esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Industrial Study: Cooked Ham, Chicken and Ready-to-Eat Vegetables Dressed with Butter  
Cooked ham, chicken, and ready-to-eat vegetable mixture seasoned with butter were the foods studied in the industrial trial, since producers of these kind of foods expressed their interest in the antioxidant packaging for their products. A large series of each food was packaged in their industrial packaging lines, using the same procedure, packaging speed, and system as the conventional multilayer. Blank and active samples were also packaged in each case and the same procedures above mentioned and optimized, were applied to monitor the behavior of the packaged food. Half the amount of the tested food was evaluated by the food companies and half by the research team at the University. All the results arrived at the same conclusions about the active nanoSe material. Drawbacks related to the packaging lines were not found, as the material behaved the same as the conventional one, because the active agent was in the middle of the multilayer, in sandwich mode, and consequently it did not affect the performance of the plastic.  
Figure 4: TBARS results expressed as mg of MDA per kg of potato chips, ham, chicken, and ready-to-eat-vegetables packaged in laminate A (control) and laminate B (actives with nanoselenium particles) for 21 days.  
Figure 5: Sensory evaluations of potato chips packaged in laminate A (control) and laminate B (actives with nanoselenium particles) after 21 days.  
Figure 4 shows the TBARS results for all of them packaged for 21 days. In the four types of foods under study, a significantly lower amount of MDA, around 50% for ham, released in the food packaged with the nanoSe were found. Therefore, this means a considerable improvement in the quality of the market produce, since less rancid products are released. This also turns into an extended shelf-life of food produce and demonstrates that the active packaging with nanoSe is a successful option. This is the first time that an industrial active multilayer containing Se nanoparticles as an antioxidant agent was studied. This study opens the door for further active materials, where nanoparticles can be introduced in a skilled manner without affecting the behavior of the material, whilst harnessing the properties and advantages of the packaging materials. Student T-test was performed, and the result was 0.04 for ham, 0.01 for chicken, and 0.06 for vegetables, respectively. The results with laminate A and B were significantly different with a 95% confidence level, for ham and chicken. Vegetables packed with laminate A and B were not significantly different.
## 5 V-HOE Based Solar Concentrators  
### Solar Concentrator and Spectral Splitting  
Since the pioneering work published by [PERSON] in 1982 [48], V-HOEs have been proposed as solar concentrators; additionally, by using DCG as a recording material, they have been employed to obtain a spatial separation of the solar spectrum allowing the use of solar cell materials with optimized band gaps achieving high PV efficiency [3; 49]. Moreover, an optimized holographic concentratingand spectral splitting systems can reduce the cooling requirements of the photovoltaic cells and, considering V-HOE design versatility, PV cells can be placed on a side of the hologram, thus avoiding shadow effects and simplifies cooling, as can be seen in Figure 2[5; 50]. In this case, the hologram not only concentrates the solar radiation but also split and directs the red and near-infrared spectrum on one PV cell and the green and blue spectrum on another one, while the far-infrared wavelengths of the solar spectrum are diffracted away from the cells, reducing the main cause of overheating.  
The authors also demonstrated that this holographic system concentrates higher power than a Fresnel-based solar concentrator and shows a larger relative efficiency; additionally, large heat sinks are not required leading to a decrease of both the bulk and the cost.  
With the aim of providing a holographic solar concentrator that reduces solar cell exposure to harmful radiations, [PERSON] et al. [51] patented a structure composed of hybrid transmission and reflection VHOEs that spectrally and spatially separate the incident solar spectrum into component wavelength bands. Using internal reflection, the separated solar radiation is propagated through a waveguide far away from the incident direction. At the end of the waveguide, band selection reflecting HOEs inject the solar radiation into PV cells of appropriate band gap energies.
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 3 Design and Implementation of Elliptic-Curve Cryptosystem F256 on FPGA
### Power Analysis and ECC  
ECC, introduced by [PERSON] and [PERSON] in the early 80s, is a preferred powerful public-key cryptosystem, especially for resource-constrained environments like smart cards, mobile phones, IoT-based devices, and RFIDs. In ECC, point multiplication is the resource-expensive operation in which a point on an elliptic-curve is added to itself successively. Let '\(P\)' be the point and '\(K\)' be the number of times '\(P\)' is required to be added, then output '\(Q\)' will be '\(K\)' times point '\(P\)' multiplication and is given by (1). Elliptic-curve point multiplication is also referred to as Elliptic-curve scalar multiplication (ECSM). Security of an elliptic-curve cryptosystem is based on the elliptic-curve discrete-logarithm problem, which relies on the fact that for an elliptic curve E and given points _P(x,y,z)_ and _Q(x,y,z)_, it is hard to find the integer k such that \(Q=kxP\).  
\[Q=kxP \tag{1}\]  
To compute ECSM, double-and-add is the simplest straightforward algorithm, in which operations are performed depending upon the 'k' key bits. If the key bit is '0' then only the point-double operation is performed. However, point-double and point-addition both are performed if the key bit is '1'. The simple double-and-add algorithm is susceptible to a simple power-analysis (SPA) attack; simply by analyzing the power consumption of the chip, scalar key 'k' can be resolved, by merely looking at the oscilloscope, without using any advanced processing. Countermeasures are proposed in the literature to help safeguard against SPA attacks. The simplest of all is to add an extra operation so that the double-and-add operations are performed always irrespective of the scalar k bit as can be seen from Algorithm 1. Double-and-add-always seems to be resistant against PA but is not secure against the safe-error attack, where an attacker introduces an error and examines if the output will show an error or not. Depending upon the output, the scalar key bit k is determined. However, double-and-add-always still seems to be feasible due to the low cost. Further details of the algorithm can be found in [30].  
```
1:Input: \(P\), \(k[n]\)
2:Output: \(Q=kP\)
3:\(R0\) = P, R1 = 0
4:for\(i=1\) to \(n-2\)do
5:\(R0=2R0\)
6:\(R1=R0+P\)
7:if\(k_{i}=1\)then
8:\(R0=R1\)
9:endif
10:endfor
11:return Q = R0
```  
**Algorithm 1** double-and-add-always
## 2 Problem Statement
### Methodology
#### 2.1.3 Scenario Reduction  
To handle the uncertainty of various stochastic parameters such as electricity price, wind speed, and load demand, the Monte-Carlo simulation task is employed in this study. Monte-Carlo simulation generates various scenarios based on the probability distribution functions. In this study, to increase the accuracy of the forecasting results, 1000 scenarios are generated in the first step. This large volume of data makes high computation cost because calculating economic dispatch for all 1000 scenarios is time-consuming. To handle this problem a scenario reduction task is employed based on the forwarding scenario selection and the backward scenario reduction approaches that are elaborated in [20]. This method eliminates the similar scenarios to 10 final scenarios which have the most probability. This procedure is applied for handling the system uncertainties. For PEV's modeling, 1000 scenarios which are generated by the Monte-Carlo simulation are reduced to 420 scenarios where 30 PEVs are considered in each bus in distribution network. Then, the economic dispatch is calculated for these scenarios and the vehicle owners' savings are derived by multiplying the optimized cost function by their relative probability.  
The boxplot in Figure 5 shows the load profile of the modeling scenarios after the scenario reduction. Each whisker shows the variability of the load profile of scenarios for each hour of the day. On average, the load profile is higher between 12 pm to 5 pm. Electricity price is at the highest point at 5 pm, when the load profile is also the highest, on average, as shown in Figure 6. Figure 7 shows the scenarios for six wind turbines which generate active and reactive power in the distribution network.
# Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development
## 3 Materials and Methods
### Methods
#### 3.1.3 Recurrent Neural Networks  
Recurrent neural networks (RNNs) provide additional synapses that connect neurons cyclically [45]. The result of the previous elaborations is reprocessed as input in the following sessions. In this way, previsions are influenced by what has been learnt from the past. The recurrent nature of these architectures makes them powerful tools for processing sequential data [51, 52].  
Let \(\mathbf{x}^{(t)}\in\mathbb{R}^{d}\), \(0\leq t\leq T\) be multivariate time series. Then, the output of an RNN layer with \(q\in\mathbb{N}\) can be formulated as:  
\[\mathbf{z}(\mathbf{x})=\phi\bigg{(}\mathbf{w}_{0}+W\mathbf{x}^{(t)}+U\mathbf{z}^{(t-1)}\bigg{)} \tag{2}\]  
where \(U=\{u_{j,k}\}_{1\leq j,k\leq q}\in\mathbb{R}^{q\times q}\) represents the weights associated with the recurrent synapses (with an initial value \(\mathbf{z}^{(0)}=\mathbf{0}^{q}\)). The RNN training is carried out using backpropagation thorugh time, which generally converges slowly and has vanishing gradient issues.  
In order to overcome these problems, two more sophisticated cell-based architectures were proposed: long short-term memory (LSTM) and gated recurrent unit (GRU) networks.
## 4 Experiment Description  
The high-speed train navigation system described here is composed of MIMU (50 Hz), GNSS module (1 Hz), odometer (1 Hz), and a Cortex-A9 + FPGA hardware platform. The integrated navigation module was fixed on the train floor; the receiver antenna was fixed on the top of the train directly above the integrated navigation module; the PC received and stored experimental data through the serial port. The system composition is shown in Figure 7. The GNSS module adopted three-mode (GPS + Beidou + Galileo) and satellite-based enhancement. The specifications of the MIMU and odometer are listed in Table 1.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background
### Handover Challenges
#### 3.5.2 Inter-Cell Interference  
With the increasing data traffic and capacity requirements, HetNets are considered an effective and efficient way to enhance user capacity and utilization of the spectrum resources. The overlapping signal coverages at a single point and splitting cells into smaller cells make optimization and network design even more complex due to several issues, especially Inter-Cell Interference (ICI) at the cell edges. The handover decision in mobile networks is mainly based on the RSRP, along with other decision-making parameters. Thus, dense deployment of small cells inside the macro cell will decrease the signal quality with the increase in the interference, which results in the degradation in HO performance. To ensure a satisfying QoS in severely affected areas, 3 GPP introduced Inter-Cell Interference Coordination (ICIC) in Release 8, later on, introduced as enhanced Inter-Cell Interference Coordination (eICIC) in Release 10. Similarly, in Release 11, 3 GPP formalized Coordinated Multipoint (CoMP) Transmission procedure for interference mitigation. To improve from interference challenges, the authors have conducted several studies on various interference reduction measures [129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139]. However, adapting the majority of these methods to 5G standards with massive network sizes and capacities will be a considerable problem for future HetNets.  
\begin{table}
\begin{tabular}{c c c c} \hline
**HO Information Collection** & **HO Decision Making** \\ \hline
**Network Related** & **UE Related** & **Strategy Related** & **Criteria Based** \\ \hline  & & Direction based \\ \hline  & & History based \\ \hline  & & User preference \\ \hline  & & Operator preference \\ \end{tabular}
\end{table}
Table 4: _Cont._
## 1 Introduction
[11] increased the amount of data by randomly applying rotation, width shift, height shift, shear, and zoom methods within a certain range to classify gastroscopy images as normal or abnormal. [PERSON] et al. [12] improved the performance of the model by generating X-ray gastritis images using a loss function-based  
Figure 1: Distribution of cases and deaths for the five most common cancers in 2018: (**a**) incidence and (**b**) mortality. conditional progressive growing generative adversarial network (GAN). [PERSON] et al. [13] proposed a method of classifying in vivo endoscopy images into normal or abnormal through VGG, DenseNet, and inception-based networks using the proposed ensemble learning. Some studies [11; 12; 13] also used gastroscopy and X-ray images of the stomach, but data were augmented by applying basic augmentation methods or a GAN. However, in the present study, for data augmentation, we mixed data generated through a deep convolutional generative adversarial network (DCGAN) with data applied using the augmentation policies of the CIFAR-10 dataset proposed by AutoAugment. In addition, we attempted to improve the performance of the model by using an image quality-measurement method for the generated images. Related studies have proposed various augmentation methods using various medical images, such as lung CT images, brain MRIs, and endoscopy images, and a method that applies classification networks based on machine learning and deep learning. In this study, two methods were used to supplement the insufficient amount of data required for learning. In our method, a gastroscopy image is generated through a DCGAN using CNNs with excellent image-processing performance, and data are augmented by applying 25 policies optimized for the CIFAR-10 dataset suggested by AutoAugment. An image quality-measurement method, with the Xception network (a deep learning-based image-classification network), is applied for the augmented data. Then, only images that are similar to real images are selected and added to the training dataset. By including the image quality-measurement process, the increased image was verified, and only data that could improve the quality of learning were selected for performance improvement. The proposed method is expected to improve the accuracy of gastric-disease diagnosis and help doctors perform the diagnosis. Therefore, the classification network, data-augmentation method, and image quality-measurement method employed in this study will be described.
## 2 Mathematical Formulation  
A two-dimensional scalar problem is considered, in which the geometry is invariant along the \(z\)-axis of a Cartesian reference system, and the electric field is \(z\)-polarized. The investigation domain \(\Omega\) is a limited portion of the \(xy\) plane and is filled with a dielectric of the known relative permittivity \(\varepsilon_{r}\). The external region is free space, whereas, inside \(\Omega\), there are \(N\) circular metallic objects, representing the section of circular cylinders whose radii are \(a\). Their positions are indicated by \(\mathbf{r}_{n}=(x_{n},y_{n})\) and \(n=1,\ldots,N\). The scene is illuminated by a \(z\)-polarized line source placed at different positions along the line \(\Sigma\). A multi-monostatic configuration is assumed in which the scattered field is observed at the same source positions, that is, at \(N_{o}\) observation points over \(\Sigma\). See Figure 1 for the geometry.  
The scattering equations are formalized as follows [31]:  
\[E_{s}=\mathbb{A}_{e}[\chi E] \tag{1}\]  
\[E=E_{i}+\mathbb{A}_{i}[\chi E] \tag{2}\]  
where \(E_{i}\) and \(E\) are the incident and the total field _inside_\(\Omega\), respectively; \(\chi\) is the normalized contrast between the internal and external region; and \(\mathbb{A}_{e}\), \(\mathbb{A}_{i}\) are linear operators whose kernel is the Green's function of the scattering problem, calculated outside and inside \(\Omega\), respectively. \(E_{s}\) is the scattered field outside \(\Omega\). It is well known that the inverse problem related to (1) and (2), that is, recovering \(\chi\) from the knowledge of \(E_{s}\) and \(E_{i}\), is non-linear. The non-linearity is due to the presence of the total field \(E\) in (1), which in turn should be recovered by solving (2).  
The usual linear approximation is the Born approximation, in which the total field in (1) is replaced by the incident field [29]. Taking into account a further term in the operator series expansion, that is, replacing the total field on the right-hand side of (2) with the incident field and then substituting the result in (1), the so-called quadratic approximation of the scattered field is achieved [31]:  
\[E_{s}=\mathbb{A}_{e}[\chi E_{i}]+\mathbb{A}_{e}[\chi\mathbb{A}_{i}[\chi E_{i}]]. \tag{3}\]
## 2 System Design
### Hardware System Design
#### 2.4.1 System Architecture Design  
In the hardware system, the accelerator is composed of three parts: the computation modules, the control modules, and the memory modules. The system architecture is shown in Figure 5. According to the description in Section 2.3, a CONV layer has three operation modules: CK and accumulator, ReLU, and maximum pooling. Owing to the substantial data dependence between the accumulator's input and the convolution kernel's output, we divide the two operations. We use CK modules to realize the convolution procedure and ARMP modules to process the rest computation part of the convolution layer. Moreover, we perform the FC layer using a FC module. The entire system has eight CK modules, four ARMP modules, and one FC module, which are all pipelined to accelerate the arithmetic speed.  
The other facet of the system design is memory modules. The input signal is preloaded into a Block RAM by a microcontroller. The trained weights and biases are programmed into two read-only memory (ROM), respectively. As the output tensor of the CK module needs to be cached, and its size is too large to be stored on the FPGA chip, we use Double-Data-Rate SDRAM (DDR) to cache the intermediate results. Because read and write operations are simultaneous, the data stream needs to be cached, we use three asynchronous First-In-First-Out (FIFO) to serve as the data buffers: a read address FIFO (RAF) as a read address buffer, a read data FIFO (RDF) as a read data buffer, and a write data FIFO (WDF) as write data buffer.  
The controller modules consist of system controller (SYS_CTRL), memory controller (MEM_CTRL), and DDR controller (DDR_CTRL). SYS_CTRL controls the work state of CNN, emit memory access request signal according to the current computation type. In addition, MEM_CTRL generates the read address and convert the width of data according to the request type and the current layer. DDR_CTRL controls the work state of the three FIFOs.
# UV System Architecture: Challenges and Evolution
## 4 UV System Architecture Version 3: Base Station Specifications
For example, in [39], an application for a search operation at sea using UAVs was described. Likewise, in [40], they presented 3D mapping using UAVs with the objective of improving the planning and execution of search and rescue operations. Regarding search planning, in [41], they designed a system for post-disaster scenes, and in [42], they referred to the importance of the user interface in UAVs and user interactions to improve results in search and rescue. Likewise, in [43], they focused on the transmission of high-quality images to be processed at the base station for search missions. Moreover, in [44], the work emphasized path planning for UGVs in search and rescue missions for mines. The above is a sample of works with different focuses, either as search and rescue operations in a specific situation or in the planning of operations. All these works have the presentation of the problem, the solution, the validation of the solution, and the conclusions in common. They do not describe the architecture or the processes in detail, although in some cases, they define the architecture as the interconnection of the components. In all cases, it is expected that what is presented will be reused as it is, without the possibilities of adaptation, improvements, changes, or optimizations since no details are given in this regard. In general, the results of the proof of concept demonstrate the correct functioning of the application and the architecture. Although its operation for this proof of concept is limited due to the battery capacity of the UAV and depends on an external connection and service for image classification, the complete system works and produces the desired results.
## 3 The Mathematical Model of Double-Coupled Duffing System
### The Mathematical Model of Double-Coupled Duffing System  
The double-coupled Duffing system is a coupled nonlinear dynamic equation established by two Duffing systems. By introducing coupling terms, it makes a certain phase difference between the two systems and improves the anti-noise performance of the system. Coupling item generally refers to coupling damping or the nonlinear items of two Duffing systems. Scholars in various fields have studied different coupling forms, and some scholars have explored triple-coupled Duffing systems, all of which have achieved certain results [24].  
In this paper, the displacement of the vibrator is selected as the coupling term to establish a double-coupled Duffing system, and its mathematical model is shown in Equation (2):  
\[\begin{cases}\ddot{x}+\dot{\xi}\dot{x}-x+x^{3}+k(x-y)&=F\mathrm{cos}(\omega t) \\ \ddot{y}+\dot{\xi}\dot{y}-y+y^{3}+k(y-x)&=F\mathrm{cos}(\omega t)\end{cases} \tag{2}\]  
Figure 1: Physical model of the Duffing system.  
where \(k\) is the coupling coefficient; \(x,y\), respectively, represent the displacement of two Duffing systems; \(\ddot{y},\,\dot{y},\,\,\,y\) are the acceleration, velocity and displacement of the second Duffing system.  
When \(k\) is 0, the two Duffing systems lose their association, and the output of the double-coupled Duffing system is exactly the same as that of the single Duffing system. When \(k\) is not 0, the actions of the two Duffing systems tend to be synchronized and have similar motion trajectories under the action of the coupling term.  
Some research has shown that, when the \(k\) value is 2, the system can show good stability [25], so in this paper, \(k\) is taken as 2.
# Energy Saving in Wireless Sensor Networks via LEACH-Based, Energy-Efficient Routing Protocols
## 4 Threshold LEACH with Sleep-Awake Scheduling Protocol
### Overview of T-LEACH\({}_{\text{SAS}}\) Architecture
Additionally, nodes receiving wake-up signals perform local synchronization updates, ensuring consistent timing alignment across the network. * Network Maintenance and Node Failure Handling Nodes report their operational status and residual energy levels to cluster heads during routine transmissions. Cluster heads use these report data to track node health. Nodes approaching energy depletion notify the network in order to enable cluster restructuring or head re-election ahead of node failures. This method of proactive maintenance reduces disruptions due to unexpected node outages and enhances overall network robustness and reliability.