esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Recognition Method for Micropores Inside Small Gas Pipelines
## 4 Results
### Ablation Experiments and Analysis of Results  
Next, we further prove that the network model proposed in this paper for the identification of micropores inside small gas pipelines is enhanced by introducing the BiFPN structure in the Neck layer of the YOLOv5s network model and building a small target detection layer in the Head layer. For this analysis, ablation experiments were performed again in this paper, and the test sets were input to the YOLOv5s, YOLOv5s + BiFPN, the YOLOv5s + BiFPN, YOLOv5s + small target detection layer, and the method in this paper. The input test sets are identified separately to compare the evaluation index values of different network models. The results of the evaluation indexes of the ablation experiments are shown in Table 4.  
From Table 4, we can see that the precision of the YOLOv5s network model is 89.6%, the recall rate is 87.6%, and the average precision is 88.4%. The network model after introducing the BiFPN structure improves the precision by 2.8 percentage points, the recall rate by 3.1 percentage points, and the average precision by 3.0 percentage points compared with the YOLOv5s network model. After constructing the small target detection layer, the network model improves the precision by 2.0 percentage points, the recall rate by 5.3 percentage points, and the average precision by 3.8 percentage points compared with the YOLOv5s network model. The precision, recall, and average precision of this method improved by 5.1 percentage points, 9 percentage points, and 7.1 percentage points,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **Precision/\%** & **Recall/\%** & **AP/\%** \\ \hline YOLOv5s & 89.6 & 87.6 & 88.4 \\ YOLOv5s + BiFPN & 92.4 & 90.7 & 91.4 \\ YOLOv5s + small & 91.6 & 92.9 & 92.2 \\ target detection layer & & & \\ Our method & 94.7 & 96.6 & 95.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation index of the ablation experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **Precision/\%** & **Recall/\%** & **AP/\%** \\ \hline SSD & 88.5 & 86.3 & 87.2 \\ YOLOv3 & 87.8 & 85.9 & 86.7 \\ YOLOv5s & 89.6 & 87.6 & 88.4 \\ Faster RCNN & 91.8 & 93.4 & 92.5 \\ Our method & 94.7 & 96.6 & 95.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation indexes of comparative experiments.  
respectively, compared with the YOLOv5s network model, reaching 94.7%, 96.6%, and 95.5%, respectively. Using ablation experiments, this paper demonstrates that the network model for identifying micropores inside small gas pipelines proposed in this paper is enhanced by introducing BiFPN structure in the Neck layer of the YOLOv5s network model and constructing a small target detection layer in the Head layer, which has higher evaluation index and possesses better identification capability.
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 4 Results and Discussion  
This section discusses the proposed model results and the effects of the divergence angle on the data rate, received power, distance, and the impact of fixed transmitted power and divergence angle on the received power. This section will attempt to calculate the effect of different train speeds, e.g., 1000 km/h and 2700 km/h. It should be noted that this study was implemented by using MATLAB(r).
## 4 Conclusions  
Applying a high-FA strategy successfully suppressed NOB and enriched AOB, leading to the stable initiation of a nitritation reactor. In PN1, starting with an initial influent NH4+-N concentration of 50 mg/L (FA 10 mg/L) did not suppress NOB, resulting in most of the NH4+ being oxidized to NO3-. In contrast, PN2 began with 300 mg NH4+-N/L (FA 50 mg/L) and gradually reduced it, achieving an ARE of 96.1% and an NAE of 95.1% at 50 mg NH4+-N/L (FA 10 mg/L), stably accumulating NO2-. The activities of AOB and NOB were 1.32 \(\pm\) 0.059 gN/gVSS/d and 0.01 \(\pm\) 0.018 gN/gVSS/d, respectively. The proportion of _Nitrosomonas_, an AOB genus, was 23.8%, while _Nitrospira_, a NOB genus, was 0.03%, indicating complete suppression of NOB. Therefore, when starting mainstream nitritation, integrating sidestream treatment and using digester supernatant to reach an FA of 50 mg/L can establish a process that stably accumulates NO2-. While further research and practical applications are needed to validate and optimize this strategy for different operational conditions, this approach ensures effective NOB suppression and AOB enrichment, enhancing nitrogen removal in mainstream WWPTs.  
**Author Contributions:** Writing--original draft, S.J.; conceptualization, S.P. (Seongjae Park); visualization, H.K. and S.Y.; investigation, S.P. (Sewon Park) and D.K.; formal analysis, J.K.; data curation, Y.K.; project administration, J.Y.; supervision and writing--review and editing, T.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Basic Science Research Program (NRF-2021R1A6A1A0309572) and the international cooperation program framework (2023K2A9A2A06060048, FY2023), both managed by the National Research Foundation of Korea.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** This work was supported by the Basic Science Research Program (NRF-2021R1A6A1A03039572) and the international cooperation program framework (2023K2A9A2A06060048, FY2023), both managed by the National Research Foundation of Korea.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Technology Development Center, Samsung Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results and Discussions
### Energy Dissipation  
Cumulative energy dissipation (\(E_{\text{d}}\)) and the equivalent viscous damping coefficient(\(h_{\text{e}}\)) are often used to assess the energy dissipating capacity. \(E_{\text{d}}\) is defined as the accumulated area under hysteretic curves, and \(h_{\text{e}}\) can be calculated using Equation (6). To comprehensively evaluate the energy dissipation capacity, \(E_{\text{d}}\) and \(h_{\text{e}}\) of specimens are calculated and compared.  
\[h_{\text{e}}=\frac{S_{\text{ABCD}}}{2\pi(S_{\text{OBF}}+S_{\text{ODE}})} \tag{6}\]  
where \(S_{\text{ABCD}}\) is the area of ABCD, and \(S_{\text{OBF}}\) and \(S_{\text{ODE}}\) represent the areas of triangles OBF and ODE, respectively, as shown in Figure 12.  
Figures 13 and 14 show \(E_{\text{d}}\) and \(h_{\text{e}}\) of each specimen. It can be seen that \(E_{\text{d}}\) and \(h_{\text{e}}\) increased with an increase in the applied displacement, indicating the better energy dissipation ability of the XMD. Note that the cumulative energy dissipation of specimens appears as linear growth due to the stable plastic stiffness.  
Figure 12: Idealized hysteretic loop.  
Specimens with cover plates are taken as examples to investigate the influence of oblique angles and weakened segment widths on the energy-dissipating ability of specimens. As shown in Figures 13a and 14a, the \(E_{\mathrm{d}}\) of specimens W30-O15\({}^{\circ}\)-LP and W30-O30\({}^{\circ}\)-LP were 575.71 kN\(\cdot\)m and 708.30 kN\(\cdot\)m at the applied displacement of 8.4 mm, which were about 14.28% and 40.60% higher than those of specimen W30-O0\({}^{\circ}\)-LP, respectively. On the contrary, the equivalent viscous damping coefficients of specimens W30-O15\({}^{\circ}\)-LP and W30-O30\({}^{\circ}\)-LP were 0.67% and 11.52% lower than those of specimen W30-O0\({}^{\circ}\)-LP, respectively.  
As shown in Table 2, it can be noted from the test results that stiffening ribs and cover plates can effectively restrict the out-of-plane deformation of X-shaped metal plates. Therefore, the \(E_{\mathrm{d}}\) and \(h_{\mathrm{e}}\) of specimens with different buckling-restrained devices were greatly similar. However, there were some certain differences between specimens with stiffening ribs and cover plates due to welding quality.  
Figure 14: Equivalent viscous damping coefficients of specimens: (**a**) specimens with cover plates; (**b**) specimens with stiffening ribs.  
Figure 13: Cumulative energy dissipation of specimens: (**a**) specimens with cover plates; (**b**) specimens with stiffening ribs.
## 3 Results and Discussion
### Antioxidant Properties  
#### 3.3.1 DPPH Assay  
For three samples of each of the extracts, the change in optical density, at 517 nm, according to previous described protocol [33], was measured (samples L_D--dried leaves  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Compounds** & **LEAVES** & **ROOTS** \\ \hline Chlorogenic acid & 49.75 \(\pm\) 5.55 & nd * \\ \hline
3,4-Dihydroyphenylacetic acid & 227.65 \(\pm\) 2.93 & 166.64 \(\pm\) 4.73 \\ \hline Gallic acid & 56.12 \(\pm\) 0.52 & 14.17 \(\pm\) 0.55 \\ \hline Caffeic acid & 7.26 \(\pm\) 0.76 & 0.60 \(\pm\) 0.04 \\ \hline Neochlorogenic acid & 44.76 \(\pm\) 6.04 & nd \\ \hline Rosmarinic acid & 166.75 \(\pm\) 1.13 & 163.65 \(\pm\) 11.01 \\ \hline Ferulic acid & 48.95 \(\pm\) 3.71 & nd \\ \hline Vanillic acid & 19.83 \(\pm\) 2.91 & 3.57 \(\pm\) 0.35 \\ \hline Apigenin & 99.26 \(\pm\) 1.86 & nd \\ \hline Quercetin & 71.55 \(\pm\) 7.25 & nd \\ \hline Rutin & 51.18 \(\pm\) 4.80 & nd \\ \hline Verbascoside & 1114.64 \(\pm\) 5.08 & 951.80 \(\pm\) 17.14 \\ \hline Isoverbascoside & 333.99 \(\pm\) 12.08 & 277.79 \(\pm\) 15.03 \\ \hline \hline \end{tabular}  
* nd—not detected.  
\end{table}
Table 3: Quantitative (mg/100 g DW \(\pm\) SD) assessment of selected compounds in freeze-dried leaf and root extracts of _A. reptans_.  
and R_D--dried roots) and also for the freeze-dried raw material (L_L--freeze-dried leaves and R_L--freeze-dried roots) (Table 4).  
From the data values shown in the Table 4, we can observe that the strongest antioxidant properties are shown by the extract of the leaves of the bugle herb obtained from the plant dried at 40 \({}^{\circ}\)C (47.76%). Similar values were obtained by extracts of the leave lyophilized at 48 h (46.13%). The extracts from the plant's roots showed significantly lower antioxidant values.
# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 3 Results and Discussion
### Experimental Environment  
An experimental environment has been designed to execute the proposed methodology, with the methods and functions developed in Python. Additionally, a set of tools was utilized, including the algorithm YOLO v8 and Roboflow ([41] for labeling the images of the dataset created in this study). The labels in each experiment were awarded in accordance with the visual assessment of two welding experts. These experts analyzed each of the welded plates and determined, based on their experience and criteria in visual inspection of welding beads, whether a weld was well manufactured. The welds were subsequently categorized as follows: GOOD, BAD, FCAW or GMAW, DEFECT (undercut (UNDER), lack of penetration (LOP), or rest of defects (OP)). Additionally, YOLO Ultralitycs [45; 46] has been used for performance metrics.  
The dataset used in this study has been presented in Section 2.1. The suitably labeled images have been selected to be validated using the YOLO algorithm. This algorithm initially needs to learn the location of the object to be searched for within the set of images. To conclude the aforementioned points, 80% of the images have been provided for training and 20% for testing. From this set of 80%, 10% has been selected as the validation set.  
Although more types of defects could have been detected in the images of the weld beads that make up the dataset, it has been decided to recognize the two most important ones, lack of penetration and underbite, leaving the rest for a class called \"other problems (op)\". The rationale behind the exclusion of other defects is that the weld beads were made by a welding robot, ensuring their correct production. In the event of defective weld beads, typically attributable to improper parameter assignment, the defects produced are predominantly those that have been previously studied, with infrequent occurrences of other defects. The consideration of these defects individually would have resulted in a significantly reduced set of samples, potentially compromising the efficacy of the detection and classification of the weld seams.  
The experiments presented in this study have been carried out on the facilities of the supercomputing center of the University of Cadiz, which has 48 nodes, each with two Intel Xeon E5 2670 2.6 GHz octa-core processors, equipped with 128 GB of RAM.
## 1 Introduction
* Flexible and Efficient Client Selection: A new algorithm is proposed to evaluate clients' local models during the FL process and select the optimal clients based on custom  
Figure 1: Federated learning scenario with LMECS that applies to real-world environments. metrics. This accelerates performance improvement speed, reduces communication costs, and enhances the overall predictive performance of the model. * Validation Using Real Multi-Institutional Emergency Room Data: The utility and validity of the proposed client selection algorithm are demonstrated through performance comparisons of centralized learning, standalone learning, and FL models using real multi-institutional emergency room data from Korea. The experimental validation strengthens the validity of the performance comparison and increases the practical applicability of the research findings. The paper is organized as follows: Section 2 covers the related work pertinent to this work. In Section 3, the data, models, and FL methods employed in the research are introduced. Section 4 presents the environment in which the research experiments were conducted. The results of the research are discussed in Section 5. Finally, Section 6 summarizes the research and proposes directions for future research.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 3 Methods
### Statistical Validation  
To ensure model generalizability and reliability, statistical validation techniques such as K-fold cross-validation with five folds were employed. This method helps prevent overfittingand provides a robust estimate of model performance on unseen data by systematically partitioning the dataset into training and testing sets. Additionally, bootstrapping techniques were applied to evaluate the statistical significance and stability of the time-effect parameter (\(\lambda\)). By generating multiple resampled datasets, this approach facilitated a comprehensive analysis of parameter variability, reinforcing the robustness of the model's time-sensitive estimates. The combination of MLE-based parameter estimation, full optimization of (\(\theta\), b, \(\lambda\)), and cross-validation frameworks ensured that the models were rigorously tested, minimizing biases and improving predictive accuracy. To ensure data quality and reliability, response times were preprocessed by normalizing values and identifying outliers using both the Interquartile Range (IQR) method and the Z-score approach. Outliers were defined as response times exceeding 1.5 times the interquartile range or falling beyond \(\pm\)3 standard deviations and were subsequently removed. Missing values were imputed using the median response time per item to minimize bias while preserving the dataset's distribution.
# Comparison between the Biofilm Desorption Abilities of T4 and MS2 Colliphages
## Material and Methods
### Treatment of Biofilms with PBS, T4, TSB, and MS2  
Pure culture _E. coli_ biofilms were formed in four wells of a 24-well plate by inoculating three wells with 1.5 mL of ATCC 11303 _E. coli_ (T4 host), and one well with 1.5 mL of ATCC C3000 _E. coli_ (MS2 host). To test the natural biofilm desorption ability of T4 and MS2 phages, 12 wells of a 24-well plate were inoculated with 1.5 mL of the biofilm samples. This experiment was performed in triplicate. The triplicates were named as biofilm 1, 2, and 3. Both natural and pure culture biofilms were allowed to form for five days. Following biofilm formation, each well of each row was treated for 24 h with 1 mL of 1X PBS, T4 phage suspension in 1X PBS, TSB, and MS2 phage suspension in 1X PBS.  
The first well for each biofilm was treated with 500 microliters of 1X PBS. Since both T4 and MS2 virions are suspended in 1X PBS, 1X PBS was used as a control. The second and third well of each biofilm was treated with 7.02 \(\times\) 10\({}^{8}\) virions of T4 and MS2 phages, respectively. The fourth well of each biofilm was treated with 500 microliters of TSB (Figure 2a,b). After 24 h of treatment, these solutions were decanted and replaced by 3 mL of TSB in all of the treatment wells.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Coliphage** & **Genome Size** & **Phage Type** & **Absorbance at 269 nm** & **Absorbance at 320 nm** & **Virions/mL** \\ \hline T4 & 168,903 bp & dsDNA & 0.437 & 0.042 & \(1.403\times 10^{11}\) \\ MS2 & 3569 bp & ssRNA & 0.283 & 0.018 & \(4.455\times 10^{12}\) \\ \hline \end{tabular}
\end{table}
Table 1: Estimation of virions per milliliter of T4 and MS2 coliphages.  
Figure 2: Effect of T4 and MS2 coliphages on biofilms. Both T4 and MS2 phage suspensions desorbed their respective host pure _E. coli_ biofilms (**a**). The T4 and MS2 coliphages displayed similar biofilm desorption of the natural biofilms (**b**). Natural biofilm desorption due to PBS was similar to the biofilm desorption displayed by both T4 and MS2 coliphages (**b**).  
The cells were resuspended by scraping off the biofilm from the wells using a pipette tip and simultaneously mixing with a 1 mL micropipette. The detachment of biofilm was confirmed by visual inspection of the wells. After resuspending the treated biofilm in each well with TSB, each sample was transferred to individual cuvettes for quantitative observations using the [PERSON](tm) DR 6000 spectrophotometer (Loveland, CO, USA) set at 600 nm. TSB was used as a blank for the spectrophotometer by setting the transmittance to 100% and the absorbance to 0.00.
## 5 Discussions and Comparisons
### Communication Cost  
Table 3 analyzes the communication cost of this scheme.  
The communication cost analysis of each phase in this scheme is shown in Table 3. We assume that the ECDSA key and signature are 160 bits, the asymmetric message or certificate is 1024 bits, and the rest of the message length such as ID is 80 bits. We analyze the authentication and issuing invoice phase (entrusted authorization) with the highest communication cost. The message sent by the system role to the blockchain center includes 1 other message. The message includes 4 ECDSA keys and signatures, 4 asymmetric messages or certificates, and 4 other messages. The total communication cost in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phase** & **Item** & & & \\ \hline System role registration phase & \(1T_{Mul}\) & N/A & N/A & N/A \\ \hline Authentication and issuing invoice phase & \(\mathrm{N/A}\) & \(7T_{Mul}+3T_{H}\) & N/A & \(7T_{Mul}+3T_{H}\) \\ (direct authorization) & & \(+2T_{Cmp}+2T_{Sig}\) & & \(+1T_{Cmp}+2T_{Sig}\) \\ \hline Authentication and issuing invoice phase & \(\mathrm{N/A}\) & \(7T_{Mul}+3T_{H}\) & \(7T_{Mul}+3T_{H}\) & \(7T_{Mul}+3T_{H}\) \\ (entrusted authorization) & & \(+3T_{Cmp}+2T_{Sig}\) & \(+2T_{Cmp}+2T_{Sig}\) & \(+1T_{Cmp}+2T_{Sig}\) \\ \hline Payment verification and browsing phase & \(\mathrm{N/A}\) & \(9T_{Mul}+3T_{H}\) & N/A & \(7T_{Mul}+3T_{H}\) \\ (direct authorization) & & \(+3T_{Cmp}+2T_{Sig}\) & \(+1T_{Cmp}+2T_{Sig}\) \\ \hline Payment verification and browsing phase & \(\mathrm{N/A}\) & \(10T_{Mul}+3T_{H}\) & \(7T_{Mul}+3T_{H}\) & \(7T_{Mul}+3T_{H}\) \\ (entrusted authorization) & & \(+4T_{Cmp}+2T_{Sig}\) & \(+3T_{Cmp}+2T_{Sig}\) & \(+1T_{Cmp}+2T_{Sig}\) \\ \hline \hline \end{tabular} \(T_{Mul}\): Multiplication operation; \(T_{H}\): Hash function operation; \(T_{Cmp}\): Comparison operation; \(T_{Sig}\): Signature operation.  
\end{table}
Table 2: Computation cost analysis of this scheme.  
system role registration phase is 5056 bits, which takes 0.361 ms under 3.5 G (14 Mbps) communication environment, 0.051 ms under 4 G (100 Mbps) communication environment, and takes 0.253 ms under 5 G (20 Mbps) communication environment [34]. The proposed scheme has excellent performance.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 1 Introduction
The association will offer fulfillment to the mobile UE in terms of QoS to the downlink as much as the uplink while favoring a specific load adjustment utilizing a self-organization process for faster convergence. Thus, our current contribution extends the DUDe association schemes-based matching game and covers some novel aspects, as follows:  
* We defined association issues as a utility function (see Section 3.1) that guarantees that each UE will meet the stringent data throughput prerequisites for uplink and downlink. * We define the QoS as a context-aware constraint to the above optimization problem. To do this, we go through the assignment step (see Section 4.2) to filter the candidate BSs for the selective handover process. * So as to enhance the network throughput and to consider a reasonable resource allocation for the UE, the proposed handover solution will boost the defined utility function (see Equation (7) in Section 3.1), which is the logarithm sum of the UE long-term rates served by the BS, to support a strategic distance from the overload circumstance and offer proportional equity. As a result, our proposed utility function encourages mobile UE association with the least loaded cells, even if they offer an instantaneous signal-to-interference-plus-noise ratio (SINR) less than the macro BS. * At that point, we formulate the handover process as a matching game with externalities (see Section 4). In the proposed scheme, we support the decrease of the algorithm's convergence time by introducing a stable dismissing-matching concept and assignment steps. During the defined assignment step, we investigate and filter the BS to keep just the helpful agent BS in terms of QoS for uplink and downlink. Along these lines, we propose a suitable cellular association calculation that achieves a local optimum with a faster convergence since the assignment step will further reduce the calculation time. * To study the performance of the proposed scheme, we elaborate a simulation scenario in network simulator-3 [7] for a two-tier heterogeneous network. The numerical results verify their potentials on utility increase and equity in comparison with the traditional cell association for the coupling and decoupling technique. The rest of this paper is arranged as follows: a prologue to the matching-game theory application for the system choice will be presented in Section 2. In Section 3, we depict the system model and frame the issue as the maximization of the utility function. Afterwards, our proposed model will be presented in Section 4, where we characterize the decoupled handover process as an assignment QoS-based technique in the primary stage and a matching-game in the second step. Then, Section 5 portrays the execution assessment scheme where the reproduction details and results are examined for the exposed schemes. Finally, we complete with the conclusion in Section 6.