esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### Simulation of Flight Cruising in a Real Wind Field  
#### 4.2.1 Flight Condition  
Airships typically cruise at a fixed altitude with a constant roll angle. In this paper, the cruising altitude of the airship is set to 20 km, and the roll angle is set to 0\({}^{\circ}\). The pitch angle oscillates according to the wind conditions and differential pressure on the envelope and is set within the range of \(-10^{\circ}\) to \(10^{\circ}\).  
The cruising dates selected are from 21 December to 24 December, which correspond to the period with the lowest solar irradiance during the year. The variations in wind speed and wind direction are depicted in Figure 12, and the variations of the pitch angle and yaw angle are depicted in Figure 13. On 21 December and 22 December, the average wind speed exceeds 60 km/h, while the wind direction undergoes a shift from 250\({}^{\circ}\) to 0\({}^{\circ}\). Therefore, the airship's maneuvering strategy is set to resist the wind and remain in position, always facing the incoming flow to minimize energy consumption. Subsequently, on the following two days, there is a significant decrease in wind speed, with an average below 40 km/h, and the wind direction varies between 0\({}^{\circ}\) and 200\({}^{\circ}\). With the wind speed decreasing, the airship's cruise strategy was switched from maintaining its position to circular cruising. The cruising radius is set to 20 km, and the cruising cycle is established to be 2 h.  
Figure 11: Estimation of fill factors for conditions 1 to 4.  
Figure 12: Wind speed and direction from 21 December to 25 December.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 1 Introduction  
It is well known that over 70% of the earth's surface is covered by ocean, which leads to the result that the waterway has to undertake more than 80% of the world's trade transportation, including the seaborne trades and the inland river navigation. In recent years, with the development of technologies such as sensing, telecommunications, computers, information, automation and smart control, a new technological revolution called internet of vessels (IoV) is proposed to meet the requirements of international and regional transportation. The IoV is defined as a network of smart interconnected vessels and the shore facilities with a series of digital entities [1]. Based on the global positioning system and wireless communication technology, the ship intelligent information service could use electronic sensing devices to exchange information on the internet and realize the extraction, supervision and utilization of each nodes' properties and dynamic information on the network platforms. The information is available in real time by way of communication (human to vessel, vessel to vessel, vessel to cargo, vessel to coast) [2]. As a result, the IoV has the ability to navigate, communicate and offer security protection to provide a more intelligent and safer navigating environment.  
In this paper, an in-depth view of the IoV is provided. Due to the dramatic increase in world trade, new challenges with efficiency, safety and energy saving have emerged in the transportation system. As one of the most important transportation modes, the shipping industry is trying to solve the key problem of improving the intelligence level of the vessels and their operating network. The emerging IoV technologies could achieve this goal with precision management, comprehensive service and humanized experience. The purpose of this paper is to analyze how to benefit from the Internet of Vessels to create a more intelligent and safer navigating environment.  
This paper is structured as follows: Section 2 introduces the definition of the IoV and its architecture from different technical perspectives; the characteristics of the internet of vessels are discussed in Section 3; in Section 4 the application and benefits of the internet of vessels are described; in Section 5 the challenges and prospects of the emerging technologies are outlined; and finally, a conclusion is drawn in Section 6.
# A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images
## 3 Proposed Framework
### Performance Parameters and Evaluation Metrics  
Several measures were used to evaluate the proposed DenseHHO, including accuracy, FI_score, Sensitivity, and Specificity. According to the ratio between the number of Dock samples correctly classified to the total number of samples, the _[accuracy]_ value is calculated. This evaluation metric can be calculated using Equations (5)-(8), where _[True Positive TP]_ represents the number of Dock, and Not-Dock images that have been correctly classified; _[True Negative TN]_ represents the number of weed images that have been misclassified. Additionally, macro average scores can be used to evaluate the overall performance of the model across several sets of data. This study uses the macro average to measure the performance of the different classes. The macro average FI-score is calculated by first computing the accuracy and recall of each class label from n classes, followed by calculating the unweighted arithmetic mean and weighted average score as shown in Equations (9)-(12). The DenseHHO is evaluated with confusion matrices (CMs). A classification method's CM represents the characteristics of that method's classification performance during its execution.  
\[Accuracy=\frac{(TP+TN)}{(TP+FP+FN+TN)} \tag{5}\]  
\[Sensitivity\ (Recall)=\frac{TP}{(TP+FN)} \tag{6}\]  
\[Specificity(Precision)=\frac{TP}{(TP+FP)} \tag{7}\]  
\[F1\_score=\frac{TP}{(TP+0.5(FP+FN))} \tag{8}\]  
\[Macro\ avg\ precision\ (MAP)=\frac{\sum_{i=1}^{n}TP}{n} \tag{9}\]  
\[Macro\ avg\ recall\ (MAR)=\frac{\sum_{i=1}^{n}FN}{n} \tag{10}\]  
\[Macro\ average=\frac{2(MAP\times MAR)}{(MAP+MAR)} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Purpose** & **Value** \\ \hline \multirow{4}{*}{Fitness\_function} & A user-defined function that takes a set of hyperparameters as input and returns a fitness value that represents the performance of a machine-learning model trained with those hyperparameters. & The maximum validation accuracy and minimum validation loss \\ \hline objf & The objective function to be optimized. & Accuracy \\ \hline \multirow{2}{*}{lb} & A numpy array representing the lower bounds of the search space. & \multirow{2}{*}{0} \\ \hline \multirow{2}{*}{ub} & A numpy array representing the upper bounds of the search space. & \multirow{2}{*}{1} \\ \hline dim & The number of dimensions in the search space. & \multirow{2}{*}{10} \\ \hline \multirow{2}{*}{SearchAgents\_no} & The number of search agents (i.e., hawks) to be used in the optimization algorithm. & \multirow{2}{*}{30} \\ \hline \multirow{2}{*}{Max\_iter} & The maximum number of iterations to run the optimization algorithm. & \multirow{2}{*}{2} \\ \hline \hline \end{tabular}
\end{table}
Table 3: HHO Parameter Setting.  
\[Weighted\ average=\frac{(1+\beta)\times(Precision\times Recall)}{(Recall+(\beta^{2} \times Precision)))} \tag{12}\]  
where \(0<\beta<1\) add weight to the _Precision_, and \(\beta>\)1 add weight to the _Recall_.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Machine-Learning-Based Predictive Modeling
#### 2.2.3 Linear Regression (LR)  
Linear Regression is a statistical technique used to model the relationship between a dependent variable \(Y\) and one or more independent variables \(X_{1},X_{2},\ldots,X_{p}\)[46]. The model assumes that the relationship between the variables is linear and can be expressed with the equation  
\[Y=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\cdots+\beta_{p}X_{p}+\epsilon \tag{8}\]  
where \(\beta_{0},\beta_{1},\ldots,\beta_{p}\) are the regression coefficients, and \(\epsilon\) represents the random error term. This method is widely used for its simplicity and ability to interpret the influence of each predictor on the outcome.
# Design of a Piezoelectrically Actuated Ultrananocrystalline Diamond (UNCD) Microantilever Biosensor
## 2 Materials and Methods
### Design of the Actuator Structure  
The metal/piezoelectric/metal actuation structure is designed as a capacitor-like configuration, consisting of an AlN film sandwiched between two platinum (Pt) electrodes, as shown in Figure 3. The top electrode measures 9.0 \(\upmu\)m in length and 20 \(\upmu\)m in width, while the bottom electrode is 10.0 \(\upmu\)m long and 20 \(\upmu\)m wide. Both electrodes have a thickness of 100 nm. The thickness of the AlN layer varies between 300 nm and 1000 nm.  
The simulations in COMSOL Version 4 employed the Solid Mechanics and Electrostatics modules, as the primary objective was to determine the resonant frequency of the actuator. Thermoelastic effects were not included since this study focused on structural performance under idealized conditions, and thermal influences were assumed negligible at this stage to simplify the model and reduce the computational cost.  
One end of the microcantilever was fixed while the other remained free, as indicated by the dotted red line in Figure 3. A relative tolerance of 0.001 was applied in the simulation. The generalized alpha solver of COMSOL Version 4 software was used, which operates similarly to the backward differentiation formula (BDF) solver but introduces a parameter called alpha to control numerical damping at higher frequencies. Displacement data were collected using an edge probe positioned at the tip of the actuator structure. The  
Figure 2: Base structure of the step microcantilever. The z – y plane refers to the coordinates in Figure 1. This design was proposed in reference [11].  
vertical displacement (in the \(z\)-direction, as shown in Figure 3) was used to estimate the resonant frequency.  
It is important to acknowledge that the current model assumes a uniform mass distribution across the active surface of the microcantilever. While this simplification is useful for establishing baseline performance and sensitivity, it does not fully capture the localized nature of biomolecular adsorption that occurs in real biosensing applications. In practice, adsorbed mass is often concentrated near the tip of the cantilever, which can result in a more pronounced shift in resonant frequency. Modeling such non-uniform distributions would require additional assumptions about adsorption site, pattern, and density--parameters that depend on the specific biochemical environment and are not readily available without experimental input. Recognizing this limitation, future work will focus on incorporating spatially varying mass loads to improve the predictive accuracy of the simulations under more realistic sensing conditions.
## 4 Case Analysis
For the working conditions with incorrect test results, the deviation between the measured position and the actual position in Case 1 and Case 2 is about three feeder rods, and the deviation between the measured position and the actual position in Case 3-5 is about six feeder rods. This is because the more the number of grounding electrode fractures, the wider the range of surface potential changes, resulting in a certain deviation between the measurement results and the actual breakage position in the region. (3) Discussion  
At present, the methods for detecting the operating state of DC grounding electrodes mainly include environmental parameter monitoring, electrical parameter detection, and direct excavation inspection. (a) In terms of monitoring according to environmental parameters, References [16; 35] used optical fiber temperature measurement technology and the wireless transmission method to realize real-time monitoring in the Puqiao DC grounding electrode. The grounding electrode has a double-ring grounding electrode with an inner-ring radius of 210 m and an outer-ring radius of 300 m. The temperature change in the soil environment is monitored by selecting temperature sensors arranged in eight deep wells of the grounding electrode. The measurement results show that this method can only realize the monitoring of the grounding electrode state in a small range near the sensor, and it is difficult to cover the entire grounding electrode field. (b) In combination with electrical parameter detection, in Reference [17], the state of the grounding electrode of the Longquan and Tuanlin circulation stations was evaluated by combining the characteristics of grounding resistance, step voltage, and current distribution. Finally, it can only be qualitatively analyzed that the grounding electrode is in good condition at present, and some electrodes may be corroded. The possibility of failure is low, but the actual breakage of the grounding electrode cannot be judged. (c) In terms of direct excavation inspection, the service life of the DC grounding electrode is about 30 years, and in practical engineering, it takes generally 5 years or 10 years to carry out an excavation inspection. On-site excavation inspection has the disadvantages of long interval period, high randomness, and huge consumption of human  
Figure 20: Case calculation results. and financial resources. Therefore, this method is difficult to realize the state detection of the grounding electrode as a conventional means. In summary, compared with the DC grounding electrode operation state detection method based on potential distribution proposed in this paper, by measuring the surface potential distribution along the grounding electrode laying line, the specific location of the breakage in the local area of the whole grounding electrode field can be accurately detected. The overall measurement accuracy is high, and the error is small. It solves the shortcomings of traditional detection methods, such as small coverage area, difficult quantitative calculation, and high randomness.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
###### Abstract  
Optimizing sparse matrix-vector multiplication (SpMV) is challenging due to the non-uniform distribution of the non-zero elements of the sparse matrix. The best-performing SpMV format changes depending on the input matrix and the underlying architecture, and there is no \"one-size-fit-for-all\" format. A hybrid scheme combining multiple SpMV storage formats allows one to choose an appropriate format to use for the target matrix and hardware. However, existing hybrid approaches are inadequate for utilizing the SIMD cores of modern multi-core CPUs with SIMDs, and it remains unclear how to best mix different SpMV formats for a given matrix. This paper presents a new hybrid storage format for sparse matrices, specifically targeting multi-core CPUs with SIMDs. Our approach partitions the target sparse matrix into two segmentations based on the regularities of the memory access pattern, where each segmentation is stored in a format suitable for its memory access patterns. Unlike prior hybrid storage schemes that rely on the user to determine the data partition among storage formats, we employ machine learning to build a predictive model to automatically determine the partition threshold on a per matrix basis. Our predictive model is first trained off line, and the trained model can be applied to any new, unseen sparse matrix. We apply our approach to 956 matrices and evaluate its performance on three distinct multi-core CPU platforms: Multiplication on Multi-core SIMD CPUs. _Appl. Sci._**2022**, 12, 9812. [[https://doi.org/10.3390/app12199812](https://doi.org/10.3390/app12199812)]([https://doi.org/10.3390/app12199812](https://doi.org/10.3390/app12199812))  
SprMV; performance; machine learning; sparse matrix format Article  
Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD
## 2 Overview of Candidate Technologies for Wearable Wireless Networks
### Overview of On-Body Communication Technologies and Existing Implementations
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline  & \multicolumn{1}{c}{**IEEE**} & \multicolumn{1}{c}{**802.11a/b/g/n**} & \multicolumn{1}{c}{**IEEE 802.15.1**} & \multicolumn{1}{c}{**IEEE 802.15.1**} & \multicolumn{1}{c}{**IEEE 802.15.4**} & \multicolumn{1}{c}{**IEEE 802.15.4a**} & \multicolumn{1}{c}{**IEEE 802.15.4j**} & \multicolumn{1}{c}{**IEEE 802.15.6**} \\  & \multicolumn{1}{c}{**(WiFi)**} & \multicolumn{1}{c}{**(Bluetooth)**} & \multicolumn{1}{c}{**(Bluetooth-LB)**} & \multicolumn{1}{c}{**(Zigbee)**} & \multicolumn{1}{c}{**(UWB)**} & \multicolumn{1}{c}{**(MBAN)**} & \multicolumn{1}{c}{**(BANs standard)**} \\ \hline Modes of Operation & Adhoc & \multirow{2}{*}{Adhoc} & \multirow{2}{*}{Adhoc} & \multirow{2}{*}{Adhoc} & \multirow{2}{*}{Adhoc} & \multirow{2}{*}{Adhoc} & \multirow{2}{*}{Adhoc} & \multirow{2}{*}{Adhoc} \\ \cline{1-2} \cline{5-5} \cline{7-9} Physical (PHY) & NB & NB & NB & & & & & \\ Layers & & NB & NB & NB & & & & \\ \hline Radio & \multirow{4}{*}{2400, 5000} & \multirow{4}{*}{2400} & \multirow{4}{*}{2400} & \multirow{4}{*}{868/915, 2400} & \multirow{4}{*}{868/915, 2400} & \multirow{4}{*}{875-724, 3000-500, 6000-10,000} & \multirow{4}{*}{2360-2400, 563-8700, 600-5600, 2360-2400, 563-8700, 600-10,000} & \multirow{4}{*}{2390-2400} & \multirow{4}{*}{950-956, 2360-2400, 2400-2438.5} \\ \cline{1-2} \cline{5-5} \cline{7-9} Power Consumption & High & (\(\sim\) 800 mW) & Medium & low & Low & Low & \multirow{2}{*}{2400-2438.5} \\ \cline{1-2} \cline{5-5} \cline{7-9} Maximum Signal & Up to 150 Mb/s & Up to 3 Mb/s & Up to 1 Mb/s & Up to 250 Kb/s & & & & \\ \cline{1-2} \cline{5-5} \cline{7-9} \cline{1-2} \cline{5-5} \cline{7-9} Communication & Up to 250 m & 100 m & & & & & & Up to 10 m \\ Range & (802.11m) & (class 1 device) & Up to 100 m & Up to 75 m & Up to 30 m & Up to 75 m & & & \\ \hline Networking & Infrastructure & Ad-hoc very & Ad-hoc very & Ad-hoc, Peer-to-Peer, & Ad-hoc, Peer-to-Peer, & & & & & \\ Topology & based & small networks & small networks & Star, Mesh & Star, Mesh & & & & \\ \hline Topology & \multirow{4}{*}{24007 devices for structured WiFi BSS} & Up to 8 devices & Up to 65,536 devices & Up to 65,536 devices per network & Up to 65,536 devices per network & Up to 65,536 devices per body, and up to 10 WBANs in 6 mT \\ \cline{1-2} \cline{5-5} \cline{7-9} Target & \multirow{2}{*}{Data Networks} & \multirow{2}{*}{Voice Links} & \multirow{2}{*}{Voice Links} & \multirow{2}{*}{Veice Links} & \multirow{2}{*}{Veice Links, \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } \\ \cline{1-2} \cline{5-5} \cline{7-9} Applications & & & & & & & & \\ \hline Target BAN & \multirow{4}{*}{Off-Body} & \multirow{4}{*}{On-Body} & \multirow{4}{*}{On-Body} & \multirow{4}{*}{Off-Body} & \multirow{4}{*}{Body-to-Body} & \multirow{4}{*}{On-Body} & \multirow{4}{*}{On-Body} \\ Architectures & & & & & & & & \\ \cline{1-2} \cline{5-5}
## 4 Limitations and Future Work  
### Scaling Up Proposed Equipment for Practical Application at Mining Sites  
The size of the supply port of the gyratory crusher and the hopper used in real-world mining sites is approximately 4.43 m in length, while the hopper model used in the experiment in this study had a 115 m supply port approximately, making it a 1/38 scale model of an actual hopper. The size of the rocks dispensed during the experiment were also reduced to a similar scale, and the experiment was carried out in a sequence imitating a practical application. However, to evaluate the performance and usability of the smart hopper system developed in this study, it needs to be applied to actual work sites. To this end, a large collaborative robot arm capable of moving unfiltered rocks from the hopper inlet and a high-resolution RGB-D camera are required to be installed. In  
Figure 15: Experimental results obtained using the proposed smart hopper system in the case involving three rocks above the hopper. (**a**) RGB image, (**b**) binary image of detected rocks, (**c**) classified rocks by depth image, (**d**) Operation of the robot arm on the first rock, (**e**) that on the second rock, and (**f**) that on the third rock.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Num. of**} & \multirow{2}{*}{**Status**} & \multicolumn{2}{c}{**RGB Image Coordinate**} & \multicolumn{2}{c}{**Robot Arm Coordinate**} \\
**Detected Object** & & \(\mathbf{X_{Cam}}\) & \(\mathbf{Y_{Cam}}\) & \(\mathbf{X_{Robot}}\) & \(\mathbf{Y_{Robot}}\) \\ \hline
1 & On the hopper & 318.565 & 243.383 & 0.296 & \(-\)0.044 \\ \hline
1 & Cross the hopper & 283.61  
addition, the image processing algorithm based on an integrated coordinate system with respect to the camera and the robot should be modified to suit the field conditions. Finally, environmental factors such as the illumination of the site and the color and size of rocks should be accounted for. Such applications may improve stability and productivity at mining sites.
# A Novel Slicing Strategy to Print Overhangs without Support Material
## 2 A Novel Slicing Strategy with Geometrical Pre- and Post-Transformation
### Back-Transformation of G-Code
#### 2.4.5 Calculation of the Nozzle Orientation  
The slicing software determines the x-, y- and z-coordinates and the necessary material flow. The orientation of the nozzle around the rotational z-axis has to be calculated within the back-transformation. Since the rotation is around the z-axis, the orientation of the nozzle is independent of the z-value and only depends on the x- and y-values. Two different approaches are suitable:  
* Radial orientation: The nozzle is always oriented towards the origin of the coordinate system, no matter in what direction it moves.  
Figure 9: Splitting of elements to achieve proper accuracy. In (**a**) long elements are not split, while in (**b**) the element is split in 5 segments.  
* Tangential orientation: The nozzle is always perpendicular to the movement in the x- and y-plane.  
Both methods have their advantages and disadvantages. However, the radial orientation has turned out to be the simpler solution with no remarkable downsides on the quality.  
Radial orientation:  
The simpler way to compute the radial angle is to take the \"new\" point and to calculate its polar angle \(\varphi\) in the x-y-plane and set the nozzle orientation to this angle \(\varphi\) (Figure 10a). Since longer elements are split into short segments, alignment to the \"new\" point is sufficient and there is no need to adjust the angle while moving in one segment. This leads to the nozzle orientation towards the origin and therefore also to the center axis of the cone. So, no collision with the printhead and the already printed part can occur. One downside of this method is the nonideal orientation of the nozzle with respect to the material flow.  
Tangential orientation: For the tangential orientation of the nozzle, the normal to the line of movement has to be calculated. Thereby, the normal pointing away from the origin is chosen (Figure 10b) to prevent the printhead of colliding with the already printed material. The major downside of this method is the increase of printing head rotation for small linear movements in different directions.
## 2 Protocols for Synchrotron X-Ray Experiments
### XAFS
#### 2.2.4 Current Challenges and Future Directions  
Currently, the preprocessing and analysis of XAFS data relies on manual verification and adjustments by researchers using specialized software. This process is time intensive and requires significant expertise, particularly for tasks such as energy calibration and background correction.  
Automating data preprocessing and analysis workflows is vital for improving the efficiency and data quality assessment, as demonstrated in the RefXAS study [12]. Automated workflows can quickly identify cases where the measurement quality does not meet the required standards, ensuring that only high-quality datasets are selected for database registration. This enhances the overall efficiency and reliability of data curation, facilitating future analysis and reuse.