esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Discussion
### Polycrystalline Silicon Solar Panel
#### 4.2.2 View Angle  
To study the effect of view angle on electroluminescence polarization imaging, Figure 11 shows DOP images at different view angles under 12 V bias voltage, and the curve of DOP value with the view angle is shown in Figure 12.  
Figure 10: Diagram of polycrystalline silicon photovoltaic panel (**a**) electroluminescence intensity of the photovoltaic panel with the forward bias voltage; (**b**) degree of polarization of the photovoltaic panel with the forward bias voltage.  
Figure 11: DOP images at different view angles under 12 V external positive bias voltage.  
Figure 12: Curve of DOP value with view angle.  
parallel components is very small, depending on the anisotropy of the crystal. With the increase of the view angle, the degree of polarization increases significantly. In other words, the polarization characteristics of the outgoing light not only depend on those of the light incident to the interface after generation of the inside panel, but also on the change of the characteristics caused by interface refraction and multi-directional scattering; this shows a similar characteristic to the infrared spontaneous radiation from the hot metal plate [7].
## 3 Results and Analysis
### Ablation Study on Framework Components  
The ablation studies systematically demonstrated the necessity of OMRoadNet's core components through quantitative and qualitative evaluations. As shown in Table 2, removing the exponential moving average unit (EMAU) attention module caused the most significant performance degradation, with the F1-score plummeting by 11.02% (from 80.77% to 69.75%). After disabling EMAU, yellow-green artifacts frequently appeared in the edge regions of the image, indicating that the model exhibited instability in semantic understanding, approaching the threshold of \"model collapse\". The model's ability to perceive road boundaries and topological structures was significantly weakened, manifesting as blurred contours and structural discontinuities. This structural degradation aligned with the 11.02% decrease in the F1-score observed in quantitative evaluations. This demonstrated that EMAU plays a crucial role in stabilizing the extraction of complex mining area shapes. This regression manifested in the visual results (Figure 6) as blurred road  
Figure 5: The open-pit mine road extraction results in comparative experiments.  
boundaries and incomplete branch structures, particularly in high-curvature sections where the model failed to propagate road context without temporal feature accumulation.  
Disabling the unsupervised domain adaptation (UDA) pre-training strategy reduced recall by 12.58% (from 71.89% to 59.31%), indicating UDA's critical role in aligning urban-to-mining feature distributions. The model trained without UDA frequently misclassified exposed ore bodies with spectral similarity to asphalt roads, generating 34.7% more false positives in iron-rich regions. The self-training strategy contributed substantially to precision optimization, evidenced by a 5.05% precision drop when omitted (from 92.16% to 87.11%). Comparative visualizations in Figure 6 revealed that static pseudo-labels derived from initial DeepGlobe masks produced overly narrow road estimates (average width error: \(\pm\)2.8 m) compared to dynamically refined annotations.  
While the TTUR learning rate strategy exhibited smaller quantitative impacts (F1-score reduction of 4.32%), its absence degraded model stability, increasing training loss variance by 38.6%. The cycle-consistency loss (\(\mathcal{L}_{\text{cyc}}\)) proved essential for maintaining geometric coherence, as models lacking this component showed 23.6% higher topological errors in complex junctions. These findings collectively validated the synergistic integration of attention mechanisms, adaptive domain alignment, and iterative label refinement in OMRoadNet's architecture.
###### Abstract  
Three broad spectrum Ag(I) complexes against MDR (multi drug resistance) and ATCC standard bacteria as well as the fungus C. albicans were presented. The three well-known structurally-related Ag(I) complexes, [Ag(pyridine-3-carboxaldhyde)2 NO3], **1**, [Ag3(2-pyridone)3(NO3)3]In, **2**, and [Ag(3-hydroxypyridine)2]NO3, **3**, were prepared by the direct combination of AgNO3 with the corresponding pyridine ligands in a water-ethanol mixture. **1** and **3** are molecular compounds while, **2** is a 2D coordination polymer with sheets bridged by strong homoleptic R2,2(8) hydrogen bonds between ligands giving the **ins** topology. Different contacts affecting the molecular packing in their crystal structures were computed by employing Hirshfeld analysis. Charge transferences from the ligand groups to Ag(I) were analyzed using natural population analysis. The effect of protonation and metal coordination on the tautomerism of 2-pyridone was analyzed using data from the Cambridge Structure Database (CSD). It was found that Lewis acid attachment to both N and O sites favor a state in between the two formal tautomers. All compounds were significantly more active than 17 tested commercial antibiotics against three clinically isolated strains of _Ps. Aeruginosa_, with **2** and **3** performing best on average against all ten tested bacterial strains but with **3** containing less Ag per weight. Finally, docking studies were carried out to unravel the inhibition mechanism of the synthesized silver(I) complexes.  
Ag(I)-pyridine complexes; Hirshfeld; ins topology; antimicrobial activity; tautomerism
# Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
## 6 Conclusions  
This paper proposes a fault detection method for rolling bearing weak faults based on an optimized LWD and the FSS algorithm. This method uses the Laplace wavelet to construct a sparse dictionary, and obtains the resonance frequency from the signal by AEWT based on the scale space method and combined component screening, then determines the morphological parameters of the Laplace wavelet. The signal-to-noise ratio is then improved, and the sparse signal is reconstructed. Simulation and experiment show that the envelope analysis of the reconstructed sparse signals obtained by this method can successfully extract and highlight the weak fault characteristic frequencies of bearings under strong background noise interference to allow the diagnosis of rolling bearing faults. At the same time, the proposed LWDFSS-AEWT algorithm is compared with the SISC algorithm based on a learning dictionary, the BPDN algorithm based on the Symlet8 wavelet packet and the WPT Kurtogram algorithm. The results of the comparison show the superiority of the LWDFSS-AEWT algorithm. As the algorithm utilizes the characteristics of the signal for feature extraction, it can be predicted that the method can be extended to the fault diagnosis of other rotating machinery, such as gears.  
Figure 21: Reconstructed signal and its envelope spectrum obtained by BPDN algorithm based on the Symlet8 wavelet packet.  
Figure 22: The reconstructed signal and its envelope spectrum obtained based on the WPT Kurtogram algorithm.  
**Author Contributions:** Conceptualization, T.L. and X.W.; methodology, Q.C. and S.Z.; validation, Q.C., S.Z. and T.L.; data curation, Q.C. and S.Z.; writing--original draft preparation, Q.C. and S.Z.; writing--review and editing, Q.C.; visualization, Q.C. and S.Z.; supervision, X.W.; project administration, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** The study did not involve humans.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data is from the bearing life test data of the IMS Center of the University of Cincinnati, and can be found here: [[[http://ti.arc.nasa.gov/project/prognostic-data-repository](http://ti.arc.nasa.gov/project/prognostic-data-repository)]([http://ti.arc.nasa.gov/project/prognostic-data-repository](http://ti.arc.nasa.gov/project/prognostic-data-repository)), (accessed on 15 August 2022)].  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China under Grants 52065030 and 51875272, and in part by the Key Research and Development Plan of Yunnan Province under grant 202102 AC080002.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Main Effects Plot for T  
Data Means  
Figure 14: Cutting zone temperatures (T\({}_{\text{max}}\)) recorded using thermal camera.  
Figure 15: Impact of input variables on cutting temperature.  
Figure 16 depicts the manifestation of the interaction effect between various cutting factors. The interaction effect between terms ap-s, ap-q, and s-q were pronounced due to the non-parallel nature of their graphs. As stated by [PERSON] et al. [35], the presence of non-parallel graphs indicates the presence of interaction among the control factors, whereas parallel lines do not exhibit such interaction; for example, while other pairs of terms such as v-q showed less interaction for the cutting temperature, ap-v and s-v exhibited negligible or insignificant interactions for the cutting temperature due to their parallel graphs. From the ANOVA (Table 5), it can be noticed that speed is the most influential factor, which affects the cutting temperature mostly with a contribution rate of 57.25%. After speed, the flow rate of MQL affects cutting zone temperature effectively with a contribution of 19.22%, followed by a depth of cut and feed with 16.73% and 6.40% contributions, respectively.
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 4 Experimental Setup and Scenario-Based Simulation
### Experiment Scenarios  
The first scenario is to submit a workload (Table 2, Rows 1-4, and Rows 5-8) for ten instances handled by the VM every 15 s. The calculated decision rule was the first and second case, where \"Definitely No Violation\" and \"Natural\" were the QoS criteria and no scalability algorithms were triggered. The experiment is replicated 20 times, with random workload submissions reaching 200.  
The second scenario is to submit a workload (Table 1, Rows 9-12) for ten occurrences, processed by the VM every 15 s. The self-checking and healing may be triggered during the simulation runtime due to \"probably violation\" of QoS. In the second scenario, the preventive USL based resource allocation algorithm will assign the task of rectifying cloud QoS violation by applying self-checking and healing techniques through task re-submission for handling faulty VM task calculation.  
The third scenario is to apply a workload (Table 1, Rows 13-16) for ten occurrences handled by the VM every 15 s. For the third case, the role of prevention is assigned for handling workload resulting in QoS \"Definitely Violated\", due to the under-provisioning. The experiment is replicated 20 times with a minimum of 500 random workload instances resulting in the \"Definitely Violation\" decision rule.  
Figure 4: Response time, Throughput to Concurrency.
## 3 Simulation  
### Validation of Assembly Error Propagation Model  
In Section 2.1, an assembly error propagation model of a multi-stage rotor was developed to predict the changes of coordinate values of any point in each rotor before and after assembly on the premise that the geometric parameters of rotors at different stages are known. To verify the calculated accuracy of the assembly error propagation model, the geometric parameters of a three-stage rotor presented in [17] (see Table 1) are used for reference for a simulation. In addition, the angle between the calibrated screw holes and the highest points is \(0^{\circ}\) by default (i.e., \(\varphi_{1}=\varphi_{2}=\varphi_{3}=0^{\circ}\)). The assembly orientations of the first rotor, second rotor, and third rotor were set to \(0^{\circ}\), \(30^{\circ}\), and \(60^{\circ}\) respectively (i.e., \(\theta_{21}=0^{\circ}\); \(\theta_{22}=30^{\circ}\); and \(\theta_{23}=60^{\circ}\)).  
The new coordinates of the center of the top mounting surface of the second and the third stage rotors after assembly were calculated using the model. SolidWorks, a 3-D software for drawing, was utilized to reconstruct the corresponding three-stage rotor model.  
Through a measurement function of the software, the coordinates of the center of the top mounting surface of the assembled rotors at different stages were measured (see Figure 4). As shown in Table 2, the calculated results achieved by the model are in good agreement with those measured by the software.
## 5 Qualitative Analysis
### Design and Implementation of Digital Twin Software Solutions for Collaborative Robots
#### 5.2.1 Communication and Synchronization Mechanisms  
Communication and synchronization mechanisms are essential components in the design and implementation of digital twin software solutions for collaborative robots [81]. These mechanisms facilitate seamless data exchange and real-time coordination between the digital twin and the physical robot, enabling effective automation and optimization.  
In cobot applications, communication between the digital twin and the physical robot involves the exchange of sensor data, control signals, and feedback information [83]. This communication allows the digital twin to continuously monitor the physical robot's state and behavior while also providing instructions and guidance based on the digital twin's analysis and simulation.  
Various protocols and interfaces are utilized to establish effective communication. These protocols ensure standardized data transmission and enable interoperability between different software and hardware components. Examples of commonly used protocols in cobot applications include OPC UA (Unified Architecture), MQTT (Message Queuing Telemetry Transport), and ROS (Robot Operating System) [84].  
The synchronization process involves updating the digital twin with real-time data received from the physical robot and feeding back the analysis and simulation results to the physical system. This two-way flow of information enables the digital twin to provide insights, recommendations, and instructions to the physical robot, contributing to improved decision-making and adaptive control.  
Effective communication and synchronization mechanisms require careful consideration of factors such as latency, reliability, and bandwidth [85; 86; 87; 88; 89]. Low-latency communication is crucial for real-time monitoring and control, ensuring that the digital twin responds promptly to changes in the physical robot's state [90]. Reliable communication ensures that data transmission is accurate and error-free, avoiding any discrepancies between the digital twin and the physical system [91]. Sufficient bandwidth is necessary to handle the exchange of large amounts of data generated by sensors and actuators in the collaborative robotic system [92].
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 3 Algorithm to Detect Vacant Parking Spaces
### Parking Space Detection Stage
#### 3.2.3 Lane Boundary Line Recognition  
Although we could simply connect classified corners to form the lane boundary line, the final set of classified corners inevitably includes outliers. Therefore, we chose to use the random sample consensus (RANSAC) algorithm [25], which is robust at detecting outliers, to identify the lane boundary line. The RANSAC algorithm is shown in Figure 9a. Because the most time-consuming portion of the algorithm is the K reiterations of the entire process, we added a criterion to accelerate the entire algorithm (the yellow rhombus in Figure 9a). The criterion is that the calculated line model must be oriented within \(\theta^{\circ}\) of the direction of travel. If a calculated line model does not meet this criterion,  
Figure 8: Results of feature classification.  
Figure 7: Maximum vertical intensity profile in the \(x\)-axis.  
testing of that particular model is skipped. This not only increases calculation speed but also decreases errors in detection. Figure 9b shows the results of the lane boundary line recognition; the red line indicates the lane boundary line.
# Stratosphere: A Fast Virtual Circuit Switch Layer for Data Center Network
## 4 Prototype of VCS
### Multi-Mode Forwarding  
VCS modules could support a variety of switching modes:  
(a) virtual circuit vs. virtual channel. Because the VCS modules cut Ethernet frames into cells, which are transmitted on the time slot of the virtual circuit. When VCS modules do not synchronize with each other, VCS descends and takes the storage-forwarding mode to realize the switching based on virtual circuit identification inserted in the preamble of cells, which is similar to ATM.  
Figure 10: Implementation of VCS prototype.  
In a stratosphere network, which consisted of several numbers of Tors in the same row or in the same aggregated switch, VCS modules could synchronize and provide the synchronized transfer model. The channel and path could be configured proactively by the controller. Cells need not be parsed and are transmitted in the cut-through model at specific time slots directly, so the latency of the stratosphere is smaller and more determined than ATM.  
(b) path vs. ID. The VCS module also supports the switching based on cell ID. When the bridge port of the source VCS module splits packets, the IP address of the end system can be mapped to the identity of cells, which is encapsulated into the cell header, so packets are forwarded flexibly based on the cell ID in the switching module.  
In the mode, the stratosphere network is full adaptive routing, in which cells are cached and matched against the forwarding table to select the output port. VCS modules need not be synchronized, and paths or channels need not be configured ahead. Each cell has a fixed size of cell header and payload, which results in lower utilization than packet switching, while the latency of processing is fixed and small.  
For the mode of virtual channel switching and cell switching, the latency of the stratospheric is smaller than Ethernet switching. VCS is suited for critical communication in the data center, such as parameter server or barrier, which is a supplement to Ethernet switching.  
(c) unicast vs. multicast. VCS supports multicast forwarding, in which cells from one channel are forwarded to multiple channels, and a multicast group is built in the stratosphere to accelerate a large number of synchronous operations in high-performance computing. Compared with circuit switching, such as Larry, VCS could also support indirect switching between Tor switches of adjacent racks and convergent switches. For the topology of a chord, there are more than two paths of different hop and latency between the source and destination of Tor switches. Packets of applications are hashed among the multiple virtual links for load balancing.
# Nonlinear Adaptive Robust Control of the Electro-Hydraulic Servo System
## 4 Simulation and Discussion
### Simulation Results
#### 4.2.3 Case 3: Parameter Variations and Load Disturbance Condition  
In order to compare the performance of the three kinds of controllers under different conditions, the maximum tracking error \(M\), the average tracking errors \(\mu\) and the tracking lag \(\phi\) are statistically analyzed, and results are shown in Table 1. It can be seen from the results that the PID controller can not deal with highly nonlinear parameters and disturbance, and the system control response is so slow that it could not track the reference signal accurately. The maximum tracking error is about 4.8 mm, and the average tracking error is about 3.2 mm. The tracking lag was also apparent than the other controller in all conditions. In addition, the dynamic performance of DRC and NARC are similar under nominal and sudden load conditions because of the same control structure. However, in the variation and load condition, the average tracking error of NARC is 0.126 mm, which is much better than the other controller with the help of the adaptive laws.  
Figure 8: The parameter estimate of the NARC controller.  
Based on these simulation results, it can be concluded that the NARC can improve the system dynamic performance and can reject external load disturbance effectively.
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 2 Preliminaries
### Weighted Average Approach for Evidence Combination  
In the D-S evidence theory, [PERSON]'s combination rule is used to fuse certain multi-source information to make a decision. However, if there is a high level of conflict between evidence, the combination rule will fail [67]. Many methods were proposed to solve this problem [66; 68; 69; 70]. Among them, [PERSON] [71] proposed a simple average approach to solve the problem. However, in [PERSON]'s approach, all bodies of evidence are equally important. Hence, a weighted average approach [56] was proposed to improve [PERSON]'s approach. The weighted average approach is given as follows.  
Suppose the distance between two BPAs \(m_{i}\) and \(m_{j}\) is calculated and denoted as \(d_{BPA}\left(m_{i},m_{j}\right)\). The similarity measure \(Sim_{ij}\) between \(m_{i}\) and \(m_{j}\) is defined as:  
\[Sim\left(m_{i},m_{j}\right)=1-d_{BPA}\left(m_{i},m_{j}\right). \tag{9}\]  
If \(n\) BPAs need to be combined, the support degree of BPA \(m_{i}\) is defined as:  
\[Sup\left(m_{i}\right)=\sum\limits_{\begin{array}{c}j=1,\\ j\
eq i.\end{array}}^{n}Sim\left(m_{i},m_{j}\right), \tag{10}\]  
Then, the credibility degree \(Crd_{i}\) of the BPA \(m_{i}\) is calculated as follows:  
\[Crd_{i}=\frac{Sup\left(m_{i}\right)}{\sum\limits_{i=1}^{n}Sup\left(m_{i}\right)}. \tag{11}\]  
The credibility degree is actually a weight that shows the relative importance of the collected evidence. According to \(Crd_{i}\), the weighted average of the evidence MAE is obtained:  
\[\bar{m}=MAE\left(m\right)=\sum\limits_{i=1}^{n}\left(Crd_{i}\times m_{i}\right). \tag{12}\]  
Finally, the weighted average evidence \(\bar{m}\) is combined \(n\)-1 times by using [PERSON]'s combination rule:  
\[m_{f}=\underbrace{\bar{m}\oplus\bar{m}\oplus...\oplus\bar{m}}_{n-1\ \ times}. \tag{13}\]  
The obtained \(m_{f}\) is the combination result of the \(n\) BPAs.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 3 Method
### Exploration Phase
#### 3.1.2 Inclusion and Exclusion Criteria  
The main inclusion criteria for this study is to include previous studies of cybersecurity in HE in the context of risk management. The literature published during 2008-2020 was taken into consideration for inclusion in the search criteria. The detailed inclusion criteria for the search are:  
* Academic studies that describe information security risk assessment or management in HE.
* Academic studies of either assets, KPI, threats, vulnerabilities, or risk in HE.
* White papers, technical reports, thesis, or websites dedicated to either topic. For these sources:  
We applied qualitative scrutiny on the latter literature category to focus on high quality sources. Articles are excluded on the following criteria:  
* Literature not written in either English or Norwegian
* Studies that do not focus on risk related topics faced by the academic industry (e.g., papers on cybersecurity education [37; 38], legal issues [21; 22], or issue specific topics in HE [26; 39].
* Studies published in the year 2000 or older.
* We have restricted the inclusion of reports from security vendors to those either containing empirical data sorted on HE or containing expert insights.
* We have not included news reports and articles.