esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 4 Experimental Research
### Experimental Results and Analysis
In Relink dataset, the performance of four pairs of experiments decreased significantly using the DCPDP-Norm method; in the cases in which the performance decreased significantly, such as in the Safe\(\rightarrow\)Apache pair, the F1 value of DCPDP was 0.714, but the F1 value of DCPDP-Norm was 0.583. Among the six experiments on Relink dataset, only two sets of experiments have improved the performance of data standardization operations. There is also such a phenomenon on the AEEEM dataset (i.e., rows 8 to 27). For example, JDT\(\rightarrow\)LC, JDT\(\rightarrow\)PDE, and PDE\(\rightarrow\)ML are all reduced in performance after data standardization operations. Generally speaking, on the two datasets, performance degradation is not rare. There are 26 groups of CPDP experiments in total, and 12 groups have performance degradation. Almost half of the experimental performances have not been improved. However, under normal circumstances, using the data after the data standardization operation to train the model can often obtain better performance. Therefore, we believe that there is a feature distortion phenomenon in the data distribution of the original feature space, which causes a decrease in the performance level of the experiment, rather than an increase. Table 3 shows the performance comparison between the MFTCPDP method and the benchmark method DCPDP, several CPDP methods, and the WPDP method. Firstly, com \begin{table}
\begin{tabular}{l l l} \hline \hline
**Source\(\rightarrow\)Target** & **DCPDP-Norm** & **DCPDP** \\ \hline Apache\(\rightarrow\)Safe & 0.787 & 0.459 \\ Apache\(\rightarrow\)Zxing & 0.606 & 0.582 \\ Safe\(\rightarrow\)Apache & 0.583 & 0.714 \\ Safe\(\rightarrow\)Zxing & 0.651 & 0.654 \\ Zxing\(\rightarrow\)Apache & 0.437 & 0.645 \\ Zxing\(\rightarrow\)Safe & 0.645 & 0.699 \\ EQ\(\rightarrow\)JDT & 0.596 & 0.422 \\ EQ\(\rightarrow\)LC & 0.843 & 0.813 \\ EQ\(\rightarrow\)ML & 0.367 & 0.344 \\ EQ\(\rightarrow\)PDE & 0.775 & 0.687 \\ JDT\(\rightarrow\)EQ & 0.716 & 0.633 \\ JDT\(\rightarrow\)LC & 0.755 & 0.884 \\ JDT\(\rightarrow\)ML & 0.788 & 0.769 \\ JDT\(\rightarrow\)PDE & 0.702 & 0.826 \\ LC\(\rightarrow\)EQ & 0.709 & 0.663 \\ LC\(\rightarrow\)JDT & 0.804 & 0.545 \\ LC\(\rightarrow\)ML & 0.770 & 0.462 \\ LC\(\rightarrow\)PDE & 0.771 & 0.789 \\ ML\(\rightarrow\)EQ & 0.711 & 0.688 \\ ML\(\rightarrow\)JDT & 0.732 & 0.578 \\ ML\(\rightarrow\)LC & 0.837 & 0.875 \\ EQ\(\rightarrow\)JDT & 0.804 & 0.809 \\ EQ\(\rightarrow\)LC & 0.722 & 0.674 \\ EQ\(\rightarrow\)ML & 0.519 & 0.571 \\ EQ\(\rightarrow\)PDE & 0.523 & 0.871 \\ JDT\(\rightarrow\)EQ & 0.384 & 0.437 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of F1 values between DCPDP-Norm and DCPDP.
## 2 Nonlinear Dynamic Modeling of Spur Gear Systems
### Derivation of the Dynamic Model
For this reason, let  
\[\omega_{n}=\sqrt{k_{0}/m} \tag{31}\]  
where \(\omega_{n}\) denotes the natural frequency of the gear system. Then, let \(\tau=\omega_{n}t\), \(b_{c}\) denote the nominal scale of displacement; \(X=x/b_{c}\), \(X_{i}=x_{i}/b_{c}(i=1,2)\), \(Y_{i}=y_{i}/b_{c}(i=1,2)\), \(X_{bi}=x_{bi}/b_{c}(i=1,2,3,4)\), \(Y_{bi}=y_{bi}/b_{c}\)\((i=1,2,3,4)\), one obtains  
\[\dot{x}=\frac{dx}{dt}=\frac{d(Xb_{c})}{d\tau}\frac{d\tau}{dt}=b_{c}\omega_{n} \dot{X} \tag{32}\]  
\[\ddot{x}=\frac{d^{2}x}{dt^{2}}=\frac{d\left(b_{c}\omega_{n}\dot{X}\right)}{d\tau} \frac{d\tau}{dt}=b_{c}\omega_{n}{}^{2}\ddot{X} \tag{33}\]  
\[\dot{\lambda}=(X_{1}-X_{2})\sin\varphi-(Y_{1}-Y_{2})\cos\varphi+X \tag{34}\]  
Substituting Equations (32) and (33) into Equation (30), one obtains  
\[\ddot{X}+\frac{c}{m\omega_{n}}\dot{\lambda}+\frac{k(t)}{m\omega_{n}{}^{2}}f( \lambda)=\frac{F(t)}{m{b_{c}}{\omega_{n}}^{2}} \tag{35}\]  
Furthermore, when the meshing displacement jumps, the meshing frequency is usually n/2 times the system's natural frequency, indicating the occurrence of a resonance phenomenon. In order to verify this inference, when the load torque \(F_{m}=27\) Nm, the system's bifurcation diagram is obtained. As shown in Figure 14, when the system's meshing frequency is almost n/2 times the system's natural frequency, the resonance phenomenon arises. In conclusion, when the system is subjected to a light load, the system motion exhibits adding periodic bifurcation accompanied by chaotic band intervals. By way of choosing the appropriate rotational speed, the system can fall into a stable periodic motion, thereby improving system stability. When the system is subjected to a large load, the resonance phenomenon is especially pronounced. The maximum vibration displacement may reach dozens of millimeters and thus damage the gear system. Therefore, for the gear systems subjected to a large load, the resonance phenomenon must be evaded.
## 4 Data Communication Scheme Derivation  
Sections 4.1-4.3 detail the mathematical formulations for deriving OCDM, SCCP, and OTFDM schemes based on the generic transmitter and receiver detailed in Section 3, while Section 4.4 discusses their nSNR, spectrogram, and computational complexities. The mathematical formulation for deriving OFDM is in [28].
## 2 Mathematical Models
### DEA Model  
Suppose there are \(n\) DMUs with input vector \(x_{j}=(x_{1j},...,x_{mj})\) and output vector \(z_{j}=(z_{1j},...,z_{sj})\) where \(z_{j}\geq 0,z_{j}\
eq 0,x_{j}\geq 0,x_{j}\
eq 0\). Then, the DEA model in the nature of the input is as follows:  
\[\begin{array}{l}\min\theta\\ s.t\quad\sum_{i=1}^{n}\varpi_{i}x_{i}\leq\theta x_{i0},\qquad i=1,...m,\\ \sum_{j=1}^{n}\varpi_{j}z_{rj}\geq z_{r0},\qquad r=1,...,s,\\ \varpi_{j}\geq 0,\qquad j=1,...,n,\end{array} \tag{5}\]  
If \(\theta=1\), then DMU is efficient, and if \(\theta<1\), then DMU is inefficient.  
In the basic DEA model, it is assumed that no specific relationship exists between inputs and outputs, and the model is non-parametric. Given our objective to extend this model for portfolio optimization, we introduce a specific assumption regarding the relationship between inputs and outputs. Since we define risk as the input and return asthe output, and considering a linear relationship between these two variables, the proposed model will consequently be a parametric model.  
**Remark 1**.: _In the DEA model, the input-oriented approach evaluates the efficiency of a DMU by assuming that the unit aims to minimize its inputs while keeping outputs constant. Conversely, the output-oriented approach in DEA assesses the DMU efficiency based on its endeavor to maximize its outputs while holding inputs constant. In the present study, we adopted a combined approach to determine the efficiency score. Specifically, we utilized the maximum efficiency value obtained from both the input-oriented and output-oriented calculations as the final efficiency index._
# Effect of Mix Proportion Parameters on Behaviors of Basalt Fiber RPC Based on Box-Behnken Model
## 3 Results and Discussion
### Influence of Parameters on the Performance of BFRPC
#### 3.2.2 Flexural Strength  
The predicted model on the flexural strength designed according to the BBD method is presented in Table 7 and given by Equation (2). According to the \(p\) values of various factors obtained from ANOVA, it could be seen that the influence degree of factors on the flexural strength was: \(B=D>C^{2}>CD>C>D^{2}>A\). It was obvious that \(B\) and \(D\) were the most significant model terms, while \(A\) was the weakest term, and there was interaction between \(C\) and \(D\).  
The effect of sand/binder ratio (\(A\)) and water/binder ratio (\(B\)) on flexural strength (\(R2\)) of the mixture is shown in Figure 4. The contour plots of \(AB\) showed that both \(A\) and \(B\) had a linear relationship with the flexural strength, regardless of the value of \(C\), \(D\). The flexural strength of specimens decreased with the increase of both \(A\) and \(B\), but the \(w\)\(b\) ratio (\(B\)) was the most significant factor, while the effect of sand volume (\(A\)) on the flexural strength is weak. When the content of quartz sand increased, the structural space of aggregate in the mixture became larger, which led to the poor compactness of aggregate and cementitious materials, forming a weak area at the interface and leading to a decline in the flexural strength of the specimen. It could be seen that when both \(A\) and \(B\) achieved a minimum value (i.e., \(A=0.9\), \(B=0.18\)), the maximum flexural strength could be obtained.  
The interaction of \(CD\) is shown in Figure 5a, which indicates that the relationship between flexural strength and basalt fiber content (\(C\)) shows different changing tendency under different silica fume content (\(D\)). When \(D=0.2\), as shown in the black curve, with the increase of fiber content (\(C\)), the flexural strength of specimens also increased. When \(D=0.4\), as shown in the red curve, the relationship between flexural strength and fiber content (\(C\)) presents a negative quadratic curve. The maximum flexural strength was obtained at the two endpoints of the curve (i.e., when \(C=0.15\), \(D=0.4\) and \(C=0.3\), \(D=0.2\)). However, it can be seen that the flexural strength at \(D=0.4\) was lower than that at \(D=0.2\), indicating that 0.2 was the optimal value of silica fume to cement ratio. Meanwhile, according to the trend of the curves, it could be judged that the strengthening effect of basalt fiber on RPC flexural strength was determined by the composition of cementitious materials. Based on the above conclusions, under the condition of \(A=0.9\) and \(B=0.18\), the contour diagram of \(CD\) and \(R2\) is shown in Figure 5b. It can be found that the maximum flexural strength can be obtained when \(C=0.3\) and \(D=0.2\).
## 2 Research Trends
### Vulnerable Case of Video Conferencing System Security  
Various video conferencing systems are being launched worldwide. Notable video conferencing systems include Zoom, Cisco Webex, Facebook, Microsoft Teams, and Google Meet. Among them, the average number of Zoom users in 2020 increased by over 40 times compared to 2019 [11].  
However, as the number of Zoom users rapidly increased and began to attract attention, the number of attackers threatening security also increased. Zoom has been subject to several attacks by attackers; particularly, Zoom members' private information has been leaked to the dark web, and Zoom Bombing/Zoom Trolling, which disrupts meetings, such as by hacking the screen, has become common.  
In addition to intentional attacks, Zoom has experienced various security vulnerability issues. Zoom was also aware of these, and took measures such as upgrading the encryption algorithm to AES-256-GCM or adding a 2-Factor Authentication method to solve it [11]. Although Zoom has addressed most of the vulnerabilities, its E2 EE function has only been partially addressed. Currently, the E2 EE security features provided by Zoom have only been implemented up to Phase 2 out of a total of four phases.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 6 Conclusions  
In summary, this review article has underscored the pivotal role of remote sensing technology in advancing our understanding of forest ecosystems, with a specific focus on the Argana forest stands (_Argania spinosa_). Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest cover, health, and responses to environmental changes. The Argana stand, known for its resilience in arid conditions and ecological significance in combating desertification, serves as a compelling case study for remote sensing applications. These technologies provide precise insights into canopy height, density, and individual tree health, enabling assessments of Argana stand populations, stress detection, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress through various indices, contributing to sustainable land management practices. Additionally, the review briefly touched on the role of remote sensing in estimating crop yield and monitoring drought conditions, showcasing the diverse applications of this technology. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argana stands, and highlights its potential for continued advancements in ecological research and conservation efforts.  
Conceptualization, M.M., M.F. and A.E.A.; writing--original draft preparation, M.M. and A.C.; writing--review and editing, M.F. and A.E.A.; visualization, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 5 Results
### Answers to RQs
#### 5.1.1 RQ1: What Approach Was Used To Apply Constraints during Software Reconfigurations in DSPL?

Table 10: Selected papers and approaches to managing runtime variability in DSPL. Figure 4: Approaches to managing runtime variability in DSPL. This loop consists of five stages [7]. The stages of MAPE-K control loop are presented in Figure 5.  
1. Monitor: Module in charge of monitoring managed resources and collecting, grouping and filtering data. Monitoring is done through sensors, e.g. IoT devices. 2. Analyze: Module responsible for analyzing the data provided by the monitor, understanding what the current status is and whether measures should be taken to mitigate needs. 3. Plan: Module responsible for developing an action plan based on the results of the analysis, this being a set of measures that will take the system from its current state to the desired state. 4. Execute: Module responsible for the execution of the action plan and the follow-up of the measures adopted in the managed element. 5. Knowledge: Knowledge is the central node of the control loop and is accessible to all components of the loop, incorporating in addition to the data collected and analyzed, additional elements such as architectural models (in the DSPL context, feature or goal models), policies and change plans [6]. Third-party software: DSPL proposals that require the use of external software for their main operation. Agent-Oriented Software Engineering: Proposals in which methodologies associated with software agents are applied, such as multi-Agent Systems Product Lines. No specific methodology. Fifty-one (60.7%) papers present a _Proprietary architecture_ for managing a DSPL execution environment. Twenty-one (25%) papers present the _MAPE-K control loop_ as a runtime reconfiguration engine. Seven papers (8.3%) present an architecture based on _Agent-Oriented Software Engineering_ to manage DSPL environments. Four papers (4.8%) use _Third-party software_ to manage a DSPL environment and one paper (1.2%) presents _No specific methodology_. See details in Figure 6 and the classification of each item according to the criteria previously defined in Table 11.  
Figure 5: MAPE-K control loop.
## 3 Results and Discussion  
### Compressive Strength Testing Results  
The compressive strength developments of concrete samples are shown in Figure 3. The ordinary Portland concrete (OPC, i.e., group A) showed high strength before 28 d age while the supplementary cementitious materials (SCMs, i.e., fly ash, slag) replaced concrete mixture (i.e., group B) and gained more strength from 28 d to 270 d. The compressive strength values at 270 d for groups A and B were almost the same, being 49.5 MPa and 49.1 MPa, respectively. The compressive strength of group A concrete samples at 28 d reached 87.1% of 270 d's value while group B was 61.7%. The reason for the low strength of group B concrete is the addition of SCMs, which decreased the cement content in the concrete. As a result, the content of hydration products such as calcium hydroxide (CH) and calcium silicate hydrate (C-S-H) is low in the early stages. Thus, the compressive strength of group B concrete is lower than group A's. However, at later stages (i.e., 28 d to 270 d), group B concrete gained more strength than group A concrete. This can be attributed to the pozzolanic reaction of SCMs and the hydration products (i.e., CH), which generate C-S-H gel and fills the interconnected pore structure. Thus, the compressive strength development of group B concrete is faster than group A concrete.  
Figure 2: The layout of sensors on a concrete surface. (**a**) The configuration of 8 sensors on a concrete surface. (**b**) The photo of the testing setup.  
The compressive strength results of pre-loaded samples were tested at 270 d age. The relative ratios of the compressive strength of pre-loaded and virgin samples at 270 d are presented in Figure 4. It is observed that the relative ratio of group B concrete is mostly higher than that of group A concrete. The results indicate that the micro-cracks induced by pre-loading were healed by the hydration products in later stage curing. Besides, group B concrete shows good post-injury repair performance, verifying that the incorporation of SCMs has a pozzolanic reaction. The pozzolanic reaction generated more C-S-H gel which refined the micro-pore structure and bridged the micro-cracks caused by pre-loading.  
It is also found that loading concrete up to 90% of its strength at the age of 8 h does not affect its later age strength development [22]. Some researchers also found that incorporating SCMs can effectively heal the cracks induced by the early age damage [23]. It was observed that the 90% pre-loading degree on concrete at 28 d caused a 27% loss of ultimate compressive strength. However, after another 30 days of curing, the compressive strength of concrete with SCMs improved by 20%, while in the normal concrete without SCMs the compressive strength only improved by 6%.  
Figure 3: Compressive strength development of concrete samples.
## 2 Materials and Methods
### Simulation  
To ensure the reliability and accuracy of our experimental findings, a computational fluid dynamics (CFD) simulation was conducted using the ANSYS software. Initially, a parametric model of the microdevice was meticulously crafted using SOLIDWORKS. This model was then seamlessly transferred to ANSYS using an IGES (Initial Graphics Exchange Specification) file. The CFD simulation was executed iteratively, with each iteration refining the model until the results achieved convergence. This iterative process allowed us to systematically fine-tune the simulation parameters and boundary conditions to closely match the real-world behavior observed in our experiments. By harmonizing the experimental data with the simulation outcomes, we were able to validate and enhance the comprehensiveness of our findings, contributing to a more robust and well-rounded analysis of the microdevice's performance. The parameters used in the simulation are summarized in Table 2.  
The software simulation method employed the following transport equations for the viscous model, utilizing the standard k-epsilon model (a two-equation model) along with the Standard Wall Function:  
\[\frac{\delta}{\delta t}(\rho\kappa)+\frac{\delta}{\delta x_{i}}(\rho\kappa\mu_ {i})=\frac{\delta}{\delta x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{ \kappa}}\bigg{)}\frac{\delta\kappa}{\delta x_{j}}\Bigg{]}+G_{\kappa}+G_{b}- \rho\epsilon-Y_{M}+S_{\kappa} \tag{1}\]  
\[\frac{\delta}{\delta t}(\rho\epsilon)+\frac{\delta}{\delta x_{i}}(\rho \epsilon\mu_{i})=\frac{\delta}{\delta x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}} {\sigma_{\epsilon}}\bigg{)}\frac{\delta\epsilon}{\delta x_{j}}\Bigg{]}+C_{1 \epsilon}\frac{\epsilon}{\kappa}\big{(}G_{\kappa}+C_{3\epsilon G_{b}}\big{)} -C_{2\epsilon\rho}\frac{\epsilon^{2}}{\kappa}+S_{\epsilon} \tag{2}\]  
\(\kappa\)--turbulence kinetic energy;  
\(\epsilon\)-- rate of dissipation;  
\(G_{\kappa}\)--turbulence kinetic energy due to mean velocity gradients;  
\(G_{b}\)--turbulence kinetic energy due to buoyancy;  
\(Y_{M}\)--fluctuating dilatation in compressible turbulence to overall dissipation rate;  
\(C_{1\epsilon}\)\(C_{2\epsilon}\)\(C_{3\epsilon}\)--constants;  
\(\sigma_{\kappa}\) and \(\sigma_{\epsilon}\)--turbulent Prandtl numbers for \(\kappa\) and \(\epsilon\), respectively;  
\(S_{\kappa}\) and \(S_{\epsilon}\)--user-defined source terms.
# Development of Continuum Robot Arm and Gripper for Harvesting Cherry Tomatoes
###### Abstract
Panasonic Co., Ltd., (also presented a commercially available tomato picker robot that takes only 2-3 s in one tomato picking cycle, which will be the fastest machine [18]. However, the above-mentioned prototypes cannot detach tomatoes accompanied by their sepal, which may cause problems in terms of tomato durability during transportation. The latest crucial part of the harvesting process is a trained tomato detection system based on a machine learning model. There are many proposed solutions by scholars and researchers to recognize the tomato and discriminate between matured and immature tomatoes [19; 20; 21; 22; 23; 24; 25]. One of the popular methods of tomato detection uses image data obtained by an RGB stereo camera [26; 27; 28; 29]. The most popular detection model is the YOLO model, the main advantage of the YOLO model is that it can work with low computation devices, such as microprocessor-based boards, such as Rasberry Pi [30]. This research presents a novel tomato harvesting robot arm based on continuum robot structure and the design of a new grasping tool with a passive stem cutting mechanism. Moreover, this research provides a machine learning model for tomato recognition and a control algorithm for the tomato harvesting process. In this research, the proposed robotic arm is named TakoBot [31; 32] (Tako in Japanese means octopus, Bot comes from the robot). This paper is organized in the following order: design concept, kinematic/kinetic formulation, development of the recognition system, and experimental results followed by some conclusive remarks.