esa-sceva/satcom-chunk-collection huggingface dataset:
# Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5
## 4 Experiments
### Experimental Results and Comparisons  
In this section, experiments were conducted with various types of YOLOv5 models, such as s (small), m (medium), l (large), and x (xlarge). Parameter values have high values in the order of YOLOv5 s, m, l, and x, and the model of the x version can be regarded as the heaviest.  
Figure 4: Parcel image dataset sample.  
YOLOv5s, m, l, x can be seen in Figure 5, which displays different performance metrics for both the training and validation sets. The improvement of YOLOv5s, m, l, and x can be seen in Figure 5, which displays different performance metrics for both training and validation sets. Training and validation results, shown in Figure 6, showed rapid improvement in terms of precision and recall, plateauing after approximately 180 epochs. The performance evaluation index also showed a sharp decrease at approximately 180.  
As a result of comprehensively analyzing s, m, l, and x of the YOLOv5 model, the precision of YOLOv5l was the highest at 0.966, followed by x, s, and m values. In the case of recall, the value of YOLOv5x was the highest at 0.910, followed by m, l, and s. In the case of mAP (0.5:0.95), YOLOv5x was the highest at 0.815, followed by l, m, and s, respectively. Finally, in the case of the F1 value that measures accuracy, YOLOv5l had the highest performance at 0.932, followed by x, m, and s, respectively. The performance values of YOLOv5 for each detailed model are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP (0.5)** & **mAP (0.5:0.95)** & **F1** & **Parameter** \\ \hline YOLOv5s & 0.952 & 0.865 & 0.946 & 0.773 & 0.906 & 7,012,822 \\ \hline YOLOv5m & 0.923 & 0.903 & 0.951 & 0.798 & 0.913 & 20,852,934 \\ \hline YOLOv5l & 0.966 & 0.899 & 0.955 & 0.801 & 0.932 & 46,108,278 \\ \hline YOLOv5x & 0.945 & 0.910 & 0.958 & 0.815 & 0.928 & 84,173,414 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment result of YOLOv5s, m, l, x.  
Figure 5: Experiment results of YOLOv5s, m, l, x.  
Figure 6: Comparison of test results of YOLOv5 models.  
As a result of learning and testing the detailed model of YOLOv5, it was concluded that YOLOv5l and YOLOv5x have the highest performance and highest Ground Truth, as shown in Figure 6. Among them, considering the parameter value and calculation speed, the YOLOv5l model is best suited for application to parcel boxes, shown in their performance and Ground Truth as analyzed in Figure 7.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 2 Related Work
### CC Algorithm Based on the End-to-End Mode  
CC algorithms based on the end-to-end mode, which refers to direct control of the data transmission speed by the data transmitter, are mainly divided into three categories: loss-based CC algorithms, delay-based CC algorithms, and hybrid CC algorithms.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 5 Experiments and Results
### Implementation of Explainable AI Techniques: SHAP, LIME, and DiCE
#### 5.4.3. LIME  
Local Behavior Analysis  
LIME are divided into three sections with a consistent color scheme:  
* The left shows the model's predicted probability for the instance. * The middle section shows key features: blue for benign (0), orange for attack (1). Bar length reflects feature impact, with longer bars indicating greater influence. * The right section shows actual feature values. The features highlighted in blue contribute negatively to the prediction and the features highlighted in orange contribute positively to the prediction. * LIME Plot for Test Sample Record 1--Normal' Traffic Prediction Figure 11 illustrates a LIME for the \"Normal\" class instance. The following conclusions can be drawn:
* Although several features contribute negatively (blue) in the LIME plot, the prediction is still classified as \"Normal\" because the model evaluates the overall interactions of all features rather than assessing them individually. * The positively contributing features (orange), such as dMaxPktSz, IMEI, and TotPkts, help to counterbalance the negative influences, leading to the final classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Full Form** & **Interpretation** & \begin{tabular}{c} **Reason for ‘Attack’** \\ **Prediction** \\ \end{tabular} \\ \hline \multirow{6}{*}{scpputures\_system} & & High CPU time for system tasks could signal attack-related resource manipulation. & \
\begin{tabular}{c} scpputures\_system represents background system tasks, and in combination with abnormal interrupts and elevated system-level resource consumption, suggests a coordinated attack manipulating system resources. \\ \end{tabular} \\ \hline \multirow{6}{*}{scpputats\_interrupts} & & High interrupt counts, combined with increased svmem\_slab (high memory usage) and CPU processes like scpputures\_user, point to network or system disruptions, often associated with DDoS or other attack strategies. \\ \cline{2-4}  & & High number of software interrupts suggest network interrupts &
\begin{tabular}{c} High \\ hardware interrupts \\ (scpustats\_interrupts) and high system process usage \\ (scputines\_system) suggest an ongoing attack, such as DDoS or resource manipulation. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._Table 5 highlights the features and their contributions to the classification of network traffic as \"Normal\", based on the LIME plot, with interpretations of the feature values and the reasons for their positive or negative influence on the final prediction. Figure 11: LIME plot for record sample 1 from test dataset (local explanation)—predicting Class 0—”Normal” traffic.
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 2 Problem Description and System Model  
Multi-UAVs cooperative target tracking problem is shown in Figure 1. A target moves on the ground while several UAVs track it cooperatively. Each UAV is equipped with an airborne sensor and a local filter to get a better estimation of the target state. Since the sensor is not perfect, it has an observation noise so it may also need information from other UAVs. Thus, each UAV platform receives information from its neighbors (we define \"neighbor\" in Section 2.3) and in the meanwhile shares its information with them. Observation information from neighbor nodes contain communication noise. Each UAV will run the information fusion and the consistent process to make use of other UAVs' information in order to get a consistent target state prediction, which is used to calculate each UAV's control input. In this paper, the target tracking problem involves three kinds of noise: Process noise of target movement, observation noise of the sensor, and communication noise during communication between UAVs.  
Figure 1: Cooperative target tracking. UAV: Unmanned Aerial Vehicle.
## 4 Discussion  
### Serum Levels of Glucose and C-Peptide  
In our study, rats with STZ-induced T1 DM, rats with ISO-induced AMI and all those with T1 DM and AMI had a higher level of glucose (Figure 2). It was observed that acute hyperglycemia is characteristic for the early phase of AMI, even in patients without a history of T1 DM, and that it exaggerates inflammation by the oxidative mechanism so that it can increase mortality [20]. Even more, hyperglycemia was observed to be capable of inducing electrophysiological alterations in myocardial tissue that might trigger fatal arrhythmias as a result of significant elongation of the QT interval [21].  
Our results show that rats with STZ-induced T1 DM had a lower level of C-peptide. C-peptide is widely used to measure the pancreatic beta-cell function of insulin secretion, as it is produced in equimolar amounts to endogenous insulin and is not influenced by therapeutically administered exogenous insulin [22]. Even if it was subsequently regarded as a waste product of insulin synthesis, since it does not affect glucose or lipid metabolism, new discoveries would support the idea that patients with T1 DM who still have a certain degree of beta-cell activity are considerably less prone to develop microvascular complications compared to those with a total deficit of C-peptide [23].  
Administration of curcumin to diabetic rats was reported to significantly reduce the plasma glucose level and increase plasma insulin and C-peptide levels substantially by promoting beta-cell regeneration [24]. Curcumin reduces the serum glucose level by stimulating insulin secretion of island \(\beta\) cells [25]. It was already reported that nanocurcinin could reduce glucose level, pro-inflammatory cytokines, and oxidative stress in STZ-induced T1 DM due to decreased inflammation and apoptosis in pancreatic islets cells [26]. Better results obtained for NC can be explained by the increased tissue distribution of the curcumin nanoparticles in the body organs [27].  
Figure 9: Serum levels of matrix metalloproteinases: (**a**) Matrix metalloproteinase 2 (MMP-2); (**b**) matrix metalloproteinase 9 (MMP-9). C—control; AMI—acute myocardial infarction; T1 DM—type 1 diabetes mellitus; S—saline (0.09%); CC—conventional curcumin solution (200 mg/Kg bw); NC—solution of curcumin nanoparticles (200 mg/Kg bw). The letters correspond to \(p\) values <0.05: (b) S-T1 DM-CC-AMI compared to T1 DM; (c) S-T1 DM-CC-AMI compared to T1 DM-AMI; (A) CC-T1 DM-CC-AMI compared to AMI; (B) CC-T1 DM-CC-AMI compared to T1 DM; (C) CC-T1 DM-CC-AMI compared to T1 DM-AMI; (D) CC-T1 DM-CC-AMI compared to T1 DM-AMI; (D) CC-T1 DM-CC-AMI compared to S-T1 DM-CC-AMI; (x) S-T1 DM-NP-AMI compared to AMI; (y) S-T1 DM-NP-AMI compared to T1 DM; (z) S-T1 DM-NP-AMI compared to T1 DM-AMI; (t) S-T1 DM-NP-AMI compared to S-T1 DM-CC-AMI; (X) NP-
## 2 Materials and Methods
### Resolving Binary Variables  
Binary parameters require careful treatment to ensure logical consistency and accurate prevalence within the simulated dataset. Although Monte Carlo simulations often rely on the Beta distribution to model bounded variables, this choice is unsuitable for truly binary outcomes. The Beta distribution performs poorly when the expected proportion is near 0 or 1 or exactly 0.5. In these cases, the distribution becomes sharply peaked or fails to support equal partitioning between the two binary states. This makes it an unreliable basis for binary simulation.  
The issue becomes more pronounced when correlation is imposed using Cholesky decomposition. If binary values are first sampled from a continuous distribution such as Beta and then transformed to introduce correlation, the resulting values often extend beyond the [0, 1] bounds. These violations must be corrected--typically by clamping--which distorts the prevalence and introduces bias.  
To avoid these problems, the patient-generator algorithm simulates binary variables using a thresholding approach applied to correlated standard normal values. During simulation, each binary parameter is treated as continuous and assigned a standard normal value as part of the Cholesky-adjusted correlation step. After correlation is imposed, the algorithm converts these values to binary outcomes based on the desired prevalence \(\mu\).  
The values for a specific binary parameter are sorted in ascending order. For a given target prevalence \(\mu\) (e.g., 62%), the threshold is determined as the value at the \((1-\mu)\) quantile of the sorted list:  
\[X_{\text{threshold}}=\text{Percentile}(X,1-\mu)\]  
In the case of 1000 patients and \(\mu=0.62\), this corresponds to the value ranked 380 th in the sorted list (i.e., the 38 th percentile). This threshold is used to binarize the variable: all values above or equal to the threshold are assigned a value of 1 (true), and all values below are assigned 0 (false). This method guarantees that the proportion of ones in the simulated dataset exactly matches the input prevalence, regardless of the applied correlation structure.  
Moreover, because the binary conversion is applied after correlation imposition, it avoids the distortions and boundary violations commonly encountered when trying to impose a correlation on binary variables directly using continuous distributions like the Beta. We validated this approach across a wide range of prevalence values--ranging from approximately 12% to 99%--using multiple binary parameters in the Framingham dataset. In all cases, the generated proportions matched the target values precisely, confirming the reliability and accuracy of the method.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 4 Case Study
### Point Cloud and BIM Registration  
To achieve the coordinate registration of the two models and obtain transformation matrix \(M_{1}\), the rigid transformation is calculated based on the poses of the BIM grid center and the virtual grid center. Figure 11 shows the registration result of the point cloud slice and the BIM grid, resulting in the approximate alignment of the BIM with the point cloud model. However, it is challenging to meet the evaluation requirements of geometric accuracy. Figure 12 (enlarged area) shows that there are still obvious gaps in the areas on both sides of the subway station (A-2 and C-38). Therefore, fine registration is used to further improve the registration accuracy. The inner wall lines are extracted from the  
Figure 10: (**a**) Scanning point cloud of the subway station; (**b**) the design BIM of the subway station.  
BIM, and the transformation matrix \(M_{2}\) is obtained after the point cloud is finely registered with the inner wall lines using the PL-ICP method. The initial point cloud coordinate is transformed using \(M=M_{2}\cdot M_{1}\) to obtain the final point cloud model. The inverse transformation of the aforesaid transformation is the coordinate transformation from the BIM to the point cloud model. Figure 13 shows the result of fine registration from the point cloud model to the BIM. The average deviation value is 17.8 \(\pm\) 0.94 mm, and the fine registration result distributes the deviation evenly on the inner wall lines. The coarse-to-fine registration of the point cloud achieves centimeter-level registration accuracy, which provides reliable results for further geometric accuracy evaluation.  
Figure 11: Registration result of point cloud slice and BIM grid.  
Figure 12: Coarse registration results between the point cloud and BIM.
## 4 Evaluation Proposal and Analysis
### Results and Analysis
#### 4.3.1 Tree Topology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \begin{tabular}{c} **Energy** \\ **Consumption** \\ **(mW)** \\ \end{tabular} & & & \begin{tabular}{c} **Energy** \\ **Consumption** \\ **(mW)** \\ \end{tabular} \\ \hline \begin{tabular}{c} **Number** \\ **of Nodes** \\ \end{tabular} & \begin{tabular}{c} **Disconnected** \\ **Nodes (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Transmission/** \\ **Interference** \\ \end{tabular} & **ETX** & **HOP** &
\begin{tabular}{c} **Transmission/** \\ **Interference** \\ \end{tabular} & **ETX** & **HOP** \\ \hline \multirow{3}{*}{10} & 0 & & 1.0352 & 0.9807 & & 1.0714 & 1.0322 \\  & 10 & 50/100 m & 1.0542 & 0.9790 & 70/90 m & 1.1173 & 1.0286 \\  & 20 & & 1.0895 & 0.9792 & & 1.1222 & 1.0230 \\ \hline \multirow{3}{*}{20} & 0 & & 1.2143 & 1.1361 & & 1.1039 & 1.0688 \\  & 10 & & 1.3056 & 1.2062 & & 1.1576 & 1.0615 \\  & 20 & & 1.5256 & 1.2476 & & 1.1650 & 1.0568 \\  & 30 & & 1.8854 & 1.2637 & & 1.1884 & 1.0500 \\ \hline \multirow{3}{*}{30} & 0 & & 1.5911 & 1.2786 & & 1.2773 & 1.1778 \\  & 10 & & 1.7239 & 1.3621 & & 1.3690 & 1.2342 \\ \cline{1-1}  & 20 & 50/100 m & 1.9176 & 1.3756 & 70/90 m & 1.4777 & 1.2500 \\ \cline{1-1}  & 40 & & 1.9779 & 1.3902 & & 1.5488 & 1.2792 \\ \cline{1-1}  & 50 & & 2.0759 & 1.4568 & & 1.6978 & 1.3020 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Energy consumption in the circular topology. Figure 10: Scenario 6, 30 nodes, circular topology: (**left**) 50/100 m, (**right**) 70/90 m.
# Enhanced Antifungal Activity of Engineered Proteins via Swapping between Thiodexin H2 and H3
## 3 Results and Discussion
### Intracellular ROS Generation in Fungal Cells  
In previous study [6], AtTrx-H5, another Trx h-type protein, exhibits antifungal activity via cellular ROS generation. Therefore, we investigated intracellular ROS generation of Trx-H2, H3, 2N3C, and 3N2C proteins in _C. albicans_ cells. As shown in Figure 3, H2 and H3 proteins produced the cellular ROS, cells treated with H2 and H3 proteins significantly produced ROS (Figure 3a,b, respectively), whereas 2N3C and 3N2C did not generate ROS (Figure 3c,d, respectively). We suggest that H2 and H3 proteins exhibit the fungal growth via intracellular ROS generation, but swapped proteins show a potent antifungal activity via another mode of action.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Minimum Inhibitory Concentration (MIC) (\(\mu\)g/mL)**} \\ \cline{2-9}
**Fungal Strains** & \multicolumn{2}{c}{**H2**} & \multicolumn{2}{c}{**H3**} & \multicolumn{2}{c}{**2N3C**} & \multicolumn{2}{c}{**3N2C**} \\ \cline{2-9}  & **pH 7.2** & **pH 5.2** & **pH 7.2** & **pH 5.2** & **pH 7.2** & **pH 5.2** & **pH 7.2** & **pH 5.2** \\ \hline \multicolumn{9}{c}{**Mold**} \\ \hline _A. flavus_ & 150 & 300 & 300 & 150 & \(>\)300 & 300 & 150 & 37.5 \\ _A. fumigatusatus_ & 150 & 300 & 300 & 150 & \(>\)300 & 300 & 150 & 37.5 \\ _C. gloeosporoides_ & 15  
Figure 3: _Cont_.  
Figure 2: Cellular distribution of Trx-H2/H3/2N3C/3N2C proteins in _C. gloeosporoides_ cells. FAM-labeled Trx-H2 (**a**), -H3 (**b**), -2N3C (**c**), or -3N2C (**d**) proteins were incubated with spores of _C. gloeosporoides_ at corresponding minimum inhibitory concentrations (MICs) in pH 5.2 for 2 h at 28 \({}^{\circ}\)C, following which, cellular populations of FAM-labeled proteins were observed under a confocal laser scanning microscope.
## 5 Emerging Trends in FSO Communication
### Artificial Intelligence-Driven FSO Systems
As research and development in this area continue to advance, the potential applications of AI and ML in FSO communication are vast, promising a future where seamless, high-speed optical communication is a reality [466, 479, 481, 492].
# Insights on Osmotic Tolerance Mechanisms in _Escherichia coli_ Gained from an _rpoC_ Mutation
## 3 Results and Discussion
### The rpoC K370_A396 dup Mutation Confer's Osmotic Tolerance in BW25113
The impact of the _rpoC_ mutation on tolerance to higher osmotic stress was tested in both aerobic and micro-aerobic conditions. Preliminary tests showed higher osmotic tolerance of the strains in micro-aerobic vs. aerobic conditions, therefore 0.8 M, 0.9 M, and 1 M NaCl were used in micro-aerobic conditions, while 0.75 M, 0.8 M, and 0.9 M NaCl were used under aerobic conditions. The higher osmotic tolerance observed under micro-aerobic conditions is likely due to higher induction of the  
Figure 1: Growth kinetics in M9 supplemented with 0.6 M NaCl. (**A**) BW25113 and EJW3 in M9 supplemented with 0.6 M NaCl (solid lines: without tryptophan, and dashed lines: with 50 \(\upmu\)g mL\({}^{-1}\) tryptophan supplementation). (**B**) Tryptophan auxotrophic strains JW1253, EJW4, HFr-2\(\times\)SFX- and G3 in M9 supplemented with 0.6 M NaCl and 50 \(\upmu\)g mL\({}^{-1}\) tryptophan. Red lines: strains without _rpoC_ mutation. Blue lines: strains with _rpoC_ mutation. Error bars are standard deviations. Figure 2: Growth and viability curves. (**A**) M9 with 0.6 M NaCl. (**B**) M9 with 0.6 M NaCl and tryptophan (50 \(\upmu\)g mL\({}^{-1}\)). Error bars are standard deviations. osmotic tolerance related genes _ompC_ and _proU_ under anaerobic conditions [37]. Relative growth results in 0.8 M (micro-aerobic) and 0.75 M (aerobic) NaCl are shown in Tables 2 and 3. In micro-aerobic and aerobic conditions, all strains with the _rpoC_ mutation showed significant growth in the presence of 0.8 M and 0.75 M NaCl respectively, while only slight growth was observed in their wild-type counterparts. In the presence of 0.9 M NaCl in micro-aerobic condition and 0.8 M NaCl in aerobic condition, only slight turbidity was observed in all strains (Tables S4 and S5), and no growth was observed in any strain cultured micro-aerobically in the presence of 1 M NaCl or aerobically with 0.9 M NaCl.
## Materials and Methods
### Outcome Measurement  
Based on the literature [29; 30; 32], the list based on the extended UTAUT model was used to measure PE, EE, and BI constructs of performing IEG. According to the studies [48; 60; 61], the measurement items of the PPS construct include feelings about the inner body and physical environment. The measurement items are presented in Table 1. PPS1, PPS2, and PPS4 measure the subjective safety of the physical body, while PPS3 focuses on the physical environment. PPS2 emphasizes the \"comfort\" feeling, and PPS4 emphasizes the \"trust\" feeling, especially about their existing worries about health issues and chronic pain, which are directly related to perceived safety. The items of each construct are rated on a 7-point Likert scale, ranging from 1 (very strongly disagree) to 7 (very strongly agree).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Constructions** & **Measurement Items** \\ \hline \multirow{7}{*}{Performance Expectation (PE)} & PE1: I find that playing VR exergames helps me with my functional mobility. \\  & PE2: I find that playing VR exergames would make me feel healthier in my daily life. \\ \cline{1-1}  & PE3: VR exergames could enhance my muscle strength. \\ \cline{1-1}  & PE4: VR exergames could enhance my balance skills. \\ \cline{1-1}  & PE5: VR exergames are helpful to enhance my confidence in exercising. \\ \cline{1-1}  & PE6: Overall, I find VR exergames would be highly helpful. \\ \hline \multirow{3}{*}{Effort Expectation (EE)} & EE1: I find that playing VR exergames would be simple. \\ \cline{1-1}  & EE2: I find that playing VR exergames would be easy to learn. \\ \cline{1-1}  & EE3: I find that VR exergames would be easily understandable and clear to me. \\ \cline{1-1}  & EE4: Overall, I find VR exergames would be convenient. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement items of hypothesis constructions.  
Figure 2: Virtual environment in Fruit Ninja VR.
## 3 Results
### Estimating Sensor Limit of Detection  
The developed sensor was not exclusively selective to the vapor of ammonia or ammonium hydroxide. Instead, it will detect a change to a high-pH environment. For this reason, pH was used to define its limit of detection. All pH measurements in both solution and headspace were seen to have pH values above the bromocresol green's stated pH range of 3.8-5.4 [34] (Figure 3 and Table 5). Initial statistical testing indicated that the acquired data were normally distributed. As such, an independent sample _t_-test was implemented for the comparison of means between two sample conditions using the IBM SPSS Statistics software (version 28.0.1.0). For each concentration, the means between the pH of headspace and solutions were found be significantly different, as noted by the _p_-values, which were all <0.001 (Table 5). Moreover, the _p_-values were also less than 0.05 when comparing pH values between subsequent NH4 OH dilutions, indicating that they are also significantly different. This highlights the differences in pH that the sensor encounters both in the headspace and in solution as ammonium hydroxide is diluted. Based on these results and when the sensor stopped reacting to NH4 OH dilutions (Table 3), the sensor's limit of detection was estimated to be at a range between pH 7.53 \(\pm\) 0.19-7.89 \(\pm\) 0.06 in the headspace of the NH4 OH dilutions.  
Figure 1: Average reaction time for BCG sensors prepared at different dye concentrations exposed to various NH4 OH dilutions (n = 3).  
Figure 2: Bromocresol green sensor color change from green (acid) to blue (basic).