esa-sceva/satcom-chunk-collection huggingface dataset:
# An Evolutionary Neural Network Approach for Slopes Stability Assessment
## 4 Results and Discussion
### Model Interpretation  
As important as the performance of a model is, its interpretability is also highly important, particularly when it is based on complex algorithms, such as ANNs. Due to their mathematical complexity, these models are typically difficult to understand and usually referred to as \"black boxes\". Therefore, it is essential to \"open\" such models in order to comprehend what they have learned. In this study, a GSA methodology [67] was used to identify the most important parameters (1-D SA, input importance bar plot).  
Figure 9 shows the relative importance of the twenty more relevant variables according to the best \"updated\" models, as described above.  
Figure 8: Best performance of ANN models (“updated” models) in EHC prediction of rock and soil cuttings, slopes, and embankments according to a nominal classification strategy and following a SMOTE approach.  
Comparing this ranking with the \"old\" models reported on [30,31], it can be observed that both rankings share around half of the more relevant features. The exceptions are the rock cuttings, where only 6 of the 20 more relevant variables were also identified as such in the \"updated\" models.  
Focusing only on the 20 more relevant variables according to the \"updated\" models for each of the slope types, it can be observed that any variable has an individual influence  
Figure 9: Relative importance bar plot for each variable according to the best ANN model (“updated” models) following a nominal classification strategy in predicting the slope stability of rock and soil cuttings and embankments.  
higher than 10.5 %. Concerning the rock cuttings, the variables \"RS Length\" and \"RS Actual Angle\" present a relative importance of 10.5%. In the case of soil cuttings, the variable \"Max Height\" stands out from the others, with an influence of 8.9%. Relating to embankments, the variable \"Validate Track Movement\" presents a relative influence of 10.4%, which is closely followed by \"End Height\", with 8.1%. Moreover, it can also be observed that at least one variable related to the slope angle or slope height appears at the top of the ranking.  
Overall, these rankings follow what is known regarding the main factors relating to slope stability [2; 15; 20; 21; 27]. Several empirical studies have shown that a high slope and its angle are among the key factors that mostly contribute to the stability of a given slope [1; 14; 22; 28].
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 2 Error Sources in PPP  
For PPP to attain centimeter-level precision, it is imperative to eliminate or significantly mitigate range errors. Many of these errors, such as the Sagnac effect (attributable to Earth's rotation), relativistic clock effects, satellite orbit and clock errors, and atmospheric delays, are also prevalent in Standard Point Positioning (SPP), leading to accuracies on the order of several meters [22]. The correction models employed for mitigating the Sagnac effect and relativistic clock errors in SPP equally apply to PPP; therefore, they are not elaborated on in this discussion.  
Conversely, the broadcast ephemeris data employed in SPP to calculate the atmospheric delays and the errors in satellite position and clock are insufficient for reaching the targeted PPP accuracy. For instance, the GPS Klobuchar's ionospheric model can correct around 50% rms of the ionospheric error [23]. Other broadcast ionospheric models also provide limited improvement to the positioning accuracy [24]. Therefore, more precise corrections and refined models are required in PPP. Furthermore, accounting for additional error sources, including antenna-induced discrepancies and site displacement errors, becomes crucial as we venture into centimeter-level precision.
## 3 Results and Discussion
### Greenness, Blueness, and Whiteness Evaluation
#### 3.1.1 AGREEprep Assessment
However, criterion 8 has a higher weight in the AGREprep metric, which causes a difference in the evaluation of both methods. The rating of both MEPS techniques is not the highest, influenced by the fact that solvents are used for extraction (low criterion 2), and it additionally reduces the criteria related to waste generation and safety for the operator (criteria 4 and 10). Dispersive micro- solid-phase extraction (DmSPE) is a solvent-free method, with a positive impact on criterion 2, which received the green status. However, the total score for this procedure is not high, and amounted to 0.47. The derivatization step of analytes reduces the overall evaluation of the method. After extraction, the analytes, together with the sorption bed, are placed in the injection port, where they are derivatized and then thermally desorbed. The use of a derivatization reagent (BSTFA) extends the sample preparation time (criterion 6--sample throughput) and reduces criteria 2 and 10 (use safer reagents and operator safety). Only two microextraction methods achieved the green status, with scores of 0.6 and 0.61--SDME and DI-SPME, respectively. The SDME method obtained such a high rating compared to the previously discussed methods thanks to the use of a very small amount of solvent (3 \(\upmu\)L of toluene) for extraction, which is injected into the injection port after extraction. This affects two green criteria--criterion 2 (use safer solvents) and criterion 4 (minimize waste). The favorable final assessment was also influenced by the use of only 2 mL of sample for analysis (criterion 5) and the consumption of a small amount of energy (criterion 8) due to magnetic stirring for 20 min. Compared to SDME, criterion 8 in the SPME method has a lower score due to the higher energy consumption during the thermal desorption step and the long operating time of the magnetic stirrer during the sorption step (45 min). However, because it is a solvent-free, waste-free method and no derivatization of analytes was used, three criteria obtained a green rating, with a result of 1 (criteria 2, 4, and 10--use safer reagents, minimize waste and operator safety), which resulted in the highest score (0.61) for the SPME method in terms of greenness.
## 2 Sound Sampling and Data Transmission
### Node Implementations
#### 2.2.2 Relay Nodes  
Relay nodes, built on ESP32 devices, are responsible for establishing and maintaining a stable Wi-Fi Mesh network, ensuring low-latency communication within the system [27]. An optimized Base64 algorithm is employed on the ESP32 to improve the efficiency of data encoding and transmission, resulting in enhanced system stability and the elimination of packet loss during laboratory tests compared to the original HWASN design [13]. The relay node prototype (Figure 3) is powered by two 2000 mAh, 3.7V batteries. It includes a step-down buck converter, an ESP32 module, and an external antenna to extend the communication range and improve signal quality. According to the ESP32 [28] and converter [29] datasheets, the ESP32's power consumption is approximately 1.74W during active Wi-Fi and BLE communication. This gives the relay node an estimated battery life of about 8.5 h under continuous operation, sufficient for short-term real-world experiments conducted in this study.
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 3 Proposed Method
### CNN Training Experiments
#### 3.4.2 Model Architecture  
Three CNN architectures were trained based on: InceptionV3, VGG16 and ResNet50V2. By means of transfer learning, the feature extraction weights of each CNN were obtained. Next, the binary classification architecture (not drowsy and drowsy) was designed by flattening the transfer learning output, followed by a 30% dropout with a dense hidden layer of 1000 neurons with ReLU activation and a 30% dropout with a dense layer of 2 outputs with SoftMax activation. The classification process was the same for all 3 CNNs. The proposed architecture is shown in Figure 5.  
The parameters used in the training are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{2}{c}{**Classes**} \\ \cline{2-3}  & **Drowsy** & **Not Drowsy** \\ \hline Training set & 2380 & 2380 \\ Validation set & 510 & 510 \\ Test set & 510 & 510 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset distribution.  
Figure 5: Architecture proposed with transfer learning.  
An example (arbitrarily chosen) of the resulting training plots for each architecture is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Value** \\ \hline Optimizer & ADAM \\ \(\beta_{1}\) & 0.001 \\ \(\beta_{2}\) & 0.9 \\ Learning rate & 0.999 \\ Epochs & 30 \\ Batch size & 32 \\ Number of experiments & 10 for each CNN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training parameters.  
Figure 6: Graphs resulting from the CNNs. (**a**) InceptionV3 accuracy and loss. (**b**) VGG16 accuracy and loss. (**c**) ResNet50V2 accuracy and loss.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## 4 Experiments and Analysis  
### Experimental Setup  
To manage the exponentially expanding number of data traffic requests, cell systems advance into heterogeneous networks (HetNets), where a substantial number of small cells (i.e., microcells, picocells, and femtocells) with broadly different transmit powers levels and physical sizes are randomly conveyed inside the coverage area of the macrocell [22]. The promising nature of HetNets has been broadly credited. Notwithstanding, using HetNets involves various difficulties, for example, mobility management.  
The theoretical model created in the previous section is assessed here. The proposed scheme was executed in Network Simulator 3 (NS3). In the scenario, we created 30 mobile UE units, with only 21 UE units moving. We configured two different technologies, the LTE and WiFi network, by designing one EnbNode for full coverage, and six overlapping APs, as presented in Figure 6. For PMIPv6 protocol we used one LMA and three MAGs, where the UE moved between different MAGs.  
Every UE unit is equipped with two interfaces enabling it to utilize the network appropriate to their needs. We assume that the UE is moving using the random walk scheme to an alternate MAG, and since the interval of medium velocity in urban and rural is (11-25 m/s), we will utilize those values for UE as irregular velocity. An uplink and downlink of user datagram protocol (UDP) traffic is transmitted amongst corresponding node (CN) and UE. The UDP packet size is equal to 100 bytes, while the time between successive packets was set at 1000 s. For the two network technologies, we utilized the Range Propagation Loss Model, with a coverage area of LTE and WiFi that is equal to 500 m and 90 m, respectively, and an overlapping area each two neighboring cells.  
Figure 5: Operation procedure of the proposed IEEE 802.21 MIH functionality assisted by PMIPv6.  
We set up a procedure that enables the mobile UE to increase or decrease its velocity while moving, in order to reproduce the issue in an urban situation. The simulation was repeated 30 times with the same movement features for each of the UE units. To enhance the exactness of the outcome, the presented results are the average value of these simulations results with a 95% confidence interval. The reenactment parameters are as delivered in Table 1.
## 3 Types and Characteristics of Sensor Faults
### Characteristics of Sensor Faults  
High spatial-temporal correlation  
The data collected by the sensor are a time series consisting of an ordered collection of measurement values collected at regular time intervals. Consequently, the influence of historical and future data should be considered during fault diagnosis. Moreover, since a large number of sensors of the same type are typically deployed in a particular area, there is a high spatial correlation between sensors, so other sensors should be consulted during the fault diagnosis process.
2. Frequent abnormal data Fault data include bias fault, drift fault, and so on, mainly caused by sensor faults or damage. The difference between abnormal data and fault data is small, but the duration is usually short. Because agricultural sensors are typically deployed in outdoor harsh environments, sudden changes in the environment can result in random changes, measurement, and recording errors, and thus make sensors produce outliers.
3. Different fault duration Due to extreme weather fluctuations or the influence of other organisms, the sensor may experience a brief period of faults. When such an error occurs, the hardware and software of the sensor are normal, and the error is quickly rectified. Due to the deterioration of sensor hardware and software or insufficient power, fault duration is lengthy, and most faults are permanent. Such faults cannot recover independently; fault diagnosis and recovery are required.
## 3 The Security Mechanism  
The core work of the mechanism is publishing and verifying the node positions. The public key of the location-based key (LBK) is composed of the identity and the location of a node. The private key is generated with identity-based cryptography by the trusted key generation entity, e.g., the sink node, of the network. The effective range of a location-based key is a circular area centered at the location included in LBK with a radius of \(\Delta\). The circular area is called a moving security area (MSA). The location-based key is valid if a node is within its own MSA. In this situation, mutual authentications and key negotiations in nodes can be ensured. Nodes apply for updates to their location-based keys with the sink node when they move out of their own MSAs. The sink node needs to verify the validity of the requests.  
The proposed scheme consists of local detection (LD) and overall detection (OD). LD is a detection algorithm which is used within MSA. In contrast, OD is used out of MSA. LD includes level 1 detection (LD-1) and level 2 detection (LD-2). LD-1 is mainly used in nodes' one-hop neighbors and LD-2 is used in nodes' two-hop neighbors.
## 2 Theoretical Background
### Smart Gates  
Smart gates at seaports refer to integrated gate systems that use various technologies, including sensors, cameras, biometrics, automatic identification, data analytics, and information systems, to automate and optimize entry and exit procedures [12; 13; 14; 39]. Some of the most important features of port smart gates are detailed below:  
* Enhanced security: Smart gates significantly enhance security measures in maritime ports by utilizing advanced identification and verification techniques. Biometric systems, such as fingerprints, ensure that only authorized individuals have access to the terminal, mitigating the risk of unauthorized entry and potential security threats [39; 22; 13].
* Improved efficiency and productivity: Smart gates streamline the entry and exit processes, reducing congestion and minimizing waiting times for trucks and cargo. Through automated systems and information systems, truck drivers can preregister their information, allowing for faster verification and seamless entry into the port. This optimized flow of vehicles and cargo results in improved operational efficiency, enhancing the overall productivity of the port [22; 40; 41].
* Accurate data collection and analysis: Smart gates generate a wealth of data that can be collected, analyzed, and utilized for operational optimization and decision-making. By capturing information such as entry and exit times, vehicle details, and cargo manifests, ports can gain valuable insights into traffic patterns, resource allocation, and demand forecasting. This data-driven approach enables port authorities to make informed decisions, allocate resources efficiently, and proactively address potential bottlenecks or capacity constraints [14; 22].
* Integration with port systems: Smart gates can be integrated into various port management systems, enabling real-time information exchange and process automation. This integration improves coordination between different stakeholders, facilitating smoother operations, accurate documentation, and faster cargo clearance processes, optimizing traffic across different transport modes [22; 40]. Smart gates have become important components of modern maritime ports, revolutionizing the way in which the process of container pick-up from trucks is conducted. With their ability to enhance security, optimize operations, and enable data-driven decision-making, smart gates play a vital role in improving efficiency, accuracy, and overall port performance [14]. As ports continue to evolve and embrace digital transformation, investing in technologies is crucial for staying competitive in the global market [16]. The emissions of pollutants from the trucks can be drastically decreased with the use of smart gates due to the organized and efficient way that the trucks can operate [13; 41; 42]. The focal point of numerous works in the literature revolves around the advantageous efficiency that terminals can bring, often neglecting to recognize the repercussions experienced by the stakeholders influenced by these systems [21].
## 3 Results
### Stability and Reusability of the Prepared Materials  
The reusability of the prepared materials is investigated and evaluated. The AC-based adsorbent is tested for various adsorption cycles, and the results are displayed in Figure 8.  
From the recyclability test, it was established that the system could work with a removal efficiency close to 50% for the studied molecules (AT, 46%; PR, 58%) for a minimum of four cycles. After each adsorption cycle, the adsorbent AC-OH is used for the desorption system, in which the leaching solvent is methanol. The histogram of the removal of AT and PR after each cycle is represented in Figure 8. As seen, for the PR removal, in the first cycle, percent recovery rates of 92% were reached in 2 h, while the second cycle shifted to 87%, which can be considered appreciable results for the recycled adsorbent. The same trend was observed for the removal of AT, which presents a slight decrease from 70% to 65%. Importantly, the developed materials had good recyclability, reaching 52% of PR removal  
Figure 7: Removal of AT (**a**) with and (**b**) without light using AC, TiO\({}_{2}\), AC-OH, AC-OH/TiO\({}_{2}\), and AC-TiO\({}_{2}\) catalysts.  
over the fourth cycle. Accordingly, the obtained results could be considered a suitable and stable adsorbent for removing pharmaceutical products.  
The adsorption capacities of the pharmaceutical molecules were compared with data already published using other adsorbents. Table 1 summarizes the maximum adsorption capacity over various adsorbents for treating AT and PR. Comparatively, it was found that AC-OH showed a better adsorption capacity for AT (288 mg/g) and PR (339 mg/g) compared to the other adsorbents, such as granular activated carbon, hematite nanoparticles, and activated carbon fiber. Interestingly, this finding can open a new prospect to evaluate the prepared materials for other toxic molecules on a large scale, like the industrial scale.  
In addition, it will be important to evaluate the efficiency of AC-TiO\({}_{2}\) on the photodegradation uptake. It was clear that AC-TiO\({}_{2}\) performs well on the photodegradation of AT and PR in liquid solution. Table 2 reports the photocatalytic degradation capacity using AC-TiO\({}_{2}\) compared to other commercial photocatalysts reported in the literature. However, the photocatalytic degradation of pollutants by TiO\({}_{2}\) has been extensively studied, while there are few reports of utilizing AC-TiO\({}_{2}\) to degrade atenol and propranolol (Table 2). It can be observed that AC-TiO\({}_{2}\) is an efficient candidate to eliminate AT and PR, as compared to the other catalysts. It is interesting to note that AC-TiO\({}_{2}\) is efficient in treating concentrated solutions of AT and PR in a short time compared to data already published in the literature. It should also be reminded that AC-TiO\({}_{2}\) is inexpensive as it is prepared from a lignocellulosic agro-waste.
# Amplitude Distribution of Partial Discharge Signals on Tunnel-Installed High-Voltage Cables
## 6 Conclusions  
Compared with the previous research on cross-boned systems, we used a more accurate expression, Tylavsky's formulas, for tunnel modeling. Therefore, a simplified characteristic admittance model has  
Figure 10: On-site partial discharge (PD) pulses in cables and their amplitude distributions. (**a**) PD pulses displayed on the oscilloscope. (**b**) Amplitude distributions of the detected PD pulses.  
been proposed for the first time. This model makes it possible to theoretically derive the amplitude distribution formula of the PD pulse current on the cross-bonding wires. Furthermore, we have studied the environmental parameters that affect the amplitude distribution of PD pulses, which is not considered in the previous research. The results are verified both by laboratorial experiments and on-site detection.  
The amplitude distribution of the PD current on the cross-bonding wires is deduced using the characteristic admittance model. The results show that the polarities of the current signals at the adjacent cross-bonding joints are the same, and the proportional relationship of their amplitudes is only determined by the characteristic admittance ratio \(\alpha\). The laying parameters of the three-phase cable system will affect the amplitude distribution of the PD pulse current at the adjacent cross-bonding joints. Through the model analysis and experimental study, it is found that the amplitude distribution is mainly affected by the cable center spacing and the distance from the cable to the inner wall of the tunnel. On the other hand, the effect of the tunnel radius and earth resistivity is limited. The smaller the cable spacing, the closer the cable is to the inner wall of the tunnel, the greater the difference in the amplitudes of the signals on the cross-bonding wires.  
Data curation, X.C.; Formal analysis, Y.Q.; Funding acquisition, X.C.; Investigation, G.S.; Methodology, Y.Z.; Project administration, X.J.; Resources, Y.Z.; Software, X.C.; Supervision, Y.Q., H.W., G.S. and X.J.; Validation, Y.Q.; Visualization, H.W.; Writing--original draft, Y.Q.; Writing--review & editing, X.C.  
This research was funded by the Project supported by Science and Technology Project of State Grid Zhejiang Electric Power Co., Ltd. (5211 DS180035).  
The authors declare no conflict of interest.