esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Optimization and Design of Railway Culvert  
Two culvert models are proposed: straight and protruding, as shown in Figure 11. They are designed to improve the structure of the original culvert and minimize sand accumulation. The straight culvert does not have wing walls or end faces. The protruding culvert includes a section that deflects the sand to the outside of the culvert. The same numerical simulation settings were used to evaluate the proposed culverts' performances in reducing sand deposition.  
Figure 12 shows the sand volume fraction of the two culverts (bottom view) at an inflow velocity of 15 m/s. The sand volume fraction of the new culverts was significantly lower than that of the original culvert (Figure 10b). The main reason is that the new culverts do not have wing walls, and the sectional area of the culvert inlet is smaller, reducing the possibility of sand sedimentation in the culvert. In addition, the sand volume fraction at the inlet was low for the new culverts, and there was almost no sand sedimentation at other places inside the culvert. The sand volume fraction was the lower inside the straight culvert. As shown in Figure 12a, the sand inside the straight culvert accumulated close to the inner wall of the culvert, and the sand sedimentation on the leeward side was far away from the slope toe. Figure 12b shows that the protruding culvert did to deflect the sand. Instead, the sand volume fraction was high at the culvert inlet. The likely reason is that thelonger length of the protruding culvert changes the flow field distribution and reduces the airflow velocity at the culvert inlet, increasing the sand deposition level.  
Figure 13 shows the distribution of the sand volume fraction on the central axis of the two proposed culvert models. The sand volume fraction and sedimentation level of the new culverts were substantially lower than that of the original culvert (Figure 9c). Moreover, the sand volume fraction at the inlet and inside the culvert was lower in the straight culvert than that of the protruding culvert.  
Figure 11: Schematic diagram of two culvert models of reducing sand deposition (Unit: m): (a) straight culvert and (b) protruding culvert.  
Figure 12: Bottom view of sand volume fraction (Unit: %): (a) straight culvert and (b) protruding culvert.  
Figure 13: Distribution of sand volume fraction (Unit: %): (a) straight culvert and (b) protruding culvert.  
In summary, the proposed culverts significantly reduced the sand volume fraction inside the culvert, demonstrating the impacts of the wing walls on the sand deposition in the culvert. The sand volume fraction was lower in the straight culvert than in the protruding culvert, indicating that these designs could increase the maintenance cycle and reduce costs. In addition, the straight culvert has a simple structure that is convenient for construction, reducing production costs. Therefore, using straight culvert may reduce problems related to sand accumulation in railway culverts.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.3 VATEX Dataset: Method Performance Overview
This variability may be attributed to differences in the attention mechanisms employed, the overall architecture design, or the specific features used for caption generation.
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 4 Proposed Work
### Simulation and Results
#### 4.4.2 Packet Delivery Ratio  
To calculate the delivery rate for the two protocols OLSR and G-OLSR, we extract the information using the following \"awk\" code:  
Figure 12: Simulation zone ”Gueliz district in Marrakech city of Morocco”.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline
**PC Simulator** & Intel® CoreTM i5 6 th generation 2.4 GHz, 8 GB \\
**Simulator** & NS3 (3.29) \\
**Number of Nodes** & 200, 300, 400 and 500 \\
**Node Speed** & 20 km/h to 60 km/h \\
**Simulation Time** & 200 \\
**Wi-Fi mode** & Ad-hoc \\
**Wi-Fi Rate** & 2 MB \\
**No. of Source/Sink** & 10 \\
**Sent Data Rate** & 2.048 Kbps \\
**Packet Size** & 64 Bytes \\
**Number of packets send** & 4 \\
**Connection Type** & TCP \\
**Node Speed** & 20 to 60 km/h \\
**Pause Time** & 0 \\
**Network size** & Carte of Gueliz Marrakech \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of simulation in NS3.  
The simulation results are shown in Figure 13:  
The simulation results show that our proposal gives the best performance in terms of packet delivery ratio. The PDR of G-OLSR increases more and more as the traffic increases; this is due to the use of the GFA approach, which gives better results compared to the GF in the case of low mobility or when the difference between the speeds of the vehicles is important. Thus, the difference is in the elected vehicles, and the use of the Dijkstra algorithm offers a high rate of convergence.
## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
Hence the two-stage optimization process with NN for query ordering is compared over the conventional methods in terms of performance measures like Latency, Throughput, and Data freshness. Ref. [95] proposed multi-weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA) protocol to increase the energy efficiency during the process of communication in the network. MWCSGA consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In [96], Q-learning-based data-aggregation-aware energy-efficient routing algorithm (Q-DAEER) uses reinforcement learning to maximize the rewards, defined in terms of the efficiency of the sensor type-dependent data aggregation, communication energy and node residual energy, at each sensor node to obtain an optimal path. In Q-DAEER, a data-type-dependent action selection and Q-table updating algorithm is incorporated. Adaptive routing algorithm for in-network aggregation (RINA) is proposed in [97]. RINA employs a reinforcement learning method called Q-learning to build a routing tree based on minimal information such as residual energy, distance between nodes and link strength. In addition, RINA finds the aggregation points in the routing structure to maximize the number of overlapping routes in order to increase the aggregation ratio. A framework for long-term monitoring applications called spatiotemporal approximate DC (STAC) is proposed in [98]. This framework depends on WSNs or IoT and utilizes data prediction, selecting work nodes, and Q-learning. In STAC, the spatio-temporal correlation among nodes is considered. First, data prediction by selecting work nodes in each work cycle is performed. Second, takes advantage of temporal redundancy and employ Q-learning to adjust the sampling interval of nodes. Finally, the BS predicts the whole data with error tolerance.
## 1 Introduction
In particular, models consisting of a non-integer order integrators in series with a resonant system of both non-integer and integer order are considered in this work. To obtain the frequency responses experimentally, the first IPMC link actuator was attached to a couple of gold electrodes and the tip deflection of the third link was measured by means of a laser distance meter. Secondly, a robust controller is designed to control the tip displacement of each link in order to generate a non-reciprocal motion for propulsion. Finally, the deflection of each link is analyzed to determine whether an AEF swimming robot based on IPMC is capable of generating a planar wave motion by distributed actuation. In summary, the two main contributions of this paper are: (1) fabrication of an IPMC-based AEF swimming robot; and (2) design of a controller robust to actuators of different lengths to propel the robot at low Re regimes. Hence, this article provides a different perspective from those presented in the aforementioned works, addressing the design of a small-scale swimming robot whose links are integrated into the same piece of material and can work as actuators or passive flexible links. In our previous works, an N-link AEF swimming robot with rotary joints was considered; the propulsion performance of such a kind of robot was studied (see [28] and references therein). The contents of the paper are organized as follows. Section 2 briefly describes the mathematical background concerning the hydrodynamics of swimming robots at low Re environments, the types of waveforms for propulsion, and IPMC technology. Section 3 focuses on the IPMC-based AEF swimming robot, as well as the setup with which the experiments will be carried out. Section 4 contains the identification process performed to obtain an appropriate model of the robot for control purposes. The control design and simulations to analyze the motion of the swimming robot are approached in Section 5. Finally, Section 6 draws the concluding remarks and perspectives on the future work.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### Covariance Shaping  
The covariance shaping approach can be achieved by using statistical beamforming, which allows users to successfully excite a sufficient fraction of all possible propagation paths between themselves and the BS. We show that our strategy works well for restoring orthogonality between users that are too near to one other. As a result, operations such as pilot decontamination and BS beamforming can benefit from this technique [189; 190; 191]. It entails applying statistical beamforming to each UE ahead of time during the downlink data transmission phase to ensure signal subspace separation between users that would otherwise be substantially overlapping. As a result, covariance shaping may be used for both pilot decontamination and statistical precoding. It offers the distinct advantage of converting the non-Kronecker nature of large channels into a benefit. Covariance shaping has not been utilised in NOMA systems previously and remains an open issue.
## 2 Material and Methods
### Field Test of AgriLogger on a Vineyard  
In order to evaluate the reliability of the system and the quality of data collection under field conditions, two sensor devices were placed manually inside a vineyard at the botanical garden of the agricultural faculty of Portici (Federico II university of Naples). Each sensor collects and stores on its own data-logger, one sample per hour of air temperature and relative humidity. Data collection was made during two different seasons, during the summer 2019 from 12 to 27 August, and during the fall from 1 to 19 October. The purpose was to characterize the microclimates outside and inside the canopy of an agricultural vineyard during these two different seasons (Figure 7). Monitoring microclimatic condition in vineyards is pivotal for an efficient plant protection of diseases such as powdery mildew (caused by _Erysiphe necator_) and downy mildew (caused by _Plasmopara vitiicola_) [21]. The sensors were placed manually due to the easy accessibility of the study site; however, the data were collected using a drone as described previously. The use of a drone to collect data is to demonstrate the reliability of the WSN-UAS combination in the agricultural field, and to show the applicability of these systems to collect data in areas that are hard to reach.  
Figure 6: Schematic illustration of Data Flow from the AgriLogger gateway to the data receiver (computer, smartphone) throughout LTE cellular tower.  
Figure 7: Pictures of AgriLogger in the vineyard: (**A**) inside canopy, (**B**) outside canopy. The pictures were taken in the winter to clarify the position of the sensor inside the canopy without leaf cover.
## 4 The Proposed Approach: DIRECT-NSGA-II  
It is important to evaluate the value of \(\min_{x\in[\lambda^{\text{low}},x^{\text{up}}]}f_{i}(\mathbf{x})\) with high accuracy. For simplicity, we let \(f_{\min,i}(x^{\text{low}},x^{\text{up}})\) denote the calculated value of \(\min_{x\in[x^{\text{low}},x^{\text{up}}]}f(\mathbf{x})\). An unfeasible box-shaped solution space could be wrongly considered to be a feasible one if \(f_{\min,i}(x^{\text{low}},x^{\text{up}})\) is much larger than \(f_{i}^{*}(x^{\text{low}},x^{\text{up}})\), while a feasible box-shaped solution space could be mistaken for an unfeasible one if \(f_{\min,i}(x^{\text{low}},x^{\text{up}})\) is much smaller than \(f_{i}^{*}(x^{\text{low}},x^{\text{up}})\). The DIRECT algorithm emerges as a leading partitioning-based method, skillfully harmonizing local and global search efforts to effectively pinpoint the global minimizer [24; 25; 26; 27]. The core procedure of the DIRECT algorithm entails dividing hyper-rectangles that are potentially optimal and assessing the performance function at their centers without relying on the detailed expression of the function [28; 35; 36; 37]. [PERSON] et al. have demonstrated that the DIRECT algorithm converges to the global minimum value when the function remains continuous in the neighborhood of the global minimum [28].  
In multi-objective optimization, the NSGA-II algorithm is widely recognized as a powerful and well-known method. According to [38], NSGA-II is a highly effective derivative-free algorithm for engineering problems.  
Therefore, in order to solve the Equation (2), we propose a novel approach that combines the DIRECT algorithm and NSGA-II algorithm. Firstly, the DIRECT algorithm evaluates \(\min_{x\in[x^{\text{low}},x^{\text{up}}]}f_{i}(\mathbf{x})\) for cases where \(f_{i}(\mathbf{x})\) is either analytically known or represented by a  
Figure 4: The first three iterations of the DIRECT algorithm applied to [PERSON]’s function. The gray-colored hyper-rectangles are potentially optimal ones. Red points represent the sample points where the performance function is evaluated.  
numerical simulation as a black box. Secondly, the NSGA-II algorithm generates Pareto-optimal solution spaces. This combined approach is referred to as the DIRECT-NSGA-II algorithm.  
This section presents customized features of the DIRECT-NSGA-II algorithm for solving the proposed multi-objective optimization problem. For a comprehensive understanding of NSGA-II, refer to [34].
## 4 A Coal Rock Strength Prediction Model Based on the AdaBoost Ensemble Method
### Model Evaluation Indicators  
To evaluate the performance and effectiveness of the prediction model, this article employs four evaluation metrics: the coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). These metrics serve as the criteria for evaluating model performance. The formulas for calculating these evaluation indicators are as follows:  
The determinable coefficient explains the ratio of the sum of squared deviations of the predicted values to the sum of squared deviations of the observed values, reflecting the degree of fit of the model.  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-\overline{y})^{2}}}{\sum \limits_{i=1}^{n}{(y_{i}-\overline{y})^{2}}} \tag{16}\]  
The average absolute error represents the meaning of the absolute differences between predicted and observed values.  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}{|\hat{y}_{i}-y_{i}|} \tag{17}\]  
The average absolute percentage error represents the mean percentage error between predicted and actual values.  
\[MAPE=\frac{1}{n}\sum\limits_{i=1}^{n}{\left|\frac{\hat{y}_{i}-y_{i}}{\hat{y}_ {i}}\right|} \tag{18}\]  
The root mean square error measures the deviation between predicted values and actual values, and it is sensitive to outliers in the data.  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}}} \tag{19}\]  
In the formula, \(\hat{y}_{i}\) represents the actual compressive strength value of the i-th lithology (where i = 1, 2, 3, \(\ldots\), n); \(y_{i}\) represents the predicted compressive strength value for the i-th lithology (where i = 1, 2, 3, \(\ldots\), n); and n denotes the total number of samples.  
Compared to RMSE, MAE emphasizes the average magnitude of model prediction errors rather than the sum of squared errors, making it more robust in handling outliers.
## 3 Research on Similar Model Experiments for Opening Cuts
### Simulation Design of Opening Cut Support Parameters  
The dimensions of the experimental model simulating the opening cut are 430 \(\times\) 210 mm. Two support cross-sections are to be simulated within the opening cut, with a spacing of 60 mm between them. Transparent epoxy resin-based cementitious material was adopted as the grouting binder for cable and rock bolts in the model. The design of the cross-section support parameters is as follows; for specific support parameter details, refer to Figure 3:  
1. The roof anchor cable support is designed in a one-beam, three-cable configuration. A 30 mm long fine iron wire is inserted into the roof, with a spacing of 144 mm between the three anchor cables.
2. A total of four roof anchor rods are arranged, utilizing 1.5 mm fine iron rods inserted into the roof of the opening cut. Two rods are inserted vertically, while the other two are inserted at a 45\({}^{\circ}\) angle at the shoulder of the opening cut. The spacing between the four anchor rods is 144 mm, with a spacing of 72 mm between the roof anchor cables.
3. Two side anchor rods, each 12.5 mm in length, are inserted into each side of the opening cut, with a spacing of 69 mm between them.
4. The entire roadway is reinforced with steel strips and metal mesh, simulated using iron sheets and fine iron wire mesh, respectively.
5. As the pressure on the model increases, when significant deformation of the opening cut is imminent, two elastic single support pillars composed of ordinary pencils and steel pipes, each 210 mm in length, are installed at the two support cross-sections of the opening cut to simulate the on-site single support pillars. The single support pillars are positioned on either side of the centerline of the opening cut, with a spacing of 215 mm between them.
6. To facilitate the monitoring of the force changes in the anchor rods (cables), five monitoring points are installed at the top of the left cross-section of the opening cut, consisting of two anchor cables, two roof anchor rods, and one side anchor rod. Strain gauge anchor rods are used for these measurements.  
Figure 2: Experimental model of the opening cut: (**a**) schematic design of the cut model; (**b**) the installed model.
# Dispatchable Substation for Operation and Control of Renewable Energy Resources
## 3 Operation of Dispatchable Substation with Renewable Generator Mix  
The primary operation objective of a DSS is to reduce uncertainties from renewable generators for TSOs. Depending on the range of accuracy that the TSO is obligated to the DSS between each dispatch period, the size and operation of the required ESS can be determined. For instance, the New York ISO requires wind turbines to follow dispatch orders within 10% of their forecast, which is 8% for MISO [4]. By smoothing out the generation from renewable generators, the DSS can  
Figure 3: Operation of ESS in a dispatchable substation.  
regulate the voltage fluctuation of interconnected buses and the neighboring transmission lines. It is also the responsibility of the DSS to calculate accurate forecast information and report it to the TSO every few minutes. As mentioned above, the accuracy of the forecast information can be increased with the support of metering and monitoring devices; these can be installed within a substation, thereby minimizing the size of the ESS required for smoothing out the generation.  
The operation and configuration of the DSS can be varied depending on the type of interconnected renewable generators and the quality of the given forecast information. The overall operation and sizing method of the ESS for DSSs are followed for a given set of meteorological data. The solar forecast information and actual measurement data for a year were obtained from Solcast ([[https://solcast.com](https://solcast.com)]([https://solcast.com](https://solcast.com))), which is a global solar forecasting and historical solar irradiance data company. Solcast's database has been recommended for use in solar research applications [25].
# Synchronous Generator Stability Characterization for Gas Power Plants Using Load Rejection Tests
## 2 Theoretical Models  
As the automatic voltage regulator (AVR) response is not fast enough to clearly show the transient dynamics of a synchronous generator (SG) during a fault event, a load rejection test is proposed to model the SG behavior. A load rejection test is a controlled and deliberate test where the SG is subjected to a sudden reduction in load. This helps to simulate a fault condition and provides valuable information about the SG's transient response.  
Understanding the transient dynamics of SGs during fault events is crucial for power grid operators as it allows them to monitor and simulate faults and other contingencies in the power system. This information can be used to improve the overall stability and reliability of the power system. The proposed model in Figure 1 illustrates the SG's behavior during a load rejection test, where the main circuit breaker (CB) is opened once the SG is running at rated speed and power, and the model can also be obtained at reduced power.  
In Figure 1, the models used in simulations and their correlation are plotted. The theoretical model consists of the SG as GENSAL, which produces as outputs the electrical power (\(P_{e}\)) and the rotor speed (\(w_{r}\)), the speed governor as GAST, whose output is mechanical power (\(P_{mcc}\)), and the AVR as SEXS, whose output is the field voltage (\(V_{f}\)). Each of the models is described in more detail below.
# Improvement of Porous GaN-Based UV Photodetector with Graphene Cladding
## 2 Materials and Methods
### UV Photodetector Characterizations  
The morphologies of the UV photodetectors were characterized using a field-emission scanning electron microscope (FEI Nova NanoSEM 450, FEI, Hillsboro, OR, USA). Meanwhile, Raman spectroscopy (Jobin Yvon HR800, HORIBA, Kyoto, Japan) was used to characterize the vibrational modes of graphene. The incident source generated by Ar\({}^{+}\) laser with a wavelength of 514.5 nm, operating at 20 mW. The UV sensing characteristics, i.e., current-time (I-T) and current-voltage (I-V) of all photodetectors, was performed using a Keithley 4200 source meter, with a UV light source obtained from a UVA black light tube with a peak wavelength of around 355 nm. The optical power density that illuminates the sensor was determined to be 13.6 mW/cm\({}^{2}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Sample/Nomenclature** & **1 Electrical Contacts Location** \\ \hline As-received & Both ends \\ Porous GaN/(Pr-GaN) & Porous (partial), non-porous \\ \({}^{2}\) CVD-graphene-GaN/CG-GaN & Graphene (partial), non-porous \\ \({}^{2}\) EC-graphene-GaN/EC-GaN & Graphene (partial), non-porous \\ \hline \hline \end{tabular} \({}^{1}\)Conductive silver paste was used as electrical contacts. \({}^{2}\) The graphene cladding is located on top of porous GaN only.  
\end{table}
Table 1: List of UV photodetectors along with nomenclature in this work.  
Figure 1: Schematic drawing (not to scale) of the UV photodetector.