esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework to Build a Big Data Ecosystem Oriented to the Collaborative Networked Organization
## 3 Proposed Framework  
In this context, the CNO-BD framework that supports the building of a big data ecosystem oriented to the CNO is proposed. It focuses on (1) the acquisition and intelligent processing of big data and (2) the establishment of good practices, techniques, tools and guidelines that support the CNO big data ecosystem.  
The first version of the CNO-BD framework was developed considering the scientific and academic advances in this line of research, and the experience acquired by the authors in the development and implementation of big data ecosystems. We used research from domains of collaborative organizations, interoperability, information systems and data fusion to design the CNO-BD framework, particularly that on big data. Thus, we conceptualized the use of big data within the CNO. The next version of this framework was evaluated by five experts in big data from different companies/institutions. They provided observations and suggestions that allowed us to improve it. The penultimate version of the framework was validated through a case study in a CNO in the education sector in Mexico. The personnel involved in the project provided feedback to improve it. Thus, the final version of the CNO-BD framework was obtained.  
In the CNO-BD framework, it was noticed that a new dimension that was not being previously considered by the other frameworks was needed. This dimension is programmable modules, which allows big data to be intelligently processed in the CNO. Therefore, the CNO-BD framework is composed of seven dimensions with their respective sub-dimensions (Figure 1): levels dimension, data sources dimension, approaches dimension, data fusion dimension, big data assurance dimension, programmable modules dimension and interoperability dimension. Highlighted in bold are (1) the sub-dimensions that are proposed to be added to each dimension, which extends the necessary big data aspects in the CNO-BD framework and (2) the seventh dimension, called programmable modules, which process big data intelligently in the CNO.  
The dimensions are interrelated. The levels dimension considers the different levels of the CNO. These provide data that are from different data sources. These data are collected by the approaches tools and methods, and their heterogeneity is solved by the data fusion techniques. Then, big data assurance provides different methods than can be used by the programmable modules to generate the proper information and knowledge for decision making at the different levels of the CNO. Finally, interoperability provides support in data extraction from the different levels and their return in the form of information and knowledge.  
All dimensions and sub-dimensions, except those indicated in bold, are considered in at least one of the ten frameworks reviewed. However, none of the existing frameworks include all of them.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 4 Materials and Methods
### Classifier Building  
After customizing the dataset to our purpose, we used the embedding approach to vectorize our dataset, generating embedded vectors for the new column, which will be the models' input. Subsequently, we partitioned the data into training and testing subsets, maintaining an 80:20 ratio. Finally, we trained the models to predict the churn status. In addition to traditional machine learning models, we experimented with zero-shot classification.  
In our experiments, we have conducted multiple experiments to select the best model (as shown in Figure 3) that can predict the churn attribute with a low error ratio. These experiments encompassed evaluating the effectiveness of different embedding techniques alongside various machine learning models to discern their impact on performance and accuracy. The embedding models we utilized included OpenAI's embedding, Google Gemini, Google Bert, Sentence transformers, Sent2 Vec, and Doc2 Vec. For classification, we trained and tested logistic regression, support vector machine, decision tree, naive Bayes, k-nearest neighbor, random forest, multiple layer perceptron, and zero-shot classification.
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 3 Compact Design of QZS Isolation
### Special Spring Type
#### 3.1.2 Wave Springs  
Multi-coil wave springs [43] can provide the same force and deflection characteristics using only half of the space of a coil spring, a property that makes them highly advantageous in space-constrained application scenarios. [PERSON] et al. [44] connected a wave spring in parallel with a pair of permanent magnet negative stiffness elements, called WQZS isolator, as depicted in Figure 5, and derived an empirical formula for the restoring force of the wave spring based on experimental data using the least squares method. The experimental results showed that the isolation frequency of the isolator was reduced by 48.89%, and the peak transfer rate was reduced by 59.33% compared with the linear isolator.
## 1 Introduction  
GaN devices have become more than just a promise to designers. Today, GaN HEMTs represent a mature technology widely used in aerospace [1], automotive industry [2], electric vehicles [3] and for the realisation of power converters where are superior to conventional silicon (Si)-based devices in terms of switching frequency, power rating, thermal capability and efficiency [4].  
The use of GaN transistors supports key RF demands such as high gain, low power consumption, high throughput and extremely fast switching speeds. The high breakdown voltage and high power density allow one to operate at higher voltages and manufacture smaller devices while reducing the overall dimensions of power converters. Their small size, together with the high carrier saturation velocity in GaN, allow operations at higher switching frequencies than MOSFETs without incurring high switching losses as the parasitic capacitances of GaN devices are significantly lower than those of MOSFETs, as well as they exhibit reduced conduction losses [5].  
However, the high speed of these devices requires great engineering skills both for the development of high-speed driver circuits and in the testing of the switching characteristics. From this point of view, a crucial point is the measurement of the current waveform. The knowledge of this quantity is very important to validate the models of the power devices and to calculate the efficiency of the converter. Moreover, it allows monitoring of circuit operation and triggering protection circuits in case of overcurrent or short circuit. Since GaN devices have very fast transients, of the order 10 A/ns, the current probes must have a very high bandwidth and small insertion impedances to avoid large insertion losses and large effects of parasitic elements on the measurement accuracy.  
The Hall effect current probe, Rogowski coil and current shunt are usually used to measure current in power converters. More recently high-bandwidth integrated probe developed for fast GaN devices has been presented [6]. The resistive shunt is by far the simplest sensor to make, cheap and easy to integrate. It can be operated over a wide bandwidth and allows the measurement of both DC and AC components [7]. For this reason, it is widely used in applications such as the characterisation of the behaviour of GaN devices during the short circuit where it is necessary to monitor the current during the entire transient [8; 9; 10; 11; 12; 13].  
Nevertheless, the insertion of a resistive shunt in a circuit involves the presence of a parasitic inductance which may play a major role in the measurement if the current has very fast transients. In fact, since the current is measured indirectly as the voltage across the impedance, the presence of a very fast gradient induces an overvoltage \(\Delta\)V = Ls \(\Delta\)I/\(\Delta\)t on the parasitic inductance that can be mistaken for an overcurrent on the device under test (DUT).  
In this paper, we show how it is possible to eliminate measurement artefacts through the insertion of a compensation network which is formed by adding an inductor to the voltage measurement circuit.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 3 Materials and Methods
### Software
#### 3.4.1 Charging Verification, Humidity, and Temperature Values  
The first phase is the phase of checking whether the system is in charging mode or not, as shown in the code presented in Figure 2. This checks whether the device is resting on the charging base, and, until it is removed, the system is in standby mode.  
When the system comes out of charging mode, it displays the following message: \"Charging complete. Start the system\". The system starts up and moves to the next phase.  
Figure 1: System flowchart.  
After the device is removed from the charging base, it must meet two conditions for the system to be able to proceed. These conditions are intended to confirm that the activation is intentional--in other words, to confirm that the device has been removed from the charging base with the intention of being used by the patient. This prevents erroneous system activations, such as the device being moved from the base accidentally. This is achieved through the temperature sensor. Since the average body temperature of a human being is between 36 \({}^{\circ}\)C and 37 \({}^{\circ}\)C, the device must comply with this condition. The sensor used has an accuracy of \(\pm\)2 \({}^{\circ}\)C; therefore, the temperature obtained by the sensor must be higher than 34 \({}^{\circ}\)C in order to account for this accuracy rate.  
Since the oral cavity is constantly bathed in saliva, it makes the mouth a place with quite high humidity. This is a complementary factor to temperature. The DHT11 is able to detect humidity rates of up to 80% and is, therefore, ideal for the inside of the mouth.  
Since an ambient temperature of 34 \({}^{\circ}\)C can easily be reached during the summer, or in countries where temperatures are higher, it is more effective for the first parameter of comparison to be humidity. The variables \"humset\" and \"tempset\" correspond to the limit values for humidity and temperature, respectively, and were defined earlier as shown in Figure 3.  
The variables \"hums\" and \"temps\" are set to zero initially, however, these assume the values read by the sensor.  
After the system verifies that the device has left the charging base, it takes a humidity reading, adding the obtained value to the \"hums\" variable, as shown in Figure 4. A comparison is made between this value and the value set initially. If the value of the \"hums\" variable is equal to or greater than the value of the \"humset\" variable, it indicates that the humidity levels are within the defined parameters, and then moves on to the temperature reading.  
The process for temperature is similar. The sensor registers the temperature value, that value is added to the \"temps\" variable and compared to the initially set \"tempset\" variable.  
If these two conditions are met, an indication is given to the system that the device is in the mouth, and ready to be used.  
Figure 3: Code 2—humidity and temperature limit values.  
Figure 2: Code1—charge verification.
## 3 Numerical Validation
### The Numerical Models  
Figure 3 shows two full-sized panel units. The glass panels in Model A and Model B are 1460 mm and 2060 mm in width, 6 mm and 8 mm in thickness, and both 1410 mm in height, respectively. These panels are bonded to the frame with structural sealant with a width of 10 mm and a thickness of 10 mm. The supporting frame of the two samples adopts an aluminum alloy box section. The cross section size of the column frame is  
Figure 2: The flow chart of the proposed two-stage method.  
Table 1 lists the mechanical parameters of the materials. In the finite element models, the glass panel and supporting frame were meshed using shell elements. The element size was set as 0.025 m. The structural sealant was modeled with spring elements, and the stiffness per unit length is given by  
\[k=\frac{E_{s}w_{s}}{t_{s}} \tag{8}\]  
where \(E_{s}\) is the elastic modulus of structural sealant and \(w_{s}\) and \(t_{s}\) are the width and thickness of the structural sealant, respectively. The four corners of the supporting frame are fixed constraints. The two finite element models are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Density (kg/m3)** & **Elastic Modulus (N/mm2)** & **Poisson’s Ratio** \\ \hline Glass panel & 2500 & 7.0 \(\times\) 10\({}^{4}\) & 0.22 \\ Sealant & 1350 & 1.12 (20 \({}^{\circ}\)C) & 0.45 \\ Supporting frame & 2800 & 7 \(\times\) 10\({}^{4}\) & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the materials.  
Figure 4: Finite element models: (**a**) Model-A and (**b**) Model-B.  
Figure 3: Dimensions of panel units design drawing: (**a**) Model-A and (**b**) Model-B.  
Mode analysis was performed to obtain the natural frequencies and associated mode shapes. Practical measurements would include noise, so random numbers were added to the calculated mode displacements, as follows:  
\[\varphi_{j,i}{}^{s}=\varphi_{j,i}+\delta(2R-1)\varphi_{j,i} \tag{9}\]  
where \(\varphi_{j,i}\) is the calculated mode displacement for mode \(i\) and point \(j\), \(\varphi_{j,i}{}^{s}\) is the mode displacement added noise, \(\delta\) is noise level, and \(R\) is a random number of uniform distribution in the interval [0, 1]. In this study, a level of 5% noise was considered.
## 2 Antenna Design and Performance
### 2 \(\times\) 2 Ka-Band Patch Antenna Array Design and Performance
In addition, the existence of the reflector affects the S-parameter performances, which are dependent upon the \(h\) values, as shown in Table 3. The best antenna performances considering both S-parameters and radiation performances, including maximum gain and FBR, were achieved when \(h\) was 3 mm (0.35 \(\lambda_{0}\) at 35 GHz). As shown in Figure 5d,e, the backlobe is dramatically reduced by the reflector in comparison with the results shown in Figure 4d,e. Figure 4: \(2\times 2\) patch antenna array with feeding network: (**a**) geometry of antenna array, (**b**) perspective view, (**c**) simulated S-parameters, (**d**) simulated radiation patterns at 35 GHz when Port 1 is excited, and (**e**) simulated radiation patterns at 35 GHz when Port 2 is excited. Figure 5: \(2\times 2\) patch antenna array with feeding network and reflector: (**a**) geometry of antenna array with reflector, (**b**) simulated S-parameters when \(h=3\) mm, (**c**) side view, (**d**) simulated radiation patterns at 35 GHz when Port 1 is excited (\(h=3\) mm), and (**e**) simulated radiation patterns at 35 GHz when Port 2 is excited (\(h=3\) mm). Figure 6a shows the proposed antenna assembled by polycarbonate (PC) bolts and nuts. The gap distance is maintained by using three 1 mm thick PC plain washers. Lumped ports were used in the simulation. To investigate the effects of the RF connector and the feeding network in actual antenna measurements, simulations with the RF connectors should be carried out. A 2.92 mm end-launch connector was used in simulation [9]. As shown in Figure 6c, the simulated 10 dB \(\ket{S_{11}}\) \(\ket{\text{ and }}\ket{S_{22}}\) bandwidths are 4.96 GHz (33.42-38.38 GHz) and 3.44 GHz (33.08-36.52 GHz), respectively. Figure 6d,e show the simulated radiation patterns of the V- and H-pol cases. In the V-pol case, the simulated maximum gain and FBR at 35 GHz are 10.94 dBi and 16.66 dB, respectively. In the H-pol case, the simulated maximum gain and FBR at 35 GHz are 10.25 dBi and 13.23 dB, respectively. Therefore, the proposed antenna has good radiation characteristics, even when considering actual measurements, and it fully covers the target band. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{\(h\) (mm)} & & & \\ \multicolumn{1}{c}{**(Wavelength at 35 GHz)**} & & & \\ \multicolumn{1}{c}{} & & & \\ \multicolumn{1}{c}{} & & & \\ \hline \multirow{2}{*}{1 (0.12 \(\lambda_{0}\))} & V & 10.81 & 20.66 \\  & H & 10.23 & 22.24 \\ \hline \multirow{2}{*}{2 (0.23 \(\lambda_{0}\))} & V & 10.89 & 23.6 \\  & H & 10.69 & 22.25 \\ \hline \multirow{2}{*}{3 (0.35 \(\lambda_{0}\))} & V & 10.89 & 27.36 \\  & H & 10.78 & 22.76 \\ \hline \multirow{2}{*}{4 (0.47 \(\lambda_{0}\))} & V & 10.88 & 15.12 \\  & H & 10.77 & 18.1 \\ \hline \multirow{2}{*}{5 (0.58 \(\lambda_{0}\))} & V & 12.39 & 8.82 \\  & H & 11 & 10.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulated radiation performances for various \(h\) values at 35 GHz.
## 4 Analysis of Security and Performance
### Performance Analysis
#### 4.2.1 Probability of Obtaining Pairing Value and Authenticating Messages  
As described in Section 3, \(R_{R}\) is the size of the secret pool in an RSU; \(N_{V}\) is the number of secrets in a vehicle that has been assigned by an RSU. Let the number of neighboring vehicles be \(N_{B}\). \(P_{NP}\) is the probability that two vehicles have no common secret. \(P_{P}\) is the probability that two vehicles have a common secret and so can generate a pairing value for message authentication or confidential communication. \(P_{RP}\) is the probability that two vehicles do not have a common secret and a common trusted neighbor, so a broadcast message cannot be authenticated.  
\[P_{NP}=C\left(R_{R}-N_{V},N_{V}\right)/C\left(R_{R},N_{V}\right)\]  
\[P_{P}=I-P_{NP}\]  
\[P_{RP}=P_{NP}\ P_{NP}^{N_{B}P_{P}}=P_{NP}^{\left(1+P_{P}N_{B}\right)}\]  
When \(V_{i}\) broadcasts a message, \(V_{j}\) has a probability \(P_{P}\) of being directly authenticated. In \(P_{RP}\), the first term is the probability that \(V_{j}\) cannot be directly authenticated with \(V_{i}\), and all trusted neighbors of \(V_{j}\) cannot be directly authenticated with \(V_{i}\) also.(in the second term). \(P_{NP}\) is small, so the probability that a message cannot be authenticated is very small.
## 4 Conclusions  
The comparisons of the chemical effect and thermal effect of CO\({}_{2}\) dilution on the flame temperature in the CH\({}_{4}\)/O\({}_{2}\) counterflow diffusion flame under high pressure (up to 5 atm) were numerically studied with detailed chemistry mechanisms. The main conclusions can be summarized as follows:  
1. Both the chemical effect and the thermal effect of CO\({}_{2}\) dilution in the oxidizer side can decrease the flame temperature significantly, while the transport effect of CO\({}_{2}\) on the flame temperature can be ignored.
2. The decreases in value of the peak flame temperature caused by both the chemical effect and the thermal effect increased with the increasing CO\({}_{2}\) dilution ratio, with linear increases for the former and exponential increases for the latter.
3. The increasing pressure can narrow the reaction zone and elevate the peak value of the flame temperature. \(RP_{chem}\) and \(RP_{therm}\) behave in opposite ways along with the pressure. The former decreases, but the latter increases with the pressure.
4. The increasing strain rate can narrow the reaction zone, but has no obvious influence on the peak value of the flame temperature. The change in \(RP_{chem}\) and \(RP_{therm}\) with the increasing strain rate shared similarities with the pressure influence.
5. The flame temperature is predominantly influenced by the chemical effect of CO\({}_{2}\), while the thermal effect increases with the increase of CO\({}_{2}\) volume fraction, the increase of pressure, and the increase of stretch rate, and tends to exceed the chemical influence.  
Conceptualization, Y.C. and J.W.; methodology, Y.C.; software, Y.C.; validation, X.Z. and C.L.; formal analysis, C.L.; investigation, Y.C.; data curation, X.Z.; writing--original draft preparation, Y.C.; writing--review and editing, C.L.; visualization, C.L.; supervision, J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 9: The comparison of \(RP_{chem}\) and \(RP_{therm}\) with different global strain rate at \(p=1\) atm and \(\beta=0\%\), 50%.
# Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
## 4 Experimental Results
With “AR” we refer to experiments with active and reactive power and with “A+” we refer to experiments with all the selected attributes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Press**} & \multicolumn{2}{c}{**Decision Tree Classifier**} & \multicolumn{2}{c}{**Random Forest Classifier**} & \multicolumn{2}{c}{**Multilayer Perceptron Classifier**} \\ \cline{2-7}  & **A** & **AR** & **A+** & **A** & **AR** & **A+** & **AR** & **A+** \\ \hline Accuracy & 0.9573 & 0.9965 & 0.9999 & 0.959 & 0.9982 & 0.9999 & 0.9998 & 0.9998 \\ Recall & 0.8854 & 0.9984 & 0.9995 & 0.7908 & 0.9899 & 0.9999 & 0.9992 & 0.9993 \\ Precision & 0.8384 & 0.9787 & 0.9999 & 0.9891 & 0.9982 & 0.9997 & 0.9997 & 0.9992 \\ F-Measure & 0.8613 & 0.9884 & 0.9997 & 0.8789 & 0.9941 & 0.9998 & 0.9994 & 0.9993 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for the ironing press. With “A” we refer to the experiments that used only active power. With “AR” we refer to experiments with active and reactive power and with “A+” we refer to experiments with all the selected attributes. very similar behavior. Finally, when using all the selected features, the Random Forest classifier had 17.256 true positives, 4 false negatives and 1 false positive. 3 out of 4 false negatives are located in the transient phases of the operation of the ironing press, while the only false positive was again found during the operation of the stove. In summary, the experimental results show clearly that by using more features the effectiveness of the chosen methods is improved. It is not imperative to use the whole set of six features, but as previously mentioned, using a subset of four features provides results that almost match the results of the full set. As a byproduct, it is observed that the Random Forest classifier (in accordance to [27]) shows the best results for classification followed by the Decision Tree classifier with somewhat worse results. Finally, the Multilayer Perceptron classifier has the worst performance. For this classifier, the results were quite bad for the electric stove and the electric oven, which have similar power consumption. On the contrary, for devices that show unique behavior, such as the vacuum cleaner, the air-conditioner, and the ironing press, all classifiers returned very good results.
## 3 Results
### Research Questions' Answers
#### 3.2.3 RQ2  
What Types of Floods in Transportation Networks Were Modeled?  
The flood types analyzed in each article are given in Table 11. Generally, floods could be divided into three types, namely pluvial, fluvial, and coastal. A pluvial flood happens when a heavy rainstorm causes flood but there is no overflowing of water bodies. On the contrary, when the water level in a lake, river, or stream rises and overflows onto the banks, shores, and adjacent land, a fluvial or river flood occurs. Coastal flooding or storm surge is the overflowing of coastal areas by seawater. Tsunamis and powerful windstorms are two common sources of coastal flooding. There are 19 articles that  
Figure 5: Frequency of articles considering their scale and analyzed continents in the current SLR.  
worked on the indirect impacts of pluvial, 17 on fluvial, and 9 on coastal floods impacts on transportation networks.