esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Formulation of the Proposed BMUA
### The New Eigenequations with Stiffness Modification (\(\Delta k\))  
Traditional BMUA adopts a classic eigenequation, e.g., \((K-\lambda M)\phi=0\), or \(\mathbf{K}\phi=M\phi\lambda\), to update stiffness while assuming mass is well known. In the proposed BMUA framework, two groups of measured data obtained from original and stiffness-modified systems (\(\Delta k\)), are used to derive new eigenequations. This aims to address the coupling effect of mass and stiffness. Two systems, namely, original and stiffness-modified systems are considered together for fundamental structural dynamics. The same equation in the original system is shown as \(\mathbf{K}\phi=M\phi\lambda\) in Equation (1), where \(\lambda\) and \(\phi\) are eigenvalue and mode shape before modification, respectively; the stiffness-modified system is expressed as  
\[(\mathbf{K}+\Delta k)\phi^{\prime}=M\phi^{\prime}\lambda^{\prime} \tag{6}\]  
where \(\lambda^{{}^{\prime}}\) and \(\phi^{\prime}\) are eigenvalue and mode shape after modification, respectively. \(\phi^{\prime T}\) is premultiplied in Equation (1), resulting in \(\phi^{\prime T}K\phi=\phi^{\prime T}M\phi\lambda\). The transposed matrix of Equation (6) is calculated, then \(\phi\) is postmultiplied in the resulting equation,  
\[\phi^{\prime T}(K+\Delta k)\phi=\phi^{\prime T}M\phi\lambda^{\prime} \tag{7}\]  
Subtracting \(\phi^{\prime T}\mathbf{K}\phi=\phi^{\prime T}M\phi\lambda\) from Equation (7), it is simplified as  
\[\phi^{\prime T}M\phi\lambda^{\prime}-\phi^{\prime T}M\phi\lambda=\phi^{\prime T }\Delta k\phi \tag{8}\]  
By defining \(\mathbf{H}=\phi^{\prime T}M\phi\) and \(\mathbf{Z}=\phi^{\prime T}\Delta k\phi\), Equation (8) is rewritten as  
\[\mathbf{H}\lambda^{\prime}-\mathbf{H}\lambda=\mathbf{Z} \tag{9}\]  
Therefore, \(\mathbf{H}\) is solved as another expression, \(\mathbf{\tilde{H}}\):  
\[\mathbf{\tilde{H}}=\left(\lambda^{\prime}-\lambda\right)^{-1}\phi^{\prime T} \Delta k\phi \tag{10}\]  
Finally, the eigenequation error \(\mathbf{E}_{m}\) is reformulated when updating mass:  
\[\mathbf{E}_{m}=\mathbf{H}-\mathbf{\tilde{H}}=\phi^{\prime T}M\phi-\left( \lambda^{\prime}-\lambda\right)^{-1}\phi^{\prime T}\Delta k\phi \tag{11}\]Similar derivation procedures for stiffness updating are employed as follows:  
\(\mathbf{\phi}^{\prime T}\) is premultiplied in Equation (1), resulting in the expression \(\mathbf{\phi}^{\prime T}\mathbf{K}\mathbf{\phi}=\mathbf{\lambda}\mathbf{\phi}^{\prime T}\mathbf{M}\mathbf{\phi}\). Then, the transposed matrix of Equation (6) is firstly calculated, the postmultiplying resulting equation by \(\mathbf{\phi}\) being  
\[\mathbf{\phi}^{\prime T}(\mathbf{K}+\Delta\mathbf{k})\mathbf{\phi}=\mathbf{\lambda}^{\prime}\mathbf{ \phi}^{\prime T}\mathbf{M}\mathbf{\phi} \tag{12}\]  
Premultiplying \(\mathbf{\phi}^{\prime T}\mathbf{K}\mathbf{\phi}=\mathbf{\lambda}\mathbf{\phi}^{\prime T}\mathbf{M}\bm {\phi}\) by \(\mathbf{\lambda}^{-1}\) gives \(\mathbf{\lambda}^{-1}\mathbf{\phi}^{\prime T}\mathbf{K}\mathbf{\phi}=\mathbf{\phi}^{\prime T}\mathbf{M} \mathbf{\phi}\).
# Mode-Locking Dynamics in an All-PM Figure-Nine Tm-Doped Fiber Laser
###### Abstract
Unlike lasers designed with material-based SAs, NALM-based lasers require a relatively high pumping power to initiate the mode-locking [28; 40]. The laser firstly operates in a multi-pulse regime, and then lowering of the pump power is required to obtain single-pulse operation. Multiple pulses propagating in a single cavity tend to interplay with each other. In solitonic fiber lasers, several phenomena of interaction between several pulses are presented [41] such as soliton rains [42; 43], soliton molecules [44], multisoliton states such as tightly bunching of soliton molecules [45], and noise-like pulses generation [46]. Tightly bunched solitons separation can be either dense or coarse indicating, in turn, the stability or instability of the state [47]. The second important element from the point of view of the dynamics of the laser operation is polarization. A number of works in which dynamics of laser in the multi-pulse state was manipulated have employed polarization controllers (PCs) and non-PM fiber architecture for this purpose. This resulted in the non-arbitrary laser settings required to operate in a given state as the PC had to be tuned to switch between states. However, in an all-PM laser based on NALM, the dynamics of operation does not depend on polarization since it is linear. Moreover, in their case, there is no concern about damaging the laser since it consists of fibers and optical components, exclusively. This makes the figure-nine laser an interesting test bed for analyzing pulse propagation dynamics in anomalous dispersion regime. Here we present an all-PM NALM-based fiber laser operating around the central wavelength of 1985 nm. We placed a NRPS in the linear arm of the cavity, which simplified the setup in contrast to previously presented oscillators with NRPS placed in the NALM loop. Thanks to the figure-nine configuration, we can observe pulses leaving the oscillator from both output and reject ports. Using the high pumping power (P\({}_{\text{pump}}\)) necessary for self-starting pulsed operation, we characterize the dynamics of the mode-locked laser from the multi-pulse states down to the single-pulse regime. We show that it is not possible to obtain instantaneous single-pulse operation after turning on the pump of the oscillator. The starting procedure of a NALM laser always requires an overdrive of the pump power to initiate multi-pulse mode-locking, and then gradual lowering of the pump to achieve single-pulsing. We observed eight different operating states of the laser which were controlled solely by the pumping power of the active fiber of the oscillator. We distinguished between these states using both time waveforms and the ratio of average output power between output and reject port.
## 2 System Model and Channel Model
### RIS-Assisted mmWave Massive MIMO System Model  
As shown in Figure 1, the BS-vehicle link is blocked due to the presence of obstructions. The BS can only communicate through the BS-RIS-vehicle link, which is equipped with hybrid beamforming technology and relies on \(N_{t}\) transmit antennas and \(M_{t}\) RF chains. At the vehicle, a structure with \(N_{r}\) receive antennas and \(M_{r}\) RF chains is used.  
The transmit signals of the BS have \(N_{s}\) data streams \(\mathbf{s_{2}}=[s_{1},s_{2},\ldots,s_{N_{s}}]^{T}\in\mathbb{C}^{N_{s}\times 1}\). At the BS, the transmit signals are firstly precoded by the baseband digital beamformer \(\mathbf{F_{BB}}\in\mathbb{C}^{M_{t}\times N_{s}}\). Then, the signal \(\mathbf{F_{BB}}\mathbf{s_{2}}\) is precoded by the analogue beamformer \(\mathbf{F_{RF}}\in\mathbb{C}^{N_{t}\times M_{t}}\). Let \(\mathbf{s_{2}}=\sqrt{P_{R}}y_{FSO}\) denote the signal forwarded by the node R and \(\mathbf{F_{all}}=\mathbf{F_{RF}}\mathbf{F_{BB}}\) be the beamforming matrix at BS, where \(P_{R}\) denotes the hybrid mmWave massive MIMO chain transmit power. Then, the transmit signal at the \(n\)-th time slot after mixing analogue and digital beamforming can be expressed as  
\[\mathbf{X}(n)=\sqrt{\frac{P_{t}}{N_{s}}}\mathbf{F_{all}}\mathbf{s_{2}} \tag{28}\]  
where \(P_{t}\) is the transmit power.  
Let \(\mathbf{H_{all}}=\mathbf{H_{RU}}\mathbf{\Phi}\mathbf{H_{BB}}\) be the signals through the BS-RIS-vehicle cascade channel, where \(\mathbf{H_{BB}}\in\mathbb{C}^{N_{RIS}\times N_{t}}\) is the BS-RIS channel, \(\mathbf{H_{RU}}\in\mathbb{C}^{N_{r}\times N_{RIS}}\) is the RIS-vehicle channel, and \(\mathbf{\Phi}=diag[\mathbf{e}]\in\mathbb{C}^{N_{RIS}\times N_{RIS}}\) is the phase shift matrix of the RIS with \(N_{RIS}\) reflecting elements. \(\mathbf{e}=(e^{j\phi_{1}},e^{j\phi_{2}},\ldots,e^{j\phi_{N_{RIS}}})\) is the vector consisting of the reflection coefficients of each RIS reflecting element.  
The signals passing through the cascade channel reach the vehicle, which applies a combination matrix \(\mathbf{W_{all}}=\mathbf{W_{RF}}\mathbf{W_{BB}}\). \(\mathbf{W_{RF}}\in\mathbb{C}^{N_{r}\times M_{r}}\) is an analogue combiner and \(\mathbf{W_{BB}}\in\mathbb{C}^{M_{r}\times N_{t}}\) is a digital combiner. Then, the signals received by the vehicle at D can be expressed as  
\[\mathbf{Y}(n)=\sqrt{\frac{P_{t}}{N_{s}}}\mathbf{W_{all}}^{H}\mathbf{H_{all}}\mathbf{F_{all}} \mathbf{s_{2}}+N \tag{29}\]  
where \(\mathbf{N}\sim\mathcal{CN}(0,\sigma^{2}I_{N_{r}})\in\mathbb{C}^{N_{t}\times 1}\) denotes additive Gaussian white noise.  
Figure 5: BiLSTM structure diagram; the arrows indicate the direction of data transfer.  
According to [38], we can further express the achievable spectral efficiency as  
\[R=\log_{2}\left(\det\left|I_{N_{t}}+\frac{P_{t}}{\sigma^{2}N_{s}}\mathbf{W}_{all}^{H} \mathbf{H}_{all}\mathbf{F}_{all}\mathbf{F}_{all}^{H}\mathbf{H}_{all}^{H}\mathbf{W}_{all}\right|\right) \tag{30}\]
## 7 Conclusions  
1. This study proposes a mechanical model for calculating the stability of TSWs under local complex loads. The model computes the stress distribution of TSWs under different operational conditions and determines the yield state of TSWs using the D-P yield criterion. 2. A comparison with the results of two existing theoretical models reveals that the accuracy of the mechanical model in this study improves by 5% and 53%, respectively. This indicates that the proposed model more effectively accounts for the influence of in situ stresses and boundary conditions. 3. Based on the analysis of the mechanical model, it is shown that the maximum principal stress (MPS) on the TSW occurs at the midpoint of the left boundary (0, h/2), the center of the lateral load on the lower boundary (LP, 0), or the center of the lateral load (LP, h/2). 4. Taking the residual ore recovery project at the Sawtooth Pit as a case study, this research investigates the changes in stress distribution, yield zone quantity, and critical thickness of TSWs under various parameters. The results indicate that to meet the mining company's requirements for residual ore recovery, it is advisable to maximize SL and minimize LP, LW, and SH. 5. Using the theoretical model presented in this study, the critical thickness of four TSWs in the residual ore recovery project was calculated as 4.6 m, 4.2 m, 2.6 m, and 11.9 m. Additionally, stability analyses of TSWs at the V3412 and V3301 mining sites, based on calculations of maximum principal stress and stability, further validate the accuracy of the mechanical model for engineering applications. X.S. : methodology, project administration, writing--original draft. Y.L. ([PERSON]): formal analysis, software, writing--review and editing. Y.L. ([PERSON]): investigation, conceptualization, writing--review and editing. X.Q. : writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation Project of China (Grant No. 52374152), and the Guangxi Key Research and Development Program of China (Grant No. 2022 AB31023). Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors acknowledge and appreciate the support received from the Central South University During the preparation of this work, the authors used ChatGPT-4o to improve the language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for this publication's content. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. List of Symbols
# An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning
## 3 System Design
### Image Analysis System
#### 3.2.5 Stem Contour Detection Using Segmentation  
The next step is to extract the contour of the stem using DeepLabv3+, a deep learning segmentation. DeepLabv3+ is an extension of the existing DeepLabv3 called semantic segmentation which labels all pixels in an image [14]. It performs better than traditional methods such as U-Net, achieving 89% performance on the test set. We created the training data for this process. An example of an image with training data in a polygon format is shown in Figure 7. The training of DeepLabv3+ was performed with a batch size of 10 and an epoch count of 200 using Adam. No data augmentation was performed. The learning rate was set to \(1\times 10^{-3}\) initially and changed to \(1\times 10^{-4}\) after 120 epochs.  
Figure 6: Reference ball detection results.  
Figure 7: Example of teacher data in segmentation.  
The extraction results of segmentation using DeepLabv3+ are shown in Figure 8. The black areas in the right image are the extracted stems.  
Due to the complexity of measuring stem diameter from stem images obtained by YOLOv5 using traditional image processing techniques, we chose to utilize deep learning-based segmentation for obtaining stem contours. After testing and evaluating various models, we found that DeepLabv3+ offered superior detection performance in comparison to alternatives such as R-CNN and was both lightweight and suitable for our system.
## 2 Proposed Models
### The Proposed Transfer-Learning-Based Model  
VGG-16 is a pre-trained CNN for the object classification task proposed by [PERSON] and [PERSON] in 2014 [19]. This network was trained with the ImageNet  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **No. of Filters** & **Kernel Size** & **Stride** \\ \hline Conv2D-A & 64 & (1,1) & 1 \\ BatchN-A & & & \\ MaxPool-A & & (3,3) & 2 \\ Conv2D-B & 32 & (3,3) & 1 \\ BatchN-B & & & \\ MaxPool-B & & (3,3) & 2 \\ Conv2D-C & 64 & (1,1) & 1 \\ BatchN-C & & & \\ MaxPool-C & & (3,3) & 2 \\ Conv2D-D & 32 & (3,3) & 1 \\ BatchN-D & & & \\ MaxPool-D & & (3,3) & 2 \\ Conv2D-E & 64 & (5,5) & 2 \\ MaxPool-E & & (5,5) & 2 \\ FC-A & 400 & & \\ FC-B & 200 & & \\ FC-C & 2 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the proposed custom architecture for the classification of colorized images.  
Figure 2: The proposed architecture. Conv is convolutional layer; BatchN is batch normalization; MaxPool is max-pooling; FC is the fully-connected layer.  
dataset which includes more than 14 million images belonging to 1000 different classes. VGG was the winning network in the 2014 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC 2014) in the classification + location category in terms of location error and was ranked second in terms of classification error [20]. It is composed of 13 convolutional layers and 3 fully connected layers with 4096, 4096 and 1000 outputs, respectively (Figure 3). It has more than 130 million parameters.  
VGG-16 has been widely used for classification tasks by means of transfer learning [21; 22]. One of the transfer-learning alternatives is to freeze the filter weights up to a specific layer in the network discarding the other layers, and then adding new fully connected layers to the frozen network. Therefore, the trainable parameters are only those corresponding to the FC layers (since the others are transferred from the pre-trained model).  
In this case, the last pooling layer (i.e., pooling_5) was selected to transfer the pre-trained weights to the new model. Like the original network, three FC layers were added, but with a lower number of outputs because the total classes of the current problem are significantly smaller than those of the original problem. Specifically, the three FC layers have 512, 200 and 2 units, respectively. Before the last FC layer, we add a dropout of 0.25. The last layer does not have one unit (unlike typical binary classification problems) but two outputs to improve compatibility with the F1 method of Tensorflow add-ons. The number of parameters in the transfer-learning-based model is lower than the pre-trained VGG-16 due to the reduction of units in the FC layers. In fact, the proposed transfer-learning-based model has about 82 million parameters.
## 4 Results and Discussion
### Influence of Initial Conditions on Wind Parameter
#### 4.1.2 Inlet Wind Speed  
Based on the previous section, in order to analyze the influence of inlet wind speed on wind parameters, the uniform oncoming flow was also set; the wind speed was 10-70 m/s, the interval was 15 m/s, and a roughness length of 0.12 m was set. Similarly, taking 102\({}^{\circ}\) oncoming flow as an example, the results are shown in Figure 6. Then, taking the condition with the inlet wind speed of 10 m as the reference condition, and according to Equations (7)-(9), the nondimensional factors are shown in Figure 5b,d,f.  
Figure 5: Influence of surface roughness on wind parameters: (**a**) Nondimensional wind speed profile; (**b**) Nondimensional wind speed factor; (**c**) Wind attack angle profile; (**d**) Wind attack angle factor; (**e**) Wind azimuth angle; (**f**) Wind azimuth angle factor.  
As can be seen from Figure 6, taking the oncoming flow of 10 m/s as a reference, the influence of inlet wind speed on the wind speed profile was not obvious, the wind speed factor fluctuated within 3%, and it tended to increase then decrease with height. However, the influence of inlet wind speed on wind attack angle and wind azimuth angle could not be ignored. Specifically, the higher the inlet wind speed, the smaller the wind attack angle in a high position. For example, at a height of 4000 m, the wind attack angle factor under 10 m/s was about twice that under 70 m/s; at the same time, the higher the inlet wind speed, the higher the low-level wind azimuth angle. For example, at 500 m height, the wind azimuth angle under 70 m/s was about 3.0\({}^{\circ}\), and the wind azimuth angle factor was about 2.5 times that under 10 m/s.  
Figure 6: Influence of inlet wind speed on wind parameters: (**a**) Nondimensional wind speed profile; (**b**) Nondimensional wind speed factor; (**c**) Wind attack angle profile; (**d**) Wind attack angle factor; (**e**) Wind azimuth angle; (**f**) Wind azimuth angle factor.
# Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production
## 3 Methodology
### Establishment of Experimental Scenarios  
The experiment involves planning for aerial photography with the UAV, the actual photography, alignment of photos, and geospatial data construction. In this study, Phantom 4 RTK (DJI), which is a rotary-wing drone, was used, and the study site was the area around Hayang-eup. The settings for the aerial photography with the drone were the following: average altitude of 100 m, flight speed of 7.9 m/s, camera angle of \(-90^{\circ}\) (vertical), horizontal overlap of 65%, and vertical overlap of 60%. In addition, the area of the drone-mapping zone was approximately 650,000 m\({}^{2}\), with a flight time of approximately 40 min. A total of 473 photos were acquired. After completion of the photography process, VRS-RTK was used to measure the geolocation for four GCPs and five checkpoints (CPs) within the study site, as shown in Figure 1. Based on the image data acquired through the UAV photography,  
\begin{table}
\begin{tabular}{c c} \hline \hline Weight & 1391 g \\ (Battery and props included) & 350 mm \\ \hline Diagonal size & \\ (Battery and props excluded) & 350 mm \\ \hline Maximum ascent speed & Automatic flight mode: 6 m/s \\  & Manual control mode: 5 m/s \\ \hline Maximum descent speed & 3 m/s \\ \hline Overview of test sites and UAV & 58 km/h (A mode) \\  & 50 km/h (P mode) \\ \hline Field of view (FOV) & Front/Back: 60\({}^{\circ}\) (horizontally), \(\pm 27^{\circ}\) (perpendicular) \\  & Downward: 70\({}^{\circ}\) (front/back), 50\({}^{\circ}\) (left/right) \\ \hline GNSS & GPS + BeilDou + Galileo \\ \hline Hovering accuracy range & Perpendicular: \(\pm 0.1\) m (vision) \(\pm\) 0.5 m (GNSS) \\  & Horizontality: \(\pm 0.3\) m (vision) \(\pm\) 1.5 m (GNSS) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the UAV used in this study.  
scenarios for experimental analysis were developed. Based on the four GCPs installed, Scenarios 1 to 5 were created with different numbers of GCPs, including the case without any GCP. For scenarios with multiple GCPs, the total number of all possible cases that could be created by varying the GCP placement could be obtained using the following: \(\sum_{\alpha=0}^{4}\)\(4\mathrm{C}_{\mathrm{x}}\,=16\); Therefore, the final number of models was 16, and PIX4D Mapper v4.5.6 (professional photogrammetry software) was used for modeling to generate an orthophoto; the RMSE values were analyzed through the derived data.
## 2 A Hypothetical Example Application of the V-GSI Chart in a Tunnel
### WestConnex M8 Tunnels--Project Overview  
The WestConnex M8 project (formerly known as the New M5 Project) consisted, in part, of twin motorway tunnels, each up to around 9 km in length, and varying from two to three lanes, respectively, for each tunnel, with the capacity to add additional lanes. The M8 Motorway opened in July 2020 and connects the M5 Motorway from Beverly Hills to a newly constructed Interchange at St. Peters in Sydney, New South Wales, Australia.  
The geological and geotechnical conditions were mapped by project geologists and engineers for each tunnel heading, with an estimated 13,000 or more geological maps being generated. The majority of the excavation on the project was accomplished by roadheader. The mainline tunnels were referred to as M110 (eastbound traffic) and M120 (westbound traffic). Mining headings for each of the mainline tunnels during construction were typically split during construction, i.e., the left-hand side of the tunnel (the leading heading) would be excavated first, then supported. Excavation and support of the right-hand side (the trailing heading) would then follow (or vice versa). Following the bulk of the excavation work, a \"bench\" was typically excavated from the tunnel invert.  
The tunnel support design consisted of several classes of rockbolts and shotcrete, designed mostly pre-construction by Aurecon-Jacobs Design Joint Venture and McMillan Jacobs Associates. Golder Associates Pty. Ltd. (now WSP Australia Pty. Ltd., Brisbane, QLD, Australia) were responsible for the geotechnical design and interpretation of the project. The construction Joint Venture consisted of CPB-Dragados-Samsung and the end client of the project being WestConnex I Transurban.