esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Analysis
### Governing Differential Equations and Transfer-Matrix Coefficient for Pile in the Passive Segment
\begin{split}\chi_{1,pi}&=b_{1}\overline{q}_{pi} \Bigg{\{}\frac{\lambda_{pi}}{b_{1}\overline{k}_{pi}}-\sum\limits_{x=1}^{4} \frac{\varepsilon^{\omega_{x,pi}^{2}pi}}{4\left(\omega_{x,pi}^{2}+2\lambda_{pi} \right)}\left[\frac{\left(\omega_{x,pi}^{2}+4\lambda_{pi}\right)}{b_{1} \overline{k}_{pi}}-\frac{1}{\kappa A_{c}G_{c}}\right]\Bigg{\}}+\frac{4\lambda_{pi }\overline{N}_{pi}\varphi_{1N}}{D_{pi}}\sum\limits_{x=1}^{4}\frac{\varepsilon ^{\omega_{x,pi}^{2}pi}}{4\omega_{x,pi}\left(\omega_{x,pi}^{2}+2\lambda_{pi} \right)}\\ \chi_{2,pi}&=\bigg{[}\ b_{1}\overline{q}_{pi}\left(\frac{4\lambda_{pi}}{D_{pi} ^{2}}\right)p_{1}+\frac{\omega_{x,pi}^{2}}{\kappa A_{c}G_{c}}\bigg{)}+\frac{4 \lambda_{pi}\overline{N}_{pi}\varphi_{1N}\omega_{x,pi}\left(\omega_{x,pi}^{2}+ \lambda_{pi}\right)}{D_{pi}}\bigg{]}\sum\limits_{x=1}^{4}\frac{\varepsilon^{ \omega_{x,pi}^{2}pi}}{4\omega_{x,pi}\left(\omega_{x,pi}^{2}+2\lambda_{pi} \right)}\\ \chi_{3,pi}&=b_{1}\overline{q}_{pi}\Big{(}-\frac{T_{pi}}{ \kappa A_{c}G_{c}}+J_{pi}\Big{)}\sum\limits_{x=1}^{4}\frac{\varepsilon^{\omega_{ x,pi}^{2}pi}}{4\left(\omega_{x,pi}^{2}+2\lambda_{pi}\right)}+\overline{N}_{pi} \varphi_{1N}\bigg{(}\omega_{x,pi}^{2}+\frac{b_{1}\overline{k}_{pi}}{\kappa A_{c}G_{ c}}\bigg{)}\sum\limits_{x=1}^{4}\frac{\varepsilon^{\omega_{x,pi}^{2}pi}}{4 \omega_{x,pi}\left(\omega_{x,pi}^{2}+2\lambda_{pi}\right)}\\ \chi_{4,pi}&=b_{1}\overline{q}_{pi}\bigg{[}\left(\omega_{x,pi}^{2}+4 \lambda_{pi}\right)-\frac{b_{1}\overline{k}_{pi}}{\kappa A_{c}G_{c}}\bigg{]} \sum\limits_{x=1}^{4}\frac{\varepsilon^{\omega_{x,pi}^{2}pi}}{4\omega_{x,pi} \left(\omega_{x,pi}^{2}+2\lambda_{pi}\right)}+\overline{N}_{pi}\varphi_{1N} \bigg{[}-1+\left(\omega_{x,pi}^{2}+4\lambda_{pi}\right)\sum\limits_{x=1}^{4} \frac{\varepsilon^{\omega_{x,pi}^{2}pi}}{4\left(\omega_{x,pi}^{2}+2\lambda_{pi} \right)}\bigg{]}\end{split}  \tag{23}\]  
Finally, the transfer matrix equation of the lateral response in the passive segment for the pile shaft can be expressed as  
\[S_{pn_{p}}=U_{pn_{p}}U_{p(n_{p}-1)}\cdots U_{p2}U_{pi}S_{p0}=U_{p}S_{p0} \tag{24}\]  
In which the notation \(U_{p}\) is the total transfer coefficient matrix in the passive segment;  
\(S_{p0}=\left[y_{fn_{p}}\ \ \varphi_{fn_{p}}\ \ \ Q_{fn_{q}}\ \ \ 1\right]^{T}\) is the mechanicals deformation parameter matrix of the interface between free and passive segments, and \(S_{pn_{p}}=\left[y_{pn_{p}}\ \ \varphi_{pn_{p}}\ \ \ M_{pn_{p}}\ \ \ Q_{pn_{p}}\ \ \ 1\right]^{T}\) is that between passive and active segments for the pile shaft.
## 5 Current Treatments to Avoid or Reduce PPO Activity in Fresh-Cut Produce
### Antioxidants and Antimicrobials
#### 5.4.3. Cysteine and Its Derivatives  
The thiol-containing aminoacid cysteine (an amioacid that has GRAS status) has been reported to effectively inhibit PPO-mediated enzymatic browning of fruits and vegetables, although some studies have remarked a neutral effect on PPO activity reduction (possibly due to pH adjustment, as explained in the following studies) whereas bioactive compounds were kept [40]. Nevertheless, three mechanisms have been reported to effectively inhibit enzymatic browning: (1) reduction of the o-quinone back to o-dihydroxyphenol. (2) direct inhibition of PPO by bounding cooper in the enzyme. (3) formation of a colorless cy-quinone adduct. When cysteine is used as an inhibitor of enzymatic browning on some fruits such as pears or apples, pinkish-red colored compounds are formed due to phenol regeneration with deep color formation [95; 106]. It was also reported that cysteine might be more effective at a neutral pH due to the acidity constant of the thiol group (pKa = 8.33). Taking into account that some tissues are acidic (especially in the case of fruits) the use of cysteine alone as an inhibitor of enzymatic browning is not effective, and consequently the use of reducing agents or a buffer at pH 7.0 are advisable [95]. Thus, many studies have been carried out using cysteine among other washing treatments. For example, the use of a dip of 2% ascorbic acid, 1% calcium lactate and 0.5% cysteine adjusted with NaOH at pH = 7.0 significantly extended the shelf-life of fresh-cut pears [95]. The use of 0.5% HCl-1 cysteine monohydrate solution was proved in combination with a gaseous ClO\({}_{2}\) treatment (the latter dramatically affecting visual quality, as some authors pointed out), being suitable for avoiding browning caused by the gaseous treatment but decreasing its decontamination effectiveness [107]. As explained before, pH not only affects PPO activity but the effectiveness of many washing treatments. Thus, as some authors have reported, pH plays a vital role in cysteine activity as in the case of fresh-cut antichokes, recommending neutral pH for the best visual quality results and the least PPO activity [25].  
On the other hand, \(N\)-acetylcysteine is specifically used as a dietary supplement, being used in order to avoid cut browning. For instance, it is worth mentioning the optimization of a washing treatment including \(N\)-acetylcysteine in minimally processed apples. Thus, the use of 100 mM isoascorbic acid, 5 mM Caascorbate, 5 mM Capropionate and5 mM \(N\)-acetylcysteine washing treatment on different cultivar apples was the optimum combination used in order to keep visual quality [104].
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 4 The 3D-LUNA Localization Framework
### Virtual Anchor Creation  
Localization relies on determining relative positions, which represent the topological relationships between network nodes. To obtain global coordinates for a node, its relative position must be linked to anchors/reference points with known coordinates. Rather than deploying tethered underwater nodes or surface nodes, our approach involves defining uniformly spaced waypoints throughout the operational area and transmitting encoded visual light beams to each of these points. The airborne node divides the water surface into a grid of square cells, hovering above to illuminate each of these cells with a light beam that encodes its current GPS coordinates. This encoded position provides the underwater nodes with the x and y coordinates where the beam hits the surface, along with the airborne node's altitude (z coordinate). Additionally, the airborne node includes other vital data like the beam angle and transmission power, enabling the underwater nodes to use this information, in conjunction with Equation (4), to calculate the full 3D position from each transmission, as explained in the balance of this section. Since the airborne node covers the entire region with sequential cell by cell transmission, necessary coverage is achieved by a single airborne node.
## 4 Experimental Results
### Parameters Setup of PSO and Classification Algorithms  
PSO uses various parameters during its configuration. During the experiments, the number of particles is set to 50, and the number of iterations is set to 100. \"\(c_{1}\)\" and \"\(c_{2}\)\", which are PSO acceleration coefficient values, are set to 2. Moreover, classifiers' training parameters were configured according to the default parameters, and as listed in Table 3.  
Figure 2: Document distribution for: (**a**) DatasetB; (**b**) DatasetC; (**c**) DatasetE.
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 3 Research Methodology
### Message Standardization
The categorization system optimizes the routing of messages to their intended recipients, reducing the quantity of extraneous data and improving the accuracy of the primary server's processing. The objective of implementing a classified structure in a broker is to ensure that information and events are organized and directed to their respective topics on both the broker and server sides. This hierarchical arrangement of topics facilitates the functioning of the smart greenhouse system, making it more streamlined. Additionally, external users can conveniently access the broker using personal computers or mobile phones, allowing them to effectively manage and control categorized data through the broker. Hence, every gateway and broker functions as an IoT hub, facilitating connectivity to the central server. Figure 3: Digital Input/Output and Analog Input topic behavior.
# Assessing Voltage Stability in Distribution Networks:
## 2 Modeling of Stochastic Variables Considering Correlation  
### Probability Model for Stochastic Variables  
#### 2.1.1 Probabilistic Model of PV Generation  
PV generation output is closely related to the solar irradiance in the area, and the solar irradiance is significantly affected by various factors including time, location, and cloud situations [28]. Therefore, its natural variability and time-varying lead to the instability of PV output. Research has shown that, over certain time scales, the stochastic variation characteristics of solar irradiance can be represented as Beta distribution, the probability density function (PDF) of which is [16]:  
\[f(r)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}\left(\frac{r}{r_ {\max}}\right)^{\alpha-1}\left(1-\frac{r}{r_{\max}}\right)^{\beta-1} \tag{1}\]  
where \(\Gamma(0)\) is the gamma function, \(r\) denotes the current solar irradiance at the location of the PV system, and \(r_{\max}\) is the maximum irradiance during the period. \(\alpha\) and \(\beta\) are shape parameters of the Beta distribution, and control the distribution curve together with the Gamma function, which can be calculated according to Equations (2) and (3):  
\[\alpha=\mu\left[\frac{\mu(1-\mu)}{\sigma^{2}}-1\right] \tag{2}\]  
\[\beta=(1-\mu)\left[\frac{\mu(1-\mu)}{\sigma^{2}}-1\right] \tag{3}\]  
where \(\mu\) represents the average solar irradiance received by the PV power field, and \(\sigma\) denotes the standard deviation.  
It is considered that the PV active power output is approximately linear and operates in a constant power factor mode:  
\[P_{\text{PV}}=A\cdot r\cdot\eta \tag{4}\]  
\[Q_{\text{PV}}=\tan\phi_{\text{PV}}\cdot A\cdot r\cdot\eta \tag{5}\]  
where \(P_{\text{PV}}\) and \(Q_{\text{PV}}\) are the actual output power of PV power generation, \(A\) denotes the area of PV array, \(r\) represents actual light intensity, \(\eta\) signifies the conversion efficiency of PV power generation, while cos\(\phi\) represents the power factor during PV operation [29].
## 2 Materials and Methods
### 3D-Convolutional Neural Network Classifiers  
Convolutional Neural Networks (CNN) are a special type of Neural Networks designed for detecting image-specific features and patterns from pixels inside the image. As illustrated in Figure 5, given an input image that may contain multiple channels (3 channels in the case of RGB images), CNN encodes image-specific features required for making inferences on inputs in image tasks. This encoding is done using fixed-sized kernels 'walking' (convolution) through the whole image space. As a result, usage of CNN provides a state-of-the-art method for tasks like image classifications, object detection, image reconstruction, etc.  
A convolutional layer with kernel \(k\) and image matrix can be formalized as Equation (1) [24]:  
\[z_{i,j}^{l}=\omega_{k}^{l\ T}x_{i,j}^{l}+b_{k}^{l} \tag{1}\]  
where \(x\) is image matrix, \(T\) stands for vector transpose, \(z\) is the feature value (_i,j_) of layer \(l\) and the kernel \(k\) of the output feature matrix and \(\omega\) and \(b\) are weight vector and bias matrix of kernel \(k\) and layer \(l\), respectively.  
The most important limitation of Artificial Neural Network (ANN) is their complexity and efficiency when dealing with high-dimensional inputs like image pixels. If each input is represented by a 64 \(\times\) 64 pixel image, the number of weights needed by a neuron in the first layer would be 12,288 [24]. This makes CNN a suitable choice for image tasks.  
In an image classification task, a model can use CNN layers to extract spatial information from images and generate encoded feature matrices (feature extraction). After some layers of CNN, it will use fully connected neural network layers to provide a predicted label given these encoded features (classification). This process is illustrated in Figure 6 [25].  
Figure 4: A sample tensor 3D hyperspectral image at a particular fixed spectrum frequency band: dataset consisted of 174 wavelength band images like the one here depicted, with a spectral resolution of 2.5 nm between frequency bands inside (550–900 nm) wavelength range, and (160 \(\times\) 120) spatial resolution pixels, totaling 19,200 spatial pixels in each spectral band image.  
Figure 5: Convolutional Neural Networks (CNN) layer with a kernel size 3 and its generated encoded output after convolution.
## 4 Numerical Analysis and Discussion
### Evaluation of Displacements Due to the Wind  
During the test, anemometric data were acquired, which showed a constant wind direction. The wind speed data make it possible to obtain the thrust on the tower. Given the geometric and mechanical characteristics of the tower, it was possible to calculate the displacements of the points at various heights. The wind pressure can be obtained by the Bernoulli Equation:  
\[p=\frac{1}{2}\rho v^{2} \tag{5}\]  
where:  
p = is the wind pressure [N/m\({}^{2}\)]  
Figure 12: Spectrum of vibration frequencies of point P on the wind tower located at H = 25 m. Data have been collected during the deactivation of the turbine (**a**). Enlargement of the low frequencies (**b**).  
Figure 13: Spectrum of vibration frequencies of point P on the wind tower located at H = 25 m. Data have been collected during the stop of the turbine.  
\(\rho=\) air density [kg/m\({}^{3}\)]  
v = air speed [m/sec]  
The air pressure, applied to the swept area of the rotor, must be multiplied to a coefficient less than 0.6 due to the Betz limit [31]. In our case, given the low wind speed, which implies a non-optimal rotor efficiency, we can use the value 0.4. It is also necessary to take into account the interference factor b (i.e., the ratio of the downstream speed v\({}_{2}\) to the upstream speed v\({}_{1}\), which in our case can be considered equal to 0.577 [32]). Given that the speed measured by the anemometers and used for our test (installed on the nacelle and behind the rotor) is the downstream speed, the thrust F acting on the top of the tower, considered as a cantilever, can therefore be evaluated as [32]:  
\[F\cong 0.4\left(\frac{1}{2}\rho 3v_{2}^{2}\pi r^{2}\right)=0.60\left(\rho v_{2}^{2 }\pi r^{2}\right) \tag{6}\]  
where r is the radius of the rotor. Geometric and material data on the tower can be found in [33]. The displacement \(\delta\) at the top of the tower, in correspondence of the connection with the nacelle, is obtained by:  
\[\delta=\Gamma\frac{L^{3}}{3 EJ} \tag{7}\]  
where:  
L = cantilever length  
E = Young's modulus  
J = moment of inertia  
For our calculations, we assumed \(\rho=1.2\) kg/m\({}^{3}\), L = 65 m, E = \(2.1\times 10^{11}\) N/m\({}^{2}\), \(J=0.323\) m\({}^{4}\). Actually, the lowering changes with the wind speed, and other effects affect the final results, so only a rough estimate can be obtained with Equation (7). Table 4 shows the displacements of a point 65 m high on the base of the tower computed for different wind speeds. By adding to the displacements, the mean value of the ground distances measured in the operating condition c, i.e., during the stop of the rotor blades, one can obtain the ground distances from TLS for the tower subject to the wind load.
# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 2 Proposed EMD-BiGRU Architecture
### Evaluation Metric  
To assess the accuracy of the EMD-BiGRU in predicting missing data, four evaluation metrics are selected, including mean squared error (MSE), root mean square error (RMSE) [33], mean absolute error (MAE) [34], and coefficient of determination (\(R^{2}\)) [35].  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}} \tag{11}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}}} \tag{12}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}{|\hat{y}_{i}-y_{i}|} \tag{13}\]  
\[R^{2}=1-\sum_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}}/\sum_{i=1}^{n}{(y_{i}-\overline {y}_{i})^{2}} \tag{14}\]  
where \(n\) is the number of missing data. \(y_{i}\) and \(\hat{y}_{i}\) are \(i\)-th true and predicted values. The smaller values of \(MSE\), \(RMSE\), \(MAE\) represent the method has high predicted accuracy. The \(R^{2}\) with high value represents that the model has a high fitting ability.
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
## 4 2-Dimensional PV System Description
### Design of 2D-PV Transmitter Part  
The conventional transmitter structure for a W-T OCDMA system is shown in Figure 4. Initially, the transmitter module consists of a multi-wavelength continuous wave (CW) laser array as an optical source. For each user, a group of wavelengths is produced by the CW laser. These short pulse trains are set to a width \(\tau\) ((\(\tau=t_{b}\)/S), which represents code length in time spread), where \(t_{b}\)/S stands for bit time period per second. The pulse train repetition rate is set equal to the system bit rate (\(1/t_{b}\)). The bit stream is modulated by the input data using an amplitude modulator. In the on-off keying process, the modulator produces an optical pulse when transmitted data bit is one, or else it produces zero output.  
Modulated signal/data pulses are demultiplexed using a demultiplexer. Pulses corresponding to data bit one are sent to a 2D W-T-based optical encoder to perform encoding operation. The W-T encoder circuit is made up of time delay circuits and Bessel optical filters. In 2D TW codes, chosen pulses are encoded by different or similar spectral components according to the PV code algorithm (Table 1). Each optical pulse corresponding to transmitted bits is divided into w optical pulses with time delay (\(t\)) between each one of them. Finally, an encoded data stream (wavelength time-spreading patterns) is transmitted to a single-mode optical fiber through a power combiner.
# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
###### Abstract  
Vehicular Delay-Tolerant Networks (VDTNs) are networks of vehicles that communicate wirelessly, where there are no permanent end-to-end connections. VDTNs have a highly variable topology, with frequent partitions, and possibly low node density. Thus, delay-tolerant routing adopts a store-carry-and-forward message transfer paradigm, where messages have a useful Time-To-Live (TTL) and are stored until a good contact opportunity arises. Multiple message replicas can be generated to improve delivery probability at the cost of increasing network congestion. In this paper, we propose the V-GRADIENT geocast routing protocol that monitors node density, buffer occupancy, and interest in geocast groups, to adapt the forwarding techniques used dynamically, to disseminate messages within the geographic region of interest. Simulation results show that the V-GRADIENT is capable of controlling network congestion and efficiently delivering messages resulting in better delivery ratios, lower latencies, and a small increase in overhead when compared with existing protocols.  
wireless communications; geocast routing; vehicular delay-tolerant networks +
Footnote †: journal: Accepted: 11 March 2020; Published: 13 March 2020
# Using Multimodal Foundation Models for Detecting Fake Images on the Internet with Explanations
## 3 Results and Discussion
### Jaccard Index for Evaluating Overlap Between Positive and Negative Regions  
The Jaccard Index, also known as the Jaccard similarity coefficient, is a statistical measure used to quantify the similarity and diversity between two sets. In the context of image analysis and model interpretability, particularly with LIME, the Jaccard Index can be employed to evaluate the overlap between regions identified as positively or negatively contributing to a model's decision.  
Significance of the Jaccard Index  
In this study, the Jaccard Index is utilized to compare the overlap between the positive regions (areas of the image that positively influence the model's prediction) and the negative regions (areas that negatively influence the model's prediction) highlighted by LIME:  
* Jaccard Index close to 1: A high Jaccard Index indicates significant overlap between positive and negative regions. This suggests that the model may rely on the same areas of the image for both positive and negative contributions, potentially indicating regions of ambiguity or mixed relevance.
* Jaccard Index close to 0: A low Jaccard Index suggests minimal overlap, indicating that the model clearly distinguishes between different parts of the image for positive and negative contributions. This separation can reflect a well-defined decision-making process where distinct regions contribute either positively or negatively, but not both.
* Moderate Jaccard Index: A moderate value suggests that while there is some overlap, there are also distinct areas that are uniquely positive or negative. This could reflect a  
Figure 13: Positive and negative regions generated by Gemini for four-shot prompting.  
balanced interpretation of the model, where certain features play dual roles depending on the context.  
The Jaccard Index thus provides a quantitative measure to assess the extent to which the model's focus areas overlap for positive and negative influences, offering insights into the model's interpretability and decision-making behavior. The results from taking each model's average Jaccard index over a sampling of images are shown in Table 2.  
The Moondream2 model is the only model with a Jaccard score that is not 0. This confirms our suspicions that this model relies on the same regions for both positive and negative contributions, which could imply that it is not great at very fine, detailed tasks. The other three models have a Jaccard score of 0, signifying no overlap, which can mean a more precise decision-making method.