esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 2 Cyber Physical Wind Energy System
### WF Power System Layer  
This layer represents the WF electric power system, which is defined as a group of wind turbines connected together and tied to the utility through a system of transformers, transmission lines, and substations. A typical wind turbine consists of a wind turbine generator, step-up transformer, and a circuit breaker. The step-up transformer is used to step-up the generation voltage of each wind turbine. Wind turbines are divided into groups, and each group is connected to the collector bus through a circuit breaker. A high-voltage transformer is used to step-up the voltage to the transmission level.
## Materials and Methods
### Datasets and Analysis
#### 2.2.5 Absorbed Fraction of the Photosynthetically Active Radiation (fPAR)  
To analyze the dynamics of the fPAR apart from SIF\({}_{757}\), we used the MODIS Aqua fPAR product (MYD15A2H) version 6 [(60; 61)], which is an 8-day composite dataset with a spatial resolution of 500 meters.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 2 Related Works
### Residual Network  
Existing studies [6; 7; 8; 9; 10; 11; 12; 29] have indicated that great features with stronger representation ability can be yield as the network depth increases. However, deep networks built in such way may suffer from gradient vanishing or exploding, resulting in network convergence difficulty and poor optimization. For the sake of resolving this issue, [PERSON] et al. [29] proposed the residual network (ResNet), which introduces cross-layer jump connections and adds identity mapping of shallow features to make the forward and backward propagation of information without a hitch, thereby promoting the feature representation ability of the network effectively. Currently, ResNet has been extensively exploited in CNN for various computer vision tasks.  
As the earliest deep learning based SR method, SRCNN [30] was a simple structure network with fewer convolutional layers, which causes the inadequate feature extraction capacity, resulting in unsatisfactory super-resolution performance. Inspired by ResNet, the residual network was incorporated into the super-resolution model to develop the deeper network to recover more abundant details [6; 7; 8; 9; 10; 11; 12; 13], such as SRResNet [7], EDSR and MDSR [8], DRCN [9], RCAN [15], etc. To fully exploit shallow and deep features, [PERSON] et al. [6] proposed an accurate image super-resolution using very deep convolutional networks (VDSR), which fastly propagates the low-level features to the high-level layers and increases the feature propagation efficiency. However, for deeper networks, the problem of gradient exploding or vanishing may not be effectively solved by using the global residual structure simply. For this reason, many SR methods [10; 11; 12; 13] jointly exploit local and global residual features. Such methods can not only make full use of the image features, but also solve the problem of feature disappearance during transmission, thereby enhancing the quality of super-resolving images performance generally. To further improve the feature utilization, [PERSON] et al. [14] proposed a residual feature aggregation network (RFAN), which aggregates the local features extracted from several residual blocks through jump layer connections to generate more representative features, thereby achieving considerable super-resolution performance.
# Development of Alfa Fiber-Based Mortar with Improved Thermo-Mechanical Properties
## 3 Results and Discussions
### Thermophysical Performance Analysis
#### 3.2.2 Thermal Conductivity Measurements  
It can be observed from Figure 8 that the thermal conductivity of AFRM composite decreases with increasing fibers weight content, and consequently increases its thermal insulation capacity. The thermal conductivity of the reference mortar of about 0.809 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) drops to 0.347 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) for AFRM5%, corresponding to an improvement in insulation performance of around 57%. A similar result was reported by [19] who used a single layer fully made of AF with a length of 15 cm as reinforcement at a specific depth inside the cementitious matrix.  
This behavior is expected and can be explained by two arguments. First, the natural fibers are known to have lower thermal conductivities than those of mortar solid matrixes. The measured bulk thermal conductivity of AF binderless board and mortar are \(0.056\pm 0.002\) W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) and 0.809 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) respectively. Second, the presence of fibers in solid matrix produces voids as shown in SEM pictures Figure 4e, causing growth in open porosity and a reduction in bulk density of AFRM composite. The latter becomes then more thermally insulating. This phenomenon is always taking place on composite with mineral matrix and vegetable fibers according to several works [41; 42; 43].
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 4 Results and Discussion
### Packet Delivery Ratio (PDR) Analysis  
The packet delivery ratio (PDR) represents the rate of successfully delivered packets regardless of the vehicles involved. It is a crucial metric that characterizes the performance of the routing network, influenced by factors such as packet volume, transmission range, and network topology. Table 4 and Figure 4b illustrate the results of the PDR analysis. The results indicate that the proposed technique successfully transmitted a greater number of packets, regardless of their volume. The introduction of PED estimation had a significant impact on the cluster formation process, contributing to this improved performance.  
Figure 3: Examples of simulation results showcasing diverse cluster formations using the innovative DA-TRPED model within the NS-2 simulation tool. Circles represents the shape of cluster.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Vehicles** & **Distance (m)** & **DA-TRPED** & **Conventional ACO** \\ \hline
20 & 0.5 & 27.23 & 18.12 \\ \hline
40 & 1.0 & 28.32 & 16.87 \\ \hline
60 & 1.5 & 28.45 & 14.23 \\ \hline
80 & 2.0 & 26.53 & 14.89 \\ \hline
100 & 2.5 & 27.23 & 15.24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative results of throughput analysis for network performance evaluation.
# Inferring Emotion Tags from Object Images Using Convolutional Neural Network
## 2 Proposed Methodology
### Data Annotation and Tagging
#### 2.2.1 Emotion Tag Assignment to Bottom-Level Object Categories  
To consider all possible emotion variations within the top-level object categories, we first performed emotion labeling at the bottom-level. In this regard, we labeled each of 256 leaf categories of the object in Caltech-256 dataset for emotion tagging. All users were asked to assign only one emotion tag to each category based on their perception. After collecting emotion responses concerning bottom-level object categories from all the users, we applied a maximum user response criterion for assigning a single emotion tag to each category. For example, some of the object categories like blimp, boom-box, comet, playing card, and dice are assigned with the amusement emotion tag as a result of the combined response from all users. Likewise, the categories of images such as picnic-table, raccoon, and Saturn are marked with contentment emotion tag. In contrast, object categories like knife, bear, and bat belong to the fear emotion class as labeled by most of the users. Subsequently, all the bottom-level object categories are marked with one out of nine emotion tags to build a strongly labeled object category dataset. Overall, our emotion-tagged dataset based on the bottom-level object categories includes a total of 6177 images for amusement, 277 for anger, 3292 for awe, 5792 for boredom, 4270 for contentment, 1569 for disgust, 6336 for excitement, 1674 for fear, and 356 images for sad emotion category, respectively.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 1 Introduction
Relevant examples in this direction are the ETSI S-MIM standard for S-band mobile interactive multimedia [21], focusing on the satellite uplink, or the mobile communications standard 5G from 3 GPP [22]. In the former case, the transmission of regular or high data rate traffic is served via demand-assignment procedures, whereas delivery of IoT messages can be attempted on dedicated resource blocks via a variation of spread spectrum ALOHA [23]. Similarly, in 5G, a large amount of the spectrum is dedicated to the enhanced mobile broadband (eMBB) traffic, while IoT messages are sent via the NB-IoT waveform in dedicated sub-bands or unused guard bands [24]. By construction, such approaches avoid interference between different services, easing the provision of proper QoS levels and simplifying waveform design. On the other hand, the reservation of orthogonal sets of resources may not be fully efficient in the presence of machine-type communications. In fact, the unpredictable nature of mMTC traffic inherently leads to significant load fluctuations, hindering the precise tuning of the amount of bandwidth to be allocated and often resulting in an either heavily congested or under-utilized channel. From a different angle, instead, a controlled level of interference may be tolerated by non-mMTC services without violating the target QoS requirements. Indeed, information theoretic results on the multiple access channel show that spectrum sharing and interference cancellation allow to achieve the corner points of the capacity region, suggesting that the coexistence of the two services over a shared band may be beneficial. Starting from these remarks, we explore in this work the possibility to serve different types of traffic over the same set of resources concurrently. Specifically, we tackle the need to provide uplink support to traffic with a specific QoS target as well as to enable transmission for a best-effort service to IoT devices. Leaning on information theoretic tools, we derive the maximum aggregate rate that can be granted to mMTC traffic without violating the rate and reliability requirements of the coexisting service. We then compare the performance of such an _overlap_ configuration to a benchmark setup where resources are orthogonally split, highlighting promising gains.
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 5 Halophyte Composition
### Ash Content  
Many halophytes can accumulate different quantitative amounts of salts/ions due to their variations in the way the plant utilize salts or rate of uptake/accumulation into the plant tissues or surfaces. Therefore, it is ideal to identify the mineral content of the biomass. According to [PERSON] et al. [51], who investigated seven different halophyte biomasses with a conventional grass, they contain 14-49% mineral content (structural ash) based on the dry matter. Following their findings, _Salicornia europaea_ contained the highest ash content of 49%. It is undeniable that halophytes contain very high concentrations of ash, as contrasted with conventional grass, which contains approximately 5-10% mineral content in dry material mainly due to their selective uptake of mineral ions as a mechanism of adaptation in saline environments [51; 73; 74; 75; 76; 77]. As reported by [PERSON] et al. [75], there were variations in ash content of the investigated wild and cultivated halophyte plants in different seasons of the year. The total ash content is directly proportional to the salinity. This is reflected in findings from [PERSON] et al. [73] and [PERSON] et al. [74], where cultivating halophytes under different salinity showed an increase in both crude ash and structural ash content with increasing salinity. As stated in [PERSON] et al. [76], a large portion of initial ash content in the raw _Salicornia bigelovii_ plant was in the form of extractable ash, as irrigating the biomass with saline water showed more salt deposits on the plant surface than being incorporated within the plant matrix. Moreover, the stem fractions of _Salicornia bigelovii_ contained less structural ash, 2-8%, as compared to its seed spikes, 5-14%. Conclusively, stems contained less ash both on the surface and incorporated in the plant matrix when compared to pods [76; 78].
# XLNet-Based Prediction Model for CVSS Metric Values
###### Abstract  
A plethora of software vulnerabilities are exposed daily, posing a severe threat to the Internet. It is almost impossible for security experts or software developers to deal with all vulnerabilities. Therefore, it is imperative to rapidly assess the severity of the vulnerability to be able to select which one should be given preferential attention. CVSS is now the industry's de facto evaluation standard, which is calculated with a quantitative formula to measure the severity of a vulnerability. The CVSS formula consists of several metrics related to the vulnerability's features. Security experts need to determine the values of each metric, which is tedious and time-consuming, therefore hindering the efficiency of severity assessment. To address this problem, in this paper, we propose a method based on a pre-trained model for the prediction of CVSS metric values. More specifically, this method utilizes the XLNet model that is fine-tuned with a self-built corpus to predict the metric values from the vulnerability description text, thus reducing the burden of the assessment procedure. To verify the performance of our method, we compare the XLNet model with other pre-trained models and conventional machine learning techniques. The experimental results show that the method outperforms these models on evaluation metrics, reaching state-of-the-art performance levels.  
Keywords:cybersecurity vulnerability assessment; pre-trained model; fine-tuning; CVSS; machine learning; predictability +
Footnote †: journal: Applied
## 2 System
### Offset Adjustment  
As mentioned earlier, the values obtained from the geomagnetic sensor are distributed on the surface of a sphere or an ellipsoid, making it necessary to calculate the center. The equation of an ellipsoid is expressed as follows:  
\[\frac{x^{2}}{a^{2}}+\frac{y^{2}}{b^{2}}+\frac{z^{2}}{c^{2}}=1. \tag{1}\]  
In the Equation (1), when \(a=b=c\), the shape becomes a sphere; in other words, if the center can be determined based on the equation of an ellipsoid, then the center of a sphere can also be obtained using the same approach. In this study, the point cloud obtained from the geomagnetic sensor is fitted to an ellipsoid, then the center coordinates are calculated by determining the equation of the ellipsoid using the least squares method. While the equation of the ellipsoid is expressed as shown in Equation (1), this assumes that the \(x\), \(y\), and \(z\) axes are the principal axes. However, because the principal axes of the point cloud are often rotated, the following equation must be used instead:  
\[Ax^{2}+By^{2}+Cz^{2}+Dxy+Eyz+Fzx+Gx+Hy+Iz-K=0. \tag{2}\]  
Here, because \(K\) does not affect any of the variables \(x\), \(y\), or \(z\), both sides of Equation (2) are divided by \(K\). The parameters obtained after division are re-expressed using the same symbols:  
\[Ax^{2}+By^{2}+Cz^{2}+Dxy+Eyz+Fzx+Gx+Hy+Iz-1=0 \tag{3}\]  
where \(J\) is defined as the least squares evaluation function. If we define the left-hand side of Equation (3) as \(f(x,y,z)\), then \(f(x_{n},y_{n},z_{n})=0\) should hold for a point \((x_{n},y_{n},z_{n})\) on the ellipsoid surface. In reality, there will be some deviation, meaning that it will not be exactly zero; nonetheless, we seek a constant that approaches zero as closely as possible for \(N\) points. Thus, the least squares evaluation function \(J\) is defined as in Equation (4). \[J=\sum_{n=0}^{N}f(x_{n},y_{n},z_{n})^{2} \tag{4}\]  
To calculate this in such a way that the evaluation function \(J\) is minimized, it is necessary to solve the system of nine simultaneous equations formed by setting the partial derivatives concerning the nine parameters from \(A\) to \(I\) equal to zero in order to find the parameters. The equations obtained by partially differentiating the evaluation function \(J\) with respect to each parameter are shown below.
# Cross-Pol Transponder with Frequency Shifter for
## 1 Introduction  
Ground-based synthetic aperture radar (GBSAR) systems are popular remote sensing instruments for detecting the ground changes of landslides [1], glaciers [2], and open pits [3] as well as for detecting small displacements of large structures, such as bridges [4] and dams [5]. These radars are able to provide displacement maps but have a major limitation: They only detect the displacement component along the range direction. Recently, in a piece published in the _IEEE Geoscience and Remote Sensing Letters_[6], the authors of this paper proposed a novel mono/bistatic GBSAR configuration for retrieving two components of the displacement vector using a transponder. The transponder proposed in that paper consisted of two co-polarized horn antennas and an amplifier. However, this solution had two possible problems: (1) The gain is limited by the poor isolation between the two co-pol antennas operating at the same frequency. (2) As monostatic and bistatic signals are in the same polarization and in the same frequency, large targets behind the transponder can give monostatic signals mixed to bistatic signals that produce artifacts in the bistatic image [6].  
The aim of this article is to propose an enhanced transponder that is able to operate at very high gain without triggering self-oscillations. It has the following features: (1) the transmitting and receiving antennas are cross-polarized, and (2) a frequency shifter decouples the transmitted and received signals of the transponder.
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 5 Preliminary Case Study
### Data Workflow  
In this subsection, we describe in detail the workflow of data in the implemented architecture and the interactions of the users with it.  
The data flow diagram of Figure 7 shows which data are collected by the basic implementation of the proposed VLE architecture and how they flow through its various layers. First of all, the lower fog layer collects heart beat rates of the students and their variations and sends them through Bluetooth technology to the smartphone of the students themselves. The smartphones are located in the fog intermediate layer, where SAMOA PIs are located. Here, the implemented app collects HRV data, together with GPS and timing data of the current studying session. GPS data are used by a proper SAMOA PI to find out a possible location through Google Maps services, such as residential districts, restaurants, libraries, etc. These data are used by other SAMOA PIs to determine an opportune list of suggested LOs for the current studying session of the student. This is done according to the vertical Hoeffding tree algorithm. The training dataset is made of different data. Timing data are provided by the university studying habits in the previous three academic years (2016-2017, 2015-2016 and 2014-2015) in the same considered faculties. HRV labeled data were the ones used in [47], while location data were labeled as more or less suited for studying by hand according to three categories: studying place, leisure place, neutral place. Finally, once the student had chosen one of the provided LOs, this information was sent directly to the teachers' smartphone, running an opportune version of the implemented app, and to the cloud part of the VLE. Teachers could verify the chosen LO and, in that case, suggest a different LO to the student via a feedback channel. In the end, data stored in the cloud can be finally used by researchers to perform analyses on the usage of the proposed architecture, as well as on the percentage of dropouts. This is described in the following subsection.
## 3 Theoretical Background
Some of the first performance analysis results from the field are pointing to a better spectral efficiency in 5G systems, compared to older 4G, which is mainly achieved with a higher Rank Indicator usage and better MIMO performance in terms of spatial multiplexing. The most probable reason behind it is the growing number of antenna elements typical for 5G radio system.
###### Abstract  
Nowadays, the use of tissue mimicking material (TMM) is widespread in both diagnostic and therapeutic medicine, as well as for quality assurance and control. For example, patient exposure evaluation during therapeutic tests has been commonly measured using TMMs. However, only a few materials have been developed for research use at the megavoltage photon energy encountered in medical radiology. In this paper, we extended our previous work to cover the photon energy range of 0.15-15 MeV for five human tissues (adipose, cortical bone, fat, lung and muscle). As a selection criterion for TMM, other than the attenuation coefficient, we introduced the computation of the buildup factor (BUF) for a given couple of energy and depth based on the geometric progression fitting method. Hence, we developed a C\({}^{++}\) program able to compute BUF for depths up to 40 mean free path. Moreover, we simulated the percentage depth dose (PDD) of a 6 MV photon beam through each tissue and their equivalent materials using the Geant4 Monte Carlo toolkit (version 10.5). After the comparison of a set of parameters (mass attenuation and mass energy absorption coefficients, BUF, equivalent and effective atomic numbers, electron density, superficial and maximal dose and dose at 10 and 20 cm depths), we found that SB3 (a mixture of epoxy and calcium carbonate) and MS15 (a mixture of epoxy, phenol, polyethylene and aluminum oxide) accurately imitate cortical bone and muscle tissues, respectively. AP6 (a mixture of epoxy, phenol, polyethylene and teflon), glycerol trioleate and LN1 (a mixture of polyurethane and aluminum oxide) are also suitable TMMs for adipose, fat and lung tissues, respectively. Therefore, this work can be useful to physician researchers in dosimetry and radiological diagnosis.  
buildup factor; PDD; TMM; GP-fitting method; Geant4
## 3 Short-Reach Systems Classification and Enabling Technologies
### Optoelectronic Oscillator
#### 3.3.4 Requirements and Challenges

Table 3: Enabling technologies and features of optical transceivers for 5G applications [70; 71; 106]. system. Therefore, the form factor plays a pivotal role in total energy consumption/usage by the system [35]. Other Challenges of Short-Reach Systems  
As previously mentioned, short-reach systems demand low-cost components. However, these kinds of components usually offer imperfect results that consequently bring about the initiation of additional transmission impairments that are mainly peculiar to the short-reach systems. For instance, when a DML is directly modulated, a signal chirp will be generated. Components such as transimpedance amplifiers (TIAs), EAMs, and PDs also generate additional nonlinear distortions. The low-pass filtering effect that is owed to the insufficient bandwidth of different components is another typical impairment of the short-reach systems. The RIN of the laser source and the finite extinction ratios of the EAM degrade the SNR, and subsequently, the link power budget margins are reduced. Moreover, equalization of linear transmission impairments such as polarization mode dispersion (PMD) and chromatic dispersion is really challenging using direct detection with non-negative signaling. This is owed to the received signal spectrum, which cannot be simply multiplied by the inverse of the PMD/CD transfer function [17; 35].