esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Application of the TFME Technique in the Analysis of Organic Compounds
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analy** & **Sorption Phase** & **Detection** & **LOD** & **Recovery** [\%] & **RSD** & **Ref.** \\ \hline \hline Frame retardants: & ZIF-8@N-rGO & HPLC & 0.03–0.14 ng L\({}^{-1}\) & 89–106 & - & [161] \\ \hline Nonsteroidal anti-inflammatory drugs & MWCNT/Agr-Ch & HPLC-UV & 0.89–8.05 ng mL\({}^{-1}\) & - & \textless{}5 & [94] \\ \hline Estrogens & C18/PAN & LC-UV & 1.2–1.6 ng mL\({}^{-1}\) & 87–109 & 3–6 & [47] \\ \hline Glucocorticoids & SDS-MWCNTs/PP & UHPLC-MS & 0.019–0.098 ng mL\({}^{-1}\) & - & 2–4 & [93] \\ \hline Carbamazepine & C18/SCX & DESI-MS & \textless{}ng L\({}^{-1}\) & - & - & [104] \\ \hline Trichosan & C18/SCX & DESI-MS & \textless{}ng L\({}^{-1}\) & - & - & [104] \\ \hline Antibiotics & & & & & \\ sulfonamides, tetracyclines, fluoroquinolones, penicillin, macrolides & p-Poly-(MMA-IL)FP & LC-MS/MS & 0.05–4.52 ugL\({}^{-1}\) & 79–127 & 1–12 & [147] \\ \hline Antibiotics & & & & & \\ Amoxicillin, enrofloxacin, tetracycline, donkey & PVA-SA-\(\beta\)CD & HPLC-UV & 0.02–0.03 ugL\({}^{-1}\) & 70–100 & 1–2 & [148] \\ \hline  & & & & For methamphetamine: & & \\  & & & 5.5 ng L\({}^{-1}\) & & & \\  & & & For ketamine: & 2.0 ng L\({}^{-1}\) & 95–111 & \textless{}6 & [162] \\ ketamine & & & For methanquadone: & & & \\  & & & 1.1 ng L\({}^{-1}\) & & & \\ \hline Chlorpyrifos, trickson, tonalide & CTA with plasticizers & GC-MS & 0.05–0.42 ugL\({}^{-1}\) & \textgreater{}80 & \textless{}10 & [163] \\ \hline Haloacetic acids & PDMS & GC-ECD & - & 51–92 & 7–19 & [164] \\  & & Carboxen/PDMS & & & & \\ \hline Sexual hormones: & 17\(\beta\)-estradiol, & CTA/NPOE & HPLC-MS/MS & 0.1–5.7 ng L\({}^{-1}\) & \textgreater{}60 & \textless{}12 & [165] \\  & & CTA/DBS & & & & \\  & & & & & \\  & & & & & \\ \hline Organic pollutants: & & & & & & \\   benzene, 2-hexanone, & & & & & \\   hexanal, \(\alpha\)-pinene, & & & & & \\   limione, eucalypt, & GO/PS-DVB s & GC-MS & 0.4–8.1 ng L\({}^{-1}\) & 78–111 & 2–6 & [166] \\  & & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Applicability of TF-SPME technique for quantitative analysis of different analytes in food matrices. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Matrix** & **Analyte** & **Sorption Phase** & **Detection** & **LOD** & **Recovery** [\%] & **RSD** [\%] & **Ref.** \\ \hline Green tea & PAHs & MWCNTs/Agr & HPLC-UV & 0.1–50 ng L\({}^{-1}\) & 91–107 & - & [95] \\ beverage & PAHs & MWCNTs/Agr & HPLC-UV & 0.1–50 ng L\({}^{-1}\) & 91–107 & - & [95] \\ \hline Vegetable juice & Codeine, acetamipid & Aptamer/cellulose & ESI-IMS & 1.8–3.7 ng mL\({}^{-1}\) & - & 2–6 & [106] \\ \hline  & Sulfonamides: & & & & & \\  & sulfathiazole, & & & & & \\  & sulfarrazine, & & & & \\  & sulfardinidine, & & & & & \\  & sulfamethoxazole, & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
## 2 Mobile Stroke Unit  
To approach the time-sensitive stroke treatment procedure, the concept of MSU was first realized in 2003, with the idea of bringing the 'hospital' to the patient [6], and was integrated in clinical reality in Homburg, Germany in 2008 [7]. However, in 2016, the PRE-hospital Stroke Treatment Organization (PRESTO) was formed, which consisted of an international consortium of medical practitioners from Europe, the U.S.A., Canada, and Australia to support research and advocacy for pre-hospital stroke treatment in MSU. The pre-hospital treatment and diagnosis provided by the MSU must adhere to current stroke guidelines and emergency medical services (EMS) legislations [8]. The MSU essentially works by delivering prehospital diagnosis and treatment to stroke patients for immediate medical evaluation and triage, and the administration of appropriate treatment (e.g., tPA) while on site. The BEST-MSU randomized trial recently provided proof of concept for significant disability reduction for tPA-eligible patients treated using an MSU care delivery paradigm [9]. A customized'stroke ambulance' used for MSU (Figure 1), which is integrated with multiple working systems, typically includes: (i) a modified mobile CT scanner and an operation console for screening procedures, (ii) a telemedicine system for transferring digital images and clinical data of the patient to the hospital's database to be evaluated by a vascular neurologist and a neuroradiologist, (iii) a point-of-care laboratory for determining the suitability of administering tPA, and (iv) a lightweight stretcher for the patient to lie on and align their head to the CT scanner. There are usually three to four seats installed for the on-board medical staff, which typically consists of but is not limited to a paramedic, a CT technologist, and a nurse with specialized stroke training [10; 11; 12; 13].  
However, most customized MSUs are commonly set up in the heart of metropolitan areas where the majority of patients are situated. As such, rural and remote communities do not have access to the same diagnostic tools [15]. A study shows promising results in expanding the catchment area of MSU by implementing a rendezvous system, where an MSU dispatched from the metropolitan area will coordinate and meet with an EMS outside of the MSU's catchment area, and the EMS unit will evaluate the patient in parallel with the MSU team before sending to the nearest stroke center [16]. However, such a system requires extensive planning and coordination between the MSU and all the EMS units involved within the catchment area and would require a robust and unified communication system to ensure an efficient collaboration.
# Magnetic Field Penetration Depth in Various Materials and Applications
## 3 Results
### Penetration Depth
\begin{table}
\begin{tabular}{l l l l l} \hline  & & & \(\lambda\) (nm) & Tc (K) \\ \hline Spearman’s Rho & \(\lambda\) (nm) & Correlation & \(1.000\) & \(-0.324\) \\  & & coefficient & & \\  & & Sig. (bilateral) & & \(0.081\) \\  & & N & \(30\) & \(30\) \\ Tc (K) & Correlation & \(-0.324\) & \(1.000\) \\  & & coefficient & \(39.081\) & \(0.081\) \\  & & N & \(30\) & \(30\) \\ \hline \end{tabular}
\end{table} 
Table 3: Depth of penetration of the magnetic field and correlation in type II superconductors. Table 5 includes conductive materials such as copper (Cu), aluminium (Al), titanium (Ti) and VT6 (ECB), used in electrical conduction and structural applications. Copper and aluminium are notable for their high thermal and electrical conductivity, while titanium and VT6 offer high mechanical and corrosion resistance and are essential in the aerospace and superconductor industries. In addition, the depth of penetration of the magnetic field in these materials is a key factor in their electromagnetic behaviour. Conductors such as copper and aluminum have a shallower penetration due to their high conductivity. But titanium and VT6 (ECB), being less conductive materials, allow a greater penetration of the magnetic field, as eddy currents are less effective in these materials, i.e., the depth of penetration is inversely related to the conductivity of the material: the higher the conductivity, the lower the penetration of the magnetic field. Likewise, the frequency of the magnetic field and the permeability of the material are also key factors affecting this behavior. There is no material that is completely insulating from the magnetic field, as magnetic fields pass through almost all materials to some extent. However, some materials can deflect, reduce, or partially block the magnetic field through mechanisms such as absorption, reflection, or shielding.
## 5 Simulation Results and Analysis
### Motor Rotary Velocity Regulation Performance Analysis
#### 5.1.4 Comparison of Different Control Algorithms  
When setting the target rotary velocity as constant (3000 rpm), the rotary velocity regulation effects of the different control algorithms (PID, PID-bang-bang, and PID-bang-bang-fuzzy) are shown in Figure 19. The different control algorithms had different effects on the rotary velocity regulation effect. The rise time and peak time of the PID-bang-bang compound algorithm and PID-bang-bang-fuzzy compound intelligent algorithms were basically the same, and they were significantly smaller than those of the PID algorithm. Furthermore, the maximum overshoot values of the PID-bang-bang compound algorithm and PID-bang-bang-fuzzy compound intelligent algorithm were higher than that of the PID algorithm, but the difference was small. In addition, the regulation time of the PID algorithm was the longest, and that of the PID-bang-bang-fuzzy compound intelligent algorithm was the shortest. After the regulation time, the rotary velocity regulation results of the PID algorithm and PID-bang-bang compound algorithm still exhibited small fluctuations, whereas the rotary velocity under the PID-bang-bang-fuzzy compound intelligent algorithm was steady. The high-frequency residual oscillation in Figure 19 (right) is mainly caused by the control overflow problem of high-frequency dynamics. In general, the steady-state response and dynamic response of the motor rotary velocity regulation process were significantly improved when the PID-bang-bang-fuzzy compound intelligent control algorithm was applied.  
Figure 18: Effect of algorithm threshold on rotary velocity regulation.
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 2 Related Work
On the other hand, increasing awareness through transmission with maximum power and rate leads to increased congestion. As noted in [27], there is an inverse correlation between transmission power and rate at a constant target load on the wireless channel, meaning that decreasing the transmission power enables an increase in the transmission rate and vice versa. The existing approaches discussed, however, only allow for a single power/rate pair, resulting in a trade-off dilemma between transmission power and rate. Paper [28] proposes an RL Q-learning approach to balance the trade-off between transmission rate and power. The authors define a Markov decision process (MDP) by making a set of reasonable simplifying assumptions which are resolved using Q-learning techniques of RL. This RL-based approach decides the next state of the agent based on the possible actions and a Nakagami model instead of the interaction with the environment. In another paper [29], the authors present two mechanisms, transmission rate and data rate adaptation combined with power control mechanisms, called Combined power and message-rate adaptation (CPMRA) and combined power and data-rate adaptation (CPDRA). In CPMRA, the transmission power and transmission rate are adapted based on the current CBR value so that the congestion is maintained within specified limits. In CPDRA, The data rate and transmission power are adapted depending on the current congestion situation. The power control mechanism is the same as the CPMRA algorithm, and the ego node changes the transmit power based on available neighbors and CBR values. The definition of CBR in this paper is not the channel busy time ratio but the ratio of message length received and channel capacity which ignored the channel busy time caused by packet collision. In their paper [30], the authors introduce a congestion control algorithm called channel-aware congestion control (CACC) that can control both the transmission power and data rate. This algorithm takes into consideration the received signal strength (RSS) to diagnose packet loss and determine the channel conditions, which could be caused by either severe fading or channel congestion. If the channel experiences severe fading, the algorithm lowers the data rate to use a more robust modulation and coding scheme that can better handle the noise. At the same time, it adjusts the transmission power to maintain an acceptable packet error rate. This approach only uses constant speed within a pre-determined mobility area and lacks techniques to estimate the channel state using packet loss more accurately in the broadcast environment. In Table 1, we present a comparison of several notable congestion control approaches. The phrases \"Tx. Rate\" and \"Tx. Power\" refer to the transmission rate and power respectively, while \"rule-based\" indicates a traditional, non-machine learning-based approach and \"learning-based\" refers to approaches that utilize machine learning algorithms.
## 2 Energy transition in North Africa
### Role of International Collaborations  
An important factor in speeding up and facilitating the region's transition to renewable energy is collaboration with other stakeholders, international organizations, and other nations. This collaboration could involve money and technology transfers, knowledge exchanges, and cooperative projects. Through regional and international electrical connections, low-carbon energy produced in North Africa will benefit not only the countries of the region but also the European Union, North Africa, and Middle East countries (Figure 13).  
Recently, during the Convention on Climate Change (COP28) organized in Dubai in 2023, Morocco and Portugal signed an agreement on electrical interconnection [34].  
Hydrogen, the flag shift of the energy transition in recent years, is anticipated to be the bridge between Europe and North Africa. The natural gas infrastructure of the Morocco-Nigeria gas pipeline could be exploited to transport hydrogen from North Africa to Europe.  
With regard to technology transfer, the German-Tunisian Energy Partnership and the German-Moroccan Energy Partnership (PAREMA), which were established in 2012 with the goal of promoting renewable energy projects and energy efficiency in North Africa, are two notable examples of the International Cooperation's (GIZ) active involvement in the energy transition of North Africa [35]. Morocco and Russia signed a Memorandum of Understanding (MoU) on 27 July 2023. The goal of the agreement is to look into the construction of water desalination plants in Morocco that will use nuclear power technology from Rosatom to deliver water for use within industry, agriculture, and domestic use [36]. Within the same framework, up to half of Egypt's electricity production capacity is anticipated to come from the 4800 GW El Dabaa Nuclear Power Plant (NPP), a nuclear power plant being developed by the Russian State Atomic Energy Corporation (ROSATOM) [37]. The Xlinks Morocco-UK Power Project will connect the Guelmim Oued Noun region to Britain via four 3800 km of high-voltage DC sub-sea cables. This project will reach 8% of Britain's electricity  
Figure 13: North African countries energy interconnection [33].  
needs; around 7 million houses will be powered by solar and wind farms installed on a surface area of 1500 km\({}^{2}\) in Morocco [38].
## 4 Experimental Results  
### Selected Features  
In the process of feature selection, irrelevant features are removed, and significant features are selected to obtain unbiased optimal results. RFE automatically adjusts the number of selected features through cross-validation, and selects the best-performing subset of features for risk prediction. It can be seen from Figure 4 that the optimal number of features is 17, and these features are: FRBMaterials, Evacuation signs, Rescue field, NO CertificatesPersonnel, Fire drills, Fire host, rFHF, nrWaterPressure, PPCSSmoke, CC  
Figure 3: Risk prediction model developed using significant features and machine learning algorithms.  
Smoke, IFP, UBMCompany, ULMTime, rIPC, rHD, BgLayout, and FSupervisor. These features are the following types of firefighting fire information:  
1. Fire acceptance: FRBMaterials, Evacuation signs, Rescue field;
2. Fire Safety Personnel Management: NO_CertificatesPersonnel, Fire drills;
3. Fire Facility Equipment Management: Fire host, rFHF, nrWaterPressure, PPCSSmoke, CCSmoke, IFP, UBMCompany, ULMTime;
4. Hazard management: rIPC, rHD;
5. Unit fire data maintenance: BgLayout, FSupervisor.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 2 Materials and Methods
### Methodology
#### 2.3.3 Metrics  
The Dice coefficient (or Dice score) is a commonly used metric to assess semantic segmentation tasks. It enables the measurement of similarity between the obtained segmentation (prediction) and the ground truth. The coefficient is calculated by determining the ratio between the overlapping pixels and the total number of pixels from both the prediction and the ground truth. The Dice coefficient is computed using Equation (1):  
\[Dice=\frac{2\times|Pred\cap Gt|}{|Pred|+|Gt|} \tag{1}\]  
Dice coefficients have values between 0 and 1. A value of 1 indicates perfect similarity between the prediction and the ground truth, while a value of 0 means that the prediction is entirely different from the ground truth (no overlapping pixels). Predictions with higher accuracy approach Dice coefficients closer to 1.  
In addition to the Dice coefficient, which assesses similarity globally, the Hausdorff distance is also used to gauge similarity locally. It involves measuring the maximum distance between the prediction and the ground truth. Spatial resolution must be taken into account for its calculation, as summarized by Equation (2):  
\[d_{H}(P,G)=max[d_{PG},d_{GP}]=max\Bigg{\{}\underset{p\in P}{max[\underset{g\in G }{min}\;d(p,g)]},\;\underset{g\in G}{max[\underset{p\in P}{min}\;d(p,g)]} \Bigg{\}} \tag{2}\]  
where lower values correspond to closer edges between prediction \(P\) and ground truth \(G\), and therefore more accurate segmentations.  
The left ventricular volume and ejection fraction are two of the most prominent clinical metrics used to assess segmentation methods. Several methods enable the extrapolation of these metrics by using the left ventricle segmentations obtained from LAX Cine-MRI images [42]. These methods can be applied to different modalities as long as the acquisitions share a similar plane of view and common anatomical features. For instance, the LVEF can be calculated by using the single-plane long-axis ellipsoid model, as was demonstrated by [PERSON] et al. [42] on echocardiography images. The same method was used in this work to determine ED and ES volumes from ground truths and predictions, using Equation (3):  
\[V=\frac{8}{3\pi}\times\frac{A^{2}}{L} \tag{3}\]  
with \(A\) corresponding to the area of the left ventricle cavity region and \(L\) being the major axis. Postprocessing was performed before computing clinical metrics to prevent errors related to outlier detection. However, as depicted in Figure 4, the left ventricle cavity is often one of the easier classes for the models to segment.  
Figure 3: Hierarchical procedure followed for data augmentation. Amount of rotations and flipping presence were varied across the different configurations.  
Figure 4: Results obtained with ENet on test sets. Darker plots correspond to data-augmented scores. Mean values are represented as red crosses. Hausdorff distances are represented on a logarithmic scale for better readability.
# Joining of Fibre-Reinforced Thermoplastic Polymer Composites by Friction Stir Welding--A Review
## 6 RFSSW of FRTP Composites  
The joining of FRTP composites by RFSSW was only reported twice. However, these two publications found are of extreme importance, as they are the only papers referring to the joining of FRTP composites reinforced with continuous fibres. While [PERSON] et al. [69] joined PA 66 reinforced with 49 vol% of CF in twill configuration, [PERSON] et al. [70] worked with polyetherimide (PEI) reinforced with 45% of CF in twill configuration. In both cases the RFSSW was performed with sleeve plunge configuration. According to [PERSON] et al. [69], the fibres are broken by the stirring and penetration movements of the sleeve. The stirred zone is therefore composed of a polymeric matrix and broken fibres oriented in the direction of the ketone of the sleeve.  
As mentioned in the Introduction, due to the reduced number of publications it has not been possible so far to address the influence of most of the welding parameters involved in the RFSSW process. Thus, it was only possible to find information about the influence of the rotational speed and of the holding pressure time. These two parameters are very important for process optimization.  
The effect of rotational speed on RFSSW was evaluated by [PERSON] et al. [70]. They observed that at low rotational speeds the thermoplastic matrix cannot be properly softened due to insufficient heat generated. As a result, uncomplete filling defects are formed at the weld surfaces. Additionally, cavities and void defects and thickness reduction of the upper plate were observed. By increasing the rotational speed, the dimensions of these defects progressively decrease, while the joining area increases. The increase of rotational speed improves the mechanical interlocking mechanism by improving the carbon fibre distribution of the smashed fibres at the interface. The authors observed that by further increasing rotational speed, flat and sound weld surfaces were obtained. On the other hand, void defects could not be completely avoided due to entrapped air.  
[PERSON] et al. [69] studied the influence of using a holding pressure time instead of the immediate removal of the welding tool at the end of the welding procedure. The results demonstrate that the existence of a holding pressure time of just 20 s results in significant improvements regarding the reduction of the formation of voids caused by differential contraction and material shrinkage during cooling. Thus, the surface finish is significantly improved.  
As it is possible to understand, the variation of these parameters has a great influence on the quality of the welds produced by RFSSW. However, the study of the referred parameters still needs further investigation. In addition, it is essential to study the other parameters involved, such as axial force, plunge depth, and dwell time.
# Optimization of Phase-Only Computer-Generated Holograms Based on the Gradient Descent Method
## 1 Introduction
[PERSON] [26] proved that the GS algorithm is mathematically equivalent to the gradient descent method, which reduces the mean square error (MSE) defined for the amplitude distribution of a reconstructed image. The gradient descent method has been widely adopted in many scientific fields, such as machine learning [27]. Although the GS algorithm reduces the error of the reconstructed image during the iteration process, the rate of error reduction decreases after several iterations. In this paper, we adopt the gradient descent method to generate phase-only CGHs. The proposed technique calculates the gradient of the MSE defined for the intensity distributions of reconstructed images. Therefore, the proposed technique directly improves the intensity distributions of the reconstructed images. Moreover, we also modify the technique to increase the optimization speed. The proposed technique directly provides the phase distributions of phase-only CGHs, whereas the GS algorithm provides the complex-amplitude distributions of CGHs, which are then approximated for phase-only distributions.
## 5 Results and Discussion  
### Ablation Analysis  
Rows \(5\)\(\sim\)\(9\) of Table 4 show the ablation studies of combining different modules elaborated in Section 3. The final performance improvement can be estimated by the average differences across the four tested benchmarks between Baseline (Row 5. Baseline) and the best-performing 9. 13D + Transformer + R-CTC, which is denoted as \(\Delta_{Base}\) hereafter for simplicity:  
\[\Delta_{Base}(9)=(\overline{\Delta_{F.Acc}}=7.74\pm 1.70,\overline{\Delta_{ mAP}}=6.76\pm 1.81,\overline{\Delta_{Edit}}=12.3\pm 3.68)\]
## 3 Multipath Parameter Estimate Algorithm
### Computation Complexity Analysis
For multiplication, the ratio is \(C_{m}/(N\log_{2}N+N)\times 100\%\approx 51.1\%\), and for addition, the ratio is \(C_{a}/(2N\log_{2}N)\times 100\%\approx 48.2\%\). We can see that for this case, the proposed method just needs approximately half of the overall computation amount.