esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions, Conclusions, and Future Directions
### Discussions
#### 4.1.3 Discussion: Limitation of the Joint Design System Approach  
As the interference power increases, the sensing performance experiences a decline. Notably, when the sensing power approaches parity with the communication power, severe mutual interfering effects arise, leading to a reduction in sensing performance. However, this performance loss can be mitigated by augmenting the number of antennas at the receiver.  
In the proposed approach, a maximum likelihood detector is employed to minimize decoding errors, treating target echo interference as noise during communication symbol detection. However, when estimating the reflection coefficient of the target, denoted as \(\alpha(n)\), the residual error \(\Delta\mathbf{y}_{s}\) is typically canceled during the construction of MMSE estimators. This cancellation, while effective for reducing error, may not hold true for MMSE estimators, potentially resulting in suboptimal sensing performance.
# An Investigation into Bol Anchoring Performance during Tunnel Construction in Bedded Rock Mass
## 6 Conclusions  
In this paper, we investigated the bolt anchoring performance for bedded rock mass under different anchoring methods, as well as the failure mode under shear and tensile stresses in bedded rock. Then, based on field tests, we analyzed the force and support effects of the different anchoring methods. The conclusions are as follows:  
1. With respect to the anchored specimen strength and strain, the reinforcing mechanism of the bolt for the rock was divided into two aspects: the anchoring agent bonding and restoring the surrounding rock near the reinforcing zone, and the bolt body resistance supporting and improving the stress state of the rock. The combined action of those two aspects increased the strength parameters of the anchored specimen.
2. Failure of the anchored specimen changed from shear splitting failure with no anchoring into shear failure where the failure plane slid parallel to the axial direction of the bolt or shear dislocation failure along the soft-hard interface.
3. Via anchoring of the joint rock mass, the bolt could significantly enhance the shear-bearing capacity of the rock mass and increase the stability of the surrounding rock. Compared to the end anchoring bolt, the bolt for full-length anchoring can form an \"anchoring area\" of a greater range and provide greater support resistance than the end anchoring bolt; therefore, it had a better coupling effect on the surrounding rock with a greater resistance increase. This occurrence enabled the surrounding rock to bear more load.
4. Full-length anchoring can provide more support resistance and have a better anchoring effect on the surrounding rock of a bedded rock tunnel. However, the grouting quality is often difficult to  
Figure 14: Arch crown settlement curve of the ZK17+365 section  
guarantee due to the impact of the bolt insertion angle, so it is necessary to pay attention to the filling quality of the anchoring agent, especially at the vault of the tunnel.  
[PERSON] analyzed the calculation results. [PERSON] carried out the laboratory test and wrote the article. [PERSON] provided the information of the construction site. [PERSON] offered useful suggestions for the preparation and writing of the paper. [PERSON] processed the data. All authors have read and agreed to the published version of the manuscript.  
The authors are very grateful for the funding from the High Speed Railway and Natural Science United Foundation (U1934213) and the General Program of National Natural Science Foundation of China (51878572).  
We also highly appreciate the reviewers for their useful comments and editors for improving the manuscript.  
The authors declare no conflict of interest.
###### Abstract  
In the face of increasing flood risks intensified by climate change, accurate flood inundation mapping is pivotal for effective disaster management. This study introduces a novel explainable deep learning architecture designed to generate precise flood inundation maps from diverse satellite data sources. A comprehensive evaluation of the proposed model is conducted, comparing it with state-of-the-art models across various fusion configurations of Multispectral Optical and Synthetic Aperture Radar (SAR) images. The proposed model consistently outperforms other models across both Sentinel-1 and Sentinel-2 images, achieving an Intersection Over Union (IOU) of 0.5862 and 0.7031, respectively. Furthermore, analysis of the different fusion combinations reveals that the use of Sentinel-1 in combination with RGB, NIR, and SWIR achieves the highest IOU of 0.7053 and that the inclusion of the SWIR band has the greatest positive impact on the results. Gradient-weighted class activation mapping is employed to provide insights into its decision-making processes, enhancing transparency and interpretability. This research contributes significantly to the field of flood inundation mapping, offering an efficient model suitable for diverse applications. This study not only advances flood inundation mapping but also provides a valuable tool for improved understanding of deep learning decision-making in this area, ultimately contributing to improved disaster management strategies.  
deep learning; Explainable Artificial Intelligence; semantic segmentation; flood inundation mapping: synthetic aperture radar; multispectral; satellite imagery; remote sensing +
Footnote †: journal: Information  
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## 4 Proposed Online Method for Impedance Characterization of Electrical Machines
### Validation and Interest of the Proposed Method  
The objective was to verify the reliability and consistency of our strategy. For this purpose, we performed about 15 measurements on the same test machine at different times during its operation (at different operating points and on different days). Interpolation was used to determine the amplitude and the frequency of the various resonances found.  
Figure 23 shows the different characteristics acquired during these tests. It can be seen that the different profiles are superimposed regardless of the machine operating point. The first two resonances can be identified. Based on the different results, we highlight that the uncertainty to characterize the first parallel resonance is less than or equal to 12.33 kHz and 4.4 dB\(\Omega\) (\(<\)3%). The frequency uncertainty to characterize the first series resonance is identical to that observed for the first parallel resonance. However, in terms of amplitude, it is less than or equal to 233 kHz.  
Furthermore, in order to validate the proposed online characterization approach, we compared the resulting plot with that measured offline using a vector network analyzer. The results are shown in Figure 24.  
There is a strong correlation between the two frequency behaviors. The relative difference between the two characteristic frequencies of the first parallel resonance is less than 10%. In terms of amplitude, the two approaches give the same result. On the other hand, there is a deviation between the behavior observed with our online method and with the impedance analyzer around the first series resonance. This is intrinsically related to the use of fixture adapters and a measurement cable between the impedance analyzer and the test machine, as explained in [58] and confirmed by Figure 25, which compares the impedance response of a traction machine as a function of the cable used. Based on these results, it is essential to emphasize the importance of using the same measurement conditions if we want to properly exploit the results of these frequency analyses and draw the correct conclusions about the health of the windings.  
Figure 22: Interpolation of the impedance frequency response.  
Finally, the benefits of this online impedance characterization approach can be summarized in three points:  
* The HF excitation is naturally provided by the voltage source inverter. The method is therefore non-invasive and has no effect on actuator operation or aging.
* No measurement cables or fixture adapters are required, eliminating the need for a calibration phase and making the characterization process highly reliable.
* Finally, health monitoring is performed at all times without removing the machine from its operating environment, allowing early detection of insulation degradation in the windings.  
Figure 23: Comparison of impedance interpolations obtained at different times and operating points.  
Figure 24: Comparison of impedance characterizations obtained with the proposed online method and with an impedance analyzer.
## 3 Link Model
### Bit Error Rate  
The bit error rate (BER), \(P_{\text{e}}\), in the case of OOK is given as [34]  
\[P_{\text{e}}=Q(\gamma), \tag{17}\]  
where \(Q\) is the Q-function, and its argument \(\gamma\) is determined by  
\[\gamma=2\mathcal{R}\frac{r_{\text{e}}-1}{r_{\text{e}}+1}\frac{P_{\text{avg}}} {\sigma_{1}+\sigma_{0}}. \tag{18}\]  
In (18), \(P_{\text{avg}}\) denotes the average optical received power, \(P_{\text{avg}}=(P_{1}+P_{0})/2\), where \(P_{1}\) and \(P_{0}\) are the optical received powers corresponding to bit \(b=1\) and \(b=0\), respectively. Additionally, \(r_{\text{e}}=P_{1}/P_{0}\) denotes the extinction ratio, and \(\mathcal{R}\) represents the responsivity,  
\[\mathcal{R}=\frac{\eta q_{\text{e}}}{h_{\text{p}}f}, \tag{19}\]  
where \(q_{e}=1.602\times 10^{-19}\) Cb is the electron charge, \(h_{\text{p}}=6.626\times 10^{-34}\) JHz\({}^{-1}\) is [PERSON]'s constant, \(\eta\) is the internal quantum efficiency, and \(f=c/\lambda\) is the optical frequency, with \(c\) being the speed of light in vacuum and \(\sigma_{1}\) and \(\sigma_{0}\) the corresponding noise standard deviations, determined by  
\[\sigma_{1}^{2}=s_{1}^{2}+\sigma_{\rm th}^{2}=4q_{\rm e}\mathcal{R}\frac{r_{\rm e }B_{\rm e}}{1+r_{\rm e}}P_{\rm avg}+\sigma_{\rm th}^{2}, \tag{20}\]  
\[\sigma_{0}^{2}=s_{0}^{2}+\sigma_{\rm th}^{2}=4q_{\rm e}\mathcal{R}\frac{B_{\rm e }}{1+r_{\rm e}}P_{\rm avg}+\sigma_{\rm th}^{2}, \tag{21}\]  
where \(s_{b}^{2}=2q_{\rm e}\mathcal{R}P_{b}B_{\rm e}\) represents the shot noise for \(b=1\) or \(b=0\) obtained by replacing \(P_{b}\) with \(P_{1}=2r_{\rm e}P_{\rm avg}/(1+r_{\rm e})\) and \(P_{0}=2P_{\rm avg}/(1+r_{\rm e})\), respectively. In this equation, \(B_{\rm e}\) is the electrical bandwidth, which is related to the data rate \(R_{\rm b}\) by \(B_{e}=R_{b}(1+\alpha)/2\), where \(\alpha\) is the roll-off factor, assumed to be zero in this study, and \(\sigma_{\rm th}^{2}\) is the thermal noise power, given by [34]  
\[\sigma_{\rm th}^{2}=\frac{4k_{\rm B}F_{\rm n}T_{\rm K}}{R_{\rm L}}B_{\rm e}. \tag{22}\]  
In (22), \(k_{\rm B}=1.38\times 10^{-23}\) JK\({}^{-1}\) is [PERSON]'s constant, \(T_{\rm K}\) the temperature measured in Kelvin (K), \(F_{\rm n}\) is the receiver amplifier noise figure, and \(R_{\rm L}\) the load resistance.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 4 INS-Assisted Open Loop Tracking Strategy  
As an opposite method against the closed loop, the idea of the open loop has been discussed for many years. In our common sense, open loop tracking is considered as block processing or batch processing. Contrary to closed loop sequential processing, the open loop batch processing has no local loop updates [19]. The use of open loop batch processing allows parallel computations on the batches of samples, and brings the benefits of improved signal observability and improved tracking robustness [20]. This section illustrates the structure of INS-assisted tracking strategy and the frequency estimation algorithm adopted in this strategy.
# Non-Centrifugal Sugar (NCS) and Health: A Review on Functional Components and Health Benefits
## 6 Biological Properties of NCS
### Non-Centrifugal Sugar and Metabolic Syndrome
#### 6.3.1 Anti-Obesity Effect  
Obesity is the pivotal point of MetS. The preventive effect of NCS against obesity has been investigated in animal studies. For example, short-term feeding for 42 days with 15 % BS caused a significant reduction of weight gain in rats compared to those fed with RS [54]. Similarly, prolonged intake of 10 % BS for 4 months showed less effect on body weight and body fat than RS or artificial sweeteners [52]. Animals fed RS had 209 g of fat mass, and rats fed sucrose had 165 g of fat mass, while animals fed BS had 125 g of fat mass, by the way, similar to the control group (125.2 g of fat mass) [52]. In another study, administration of BS for 4 months effectively averted overall weight gain in adult rats compared to the rat's receiving RS [50]. Indeed, consumption of BS in the rat model resulted in a smaller adipocyte size of white and brown adipose tissue than RS [50]. Enlarged adipocytes recruit macrophages and trigger inflammation and the release of factors that reduce insulin sensitivity. BS also increased the functionality of adiposity by increasing adiponectin in white adipose tissue and an increase in uncoupling protein-1 (UCP-1) in brown adipose tissue [50]. Dysregulation of signaling pathways in adipose tissue can contribute to the development of obesity. The potassium content of this natural sweetener is also believed to assist in weight management, since it prevents water retention in the body improve food metabolism [14]. Due to the promising effects of NCS in weight management observed by different studies and its role in improving the functionality of adiposity, further studies would aid the clarification of the role of NCS consumption on weight maintenance.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 4 TSAER: Protocol Description  
### Proposed Parameters  
The main parameters considered in this paper are the node temperature, the remaining node energy, and the distance to sink, which are described in detail as follows.  
Temperature T: Estimate the current node temperature using the Pennis bioheat equation. When the node forwards each data packet, it will cause its temperature to rise by one unit.  
Remaining energy: the remaining energy refers to the difference between the initial energy of the node and the energy consumed. The calculation expression of the remaining energy of the node is as follows:  
\[\mathrm{E_{res}(i)=E_{initial}(i)-\sum[E_{\mathrm{rc}}(i)+E_{rx}(i)+E_{ax}(i)]. \tag{7}\]  
where \(E_{initial}(i)\) represents the initial energy of node i, \(E_{tx}(i)\) represents the energy consumption of node i to send data, \(E_{rx}(i)\) represents the energy consumption of node i to receive data, and \(E_{ax}(i)\) represents the energy consumed by node i to process and aggregate the data it has collected or received. This aggregation process, typically involving computational tasks, such as in reducing data volume and redundancy, consumes energy for processing and memory access. The forwarding node has the ability of data aggregation to calculate the energy consumption of data aggregation, while other nodes do not need the data aggregation function, so the energy consumption of data aggregation is not calculated.  
Distance between node and sink: the expression of the distance between node i and the sink is as follows:  
\[\mathrm{D(i,Sink)=((X_{i}-X_{Sink})^{2}-(Y_{i}-Y_{Sink})^{2})^{1/2}. \tag{8}\]  
where \(\mathrm{D(i,Sink)}\) represents the distance between node i and the sink, \(X_{i}\) and \(Y_{i}\) represent the position coordinates of node i, and \(X_{Sink}\) and \(Y_{Sink}\) represent the position coordinates of the sink.
## 3 Results
### High-Resolution Skin Ultrasound Images
#### 3.2.1 Skin Layer Thickness  
By analyzing the skin ultrasound images, we can obtain the thickness information of the different skin layers. Figure 4 shows the skin layers at different cross-sections in the high-resolution ultrasound image [6].  
In this experiment, a sample of 605 color images was obtained from eight different areas of the body: the arm, cheek, chin, eyelid, forehead, lips, neck and nose.  
Table 3 shows the skin layers' thickness measured at the different skin sites. The results show that the cheek contains the highest percentage of dermis among all facial sites, about 95.3%, while the neck's dermis percentage was the lowest at 92.0%. The value for the stratum corneum was found to be highest on the chin, at 0.038 mm, and lowest on the arm--merely 0.024 mm. Likewise, the epidermis thickness measurements recorded for the arm and chin were lowest (0.047 mm) and highest (0.083 mm), respectively. A deviation from the mean indicates poor skin condition. The risk of skin damage will be higher when the standard deviation is high.  
\begin{table}
\begin{tabular}{c c c} \hline
**Deep Learning Networks** & **Accuracy** & **Training Time** \\ \hline AlexNet & 64.3\% & 15 min \\ GoogLeNet & 73.5\% & 21 min \\ VGG16 & 59.7\% & 114 min \\ ResNet-50 & 73.9\% & 64 min \\ InceptionV3 & 71.0\% & 78 min \\ MobileNetV2 & 72.8\% & 46 min \\ DenseNet 201 & 83.8\% & 110 min \\ SqueezeNet & 61.1\% & 8 min \\ InceptionResNetV2 & 64.7\% & 168 min \\ Xception & 71.0\% & 164 min \\ \hline \end{tabular}
\end{table}
Table 2: The image classification results of different Deep Learning Neural Networks.  
Figure 3: The typical high-resolution ultrasound images at the different skin sites: cheeks, chin, forearm, forehead, lips and nose.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Skin Sites** & **Count 1** & **Full Skin** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **Stratum** \\ **Corneum** \\ \end{tabular} & **Epidermis** & **Dermis & **Dermis (\%)** \\ \hline Arm & 498 & 1.306 & 0.194 & 0.024 & 0.047 & 1.185 & 90.7\% \\ Eyelid & 453 & 1.309 & 0.226 & 0.025 & 0.052 & 1.238 & 94.5\% \\ Neck & 490 & 1.378 & 0.187 & 0.026 & 0.054 & 1.267 & 92.0\% \\ Lips & 477 & 1.426 & 0.294 & 0.027 & 0.064 & 1.320 & 92.5\% \\ Forehead & 451 & 1.647 & 0.173 & 0.028 & 0.065 & 1.569 & 95.2\% \\ Cheek & 551 & 1.690 & 0.437 & 0.032 & 0.074 & 1.611 & 95.3\% \\ Nose & 487 & 1.912 & 0.316 & 0.033 & 0.077 & 1.821 & 95.2\% \\ Chin & 339 & 1.929 & 0.271 & 0.038 & 0.083 & 1.837 & 95.2\% \\ \hline \hline \end{tabular}  
* 1 Count is number of measurements for each skin site.  
\end{table}
Table 3: The measured skin layer thickness of different volunteers at the different skin sites.  
Figure 5 shows the average stratum corneum thickness at the different skin sites with \(+1\) sigma and \(-1\) sigma.
# Leather Defect Detection Based on Improved YOLOv8 Model
## 4 Improved YOLOv8-AGE Algorithm
### Afpn  
The scale of different defects in leather varies significantly. Moreover, the same type of defects also exhibits multiple aspect ratios. Therefore, enhancing the model's performance in handling multiple scales can be effective in detecting leather defects. YOLOV8 adopts the PA-FPN concept, which utilizes top-down and bottom-up Feature Pyramid Networks. However, this approach leads to the loss and degradation of feature information during the transmission and fusion process, impairing the fusion of features at non-adjacent levels. Therefore, this study introduces the AFPN structure to improve the utilization efficiency of feature information. The AFPN structure is illustrated in Figure 4.  
Two low-level features are first input for Adaptive Spatial Fusion, followed by high-level features one by one. At the end of the feature fusion, a set of multi-scale features is output. To ensure the consistency of dimensionality during feature fusion, AFPN adopts convolution kernels with different step sizes to achieve multiple scales of downsampling. Specifically, a 2 \(\times\) 2 convolutional kernel with a step size of 2 was used for 2-fold downsampling, a 4 \(\times\) 4 convolutional kernel with a step size of 4 for 4-fold downsampling, and an 8 \(\times\) 8 convolutional kernel with a step size of 8 for 8-fold downsampling. The AFPN structure takes four sets of input features [C2, C3, C4, C5] and processes them to output a set of features [P2, P3, P4, P5]. Since YOLO only inputs [C3, C4, C5] into the feature pyramid, the improved structure proposed in this study is shown in Figure 5.  
The keyword of AFPN is progressive fusion, utilizing the ASF (Adaptive Spatial Fusion) module. After the different feature inputs are aligned dimensionally through sampling, the weight vectors for each feature are computed using a 1 \(\times\) 1 convolution. These vectors are then concatenated, and a 1 \(\times\) 1 convolution computes the fusion weights, which are then normalized using softmax. The output weights are then used to weight and sum the feature maps to obtain the fused feature map. Finally, the feature map is processed using a 3 \(\times\) 3 convolution and then output.  
Figure 4: Structure of the AFPN.  
The adaptive fusion process set \(x^{n\to l}_{ij}\) to be the feature vector from layer n to layer l at position (i, j) and \(y^{l}_{ij}\) to be the result of the fusion. For example, in Equation (1), \(y^{l}_{ij}\) is defined by a linear combination of \(x^{1\to l}_{ij}\), \(x^{2\to l}_{ij}\), \(x^{3\to l}_{ij}\), and \(\alpha^{1}_{ij}\), \(\beta^{1}_{ij}\), \(\gamma^{1}_{ij}\) when performing feature fusion of three layers as in Equation (1).  
\[y^{l}_{ij}=\alpha^{l}_{ij}.x^{1\to l}_{ij}+\beta^{l}_{ij}.x^{2\to l}_{ij}+ \gamma^{l}_{ij}.x^{3\to l}_{ij} \tag{1}\]  
\(\alpha^{1}_{ij}\), \(\beta^{1}_{ij}\) and \(\gamma^{1}_{ij}\) are the weights of the three layers in level l and satisfy Equation (2).  
\[\alpha^{1}_{ij}+\beta^{1}_{ij}+\gamma^{1}_{ij} \tag{2}\]
## 3 Proposed Approach
### Tracking the Mobile Platform
#### 3.3.2 Height-Adaptive, Predictive PID Controller
We presented two variants for the tracking module: a non-predictive height-adaptive PID controller and its predictive counterpart, which leverages a Kalman filter to predict the future position of the landing platform. By doing so, not only do we filter out noise in the measured relative 3D position of the landing platform with respect to the UAV, but also allow the UAV itself to stay slightly ahead of the UGV by directly feeding this virtual future position to the height-adaptive PID. This approach will prove to be crucial to accomplish successful landings, especially when the landing platform describes non-linear trajectories.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
###### Abstract
We evaluated our scheduler in the Cooja [3] simulator for two node placements for multiple traffic loads. The performance analysis shows that our solution adapts to sudden changes in traffic by allocating additional cells if there is a burst and deallocating them when the burst ends. It increases the PDR, decreases the delay, and preserves the stability of the network in case of a sudden increase in traffic. It performs similarly to Orchestra outside the burst. Allocating additional cells clearly leads to a rise in the radio duty cycle. To illustrate the strengths of our scheduler and its usefulness, we compare it with one of the state of the art approaches. The rest of the paper is organized as follows: Section 2 describes the protocols related to the implementation of the new scheduler and discusses the Orchestra scheduler and its shortcomings. Section 3 introduces related work. Section 4 outlines the design and implementation of the subtree-size and traffic-aware scheduler. Section 5 describes the performance evaluation methods. Section 6 presents the results, and Section 7 concludes the paper.