esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Hybrid Optical OFDM with High Spectrum Efficiency for Dimming Compatible VLC System
## 3 Proposed Dimming Compatible Hybrid O-OFDM
### Complexity Analysis  
In this section, the complexity of the proposed DCHO-OFDM is discussed. For comparison, the conventional HACO-OFDM is invoked as a benchmarker. At the transmitter side, the main difference between the conventional HACO-OFDM and the proposed scheme lies in the signal component. To elaborate, the zero-clipping operation of the ACO-OFDM branch is required in HACO-OFDM, while the time-varying biasing process is introduced in the proposed DCHO-OFDM. Remarkably, the zero-clipping operation of ACO-OFDM branch is based on computing the maximum value between zero and the signal sample, which is similar to the calculation process of the time-varying biasing scheme in the proposed DCHO-OFDM. Therefore, the proposed DCHO-OFDM is capable of achieving the similar complexity as the conventional HACO-OFDM at the transmitter.  
At the receiver side, due to the undesired clipping distortion generated by the ACO-OFDM branch, an additional process including the clipping noise re-generation and cancellation is required in HACO-OFDM. Therefore, the associated computational complexity mainly relies on the sum of two real-valued \(N\)-point FFT operations and of one complex-valued \(N\)-point IFFT operation [14]. On the contrary, a benefit to the orthogonal interference, the detection of DCHO-OFDM, can be achieved upon relying on a standard OFDM receiver seen in Figure 3, as that of ACO/DCO-OFDM. Hence, the computational complexity of DCHO-OFDM is dominated by a single real-valued \(N\)-point FFT operation, which is significantly reduced compared to the HACO-OFDM counterpart [28]. Moreover, for the HACO-OFDM-based dimming control scheme [22], an additional operation is required at the receiver side to distinguish if either HACO-OFDM or NHACO-OFDM is transmitted, since they introduce different clipping distortions. Instead, the proposed DCHO-OFDM signal is invulnerable to the varying dimming level and can be directly recovered without detecting the PWM signal, which futher reduces the complexity compared to that of the HACO-OFDM scheme.  
To this end, superior to the conventional HACO-OFDM scheme, the complexity and the process delay of the proposed DCHO-OFDM scheme at the receiver is significantly reduced, without increasing the complexity at the transmitter side.
## 5 Conclusions  
The proposed method for strain reconstruction in stiffened ship panels, employing optical fiber sensors and the strain function-inverse finite element principle (SF-iFEM), addresses the crucial need for monitoring the health of ship structures. The key contributions of this research can be summarized as follows:  
1. A novel strain field reconstruction method based on the nodal strain vector has been developed, resulting in improved accuracy compared to conventional iFEM. The simulation's results demonstrate significant enhancements in strain reconstruction accuracy, particularly for bending and bending-torsion deformations.
2. This paper introduces a method for calculating the equivalent neutral layer of stiffened ship panels. This approach reduces the number of elements and establishes a strain mapping function between the inner and outer surfaces of the structure. This breakthrough addresses the limitations of conventional iFEM related to sensor arrangement. Experimental results indicate average relative errors of 4.45% and 4.87% for bending and torsion deformations under different support conditions.  
In conclusion, the outcomes of this research provide valuable data for strain which are required by technologies such as radon transform. Furthermore, they significantly enhance the engineering applicability of iFEM in ship structural health monitoring, load identification, and structural morphology perception. This work holds promise for advancing the state-of-the-art research in these fields and lays a foundation for future research endeavors.  
Formal analysis, Q.Z.; Funding acquisition, J.Z. ([PERSON]); Investigation, G.W.; Methodology, Z.J.; Project administration, Y.Y.; Resources, C.X.; Validation, J.Z. ([PERSON]) and B.Z.; Writing--original draft, Q.Z.; Writing--review and editing, J.Z. ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 52275536), the Innovative Research Foundation of Ship General Performance (Grant No. 32822229), the Fund of Aeronautics Science (Grant No. 20220028052002), the National Key Research and Development Program of the Ministry of Science and Technology (Grant No. 2021 YFC2802300), and the National Key Laboratory of Helicopter Rotor Dynamics Fund project (Grant No. 6142220207).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Travel-Time Inversion Method of Converted Shear Waves Using RayInvr Algorithm
## 3 Types and Applications of Converted S-Waves Phases
### PSS Phases and Their Travel-Times Correction  
The PSS phases result from P-waves are converted to S-waves at the sediment interface on the way down, so the entire path through the crust was at S-wave velocities (Figures 5 and 6). The PSSuc and PSSuc phases originated from the Pg phase refracted in the upper and lower crust, respectively, which were converted to S-waves at the bottom of sedimentary layer 1 on the way down (Figure 6). The PSSm phase was generated by Moho reflected phases (Pmp) converting to S-waves at the bottom of sedimentary layer 1 on the way down, while the Pmp phases converted at the basement formed the PSSmb phase.  
The Vp/Vs ratios of the crust were constrained using the PSS phases recorded on the radial component. Since the converted interface of the PSS phases was within the sediments, its ray path included both P- and S-wave velocities segments. However, there is not a travel-time inversion method for the joint inversion of P- and S-wave velocities. The travel-time of the PSS arrivals can be corrected when the P-wave velocity and Vp/Vs ratios of the sedimentary layer are determined [24; 25], because RayInvr allows the flexibility to model different arrival phases and conversions. Therefore, the conversion interface of the PSS phases was corrected from the sediments' interfaces to the seafloor. In that case, the velocity models have S-wave velocity below the seafloor, so the S-wave velocity inversion can be performed by the RayInvr software.  
We verified the precision of the travel-time correction by ray-tracing the model using the corrected travel-times. The travel-time difference refers to the propagation time difference between the seafloor and the conversion interface at the velocities of P- and S-waves,  
Figure 7: (**a**) P-wave velocity model, (**b**) Vp/Vs model, (**c**) S-wave velocity model of sedimentary layers. S1, S2 and S3 represent sedimentary layers 1, 2, and 3, respectively.  
respectively. The fitting of the initial travel-time and corrected travel-time were compared by ray tracing the forward and composite model, which shows very small differences in the RMS. Corrected travel-times gave a fit with an RMS of 188 ms, compared to 185 ms for the initial travel-times (for details, see Table 1). In addition, the ray path of corrected travel-time is almost consistent with the initial (Figure 8), indicating that the correction of travel-time is reasonable.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 7 Performances
### Modulation Speed  
Steering based on wavelength sweeping due to grating dispersion has its speed determined by tunable lasers [96]. Phase-based steering speeds are determined by dynamical modulations of phase shifters [46]. The phase shifters should be compact (ideally with sub-wavelength pitches), low-power, and capable of a full 2\(\pi\) coverage [142].  
For OPAs based on TO shifters, contact TO phase shifters were used to enhance heating efficiencies and steering speeds [64]. Graphene has high thermal conductivity and a high steering speed. With the combination of deeply etched trenches and graphene phase shifters, an OPA operated at 0.2 MHz was realized [75].  
Compared with TO phase shifters, EO phase shifters based on the carrier induced index effect provide high-speed steering [64; 79] with low power consumption [64]. Steering speeds of MHz level [44; 79; 97] and even GHz level [42; 52; 64] have been achieved by OPAs with EO phase shifters. An OPA utilizing the EO effect of InP achieved a sweeping speed of 1 MHz [79]. Another OPA with p-i-n EO phase shifters showed a beam steering speed of 20 MHz [44]. An 8-channel EF OPA with EO polymer phase shifters enabled an optical beam scanning of 2 MHz [97]. In most cases, phase modulation speeds are not a major concern of OPAs for microsecond scale scanning abilities of phase shifters [142].
## 3 Major Agriculture Applications and Services
### Crop Harvesting Monitoring and Forecasting  
Moisture level, the flow of grain mass, crop yield, and quantity or number of grains per crop are monitored and diagnosed by the yield monitoring system. To analyze the crop's overall performance, it is necessary to check the level of the moisture and crop yield. Crop monitoring during the growing period is one of the essential processes. The measurement of crop yield is not only critical at the time of harvest, but it is also important to measure crop yield before the harvesting and during the crop's developmental processes. Various aspects are required for monitoring crop yield, including a high level of pollination, particularly when the environment is changing; there is a need for the prediction of seeds [56; 57; 58].  
Crop forecasting is a technique that predicts yields before harvest occurs. Such forecasts help farmers make near-term plans and decisions. Moreover, the accurate harvesting time can be estimated by the analysis of crop maturity and its quality. The monitoring process can also judge many of the fruit factors such as fruit color, size, quality, and developmental stages. Crop disease management strategies and other developmental stages such as crop yield and its quality can be enhanced by crop prediction. For this, awareness about the appropriate harvesting schedule is necessary for the betterment of crops. Figure 3 indicates a typical snapshot of the farm area network (FAN), which gives real-time portraits of the whole agricultural field to the farmer.  
A new technology, such as a developed yield monitor, can be correlated with any machine which is used for harvesting. The yield monitor tool can also be connected with a smartphone application named FarmRTX, which shows accurate harvesting data. It ultimately processes data to the manufacturer's web-based program [59]. This smartphone application can be used to generate high-defined mapping tools and transfer them with other experts and growers to export to the other farming tools for monitoring crop yield.
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 1 Introduction
The trained-DNN approach can generate approximate solutions quite near the optimum by immediately achieving a satisfactory quality of the outputs. The numerical of simulation shows the superiority of the DNN-driven solution in terms of the time-cost of the operations involved respect to iterative optimization algorithms. Our main contribution can be summarized as follows:  
1. In this paper, we propose a cutting-edge learning-based method for solving the maximum D2D links problem. Using the training samples generated by a conventional method, we train a deep learning model to simulate the complex relationship between link information and link activity. Due to computational burden transferred to the training phase, the algorithm significantly reduces the computational overhead. 2. We introduced a sizable D2D communication network under an SINR interference model. Furthermore, we present the value of a two-layer interference--a D2D layer and cellular layer--and then formulate a maximum active D2D links problem subject to SINR constraints. 3. We adjust the parameters of the learning model depending on simulation results to get an optimal output. The experiments show that our algorithm can reduce more than 90% the time-spent. 4. Through simulation results, we analyze how some factors, such as SINR and the coverage of the BS, affect the number of active D2D links. Based on such analysis, we can efficiently schedule D2D links under different wireless network environments.
# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 4 Experiment and Result Analysis
### Evaluation Index  
This study evaluates model performance using five metrics: precision (P), recall (R), mean average precision (mAP), number of parameters (Parameters, Para), and model size (Model Size).  
Precision (P) represents the proportion of correctly classified positive samples, reflecting the model's ability to classify accurately. Here, TP (True Positive) represents correctly classified positive samples, while FP (False Positive) denotes negative samples mistakenly identified as positive. The formula for precision is given by Equation (13):  
\[P=\frac{TP}{TP+FP} \tag{13}\]  
Recall (R) represents the proportion of correctly predicted positive samples out of the total number of actual positive samples, reflecting the model's ability to comprehensively detect targets. It quantifies the samples missed during target identification. FN (False Negative) represents positive samples incorrectly classified as negative, i.e., missed positives. The formula for recall is given by Equation (14):  
\[R=\frac{TP}{TP+FN} \tag{14}\]  
mAP (mean average precision) refers to the average precision of all targets detected by the model, reflecting the ability of the predicted bounding boxes to overlap with the ground truth labels. A higher mAP value indicates better detection performance across different categories. AP (average precision) quantifies the model's precision for each category at a given IoU (Intersection over Union) threshold, where n represents the number of categories. Note that mAP in this paper refers to mAP50, indicating the mAP at IoU = 0.5. The formula for mAP is given by Equation (15):  
\[mAP=\frac{1}{n}\sum_{i=1}^{n}AP,AP=\int_{0}^{1}P(R)dr \tag{15}\]  
Parameters refer to the total number of trainable parameters in the model, including the weights and biases of convolutional layers, as well as parameters from other specific layers, such as fully connected layers and normalization layers.  
Model size is used to evaluate the complexity of the model. Generally, a smaller model size indicates that the model requires less computational power, making it easier to deploy on edge devices.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Names** & **Settings** \\ \hline Epoch & 100 \\ Pretrain & Closed \\ Input images size & 640 \(\times\) 640 \\ Batchsize & 16 \\ Workers & 8 \\ lr0 & 0.01 \\ Optimizer & SGD \\ Momentum & 0.937 \\ Weight\_decay & 0.0005 \\ Data augmentation & mosaic \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental training parameters.
## 4 Discussion  
The precision irrigation system was designed as a closed-loop control system. In the designed control system, the data obtained with the reference data were compared and thus real-time monitoring is provided in addition to the water efficiency. Reference values in the study were determined according to Equations (1) and (2). This data indicates the seasonal water needs of the walnut tree. The amount of rainfall in previous years was used as data when water demand was determined. This data was taken from Turkish State Meteorological Center. At the same time, daily water needs were determined at a lower level. The obtained data were arranged in an algorithmic way into the PLC program. The data obtained from the sensors were compared with the periodical water requirement table and the working status of the system was revealed. While studies reported before in the literature mostly focus on the opening and closing operations of drip irrigation system, in this study, the opening and closing operations were determined according to the seasonal water need. In the studies shown in the references, especially those related to the drip irrigation system, only water irrigation was done from the sensors based on the humidity condition of the soil.  
Dating from October to harvest, average monthly moisture results obtained from 16 sensors in five different laterals are given in Figure 10. As can be seen in Figure 10, the moisture rate decreases especially during the summer season. When this situation is compared with climatic data, it is shown that these results are close to average values.  
Figure 9: Smart phone monitoring page. **(a)** Home screen, **(b)** Sensor Menu, **(c)** Sensor 1’ e moisture value.  
Moisture values that were obtained from sensors during the day and depend on four separate stages are given in Figure 11.  
Values were obtained from equalities, which were used to define the amount of water needed in the product growth stage, and given daily in Figures 12-15 respectively.  
Figure 11: The average moisture values are occurring in the sensor during the day (Mid. Stage).  
Figure 12: Int. Stage water requirement.  
Figure 10: Monthly average moisture.  
In Figure 16, a comparison was done between the total amount of water to be supplied and that which was actually supplied to plants. It can be seen in Figure 16 that plants' water requirement especially increases after the development stage. Moreover, it is clear that calculated values and real values are close to each other and a precision irrigation process is achieved.  
Resulting from the leveling of product growth area, it can be seen that average moisture values in each lateral are close together. This situation was effective in reducing energy and water consumption.  
Figure 14: Mid. Stage water requirement.  
Figure 13: Dev. Stage water requirement.  
Figure 15: End stage water requirement.
## 3 Computational Model
### Stator Blade Load Analysis Model  
In this paper, a numerical model of unsteady flow field was constructed to obtain the flow load on stator blades. Due to the high complexity of the transient solution, in order to minimize the computing resources and time under the condition of enough accuracy, multi-core calculation was used to improve efficiency and the number of blades in the model was fine-tuned. The number of guide blades, rotor blades and stator blades were 50/25/50, respectively. The transient analysis model of 1.5 stage compressor flow field established in this way can quickly and accurately obtain the flow load on the compressor stator blade surface. The rotating speed of the rotor blade was 3000 rpm. The unsteady flow excitation data of compressor stator blades can be obtained under the given working conditions. The research model is shown in Figure 7.  
The grid division of the numerical model was completed using TurboGrid. It should be noted that axial extension was carried out appropriately for the downstream of the stator blade model to improve the convergence of the numerical simulation. The O-mesh method was mainly used for meshing, which showed good boundary layer quality improvement for the area near the blade surface. At the same time, in order to effectively improve the accuracy of the solution, the density of the local grid was improved, especially at the leading edge and trailing edge of the blade. The number of grids in the whole transient flow calculation model was 4.4 \(\times\) 10\({}^{5}\). Figure 8 shows a grid diagram of the fluid domain during CFD calculation. The boundary conditions were calculated using total temperature and pressure at the entrance and mass flow at the exit. Corresponding to a working condition point of the compressor model, the total inlet pressure was 75,000 Pa. The total temperature of import was 300 K. The outlet mass flow rate was 72 kg/s.  
Figure 6: Stator blade finite element model.  
Figure 7: The transient flow numerical model.
## 4 Discussion
Future studies should include data from various regions and different types of milk to evaluate the robustness of the model more comprehensively [16; 17]. Additionally, while the AS7265x multi-spectral sensor provides accurate spectral data, the device's performance may still be influenced by minor environmental variations that were controlled in this study. Temperature fluctuations, variations in humidity, and differences in light conditions could potentially impact the accuracy of the spectral readings, affecting the reliability of the predictions. Conducting additional testing under varied environmental conditions would help to assess the robustness of the device and the model's performance in real-world settings [18]. The results obtained show that the XGBoost model offers high accuracy in predicting protein and fat content. The model's R\({}^{2}\) and MSE values reflected superior performance compared to other methods in the literature [19]. Comparative analyses with other machine learning algorithms like GBM and random forest have further demonstrated XGBoost's superior capability in modeling complex, non-linear relationships, such as those found in milk components [11]. While XGBoost proved effective in this study, other machine learning models, such as deep learning techniques, may further enhance predictive accuracy, particularly with larger and more complex datasets. Exploring alternative algorithms could potentially improve performance, especially in identifying subtle differences in milk composition that might not be as easily captured by gradient boosting models [20]. Lastly, this study's focus on spectral analysis provides a promising approach for detecting milk components, but integrating additional biochemical and physical measurements could enhance the model's predictive power and reliability. For instance, combining spectral data with chemical indicators or viscosity measurements may provide a more holistic analysis, strengthening the model's capability to assess milk quality under various processing conditions. In summary, this research highlights the potential of combining multi-spectral sensing and machine learning algorithms to revolutionize quality control processes in the dairy industry. The portability, energy efficiency, and cost-effectiveness of the device are key factors that could facilitate widespread adoption. However, future research incorporating diverse data sources, alternative algorithms, and robust environmental testing will be essential to optimize its reliability and applicability in diverse dairy industry contexts.