esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conditional Diffusion Model for OFDM-Based Image Reconstruction
### Performance Metrics  
In this paper, a peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were used to evaluate the performance of the OFDM-based image reconstruction. The PSNR is a widely used metric for quantifying the quality of reconstructed images compared to their original versions. It measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects its representation. The PSNR is expressed in dB and can be calculated using the following formula:  
\[\text{PSNR}=10\text{log}\left(\frac{\text{MAX}_{\text{I}}^{2}}{\text{MSE}} \right), \tag{32}\]where MAXI is the maximum possible pixel value, and MSE is the mean squared error between the original and the reconstructed images. A higher PSNR value indicates a better quality of reconstruction. The SSIM is a more sophisticated metric that assesses the visual quality of images based on structural information. Unlike the PSNR, the SSIM considers changes in texture, contrast, and structural patterns. The SSIM index ranges from 0 to 1, where 1 indicates perfect structural similarity. It is defined as:  
\[\text{SSIM}(x_{0},\ \hat{x}_{0})=\frac{\left(2\varphi_{x_{0}}\varphi_{x_{0}}+C_{1} \right)\left(2\psi_{xx_{0}}+C_{2}\right)}{\left(\varphi_{x_{0}}^{2}+\varphi_{x }^{2}+C_{1}\right)\left(\psi_{x_{0}}^{2}+\psi_{\hat{x}_{0}}^{2}+C_{2}\right)}, \tag{33}\]  
where \(\varphi\), \(\psi^{2}\), and \(\psi_{xx}\) denote the average pixel value, the variance, and the covariance, respectively. The constants \(C_{1}\) and \(C_{2}\) are small values added to stabilize the division.
## 3 Results
### Correlation Analyses  
The percentage of correlations above 0.4 among the sagittal parameters was 22.2% in the whole sample, with no difference between sexes; this number significantly increased after age 14, reaching 50% (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **N (Femiled)** & **Age** & **TK** & **LSA** & **LL-S1** & **TL-S1** & **rn** & **ss** & **rr** & **SSA** & **SPA** \\ \hline
**Total** & 222 (212) & 12.04 \(\pm\) 2.05 & 46 \(\pm\) 12 & 12 \(\pm\) 6 & 48 \(\pm\) 12 & 60 \(\pm\) 12 & 40 \(\pm\) 11 & 38 \(\pm\) 8 & 5 \(\pm\) 7 & 265 \(\pm\) 48 & 342 \(\pm\) 50 \\ \hline
**Malus** & 101 & **12.05 \(\pm\) 2.08\({}^{*}\)** & 46 \(\pm\) 12 & 12 \(\pm\) 6 & 47 \(\pm\) 2\({}^{*}\) & 59 \(\pm\) 12 & 40 \(\pm\) 10 & 37 \(\pm\) 8 & 6 \(\pm\) 7 & 266 \(\pm\) 50 & 342 \(\pm\) 9 \\ \hline
**Femiled** & 121 & **13.11 \(\pm\) 2.08\({}^{*}\)** & 46 \(\pm\) 12 & 12 \(\pm\) 6 & 80 \(\pm\) 12 & 42 \(\pm\) 11 & 46 \(\pm\) 12 & 39 \(\pm\) 8 & 5 \(\pm\) 7 & 264 \(\pm\) 53 & 339 \(\pm\) 57 \\ \hline
**Age + 10** & 41 (25) & **8.09 \(\pm\) 0.25** & 44 \(\pm\) 11 & 12 \(\pm\) 7 & 43 \(\pm\) 12 & 56 \(\pm\) 19 & 39 \(\pm\) 9 & 35 \(\pm\) 9 & 4 \(\pm\) 7 & 294 \(\pm\) 43 & 345 \(\pm\) 41 \\ \hline
**Age + 10-11** & 59 (88) & **11.04 \(\pm\) 0.07** & 45 \(\pm\) 12 & 12 \(\pm\) 6 & 45 \(\pm\) 10 & 39 \(\pm\) 19 & 42 \(\pm\) 10 & 36 \(\pm\) 8 & 5 \(\pm\) 7 & 330 \(\pm\) 51 & 351 \(\pm\) 80 \\ \hline
**Age + 12-14** & 94 (48) & **12.21 \(\pm\) 67** & 46 \(\pm\) 12 & 11 \(\pm\) 6 & 50 \(\pm\) 11 & 61 \(\pm\) 10 & 44 \(\pm\) 10 & 38 \(\pm\) 7 & 6 \(\pm\) 7 & 288 \(\pm\) 65 & 344 \(\pm\) 69 \\ \hline
**Age + 15-8** & 25 (0) & **18.05 \(\pm\) 0.15** & 48 \(\pm\) 14 & 9 \(\pm\) 5 & 56 \(\pm\) 14 & 65 \(\pm\) 15 & 47 \(\pm\) 10 & 42 \(\pm\) 10 & 42 \(\pm\) 10 & 6 \(\pm\) 8 & 338 \(\pm\) 61 & 551 \(\pm\) 92 \\ \hline \hline \end{tabular} \({}^{*}\) Statistically significant difference between sexes. \(\lx@sectionsign\) Statistically significant difference for the age group. Data are average \(\pm\) standard deviation. Age is reported as years and months. TK: Thoracic Kyphoosis; ISA: Lumbo-Sacral angle; LL-L5: Lumbor Lordosis measured at the lower plate of L5; LL-S1: Lumbar Lordosis measured at the upper plate of S1; PI: Pelvic Incidence; PT: Pelvic Tilt; SS: Sacral Slope; SSA: Spino-Sacral Angle; SPA: Spino-Pelvic Angle.  
\end{table}
Table 1: Characteristics of the study population according to gender and age groups.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **LSA** & **LL-L5** & **LL-S1** & **PI** & **PT** & **SS** & **SSA** & **SPA** \\ \hline \multicolumn{8}{c}{**Total**} \\ \hline
**TK** & 0.12379 & 0.27181 & 0.33752 & \(-\)0.02800 & 0.00729 & 0.02145 & 0.24136 & 0.23914 \\ \hline
**LSA** & & \(-\)0.27042 & 0.23933 & \(-\)0.05734 & \(-\)0.28005 & 0.14565 & 0.02042 & 0.03279 \\ \hline
**LL-L5** & & **0.87004** & **0.46718** & \(-\)0.05265 & **0.69862** & 0.12490 & 0.01364 \\ \hline
**LL-S1** & & & **0.44180** & \(-\)0.19
# The Marine Bacterial Genus _Euzebya_ Is Distributed Worldwide in Terrestrial Environments: A Review
## 2 Metagenomic Detection of _Euzebya_ in the Environment: Caves
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Cave** & **Abundance \%** & **Taxa** & **Type of Sample** & **References** \\ \hline Altamira Cave, Spain & n.a. & _Euzebya_ & Grey biofilms & [18] \\ \hline Volcanic caves, Portugal, USA & n.a. & _Euzebyales_ & Biofilms & [19] \\ \hline Hawai’i lava caves, USA & n.a. & _Euzebya_ & Biofilms & [20] \\ \hline Heshang Cave, China & n.a. & _Euzebya_ & Weather rocks & [21] \\ \hline Laugerie-Haute shelter, France & 2.6 \({}^{\star}\) & _Euzebya_ & Biofilms & [22] \\ \hline Scarisoara Ice Cave, Romania & n.a. & _Euzebya_ & Ice core & [23] \\ \hline Honda del Bejenado Cave, Spain & 43.9 \({}^{\star}\) & _Euzebya_ & Yellow biofilms & [24] \\ \hline B2 Cave, India & 1.16 & _Euzebyales_ & Cave wall & [25] \\ \hline Honda del Bejenado Cave, Spain & 31.7 & _Euzebya_ & Yellow biofilms & [26] \\ \hline Fuente de la Canaria Cave, Spain & 0.2–2.7 & _Euzebya_ & Yellow biofilms/moonmilk & [26] \\ \hline Bucara II Cave, Spain & 2.9–11.0 & _Euzebya_ & Pink deposit/moonmilk & [26] \\ \hline Llano de los Carlos Cave, Spain & 3.0 & _Euzebya_ & Speleothem & [26] \\ \hline Yixing Shanjuan Cave, China & 4.2 & _Euzebya_ & Speleothem & [27] \\ \hline Chimalacatepec lava tube, Mexico & 6.2 & _Euzebya_ & Stromatolite & [28] \\ \hline RN5 Cave, USA & 30.7 & _Euzebya_ & Biofilms & [29] \\ \hline RN5 Cave, USA & 13.8 & _Euzebyacene_ & Biofilms & [29] \\ \hline KN14 Cave, USA & 0.1 & _Euzebya_ & Biofilms & [29] \\ \hline Royal Palm Cave, Galapagos & n.a. & _Euzebya_ & White biofilms & [30] \\ \hline Pukzing Cave, India & n.a. & _Euzebya_ & Cave sediments & [31] \\ \hline Altamira Cave, Spain & 33–79 & _Euzebyacene_ & White biofilms & [32] \\ \hline Altamira Cave, Spain & 20–42 & _Euzebyacene_ & Light brown biofilms & [32] \\ \hline Heshang Cave, China & n.a. & _Euzebya_ & Weather rocks & [33] \\ \hline Moon National Monument, USA & n.a. & _Euzebya_ & Biofilms/Speleothems & [34] \\ \hline Geomunoreum lava tubes, Korea & 0.3–0.7 & _Euzebya_ & Microbial mats & [35] \\ \hline Pindal Cave, Spain & 7.0–16.0 & _Euzebyacene_ & Pink biofilms & [36] \\ \hline Covadura Cave, Spain & 48.4–52.5 & _Euzebyacene_ & White biofilms (2010) & \\ \hline  & 0.1–21.7 & _Euzebyacene_ & White biofilms (2022) & Unpublished \\ \hline  & 0.7–10.4 & _Euzebyacene_ & Yellow biofilms (2010) & report \\ \hline  & 3.4–9.0 & _Euzebyacene_ & Yellow biofilms (2022) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Occurrence and relative abundance of _Euzebyales/Euzebyacene/Euzebya_ in caves.
# Assessing Database Contribution via Distributed Tracing for Microservice Systems
## 3 Methodology
### Method Framework  
We propose a business-scenario-oriented database contribution assessment approach, and the framework is illustrated in Figure 3. First, the importance weight of business scenarios is determined by AHP, which includes constructing a business scenarios hierarchy, generating pairwise comparison matrices, hierarchical sorting, and consistency test. Second, we reproduce business scenarios, and the corresponding traces are generated by distributed tracing, which contain behavioral information about databases in span tags or span logs. At the same time, according to each business scenario and aggregate rules, the numbers of records with the same operation on the same database are aggregated.  
Third, we define operators for four basic database operations (creating, retrieving, updating, and deleting). Meanwhile, based on the business scenarios' importance weight and the aggregated matrices obtained in the second step, the database contribution assessment is formalized as a nonlinear programming problem, which some corresponding optimization algorithms or tools can solve.
# Catalytic Advanced Oxidation Processes for Sulfamethoxazole Degradation
## 2 Materials and Methods
### Analytic Methods  
SMX and primary degradation products, AMI and BZQ, as shown in Table 1, were detected by high-performance liquid chromatography (HPLC) with a Hitachi Elite LaChrom equipment with diode detector (DAD). The stationary phase was composed by LiChroCART Purospher STAR RP-18 (Merck Millipore, Burlington, MA, USA) column (\(250\times 4.6\) mm), and the mobile phase was a gradient of water and methanol with a flow rate of 1 mL min\({}^{-1}\) and an injection volume of 50 \(\mu\)L. The wavelengths used for SMX, AMI, and BZQ were 265, 210, and 225 nm, respectively.  
The determination of final by-products of SMX degradation, OXM and OXL, as shown in Table 1, was performed by HPLC Hitachi Elite LaChrom (Hitachi High-Technologies Corporation, Kagoshima, Japan) equipped with a UV detector, using an Alltech AO-1000 (Fisher Scientific, Hampton, NH, USA) column (\(300\times 6.5\) mm) and a mobile phase composed by 5 mM H\({}_{2}\)SO\({}_{4}\) solution at a flow rate of 0.5 mL min\({}^{-1}\) and injection volume of 15 \(\mu\)L. The wavelength used was 200 nm.  
Ion chromatography was used to analyze the formation of ion species (SO\({}_{4}\)\({}^{2-}\), NO\({}_{3}\)\({}^{-}\), and NH\({}_{4}\)\({}^{+}\)) that may be released during SMX degradation. This analysis was performed on a Metrohm 881 Compact IC Pro equipment. The SO\({}_{4}\)\({}^{2-}\) and NO\({}_{3}\)\({}^{-}\) ions were detected in the Metrosep A Supp 7 column (250 \(\times\) 4 mm) with a Metrosep P2 Guard/3.5 pre-column. The mobile phase was composed by a solution of 3.6 mM Na\({}_{2}\)CO\({}_{3}\), flow rate of 0.7 mL min\({}^{-1}\), and injection volume of 20 \(\mu\)L. The analysis was performed at 45 \({}^{\circ}\)C. A Metrosep C4 column (250 \(\times\) 4.0 mm) from Metrohm with Metrosep C4-S-Guard/4.0 pre-column was used for NH\({}_{4}\)\({}^{+}\) determination. The eluent was a solution of 0.7 mM of 2,6-pyridine dicarboxylic acid and a solution of 1.7 mM of nitric acid at 0.7 mL min\({}^{-1}\) flow rate. The analysis was performed at 25 \({}^{\circ}\)C and with an injection volume of 20 \(\mu\)L.  
The determination of total organic carbon (TOC) was performed in a L-TOC Total Organic Carbon Analyzer of Shimadzu coupled to an ASI-L auto sampler of the same brand, by the Non-PurgableOrganic Carbon (NPOC) method. The collected samples, already centrifuged, were analyzed without any previous preparation.  
The toxicity of treated samples was evaluated by the Microtox(r) (Guildford, UK) acute toxicity test according to the procedure established by standard methods (ISO/DIS 11348-3) [37].
# Future of IoT Networks: A Survey
## 4 Research Challenges of the IoT Network
### Software-Defined Network & Network Function Virtualization  
In an IoT environment, devices and services have their own policies. To seamlessly operate the devices, services, and platforms, the network must manage the network flow. Specifically, the network manager must control and apply flow rules to the devices and platforms. One of the solutions for this issue is to modify the devices and improve the networking capability. Nonetheless, this approach may increase the cost, and it is time consuming. Specific functions of network equipment are not opened to the network manager, so the network manager cannot easily modify the existing flow rules as the manager wants. For instance, the network manager must access each of the devices to customize the network flow rule. Furthermore, in a conventional network configuration, the router or switch has customized hardware parts or chips that are suited for the network environment. To modify the network environment, all chips and parts of the switch must also be modified, and this process requires many efforts and costs to replace the hardware parts of a router or switch.  
To overcome these challenges, the concept of the programmable network has been introduced as the software defined network (SDN). The fundamental idea of SDN is to provide a feature to manage the traffic of the network in a software manner. SDN can be an essential technical feature of IoT, and research related to institute SDN into an IoT environment is currently underway [61; 62]. SDN separates the control plane and data plane from the network flow to enable programming of the network. SDN can substitute control software for hardware parts. Additionally, the network manager can modify the network configuration according to the current network status.  
It is certain that the size of the IoT network will increase. In this particular case, a plurality of network equipment is needed to cover the network. When the network managers want to add network functions, they must buy the functions or new equipment to embed the wanted functions into the current network. To overcome this challenge, the concept of network virtualization [63] was introduced to easily connect different clients without making a new physical connection. Further, network function virtualization (NFV) supports various services in the network device [64; 65]. The concept of NFV is to virtualize various functions such as the firewall, DNS, and caching and operate these functions in a software manner. The NFV can provide a different service to each flow with ease, which reduces the network manager's overhead and cost to change the network service.
# Implementing YOLO Convolutional Neural Network for Seed Size Detection
## 6 Results and Discussion
image brightness is essential for a comprehensive evaluation of CNN models. Ignoring these factors can lead to overly optimistic conclusions. Therefore, future research should include these additional variables to obtain more realistic and practical results that can be applied in practice. The proposed solution demonstrates impressive efficiency but requires significant computational resources. In the future, it will be crucial to conduct research on the application of dilation techniques in the context of segmentation and classification, which will enable the practical use of algorithms for seed detection and segmentation [29, 30]. Such an approach will not only expand the capabilities of these methods but also enhance their accessibility and efficiency in various industrial and scientific applications. To evaluate how the proposed YOLO-based method compares with other standard signal processing and analysis techniques, additional studies were conducted. Various edge detection and image segmentation methods were employed to identify and extract the regions of interest for the seeds. Methods such as Roberts, Prewitt, Sobel, thresholding, and adaptive thresholding were applied. A comparison of the results between the YOLO method and typical seed area detection methods is presented in Figure 13. The [PERSON] method involves using small masks to detect edges by calculating the intensity gradient horizontally and vertically [31]. The Prewitt method is similar but uses larger masks, making it more robust to noise and better suited for detecting edges in medium-quality images [31]. The Sobel method, an extension of the [PERSON] and Prewitt methods, also uses larger masks and places greater emphasis on central pixels, making it one of the most commonly used edge detection methods due to its efficiency [31]. The simplest image segmentation method is thresholding, which involves applying a single global threshold. All pixels with values above the set threshold are classified as seed areas, while the rest are classified as the background. This method is quick, but its effectiveness depends on the appropriate choice of the threshold value, which can be challenging in images with uneven lighting. During the experiments, [PERSON]'s method was used to calculate the pixel threshold [32]. Adaptive thresholding applies different thresholds in different parts of the image, depending on local pixel properties. This method is particularly useful in scenarios where the image has uneven lighting. Popular approaches include mean thresholding, where the threshold is set as the average intensity value in the local neighborhood of the pixel. During the experiments, a block size of 11 was used to calculate the local mean pixel value, with the resulting threshold applied only to that block of the image. Figure 13: Comparison of YOLO methods and typical seed area detection methods.
## 1 Introduction
The presentation in [23] focusing on three cities of China proposed a combination of the rolling mechanism and gray model in the data preparation process and the ANN model was used in the prediction process. The result was a prediction of the daily average values of PM\({}_{10}\) concentrations and PM\({}_{10}\) classes, calculated from the China air quality index. A research article presented in [24] applied ANN to predict the highest daily PM\({}_{10}\) concentration in Santiago, Chile. The rule-based classification is used from a combination of two models, ANN and K-nearest neighbor (K-NN), to improve model performance in the minor classes. There is another type of ANN, long short-term memory (LSTM), used by [25]. The research presented an appropriate LSTM structure to predict the daily average PM\({}_{10}\) concentration in Seoul, South Korea. Another type of ANN is a combination of ANN and fuzzy logic called neuro-fuzzy. Two research articles used neuro-fuzzy with the Tagaki-Sugeno system to predict daily average PM\({}_{10}\) concentrations in Turkey. The output data from fuzzy logic was used as an input feature for ANN. In the fuzzy logic part, in [26], a bell-shaped membership function was selected, while in [27], the Gaussian membership function was selected. Moreover, neuro-fuzzy is more effective than the other classifiers, such as NN and the support vector machine, when using the standard datasets from UCI reported by [28; 29; 30]. Neuro-fuzzy was selected to be applied in various applications, such as the diffuse large B-cell lymphomas classification [31]. In addition, in [32], it was reported that the positions for changing slope in the fuzzy membership function are very important, so the minimum entropy principle (MEP) is applied to find these values. This research proposes the neuro-fuzzy with the minimum entropy principle model for data transformation to create new informative features that are used to represent historical data. Moreover, the proposed transformation model can reduce concerns about bias in raw data. Finally, an ANN model is created for new informative features. The three- and five-class output data of this model are the hourly PM\({}_{10}\) and PM\({}_{2.5}\) classes associated with the Thailand standard. The results of the model can be an application implemented to alert people and for short-term outdoor activity planning up to three hours in advance.
## 3 Materials and Methods
### Multi-Model Optimization Procedure  
We extended our single-model optimization framework to a multi-model setting. Let \(F_{1},F_{2},\dots,F_{N}\) represent a set of \(N\) object detection models (e.g., YOLOv5, YOLOv8, and YOLOv3). Our goal was to generate an adversarial texture \(T_{\text{adv}}\) that minimized the detection confidence across all models in this set. To achieve this, we redefined our total loss \(\mathcal{L}_{\text{total}}\) as the mean attack loss across all models. Specifically, the attack loss \(\mathcal{L}^{n}_{\text{atk}}\) for each model \(F_{n}\) was computed individually and then averaged to produce a unified objective function:  
\[\mathcal{L}_{\text{total}}=\frac{1}{N}\sum_{n=1}^{N}\mathcal{L}^{n}_{\text{atk}} \tag{10}\]  
where \(\mathcal{L}^{n}_{\text{atk}}\) represents the attack loss for model \(F_{n}\), as defined in Equation (9) for the single-model setup. This multi-model attack loss formulation encouraged the adversarial texture \(T_{\text{adv}}\) to minimize the detection confidence consistently across all models.
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
## 3 Results and Discussion
### Evaluation of Leaf Transmission Response  
In this section, transmission responses of all leaves were observed on day 1 and 4 as shown in Figure 7. It was noticed that on day 1, attenuations of all leaves were substantially high due to the presence of higher WC in tissues of leaves, which resulted in a higher absorption and lower transmission response. Moreover, on day 4, a substantial degree of increment in transmission response was observed as WC in leaves had evaporated to large extent, which resulted in a decrement of weight and thickness of leaves and eventually, less absorption occurred at this time. Figure 7 exhibited a strong correlation of transmission response with WC, weight and thickness of leaves. Baby-leaf exhibited a lower transmission response on day 1 compared to others, reflecting a higher presence of WC in leaf, which resulted in higher absorption. Contrarily, parsley displayed an increment in transmission response due to the presence of less WC in the leaf and hence, producing low absorption compared to other leaves.