esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 3 Preservation Technologies and Optimization
### Chemical and Biological Preservation
#### 3.2.2 Ozonation  
Ozonation applies ozone gas or ozonated water, typically at 0.5-2.0 ppm, to sanitize seafood. Ozone is generated by feeding oxygen or air through a corona-discharge reactor or exposing it to ultraviolet light. In practice, either ozone gas is introduced into cold-storage rooms and packaging headspace, or ozonated water is used in immersion or slurry-ice systems to deliver both cooling and microbial control [22].  
Once formed, ozone decomposes rapidly to generate reactive oxygen species (ROS), such as hydroxyl radicals (\(\bullet\)OH), superoxide anions (O\({}_{2}\bullet^{-}\)), and singlet oxygen (\({}^{1}\)O\({}_{2}\)). These ROS attack microbial cells by abstracting hydrogen atoms from unsaturated fatty acids in membrane lipids, initiating lipid peroxidation. They also oxidize membrane proteins and intracellular thiol groups, disrupting enzyme function and compromising membrane integrity. The combined damage to membranes, proteins, and DNA leads to microbial inactivation and cell lysis [22; 89]. Unfortunately, the same ROS and ozone molecules can react with fish tissue lipids, accelerating hydroperoxide breakdown and generating secondary oxidation products, such as aldehydes and ketones, which raise thiobarbituric-acid-reactive substances (TBARS) and can produce off-flavors [23; 89].  
In one study, whole freshwater tilapia immersed in 1.5 ppm ozonated water for 15 min exhibited an 88.3 percent reduction in total microbial load without altering the pH or color. When tilapia fillets were treated at 1.0 ppm and 1.5 ppm, microbial counts decreased by 77.2 percent and 79.5 percent, respectively, although TBARS values increased, indicating enhanced lipid oxidation [23]. Optimized ozone treatments can achieve up to a 3 log CFU/g reduction in spoilage organisms, but exposure parameters must be carefully controlled to balance sanitization and lipid stability [22].  
Commercially, ozonation is integrated into slurry-ice systems for species such as cod and tilapia to combine effective temperature control and microbial reduction while minimizing drip loss. Ozone gas is also applied in controlled atmospheres within cold-storage rooms and packaging headspaces to suppress surface contamination on fillets. To counteract oxidative side effects, ozonation is often followed by antioxidant interventions, such as dipping fish in polyphenol-rich extracts or applying antioxidant-releasing edible coatings, thereby preserving sensory quality while maintaining microbial safety.
## 3 Obstacle Avoidance Model by Harmonic Potential Field with Curvature Constraint
### Setting Lookahead Distance  
Look-ahead distance is the only parameter in the pure pursuit algorithm, and the reason for the look-ahead distance is that non-holonomic robots cannot correct errors directly with respect to the nearest point on the path [20]. The pure pursuit procedure is summarized in Algorithm 2. The common practice for setting look-ahead distance takes into consideration its effect on path geometry and tracking performance. A longer look-ahead distance results in smoother paths but a worse tracking accuracy. In contrast, a shorter look-ahead can reduce tracking errors more quickly. Yet, due to the curvature constraint, the pure pursuit controller may not be able to follow steering commands, and the robot motion becomes unstable. Therefore, a suitable look-ahead is needed for both stability and tracking performance.  
Figure 13 illustrates that the pure pursuit path is sensitive to the setting of look-ahead distance. In this example, there is an angle between the \(x\)-axis and the line connecting the point robot and the obstacle center. The path with a look-ahead of 0.5 m has an effective and efficient tracking ability. However, the path with too large of a look-ahead distance, 1 m, for example, is smoother but unable to track the path accurately. On the contrary, the path with a shorter look-ahead 0.1 m responds to tracking errors quickly, but the robot's motion is unstable and overdamping because of the curvature constraint. Both paths obtained with a look-ahead of 1 m and 0.1 m lead to a collision with the obstacle.
# Experimental Investigation of TR-UHPC Composites and Flexural Behavior of TR-UHPC Composite Slab
## 2 Axial Tensile Performance of TR-UHPC
### Specim Manufacture Process and Experiment Program  
Through the axial tension test, the impact of the type and amount of fiber textiles on the tensile properties of the textile-reinforced UHPC (TR-UHPC) was investigated. The tensile specimen was named \"T\". The capitals \"C\", \"G\", and \"B\" refer to the fiber type, which are the CFT, GFT, and BFT, respectively. The numbers \"2\" and \"4\" refer to the number of fiber textile layers. For instance, in \"T-B2\", \"T\" refers to tensile test, \"B\" indicates BFT, and \"2\" means two layers of textile. The series of tensile specimens are shown in Table 4, with three specimens for each series.  
The casting process is shown in Figure 2. Axial tensile specimens (dog-bone shaped) made of TR-UHPC were manufactured with a tensile section size of 80 \(\times\) 50 \(\times\) 150 mm. The spacing between the two-layer and four-layer textile specimens was 10 mm, as shown in Figure 3a. The specimens were cast layer by layer, with the first layer of UHPC cast into the mold up to the marked height. When laying the first layer of the fiber textile, the textile was slightly pressed to make the UHPC surround the textiles. Then, the next layer of UHPC was cast and repeated until the casting was complete. After 48 h from casting, the specimen was demolded and cured in steam at a temperature of 90 \({}^{\circ}\)C for 48 h.  
Carbon fiber reinforced plastic (CFRP) cloths were attached to the sectional area changing part of the dog-bone shaped specimen to make sure the specimen failed at the middle section. A universal testing machine (SHT4605-G, stroke and force limits of 710 mm and 600 kN, respectively) was used to apply tension to the specimen, as shown in Figure 3b.  
The two ends of the dog-bone specimen were connected to the testing machine through jigs, and the loading method used was displacement-controlled loading with a loading rate of 0.05 mm/min. The strain before cracking was measured by strain gauges, and the tensile displacement of the specimen after cracking was measured by an electronic extensometer  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **Fiber Type** & **Textile Layer Number** & **Textile Reinforcement Ratio** \\ \hline T-C2 & Carbon fiber & 2 & 0.20\% \\ T-G2 & Glass fiber & 2 & 0.21\% \\ T-B2/4 & Basalt fiber & 2/4 & 0.25\%/0.50\% \\ T-U & – & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the TR-UHPC axial tensile test.  
Figure 2: Manufacturing process of axial tensile specimens.  
with a measurement scale of 150 mm. The axial tension test was terminated when the specimen completely fractured.
## 7 Simulation and Evaluation
### Switching Authentication Time  
We set the maximum number of simultaneous online users of the Iridium satellite system to 150,000 [25]. Assuming that users are evenly distributed, it takes approximately 2 h for a satellite to orbit the earth, and the available service time for a stationary ground user is approximately 10 min in Iridium. Therefore, ground users need to initiate a switching authentication request to the satellite every 10 min, and a single satellite needs to process four user authentication requests every second. The simulation experiment is set to run for 1 h, and the number of users that a single satellite needs to process per second is successively considered in four cases \(\lambda\): 4, 8, 16 and 32. The simulation results are shown in Figure 8. When processing 4 user requests per second, the response time is 0.026 s for the BAPC protocol, 0.019 s for the Fast-access protocol and 0.044 s for the Handover protocol. As the number of users increases, the response time of each protocol increases linearly. BAPC reduces the response time by 39% to 43% compared with the Handover protocol in terms of the interval between 4 and 32 users.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field** & **Length/Byte** & **Field** & **Length/Byte** \\ \hline Pk & 64 & Server & 4 \\ Address & 20 & Duration & 4 \\ Signature & 65 & Timestamp & 4 \\ Session Key & 128 & Amount & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Settings of the simulation.  
Figure 8: Comparison of the authentication response time.  
In general, our BAPC authentication protocol shows a better comprehensive performance other protocols. Compared with the Fast-access authentication protocol, it transmits less data and has no intersatellite communication; therefore, it is slightly superior to BAPC. However, the restrictions are more stringent, requiring satellites in the same orbit. As the satellite moves, satellites in the same orbit do not keep circular coverage of the same location. In practice, users most likely need handover authentication between different orbits. BAPC is better than its Handover authentication protocol.  
At the same time, compared with other satellite access authentication protocols, BAPC does not distinguish between intraregional authentication and cross-regional authentication, nor does it require that the satellite for access authentication be in the same orbit or adjacent satellite across orbit. BAPC can be used for different types of switching authentication, which is more suitable for LEO satellite networks with constantly changing links. In addition, user authentication information is basically stored on the blockchain, which has high scalability. Based on blockchain and cryptocurrency, BAPC securely stores user authentication information in a distributed ledger, reducing the cost of communication between users and satellites and bringing lower communication cost with tolerable storage cost. For an increasing amount of data, a certain mechanism can be used to clear the data and maintain sufficient storage space.
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 5 Analyzing Influencing Parameters in Wind Energy with ML  
ML models have shown potential in the specialized literature for improving wind energy production processes by identifying and analyzing parameters that influence this process. The following analysis aims to review the ML models used in the specialized literature, focusing on the factors that affect the optimization of the efficiency and performance of wind turbines and, therefore, the overall wind energy production process.  
The parameters identified as strong influencers in this process are wind speed, wind direction, climatic conditions, meteorological forecasting, turbine design, grid connectivity, turbine height, and terrain topology. These will be analyzed concerning wind energy production.
## 3 Alkaloids
### Indole Alkaloids
#### 3.1.1 Tetracyclic Indoloquinolizidine Alkaloids  
There are six tetracyclic indolequinolizidine alkaloids that have been identified in _U. tometosa_ (Figure 1, **1-4**). These compounds are distinguished by a quinolizidine ring fused to an indole to form a tetracyclic indolequinolizidine highlighted in blue in Figure 1. These alkaloids are commonly identified as corynanthe-type monoterrepenoid alkaloids in other literature sources [38].  
The six compounds share several similar structural motifs besides the tetracyclic indolequinolizidine structure. An alkoxy and carboxylate ester within the monoterpene skeleton exists for all compounds within this subclass. Compounds **1a-2b** were isolated and quantified in a study of sixteen plant samples utilizing high-performance liquid chromatography (HPLC) retention times, mass spectrometry (MS), proton (\({}^{1}\)H) and carbon (\({}^{13}\)C) nuclear magnetic resonance (NMR) spectroscopy, and ultraviolet-visible (UV-Vis) spectroscopy, with **2b** reported in trace amounts [10]. Of the citations from this source that reference structural determination of compounds **1-4**, a single chromatographic method utilizing retention time and mass spectroscopy is the only report that confirmed structural identification of these molecules [39]. Molecules **1a-2b** are structurally related. Hirsuteine **1a** and corynantheine **1b** are epimers with a vinyl group at C-20, while the compounds hirsutine **2a** and dihydroorynantheine **2b** are epimers with ethyl substitutions at C-20. The structural determination of these compounds was completed utilizing--chromatographic method with the addition of MS [39].  
\(N\)-oxide derivatives of alkaloids are known to naturally occur in plants [40]. Two \(N\)-oxide tetracyclic indolequinolizidine compounds have been identified from the leaves of _U. tometosa_[41]. The \(N\)-oxide tetracyclic indolequinolizidine alkaloids **3** and **4** possess a structure identical to **1a** and **2b**, respectively, with the addition of an oxygen atom bound to the nitrogen atom of the quinolizidine structure. Heterocyclic \(N\)-oxides are currently being investigated as an emerging class of therapeutic agents with antibacterial activity [42, 43]. However, antibiotic data for **3** and **4** have not been reported to date. Examples of \(N\)-oxides exhibiting antibacterial activity are the quinoxaline 1,4-di-\(N\)-oxide derivatives [42]. The exact mechanism of antibacterial activity is unknown; however, it has been established that the \(N\)-oxide moiety improves antibacterial activity [44], and that the mechanism proceeds though oxidative damage of bacterial DNA [45].  
Figure 2: Structure of \(\beta\)-carboline alkaloids (**10–14**). Sugars in red are \(\beta\)-D-glucose.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 5 Performance Evaluations  
### Simulation Environment and Performance Metrics  
We use a MATLAB simulator to compare the performance of SAGREH with that of EHWA. The following performance metrics will be used:  
* **Packet delivery ratio (\(PDR\))** is defined as \[PDR=\sum_{1}^{\Omega}\frac{\#\text{ {packet received at sink}}}{\#\text{ {packet transmitted from source}}}\] (16) where \(\Omega\) is the number of topologies. In this paper, the PDR of the EHWA protocol-based EH-WSNs and SAGREH were comparatively studied as a function of the radio transmission range and number of nodes.
* **Average consumption energy (\(\overline{CE_{i}}\))** is defined as the average energy consumed to transmit a packet from the source node to the sink node and is given by \[\overline{CE_{i}}=\frac{1}{\Omega}\sum_{i=1}^{\Omega}CE_{i}\] (17) where \(CE_{i}\) is the energy consumed over a route to transmit a data packet from a source node to a sink node in the \(i_{th}\) network topology, and \(\Omega\) is total number of network topologies.
* **Average hop count of successful routing path from source to sink (\(\overline{HC_{i}}\))** is the average number of hops over a path between a source node and a sink node, as follows \[\overline{HC_{i}}=\frac{1}{\Omega^{\prime}}\sum_{i=1}^{\Omega^{\prime}}HC_{i}\] (18) where \(HC_{i}\) is the number of hops of a successful routing path from a source node to a sink node in the \(i_{th}\) network topology, and \(\Omega^{\prime}\) is the total number of topologies with a successful routing path from the source to the sink, \(\Omega^{\prime}\in\Omega\).  
Network parameters used in the simulations are presented in Table 1. For this evaluation, it was assumed that all the packets were available in the buffer.  
In all scenarios, the sink node location was the same, while the source node was randomly located in the network area. The mobility of the source node and intermediate nodes was represented by different random locations in each network topology.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network Parameters** & **Value** \\ \hline Simulator & MATLAB R2014a \\ Simulator time & 500 s \\ Network area & 500 m \(\times\) 500 m \\ Radio transmission range & 50–100 m \\ Number of nodes & 50–100 \\ MAC protocol & IEEE 802.11 \\ Data rate & 20,000 bps \\ Number of topologies & 10,000 \\ Packet size & 512 bytes \\ Maximum harvested energy \(E_{max}\) & 25 \(\times\) 10\({}^{-6}\) J/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network parameters.  
Figure 5a,b show the accumulated routing paths from a source to a sink node, which were obtained after transmitting 15 packets using the EHWA and SAGREH. The data packets that traveled over the edges are represented by the thickness of the corresponding line. In Figure 5a, some disconnect is evident in the path, indicating that some packets could not reach the sink node. However, the routing path between the source and sink nodes can be established. Figure 5b presents an evaluation of the performance of the proposed SAGREH protocol.
## 3 Case Study and Numerical Modeling
### Finite Element Modeling  
In this study, the section A-A of the foundation pit near the subway tunnel (Figure 2) was selected for finite element modeling by PLAXIS 2D. This section is located in the middle of the pit, and thus the deformation of the retaining structure and the soil at this section can be treated as plane strain. According to the literature review, the HS and HSS constitutive models can accurately characterize the stress-strain changes in soil caused by braced excavation. Therefore, these two constitutive models were selected. In the established finite element model, the HSS model was adopted to simulate muddy clay, silty clay, and miscellaneous fill, and the HS model was used for the remaining soil layers. The initial soil parameters are shown in Table 1, where the unit weight \(\gamma\), cohesion \(c\), and friction angle \(\varphi\) were selected from the soil survey report. The initial values of the constitutive parameters were determined according to the literature and engineer's design experience [14; 15; 16; 30].  
The schematic diagram of the finite element model is shown in Figure 3, where the retaining wall and the operating subway tunnel were simulated by plate elements. The three concrete supports in the pit were simulated by anchor elements. The material properties are listed in Table 2. There is contact between the underground structure (retaining structure and tunnel lining) and the soil. The contact between the structure and the soil was set by the reduction coefficient \(R_{\text{inter}}\) to reduce the strength and stiffness, as defined in Equation (6). In this study, the value of \(R_{\text{inter}}\) is 0.7.  
\[\tan\varphi_{i}=R_{\text{inter}}\cdot\tan\varphi_{\text{soil}} \tag{6}\]  
where \(\varphi_{i}\) is the friction angle between the structure and soil; and \(\varphi_{\text{soil}}\) is the friction angle of the soil.  
According to the excavation sequence of the foundation pit, the numerical simulation was divided into 10 construction stages, as shown in Table 3. All calculations were carried out under the assumption of undrained conditions, without considering the time effect or material effect of pore water pressure changes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Soil Layer** & **Constitutive** & **\(\gamma\)** & **\(c\)** & **\(\varphi\)** & **\(m\)*** & **\(R_{t}\)*** & **\(p\)****net*** & **v*** & **\(K_{0}\)******* & \
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### FET Device Biasing  
When used in switch applications, pHEMT devices are generally operated around 0 V DC bias. The gate control voltage for the \"ON\" state is 0 V, and for the \"OFF\" state it is driven below the pinch-off value (i.e., \(V_{g}<V_{p}\)). In this work, the pinch-off voltage \(V_{p}\) is approximately \(-1.5\) V. Thus:  
* **ON State:** Gate voltage \(V_{g}=0\) V. The low gate-source voltage produces a low channel resistance (\(R_{\text{on}}\)) and enables the RF signal to pass from drain to source.
* **OFF State:** Gate voltage \(V_{g}=-1.5\) V. The transistor channel is pinched off, creating a high channel resistance (\(R_{\text{off}}\)), thereby blocking the RF signal path.  
Since the RF signal traverses from drain to source, the gate serves as the control terminal. Large-value resistors are typically employed in the gate bias network to isolate RF signals from the DC supply lines.  
Figure 17 illustrates typical I-V characteristics of a depletion-mode FET at various negative gate-source bias levels near the 0 V operating point. As \(V_{gs}\) decreases below pinch-off, the drain current \(I_{d}\) significantly drops, corresponding to the \"OFF\" state.  
Designing an SPDT switch for high-frequency operation (from DC up to 50 GHz) requires a thorough understanding of scattering parameters (S-parameters). S-parameters quantitatively describe how incident waves at each port of a multiport network are transmitted or reflected.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref.** & **Process** & **Application** & **Freq. (GHz)** & **Gain (dB)** & **Noise (dB)** & **Power (mW)** \\ \hline
[24] & 0.1 \(\upmu\)m GaAs mHEMT & 5G & 25–34 & 24.2 & 3.04 & 2.8 \\
[25] & 0.1 \(\upmu\)m InP HEMT & SATCOM/5G & 16–28 & 32.3 & 4.11 & 3.5 \\
[32] & GaAs pHEMT & SATCOM & 18–21.6 & 30.3 & 1 & 60 \\
[33] & 0.1 \(\upmu\)m GaAs pHEMT & SATCOM/5G & 18–43 & 21.6 & 1.8–2.7 & 140 \\
[34] & 0.25 \(\upmu\)m SiGe & 5G & 24–34 & 26.4 & 3.1–3.5 & 134 \\
[35] & 0.4 \(\upmu\)m CMOS & 5G & 26–33 & 27.1 & 3.3–4.3 & 31.4 \\
[18] & 0.15 \(\upmu\)m InGaAs pHEMT & 5G & 23–28 & 30.8 & 1.7 & 43 \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._
## 2 Methodology
### Grey Prediction Modelling
#### 2.2.1 Single-Step Grey Prediction  
The process of the single-step grey prediction strategy is shown in Figure 3. The sample period of the power was set as 15 s. The whole power series obtained by sampling cover data of the current moment \(\text{t}_{\text{m}}\) and the three previous moments (\(\text{t}_{\text{m}-1}\), \(\text{t}_{\text{m}-2}\), \(\text{t}_{\text{m}-3}\)). Based on a prediction step size of 1, which is called single-step prediction, the prediction of the power demand at a \(\text{t}_{\text{m}+1}\) moment can be obtained.