esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast Maritime Target Identification Algorithm for Offshore Ship Detection
## 5 Conclusions  
This paper addressed an encoder, known as SFMF, which enables multi-scale feature fusion on a map. A cross-sectional assessment of the different component compositions was conducted prior to the experimental application of model choices, then the YOLOF model was selected for comparison with the FMTI model. Although the FMTI model had a slightly lower speed evaluation metric of FPS than the YOLOF model, it had more computational power in the COCO dataset, so the two models mentioned above were chosen for the next experimental comparison. Combining speed and processing power, the FMTI algorithm was able to outperform the previous YOLOF in the marine ship detection data, so it has the potential for future applications.  
The FMTI algorithm could offer technical support in the areas of smart coastal transit, naval defense, and smart maritime construction. It could be employed on the video surveillance equipment to detect offshore ships, detecting the ships entering and departing ports, the field of illegal fighting or military defense by recognizing and pre-warning dangerous boats along the shoreline.  
However, the image data for the majority of the training data set are captured during good weather conditions in this paper, so further studies may still be done to ensure better performance of the model. Future work will focus on the diversity of test samples.  
Conceptualization, J.W. and X.X.; methodology, J.W., D.G. and X.X. soft-ware, X.X.; validation, X.X. and D.G.; investigation, J.W.; resources, J.W.; writing--original draft preparation, J.W.; writing--review and editing, J.W., J.L. and R.L.; visualization, X.X.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52171346), the Natural Science Foundation of Guangdong Province (2021A1515012618), the special projects of key fields (Artificial Intelligence) of Universities in Guangdong Province (2019 KZDZX1035), and the program for scientific research start-up funds of Guangdong Ocean University (R19055).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Name** & **FMTI** & **YOLOF** \\ \hline
**FPS** & 36.66 & 37.53 \\
**FLOPs** & 98.016 G & 93.52 G \\
**mAP** & 0.47529 & 0.40382 \\
**Model\_parameter** & 47.137 M & 42.488 M \\ \hline \hline \end{tabular}
\end{table}
Table 5: FMTI and YOLOF comparison results.
## 2 Materials and Methods  
### Details of the Analyzed Campaign and Evaluation Methodology  
The BOF campaign under analysis comprised 17,669 heats (campaign duration: 946 days), during which 751 measurements of the BOF refractory lining were taken using a non-contact laser device FERROTRON LaCam M from Minerals Technologies Inc., New York, NY, USA. This equipment employs pulsed laser time-of-flight (ToF) technology combined with advanced signal processing to perform non-contact 3D measurements of refractory linings in BOF vessels. The measurement is performed using a class 1 near-infrared (NIR) laser that emits pulses at a frequency of 300 kHz, generating up to 125,000 measurements per second. Deflection of the laser beam by a rotating mirror facilitates scanning of the BOF interior, producing a dense point cloud of the refractory surface. Measurement accuracy (<5 mm) is maintained in harsh environments by utilizing echo-digitization and online waveform analysis to discriminate between reflections from dust/smoke and the refractory lining. The analysis was focused on the section of the vessel from 75\({}^{\circ}\) to 105\({}^{\circ}\) (known as the 3 o'clock position and labeled as S3 zone in this research) and from 255\({}^{\circ}\) to 285\({}^{\circ}\) (known as the 9 o'clock position and labeled as S9 zone in this research). In addition, the research was focused on the horizontal direction, specifically on field 4-5.2 m measured from the top of the vessel, which is located in the area of the trununions of the vessel (Figure 2).  
Out of a total of 751 refractory lining measurements, 160 were selected for detailed processing and in-depth analysis within the investigated areas. Measurements were randomly selected to ensure that they covered the entire campaign period, with approximately equal time intervals between the selected data points throughout the campaign.  
In order to determine the amount of refractory material to be applied to repair the lining, it is necessary to know the results of the laser measurement of the residual thickness of the lining. Gunning is controlled by adjustable feed rates, with automated systems linking output to laser-based thickness goals. Material loading tracks total consumption. It is important to note that the material sprayed does not equal the material deposited due to rebound. The rate of rebound, or unadhered gunning material, varies significantly with gunning parameters, substrate condition, and mix properties. The data obtained on the consumption of gunning refractories in each area were processed and evaluated using Microsoft Excel 365 with the Lumivero XLSTAT 2019 statistical add-in. The graphs were also generated in Microsoft Excel 365.
## 3 Methodology  
This paper has two primary objectives: reducing output voltage ripple and minimizing the charging and discharging cycles of EV batteries. To achieve these goals, an objective function is utilized. The first part of this function focuses on minimizing the square value of the ripple, guiding the controller to generate signals that activate the EV battery and supercapacitor, assisting the source of the DC microgrid in tracking the reference output bus voltage. The primary controller is a fuzzy controller, discussed in the next subsection. The second objective aims to minimize the number of charge and discharge cycles of the EV battery to extend its lifespan, as the lifespan is directly related to these cycles. The second part of the objective function reduces the square sum of the EV battery current, thus minimizing the charging and discharging cycles. This objective is achieved by activating the supercapacitor to handle short-time ripples and engaging the battery only in cases of longer-time ripples in the output voltage. Consequently, the objective function is defined as follows:  
\[\text{OF}=\int_{0}^{\tau}(v_{\text{Ripple}})^{2}dt+0.1\int_{0}^{\tau}l_{\text{ battery}}^{2}dt \tag{1}\]  
\[v_{\text{Ripple}}=v_{\text{bus}}-100 \tag{2}\]  
\(v_{\text{bus}}:\) The voltage of DC microgrid bus voltage;  
\(I_{\text{battery}}:\) The battery current;  
\(v_{\text{Ripple}}:\) The difference between bus voltage and reference voltage.  
The optimization and adjustment of the controller to meet these goals will be explained in the following subsections.  
Figure 4: Stabilizer model, where \(R\) is the buck–boost converter resistor, \(L\) is the buck–boost converter inductance, \(C\) is the DC link capacitor, \(R_{h}\) is the connection resistor, and \(V_{\text{Bus}}\) is the DC microgrid bus voltage [35].
# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 2 Materials and Methods
### Basic Description of Visual-Inertial
#### 2.2.1 Coordinate System Normalization  
The different positions of the device placed on the wearable undershirt and the orientation of the device itself can have important effects on training. IMU sensor measurements are taken from the mobile device coordinate system (b-system), while ground truth motion trajectories are taken from the global coordinate system (n-system). In this paper, the coordinate system is defined in terms of the chest camera device orientation, using a coordinate system in which the \(Z\)-axis is aligned with gravity. In other words, any such coordinate system can be chosen as long as it remains consistent throughout the sequence. Coordinate conversion to a coordinate system with \(Z\)-axis aligned with gravity does not result in singularities or discontinuities, such as quaternions, as long as there is an appropriate rotational representation. The rotation of the IMU device direction \(R_{gry}\) is in the same direction as the \(Z\)-axis (\(R_{z}\)) rotation:  
\[\overset{\rightarrow}{a}=R_{z}R_{gry}\overset{\rightarrow}{a}_{dev} \tag{12}\]  
During testing, the coordinate system was defined using the orientation of the camera equipment fixed on the chest with its \(Z\)-axis aligned with gravity.  
\[\overset{\rightarrow}{a}=R_{gry}\overset{\rightarrow}{a}_{dev} \tag{13}\]
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 3 Comparison of Calculation Results
### Height of Shear Key  
Shear key height \(h\) has a leading influence on shear strength \(f_{\rm bk}\) of the grouted joint. When the minimum value of \(h\) is taken, it means there is no shear key on the grouted joint, and the maximum value of \(h\) is generally half the thickness of the grouted material, which is generally greater than the value in an actual project. The curve of shear strength \(f_{\rm bk}\) calculated by different codes and shear key height \(h\) is shown in Figure 3, where the specifications of API, DNV, and DNV-GL do not set a maximum value of \(h\), and only the case when \(h\) less than 50 mm is compared. It can be seen from the data that the DNV takes \(f_{\rm bk}\) as 0, when \(h=0\), ignoring the cohesive force and friction force between grouted material and the steel pipe surface. With increased \(h\), the slope of the HSE curve becomes the largest and \(f_{\rm bk}\) grows the fastest. However, the application scope of \(h\) in HSE is limited. Compared with DNV and DNV-GL, the API curve increases faster with increased \(h\), reflecting that the influence of \(h\) becomes more obvious. The DNV-GL code requires that the height of shear key \(h\) is greater than 5 mm, to avoid slippage damage on the interface of steel pipe and grouted material in the grouted section. The slope of the curve of the DNV code is the smallest, and shear strength \(f_{\rm bk}\) of the calculation is the smallest with increased \(h\). In addition, the API, DNV, and DNV-GL codes assume that shear strength \(f_{\rm bk}\) will continue to increase with the growth of \(h\), which disagrees with the actual situation.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 3 Methods
### Loss Function  
The loss function used in the SISnet network is the cross-entropy loss function. The formula is as follows:  
\[L=-\sum_{i=1}^{N}y_{i}log\hat{y_{i}}+(1-y_{i})log(1-log\hat{y_{i}}) \tag{5}\]  
Here, \(L\) is the training loss, \(N\) is the number of samples, \(y_{i}\) is the real sample label, and \(\hat{y_{i}}\) is the prediction label. The smaller the \(L\) value, the closer the prediction label is to the real sample label, and the more accurate the network segmentation result is.
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
###### Abstract  
In order to improve the detection performance of the radar constant false alarm detector in a multiple-target environment, a Kaigh-Lachenbruch Quantile constant false alarm rate detector based on composite fuzzy fusion rules (CFKLQ-CFAR) is designed by combining fuzzy fusion rules and the Kaigh-Lachenbruch Quantile constant false alarm rate detector. Two sensors are used to collect environmental information, and the membership function value is calculated based on the collected information. Furthermore, the presence or absence of the target is judged compositively by four fuzzy fusion rules. CFKLQ-CFAR is applied to the variability index CFAR (VI-CFAR) detector, and an adaptive constant false alarm rate detector based on the composite fuzzy fusion rules (CFVI-CFAR) is designed to improve the performance of the radar constant false alarm detector in different environments. The simulation experiment results show that the average detection probability of CFKLQ-CFAR is 2.67% and 1.00% higher than that of KLQ-CFAR and the fuzzy logic fusion detector (FUMCA-CFAR) in a multiple-target environment. The average detection probability of CFVI-CFAR is 3.66% higher than that of the variability index heterogeneous clutter estimate modified ordered statistics CFAR (VIHCEMOS-CFAR) in a multiple-target environment, while in a clutter edge environment, the average false alarm probability of CFVI-CFAR is only 1.65% of that of VIHCEMOS-CFAR. Therefore, the performance of the radar constant false alarm detector has been effectively improved.  
CFAR detection; fuzzy fusion rules; multiple-target environment +
Footnote †: journal: Applied sciences  
1
## 2 Materials and Methods
### Isometric Torque Measurement  
The maximum isometric torques for knee flexion and extension were measured in a sitting position using an isometry measurement device (Easytorque, [PERSON], Zemmer, Germany). The knee angle was set to 80 degrees for measuring knee flexion and to 90 degrees for knee extension (Figure 3). The thighs were fixated with pads and the calves pressed isometrically against the pads, and thus against the force transducers. The test persons had to build up strength for five seconds and hold it for five more. For familiarization purposes, a trial was performed for each measurement. Both sides were tested alternatingly. At least three isometric measurements were performed for each side. Whenever the third value turned out to be the maximum value, further tests were performed until the last measurement value was smaller than the penultimate value. Only the maximum value achieved was included in the subsequent calculations. The reproducibility of this type of isometric measurement has been shown in other studies (intraclass correlation coefficient 0.97-0.99, [PERSON]'s correlation coefficient 0.95 [23; 24].  
Figure 1: Position of the marker spheres. 1: Trochanter major right, 2: spina iliac anterior superior (ASIS) right, 3: symphysis, 4: ASIS left, 5: trochanter major left, 6: spina iliac posterior superior (PSIS) left, 7: PSIS right, 8: S1.  
Figure 2: Simplified geometrical model: representation of the left and right hemi-pelvises by triangles between ASIS, PSIS, and symphysis. (**a**) Frontal view; (**b**) view from the right side; (**c**) angles W1 and W2 between the horizontal (transversal) plane and the triangles are the basis for the calculation of the pelvic torsion. Dotted lines: pelvic anteversion.
## 1 Introduction
The results indicated that the bearing capacity of gravel soils could be significantly affected by factors such as particle size distribution, density, and the presence of fines. It was found that increasing the proportion of fines in the soil could lead to a reduction in bearing capacity, while proper compaction and moisture content can improve the strength of the soil [48]. Another study focused on the deformation capacity of gravel soils in Turkiye. They conducted laboratory experiments to investigate the deformation behaviour of gravel soils under different loading conditions. The results showed that gravel soils have a relatively high deformation capacity, which can be attributed to the interlocking of coarse particles and the presence of void spaces within the soil matrix. The researchers also found that the deformation behaviour of gravel soils is influenced by factors such as particle size distribution, moisture content, and compaction. Overall, the strength and deformation capacity of gravel soils in Turkiye can vary depending on various factors. Proper characterization and testing of these soils are essential for ensuring the stability and safety of structures built on them. However, further research is needed to fully understand the behaviour of these soils under different loading conditions and to develop appropriate design guidelines for their use in geotechnical projects [49]. This study focuses on investigating the engineering parameters of soils through extensive field and laboratory tests conducted in Duzce Province, located in the northwestern part of Turkiye. Data obtained from 27 boreholes drilled to various depths were used to analyse the bearing capacity and settlement properties of soils. In the laboratory tests, excavation depth, groundwater levels, unit weight, water content, particle size distribution (No. 10 and No. 200 sieves), internal friction angles, and cohesion coefficients were evaluated. Statistical methods were used to develop predictive models for bearing capacity and settlement, and high accuracy was demonstrated based on soil parameters. The results of this study show that there were strong correlations between soil parameters and geotechnical properties. Using Regression methods, a cubic predictive model predicted bearing capacity with R\({}^{2}\) = 0.79, while a quadratic model predicted settlement with R\({}^{2}\) = 0.90. Validation at 95% confidence interval confirmed the reliability of these models. These findings highlight the potential of statistically validated models as robust tools for predicting soil behaviour based on field and laboratory test results.
## 2 Methods
### Ray Casting Algorithm Optimized with Early Ray Termination and Adaptive Sampling Methods
The core principle of early ray termination is that, as the ray traverses the volume data, the contribution of subsequent sampling to the pixel'sfinal color can be ignored if the accumulated opacity value at a particular sampling point is already sufficiently large. Once the accumulated opacity value exceeds a specified threshold, the sampling process terminates immediately, avoiding unnecessary computations and reducing the overall computational load. Furthermore, since seabed topography and islands typically exist within the ocean modeling computational domains, a large number of grid points are assigned null values, generating continuous null-value regions where the opacity of the data points is zero. Given this characteristic of the ocean scalar data, this study also employs an adaptive sampling method to leap the null-value space quickly. As shown in Equation (5), if the absolute value of the difference between \(A_{i}^{\Delta}\) and \(A_{i-1}^{\Delta}\) is less than the sufficiently small pre-set threshold \(\epsilon\) (\(\epsilon>0\)), the ray is considered to have entered the null-value region, and the sampling step size is appropriately increased. This optimization can efficiently accelerate the sampling process while preserving the original high-quality visual effects. \[\left|A_{i}^{\Delta}-A_{i-1}^{\Delta}\right|<\epsilon \tag{5}\]
# Ransomware Detection Model Based on Adaptive Graph Neural Network Learning +
## 2 Materials and Methods
### Acquisition of API Call Sequences  
API call sequences are derived from the behavioral logs within the Cuckoo Sandbox, where both ransomware and benign software are executed. Based on the behavior observed and documented in studies such as the execution process of the WannaCry virus [27; 28], the ransomware attack can be broken down into five key steps: establishing network connectivity, generating asymmetric keys for encryption, establishing communication with remote command and control (C&C) hosts, encrypting data on the host system, and finally, releasing ransom information to the victim.  
Through the analysis of these steps, sensitive API functions are identified. These sensitive API call functions are then integrated with the dynamic features obtained in the Cuckoo Sandbox to form an API call sequence text graph. During the construction of this text graph, the weight between the word nodes is adjusted using the identified sensitive APIs, thereby amplifying the features related to these APIs. Simultaneously, while calculating the weight between word nodes, the statistical rules of the Pointwise Mutual Information (PMI) theory are modified to preserve both word order information and word co-occurrence information. Finally, the constructed API call sequence text graph is input into the ADC-TextGCN, and the classifier subsequently produces results after discrimination. This methodical approach ensures a comprehensive and effective evaluation of potential ransomware threats.
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 4 Results
### Yield Estimations Models Built in Jilin and Liaoning Provinces
#### 4.2.2 Model Built Using Variables Selected by a Two-Stage Importance Evaluation Method  
Figures 5 and 6 show the modeling process according to the second method described in Section 3.2 in Jilin and Liaoning provinces, respectively. In Jilin Province, the R\({}^{2}\), CCC, and MARE of the yield estimation model (Yield Model 3) built with the variables from the GPP group were 0.8201, 0.9761, and 0.0943, respectively. The R\({}^{2}\), CCC, and MARE of the yield estimation model (Yield Model 4) built with variables from the NDVI group were 0.8019, 0.9599, and 0.0961, respectively. The modeling accuracy using only the remote sensing variables was lower than that using only the agrometeorological indexes. The model built using only NDVIa variables had the lowest accuracy (R\({}^{2}\) = 0.6622; CCC = 0.8282; MARE = 0.1154). In Liaoning Province, the R\({}^{2}\), CCC, and MARE of the yield estimation model (Yield Model 3) built with variables from the GPP group were 0.7075, 0.8435, and 0.1247, respectively. The R\({}^{2}\), CCC, and MARE of the yield estimation model (Yield Model 4) built with variables from the NDVI group were 0.7353, 0.8783, and 0.1128, respectively. The accuracy of models using EDD variables, AAT variables, SPI variables, and GPPa (NDVIa) variables together was higher than that of using any one of them alone (Table 2). The model built using only GPPa variables had the lowest accuracy (R\({}^{2}\) = 0.5509; CCC = 0.6969; MARE = 0.1710).  
Figure 5: The modeling process according to the two-stage importance evaluation method in Jilin Province.  
Figure 6: The modeling process according to the two-stage importance evaluation method in Liaoning Province.