esa-sceva/satcom-chunk-collection huggingface dataset:
# Scaling of Rain Attenuation Models: A Survey
## 4 Comparison of Scaling Models  
The scaling of rain attenuation models has been developed in several parts of the world. Since the climatic parameters are different in different parts of the world, the scaling parameters are also limited for the best fit in a particular geographical area. However, scaling models are always needed for better applicability, whose performance can be \"fine-tuned\" by the local climatic parameters. Among the climatic parameters, the rain rate is the frequently used parameter. However, in recent years, it has been validated that the DSD-based distribution gives more accurate results than the rainfall rate. However, the problem of the DSD-based model is that the rainfall distribution can change in different rain events. Furthermore, the availability of DSD-based measurement data is limited compared to rain-rate-based data. Among the developed models of scaling, the frequency-scaled models are the most common in the literature. Some scaling models consider only the ratio of frequencies, while other methods consider a more complicated one, including attenuation and frequency in the ratio-based scaling factor. Another critical feature of scaling techniques is the temporal domain perspective: \"long-term\" and \"short-term\" applicability. The \"short-term\" applicability is also called \"instantaneous scaling [51]\". The \"instantaneous scaling\" techniques are sensitive to the variations of the same rain event due to the variations of the drop size distribution on the slant path [41]. In Tables 1 and 2, all the rain attenuation models' innovative ideas, long or short applicability, reported frequency range, and supported link are discussed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Location** & **Link** & **Atten.** & **DSD** & **Camp.Time** & **Freq. (GHz)** \\ \hline
[14] & Italy & Satellite & & & 06/2014– & 20/40 \\ \hline
[52] & Italy & Satellite & ✓ & & 72/36 h & 11.6/17–17.8 \\ \hline
[53] & Belgium & Satellite (Olympus) & ✓ & & 7 mo. & 12.50/19.77–29.66 \\ \hline
[54] & Italy & Satellite (ACTS, ITALSAT) & ✓ & & – & 20.2/27.5 (ACTS); 39.6/49.5(ITALSAT) \\ \hline
[21] & Worldwide & Satellite & ✓ & & – & 10–100 \\ \hline
[55] & Collected & Satellite & & ✓ & – & 10-50 \\ \hline
[51] & U.K. & Satellite & ✓ & & 8–11 mo. & 12.5/20/30 \\ \hline
[56] & Collected & Satellite/ Terrestrial & ✓ & & – & 13/19/30 \\ \hline
[57] & Czech Rep. & Terrestrial & ✓ & & 9 mo. & 58/93 \\ \hline
[58] & Malaysia & Terrestrial & ✓ & & 12 mo. & 23/26/38 \\ \hline
[6] & USA & Satellite & ✓ & & 12 mo. & 12/20/30 \\ \hline
[7] & India & Satellite & ✓ & & 5 d & 20.2/30.5 \\ \hline
[59] & India & Satellite & & ✓ & 2 y & 20/30 \\ \hline
[13] & India & Satellite & ✓ & & & 20/30 \\ \hline
[8] & Italy & Satellite (ITALSAT) & ✓ & ✓ & 12 mo. & Ka/V bands (18.7/39.6/49.5) \\ \hline
[49] & Italy & Satellite (ITALSAT) & & & 8 y & Ka/V (18.7/39.6/49.5) \\ \hline
[11] & Synthetic & Satellite & ✓ & & – & 18.7/39.6 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison among the scaling of propagation links.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 5 Results  
Packet latency is first calculated in the ideal scenario. The only delay is caused by the finite throughput, 1.76 Gsps. Processing delays in the RX DBB are added, as data transmission using a latency inducing PHY is simulated. Further simulations are carried out by relaxing the number of allowed LDPC decoder iterations to 1-100 and by substituting the demapper with one of three possible instances.
## 5 Experiments and Results
### Datasets  
To assess the effectiveness of our novel extractive summarization framework, we conducted experiments by using three benchmark datasets, each tailored to different textual lengths and complexities. These datasets are widely recognized in the domain of automatic text summarization, providing diverse challenges ranging from short news articles to lengthy scientific reports:  
* **XSum**: This dataset comprises news articles sourced from the British Broadcasting Corporation (BBC), known for their concise single-sentence summaries. It includes approximately 203,028 articles for training, 11,273 for validation, and 11,332 for testing. Each document is accompanied by a professionally written summary, making it ideal for evaluating summarization on short texts [36].
* **CNN/DailyMail**: This dataset features news articles paired with multi-point bullet summaries, serving as a moderate-length document source for summarization tasks. The non-anonymized version of the dataset was used, segmented into 287,084 training samples, 13,367 validation samples, and 11,489 test samples. The structure of these summaries provides a unique challenge in capturing essential narrative threads across multiple bullet points [37].
* **PubMed**: As a representative of scientific and technical document summarization, the PubMed dataset contains lengthy abstracts of biomedical articles. It is composed of 83,233 training documents, 4946 for validation, and 5025 for testing. The comprehensive and technical nature of these summaries tests the ability of summarization models to handle complex, information-dense content [38].  
These datasets provide a robust platform for demonstrating the capabilities of our framework across various dimensions of content length and complexity. Table 2 provides an overview of the domain, the number of document-summary pairs, and the average token counts for documents and summaries in each dataset.
# Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
## 2 Preliminaries
### Potential Games  
The concept of a potential game was first conceived by [PERSON] [17] and lays the necessary foundations in most of today's work in game theory.  
**Definition 3**.: _(Ordinal potential game) [18] A game is an ordinal potential game if and only if a potential function \(\phi(\mathcal{A}):\mathcal{A}\mapsto\mathbb{R}\) exists such that:_  
\[U_{i}(a_{i}^{\prime\prime},a_{-i})-U_{i}(a_{i}^{\prime},a_{-i})>0\Leftrightarrow \phi(a_{i}^{\prime\prime},a_{-i})-\phi(a_{i}^{\prime},a_{-i})>0\qquad\forall a _{i},a_{i}^{\prime}\in\mathcal{A}_{i}\quad\forall i\in\mathcal{N} \tag{1}\]  
_where \(\mathcal{A}_{i}\) is player \(i\)'s action set, \(\mathcal{N}\) is the set of all players, \(U_{i}\) is the local utility of player \(i\), \(a_{i}^{\prime}\) and \(a_{i}^{\prime\prime}\) are the actions of player \(i\), and \(a_{-i}\) is the action of all other players except \(i\)._  
Effectively, a game is an ordinal potential game if and only if any unilateral change in actions by an individual that leads to a change in its local utility affects the potential function in the same manner. The suitability of a game-theoretic framework for task allocation problems lies in the characteristics of a potential game. Foremost, every finite potential game possesses the finite improvement property and has at least one pure-strategy equilibrium. Additionally, for every game with a finite improvement property, it will always converge to a Nash equilibrium [17; 19; 20; 21]. Therefore, by modeling a task allocation problem as a finite potential game, we can guarantee the existence of a feasible pure-strategy allocation that is socially acceptable.
###### Abstract  
With the rapid development of the photovoltaic industry, fault monitoring is becoming an important issue in maintaining the safe and stable operation of a solar power station. In order to diagnose the fault types of photovoltaic array, a fault diagnosis method that is based on the Least Squares Support Vector Machine (LSSVM) in the Bayesian framework is put forward. First, based on the elaborate analysis of the change rules of the output electrical parameters and the equivalent circuit internal parameters of photovoltaic array in different fault states, the input variables of the photovoltaic array fault diagnosis model are determined. Second, through the LSSVM algorithm in the Bayesian framework, the fault diagnosis model based on the output electrical parameters and the equivalent circuit internal parameters of the photovoltaic array is built, which can effectively detect the photovoltaic array faults of short circuit, open circuit, and abnormal aging. Then, the simulation model is built to verify the validity of the LSSVM algorithm in the Bayesian framework by comparing it with the model of LSSVM and the Support Vector Machine (SVM). Moreover, a 5 \(\times\) 3 photovoltaic array and a reference photovoltaic string are established and experimentally tested to validate the performance of the proposed method.  
fault monitoring: photovoltaic array; the LSSVM algorithm; Bayesian framework +
Footnote †: journal: Applied Sciences
###### Abstract  
The purpose of this research is to study the effect of both transverse impact, and ratio of outer diameter to thickness of outer steel tube (_D/t_), on the residual axial bearing capacity of concrete-filled circular steel tubular columns (CFCSTCs). A total of sixteen samples, including four samples left untreated for comparison, are experimentally studied to investigate the effect of both drop-hammer transverse impact height (_H_), and _D/t_ ratio, on the residual axial bearing capacity of CFCSTCs. The failure mode, load-displacement curves, load-strain curves, and residual axial bearing capacity of those samples are extensively investigated. A finite element analysis (FEA) model is established to predict the effect of _D/t_ ratio on the residual axial bearing capacity of CFCSTCs. The results indicate that the \(H\) and the _D/t_ ratio have noticeable effects on the axial compression performance of CFCSTCs. Failure mode of samples is commonly local buckling. In addition, maximum reduction of the axial bearing capacity of columns reaches about 35% compared with that of untreated columns. The results also show that the bearing capacity of the column increases with a decreasing _D/t_ ratio of the same diameter (_D_).  
Keywords:concrete-filled circular steel tubular columns (CFCSTCs); transverse impact; bearing capacity; compression; finite element analysis (FEA) +
Footnote †: journal: Applied Sciences
# Pseudo-Spin Polarized One-Way Elastic Wave Eigenstates in One-Dimensional Phononic Superlattices
## 2 Model and Method
### SSH Limit  
In the SSH scenario (\(n=p=1\)), \(\sin k_{1}d_{1}\sin k_{2}d_{2}=\sin k_{1}a\sin k_{2}a\) and \(\cos k_{1}d_{1}\cos k_{2}d_{2}=\cos k_{1}a\cos k_{2}a\). Using the dispersion relations given by Equation (3) and basic trigonometric relations, Equation (10) reduces to:  
\[\omega^{4}-2(K_{1}+K_{2})\omega^{2}+2K_{1}K_{2}(1-\cos qL)=0 \tag{11}\]  
This leads to the usual SSH dispersion relation:  
\[\omega^{2}=K_{1}+K_{2}\pm\sqrt{K_{1}^{2}+K_{2}^{2}+2K_{1}K_{2}\text{cos}\,qL} \tag{12}\]  
Equation (8) can be manipulated algebraically to obtain a relationship between \(A=A_{+}+A_{-}\) and \(B=B_{+}+B_{-}\), namely:  
\[A\Big{(}-\omega^{2}+K_{1}+K_{2}\Big{)}=\Big{(}K_{1}+K_{2}e^{-i\theta L}\Big{)}B \tag{13}\]  
The amplitudes \(A=A_{+}+A_{-}\) and \(B=B_{+}+B_{-}\) do not differentiate between positive or negative \(k_{j}\), since \(m=0\) and \(l=0\) in Equation (2a,b).  
Employing Equation (12) in the form of \(\big{(}-\omega^{2}+K_{1}+K_{2}\big{)}^{2}=\big{(}K_{1}+K_{2}e^{-i\theta L}\big{)}\)\(\big{(}K_{1}+K_{2}e^{+i\theta L}\big{)}\), we recover the complex SSH amplitudes [14]:  
\[\begin{pmatrix}A\\ B\end{pmatrix}\propto\begin{pmatrix}\sqrt{K_{1}+K_{2}e^{-i\theta L}}\\ \sqrt{K_{1}+K_{2}e^{+i\theta L}}\end{pmatrix} \tag{14}\]  
The dispersion relation (Equation (12)) exhibits band inversion when varying the spring stiffness. The complex amplitudes (Equation (14)) support a non-conventional topology over the Brillouin zone, \(q\in\big{[}-\frac{\pi}{L},\ \frac{\pi}{L}\big{]}\), when \(K_{1}<K_{2}\). It is worth noting that these amplitudes cannot be zero for any value of the wave number \(q\).
# Effect of Unit-Cell Size on the Barely Visible Impact Damage in Woven Composites
## 3 Experiment and Characterisation
### Nondestructive Testing of the Impact-Induced Damage
#### 3.3.3 X-ray Computed Tomography (CT)  
X-ray CT scans were performed for the impacted specimens, using a Nikon XTH-320 system, to determine the extent of the internal damage. With a 0.125 mm copper filter, the 225 kV source, with a reflective target, was used. The volume captured by the field of view had the dimensions of 22.5 \(\times\) 19 \(\times\) 5.5 mm\({}^{3}\), resulting in a resolution of \(\sim\)13.2 \(\upmu\)m. The source current and voltage were set to 59 \(\upmu\)A and 220 kV, respectively. Each radiograph had an exposure time of \(\sim\)1.4 s. With \(\sim\)3140 radiographs being collected over 360\({}^{\circ}\), the total data acquisition time was \(\sim\)1.25 h. After scanning, VGSTUDIO MAX software was used to reconstruct the 3D volume from the raw data.
## 1 Introduction
### Significance of IoT in the Maritime Industry  
The Internet of Things (IoT), with its capacity to connect a multitude of devices and systems over networks to gather, exchange, and analyze data, has significant implications for the maritime industry. By leveraging IoT, maritime operations can realize unprecedented levels of efficiency, safety, and environmental sustainability.  
IoT technologies are transforming traditional maritime operations into a connected ecosystem. Ship-to-ship and ship-to-shore communication, real-time tracking of vessels and cargo, condition monitoring of critical equipment, and predictive maintenance are just some examples of IoT maritime applications. These technologies provide operators with a wealth of real-time information, enabling them to make data-driven decisions and take proactive measures [13; 14; 15; 1].  
For instance, IoT-enabled smart shipping involves embedding sensors and devices throughout the ship to monitor various parameters, such as location, speed, fuel consumption, engine performance, and weather conditions. This data can be used to optimize routes, improve fuel efficiency, and prevent equipment failure, significantly enhancing operational efficiency and cost-effectiveness [16; 17; 18; 19; 20].  
Similarly,'smart ports' leverage IoT technologies to streamline and automate operations. IoT devices monitor and control cargo handling, manage logistics, track vehicles and equipment, and automate processes, reducing waiting times, improving cargo turnover, and minimizing human errors [19; 20].  
Furthermore, IoT plays a crucial role in enhancing maritime safety and security. It aids in piracy control, collision avoidance, emergency response, and environmental protection. On the sustainability front, IoT-driven data can help reduce greenhouse gas emissions and mitigate the environmental impact of maritime operations [21].
###### Abstract  
The direction of arrival (DoA) estimation of Lamb waves is a fundamental task to locate acoustic events, such as those caused by impacts in plates or shells. To perform this task, a novel cluster of piezoelectric sensors is presented in this work. The designed cluster is composed by three irregularly shaped patch transducers (P1, P2 e P3). This is in contrast with the approaches that are typically presented in literature which are based on isotropic piezo-disks. In our approach, the transducers are shaped with a procedure based on the Radon Transform, so that the difference in time of arrival (DToA) of the Lamb waves at patches P1 and P2 is linearly related to the DoA, while P3 is designed so that it is possible to perform the estimation of DoA without knowing the actual wave velocity. The numerical validation shows that the performance in the DoA estimation achieved by means of the proposed cluster compares favorably with respect to clusters of conventional sensors, even in the case of noise-affected measurements.  
Keywords:direction of arrival estimation; Acoustic source location; piezoelectric sensors; shaped sensors; Lamb waves; Radon transform +
Footnote †: journal: Applied Sciences
## 3 Experiments and Analysis
### Comparison with Other Methods  
To verify the effectiveness of CDFFNet in remote sensing road extraction, comparative experiments were conducted using the PSPNet [39] model, U-Net model, HRNet [40] model, SegFormer [41] model, RoadCNN [6] model, and the proposed CDFFNet model. The accuracy comparison experiment will compare the differences between the models in the accuracy of road extraction, while the inference efficiency comparison experiment will compare the differences between the models in the number of parameters and the inference speed. Both experiments performed a series of rigorous quantitative and qualitative analyses to better demonstrate their respective strengths and weaknesses in the road extraction task.  
To ensure a convenient and fair comparison of performance differences between different road extraction models, all models shared a common training and testing framework. A significant amount of time was invested in debugging the comparative experimental models within the PyTorch 1.10.2. For models originally implemented in Caffe or TensorFlow, where no PyTorch-based source code was available from the original papers, we utilized the implementations of these models found in MMSegmentation (an open-source semantic segmentation framework developed by OpenMMLab), such as PSPNet, UNet, and DeepLabV3+. If a model was not available in MMSegmentation, we searched for its implementation in the source code provided by relevant literature that used the model, for example, RoadCNN, which is sourced from MSMDFF-Net [7]. For models whose original papers directly provided PyTorch framework-based code, we directly used the code for those model architectures, such as HRNet and Segformer.
## 2 Materials and Methods
### Low Molecular Weight Compounds  
Twelve low molecular weight compounds (LMWC) were chosen based on their effectiveness, demonstrated in previous works, in differentiating between aged WSs [4]: ellagic acid, gallic acid, syringe acid (phenolic acids), vanillin, syringaldehyde, coniferaldehyde, sinapaldehyde (phenolic aldehydes), scopoletin, umbelliferone (coumarins), 5-hydroxymethylfurfural (HMF), furfural and 5-methylfurfural (furanic aldehydes).  
They were quantified in the WSs by a validated HPLC method [37], using a Lachrom Merck Hitachi system (Merck, Darmstadt, Germany) equipped with a quaternary pump L-7100, a column oven L-7350, a UV-Vis detector L-7400, a fluorescence detector L-7480 (connected to the UV-Vis detector) and an autosampler L-7250, and coupled to HSM D-7000 software (Merck, Darmstadt, Germany) for the management, acquisition and treatment of data. The stationary phase was a 250 mm \(\times\) 4 mm i.d. LiChrospher RP 18 (5 \(\upmu\)m) column (Merck, Darmstadt, Germany). UV detection was carried out at 280 nm for phenolic acids and furanic aldehydes, and at 320 nm for phenolic aldehydes. Fluorescence detection was carried out at 325 nm (excitation)/454 nm (emission) for coumarins. Samples were spiked with 4-hydroxybenzaldehyde (internal standard, 20 mg/L), filtered through 0.45 mm membrane (Filter-Bio, Nantong, China), and analysed by direct injection of 20 \(\upmu\)L.  
The analysis was performed in duplicate. Calibration curves made with the corresponding commercial standards were used for the compounds' quantification. The results were expressed as mg of compound per litre.
## 3 Results and Discussion
### Characterization of Essential Oils
#### 3.1.4 NMR Spectroscopy  
NMR spectroscopy is a powerful tool for the structural characterization of complex mixtures like essential oils, enabling the identification of key compounds [7; 65]. In this work, \({}^{1}\)H NMR spectroscopy was used to characterize different types of essential oils and their main compounds. The assignment of the main components of the essential oil samples studied was achieved by analyzing the resonances in the \({}^{1}\)H NMR spectra and comparing them with previously published data [5; 65; 66; 67; 68] and spectra of major components. \({}^{1}\)H NMR spectra with the assignment of the main components of the essential oils are shown in Figures 8-13. The majority of the chemical shifts in the \({}^{1}\)H NMR spectra of the extracted essential oils can be assigned to the main compounds. Peaks with lower intensity correspond to other components that are present in a larger proportion in the essential oils. The chemical shifts of some compounds are as follows:  
1. LEO: * \(\gamma\)-Terpinene (1.01, 1.24, 2.60 ppm); * \(\beta\)-Pinene (0.72, 1.23, 1.27, 1.50-2.50, 4.54-4.66 ppm); * \(\alpha\)-Pinene (0.83, 1.15, 1.50-2.50 ppm); * \(\alpha\)-Myrcene (-1.70, 4.7-5.35 ppm); 2. BLEO: * \(\bullet\)Sabinene (0.64, 0.82-0.98, 1.16, 3.33, 3.85, 4.55-4.85 ppm); * \(\bullet\)Methyl eugenol (3.63, 3.87, 5.01-5.17, 5.94, 6.64-6.92); * \(\bullet\)Terpinyl propionate (1.11-1.22, -1.40, 1.60 ppm); 3. CEO: * \(\bullet\)Caryophyllene (0.98, 1.00, 1.61, 1.84-2.56, 4.82, 4.95, 5.30 ppm); * \(\bullet\)Eugenol acetate (2.29, 3.36, 3.81 ppm); * \(\bullet\)Humulene (1.07 ppm); 4. OEO: * \(\bullet\)\(p\)-Cymene (1.25, 2.32, 3.17, 7.11 ppm); * \(\bullet\)Thymol (1.25, 2.26, 6.52-7.42 ppm); 5. NEO: * \(\bullet\)\(\alpha\)-Pinene (0.84 ppm); * \(\beta\)-pinene (0.72 ppm).  
Due to overlapping signals in the NMR spectra, some of functional groups could not be distinctly identified. For example, most of the aliphatic groups resonated in a narrow range of the spectra and the signals could not be separated well.  
Figure 10: \({}^{1}\)H NMR spectra of CEO and eugenol.  
Figure 9: \({}^{1}\)H NMR spectra of BLEO and eucalypt.  
Figure 11: \({}^{1}\)H NMR spectra of SAEO and _trans_-anethole.  
Figure 12: \({}^{1}\)H NMR spectra of OEO and carvacrol.  
Figure 13: \({}^{1}\)H NMR spectrum of NEO.