esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Microscopy Module Fabrication and Implementation  
The CAD model of the microscopy module assembly is shown in Figure 1A. The model integrates (1) an AM751 SMT8A brightfield/darkfield or AM7515 MT8A-GFBW fluorescence 5 MP Dino-Lite Edge Series microscope [9] (AnMo Electronics Corporation, New TaipeiCity, Taiwan, ROC, through Dunwell Tech, Torrance, CA, USA) (or can accommodate any digital handheld microscope with a length <10.5 cm), (2) a WF-10 WiFi adaptor for live streaming, and (3) a BL-CDW Brightfield/Darkfield Backlight Illuminator (AnMo Electronics Corporation) that makes a collimated beam, which reduces scattered light. The pictured module is designed to accommodate biological cell and tissue culture samples contained in ibidi \(\upmu\)-Slide \(\mathrm{I}^{0.8}\) Luer slides (ibidi GmbH, Grafelfing, Germany, 80196) or within Thermo Scientific Nunc SlideFlasks (ThermoFisher, Waltham, MA, USA, 170920). The design of the module can be easily modified to accommodate other tissue culture holders within acceptable dimensions.  
All CAD models were designed using either SolidWorks (Dassault Systemes, Waltham, MA, USA, 2018) or DesignSpark Mechanical (RS Components Ltd., Fort Worth, TX, USA, 2019) software. The components were 3D-printed using ABS-M30 black thermoplastic material with soluble supports on a Stratasys Fortus 450 mc housed in the Prototype Development Lab at the NASA Kennedy Space Center. Compared to standard ABS, ABS-M30 is stronger and exhibits better layer bonding for the creation of a more durable part. The thermal properties of the material are also compatible for use in the 37 \({}^{\circ}\)C environment of the CO\({}_{2}\) incubator, as the 99 \({}^{\circ}\)C Vicat softening temperature for ABS-M30 is considerably greater than the temperatures that the module will reach during experimentation within the incubator. The black opaque color of the material was selected to mitigate the effects of light refraction and reflection that may interfere with the microscopy image. Once printed, the components were treated in lye to remove the support material, rinsed with water, dried with compressed air, and air-dried overnight. The final 3D-printed pieces of the module were opaque, black, and rigid. We installed #6-32 steel helical threaded inserts on the attachment points of the microscope clamp and the stage legs. All other attachment points were tapped using a #35 drill bit to accommodate #6-32 screws. A 144 g steel block counterbalance was installed on the module to ensure that the field of view of the microscope was located at both the center of gravity and the center of axis of rotation. The overall weight of the system was 1.1 kg and the dimensions were approximately 14.7 cm \(\times\) 15.9 cm \(\times\) 15 cm. The final printed and assembled microscopy module is shown in Figure 1B. The system was designed with modular components to allow for greater versatility and customization of the platform and to be integrated with other microgravity simulation devices such as 2D and 3D clinostats.
## 2 Materials and Methods
### Rationale
#### 2.1.2 Open Access and \"Tunable\" Data
In particular, the methodology proposed in this paper also encompasses the use of satellite images that can be specifically tasked to be collected over the area of interest, by selecting the appropriate timing for acquisition, and spatial and temporal resolutions to suit the specific use-case and user requirement. For the sake of brevity, we hereinafter refer to these images as \"tunable\" data. This definition applies to the above VHR optical imagery from commercial providers, in case the research team can afford to pay for collection of new data (e.g., a panchromatic WorldView-3 not yet available in the archive catalogue, just to make an example), as well as to SAR missions that offer users with tasking opportunities, instead of predefined routine acquisition plans such as in the case of Sentinel-1. In the present research, this is achieved with COSMO-SkyMed SAR constellation of the Italian Space Agency (ASI) and its StripMap HIMAGE (3 m spatial resolution), and Enhanced Spotlight (1 m) imaging modes that were proved to be the most advantageous for archaeological and cultural heritage applications [72] (see the same reference for accessibility terms and conditions in the case of applied research and purely commercial applications). Such sets of data provide an observation capability that becomes crucial for documenting specific phases of the dam construction and cannot be otherwise achieveddue to either the lack and discontinuity of Google Earth and Bing Maps images, or cloudiness or limited spatial resolution with Sentinel-2 time series. This is also thanks to the agility of the COSMO-SkyMed constellation satellites that can be tasked to collect during the temporal windows of interest regardless of the weather conditions, at different scales and associated spatial and temporal resolutions (see [72] for an overview). These features of COSMO-SkyMed are definitely advantageous compared to Sentinel-1, with which it otherwise shares similarities in terms of difficulty of image processing, data handling and interpretation. Therefore, COSMO-SkyMed data may better serve the purposes of rescue archaeology, depending on the specific scale and scope, as we demonstrate in this paper. The combination of SAR and optical satellite imagery aims to make the best out of their diverse capabilities, and perform observations at different scales, from landscape to small archaeological features [69; 72].
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Applications and Implications  
The robust performance of the machine learning models developed in this study has significant implications for the management of urban water bodies. Their ability to predict the water quality of an urban lake based on environmental and anthropogenic factors provides a critical tool for urban planners, environmental scientists, and policymakers.
# A Numerical Study of the Behavior of Micropile Foundations under Cyclic Thermal Loading
## 3 Results and Discussion
### Mechanical Behavior
#### 3.1.2 Pile-Soil-Pile Interaction under Mechanical Load  
Following the same approach as [34] and [59], amongst others, this study considered group effects through a simple, unit cell approach, adjusting the finite element model radius, R in order to investigate the effect of micropile spacing in mechanical, thermal, and thermo-mechanical pile-soil-pile interactions. Figure 7 illustrates the response of the micropiles under mechanical loading only, but with differing domain radii (R/D = 30 and 4). In this figure, the vertical displacement (y) was normalized by the pile diameter, D, and the axial load (N) by the relevant allowable load, F\({}_{\text{a}}\).  
Figure 6: Comparison of MP and LD pile response under allowable load conditions.  
The results were broadly as expected, i.e., reduced pile spacing resulted in greater mechanical interference between the piles, leading to increased pile vertical settlement, and changes in the load transfer to the ground with more load passing to the pile base. This pile group interaction effect is well documented and has been described by, e.g., [60, 61], among others. Another feature was the increase in mobilized shear stress below about z/L = 0.8, which developed as R/D reduced: this was an additional interference effect which leads to an increase in horizontal stress resulting from load transfer to, and compression of the ground between, the piles in the group, increasing the normal stress acting on the pile shaft.  
As noted in the previous section, when R/D = 30 (isolated pile), the mobilization factor, M was 54% and 50% for MP-15 and MP-30, respectively. As R/D reduced to 4, interaction between the piles altered M by a small amount, reducing to 49% and 48%, respectively.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 3 Methods
### Analyzing Temporal Remotely Sensed Imageries
#### 3.2.2 Detecting Cropping Structure Based on Phenological Metrics  
The time-series NDVI for each cluster center was fitted by the shape model (an asymmetric logistic curve in this study) to identify the cropping structure of crops (Equations (9) and (10)).  
\[f(x)=a+\left(\frac{b}{k}\right)(1+n)^{-\frac{k+1}{k}}n(k+1)^{\frac{k+1}{k}} \tag{9}\]  
\[n=exp\left[\frac{x+dln(k)-c}{d}\right] \tag{10}\]  
where \(f(x)\) is the shape model with five fitting parameters: \(a\), \(b\), \(c\), \(d\), and \(k\) (Figure 4a). The shape model represents the typical temporal VI pattern for a specific crop [33; 35]. \(a\) refers to the base value of the NDVI. \(b\) denotes the difference between the maximal NDVI and \(a\). \(c\) is the DOY when the NDVI reaches the peak. \(d\) and \(k\) control the wideness and the skewness of \(f(x)\). The two inflection points of \(f(x)\) are:  
\[\text{Inflection}_{\text{points}}=c+\text{dln}\Big{(}\text{k}+3\pm\frac{\sqrt {k^{2}+6\text{k}+5}}{2}\Big{)} \tag{11}\]  
where the left inflection point denotes the fastest growth stage and the right inflection point corresponds to the senescence stage. Here, we introduced the growth active period (GAP) calculated as follows to represent the growing season length for each plant.  
\[\text{GAP}=\text{dln}\Big{(}\text{k}+3+\frac{\sqrt{k^{2}+6\text{k}+5}}{2} \Big{)}-\text{dln}\Big{(}\text{k}+3-\frac{\sqrt{k^{2}+6\text{k}+5}}{2}\Big{)} \tag{12}\]  
The optimal fitting parameters were retrieved by fitting \(f(x)\) on the discrete VI points via the least square error method using the \"scipy.optimize\" module in Python. The fitted shape models for the dominant land cover types are shown in Figure 4b. Spring wheat was planted ahead of other crops, and thus, parameter \(c\) was significantly smaller. The forest area shows a longer GAP than the crops, and an infinite GAP was observed for non-vegetative land cover types. However, distinguishing corn and sunflower is challenging due to the similar VI dynamics, which require data with a higher temporal resolution [13]. As a result, this study did not distinguish corn from sunflower but only classified them into one category containing plants with tall stems.  
Figure 4: Schematic diagram of phenological metric extraction by shape-model-fitting method.
# FEM Based Preliminary Design Optimization in Case of Large Power Transformers
## 2 Proposed Methodology
### GP Based Embedded Winding Model  
#### 2.11.1 Eddy Losses in the Windings  
The objective function of this embedded geometric program to minimise the loss of the winding:  
\[P_{loss} =P_{dc}+P_{ax}+P_{rad}, \tag{19}\] \[P_{dc} =\rho\cdot\frac{2\cdot\pi\cdot r_{m}}{A_{Cu}}\cdot l^{2}. \tag{20}\]  
The load loss of the winding consist of the dc loss (\(p_{dc}\)) and the axial and radial components of the eddy losses (\(p_{ax},p_{rad}\)). Where \(\rho\) represents the specific conductivity of the conductor, and \(r_{m}\) represents the mean radius and \(I\) is the phase current of the winding. (\(B_{rad}\)) and (\(B_{ax}\)) are represents the radial and the axial components of the flux density, they are input parameters in this method, their value is calculated by the FEM part of the algorithm.  
\[P_{\text{ax}} =\frac{1}{24\rho}(\omega d^{*}B_{ax})^{2} \tag{21}\] \[P_{\text{rad}} =\frac{1}{24\rho}(\omega h^{*}B_{rad})^{2} \tag{22}\]  
This calculation of eddy current losses in the winding segments assumes that the eddy currents do not modify the magnetic field around the winding segments [20].
# Objectively Scoring the Human-Likeness of Artificial Driver Models
## 5 Results
### Objective Metric Results versus Subjective Human Ratings  
When investigating the results of the survey, two aspects are of interest. First, subjects were able to distinguish between real and artificial behavior, and second, participants' subjective ratings correlated with objective scores calculated by the proposed metric. The mean value of the Turing test (6.37), indicated that participants' ability to distinguish  
Figure 4: Exemplary screenshots of a video shown to participants for rating human-likeness of a subject vehicle (marked red).  
Figure 3: Locations for data gathering—**Left**: synthetic intersections for creating artificial driving behavior. **Right**: locations from inD Data [35].  
between artificial and real drivers is only slightly higher than random responses or the result (exactly 6.0) when selecting \"Not sure\" for all vehicles. Figure 5 visualizes the subjective ratings for all test vehicles shown during the survey associated with the objective scores calculated by the proposed metric. Real vehicles are green, whereas artificial vehicles are colored red. The y-axis shows the rating scale presented during the survey. The blue value above each vehicle rating describes the objective human-likeness score obtained by the proposed metric. During the survey, artificial vehicles were selected that exhibited high and low levels of human-likeness. Furthermore, for the real vehicles, samples with more and less objective human-likeness scores were selected to determine if the metric is able to detect both good and bad results.  
Comparing the objective scores from the metric to participants' subjective ratings from the survey, a positive correlation could be observed with a Spearman correlation coefficient of 0.62 and a \(p\)-value of 0.030, and a Pearson correlation coefficient of 0.65 and a \(p\)-value of 0.023 shown in Figure 6. The \(p\)-values indicate that there is a moderately monotonic positive relationship at the 97% confidence level and a moderately linear positive relationship at the 97% confidence level, which can be considered statistically significant.  
Based on the insights into which vehicles were rated as more human-like by the participants and the multidimensional quality function, the individual parameters could be analyzed in terms of the extent to which they contribute to the decision for real or artificial behavior. Table 4 provides the results of this analysis using the Spearman correlation, which is further transformed into a weighting of the individual parameters with the aim of reflecting how people prioritize differences in the individual behavioral parameters.  
Figure 5: Subjective human-likeness rating obtained from participants (y-axis) associated with objective human-likeness scores obtained by the proposed metric (blue value above). Vehicles are sorted by the average rating assigned by participants, in descending order from left to right.
## 3 Reclassification of Emergency Messages Dissemination Approaches
### Simulation Parameters and Setup
Figure 5: Area (approximately 20,566.21 m\({}^{2}\)) where EMs are disseminated via the pheromone mechanism during the pheromone’s lifetime. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Pheromone with Degradation** & **Flooding with Control (Single Packet Relay)** & **Flooding with Relay (Multiple Packet Relay)** \\ \hline Radio standard & 802.11 p & 802.11 p & 802.11 p \\ \hline Radio frequency band & 5.9 GHz & 5.9 GHz & 5.9 GHz \\ \hline Radio channel (literature/FCC) & 3/176 & 3/176 & 3/176 \\ \hline Radio channel central frequency & 5.880 GHz & 5.880 GHz & 5.880 GHz \\ \hline Radio bandwidth & 10 MHz & 10 MHz & 10 MHz \\ \hline Radio Tx power & 0.50 mW & 20 mW & 20 mW \\ \hline Ideal range (approximate) & 80.91 m * & 511.74 m * & 511.74 m * \\ \hline \hline \end{tabular}

* Computed from frequency, Tx power, and isotropic antenna under the Free-Space Path Loss model with GatcomSUMO [45] v 1.06.

\end{table} 
Table 7: Simulation parameters: radio. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Pheromone with Degradation** & **Flooding with Control (Single Packet Relay)** & **Flooding with Relay** \\ \hline Global simulation space (area approximate) & 47,700 m\({}^{2}\) & 47,700 m\({}^{2}\) & 47,700 m\({}^{2}\) \\ \hline Potential communication area (approximate) & 20,566.21 m\({}^{2}\) & 47,700 m\({}^{2}\) & 47,700 m\({}^{2}\) \\ \hline Global simulation period & 300 s & 300 s & 300 s \\ \hline Incident period duration & 200 s & 200 s & 200 s \\ \hline Overall pheromone lifetime & 210 s & - & - \\ \hline Period of EM retransmission & 5 s & 5 s & 5 s \\ \hline Overall EM transmissions (occurrence) & 42 & 41 & 41 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Simulation parameters: space and time for communication. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Pheromone with Degradation** & **Flooding with Control (Single Packet Relay)** & **Flooding with Relay** \\ \hline Communicated types of packets & Emergency only & Emergency only & Emergency only \\ \hline Relay counts & Only RSU relays packets & 1 & \(\geq\)1 \\ \hline Transport protocol used & UDP & UDP & UDP \\ \hline Packet header length & 20 B & 20 B & 20 B \\ \hline Packet payload length (average) & 93 B & 42 B & 45 B \\ \hline \multirow{4}{*}{Payload contents} & Entities’ IDs, space–time & \multirow{2}{*}{Entities’ IDs, space–time data for incident, data for incident, data for incident, consecutive IDs} \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table} 
Table 9: Simulation parameters: packets or emergency messages (EMs). Figure 6: Area (approximately 47,700 m\({}^{2}\)) where EMs are disseminated through both flooding mechanisms during the pheromone’s lifetime. All experiments were made with a machine with the following specifications:

* Hardware: Intel@i7-7700k microprocessor generic machine at Puebla, Mexico, with 64 GB RAM, 4 GB VRAM, and a single 120 GB SSD for storage.
## 6 Results and Discussion
Table 5 shows the newly obtained results of the experiment (Computer Vision Method) and FEA. The results presented in this way clearly confirm the similarity between FEA and experimental results in the area up to 70 degrees for curved flexure hinge with a 21-degree bend and up to 80 degrees for curved flexure hinge with a 90-degree bend. In the area larger than the above-mentioned angles, the results diverge. This outcome can be attributed to the behavior of the material, which in the area of large deformations tends to creep after the addition of weights heavier than 40 g. With the graphic in Figure 13, this can be confirmed because, in these areas, the stress is greater than 12 MPa for both compliant fingers. This also refers to Figure 11, where, as already noted, the material enters the non-linear deformation region. The difference between the previously performed experiment (Table 3) and the newly performed experiment (Table 5) should also be explained. The conclusion of the difference in results is explained by the fact that the previous experiment was performed after the bending test of 3D-printed compliant finger prototypes. This means that the prototypes were previously brought to a state of large deformations, whereby plastic deformation remained in the material. It should be noted that these permanent deformations were not seen and that the prototypes had the ability to return to their initial state, but the experiment performed with such plastic deformations in the material cannot be considered valid.
## 4 Conclusions, Recommendations, and Implications  
As indicated in the literature of other countries, the findings of the study confirm that plagiarism continues to remain a challenge at the higher education level in Pakistan. The findings of this study identify various determinants, such as an omission of citation conventions from course content, a lack of a proactive approach to create awareness, untrained teachers, a lack of strict penalties, poor time management, a fear of failure, a lack of confidence, laziness, and a culture of plagiarism. The students also questioned the role of universities and teachers, which ranged from reactive and laidback to promoting plagiarism. All of these factors appear to have created a culture of plagiarism in Pakistan.  
Based on the suggestions given by the students, and consulting the literature on the same issue; this study provides a theoretical model, i.e., the AS&P model (Awareness, Support, and Prevention), to address this issue. There is a strong need to adopt a more proactive approach to create an awareness and understanding of plagiarism, the policies about it, and its consequences. Universities must first educate and train their teachers about it. Later, the students should be educated through seminars, workshops, and training sessions. Plagiarism and citing conventions should also be made a part of different course outlines. The students should be provided with support to combat different academic, technical, psychological, emotional, and other nonacademic issues, which often lead them to plagiarize. Teachers should adopt positive and caring behavior so that students can share their issues freely and seek solutions. Finally, both universities and teachers should adopt a zero-tolerance policy for it. Students and teachers involved in this practice should be punished so that others will be deterred. Plagiarism should also be checked for in students' assignments and research proposals/synopses so that they can learn to avoid it in early stages of the university career. It is worth mentioning that this model has been developed based on research findings and has not been tested in practice. Thus, researchers are encouraged to further investigate the experimental effectiveness of this model in reducing plagiarism in higher education settings.  
Although this study is exclusively about Pakistan (more specifically about an online university in Pakistan), it has far-reaching implications. This model can be used in other countries, especially developing ones, with a similar context, a similar education system, and the same issues. Based on this model, universities can devise specific steps and strategies to reduce plagiarism and can emphasize different aspects of the model based on their context, issues, and needs.