esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Biotechnological Approaches for Sensing and Actuation
### Biotechnology for Energy Harvesting
supply to increase the distance of transmission (Figure 2B) or the amount of information (Figure 2C). Additionally, a combination of those variants can be used with respect to each project feature. An interesting application of the variant presented in Figure 2C can be found in green streets or rain gardens. As mentioned in [10], such systems can use several sensors for water flow and quality control. Those sensors should operate during water flow, so there is no need to use it actively in dry periods. MFCs can also effectively operate when water flow with organic compounds has taken place. Therefore, in this case, MFCs can be used as a special power booster to supply sensors and increase the frequency of data transmission. Additionally, green streets and rain gardens can be a good place for PMFC testing and applications. Thus, it is possible to develop a bioenergy supply sensor network for green streets or rain gardens. An LPWAN's basic station usually requires energy equal to tenths of W. Theoretically, it is possible to build MFCs that can generate such a power output in the case of the availability of organic substrate. Solving this issue and finding specific environmental conditions is the goal for further research. MFCs can be applied for the power supply of the low-power wireless transmitter. Low-power Bluetooth transmitters usually require less than 1 W of energy that can be effectively generated by MFCs. This means that MFC-supplied transmitters can be used for heterogeneous wireless networks. Such networks that include different transmitters have become very popular for wide spaces and IoT applications [56; 57]. Finally, it is necessary here to mention several issues related to MFCs in the applications mentioned above:  
* Unstable generation related to substrate availability, which leads to a necessary power control system and battery;
* The possibility of microorganism death for several reasons that require additional research on long-time BFC operation in natural environments [33];
* Low power output that leads to the necessity of its combination with other energy-harvesting technologies. Nevertheless, there is research focused on all these issues, and MFCs are still a promising energy harvesting technology for low-power devices for a city's IoT. Figure 2: Variants of microbial fuel cell (MFC) applications for low-power wide-area networks (LPWANs). (**A**) MFCs used as a power supply for wireless sensors to increase the time of operation; (**B**) MFCs as an additional power source for sensor transmitters to increase the distance of transmission; (**C**) MFCs as an additional power source to increase the amount of data from sensors.
## 2 Conductor Resistance under DC Supply
### Multi-Stranded Bimetallic Conductors of Circular Cross-Section  
For stranded bimetallic conductors with \(n_{Al}\) aluminum layers and \(n_{steel}\) layers, as is the case for aluminum conductor steel-reinforced (ACSR) conductors, \(r_{D,20^{\circ}\text{C}}\) can be calculated as Cigre 345 [1] by the following formula:  
\[\frac{1}{r_{DC,20^{\circ}\text{C}}}=\frac{\pi d_{steel}^{2}}{4\rho_{steel,20^{ \circ}\text{C}}}\;\left[1+\sum_{j=1}^{n_{steel}}\frac{6j}{\sqrt{1+(\pi D_{steel,j}/\lambda_{steel,j})^{2}}}\right]+\frac{\pi d_{Al}^{2}}{4\rho_{Al,20^{\circ} \text{C}}}\;\left[1+\sum_{i=n_{steel}+1}^{n_{steel}+n_{Al}}\frac{6i}{\sqrt{1+( \pi D_{Al,j}/\lambda_{Al,j})^{2}}}\right]\;[\text{m}/\Omega] \tag{4}\]  
Figure 1: Stranded conductor and lay length.  
where \(d_{AI}\) [m] and \(d_{stel}\) [m] are the diameters of the aluminum and steel strands, respectively; \(i\) = 1, 2,... \(n_{Al}\) is the aluminum layer number; \(j\) = 1, 2,... \(n_{stel}\) is the steel layer number, \(D_{AI,i}\) [m] and \(D_{stel,j}\) [m] are the mean diameters of the \(i\)-th aluminum and \(j\)-th steel layers; and \(\lambda_{AI,i}\) [m] and \(\lambda_{stel,j}\) [m] are the lay lengths of the \(i\)-th aluminum and \(j\)-th steel layers. Note that the lay length is the axial distance required for the wire to complete one full revolution around the conductor diameter.  
However, according to Cigre Working Group 22.12 [13], for stranded bimetallic conductors with \(n_{Al}\) aluminum layers and \(n_{stel}\) layers, \(r_{DC,20^{\circ}\text{C}}\) should be calculated as follows:  
\[\frac{1}{r_{DC,20^{\circ}\text{C}}}=\frac{\pi d_{stel}^{2}}{4\rho_{stel,20^{ \circ}\text{C}}}\left[1+\sum_{j=1}^{n_{stel}}\frac{6j}{\sqrt{1+\left(\pi D_{ stel,j}/\lambda_{stel,j}\right)^{2}}}\right]+\sum_{i=1}^{n_{Al}}\frac{\pi d_{AI,i}^{2}n _{AI, strands,i}}{4\rho_{AI,20^{\circ}\text{C}}\sqrt{1+\left(\pi D_{AI,i}/ \lambda_{AI,i}\right)^{2}}}\left[\text{m}/\Omega\right] \tag{5}\]  
where \(n_{AI, strands,i}\) [-] is the number of strands in the \(i\)-th aluminum layer.  
From a simple observation of Equations (3)-(5) it can be deduced that in order to determine \(r_{DC,20^{\circ}\text{C}}\), many parameters of the conductor are required, which are not always available; they are difficult to measure or at least are not known with accuracy. The results are also highly dependent on the exact value of the electrical resistivity of the specific composition of the aluminum strands, which is not exactly known a priori, because it requires a specific measurement.
## 3 Results and Discussion
### Identification of Potential Characteristic Volatile Compounds  
To assess the discriminatory power of each compound, variable importance in projection (VIP) scores derived from the PLS-DA model were employed. Only compounds with VIP scores greater than one were considered significant contributors to group separation and were thus selected as key volatile markers. The VIP (variable importance in projection) scores, which provide the volatile compounds of three experimental groups in Figure 3A-C (VIP > 1), highlight the top 10 volatile compounds contributing to the separation in the PLS-DA model, with VIP values greater than one (as explained in Table 1). Among these volatile compounds, 2-methylpropanoate, triethyl phosphate, and 1,2-propanediol are the top three significantly different volatile compounds, as illustrated in Figure 3D.  
Based on the analysis of these volatile compounds, we found that the three treatment methods significantly influenced the composition of volatiles in large yellow croaker during storage. These changes primarily involved nitrogen compounds, aldehydes, ketones, esters, phenols, alcohols, ethers, hydrocarbons, and other compounds. Among these, only volatiles with a VIP (variable importance in projection) score above one were deemed significant contributors to the fish's volatile profile. From the loadings and VIP plots, specific compounds emerged as key differentiators among the treatment groups. In Group C, 1,3-dimethylbenzene (H2), dodecane (H4), nonanal (D2), and toluene (H1) were the primary contributors to variations in volatile profiles. For Group L, pentadecane (H7) and 2,5-cyclohexadiene-1,4-dione, 2-(1,1-dimethylethyl)-5-(2-methyl-2-propen-1-yl) (R7) were identified as the main distinguishing compounds. Meanwhile, Group P was characterized by nonanal (D2), 2,5-cyclohexadiene-1,4-dione, 2-(1,1-dimethylethyl)-5-(2-methyl-2-propen-1-yl) (R7), n-dodecane (H4), n-pentadecane (H7), and n-hexadecane (H8). Although 10 characteristic volatile compounds were identified in this study, VIP plot analysis suggested that only benzene,1,3-dimethyl (H2) and 2,5-cyclohexadiene-1,4-dione, 2-(1,1-dimethylethyl)-5-(2-methyl-2-propen-1-yl) (R7) were key in distinguishing different groups of large yellow croaker samples.
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
###### Abstract  
Ubiquitous learning is an evolution of educational learning processes that implements the concept of ubiquity. That is to say, it is found at all times and in all places. This article summarizes our previous works and proposes an alternative to answer our main research question: how can we develop a U-Learning model that integrates connective learning and xAPI user experiences? This paper presents the U-Learning Model Supported by Learning Experiences and Connective Learning for virtual higher education (U-CLX Model) to measure U-Learning in virtual institutions. The U-CLX Model measures ubiquitous learning in four dimensions: time, place, medium, and context. To develop the model, we proposed a theoretical and technological framework, a definition of the U-Learning concept, a unit of measurement for ubiquitous learning (UbiquoL), and a description of the measurement process. We validated the proposal by thematic specialists and applied the instrument in two universities. The model aims to assess the level of ubiquitous learning in virtual higher education institutions and to suggest how these institutions can improve within their current operations.  
ubiquitous learning; virtual higher education; information technology; learning model; interactive techniques +
Footnote †: journal: _Applied_  
We reviewed other models and approaches to obtain different contexts and appreciation in order to achieve this. We carried out two different literature reviews and summarized the findings. Our proposal measures the level of ubiquitous learning achieved by virtual higher education institutions (U-CLX Model), considering four dimensions: time, place, medium, and context. We propose a method for measuring U-Learning using the volume equation of a hypersphere in 4 dimensions (R4).  
For its validation, we worked with higher education specialists, IT researchers, and mathematical professionals who supported the proposal. Finally, we presented the application in two formal institutions of virtual higher education.  
The paper describes the conceptual mathematical basis, the validation, and application of the model, and the results obtained are structured as follows: Section 2 provides the basic methodology of the theoretical proposal; Section 3 provides the fundamental concepts; Section 4 describes the general model; Section 5 discusses the model evaluation environment, the general process, self-assessment, and the indicators of the U-CLX Model. Finally, Section 6 discusses the proposal's validation and results, and Section 7 exposes conclusions and future work.
## 2 Nanodiamonds Synthesis Techniques
### Synthesis of Nitrogen-Vacancy (NV)Centers-Enriched Nanodiamonds (NDs)  
Recently, [PERSON] et al. [55] reported the observation of NV\({}^{-}\) emissions from unprocessed detonation NDs, with a brightness comparable to what is obtained with highly processed fluorescent 100 nm HPHT NDs. Moreover, diamond CVD synthesis of highly-oriented NV centers was reported [56], with the possibility of crushing fluorescent bulk diamond into NDs [57].  
Despite that, the standard way to produce NV-centers-enriched NDs is a complex, multi-step, and expensive process. Synthetic HPHT diamonds typically contain 100 ppm of nitrogen atoms as an impurity in the diamond structure [58] and to form NV centers, vacancies are created in the lattice by high-energy particle irradiation (electrons, protons, He+) followed by a vacuum annealing at 700-1000 \({}^{\circ}\)C [59]. The role of the annealing is to increase the mobility of the nitrogen atoms inside the lattice, which are then trapped by the carbon vacancies to form NVs. One could directly irradiate NDs to increase the NV-fluorescence, but it has been proven by density functional theory [60] and Monte Carlo simulations [61] that NV concentration increases non-linearly with crystal size. In particular, the probability of forming NV centers in 5 nm sized NDs is 4.5 and 25 times lower compared to 20 and 55 nm NDs, respectively [61]. Thus, it is more efficient to increase NV concentration in micro-sized diamonds and then reduce their size to obtain fluorescent NDs. This was done by [PERSON] et al. [23], in what has become the standard fluorescent NDs synthesis technique. The procedure is shown in Figure 6. The starting material is HPHT-synthesized microdiamonds that are then irradiated with a 10 MeV electron beam to form vacancies. The following step is annealing at 750 \({}^{\circ}\)C to form fluorescent microdiamonds. The next step is the reduction of diamond size, achieved by nitrogen jet milling and ball milling to obtain NDs with a crystal size smaller than 10 nm. For bioimaging, which is the main field of application for fluorescent NDs, the number of NV centers should be as high as possible. For this reason, only HPHT diamonds that contain a relevant amount of nitrogen impurities are used. In contrast, CVD diamond films contain a very small number of nitrogen atoms [62]. Consequently, highly fluorescent NDs are not produced by this mechanism; rather CVD is used to produce nanocrystalline diamond films with single NV centers, suitable for quantum information technologies.
# Bit-Level Construction for Multiplicative-Repetition-Based Non-Binary Polar Codes +
## 1 Introduction
Inspired by this, we consider bit-level construction for MR-based non-binary polar codes and propose an improved construction based on the CD method and MC method. The main contributions of this paper are summarized as follows:  
First, we revisit CD construction for non-binary polar codes. We then calculate the lower bound of the channel capacity and the symbol error probability for each synthesized channel, which are then used to determine the number of bits each channel can carry. Additionally, we compute the error probability for all transmission patterns and select the optimal pattern that minimizes the error probability of the synthesized channels. Compared with the conventional symbol-level construction, the proposed bit-level construction achieves better BLER performance with the same complexity. Second, due to the high complexity of CD construction, we also explore the MC method to reduce construction complexity. We compute the error probability of each synthesized channel carrying \(i\) information bits, \(1\leq i\leq q\). Based on this, we select the optimal information channels (\(i=q\)) and partial information channels (\(i<q\)) to minimize the error probability of the non-binary polar code. Recently, we noticed that the construction in the appendix of [14] is a special case of our bit-level construction-based MC method. Simulation results demonstrated that the proposed construction method improved the BLER performance compared with the original construction and the scheme in [12]. Third, for high spectral efficiency, MR-based non-binary polar codes combined with BICM and a hybrid MLC and BICM framework [28] are also considered. To improve BLER performance, we integrate probabilistic shaping with BICM and introduce a bit-level construction for shaping MR-based non-binary polar codes. The article significantly extends the work presented in preliminary form in [29], where we demonstrated the potential of bit-level construction for non-binary polar codes. We provide a more detailed description of bit-level CD and MC constructions. Furthermore, we propose a novel framework combining non-binary polar codes with hybrid MLC and BICM coded modulation, along with a bit-level construction for shaping MR-based non-binary polar codes. The rest of this paper is organized as follows: Section 2 introduces the notations, definitions, and preliminaries of non-binary polar codes. Section 3 presents the improved construction scheme. In Section 4, the probabilistic shaping method based on the improved construction for multiplicative repetition non-binary polar codes is described. Section 5 provides simulation results for our scheme. Finally, the conclusions are given in Section 6.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 3 Overview of Big Data
### Areas of Utility of Big Data
#### 3.4.4 Transport and Energy Domain  
In the field of transport, population movements can be modeled to adapt infrastructure and services such as train schedules. For this purpose, data from transit passengers, bicycles, and shared cars, as well as from the geolocation of people or cars, are used, such as cellular data and satellite tracking systems. In the field of energy and sustainable development, smart metering systems (electricity, gas, water) generate Big Data that can rationalize energy consumption [71]. In addition to offering citizens the opportunity to better control their consumption, these meters allow remotely cutting, with the agreement of customers, the supply of equipment to avoid overloading the network. In air transport, by combining sensor data on airplanes with weather data, air corridors can be modified to save fuel, improve aircraft design, maintenance, or safety [72].
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 3 Receiver Comb Technologies
### Regeneration from Two Pilots
For example, in the case of the PLL, one could consider transmitting the central lines and an outer line. The regenerated comb would use the central line as the seeding wave. To obtain the frequency spacing, one could detect the phase variations between the other pilot tone and a carrier of the regenerated comb neighboring or at the same location as this pilot tone. The use of two unmodulated lines at each side of the comb has been used to improve the phase noise of the carriers generated by a gain-switched laser [90]. Similarly in the case of obtaining the frequency spacing from a data channel processed through DSP, this channel is not required to be at any specific location, although its location must be accounted for. In the context of SDM, comb regeneration has been demonstrated by dedicating one core of a multi-core fiber to transmission of a set of comb pilots [91], whereas the data were transmitted in the remaining cores. The comb was detected in a photodiode in order to obtain the frequency spacing, and the obtained RF signal then drove a modulator seeded by the pilot comb. Frequency estimation was not included in the DSP, but carrier phase estimation was performed. We should note that this demonstration was performed with QPSK modulation only and that the comb regeneration was enabled by the high OSNR of the comb at the receiver side.
## 3 Database of Water Content and Hyperspectral Information
### Data Acquisition Method
#### 3.1.5 Drone Flight and Sampling of Soil  
The actual width of the shooting area is 20 m, but considering coordinate errors, among other things, the shooting width was set to 30 m. The shooting width is affected by the altitude of the drone, and considering the overlap of 1.6, the shooting altitude is 60 m and the speed is 5.49 m/sec, shown in Table 2. Accordingly, the time-of-flight path based on a length of 60 m is 22 s, and when landing, takeoff, departure, turning, and arrival points are comprehensively considered, the experiment time for one cycle (Figure 2) is about 1 min.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specification** & **Drone** & **Camera** & **GNSS Sensor** & **Battery** \\ \hline Model & \begin{tabular}{c} Matrice 300 Pro \\ 6.3 \\ 810 \(\times\) 670 \(\times\) 430 \\ \end{tabular} & \begin{tabular}{c} Shark microHSI 410 \\ 0.7 \\ 90 \(\times\) 70 \(\times\) 200 \\ \end{tabular} & \begin{tabular}{c} TW4721 \\ 0.1 \\ 25 diameter \\ \end{tabular} & \begin{tabular}{c} - \\ 0.5 \\ 35 \(\times\) 70 \(\times\) 50 \\ \end{tabular} \\ \begin{tabular}{c} Continuous flight is \\ possible for at least 30 \\ \end{tabular} & \
\begin{tabular}{c} Providing optimized \\ analysis images of the \\ near-infrared (NIR) \\ region of the spectrum \\ \end{tabular} &
\begin{tabular}{c} Providing true response \\ over its entire bandwidth, \\ thereby producing superior \\ multi-path signal rejection \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of equipment.  
Figure 6: Drone flight: (**a**) Composition of drone; (**b**) Push-broom method modified as in [32].  
At road construction sites, the filling height of the subgrade soil is generally 20 cm. In tests for the quality control of compaction [33], about 6 to 13 cm of soil is sampled, and water content is measured at this time. However, the hyperspectral information to be converted to water content is measured from a drone and can also be called the surface water content, because the camera measures only the reflectance of the ground surface. Therefore, it is necessary to confirm whether the hyperspectral information can reflect the inside water content such as Figure 7. Soil was sampled from the surface and 6 cm or less below the upper part, underneath the number indicated by spray lacquer, and the relationship between the surface and inside water content was confirmed. The amount of soil sampled was at least 50 g. At this time, the water content of the sample was measured after obtaining hyperspectral information from the drone flight.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 3 Perturbation Observer-Based Nonlinear Adaptive Controller Design
### NAC Design of WT  
#### 3.1.1 Input/Output Linearization  
Input/output linearization of WT speed dynamics in system Equation (6) can be represented as  
\[y_{1}^{(2)}=\frac{1}{I_{\mathrm{tot}}}(\hat{T}_{\mathrm{m}}-\hat{T}_{\mathrm{e }}) \tag{8}\]  
As the electromagnetic torque has much faster response than the mechanical torque, from the perspective of control of WT, \(\hat{T}_{\mathrm{e}}\simeq 0\). Equation (8) can be expressed as  
\[y_{1}^{(2)} = \frac{1}{I_{\mathrm{tot}}}\hat{T}_{\mathrm{m}}\] \[= F_{1}(x)+B1(x)u_{1}\]  
where  
\[\begin{array}{rcl}F_{1}(x)&=&A[-\frac{C_{\mathrm{P}}}{\omega_{\mathrm{m}}}- \frac{RV}{F^{2}}E]\frac{\mathrm{d}\omega_{\mathrm{m}}}{\mathrm{d}t}\\ &&-\frac{AE\beta}{\tau_{\beta}}[-0.088e^{-125\tau}-\frac{0.08V^{2}}{F}+\frac{0. 105\beta^{2}}{(1+\beta^{3})^{2}}]\frac{\mathrm{d}\beta}{\mathrm{d}t}\\ \end{array} \tag{10}\]  
\[\begin{array}{rcl}B1(x)&=&\frac{AE\beta}{\tau_{\beta}}[-0.088e^{-12.5\tau}- \frac{0.08V^{2}}{F}+\frac{0.105\beta^{2}}{(1+\beta^{3})^{2}}]\end{array} \tag{11}\]  
where  
\[\begin{array}{rcl}A&=&\frac{\rho\pi R^{2}V^{3}}{2\omega_{\mathrm{m}}}\\ E&=&(39.27-319\tau+1.1\beta)e^{-12.5\tau}\\ F&=&\omega_{\mathrm{m}}R+0.08\beta V\\ \tau&=&\frac{1}{\lambda+0.08\beta}-\frac{0.035}{\beta^{3}+1}\end{array} \tag{12}\]  
Figure 3: Brief overall control structure of the PMSG-WT.  
Please note that \(\dfrac{\mathrm{d}V}{dt}\) is not included in the FLC design, which cannot be directly measured.  
As \(\det[B1(x)]=\dfrac{AE\beta}{\tau_{\beta}}[-0.088e^{-12.5\tau}-\dfrac{0.08V^{2}}{F }+\dfrac{0.105\beta^{2}}{(1+\beta^{3})^{2}}]\
eq 0\) when \(V\
eq 0\) and \(\beta\
eq 0\), i.e., \(B1(x)\) is nonsingular for all nominal operation points. Therefore, the FLC is expressed as  
\[u_{1} = B1(x)^{-1}(-F_{1}(x)+v_{1}) \tag{13}\]  
And the nonlinear system is linearized as  
\[y_{1}^{(2)}=v_{1} \tag{14}\]  
\[v_{1} = \dot{y}_{1\mathrm{r}}+k_{11}e_{1}+k_{12}e_{1} \tag{15}\]  
where \(v_{1}\) is input of linear systems, \(k_{11}\) and \(k_{12}\) are gains of linear controller, \(y_{1\mathrm{r}}\) is the output reference, and \(e_{1}=y_{1\mathrm{r}}-y_{1}\) as tracking error. The error dynamic is  
\[\ddot{e}_{1}+k_{11}e_{1}+k_{12}e_{1}=0 \tag{16}\]