esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
## 3 Experiment and Analysis
### Experiments on Different Datasets  
To validate the effectiveness of the model, the experimental environment was kept consistent, with the dataset randomly split into a training set and a validation set in a 7:3 ratio. The model was trained for 120 epochs, with an image resolution of 640 \(\times\) 640 pixels, a batch size of 16, and an initial learning rate (lr0) of 0.01. The model was tested using both the Kaggle steel strip dataset and defect data collected from an actual factory. The Kaggle dataset contains four types of defects, as shown in Figure 16. The dataset includes a total of 6666 defect images, with 4666 images in the test set and 2000 images in the validation set.  
The defect dataset collected from the factory includes two types of defects were flaw (FL) and scratch (Sc), as shown in Figure 17. The dataset contains a total of 608 defect images, with 426 images in the test set and 182 images in the validation set.  
Figure 16: The four types of defects in the Kaggle dataset: (**a**) cracks; (**b**) inclusion; (**c**) patches; (**d**) scratches.  
Figure 17: The two types of defects in the iron dataset: (**a**) flaw; (**b**) scratch.  
The validation experiment results are shown in Tables 6 and 7, where the four defect types in the Kaggle dataset correspond to Cr, In, Pa, and Sc in Table 6, and the two defect types in the factory dataset correspond to FL and Sc in the table.  
As can be seen from Tables 6 and 7, for the Kaggle dataset, the improved Yolov8 model has the precision, recall rate, and mean average precision increased by 2.1%, 2.1%, and 2.3%, respectively, and the mean average precision of cracks, inclusions, patches, and scratch defects increased by 4.7%, 4.0%, 0.9%, and 7.2%, respectively. For the factory dataset, the mean precision, recall, and average precision of the improved model are increased by 10.0%, 1.9%, and 5.5%, and the mean average precision of scratch and flaw defects increased by 11.0% and 3.4%. In summary, the improved model has better generalization.  
To more clearly and intuitively observe the performance improvement after mod-el enhancement, we provided visual images of YOLOv8-BSPB on two datasets. As shown in Figure 18, the improved YOLOv8-BSPB model demonstrates significantly better defect detection performance on both datasets compared to the original model. The enhanced model more accurately identifies and classifies defects in complex areas, particularly in cases where the original model missed or incorrectly detected defects. Additionally, the improved model shows fewer false positives in detecting scratches and edge losses. These enhancements not only increase the comprehensiveness and reliability of defect detection but also effectively reduce the false detection rate, validating the potential advantages of the improved model in practical applications.
## 3 Results and Discussion
### Porosity  
The porosity of the samples before and after exposure cycles is presented in Figure 8. There was an overall increase in the porosity of the samples after the cyclic exposures, with the most notable increment observed in samples after the first cycle for 1Y2, 18M1, and 18M2 samples. An overall increase in the porosity of LW2 is observed, and there is an overall reduction in LW1 porosity values. However, the changes in the LW1 and LW2 samples are not significant. After the first exposure cycle, approximately 29% and 21% change is observed in the porosity values of sample 18M1 and 1Y2, respectively. In contrast, sample 18M2 exhibits a porosity increase of 18% after the first cycle. Through micro-CT imaging, [PERSON] et al. (2018) evaluated the changes in porosity and concluded that there was a minimal decrease in porosity that can be attributed to the precipitation of the calcite and other high-density minerals in the cement. The observations were made through a spot analysis calculating the total porosity of Class G cement, whereas, in this study, effective porosity was measured for Class H cement.  
The results confirm the occurrence of geochemical reactions within the cement samples when exposed to hydrogen. The minimal decrease in porosity suggests a near-equilibrium between dissolution and precipitation rates, resulting in insignificant porosity changes. Hydrogen-driven reduction in sulfate and ferric iron can disintegrate cement minerals, namely hematite and ettringite. As a result, sulfurides and ferrous iron are created, eventually precipitating iron oxide and sulfide minerals. However, because the minerals involved make up a minor part of the material, these reactions did not significantly affect the cement's porosity. Our study's central hypothesis is that these redox reactions achieved chemical equilibrium. However, we also recognize that their kinetics are constrained and that catalysis--from hydrogenotrophic bacteria, for example--is necessary for them to proceed. Thus, it can be inferred that the geochemical interactions (dissolution) involving hydrogen and cement remain relatively modest after the second and third exposure cycles.  
Figure 8: Effect of hydrogen exposure on porosity.
## 4 Implementation
### Server Side
#### 4.3.2 Inference Time  
We measured inference times of our self-trained model for the ROI-filtered video based on DSSIM values. With an object-detection model based on the Region Proposal Network (RPN), the RPN score threshold can be set to configure the objectness level to select regions in advance to be suggested to the object classification stage [5; 25]. In a fast r-cnn fpn v2 model, the default RPN score threshold is 0.05. In our study, we compared two scenarios that the RPN score threshold was set to 0.0 and 0.05 while the DSSIM threshold value varies from 0.0 to 0.4. Figure 9 shows the inference times taken in those two scenarios.  
In Figure 9, looking at the results, the average inference time per frame was around 27.652 ms when both DSSIM threshold and RPN score threshold were set to 0.0. The inference time continuously decreased as the DSSIM threshold was set higher.  
To apply the RPN score threshold also can reduce the inference time, as shown in the results. But the enhancement did not change significantly depending on how the RPN score threshold was set. In our experiment, the inference time scarcely changed while we gradually increased the RPN score threshold from 0.1 to 0.5. Thus, we presented the results only when the RPN score threshold was set to 0.05, which is the default value.  
When the DSSIM threshold exceeded 0.3, the case only with the DSSIM threshold required less inference time than the case only with the RPN score threshold. This implies that the DSSIM filtering may affect more the inference time than the RPM score filtering.
## 5 Conclusions  
In this paper, we build a new network structure with a multi-level attention model by combining a two-level attention model (TA-model) and a grouping attention model (GA-model). At the same time, we adopt the multi-level feature fusion method for the unified learning of low-level visual features and high-level semantic information of the network. The entire network subtly completes the fine-grained image classification task under small-data conditions by adding attention modules and feature fusion. The proposed structure does not need a bounding box or partial annotation for training, and can be easily embedded into all convolutional networks. The experimental results demonstrate good performance on fine-grained classification. In the future, we will conduct the research in two directions. First, we will use more efficient similarity measurement methods and multi-stage grouping attention to obtain more distinguishing features. Second, we will try to borrow object detection mechanisms to locate and capture more precise fine-grained features.  
[PERSON] and [PERSON] guided the theoretical and experimental design; [PERSON] and [PERSON] participated in the environmental configuration and data collection; [PERSON] completed literature collection, experimental design, and paper writing.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61872231, 61701297, 61473239) and Graduate Student Innovation Project of Shanghai Maritime University 2017 ycx083.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{2-3}  & **Cars** & **Aircrafts** \\ \hline FV-CNN & 87.79 & 81.46 \\ DVAN & 87.10 & – \\ Random Maclaurin & 89.54 & 87.10 \\ Tensor Sketch & 90.19 & 87.18 \\ LRBP & 90.92 & 87.31 \\ MA-CNN & 92.80 & 89.90 \\
**TGA-CNN(Ours)** & **91.05** & **87.93** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The classification accuracies of these models on the two datasets.
## 3 Validation  
The calculation method for the maximum crack width of linings was validated by comparing it with a physical model test of a reinforced concrete pressure pipeline. The model of the pressure pipeline is shown in Figure 7[32], with a clear internal diameter of 400 mm. The innermost layer consists of a steel lining with a thickness of 1 mm, while the lining is reinforced on both the inner and outer sides with 6 mm diameter steel bars, achieving a reinforcement ratio of 1.286%. The lining is made of standard C30 concrete specimens. The internal water pressure is 1.3 MPa, and the normal displacement at the bottom of the model is constrained, with the rest of the boundaries being free.  
The concrete tensile damage factor (DAMAGET) distribution obtained from the simulation is shown in Figure 8a. The values of DAMAGET range from 0 to 1, with higher values indicating more severe damage. The red regions, where the damage factor reaches 0.97, represent fully damaged areas, corresponding to the crack distribution zone. This closely matches the crack distribution area observed in the experiment. Additionally, the maximum tensile stress in the reinforcement obtained from the simulation is 156.0 MPa, which is in good agreement with the experimental result of 144.8 MPa. The calculated maximum crack width in the concrete is 0.092 mm, which is only 6% different from the experimental result of 0.087 mm [32]. This confirms the accuracy of both the numerical simulation method and the calculation approach for the maximum crack width of the lining.  
A key limitation of the laboratory setup is the simplification of boundary conditions, where the confining effects of the surrounding rock mass and the initial support were not considered when evaluating the mechanical behavior of the concrete lining. While the simplified test setup can, to some extent, reproduce the working condition of the lining, it inevitably introduces several deficiencies. First, the concrete lining in the test was subjected to uniaxial tensile loading, whereas in real conditions, the lining may experience tensile stress in one direction and compressive stress in another, which typically leads to lower effective strength than uniaxial tension alone. Therefore, the experimental results may overestimate the actual load-bearing capacity of the lining. Second, the laboratory model cannot represent the shear stress transfer at the interface between the lining and the surrounding rock; only smooth contact conditions can be simulated. These limitations imply that the laboratory tests provide an idealized assessment of crack development, which may differ from actual in situ behavior due to the absence of confinement. Future experiments should incorporate more realistic boundary constraints and interface behavior to better simulate the in situ mechanical behavior of underground workings for CAES.  
Figure 7: Reinforced concrete pressure pipeline [30].
# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
## 3 Our Proposed Approach
### Finding the Corresponding Road Segment  
Roads are multi-lines. Usually, the left and right road boundaries are represented by two multi-lines. The road marks separating lanes are also shown by multi-lines. A single lane of a multi-lane feature is called road segment.  
One of the important steps in our approach is to localize the corresponding road segment from the input image. For this work, LaneNet [24] is employed to produce the lane segmentation. We use binary image output of the LaneNet and perform post-processing. The result of LaneNet is in form of multi-line based upon the number of road segments. In order to obtain the corresponding lane, output of LaneNet requires further processing to find the correct road segment, which is the segment where the vehicle is passing through it. To do so, we use a simple technique involving line Hough transform in order to obtain multi-line on the image. We divide the image into two sides, left and right and then we select one line on the left side and one line on the right side whose their bottom positions are closest to the bottom center of the image. These two lines are supposed to be the borders of the corresponding lane. Figure 5 shows the examples of the process of obtaining the corresponding road segment.  
Figure 4: Reference map.  
Figure 5: Process of finding the corresponding road segment.
###### Abstract  
The paper defined the laser-induced damage threshold from the fluence and the peak power of GaSe and GaSe:In single crystals upon exposure to nanosecond radiation in the two micron range and assessed the influence of test radiation energy parameters (pulse repetition rate, pulse duration) on the damage threshold. Laser-induced damage threshold was determined with the parameters of the incident radiation close to the pump radiation parameters of promising dual-wavelength optical parametric oscillators (effective pump sources for THz difference frequency oscillators): wavelength was \(\approx\)2.1 um; pulse repetition rates were 10, 12, 14, and 20 kHz; and pulse durations were 15, 18, 20, and 22 ns. The obtained results made it possible to conclude that the value of GaSe damage threshold at a wavelength of 2.091 um of the incident radiation was influenced by the accumulation effects (the damage threshold decreased as the pulse repetition rate increased). The accumulation effects were more significant in the case of the In-doped sample, since a more significant decrease in the damage threshold was observed with increasing frequency in terms of the peak power and the fluence.  
laser-induced damage threshold; GaSe; nonlinear crystals +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Sec  
Table 2 lists the results of the influence of different parameters (_MP_ and _ST_) in different pretreatments on the _SEC_ of the microwave drying process. As can be seen, the _MP_ and the _ST_ have a significant effect (\(p<0.0001\)) on the _SEC_ value of carrot slices, while the interaction effect of the _MP_ and the _ST_ had no significant effect on the _SEC_ value. Figure 4 also depicts the variations of the _SEC_ for (a) control, (b) ultrasonicated, and (c) blanched samples. The value of the _SEC_ decreases linearly or quadratically by increasing the _MP_ and decreasing the _ST_. The use of the _MP_ can accelerate the drying due to changes in the cell structure and faster moisture diffusion. Such a reduction in the \(D_{t}\) can decrement the _SEC_. In other words, the thermal gradient and the acceleration of moisture extraction from the samples are improved by increasing the _MP_[50]. Additionally, in thicker samples, the transfer of product moisture to the surface for final evaporation is associated with a slower movement, hence, the \(D_{t}\) is prolonged, leading to a higher _SEC_[51]. Similar findings were shown for drying potato [8], banana [52], and kiwi [34] slices.  
Figure 3: Three-dimensional counterplots for \(D_{\mathit{eff}}\) against microwave power and thickness: (a) control, (b) ultrasonicated, and (c) blanched samples.  
According to Figure 4, the application of a pretreatment reduced energy consumption as compared to the untreated samples. It is worth mentioning that sonication (damaging carrot tissue due to creation of microscopic channels and cavitation) before drying is effective in reducing the \(D_{t}\) and the \(SEC\)[37]. Additionally, the use of the blanching pretreatment had a greater effect on the \(SEC\) reduction compared to the ultrasound pretreatment. This phenomenon may be assigned to the pressure difference between the surrounding environment and the sample tissue during the blanching stage. The blanching process leads to the formation of a porous structure, resulting in a better and faster moisture transfer during the microwave drying [53]. The highest \(SEC\) value (28.06 MJ/kg) was achieved at the \(MP\) of 100 W for control samples with the thickness of 6 mm, while the lowest \(SEC\) value (9.51 MJ/kg) was observed at a power of 300 W in the blanching-pretreated samples with the thickness of 2 mm. Similar findings were reported on the reduction of the \(SEC\) by using blanching and ultrasound pretreatment for different products, such as mushroom slices [15], parsley leaves [37], and blackberry [54].
## 6 Conclusions  
Energy efficient routing and node activity scheduling in wireless sensor networks constitute a challenging research area. The OCARI project aims at designing and developing solutions for an industrial environment. The proposed solutions include EOLSR and SERENA. EOLSR selects routes that minimize the energy consumed by an end-to-end transmission while avoiding nodes with low residual energy. SERENA schedules node activity based on node coloring. It is a generic algorithm able to adapt to specific application requirements, environment constraints and various types of communications. For instance, to support immediate acknowledgement, often needed in an unreliable wireless communication, three-hop coloring is required. Simulation results show that both SERENA and EOLSR contribute to a more efficient use of network resources and increase network lifetime while delivering a higher amount of user data. Benefits resulting from the integration of EOLSR and SERENA are higher than the sum of the benefits obtained by each separately.  
Cross-layering with the application layer significantly contributes to improve the performances of both SERENA and EOLSR. For instance, in data gathering applications, EOLSR uses the knowledge of potential destinations to considerably reduce the overhead. Similarly, SERENA is able to reduce the end-to-end delays in such applications, adapting the coloring rules to a tree topology or a hierarchical DAG.  
A prototype integrating the physical layer, MaCARI, SERENA and EOLSR is under implementation. We will collect measures made on this prototype and compare them with other solutions, before experimenting our solution in a larger industrial network.
## 4 Results
### CovidDetNet Experimental Setup
#### 4.3.4 COVID-19 Detection Comparison with State-of-the-Art Methods
The comparison results are shown in Table 11, elaborating the success of the proposed CovidDetNet model in identifying COVID-19 from chest radiograph images compared to existing alternatives. It is important to mention that the proposed model outperformed the approaches [43; 44] using the same dataset (COVID-19 radiography database) for COVID-19 detection. It is worth noting that these approaches are more computationally challenging than the proposed approach because they use deeper models, leading to overfitting. The proposed CovidDetNet model, on the other hand, is ten layers deep and uses modest \(1\times 1\) filters to extract in-depth high-level and more complex features for COVID-19 identification.
## 3 RTS/CTS Scheme for IEEE 802.15.6
### Hidden Node Problem in IEEE 802.15.6  
The hidden node problem occurs when some nodes are not in the wireless range of other nodes. In single-hop IEEE 802.15.6 CSMA/CA based networks, the hidden node problem may occur when some nodes deployed on the human body are in the range of the hub but not in the range of each other. This is possibly due to body shadowing effects or when the nodes are not in the line of sight, e.g., nodes deployed on the back of a human body may have a chance of becoming hidden nodes. Figure 1 shows a description of a hidden node in the IEEE 802.15.6 CSMA/CA based networks. As shown in this figure, node A is in the wireless range of the hub H1, and the same hub is in the wireless range of node B. However, node A and node B are not in each other's wireless ranges and so are unable to sense their respective transmissions. Therefore, when node A transmits data to the hub, node B cannot hear this transmission and transmits a data packet to the same hub after sensing the channel. Since node B is not in the transmission range of node A, the channel is perceived to be free even though node A hasalready sent data to the hub. This scenario results in collision at the hub. The hidden node problem may also occur when multiple IEEE 802.15.6 based networks coexist with each other by sharing the same spectrum [11].
## 4 Scenario and Parameterization
### Parameterized TDM Pattern
For example, the backhaul micro-phase in Sector \((1,3)\) is \(\frac{2}{3}T\), while in the linked Sector \((2,1)\), it is \(\frac{1}{2}T\), which results in a downtime of \(\frac{1}{6}T\) TTIs for both antennas in each pattern run. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Start Time** & **End Time** & **Sector \((i,M_{i,j})\)** & **Sector \((j,M_{j,i})\)** \\ \hline \(2 nT\) & \(D_{i,j}T+2 nT\) & transmit backhaul & receive backhaul \\ \(D_{i,j}T+2 nT\) & \((2n+1)T\) & transmit access & receive access \\ \((2n+1)T\) & \(D_{j,i}T+(2n+1)T\) & receive backhaul & transmit backhaul \\ \(D_{j,i}T+(2n+1)T\) & \(2(n+1)T\) & receive access & transmit access \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The TDM pattern specified by matrix \(\mathbf{D}\) for any linked sectors \((i,M_{i,j})\) and \((j,M_{j,i})\); node \(i\) belongs to an odd tier, \(n\in\mathbb{N}\).
## 2 Basic Structure and Key Technologies
### Deadweight Balance Structure
#### 2.5.1 \(Z\)-Stage Deadweight Balance System  
The \(Z\)-stage can move in the \(Z\) direction alone and in \(X\) and \(Y\) directions together with \(X\)-Stage and \(Y\)-stage, respectively. Its motion is driven by a commercial linear piezo-motor provided by Physik Instrumente. The deadweight of the \(Z\)-stage will induce more actuation force if it is moved up. In order to overcome this problem, a force balance system for \(Z\)-stage deadweight is particularly designed. Figure 4 shows the schematic structure. The \(Z\)-stage (1) and its supporting frame (5) are fixed together and driven by the linear piezo-motor (8). The \(Y\)-stage (2), pulley (3) and outer frame (6) are fixed together. Two counterweights (7) and the supporting frame (5) are connected by two thin steel strips (4). The total weight of two counterweights is equal to the net weight of the \(Z\)-stage and its accessories. In this mechanical structure, the \(Z\)-stage can be driven by the actuator with a low and equal driving force in both up and down motions.
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks
### Model Assumptions
#### 3.1.2 Channel Assumptions  
* The \(MCS\) coding scheme used by a given mobile can, very often, change due to the high variability of the radio link quality. Since the mobile sends its channel estimation frame by frame, the base station will, therefore, have different coding schemes for each frame. On the other hand, we associate a probability \(p_{k}\) for each \(MCS_{k}\) coding scheme and assume that at each \(T_{F}\) frame, any mobile has a probability \(p_{k}\) of using \(MCS_{k}\) (including those in a state of failure: a station is in a state of failure if it is unable to emit/transmit because of the poor quality of its channel: \(k=0\)).