esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 5 Experimental Results  
### Evaluation Protocol  
We experimented on the UCF101 dataset, which is the benchmark for action videos and is currently the largest dataset available in this field of computer vision. It contains 101 different classes, which can be split into four categories. There are 13,320 videos in the entire dataset. For evaluation, we used \(k\)-folds cross-validation, where \(k=3\). The training set contains around 9324 videos, and the test set contains around 3996 videos. The classification accuracy is used as the performance measure, and the reported metric is the mean classification accuracy across three splits. Comparisons are performed with different architectures based on the accuracy of split 1. The mean classification accuracy across three splits of data was compared with the state of the art.
# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 2 Materials and Methods  
We conducted a comprehensive evaluation of seven traditional machine learning models for cell type annotation using single-cell gene expression data. The dataset was pre-processed and split into training (80%) and test (20%) sets. We evaluated random forest (with 10 estimators), gradient boosting (300 estimators), support vector machine (with an RBF kernel), logistic regression (max 100 iterations), k-nearest neighbours (k = 5), decision tree, and Gaussian naive Bayes models with default parameters. Each model was trained on the training set and used to predict cell types in the test set. We also implemented a Transformer-based model comprising an embedding layer that projected the input gene expression data into a 512-dimensional feature space, followed by a transformer encoder with 6 layers and 8 attention heads, and a final classification layer. The number of output classes corresponded to the unique cell types in the dataset. We trained the model for 10 epochs using the Adam optimizer with a learning rate of 0.0001 and implemented a scheduler to adjust the learning rate based on validation loss. Cross-entropy loss was used as the optimization criterion. This experiment offered insights into the performance of various traditional machine learning methods for cell type annotation, highlighting differences between simple, interpretable models and complex ensemble methods.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Event** & **Aroma Descriptor** & **Weighted** & **Retention** & **Aroma Event** & **OD \({}^{*}\)** & **Mass Spectral Library** & **Chemical Abstracts** & **Significant Ions (Number of Ions** & **Listed: Ions listed in the Order** & **Listed: Ions listed in the Order** & **of Intensity** \\ \hline
3 & Honey, caramel, butterscoth & 431 & 5.86 & 0.1 & 32 & Ethyl isobutyrate & 97-62-1 & 11: 43 71 41 88 16 89 72 44 87 55 70 \\
4 & Honey & 122 & 6.53 & 0.04 & 32 & Isobutyl acetate & 110-19-0 & 9: 43 56 73 41 39 71 61 57 37 \\
5 & Unknown pleasant & 307 & 7.09 & 0.07 & 32 & Ethyl butyrate & 105-54-4 & 19: 71 88 43 73 41 60 70 14 2 45 61 \\
6 & Solvent & 291 & 7.38 & 0.07 & 32 & Unknown & & 39 16 55 44 57 69 117 \\
7 & Body odor & 277 & 7.68 & 0.14 & 32 & Isoamyl alcohol & 123-51-3 & 16: 55 70 47 37 26 35 04 73 26 & 50 47 37 26 \\
8 & Entity 1 & 314 & 8.2 & 0.09 & 32 & Ethyl methylbutyraterate & 7452-791 & 7: 10 28 58 85 115 103 73 75 \\
9 & Entity 2 & 112 & 8.4 & 0.09 & 32 & Ethyl isovalerate & 108-64-5 & 7: 88 56 80 61 515 95 103 \\
10 & Barana & 337 & 9.16 & 0.08 & 32 & Isoamyl acetate & 123-92-2 & 20: 43 70 55 76 61 41 42 73 69 37 & 58 56 88 48 57 58 54 53 54 \\
11 & Woody 1 & 55 & 10.31 & 0.06 & 32 & Ethyl lactate & 97-64-3 & 5: 45 47 54 476 56 \\
12 & Vinegar & 57 & 11.45 & 0.06 & 32 & Acetic acid & 64-19-7 & 4: 43 45 04 2 \\
13 & Cereal & 138 & 11.75 & 0.39 & 32 & Unknown & & 20: 88 99 43 10 60 70 73 71 41 61 55 \\
14 & Fruity & 333 & 12.43 & 0.11 & 32 & Ethyl hexanoate & 123-66-60 & 2: 88 89 69 11 67 89 74 \\
15 & Garlic & 129 & 12.89 & 0.11 & 32 & Not detected & & & \\
16 & Mashroom & 55 & 14.95 & 0.06 & 32 & Unknown & & & \\
17 & Sweaty & 250 & 15.74 & 0.45 & 32 & Methyl octanoate & 111-11-5 & 15: 74 87 127 43 57 55 115 59 41 75 & 11 129 84 39 98 \\
18 & Rose 1 & 27 & 16.44 & 0.05 & 32 & Unknown & & & \\
19 & Match, sulfurty & 136 & 16.74 & 0.08 & 32 & Not detected & & & \\
20 & Cut grass, fruity & 348 & 17.26 & 0.19 & 32 & Ethyl octanoate & 106-32-1 & 20: 88 101 127 57 37 05 56 41 61 & 129 43 415 83 45 39 \\
21 & Woody 2 & 52 & 18.23 & 0.12 & 32 & Unknown & & & \\
22 & Rose 2 & 290 & 20.05 & 0.3 & 32 & Phenethyl alcohol & 60-12-8 & 20: 91 92 122 65 51 77 39 63 78 89 & 10 35 06 29 52 66 79 64 102 \\
23 & Strawberry, honey & 268 & 21.45 & 0.3 & 32 & Ethyl decanoate & 110-38-3 & 20: 88 101 155 157 37 05 41 43 60 & 61 69 115 99 57 1143 84 24 \\
24 & Strawberry & 458 & 22.1 & 0.11 & 32 & Octanoic acid & 124-07-2 & 20: 60 673 35 101 41 85 84 87 69 & 115 61 45 39 24 55 76 74 83 82 \\
25 & Carrots, woody & 156 & 22.61 & 0.69 & 32 & Unknown & & & \\
26 & Fecal & 61 & 23.4 & 0.02 & 32 & Unknown & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Event**} & \multirow{2}{*}{**Aroma Descriptor**} & \multirow{2}{*}{**Weighted**} & \multirow{2}{*}{**Retention**} & \multirow{2}{*}{**Aroma Event**} & \multirow{2}{*}{**OD***} & \multirow{2}{*}{**Mass Spectral Library**} &
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Bacterial Aegerolysins from Proteobacteria
#### 2.6.2 Aegerolysins from _Pseudomonas aeruginosa_  
_Pseudomonas aeruginosa_ ([PERSON] 1872) Migula 1900 is a known ubiquitous opportunistic bacterial human pathogen that is widely distributed in patients with cystic fibrosis or acquired immune deficiency syndrome. It infects a remarkably evolutionarily diverse hosts, namely vertebrates, plants, and also insects; it is an important drug-resistant pathogen and has shown innate potential to promote plant growth and biological control in vitro [42; 46].  
The quorum-sensing receptor RhlR has been shown to trigger expression of the _P. aeruginosa_ aegypti **RahU** as a complex with its cognate autoinducer N-butanol-homoserine lactone [145]. Expression of **rahU** gene is increased eightfold in the _P. aeruginosa_ isolates with a nonmucoid phenotype, associated with the initial phase of lung infection. The biological role of **RahU** remains enigmatic, but it has been suggested that it may play a role in host-pathogen interaction [146]. **RahU** has been shown to associate with both the inner and outer membranes of the mucoid isolate [147]. **RahU** specifically interacts with CPE [148]; this CPE-specific interaction was demonstrated both on the artificial membranes containing physiologically relevant concentrations of CPE and on CPE-producing insect cell line. However, no interaction of **RahU** with the mammalian cell membrane was observed. The crystal structure of **RahU** (PDB ID: 6 ZC1) showed the conserved aegerolysin folding as well as a ligand binding cavity [148]. Further mutational analysis revealed that residues within and around the binding site are important for the interaction of **RahU** with CPE-containing membranes, providing insights into the molecular discrimination of host cell membranes by **RahU**[148].  
For **RahU**, there are no data on whether it acts alone or with a partner protein. There does not appear to be a gene for a MACPF-like partner adjacent on the chromosome of _P. aeruginosa_ PAO1 or any other aegerolysin gene. In several strains, the **RahU** sequence is invariant; in more distant strains (according to whole genome comparison), such as PA7, only 77% identity (27 amino acid changes) was detected in the C-terminal shorter protein.
# Digital Photoelastic Analysis of TAD-Supported Maximly Arch Distalization
## 4 Discussion
### Biomechanics of Dentition  
From the viewpoint of biomechanics, the moment of center of resistance (CR) in the maxillary dentition, the moment at the junction of the retraction hook and main arch, and the direction of retraction force should be considered during the distalization maxillary dentition (Figure 12). These factors work in a complex manner to determine the retraction condition for dentition. [PERSON] et al. [16] used a 3 DFEM model to report that changes in moments occur to CR depending on the retraction hook length, TAD position, and changes in the movement patterns of anterior teeth. Their study showed similar stress distribution results. However, in their study, molar intrusion occurred when the tensile direction from the TAD was from the apex, whereas molar extrusion occurred when retracted from the cervical region. [PERSON] et al. [24] used a more sophisticated simulation of long-term orthodontic tooth movement in 3 DFEM and reported that the lingual tipping of anterior teeth occurred during anterior retraction. These results are also consistent with our results. There are several reports on the location of CR in the maxillary dentition. In this study, since the stress at the second premolars differed significantly in the six models, CR in the maxillary dentition appeared in the cervical half or third of the distal root surface of the second premolars. [PERSON] et al. [16] performed a simulation of anterior maxillary retraction using a 3D finite element model. They reported that the CR on the distal root surface of the second premolar was 6.5-7 mm above the archive. Our study is not 3 DFEM, but these results support our experimental results.  
Figure 11: The average tendency of the stress direction in all six combinations. The stress direction of all combinations showed the same tendency, as follows: oblique in the anterior teeth root apex, horizontal at inter root space between the premolars and molars, and vertical in the apical area of the molars.
## 3 Results
### Effect of Water Blocking Rate  
As a water retaining structure, the water blocking rate of a TCC is the main feature of its water retaining capacity. The water blocking rate of the bottom TCC is generally between 0.3 and 0.8 [31], which will affect the temperature increase effect if it is too low and will affect the water flow if it is too high. Assuming that when water blocking rate (\(Pr_{h}\)) varies, the other main parameters remain constant. According to Table 3, the inflow velocity (\(v\)) is uniform and the value is 0.05 m/s, the area ratio (\(Pr_{a}\)) is 1.0, the inclination ratio (\(Pr_{l}\)) is 0, and the water temperature stratification ratio (\(\beta\)) is 0. The TCC load (\(F\)) of various \(Pr_{h}\) is measured at the same inflow velocity (\(v\) = 0.05 m/s). From resistance equation \(F=q*A=C_{d}/2\rho v^{2}*A\), Where \(A=Pr_{h}*A_{T}\) (\(A_{T}\) is the cross-section area (m\({}^{2}\))); \(\mathrm{C_{d}}\) for each \(Pr_{h}\) is calculated according to the given force (F), inflow velocity (v) and cross-section area (\(A_{T}\)). Then from that we can further calculate F for any velocity using the \(\mathrm{C_{d}}\) obtained. Figure 9 shows that as the water blocking rate increases, the drag coefficient and the load increase significantly. To analyze the effect of the water blocking rate, a fitting formula is investigated to quantify the relationship between the water blocking rate and the drag coefficient. The results of the fitting formula show that the change in the drag coefficient obeys an exponential growth function at different water blocking rates. According to the fitting formula, the drag coefficient can be calculated based on the corresponding water blocking rate.  
Figure 8: Comparison of the calculated and measured values of discharged water temperature.  
Figure 9: (**a**) Drag coefficient and (**b**) force at different water blocking rates (data sources: C1–C11).
# Research on Image Feature Extraction and Environment Inference Based on Invariant Learning
## 1 Introduction
Subsequently, based on the principle of gradient-weighted class activation mapping (Grad-CAM), we extract environmental features from the results of the ResNet18 network, which represent the spurious features in the classification task. Finally, we employ the K-means clustering algorithm to categorize the extracted environmental features, grouping the image data according to environmental classes to mitigate the interference of environmental factors on the model and enhance the robustness of the classification task. In summary, the innovations of this paper are summarized as follows:  
1. An image invariant feature extraction method based on invariant learning is proposed, enhancing the model's out-of-distribution generalization ability. 2. Comparative experiments demonstrate the significant advantages of IRM-ResNet18 in extracting key invariant features, thereby improving the understanding of classification targets. 3. Environmental partitioning algorithm: By integrating invariant learning with Grad-CAM, an effective environmental partitioning algorithm is proposed, which employs clustering to address spurious features in confounded image datasets, thereby improving the accuracy of environmental partitioning. The structure of this paper is arranged as follows: Section 2 introduces the invariant learning framework for adaptive environment partitioning, detailing the theoretical foundations of the framework. Section 3 describes the image feature extraction process based on invariant learning, including the dataset, environmental configuration, training of the IRM-ResNet18 network, and validation of its feature extraction effectiveness. Section 4 presents a method for image environment partitioning that combines invariant learning with Grad-CAM theory, and validates its partitioning effectiveness. Finally, Section 5 concludes the study.
## 3 Transmitter and Receiver Structure  
In this section, we describe the transmitter and receiver structures adopted. Section 3.2 addresses the use of complex rotation matrices (CRMs) so as to integrate SSD into PT-GSFIM symbol design. The last subsection presents the adopted receiver algorithm.
# Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)
## 1 Introduction
* To protect the network from the malicious activities and to reduce the energy consumption and delay in the proposed method, triple-layer trust construction, congestion-aware community construction, and hybrid optimization is performed. Initially, to improve the security, triple-layer trust construction is performed through a dynamic trust estimation process. Here, the trust for vehicle to CH, for CH to drone, and for drone to BS are concentrated. * Secondly, congestion is controlled through a congestion-aware community construction method. Finally, in order to reduce energy consumption and routing overhead, hybrid optimization is performed with the combination of ACO and GWO algorithms. * In the performance analysis section (Section 5), the evaluation of the proposed work is performed using various attacks such as flooding attack, insider attack, wormhole attacks, and position falsification attack. The considered parameters for the performance analysis are energy efficiency, packet delivery ratio, routing overhead, end-to-end delay, packet loss, and throughput.
# 25 Years of Bluetooth Technology
## 6 Recent Research Results on Bluetooth
### Location Tracking  
The research community is also currently exploring the usability of BLE for _location tracking and indoor positioning systems_[49; 50; 51; 52; 53]. For example, BLE proximity beacons can be utilized for determining the location of buses without requiring the usage of any Global Positioning System (GPS) devices. BLE beacons are being deployed on buses so that they can be tracked, and by installing BLE detection devices at selected bus stops along the route, bus arrivals can be detected. The authors of [49] showed that the detection of BLE beacons is very accurate and tracking bus locations without using a GPS device is a cost effective method. BLE beacon technology is also being explored for position tracking and for identifying people in micro-locations such as buildings [50]. Indoor navigation systems with BLE technology use Bluetooth beacons that emit radio frequency signals.  
One issue with BLE-based indoor positioning systems is the fluctuation in the Received Signal Strength (RSS) because of fading. Recent research [51] has proposed the use of spatial diversity and frequency diversity to handle the fluctuations and provide better positioning accuracy. There is also a lot of focus on developing new fingerprinting-based algorithms to address issues of accuracy, precision, and time complexity when implementing positioning using BLE-beacons [52]. Efforts are being focused on improving the time-complexities of these algorithms [52] as well as employing machine learning algorithms together with BLE to improve location accuracy [53]. Some BLE systems have shown an improvement in the positioning accuracy by about 15% compared to other positioning alternatives such as GPS, and so on [54].
## 3 Thermal Sensing
Since the electrical resistivity is the inverse of the DC conductivity, it is also proportional to the temperature. Thus, the damping frequency can be modified as:  
\[\omega_{\tau}(T)=\omega_{\tau}\frac{1+\alpha_{R}\Delta T}{1+3\alpha\Delta T} \tag{10}\]  
where \(\alpha_{R}\) is the temperature coefficient of the resistivity at \(T_{0}\). Considering the temperature modified Drude model, and the thermal expansion of Au, various parameters were set in the RF module of COMSOL Multiphysics. These parameters include the temperature-dependent dielectric constant of Au, as well as the period and radius for different temperatures. For the NBDS, simulated results for different temperatures are shown in Figure 3. As can be seen in this figure, as the temperature increases, the transmission peaks gradually shift to lower frequencies. Additionally, the width of the peaks becomes wider, and the transmittances will gradually decrease. The frequency shift is, thus, a very promising parameter to measure in the determination of the temperature variation in the environment (i.e., for sensing). More generally, the sensitivity and the figure of merit (FOM) are two important parameters that reflect the performance of a sensor. The definition of sensitivity can be expressed as \(\Delta f/\Delta T\), where \(\Delta f\) is the frequency shift of the transmission peak and \(\Delta T\) is the numerical shift in temperature. By applying this definition of sensitivity on the results presented in Figure 3, it is obvious that the sensitivity of this THz thermal sensor can reach 11.5 MHz/K. Moreover, according to reference [41], the FOM of a terahertz thermal sensor is the ratio of the sensitivity to the full width at half maximum (FWHM): FOM = (Sensitivity \(\times\) _T_)/FWHM. The FOM of the here presented waveguide-based thermal sensor has a maximum value of 142.7 (at \(T\) = 273 K), which is larger than the FOM of 117, as presented in Ref. [39]. As shown in Figure 3, the FWHM of the transmission peaks increases sharply with an increase in temperature. This makes FOM smaller, but it can still reach a FOM value of 28.9 (at \(T\) = 353 K). The observation that FOM becomes smaller as the temperature rises is a problem to be solved in the near future. The most important result is, though, that we have constructed a waveguide-based THz thermal sensor by measuring the frequencies of the transmission peaks.
## 2 Materials and Methods
### ORR Electrocatalytic Activities  
For the cyclic voltammetry (CV) and linear sweep voltammetry (LSV) experiments, an Autolab PGSTAT 302N potentiostat/galvanostat (EcoChimie B.V. Netherlands) was used. A modified glassy carbon rotating disk electrode, RDE (\(d\) = 3 mm, Metrohm, Switzerland) was used as the working electrode, a carbon rod (\(d\) = 2 mm, Metrohm, Switzerland) was used as the counter, and an Ag/AgCl (3 mol dm\({}^{-3}\) KCl, Metrohm, Switzerland) was used as the reference electrode.  
Before any type of modification, the electrode was cleaned (detailed information in ESI). The electrode modification was achieved by dropping 2 \(\times\) 2.5 \(\upmu\)L of electrocataly dispersion onto the RDE surface followed by solvent evaporation under a flux of hot air. To prepare the electrocatalyist dispersion, 1 mg of material was mixed with 20 \(\upmu\)L of Nafion, 125 \(\upmu\)L of ultrapure water, and 125 \(\upmu\)L of isopropanol. Then, the mixture was sonicated for 15 min.  
The CV and LSV tests were performed in KOH electrolyte (0.1 mol dm\({}^{-3}\)) saturated in O\({}_{2}\) and N\({}_{2}\). (30 min purge for each gas). The potential range used for CV and LSV tests was between 0.26 and 1.46 V versus reversible hydrogen electrode (RHE) at a scan rate of 0.005 V s\({}^{-1}\). In addition, for the LSV tests, rotation speeds between 400 and 3000 rpm were used. The effective ORR current was determined through the subtraction of the current obtained in KOH saturated with N\({}_{2}\) by that saturated with O\({}_{2}\). The stability was evaluated by chronoamperometry (CA) for 20,000 s at 0.5 V versus RHE and 1600 rpm. CA was also used to determine the tolerance to methanol crossover by applying a \(E\) = 0.5 V versus RHE for 2000 s and a 1600 rpm rotation speed.  
All relevant ORR parameters (effective currents, diffusion-limiting current densities (\(j_{\text{L}}\)), onset potential (\(E_{\text{onset}}\)), Tafel slope, and the number of electrons transferred for each O\({}_{2}\) molecule (\(\hat{n}_{\text{O}2}\))) were calculated as described in the ESI file.  
Experiments with the rotating ring disk electrode (RRDE) were also conducted in KOH electrolyte saturated with O\({}_{2}\) to determine the amount of H\({}_{2}\)O\({}_{2}\) produced. This was achieved using Equation (1) where \(i_{\text{R}}\) and \(i_{\text{D}}\) correspond to the ring and disk currents, respectively, and N is the current collection efficiency of the Pt ring (\(N\) = 0.25) [39].  
\[\%\;\text{H}_{2}\text{O}_{2}\;=\;200\;\times\;\frac{i_{\text{R}}/\;N}{i_{ \text{D}}+i_{\text{R}}/N}, \tag{1}\]
## 4 Evaluation and Results
### Dataset  
We used a publicly available real-world EUA dataset for the evaluation of the proposed VNF-AS model [31; 32]. The dataset shows the exact locations of the eNBs/MECs and the cellular users of different MNOs across Australia. We chose the Melbourne Central Business District (CBD) as the Region of Interest (ROI) for our experimentation as it depicts a dense 5G cellular scenario. Using the precise locations taken from the dataset, we calculated cellular traffic load and resource usage at various levels.  
From the ROI, i.e., Melbourne CBD, we selected six distant eNBs covering an area of 2.05 square kilometers depicting a dense cellular deployment. Each UE was attached within the ROI with one of the six eNBs depending on the cell radius of the eNBs. Each UE was assigned a CQI relative to its distance from the attached eNB. We streamed a 720p video of 1 h in length in time to all of the six eNBs with requesting users in both unicast and multicast modes during simulations. For the calculation of the traffic load, bandwidth, RB usage, and QoE score, we took a scenario of streaming a lecture video to various students within the ROI. We selected a random number of users from all the attached users within each cell not to lose the concept of generality. The peak hours in a cellular network has changed dramatically due to the novel COVID-19 pandemic situation. Therefore, the burden on each layer of the cellular networks has undoubtedly increased significantly. Consequently, the MNO cannot provide a higher number of resources to each user on a dedicated channel, i.e., unicast mode. In such situations where several students attend an online lecture within the radius of one cell, multicast transmission instantiated by VNF-AS can be exploited to manage the MNO resources efficiently, reducing the cost of fetching the content from the CDN, and increasing the QoE of the users.