esa-sceva/satcom-chunk-collection huggingface dataset:
# Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions
## 4 Computer Vision System
### Classification and Growth Monitoring  
In 1994, [PERSON] et al. developed a mushroom classification system using image analysis to assess the parameters, such as color, shape, stem cut, and cap veil opening, with the goal of creating an algorithm to evaluate mushroom quality [78]. [PERSON] et al. later designed an image processing algorithm based on the watershed method, Canny operator, OR operation, and closed operation to classify mushrooms based on their pileus diameter according to industrial standards, categorizing them as small (less than 2.5 cm), medium (2.5-4.5 cm), or large (more than 4.5 cm) [79]. Their work demonstrated significant potential in determining when mushrooms are ready for harvest. [PERSON] et al. proposed a smart mushroom measurement system using image processing and deep learning to automatically measure the mushroom's cap size and growth rate, aiding in harvest time estimation and production management. With strong performance in mushroom localization and positioning correction, the system was suggested as having the potential for integration with greenhouse control systems to optimize growth conditions and could contribute to the development of AI in agricultural systems [80]. [PERSON] et al. also used a novel image detection algorithm to calculate the circle diameter of mushroom caps using YOLOv3 for positioning and an SP (Score Punishment) algorithm for circle estimation. The algorithm proved robust across various conditions without needing specific parameters, outperforming the CHT (circle Hough transform) in accuracy [81]. They highlighted its potential applications in optimizing greenhouse climate control, tracking mushroom growth, and improving harvest management.  
[PERSON] et al. conducted a study monitoring the growth stages of 86 mushrooms using time-lapse imaging, identifying maturity through image processing and machine learning. By utilizing color, NIR, and depth images captured by a Kinect camera, the faster RCNN model was able to localize mushrooms, and their morphological traits were quantified. An SVM classifier achieved a 70.93% accuracy in determining crop maturity, with the potential  
Figure 7: Algorithm recognition comparison: (**a**) YOLOv5, (**b**) YOLOv5s-CBAM, and (**c**) YOLOv7. Red rectangles show the targets detected, whereas circles in yellow show the targets missed. (Reprinted with permission from ref. [72]).  
for extension to commercial-scale mushroom farming [82,88]. Figure 8 shows the RGB image and the image with RGB depth.
# Seismic Responses of GRS Walls with Secondary Reinforcements Subjected to Earthquake Loading
## 4 Results
### Effects of Peak Ground Acceleration (PGA)  
Figure 19a-d show the effect of PGA on the relative lateral displacement, connection load, maximum reinforcement load, and acceleration amplification of walls with/without secondary reinforcement, respectively. Four different PGAs of 0.2, 0.4, 0.6, and 0.8 g were compared. As shown in Figure 19a, with increasing input PGA, the relative lateral facing displacement of the wall increases. The relative lateral facing displacements are nearly linear on the upper part of the wall under PGAs of 0.2 and 0.4 g. Under PGAs of 0.6 and 0.8 g, the distributions of relative lateral facing displacements are nonlinear over the wall height. The results demonstrate that, for the same wall configuration, under a smaller PGA, secondary reinforcement has a slightly greater effect on reducing the relative lateral facing displacement. Under PGAs of 0.2 and 0.8 g, the relative lateral facing of the wall with secondary reinforcement is 17.6% and 3.4% lower, respectively, than the wall without secondary reinforcement. This suggests that the benefit of secondary reinforcement is lower during strong earthquakes.  
Figure 19: _Cont._  
As shown in Figure 19b,c, installing secondary reinforcement can reduce the connection load and maximum reinforcement load under various PGAs. To compare the effect of secondary reinforcement under different PGAs, the maximum reinforcement loads in each layer after the shaking were added together. Similar to the results for relative lateral facing displacement, it was found that secondary reinforcement is more effective at reducing the maximum reinforcement load under smaller PGA. As shown in Table 7, for PGAs of 0.2 and 0.8 g, the maximum reinforcement load was lower by 22.8% and 12.9%, respectively, in walls with secondary reinforcement compared to those without secondary reinforcement.  
Furthermore, as shown in Figure 19d, a smaller PGA was found to give a higher acceleration amplification. Additionally, the acceleration amplifications for various PGAs are all slightly smaller in walls with secondary reinforcement compared to those without secondary reinforcement.
# Tunable and Switchable Erbium-Doped Fiber Laser Using a Multimode-Fiber Based Filter
## 1 Introduction
The three-paddle PC is coiled with a piece of MMF to form a PC-based SMF/MMF/SMF structure for an interference effect. In contrast to a conventional SMF/MMF/SMF mode beating filter with a fixed transmission curve, the proposed simple and compact MMF-based filter has shown to be tunable for a sensing application in our recent work [22]. In this work, the tunable capability of the filter is exploited to realize the tuning and switching functions in the EDFL. By properly adjusting the PC, a stable single/dual wavelength can be achieved and switched; for the single-wavelength operation, the laser can be tunable continuously, with a tuning range of 9.3 nm, while switching to the dual-wavelength state, variable wavelength spacing is also achievable. Compared to the reported tunable single and multiwavelength EDFLs with using special fibers (such as PCFs [18; 20]) or LPGs [19], where at least two PCs or one PC on top of controlling the curvature of the PCF must be implemented, the proposed tunable filter based on one PC and standard MMF is simple, compact, and low-cost. Furthermore, the PCF based filter suffers from a high insertion loss [20]; the use of LPG cladding modes for interference [19] and mechanically stretching the PCF [20] could lead to the issue of long-term stability of the optical filter.
# Compressive Behavior of Oil Shale with Calcareous Concretion: Parametric Study
## 4 Results and Discussions
### Effect of the Size of Concretion  
The typical failure mode of the specimens with different concretion sizes are presented in Figure 7a. It is obvious in Figure 7 that all specimens with concretion are failed with the separation of the rock mass and the concretion along the interface in between, which agrees well with the experimental observation presented earlier. No obvious damages can be seen from the surface of the concretion itself, indicating that the concretion size will not significantly affect the failure mode when other parameters are the same. However, with the increase in the concretion size, the affected zone with micro fractures was enlarged, which can be found from Figure 7b. It is not difficult to explain because the integrity of the specimen is relevant to the size of the concretion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Rock Mass** & **Concretion** & **Concretion \({}^{*}\)** \\ \hline Effective modulus, emod (GPa) & 3.8 & 3.8 & \(3.8\times\gamma\) \\ Bond effective modulus, pb\_emod (GPa) & 3.8 & 3.8 & \(3.8\times\gamma\) \\ Tensile strength, pb\_ten (MPa) & 1.0 & 5.0 & \(1.0\times\gamma\) \\ Cohesion strength, pb\_coh (MPa) & 1.5 & 7.5 & \(1.5\times\gamma\) \\ Reference gap & 0.75e\({}^{-4}\) & 0.75e\({}^{-4}\) & 0.75e\({}^{-4}\) \\ Friction angle (\({}^{\circ}\)) & 35 & 35 & 35 \\ Friction coefficient & 0.577 & 0.577 & 0.577 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Microscopic parameters of the linear model.  
Although the concretion size will not affect the failure mode of the specimen, it is much different when the axial stress and axial strain curves of these specimens with various concretion sizes are compared, as shown in Figure 8. Compared with the base model (reference specimen) without the concretion, the existence of the concretion results in the enhancement of the overall compressive strength of specimens. This is mainly attributed to the higher strength of the concretion.  
It can also be found from Figure 8 that the peak stress of these specimens with concretion is similar, although the post-peak behavior is not always constant as depicted in Figure 8. That is, the ductility of these specimens with large concretion seems to be superior to some extent.
## 4 Model Formulation
### Lower-Layer Model
#### 4.3.1 CAV Queue Adjustment  
When the hold time of every phase in barrier group \(j\) is informed state, the passable CAV queue in barrier group \(j\) can be determined. Given the preset phase sequence used in this study, the phase sequence setting, or go-straight-ahead and turn-left-behind sequence, is presented in Figure 3.  
The straight-ahead and left-turn CAVs are regrouped into straight-ahead CAV groups and left-turn CAV groups in the adjustment zone when the go-straight-ahead and turn-left-behind sequence is utilized. The first vehicle is subject to a different speed restriction, and the CAV queue may wait there until it enters the passing and variable virtual waiting zones. The CAV group turning left then follows the CAV group moving straight into the passing zone. There are two cases, depending on the time of the signal.  
Case 1: Two queues. As seen in Figure 9(a), a stop-and-wait situation will result if the first left-turn CAV follows the final CAV in the straight-ahead group because of the extended red light time. The group moving straight ahead and the group making a left turn through the intersection can be thought of as two queues in one barrier group to prevent a prolonged stopping time for the left-turn group. The queue can be optimized by Section 4.3.2. The vehicle-following model is determined by Section 4.3.3.  
Figure 10: _Cont._  
Case 2: A single queue. The first left-turn CAV will not stop at the virtual left-turn waiting zone if it follows the final straight CAV, according to Figure 10b, allowing the entire left-turn CAV group to travel through the junction without stopping. As a result, the convoys making a left-turn and going straight ahead can be thought of as one convoy in one barrier group as they pass through the intersection.  
Similar to case 1, the passage time \(t_{j}^{\omega}\) can be calculated for the vehicles in the following queue of the straight-ahead CAV group and the left-turn CAV group. For the CAVs in barrier group \(j\) that cannot cross the intersection, the passage time is set to \(t_{j}^{\omega}=t_{free}^{\omega}\).
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 2 Background and Related Work  
### MI Coding  
The gold standard for examining counsellor adherence to therapy protocols is behavioural observation and coding [15], which provides feedback and evaluation of therapy sessions. During the coding process, trained annotators assign labels to therapist skills/behaviours such as reflection and client behaviours such as change talk. Session-level ratings on qualities such as empathy are often also included.  
A variety of coding schemes have been proposed, including the Motivational Interviewing Skill Code (MISC) [3] and the Motivational Interviewing Treatment Integrity Code (MITI) [4]. However, as manual coding is costly and time-consuming, automatic coding of utterance-level behaviour and related tasks such as automatic rating of therapist empathy have garnered significant research interest in recent years.
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 3 Simulation of Hard Roof Blasting
### Analysis of Simulation Results  
#### 3.3.1 Crack Propagation under Different Borehole Diameters  
Figure 3 shows the shock wave stress nephograms under different diameters at \(t=1\) ms after the main explosive was detonated. The detonation energy first acts on the surrounding rock of the hole wall and then forms a high-intensity blasting dynamic load, rapidly diffusing from the hole wall to the deep surrounding rock. The detonation energy decays rapidly during the propagation. The numerical results indicated that the crack propagation is governed by the detonation energy in the vicinity of the borehole, and the confining pressure can affect far-field crack propagation. The cracks tend to propagate towards the region of high initial pressure [24]. Moreover, under the same in situ stress conditions, the stress wave propagation range linearly increases with the charge diameter.
## 3 Results and Discussion
### Validation of Vaporisation Enthalpies Using Structure-Property Relationships
#### 3.3.2 Empirical Correlations: Vaporisation Enthalpies versus Solution Enthalpies  
Another empirical method based on the GC technique is to correlate enthalpies \(\Delta_{1}^{\rm g}H_{\rm m}^{\rm o}\)(298.15 K) with enthalpies of solution, \(H_{sol}\), of a compound of interest in the GC stationary phase. The measurable value in this method is also the retention time and the enthalpy of solution results from the temperature dependence of the retention times [50; 51]. We found a comprehensive compilation of the experimental solution enthalpies in the non-polar stationary phase OV-101 [52] and used these data (see Table 6) in this work to correlate with the vaporisation enthalpies \(\Delta_{1}^{\rm g}H_{\rm m}^{\rm o}\)(298.15 K).  
\begin{table}
\begin{tabular}{l c c c c} \hline  & \(J_{x}\)a & \(\Delta_{1}^{\rm g}H_{\rm m}^{\rm o}\)(298.15 K), & \(\Delta_{1}^{\rm g}H_{\rm m}^{\rm o}\)(298.15 K), & \(\Delta_{1}^{\rm g}H_{\rm m}^{\rm o}\)(298 K)\({}_{\rm calc}\)c & \(\Delta\)d \\
**Compound** & & **kJ\(\cdot\)mol\({}^{-1}\)** & **kJ\(\cdot\)mol\({}^{-1}\)** & **kJ\(\cdot\)mol\({}^{-1}\)** \\ \hline iso-propylbenzene & 920 & 45.2 & 44.7 & 0.5 \\
2-Me-iso-propylbenzene & 1032 & 49.3 & 50.0 & \(-\)0.7 \\
3-Me-iso-propylbenzene & 1012 & 49.4 & 49.1 & 0.3 \\
4-Me-iso-propylbenzene & 1018 & 49.6 & 49.4 & 0.2 \\
1,2-di-iso-propylbenzene & 1152 & - & **55.8** & - \\
1,3-di-iso-propylbenzene & 1143 & - & **55.4** & - \\
1,4-di-iso-propylbenzene & 1162 & 56.2 & 56.3 & \(-\)0.1 \\
1,3,5-tri-iso-propylbenzene & 1325 & - & **64.1** & - \\ ter-butylbenzene & 988 & 47.5 & 47.9 & \(-\)0.4 \\ ter-anylbenzene & 1086 & 52.3 & 52.6 & \(-\)0.3 \\
3-Me-tert-butylbenzene & 1062 & - & **51.5** & - \\
4-Me-tert-butylbenzene & 1085 & 52.2 & 52.6 & \(-\)0.4 \\
3,5-di-Me-1-tert-butylbenzene & 1152 & 56.6 & 55.8 & 0.8 \\ \hline \end{tabular} a
[FOOTNOTE:a]Footnote a: footnotetext: Kovats indices, \(J_{x}\), on the standard non-polar column OV-101 [46]; b
[FOOTNOTE:b]Footnote b: footnotetext: Evaluated experimental data (given in bold in Tables 1 and 2); c
[FOOTNOTE:c]Footnote c: footnotetext: Calculated using Equation (7) with the assessed uncertainty of \(\pm\)1.0 kJ\(\cdot\)mol\({}^{-1}\) (expanded uncertainty 0.95 level of confidence); d  
\end{table}
Table 5: Correlation of the vaporisation enthalpies, \(\Delta_{1}^{\rm g}H_{\rm m}^{\rm o}\) (298.15 K), of alkyl-substituted benzenes with their Kovats indices on (\(J_{x}\)) on OV-101.  
The vaporisation enthalpies, \(\Delta_{\rm I}^{\rm g}H_{\rm m}^{\rm o}(298.15\;{\rm K})\), and solution enthalpies, \(H_{sol}\), on OV-101 correlate according to the following equation:  
\[\Delta_{\rm I}^{\rm g}H_{\rm m}^{\rm o}(298.15\;{\rm K})/\left({\rm kJ}\cdot{ \rm mol}^{-1}\right)=9.6+0.9039\;\times\;H_{\rm sol}\;{\rm with}\;\left(R^{2}=0.9 93\right) \tag{9}\]  
The \"empirical\" vaporisation enthalpies of alkylbenzenes according to Equation (9) are denoted as \(H_{sol}\) in Tables 1 and 2 and agree well with the experimental values obtained with conventional experimental methods.
## 5 Performance Evaluation  
In this section, we evaluate the accuracy of the proposed network bandwidth prediction model and QoE of the buffer-based ABR technique using the predicted values. We used the PyTorch framework ([31]) for the implementation of the GRU-based network bandwidth prediction model. Model training and simulation were conducted on a desktop equipped with an NVIDIA RTX 3060, AMD Ryzen 7 5800X 8-core 3.80 GHz processor, and 32 GB of RAM.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 1 Introduction
### Contribution  
This study is focused on improving the network performance by introducing the Mobility, Battery, and Queue length Multipath-Aware (MBMQA) routing scheme for the IoT 5G network based on D2D communication. This study aims to discover several ultimate routing paths to the destination while balancing workload to all the devices so that the battery consumption of specific devices could be reduced and the mobility would not affect the routing of the packets. An algorithm for route computations was developed based on each node's available parameters, including the battery's energy level, mobility information, and queue length size of the devices. A multiple attributes route selection metric is also presented to quantify the stated information into making a decision for selecting the best path among multiple paths towards the destination device. Therefore, this proposed MBMQA routing scheme aims to select the optimal route from source to destination devices which mitigates devices energy consumption, balances devices traffic load, and improves the network and route stability during data transmission.  
The rest of the paper is organized as follows: Section 2 presents the related literature, while Section 3 consists of the details of the protocol and mechanisms that have been employed to compute the multi-path routes, simulation setup, and measurements that have been collected. Section 4 highlights the collected results and presents the evaluation of the proposed method compared to other methods. Finally, Section 5 contains the conclusion along with future work and recommendations.
# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 3 Design of Imbalanced AD Medical Data Analysis Approach
### The Proposed RF-AUC-Cor Algorithm
The elements of the \(i\)th row and the \(j\)th column of the correlation matrix are the correlation coefficients of the \(i\)th column and the \(j\)th column of the original matrix, which corresponds to the degree of redundancy between the corresponding variables;  
1. **RF-AUC-Cor algorithm. ** To optimize the process of feature selection, the RF-AUC-Cor algorithm is proposed in this paper, as shown in Figure 4. The procedure of the RF-AUC-Cor algorithm is divided into two steps. In the first step, the random forest is used to classify samples when evaluating the features. The AUC of each feature is calculated, and the features are sorted in descending order based on their AUC values. In the second step, the correlation between all features is calculated. Moreover, according to the correlation matrix and the sorted features, the feature subsets are generated by feature selection. Finally, the random forest is used to verify the feature subset. The specific process of the RF-AUC-Cor algorithm is as follows:  
**Step 1:** Utilize random forest to classify samples, calculate the AUC value of each feature, sort the features by AUC value as the score, and obtain a new sorted feature set;  
**Step 2:** For the sorted feature set, calculate the correlation between the features and obtain the correlation matrix;  
Figure 4: The overall framework of the RF-AUC–Cor algorithm. **Step 3:** The initial state of the subset is empty, and in the process of selecting the feature subset, one feature is selected to add to the existing feature subset each time. The selected feature is the feature with the highest score in the feature ranked set, or the feature which has the minimal redundancy with the highest score feature that was selected last time;  
**Step 4:** Based on the above analysis, in the feature selection process it is necessary to determine whether to continuously select the \(K\) highest ranked features, or the consecutive \(K\) features that have the minimum redundancy with the highest score feature that was selected last time;  
**Step 5:** Each selection obtains a new feature subset, and the random forest is employed to classify the samples. The feature subset with the highest AUC value is the optimal feature subset to be selected.
# Structural Damage Localization by the Principal Eigenvector of Modal Flexibility Change
###### Abstract  
Using the principal eigenvector (PE) of modal flexibility change, a new vibration-based algorithm for structural defect localization was presented in this paper. From theoretical investigations, it was proven that the PE of modal flexibility variation has a turning point with a sharp peak in its curvature at the damage location. A three-span continuous beam was used as an example to illustrate the feasibility and superiority of the proposed PE algorithm for damage localization. Furthermore, defect localization was also performed using the well-known uniform load surface approach for comparison. Numerical results demonstrated that the PE algorithm can locate structural defects with good accuracy, whereas the ULS approach occasionally missed one or two defect locations. It was found that the PE algorithm may be promising for structural defect assessment.  
defect localization; principal eigenvector; modal flexibility variation; deflection; vibration Article
## 2 Data and Methods
The upper label in Figure 1 shows that the slot region can present several space-weather concerns, including low- and medium-energy electron enhancements, multi-MeV electrons (on rare occasions), and strong solar energetic particle events (again on relatively rare occasions). The slot region extends from L-2.0 to L-3.0. The outer zone of the radiation belt extends from L-3.0 to L-6.5. It comprises mildly to highly relativistic electrons and varies widely in time and particle intensity. Due to geomagnetic storms, the high-energy electrons can spill down from the outer radiation belt and form a new radiation belt (storage ring) around certain geomagnetic lines in the inner magnetosphere [38; 39; 40; 41; 42; 43; 44; 45; 46]. The new radiation belts normally last for several days but very rare, they may last for up to several weeks, months, or even years. We analyze the response of seismic activity to four widely discussed geomagnetic storms in the scientific literature, which were all followed by a newly formed storage ring in the inner magnetosphere. Three of them lasted for up to several months; they were created by geomagnetic storms on 24 March 1991 [41; 42], 1 September 2012 [43], and 21 June 2015 [44]; one of them, created by a giant storm on 31 October 2003, lasted for more than two years [40]. To compare seismic data variations with solar activity variations, we used sunspot numbers from the World Data Center SILSO, Royal Observatory of Belgium, Brussels ([[https://wwwbis.sidc.be/silso/datafiles](https://wwwbis.sidc.be/silso/datafiles)]([https://wwwbis.sidc.be/silso/datafiles](https://wwwbis.sidc.be/silso/datafiles)), accessed on 30 September 2023). The sunspot number is one of the space weather parameters usually used to investigate long-term solar activity variations, including 11-year variations. Figure 1: Schematic diagram of Earth’s radiation belt, reprinted from Ref. [37].