esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The present work focused on the buckling behavior of FG-saturated porous rectangular plates subjected to normal and shear loads, while adopting [PERSON]'s constitutive law and proposing a combined FE-GDQ as an efficient computational tool to solve the problem. This means that the in-plane problem has been discretized in the \(x\)-\(y\)-directions by means of classical FEs, and follows a weak formulation. Along the thickness direction (the \(z\)-direction), instead, the problem is defined in a strong form based on a GDQ approximation. This mixed method deals with a three-dimensional theory of elasticity without any additional kinematic assumption for the plate deformability. Various numerical examples have been considered and solved systematically to check for the reliability of the proposed method against a pure FE response, as well as to study the sensitivity of the response to some input parameters, i.e., the geometrical aspect ratio, the Skempton coefficients, the porosity distribution and coefficient and the BCs. Based on the parametric analysis, the main conclusions can be summarized as follows:  
* The porosity coefficient more significantly affects the buckling load than the Skempton coefficient. In detail, an increased porosity coefficient and a decreased Skempton coefficient yield an overall decrease of the buckling load.
* Among different boundary and loading conditions, the maximum and minimum values of the buckling load are reached for a FCFC plate under a shear loading and a SSSS plate under a biaxial loading condition, respectively.  
Figure 8: First four buckling mode shapes of a FG-saturated porous plate subjected to a biaxial load (SSSS, \(a=2\) m, \(b=1\) m).  
* The influence of the porosity coefficient on the buckling load for a uniform distribution is larger than other types of non-uniform porosity distributions.
* The effect of the Skempton coefficient on the buckling load, for a uniform distribution, is larger than other types of porosity distributions.
* By increasing the ratio, the buckling load generally decreases, except for a FCFC plate under a shear load and a SSSS plate under a normal uniaxial load, because of the variability in stiffness of the overall structure.
* The proposed method is verified to be a reliable tool for the computational study of saturated porous materials and structures, even from a design standpoint.  
Conceptualization, F.T.; Data curation, F.T.; Formal analysis, F.K., M.B., K.A., R.D. and F.T.; Investigation, F.K., M.B., K.A., R.D. and F.T.; Methodology, R.D. and F.T.; Supervision, R.D. and F.T.; Validation, R.D. and F.T.; Writing--original draft, F.K., M.B. and K.A.; Writing--review & editing, R.D. and F.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
## 3 Proposed Approach Towards the Definition of a Space Sustainability Index  
This Section presents the proposed preliminary framework for establishing a novel KPI that takes into account in a compact way the main parameters and design choices that have an impact on the space sustainability of the designed constellation/mission. A key characteristic of such a KPI is that it also takes into account the performance requirements of the specific mission, thus giving higher scores to the design that achieves the best trade-off between performance and impact on space sustainability.  
The Space Sustainability Index (\(SSI\)) is defined as an integrated metric comprising three key components:  
\[SSI=w_{1}e_{debris}+w_{2}e_{p}+w_{3}e_{bf} \tag{1}\]  
where  
* \(e_{debris}\) is a measure of how the designed constellation manages to keep low the probability of further increasing the space debris.
* \(e_{p}\) is the efficiency related to the performance, i.e., a constellation is well designed from the space sustainability point of view, if it manages to meet the performance requirements of the specific service for which it is designed, with a low number of satellites.
* \(e_{bf}\) represents the degree to which the designed constellation aligns with the compatibility of BW or FW, as detailed in Section 2.
* \(w_{i}\) parameters are weights, with unitary norm, used to properly tailor the impact of the previously described metrics on SSI.  
The parameters \(e_{debris}\) and \(e_{bf}\) are associated with the characteristics of the constellation, including the number of satellites, orbital configurations, and altitude. These parameters also account for mechanisms aimed at mitigating the probability of exacerbating space debris, such as debris removal systems and deorbiting strategies, as well as ensuring BW and FW compatibility, for instance, through the implementation of ISLs or a software-defined payload. In contrast, \(e_{p}\) refers to the specific service being provided. In the following sections, we define the three components of the \(SSI\) under the assumption that the service in question is broadband communications. For this scenario, the primary performance metrics considered are data rate and coverage.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 2 State of the Art Research
### Linear Equalization  
One of the advantages of OFDM is that it simplifies the equalization in the frequency domain. The literature is full of equalization techniques for OFDM. A few are covered in this section. In [12], the authors proposed a zero-padded OFDM that used a mean square error combining technique to estimate the transmitted data after obtaining the channel estimates. A high data rate acoustic modem for coherent OFDM was proposed by [PERSON] et al. [11]. In this work, the received signal was equalized using a scalar inversion of the channel estimates. Using a decision feedback equalizer along with time reversal was employed for an OFDM system over a UWA channel by [PERSON] et al. [20]. When comparing the computational complexity of the proposed equalizer, it was of the same order as that of a single carrier system. In [21], it was argued that as zero forcing equalizers have a tendency to enhance noise, they should not be used for a UWA OFDM system. They also argued that a minimum mean square error equalizer is not only complex but it also needs an estimate of the SNR. Consequently, the authors proposed a low complexity zero forcing equalizer that addressed these concerns. In another work [22], a combination of matching pursuit and maximum a posteriori algorithms was used to estimate a UWA channel. The authors also employed a linear minimum mean square equalizer at the receiver. An iterative least squares equalizer was proposed in [23] and its performance was compared with a conventional frequency domain equalizer. In [24], a channel was divided into several sub-bands. Each sub-band was treated as a narrowband channel. A minimum mean square error equalizer was employed with soft information. The complexity was reduced by exploiting the narrowband sub-bands. An equalizer that had a single tap and used a least mean square algorithm was reported in [25]. The proposed equalizer was also compared with a mean square error equalizer implemented in an OFDM system over a UWA channel. In [26], turbo equalization was used in field experiments performed in shallow water. The trials were conducted for three months with a negligible Doppler but a variable multipath channel. It was shown that turbo equalization with a 4-PSK modulation outperformed a decision feedback equalizer.
# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 3 Materials and Methods
### Summary of Data Analysis Tools and Techniques Utilized  
1. ETL Methodologies: Extract, Transform, Load processes for data acquisition and preprocessing.
2. Bibliometric Analysis: Evaluation of research output, collaboration networks, and publication trends.
3. Natural Language Processing (NLP): Extraction and categorization of keywords and thematic analysis.
4. Machine Learning Models: Random forests and deep learning for pattern detection and outcome prediction.
5. Latent Dirichlet Allocation (LDA): Topic modeling to identify core research themes.
6. Sentiment Analysis: Assessment of emotional tone in research abstracts using TextBlob.
7. Hierarchical Agglomerative Clustering: Clustering based on bibliographic coupling to identify related research topics.
8. Co-occurrence Matrices: Analysis of relationships between algorithms and application areas.
9. Python Libraries: Pandas, Matplotlib, NetworkX, and Regular Expressions for data processing and visualization.
10. Knowledge Graphs: Visualization of international research collaborations and networks.  
These comprehensive tools and methodologies facilitated an exhaustive evaluation of the scientific literature, identifying trends, international collaborations, and predominant areas of focus in the integration of AI within the Spanish agrifood industry. The combination of quantitative and qualitative methods ensured a deep understanding of the application and challenges of AI technologies in this sector.
# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 3 Proposed Methodology
### Fully Connected Layers  
Lastly, the high-level reasoning in CNN was done via fully connected layers after numerous convolutional and the pooling layers. The neurons of these layers have connections to all the activation in the previous layers. In our CNN model, we had five convolutional layers having almost the same kernel size, the first convolutional layer had 96 kernels with size 1. The convolutional output was received through a pooling layer of size 2 where it was normalized and sent into the second convolutional layer as i/p and filtered with same kernel size. Similarly, the convolutional output from the second layer was reduced through the pooling layer of size \(2\times 1\) and connected to the kernel size 1 in the third convolutional layer. The third and fourth convolutional layers comprised 384 kernels whereas the fifth convolutional layer had 256 kernels. A total of 4096 neurons were present in fully connected layers.
# Assessment of Cardiovascular Disease in Autosomal Dominant Polycystic Kidney Disease
## 4 Echocardiography
### Novel Echographic Parameters for Cardiovascular Risk  
Beyond the classical echocardiographic parameters, some studies show that PC1 and PC2 mutations are also related with novel risk factors and novel radiological markers [7]. In recent years, several studies have been focused on the correlation between epicardial adipose tissue and other possible cardiovascular risk factors. In three studies, ADPKD patients had more epicardial adipose tissue (EAT), associated with a higher risk of cardiovascular events [37; 38]. EAT is considered a proper tissue with neuroendocrine functions, and its thickness is related with all the classical cardiovascular risk factors such as IMT and LVH.  
In conclusion, the prevalence of cardiac abnormalities seems to be particularly high in ADPKD patients compared with the healthy population and hypertensive population.  
An early assessment of these abnormalities is clinically relevant, considering the high rate of cardiovascular events in the ADPKD population. An adequate diagnostic pathway could improve therapeutic choices. Thus, we can suggest performing echocardiography in the affected patients, despite the presence of hypertension, to more accurately stratify the cardiovascular risk.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 2 Background
### An Overview of 3 GPP Releases
The 5G-NR phase 1 started in _Rel-15_ and the 5G-NR phase 2 in _Rel-16_. The 5G URLLC network slicing supports the advanced functions of Level-3 and Level-4 AD with a higher quality-of-service (QoS) profile. Though the 3 GPP NR _Rel-17_ provides new sidelink communication relaying architecture aiming to support some of the aV2X services, the _Rel-18_, yet to be published, aims to introduce new features and services such as 5G advanced, evolved 5G in AI and extended reality, etc. The depicted V2X phases in the evolution provide a clear path to the research community for putting efforts in the right direction toward providing solution components for V2X services.
# Resource Allocation Schemes for 5G Network: A Systematic Review
## 6 Open Research Issues and Trends in 5G
### Energy Efficiency  
In this regard, it is essential to measure and analyze the tradeoff between an application's performance and familiarizing power allocation as a power-saving mode on cellular devices. Additionally, analyzing the effectiveness of beamforming algorithms across a large scale demands more attention. Harvesting energy from renewable resources can increase the ultra-dense CRANs' performance from a perspective of energy efficiency. It is also imperative to enquire about efficient RRH switching-off schemes to minimize the consumption of energy using fewer traffic scenarios.
## 2 Materials and Methods
### Functional Annotation  
DEGs were searched against proteins from the NCBI non-redundant (nr) database using the Basic Local Alignment Search Tool (BLASTX) implemented in BLAST+ (v.2.2.29) [47], with an E-value threshold of \(10^{-5}\), using sequences from which probes were designed. For functional annotation, gene ontology (GO) terms were assigned to the DEGs using Blast2 GO software (version 6.0.3) [48] with the same E-value. Further, gene symbols were assigned to each DEG using the Zebrafish Information Network (ZFIN) and the HUGO Gene Nomenclature Committee (HGNC) databases. GO and pathway enrichment analyses were performed using _Kyoto Encyclopedia of Genes and Genomes_ (KEGG) Orthology-Based Annotation System (KOBAS v.3.0) for the human data (corrected \(p\)-value \(<\) 0.05) [49].
### Motivation and Contribution  
USWNs, used to monitor the marine activities of marine species by using acoustic device networks, are preferred by researchers due to their self-organization and transmission. In this context, the choice of UWSN protocols can communicate information from one sensor to another to transmit maritime environmental conditions. The information gathered can then be utilized to create ecosystem models that can forecast changes in the undersea environment and climate changes. Such UWSNs have an application in monitoring seismic activities such as oil extraction from fields under the water. A 4D model is used to study the oil reservoir's fluctuation over time to evaluate the oil field operation and apply ad hoc treatments. Onshore fields are often routinely monitored using permanent  
Figure 1: Organization of the paper.  
instruments on a daily, quarterly, or annual basis [9; 10]. Conversely, sub-merged oil fields are more demanding because the deployment of sensors is not presently permanent in the oil fields underwater.  
The main contribution of the manuscript is highlighted as follows: Firstly, the review of a UWSN system, including sensing capabilities, wireless conveyance of information to a distantly situated station on the ground, a visualization of measured data, and an alert system in the event of anomalies, was analyzed. Secondly, a rudimentary system, which uses a cluster or a topology of stars for direct information transfers to a buoyant gateway, is discussed. Thirdly, underwater acoustic communication characteristics, namely, sound propagation speed, transmission loss, noise, and propagation delay models, are reviewed. Finally, the underwater localization method is classified into three key categories; namely, the distance measurement, network scale, and anchor use are described. In addition, future research directions for localizing underwater sensor nodes utilizing UWSNs are described in detail.
## 4 Numerical Simulation
### Simulation Results  
#### 4.2.1 Horizontal Displacement Analysis of the Enclosure Wall  
The monitoring point W1 was selected in the constructed model for the horizontal displacement analysis of the enclosure wall along the depth direction, and its simulated values are shown in Figure 9. Step 0 in the figure is the deformation value after ground stress balance in the simulation, and Step 2-Step 14 are the whole excavation process simulated by zoned excavation steps.  
Upon comparing Figure 9 with Figure 4a, it can be observed that the trend of the measured horizontal displacement values along the depth direction initially increased rapidly and then decreased gradually. However, the simulated values of the horizontal displacement along the depth direction increased gradually, stabilized, and then decreased gradually. This discrepancy can be attributed to the difference between the formation of the ground diaphragm wall in the actual construction using bored piles, and its direct imposition in the numerical simulation, leading to a higher stiffness of the simulated enclosure wall. Thus, the simulated results show a continuous equal phase of horizontal displacement, unlike the actual construction where the displacement increased rapidly and then gradually became smaller.  
It was observed both in the actual construction and the numerical simulation that the horizontal displacement is relatively small near the top of the enclosure wall, as there are top beam and horizontal cross bracing in that region. Additionally, as the excavation depth of the foundation pit is small, but the height of the enclosure wall is larger than the excavation depth, the bottom deformation of the enclosure wall is also small.  
The measured and simulated values both show an increasing trend in horizontal deformation of the enclosure wall as the excavation proceeds, but the measured values are significantly larger than the simulated values. This is likely due to ideal modeling conditions in the numerical simulation. In the construction process, the measured horizontal displacement gradually increased in each step, whereas the simulated values increased differently. Specifically, the simulated values show a clear increase when excavating the  
Figure 9: Horizontal displacement of the diaphragm wall along the depth direction.  
area where the observation point W1 is located, but do not show an obvious increase when excavating areas 2, 3, or 4.
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 3 Approach
### Hardware Platform  
The proposed platform consists of an energy harvesting sensor node, two capacitors and a switching circuit. The switching circuit consists of one bi-stable relay, two bipolar junction transistors (BJT) and two resistors, as shown in Figure 3. The P1 relay is bi-stable single pole double throw relay, which operates at 3 V and consumes 69 mW. The set and reset take 2 ms, which means it consumes 138 \(\upmu\)J energy per switch. The advantage of using a relay is that once set, it does not consume energy and maintains its state across the reboots. This is particularly suitable for sparse charging networks where the mobile node roams between high and low energy areas as the circuit does not consume energy once it moves into a certain area. The circuit is controlled by sensor node's General Purpose Input Output (GPIO) ports using a switching algorithm as described in the following subsection.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 2 Related Work
### Multipath Routing
The literature [17] first formalizes the maximum survival time of the network, and then presents an optimal load balancing solution. Load balancing is achieved by transmitting power control to maximize the network lifetime. Although their approach is designed for converged transmissions, they assume that the link quality is constant and homogeneous, which is rare in actual deployments. In [18], the authors find bottleneck nodes by analyzing the residual time of nodes. Depending on their residual time, a different proportion of the traffic can be distributed for traffic balance. For the nodes with much residual energy, their cache is not sufficient, because they undertake more traffic. This will lead to frequent data loss, frequent retransmission of data and extra energy consumption. This method cannot achieve the purpose of extending the network survival time. Therefore, a good routing algorithm should take into account residual energy and cache size to get a dynamic balance.
# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
## 3 Methodology
### Evaluation Criterion  
To assess the segmentation performance of the algorithm proposed in this paper, we employed the Dice Similarity Coefficient (DSC), the area under the receiver operating characteristic curve (AUC), and sensitivity.  
The DSC is an indicator that measures the overlap between the model's segmentation result and the ground truth, with values ranging from 0 to 1. A higher value signifies greater similarity and, consequently, higher accuracy. The formula is as follows, where Prediction represents the total sum of pixels in the predicted region, and Ground Truth represents the total sum of pixels in the ground truth region.  
\[DSC=\frac{2\times|Prediction\cap GroundTruth|}{Prediction+ GroundTruth} \tag{8}\]  
The AUC is a widely used metric for evaluating the performance of binary classification models. The ROC curve plots the true positive rate (recall) on the vertical axis against the false positive rate on the horizontal axis. The AUC represents the area beneath the  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Configuration** & **Settings** & **Configuration** & **Settings** \\ \hline Hardware & NVIDIA GeForce GTX1650 & Image Size & 244 \(\times\) 244 \\ Framework & PyTorch & Patch Size & 4 \\ GPU & 4.295G & CUDA Version & 11.7 \\ Optimizer & SGD Optimizer & Momentum & 0.9 \\ Batch Size & 24 & Weight Decay & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration details for the experimental setup, including hardware specifications, software framework, and model training parameters.  
Figure 3: The loss and score curve during the model training process indicates that the loss function stabilized when epoch = 150.  
receiver operating characteristic (ROC) curve, with values that range from 0 to 1. In image segmentation, AUC is typically used to assess a model's ability to distinguish between the target region and the background based on pixel classification.  
Sensitivity measures the proportion of actual positive instances correctly identified by a model. A higher sensitivity indicates better performance in identifying the positive class and is calculated as follows.  
\[Sensitivity=\frac{TP}{TP+FN} \tag{9}\]  
where we have the following:  
* TP is the true positives, i.e., the number of correctly predicted positive instances.
* FN is the false negatives, i.e., the number of actual positive instances that are incorrectly predicted as negative.