esa-sceva/satcom-chunk-collection huggingface dataset:
# Pavement Disease Visual Detection by Structure Perception and Feature Attention Network
## 4 Experiments
### Results Analysis
#### 4.4.4 Comparison of Different Loss Functions  
GIoU can enhance the localization accuracy by fine-tuning the overlapping and non-overlapping regions of the prediction and ground truth, especially for those disease areas with complex shapes and variable sizes. To further validate the superiority of the GIoU loss function, a series of experiments of SPFAN with different loss functions are conducted. The used loss functions include GIoU, wise-IoU (WIoU) [44], distance-IoU (DIoU) [45] and complete-IoU (CIoU) [45], and the corresponding results are listed in Table 7.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & **Network** & **[EMAIL_ADDRESS]** & **F1-Score** & **Params/M** & **GFLOPs** \\ \hline Base network & YOLOv8n & 0.662 & 0.642 & 3.01 & 8.1 \\ \hline \multirow{3}{*}{Insert Positions} & +CBAM (a) & 0.670 & 0.680 & 3.07 & 8.1 \\  & +CBAM (b) & 0.652 & 0.647 & 5.37 & 19.4 \\  & +CBAM (c) & 0.683 & 0.697 & 3.07 & 8.1 \\ \hline \multirow{3}{*}{Attention Modules} & +GAM & 0.682 & 0.680 & 4.68 & 9.5 \\  & +CA & 0.678 & 0.675 & 3.01 & 8.1 \\ \cline{1-1}  & +ECA & 0.678 & 0.679 & 3.01 & 8.2 \\ \cline{1-1}  & +CBAM & 0.683 & 0.697 & 3.07 & 8.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of different types and insert positions of attention modules.  
It can be found that WIoU reduces the [EMAIL_ADDRESS] from 0.682 to 0.574, leading to a decrease of 15.7%. Under the same conditions, the effect of DIoU is lower than that of CIoU, and its [EMAIL_ADDRESS] is 0.664. In contrast, GIoU improves the [EMAIL_ADDRESS] from 0.682 to 0.712, achieving a 3.0% improvement. Without increasing the computational cost of the model, the greatest improvement in the detection accuracy of the model is achieved by using the GIoU classification loss function. For the special scenario of road disease target detection, which has fewer overlapping targets and irregular shapes, GIoU is more robust to changes in the shape and size of the bounding box by considering the minimum closed rectangle, which helps to detect disease targets of various shapes more accurately. Above all, GIoU demonstrates the most significant performance enhancement in detecting pavement diseases compared to other similar loss functions, and this explains why SPFAN adopts it as the default loss function.
# Passive Earth Pressure and Soil Arch Shape: A Two-Dimensional Analysis
## 4 Parametric Analysis  
### Soil Internal Friction Angle  
Figure 9a-c illustrates the influence of the internal friction angle \(\varphi\) on the passive earth pressure distribution, the passive earth pressure coefficient \(K_{p}\), and the normalized height of the resultant passive force application point \(h_{p}/H\), respectively.  
Figure 8: Comparison of the proposed soil arch shape with the assumed circular and parabolic arches: (a) \(q=0.2\gamma H\); (b) \(q=\gamma H\).  
Figure 9: Effect of internal friction angle on passive earth pressure: (a) passive earth pressure distribution; (b) passive earth pressure coefficient; (c) the height of the application point.  
As the internal friction angle increases from 0 to 40\({}^{\circ}\) (Figure 9a), the earth pressure distribution transitions from a linear to a distinctly nonlinear form, with the passive earth pressure gradually increasing. When \(\varphi=0\), the earth pressure distribution remains linear, and the passive earth pressure coefficient equals 1, a condition that does not exist in practical applications. Additionally, the earth pressure distribution is approximately linear in the upper and middle sections of the wall but becomes nonlinear near the wall heel, where the magnitude of earth pressure increases. The larger the internal friction angle, the more pronounced the curvature of the passive earth pressure distribution. This phenomenon aligns with the enhanced soil arching effect observed with increasing soil-wall friction, where soil arch concentrate load transfer toward the lower wall section.  
Figure 9b illustrates the effect of internal friction angle on the passive earth pressure coefficient. As the internal friction angle increases from 0 to 45\({}^{\circ}\), the passive earth pressure coefficient increases monotonically. In addition, a higher surface surcharge \(q\) results in a larger \(K_{p}\) value, and the sensitivity from \(K_{p}\) to \(\varphi\) becomes more pronounced at higher surcharge levels.  
As shown in Figure 9c, for a given soil-wall friction angle \(\delta\), the normalized height of the application point initially decreases and then increases as the internal friction angle increases. This initial decrease is more evident for smaller values of \(\delta\). Notably, the application point starts at \(H/3\) for \(\varphi=0\), decreases to a minimum, and returns to \(H/3\) at \(\varphi=90^{\circ}\). This indicates linear pressure distributions for the theoretical limits of \(\varphi=0\) (frictionless, similar to hydrostatic) and \(\varphi=90^{\circ}\) (infinitely frictional, unrealistic), both of which are rarely observed in practice. As \(\delta\) increases, the value of \(\varphi\) corresponding to the minimum \(h_{p}\) shifts to lower angles.  
For typical engineering soils where \(\varphi\) generally ranges from \(20^{\circ}\) to \(40^{\circ}\), the height of the application point generally decreases with increasing \(\varphi\).
# Influences of Extreme Weather Conditions on the Carbon Cycles of Bamboo and Tea Ecosystems
###### Abstract  
Tea plantations have expanded rapidly during the past several decades in China, the top tea-producing country, as a result of economic development; however, few studies have investigated the influence of tea plantations on the carbon cycle, especially from the perspective of climate change and increases in extreme weather events. Therefore, we employed combined observational and modeling methods to evaluate the water and carbon cycles at representative bamboo and tea plots in eastern China. Green tea growth and the corresponding water and carbon cycles were reproduced using the Community Land Model after applying fertilizer. Old-growth bamboo was reasonably simulated as broadleaf evergreen forest in this model. The mean observed soil respiration ranged from 1.79 to 2.57 and 1.34 to 1.50 umol m\({}^{-2}\) s\({}^{-1}\) at the bamboo and tea sites, respectively, from April 2016 to October 2017. The observed soil respiration decreased by 23% and 55% due to extreme dryness in August 2016 at the bamboo and tea plots, respectively, and the model reproduced these decreases well. The modeling results indicated that tea acted as a stronger carbon sink during spring and a stronger carbon source during autumn and winter compared with old-growth bamboo. The carbon cycle was affected more by extremely dry weather than by extremely wet weather in both the bamboo and tea plots. Extremely dry periods markedly reduced the carbon sink at both plots, although this trend was more pronounced at the tea plot.  
carbon cycle; soil respiration; green tea; moso bamboo; drought; Taihu Basin 2018 9 629; doi:10.3390/f9100629
## 2 Materials and Methods
### Calculation of the Stress Distribution in the Measurement Phase
#### 2.3.2. Prestress Method  
This method was performed in an iterative process implemented in Python 3.8 (Python Software Foundation). Ansys files (Swanson Analysis System Inc., Canonsburg, PA, USA) were read and updated following the iteration method shown in Figure 3. The objective of this method is to calculate a stress distribution in the cornea that equilibrates the applied IOP. From the model based on the geometry obtained with the topographer, IOP was applied, and the deformation gradient of the previous case was applied up to the displacement level was lower than a previously established tolerance. If the displacement level evaluation was higher than tolerance, the process continued until the tolerance criteria was fulfilled or a maximum number of iterations was reached (Figure 3).  
Figure 3: Calculation of stress distribution in measurement phase by prestress method.  
Figure 2: Preliminary stress calculation with displacement method.  
The prestress method allows the stress distribution in the measurement phase to be computed. However, the stress-free geometry is not directly obtained as in the displacement method. To obtain the free stress geometry, a direct inverse calculation can be applied to the finite element model based on the corneal topography by considering only the stress distribution in the measurement phase without IOP.
## 3 Time Division Operation of WiFi and WiMAX Systems  
We exploit the time division operation (TDO) to avoid or reduce the mutual interference between WiFi and WiMAX systems, as shown in Figure 2. The mutual interference can be avoided, since the TDO means that only one of two systems transmits, and the other system pauses its transmission [14,15]. Master devices of the systems, such as a WiFi AP and a WiMAX base station (BS), should control the signal transmission of its terminals, such as WiFi STAs and WiMAX mobile stations (MSs), respectively, to achieve the TDO, as shown in Figure 2. A little information exchange between WiFi and WiMAX systems is needed for timing synchronization of the transmission periods of two systems.  
In this paper, we only focus on the coexistence of collaborative WiFi and WiMAX systems. Thus, we assume the minimum collaboration between a WiMAX BS and a WiFi AP for timing synchronization of the transmission period of each system is acquired. Information exchange among the WiMAX BS and WiFi STAs, among WiMAX MSs and the WiFi AP, and among WiMAX MSs and WiFi STAs, is unnecessary. However, the WiMAX BS and the WiFi AP should independently provide information about the transmission period to their terminals. If the WiMAX BS and the WiFi AP are in one device, as shown in Figure 1(b), the collaboration can be easily implemented.  
A WiMAX BS based on IEEE802.16e specification can control the signal transmission of all of its WiMAX MSs through the DL-MAP (Downlink map) and UL-MAP (Uplink map). However, a WiFi AP based on IEEE802.11g cannot control the signal transmission of all its WiFi STAs. Thus, the desired structure of the TDO, as shown in Figure 2, cannot be achieved at the WiFi STAs, even though the WiFi AP can be operated based on the TDO structure due to the minimum information exchanged with the WiMAX BS. We propose a PS-Request protocol, the so called original PS-Request protocol, which makes the WiFi AP control the signal transmission of all of its WiFi STAs, to solve the problem.
# Shaping Goose Meat Quality: The Role of Genotype and Soy-Free Diets
## 3 Results
### pH Value  
The ultimate pH measured 24 h post mortem (pH24) in the goose breast muscles was significantly influenced by both genotype (\(p<0.01\)) and dietary protein source (\(p<0.001\)) (Table 2). The highest pH24 values were observed in the birds from the LPS diet, with the ET LPS and TE LPS groups reaching 5.83 and 5.81, respectively, compared to 5.76 in the ET SBM group and 5.72 in TE SBM. The applied diet had a more pronounced effect than genotype, and no significant interaction between the two factors was detected.  
The L* parameter, indicating meat lightness, showed significant differences between the groups (\(p<0.001\)) (Table 3). The highest L* value was recorded in the TE SBM group (50.69), indicating a lighter meat colour. In comparison, the L* values in the ET SBM (47.39), TE LPS (47.32), and ET LPS (47.55) groups were significantly lower and did not differ statistically from each other (\(p>0.05\)). An ANOVA revealed a highly significant effect of genotype, feeding system, and their interaction (\(p<0.001\)) on L* values, indicating a complex mechanism influencing meat lightness. The a* parameter, representing the intensity of red colouration, also showed significant differences between the groups (\(p<0.001\)). The highest a* value was observed in the ET SBM group (13.18), while the lowest was found in the ET LPS group (12.07). The TE SBM (12.88) and TE LPS (12.65) groups showed intermediate values. Both the feeding system and the genotype by feeding interaction had a highly significant effect (\(p<0.001\)) on a*, whereas the genotype alone did not show a statistically significant influence. The b* parameter, reflecting the intensity of yellow colouration, also showed significant differences between the groups (\(p<0.001\)). The highest b* values were found in the ET SBM group (2.08) and TE SBM group (2.02), while the lowest were in the ET LPS (1.69) and ET LPS group (1.22), indicating a significant decrease in the yellowness. A statistical analysis showed a significant effect of the feeding system (\(p<0.001\)) and the genotype \(\times\) feeding interaction (\(p<0.05\)), while the genotype alone had no significant influence.  
The results indicate that both the genetic and feeding factors affect the colour of the goose breast muscles, with the feeding system playing a dominant role in all the colour parameters (L*, a*, b*). Genotype had a significant effect on lightness (L*), while the interaction between the genotype and feeding system significantly affected all analysed parameters.
## 3 Network Anomaly Detection
### The Algorithm of Network Anomaly Detection
#### 3.3.2 The Inference Process  
During the inference phase, the input data are represented by an unknown window data, denoted by \(\tilde{\mathcal{W}}\). We propose to consider the reconstruction error obtained during this process as the anomaly score. This score can be mathematically expressed as:  
\[S(\tilde{\mathcal{W}})=\alpha\big{|}\tilde{\mathcal{W}}-AE_{1}(\tilde{ \mathcal{W}})\big{|}_{2}+(1-\alpha)\big{|}\tilde{\mathcal{W}}-AE_{2}\big{(} AE_{1}(\tilde{\mathcal{W}})\big{)}\big{|}_{2} \tag{14}\]  
The specific values of \(\alpha\) greatly influence the quantity of results that we deem positive predictions. Setting a smaller \(\alpha\) value tends to increase the count of instances classified as positive. This stringent criterion is particularly suitable for scenarios requiring high detection sensitivity. Conversely, if a lower sensitivity is appropriate for the situation, we can decrease the instances predicted as positive by tuning these parameters.  
These adjustable parameters allow the anomaly detection algorithm to be well suited for various circumstances, notably those of a 5G private network quality monitoring system, which often requires scenario-based adjustments. By effectively manipulating \(\alpha\), the algorithm can better meet the distinctive needs of various anomaly detection applications within this context. For the ease of understanding, we summarize the anomaly detection algorithm in Algorithm 1.  
```
0: Historical window data \(\mathcal{W}=\{\mathbf{w}_{t}\}_{t=1}^{T}\), new window data \(\tilde{\mathcal{W}}=\{\tilde{\mathbf{w}}_{t}\}_{t=1}^{\tilde{T}}\), epoch \(N\), threshold \(\eta\), weight parameter \(\alpha\)
0: Anomaly labels \(\mathcal{L}=\{l_{t}\}_{t=1}^{\tilde{T}}\)
1: Initialize \(E\), \(D_{1}\) and \(D_{2}\)
2:for\(n=1\to N\)do
3:for\(t=1\to T\)do
4:\(Loss_{AE_{1}}=\frac{1}{n}\|\mathbf{w}_{t}-AE_{1}(\mathbf{w}_{t})\|_{2}+\left( 1-\frac{1}{n}\right)\|\mathbf{w}_{t}-AE_{2}(AE_{1}(\mathbf{w}_{t}))\|_{2}\)
5:\(Loss_{AE_{2}}=\frac{1}{n}\|\mathbf{w}_{t}-AE_{2}(\mathbf{w}_{t})\|_{2}-\left( 1-\frac{1}{n}\right)\|\mathbf{w}_{t}-AE_{2}(AE_{1}(\mathbf{w}_{t}))\|_{2}\)
6: Update the weights of \(E\), \(D_{1}\) and \(D_{2}\) using \(Loss_{AE_{1}}\) and \(Loss_{AE_{2}}\)
7:endfor
8:endfor
9:for\(t=1\to\tilde{T}\)do
10:\(S(\tilde{\mathbf{w}}_{t})=\alpha\big{\|}\tilde{\mathbf{w}}_{t}-AE_{1}(\tilde {\mathbf{w}}_{t})\big{\|}_{2}+(1-\alpha)\|\tilde{\mathbf{w}}_{t}-AE_{2}(AE_{1 }(\tilde{\mathbf{w}}_{t}))\|_{2}\)
11:if\(S(\tilde{\mathbf{w}}_{t})>\eta\)then
12:\(l_{t}=1\)
13:else
14:\(l_{t}=0\)
15:endif
16:endfor
17:return\(\mathcal{L}=\{l_{t}\}_{t=1}^{\tilde{T}}\)
```  
**Algorithm 1** Anomaly detection algorithm.
## 4 Numerical Results and Discussion
### Analysis of Numerical Simulation Results of Pilot Tunnel Excavation
#### 4.2.2 Lining Deformation Analysis
With the increase of excavation footage, the vault settlement of the three pilot tunnels increased first and then decreased. The settlement and defamation laws of the vault were different at different positions. Within the range of 0-50 m, the vault settlement of the left pilot tunnel was significantly greater than that of the middle pilot tunnel and the right pilot tunnel. Within the range of 50-80 m, the vault settlement of the right pilot tunnel was greater than that of the middle pilot tunnel and the left pilot tunnel. In the range of 80-140 m, the vault settlement of the middle pilot tunnel was significantly greater than that of the left pilot tunnel and the right pilot tunnel. The maximum vault settlement of the three pilot tunnels was obtained near the range of 90-100 m, which was located in the fault fracture zone. The maximum vault settlement of the middle pilot tunnel was significantly greater than that of the two side pilot tunnels, and the maximum vault settlement of the left pilot tunnel and the right pilot tunnel were similar.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Problem Description  
To generalize our goal, we would like to establish _lightweight_ symmetric key(s) between one or more edge IoT devices (referred to as a _Child_ device), such as sensors, RFID tags and actuators, and a computing device (referred to as a _Guardian_ device) within an IoT network. Here, we emphasize the lightweight nature of the output key and involved cryptographic operations associated with the Child device because of the intrinsic characteristics of the IoT systems as discussed in previous sections.  
Trust does not come from nowhere. To build a mutual trust between the Child and the Guardian, we need to extract the new trust relationship from an existing trust relationship. With that being said, we start from a pre-established trust relationship, with which we can attest the identity and integrity of the target Child. As a result of this reasoning, a third party is introduced into our protocol, known as the _Parent_ device. The Parent, for example, can be the owner of the Child device or the manufacturer of the Child. Essentially, the Child and the Parent may have a pre-established trust relationship, in which the Child device stores therein a _parent key_ that indicates the Parent's privilege on the Child.  
Now the challenge rests with how to leverage a device's Parent key and Parent trust establishment to delegate certain privileges to a new party, referred to herein as a Guardian, to enable the IoT edge device (Child) to establish a shared key securely with the new party, which thus acts as the edge device's Guardian. In other words, taking advantage of a pre-existing trust relationship between a Parent device and a Child device, we would like to yield a lightweight symmetric key shared between the Child device and the Guardian device.
## 4 Measurement Results, Analysis, and Discussion  
Figure 18 shows a chip microphotograph of the four-lane optical receiver. The optical receiver was fabricated via GlobalFoundries 0.13 uO SiGe BiCMOS (GlobalFoundries, Santa Clara, CA, USA) technology, and the one-lane area is about 0.54 mm\({}^{2}\). Meanwhile, one lane consumes 84.97 mW in the low-power mode and 123.75 mW in the high-power mode, both with a 3.3 V supply voltage.  
Figure 17: Simulated input-referred noise current introduced by the sink currents.  
In order to explore the eye diagram performance of the optical receiver, an INOPOTICALS Bit Analyzer BA8042 (INOPOTICALS, Taiwan) was used to produce \(2^{7}-1\) pseudo-random bit sequence (PRBS7) data patterns to the optical receiver, while a Keysight digital signal analyzer DSAZ254A (Keysight, Santa Rosa, CA, USA) was used to measure the output eye diagram. The Bit Analyzer BA8042 was also implemented to measure the BER performance with a given PRBS7 input signal.  
The measured eye diagrams with a 300 \(\upmu\)App input signal (for PRBS7) are presented in Figure 19a,b, where the receiver works in high-power mode. The measured results of the eye diagrams in Figure 19a,b are given in Table 1. The eye heights with input signals at 25 Gb/s and 28.9 Gb/s are 154.2 mV and 114.8 mV, respectively. The eye diagram at 25 Gb/s has a much larger eye height than the eye diagram at 28.9 Gb/s. The crossing of the eye diagram become larger with higher data rates, as shown in Table 1. Additionally, the measured BER of the optical receiver with a 200 \(\upmu\)App input signal varying from 25 Gb/s to 28.9 Gb/s (for PRBS7) is \(10^{-12}\) when the receiver works in both high-power mode and low-power mode.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **25 Gb/s** & **28.9 Gb/s** \\ \hline Eye height (mV) & 154.2 & 114.8 \\ Eye width (UI) & 0.65 & 0.65 \\ Eye one level (mV) & 126.7 & 116.8 \\ Eye zero level (mV) & \(-\)129.0 & \(-\)119.5 \\ Crossing (\%) & 51.2 & 51.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured results of the eye diagrams.  
Figure 19: Measured eye diagrams of the optical receiver. (a) 25 Gb/s; (b) 28.9 Gb/s.  
Figure 18: The microphotograph of the four-lane optical receiver.  
A performance summary of the presented optical receiver and comparison with previous works is given in Table 2. The measured minimum input current of the proposed receiver is 110 \(\upmu\)App with minimized noise of the DCOC loop and the input TIA stage. The minimum input current is kept low by the improved stability of the input TIA. Meanwhile, the one-lane area of the receiver is reduced to 0.54 mm\({}^{2}\), almost half that of prior works in SiGe BiCMOS technology, with parasities utilization in the DCOC loop and capacitive emitter degeneration in the VGA stage. The one-lane area of the receiver is even smaller than the arts inadvanced CMOS technology. We simplified the power supply solution with a single power supply. Compared with previous works, this work provides a wider range of data rates and transimpedance, which suits different communication environments.