esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or \(CO_{2}\) emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios.  
keywords:microgrids; wind turbines; power management; metaheuristics optimization; operating costs; \(CO_{2}\) emissions; energy losses
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Short Range Communication Technologies
#### 4.3.5 Radio Frequency Identification (RFID)  
RFID is a wireless technology that uses different frequency bands from both licensed and unlicensed (ISM band) bands. The different carrier frequencies used for RFID are:  
1. 125-135 KHz (Low Frequency)
2. 23.56 MHz (High Frequency)
3. 868-930 MHz (Ultra-High Frequency)
4. 2.45 GHz (Microwave Frequency)
5. 5.8 GHz (Microwave Frequency)  
The two components that make up an RFID device are:  
1. A reader
2. A tag  
The reader has one or more antennas, which are used to emit radio waves, while the RFID tag is used to receive signals. RFID technology has been used for various applications, such as patient tracking and monitoring. The major drawbacks, as hinted at by [16; 21], include the high cost of adoption, patient privacy and security issues, and Electromagnetic Interference (EMI) between RFID tags and other biomedical sensors.
## 3 Results and Discussions
### Pore Structures
#### 3.2.2 LP-NA and LP-CA Tests Results
The joint PSDs are listed in Table 4.  
The combined PSD showed that the TPV and SSA of anthracite were in the ranges 0.078-0.110 cm\({}^{3}\)/g and 242.283-359.818 m\({}^{2}\)/g, respectively. The super-micropores were the most developed; their pore volume varied in the range 94.39%-96.36%, with an average of 95.12%. The super-micropores were followed by micropores, transitional pores, mesopores, and macropores. The super-micropores provided the largest proportion of the pore SSA (\(>\)99%) (Figures 7 and 8). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{\(V_{\text{t}}\)} & \multirow{2}{*}{\(S_{\text{t}}\)} & \multicolumn{5}{c}{**Pore Volume Ratio (\%)**} & \multicolumn{5}{c}{**Specific Surface Area Ratio (\%)**} \\ \cline{3-14}  & & & & & & & & & & & \\ \cline{3-14}  & & & & & & & & & & & \\ \hline A1 & 0.078 & 242.823 & 1.786 & 0.765 & 1.786 & 1.276 & 94.388 & 0.001 & 0.003 & 0.085 & 0.386 & 99.525 \\ A2 & 0.088 & 278.051 & 1.923 & 1.244 & 0.905 & 0.905 & 95.023 & 0.001 & 0.004 & 0.039 & 0.278 & 99.68 \\ A3 & 0.094 & 298.250 & 2.455 & 0.427 & 1.174 & 0.961 & 94.984 & 0 & 0.002 & 0.046 & 0.304 & 99.648 \\ A4 & 0.101 & 320.121 & 1.482 & 2.273 & 0.692 & 0.692 & 94.862 & 0.001 & 0.014 & 0.032 & 0.222 & 99.731 \\ A5 & 0.110 & 359.818 & 0.727 & 0.727 & 0.727 & 1.455 & 96.364 & 0 & 0.003 & 0.04 & 0.474 & 99.484 \\ Average & 0.094 & 299.81 & 1.675 & 1.087 & 1.057 & 1.058 & 95.124 & 0.001 & 0.005 & 0.048 & 0.333 & 99.614 \\ \hline \end{tabular} Note: \(V_{\text{t}}\) = Total pore volume (TPV); \(S_{\text{t}}\) = Total specific surface area (SSA); \(V_{1}\) = Macrorope volume (\(>\)1000 nm); \(V_{2}\) = Mesopore volume (10–1000 nm); \(V_{3}\) = Transitional pore volume (10–100 nm); \(V_{4}\) = Microrope volume (2–10 nm); \(V_{5}\) = Super–micropore volume (\(<\)2 nm); \(S_{1}\) = Macropore SSA; \(S_{2}\) = Mesopore SSA; \(S_{3}\) = Transitional pore SSA; \(S_{4}\) = Microrope SSA; \(S_{5}\) = Super-micropore SSA.  
\end{table} 
Table 4: Combined results of MIP, LP-NA, and LP-CA. Figure 8: Incremental pore volume and SSA in relation to pore diameter (A, Sample A1; B, Sample A2; C, Sample A3; D, Sample A4; E, Sample A5). Figure 7: Contribution of pore structures to (**A**) pore volume and (**B**) SSA.
## 5 Fracture Analysis
### Effect of Adhesion Strength  
As analyzed in the previous sections, stress concentration around angular aggregates may induce excessive stress at the aggregate-asphal interface. Therefore, the adhesion strength of the interface may have significant effects on the fracture behavior of asphalt concrete. In this section, a series of simulations were conducted to investigate the influence of adhesion strength under a constant temperature of 0 \({}^{\circ}\)C and a loading rate of 5 mm/min. The interface adhesion strength was taken as 60%, 100%, and 140% of the initial value used in the model validation. The simulated crack propagation paths are shown in Figure 16. It can be seen that when the interface adhesion strength decreased to 60% of the initial value, local cracks were first observed around aggregates. Then the connection of these local cracks finally formed the main crack propagation path. When the interface adhesion strength increased to 140% of the initial value, the crack only propagated through the asphalt mortar. In general, the interface adhesion strength has an important effect on the  
Figure 14: Specimens with the same aggregate distribution but different aggregate shapes: (**a**) ellipsoidal aggregate; (**b**) polyhedral aggregate.  
crack propagation path. When the interface adhesion strength is relatively small, the cracks are more likely to propagate around the interface. In this case, the asphalt concrete cracking is controlled by the interface adhesion strength. When the interface adhesion strength is relatively large, the asphalt mortar becomes the weaker part, and the asphalt concrete cracking is controlled by the cohesive strength of the asphalt mortar.
## 4 Experimental Results and Discussion
### LP Characters Recognition Results Analysis
#### 4.2.1 Layers Aggregation Module  
The CNN module is the backbone of the proposed method as it plays a vital role in extracting the features from license plate images, which greatly affects the recognition ability of the proposed framework. This is the reason behind proposing and evaluating four different suggested modified  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Country** & **T\({}_{\text{LPs}}\)** & **NMC\({}_{\text{LPs}}\)** & **CSc** & **FS\({}_{\text{C}}\)** & **Accuracy, \%** & **Precision, \%** \\ \hline Australia & 247 & 242 & 1364 & 18 & 97.98 & 98.70 \\ Canada & 329 & 325 & 1813 & 22 & 98.78 & 98.80 \\ England & 150 & 144 & 856 & 12 & 96.00 & 98.62 \\ Mexico & 106 & 103 & 735 & 07 & 97.17 & 99.06 \\ Pakistan & 397 & 382 & 2387 & 37 & 96.22 & 98.47 \\ Europe & 747 & 732 & 4445 & 75 & 97.99 & 98.34 \\ UAE & 46 & 44 & 237 & 05 & 95.65 & 97.93 \\ USA & 1696 & 1630 & 9880 & 191 & 96.11 & 98.10 \\ \hline Total & 3718 & 3602 & 21717 & 367 & 96.88 & 98.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy and precision of segmented characters.  
CNN structures by using layer aggregation models to fuse low- and high-level features. These four models are referred to as CNN\({}_{\text{C15}}\), CNN\({}_{\text{C25}}\), CNN\({}_{\text{C35}}\) andCNN\({}_{\text{C45}}\) and concatenate the feature map of convolution layers 1, 2, 3 and 4 with convolution layer 5 respectively. CNN\({}_{\text{B}}\) denotes the base structure and outputs only convolution layer 5 feature map as traditional structure works. The proposed layers aggregation models for CNN structure can be seen in Figure 15, where C represents the convolution and P represents the pooling layer.  
Table 4 demonstrates the comparative study of four different layer aggregation and traditional models for 8 different countries. It is observed that network with CNN\({}_{\text{C45}}\) model gives better character recognition as compared to CNN\({}_{\text{B}}\), CNN\({}_{\text{C15}}\), CNN\({}_{\text{C25}}\) and CNN\({}_{\text{C35}}\). Probably, CNN\({}_{\text{C15}}\), CNN\({}_{\text{C25}}\) and CNN\({}_{\text{C35}}\) still contain excessive background noise as compared to CNN\({}_{\text{C45}}\), which would lead to reducing the performance of the system. Table 4 shows that CNN\({}_{\text{C45}}\) got 90.30% recognition accuracy, which is highest among all other structures even it beats the state-of-the-art network (CNN\({}_{\text{B}}\)).
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 5 Simulation Results and Discussion
### Model Hyper-Parameters and Training Conditions  
We conducted a thorough analysis, running the DL models on various occasions to ensure robustness and to explore potential variability in the results. Prior to reporting the results, the models were executed multiple times to identify the optimal configurations and assess the stability of their performance.  
We trained our models using different filter numbers and assessed their performance on a validation set. Our decision-making process was informed by the validation performance, leading us to select configurations that yielded optimal performance without overfitting. The selection of filter numbers was influenced by various factors, including the data characteristics, task complexity, model architecture and resource constraints. Ultimately, identifying the optimal set of hyper-parameters, including the filter number, entails a combination of educated guesswork and empirical experimentation based on experience. Table 2 summarizes the model hyper-parameters used for the different proposed architectures.  
During the training phase, the dataset incorporated several SNR values, and the instances were shuffled to expose the models to various SNR conditions. Specifically, we generated examples with SNR values in the set \(\{-20\text{ dB},-19\text{ dB},\ldots,-1\text{ dB},0\text{ dB}\}\), which contained discrete SNR values in the range between \(-20\text{ dB}\) and \(0\text{ dB}\) with a step of \(1\text{ dB}\). This strategy aimed to enhance the model's adaptability to a wide spectrum of SNR scenarios, ensuring robust performance across different signal quality levels.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 2 Proposed Method
### Gamma-Ray Measurement Using All-Around View Compton Camera
The all-around view Compton camera projects Compton cones on both sides of a straight line connecting the two interacting elements, as illustrated in Figure 4. In addition, an energy window is set to identify the type of radionuclide based on the gamma-ray energy spectrum emitted from the radiation source. Through this approach, the effect of natural radioactive materials contained in the target area can be reduced. Our previous paper comprehensively describes a sensing mechanism that adopts the all-around view Compton camera [14]. Figure 3: Principle of Compton camera: the Compton camera comprises two layers: scatterer and absorber. Let \(A\) and \(B\) be the positions at which the gamma ray is deposited on the scatterer and absorber, respectively, while \(\theta\) is the scattering angle of the gamma ray relative to the straight line connecting \(A\) and \(B\).
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 2 Security Threats in the Post-Quantum Era
Attackers' abilities to capture transmissions have been considered as granted in classical (Dolev-Yao based [27]) threat models for cryptographic solutions. However, in the case of physical layer security, this assumption is sometimes considered too strong. In the second scenario, we have the active man-in-the-middle attacker [PERSON]. This threat model requires an additional authentication approach resistant against the man-in-the-middle attacker. This requirement can be fulfilled e.g., with cryptographic authentication (but not with secrecy coding in the physical layer that lacks strong authentication capabilities). When [PERSON] is quantum capable, she will be able to break classical asymmetric algorithms at run-time using [PERSON]'s algorithm. Hence, if the key agreement uses classical asymmetric algorithms for authentication, [PERSON] is able to break the protection and masquerade as [PERSON] to [PERSON] and as [PERSON] to [PERSON]. However, as noted by [PERSON] et al. (2016), classical authentication methods also work in the pre-quantum era as [PERSON] cannot break them in real time. Consequently, the time when security solutions must be quantum immune is the quantum era (the time when quantum computers for [PERSON]'s algorithm exist) minus \(N\) years, where \(N\) is the time that the protected information must be kept secret. In authentication \(N\) is zero because, to break an authentication scheme, the attacker must have quantum computer capabilities during the authentication procedure (and active attack). In confidentiality protection, on the other hand, the attacker can just store the ciphertext and then wait until the quantum computer emerges. This insight is relevant for quantum immune solutions that do not provide authentication. In the era where we are waiting quantum computers, it is safe to use classical authentication mechanisms as long as other parts of the security system are quantum immune (for instance, the key distribution is protected with physical layer confidentiality and the application data is protected using AES with long keys).
## 4 Case Study
### Use Case Description
On the last point, a FlexSim simulation environment could be added to a collection of environments used for validation and benchmarking of RL algorithms, such as the one available for the Python Gym library, which is presented below. ```
1:Input: Item, Locations Output: Location \(\triangleright\) Location \(\in\) Locations to which the Item is assigned
2:while isAssigned\((Item)\
eq\)true do
3:\(k\leftarrow\) randomUniformBetween\((1,count(Locations))\)
4:\(Location\leftarrow\)selectLocation\((Locations,k)\)
5:ifisFull\((Location)=\)true then
6:\(Locations\leftarrow\)removeLocation\((Locations,Location)\)
7:else
8:\(Location\leftarrow\)assignItem\((Location,Item)\)
9:return Location
10:end
```  
**Algorithm 2** Random policy. ```
Input: Item, Locations Output: Location
1:while isAssigned\((Item)\
eq\)true do
2:\(k\leftarrow\) minDistance\((Locations,Item)\)
3:\(Location\leftarrow\)selectLocation\((Locations,k)\)
4:ifisFull\((Location)=\)true then
7:\(Locations\leftarrow\)removeLocation\((Locations,Location)\)
8:else
9:\(Location\leftarrow\)assignItem\((Location,Item)\)
10:return Location
11:end
```  
**Algorithm 3** Greedy policy. Figure 3: Schematic representation of the use case (**top**) and screenshot of the simulation set-up (**bottom**). It can be seen that the positions of the input (IN) and output (OUT) points make the first storage location the most appropriate for reducing the traveled distance of the operators, if no other restriction is considered.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 4 Biobased Monomers for Polyester and Polyamide Synthesis
### Biobased Aliphatic Diols and Polyols  
1,3-Propanediol (1,3-PDO) is a commodity chemical used for the production of various polymers, with an annual global demand of around 1 million tons [99]. At present, there are two chemical processes for the industrial production of 1,3-PDO, starting from petrol-based acrylaldehyde or ethylene oxide [100; 101]. Nowadays biobased 1,3-PDO is commercially synthesized via fermentation of D-glucose based on corn using a genetically engineered _E. coli_. [100] In addition, it is promising to produce 1,3-PDO from biomass-derived glycerol using a bacterial fermentation process [99; 100; 101; 102; 103].  
1,4-Butanediol (1,4-BDO) is widely used as a building block for polymer synthesis, with an annual global market of over 2.5 million tons [104]. The industrial production of 1,4-BDO dominantly depends on petrol-based chemicals such as maleic anhydride, acetylene, butane, propylene and butadiene. Since late 2007, Genomica (USA) started to develop a biological process for the synthesis of biobased 1,4-BDO from sugars using a genetically-modified strain of _E. coli_ bacteria [99; 104; 105; 106]. This process has already been commercialized [31]. Alternatively, biobased 1,4-BDO can be produced by reduction of sugar-derived succinic acid and this process is under commercialization preparation stage [31].  
1,4:3,6-Dianhydrohexitols (DAHs) are sugar-derived aliphatic diols with rigid and chiral structures [107]. It is of great interest to synthesize DAH-based polymers with high glass transition temperatures (\(T_{g}\)) and/or with special optical properties [108]. According to the chirality, DAHs have three possible stereoisomers: isosorbide, isomanide and isoidide (Scheme 4). Due to the different positions of the hydroxyl groups, the reactivity of these isomers are different, showing the following sequence: isomannide < isosorbide < isoidide [107; 108]. Nowadays, only isosorbide is produced at an industrial scale using sugars as the starting materials [26; 107]; and Roquette (France) is a leading producer. However, the purity and high price of the commercial isosorbide are two major concerns when used for polyester synthesis.  
Other aliphatic diols such as 2,3-butanediol, 1,6-hexanediol, 1,8-octanediol and 1,10-decanediol, are (potentially) biobased monomers [5; 109; 110].  
Moreover, glycerol and D-sorbitol are abundant and inexpensive biobased aliphatic polyols. Glycerol is obtained as a byproduct in the production of biodiesel from vegetable oils and fats [5; 111], while D-sorbitol is produced industrially on large scale by reduction of glucose derived from biomass feedstocks [33].  
Furthermore, sugars like glucose and sucrose, and sugar alcohols such as erythiritol, xylitol and sorbitol, are polyols with multi hydroxyl groups. They are naturally occurring compounds which can be produced via fermentation of various sources of biomass feedstocks [112].
## 3 Proposed Algorithm
### Directional forwarding  
Figure 1 shows the proposed directional forwarding algorithm. Each node recognizes its location through GPS. A one-hop neighbor node, which is at a distance of one hop from the main route, recognizes the location of the main route node through a beacon message. In the figure, node A contains information about its location (Xa, Ya). In this situation, node A broadcasts its location information using a beacon message. Nodes B and C store the location of node A in each routing table. Four-directional forwarding, directions 1 to 4, is then applied. Direction 1 is (+, +), Direction 2 is (+, -), Direction 3 is (-, -), and Direction 4 is (-, +). In Figure 1, Node B locates direction 1 from node A, and node A sets the position of direction 3 from node B. In the same manner, node C locates direction 2 from node A and node A locates direction 4 from node C. Though two-, four-, eight-, and 16-way directions can be applied, four-way direction shows the best performance with the least routing overhead. Using two-way direction, it causes many routing overhead as similar data packet delivery ratio. Furthermore, using eight- and 16-way direction, they result very low data packet delivery ratio because there are not enough control messages to trace the destination's location.  
Figure 1: Four-way directional forwarding.
## 3 Results and Discussion
### High Temperature Properties
#### 3.2.3. Macrostructure and Microstructure
Figure 13: Microstructures of the cross-sectioned areas according to each condition after high-temperature compression of Ti-6 Al-4V alloy; (**a**,**d**) 800 \({}^{\circ}\)C, \(1\times 10^{-2}\)/s, (**b**,**e**) 800 \({}^{\circ}\)C, \(1\times 10^{0}\)/s, (**c**,**f**) 850 \({}^{\circ}\)C, \(1\times 10^{-2}\)/s; (**a**–**c**) Dead Zone, (**d**–**f**) Severe Plastic Deformation region. Figure 14 shows the \(\alpha^{\prime}\) martensite structure throughout the specimen by compressing it at a temperature above the \(\beta\)-transus. This shows that when Ti-6 Al-4V alloy with \(\alpha+\beta\) microstructure is heated to a temperature higher than the \(\beta\)-transus, it changes to a fully \(\beta\) phase, and an \(\alpha^{\prime}\) martensite structure grown at the \(\beta\) grain boundary was revealed by the rapid cooling rate. In the EBSD data presented in Figure 15a,c, it was confirmed that fine grains were observed by dynamic recrystallization. Due to the structure with a thinly layered space, it may be seen that the stress required for the deformation is reduced due to the advantageous diffusion described above, and thus, low flow stress is occupied, as shown in Figure 6. Then, Figure 15b,d shows the reconfiguration of the prior \(\beta\) grain based on Figure 15a,c. In the results of the reconfiguration of the prior \(\beta\) grain, small-sized prior \(\beta\) grains were observed in the upper part of the specimen, as shown in Figure 15b, due to a relatively fast cooling rate and a small deformation energy because of the contact with the die. On the other hand, in Figure 15d, it was confirmed that the uniform microstructure deformation occurred without significant differences in the size of the prior \(\beta\) grains. After the ring-rolling process, however, it is expected that the difference in the grain size for fabricating actual products will not be significantly affected according to the cutting and processing of the final dimension and shape calibration. Thus, it is determined that AMS4928, which is one of Ti-6 Al-4V alloys, used in aircraft parts through applying a high-temperature compression test is suitable for the ring-rolling process under the conditions of 1050 \({}^{\circ}\)C and \(1\times 10^{-2}/\)s without the plastic unstable region proposed in Figure 11.  
Figure 14: Microstructures of the cross-sectioned areas according to each condition after high-temperature compression of Ti-6 Al-4V alloy; (**a**,**c**) 1050 \({}^{\circ}\)C, \(1\times 10^{-2}/\)s, (**b**,**d**) 1100 \({}^{\circ}\)C, \(1\times 10^{-2}/\)s; (**a**,**b**) Dead Zone, (**c**,**d**) Severe Plastic Deformation region.