esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### SDG Connections  
To evaluate the different contributions of the technologies in the EMP to the UN SDGs [59] (Table 11), the driving forces of the shared socioeconomic pathways (SSPs: the scenarios of projected socioeconomic global changes up to 2100) that are related to the SDGs were considered following the approach in [60]. The SSPs include six driving forces: demographics; economy and lifestyle; technology; environment and natural resources; human development; and policies and institutions. Following the methodology proposed in [60], the two driving forces that were most relevant to the paper were selected; namely, technology and environment and natural resources. The relationship between the technologies in the EMP and the SDGs were mapped based on the SSP driving forces (Figure 5). By matching the SDGs to the technologies, policy makers can align the sustainable development of FB with the UN's standards and proposals.  
The development and implementation of the technologies that were identified through the process of EMP related to the local driving forces of the technology and environment and natural resources SSPs. In particular, the relevant driving forces that were considered when mapping the SDGs were: development and transfer; carbon intensity and energy intensity; fossil constraints; and environment and resilience against climate change impacts. Each of the technologies in the selected case study were analysed against the driving forces. Figure 5 shows that the implementation of the technologies that were identified and prioritised in the EMP and also satisfied the performance objectives, thereby contributing to the achievement of some global SDGs (Table 11).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SDG** & **Description/Title** \\ \hline Goal 1 & No Poverty \\ \hline Goal 2 & Zero Hunger \\ \hline Goal 3 & Good Health and Well-Being \\ \hline Goal 4 & Quality Education \\ \hline Goal 5 & Gender Equality \\ \hline Goal 6 & Clean Water and Sanitation \\ \hline Goal 7 & Affordable and Clean Energy \\ \hline Goal 8 & Decent Work and Economic Growth \\ \hline Goal 9 & Industry, Innovation and Infrastructure \\ \hline Goal 10 & Reduced Inequalities \\ \hline Goal 11 & Sustainable Cities and Communities \\ \hline Goal 12 & Responsible Consumption and Production \\ \hline Goal 13 & Climate Action \\ \hline Goal 14 & Life Below Water \\ \hline Goal 15 & Life on Land \\ \hline Goal 16 & Peace, Justice and Strong Institutions \\ \hline Goal 17 & Partnership for the Goals \\ \hline \hline \end{tabular}
\end{table}
Table 11: The UN SDGs.
## 3 Mathematical Model
### Surrogate Modeling
#### 3.1.1 Deep Neural Networks  
Deep neural networks (DNNs) are a class of artificial neural networks (ANNs), similar to a human brain's neuronal network. The basic unit of an ANN is called a node, which collects information from one end and passes it to another node at the other end. A node contains the values of inputs, sums their weighted values, and then uses an activation function to produce an output [29]. A simple structure of a neural network is shown in Figure 2a. The nodes are arranged in a number of layers. Each layer is fully connected with its subsequent layer, but the nodes in a layer are not interconnected. The first and last layers of an ANN structure are called the input and output layers. The number of nodes in the input and output layers is equal to the number of input and output variables, respectively. After the first layer, there are one or more layers called the hidden layers [30]. A neural network model with more than two hidden layers and many nodes is referred to as a deep neural network [31].  
The values from each layer are mapped to the nodes in the successive hidden layers by being multiplied by some weights. The new value in each node is given by the equation:  
\[d_{j}^{k}=g(\sum_{i=1}^{n}W_{ji}^{k}x_{i}+b_{j}^{k}) \tag{10}\]  
where \(k\) represents the number of layers and \(W_{ji}^{k}\) are the weight parameters associated with each node \(i\) whose state is given by \(x_{i}\). The \(g\) is the activation function that helps to introduce the non-linearity and \(b_{j}^{k}\) are the biases parameters. The sigmoid and hyperbolic tangents are the most widely used activation functions. Once the activation function is selected, we need to determine the optimal weights and biases by minimizing the loss function. The mean square error (MSE) is used as a loss function in the present study:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\left\{\sum_{j=1}^{m}\left[y_{j}^{i}-y_{p,j}^{i} \right]^{2}\right\} \tag{11}\]  
where \(N\) represents the total number of input parameters: \(x_{p}^{1},x_{p}^{2},\ldots,\ x_{p}^{N}\). The \(P\) represents a dataset for a given input sample vector. The corresponding target outputs are representedby \(y_{p}^{1},y_{p}^{2},\ldots,\ y_{p}^{N}\), and \(y_{p,j}^{i}\) represents the desired output. In the MSE equation, a regularization term is added to prevent the neural network model from overfitting. Thus, a complete MSE equation can be written as:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\left\{\sum_{j=1}^{m}\left[y_{j}^{i}-y_{p,j}^{i} \right]^{2}\right\}+\ \lambda\sum_{l,\alpha,\beta}\left[W_{\alpha\beta}^{l}\right]^{2} \tag{12}\]  
where \(\lambda\) represents the regularization hyperparameter. In the current study, a total of six hidden layers were considered as shown in Figure 2b, where the first and the last layer had 800 neurons, and the middle four layers had 500 neurons each. To build our neural network model, we used Matlab's deep learning toolbox [32].
## 2 Materials and Methods
### Analysis of Phenolic Compounds  
Phenolic acid, flavanols and flavonoids analysis [16] was carried out in a HPLC instrument equipped with a quaternary gradient pump Jasco PU-2089, an autosampler Jasco AS-2057 Plus Intelligent Sampler and two detectors: A Jasco UV/Vis MD-910 PDA detector and a Jasco FP-2020 Plus Fluorescence detector (Jasco, Tokyo, Japan). The column was a C18 Poroshell 120 (Agilent technologies, Santa Clara, CA, USA), 2.7 um, (4.6 \(\times\) 150 mm), operating at 35 \({}^{\circ}\)C with a flow of 0.8 mL/min. Elution solvents were 2% acetic acid in HPLC grade water (Eluent A) and 2% acetic acid in HPLC grade acetonitrile (Eluent B). Gradient elution was as follows: from 98% to 95% A in 10 min, 95% to 90% A in 7 min, 90 to 82% A in 6 min, 82% to 80% A in 3 min, 80% to 70% A in 3 min, 70% to 50% A in 3 min, 50% to 0% A in 4 min and finally to 98% A in 1 min. Quantification was performed by means of calibration curves previously obtained by duplicate injections of pure standards solutions at known concentrations. All the analyses were carried out by triplicate. For compounds lacking pure standard, the calibration curve of structurally similar compound was used.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 2 The Time-Varying ISI Model of NLIN
### Phase and Polarization Rotation Noise (PPRN)  
Separating the 0-th ISI term from the summation in Equation (4), we obtain  
\[\tilde{s}_{n}=\tilde{a}_{n}+i\mathbf{R}_{0}^{(n)}\tilde{a}_{n}+i\sum_{l\
eq 0} \mathbf{R}_{l}^{(n)}\tilde{a}_{n-l}+\tilde{w}_{n}, \tag{5}\]  
where, as shown in [8; 23], the ISI matrix \(\mathbf{R}_{0}^{(n)}\) is Hermitian. Since the entire perturbation analysis is only meaningful to first order in the nonlinearity, the above can be rewritten as  
\[\tilde{s}_{n}=e^{i\mathbf{R}_{0}^{(n)}}\tilde{a}_{n}+i\sum_{l\
eq 0}\mathbf{R}_ {l}^{(n)}\tilde{a}_{n-l}+\tilde{w}_{n}. \tag{6}\]  
The Hermiticity of \(\mathbf{R}_{0}^{(n)}\) implies that \(e^{i\mathbf{R}_{0}^{(n)}}\) is unitary, and hence it can only represent phase and polarization rotation. Naturally, these rotations are time dependent because \(\mathbf{R}_{0}^{(n)}\) depends on the IC's data, and hence this phenomenon can be referred to as phase and polarization-rotation noise, or PPRN. The PPRN element is the most significant component in the time-varying ISI model, as it has the largest variance and longest correlation length [28]. For this reason, most of the initial efforts in the field of nonlinearity equalization have been directed towards PPRN mitigation. Moreover, it must be mentioned that since all coherent systems are inevitably equipped by phase-recovery and polarization demultiplexing algorithms, some of the PPRN contribution is compensated intrinsically, even when no dedicated NLIN mitigation algorithms are introduced.
## 2 Materials and Methods
### Emissions Reduction vs. Air Quality Improvement  
We analyzed the improvements in air quality to relate them to the emission reductions analyzed during the quarantine period. We used data from five air quality traffic stations in Bogota (Carvajal-Sevillana, Estacion Movil, Fontibon, Las Ferias, Minambiente) available in the Bogota Air Quality Monitoring Database (BAQMD) [54]. These data were used to assess the air quality concentration of CO, SO\({}_{2}\), NO\({}_{2}\) and O\({}_{3}\); the equipment used by BAQMD is specified in Table 11. For each station, data from April, May and June of 2018 were used to calculate the mean concentrations of each pollutant for each month. Similarly, data from April, May and June of 2020 were used to calculate mean levels of each pollutant during the lockdown. It is worth clarifying that BAQMD reports pollutant concentrations under standard conditions (1 atm and 25 \({}^{\circ}\)C). Thus, this allowed us to perform a comparison with concentrations during the same period of a base year (2018). This base-year comparison was also performed to control for meteorological conditions. We used tropospheric NO\({}_{2}\) data for April to June 2018 and 2020, retrieved from the ozone monitoring instrument (OMI), a visual and ultraviolet spectrometer aboard the NASA Aura spacecraft [55]. This information enabled the emissions analysis and estimation associated with road transportation in the four groups of pollutants previously cited. In addition, the average NO\({}_{2}\) retrieved from OMI data was estimated for the period of April to June 2018 and 2020 to evaluate the NO\({}_{2}\) level variation during the pandemic lockdown in Colombia [56].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fuel**} & \multicolumn{5}{c}{**Consumption (\%)**} \\ \cline{2-6}  & **Cars** & **Cargo** & **Public Transport** & **Motorcycles** & **Others** \\ \hline Gasoline & 77 & - & - & 22 & 1 \\ Diesel oil & 18 & 53 & 26 & - & 3 \\ CNG & 91 & 7 & 2 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuel consumption by vehicle category [51].
## 5 Design of the Other Key Devices
### Design of the Seedling Supply System  
The rice seedling-supply system comprises a horizontal box-transfer system and a longitudinal rice-feeding system. These two systems work together to supply seedlings to the transplanting mechanism from both the horizontal and longitudinal directions.  
The horizontal box-transfer system employs the box-transfer method of the blanket-seeding transplanter [19] (where a spiral axis drives the slider for a reciprocating linear motion) to achieve a lateral rice-seeedling supply. To enhance the success rate of picking seedlings and to minimize the rate of seedling injury in the picking-type transplanting mechanism, a traditional transplanter based on the lateral transfer of the seedling box at a constant speed was redesigned for variable speed. During the seedling-picking stage, the seedling box moves slowly, which is conducive to the completion of the seedling-picking action by the transplanting mechanism. On the other hand, the seedling box moves quickly during the process of rice conveying and planting, thereby improving the efficiency of transplanting. In the development of the horizontal box-transfer system, the circular gear pair that drives the rotation of the helical shaft was improved to an elliptical gear pair (as shown in Figure 17). This modification allows the spiral shaft to rotate at a variable speed during the working process, thereby enabling the variable-speed movement of the seedling box. Through the use of the variable-speed transfer box, the movement of the seedling box in the seedling-picking stage was 2.05 mm, which is 1.28 mm less than the lateral movement of the seedling-picking stage of the traditional horizontal seedling box-transfer mechanism. This improvement effectively ensures the success rate of picking seedlings.  
Figure 16: Seedling box design. (**a**) Size of the seedling tray; (**b**) Seedling box.  
Figure 17: Horizontal seedling box-transfer system. (1. Crescent-moon dowel; 2. Reciprocating lead screw; 3. Gear pair; 4. Transmission shaft).  
The longitudinal system operates by moving a row of seedlings in a longitudinal direction after they have been transplanted transversely (14 holes). This system features the intermittent feeding of seedlings (with seedlings being fed when the box is moved to both ends) and a high-precision feeding of seedlings (since the longitudinal delivery of seedlings has a significant impact on picking seedlings). To address these needs, a ratchet-type seedling-feeding mechanism [20] (as shown in Figure 18) was employed in this study. This mechanism not only guarantees that an accurate number of seedlings is fed at low speed but also prevents excessive seedling delivery due to inertia during high-speed operations.
# Effect of Camera Choice on Image-Classification Inference
###### Abstract  
The field of image classification using Convolutional Neural Networks (CNNs) to predict the principal object in an image has seen many recent innovations. One aspect that has not been extensively explored is the effect of the camera employed to acquire images for inference. We investigate this by capturing comparable images of five drinking vessels using six cameras in various scenarios. We examine the classification ranking of object classes when these images are input to an independently pretrained Resnet-18 model based on the ImageNet-1k dataset. We find that the camera used can affect the top prediction of object class, particularly in scenarios with a more complex background. This is the case even when the cameras have similar fields of view. We also introduce a metric called selectivity, defined as the mean absolute difference between prediction probabilities of similar relevant object classes (such as cups and mugs). We show that the effect of the camera is largest when the selectivity of the pretrained model between these object classes is small. The effect of camera choice is also demonstrated quantitatively by examining [PERSON]'s Kappa (\(\kappa\)) statistic. Finally, we make recommendations on mitigating the effect of the camera on image-classification inference.  
image classification; computer vision; inference; prediction; camera Article
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 4 Simulations
### BER Performance under Non-Ideal Synchronization Conditions with Constant Time Offset  
The following is the numerical simulation of the BER performance under non-ideal synchronization conditions with constant time offset. The simulation parameters are as follows: the \(Q\) of the \(Q\)-ary TH-PPM is 16; the pulse repetition period \(N_{s}\) is set to 10; and the time-hopping sequence period \(N_{h}\) is 10; the time synchronization error factor \(\mu\) is set to 0, 0.1, 0.4; the \(E_{b}/N_{0}\) is set to 0-12 dB; the BER is calculated to \(1\times 10^{-4}\); the simulation channel is a Gaussian white noise channel. The simulation result is shown in Figure 11.  
The simulation result shows that as the time synchronization accuracy deteriorates, the BER performance of the conventional TH-PPM rapidly deteriorates. The simulation result is worse than the theoretical analysis mentioned above. The reason is explained in the analysis process. This is because the theoretical analysis belongs to semi-quantitative analysis, which only considers the signal power decrease caused by the decrease, without considering the noise power increase caused by synchronization error. Therefore, the actual SNR deterioration is worse than the analysis mentioned above. However, the trend of the analysis is consistent with the simulation results, which require extreme time synchronization accuracy for the conventional TH-PPM. When the time synchronization error factor is 0.1, the BER performance decreases by about 1 dB, and when the time synchronization error factor is 0.4, the BER performance decreases by about 10 dB. The time synchronization error is larger, the BER performance deteriorates more rapidly. From the simulation, it is shown that the BER performance deterioration does not exceed 1 dB required \(\mu\leq 0.1\), which corresponds to a time synchronization error less than 0.1 of a time slot \(\Delta T\). While the \(\Delta T\) is a nanosecond, even for sub-nanosecond time synchronization is required, which is difficult to achieve.  
For the new TH-PPM, the simulation result shows that the time synchronization error has a small impact on the BER performance, which is consistent with the analysis. Compared to the conventional TH-PPM, the new TH-PPM can achieve better BER performance under non-ideal synchronization conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mu\) & **0** & **0.1** & **0.4** \\ \hline
**BER deterioration/dB** & & & \\
**(new TH-PPM)** & & & \\
**BER deterioration/dB** & & & \\
**(conventional TH-PPM)** & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: BER performance under non-ideal synchronization conditions with constant time offset.  
Figure 11: BER performance under non-ideal synchronization conditions with constant time offset.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 2 Materials and Methods
### Fundamental Concepts
#### 2.1.3 Geometrical Arrangements
From the PDs point of view, the solid angle of the TX lens part is not used for optical concentration. In Equation (4) this is expressed by a reduced \(\eta_{\text{RX}}\). In summary, using a hybrid lens for radial separation is most promising: besides the potential for \(e_{\text{c}}\approx 1\), commercial emitter and PD chips can be used. Since the hybrid lens can be fabricated at low cost by injection molding, the system costs are expected to be low.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 4 Methodology  
### Altimetry Data Processing  
As indicated above, current coastal altimetry products differ in terms of content. Thus a first step consists of processing the different data sets to obtain homogeneous variables for further comparison. Because there is no waveform data in PISTACH, we used the waveforms provided in ALES, and merged PISTACH and ALES using the measurement time common to all products. In Section 5.4, we also projected all along-track, cycle-by-cycle L2 data onto the X-TRACK 1-Hz reference grids to benefit from the XTRACK improved geophysical corrections.  
Figure 4: HK tide gauge sea level time series (in meters) interpolated to Jason-2 observations.
## 1 Introduction  
The cement manufacturing industry is one of the largest consumers of fuel and raw materials, and one of the main producers of CO\({}_{2}\) emissions [1]. In 2019, worldwide production of Portland cement was estimated at 4.10 billion tonnes, producing 8% of global anthropogenic carbon dioxide emissions [2]. The cement industry needs to reduce its carbon footprint to 1.55 billion metric tonnes per year by 2050 [3]; in order to do this, the cement producers and the research community have been striving to develop new environmentally friendly binders (low-CO\({}_{2}\) cements [4, 5, 6, 7, 8, 9]). Three different approaches have been proposed for the manufacture of low-CO\({}_{2}\) cements: (1) the utilization of non-carbonated sources of CaO instead of limestone as a constituent of Portland clinker (PC)-generating raw mix [10, 11]; (2) the increased production of blended cements, obtained by mixing ordinary Portland cement (OPC) with high amounts of supplementary cementitious materials (SCMs) [12, 13, 14, 15, 16, 17]; (3) the larger use of special cements (SCs), namely, hydraulic binders obtained from non-PCs [18, 19, 20, 21, 22, 23, 24, 25]. Calcium sulfoaluminate (CSA) cements have prompted the interest of the international cement research community for both their valuable technical properties and environmentally friendly features mainly associated with their manufacturing process [26, 27]. CSA cements contain ye'elimite (3 CaO-3 Al\({}_{2}\)O\({}_{3}\)-CaSO\({}_{4}\)) as the main component, and, depending on the synthesis temperature and the type and proportioning of raw materials (usuallylimestone, bauxite and natural gypsum), calcium sulfates (e.g., CaSO4, CaSO4 2H2O), belite (2 CaO-SiO2), brownmillerite (4 CaO-Al2O3 -Fe2O3), ternesite (4 CaO-SiO2 -CaSO4) and various calcium aluminates.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 2 System Models for RIS-Assisted Systems
### MIMO Systems
#### 2.2.2 Cell Communication System
The \(N_{j2}\) elements are defined by the diagonal RBMs \(\Theta=diag\left(\mu_{1}e^{\theta_{1}},\dots,\mu_{J}e^{\beta_{N_{j2}}}\right)\in \mathbb{C}^{N_{j}\times N_{j}}\), where \(\theta_{N_{2}}\in[0,2\pi]\) and \(\mu_{N_{2}}\in[0,1]\) are the phase and amplitude coefficient for the RIS element \(N_{j}\), accordingly. Maximum signal reflection is, i.e., \(\mu_{L2}=1\) V \(N_{j}\)[4]. The overall channel vector \(h_{k,j}=g_{k,j}+H_{1}\Theta h_{2k,j}\), conditioned on \(\Theta\) is delivered as \(h_{k,n}\sim\mathcal{CN}(0,R_{k})\), where \(R_{k}=\beta_{g,k}R_{BS,k}+\beta_{h_{2k,j}}H_{1}\Theta R_{RIS,k}\Theta^{H}H_{1}^ {H}\). Because \(R_{k}\) depends on the path-losses, the correlation matrices can also be identified by the network and \(H_{1}\), which are all expected to be known as clarified earlier [3]. * Channel Aging  
In practice, the UE mobility causes channel aging phenomenon [84; 85; 86; 87]. In [3], authors assume that the RIS's components move the same rating to a unique UE and this results in a Doppler shift that changes channel with time. Therefore, unlike the conventional block fading channel model, the flat fading channel coefficients differ from symbol to symbol. Still, they are fixed within a symbol. The symbol duration is expected to be less than or even to the coherence time of all UEs. Studies on the impact of channel aging assume the same in [84; 85; 86; 87]. The channel use is represented by \(n\in\{1,\dots,\tau_{c}\}\)[3]. Arithmetically, the channel \(h_{k,j}\) at the \(n\)-th time instant is denoted as a function of its initial state \(h_{k,0}\). The advanced component is [87]  
\[h_{k,j}=\alpha_{k,j}h_{k,0}+\bar{a}_{j,k}e_{k,j}, \tag{16}\]  
where \(e_{k,j}\sim\mathcal{CN}(0,R_{k})\) is the separate innovation component at the \(n\)-th time instant and \(\alpha_{kj}=J_{0}(2\pi f_{D}T_{s}n)\) is the sequential correlation coefficient of UE \(k\) in the middle of the channel at time \(0\) and \(k\) with \(J_{0}(\cdot)\) are the zeroth-order Bessel function of the first kind, \(T_{s}\) is the channel testing duration, \(f_{D}=\frac{vf_{D}}{c}\) is the maximum Doppler shift and \(\bar{a}_{j,k}=\sqrt{1-\alpha_{k,j}^{2}}\)[3].