esa-sceva/satcom-chunk-collection huggingface dataset:
# Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water
## 4 Experimental Results  
### Experiment Introduction  
The experiment was an acoustic field trial conducted in the South China Sea in 2016, as shown in Figure 3a. The vertical hydrophone line array was composed of 16 elements with an interval of 5 m. The top element was deployed at a depth of 370 m, and the bottom element was about 445 m. The sound source loaded on the ship was at point A and B, where the horizontal range was 2.4 km and 10.3 km, respectively, and the source depth was 30 m. The depth of the whole measurement area was about 1800 m. There were 5 TD sensors (RBRduet T.D) used to record the element depths of the vertical line array. The top hydrophone element was equipped with an electronic compass to record the underwater attitude of the vertical line array. The change of array tilt angle during the experiment is shown in Figure 3b. The narrow-band sound source radiated a single-frequency signal at 350 Hz. The duration of one pulse signal was 5 s, and the interval between the two adjacent pulses was 1 s. The measurement time for each point was half an hour. Considering the time (one hour) for the boat from point A to the point B, the total time for the whole experiment was two hours. The sampling rate was 16 kHz. The sea condition was level 3 and there were no other ships around the experiment site.  
The sound velocity profiler (AML-Minos X SVP) was used to measure the sound speed profile (SSP) at both points, and the difference was very small. It can be considered that the variation of the SSP can be ignored in a short time. Therefore, we used the SSP shown in Figure 4a to analyze the  
Figure 3: (a) Experiment configuration; (b) Tilt angles recorded by an electronic compass.  
sound field. The depth of the deep sound channel is 1041 m with the minimum sound velocity of 1482 m/s. The sound speed near the seafloor is 1491 m/s while that near the surface is 1546 m/s. This is a typical non-full deep water sound channel, where the sound rays reflected by the bottom have a profound influence on the sound field in the shadow zone. The seabed-state parameters obtained after consulting the information are shown at the bottom of Figure 4a.  
According to the experiment configuration and measured SSP, the transmission loss is calculated and presented in Figure 4b by using the BELLHOP model. It is indicated that the VLA with a length of 75 m is in the direct-arrival zone when the horizontal range is 2.4 km and in the shadow zone when the range is 10.3 km.
# Simulation-Guided Path Optimization for Resolving Interlocked Hook-Shaped Components
## 4 Our Approach
### Modeling Interlocking  
Using the work of [5], we utilize IsaacGym [22] as the dynamic model for rollout purposes, specifically to predict how the robot's movements affect the task of separating components. To accomplish this, we create a simulation that represents the current state of the environment, including the robot's joint configuration and the poses of all detected components. A straightforward approach involves forward-simulating the sequential movements for the entire pick-and-place task. This includes approaching an object, grasping it, and moving away in a manner that separates the component from the rest of the objects. However, this approach faces a significant bottleneck. To accurately simulate the contacts between the gripper and objects, the simulator must use a very small timestep, typically between 10 and 20 milliseconds or even lower, to properly capture the contact dynamics. However, this greatly increases the computational time required for rollouts. Additionally, the complex shapes of the components further exacerbate the issue by reducing the likelihood of the robot successfully grasping an object, ultimately diminishing the benefits of using the simulator.  
Since we are only interested in simulating the interaction with objects during the separating stage of the pick-and-place task, we can disregard the objects' poses in all other stages by manually placing them outside the robot's workspace. This allows us to directly attach the component to the robot's end-effector by extending the kinematic chain of the modeled robot through a fixed joint, as shown in Figure 3. With all components placed outside the robot's reach, we can safely simulate the approaching or placing stages of the task without disturbing the environment or violating collision constraints with other objects. Furthermore, by assuming the robot is already holding the object at the start of the separating subtask, we eliminate the previously mentioned issue of the simulator struggling to grasp complex components, as the simulation of the grasping process is no longer required.  
Figure 3: Simulating interlocked objects. The green object is treated as an extension of the robot’s kinematic chain, meaning the robot is attempting to pick it up. The blue object, which is interlocked with the green one, represents the object that the robot is trying to separate from the green object.  
In the simulations, model parameters and joint limits are inherited from the robot's URDF file, ensuring that joint limit constraints are satisfied even if a control input violates them. The inertia matrix and component mass are derived from the CAD model and incorporated into the simulator. The joints of the simulated robots are controlled by low-level position controllers.
## 3 Method  
### Overall Design  
First, the data were pre-processed by (1) coordinate system harmonisation of multiple types of data, (2) reclassification of POIs, and (3) generation of road meshes. Second, the spatial relationships between POIs, buildings, and road meshes were analysed. Buildings were classified into those containing/not containing POIs. The ratio of the frequency density of the types of POIs within buildings containing POIs was calculated to identify their functional types. Third, the distance, area, orientation, and shape similarities between buildings containing/not containing POIs in the same road network mesh were calculated. The entropy weighting method was utilised to determine the weights of each similarity index. Finally, the functional types of buildings not containing POIs were inferred from the maximum similarity between buildings containing/not containing POIs in the same road network mesh. The technical process is shown in Figure 2.  
\begin{table}
\begin{tabular}{l l l} \hline
**Data Type** & **Source** & **Data Format** \\ \hline POIs & Gaode map & csv \\ Building footprints & Gaode map & shp \\ Road network & Open Street Map & shp \\ \hline \end{tabular}
\end{table}
Table 1: Data sources.  
Figure 1: Satellite image of the study area: Qingxiu District, Nanning, China.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 2 Materials and Methods  
### Study Site  
The study was carried out at the end of the 2016/17 growing season in a drip-irrigated Shiraz vineyard at the Welgevallen Experimental Farm located in Stellenbosch, South Africa (33 deg56'26'' S; 18 deg51'56'' E). The vineyard was planted in the year 2000, with a grapevine spacing of \(2.7\times 1.5\) m in a North-South orientation, and lies approximately 157 m above sea level. A seven-wire hedge VSP trellis system with three sets of moveable canopy wires is used in the vineyard. The Stellenbosch area falls within the coastal wine grape region of the Western Cape, which is characterised by a Mediterranean climate with long, dry summers [23]. Thirty-one individual vines were selected across three rows and sampled for this study (Figure 1).
# On the Use of Neuro-Swarm System to Forecast the Pile Settlement
###### Abstract  
In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R\({}^{2}\)) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique.  
pile settlement; neural network; particle swarm optimization; hybrid intelligence technique +
Footnote †: journal: Applied Sciences
## Abstract  
Lightweight structures with an internal lattice infill and a closed shell have received a lot of attention in the last 20 years for satellites, due to their improved stiffness, buckling strength, multifunctional design, and energy absorption. The geometrical freedom typical of Additive Manufacturing allows lighter, stiffer, and more effective structures to be designed for aerospace applications. The Laser Powder Bed Fusion technology, in particular, enables the fabrication of metal parts with complex geometries, altering the way the mechanical components are designed and manufactured. This study proposed a method to re-design the original satellite structures consisting of walls and ribs with an enclosed lattice design. The proposed new structures must comply with restricted requirements in terms of mechanical properties, dimensional accuracy, and weight. The most challenging is the first frequency request which the original satellite design, based on traditional fabrication, does not satisfy. To overcome this problem a particular framework was developed for locally thickening the critical zones of the lattice. The use of the new design permitted complying with the dynamic behavior and to obtain a weight saving maintaining the mechanical properties. The Additive Manufacturing fabrication of this primary structure demonstrated the feasibility of this new technology to satisfy challenging requests in the aerospace field.  
laser powder bed fusion; design for additive manufacturing; satellite platform; lattice structure; aerospace application
## 4 Experiments and Results
### Experiment Setup  
We introduce various hyperparameters settings in detail in this section. In simulation reasoning, the predict length K is 15. There is a CNN network, LSTM network, and fully connected network in the AEN. In the CNN, the convolution kernel size is 6 and the dimensional of the fully connected layer is 64. The times of recurrence is 15 in LSTM. The action contribution evaluation part is a fully connected neural network with two hidden layers of size \(128\times 64\). The agent model includes actor network and critic network, and they are also fully connected neural networks. In partial observable environments, the agent's observation range is \(5\times 5\). The discount factor is 0.95. In parallel processing, the batch size is 8000.
## 2 Detection Methods
### Image Preprocessing  
The flow chart of image preprocessing is shown in Figure 4. The image is captured using color industrial cameras, so the first step is to convert the color image to a gray image. The second step is image segmentation. The purpose of image segmentation is to separate the target region from the background region, so image segmentation holds a crucial position in image detection techniques. The gray image is defined as a two-dimensional function f(x,y), where the output of the function represents the grayscale value of a pixel point. The region of the target is dark colored while the background region is light colored. Based on this characteristic of the image, it is possible to find a suitable threshold value T to separate the target region from the background, thereby achieving binarization. Its mathematical expression is as follows:  
\[\text{f}_{1^{(xy)}}=\left\{\begin{matrix}0&\text{f}(\text{x},\text{y})\leq \text{T}\\ 255&\text{f}(\text{x},\text{y})\geq\text{T}\end{matrix}\right. \tag{2}\]  
where \(\text{f}_{1^{(xy)}}\) is the binarized image.  
During the image acquisition process, factors such as uneven lighting and electromagnetic interference can lead to the presence of a lot of noise in the image, which can significantly affect subsequent image processing. Because the median filter can remove noise while ensuring edge details, the effect is better, so we use the median filter [26]. After filtering and denoising, the image may still contain some small objects. The fourth step involves utilizing mathematical morphology techniques to remove small connected regions, thus obtaining a clean and clear target region. Compared to first-order differential operators, second-order differential operators can more accurately locate edges, and the obtained edge contour is basically the unit pixel width. Therefore, the Laplacian of Gaussian operator is used for edge detection in the final step.  
Figure 3: Flow chart of detection method.
# Optical Amplifiers for Access and Passive Optical Networks: A Tutorial
## 3 Optical Amplifiers in Telecommunications Networks
### Erbium-Doped Fiber Amplifiers
EDFAs also have the following disadvantages [64]:  
* Amplified spontaneous emission, there is always some output even with no signal input due to some de-excitation of ions in the fiber--spontaneous noise. * necessary to use flat-top filters for WDM systems,
* not possible to use for the O-band,
* problematic miniaturization,
* inability to be integrated with other semiconductor devices,
* gain saturation effects.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 2 Materials and Methods  
### Preparation of Materials  
The AS fruits were harvested at the commercial maturity stage, characterized by the deep purplish-black color and firm texture shown in Figure 1. The maturity (BBCH 93) of the harvested fruits was determined based on both manual experience and the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale. All AS fruits were collected from the garden located at 126\({}^{\circ}\)38\({}^{\prime}\)17.268\({}^{\prime\prime}\) E, 45\({}^{\circ}\)43\({}^{\prime}\)26.684\({}^{\prime\prime}\) N, within the premises of the College of Chemical Engineering and Resource Utilization, Northeast Forestry University.  
Figure 1: Morphological characteristics of AS fruits at different stages: (**a**) AS fruits during the fruiting period; (**b**) freshly harvested AS fruits.  
After harvesting, the fruits were promptly transported to the laboratory within 1 h. Only intact and fresh fruits were selected and mixed together to minimize the impact of sample heterogeneity. All samples were rinsed with purified water at 20 \(\pm\) 2 \({}^{\circ}\)C to remove surface impurities. No detergents or chemical cleaning agents were used. The rinsed fruits were gently blotted with sterile cotton fabric to remove surface moisture. They were then spread in a single layer on perforated stainless-steel trays and left at room temperature with natural air circulation for 1 h before different drying treatments (VFD, HD, VD, MD, and ND).
## 3 Methodology  
We initially present our SF-ICNN technique in this section. Subsequently, the principal elements of our suggested approach--PCA dimension reduction, fractal-based spatial features, and CNN--are presented in the following three subsections.  
The suggested method's flowchart is displayed in Figure 1. The phases of this strategy are as follows:  
1. The HSI's dimensionality is reduced in the first stage using the well-known principal components analysis (PCA) method. This method efficiently eliminates data redundancy and extracts compact, informative 3D data cubes.
2. The first three PCA characteristics are used to create FFs in the second stage.
3. The PCA approach is then used to reduce the fractal data cube's dimensions.
4. In the fourth step, the reduced fractal and spectral PCA features with five dimensions are stacked to create a combined 10-band spatio-spectral data cube. This number of bands is empirically chosen as input to balance speed and accuracy. The important aspect is that the 10-band input with spectral and fractal features is a compact and information-rich representation that is used as input to the CNN.
5. This dense and rich spectral-fractal data cube is fed to a CNN with a simple structure, and primitive PMs are generated. The generated PMs have dimensions equal to the number of classes. The accuracy of the classification process is increased by the two-step use of spatial information resulting from the FFs and PMs.
6. The PMs are then stacked onto the combined data cube of spectral and fractal features. This new data cube is then used again as input for the CNN to create new PMs.
7. Step 6 is repeated until it reaches the desired results. It is worth noting that in each step of the iterative process, old PMs are replaced by new PMs. Also, the best iteration number is determined based on the classification accuracy of the validation samples.
## 3 Properties and Characteristics of LoRaWAN Technologies
### Coding Rate (CR)  
CR is considered an important LoRaWAN characteristic that has an impact on both transmission rate and error correction performance. There are six CR values that are supported by LoRaWAN; these include 4/5, 4/6, 4/7, 4/8, 2/3, and 4/5. These values determine the ratio of redundant bits needed for error correction. Higher CR values enhance error correction but extend the time on air, while lower values optimize transmission efficiency but compromise error correction. For instance, CR value 4/5 means that 1 in 5 bits is for error checking [3]. The higher coding rates, such as 4/5 and 4/6, offer high rates in terms of throughput, but they also offer less resistance to nose and inference. Meanwhile, the lower coding rates, such as 4/7 and 4/8, offer more resistance, but the throughput rate is lower, which shows the potential tradeoff [22].  
The choice of CR depends on application needs as well as interference levels and available bandwidth, with careful consideration, which can further involve testing and simulation for optimal deployment. LoRaWAN devices must transmit data at constant coding rates in both the uplink and downlink directions [21].
## 6 Conclusions  
For the liquid fertilizer pH regulation system, this paper fitted its mathematical model, got the transfer function of the pH regulation system, combined BP neural network algorithm with the Smith prognosticator, designed a BP-PID-Smith prognostication compression controller, and compared the performance of three controllers, BP-PID-Smith, PID-Smith, and Smith, in both simulation and practical application. The results showed that the BP-PID-Smith predictive compensation controller was able to bring the pH to the set value at a faster rate in both cases with a smaller overshoot compared to the other two controllers.  
According to the experiments, the BP-PID-Smith controller showed excellent dynamic performance at different fertilizer application flow rates and was able to respond to the input signal at a faster rate and achieve the desired target pH value. The average maximum overshoot was 0.27% and the average regulation time was 71.39 s, which was significantly better than the PID-Smith and Smith controllers.  
The BP-PID-Smith predictive compensator can reduce the adverse effects of the system in the fertilization process due to time lag and nonlinearity in practical applications while possessing excellent dynamic performance and robustness to meet the control requirements in practical applications.  
This study was conceptualized by [PERSON] and [PERSON]. The software was designed by [PERSON] and validated by [PERSON], [PERSON] and [PERSON]. [PERSON] provided resources and [PERSON] curated data. The original draft of the manuscript was prepared by [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. [PERSON], [PERSON] and [PERSON] assisted with project administration. [PERSON] and [PERSON] managed funding acquisition. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controller Type** & **Rise Time (s)** & **Peak Time (s)** & \
\begin{tabular}{c} **Regulation** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Overshoot** \\ \end{tabular} \\ \hline Smith & 235.547 & 288.110 & 174.819 & 0.05\% \\ \hline PID-Smith & 139.638 & 178.810 & 110.445 & 0.37\% \\ \hline BP-PID-Smith & 94.403 & 121.180 & 70.695 & 0.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the dynamic performance of the three controllers at a fertilizer flow rate of 0.58 m\({}^{3}\)/h.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controller Type** & **Rise Time(s)** & **Peak Time(s)** & \
\begin{tabular}{c} **Regulation** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Overshoot** \\ \end{tabular} \\ \hline Smith & 178.436 & 235.560 & 111.320 & 0.02\% \\ \hline PID-Smith & 107.892 & 156.370 & 80.897 & 0.46\% \\ \hline BP-PID-Smith & 80.949 & 111.602 & 62.578 & 0.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the dynamic performance of the three controllers at a fertilizer flow rate of 0.73 m\({}^{3}\)/h.  
Funding: This research was funded by the National Natural Science Foundation of China, grant number 52065055.