esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Pressure Sensor Calibration Test  
In order to accurately determine the weight change of fertilizer in the fertilizer tank, it was necessary to calibrate the pressure sensor of the fertilizer tank. First, the China Machinery Miele 1304 field cultivating fertilizer applicator was placed on the flat ground, and the pressure sensor measurement value was recorded when the fertilizer tank was empty; next, 5 standard weights (50 kg) were put into the fertilizer tank in turn, and the pressure sensor measurement value was recorded after each load; finally, the 5 standard weights were taken out from the fertilizer tank in turn, and the pressure sensor measurement value was recorded after each unloading. The repeatability of the above measurement test was carried out 3 times, and the measurement results are shown in Table 1. The calibration coefficient \(K\) of the pressure sensor was calculated based on this:  
\[K=\frac{m_{f}}{|m_{n}-m_{n-1}|} \tag{7}\]  
where \(m_{f}\) is the weight value of the standard weight (50 kg), kg; \(|m_{n}-m_{n-1}|\) is the absolute value of the difference between the measured value of the pressure sensor when loading or unloading a standard weight (\(2\leq\) n \(\leq\) 5, n is an integer), kg.  
Figure 7: Program flow diagram of software system.  
From this, the real-time weight value of the fertilizer in the fertilizer tank could be obtained:  
\[\left\{\begin{array}{l}m_{s1}=Km_{c}-m_{k1}\\ m_{s2}=Km_{c}-m_{k2}-3m_{k1}\end{array}\right.\left(\text{kg}\right) \tag{8}\]  
where \(m_{c}\) is the real-time measured value of the pressure sensor, kg; \(m_{k1}\) is the weight value of no-load top fertilizer tank, kg; \(m_{k2}\) is the weight value of no-load bottom fertilizer tank, kg; \(m_{s1}\) is the real-time weight value of top fertilizer, kg; \(m_{s2}\) is the real-time weight value of bottom fertilizer, kg.  
After the calibration of the pressure sensor was completed, the measurement accuracy of the pressure sensor needed to be checked here. According to the above test method, the standard weight (50 kg) was loaded and unloaded into the fertilizer tank in turn. The measured value of the pressure sensor was recorded and the above measurement test was repeated 3 times. The measurement results are shown in Table 1. After calculation, it could be known that the error range of the measured value of the pressure sensor was 0.005-0.03%, which met the requirements of weighing measurement accuracy.
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 3 Test Results and Discussion
### Discussion on Test Results  
#### 3.2.1 Hysteresis Loops  
The hysteresis curve shows the deformation performance of structures (or members), which provides an important reference for analyzing their seismic performance. The geometrical shape of the hysteresis loop represents the capacity of structures to be restored to their original status when loads are removed. Based on the shape of the hysteresis curve, not only the seismic performance of structures can be qualitatively determined but also the load-carrying capability, stiffness, failure status, deformation, ductility, energy-dissipating ability, and nonlinear properties of structures are able to be obtained. In this section, the typical specimens 1# and 5# are selected and their hysteresis loops are shown in Figures 10 and 11.  
Figure 10: _Cont._  
Figure 9: (**a**) Crack distribution of the cast-in-situ columns; (**b**) crack distribution of the precast columns; (**c**) schematic of the first reason; (**d**) schematic of the second reason.  
Several common features of the specimens are summarized as follows:  
1. In the preload stage of low cyclic repeated loading, the hysteresis loops are relatively full and show shuttle shapes, whereas in the first stage, the hysteresis loops begin to show the rheostriction effect due to the generation and development of cracks in the concrete. In the second stage, the rheostriction effect of the curve becomes obvious and the hysteresis loops present arch and reversed S shapes.
2. Under the load with the same level, the area that the hysteresis loops envelop is smaller and smaller, indicating that the energy-dissipating capacity is degrading. Moreover, the residual  
Figure 11: Hysteresis loops of column 5#: (**a**) hysteresis loops at the preloading stage; (**b**) hysteresis loops at the first stage; (**c**) hysteresis loops at the second stage; (**d**) hysteresis loops of the overall cyclic loading process.  
Figure 10: Hysteresis loops of column 1#: (**a**) hysteresis loops at the preloading stage; (**b**) hysteresis loops at the first stage; (**c**) hysteresis loops at the second stage; (**d**) hysteresis loops of the overall cyclic loading process.  
deformation and the area enveloped by the hysteresis loops is increasing with load boosting; meanwhile the curve is inclined to the displacement axis.
3. For the loading curve, under the load with the same level, the displacement increases with the increasing number of loops, while the slope of the curve diminishes, indicating the stiffness degradation of the specimens when they suffer repeated loads. The curve slope reduces significantly when the loading enters into the second stage. Regarding the unloading curve, it has the same change law in terms of the curve slope with the increasing number of loops. When all the loads are removed, there is a residual deformation in the specimens and it can be intensified with the repeated loading and unloading process.
## 3 Simulation Results and Discussions  
In this study, the CMOR wavelet transform is performed using Python's module \"PyWavelets\" [30] and with the optimal \(f_{b}\) and \(f_{c}\):  
\[f_{b}=\frac{\sqrt{2\ln 2}f_{k}}{\pi\delta f^{\prime}},f_{c}=\sqrt{2f_{b}} \tag{17}\]  
Equation (17) is derived from the separation of \(f_{c}\sqrt{f_{b}}=\frac{2\sqrt{\ln 2}f_{k}}{\pi\delta f^{\prime}}\). With the definite \(f_{b}\) and \(f_{c}\), we further perform the QTF analysis of water molecule vibration by CMOR wavelet transform based on the atomic trajectories from our first-principles molecular dynamics (FPMD) simulation. Under the framework of density functional theory, the FPMD simulation was performed with the quickstep module of CP2K code [31; 32] with microcanonical ensemble. The triple-zeta valence Gaussian basis sets with two sets of polarization functions optimized for Goedecker-Teter-Hutter pseudopotentials [33] and the local density approximation exchange-correlation functional were used for H and O elements in H\({}_{2}\)O molecule, with the cutoff energy of 280 Ry for the plane waves expansions. Under the periodic boundary conditions, a water molecule was placed in the center of a vacuum cube box, and the edge of the box was set to 15A. The FPMD timestep is 0.1 fs. The total time of the FPMD simulation is 0.29 ns, which corresponds to 2.9 million MD steps. Here, the atomic trajectories of 1 million MD steps of H\({}_{2}\)O molecule were used to perform the QTF analysis. Furthermore, details of the FPMD can be referred to in our previous study [34].
# A Novel Connected-Components Algorithm for 2D Binarized Images
## 1 Introduction
### Classical Labeling Approaches  
The earliest algorithms for CCL relied on raster-scanning strategies, where the image is processed row by row to assign labels to connected pixels. The two-pass labeling algorithm, introduced by [PERSON] [13], remains one of the most well-known classical approaches. It operates as follows:  
* First Pass (Initial Labeling and Equivalence Table Creation): The binary image is scanned in raster order (left to right, top to bottom). Each black pixel is assigned a provisional label based on its neighboring pixels. If a pixel has multiple labeled neighbors, an equivalence table is updated to record that the labels belong to the same connected component.
* Second Pass (Label Resolution): Once the entire image has been processed, the equivalence table is resolved using a union-find data structure [14]. Each pixel is updated with the final label corresponding to its connected component.  
The two-pass algorithm is widely used due to its simplicity but suffers from high memory overhead and poor cache efficiency, particularly when dealing with large images.  
Another approach to CCL involves contour tracing, where connected components are identified by following their external boundaries. The algorithm introduced by [PERSON] and [PERSON] [15] follows a border-following technique, tracing connected pixel boundaries and assigning labels accordingly. This method is particularly useful for shape analysis but is computationally expensive for dense, highly connected images.  
The union-find approach [14] builds on the classical two-pass algorithm but optimizes label resolution using path compression and union by rank. This results in near-constant-time complexity for label merging, significantly reducing the overhead associated with equivalence table resolution. In this method:  
* Each connected component is treated as a disjoint set, and pixel labels are managed using find and union operations.
* The find operation determines the root label of a component, while the union efficiently merges components.  
Although union-find is more memory efficient than classical two-pass algorithms, it still requires multiple scans of the image and is difficult to parallelize effectively.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 4 Performance Evaluation  
### Experimental Setup  
For implementation, we used the Mask-RCNN model provided by Matterport Inc. [60] released under MIT license based on open-source Tensorflow and Keras Libraries. The Mask-RCNN model with both ResNet-50 and DenseNet-41 frameworks was implemented. In our work, rather than training our model from scratch, we initialized the model using pre-trained weights obtained from MS-COCO and incorporated transfer learning to fine-tune the model on the Brain MRI dataset for tumor segmentation and classification. The motivation to use pre-trained models is that the model has been trained on massive, publicly accessible datasets, such as ImageNet and MS-COCO, and is therefore capable of learning important features. During training, the initial layers learn low-level features; as the network goes up, the layers can learn task-specific patterns. Thus, when the pre-trained models are trained for a new task such as the segmentation and classification of brain tumors, the training speed and accuracy of the new model increase. As the important image features have already been learned, they do not have to be learned again and are transferred to the new task. This process is known as 'transfer learning'. For training, the given data are randomly split into training and test sets containing 70% for training and 30% images for testing. The training parameters for the proposed model using custom Mask-RCNN are displayed in Table 1.
###### Abstract  
This work presents the first results obtained by applying in situ and remote-sensing methodologies to monitor the Ponte della Musica-Armando Trovajoli located in Rome, within the activities of the W76 \"Structural Health Monitoring and Satellite Data\" 2019-21 Reluis Project. In particular, the use of remote-sensing Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) measurements provided a spatial map of the displacement of the investigated infrastructure and the corresponding time-series, with the aim of monitoring deformation phenomena, focusing on the local scale analysis, which produces suitable results for urban monitoring and damage assessment. The DInSAR results have been integrated with the identification of the dynamic characteristics of the bridge, performed through an experimental campaign of ambient vibration measurements carried out in October 2020 and with the local-scale definition of the engineering geological setting of the foundation soil. The subsoil of the bridge is constituted by more than 50 m of recent alluvial deposits resting on Pliocene stiff clay acting as a geological bedrock. A substantially stable behavior of the bridge structural elements has been observed based on the analysis of both satellite and velocimetric data. This case represents a good example about how the integration of in situ sensors with remotely sensed data and the exploitation of a detailed knowledge regarding the on-site conditions represent a key factor for a sustainable structural and infrastructural monitoring and can support the planning both of maintenance and safety management.  
structural health monitoring; remote sensing; Differential SAR Interferometry; deformation time series; in-situ sensors; transport infrastructures +
Footnote †: journal: Applied Sciences  
0021
## 6 Outdoor Testing Procedure
Next, the V2V distance was gradually increased up to a maximum distance of 75 m.  
As the VLC receiver's field-of-view significantly influences the communication parameters, the VLC prototype has been evaluated for a wide \(\pm\)53\({}^{\circ}\) FOV and a narrower one, of \(\pm\)20\({}^{\circ}\) FOV. For both these cases, the measurements were made in day conditions, under the influence of the sunlight, and in right conditions under the influence of fluorescent light sources that are illuminating the parking lot. For each longitudinal distance and for each scenario, the BER was determined for the VLC receiver alternately placed on each of the two lanes. In order to determine the BER, a predefined message has been cyclically transmitted. The VLC receiver processes the incoming light beam, decodes the data in real-time and computes the BER by matching the received bits with the bits of the original message. In accordance with the specifications of the IEEE 802.15.7 standard, the data were transmitted using OOK modulation, Manchester coding and a 100 kb/s data rate [67]. A summary of the testing parameters is available in Table 3, whereas the experimental testing setup is shown in Figure 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**Feature/Values**} \\ \hline Testing conditions & \multicolumn{2}{c}{Outdoor, uncontrolled conditions} \\ \hline Testing scenario & \begin{tabular}{c} Two lane V2V communication with: \\ \(\bullet\) \\ \end{tabular} & \begin{tabular}{c} Two lanes V2V communication with: \\ vehicles on the same lane \\ vehicles on adjacent lanes \\ \end{tabular} \\ \hline Lighting conditions & \begin{tabular}{c} - \\ - \\ - \\ - \\ \end{tabular} & \begin{tabular}{c} Day conditions with variable irradiance \\ of sunlight between 400–22,000 \(\upmu\)W/cm\({}^{2}\) \\ Night conditions under the influence of \\ fluorescent lights \\ \end{tabular} \\ \hline VLC emitter & \multicolumn{2}{c}{LED-based vehicle rear lights} \\ \hline VLC emitter height & \multicolumn{2}{c}{74 cm (i.e., similar as commercial vehicles)} \\ \hline Distance between the two vehicle rear lights & \
\begin{tabular}{c} 90 cm (i.e., similar to the case of a commercial \\ vehicle) \\ \end{tabular} \\ \hline Width of a lane & 3.5 m \\ \hline Emitter–Receiver (V2V) distance & 1–75 m \\ \hline Emitter–Receiver (V2V) angle & 0\({}^{\circ}\)–49\({}^{\circ}\) \\ \hline VLC receiver & Photodiode-based \\ \hline VLC receiver height & 74 cm \\ \hline Modulation technique & OOK \\ \hline Coding technique & Manchester \\ \hline Data rate & 100 kb/s \\ \hline Measured parameter &
\begin{tabular}{c} Real-time BER determination without the \\ usage of forward error correcting protocols \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the experimental parameters.
## 4 Results
### Experimental Comparison with Existing Techniques
#### 4.2.3. Experiments on FT-UMT Dataset  
On the FT-UMT dataset, the proposed approach, 'PA_B-J48', shows the best performance, as can be seen in Table 4. The reason is that geometric features of 'PA_B-J48' work well on a subject with the beard as in the FT-UMT dataset. The second-best percentage accuracy is shown by the approaches EM-Stat and Ali.
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Agro-Waste Types Used in RC Elements  
Studies have identified the use of various AW types in the production of RC beams. Materials such as flax-reinforced coconut fibers [76], PO shells [69; 74], palm kernel shells [77; 78], PO fuel Ash [45], CS [38; 73], WS Ash [21], banana fibers [79; 80], natural fibers [41; 81], and other agricultural by-products have been considered within this scope. These materials have led to significant modifications in the mechanical properties of RC beams. Recent studies, such as [81], highlight the potential of advanced natural fiber composites in improving structural behavior and promoting sustainability in RC systems.  
Particularly, research has focused more extensively on the application of PO shells, palm kernel shells, and PO clinker in concrete beams. These materials have been found to exert notable effects on both the mechanical and physical properties of the resulting RC elements [75; 82; 83; 84].
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## Appendix A  
### Proof of Theorem 1  
The proof of Theorem 1 follows the approach outlined in Theorem 2 from [12]. Figure 4: (**a**), Original image; (**b**), undersampling template with 30% sampling rate; (**c**–**e**), reconstructed images using \(\ell_{1}\) regularization, \(\ell_{1/2}\) and GMC regularization, respectively; (**f**), difference between (**a**) and (**c**); (**g**), difference between (**a**) and (**d**); (**h**), difference between (**a**) and (**e**). Proof. : Firstly, we consider the first order optimality conditions of the convex problems (2). According to the chain rule of subdifferential, we can derive the subdifferential the \(\ell_{1}\) term is  
\[\partial\|\hat{p}\|_{1}=\text{sgn}(\beta),\]  
where  
\[\text{sgn}(\beta)= \begin{cases}\{1\},&\beta>0,\\ -1,1,&\beta=0,\\ \{-1\},&\beta<0.\end{cases} \]  
Using the Karush-Kuhn-Tucker conditions, we have  
\[\frac{1}{n}\beta X^{\top}(y-X\hat{\beta})\in\lambda\text{sgn}(\beta).\]  
This implies that for any \(\tilde{\beta}\in\mathbb{R}^{p}\), we obtain  
\[\frac{1}{n}\hat{\beta}^{\top}X^{\top}(y-X\hat{\beta})=\lambda\|\hat{p}\|_{1}, \tag{10}\]  
and  
\[\frac{1}{n}\hat{\beta}^{\top}X^{\top}(y-X\hat{\beta})=\lambda\|\tilde{p}\|_{1}. \tag{11}\]  
Subtracting (10) from (11), we obtain  
\[\frac{1}{n}(\tilde{\beta}-\hat{\beta})^{\top}X^{\top}(y-X\hat{\beta})\leq \lambda(\|\tilde{\beta}\|_{1}-\|\hat{\beta}\|_{1}). \tag{12}\]  
By utilizing the observation model \(y=X\beta^{*}+\epsilon\) and considering polarization equality, i.e., \(2u^{\top}v=\|u\|_{2}^{2}+\|v\|_{2}^{2}-\|u-v\|_{2}^{2}\), the above Equation (12) can be rewritten as  
\[\frac{1}{n}\|X(\tilde{\beta}-\hat{\beta})\|_{2}^{2}+\frac{1}{n} \|X(\beta^{*}-\hat{\beta})\|_{2}^{2} \leq\frac{1}{2n}\|X(\tilde{\beta}-\hat{\beta})-X(\beta^{*}-\hat{ \beta})\|_{2}^{2}+\frac{1}{n}\epsilon^{\top}X^{\top}(\hat{\beta}-\tilde{\beta })\] \[+2\lambda\|\tilde{p}\|_{1}-2\lambda\|\hat{p}\|_{1}. \tag{13}\]  
Let us first consider \(\epsilon^{\top}X^{\top}(\hat{\beta}-\tilde{\beta})\) and apply [PERSON]'s inequality, we have  
\[\epsilon^{\top}X^{\top}(\hat{\beta}-\tilde{\beta}) =(X\hat{\beta}-X\hat{\beta})^{\top}(I-\Pi_{T})\epsilon+(X\hat{ \beta}-X\hat{\beta})^{\top}\Pi_{T}\epsilon\] \[\leq\|X^{\top}(I-\Pi_{T})\epsilon\|_{\infty}\cdot\|\hat{\beta}- \tilde{p}\|_{1}+\|\Pi_{T}\epsilon\|_{2}\cdot\|X(\hat{\beta}-\tilde{\beta})\|_ {2}. \tag{14}\]  
The classical results for the standard Gaussian tail distribution and the \(\chi^{2}\) distribution demonstrate that, given any \(T\), there exists a probability of at least \(1-2\delta\) satisfying the following two inequalities [46]  
\[\|X^{\top}(I-\Pi_{T})\epsilon\|_{\infty}\leq\gamma\sigma\sqrt{2\log(\frac{p}{ \delta})}=\lambda\sqrt{n}, \tag{15}\]  
\[\|\Pi_{T}\epsilon\|_{2}\leq\sigma^{*}\bigg{(}\sqrt{|T|}+\sqrt{2\log(\frac{1}{ \delta})}\bigg{)}. \tag{16}\]  
Next, note that for any set \(T\), \(\|\hat{p}\|_{1}=\|\beta_{T}\|_{1}+\|\beta_{T}\|_{1}\).
## 2 Algorithm Principle
### State Equation
#### 3.1.2 The State Equation of GNSS  
The random error model of the GNSS system can be described as [25]:  
\[\left\{\begin{array}{c}\delta{i_{u}}=\delta{t_{ru}}+{w_{tu}}\\ \delta{i_{ru}}=-{\beta_{tru}}\delta{t_{ru}}+{w_{tru}}\\ \end{array}\right. \tag{9}\]  
where \(\delta{t_{u}}\) is the equivalent distance error of the clock offset, \(\delta{t_{ru}}\) is the equivalent distance error of the clock drift of the first-order Markov process, \(\delta{i_{u}}\) and \(\delta{i_{ru}}\) are the corresponding differential, \({w_{tu}}\) and \({w_{tru}}\) are Gaussian white noise with a mean value of 0, \({\beta_{tru}}=\frac{1}{{\upsilon_{r}}}\) is the anti-correlation time, and \({\upsilon_{r}}\) is the correlation time.  
The state equation of the GNSS system can be described as [19]:  
\[\dot{\mathbf{X_{G}}}(\mathbf{t})\mathbf{=}\mathbf{F_{G}}(\mathbf{t})\mathbf{X_ {G}}(\mathbf{t})\mathbf{+}\mathbf{G_{G}}(\mathbf{t})\mathbf{W_{G}}(\mathbf{t}) \tag{10}\]where \(\mathbf{F_{G}}(\mathbf{t})=\left[\begin{array}{cc}1&0\\ 0&-\beta_{ttn}\end{array}\right]\), \(\mathbf{X_{G}}(\mathbf{t})=\left[\begin{array}{cc}\delta t_{u}&\delta t_{ ru}\end{array}\right]^{T}\), \(\mathbf{G_{G}}(\mathbf{t})=\left[\begin{array}{cc}1&0\\ 0&1\end{array}\right]\), \(\mathbf{W_{G}}(\mathbf{t})=\left[\begin{array}{cc}w_{tu}\\ w_{ttn}\end{array}\right]\). According to our hypothesis, \(\delta t_{u}\) and \(\delta t_{ru}\) can be eliminated by the FD system, so \(\mathbf{X_{G}}(\mathbf{t})=\left[\begin{array}{cc}0&0\end{array}\right]^{T}\), and Equation (10) becomes:  
\[\mathbf{\dot{X_{G}}(\mathbf{t})}\mathbf{=}\mathbf{G_{G}(\mathbf{t})}\mathbf{W_ {G}}(\mathbf{t}) \tag{11}\]  
Combining Equations (6), (10) and (11), we can obtain the state equation of the combined system as:  
\[\left[\begin{array}{c}\mathbf{\dot{X_{S}}(\mathbf{t})}\\ \mathbf{\dot{X_{G}}(\mathbf{t})}\end{array}\right]=\left[\begin{array}{cc} \mathbf{F_{S}}(\mathbf{t})&0\\ 0&\mathbf{F_{G}}(\mathbf{t})\end{array}\right]\left[\begin{array}{c}\mathbf{ X_{S}(\mathbf{t})}\\ \mathbf{X_{G}(\mathbf{t})}\end{array}\right]\mathbf{+}\left[\begin{array}{cc} \mathbf{G_{S}(\mathbf{t})}&0\\ 0&\mathbf{G_{G}(\mathbf{t})}\end{array}\right]\left[\begin{array}{c}\mathbf{ W_{S}(\mathbf{t})}\\ \mathbf{W_{G}(\mathbf{t})}\end{array}\right] \tag{12}\]