esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Analytical Chemistry--Recent Innovations
## 5 Application of Green Materials
### Sustainable Materials in Sample Preparation  
In sample preparation, biodegradable sorbents and natural polymer-based adsorbents have emerged as effective alternatives to traditional materials. Cellulose, chitosan, and biochar-based sorbents have been successfully employed in solid-phase extraction (SPE) and dispersive solid-phase extraction (d-SPE) due to their high adsorption capacities, reusability, and biodegradability. Additionally, magnetic nanoparticles coated with ecofriendly polymers offer selective and efficient analyte extraction, reducing the need for organic solvents.  
[PERSON] et al. [85] have provided a comprehensive review on the role of cellulose-based materials in the removal of pesticides from contaminated water, highlighting their effectiveness as green adsorbents in environmental remediation and sample preparation. The review discusses pristine and functionalized cellulose sorbents, biochar, and cellulose nanocomposites, emphasizing their adsorption efficiency, sustainability, and recyclability. These materials not only reduce the dependence on hazardous synthetic sorbents but also contribute to green analytical chemistry by minimizing solvent use and promoting waste reduction. Recent advancements have explored hybrid bio-based sorbents that integrate natural and synthetic materials for enhanced extraction efficiency. [PERSON] and co-workers [86] developed an MIL-68(Al)/Chitosan-coated melamine sponge for vortex-assisted solid-phase extraction (V-SPE) of parabens in water samples, offering a solvent-free, biodegradable alternative to conventional extraction methods. This approach eliminates organic solvents in the preparation process while utilizing chitosan, a renewable biopolymer, as an adsorbent and adhesive, further reinforcing sustainability.  
[PERSON] et al. [87] developed a magnetic biochar-based imprinted polymer (FeNi@Mct-MIPs) for solid-phase extraction (SPE) of monocotaline (Mct) from herbal medicine samples, utilizing shrimp shell-derived biochar functionalized with iron-nickel oxide. The sorbent demonstrated high selectivity and sensitivity, with recovery rates of 85.17-97.81% and a limit of detection (LOD) of 0.60 \(\upmu\)g/mL. Compared to conventional C18 and resin adsorbents, it offered greater efficiency, better selectivity, and lower solvent consumption, making it a more sustainable alternative.  
This biochar-based sorbent exemplifies the integration of renewable and eco-friendly materials in sample preparation, aligning with green analytical chemistry principles by minimizing waste and enhancing extraction efficiency.
# Design and Investigation of Capsule Networks for Sentence Classification
## 4 Experiments, Results and Discussion  
In this section, we report the results of a series of experiments conducted on various datasets using the proposed CapsNet architecture in Figure 1 and two baseline CNN and LSTM models for comparison. We present a comparison of the accuracy of our CapsNet model with other state-of-the-art models, a visualization of the capsule outputs, and a detailed error inspection in the inference stage.  
The baseline CNN model is shown in Figure 4. As the baseline LSTM model, we used Keras' LSTM model with the following layers: Input layer, Embedding layer of shape (None, Sentence Length, 300), LSTM layer of shape (None, 50) and Dense layer of shape (None, Number of Classes), where 'None' refers to the number of samples and '50' is the dimensionality of hidden units/output units in the LSTM network. All of the experiments were performed using Keras with TensorFlow backend in a machine with Intel Core i7-8700 CPU at 3.20 GHz, two GeForce GTX 1080 Ti GPUs and 32 GB RAM.  
The datasets used in the experiment are the following and details of these datasets are given in Table 1.  
* MR Dataset: movie review dataset (positive/negative review) [23].
* TREC Dataset: 6 class Text REtrieval Conference dataset (Entity, Human, Location, \(\cdots\)) [24].
* SST-2 Dataset: modified version of the MR dataset provided on a phrase and sentence level [25].
* Subj Dataset: Subjectivity dataset (subjective/objective sentence) [26].
* MPQA Dataset: contains mostly short news articles from a variety of sources manually annotated for opinion polarity [27].  
In the experiments, data augmentation was used in the training stage. This augmented data were generated by using Word2 vec. To generate synonyms for the data augmentation task, WordNet with the help of Natural Language Toolkit (NLTK) was also tried, but Word2 vec usually gives more replacement options (synonym words for a given word) than WordNet.
###### Abstract  
A circularly polarized (CP) and wide-band monopole antenna with a miniaturized size is suggested in this study. The suggested structure is composed of a U-shaped radiator on the front side, a partial ground plane with two rectangle slots, and a quadrilateral-shaped parasitic strip on the back side of the FR4 substrate. A wide-band operation with \(S_{11}\leq-10\) dB was achieved by regulating the radiator and the partial ground that was placed on the second side of the antenna substrate. The CP was achieved when excited two modes with the same amplitude and a 90\({}^{\circ}\) phase difference. This could be generated by regulating the slots' dimensions in the ground plane. Moreover, a quadrilateral-shaped parasitic strip placed on the second side with the partial ground was utilized to extend the 3 dB axial ratio (AR) bandwidth. The suggested structure is simulated, prototyped, and measured to confirm the desired requirements with a total size of 30 \(\times\) 32 mm\({}^{2}\) (0.4 \(\times\) 0.42 \(\lambda_{0}\) at 4 GHz). The tested outcomes have a bandwidth of \(S_{11}\leq-10\) dB (81.25%) (5.2 GHz, 3.8-9 GHz) and a 3 dB axial ratio (AR) bandwidth (30.7%) (1.63 GHz, 4.48-6.11 GHz). The antenna's different parameters are discussed, which recommend the suggested antenna to be used in UWB, sub 6 GHz, and WLAN wireless applications.  
circular polarization (CP); compact antenna; UWB; AR; wireless systems 2023
# Wideband Millimeter-Wave Perforated Hemispherical Dielectric Resonator Antenna
## 2 Antenna Configuration  
Figure 1 illustrates the two-layer hemispherical DRA and feed network configurations proposed in this study, where the inner and outer layers have respective dielectric constants and radii of \(\varepsilon_{r1}\), \(a_{1}\) and \(\varepsilon_{r2}\), \(a_{2}\). For a single-layer HDRA, i.e., \(\varepsilon_{r2}=1\), the resonant frequency of the fundamental mode TE\({}_{111}\) can be determined by solving the mode characteristics equation [20; 21].  
\[\frac{\hat{J}_{1}(ka)}{\hat{J}_{1}^{r}(ka)}=\sqrt{\varepsilon_{r1}}\frac{\hat{ H}_{1}(k_{o}a)}{\hat{H}_{1}^{r}(k_{o}a)} \tag{1}\]  
in which \(k=\sqrt{\varepsilon_{r1}}\ k_{o}\), \(\hat{J}_{1}(x)=x\hat{J}_{1}(x)\), \(\hat{H}_{1}(x)=x\hat{h}_{1}^{(2)}(x)\), where \(j_{1}(x)\) and \(h_{1}^{(2)}(x)\) represent the spherical Bessel and Hankel functions of the 1 st kind and the 2 nd kind, respectively. To find the resonant frequency, Equation (1) is solved for a given \(a\), from which \(k_{0}\) can be determined. Finally, the resonant frequency can be calculated as [22]  
\[f_{r}=\frac{4.7713R_{e}(k_{o}a_{1})}{a_{1}} \tag{2}\]  
The feeding spiral is illustrated in Figure 2 with dimensions defined by the well-known Archimedean spiral function of [23]  
\[\mathrm{p}=\alpha\ \varphi \tag{3}\]  
where \(\mathrm{p}\)\(\mathrm{p}\) represents the radius of the spiral-slot arm, \(\alpha\) is the spiral constant, and \(\varphi\) is the winding angle. The chosen parameters are \(\alpha=0.08\) mm/rad, \(0.138\pi\leq\varphi\leq 2.16\pi\) and the width of the spiral-slot arm is \(w_{s}=0.38\) mm, which results in a spiral-slot arm length of 3.9 mm that is approximately 0.7\(\mathrm{x}_{\mathrm{eff}}\) at an operating frequency of 29 GHz. The variations of the above spiral's parameters and the separation between the spiral slot arm tuns (g) can change the spiral's arm length and provide optimum impedance matching.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 1 Introduction  
The new concept of Logistics 4.0 requires higher quality standards of demand, distribution, and inventory management [1]. Meanwhile, the prosperity of e-commerce and mobile business requires more efficient logistics [2]. With the rapid development of drone technology, drones have been widely applied in health care, agriculture, aerial photography, mapping, etc. In the last few years, some e-commerce and logistics companies started to adopt drones in deliveries, which greatly reduces delivery costs and improves delivery efficiency. DHL company reported in 2015 that it successfully delivered medals and other goods considered as urgent on a small island in northern Germany using drones. Amazon carried out its first drone delivery practice in 2017. However, drone-only delivery has limitations in delivery coverage and carrying capacity due to the drones' endurance. Hence, the coordinated delivery of trucks and drones (or truck-drone delivery) attainedconsiderable attention [3]. Accordingly, there is a growing demand for developing an efficient route planning method for the truck-drone delivery problem [4].  
In the literature, many studies focus on the route planning for truck-drone delivery of goods from warehouses to customers, which is defined as a traveling salesman problem with drone (TSP-D) [5]. In summary, according to the functions of drones in the delivery process, the TSP-D can be classified into four categories: (1) flying sidekick traveling salesman problem (FSTSP) [6; 7], (2) parallel drone-scheduling traveling salesman problem (PDSTSP) [8; 9; 10], (3) heterogeneous delivery problem (HDP) [11; 12; 13], and (4) vehicle routing problem with drone resupply (VRPDR) [14]. This study mainly studies FSTSP. The existing FSTSP assumes that the drones arrive at rendezvous points ahead of the trucks. But the drones may arrive at rendezvous points later than the trucks, which would result in truck waiting time.  
To this end, this paper aims to investigate the optimal route planning for truck-drone delivery, considering truck waiting time at rendezvous points. Our work makes the following three contributions. (1) We build an optimal route planning model with time constraints and route constraints, aiming at the minimization of the total delivery time. (2) We design a variable neighborhood tabu search algorithm as the model solution. (3) We demonstrate the proposed mathematical model and algorithm on a delivery network and analyze the effects of model parameters on the delivery efficiency.  
The remainder of this paper is organized as follows. Section 2 reviews the previous studies on TSP-D and the solution approaches. Section 3 develops a mathematical model to formulate the optimal route planning for truck-drone delivery. Section 4 presents the model solution. Section 5 utilizes a case study to test the proposed method framework and analyze the sensitivity of model parameters. Conclusions and future work are discussed in Section 6.
## 3 Results and Discussion
### Analysis of the Simulation Results of the Different Scaffold Patterns  
Figure 9 shows the velocity and pressure distribution of the nutrient and cell mixture flowing in the concave hexagon, orthogonal and triangular scaffolds with the same porosity. From the analysis of the pressure distribution diagrams in Figure 9a-c, it can be calculated that the average pressure of the concave hexagon is 87.26 Pa, while the average pressure of the orthogonal and triangular scaffolds reaches 314.83 Pa and 349.33 Pa, respectively. While the concave hexagon scaffolds have low pressure and resistance and good flow, which is suitable for the transport of cells and nutrients. From the velocity distribution diagram, the flow pattern is followed by the three different shapes of the scaffold flow field. By comparing the velocity distribution diagrams (d, e, f), the maximum flow velocity of the fluid in the orthogonal and triangular-shaped scaffold tubes reached 0.73 m/s and 0.74 m/s, respectively, with a large velocity difference, which is not conducive to the transport of cells and nutrient fluid and adversely affects the deposition of cells on the scaffold. The maximum flow velocity of the fluid in the concave hexagon scaffold tube was 0.505 m/s with a small velocity difference, which was very beneficial for the transport of cells and nutrient fluid [38]. To sum up the above analysis, in the range of this simulation, the concave hexagon fluid flow rate relatively uniform, and the average flow rate can basically reach 0.25 m/s, which is suitable for the transport of cells and nutrients, and the cells are also easy to deposit on the scaffold, which conducive to the growth of cells.
## 3 Data  
### TEC Observations  
Throughout the SVR process, data are fundamental, and collecting data is the first step in modeling. In this study, we take Gakona station as an example. The TEC obtained from observations is processed into monthly averages, i.e., the mean values at corresponding times are calculated hourly for each month. As shown in Figure 1, the Gakona station is located at a latitude of 62.38\({}^{\circ}\)N and a longitude of 145.00\({}^{\circ}\)W, situated in Alaska, USA, in a high-latitude region. These data were sourced from the GLOBAL IONOSPHERE RADIO OBSERVATORY (GIRO), accessible at [[https://giro.uml.edu/didbase/](https://giro.uml.edu/didbase/)]([https://giro.uml.edu/didbase/](https://giro.uml.edu/didbase/)) (accessed on 12 May 2022). GIRO automatically observes vertical electron density profiles using ionospheric digital sounders such as DSP-1, DGS-256, and DPS-4 [24] deployed worldwide, providing TEC and other classic ionospheric parameters [25]. The calculated monthly average TEC value is shown in Figure 2. The unit of TEC is TECU, where 1 TECU = 1 \(\times\) 10\({}^{-6}\) e/m\({}^{2}\).
# Damage Diagnosis of Frame Structure Based on Convolutional Neural Network with SE-Res2 Net Module
###### Abstract  
The complex application environments of frame structures and the similar vibration signals between different locations make it difficult to accurately diagnose damage using traditional methods. Based on modifying the parameters and configuration of the convolution neural network with training interference (TICNN), this paper proposes a new model for damage diagnosis of frame structures by implanting a squeeze-and-excitation neural network (SENet) and Res2 Net modules. Taking the frame structure model from the University of British Columbia as the research object, the proposed damage diagnosis model was used to diagnose its damage type. The proposed new model was compared with other models in terms of accuracy and anti-noise ability. The experimental results show that the accuracy of the proposed model was 99.44% when the training epoch was 30 and 99.78% when training epoch was 100. It is superior to other similar models in terms of convergence speed and accuracy. At the same time, the proposed model also has an excellent advantage in anti-noise ability. Therefore, the proposed damage diagnosis model has the advantages of fast convergence and higher damage diagnosis accuracy under a strong noise environment. It can realize the accurate damage diagnosis of structural frames.  
damage diagnosis; frame structure; convolutional neural network; SENet; Res2 Net; anti-noise ability +
Footnote †: journal: Applied Sciences
# A Subcell Finite-Difference Time-Domain Implementation for Narrow Slots on Conductive Panels
## 4 Results
### Cage with Curved Slots: Numerical and Experimental Data  
The final test setup evaluates the shielding effectiveness in a real test case proposed in ref. [27], measured at INTA facilities in an RC, following the standards [16; 28]. The box used in the experiments has dimensions of 0.48 m (width), 0.48 m (height), and 1 m (length), and it is primarily made of galvanized steel. One face of the box is removable, allowing for different material panels to be placed inside (Figure 10). In the first test case, the removable panel is made of aluminum and features two semicircular slots, as indicated in Figure 11. In the second test case (Figure 12), the panel is made of CFC material (referred to as CFC-\"blue\" in ref. [27; 29]), and it has two pairs of semicircular slots. The box has been tested in the reverberation chamber at INTA facilities. This setup exposes the apertures to waves with all possible polarizations and directions of incidence. The metals like aluminum and galvanized steel are considered as PEC. The CFC material has a thickness of 2 mm, and we use a SGBC numerical model with an equivalent conductivity of \(\sigma=10\) kS/m at the frequencies of interest. To simulate the reverberating environment, we use a statistical superposition of plane waves, with random propagation and polarization [25].  
Figures 13 and 14 compare results for these configurations using DMMA, CA, standard FDTD, and SG. The minimal cell size for SG is 1.25 mm, and the maximum is 2.5 mm. Results show that DMMA underestimates the SE, significantly below the first resonance. We can attribute it to the stochastic nature of the incident waves, making the DMMA fail  
Figure 14: Electric field shielding effectiveness (SEe) evaluated at the central position of the rectangular enclosure. These results correspond to the Test Case shown in Figures 10 (right) and 12.  
Figure 12: Sketch of a rectangular enclosure with a pair of semicircular slots.  
Figure 13: Electric field shielding effectiveness (SEe) evaluated at the central position of the rectangular enclosure. These results correspond to the Test Case shown in Figures 10 (left) and 11.
# Cybersecurity for Blockchain-Based IoT Systems: A Review
## 5 Discussion
### Blockchain for IoT: Potential Applications for Smart Cities
#### 5.2.2 Identity and Access Management  
In IoT systems, ensuring secure and authorized access to devices and services is essential. Blockchain can serve as a decentralized identity management system, where each device or entity is assigned a unique cryptographic identity. These identities can be stored on the blockchain, enabling secure and verifiable authentication and access control mechanisms [47; 48]. By utilizing blockchain for identity management, smart cities can  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & & \multicolumn{2}{c}{**Security Challenges**} & \multicolumn{2}{c}{**Security Solutions**} \\ \hline Device Layer & & \(\bullet\) & Weak authentication and authorization & \(\bullet\) & Strong authentication mechanisms \\  & \(\bullet\) & Outdated firmware & \(\bullet\) & Regular firmware updates \\  & \(\bullet\) & Physical tampering & \(\bullet\) & Physical security measures \\ \hline Communication Layer & & \(\bullet\) & Insecure communication protocols & \(\bullet\) & Encryption and secure communication protocols \\  & \(\bullet\) & Man-in-the-middle attacks & \(\bullet\) & Use of digital certificates and secure channels \\  & \(\bullet\) & Denial-of-Service (DoS) attacks & \(\bullet\) & DoS attack prevention and detection mechanisms \\ \hline Blockchain Layer & & \(\bullet\) & 51\% attack & \(\bullet\) & Consensus mechanisms to prevent \\  & \(\bullet\) & Smart contract vulnerabilities & \(\bullet\) & 51\% attacks \\  & \(\bullet\) & Privacy risks & \(\bullet\) & Code audits and security testing for smart contracts \\  & & & & Privacy-preserving techniques and encryption \\ \hline Smart Contract Layer & & \(\bullet\) & Smart contract bugs and vulnerabilities & \(\bullet\) & Thorough code review and testing \\  & \(\bullet\) & Lack of legal enforceability & \(\bullet\) & Use of established coding standards \\ \hline Application Layer & & \(\bullet\) & Insecure application interfaces & \(\bullet\) & Secure coding practices and input validation \\  & \(\bullet\) & Data privacy breaches & \(\bullet\) & Encryption and access control for sensitive data \\  & \(\bullet\) & Social engineering attacks & \(\bullet\) & User awareness and education against social engineering \\ \hline \hline \end{tabular}
\end{table}
Table 2: Security challenges and solutions for each layer of the IoT system.  
establish a trustworthy framework for managing the identities of devices, users, and other entities participating in the IoT ecosystem.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 7 Power Budget
### Scenario 1  
Considering a lunar circular polar orbit at 600 km, a change in the maximum transmission angle greatly affects the power the system is capable of transmitting, as shown in Table 2. With three satellites at 600 km, it is possible to transmit on average until 5.68 kW of power, which correspond to 136 kWh in one day. This power would not be enough to completely power a lunar base but would certainly be a great resource for lunar night and rover use. To increase this power, the trade-off consists of the number of satellites, the orbit of the constellation and the power of the laser and its efficiency. In particular, the laser power could potentially reach hundreds of kW, but it would greatly increase the weight and complexity of the thermal dissipation system. With 10 satellites, the energy transmitted increases considerably. In the best case, the constellation would be able to energetically help a possible lunar base for almost 20% of its total needs (set to 2.4 MWh). Indeed the ten-satellite constellation could provide 454 kWh of energy to a lunar base in one day. In Figure 13, we can note that the base load that a single satellite can transmit is equal to 10.34 kW, which exactly corresponds to the following calculation  
\[P_{r}=P_{L}\cdot\eta_{L}\cdot\eta_{cell} \tag{7}\]  
In this way, when two satellites are at the same time into the visibility cone, the power peak increase to 20.68 kW as shown in Figure 14.  
Figure 13: Power vs. time with a three-satellite constellation placed in lunar circular polar orbit with different maximum transmission angles. (**a**) \(\alpha_{max}=50^{\circ}\); (**b**) \(\alpha_{max}=60^{\circ}\); (**c**) \(\alpha_{max}=70^{\circ}\); (**d**) \(\alpha_{max}=80^{\circ}\).