance from the antenna quarter-wavelength range according to the calculation using a radiation cylinder air box. In the establishment of the radiation cylinder, the radiation surface was added and set to Rad1.  
Feed point settings: To ensure the circular polarization of the antenna's radiation, the current amplitude of the ports of the feed to the four arms should be equal, and the phase should be two by two with a 90\({}^{\circ}\) difference. Four points were set at the bottom of the four-arm spiral antenna to feed each spiral arm separately.  
After the setup was completed, the calculated parameters were substituted. The whole structure of the four-arm spiral antenna is shown in Figure 7.
# Estimation of Dose Enhancement for Inhomogeneous Distribution of Nanoparticles: A Monte Carlo Study
## 2 Materials and Methods
### Simulation Output  
GATE provides tools termed as \"actors\" to collect various types of information from the simulation. The dose distribution in the phantom was calculated with the use of the dose actor. This actor produced 3-dimensional images representing the dose and dose uncertainty distributions. The resolution of each image was set to 100 \(\times\) 100 \(\times\) 300 voxels (the size of each voxel is 1 \(\times\) 1 \(\times\) 1 mm\({}^{3}\)). The use of 10\({}^{9}\) particles ensured the uncertainty in dose calculations to be less than 2% within the beam field of view for all simulations.  
The dose enhancement factor (DEF) was calculated by dividing the dose distribution images obtained with the presence of NPs by those obtained with no added NPs. The resulting image represents a 3D distribution of the DEF. The depth DEF is determined at each depth by averaging the 4 central voxels located at the horizontal plane center at that given depth. In this study, as shown in Figure 2, the maximum voxel depth will be used to represent the location of the measurement along depth.  
The energy spectrum actor was used to collect the primary and secondary particle flux, energy deposition, and energy spectrum. The output of this actor was essential in calculating the characteristic X-ray production in the tumor. The percentage of characteristic X-rays produced in the tumor, escaping without interaction, is denoted by \(\varepsilon\).
## 4 Results and Discussion  
This section first describes a simulation experiment that used the proposed method for TVCFO estimation. Then the results are analyzed. To verify the suitability of using DE to estimate the TVCFO in an OFDM system, it was compared with representative intelligent optimization algorithms applied to channel estimation (the genetic algorithm (GA) [39] and Simulated Annealing Mutated-Genetic Algorithm (SAMGA) [36]) on the same model. We gathered the statistics and results of the proposed scheme, as well as the received signal with and without the actual TVCFO compensation, using the bit error rate (BER). Finally, the performances of the proposed scheme for four different modulation types were examined given to test its universality.  
An OFDM system with eight blocks in one frame was considered. Each block contained \(k=64\) subcarriers. Binary phase-shift keying (BPSK) modulation was adopted if there was no additionally specified. A channel fading diagonal matrix was obtained based on the channel impulse response, as discussed in [33] (i.e., \(h(n)=0.6708\delta(n)+0.5\delta(n-1)+0.3873\delta(n-2)+0.3162\delta(n-3)+0.2236 \delta(n-4)\)) [33]. Two different types of TVCFO were considered for simulation analyses i.e., fourth-order polynomial function and sinusoidal function [9]. One of the CFO variations was a fourth-order polynomial function (19):  
\[\epsilon[n]=\sum_{l=0}^{\infty}\epsilon_{l}n^{l},\quad 1\leq n\leq K \tag{19}\] \[b_{l}=K^{l}\epsilon_{l},\]  
where \(b_{l}\) are uniformly distributed and randomly taken from the limits of \([-0.25,0.25]\), that is \(b_{l}\sim\mathcal{U}[-0.25,0.25]\), and \(K\) is the number of blocks. The other variation form was the sinusoidal function (20) :  
\[\epsilon(n)=\Delta f\bigg{[}A_{0}+A\sin\bigg{(}2\tau n\frac{f_{\sin}}{K}\bigg{)} \bigg{]}, \tag{20}\]  
where \(A_{0}\), \(A\sim\mathcal{U}[-0.25,0.25]\) and \(f_{\sin}\sim\mathcal{U}[0.25,2]\). The limits of the TVCFO estimation values were modified as \([-0.5,0.5]\) for convenience.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 2 Related Work
Reference [15] provides a robust system for tracking GPS in such situation, that the Navigation System can be based on the Wi-Fi signal to improve the accuracy of uses. The same concept can be applied in the I/O detection scheme and improve its performance in the emergency situation, when the GPS signal becomes unstable and weaker.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 2 Mathematical Model
### Operational Constraints  
Based on the above discussion, we establish a multi-constraint satisfaction model of the LEO satellite network. Some assumptions are made as follows:  
1. The on-board power consumption of satellites is only affected by the number of ISL;
2. Attenuation of microwave is ignored due to other reasons such as outside interference;
3. Once satellite communication starts, it cannot be interrupted;
4. During the entire period \(N_{T}\), the quantity of satellites remains the same.  
According to the characteristics and operational regulations of the satellite formation, some constraints can be listed as follows:  
* (1) Communication constraints between satellite and gateway station.  
If the \(i\)th satellite is visible to the \(p\)th gateway station, the angle \(\alpha_{pi}\) between the vector of the satellite's centroid pointing to the gateway station and connecting the satellite to the center of the Earth cannot exceed the satellite's maximum offset capability \(\alpha_{s\,\rm max}\), expressed as:  
\[\alpha_{pi}\leq\alpha_{s\,\rm max} \tag{14}\]
* (2) Communication constraints between satellite and user terminals.  
If the \(i\)th satellite is visible to the \(q\)th user, the angle \(\alpha_{qi}\) between the vector of the satellite's centroid pointing to the terminal and connecting the satellite to the center of the Earth cannot exceed the satellite's maximum offset capability \(\alpha_{o\,\rm max}\). It is expressed as:  
\[\alpha_{qi}\leq\alpha_{o\,\rm max} \tag{15}\]
* (3) Satellite's microwave communication capability constraints.  
When the satellite communicates with ground gateways and terminals, data uploads and downloads occur. The data upload speed \(p^{i}_{up}\) and download speed \(p^{i}_{down}\) of satellite \(i\) cannot exceed the maximum values \(p_{up\,\rm max}\) and \(p_{down\,\rm max}\), expressed as:  
\[p^{i}_{up} \leq p_{up\,\rm max} \tag{16}\] \[p^{i}_{down} \leq p_{down\,\rm max} \tag{17}\]
* (4) Satellite laser communication capability constraints.  
The number of inter-satellite links established by satellites at the same time is limited; that is, the out-degree \(o_{i}\) and in-degree \(l_{i}\) of the satellite \(i\) cannot exceed their maximum values \(o^{i}_{\rm max}\) and \(l^{i}_{\rm max}\). This is expressed as:  
\[o_{i} \leq o^{i}_{\rm max} \tag{18}\] \[l_{i} \leq l^{i}_{\rm max} \tag{19}\]
* (5) ISLL communication range restriction.  
The communication distance \(d_{ij}\) between satellite \(i\) and \(j\) cannot exceed the maximum \(d_{\rm max}\), expressed as:  
\[d_{ij}\leq d_{\rm max} \tag{20}\]
# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 2 Methodology
### Classification
#### 2.4.3 Vision Transformer (ViT)  
The Vision Transformer model, originally proposed for image classification [41; 42], was adapted for cry sound analysis. The architecture comprises a stack of transformer layers, which consist of self-attention mechanisms and feed-forward neural networks. In the cry sound analysis context, the input cry spectrograms are reshaped into a sequence of patches, denoted by:  
\[P\in\mathbb{R}^{N\times D}. \tag{4}\]  
where \(N=\frac{H\times W}{D}\) and \(D\) is the patch size. Each patch \(p_{i}\) is then embedded using an embedding layer, resulting in an embedded patch. The transformer layers enable the model to attend to different patches and capture the relationships between them. Given an embedded patch sequence \(E=(e_{1},e_{2},\dots,e_{N})\), the self-attention mechanism calculates the attention scores which are then passed through a feed-forward neural network (FFN) layer. The transformer layers are stacked to attend to different patches and capture their relationships. The output of the last transformer layer is passed through a classification head, typically consisting of one or more fully connected layers and a softmax activation function, to produce the predicted cry class probabilities.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 3 BLE Signal Analysis  
During signal measurement, signals emitted by the BLE devices (positioned in the front pocket, back pocket, top pocket, shoulder, and wrist) were recorded in distinct time segments. Each segment corresponds to the signal emitted over a 180 s period, with the signal transmitted once per second (1 Hz). The length of these segments was determined by the number of signal measurements recorded during this 180 s interval.  
To categorize the segments as either short or long, threshold values were applied, ranging from 10% (equivalent to 18 measurements) to 100% (equivalent to 180 measurements). The goal of this analysis was to examine the variability and stability of signal values across different body positions.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 3 Core Technologies in Vision-Based Collaborative Robotics
### Human Pose Estimation and Activity Recognition  
Effective human-robot collaboration requires robots to not only detect humans but also understand their poses, gestures, and activities. Human pose estimation is the task of localizing body key points. Human pose estimation is pivotal for recognizing human intent, predicting actions, and ensuring safe interactions [43; 44]. Pose estimation accuracy significantly affects downstream tasks such as intention prediction and cooperative planning in shared workspaces [44; 45]. Pose detection errors greater than 10-15 pixels at the wrist or shoulder joints substantially degrade the collaborative task efficiency [46].  
State-of-the-art techniques, including OpenPose, HRNet, and vision transformers, have enabled real-time multi-person tracking in cluttered environments [47]. Coupled with activity recognition models, such as 3D convolutional neural networks (3D-CNNs) and LSTM (Long Short-Term Memory)-based architectures, cobots can proactively adjust their behaviors in response to human movements [48; 49].