esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromechanical Behavior of Afyonkarahisar Clay Under Varying Stress and Moisture Conditions
## 3 Test Results and Discussions
### Stress-Strain and Conductivity Through Uniaxial Compression Test  
The strain-stress data were obtained through uniaxial compression tests, where an increasing axial load was applied to the clay sample until failure. The corresponding strain was recorded using a displacement sensor, while stress values were derived from the applied force divided by the initial cross-sectional area of the sample.  
Simultaneously, electrical conductivity was measured using electrodes, which recorded resistivity changes in real time as the sample was compressed. The graph shows that both curves exhibit a similar trend, indicating a correlation between mechanical deformation and electrical conductivity changes. In the initial loading phase, both stress and conductivity increase gradually with strain. As strain progresses, the stress-strain curve exhibits a nonlinear increase, while the conductivity-strain curve follows a comparable pattern. This suggests that changes in microstructure (such as pore compression and particle rearrangement) influence both mechanical and electrical properties.  
The slopes of both curves are notably close, implying a strong relationship between stress development and conductivity variation in clay with moderate water content. These findings suggest that electrical conductivity measurements could serve as an indirect method for monitoring strain-induced changes in clay under compression.  
Figure 3: Electrical Resistivity and Conductivity vs. Time in different water contents.  
Figure 4 illustrates the Stress-Strain and Conductivity-Time responses obtained from uniaxial compression and electrical resistance tests conducted at different water contents. These measurements provide insight into how clay with varying water contents responds to mechanical loading and how its electrical resistance evolves during the compression process.  
When the graphs in Figure 4 are analyzed overall, it can be concluded that the stress-strain graphs and the time-dependent electrical conductivity graphs exhibit similar behavior across all three water content levels. For instance, in clay samples with 10% water content, electrical conductivity increases as stress rises and decreases as stress reduces.  
In Figure 5, strain-stress and strain-conductivity relationships are shown for clay samples with 10-20-30% water content. In addition, since the experiments were conducted in the same time interval, time-stress and time-conductivity relationships are also shown. When Figure 5 is examined, it is concluded that the strain-stress and strain-conductivity graphs are similar to each other. In addition, when the time-stress and time-conductivity relationships are examined, it is seen that similar curves are obtained.  
Figure 4: Stress–Strain and Conductivity–Time figures obtained from the uniaxial compression test and electrical resistance test at different water contents.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.24 Track Control System (TCS)  
The Track Control System steers the ship towards a waypoint or a sequence of waypoints. The Track Control System may also have a heading control mode; either way, the performance standards for heading control systems in the SOLAS should be met [81].
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Datasets
#### 2.2.2 Auxiliary Data  
Auxiliary data used in this study included the Google image, the Global Forest Change (GFC) land cover [18], the JRC annual water surface data [19], the ESA WorldCover 2020 land cover [20], and the global ESRI 2020 land cover [21], all of which are available on GEE. Furthermore, the Global land cover product with a fine classification system at 30 m (GLC_FCS30) in 2020 [43] and China Land Cover Dataset (CLCD) in 2020 [44] were used. Auxiliary data were used to select the training and validation samples for the pre-classification and compared with the classification result of our method in the study area.
## 3 Results and Discussion
### Correlation between GC-MS and Lipid  
Lipids were one of essential flavor precursors and taste contributors that affect meat flavor and palatability. Proper lipid oxidation is a major cause for changes of flavor during storage and processing [42]. The volatile compounds are mainly produced by lipid oxidation [21]. The TBARS' value is an index of secondary lipid oxidation, the POV value is an index of primary oxidation. Correlation analysis was used to explore the impacts of volatile flavor compounds of the Zhenba bacon during different stages and the lipid oxidation (Figure 5). A positive correlation was observed between lipid oxidation and some VOCs. Alcohols include ethanol, phenethyl alcohol, 3-octenol, 2-methyl-1-propanol, 3-methyl-1-butanol, 1-hexanol, and 1-pentanol. Aldehydes include pentanal, hexanal, 1-nonanal, 5-methyl furfural, and acetal. Esters include ethyl acetate, ethyl valerate, ethyl caproate, methyl hexanoate, and ethyl lactate.
## 4 Conclusions  
In this paper, a type of Cyber-physical Systems (CPS), i.e., an AHS in the mining environment, was discussed in terms of cybersecurity, reliable communication, and safety. AHS-based trucks have been shown to be very useful in the mining industry. However, the safety of using such trucks has not been thoroughly investigated. The literature was then surveyed to identify and discuss the relation between safety of AHS in the mining environment and both cybersecurity and communication. The relation between cybersecurity and safety in AHS systems was discussed and it was found that compromising cybersecurity would threaten the safety of mining operations leading to loses in people and equipment. In addition, we have shown that the reliability of wireless communication is mandatory for the safety of AHS operations. Through this survey, there are several open issues and challenges that may entail further studies and investigations. It was discussed that new technologies such 5G, pLTE and DSRC-WAVE could be good alternatives for the currently used IEEE 802.11. Although these communication technologies would outperform current technologies in terms of bandwidth and latency, they would be exposed to traditional or new cybersecurity threats or attacks, which still need to be considered while designing AHS systems and studied in the mining environment.  
Conceptualization, T.G. and Y.E.J.; methodology, T.G.; E.E.; and A.A.; validation T.G.; analysis, T.G.; E.E.; and A.A.; investigation, T.G. and Y.E.J.; E.E.; and A.A.; resources, T.G. and Y.E.J.; writing--original draft preparation, T.G. and Y.E.J.; writing--review and editing, T.G.; E.E.; and A.A.; supervision, T.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We would like to acknowledge with much appreciation Prince Sattam Bin Abdulaziz University for its ongoing support to our research.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CPS & Cyber-physical Systems \\ AHS & Autonomous Haulage System \\ OT & Operating Technology \\ ATs & Autonomous Trucks \\ EMV & Equipped Manual Vehicle \\ ICS & Industrial Control Systems \\ AHT & Autonomous Haulage Trucks \\ RoT & Root of Trust \\ IT & Information Technology \\ AI & Artificial Intelligence \\ H2S & Hydrogen Sulfide \\ GPS & Global Positioning System \\ WNs & Wireless Networks \\ WSNs & Wireless Sensor Networks \\ LTE & Long-Term Evolution \\ DSRC & Dedicated Short Range Communications \\ WAVE & Wireless Access for the Vehicular Environment \\ pLTE & private LTE \\ QoS & Quality of Service \\ \end{tabular}
# Calculation Method for Electricity Price and Rebate Level in Demand Response Programs
## 3 Calculation Methodology of Electricity Prices and Rebate Levels  
The power suppliers bring the electricity consumption closer to the target value, which is preferable in the power supply-demand management by changing the electricity price or rewarding the money rebate to the consumers. The target electricity consumption in DRs is defined as:  
\[d_{t}^{\prime}=d_{t}^{*}+\Delta d_{t}, \tag{10}\]  
where \(d_{t}^{*}\) is the standard electricity consumption, which is the actual consumption without DRs; \(\Delta d_{t}\) is the change in electricity consumption by the DR request.  
In the actual power grids' operations, \(d_{t}^{*}\) is replaced with the estimated value, because we cannot know it. This replacement brings an uncertainty to Equation (10), and therefore, can lead to new challenges in the design of DRs. Detailed discussion on the issues remains as a future work of this study.  
Under the following assumption, the authors set the acceptable conditions of electricity prices and rebate levels and define their optimal values.  
**Assumption 1**.: _Consumers buy electricity to maximize their utility._This assumption activates Equation (11), which is derived by Equations (8) and (9):  
\[p_{t}^{*}=\left.\frac{\partial F_{t}}{\partial d_{t}}\right|_{d_{t}=d_{t}^{*}}, \tag{11}\]  
where \(p_{t}^{*}\) is the standard electricity price, which is the actual price without DRs.
## 3 Structure Design
### Leakage Prevention Design  
When the level is not in use, it will be put in a carrying case. Hence, the container needs to be sealed to prevent liquid leakage. However, if we use the optical window to seal the container on the top, the liquid will inevitably remain on the optical window after the sensor returns from the rest  
Figure 3: The influence of the surface adhesion effect. (**a**) Too small container size; (**b**) too shallow liquid depth.  
Figure 2: Structure design of the refractive precision level sensor: (1) micro autocollimator unit, (2) reference laser beam, (3) container, (4) transparent liquid, (5) optical window, (6) laser diode.  
pose, especially for silicone oils with a higher viscosity. This is the natural phenomenon of surface wettability. That will significantly reduce the measurement accuracy and the stability of the precision level. In order to avoid this problem, the container has been specially designed to ensure that the liquid will not leak out in any posture without using an optical window to completely seal the container [20]. A tube with a certain length is mounted on the top of the container. The hollow tube allows the reference laser to pass through. When the level is tilted, the tube wall can prevent the liquid in the container from leaking out, as shown in Figure 4.  
Considering the influence of the above factors on the structure design, the final designed container size is \(30\times 30\times 25\) mm\({}^{3}\), as shown in Figure 4, and the size of the prototype sensor is \(60\times 37\times 55\) mm\({}^{3}\).
## 4 Sensing-Oriented Issues and Solutions
### Sensing Variables and Measurement Uncertainties
Even without recalibrating demodulated backscatter from a spectrum analyzer, AID would have the uncertainty of an order of 1 dB, which has a 1-dB improvement compared with traditional RCS measurement [140]. AID can be measured to within 0.5 dB of absolute uncertainty with calibrated modulation power measurements [17]. This propagates to about the same uncertainty in estimates of the minimum bound for backscattered power. Therefore, compared with \(\mathrm{P_{in}^{to}}\), RSSI, and \(\Delta\)_RCS_, AID is preferred among power metrics in terms of repeatability [136].
## 5 Theoretical Analysis
### Query Efficiency Analysis  
This scheme provides a hybrid query mechanism. When a node initiates a transaction data query request, the best case scenario is that the data requested by the node were requested a short time ago and are cached in the master node cache database. In such a case, the node only needs to query the neighboring master node cache database to obtain the requested data, and the whole query process requires only one or two hops. The worst case is when a node initiates a query request, the master node adjacent to the node is in a fault state and neither the cached data nor the routing information can be provided. The system can only use the flood query algorithm to query data on the nodes in the network, whereby the query process can reach up to six hops according to the six-degree separation theory. The specifics of query efficiency are described in the reports of the simulation experiments, in Section 6.
## 7 Experimental Evaluation
### Benchmarks
#### 7.1.3 V-DPAP and R-DPAP Generation  
We describe here how the function \(\phi\) is implemented in the case of the orbit slot problem for generating V-DPAP and R-DPAP instances.  
* For generating the set of conflicts \(\mathcal{C}\) associated with a V-DPAP, we define a conflict for each pair of nodes corresponding to orbit slots that: 1. belong to the same satellite; 2. temporally overlap; and 3. are from different users. For the last assumption, we consider that it is possible to allocate to some user two orbit slots from the same satellite that overlap. In fact, as the allocation of an orbit slot consists in allowing an agent to dispose of the satellite during the associated time interval, two overlapping orbit slots \(o_{1}\) and \(o_{2}\) can be seen as a unique orbit slot that is the union of \(o_{1}\) and \(o_{2}\). With this conflict generation scheme, all the conflicts obtained are binary. Note that it would be possible to compute these conflicts more finely, for instance, by following the approach proposed in [26].
* In the case of the \(\phi\) function in R-DPAP, we follow the same process as in Example 4. More precisely, we create a resource \(\rho_{s}\) for each satellite \(s\) of the constellation. Then, for each graph \(g_{r}\) associated with an observation request \(r\), for each vertex \(v\) in \(V_{g_{r}}\) that corresponds to an orbit slot \(o\) (i.e., all vertices except source, sink, and the ones added between successive RTs), we define \(ws_{g_{r}}(v)=start_{o}\), \(we_{g_{r}}(v)=end_{o}\) (i.e., the temporal window associated with vertex \(v\) is exactly the temporal window associated with orbit slot \(o\)), \(d_{g_{r}}(v)=minD\) (i.e., the duration associated with \(v\) is the minimum duration required) and \(c_{g_{r}}(v)=\rho_{s}\) where \(s\) is the satellite associated with orbit slot \(o\). For each vertex \(v\) that is a virtual node, we consider that \(ws_{g_{r}}(v)=we_{g_{r}}(v)=0\), \(d_{g_{r}}(v)=0\), and \(c_{g_{r}}(v)=r_{\emptyset}\). Note that R-DPAPs are next transformed into V-DPAPs as explained in Section 5.3, and in this case the conflicts obtained are not necessarily binary ones.
## 4 Experimental Results
### Multi-Person Expression Recognition  
Since there is no formal dataset available for multi-person expression recognition, we utilized randomly collected multi-person images from the internet as test data. The results of the actual experiments are presented in Figure 12. The system detects facial regions and recognizes the emotions displayed on their faces, which are displayed above the bounding boxes. In this experiment, 10 images were selected as the test dataset, containing a total of 102 people. Among these, our system detected 75 people with an accuracy of 73.53%. It correctly identified the emotions of 57 people with an accuracy of 55.88%.
## 5 Experimental Results and Analysis
### Ablation Experiment  
This paper conducts ablation experiments by individually removing the SFA, SPPF, and C2 PSA modules to evaluate their respective effectiveness.  
After removing the SFA module, the FPS of SFA-YOLO drops to 130. It is a significant decrease. Meanwhile, precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 all showed a decline. This indicates that the SFA module effectively enhances the model's feature extraction capability and contributes to detection speed.  
Removing the SPPF module reduces the FPS of SFA-YOLO to 145. The difference from the original FPS is not significant. The recall rate slightly increased. Precision and [EMAIL_ADDRESS] decreased. [EMAIL_ADDRESS]:0.95 remained almost unchanged. This indicates that SPPF affects the metrics but is not the main contributing factor. It can enhance recall while leading to a reduction in FPS.  
Removing the C2 PSA module increased the FPS of SFA-YOLO to 154, with only a small difference from the original FPS. Precision slightly improves, while recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 remain largely unchanged. This indicates that C2 PSA primarily enhances precision and FPS but is still not the main influencing factor.  
Overall, all three core modules have an impact on the performance of SFA-YOLO. However, its performance is primarily determined by the SFA module. SPPF and C2 PSA help SFA-YOLO balance FPS, precision, and recall, preventing significant degradation of the metrics. The impact of different modules on performance is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **FPS** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline Without SFA & 130 & 85.8\% & 77.1\% & 84.2\% & 45.3\% \\ Without SPPF & 145 & 88.4\% & 84.2\% & 89.1\% & 50.4\% \\ Without C2 PSA & 154 & 89.7\% & 81.2\% & 88.4\% & 49.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of different modules.
## Materials and Methods  
A new GD&T-based test artifact is presented in this section. The model is designed for interlaboratory comparisons of CMMs. Figure 2 provides a visual representation and a description of the proposed artifact, as well as its sub-elements. To ensure the measurement of different shapes and geometrical tolerances, the artifact included basic geometric features (primitives), such as rectangular, planar, cylindrical, and conical surfaces; bore and hole patterns; and free-form surfaces. As shown in Figure 2 and Table 1, a total of ten different features (items) were selected in the artifact, and five main categories related to GD&T were proposed to be characterized and controlled based on ASME Y14.5 (2009) [1]:  
* The size tolerances on slab features and cylinder bore/hole diameters. * The form tolerances, which control the shape of surfaces, such as the flatness of datum plane A, and the cylindricity of cylinder bores. * The orientation tolerances, which control the tilt of the surfaces and axes for size and non-size features, such as the perpendicularity of datum plane B related to datum plane A.  
Figure 2: Description of the proposed geometric dimensioning and tolerancing (GD&T)-based artifact. The overall dimensions of the artifacts were 138 \(\times\) 90 \(\times\) 50 mm. They were conveniently transportable and could fit into small CMM metrology systems. The artifacts were made from aluminum: Part #1, with no intentional defects, and parts #2 to #5, with some predefined and intentional geometrical imperfections. The geometrical imperfections were performed in accordance with the procedure described in Table 2.  
Table 1 presents the predefined defects, which were considered as the reference values (nominal defects). Their respective amplitudes were approximately in the same order of magnitude of tolerance. The final real geometry of the part 'as manufactured' was unknown and the actual values were calculated from the measurement points. The artifact parts were manufactured on three-axis CNN milling machines at the Ecole de technologie superieure's Products, Processes, and Systems Engineering Laboratory (P\({}^{2}\)SEL). Figure 3 presents one of the five manufactured artifacts. A total of 15 fixed and portable CMMs from different and independent industrial and academic collaborators in North America (Canada and the USA) were included in this investigation, and are presented in Figure 4. The CMMs used were named according to ISO 10,360 [40]. The accuracy of the CMMs used (equipment variation) in this study typically ranged between 0.7 and 45 \(\upmu\)m (\(\pm\)2 \(\sigma\) level). All the induced defects for artifacts #2 to #5 were significantly higher than the aforementioned accuracies (Table 1).
## 3 Energy Management Strategies for HEVs
### Optimization-Based Control Strategy  
The central goal of an optimization-based control strategy is to minimize a cost function by precisely tuning various parameters to achieve a specific control effect. When it comes to HEVs, the cost function is not defined by a single criterion, but rather is determined comprehensively based on a set of constraints and optimization objectives [113], including factors that are closely related to the application, such as emission levels, fuel consumption rate, and torque output [68]. This diversified evaluation system ensures that the control strategy is able to consider various key factors affecting HEV performance in a comprehensive and balanced manner. The global optimal solution, as a desired control state, is usually obtained by executing exhaustive optimization algorithms under specific, fixed design conditions. However, although this global optimization method can provide a theoretically optimal solution, it does not directly involve real-time energy management and thus may have some limitations in practical applications. To make up for these deficiencies, instantaneous cost function-based real-time control solutions have been created. In order to accomplish more precise and timely energy management, this technique can  
Figure 6: Schematic diagram of a typical fuzzy logic control strategy [113].  
modify the control parameters in real time based on the system variables at the moment, such as vehicle speed, battery status, and load condition. This real-time capability enhances energy efficiency and allows HEVs to maintain stable performance under diverse and complex conditions. Based on the distinctive characteristics and application scenarios of optimization strategies, researchers can broadly categorize them into two primary types: global optimization and real-time optimization. Global optimization methods predominantly utilize static data tables or historical datasets to achieve the global optimum in energy usage under specific operating conditions. Although this method is complicated to calculate, it can provide a more accurate global optimal solution and provide theoretical support for the energy management of HEVs. The real-time optimization method, on the other hand, pays more attention to online control based on the real-time state or current parameters of the vehicle to ensure that the local or instantaneous optimal energy management effect can be achieved at any moment. This method is less computationally intensive, but it can respond to changes in vehicle state in real time and improve the adaptability and flexibility of HEVs.
## 4 Kinematic Model
### Workspace  
Given that the joints in the current design are limited to 50 degrees of rotation per side, we analyzed the serial arm's work volume in 3D space. The work volume was determined through an exhaustive search of the total area for possible solutions using the motion planning algorithm presented in Section 5.  
Figure 9B illustrates the arm's work volume when the gripper can be positioned anywhere on the arm (AL). Notably, when the gripper is located only at the edge (LL), the work volume is included within the volume of AL but is significantly smaller.  
To make the work volume easier to understand, we first illustrate the work plane (before the rotation of the first joint) in Figure 9A. This plane rotates around the \(x\)-axis using the first joint (\(\theta_{0}\)), thus creating a three-dimensional workspace as described in the kinematics equations.