esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### E-Tongue Analysis  
E-tongues turn electrical signals into relish signals to reflect the taste information of food. They have a small threshold of sensation, and the subjectivity of sensory evaluation can be excluded [41]. As shown in Figure 8A, the cumulative contribution rates of PC1 (74.9%) and PC2 (19.6%) were 94.5%, indicating that most taste information was reflected. Samples L1 and L2 were close to each other, indicating similar taste characteristics while samples L3 and L4 were far from each other, indicating different taste characteristics. Both samples L3 and L4 were far from samples L1 and L2, indicating that the chicken taste had changed significantly during braising.  
A radar graph was also used to illustrate the changes of various tastes during processing (Figure 8B). There were significant differences (_p_ < 0.05) among the samples in terms of saltiness, astringency and richness. However, no significant distinction (_p_ > 0.05) was observed for bitterness, umami, bitter aftertaste and astringency aftertaste. Compared to samples L1 and L2, the saltiness, astringency and richness in samples L3 and L4 were higher (especially saltiness), and increased during the braising process. From Figure 8C, we can see that the richness and saltiness sensors contributed more to samples L3 and L4. Long braising time was beneficial to salt penetration and umami release, but could lead to bitterness and astringency. The value of astringency in this study was less than minus 2, i.e., lower than that of the reference solution, and as such, had little effect on the taste. However, we still need to pay attention to the possible adverse effects of braising for a long time on taste, and apply appropriate braising times in the preparation of Daokou braised chicken.  
Figure 8: E-tongue analysis. (**A**) PCA plot of the samples at different processing stages. (**B**) Radar graph of the samples at different processing stages. (**C**) PCA loading plot of the samples at different processing stages.
## 4 Bending Effect on Wearable Antennas
### Primary Planes  
Bending an antenna in the E- and H-planes changes electric and magnetic fields. Bends or distortions in the E- and H-planes can impact the antenna's signal output. Authors tested antennas with 45, 35, 55, and 15 mm radii. The simulation and understanding of antenna performance with varied bending degrees were achievable. Bending an antenna alters its radiation bandwidth and return loss, allowing for the study of frequency effects. It indicates the antenna's versatility and longevity. The durability and flexibility of antennas were strictly tested through rigorous test protocols. One of the crucial design aspects is that the phase difference had to be 90 degrees between synchronized elements of the antenna. Bending affects performance and impedance matching, as return loss models describe the degradation in signal reflection under various bending conditions.  
These challenges allow the development of conformal and reliable antenna designs suitable for real-world applications. As shown in Figure 7, the antennas were bent along both principal planes with bending radii of 45 mm, 35 mm, 25 mm, and 15 mm. The results provide an understanding of the mechanical flexibility of the antennas and the stability of their performance. These tests give a way to optimize the function of antennas under practical usage scenarios [11].
## 4 IoT for Geospatial Research
### IoT Data Transmission Standards  
From the IoT projects where mobile phones were employed, Wi-Fi [13; 51] and/or telecommunication protocols were used, such as 3G/4G [19] and GPRS/UMTS [66; 20], or even SMS/MMS messages [61; 65]. Bluetooth was also used, together with mobile phones [9; 25; 57].  
Closed systems, probably wired ones for reasons of security, were used in transportation systems [12; 56] and smart traffic lights [55]. On the other hand, custom-made IoT sensors made use of the IEEE802.11 wireless transmission standard, such as ultrasonic water sensors [21] and vehicular sensors [54], or a combination of GPRS/Wi-Fi at an air quality sensory system [46]. Moreover, a low power wide area network was observed in [59], for recording animal activity and in [63], for video transmission from IR cameras. In particular for wildlife animal monitoring, a combination of Bluetooth low energy (BLE) and LoRa was proposed. For landslide displacements monitoring [26], geocube sensors were selected, measuring meteorological and hydrological parameters based on a 2.4 GHz wireless protocol. The ZigBee protocol was used in the wireless sensor network of [66]. Satellite communications were involved in deep-ocean tsunami measuring [27] and asset management [58].  
Some papers did not involve any wired/wireless transmissions, recording the sensory measurements at the measuring spot, and this was the case in the majority of the papers dealing with surface analysis and geostatistics (e.g., [68; 69; 70; 71]).
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Topology-Based Simulations in DEN2 DE  
Once the topologies were generated, they were imported into the DEN2 DE framework, and the algorithm executed simulations to model the energy distribution across the nodes. These simulations incorporated the real load profiles that were extracted and processed in Section 3.3 allowing for the simulation of power distribution processes in realistic SG environments.  
Considering that the objective was to discover energy transactions between nodes that exceeded their link's capacity, the scenario of lossy-constrained link capacity was defined. Also, as specified for BRITE, five different seed files were applied to DEN2 DE to obtain different simulations from the same input topology. Based on the configured parameters and the 8760 real-data rows available (representing one year of hourly data), the algorithm could potentially run 47,304,000 unique simulations. As it was inefficient and impractical to execute all of them, it was essential to decide which tests to conduct in DEN2 DE, and 12 specific time points were chosen, each representing a specific hour of a day for each month. To simplify the process, since the sample time frame started on 28 November 2010, the 28 th day of each month was selected. The time chosen for the extraction of load profiles was 11:00 a.m., as this was when average energy production tended to be highest. This selection strategy increased the likelihood of encountering energy exchanges that exceeded the link capacity, leading to a higher number of errors in the load distribution process. This was important for effectively training the ML and DL models.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 1 Introduction
### Our Contributions  
(1) By introducing the method of deep learning, the precise segmentation of the aquaculture cages in a specific aquaculture sea area can be achieved, so as to realize the efficient statistics of the cage culture density and reduce the cost of manual statistics.  
(2) To address the problem of lack of marculture cage dataset, we construct a remote sensing cage image dataset and expand the data of the training set to effectively improve the training effect of the model.  
(3) An ensemble learning strategy is used to fuse the feature extraction capabilities of different deep learning network frameworks on different pixel size datasets, thereby improving the generalization capability of the model and the overall prediction accuracy.
# Hybrid Self-Protected Fiber-FSO WDM-PON System with Fiber Breakage Prevention
## 3 Experiment  
First, we will confirm the bit error rate (BER) performance of fiber and FSO WDM signals. In the measurement, 24.3 Gbit/s on-off keying (OOK) modulation with a pattern length of \(2^{15}-1\) is applied on 40 GHz MZM to produce the symmetric downstream and upstream signals in the presented fiber-FSO PON system. Here, a tunable laser (TL) is applied to represent each LD for the experiment in a convenient manner. The TL has 13 dBm output power and >35 dB optical signal-to-noise ratio (OSNR). The PC is adjusted to tune the properly polarization status in order to achieve the optimal output power.  
Four wavelengths of 1528.77 (\(\lambda_{\text{A1}}\)), 1529.55 (\(\lambda_{\text{A2}}\)), 1530.33 (\(\lambda_{\text{A3}}\)) and 1531.12 nm (\(\lambda_{\text{A4}}\)) are used to act as the downstream (\(\lambda_{\text{A1}}\) and \(\lambda_{\text{A2}}\)) and upstream baseband traffics (\(\lambda_{\text{A3}}\) and \(\lambda_{\text{A4}}\)) in A-band range, respectively. Identically, the other four wavelengths of 1531.90 (\(\lambda_{\text{B1}}\)), 1532.68 (\(\lambda_{\text{B2}}\)), 1533.47 (\(\lambda_{\text{B3}}\)) and 1534.25 nm (\(\lambda_{\text{B4}}\)) are served as the downstream (\(\lambda_{\text{B1}}\) and \(\lambda_{\text{B2}}\)) and upstream FSO signals (\(\lambda_{\text{B3}}\) and \(\lambda_{\text{B4}}\)) in the B-band window, respectively. Here, the channel spacing of downstream and upstream traffics is 0.8 nm (100 GHz) in the measurement. Due to the wide channel spacing, the inter-channel crosstalk can be ignored. Owing to the 24 and 25 km single-mode fiber (SMF) link, the 24.3 Gbit/s OOK modulation signal is enough to resist fiber dispersion for data transmission. The amplified spontaneous emission (ASE) noise can also filter the AWG. The whole output powers of downstream wavelengths are set at 7.5 dBm after leaving the OLT\({}_{\text{A}}\) and OLT\({}_{\text{B}}\), as seen in Figure 1. The type optical carrier power of 7.5 dBm is exploited to avoid the non-linear effect in the experiment. As illustrated in Figure 1a, the 16.8 dB total downstream insertion loss of baseband WDM-PON is induced by an AWG\({}_{\text{A(B)}}\) (6 dB), a CP (3 dB), a WF (1.5 dB), a SW\({}_{\text{A(B)}}\) (1 dB), an OC (0.5 dB) and 24 km fiber length (24 \(\times\) 0.2 = 4.8 dB), respectively. To access the FSO signal, the CM, which has a diameter of 20 mm, divergence angle of 0.016\({}^{\circ}\) and focal length of 37.13 mm, respectively, is applied in the execution. Basically, the link loss between two CM is 2.8 dB, when the optimal alignment is achieved. So, the total insertion loss of the FSO system will be 19.6 dB, including the additional FSO link loss. In this setup, in addition to transmitting wireless FSO signals, the CM can also be used to collect the FSO signal. The relevant experimental parameters are listed in Table 1.  
Figure 3: Assume the fiber breakage occurs at the “B1” and “B2” point in the proposed fiber-FSO WDM-PON system, respectively.
## 3 Results and Discussions  
### Yeast Cells Disintegration  
Effectiveness of sonication followed by thermal treatment on yeast cells was quantitively estimated based on conductivity disintegration index Z. The use of ultrasound directly impacted yeast cell disruption and mass transfer between the yeast cells and water.  
The mechanism of cell disruption is based on acoustic power, normally associated with the cavitation phenomena, which has been applied to obtain intracellular proteins from microbial cells [30].  
During sonication treatment performed at a 30% amplitude for 80 min, the ultrasound frequency triggered shear forces which highly disintegrated yeast cell walls, with the conductivity reaching 1.63 \(\pm\) 0.17 (mS/m), approximately four times higher compared to that of the untreated yeast suspension (Table 1).  
A direct correlation between conductivity and the disruption degree of yeast cell damage was reported by [18]. Considering the cumulative effect of sonication with the thermal treatment at 92 \({}^{\circ}\)C for 32 min, when the maximum conductivity reached 1.87 \(\pm\) 0.12 (mS/m), the disintegration index calculated with Equation (1) was 0.84 \(\pm\) 0.02, out of the maximum value of 1. Similar values were reported for spent brewing yeast sonicated at a 50% amplitude for 5 min [19], as well as for dry baker's yeast treated by a high-pressure homogenizer at 800 bar for one or two passes [31]. The estimated disintegration index of sonicated yeast suspension used for casting yeast films indicated a high level of cell wall disruption and intracellular components extraction. Considering the disruption degree of the yeast cells and the high content of mannoproteins present in the external cell wall layer representing approximately 35%-40% cell wall dry matter, along with the \(\beta\)-1,3 and \(\beta\)-1,6-glucan content from the internal yeast wall representing 50%-65% of cell wall dry matter [32], it can be presumed that the obtained film-forming emulsions were based on \(\beta\)-glucan and mannoproteins.
## 3 Integrated Adaptive Security Framework for IoT (IASF-IoT)
### Blockchain-Based Identity and Access Management  
This component comprises a blockchain-based identity and access management layer. It is based on the Heterogeneous Remote Anonymous Authentication Protocol (HRAAP) and uses a Merkle tree structure to efficiently manage device identities and permissions. The Merkle tree's hierarchical structure enables the quick verification of IoT devices' identities and permissions without traversing the entire blockchain, reducing computational overhead and latency. The root hash of the Merkle tree is periodically recorded on the blockchain, typically every \"n\" block or time interval, providing an immutable record of the network state. One significant disadvantage is the potential for increased storage requirements, especially in large-scale IoT networks, as it requires storing additional hash values at each tree level. Solutions to address this include pruning [61], sparse Merkle trees [62], and Bloom filters [63].  
The identity management system is designed as a permissioned blockchain environment, suitable for enterprise or industrial IoT deployments. Transactions related to device join, exit, or credential updates are validated through consensus among authorized nodes and appended to the ledger, ensuring traceability and tamper resistance without incurring the latency of public blockchain systems.  
The choice depends on specific IoT network requirements, balancing scalability, security, verification speed, and device resource constraints. For instance, pruning might suit networks with limited storage but less need for historical data, while Bloom filters could  
\begin{table}
\begin{tabular}{c c c} \hline \hline  
benefit networks requiring rapid verification despite a small false positive risk. The Merkle tree is constructed as follows:  
\[H(\text{root})=H(H(H(L1)+H(L2))+H(H(L3)+H(L4))), \tag{2}\]  
where \(H()\) is a cryptographic hash function, and \(L1,L2,L3,L4\) are leaf nodes representing device identities.
# Inspection of Coated Hydrogen Transportation Pipelines
## 3 Pipeline Inspection  
### An Overview of Pipeline Integrity Management  
Pipeline inspection is one aspect of overall efforts to ensure the pipeline is safe. Pipeline integrity management is proposed to consist of three basic steps [11]. These include:  
* _\"Defect detection and identification\"_: This is done through \"inspection, monitoring, testing and analysis techniques.\"
* _\"Defect growth prediction\"_: The data collected is used alongside damage prediction models for this.
* _\"Risk-based management\"_: The origins of risks are studied, the likelihood of failure is estimated and the consequences of failure are examined.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pipeline Material** & **Material** & **Mechanical** & **Blended Pipeline** & **Processing** & **Environmental** & **Testing \&** **Conditions** & **Analysis** \\ \hline X52 Steel & - & [24; 25] & - & - & - & - \\ X56 Steel & - & [26] & - & - & - & - \\ X60 Steel & - & - & - & [27; 28] & [29] & - \\ X65 Steel & - & [30] & - & - & [31; 32] & - \\ X70 Steel & [33; 34; 35] & [25; 36; 37; 38; 39; 40] & [37; 41; 42] & [43] & [36; 44; 45] & [46; 47] \\ X80 Steel & [48; 49] & [50; 51; 52] & - & [49; 53; 54; 55] & [56; 57; 58; 59; 60; 61; 62; 63; 64] & [65] \\ X90 Steel & - & [66] & - & - & - & - \\ X100 Steel & - & - & - & - & [67] & - \\ Super Duplex Steel & - & - & - & - & [68; 69] & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: An overview of findings in the existing literature about the properties of each pipeline steel grade.  
There have also been alternative integrity management processes proposed [8]. The process of integrity management is also said to be a cyclical one, consisting of \"Integrity Assessment\" via inspection, \"Managing Discovery\" of information received from the inspection, \"Remediation\" which involves repairs and \"Documentation\" where data is used to assess performance, before moving on to \"Integrity Assessment\" again [8].  
It appears that ideas in the steps outlined by both processes have significant overlap. Therefore, in further discussions, the more succinct terminology proposed by [PERSON] and [PERSON] [11] shall be used. Since this review focuses on the inspection of pipelines, we shall limit our attention only to \"Defect detection and identification\" with some discussion of \"Defect growth prediction\" as it tends to depend on data collected from inspection.
## 2 Silymarin Extraction Methods: From Conventional to Novel
### Microwave-Assisted Extraction  
Microwave-Assisted Extraction (MAE) utilises microwave irradiation to enhance the extraction process. Microwaves generate heat within the plant material, leading to the disruption of cell walls and improved extraction efficiency. This method is known for its speed and reduced solvent consumption. Compared with conventional methods, MAE offers a number of advantages, including a shorter processing time, the use of reduced quantities of solvents, an increased extraction rate, and superior product quality at a lower cost. The mechanism of MAE is based on the impact of microwaves on molecules by ionic conduction and dipole rotation inside target materials [38]. Ionic conduction is the electrophoretic migration of ions caused by an electromagnetic field applied. Furthermore, the solution is subjected to heating as a consequence of the resistance friction experienced by the solution in relation to the ion flow. Dipole rotation leads to the realignment of polar molecules under an applied electromagnetic field. At the commercial frequency of 2.45 GHz, the dipoles undergo 4.9 \(\times\) 10\({}^{9}\) random alignments per second, which results in rapid heating. Thus, the microwaves heat the solvent or solvent mixture directly, and interact directly with the free water molecules present inside the materials, resulting in a rapid rise of pressure within cells and a pressure-driven enhanced mass transfer of target compounds out of the source material, which causes rupture of the plant tissue and release of the active compounds into the organic solvent [38; 39].  
[PERSON] et al. [26] conducted research on the extraction of silymarin using MAE, and highlighted the impact of four independent variables--namely extraction time, temperature, ethanol concentration, and solid-liquid ratio--on the yield of silymarin. The study revealed that the independent variables had significant effects on and interactions with silymarin yield, with the exception of extraction time. This was determined through analysis of the data. The optimal extraction parameters were identified as follows: 60 min duration, 112 \({}^{\circ}\)C temperature, 81.5% ethanol concentration (\(\
u/\
u\)), and a solid-liquid ratio of 1:38 (g/mL). Under these conditions, the average experimental silymarin yield was 56.67 \(\pm\) 1.36 mg/g, closely aligned with the predicted value of 57.40 mg/g [26].
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results
### Future of AV Deployment in Rural Areas
#### 3.3.3 Necessary Infrastructure Modifications
Road curves also impacted data rates, with crest curves reducing them by 4.2% and sag curves increasing them by 7%. Minor infrastructure changes, like lane additions, raised data rates by 12% to 16%. These insights offer guidance for optimizing AV navigation and rural infrastructure design. [PERSON] et al. (2023) [42] addressed safety and control challenges for AVs and CAVs on rural roads, particularly on horizontal curves, where inconsistent road designs often cause crashes due to speed and steering errors. Using a custom microscopic traffic simulation, we tested four configurations: human-driven vehicles, non-connected AVs, AVs following human-driven vehicles, and CAVs receiving road data via inter-vehicle communication. Safety metrics, including jerk, time-to-line crossing, and cross-track error, were analyzed. Results showed CAVs improved longitudinal safety by anticipating road conditions and reducing abrupt deceleration. However, lateral safety improvements were limited due to fixed look-ahead distances. The study recommended adaptive lateral control models to enhance CAV performance on rural curves.
## 5 Discussion  
The experimental results in Table 2 show that, among the lightweight models, RPS-YOLO achieves the best detection accuracy. Compared to YOLOv6s, RPS-YOLO provides an 8.9% higher mAP0.5 while maintaining fewer parameters and computational costs. Additionally, compared to YOLOv8s, RPS-YOLO improves mAP0.5 by 8.2% on the VisDrone-DET2021 dataset, with an increase in the parameter count of less than 20%, which is acceptable.  
The experimental results in Table 3 indicate that RPS-YOLO demonstrates a clear advantage over other lightweight networks in the UAVDT dataset, particularly in terms of accuracy, achieving an mAP0.5 of 35.3%. It outperforms other networks, showcasing its strong capability in handling multi-scale object detection. Compared to YOLOv3-tiny and PP-PicoDet-L, RPS-YOLO provides higher object detection accuracy, especially excelling in complex scenes and small object detection, making it highly effective in tasks requiring high-precision detection.  
We further assessed the impact of each component in the RPS-YOLO model through ablation experiments conducted on the VisDrone2021-DET validation dataset. Table 4  
Figure 13: YOLOv8s inference results on the VisDrone-DET2021 dataset (top) and RPS-YOLO inference result (bottom) for (**a**) a night scene and (**b**) a densely populated scene.  
shows the results of these experiments. YOLOv8s was chosen as the baseline model, and the input image size was fixed at 640 \(\times\) 640. A checkmark (\(\surd\)) denotes the application of each respective enhancement strategy.  
As shown in Table 4, it is evident that each enhancement strategy applied to the baseline model leads to varying degrees of improvement in detection performance. By integrating the RFP structure into the baseline model, the mAP0.5 increased by 5.9%. The analyzed dataset contains a significant number of small targets, so prioritizing shallow features during the initial \"processing\" phase of the image and incorporating scale fusion of shallow feature information helps to effectively reduce the false negative rate for small targets.  
The SCC2f module was incorporated into the backbone network, substituting the original C2f module. Thanks to its efficient attention mechanism, the SCC2f module improved the focus on smaller targets within the feature maps, resulting in a 1.9% boost in mAP0.5. Additionally, the introduction of LIOU into the regression loss function for bounding boxes addressed the angle loss issue during bounding box regression and decoupled the aspect ratio factor. This improvement boosted the localization accuracy of the bounding boxes, resulting in a 0.4% increase in mAP0.5.  
The improved model achieved an 8.2% increase in mAP0.5 on the VisDrone-DET2021 dataset, with most detection metrics showing significant improvement. The use of the recursive feature pyramid structure makes the model more complex, but this is acceptable in comparison to the improved detection accuracy.