esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**N\({}^{\circ}\)**} & **Kovats Indices** & \multirow{2}{*}{**Components**} & \multicolumn{4}{c}{**(\%) of Components**} \\ \cline{3-6}  & & & **March** & **June** & **September** \\ \hline
46 & 1480 & germacrene D & 1.12 & 1.87 & 0.50 \\
47 & 1499 & \(\alpha\)-muurolene & 0.16 & 0.47 & --- \\
48 & 1524 & \(\delta\)-cadinence & --- & 0.15 & --- \\
49 & 1574 & germacrene D-4-ol & --- & 0.21 & --- \\
50 & 1586 & **davanone** & **3.87** & 1.54 & 1.4 \\
**Hydrocarbon monoterpenes (\%)** & **6.17** & **7.89** & **9.72** \\
**Oxygenated monoterpenes (\%)** & **84.66** & **84.43** & **80.92** \\
**Total (\%)** & **96.96** & **97.53** & **93.02** \\
**Sesquietpenes** & **6.09** & **5.17** & **2.51** \\
**Aliphatic hydrocarbons** & **8.44** & **11.23** & **10.17** \\
**Ketones** & **44.55** & **33.84** & **42.19** \\
**Esters** & **30.17** & **27.11** & **28.3** \\
**Alcohols** & **12.94** & **12.15** & **9.64** \\
**Ethers** & **1.90** & **11.83** & **11.98** \\
**Aldehydes** & **1.57** & **1.30** & **0.77** \\ \hline
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 1 Introduction  
With the growing popularity of automatic speaker verification (ASV) systems, the attacks on them have posed significant security threats, which requires that the ASV systems have the ability to detect spoofing attacks. Generally, spoofing attacks can be categorized into four types: impersonation, synthesis, conversion, and replay [1]. In order to promote research on anti-spoofing, the automatic speaker verification spoofing and countermeasures (ASVspoof) challenge [2] was first launched in 2015, which focused on discriminating between synthesized or converted voices and those uttered by a human. The second challenge, i.e., the ASVspoof 2017 challenge [3], focused on detecting replay spoofing to discriminate whether a given speech was the voice of an in-person human or the replay of a recorded speech.  
Replay attack refers to when an attacker uses a high-fidelity recording device to record the voice of a legitimate authentication system user and then uses the recorded playback through the device on theASV system, thereby achieving an attack behavior [4]. With the development of electronic technology, the performance of high-fidelity recording and playback equipment has been considerably improved. A replay attack is not difficult to implement and does not require any specialized knowledge, which poses a significant threat to ASV systems [5]. Therefore, the research on replay attack detection has become increasingly important. [PERSON] et al. [6] used far-field replay speech recordings to investigate the vulnerability of an ASV system and showed that the equal error rate (EER) of the ASV system increased from 1% to nearly 70% when the human voice was recorded directly and replayed to the device. [PERSON] et al. [5] further evaluated the risk of replay attack by using the 2005 and 2006 National Institute of Standards and Technology Speaker Recognition Evaluation dataset (NIST' 05 and NIST' 06) corpus and six kinds of ASV systems. Their results showed that the low-effort replay attack posed a significant risk to all the ASV systems tested. However, the work presented above only reported the results in conditions without background noise, thus it is still not clear how background noise affects the performance of spoofing detection. Therefore, in order to enhance the security of ASV systems, spoofing detection in noisy environment is investigated in this paper.
# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 4 Experimental Results and Analysis
### Parameter Analysis
#### 4.5.3 Analysis of the Number of Classifiers  
To verify whether subclassification has an effect on the performance, we designed experiments with different numbers of sub-classifiers and the results are shown in Figure 11. From Figure 11 it is clear that the accuracy of the different numbers of sub-classifiers did not differ significantly.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**SVM (\%)**} & \multicolumn{3}{c}{**RF (\%)**} & \multicolumn{3}{c}{**ELM (\%)**} \\ \cline{2-10}  & Acc & Spec & Sens & Acc & Spec & Sens & Acc & Spec & Sens \\ \hline PID & \(\mathbf{84.54}\) & \(88.11\) & \(\mathbf{80.99}\) & \(83.61\) & \(85.85\) & \(81.38\) & \(80.83\) & \(\mathbf{92.54}\) & \(69.14\) \\ Maxlettle & \(\mathbf{100}\) & \(\mathbf{100}\) & \(\mathbf{100}\) & \(98.89\) & \(100\) & \(97.78\) & \(99.00\) & \(98.00\) & \(100\) \\ Heart & \(\mathbf{96.67}\) & \(\mathbf{97.50}\) & \(\mathbf{95.83}\) & \(96.67\) & \(97.50\) & \(95.83\) & \(96.67\) & \(97.50\) & \(95.83\) \\ PD & \(\mathbf{81.73}\) & \(\mathbf{81.35}\) & \(82.12\) & \(81.44\) & \(78.27\) & \(\mathbf{84.62}\) & \(80.67\) & \(80.00\) & \(81.35\) \\ AD & \(\mathbf{71.11}\) & \(\mathbf{85.56}\) & \(\mathbf{71.11}\) & \(61.11\) & \(80.56\) & \(61.11\) & \(67.78\) & \(83.89\) & \(67.78\) \\ Wisconsin & \(\mathbf{99.37}\) & \(98.74\) & \(\mathbf{100}\) & \(99.16\) & \(98.32\) & \(100\) & \(99.16\) & \(\mathbf{99.15}\) & \(99.15\) \\ Vehicle & \(\mathbf{87.80}\) & \(\mathbf{95.93}\) & \(\mathbf{87.82}\) & \(87.06\) & \(95.67\) & \(87.06\) & \(87.55\) & \(95.16\) & \(87.55\) \\ WDBC & \(\mathbf{99.76}\) & \(\mathbf{100}\) & \(\mathbf{99.53}\) & \(97.41\) & \(97.63\) & \(97.16\) & \(98.82\) & \(98.58\) & \(99.06\) \\ \hline \hline \end{tabular}
\end{table}
Table 17: Classification accuracy of proposed algorithm with different classifiers.  
Figure 10: Effect of penalty term coefficients on FESSAE performance: (**a**) single sample of PID; (**b**) sample pair of PID; (**c**) single sample of Heart; (**d**) sample pair of Heart.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Other Works Analyzing Different Aspects of LoRaWAN
#### 3.3.3 Performance Improvement
Because this scheme depends on the intensity of the channel traffic, this work endeavored to find an optimal traffic distribution using dynamic programming. Another interesting work is [61], where a large number of smart meters are used to manage an electrical network in real time; device management in the LoRaWAN network was optimized through the use of frequency and time multiplexing, thus allowing 98.45% of the packets sent to be received.
# Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning
## 2 Experimental Details
### Lacunarity Analysis and Classification Using Deep Learning
#### 2.6.1 Image Processing and Lacunarity Calculation  
High-resolution images (4000 \(\times\) 4000 pixels) of dried deposits were processed using OpenCV and the scikit-image library. Initially, each image was converted to grayscale and binarized using [PERSON]'s thresholding method to facilitate the segmentation of relevant structures. Then, the images were systematically segmented into consecutive, non-overlapping regions of interest (ROIs) of dimensions \(128\times 128\) pixels, covering the entire image to ensure a detailed analysis of the relevant substructures.  
Lacunarity (\(\lambda\)) was calculated using a sliding box counting method with a box size of 4 pixels, following Equation (1), where \(\mu\) and \(\sigma\) represent the mean and standard deviation of pixel intensity values within each ROI. Only ROIs with lacunarity values exceeding a threshold of 1.5 were selected.
## 3 Results
### Biocompatibility Assessment  
Samples were incubated in complete DMEM for 24 h, which was then added to cell cultures for indirect biocompatibility testing, as recommended in ISO 10993-5 guidelines [22]. The addition of a biomaterial-conditioned medium did not impair cell viability, and no morphological alterations were observed (Figure 2). Cells grew up to confluence and formed well-organized nodules, a typical feature of osteoblast cultures, in all samples, and nodules appeared larger and more numerous in samples conditioned with SrHA (Figure 2C,G) and MgSrHA (Figure 2D,H) granules by day seven.  
Figure 1: Scanning electron microscope microphotographs of MgSrHA granules. The surface displays a high degree of roughness, with numerous cavities, because of the sintering process. Most cavities are a few microns wide, though smaller pores are present. (**A**) Bar = 200 \(\upmu\)m. (**B**) Bar = 2 \(\upmu\)m.
## 2 Experimental Setup  
Figure 1 illustrates the experimental schematic diagram of the C-band SOA-based tunable fiber laser with two nested ring cavities (i.e., compound-ring resonator) and two broadband Sagnac loop mirrors that serve as either dual-output ports or a single output port according to the reflectivity settings of each of the Sagnac interferometers (i.e., SLMs). Each ring cavity consists of two branches, I-II and I-III, for the inner and the outer ring cavity, respectively. Both ring cavities share a common branch, I, which contains an SOA\({}_{1}\), (Kamelian, OPA-20-N-C-SU), a tunable optical filter (TF-11-11-1520/1570), and a polarization controller, PC\({}_{1}\). Branch II contains an SOA\({}_{2}\) (Thorlabs, S1013S), and a polarization controller, PC\({}_{2}\). Branch III contains only a polarization controller, PC\({}_{3}\). Note that the branch has no SOA due to the limited number of SOAs available during the time of our experiment. All the three branches, I, II and III, are connected by two 3-dB fiber couplers, C\({}_{1}\) and C\({}_{2}\). Each 3-dB fiber coupler, C\({}_{1}\) and C\({}_{2}\), is also connected to a Sagnac loop mirror, SLM\({}_{1}\) and SLM\({}_{2}\), respectively, as shown in Figure 1. These Sagnac loop mirrors, with a polarization controller placed in each loop, act as variable reflectors. By adjusting the polarization controller (i.e., PC\({}_{4}\) or PC\({}_{5}\)), one can change the reflectivity of the loop mirrors and thereby can switch from single to dual-output port configurations [26; 27]. The low power tunable optical filter (TF), which is placed in the common branch I, is used for selecting and tuning the operating wavelength of the proposed fiber laser. It operates between 1520 and 1570 nm wavelength. There are three PCs (PC\({}_{1}\), PC\({}_{2}\) and PC\({}_{3}\)) in the compound-ring resonator.  
Figure 1: Experimental setup of the dual Sagnac loop mirror semiconductor optical amplifier (SOA)-based tunable fiber compound-ring laser. PC: polarization controller; cw: clockwise; ccw: counter-clockwise, SLM: Sagnac loop mirrors; PM: power meter.
## 4 Results and Analysis
### Predicted vs. Measured Enhancement
This could indicate that the correlation length of the aluminum samples is too small for the focal-plane system to be effective in the given laboratory conditions. Figure 7: Enhancement (\(\eta\)) vs. Lateral Surface Dimension (\(\ell_{\lambda/2}\)). Simulations are conducted with a random shift in the sample prior to each intensity measurement. The solid lines are experimental data trends, and dashed lines represent simulation data trends. The simulated focal-plane system is subject to a random 20.6-um shift; maximum enhancement is shown in red, while average enhancement is shown in magenta. The simulated imaging system is subject to a random 23.4-μm shift; maximum enhancement is shown in blue, while average enhancement is shown in cyan.
## 2 Automatic Segmentation Using U-Net  
### U-Net  
U-Net [26], as shown in Figure 4, is a neural network architecture proposed in deep learning; it is currently being studied in the field of medical segmentation. The U-Net structure used in this study is shown in Figure 4. The medical imaging modality used in this study is CTA and the CTA images were obtained from SOMATOM Definition AS + SIEMENS. The image obtained from the CT instrument had a size of 512 \(\times\) 512 pixels and in this study, the patient CTA image dataset was provided to our U-Net model, which, according to previous research [23; 24], has surpassed the performance of conventional segmentation methods when applied to medical image segmentation.  
Figure 4: U-Net neural network architecture.  
U-Net architecture has two different paths--the contracting path and the symmetric expanding path. U-Net's contracting path captures context, whereas its expanding path facilitates precise localization, enabling U-Net to overcome the trade-off relationship between context and localization observed in conventional neural networks. During up-sampling in the expanding path, image data are copied and cropped into the contracting path and are then concatenated for precise localization. This method of copying and concatenation into the contracting path was employed in our study, as shown in Figure 4. U-Net has high learning speed as there is no fully connected layer and the patch overlap rate is low. As reported in Reference [26], U-Net can also achieve good segmentation performance when trained using augmentation data. It was selected for use in this study because of its suitability for the single-graphics processing unit (GPU) environment and its ability to overcome limitations caused by the number of patient images.  
In the neural network learning stage, each label data should further inform the neural network, according to the nature of supervised learning. Therefore, we needed to prepare CTA images and ground truth (label) the learning targets of this study when creating the dataset. The preparation of the dataset is explained in the next section.
# A Robust Localization System Fusion Vision-CNN
###### Abstract  
Map-based, high-precision dynamic pose tracking and rapid relocalization in the case of unknown poses are very important for indoor navigation robots. This paper aims to propose a robust and high-precision indoor robot positioning algorithm that combines vision and laser sensor information. This algorithm mainly includes two parts: initialization and real-time pose tracking. The initialization component is mainly to solve the problem of the uncertainty of a robot's initial pose and loss of pose tracking. First, the laser information is added to the posenetLSTM neural network that only considers image information as a geometric constraint, and the loss function is redesigned thereby improving global positioning accuracy. Second, on the basis of visual rough positioning, the branch and bound method is used to quickly search the high-precision pose of the robot. In the real-time tracking component, small-scale correlation sampling is performed on the high-resolution environment grid map, and the robot's pose is dynamically tracked in real time. When the score of the tracking pose is lower than a certain threshold, the method of nonlinear graph optimization is used to perform the pose optimization. In order to prove the robustness, high precision, and real-time performance of the algorithm, this article first builds a simulation environment in Gazebo for evaluation, and then verifies the relevant performance of the algorithm through the Mir robot platform. Both simulations and experiments show that the introduction of laser information into the neural network can greatly improve the accuracy of vision relocalization and the system can quickly perform high-precision repositioning when the camera is not severely blocked. At the same time, compared with the pose tracking performance of the adaptive Monte Carlo localization (AMCL) algorithm, the proposed algorithm has also improved in accuracy and in real-time performance.  
mobile robot; relocalization; correlation sampling; graph optimization Article
## 3 Experimental Results and Discussions
### Particulate Emission Comparison  
#### 3.2.1.  
Figures 11 and 12 show the variation in soot emissions measured by the opacimeter (OPA-102) during vehicle driving in the KD-147 mode. In the case of a normal DPF device, the emission is less than 10% of the permissible exhaust emission for diesel vehicles in Korea. Meanwhile, the values for the three damaged DPFs exceed this permissible value. Because the filter function cannot be expected under severe carrier-damage conditions, such as melting or hollow, the emission concentration was at least 20%. Crack damage is characterized by a level of emission concentration similar to that for melting. It is inferred that the damage is more severe than that observed in X-ray images.  
Figure 12 shows the soot emission concentrations of the four types of DPF samples. The hollow and melting-damaged DPF samples are considered the most severely damaged. These are essentially incapable of capturing particulate emissions. However, the particulate emission levels for the cracked DPF sample (which is conjectured to be a relatively marginal damage) were similar to those of the hollow and melting-damaged DPF samples. This  
Figure 11: Variation in soot emission during KD-147 driving mode (black line) for 4 types of DPF sample.  
Figure 12: Smoke concentration with each with various DPF samples.  
is considered to be owing to the damage inside cordierite DPF substrates that were not identified effectively by X-ray imaging.
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 3 The Improved Clustering Scheme
### Cluster Formation
#### 3.2.2 Selecting MCHs
Due to the heterogeneity of nodes' approximate maximum transmission ranges, we employ Equations (5) and (6) to determine the size of clustering radius (see line 3). Also, the selection criterion for MCHs is the node's MCH eligibility metric, and the node with the highest MCH eligibility metric in its neighborhood can become MCH, so node \(u\) firstly needs to compute its own MCH eligibility metric (see line 4) and then compares it with those of its 1-hop neighbors (see lines 6-11). The boolean variable \(FLAG_{1}\) is initialized as \(true\) (see line 5) for future use. If the MCH eligibility metric of node \(u\) is not highest, \(FLAG_{1}\) is changed as \(false\) (see line 9). If node \(u\) has the highest MCH eligibility metric in its neighborhood, it declares itself as the MCH and announces that to its neighbors at the clustering radius \(R_{u,intra}\) (see lines 12-14). Otherwise, it will wait for the _\"decided\"_ messages from its neighbors during the time \(\tau\), and make the corresponding response (see lines 16-28). At this moment, upon hearing a MCH announcement from any neighbor, \(u\) switches to a _\"decided\"_ state and announces this new state to its neighbors at the clustering radius \(R_{u,intra}\) (see lines 20-22). Also, the boolean variable \(FLAG_{2}\) initialized as _true_ (see line 17) is changed as _false_ (see line 23), which makes \(u\) stop competing for the role of MCH. However, upon hearing any non-MCH announcement, \(u\) must keep it (see line 24), which makes the remaining undecided node \(u\) repeat the above MCH selection process but without considering non-MCH neighbors who are already in the _\"decided\"_ state. After the time \(\tau\), if \(FLAG_{2}\) is _true_ (see line 28), \(u\) thinks that it still remain the _\"undecided\"_ state, and thus continues to compete for the MCH.
## 4 Results
### Verify the Downscaled SIF Result  
#### 4.2.1 Correlation Analyses between Downscaled SIF and GOSIF  
The downscaled SIF results were resampled to the same spatial resolution (0.05 degrees) as the GOSIF data using the pixel aggregate resampling method. The scatter diagram of the GOSIF and resampled downscaled SIF were obtained (Figure 4). Additionally, the correlation coefficients (R) between downscaled SIF and GOSIF were calculated. The downscaled SIF data exhibited a strong correlation with GOSIF, with a correlation coefficient of 0.74. The distribution of the downscaled SIF and GOSIF data points was observed to be close to the 1:1 line. The high correlation implies that the downscaled SIF effectively preserves the essential information from the original GOSIF products.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Column Mobility Model (CMM)
#### 6.1.4 Expected Transmission Count  
Figure 5 provides a thorough examination of the ETX metric within a mobile network environment employing the CMM mobility model, with a focus on variations in node count and the inclusion of attacker nodes. Both scenarios with and without the Hello Flood attack are considered, with mobile nodes representing unaffected nodes and attacker nodes representing those subjected to the attack. Across different node counts, there is a consistent trend of increasing ETX values, indicating a heightened transmission count required for successful packet delivery as the network size grows. This trend persists regardless of changes in the percentage of attacker nodes, suggesting that network size strongly influences transmission efficiency. Additionally, the presence of attacker nodes correlates with higher ETX values, signaling a decline in overall link quality and reliability. Notably, the graph shows that at the maximum node count, with 40% attacker nodes, the ETX peaks, reflecting the highest transmission count requirement under such conditions. These observations highlight the significant impact of both network density and attacker presence on ETX values, emphasizing the importance of considering these factors in mobile network design and optimization.  
The relationship between the CMM and the Hello Flood attack on increasing the ETX metric is intricate and significant. The structured movement patterns introduced by the CMM can lead to congestion in network areas, causing packet collisions and retransmissions, thus elevating the ETX metric. Similarly, the Hello Flood attack inundates the network with excessive traffic, leading to interference and further exacerbating packet loss and retransmissions, consequently increasing the ETX values. Furthermore, the CMM's dynamic movement patterns can destabilize routing paths, leading to suboptimal routing decisions and higher ETX values. The attack also disrupts routing protocols, introducing instability and exacerbating routing inefficiencies, thus contributing to elevated ETX metrics. Additionally, as nodes converge within columns in the CMM, resource consumption increases, leading to bottlenecks and queuing delays that further elevate the ETX metric. The attack compounds this by consuming network resources and exacerbating resource contention, thus increasing ETX values. In addition, the CMM's boundary handling mechanisms cause packet transmission delays. These delays, along with possible Hello Flood attack exploits, cause more packet loss and retransmissions, which ultimately leads to higher ETX metrics.  
Figure 5: ETX without and with attack using CMM.