esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation
### Hardware Setup
#### Real-Time Inference  
The proposed vision models are evaluated in real tobacco fields on a mobile robot spraying platform. For obtaining higher inference in real-time, NVIDIA's optimized library for faster deep-learning inference, i.e., NVIDIA TensorRT, was used. The modified Faster R-CNN and YOLOv5-based vision models identified tobacco plants at 10 and 16 fps, and with classification accuracies of 98% and 94%, respectively, at a robot speed of approximately 3 km/h. The modified YOLOv5-based model can process images at a higher frame rate compared to the Faster R-CNN model, thus making it a better choice for real-time deployment on a spraying robot. Real-time detection results for both models are presented in Figures 13 and 14.  
Table 5 presents each model's inference results in real time. YOLOv5 outperformed the Faster R-CNN model in terms of inference speed.  
Figure 11: YOLOv5 detection results of tobacco from testing data in varying scenarios: (**a**) high intra-row plant distance. (**b**) high weed density. (**c**) low weed density. (**d**) low intra-row plant distance.  
Figure 12: YOLOv5 detection results with (**a**) undetected targets, and (**b**) misidentified regions.  
Figure 13: Real-time Faster R-CNN detection of tobacco crop and weeds in scenarios with **(a)** low intra-row plant distance, and **(b)** high intra-row plant distance.
## 3 Results and Discussion
### Colloidal Stability of Various Late Dispersions  
#### 3.3.1 Influence of Particle Size on Colloidal Stability  
After synthesizing polystyrene nanoparticles of various sizes and stabilized with different oligoglycidol surfmers, we analysed the colloidal stability of the nanoparticle dispersions in aqueous NaCl solutions. Therefore, we used nanoparticle dispersions at a distinct solid content and added those to NaCl solutions of several concentrations. Afterwards we immediately conducted DLS measurements to analyse the average particle size, which was recorded 3 min after sample preparation. Firstly, we analysed the colloidal stability as a function of the particle size. For this, we used three particle dispersions functionalised with **2a** and an average particle size between 195 and 369 nm (**3e-g**, respectively). We analysed the colloidal stability of each of these particle dispersions at NaCl concentrations between 0.01 and 5 mol:L\({}^{-1}\). The particles with the smallest particle size of 195 nm were stable up to a NaCl concentration of 0.05 mol:L\({}^{-1}\) (Figure 6, blue squares). Upon a further increase in NaCl concentration the particles immediately started to form large agglomerates. Larger particles, which are prepared with less surfmer, displayed an improved colloidal stability (Figure 6, orange diamonds and grey triangles). The polystyrene particles with an average size of 243 nm (Figure 6, orange diamonds) were still stable at an NaCl concentration of 0.1 mol:L\({}^{-1}\) and first started to form medium sized agglomerates at an NaCl concentration of 0.25 mol:L\({}^{-1}\) before large agglomerates were formed at 0.5 mol:L\({}^{-1}\). This trend continued further for increasing particle size. The nanoparticles with an average diameter of 369 nm (Figure 6, grey triangles) were still stable at 0.25 mol:L\({}^{-1}\) NaCl, only started to form clusters of 2-3 particles at a concentration of 0.5 mol:L\({}^{-1}\) and further increase of NaCl concentration did not impact the colloidal stability further. Up to a NaCl concentration of 5 mol:L\({}^{-1}\) only clustering of 2-3 particles occurred without formation of large agglomerates.  
Figure 5: Average polystyrene particle size (determined by DLS) as a function of the oligoglycidol chain length at surfmer concentrations around 1 mmol:L\({}^{-1}\) and surfmer:monomer ratios around 1:500 (datapoint at \(n=0\) represents control experiment without a surfmer).  
Figure 6: Colloidal stability of different sized nanoparticles with oligoglycidol\({}_{6}\) (**2a**) surface functionality in various NaCl concentrations.
## 3 Experimental Section
### Synthesis of Compounds
#### 3.2.5 Methyl 2-(Nonanoyllamino)propanoate (\(N\)-Nonanoyl Alanine Methyl Ester)  
Amide coupling was performed according to the method of [PERSON] [46] and [PERSON] _et al._[47] using carbonyl diimidazole (CDI). To a stirred solution of nonanoic acid (322.9 \(\mu\)L, 2 mmol) in anhydrous THF (4.39 mL) under N\({}_{2}\) was added CDI (324.3 mg, 2 mmol), causing effervescence from liberated CO\({}_{2}\). Stirring was continued at room temperature for 2 h and methyl l-alanine (279.2 mg, 2 mmol) was added, resulting in separation of a brown oily residue. Stirring at room temperature continued overnight, by which time a white precipitate had formed. The solvent was removed _in vacuo_, resulting in the formation of white crystals in an oily residue, which were then taken up in Et2O (10 mL), EtOAc (20 mL), and H\({}_{2}\)SO\({}_{4}\) (1 M, 15 mL). The phases were separated and the organic phase was washed successively with H\({}_{2}\)SO\({}_{4}\) (1 M, 3 \(\times\) 15 mL), water (20 mL), and brine (20 mL). The organic phase was dried (MgSO\({}_{4}\)), filtered, and concentrated _in vacuo_, producing a white crystalline solid, (551.3 mg, 2.27 mmol, 113%) which was used crude in the subsequent reaction. A small portion was purified for characterisation by column chromatography on silica (80% CH\({}_{2}\)Cl\({}_{2}\)/EtOAc, R\({}_{\rm f}\) = 0.51) to give the title compound as a pale yellow powder, which was 98% pure (HPLC-MS), mp 36.9-38.8 \({}^{\circ}\)C. [\(\alpha\)]\({}_{\rm 0}^{\rm 20}\)= +4.8 (\(c\) 0.620, CHCl\({}_{3}\)). \({}^{1}\)H-NMR (MeOH-\(d_{4}\)) 8 6.05-6.12 (1H, m, N\(H\)); 4.61 (1H, app q, \(J\) = 7.2 Hz, H\({}_{11}\)); 2.20 (2H, t, \(J\) = 7.6 Hz, H\({}_{2}\)); 1.63 (2H, app q, \(J\) = 7.4 Hz, H\({}_{3}\)); 1.40 (3H, d, \(J\) = 7.1 Hz, H\({}_{12}\)); 1.35-1.22 (10H, m, H\({}_{4}\)-8); 0.87 (3H, t, \(J\) = 7.0 Hz, H\({}_{12}\)); \({}^{13}\)C-NMR (MeOH-\(d_{4}\)) 8 173.72 (C\({}_{10}\)); 172.65 (C\({}_{1}\)); 52.42 (C\({}_{13}\)); 47.81 (C\({}_{11}\)); 36.54 (C\({}_{2}\)); 31.77 (C\({}_{7}\)); 29.25 (C\({}_{4}\)); 29.17 (C\({}_{5}\)); 29.09 (C\({}_{6}\)); 25.53 (C\({}_{3}\)); 22.60 (C\({}_{8}\)); 18.56 (C\({}_{12}\)); 14.06 (C\({}_{9}\)). ESI-HRMS (_m/z_): Calcd for C\({}_{13}\)H\({}_{26}\)NO\({}_{3}\)\({}^{+}\) ([M+H]\({}^{+}\)), 244.1985; found, 244.1907.
## 6 Conclusions  
A parametric study was made by employing CFD modeling for a real data center room comprising more than 200 server racks with the purpose of studying the thermal performance and airflow. Four case studies with distinct thermal loads were addressed in this study. Several scenarios were simulated for the heat flow limit condition and the results for four case studies were obtained and then analyzed for the maximum and minimum air flow velocity. The main results are as follows:  
* For all four case studies, the results predict the occurrence of several hot spots in the case of the minimum air flow velocity.
* By analyzing the results, no hot spots materialize in the case of the maximum air flow velocity except in Case 1.
* Case 4 shows the best solution. Conversely, refrigerating through these means is unsustainable and consumes a great amount of energy.
* From the results, it can also be deduced that every predicted hot spot is situated at the end of the aisles. The reason behind this occurrence is the ineffective refrigeration of the data center server racks as a result of an inappropriate position of the CRAC units and this happens in every case study.
* Having to always use the maximum air flow velocity, as in Cases 1-3, is not the best solution. The airflow is suboptimal close to the end of the rows since the layout of the data center room is imperfect. This challenge can be overcome by withdrawing a few server racks located at the end of the aisles. Such a solution could signify a much better airflow and could even allow a reduction of the air flow velocity, thus diminishing the overall cost with entire cooling operation.  
**Author Contributions:** D.M.: Writing and simulating original paper; R.G.: conceptualization, methodology and revising; P.D.G.: editing, data analyzing, funding acquisition, supervision; P.D.d.S: editing, M.T.C.: revising the paper, validation.  
**Funding:** The current study was funded in part by Fundacao para a Ciencia e Tecnologia (FCT), under project UID/EMS/00151/2019 C-MAST, with reference POCI-01-0145-FEDER-007718. [PERSON] would like to acknowledge the financial support from Fundacao para a Ciencia e Tecnologia (FCT), (grant UID/EMS/00667/2019).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Reference Architecture
### Layered View  
Figure 5 represents a layered view for the GLAM sector (galleries, libraries, archives, museums) virtual facility platform. It illustrates the various components, services, and management layers involved in creating and maintaining a virtual gallery using advanced technologies. Each layer is presented with the corresponding components required for consideration.  
This architecture outlines the comprehensive structure for managing and operating a virtual gallery platform. It includes various management and service layers to ensure smooth operation, communication technologies to connect different parts of the system, application layers to support user interactions, IoT services for integrating smart devices, and security measures to protect the platform and its data. The use of advanced visualization  
Figure 4: Business Process Model.  
Figure 5: Layered View Composition.  
and interactive tools highlights the platform's capability to create immersive and engaging virtual experiences.
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed
### Atomic Mass Spectrometry
Based on the assumption of 5 g dry weight of seaweed consumption per week, by comparison with current regulations or recommendations on the upper limits or tolerable intakes for these elements, the authors concluded that seaweed consumption poses low health risks with respect to Cd, Hg, and Pb, though more studies on this topic are needed, especially in regard to species-specific consumption data in Europe. Researchers in Greenland collected 77 samples belonging to 10 species of seaweed and analyzed them for 17 elements (As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, and Zn) to study their nutritional profiles and potential food safety risks [24]. A quantity of 0.2 g of each sample was extracted with microwave-assisted acid digestion with a HNO\({}_{3}\) (4 mL): H\({}_{2}\)O (1 mL) mixture. Concentrations of these elements were measured with ICP-MS: Pb was analyzed in standard mode, As and Se were analyzed in reaction cell mode using O\({}_{2}\) as the reaction gas, and the other elements were analyzed in KED mode with He as the collision gas. Measurement of these elements was also performed with different parts of the seaweed. The authors observed that element concentrations vary with thallus parts, species, as well as geological locations, though samples from the same family of seaweed tend to have similar profiles. A portion-size survey was conducted to estimate consumption amounts and compare these with recommended intake values. The authors concluded that all species included in this study serve as good sources of essential minerals and trace elements. Concentrations of toxic elements, such as Hg, Cd, and Pb, were below current EU maximum levels. As reported by [PERSON] et al., pairwise correlation was investigated and strong correlation was found between Cr-Fe, Fe-Pb, and Ni-Mn [7]. Thirty-three purchased samples belonging to nine species of edible seaweed were analyzed for As (total and inorganic) and heavy metals (Cd, Hg, Ni, and Pb) to address food safety concerns [23]. Quantities of 0.5 g of dry seaweeds were microwave-digested in a HNO\({}_{3}\) (3 mL), H\({}_{2}\)O (5 mL), HCl (0.5 mL), and H\({}_{2}\)O\({}_{2}\) (2 mL) mixture. HPLC was used to purify the inorganic As species prior to measurement. In addition, 0.4 mg/L of gold was added to stabilize the Pb signal. Element concentrations were measured with ICP-MS in KED mode, with He as the collision gas. The results showed that the levels of Hg and Ni do not pose a threat to human health. On the other hand, Cd and Pb may contribute to health risk in some seaweed species, such as kelp, nori, wakame, and dulse. Inorganic As was especially high in hijiki seaweed and greatly exceeded the tolerable daily intake (TDI) level, while the levels in the other species were much lower.
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 4 Proposed PrOEXPRule Scheduler Design
### Simulation Environment  
To assure our objective, the novel proposed PrOEXPRule schema was validated in one cell in an interference environment populated with different user numbers using the LTE-Sim simulator [24]. This module comprises open-source simulation software that is designed to model dissimilar scheduling strategies for both uplink (UL) and downlink (DL). User demand settings are provisioned as multi-user/multi-cell environments that cover user mobility, radio resources optimisation, frequency usage reconfiguration, adaptive modulation coding (AMC), and other relevant industrial and scientific aspects [25].  
User mobility was analysed for every user; a simulation was performed to understand the average packet loss ratio, fairness, latency, and data rate. Every LTE downlink scheduling technique was simulated 150 times, and the data were used for the outcome. The proposed algorithm is constructed in a frequency domain experiment compares real-time video stream simulation outcomes beginning from the eNodeB base station and terminating at the user equipment. The schedulers used for this experiment employed the EXP_PF, PF, MLWDF, and EXPRule algorithms, using LTE-Sim to compare and measure performance.  
The objective of this trace-specific traffic use case is to employ the video trace to send data to the streaming application. In this case, it is simulated using four downlink sequences. Understanding of the Media Access Control (MAC) components Queue, QoS, Radio Bearer, and Packet is required when executing the newly developed LTE-Sim algorithms.  
LTE-SIM enables network simulation based on scenarios set by users. As video streaming was involved in our model, a single cell with interference case was utilised in a single-cell setting wherein a single base station was situated at the centre among four clusters with a 1 km radius. User equipment (UE) ranged from [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30] at a uniform mobility speed of 120 km/h, with user intervals of 5. A video stream (encoded at 242 kbps) was analysed for each UE, with every stream active in simulated time. The simulation parameters are shown in the Table 1:
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 3 Proposed Three-Coil PT-WPT System
### Variable Negative Resistance Control Strategy Based on Primary Side Regulation  
Building upon the analysis in Section 2.2, it is evident that the system's output power solely relies on the system's equivalent AC load \(R_{Leq}\) and the inverter output voltage \(U_{in}\) when functioning within the PT-exact symmetry region. Consequently, adjusting the duty cycle of the boost circuit alters the negative resistance and, consequently, the inverter output voltage \(U_{in}\).  
By combining (10) and (13), the equivalent AC load output power of the system is obtained as follows:  
\[P_{Leq}=\frac{\frac{8}{\pi^{2}}\frac{U_{DC}^{2}}{\left(1-D\right)^{2}}R_{Leq}} {\frac{L_{T}}{L_{R}}\big{(}R_{Leq}+r_{R}\big{)}^{2}+2r_{T}\big{(}R_{Leq}+r_{R} \big{)}+\frac{L_{R}}{L_{T}}r_{T}^{2}} \tag{17}\]  
Equation (17) depicts that output regulation can be attained by adjusting the boost converter's duty cycle. Assuming the power loss of the full-bridge rectifier is negligible, the output power \(P_{L}\) of the DC load satisfies \(P_{L}=I_{L}^{2}R_{L}=P_{Leq}\). By combining (6) and (11), the estimated DC output power of the system can be calculated as follows:  
\[P_{L-est}=\frac{8L_{T}}{\pi^{2}L_{R}}I_{T}^{2}R_{L-est} \tag{18}\]  
Upon determining the estimated system's output power, the system's output power is regulated through the closed-loop PI control shown in Figure 7. As the load changes, the real-time assessment of output current \(i_{T}\) and output voltage \(u_{T}\) are obtained, and their RMS values of the fundamental wave are individually computed. The DC load resistance and output characteristics are estimated using (14) and (18), while the estimated output value \(P_{L-est}\) is compared with a preset reference value \(P_{L-ref}\) representing the desired output characteristics. The error \(\Delta P_{L}=P_{L-ref}-P_{L-est}\) is fed to the PI controller, which generates the duty cycle \(D\). The switching driver then produces the PWM signal and propels the boost converter. This control method can be effectively implemented within the exact state of the system PT using a straightforward control algorithm and obviates the necessity for system resonant cavity topology switching.
# Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks
## 3 Design of Distributed Unequal Error Protection Rateless Codes
### Analysis of RC and NC Degree Distributions by the And-Or Tree Technique
When \(n_{1}\), \(n_{2}\) and \(n_{NC}\) are given, the DER \(y_{l}\) in (2) and (3) monotonically decrease with \(l\), and converge to fixed values, which can be regarded as the final DER.
## 4 Discussion
The achieved RMSE ranges from 0.04 to 0.8, which is very competitive compared to other experimental results obtained in the same region. As for the research works carried out in North America and Hawaii [15; 16; 19], the analysis time length ranged from 6 to 20 months, obtaining RMSE values equal to 6.11 and 0.086, respectively. This means that the short-term forecast (only six months) provides a lower outcome prediction with respect to the longer ones (up to 20 months). In these studies, the DL LSTM method was employed in [15] and [16]; up to five forecasting parameters were studied, including wind, clouds, longitude, and latitude. Compared to these published results, our research work presents some advantages, such as the longer time length (5 years), significantly lower RMSE values (in the range 0.04-0.86), and a particularly high number of studied parameters (up to 24), some of which were analyzed for the first time, to our knowledge. In [56], the authors proposed a new short-term load forecasting model that integrates different machine learning methods, such as support vector regression (SVR), grey catastrophe, and RF modeling. The developed model, focusing on characteristics of electric load sequence as stability and flexibility sequence, can help systems to balance power supply and demand, to avoid possible catastrophes, to rationally allocate resources, and to capture trends in power system loads. In studies conducted in East-Asian countries with a time length from 6 months to 3 years, the obtained RMSE values varied from 0.086 to 1.39 [17; 18; 21; 22]. The number of forecasting parameters was five, including the dew point and wind speed. The best RMSE (i.e., 0.086) was achieved using the LSTM model and a three-year analysis. A six-month short-term study for solar irradiation forecasting was carried out through ML methods in Mexico [27]. The best MSE values, by processing acquired data related to six ambient parameters, were obtained by MLP (Multi-layer perceptron) and RF (Random Forest) algorithms, respectively 0.222 and 0. In the study presented in [25] and conducted in Scotland for a four-year period, the determined MAE was equal to 0.525 for day-ahead and 0.708 for week-ahead forecasting by using only three parameters as inputs to the stacked bidirectional LSTM neural network. In [PERSON] et al. [30], the minimum MSE value obtained relative to the solar radiation prediction was equal to 0.00237. Compared to these reported performances, in this research work, we obtained better results, as shown in the following Table 5 (data shown in the last line). Based on the results reported in Section 3 in relation to the solar radiation prediction, we have obtained RMSE values in the range 0.04-0.8, MSE values in the range 0.0016-0.64 and MAE values between 0.034 and 0.86.
# A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals
## 6 Proof of Concept Prototype
As the prototype works on a 1 ms basis, it is possible to have a mix of streaming and burst-based transmissions as shown in Figure 8.  
The only inconvenience found in the proposed architecture is that it requires that all the involved PHY implementations be jointly modified (although large parts of the implementations suitable for physical RF front ends can be directly reused). This means that in practice, the proposed approach is only valid when the researchers/developers have access to the source code (either open or proprietary source code) of all involved OFDM-based PHYs, since they need to have their code modified in order to be connected to the hypervisor. However, we would like to point out that presently the open-source community offers several full-blown radio stack projects, being the most important and well-known ones srsLTE [49] and Open Air Interface (OAI) [56] projects. Both of them offer mature, well-organized, well-documented, well-written and easily modifiable open-source code for LTE, NB-IoT and 5G-NR radio stacks, including PHY, MAC, RLC, PDCP, RRC, NAS, SIAP and GW layers besides the implementations of EPCs. Additionally, they also have very active mailing lists for researchers/developers to ask questions and report issues with the code. Figure 9: Internal architecture of the Hypervisor Tx module.
## 1 Introduction
The proposed state encoding methods differ from existing methods in several key ways:  
* They consider the internal relationships between FSM states, including both already encoded and not yet encoded states;
* They account for the number of input variables influencing the transitions to each state;
* They consider the architectural features of the electronic device in which the FSM is implemented.