esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validation  
### Measurement Texperiment of Two Horizontal Parallel Surfaces  
#### 5.1.1 Experimental Step  
In order to verify the correctness of this measurement method, the measurement experiment was carried out. Before measuring, a marble flat ruler with class 000 precision as shown in Figure 2 was selected as the datum surface, the size of the marble flat ruler selected in this paper is \(500\times 100\times 50\) mm, and its flatness is \(1.5\upmu\)m. Therefore, it can be regarded as an approximately absolute horizontal plane. Then, the measurement mechanism was placed on the surface of marble ruler to calibrate the initial value \(X_{0}\). It is assumed that contact line \(A\) and detection line \(C\) are on the same horizontal surface. Place the detection mechanism at different positions on the marble flat ruler and find the average of the five measurements, then the initial value \(X_{0}\) can be obtained, and \(X_{0}\) is \(32.92014\) mm.  
Taking \(PA\) and \(PB\) surfaces with a length of 500 mm and a width of 50 mm, the distance between the two surfaces is 60 mm. Calibrating the starting and ending positions, ensure that the spring is in compression. Firstly, the \(PB\) surface was detected by using \(PA\) surface as the datum surface, as shown in Figure 15a, and then the \(PA\) surface was detected by using \(PB\) surface as the datum surface, as shown in Figure 15b. The measurement method is described in Section 3.1. Each surface was measured four times. During the measurement process, the measurement mechanism moves along the auxiliary surface toward the arrow direction. Under the constraint of an auxiliary surface, the detection mechanism will not slide.
## 3 Results and Discussion
### Metabolic Pathways of Key Characteristic Aroma Compounds  
The changes in aroma characteristics during tea processing are affected by various factors. The six key characteristic aroma components identified in this study can be divided into fatty acid derivatives (FADVs), carotenoid-derived volatiles (CDVs), and methoxybenzenes (MPCs), as shown in Figure 6. FADVs can produce some short-chain C6-C9 fatty aldehydes through the action of lipoxygenase (LOX) and hydroperoxide lyase (HPL) as a result of microbial activity [39]. Nonanal and octanal, as FADVs, have floral and  
Figure 5: The key volatile compounds in the four types of samples (OAV \(>\) 10).  
fruity properties and are generated by the oxidative degradation of oleic acid [40]. CDVs are derived from different carotenoid cleavage dioxygenases (CCDs), including \(\beta\)-carotene, \(\alpha\)-carotene, phytoene, lutein and lycopene, during processes such as enzymatic oxidative degradation, photooxidation, autooxidation, or thermal degradation in fermentation and drying [41]. The key characteristic aromas in this study include two CDVs: \(\alpha\)-ionone, which is mainly produced by the oxidative degradation of \(\alpha\)-carotene, and dihydro \(\beta\)-ionone, which is a secondary oxidation product derived from \(\beta\)-carotene as well as a biotransformation product of \(\beta\)-ionone [42]. In addition, GA is methylated under the action of microbial enzymes to produce MPCs, such as 1,2,3-trimethoxybenzene and other fragrance compounds [43]. Cedrol, a sesquiterpene alcohol known for its woody aroma, has been identified as a key aroma compound in Liupao tea [44]. Previous research has shown a significant increase in ecdf during the early stages of fermentation of Fuzhuan brick tea, stabilizing towards the end of the process. At present, the metabolic pathway of cedar alcohol is not clear, and it is speculated that it may be related to high-temperature steam treatment or microbial activity [45; 46]. The fermentation of Liupao tea caused significant changes in its flavor profile, influenced not by a single factor but by the combined effects of microbial metabolism, enzymatic oxidation, and moist heat. In the future, the key microbial flora involved in the fermentation process and their impact on the quality and flavor development of Liupao tea will be further explored.
# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
## 3 Materials and Methods
### PDR Technology  
Pedestrian dead reckoning (PDR) is a self-positioning method for indoor navigation. The key technologies of PDR are step detection (or counting), step length estimation, and heading angle of pedestrian estimation. After a pedestrian step was detected, the position can be obtained using step length and heading angle based on the previous position [20]. The general process of PDR is illustrated as Equation (5):  
\[\left\{\begin{array}{l}N_{k+1}=N_{k}+s_{k}\cdot cos\psi_{k}\\ E_{k+1}=E_{k}+s_{k}\cdot sin\psi_{k}\end{array}\right. \tag{5}\]  
Figure 2: The scanning RSSI number of the per real-time fingerprint in 60 s.  
where \(N_{k}\) and \(E_{k}\) refer to the coordinate of the pedestrian at the north and east direction at time \(k\), respectively. \(s_{k}\) is the step length and \(\psi_{k}\) is the heading angle at time \(k\).  
In the PDR method, step detection and step length are estimated according to the accelerometer readings. Many step detection algorithms have been proposed by researchers, including peak detection [47], threshold setting [29], zero velocity update [48], auto-correlation [49] and finite-state machine (FSM) [50]. Among them, the FSM method is easy to implement and more resistant to interference from errors. After detecting a step, step length is estimated by different models. Studies have shown that the step length is related to the acceleration, heights, and strides of different people. Linear models [51], constant models [52], and nonlinear models [53] are the most common methods which are used to estimate the step length. The step length estimated by different methods differs little. For simplicity, we choose the Weinberg model [54] to estimate the step length in our research and the expression for the model is as follows in Equation (6):  
\[s_{k}=K\cdot\sqrt[4]{a_{max}-a_{min}} \tag{6}\]  
where \(K\) is the scale factor of the step length, \(a_{max}\) and \(a_{min}\) are the maximum and minimum acceleration in one step cycle.  
Apart from step detection and step length estimation, heading estimate is another important component of PDR. The compass [55] or the gyroscope [56] are usually used to estimate the heading angle. Because of the inherent sensor noise in the smartphone, the accuracy of the heading obtained by the compass is not high but it will not drift for a long time. In contrast, using the gyroscope to estimate the heading, the accuracy in a short time will be high, but it will suffer a drift problem. We also found that different motion states of pedestrians would affect the accuracy of heading estimation. To avoid the shortcomings of compass and gyroscope and take into account the different motion states of people, an improved Mahony complementary filter (AMMCF) based on the motion states had been proposed in our research. The parameter \(K_{p}\) in the filter can adaptively change based on the motion states which are judged according to the acceleration readings. The principle of PDR is illustrated in Figure 3.
# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
## 5 Stream Data Load Prediction for Cloud Recourse Scaling
With the application \(A\), the time window interval \(T\), the response time limit \(R_{SLA}\), the application average response time \(R(A,T)\), the payload prediction value \(F\) together as the input of the flexible distribution algorithm; the output is the number of extended virtual machine \(UbestVMs\) or the contractile virtual machine \(DbestVMs\). The algorithm first initializes the application average response time, and then compares it with the load prediction value \(F\) obtained by the load forecasting algorithm. If the \(F\) value is larger, then theexisting cluster resources are unable to handle the overloaded stream data. We need to add virtual machine source processing data to the cluster. If the \(F\) value is smaller, it shows that the system can handle the data normally, but the data is delayed in the process of waiting for processing and we can calculate the average time of delay using \(R(A,T)\). If it is larger than the set \(R_{SLA}\), some data cannot be processed for a long time, and we assign the value one. The platform virtual machine handles these data and reduces the delay in application execution. On the contrary, if the latency is not exceeded, the virtual machine is reclaimed to reduce the waste of resources, and \(\min VMs\) is the minimum number of virtual machines that can ensure that the \(MSTW_{SLA}(m,T)\) is greater than \(F\). ```
Input: A, T, \(R_{SLA}\), \(R_{(A,T)}\), F, \(MSTW_{SLA}(m,T)\) Output:\(\mathit{UbestVMs}\) or \(\mathit{DbestVMs}\)
1) Begin
2)\(r=R(A,T)\)
3) if \(F\geq MSTW_{SLA}(m,T)\)
4)\(\mathit{UbestVMs}\leftarrow\min VMs-m\)
5) else if \(F<MSTW_{SLA}(m,T)\)
6) if \(r>R_{SLA}\)
7)\(\mathit{UbestVMs}\gets 1\)
8) else
9)\(\mathit{DbestVMs}\gets m-\min VMs\)
10) End
```  
**Algorithm 1** Time Window Resource Elasticity Scaling Algorithm
## 4 Results and Discussion
### Analysis of the Computational Complexity
#### 4.5.1 The Orders of Computational Complexity: Mathematical Derivation  
The four methods contemplated exhibit unique computational complexity properties, discernible from their respective algorithms:  
* **ACRLPC Framework:** The computational complexity of the ACRLPC framework mainly relies on the number of operations needed to measure energy consumption, throughput, accuracy and latency. The complexity for a system to collate \(N\) data points is \(O(N\log N)\) owing to how sorting and aggregate operations are implemented in the system--in other words, how the system is designed. The ACRLPC architecture has been tuned to minimize overhead, resulting in decreased computational cost compared to legacy systems.
* **FDU-MAC:** A frequency division multiplexing-based framework with a complexity of \(O(N^{2})\) was mainly constructed because of the need for parallel frequency assignment and coordination between nodes in the network. The fundamental overhead associated with handling various frequency bands makes it more computationally intensive than the ACRLPC framework.
* **TCH-MAC:** The channel multiplexing techniques based on time division cause a computational burden of \(O(N^{2})\) since this is a resource allocation-based scheme, where time slots are assigned to each user in the network. While the time-slot allocation process, as well as synchronization, introduces additional complexity, our protocol is still less efficient than the ACRLPC framework.
* **UW-ALOHA-QM:** The computational complexity of the UW-ALOHA-QM framework is \(O(N)\) since it adopts a simpler random access mechanism. But the visual comparison metrics between the throughput and accuracy showed that the performance was lower due to the simplicity that it offers.
## 7 Robustness
### Evaluation Metrics and Baseline Performance
#### 7.2.2 Noise Attacks  
**Gaussian Noise:** The proposed algorithm was tested under Gaussian noise (variance 0.01), and the watermark was perfectly recovered in all image dimensions, with DCT_BER values of 0 and NC values of 1.  
**Speckle Noise:** Similarly, the algorithm demonstrated strong resistance to Speckle noise. For variances of 0.02 and 0.03, larger image sizes maintained perfect NC values. However, for the 500 \(\times\) 500 size, the method continued to perform well with NC values close to 1.0.  
**Salt & Pepper Noise:** The algorithm showed excellent robustness to Salt & Pepper noise, with a density of 0.05. Across all image sizes, the watermark was perfectly recovered with DCT_BER values of 0 and DCT_NC values of 1.  
A direct comparison with state-of-the-art methods shows our technique achieves superior performance across all noise types tested. Table 7 presents comparative results against the method of [PERSON] et al. (2017) and demonstrates the strength of our approach in maintaining watermark integrity under various noise conditions.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Image Dimension** & **QF 90** & **QF 80** & **QF 70** & **QF 60** & **QF 50** & **QF 40** \\ \hline \(640\times 480\) & 0.0 & 0.0 & 0.0 & 0.0 & 0.0156 & 0.1094 \\ \(600\times 640\) & 0.0 & 0.0 & 0.0 & 0.0469 & 0.0312 & 0.1094 \\ \(500\times 500\) & 0.0 & 0.0 & 0.0 & 0.0156 & 0.0313 \\ \(500\times 400\) & 0.0 & 0.0156 & 0.0312 & 0.0156 & 0.1406 & 0.2500 \\ \(480\times 640\) & 0.0 & 0.0 & 0.0 & 0.0156 & 0.0781 & 0.0781 \\ \(335\times 500\) & 0.0 & 0.1406 & 0.1094 & 0.1563 & 0.2656 & 0.3750 \\ \hline \hline \end{tabular}
\end{table}
Table 6: COCO2017 dataset: Bit Error Rate (BER) under JPEG compression attacks.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Image Dimension** & **QF 90** & **QF 80** & **QF 70** & **QF 60** & **QF 50** & **QF 40** \\ \hline \(335\times 500\) & 1.0 & 0.8581 & 0.8924 & 0.8442 & 0.7464 & 0.6473 \\ \(480\times 640\) & 1.0 & 1.0 & 1.0 & 0.9847 & 0.9284 & 0.9232 \\ \(500\times 500\) & 1.0 & 1.0 & 1.0 & 1.0 & 0.9847 & 0.9692 \\ \(500\times 400\) & 1.0 & 0.9852 & 0.9962 & 0.9847 & 0.8553 & 0.7259 \\ \(640\times 480\) & 1.0 & 1.0 & 1.0 & 1.0 & 0.9847 & 0.8899 \\ \(600\times 640\) & 1.0 & 1.0 & 1.0 & 0.9539 & 0.9696 & 0.8882 \\ \hline \hline \end{tabular}
\end{table}
Table 5: COCO2017 dataset: Normalised Correlation (NC) under JPEG compression attacks.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 5 Application to Face Contour Approximation Model
### Proposed Model and Parameters  
In this paper, we present the use of _OpenPose_ to assist in drawing the human face contour by beginners. _OpenPose_ is the popular open software that can jointly detect the coordinates of the _keypoints_ in the human body, hands, face, and foot from a single image [23]. A _keypoint_ represents a feature point in them such as a joint, a fingertip, and a nostril. Since _OpenPose_ will extract the contour of the chin, it can help extract the face contour. However, _OpenPose_ cannot extract the contour of the upper part of the face including the forehead due to the hair.  
For solving this limitation, we propose a _simple face contour approximation model_ that consists of two half circles and line segments. The upper half circle will draw the forehead and the lower half circle will draw the chin. The two line segments that connect the ends of the two half circles will draw the edges of the face contour. Then, the parameters of this model including the center coordinates and the radii of the half circles should be properly selected so that the resulting model is well matched with the keypoints by _OpenPose_. _paraOpt_ is applied to the optimization of the parameters. Figure 8 illustrates the face contour approximation model and the related keypoints by _OpenPose_, which is obtained from [24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Iterations** & **GA** & **paraOpt** \\ \hline
100 & 11.96 s & 11.21 s \\ \hline
1000 & 117.43 s & 107.21 s \\ \hline
10,000 & 1162.07 s & 1030.53 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: CPU time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Iterations** & **GA** & **paraOpt** \\ \hline
100 & 62.5\% & 68.9\% \\ \hline
1000 & 76.5\% & 89.8\% \\ \hline
10,000 & 92.3\% & 95.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between _GA_ and _PanaOpt_.
## 3 System Design and the Proposed HPR-DCC Methodology
### Proposed HPR-DCC Algorithm
Additionally, the HPR-DCC is designed to manage channel load by preventing significant increases in channel load caused by a sudden growth in the target awareness range. However, the proposed HPR-DCC allows safety-critical messages generated during hazardous events to be transmitted at high power and rate, by passing the standard restrictions. This ensures that crucial information is promptly communicated in emergency situations, thereby enhancing the overall safety and reliability of the vehicular communication system. In this study, the parameter settings of the proposed HPR-DCC algorithm are calibrated to account for the multifaceted aspects of V2V communication systems, thereby ensuring an accurate representation of scenarios, with the main parameters shown in Table 2. The time step duration is set at 200 milliseconds, providing a suitable time resolution for capturing the dynamic interactions within V2V networks. Both the target range and target awareness are defined as context-dependent variables, which typically fluctuate between 20 and 500 m, and 50% to 100%, respectively, depending on the specific application context. This flexible approach allows the algorithm to adapt to various V2V communication scenarios and capture the nuances of different vehicular environments. Furthermore, the maximum transmission power is confined to a range of 0 to 23 dBm [25], adhering to the standard constraints for V2V communication radios. This restriction ensures that the proposed algorithm operates within the acceptable power limits established for vehicular communication systems, mitigating potential interference or signal degradation issues. And, the maximum beacon rate (BR) is specified within a range of 1 to 10 Hz, representing a typical range for cooperative messages in V2V communication systems. By constraining the beacon rate within this range, the algorithm effectively accommodates the requirements of V2V communication, facilitating efficient and reliable data exchange between vehicles. Then, set the subcarrier spacing as 15 kHz for providing better time domain resolution, reduce the influence of multipath fading on the signal, and improve the anti-interference ability of the system.
## 4 Application to Quantum Computing Devices: Mach-Zehnder Interferometer
### Implementation of Condition 1 and Condition 2  
As with Section 3, here too we ensure that our modified quantum device satisfies the conditions mentioned in Section 2.2. Here **condition 1** can be obtained with a different strategy than the one used for the RTD since electrons are entering in the MZI one by one (as in a quantum wire). The strategy is enlarging the length \(L\) of the arms of the active region of the device (between \(S1\) and \(D1,D2\)) to increase the number of simultaneous single particle wave packets that fit inside the device. We argue in the Appendix B that a _natural_ way in the injection of electrons from the contacts in the device is at time intervals \(\tau_{in}\), as defined in Equation (A8) in Appendix B. Neglecting the thermal noise of the contacts the spatial separation between electrons is therefore given by \(\tau_{in}v_{x}\) as plotted in Figure 4. Notice that such spatial separation ensures that the consideration of non-interacting electrons required in Equation (1) is more accurate for our modified MZI than for the RTD mentioned before.  
As indicated in Appendix C, the consideration of metallic contacts with a lateral area \(A\) satisfying \(\sqrt{A}\gg L\) is necessary condition to be able to successfully use Equation (7) in the computation of the experimental current. Notice that enlarging the lateral area \(A\) does not imply an increment of the number \(N\) of electrons here since electrons can only enter inside the device, one by one, in the 1D edge channels. Besides this **condition 2** can again be obtained by post-processing the experimental current \(I_{exp}\) as indicated in Section 3, by fixing the total time \(T\) that we allow the electrons to enter inside the enlarged active device region, \(N=T/\tau_{in}\).
## 4 Experiments and Analysis
### Ablation Experiments
#### 4.4.2 The Impact of iAFF on the Network  
Comparative analyses were conducted to assess iAFF's impact on cross-scale feature fusion, employing both the initial feature fusion (direct concatenation) and iAFF for multi-scale feature fusion in the FPN-PAN, as illustrated in Figure 9.The results indicate that the simple channel concatenation causes large-scale background features to obscure small-scale target features, which further complicates small-scale target detection and recognition. Conversely, our approach compiles contextual data across multiple scales along the channel's length, highlighting both extensive targets with widespread distribution and minor targets with localized distribution, facilitating target identification amidst significant scale fluctuations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Backbone** & **iAFF** & **OAAM** & **Dy-Head** & **NWD** & **Precision** & **Recall** & **mAP50** \\ \hline \multirow{6}{*}{YOLOv8m} & ✗ & ✗ & ✗ & ✗ & 79.5\% & 68.2\% & 73.7\% \\  & ✓ & ✗ & ✗ & ✗ & 86.1\% & 74.1\% & 81.9\% \\  & ✓ & ✓ & ✗ & ✗ & 87.3\% & 80.9\% & 83.7\% \\  & ✓ & ✓ & ✓ & ✗ & 88.6\% & 82.8\% & 84.9\% \\  & ✓ & ✓ & ✓ & ✓ & 91.7\% & 87.4\% & 89.5\% \\ \hline \multirow{6}{*}{RetinaNet} & ✗ & ✗ & ✗ & ✗ & 78.4\% & 67.8\% & 72.6\% \\  & ✓ & ✗ & ✗ & ✗ & 84.3\% & 76.3\% & 78.9\% \\ \cline{1-1}  & ✓ & ✓ & ✗ & ✗ & 85.9\% & 78.7\% & 80.6\% \\ \cline{1-1}  & ✓ & ✓ & ✓ & ✗ & 86.3\% & 80.5\% & 81.4\% \\ \cline{1-1}  & ✓ & ✓ & ✓ & ✓ & 90.1\% & 87.4\% & 88.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Loss**} & \multicolumn{3}{c}{**IRMT-UAV**} & \multicolumn{3}{c}{**IRSTD-1k**} \\ \cline{2-6}  & **Precision** & **Recall** & **mAP50** & **Precision** & **Recall** & **mAP50** \\ \hline IoU & 87.8\% & 86.4\% & 87.2\% & 86.3\% & 83.7\% & 85.4\% \\ GIoU & 87.9\% & 86.3\% & 88.3\% & 86.7\% & 84.2\% & 85.9\% \\ DIoU & 88.9\% & 87.2\% & 89.3\% & 87.9\% & 85.3\% & 86.2\% \\ CIoU & 90.1\% & 88.4\% & 89.1\% & 88.4\% & 85.2\% & 86.9\% \\ NWD & 92.8\% & 89.5\% & 91.7\% & 90.3\% & 89.1\% & 88.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative analysis comparing various loss functions.
## 6 Simulation Results
### Communication Overhead  
The main communication overhead of FESA scheme is the MAC transmission for data transmission and false data detection during data aggregation.
###### Abstract  
Southeast Asia is the epicentre of world palm oil production. Plantations in Malaysia have increased 150% in area within the last decade, mostly at the expense of tropical forests. Maps of the aboveground carbon density (ACD) of vegetation generated by remote sensing technologies, such as airborne LiDAR, are vital for quantifying the effects of land use change for greenhouse gas emissions, and many papers have developed methods for mapping forests. However, nobody has yet mapped oil palm ACD from LiDAR. The development of carbon prediction models would open doors to remote monitoring of plantations as part of efforts to make the industry more environmentally sustainable. This paper compares the performance of tree-centric and area-based approaches to mapping ACD in oil palm plantations. We find that an area-based approach gave more accurate estimates of carbon density than tree-centric methods and that the most accurate estimation model includes LiDAR measurements of top-of-canopy height and canopy cover. We show that tree crown segmentation is sensitive to crown density, resulting in less accurate tree density and ACD predictions, but argue that tree-centric approach can nevertheless be useful for monitoring purposes, providing a method to detect, extract and count oil palm trees automatically from images.  
oil palm plantation; aboveground carbon density; laser scanning, LiDAR; crown segmentation; canopy cover; top of canopy height Article