esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential Study of Solar Thermal Cooling in Sub-Mediterranean Climate
## 4 Economic Feasibility
### CO\({}_{2}\) Emissions Reduction  
For the calculation of the total CO\({}_{2}\) emission reduction, it is important to know the emission factors. The emission factor used in this study was derived based on the numbers provided for Mediterranean cities that carried out their sustainable energy action plan with the only exception of the electricity emission factor, which is a characteristic of the country [30]. It was not possible to acquire the electricity emission factor for Jordan directly from the Ministry of Energy and Mineral Resources or any of the utilities servicing the country. Therefore, the utilization of available statistical energy data from the Ministry of Energy and Mineral Resources [31] was considered the best approach. Using the Energy data of 2015 and the energy production from different sources including renewable energy, as shown in Table 4, the Electricity Emission Factor (EEF) was calculated.  
The main source of electricity generation in Jordan is the natural gas which has an emission factor of 0.4 kg/kWh, followed by heavy fuel oil with an emission factor of 0.7 kg/kWh. The average Electricity Emission Factor in Jordan for the year 2015 is calculated by using Equation (4), and it was equal to \(EEF=0.54\,kgCO_{2}/kWh\)  
\[EEF=\frac{CO_{2}\,\,Emission\,\,(kg)}{Electrical\,\,energy\,\, generated\,\,(kWh)}=\frac{10,120\times 10^{6}kg\,\,CO_{2}}{19,013\times 10^{6}kWh} \tag{4}\]  
The electrical energy (kWh) saved by operating the absorption chiller was compared against operating the conventional vapor compression chiller with a COP of 2.5 which corresponds to 19,980 kWh in electricity savings. Thus, for the operating with COP of 0.32, the reduced CO\({}_{2}\) emissions is equal to 10,789 kg, as shown in Equation (5).  
\[Reduced\,\,CO_{2}\,\,emission\,=\,EEF\times\,Saving\,\,in\,\,electrical\,\,energy =0.54\frac{kg\,\,CO_{2}}{kWh}\times 19,980\,\,kWh \tag{5}\]  
Also, the reduction in CO\({}_{2}\) emissions is equal to 26,500 if the system reaches the claimed COP of 0.79. The annual savings for the reduction of CO\({}_{2}\) emissions with an assumption of 28 \(\frac{JD}{ton\,\,of\,\,CO_{2}}\) was equal to 300 JD and 742 JD for COPs of 0.32 and 0.79, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **GWh** & **CO\({}_{2}\) Factor kg/kWh** & **Emission (Tons)** \\ \hline Natural gas & 9211 & 0.4 & 3,684,400 \\ Heavy fuel oil & 6644 & 0.7 & 4,650,800 \\ Diesel & 2974 & 0.6 & 1,784,400 \\ Renewable & 184 & 0 & 0 \\ Total & 19,013 & - & 10,119,600 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Electrical energy production shares from different sources including renewable energy resources in Jordan [31].
## 4 Evaluation of Small-Signal Characteristics of Single-Phase CMIs
### Evaluation of the Small-Signal Characteristics
Figure 15c,b show that the frequency at which the CM attenuation begins to deteriorate does not change from around 1 MHz, even when the CC method is applied. As already shown in Figure 4b, the real part of the complex permeability of MnZn ferrite decreases rapidly, and the imaginary part peaks around 1 MHz. Furthermore, the real part of the complex permeability shows negative polarity from about 1.5 MHz and converges slowly to zero. In the frequency range where the real part of the complex permeability shows negative polarity, the inductor behaves as a capacitive impedance component. Therefore, as shown in Figure 15b, even if the CC method is applied to cancel the effect of parallel stray capacitance, the frequency at which the CMI-2 attenuation characteristic begins to deteriorate does not change from around 1 MHz due to the frequency dependence of the complex permeability. As a result, the CM attenuation improvement by the CC method is smaller than that of CMI-1 based on the nanocrystalline core. Since the proposed model implements both the frequency dependence of complex permeability and parallel stray capacitance, the proposed model can simulate the CM attenuation characteristic of the CMI-2 based on the MnZn ferrite core when the CC method is applied. Note that Figure 15a shows that a slight discrepancy between the measured and simulated CM attenuation from 10 to 30 MHz is observed. This is possible because of the error of calculated stray capacitance.
# Three-Dimensional Broadband Electric Field Sensor Based on Integrated Lithium Niobate on Insulator
## 4 Conclusions  
In conclusion, a broadband LNOI-based 3D EFS has been fabricated and demonstrated. Three-dimensional electric field sensing is realized by packing three LNOI-based sensor chips with an optical waveguide-based asymmetrical Michelson interferometer architecture in a triangular-prism-type clamp. The tapered dipole antennas on each sensor chip have an incline angle of 54.7\({}^{\circ}\) from the horizontal plane, which guarantees that the three pairs of antennas are mutually orthogonal. The packaged 3D sensor has a small volume of 9.5 cm\({}^{3}\), which is beneficial for achieving high-accuracy measurements of EM waves in free space. In the experiment, a flat response in the frequency range of 10 MHz to 3 GHz is demonstrated. In addition, a 3 \(\times\) 3 response calibration matrix is obtained and utilized to reduce the measurement error. After calibration, the relative measurement error of the electric field amplitude is smaller than 5.1% for every polarization direction. The proposed 3D EFS scheme is a promising candidate to realize distributed broadband EM measurement.  
Writing--original draft preparation, B.S. and Z.L.; investigation, Z.L., L.Q. and L.L.; formal analysis, Z.L. and L.Z. ([PERSON]); validation, Z.L., Y.L. and W.D.; writing--review and editing Z.L., L.Z. ([PERSON]) and Z.Z.; resources, B.S. and Y.L.; supervision, S.Z. and B.S.; methodology, Z.L.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2019 YFB2203800), Fundamental Research Funds for the Central Universities (ZYGX2020 ZB012), and the National Natural Science Foundation of China (NSFC) (61927821).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Gauge Section** & **Gauge Section** & & & \\
**Specimen ID** & **Width (W)** & **Thickness (T)** & **Fabrication** & **Heat** & **Barrel** \\  & **(mm)** & **(mm)** & **Direction** & **Treatment** & **Finishing** \\ \hline _BF 00 00_ & 5.74 & 4.08 & Horizontal & - & - \\ _BF 00 45_ & 5.58 & 3.94 & Diagonal & - & - \\ _BF 00 90_ & 5.54 & 3.77 & Vertical & - & - \\ _BF 30 00_ & 5.30 & 3.84 & Horizontal & - & 30 rpm \\ _BF 30 45_ & 5.30 & 3.80 & Diagonal & - & 30 rpm \\ _BF 30 90_ & 5.40 & 3.64 & Vertical & - & 30 rpm \\ _BF 42 00_ & 5.30 & 3.80 & Horizontal & - & 42 rpm \\ _BF 42 45_ & 5.30 & 3.74 & Diagonal & - & 42 rpm \\ _BF 42 90_ & 5.30 & 3.60 & Vertical & - & 42 rpm \\ _TT 482 06 1_ & 5.75 & 4.03 & Vertical & 482 \({}^{\circ}\)C—6 h & - \\ _TT 482 06 2_ & 5.77 & 4.02 & Vertical & 482 \({}^{\circ}\)C—6 h & - \\ _TT 482 06 3_ & 5.76 & 4.06 & Vertical & 482 \({}^{\circ}\)C—6 h & - \\ _TT 482 08 1_ & 5.76 & 4.04 & Vertical & 482 \({}^{\circ}\)C—8 h & - \\ _TT 482 08 2_ & 5.78 & 4.02 & Vertical & 482 \({}^{\circ}\)C—8 h & - \\ _TT 482 08 3_ & 5.77 & 4.08 & Vertical & 482 \({}^{\circ}\)C—8 h & - \\ _TT 482 10 1_ & 5.76 & 4.02 & Vertical & 482 \({}^{\circ}\)C—10 h & - \\ _TT 482 10 2_ & 5.76 & 4.03 & Vertical & 482 \({}^{\circ}\)C—10 h & - \\ _TT 482 10 3_ & 5.79 & 4.03 & Vertical & 482 \({}^{\circ}\)C—10 h & - \\ _TT 704 04 1_ & 5.79 & 4.05 & Vertical & 704 \({}^{\circ}\)C—4 h & - \\ _TT 704 04 2_ & 5.76 & 4.03 & Vertical & 704 \({}^{\circ}\)C—4 h & - \\ _TT 704 04 3_ & 5.79 & 4.04 & Vertical & 704 \({}^{\circ}\)C—4 h & - \\ _TT 704 05 1_ & 5.76 & 4.05 & Vertical & 704 \({}^{\circ}\)C—5 h & - \\ _TT 704 05 2_ & 5.78 & 4.05 & Vertical & 704 \({}^{\circ}\)C—5 h & - \\ _TT 704 05 3_ & 5.80 & 4.03 & Vertical & 704 \({}^{\circ}\)C—5 h & - \\ _TT 704 06 1_ & 5.77 & 4.02 & Vertical & 704 \({}^{\circ}\)C—6 h & - \\ _TT 704 06 2_ & 5.79 & 4.04 & Vertical & 704 \({}^{\circ}\)C—6 h & - \\ _TT 704 06 3_ & 5.76 & 4.04 & Vertical & 704 \({}^{\circ}\)C—6 h & - \\ _TT 788 03 1_ & 5.78 & 4.04 & Vertical & 788 \({}^{\circ}\)C—3 h & - \\ _TT 788 03 2_ & 5.81 & 4.03 & Vertical & 788 \({}^{\circ}\)C—3 h & - \\ _TT 788 03 3_ & 5.80 & 4.02 & Vertical & 788 \({}^{\circ}\)C—3 h & - \\ _TT 788 04 1_ & 5.80 & 4.02 & Vertical & 788 \({}^{\circ}\)C—4 h & - \\ _TT 788 04 2_ & 5.78 & 3.99 & Vertical & 788 \({}^{\circ}\)C—4 h & - \\ _TT 788 04 3_ & 5.78 & 4.03 & Vertical & 788 \({}^{\circ}\)C—4 h & - \\ _TT 788 05 1_ & 5.77 & 4.02 & Vertical & 788 \({}^{\circ}\)C—5 h & - \\ _TT 788 05 2_ & 5.76 & 4.06 & Vertical & 788 \({}^{\circ}\)C—5 h & - \\ _TT 788 05 3_ & 5.79 & 4.03 & Vertical & 788 \({}^{\circ}\)C—5 h & - \\ _TT 800 04 1_ & 5.86 & 4.04 & Vertical & 800 \({}^{\circ}\)C—4 h & - \\ _TT 800 04 2_ & 5.85 & 4.02 & Vertical & 800 \({}^{\circ}\)C—4 h & - \\ _TT 800 04 3_ & 5.87 & 4.01 & Vertical & 800 \({}^{\circ}\)C—4 h & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specimens IDs, actual dimensions and postprocessing information.
## Discussion
### Key Findings
Remarkably, 61% of the proposed models did not benchmark their performance against other baseline classifiers. Finally, five studies re-examined the validity of their models on multiple datasets. * RQ3-Academic Performance Predictors. What dominant predictors of student performance using learning outcomes are reported? Learning analytics insights about student outcomes in the domain of education necessitate the investigation of features impacting academic performance [107]. Such understanding empowers the implementation of personal recommendations by the concerned education stakeholders [112]. However, our systematic survey demonstrated a lack of explanatory models that go beyond predicting the student performance to pinpointing the features that genuinely impact the attainment of course and program-level outcomes. Approximately a third of the studies listed no more than three dominant factors to be influencing the accuracy of the academic outcome predictions. Similarly, nearly 30% of the studies were inconclusive about the effects of the features they explored. The thematic analysis revealed that student online learning patterns, term assessment scores, and student academic emotions are the top three predictors of learning outcomes. The sample size of the synthesized studies differed significantly, as well as the number of courses used for understanding the impact of some features on the student learning outcomes. What works for one course might not work for another, and what works for one batch of students might behave adversely for another. In fact, student performance predictive models are known to work well, particularly for the datasets on which they were trained (i.e., model overfitting) and therefore have limited generalizability to new students and disciplines [111].
## 4 Results
### Simulation Results
#### 4.1.4 CoMTUS through Layered Media
In this case, the worst lateral resolution corresponds to the one probe system with 1.78 mm, which is the one with the smallest aperture size, while the 2 probes and CoMTUS images are similar with 0.40 m. The results are shown in Figure 13.  
Figure 11: Simulated delayed RF data for a medium with a fat layer of 35 mm in thickness and backscattered from a point-like target, obtained by coherently adding the four delayed backscattered echoes from the same point-like target (\(T_{1}R_{1},T_{1}R_{2},T_{2}R_{1},T_{2}R_{2}\)) using different beamforming parameters: (**a**) 2 probes; (**b**) CoMTUS. Figure 10: Comparison of simulated images for the control case (**a–c**) and imaging through fat and muscle layers of 35 mm and 8 mm in thickness, respectively (**d–f**). Different methods are compared: one probe, 2 probes and CoMTUS. mm. The trends show that if aberration is not corrected, there are no significant improvements in the imaging metrics related to the aperture size for thicker fat layers. At a clutter thickness greater than 10 mm, image quality of the system formed by two transducers without aberration correction (2 probes) is significantly degraded, while CoMTUS imaging metrics are not affected by aberration errors, following the same trend as a conventional aperture (one probe) and providing a constant value of resolution over clutter thickness without any significant loss of contrast. At the thickest fat layer simulated, resolution is 1.7 mm and 0.35 mm for the one probe and CoMTUS images, respectively, while in the case of 2 probes images, it is no longer possible to reconstruct point-targets with sufficient definition to measure resolution. Contrast and CNR also show a similar significant loss for the 2 probes images. With the thickest aberrating layer, the 2 probes case gives a contrast of \(-10.84\) dB and a CNR of 0.69, while those values are significantly better for the one probe (\(-18.44\) dB contrast and 0.87 CNR) and CoMTUS (\(-17.41\) dB contrast and 0.86 CNR) images.
## 3 Experimental Results and Discussion
### Experimental Setup
We selected the ADAM algorithm to train the traditional 2D CNN-based classifier. In addition, we used a multi-core personal computer (PC) (Intel(r) Q370, Intel(r) Core(tm) i7 8700, DDR4 2400 MHz 8C*3)-based platform to implement two classifiers, as shown in Table 1, and also used the graphics processing unit (GPU) (NVIDIA(r) GeForce(r) RTX(tm) 2080 Ti, 1755 MHz, 11 GB GDDR6) to speed up the CPU execution time for digital image processing and classification tasks. In the MIAS image database [22; 23], we selected the image size of 4320 pixels \(\times\) 2600 pixels (600 dpi for the vertical and horizontal resolutions, a bit depth of 24 bits) for breast lesions screening. According to the MIAS database's biomarkers, the categories and tumor locations could be identified. A total of 118 mammography images (right and left breasts, 59 subjects: 35 normal subjects and 24 abnormal subjects), including 70 normal subjects' images and 48 abnormal subjects' images, were obtained to extract the feature patterns for the training dataset and testing dataset; the training dataset was used to train the classifier for abnormality detection; and the testing dataset was used to validate the classifier performance. This study used a K-fold cross-validation (\(K_{f}\) = 10) to evaluate the classifier's performances with the four indices, including precision (%), recall (%), accuracy (%), and F1 score [39; 40; 41]. The feasibility study was validated as described in detail in the subsequent sections.
## 3 Results and Discussion
### Data Processing using Machine Learning Methods
#### 3.4.5 Feature Engineering  
The trained inner layers consist of 8 neurons, each of which has 64 values. Then the average, standard deviation and the maximum value are calculated for all the values of each neuron. The idea was to obtain reduced dimensional data, which is more selective to a specific substrate property due to each neuron uniqueness. The resulting dataset of 6000 images per 24 characteristics was used to train the K-means algorithm in a similar way to the basic features approach. The results showed that the content of SERS-active surfaces is 90% concentrated in two of the clusters. The optimal number of clusters was equal to four. The proposed approach made it possible to define the technological mode of formation silver-on-glass substrates with maximum spectral intensity for myoglobin.
## 2 The Test Case  
In Figure 1, the layout of the wind farm of interest is reported. The upgraded wind turbine (T7) is indicated in blue.  
The wind farm under investigation has recently attracted some attention in the wind energy scientific literature because it is an interesting test case for the study of wind flow, wake-terrain interactions and wind turbine performances in complex terrain [32; 33]. In particular, it has been one of the test cases of the International Energy Agency project about wind flow over complex terrain at the microscale level [34].  
The wind turbines have 93 meters of rotor diameter and 80 meters of hub height above ground level. The cut-in wind speed is 4 m/s and the cut-out wind speed is 32 m/s [24]. The rated power is 2.3 MW. Notice that, in the following, all the data in the graphs will be normalized on the rated values (for example power, rpm, etc.).  
On 1 March 2018, the control system upgrade was installed on the turbine T7 and, therefore, the rotor rpm-power curve changed: The idea underlying this intervention was that the rotor rotational speed schedule is adjusted in order to reach the most appropriate induction level, while maintaining adequate angles of attack along the blade. The resulting operational curve is reported and compared against the pre-upgrade one in Figure 2.
# Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network
## 2 Related Work  
In WBAN, routing is a crucial process due to restricted available resources, i.e., less computational power, energy source and memory [28]. In WBAN, the usage of energy consumption is directly proportional to the Radio Frequency (RF) at sensor nodes' physical layer. Moreover, the sensor nodes' residual energy could be enhanced via MAC protocols by controlling the RF part's duty cycle. Several studies exist in the literature regarding energy efficient protocols in WSN [41; 42]. The protocols of MAC and physical layer enhance the network communication irrespective of the certitude. In the contemporary studies do not handle the necessities like addressing schemes, end-to-end packet delivery and determination methods of routes. Probably, these issues could be pretty much handled with assistance of the network layer. Moreover, network layer routing protocol plays a pivotal role when it comes to conserve the energy and temperature of WBAN sensor nodes [43]. Though, the subset of WSN is WBAN but WBAN is different than WSN in terms of the specifications and nature due to which the transformation of coherent WBAN routing protocol demands more attention than WSN (Wireless Sensor Network).  
Some aspects of WBAN demand special attention, such as limited bandwidth (because of duty cycle mechanism or interference), nature of heterogeneous network (because of specific physiological data), consumption of energy (because of higher rate of transmission), increased temperature (because of high number of transmissions), coverage area (because of small range of range), Quality of Service (QoS--because of priority-based data) and mobility (because of postural movements) [44].  
The most quintessential factor among all depicted issues is the increased temperature of sensor nodes that negatively affects the performance of WBAN. Such high temperature is reported due to energy consumption and assimilated radiation by the antenna that result in damaging the heat sensitive areas of the body [45]. Moreover, high temperature can also hit the body tissues around the sensor nodes [26]. All these stated issues demand a significant research to propose a routing algorithm for WBAN to avert the damage of body tissues that occur due to rise of node's temperature. Consequently, insightful routing algorithm that contemplates temperature rise can positively affect the life span of a network.
## 4 Measurement Setup and Results  
This section presents the real-world measurement campaign conducted to validate the theoretical and simulation-based analysis of phase noise effects on range estimation in harmonic FMCW radar systems. The experimental setup was based on the implemented radar system introduced in [33] and of 2.4-2.5 GHz and a harmonic reception band of 4.8-5.0 GHz. The radar utilized a passive harmonic tag and dual circularly polarized antennas for transmission and reception, as illustrated in [33,34].
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 4 Redundancy for Collaborative UVs  
Within the OWT IMR operations, the reliability and availability of unmanned systems are very important to minimize the risks associated with safety, poor performance, and data security. While a collaborative system may improve the operation of current unmanned systems, it may also replicate several failure modes that exist in a single operating UV. In the worst-case scenario, it may amplify the degree of severity of the failure modes which could result in more severe damage to the vehicles or assets, loss of operating time, or critical data breach. To prevent or reduce the risks of these failure modes, a redundant system for collaborative UVs should be introduced.
# Estimating Flood Inundation in Urban Areas Using a Scenario Generation Method and Inundation Graphs
###### Abstract
Following the previous research related to flood scenarios used for vulnerability analysis and the hydraulic performance graph, a new scenario generation algorithm tool for estimating flood inundation was developed to analyze the flood effects on urban areas to prepare for disasters and minimize damage. This algorithm generates synthetic or hypothetical flood events that may occur in a given area, based on measured precipitation data from former events and other relevant information such as terrain and topography. By generating multiple scenarios using a two-dimensional flow model and storing its results in a graph-form database called an inundation graph, it is possible to assess the probability and potential impact of different flood events quickly. This can improve decision-making regarding flood protection measures. Additionally, in the event of an emergency involving flash floods, the user can quickly and conveniently estimate the inundation water level using the stored database instead of utilizing a two-dimensional flow model during the event.