esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Actual Sample Determination  
The experimental data in Table 3 observed that the sample recoveries ranged from 96.29% to 103.22% with a relative standard deviation of 1.78% to 3.26%. Furthermore, it shows that the newly prepared MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE is suitable for the determination of trace uranyl ions in actual samples within a 95% confidence level and an acceptable range of error.
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 2 Related Works
### Most-Used SAR Datasets  
SAR images are obtained from air and satellite platforms. The most-utilized SAR dataset for image processing is the MSTAR [27], created by the U.S. Air Force based on HH polarization X-band images. This dataset has images of armored vehicles of 15 different types. There are a few hundred images, all of which are convenient for image processing.  
Some of the most recent SAR datasets are OpenSARShip 2.0 [28], JAXA's ALOS PALSAR [29] forest/non-forest, and the SARptical [30] dataset. OpenSARShip 2.0 includes 41 images of Sentinel-1 and nearly 11,000 various ships. JAXA's ALOS PALSAR forest/non-forest dataset is used for land-cover analysis. Additionally, the SARptical provides TerraSAR-X data.  
The main differences between SAR datasets are that they can be obtained from different sources using separate bands, and that they have different resolutions. Resolution and noise of the datasets are some of the main factors affecting success.
## 3 Colpitts Chaos Oscillator
### Study on the Colpitts Chaos Oscillator in a Simulation Environment and Prototype  
The chaotic system is characterized by sensitivity to the initial conditions. In the case of the analog circuit, the sensitivity to the electronic component's nominal values and parasitic characteristics can be considered an implication of this property. Even for identical initial conditions, the slight deviations in nominal values imply changes in the system's state after a short time interval. Simulation and experimental studies aim to determine the Colpitts chaos oscillator parameters that provide chaotic dynamics for the fundamental frequency of 96.86 kHz.  
The simulations were run in the LTspice simulation environment, and the prototype measurements were taken with Analog Discovery Pro. MATLAB was used to process the signals for both the simulation and the prototype. The first stage of model verification involved comparing the attractors' two-dimensional projections to those reported in the original publication [36]. Following that, the Z1 TEST was used to evaluate the system's signal behavior, quantifying it on a scale of 0 to 1 [62]. In this case, 0 indicates periodic behavior, while 1 represents chaotic behavior.  
The Z1 TEST consists of four primary steps. The first step is calculating the Fourier decomposition of the input signal \(X\)(n). The next step is determining the mean square displacement \(M_{c}\)(n) using the Fourier decomposition results. The final stage involves determining the correlation coefficient \(K_{c}\) between \(M_{c}\)(n) and the linearly growing array. For precise results, this procedure is conducted several times. The Z1 TEST returns the median of all estimated \(K_{c}\) values.  
The nominal values of the reactive components were reduced to achieve a higher fundamental frequency for the Colpitts chaotic oscillator. Modifications were made to the original working frequency of 96.86 kHz, resulting in an increased fundamental frequency of 968.59 kHz. This improvement was achieved by changing the reactive component values to \(L_{1}\) = 10 uH and \(C_{1}\) = \(C_{2}\) = 5.4 nF. [PERSON]'s formula was used to compute the fundamental frequency, which considered the equivalent capacitance produced from the series combination of \(C_{1}\) and \(C_{2}\). The change is critical because it provides the path for faster data transfer speeds inside the QCSK communication system.
# End-To-End Convolutional Neural Network Model for Fear Fault Diagnosis Based on Sound Signals
## 2 Model Building
### Fault Pattern Recognition Based on CNN  
The end-to-end stacked CNN has the capability to establish the inner relationship between the time and frequency domain signals and fault types. The network consists of three functional layers: the convolutional layer, the batch normalization layer, and the fully connected layer.  
The end-to-end convolutional layer can be viewed as a feature extraction representation jointly with classification to realize the adaptive feature extraction and feature identification. Convolution kernels are used to do convolution operations over multi-dimensional input matrices to extract different features and increase the bias to results. The equation is defined as followed:  
\[x_{j}^{l}=\sum_{k\in M_{i}}x_{k}^{l-1}\cdot\omega_{kj}^{l}+b_{j}^{l} \tag{2}\]  
where \(x_{j}^{l}\) represents the \(j\) feature map of layer \(l\), \(x_{k}^{l-1}\) represents the \(k\) output feature map of former layer, and \(\omega_{kj}^{l}\) represents the convolution operation between the \(k\) output feature map of former layer and the \(j\) feature map of layer \(l\). \(M_{i}\) represents the input feature atlas and \(b_{j}^{l}\) is a bias corresponding to \(x_{j}^{l}\).  
Figure 2: Multi-channel information fusion flowchart.  
The output feature of each convolutional layer is normalized by the batch normalization layer such that the mean and variance of the feature become 0 and 1 according to Equation (2).  
\[x_{2}=\frac{x_{1}-\mu_{B}}{\sqrt{\sigma_{B}^{2}+\epsilon}} \tag{3}\]  
where \(\mu_{B}\), \(\sigma_{B}^{2}\) represent the mean and variance of the mini-batch, respectively. However, the normalization processing between layers will change the distribution of data and learned features. Therefore, Equation (3) is used to restore the certain level features by transformation and reconstruction.  
\[x_{3}=f(\gamma x_{2}+\beta) \tag{4}\]  
where \(\gamma\), \(\beta\) are the two parameters that need to be learned, and the activation function \(f\) is used to realize a nonlinear transformation for output features of each batch normalization layer.  
Finally, the fault pattern recognition is realized by the fully connected layer. The calculation process of the fully connected layer can be expressed as follows:  
\[h\left(x^{l}\right)=f\left(wx^{l-1}+b\right) \tag{5}\]  
where \(x^{l-1}\) represents the input of the connected layer \(l-1\), \(h\left(x^{l}\right)\) represents the output of connected layer \(l\), and \(\omega\) and \(b\) represent weight and bias respectively.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 1 Introduction
Compared to transmission tower re-identification, facial recognition exhibits a more pronounced difference for distinct faces. Additionally, pedestrian re-identification can rely on some characteristics of different individuals for identification, such as facial features and the relative positioning of human body key points [38; 39]. Landmarks, due to their specific geographical locations, often come with discernible reference objects, making their identification process relatively stable. Therefore, transmission tower re-identification is a challenging issue. Considering the limitations of nameplate recognition and POS, this paper proposes a system framework for transmission tower re-identification, which contains two parts. These are the construction of the transmission tower identity matching template library and the identity matching stage (the identity matching of the transmission tower in the input images). Both parts require the implementation of transmission tower detection and the generation of feature vectors. Given our aspiration to deploy this algorithm in edge devices such as UAVs, we prioritize accuracy during the establishment of the template library. During the identity matching stage, we intend to comprehensively consider both speed and accuracy. Consequently, optimizations are made for the transmission tower detection and matching networks. Within the framework of our newly proposed transmission tower re-identification system, the principal contributions are enumerated as follows:  
1. During the identity matching stage, we propose using the YOLOv5-conv head network to detect transmission towers. While maintaining detection accuracy, the speed of transmission tower detection is enhanced;
2. During the training of the transmission tower matching network, we introduce an online triplet sample generation strategy. During the training process, we fix the anchor and positive samples in the triplet and employ the Hungarian algorithm to optimize the selection of negative samples in all triplets. The online triplet generation for triplet sampling strengthened model convergence stability, accelerated the convergence speed, and improved the rank-1 accuracy of transmission tower identity matching. 3. We propose a method to establish a transmission tower identity matching template library. The matching template library for transmission towers is constructed based on the feature vectors generated from transmission tower images. Additionally, GPS information can be included in the database, which can be neglected in the absence of GPS signals. 4. Our proposed method does not rely on GPS information. During the process of matching transmission towers, on the one hand, if the input image is equipped with GPS information, the matching accuracy and speed can be improved by narrowing the template library. On the other hand, the method is still capable of performing transmission tower identity matching even in the absence of GPS information in the input images.
# Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings
## 3 Results
### Residential PVSCS
#### 3.1.3 Life Cycle Assessment  
The carbon balance of the PVSCS is calculated according to Equation (14) and the greenhouse gas and the energy payback according to Equations (15) and (16). The emissions factor for the Spanish electricity production is 0.19 \(tCO_{2}/MWh\)[51], and the emissions for a Chinese-made PV module of 811 \(kg\)\(CO_{2}-eq/KWp\)[52] and 519 \(kg\)\(CO_{2}-eq/kWp\) for the balance-of-system (BoS) [53]. The results are shown in Table 5 and show the good footprint of all the configurations. For the more conventional installations on the roof, the greenhouse gas payback time (GGPBT) is between 4.1 and 4.4 years, and the energy payback time (EPBT) between 1.6 and 1.8 years. For the installation on facades, the GGPBT is between 5.1 and 6.5 years and the EPBT between 2.0 and 2.5 years. Considering the projected lifetime of 25 years, all these installations will produce between 9 and 14 times the energy invested in their deployment (EROI, energy return on investment).
# Comparison of Radio Frequency Path Loss Models in Soil for Wireless Underground Sensor Networks
## 4 Conclusions  
The two main models of estimation of attenuation in soil were described and compared with each other. These comparisons showed a large difference between the estimation values of the modified-Friis and CRIM-Fresnel models. The difference between the two models is largely caused by the method that these models were based on. Both models were developed based on limited soil samples and both are semi-empirical models. They also used different mixing models for estimation of soil properties during the development of the RF attenuation models (Peplinski mixing model is used in Modified-Friis and CRIM mixing model is used in the CRIM Fresnel model), which further contributes towards the difference between these models. These differences showed a need for further investigation into the accuracy of these models through comparison of the estimated values with actual measured values from laboratory or field trials in varied soil conditions and compositions. A laboratory test setup for measurements of signal attenuation in soil was developed. Signal attenuation at 434 MHz was measured at five different soil compositions and three different moisture contents. Results from these measurements were compared with the estimated values provided by modified-Friis and CRIM-Fresnel models. These comparisons showed that in soils with higher permittivity value (i.e., higher water content/higher clay content) the modified-Friis model provided a more accurate estimation compared to the CRIM-Fresnel model. In contrast, the results from these investigations showed the CRIM-Fresnel model has a higher accuracy in estimation of attenuation in soils with lower permittivity.  
This paper only investigates the attenuation of signal in five different soil compositions at three gravimetric water contents. Therefore, more tests on different soil compositions and conditions are required to fully validate the findings. More varied soil types and conditions are currently being investigated as a part of this research and research on the effect of soil parameters on attenuation and accuracy of the models is ongoing. Furthermore, investigation of the accuracy of these models in different frequency bands can also be helpful in applications were higher frequencies are used (i.e., Ground Penetrating Radar, GPR).  
**Author Contributions:** conceptualization, D.A. and A.S.; methodology, D.A.; software, D.A. and A.S.; validation, D.A., A.S.; formal analysis, D.A.; investigation, A.S.; data curation, D.A.; writing--original draft preparation, D.A.; writing--review and editing, D.A and A.S.; supervision, A.S.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors of this paper would like to thank [PERSON] for his help with the TDR measurements. The authors also would like to thank the Civil Engineering laboratory staff for their help and assistance.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Parallel Accelerated Fifth-Order WENO Scheme-Based Pipeline Transient Flow Solution Model
## 3 Numerical Calculation Method
### Boundary Condition Processing
#### 3.4.2 Downstream Boundary Conditions  
The downstream boundary can be the flow rate boundary, flow boundary, or valve boundary. The downstream flow rate is given as \(v_{(i,N(i))}\) or flow rate as \(\Big{(}v_{(i,N(i))}=Q_{(i,N(i))}/A_{i}\Big{)}\)for the flow rate boundary or flow boundary (Here \(i\) represents the pipe number of the pipe network, the same as below). Now, the head at the end of the pipe can be calculated as  
\[h_{(i,N(i))}=h_{(i,N(i)-1)}^{n-1}-v_{(i,N(i))}\frac{c_{i}}{g}+v_{(i,N(i)-1)}^{n- 1}\left(\frac{c_{i}}{g}+\Delta t\sin\theta-\frac{f\Delta x\Big{|}v_{(i,N(i)-1)} ^{n-1}\Big{|}}{2 gD_{i}}\right) \tag{21}\]  
Based on the MOC at the boundary and the orifice outflow law, the relationship between head and flow velocity under a valve boundary condition can be determined as  
\[v_{(i,N(i))}=\frac{v_{m}}{\sqrt{h_{0}^{\prime}}}\tau_{j}\sqrt{h_{(i,N(i))}} \tag{22}\]  
where \(v_{m}\) stands for the valve fully open valve at the average flow rate, \(h_{0}^{\prime}\) stands for the pipeline constant flow state at the valve head, and \(\tau_{j}\) is the valve opening degree.  
To obtain the upstream and downstream boundary flux values \(\mathbf{F}_{-1/2}\) and \(\mathbf{F}_{N+1/2}\), the virtual boundary values outside the cell inside the pipe should be determined. The dummy grid technique proposed by [PERSON] et al. [31] is employed in this paper to improve the computational efficiency while ensuring the accuracy of the results. As shown in Figure 1, for the upstream constant pressure boundary, we have \(\mathbf{U}_{-2}=\mathbf{U}_{-1}=\mathbf{F}_{-1/2}\), while for the downstream flow velocity boundary or valve boundary, we have \(\mathbf{U}_{N+2}=\mathbf{U}_{N+1}=\mathbf{F}_{N+1/2}\).
## 2 Literature Review
### Progress Beyond the State of the Art  
Currently, in manufacturing plants, mobile robots navigate from workstation to workstation using shopfloor perception methods that are based on sensors, which often add an error to the final position. This error is an obstacle to any object manipulation in those workstations. To compensate for that error, they often facilitate a physical connection with the workstations, enabled by physical clamping mechanisms, in what is called physical docking. This requires a specific geometry from the robots and workstations, which adds physical constraints to the execution of the production.  
In this paper, a framework is proposed for the proper functionality of a mobile robot in a fenceless manufacturing environment with several workstations. First, the navigation is performed using data from laser sensors, then a much more accurate localization procedure, based on a camera mounted on the robot, is performed to compensate for the error in navigation, in what is called virtual docking of the mobile robot in front of the workstations. Finally, an object detection method is used using cameras mounted on the manipulator of the mobile robot to detect the object to be manipulated with even greater accuracy. The result is a system that minimizes the error, while forgoing the constraints a physical clamping system would add to the production at the same time. These functionalities also allow for easier reconfiguration of the execution in case of error, or in case of rearrangement of the shopfloor, that with a physical clamping system would be much more costly in time and actual cost.
# A Literature Survey on Electrical-Current-Assisted Friction Stir Welding
## 6 EA-FSW of Dissimilar Joints
### EA-FSW of Dissimilar Steels  
[PERSON] et al. [31] investigated EA-FSW of 2 mm-thick plates of 2 Cr13 Mn9 Ni4 stainless steel and Q235B steel in lap configuration. Tool rotational speed of 1000 rpm, tool traverse speed of 50 mm/min, and current intensity of 1000 A were the welding parameters used in this case. They indicated that it is feasible to join high-strength alloys without tool damage. Due to the high shear strength of steels, the wear rate in the FSW process is high. [PERSON] et al. [31] showed that EA-FSW could minimize tool damage during welding. They stated that no gap or voids formed at the EA-FSW joint interface, and the mechanical interlock's hardness was higher than the base metals. The mechanical interlock in the 2 Cr13 Mn9 Ni4 stainless steel and Q235B steel joint welded by EA-FSW was greater than the FSW case.  
[PERSON] et al. [38] studied the EA-FSW of IS 2062 Grade B and 316 stainless steel. The aim of their research was the optimization of process parameters in order to obtain the maximum possible tensile strength and lowest tool wear. They used the same process parameters for FSW and EA-FSW to compare the effects of electric current. The rotational tool speed, traveling velocity, and electrical current were 500-1050 rpm, 11-24 mm/min, and 50-150 A, respectively. This research was based on statistics, and they did not report deep analysis or characterization of their welded samples. The maximum tensile strength was achieved at 650 rpm, 20 mm/min, and 150 A has 452 MPa. The lowest tool wear was recorded in the same process parameter by 0.07%, while the wear of the tool in the FSW sample with the same process parameter was 0.11%.
## 4 Discussion
This lack of consistent satellite observations creates uncertainty when interpreting vegetation trends across space, as environments with consistent cloud cover have fewer observations [23]. Additionally, the increase in observations over time may skew the interpretation of long-term multidecadal vegetation trends [125]. Recommendations for future research include the use of high-temporal-resolution nanosatellites to analyze the daily patterns in plant senescence at higher spatial resolutions across the trail system (e.g., Planet, MAXAR). In addition, a comparative analysis of the local phenological shifts within isolated environments throughout the trail system is required. As savannas and grasslands may be misclassified at the boundaries of their environmental ranges, implementation of high-resolution land cover classification would help land managers track the decline in forested land cover [126].
## 3 Results
### Earthquake Grouping Characteristics  
To facilitate the discussion of the grouping characteristics of earthquakes occurring after anomalies, strong earthquakes with a magnitude Mw \(\geq\) 5.8 that occurred within the high-value anomaly area and near its edges within 1 year after a group of anomalies are called a group of earthquakes. Earthquakes of magnitude 5.8 or more that occurred within 1 year after the anomaly appeared are marked on the anomaly distribution map (Figure 2).  
From our analysis, 100% of the grouped earthquakes occurred within 1 year after the appearance of the grouped anomaly, and the epicenter location is still located inside or near the boundary of the high-value zone. However, there are two cases of earthquake groupings: first, all the earthquakes occurred within the same anomaly area, and no earthquakes occurred in other anomaly areas (e.g., [18]), such as Anomalies 7 and 8 in Table 1; second, earthquakes occurred in multiple anomaly areas of grouping anomalies, except for Anomalies 7 and 8, and the remaining seven groups of anomalies belong to this case, accounting for 70% of grouping anomalies.  
In addition, as for the subsequent year of anomaly occurrences, multiple earthquakes of magnitude 5.8 or greater occurred in the subsequent year of five anomalies of 18 January 2015, 15 September 2016, 2 December 2016, 24 September 2019, and 12 November 2019. Specifically, on 15 September 2016 (Anomaly 4) and 24 September 2019 (Anomaly 7) had four earthquakes of magnitude 5.8 or greater in the anomaly area (e.g., [19; 20]).  
Among the 28 anomalous zones discussed in this section, 10 had more than two strong earthquakes in the follow-up, six had only one strong earthquake in the follow-up, and 12 had no strong earthquake in the follow-up.
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 6 Improving Mechanical Property  
Experimentally, in-situ nanoparticle strengthening method is an effective strategy to enhance the strength of steel and Cu alloy without sacrificing plasticity, as seen in Figure 16. With appropriate microalloying elements and preparation technology, in-situ nanoparticles appear and the grains are refined. Therefore, the principle of strengthening is due to fine grains, precipitation strengthening and solution strengthening. It should be noted that the uniformly distributed Ti\({}_{3}\)O\({}_{5}\), together with the optimized inclusion (seen in Figure 15a) pinning at the grain boundary in steels, will restrain the growth of grains and the movement of grain boundary according to [PERSON] pinning theory, which contribute to strengthening effect [92; 93; 94].  
Based on Orowan hardening mechanism, precipitation strengthening effect is mainly caused by the interaction between precipitates and dislocations during deformation [95; 96; 97]. While hindering the movement of dislocation, stress concentration occurs near the second phase which always leads to cracks initiation and rupture, indicating sacrificing the ductility. However, in-situ nanoparticle strengthening method can overcome this problem. Possessing coherent or semi-coherent interface of nanometer scale with the matrix, in-situ nanoparticles make contribution to strength and plasticity simultaneously. Dislocations are permitted to pass through the coherent matrix-particle interface, so stress concentration can be efficaciously relieved and considerable plastic strain turns to be possible, as seen in Section 2 and Figure 1. Specifically, it should be noted that the elimination of \(\delta\) phase segregation is also advantageous to distinctly improve the ductility of tin bronze alloys.  
Figure 15: (**a**) TEM micrograph and EDS result of inclusions in the investigated plain carbon steel with and without in-situ nanoparticles [56], (**b**) TEM and EDS analysis of the composite nano-precipitated phase in Ni-Cr-Mo HSLA steel [58].