esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Design
#### 2.7.6 Baseline Models  
To provide a comprehensive evaluation of the RL agent configurations, it is essential to compare their performance not only against each other but also relative to established market benchmarks. This ensures that the results are meaningful and realistic within the broader financial context, allowing for a more accurate assessment of the RL-based strategies.  
Financial markets are dynamic, experiencing various economic cycles with bullish, bearish, and sideways trends. To account for this variability, we compare the RL agents' performance against well-recognized benchmarks and simple portfolio models that capture overall market movements. This contextualizes the agents' performance by relating it to market conditions, providing a point of reference for evaluating the robustness of each model.  
One straightforward baseline approach is the equally weighted portfolio (EWP), where each asset in the portfolio is assigned an equal weight. This strategy assumes equal currency allocations across all instruments, simplifying the process by automatically adjusting to market movements without introducing additional complexity. It assumes that the assets are fully liquid and fractional. Additionally, we employ the S&P 500 index as an industry-standard benchmark. This index, which tracks the top 500 U.S. companies, serves as a widely accepted reference for evaluating portfolio performance.  
For the EWP, given that there are 6 instruments (Section 2.7.1: *GSPC, *GDAXI, *IXIC, *RUT, *N225, *FTSE) plus an additional risk-free asset, the portfolio weights are a constant vector of dimension \(1\times n_{\text{instruments}}\), where each element of the vector is \(\frac{1}{n_{\text{instruments}}}\). With \(n_{\text{instruments}}=7\), the weights are equally distributed across all instruments:  
\[\widetilde{w}_{\text{ewp}}=\left[\frac{1}{7},\frac{1}{7},\frac{1}{7},\frac{1} {7},\frac{1}{7},\frac{1}{7},\frac{1}{7},\frac{1}{7}\right] \tag{20}\]  
For the S&P 500 benchmark (market portfolio), the portfolio is fully invested in the *GSPC instrument, representing the market index. The portfolio weights are a constant vector, where only the S&P 500 index receives a non-zero weight:  
\[\widetilde{w}_{\text{market}}=\left[1,0,0,0,0,0\right] \tag{21}\]  
These two portfolio models--the equally weighted portfolio and the market portfolio--serve as baselines for evaluating the RL agent configurations. By comparing their performance to these benchmarks, we gain insight into how well the RL agents perform in relation to standard, market-based investment strategies. The cumulative reward of the market and EQP portfolio for the period 2016-April 2024 is shown in Figure 4.
# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 3 Experiment and Results
### Datasets and Pre-Processing  
Three publicly available retinal fundus image datasets: DRIVE, STARE, and CHASE_DB1 were applied to evaluate our method. The DRIVE dataset contains 40 color fundus images with a resolution of 565 \(\times\) 584 pixels from the Netherlands diabetes retinopathy screening project [40], where 20 images are performed for training and 20 images for testing. To assist ophthalmologists in diagnosing eye diseases, 20 images with a resolution of 700 \(\times\) 605 pixels are selected from the STARE dataset [41]. As the official does not provide a clear division of training and testing sets, the leave-one-out method is applied for cross-validation. Specifically, each time 19 images are selected for training, and the remaining one is for testing. The above operation is repeated and finally the average of the 20 trained model results is calculated. The CHASE_DB1 dataset contains 28 images with a resolution of 999 \(\times\) 960 pixels, corresponding to two images per patient of 14 children in the UK Children's Hearing and Health Research Project [42], where the first 20 images are employed to form the training set and the last eight images to form the test set.  
Due to the optical effect during photography, fundus images often exhibit low contrast between blood vessels and the background, making it challenging to distinguish between them effectively. To address this issue, three preprocessing methods are utilized in this study.  
* In this study, the fundus images are separated into RGB three-channel feature maps. It was found that the green channel exhibited moderate brightness and better contrast between blood vessels and the background, in comparison to the other channels [43; 44]. Consequently, the fundus vascular images with green channels were selected as the post-processing images.
* The contrast-limited adaptive histogram equalization (CLAHE) improves the contrast between blood vessels and the background while minimizing noise amplification, resulting in smoother processed fundus images [45].
* The gamma transformation is a nonlinear method commonly employed for image enhancement [46]. It can enhance the brightness of the darker areas in an image, thereby improving its visibility. In this study, the gamma value is set to 1.2.  
Figure 5 illustrates the original retinal image and its corresponding preprocessed image. The enhanced image exhibits richer details and higher contrast compared to the original image, thereby facilitating the subsequent segmentation steps.  
To expand the limited number of training samples, two data augmentation operations are employed, including random horizontal and vertical flipping with a 50% probability, and random rotation with a 40% probability (angle ranges from \(-10\) to 10 degrees). These operations aim to improve the generalization ability of the model.
## 4 SRF-IoT Design
### External SRF-IDS  
A main component of SRF-IoT scheme is the external SRF-IDS. It consists of two types of devices: an SRF-IDS root and SRF-IDS detectors. The SRF-IDS root is actually a router responsible for taking final decisions for malicious nodes, whereas SRF-IDS detectors are sensor devices operating in promiscuous mode to gather information from the monitored network.  
One of the novelties of this work is the ability of SRF-IDS to operate independently in a different RPL instance without interfering with the monitored network. This is achieved by using a unique RPL InstanceID in packets, only for SRF-IDS nodes. We revised our hybrid-based IDS from previous work [12, 30, 31] by adding more detection mechanisms for RPL-based attacks, allowing local decision-making by SRF-IDS detectors, and enabling communication between SRF-IDS detectors and neighbour RPL networks. Specifically,  
Figure 2: Network topology.  
the new SRF-IDS can monitor a network for blackhole and rank attackers. However, the actual detection is done by the monitored nodes themselves and not by SRF-IDS. A malicious actor usually combines blackhole and rank attacks so that nodes select the attacker as parent, and then all packets going through the attacker are dropped. To detect blackhole attackers, SRF-IDS detectors are deployed near the devices of the monitored network so that network packets are captured and analysed. After processing network traffic, SRF-IDS detectors can decide locally if a suspicious node exists or not, and in the former case an alert with useful metrics is sent to nearby monitored devices. Hence, SRF-IDS operates in a decentralised way to combat malicious actors more efficiently.  
Another feature is that SRF-IDS detectors are able to send RPL control messages to IoT devices in the monitored network to inform about the possible attacker. Implementing this required a minor modification in the monitored devices so they can correctly parse the SRF-IDS RPL packet. Also, a different RPL InstanceID was needed as discussed in Section 3.2. In this way, a communication between the SRF-IDS network and the monitored network is achieved and malicious nodes are isolated. As an extra security feature, the SRF-IDS root has an embedded firewall to block malicious IPs. In case a malicious node is detected, its IP is forwarded to the SRF-IDS root for blacklisting. For other attacks, such as DIS flooding, SRF-IDS detectors report suspicious nodes to the SRF-IDS root for further decision making [12].
## 3 Experimental Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Fold** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **F1 Score** \\ \hline \multirow{3}{*}{4} & 97.00 & 95.10 & \multirow{3}{*}{96.00} & \multirow{3}{*}{0.960} \\  & (TP: 97, FP: 3) & (TP: 97, FN: 5) & & \\ \hline \multirow{3}{*}{5} & 97.00 & 96.04 & \multirow{3}{*}{96.50} & \multirow{3}{*}{0.9652} \\  & (TP: 97, FP: 3) & (TP: 97, FN: 4) & & \\ \hline \multirow{3}{*}{6} & 96.00 & 97.96 & \multirow{3}{*}{97.00} & \multirow{3}{*}{0.9697} \\  & (TP: 96, FP: 4) & (TP: 96, FN: 2) & & \\ \hline \multirow{3}{*}{7} & 97.00 & 96.04 & \multirow{3}{*}{96.50} & \multirow{3}{*}{0.9652} \\  & (TP: 97, FP: 3) & (TP: 96, FN: 4) & & \\ \hline \multirow{3}{*}{8} & 97.00 & 95.10 & \multirow{3}{*}{96.00} & \multirow{3}{*}{0.9604} \\  & (TP: 97, FP: 3) & (TP: 97, FN: 5) & & \\ \hline \multirow{3}{*}{9} & 97.00 & 96.04 & \multirow{3}{*}{96.50} & \multirow{3}{*}{0.9652} \\  & (TP: 97, FP: 3) & (TP: 96, FN: 4) & & \\ \hline \multirow{3}{*}{10} & 97.00 & 97.00 & \multirow{3}{*}{97.00} & \multirow{3}{*}{0.9700} \\  & (TP: 97, FP: 3) & (TP: 97, FN: 3) & & \\ \hline Average & 96.70 & 96.13 & 96.40 & \multirow{3}{*}{0.9641} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._critical standard to evaluate the value of its results or understand whether the classifier's findings were accurate or useful.
## 3 Direct Shear Test of the Jagged Sandstone Joints
### Numerical Simulation Results Analysis
#### 4.4.2 Estimation of Shear Strength Parameters Based on Numerical Simulation Results  
The numerical model for direct shear tests was used to apply a standard contour line with JRC values of 0.4, 2.8, 5.8, 6.7, and 9.5 as rough rock joints. Direct shear numerical tests were carried out at normal pressures of 2, 4, 6, 8, and 10 MPa to determine the corresponding peak shear strengths, which are summarized in Table 5.  
The data from Table 5 were analyzed using least squares estimation (Figure 12). It was observed from the \(\tau\)-\(\sigma_{\rm n}\) scatter plot that the peak shear strength corresponding to a normal stress of 10 MPa exhibited a more significant dispersion. With JRC values of 2.8, 5.8, and 6.7, the results deviated from the point group; therefore, they are excluded from parameter estimation.  
Based on the shear strength parameter estimates in Table 6, it was observed that the cohesion of the rock joint decreased with an increase in JRC, while the internal friction angle increased with an increase in JRC. This trend is consistent with the results from physical tests.  
Generalized Shear Strength Parameter and Empirical Formula between Roughness and Shear Strength Parameters  
The scatter plot (Figure 13) is plotted with JRC as the horizontal coordinate, and cohesion \(c\) and the internal friction angle were plotted as vertical coordinates. It was observed that they have a good linear relationship, so a linear fit was performed to obtain empirical equations for the generalized shear strength parameter values that were applicable to the subject of this study.  
\[\begin{array}{l}\varphi=1.2175\mathrm{JRC}+35.284\\ c=-0.0383\mathrm{JRC}+1.8558\end{array} \tag{3}\]  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **JRC = 0.4** & **JRC = 2.8** & **JRC = 5.8** & **JRC = 6.7** & **JRC = 9.5** \\ \hline Cohesion \(c\)/MPa & 1.861 & 1.695 & 1.685 & 1.5927 & 1.4800 \\ Internal friction angle \(\varphi/^{\circ}\) & 34.5 & 39.6 & 42.8 & 44.8 & 45.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of shear strength parameters of JRC0.4–JRC9.5 rock joints.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**JRC** & \(\sigma_{\text{n}}\)/MPa & \(\tau\)/MPa & **JRC** & \(\tau\)/MPa & **JRC** & \(\tau\)/MPa & **JRC** & \(\tau\)/MPa \\ \hline  & 2 & 2.8 & & 2.59 & & 2.87 & & 3.43 & & 3.99 \\  & 4 & 5.2 & & 5.81 & & 6.28 & & 6.42 & & 6.52 \\
0.4 & 6 & 6.14 & 2.8 & 7.32 & 5.8 & 7.43 & 6.7 & 7.86 & 9.5 & 8.42 \\  & 8 & 7.07 & & 7.6 & & 8.  
Figure 12: Estimation of shear strength parameters of JRC0.4–JRC9.5 rock joints. The points with red cross are eliminated from group.  
Figure 13: Linear fitting of the \(c\) and the \(\varphi\) with the JRC.
# Research on the Application Status of Machine Vision Technology in Furniture Manufacturing Process
## 2 Methods
### Quality Detecting
#### 3.2.1 Dimensional Defect Detection  
The dimensional defect detection technology based on MV can accomplish the detection work with high real-time, high accuracy, high efficiency, low error, and non-contact. The most common dimensional defect detection method involves pre-processing the image, using the Hough transform (HT. Its basic idea is to convert the detection problem from the image space into the parameter space, complete inspection tasks using straightforward statistics accumulated in the parameter space, and describe the edge of the image by some form of parameter that satisfies most of the edge points [31].) to identify the workpiece's outline information, finding the edge line and transforming it, rotating the image of workpiece's edge to the horizontal position in accordance with the slope of the line, and then calculating the actual physical distance between the pixels using camera calibration data. The measurement data is uploaded to the database, and compared with the database data. If the error is within the range, the workpiece will be retained and entered the next process. Otherwise, it will be eliminated. However, the Hough transform method has a large amount of calculation and is seriously affected by noise [32], which requires high-quality collected pictures. Only applying the Hough change cannot meet the variety of detection needs due to the complexity of workpiece and the enhancement of dimensional defect detection requirements. In recent years, many scholars have made more and more in-depth research on dimensional defect detection by MV. Edge detection algorithms (such as the Canny operator, and Sobel operator) and particle swarm algorithms have been proposed and applied to the field of dimensional defect detection [33; 34]. The detection range extends from length and width to angle, radian, diagonal length, and even thickness. Table 2 summarized and compared the advantages and disadvantages of several commonly used edge detection algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & & **Advantages** & & **Disadvantages** \\ \hline \hline \multirow{6}{*}{Canny} & \(\bullet\) & High precision & \(\bullet\) & Large amount of \\  & \(\bullet\) & Accurate & & calculation \\ \cline{1-1}  & \(\bullet\) & Anti-noise & \(\bullet\) & Poor real-time \\ \cline{1-1}  & \(\bullet\) & Suppresses false edges & & performance \\ \cline{1-1}  & & & \(\bullet\) & Less edge detail \\ \cline{1-1}  & & & \(\bullet\) & Poor adaptability \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of edge detection algorithms.  
Figure 5: Technical roadmap of quality detecting technology based on MV.
## 3 Results
Using the discrete control surface case as a baseline, this can be found from  
\[\Delta\big{(}C_{D_{i}}\big{)}_{\text{opt}}\equiv\frac{\big{(}C_{D_{i}\text{ cont}}\big{)}_{\text{opt}}-\big{(}C_{D_{i}\text{disc}}\big{)}_{\text{opt}}}{ \big{(}C_{D_{i}\text{disc}}\big{)}_{\text{opt}}}=\frac{\kappa_{D\text{0}\text { cont}}-\kappa_{D\text{0}\text{disc}}}{1+\kappa_{D\text{0}\text{disc}}}=\frac{ \kappa_{P}(\varepsilon_{T\text{disc}}-\varepsilon_{T\text{ cont}})}{1+\kappa_{D \text{0}\text{disc}}} \tag{22}\]  
Results for Equation (22) are shown in Figure 5. From Figure 5, we see that a continuous control-surface design will reduce induced drag by less than 5% relative to an equivalent planform with discrete control surfaces for nearly all cases. Also note that the difference in drag between the two control surfaces diminishes with increasing number of actuators with less than 3% benefit for the three-actuator cases, less than 2% benefit for the four-actuator cases, and less than about 1% for the five-actuator cases. This is because there is an absolute minimum induced drag possible for any given aspect ratio, and as the number of actuators increase, both control-mechanism designs are better able to match the twist distribution given by Equations (15) and (16). Figure 4: The term \(\kappa_{D\text{0}}\) for a (**a**) 2-, (**b**) 3-, (**c**) 4-, and (**d**) 5-actuator control mechanism, shown as a function of taper ratio for aspect ratios ranging from 4 to 20 in increments of 2.
# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
## 2 Background and Related Work
### How Human Operators Excavate  
The excavation process is the core of the loading maneuver; it is the crucial step and, since bucket-rock interactions cannot be predicted in advance, there is no optimal method for performing this operation. Nevertheless, human operators have had to deal with this situation by learning the loading technique from experience and shared good practices. It is natural, therefore, to analyze which ideas can be borrowed from them. Through several interviews with LHD operators of Chilean block caving and sublevel stoping mining companies, and discussions and analysis of data from mining operation manuals, some common points in their practices were identified:  
* Before engaging with the rock pile, the bucket must be fully extended.
* Forward motion is selected for maximum traction (first gear).
* Intermittent tilt commands are the basis of the excavation process.
* Lift commands are mainly issued to prevent or correct wheel skidding.  
Figure 4 shows a graphical representation of an excavation maneuver. First, the LHD engages with the rock pile, then the bucket is tilted in order to excavate on the muck pile. When the wheels lose traction, the boom is lifted to regain traction. After filling the bucket, the LHD pulls back. Figure 5 shows a graphic of some of the relevant variables during a line-of-sight remote-controlled loading maneuver. As mentioned, bucket tilting (\"tilt command\") is activated intermittently. Its duration and shape vary among operators and loading attempts. Techniques also using negative tilt commands (downwards tilting of the bucket) were also encountered in combination with positive lift commands (\"lift command\"). The common factor in all of them was the intermittency of the commands. As for the lift command, its use was seen to be highly variable, which correlates with the notion of using it mainly to prevent wheel skidding. In the example of Figure 5, the lift command was not used at all. The \"pedal command\" is the one used for machine acceleration and thus, in turn, engine revolutions per minute (RPM). \"Tilt angle\" and \"lift angle\" are the  
Figure 3: Sublevel stoping draw point.  
bucket tilt and boom lift angle values, estimated through the encoders installed in the machine. \"Transmission pressure\" is the hydrostatic transmission pressure of the machine, a variable that can be associated with the engine's power output. \"Skidding factor\" is an estimated skidding factor that is explained in Section 3.3.
# Steady State Response of Linear Time Invariant Systems Modeledby Multibond Graphs
## 6 Conclusions  
The steady state response of LTI systems represented by multibond graph models is presented. The multiport junction structure of a multibond graph in an integral causality assignment (MBGI) that determines the state space of an LTI system is proposed. This MBGI admits multiport storage elements in integral and derivative causality assignments that represent linearly independent and dependent state variables, respectively. By assigning a derivative causality to all storage elements, a multibond graph in a derivative causality assignment (MBGD) is obtained. The relationships between MBGI and MBGD give the direct determination of the steady state of a multiport system.  
Two case studies are modeled by multibond graphs: a three-phase electrical system and a synchronous generator. In both cases, the steady state using this approach is obtained. Finally, in order to verify the steady state behavior of the state variables, the simulation results are shown. The advantages for applying multibond graphs with respect to bond graphs for multibody systems are clear: the short notation, junction structure, and mathematical model are compact. Furthermore, the proposed junction structure to determine the characteristics (structural observability, structural controllability, stability, control design) in the physical domain can be key for new results.  
This research received no external funding.  
Not applicable.  
Figure 21: Three-phase electrical current: \(i_{a}\), \(i_{b}\), and \(i_{c}\).
## 2 Materials and Methods
### Rapid 2-AB Method  
The conventional 2-AB method usually uses conventional reagents and approaches for glycan release, 2-AB labelling, and purification, which is time consuming and still widely used in mAb industry. In this study, the Agilent AdvanceBio Gly-X N-Glycan Prep kit was used for more rapid sample pretreatment than the conventional 2-AB method, referred to as the rapid 2-AB method. Firstly, a 40 microgram mAb sample was buffer exchanged and diluted to about 2 mg/mL. Then, denaturation, reduction, and deglycosylation (2 \(\upmu\)L of PNGase F working solution: 40 \(\upmu\) antibody) were performed to release glycans from mAbs [26]. The following step was to load the sample onto a cleanup plate to collect the released glycans. The 2-AB labelling solution was added to a cleanup plate and incubated in the dark at 65 \({}^{\circ}\)C for 1 h for 2-AB labelling. Then, two purification steps were used to purify the 2-AB labelled glycans. Finally, the purified 2-AB labelled glycan samples were injected into UHPLC for glycan analysis.  
Glycan BEH Amide column (130 A, 2.1 \(\times\) 150 mm, 1.7 \(\upmu\)m) from Waters (Milford, MA, USA) was chosen for 2-AB labelled glycan analysis. The column temperature was set as 60 \({}^{\circ}\)C. A fluorescence detector was used for signal acquisition with excitation wavelength at 330 nm and emission wavelength at 420 nm. The mobile phase A was 100 mM ammonium formate buffer at pH 4.5, and the mobile phase B was acetonitrile. The gradient elution was decreased from 78% mobile phase B to 60% mobile phase B in 30 min, with a 5 min regeneration using 40% mobile phase B and a 20 min equilibration using 78% mobile phase B. The flow rate was 0.4 mL/min for gradient elution and equilibration while 0.2 mL/min for regeneration. The qualitative analysis of different glycans was based on the retention time of glycan standards. The percentage of different glycans was calculated from the relative fluorescence peak area.