esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Introduction  
The AS was produced at the Arjan Prinsen Farm (APF), a dairy farm in Haarlo, The Netherlands, with about 60-80 cows and an anaerobic mono-digester for the dairy manure. The digested manure was separated by a screw press and the liquid fraction was further treated. Residual heat from the combined heat-and-power unit (CHP) was used to increase the temperature of the liquid digestate in the N stripper (45 \({}^{\circ}\)C) in order to promote NH\({}_{3}\) transfer to the air. Concentrated sulfuric acid was used to recover the NH\({}_{3}\) emitted from the liquid fraction of the digestate, according to methods described earlier [2]. The target pH for the AS product was <5.5, in line with the criteria developed for processed animal manure products (RENURE) [1; 13].  
Three experiments were performed: (1) a full factorial greenhouse experiment using two crops, maize and grass, and two soils, a sandy soil and a clay soil (Table A1, in Appendix A). Both the yield and gaseous emissions of the GHGs and the NH\({}_{3}\) were measured, (2) a field demonstration with both crops (maize and grass) and both soils (clay and sand) focused on maize yields in 2021, and grass yields in 2022 on both soil types. GHG and NH\({}_{3}\) emissions as well as the leaching of S were also measured, and (3) a sheltered flux chamber experiment was conducted to test the NH\({}_{3}\) emission from AS.  
In the greenhouse experiment and the field demonstration, AS was compared to calcium ammonium nitrate (CAN) (Table A2) at equal rates of N and S fertilization by correcting for the S content of CAN by supplementing it with gypsum. This resulted in S fertilization rates of 296-588 kg SO\({}_{3}\) ha\({}^{-1}\), depending on soil and crop (see Section 2.3, or Table A3), that are in exceedance of crop needs, but are excellent to test the applicability and safety of the AS. The greenhouse experiment was designed to test the nitrogen fertilizer replacement value (NFRV) [5]. The field demonstration was used to test the application of AS at a field scale and at a fertilization rate of 100% of the N fertilization advice, and therefore control plots were assumed to be less relevant. The method of application of the AS was liquid injection to approximately 5 cm soil depth, while the application method for CAN, a granular fertilizer, was broadcast spreading. We use CAN as a reference fertilizer as this is the most used N fertilizer in NW Europe.
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 2 Methods
### Deep Neural Network  
A deep neural network (DNN) is a simple network with many hidden layers. A large number of hidden layers is advantageous for dealing with time-series data [28]. Our DNN model contains 11 hidden layers. The first four hidden layers each contain 256 neurons, the middle three layers have 128 neurons, and the last four layers have 64 neurons. This decrease in neuron count helps DNN to compress the information into fewer neurons. The last layer, the output layer, contains four neurons representing the four classes in our model (Figure 2). Before each decrease in the size of the hidden layer, we apply batch normalization to avoid internal covariate shifts [35]. The details of the DNN model architecture are given in the Supplementary Material (Table S1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DNN** & **CNN** & **RNN** \\ \hline Number of dense layers & 11 & 4 & 2 \\ Special layer & None & Convolutional layer & LSTM \\ Activation function after dense layers & ReLU & ReLU & ReLU \\ Activation function after the final layer & SoftMax & SoftMax & SoftMax \\ Trainable parameters & 605,572 & 87,170 & 871,684 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the three neural network architectures used.  
Figure 2: The DNN architecture used in this study. The 5 s waveform is discretized as 1251 samples and fed to 11 dense layers, including two batch normalization (B.N) operations between hidden layers 4 and 5 and hidden layers 7 and 8. The model produces four values indicating the probability of each vehicle class.
# Computer-Aided Diagnosis of Skin Diseases using Deep Neural Networks
## 1 Introduction
### Related Work  
Convolutional Neural Networks (CNNs) are computer models inspired by biological visual cortex. These models have been proven to be very efficient, accurate and reliable in image classification. They have already achieved near-human performance in many challenging natural image stratification tasks [18; 19; 20; 21] and have also been used to classify diseases from medical images [4].  
Towards automated skin disease classification, [PERSON] et al. [22] employed CNNs to extract features and trained a linear classifier on them using 1300 images of Dermofit Image Library to perform 10-ary classification. Similar approach was used by [PERSON] et al. [23] on MoleMap dataset to do 15-ary classification. [PERSON] et al. [24] used a pre-trained Inception v3 on around 130,000 images. Although their results for two binary-classification tasks are merely \"on par with all tested experts\", yet this work was the first credible proof-of-concept based on a large dataset that DL can make a practical contribution in real-world diagnosis. Following their steps, [PERSON] et al. [25] pitched their fine-tuned Inception v4 model against 58 dermatologist after evaluating binary classification performance of their model on two test sets of size 100 and 300 only. The sensitivity and specificity of their Deep Neural Network (DNN) model is certainly higher than that of dermatologists' mean performance on two private test sets, however, their performance on publicly available International Symposium on Biomedical Imaging (ISBI) 2016 Challenge [26] test data is below the performance of first two winning entries in that challenge.  
To address the scarcity of available data for tracking and detecting skin diseases, [PERSON] et al. [27] developed a domain-specific data augmentation technique by merging individual lesions with full body images to generate large volume of synthetic data. [PERSON] and [PERSON] [28] also used DNN to segment lesions, extract their dermoscopic features and classify them.
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 5 KAFIN for Option Pricing
### KAFIN for Option Pricing
#### 5.2.2 Initial Condition for European Call Option  
The initial value loss for call option pricing, using a neural network, ensures that the network's output satisfies the initial conditions of the option pricing model. For a European call option, the initial condition is determined by the payoff function at the time of option issuance (time \(t=0\)). The initial condition for a European call option at \(t=0\) is given by the payoff function:  
\[C(S,0)=\max(S-K,0), \tag{20}\]  
where \(C(S,t)\) is the option price, \(S\) is the underlying asset price, and \(K\) is the strike price.  
The initial value loss \(\mathcal{L}_{\text{initial}}\) ensures that the neural network output at \(t=0\) matches the initial condition \(\max(S-K,0)\). Mathematically, this loss can be defined as the mean squared error between the neural network's predicted option prices and the actual initial condition over a set of sampled asset prices \(S_{i}\). Let \(u_{\theta}(S,t)\) be the output of the neural network with parameters \(\theta\), representing the option price, and the initial loss can be formulated as  
\[\mathcal{L}_{\text{initial}}=\frac{1}{M}\sum_{i=1}^{M}(u_{\theta}(S_{i},0)- \max(S_{i}-K,0))^{2}, \tag{21}\]  
where \(M\) is the number of sampled points \(S_{i}\).
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 6 VLC Application
### Underwater Communication  
Due to the extremely high attenuation of radio frequency waves in water, RF or near-infrared OWC cannot propagate well in sea water [130]. Therefore, an underwater optical communication (UWOC) network should adopt VLC communication as shown in Figure 16.  
For underwater application, the attenuation of the light can be evaluated through Equation (23) [132]:  
\[P_{d}=P_{0}e^{-c(\lambda)d} \tag{23}\]  
where \(P_{0}\) is the output power of the light source, \(P_{d}\) is the received power over certain distance \(d\), and \(c(\lambda)\) is coefficient of water associated with attenuation. By evaluating Equation (23) and by assuming the output power to be 1 mW we can obtain the propagation  
Figure 16: Illustration of application scenarios of duplex UWOC and charging technologies based on a micro-LED array [131]. Figure reused with permission from John Wiley and Sons.  
Figure 15: LiFi application in an airplane cabin. Reprinted from [28], Copyright 2019, with permission from Elsevier.  
distance of visible light in ocean water, as illustrated in Figure 17. In clear ocean water, blue and green light are usually preferable to red light, as illustrated in Figure 17a; however, the attenuation and scattering of red light are lower for certain environments such as a highly turbid harbor [133], as illustrated in Figure 17b.
## 4 Experimental Results and Analysis
### Validation of Module Structure  
Based on the full-attention module, we tested the impact of RFAC blocks with different structures on feature-extraction ability, primarily examining five structures as illustrated in Figure 7. The test results are shown in Table 3. Among them, the effect of the base and adding a res path is not much different, because the \(1\times 1\) convolution is equivalent to linear mapping in nature, and its role is equivalent to the res path. Analyzing the experimental results of adding the attention mechanism in different locations, we can see that adding in the external effect is better, and adding the residual path after the attention module reduces the possible gradient disappearance problem.
###### Abstract  
Compressive sensing (CS)-based data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). Traditional CS-based data-gathering approaches require a large number of sensor nodes to participate in each CS measurement task, resulting in high energy consumption, and do not guarantee load balance. In this paper, we propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings' spatial correlation in WSNs. In particular, a novel data-gathering scheme with joint routing and CS is presented. A modified ant colony algorithm is adopted, where next hop node selection takes a node's residual energy and path length into consideration simultaneously. Moreover, in order to speed up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. More importantly, theoretical proof is given that the equivalent sensing matrix generated can satisfy the restricted isometric property (RIP). The simulation results demonstrate that the modified diffusion wavelets' sparsity affects the sensor signal and has better reconstruction performance than DFT. Furthermore, our data gathering with joint routing and CS can dramatically reduce the energy consumption of WSNs, balance the load, and prolong the network lifetime in comparison to state-of-the-art CS-based methods.  
compressive sensing; wireless sensor networks (WSNs); diffusion wavelets; ant colony algorithm; data gathering +
Footnote †: journal: Sensors
## 2 Multiple Types of Non-Probabilistic Representation Approaches for Epistemic Uncertainties  
### Interval Approach  
Two interval representation approaches for epistemic uncertainty are considered: (1) Single interval, and (2) multiple intervals. In some situations, the sampling information of epistemic uncertainty cannot be obtained, and only possible boundaries of epistemic uncertainty are acquired through theoretical calculation or physical analysis, the single interval is used to represent epistemic uncertainty. In other situations, the uncertain information can be determined using experiments or measurements, but the observation data are intervals because of limitations of the measuring equipment. Therefore, multiple interval information is available for the representation of epistemic uncertainty.  
In the single interval approach, the epistemic uncertainty \(x\) is represented using an interval vector:  
\[x\in[\underline{x},\overline{x}],\underline{x}=x^{c}-\Delta x,\overline{x}=x^{ c}+\Delta x, \tag{1}\]  
where \(\underline{x}\), \(\overline{x}\), \(x^{c}\), and \(\Delta x\) are the lower bound, upper bound, midpoint, and radius of epistemic uncertainty, respectively.  
For an engineering problem whose uncertain input variables \(\mathbf{x}\) are represented using the single interval method, the system response function \(y=f(\mathbf{x})\) is also an interval variable. The uncertainty of \(y\) can be calculated using a series of available methods, such as the perturbation analysis method and reliability index method.  
In the multiple interval approach, the epistemic uncertainty \(x\) is represented using \(m\) intervals \([a_{i},b_{i}],i=1,\cdots,m\), where \(a_{i}\) and \(b_{i}\) are the lower and upper bounds of the \(i\)-th subinterval, respectively. These subintervals can be overlapping or non-overlapping intervals. These multiple intervals include partial uncertainty information of \(x\). In actual engineering problems, many uncertain variables \(\mathbf{x}\) represented using the multiple interval approach should be included, and then the uncertainty of the response function \(y=f(\mathbf{x})\) can be analyzed.
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 5 Conclusions and Future Work  
Routing in multi-hop ad hoc networks is a challenging task. Thus, analyzing routing protocols is essential to maintaining their development, modernization and maintenance. Various methods have been proposed to analyze routing protocols and evaluate their performance in a variety of scenarios. The majority of these methods used simulation testbeds; only very few contributions applied mathematical modeling to analyze reactive routing protocols. In this study, we have proposed a mathematical-based model targeting the DSDV protocol; it is one of the main widely deployed proactive routing protocols, with many new extensions and variants.  
We tested the validity of the proposed model using simulations, and the results have shown that the DSDV path duration is inversely proportional to the speed of nodes, especially when this value exceeds 10 m/s. The path duration has the same curve, with better results due to the implementation of the settling time concept. Furthermore, we conclude that the rate of path utilization decreases with the increase of nodes' speed, as it decreases in the path duration as the speed increases, and the periodic update interval remains constant. The path duration was significantly decreased when the number of hops becomes larger (more than three hops), even if the node's speed was low. Furthermore, it has been shown that the settling time and keeping the periodic update interval fixed have a significant impact on the path duration and the path utilization rate. We have noticed that the protocol has a negative effect on the path duration when the number of hops increases--in contrast to the increase of the nodes' speed, this effect remains limited.  
As a future work, we will analyze both factors and try to reduce their impact without affecting other performance factors, such as routing overhead. Another research line is to study link duration, path duration and path utilization of routing protocols using other mobility models.  
Conceptualization and methodology, S.S., M.A. and R.Z.; software, M.A. and R.Z.; validation, formal analysis and investigation, S.S., M.A. and R.Z.; writing--original draft preparation, S.S.; and writing--review and editing and visualization, S.S., M.A. and R.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 A Problem Setting and Terminology for Formulations
### A Problem Setting for Solving Tantrix by a Computer  
Humans may play Tantrix on the table, on the floor or at any other places they like. For computers, however, we have to prepare artificially and appropriately a space where solutions are made. Consider an infinite hexagonal lattice plane (as shown in Figure 4), where the size of each hexagon is as the same as the one of a tile. When Tantrix is played by a computer, a tile is placed to fit on each hexagon, which we call a _place_ (Figure 4(a)). In addition, a collection of multiple places where a solution is supposed to be made is called a _board_ (Figure 4(b)). The _size_ of a board is the number of hexagons that constitute it.  
A place (left) and an example of a board (of size 5) (right) on a hexagonal lattice plane.  
A _solution_ (of _Tantrix solution_) is an arrangement how \(n\) tiles are placed, and a _Tantrix solution_ is the one that satisfies all the above conditions (Figure 3). A _shape_ of a solution is its boundary formed by \(n\) hexagonal tiles.  
When a human solves Tantrix, one may connect the lines of the designated color to make a single loop, while connecting lines of the other colors simultaneously, as well as arranging tiles so that they do not create a hole in its shape at the same time. Especially, one cannot supposed to imagine the final shape of a loop (and thus, a solution) in advance. In other words, even if once one determined the challenge number, the shape of a solution cannot be fixed uniquely. It is not so easy for computers to deal with this situation since they have to assume unfixed shapes of boards on an infinite hexagonal lattice plane. To avoid this difficulty, in this paper, we consider a following simplified setting of Tantrix, in which we also give as an input a fixed board of the same size as a challenge number and place tiles on that board.  
Fixed_Board TANTRIX  
Input: a challenge number \(n\) and a board of size \(n\) that satisfies the rules of Tantrix,  
Question: a Tantrix solution if there is on the input board, or no, otherwise.  
Remark that on this problem setting, the shape of a solution always matches the shape of an input board. Furthermore, to concentrate on developing integer program formulations for making a loop that satisfies rules of Tantrix, we give a board whose shape is known to have a Tantrix solution (confirmed by humans) as an input. This implies that we do not have to consider \"no\" instances under this assumption.  
In the subsequent sections, we solve this problem Fixed_Board TANTRIX by utilizing a mathematical programming solver after formulating it as an IP. We use IBM ILOG CPLEX 12.2 [19] as a solver, which is installed on a single PC of Intel Pentium Dual E2200 processor (2.2 GHz) with 1 GB RAM.
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 5 Research Challenges and Future Directions
### Privacy-Preserving Schemes  
Most of the data in the DigAg are related to field work, which users might overlook. Privacy-preserving schemes for DigAg are an emerging area [57]. New privacy-preserving schemes need to be developed tailored for digital agriculture to protect the data from the malicious user in all aspects such as data privacy, data analytics, data utility, and overall system efficiency. New privacy-preserving schemes would mitigate IP theft, PII, and commercially sensitive information.
## 3 Results
### PLS Calibration
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Matrix**} & \multirow{2}{*}{**Analyte**} & \multirow{2}{*}{**Preprocess 1**} & \multirow{2}{*}{**LV**} & \multicolumn{2}{c}{**Variance of**} & \multicolumn{2}{c}{**Variance of**} & \multirow{2}{*}{**Out. 2**} & \multirow{2}{*}{**RMSEC**} & \multirow{2}{*}{**RMSEC**} & \multirow{2}{*}{**RMSECV**} & \multirow{2}{*}{**RMSEP**} \\  & & & & & **(\%)** & & & & & \\ \hline \multirow{2}{*}{Vogurt} & Fat (\%) & SNV + 2D (2, 9) + MC & 6 & 99.31 & 98.60 & 7 & 0.292 & 0.360 & 0.315 \\  & Protein (\%) & SNV + MC & 7 & 99.95 & 96.40 & 2 & 0.177 & 0.207 & 0.215 \\ \hline \multirow{2}{*}{Olive oil} & Refined olive & SNV + 2D (2, 15) + MC & 4 & 94.99 & 94.13 & 5 & 2.896 & 3.620 & 2.872 \\ \hline \multirow{2}{*}{Olives} & Diflurican & SNV + 2D (2, 7) + MC & 7 & 97.09 & 94.90 & 2 & 0.317 & 0.484 & - \\  & Pinetrin (mg kg\({}^{-1}\)) & SNV + 2D (2, 7) + MC & 7 & 92.09 & 95.93 & - & 0.971 & 1.644 & - \\ \hline \hline \end{tabular}  
* \({}^{1}\) SNV: standard normal variate; iD (y, z): where iD refers to the “first” or “second” “derivative”, y is the polynomial degree, and z is the window size; MC: mean centering. 2 Out: outliers. The number indicated in each row of this column refers to the total number of outlier spectra.  
\end{table} 
Table 2: \(Cont.\) \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Matrix**} & \multirow{2}{*}{**Analyte**} & \multirow{2}{*}{**N**} & \multicolumn{2}{c}{**Analyte Range**} & \multirow{2}{*}{**Intercept**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**s\({}_{\text{pr}}\)**} & \multirow{2}{*}{**\(p\)-Value \({}^{*}\)**} \\  & & & & **Min** & & & & & \\ \hline \multirow{2}{*}{Butter} & Fat (\%) & 66 & 81.10 & 86.60 & 4.109 & 0.951 & 0.293 & \textless{}0.0001 \\  & Salt(\%) & 66 & 0.00 & 1.20 & 0.008 & 0.965 & 0.083 & \textless{}0.0001 \\ \hline \multirow{2}{*}{Flour} & Protein (\%) & 484 & 9.41 & 14.58 & 0.575 & 0.952 & 0.272 & \textless{}0.0001 \\ \hline \multirow{2}{*}{Milk} & Fat (\%) & 192 & 3.65 & 6.16 & 0.166 & 0.961 & 0.110 & 0.0058 \\  & Protein (\%) & 190 & 3.09 & 4.27 & 0.339 & 0.904 & 0.100 & \textless{}0.0001 \\ \hline \multirow{2}{*}{Yogurt} & Fat (\%) & 137 & 0.1 & 9.4 & 0.038 & 0.986 & 0.292 & \textless{}0.0001 \\  & Protein (\%) & 142 & 2.8 & 6.4 & 0.137 & 0.964 & 0.174 & \textless{}0.0001 \\ \hline \multirow{2}{*}{Olive oil} & Refined olive oil (\%) & 76 & 61 & 100 & 4.794 & 0.941 & 2.847 & \textless{}0.0001 \\ \hline \multirow{2}{*}{Olives} & Diflurican (mg kg\({}^{-1}\)) & 38 & 0.00 & 3.42 & 0.047 & 0.949 & 0.281 & \textless{}0.0001 \\  & Pinetrin (mg kg\({}^{-1}\)) & 40 & 0.00 & 11.40 & 0.126 & 0.9593 & 0.869 & \textless{}0.0001 \\ \hline \hline \end{tabular}  
* \({}^{*}\)H\({}_{\text{d}}\): the regression is not significant versus H\({}_{\text{d}}\): the regression is significant.  
\end{table} 
Table 3: Number of samples, N, intercept, and slope of the accuracy lines, and _p_-value for the significance test. With these data, it is possible to determine the capability of detection and/or the capability of discrimination as explained in the following section.