esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### MCA Identified on Simulated Data

Table 1: Performance metrics for addressed proxies fitted on data simulated by the procedure of Equation (43) (non-normal errors). \(E_{\lambda 0}(y|x)\) stands for back transformed TAMA, \(E_{\lambda 2}(y|x)\) is for back transformed \(\lambda_{2}(u)\) form. Huxley–DNLR refers to [PERSON]’s model fitted by direct non-linear regression, \(\text{H}_{TSK}(x)\) stands for the direct scales Takagi–Sugeno–Kang (TSK) fuzzy model regression protocol of Equation (34). \(E_{\Omega\text{TSK}}(y|x)\) denotes backtransformed form of the geometrical space TSK form \(\Omega_{\text{TSK}}(u)\). Finally, \(E_{\pi m}(y|x)\), associates to back transformation of \(\pi_{m}(u)\) a direct polynomial representation of MCA in geometrical scales. AIC stands for [PERSON]’s information criterion, \(\rho\) symbolizes [PERSON]’s concordance correlation coefficient, \(R^{2}\) denotes determination coefficient, SEE standard error of estimation and MPE mean prediction error. Figure 3: Comparison of \(\lambda_{2}(u)\) and \(\Omega_{TSK}(u)\) fits on lognormally distributed data. Panel (**a**) comparison of mean response functions fitted by means of a \(\lambda_{2}(u)\) and a \(\Omega_{TSK}(u)\) proxies. Panel (**b**) compares related retransformed forms \(E_{\lambda 2}(y|x)\) and \(E_{\Omega\text{TSK}}(y|x)\). Panel (**c**) compares \(E_{\lambda 2}(y|x)\) to \(E_{\text{HTSK}}(y|x)\) fitted by the regression model of Equation (33) in direct arithmetical scales. Panel (**d**) is a comparison of \(E_{\Omega\text{TSK}}(y|x)\) to \(E_{PQ2}(y|x)\), the directly acquired projection function. This can be obtained by replacing the fitted parameter vector \(PQ\) into addressed MCA form in arithmetical space. We can be aware of reliable projections even without a CF.By acquiring non lognormally distributed replicates \(y_{k(i)}(\xi)=\beta\exp(\alpha lnx_{i}+\xi_{k})\) according to procedure of Equation (44), we examined error structure effects on performance of addressed proxies. Figure 4 displays plots corresponding to retransformed forms. Table 2 presents related model performance statistics. Results reveal that reliability of the MCA and TSK approaches does not depend in error structure for the present assay.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 1 Introduction
### Related Work
In [28], the authors analyzed the massive MIMO channel model combined with LEO satellite characteristics and proposed an approach based on the LSTM neural network for a prediction scheme to mitigate the effects of outdated CSI. The proposed architecture achieved higher prediction accuracy compared to the adopted outdated CSI. A review of the methods used for communication optimization in the work presented in the literature is given in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Communication Method** & **Optimization Criteria** & **Optimization Method** \\ \hline
[20] [PERSON] et al. & LEO satellites and DVB S2X & energy efficiency &
\begin{tabular}{c} hierarchical clustering, the \\ semidefinite programming \\ algorithm and the CCCP \\ \end{tabular} \\ \hline
[21] [PERSON] et al. & satellite communications and DVB-S2X & \
\begin{tabular}{c} maximum sum rate under the \\ per-beam power constraint and \\ minimum SINR requirement of \\ scheduled users \\ \end{tabular} &
\begin{tabular}{c} convex–concave procedure-based \\ algorithm \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A review of related work.
# Blockchain and Smart Contracts for Insurance: Is the Technology Nature Enough?
## 3 Blockchain Applications in Insurance: Selected Use Cases
### Peer-to-Peer Insurance  
Several peer-to-peer insurances already exist [52; 53; 54], though it must be said that, at present, they are not \"real\" peer-to-peer models, as they have a traditional insurance model or risk carrier behind them, supporting the heavy part of the insurance business.  
In this context, smart contracts could represent an important innovation, as they would enable the creation of DAOs, where self-insured groups' functioning rules could be hard-coded.  
A prototype solution named DYNAMIS and based on the Ethereum blockchain has already been implemented [55]. This solution aims to provide supplemental unemployment insurance for a community of self-managed people in terms of underwriting and claims acceptance and processing.  
Even though in peer-to-peer insurance the blockchain could really become the key technology, from the insurance company's perspective it must be underlined that the objective of peer-to-peer insurance is the removal of intermediaries (i.e., the insurance companies themselves). Hence, a wise choice insurance companies could make here is to recognize this risk, and turn it from a threat into a business opportunity, e.g., by providing the infrastructure for peer-to-peer insurance.  
From the architectural point of view, since this scenario requires a high amount of decentralization, a public blockchain would be more suited.  
It should be underlined, however, that the adoption of peer-to-peer insurance models by the wider public is not imminent yet. In fact, apart from a small amount of technology enthusiasts who aim at reducing insurance costs, a high number of customers still considers the interaction with intermediaries important and worth of extra costs [56].
## 2 Materials and Methods
### Numerical Analysis  
Numerical finite element analyses of all configurations studied in the experimental campaign (Figure 2) were carried out using ABAQUS v2021 software [53]. Brittle splitting failure is addressed as simply as possible and in a way that provides sufficiently safe results from a structural design point of view. Since the dovel had low slenderness, it showed very small deformations, and the authors did not observe any yield hinges, so a simplified 2D model was developed in which a cohesive zone model (CZM) was implemented to reproduce the crack growth. The timber beam and the steel elements (supports and dowels) were defined by 8-node quadratic plane stress elements (CPS8R). The model mesh was refined in the crack propagation region, as well as in the dowel and support locations (Figure 3a,b), using a minimum and maximum mesh size of 1 mm \(\times\) 1 mm and 4 mm \(\times\) 4 mm, respectively. To simulate crack growth and propagation, 6-node quadratic cohesive surfaces were used together with the Newton-Cotes integration scheme [54]. To reduce the computational time and in accordance with the findings in [55], only one fracture plane at mid-height of the dowel was modelled. The contact between the steel elements and wood was introduced using 6-node quadratic contact surfaces with a friction coefficient of 0.15 [56]. The analyses were carried out considering a non-linear geometrical behaviour of the beams. The Newton-Raphson convergence method was applied to solve the systems of non-linear equilibrium equations. An isotropic material was considered for steel elements and an orthotropic material for wood. The yielding behaviour of wood was neglected for the sake of the simplification intended by the model. Consequently, a linear elastic constitutive response of the materials was assumed. The application of the load was performed by a uniform vertical displacement imposed at the central node of the dowels in the range of 6 and 20 mm introduced in increments of no more than 0.01 mm to reproduce stable crack growth and to obtain accurate results. Figure 3: Numerical model: (**a**) single connection; (**b**) double connection.
## 2 Carbon Nanostructures as Templates for Titania Nanomorphologies

Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Carbon** & **Nanocarbon** & **Distinctive** & **Specific** & **TiO2** & \\
**Nanostructure** & **Surface** & **Raman** & **Surface Area** & **Crystallite** & **Ref. ** \\ (CN)** & **Functionalization** & **Peaks (cm\({}^{-1}\))** & **(m\({}^{2}\) g\({}^{-1}\))** & **Size (nm)** & **Ref. ** \\ \hline SWCNTs-C\({}_{60}\)[2; 4] & Fullerodendron & 425, 610 (titania) & & & \\  & & 1139, 1610 (CN) & & & \\ \hline MWCNTs [5] & Pristine/ & 143 (A) [1] & & & \\ oxidation/N-doping & D and G bands (CN) & & & & \\ \hline MWCNTs [5] & Oxidation & 145, 398, 517, 641 (A) [1] & & & \\  & & 1351, 1582 (CN) & & & \\ \hline MWCNTs [5] & Oxidation & \(n.\)a. & 104–190 & 9–17 & [110] \\ \hline MWCNTs [5] & OH & 1342, 1576 (CN) & \(n.\)a. & \(n.\)a. & [111] \\ \hline MWCNTs [5] & SDS [6] (non-covalent) & \(n.\)a. & 50 & 14 & [113] \\ \hline MWCNTs [5] & Oxidation & 234, 432, 612 (R) [7] & & & \\  & 1358, 1580, 2711 (CN) & & & & \\ \hline MWCNTs [5] & SDS [6] (non-covalent) & 448, 611 (R) [7] & & & \\  & 1355, 1600 (CN) & & & & \\ \hline MWCNTs [5] & OH & 161, 388, 516, 634 (A) [1] & & & \\  & 1338, 1578, 2673 (CN) & & & & \\ \hline MWCNTs [5] & Oxidation, magnetization & Anatase, D and G band (CN) & 30 & \(n.\)a. & [101] \\ \hline MWCNTs [5] & PVP [8]-PAN [9] (non-covalent) & 143 (A) [1], 245, 421, 602 (R) [7] & & & \\  & 1325, 1592 (CN) & & & & \\ \hline MWCNTs [5] & Ammonia treatment & 153, 202, 394, 207, 512, 631 (A) [1] & & & \\  & D and G bands (CN) & & & & \\ \hline MWCNTs [5] & \(n.\)a. & Anatase, D and G bands (CN) & 221 & 2 & [120] \\ \hline MWCNTs [5] & Benzoic acid & 146, 198, 395, 513, 639 (A) [1] & & & \\  & D, G, 2D bands (CN) & & & & \\ \hline MWCNTs [5] & Oxidation & 153, 396, 513, 635 (A) [1] & & & \\  & 1329, 1597 (CN) & & & & \\ \hline MWCNTs [5] & N-doping, acid treatment & \(n.\)a. & 160–295 & 10 & [123] \\ \hline MWCNTs [5] & Oxidation & \(n.\)a. & \(n.\)a. & 15 & [124] \\ \hline MWCNTs [5] & OH & Anatase, D and G bands (CN) & \(n.\)a. & \(n.\)a. & [126] \\ \hline MWCNTs [5] & \(n.\)a. & 142, 446, 610 (R) [7] & & & \\  & 1365, 1591 (CN) & & & & \\ \hline MWCNTs [5] & Oxidation & \(n.\)a. & 71–266 & \(n.\)a. & [130] \\ \hline MWCNTs [5] & \(n.\)a. & Anatase, 1342 and 1574 (CN) & 62–115 & 8 & [131] \\ \hline MWCNTs [5] & Oxidation & \(144, 515, 640\) (A) & & & \\  & 1356, 1591, 1754, 1775, 2704 (CN) & & & & \\ \hline MWCNTs [5] & Oxidation & \(n.\)a. & 137 & 5–6 & [133] \\ \hline MWCNTs [5] & Oxidation, PVP [8] & 144, 197, 399, 515, 519, 639 (A) [1] & & & \\  & 447 and 612 (R) [7] & \(n.\)a. & 12 & [89] \\  & & 1345 and 1594 (CN) & & & \\ \hline MWCNTs [5] & \(n.\)a. & 244 & 5–10 & [134] \\ \hline MWCNTs [5] & Oxidation & 149, 199, 390, 512, 639 (A) [1] & & & \\  & 289 (B)[10], 1346 and 1578 (CN) & & & & \\ \hline MWCNTs [5] & Oxidation & \(394, 515, 636\) (A) [1] & & & \\  & 1352 and 1585 (CN) & & & & \\ \hline  & 1362 and 1585 (CN) & & & & \\ \hline  & 1363
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
###### Abstract  
Industry 4.0 is the fourth generation of industry which will theoretically revolutionize manufacturing methods through the integration of machine learning and artificial intelligence approaches on the factory floor to obtain robustness and speed-up process changes. In particular, the use of the digital twin in a manufacturing environment makes it possible to test such approaches in a timely manner using a realistic 3D environment that limits incurring safety issues and danger of damage to resources. To obtain superior performance in an Industry 4.0 setup, a modified version of a binary gravitational search algorithm is introduced which benefits from an exclusive or (XOR) operator and a repository to improve the exploration property of the algorithm. Mathematical analysis of the proposed optimization approach is performed which resulted in two theorems which show that the proposed modification to the velocity vector can direct particles to the best particles. The use of repository in this algorithm provides a guideline to direct the particles to the best solutions more rapidly. The proposed algorithm is evaluated on some benchmark optimization problems covering a diverse range of functions including unimodal and multimodal as well as those which suffer from multiple local minima. The proposed algorithm is compared against several existing binary optimization algorithms including existing versions of a binary gravitational search algorithm, improved binary optimization, binary particle swarm optimization, binary grey wolf optimization and binary dragonfly optimization. To show that the proposed approach is an effective method to deal with real world binary optimization problems raised in an Industry 4.0 environment, it is then applied to optimize the assembly task of an industrial robot assembling an industrial calculator. The optimal movements obtained are then implemented on a real robot. Furthermore, the digital twin of a universal robot is developed, and its path planning is done in the presence of obstacles using the proposed optimization algorithm. The obtained path is then inspected by human expert and validated. It is shown that the proposed approach can effectively solve such optimization problems which arises in Industry 4.0 environment.  
Keywords:physic inspired optimization algorithms; gravitational search algorithm (GSA); discrete binary optimization problems; assembly task planning; universal robot +
Footnote †: journal: Applied Mechanics and Physics Letters
## 4 Experiments
### Skin Lesion Classification  
#### 4.2.1 Datasets  
HAM10000 dataset: The HAM10000 dataset consists of 10,015 dermoscopy images and is classified into the following seven lesion classes in Figure 8 [87]: (1) actinic keratoses and intraepithelial carcinoma/Bowen's disease (akiec), (2) basal cell carcinoma (bcc), (3) benign keratosis-like lesions (solar lentigines/seborrheic keratoses and lichen-planus-like keratoses, bkl), (4) dermatofibroma (df), (5) melanoma (mel), (6) melanocytic nevi (nv), and (7) vascular lesions, including angiomas, angiokeratomas, pyogenic granulomas, and hemorrhage (vasc). The HAM10000 dataset was used for training and testing for skin lesion classification.  
International Skin Imaging Collaboration (ISIC) dataset: The \(T_{sim}\) transfer learning of skin lesion classification used the dataset collected by the ISIC, which is sponsored by the International Society for Digital Imaging of the Skin to improve the diagnostic performance of melanoma, commonly known as skin cancer, and provides the largest publicly available skin lesion image dataset [93]. In this study, the ISIC 2017 dataset was used for \(T_{sim}\) training, and the data were classified into the following three types: (1) melanoma, (2) nevus, and (3) seborrheic keratosis. The total number of images in the dataset is 2750, as shown in Table 4.
## 1 Introduction
Finally, we examined how VIs in combination with environmental factors could better explain the variations in GPP. Our findings can reveal the performance of satellite-derived VIs for approximating GPP and the influence of the BRDF effect and informing future ecosystem functioning, vegetation productivity, and carbon cycle studies based on VIs.
## 4 Implementation and Results
### Use Cases
#### 4.2.2 GDXray Dataset  
As the above-mentioned dataset was privative, we also considered evaluating the methods on a benchmark dataset. GDXray [20] is a publicly available dataset of X-ray images. It contains 19,407 images organized into five groups: castings, welds, baggage, natural objects, and settings, each with multiple series. As we were dealing with manufacturing defect detection, we focused on the castings group, which is related to manufacturing and which comprised 2727 X-ray images from 67 series primarily featuring automotive parts like aluminum wheels and knuckles. These images are annotated with bounding boxes, similar to in the first dataset, offering a benchmark for assessing our methods in manufacturing defect detection. However, it is noteworthy that, unlike our industrial target dataset, GDXray does not categorize defects into different types. Additionally, considering that the existing literature utilizing GDXray primarily addresses defect detection rather than classification, we opted to refrain from performing the linear classification evaluation for this dataset. Consequently, our analysis of the GDXray dataset was confined to fine-tuning evaluation.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 4 Dataset
### Dataset B  
Dataset B comprises data on signal samples from two navigation satellites of the Global Positioning System (GPS). These signals were observed using an Agilent spectrum analyzer, received using a 40 m antenna, and collected using a signal-acquisition device with a 250 MHz sampling rate at the Luonan Haoping Station of National Time Service Center, Chinese Academy of Sciences. Similar to the signal-processing method in Dataset A, Figure 6c shows the spectrum of a navigation signal broadcasted by a GPS satellite at 1575.42 MHz frequency. The spectrum was also calculated and plotted using 10 M data points. The quality of GPS satellite navigation signals collected using a 40 m large-aperture antenna was higher. We conducted 2 years of observation on two GPS satellites and collected multiple long time span signals. Specifically, we constructed 14 data subsets using these navigation signals and labeled them as B1, B2,......., and B14, respectively. The time span between these data subsets is approximately in the range of 1-2 months, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Subset** & **Specific Emitter 1** & **Specific Emitter 2** \\ \hline B1 & 8 May 2021 & 8 May 2021 \\ B2 & 24 May 2021 & 22 May 2021 \\ B3 & 9 June 2021 & 24 June 2021 \\ B4 & 10 July 2021 & 6 July 2021 \\ B5 & 19 August 2021 & 16 August 2021 \\ B6 & 8 September 2021 & 5 September 2021 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GPS navigation signal acquisition time.  
Figure 6: Spectrum of navigation signals from various satellite systems: (**a**) QZSS, (**b**) NAVIC, and (**c**) GPS.
## 4 Core Insights and Critical Reflections
### Evolution and Breakthroughs of Key Technologies
#### 4.1.2 Sensor Technology Innovation  
The innovation of sensor technology is the key to enhancing the capability of UAV-RS information acquisition. A significant advantage of UAV-RS over satellite remote sensing is its flexible sensor configuration ability. It can carry different types of sensors according to mission requirements, thereby obtaining diversified ground target information [24]. Early UAV-RS mainly relied on visible light cameras to acquire RGB images. With the development of technology, advanced sensors such as Multispectral, Hyperspectral, Thermal Infrared, LiDAR, and Synthetic Aperture Radar (SAR) have gradually been integrated into UAV platforms. Multispectral and hyperspectral sensors can acquire spectral information of ground objects in different bands, providing a data basis for refined ground object classification, vegetation health status assessment, mineral identification, etc. For example, in crop pest and disease monitoring, multispectral data are the most widely studied type of data. Thermal infrared sensors can detect the temperature distribution of ground objects and have unique advantages in fields such as forest fire monitoring, urban heat island effect research, and power line inspection. LiDAR measures distance accurately by emitting laser pulses and receiving echoes, and can quickly obtain high-precision 3D point cloud data for topographic mapping, forest structure parameter inversion, 3D building modeling, etc.  
The emergence of Solid-state LIDAR technology, with its more compact and lightweight characteristics, is particularly suitable for UAV platforms. The continuous miniaturization, light-weighting, high-precision, and low-cost of sensor technology have greatly expanded the depth and breadth of UAV-RS applications. Polarization remote sensing, as an emerging direction, acquires multi-dimensional information such as the degree of polarization and polarization azimuth angle of ground objects. It shows great potential in sky polarization navigation, image dehazing, rock density inversion, etc. The Hefei Institute of Optics and Fine Mechanics of the Chinese Academy of Sciences and Peking University have made important progress in the development of UAV-borne polarization CCD cameras [157].
## 3 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & **EO** & **G** & **CVG** & **MGT** & **Shoot** \\
**Species** & **(\(\mu\)L/mL)** & **(\%)** & **CVG** & **MGT** & **(mm)** \\ \hline \multicolumn{5}{c}{**Interaction species \(\times\) treatment**} \\ \hline \hline \(F\) & 1.155 & 1.140 & 1.387 & 17.195 \\ \(p\)-value & 0.347 & 0.353 & 0.271 & 0.000 * \\ \hline \hline \end{tabular} Values are mean \(\pm\) standard deviation; asterisk and different letters indicate statistically significant differences at \(p\)-value \(\leq\) 0.05 among treatments in each species. \(F\)-value and \(p\)-value of the ANOVA test. Abbreviations: G\(\%\), Germination percentage; CVG, Coefficient of Velocity of Germination; MGT, Mean Germination Time.  
\end{table} 
Table 9: _Cont._strmonium_ L., attributing it to its major group of constituents, namely, monoterepenes hydrocarbons. Herbicidal effects were also reported for _J. oxysporum_ L. subsp. _macrocarpa_ and _J. phonicae_ EOs, which were able to strongly reduce the germination and seedling growth of all tested weeds, in a dose-dependent manner [13; 59]. In the case of _J. phonicaea_, its EO also increased the proline level and caused severe electrolyte leakage from the roots of all target weeds, indicating membrane disruption and loss of integrity [59]. Finally, the _J. communis_ EO exhibited no phytotoxic effect against _Ailanthus altissima_ (Mill.) Swingle, resulting in 0% seedling mortality [23]. Few data are also available on the phytotoxicity of EOs from the genus _Larix_. The most recent work [17] studied the herbicidal effects of _L. kaempferferi_ (Lamb.) Carriere, demonstrating its capacity to inhibit the growth of _Brassica napus_ L. by 50% in a seed bioassay and its inability to stop the development of new shoots after a foliar application of 10% EO in a greenhouse experiment. Previously, the negative effect of volatile substances of _L. gmelini_ (Rupr.) Kuzen. EO on the growth of _Frazimus mandshurica_ Rupr. was mainly attributed to \(\alpha\)-pinene [15], while the EO from leaves and branches of _L. principis-rupprechtti_ affected its own regeneration with significant inhibitory effects on the germination rate, radicle and hypocotyl length, and fresh mass [16]. In general, our data, in addition to highlighting a greater efficacy of the _L. decidua_ EO, showed the different susceptibility of the two target species. _L. multiform_ (monocotyledon) was more sensitive to treatments than _S. alba_ (dicotyledon). Furthermore, the effects of both EOs were reduced by the interaction with the soil, with significant results still being obtained. In this type of substrate, we wanted to check for the possible presence of residual volatile terpen. As expected, after 7 days, most of them were not detected, with differences between the two tests. Their absence, which may be due to the ability of soil particles to adsorb the volatile terpen and subsequently release them to penetrate the seeds and exert their possible toxicity, deserves to be further investigated [60].