esa-sceva/satcom-chunk-collection huggingface dataset:
# Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention
## 4 Experiments
### Experimental Settings
#### 4.1.2 Evaluation Metrics  
We use the following common evaluation metrics to assess the model's overall performance. Specifically, these include classification accuracy metrics: Precision, Recall, F1 score, mean average precision (mAP), and operational speed metrics (frames per second (FPS)).  
Precision measures the degree of false detection, and Recall indicates the situation of missed detection by the model. The calculation methods are as follows:  
\[Precision=\frac{TP}{TP+FP}, \tag{21}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{22}\]  
among them, TP refers to samples that the detector predicts as true and are indeed true; TN refers to samples predicted as false that are indeed false; FP refers to samples that are predicted as true but are actually false; FN refers to samples predicted as false that are actually true.  
\begin{table}
\begin{tabular}{c c} \hline
**Software Environment and** & **Parameter** \\
**Hardware Configuration** & **Parameter** \\ \hline Operating system & Windows 10 64-bit \\ Integrated Development Environment (IDE) & PyCharm Professional 2023.1.3 \\ Runtime framework & Pytorch 1.13.1(torchvision 0.14.1) \\ Development tool & CUDA 11.1(cuDNN 8.0) \\ Processor & Inter Core i7-12700F @2.10 GHz \\ Memory & 32 G \\ Graphics Processing Unit(GPU) & NVIDIA GeForce RTX3080(10 G) \\ \hline \end{tabular}
\end{table}
Table 2: Experimental software environment and hardware configuration.  
\begin{table}
\begin{tabular}{c c} \hline
**Model Hyperparameter** & **Value** \\ \hline Depth multiplier & 0.33 \\ Width multiplier & 0.50 \\ Batch size & 16 \\ Image size & 640 \(\times\) 640 \\ Number of training epochs & 500 \\ Number of worker processes & 4 \\ Momentum & 0.937 \\ \hline \end{tabular}
\end{table}
Table 3: Model hyperparameter settings.  
The F1 score is the harmonic mean of _Precision_ and _Recall_, calculated as follows:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}. \tag{23}\]  
All kinds of average precision metrics take both Precision and Recall into account. The P-R curve is formed by plotting Precision against Recall as co-ordinates. The area enclosed by this curve and the co-ordinate axis at different confidence levels equals the AP value. The calculation method for AP is as follows:  
\[P=\int_{0}^{1}P(r)dr, \tag{24}\]  
where \(P\) is the Precision of the prediction result, and \(r\) represents the Recall of the prediction result. AP is used to evaluate the classification effect of a certain class, while mAP averages AP from the class dimension so it can evaluate the performance of multi-classifiers. Its calculation method is as follows:  
\[mAP=\frac{1}{n}\sum_{i=1}^{n}AP_{i}, \tag{25}\]  
where \(n\) is the number of classes of the detector, and \(AP_{i}\) represents the AP value of the \(i\)-th class. The value of mAP must be within \([0,1]\). A larger value indicates a better detection effect.  
FPS refers to the number of image frames processed per unit of time, typically measured in frame/s.
## 4 Discussion
### Analysis of Component Reliability and Maintainability
#### 4.2.3 General Wear Analysis  
The wear test results presented in Tables 4 and 5 demonstrate significant wear of the bucket tooth and bucket blade, highlighting the need for reconditioning. Tables 7 and 8 present the weld reconditioning data for these components, illustrating an effective method to restore the functionality of worn equipment. By comparing the wear data with the reconditioning data, a direct link between material degradation and maintenance requirements is observed, emphasizing the relevance of our study in the context of optimizing maintenance processes in the mining industry.  
Compared to the results of other research [12] conducted under laboratory conditions, regarding the wear of metallic materials in technological equipment in contact with various rocks, we can assert the following aspects: the value of wear of metal elements depends on the homogeneity and abrasiveness of the rocks they contact; the variation curves of wear of wear-resistant steel, as a function of quartz content in different types of rocks examined and quartz sand (behavior established for loads of 0.094 MPa and 0.125 MPa), show that in the 80-88% range of SiO\({}_{2}\) content in the rock, the increase in metal wear is relativelysmall, and only when the quartz content approaches 100%, does the abrasive material cause significantly higher wear. We should seek the reasoning behind the above observation in the potential interactions between the hard particles of the abrasive material (crushed quartz grains) and the relatively soft cement or carbonaceous inclusions.  
The aforementioned findings align with the results presented in this paper, which refer to the different evolutions of wear of metal elements in friction processes with rocks of known properties, thus confirming the need to correctly establish the wear of metal elements in contact with rocks of varying physical-mechanical properties through laboratory tests, complemented by in situ experiments of metal prototypes of subassemblies of technological equipment intended for quarries, underground mining, railway tunnel excavation, or road construction.
## 3 Dataset
### Data Collection  
Road images were compiled in collaboration with maintenance employees in the municipality of Rabigh Governorate using a smartphone device, as shown in Figure 1. The images were saved in JPG format with different resolutions and were resized to 650 \(\times\) 650 to obtain square images and maintain uniformity in the dataset. Furthermore, it can be noted that the proposed data majorly captures the images of flexible (asphalt) pavements. A total of 400 images were collected between January and March 2022 under varying weather and lighting conditions, including sunny, overcast, light rain, and sunset. In addition, the images were taken from various angles. However, only 242 images were found suitable for the study. The remaining images were either blurred or did not cover the required road damage categories or significant road portions, and they were discarded.
## 7 Application to an Existing Bridge with Extended Service Life-Based State Function
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inspection** & **Probability of Failure** & **MCS \({}^{*}\)** & **KDE** & **Combined** \\
**Application Times** & **\(p_{f}\)**/**Reliability Index \(\beta\)** & & **KDE** & **KDE** and GPD** \\ \hline \(t_{\text{{\it insp1}}}\) = 10 years & \(p_{f}\) & 0.0038 & 0.0038 & 0.0053 \\  & \(\beta\) & 2.67 & 2.67 & 2.56 \\ \hline \(t_{\text{{\it insp1}}}\) = 7 years & \(p_{f}\) & \(1.30\times 10^{-4}\) & \(1.30\times 10^{-4}\) & \(1.89\times 10^{-4}\) \\ \(t_{\text{{\it insp2}}}\) = 14 years & \(\beta\) & 3.65 & 3.65 & 3.56 \\ \hline \(t_{\text{{\it insp1}}}\) = 6 years & \(p_{f}\) & \(2.40\times 10^{-5}\) & \(2.40\times 10^{-5}\) & \(1.64\times 10^{-6}\) \\ \(t_{\text{{\it insp2}}}\) = 12 years & \(\beta\) & 4.07 & 4.07 & 4.65 \\ \hline \hline \end{tabular}  
* Number of samples for MCS = 10\({}^{6}\).  
\end{table} 
Table 3: Probability of failure (\(p_{f}\)) and reliability index (\(\beta\)) associated with the extended service life-based state function when MCS, KDE, and the combination of KDE and GPD are applied. Figure 8: Extended service life-based state function values when the inspection is applied at 10 years: (**a**) entire representation of PDF; (**b**) representation for detail D in (**a**); and (**c**) CDF associated with detail D.  
Figure 9: Extended service life-based state function values when the inspections are applied at 7 years and 14 years: (**a**) entire representation of PDF; (**b**) representation for detail E in (**a**); and (**c**) CDF associated with detail E.  
The PDFs and CDFs of the state function values when inspections were applied at 7 and 14 years are shown in Figure 9. As shown in Figure 9b, two peaks near -6 years and -14 years were represented using MSC and KDE. However, the PDF obtained using the combination of KDE and GPD had an exponentially decreasing left tail without any peaks. As shown in Figure 9c and Table 3, among MCS, KDE, and the combination of KDE and GPD, the combination of KDE and GPD produced the largest \(p_{f}\). The PDFs and CDFs of the state function values under three inspections at 6, 12, and 18 years are shown in Figure 10. In addition, the \(p_{f}\) computed using the combination of KDE and GPD was the smallest (see Figure 10 and Table 3). As shown in Figures 8c and 9c, \(p_{f}\) (or \(\beta\)) values based on MCS and KDE were the same, as the CDF based on MCS is appropriately represented by the CDF based on KDE.
## Materials and Methods
### Eddy Covariance Measurement and the LUE Calculation  
The flux tower was installed at 27 m, consisting of a three-dimensional ultrasonic anemometer (CSAT3, Campbell Scientific Ltd., Logan, UT, USA) and fast response infrared CO\({}_{2}\)/H\({}_{2}\)O analyzer (Li-7500, Li-CorInc., Lincoln, NE, USA) composition. The CO\({}_{2}\) and H\({}_{2}\)O flux data were automatically archived with a CR500 data logger at a frequency of 10 Hz, and the average flux data was calculated at 30 min intervals. In addition, meteorological measurements above the vegetation canopy included canopy incident radiation, air temperature, saturated water vapor pressure, precipitation, soil moisture, etc. The net CO\({}_{2}\) exchange (NEE) was calculated from the CO\({}_{2}\) storage flux and turbulent flux obtained from flux observations, and the ecosystem respiration (Re) was obtained using an empirical relationship between nighttime requirements at a 50 mm depth and at soil temperature. Re is the ecosystem respiration during the day, obtained by deducting the exponential relationship between NEE at night and soil temperature at a 5 cm depth. _GPP_ and _LUE_ were estimated using the following equations:  
\[GPP=R_{e}-NEE \tag{3}\]  
\[LUE=GPP/(PAR*FPAR) \tag{4}\]  
Photosynthetically active radiation (_PAR_) refers to the incident radiation at the top of the vegetation canopy, which can usually be obtained from meteorological observations. A fraction of the absorbed photosynthetically active radiation (_FPAR_) refers to the ratio of _PAR_ absorbed by the green part of the  
Figure 2: Distribution of the TLS scanning stations.  
vegetation canopy to total the _PAR_. It can be obtained from remote sensing data [5,44]. _FPAR_ can be calculated as follows:  
\[FPAR=\frac{(NDVI-NDVI_{min})(FPAR_{max}-FPAR_{min})}{NDVI_{max}-NDVI_{min}}+FPAR_{min} \tag{5}\]  
where \(NDVI_{min}\) and \(NDVI_{max}\) are NDVI values for vegetation coverage of 5% and 98%, respectively. According to the characteristics of the vegetation canopy structure in the observation area, \(NDVI_{min}=0.01\), and \(NDVI_{max}=0.5\). \(FPAR_{min}\) and \(FPAR_{max}\) represent the minimum and maximum FPAR values, respectively, which are generally assumed to be 0.001 and 0.95, respectively. The _FPAR_ was obtained by the normalized difference vegetation index (\(NDVI\)) estimation method [44], and the vegetation reflectivity in each band was obtained by an automatic multi-angle spectroscopy system. The \(NDVI\) can be calculated as follows:  
\[NDVI=\frac{R_{850}-R_{680}}{R_{850}+R_{680}} \tag{6}\]  
where \(R_{850}\) and \(R_{680}\) represent the reflectance of plant leaves at 850 and 680 nm, respectively, observed by an automated multi-angle spectroscopy system.
## 3 Results and Investigations
### Thermal Environment of the Ground-Water Temperature  
Figure 7 shows the results of analyzing the thermal environment characteristics of the groundwater through the cross mixing of the SCW and balancing well methods. Figure 7a shows the characteristics of the circulating water intake temperature, circulation flow rate, bleed flow rate, input, and exit temperature difference, and injected heat caused by the change to the balancing well method from the SCW method after 24 h of operation. The SCW method initially showed the characteristics of a continuous increase in the temperature of the In/Out during operation. However, utilizing the SCW method instead of the balancing well method resulted in a decrease in temperature difference between the inlet and out temperatures of the circulating water. The possible reason is the activation of the underground aquifer by the water level difference of the well by the injected bleed flow and the activation of the flow of the underground aquifer. Consequently, the recovery of the temperature of the underground circulating water might increase. Figure 7b shows the results of the circulation water temperature, circulation flow rate, bleed flow rate, and injection heat, due to the utilization of the SCW method, instead of the balancing well operation, after 24 h of operation. When operating the balancing well method, the temperature difference between the inlet and exit temperatures of the circulating water was observed to be slightly higher than that shown in Figure 7a. However, when changing the operation from the balancing well method to the SCW method, the temperature of the In/Out increased rapidly. Through this technique, it is deemed that the heat accumulation phenomenon occurred because of the increase in the temperature of the circulating water in the ground. As shown in Table 5, when driving balancing well, the thermal efficiency increased more than that of the SCW, and the thermal environmental conditions of the groundwater were optimized, which could be a condition for long-term operation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**COP**} & \multicolumn{3}{c}{**Cooling Operation**} & \multicolumn{3}{c}{**Heating Operation**} \\ \cline{2-7}
**Performance** & **SCW** & **BW SCW** & **Remarks** & **SCW** & **BW SCW** & **Remarks** \\ \hline Minimum & 2.58 & 3.45 & & 2.08 & 2.21 & \\ Maximum & 3.96 & 4.29 & & 3.44 & 3.66 & \\ \hline Average & 3.05 & 3.76 & 23\% \(\uparrow\) & 2.92 & 3.27 & 12\% \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the performance factors of COP.  
Figure 7: _Cont._  
Figure 7: Operating characteristics of the SCW and cross-mixing balancing well geothermal heat exchanger affecting the thermal environmental characteristics of the ground.
## 4 Performance Analysis of Closed-Form Estimator
### The Simulation for Estimator Accuracy Analysis
Ideally, the subtraction of two numbers with close values should be  
Figure 2: Estimation accuracy analysis of \(k\), \(\mu\) parameter estimation methods based on Monte Carlo method generated \(k\)-\(\mu\) channel data; (**a**) Comparison and error of theoretical PDF and estimated PDF curves at \(k=2\), \(\mu=1\); (**b**) Comparison and error of theoretical PDF and estimated PDF curves at \(k=4\), \(\mu=1\); (**c**) Comparison and error of theoretical PDF and estimated PDF curves at \(k=4\), \(\mu=2\); (**d**) Comparison and error of theoretical PDF and estimated PDF curves at \(k=6\), \(\mu=2\). avoided. However, in the denominator of term \(\hat{k}_{yacoub}\) in Equation (6), the values of \(E[\alpha^{6}]\) are very close to that of \(2E[\alpha^{4}]^{2}-E[\alpha^{4}]\). Last, it is preferable to avoid dividing a large number by a small one. Nonetheless, in Equation (6) \(\hat{k}_{yacoub}\), the numerator \(\sqrt{2}(E[\alpha^{4}]-1)\) is several orders of magnitude larger than the denominator \(\sqrt{2E[\alpha^{4}]^{2}-E[\alpha^{4}]-E[\alpha^{6}]}\). Therefore, under the experimental scenarios presented in Table 4, Estimator \(\hat{k}\)-\(\hat{\mu}_{\Delta}\) and \(\hat{k}\)-\(\hat{\mu}_{\Delta}\) outperforms other estimators. Moreover, the computation time of Estimator \(\hat{k}\)-\(\hat{\mu}_{\Delta}\) is smaller than that of Estimator \(\hat{k}\)-\(\hat{\mu}_{\Delta}\), making Estimator \(\hat{k}\)-\(\hat{\mu}_{\Delta}\) the most optimal choice. Additionally, we conducted another set of comparative experiments. Assuming \(k=0\) and \(\mu\) taking values of 1, 1.5, and 2, we generated channel data using the Monte Carlo method. In each estimation experiment, the four estimators used the same data samples, with a sample size of 10,000. The estimated PDF curves, theoretical PDF curves, and their errors \(\left\|f_{PDF}(k,\mu)-f_{PDF}(\hat{k},\hat{\mu})\right\|_{2}\) are depicted in Figure 3. Furthermore, the estimation results and root mean square errors (RMSE) are recorded in Table 5.  
In Figure 3, the experiments for different \(\mu\) values under the condition of \(k=0\) are presented. The left image displays a comparison between the estimated PDF curves and the theoretical PDF curves, while the right image illustrates the errors between the estimated PDF curves and the theoretical PDF curves. In Table 5, the RMSE\({}_{pdf}\) column shows the root mean square errors between the PDF curves and the theoretical PDF curves; values closer to 0 indicate higher estimation accuracy. The last column demonstrates the ranking of estimation accuracy for each estimator in every experiment. In Table 5, we still observe that the \(k\), \(\mu\), and RMSE\({}_{pdf}\) values of Estimator \(\hat{k}\)-\(\hat{\mu}_{Yacoub}\) and \(\hat{k}\)-\(\hat{\mu}_{Yacoub}\) are almost identical, with differences only occurring after the seventh decimal place. Consequently, we can assert that these two estimators have the same estimation accuracy, regardless of the variations in \(k\) and \(\mu\).
## 4 GPSR Routing Mechanism
### Beaconing Approaches: Overview  
To achieve high-reliability beacon communication in various network environments like MANET or VANET, many adaptive beaconing strategies have been developed to individually handle each beacon's transmission power, transmission rate, or contention window (CW) at the MAC layer, or any combination of these as hybrid techniques [38; 39; 40; 41; 42; 43; 44; 47; 49]. For further details, the state-of-the-art beaconing approaches with key features were addressed in [17]. Notably, the broadcasting beacons over MAC IEEE 802.11 have unique characteristics that make the broadcast more critical in finding the accurate position [43; 56]:  
Figure 2: GPSR mechanism: (**a**) greedy forwarding mode; (**b**) perimeter mode.  
1. There is no acknowledgment system for the short beacon message to let the sender know when it is successfully received.
2. A request to send/clear to transmit (RTS/CTS), a handshaking mechanism, is not employed prior to beacon transmission in order to prevent beacon broadcast collisions at the MAC layer.
3. The fixed contention window (CW) may increase the chances of collision and degrade the performance of beacon broadcast.  
Moreover, the redundant broadcasting of the beacon packets increases the amount of control traffic overhead in the network and the energy consumption at each node. This causes early disconnections in data-routing paths in wireless networks. Additionally, excessive control overhead increases bandwidth consumption and congestion in the network [35]. Therefore, beaconing techniques are classified depending on the nature of routing protocols or the emergency network status as follows:  
* Periodic Beaconing: Nodes send beacons at regular intervals. For instance, this method can be utilized in topological routing such as DSDV (Destination-Sequenced Distance Vector) and AODV (Ad hoc On-Demand Distance Vector) algorithms.
* Adaptive Beaconing: Beaconing intervals are constantly modified according to the network traffic load, connection quality, or mobility patterns.
* Beaconing triggered by events: Nodes transmit beacons in response to certain events or triggers (substantial change in their network state).
* Geographic Beaconing: Nodes use their geographical position to determine when and to whom beacons should be sent.
# Computed Tomography-Driven Design and Fused Filament Fabrication of Graded Density Bone Scaffolds
## 2 Materials and Methods
### Cad
#### 2.1.1 Volumetric Models  
The CT scan data are stored as DICOM files. The volumetric HU 3D scalar field is read from the DICOM file as a voxel model data structure. The voxel model is defined by the number and size of elements in the X, Y, and Z directions, along with pixel data for each element. The information, related to the voxel model data structure, is stored as metadata in the DICOM file header. Here, tag {0028;0011} defines the number of columns, i.e., number of voxels in the X direction (nX), while tag {0028;0010} defines the number of rows, i.e., the number of voxels in the Y direction (nY), and the number of voxels along the Z direction corresponds to the number of acquisitions (nZ), with attribute {0020;0013} being the \"instance number\". The voxel sizes (vS) are saved in the attribute {0028;0030} as  
Figure 1: Workflow schematic.  
\"pixel spacing\" for the XY dimensions (vSX, vSY), while the Z dimension can be found in the attribute [0018;0050] as the slice thickness (vSZ). The rescale slope and intercept values and type are checked and applied to retrieve the correct HU values from the images.  
Attribute [0020;0032] for the \"image position (patient)\" defines the x, y, and z coordinates of the center of the first voxel transmitted relative to the scanner table reference frame, while the orientation is defined in tag [0020;0037] for the \"image orientation (patient)\" and eventually used to correctly rotate the volumetric model with respect to the printing direction.  
Once the HU 3D scalar field is reconstructed, correctly positioned, oriented, and visualized in the 3D space, the user defines a region of interest (ROI) using a box. An ROI is a subset of the 3D scalar field, used to isolate and evaluate regions such as bones, organs, or lesions--allowing for measurements (e.g., density, volume, shape) or further image processing. This ROI is used in the following step to perform Boolean operations.
# Review of Physical Layer Security in 5G Wireless Networks
## 5 Future Research
### New Transmission Methods  
Research and develop innovative transmission methods that can enhance security in 5G networks. Investigating innovative modulation and coding techniques, multiple antenna systems, and beamforming techniques designed especially for physical layer security can all be part of this [45]. To improve the secrecy performance, it is also possible to investigate the integration of innovative technologies such as massive MIMO, millimeter-wave communication, and full-duplex communication.