esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Behavior of Track Bridge on Serviced Long-Span Road/Railway Bridge
## 5 Prediction of Dynamic Behavior of Bridge  
### Natural Frequency  
An eigenvalue analysis of the Yeongjong Bridge (truss bridge and suspension bridge) was performed through a numerical analysis. Figures 11 and 12 show the mode shapes of the Yeongjong Bridge, and Table 1 summarizes the eigenvalues by mode.  
Figure 10: Natural frequency calculation results for the track girder system.  
Figure 9: Analysis of natural frequencies of track girders by mode. (**a**) 1 st bending mode (25.94 Hz); (**b**) 1 st torsional mode (33.68 Hz); (**c**) 2 nd torsional mode (42.04 Hz).  
Figure 11: Yeongjong Bridge suspension bridge mode shapes. (**a**) 1 st bending mode (0.480 Hz); **(b)** 2 nd bending mode (0.725 Hz); **(c)** 1 st torsional mode (0.887 Hz); **(d)** 2 nd torsional mode (0.973 Hz).  
Figure 12: Yeongjong Bridge truss bridge mode shape. (**a**) 1 st bending mode (1.171 Hz); **(b)** 2 nd bending mode (1.465 Hz); **(c)** 3 rd bending mode (1.928 Hz).  
According to the results of the eigenvalue analysis shown in Table 1, in the main modes, eigenvalues similar to those of the completion data, initial diagnosis data, load test and precision safety diagnosis, were derived, demonstrating the appropriateness of the analytical model used in this study.  
To examine stability with regard to resonance (critical speed), we performed calculations based on the natural frequency of the first bending mode of the structure. According to the examination of the resonance effect by bridge type, the natural frequency of the suspension bridge was 0.48 Hz. The critical speeds for KTX and AREX were 32.31 km/h and 23.85 km/h, respectively. For the truss bridge, the natural frequency was 1.17 Hz, and the critical speeds for KTX and AREX were 78.83 km/h and 58.18 km/h, respectively. Based on the evaluation of the bridge structure resonance effect, the critical speed of the suspension bridge and truss bridge calculated by reflecting the first bending mode natural frequency was less than 80 km/h. This indicates no influence of resonance when train speeds are above 150 km/h.
## 3 Preliminaries
### Run-Length Encoding  
Run-length encoding (RLE) is a lossless data compression method that analyzes a sequence with the same value occurring many consecutive times. It helps to compress the data by encoding the sequence by storing a single data value and its count.  
Two examples are given below to understand the RLE process. The first example considers a sequence of ASCII characters, and the second example assumes a bit sequence.  
Let us assume that the given character sequence \(C\) is as follows:  
\[C=(AAAAAAXXXXXBBBBBBBB) \tag{2}\]  
The RLE sequence \(\hat{C}\) that corresponds to \(C\) is given below:  
\[\hat{C}=(A\;6\;X\;5\;B\;9) \tag{3}\]Similarly, let us assume that we have a binary sequence \(B\), as given below:  
\[B=(1111110000011111111111) \tag{4}\]  
The RLE sequence \(\hat{B}\) that corresponds to \(B\) is given below:  
\[\hat{B}=(1\;6\;5\;10) \tag{5}\]  
The RLE for the bit sequence is slightly different when we compare it with the RLE of ASCII characters. While applying RLE for ASCII characters, we need to keep each character in the run and its count. But while performing RLE in a binary sequence, we need to keep the first bit of the original arrangement; then, we need to keep counts. Let us denote the \(\hat{B}=(B_{0},B_{1},\ldots,B_{K})\) where \(B_{0}\) indicates the starting bit in the original series (0 or 1); then, \(B_{1}\) indicates how many times \(B_{0}\) consecutively appears in the original series, and \(B_{2}\) indicates how many times \(\hat{B}_{0}\) occurs in consecutive positions and so on.
## 5 A Case Study on Reliable Pipeline Monitoring Using Cyber Physical Sensor Networks
### Sensing Techniques for Pipeline Monitoring
Meanwhile, work described in [48, 49] uses pressure signals to measure the events like leakages and bursts by monitoring the signal states using stochastic HMM processes. SWATS [50], a system for monitoring steam flood and water flood pipelines, makes use of all common measurements such as pressure, flow and temperature. Work in [51] describes a thermal video technology for leak detection. Thermal cameras are used to exploit temperature differentials which provide greater accuracy than methods that rely on color and size characteristics.
## 4 Materials and Methods
### Experiment Design
Net CO\({}_{2}\) assimilation rate (\(A_{\text{net}}\), umol m\({}^{-2}\) s\({}^{-1}\)) and stomatal conductance to water vapor (G\({}_{\text{S}}\), mol m\({}^{-2}\) s\({}^{-1}\)) provided by the gas-exchange system were automatically stored every 5 s. To ensure that the gas-exchange conditions were stable, the measurements for light curves and CO\({}_{2}\) curves were made after waiting at least 5 min, and up to 20 min, between each light intensity or CO\({}_{2}\) concentration change. The filtered incident radiation of the LED light source at each light intensity were measured with a standard reflectance panel (Spectralon; Labsphere, NorthSutton, NH, USA). The above measurements were taken at day 0 (before the water stress treatment started, WS0) and at days 2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28 after withholding water (WS2, WS4, etc., respectively). At WS0, the unstressed maximum carboxylation capacity of Rubisco (\(V_{\rm{cmax,0}}\), \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) and the unstressed maximum electron transport rate (\(J_{\rm{max,0}}\), \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) were estimated by fitting the FvCB model [64] to the CO\({}_{2}\) response curves. According to the photosynthetic light-response curve of winter wheat at WS0, \(A_{\rm{net}}\) increased steeply with PAR when PAR \(\leq\) 350 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), and increased slowly with increasing PAR when PAR \(\geq\) 1000 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) (see below). Thus, a PAR level between 350 and 1000 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) was assumed to be the intermediate light condition, and PAR levels lower than 350 or higher than 1000 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) were taken to be low and high light conditions, respectively.
## 3 Samples and Methods
### Basin and Petroleum System Modeling (BPMS)
#### 3.3.2 Input Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{3}{*}{**Formation**} & \multicolumn{2}{c}{**Deposition Age**} & \multicolumn{2}{c}{**Erosion/**} & \multirow{3}{*}{**Lithology**} & \multirow{3}{*}{**Lithology**} & \multirow{3}{*}{**Hydrocarbon**} & \multirow{3}{*}{**PWD**} & \multirow{3}{*}{**TOC (\%)**} & \multirow{3}{*}{**HI (mg/g**} \\  & **From** & **To** & **(Ma)** & & & & & **Element** & \\  & **(Ma)** & **(Ma)** & & & & & & & \\ \hline Q & 1.8 & 0 & & & & Conglomerate (typical) & Overburden Rock & 0 & & \\ N\({}_{2}k\) & 5.3 & 1.8 & & & Shale and Sand. & Overburden Rock & 0 & & \\ N\({}_{1}\)-\({}^{2}k\) & 12 & 5.3 & & & Shale and Sand. & Overburden Rock & 10 & & \\ N\({}_{1}\)/\({}^{2}\) & 23.3 & 12 & & & Gyp. and Sand. and Congl. & Overburden Rock & 10 & & \\ E & 65 & 34 & 34 & 23.3 & & Congl. and Sand. and Lime. & Overburden Rock & 5 & & \\ K\({}_{1}b\) & 125 & 112 & 112 & 65 & & Shale and Sand. & Overburden Rock & 0 & & \\ K\({}_{1}\)\({}^{8}\) & 133.9 & 125 & & & Shale and Sand. & Overburden Rock & 5 & & \\ K\({}_{1}\)\({}^{4}\) & 145.6 & 133.9 & & & Sand. (typical) & Reservoir Rock & 5 & & \\ J\({}_{2}k\)-SD & & & & Sand. (typical) & Reservoir Rock & 2 & & \\ J\({}_{2}\)\({}_{2}\)-DM & 178 & 165 & 165 & 145.6 & & Shale (organic rich, 3\({}^{\circ}\)TOC) & & & \\ J\({}_{2}\)\({}_{2}\)-CM & & & & & Shale (organic rich, 3\({}^{\circ}\)TOC) & & & \\ J\({}_{2}\)\({}_{2}\)-CO & & & & & & 20\({}^{\circ}\)TOC) & & & \\ J\({}_{1}\)-SD & & & & & Coal (pure) & & & \\ J\({}_{1}\)-DM & 198 & 178 & & & Sand. (typical) & Reservoir Rock & 2 & & \\ J\({}_{1}\)-CM & & & & & Shale (organic rich, 3\({}^{\circ}\)TOC) & & & & \\ J\({}_{1}\)-\({}^{2}\)CO & & & & & & 20\({}^{\circ}\)TOC) & & & \\ J\({}_{1}\)-\({}^{2}\) & 208 & 198 & & & Sand. (typical) & Reservoir Rock & 7 & 6.00 & 500.00 \\ T\({}_{3}\)\({}^{2}\) & 235 & 216 & & Shale and Sand. & Source Rock & 20 & 10.00 & 500.00 \\ T\({}_{2}\)\({}^{2}\)\(k\) & 245 & 235 & & Shale and Sand. and Congl. & & Rock & & & \\ P & 295 & 245 & & & Congl. and Sand. and Lime. & Underburden & 10 & & \\ C & 438 & 295 & & Sand. and Shale and Lime. & Underburden & 10 & & \\ \hline \end{tabular}
\end{table} 
Table 3: Input parameters used for the 2D basin modeling: SD = Sandstone, DM = Dark mudstone, CM = Carbonaceous mudstone, CO = Coal, Congl. = Conglomerate, Sand. = Sandstone, Lime. = Limestone, Gyp. = Gypsolyte, PWD = Palaeo-water depth.
# Performance of Concrete with Waste Granite Powder: The Effect of Superplasticizers
###### Abstract  
Granite stone is highly demanded by construction sector. In the processing stage, great amounts of waste powder are produced that can be used as cement replacement in concrete production. Reduced workability is the first problem of this replacement and it can be mitigated by using superplasticizers (SP). This study was carried out to limit the disadvantages related to waste granite powder (WGP) use through the use of SP. For this purpose, 19 concrete mixes containing various types of SP with different contents and WGP as cement replacement were appraised. Fresh concrete density, splitting tensile strength, electrical resistivity, compressive strength and resistance to sulphate attack tests were conducted. The mixes were assessed based on their environmental impact, economic, and mechanical performance. The results have shown that SP can eliminate most disadvantages caused by using stone waste, particularly negative effects on the mechanical properties. They can even improve some properties of concrete, including electrical resistivity by up to around 90%. Furthermore, the concrete mixes PT5, PF5, and PF10, which were made with both SP and stone waste, obtained the best results, with over 30% improvement in the multi-criteria evaluation index values compared to the mix W0 (reference mix). The proper use of WGP as replacement for cement would have environmental benefits and help the economy of the granite stone industry.  
waste granite powder; superplasticizer; cement replacement; multi-criteria index; concrete +
Footnote †: journal: Applied Sciences
# Robotic System for Post Office Package Handling
## 3 Architecture
### Communication and Synchronization  
Since the whole system is controlled by a state machine, the system functions by transitioning from one definite state to the next. As mentioned in Section 3.1.4, the robot control and workspace sensing are processed on two different units. This provides an extra layer of complexity to the system in the form of communication and synchronization.  
The signal to start detection can be sent once the robot is no longer blocking the workspace, but this happens in multiple states before the robot is ready to begin a new cycle. Since the grasp point detection takes an unpredictable amount of time, the robot system can both run forward or fall behind during the process in terms of synchronizing when the new grasp point is sent. To make the process more time efficient, the communication between processing units happens in parallel with the robot's motion. When a new grasp point is detected, the point is not sent as a message but, rather, it is set as a new value on the ROS parameter server with the tag new, describing that the robot has not yet used this point. Once the robot has reached a state where it can act upon a new point, it checks this new tag, which can be set to either true or false. Depending on when both of these parts of the system reach this point, the robot either waits until a new pose with this tag set to true appears, or reaches the new cycle starting pose and instantly starts working on the new cycle. In this way, the two parts of the system are both co- and independent in their functionality.
## 4 Discussion
A further advantage of the implementation of a satellite hospital dedicated to COVID-19 patients is that it aids in the decongestion of the main hospital, allowing the theoretical elective caseload to remain unaffected. EH19 also facilitated an environment where an in-depth anamnestic investigation of the patients' medical history, with a focus on comorbidities, was possible. This could explain the higher comorbidity rate among patients in the second wave when compared to the first. In other words, it avoids delaying the care of time-dependent pathologies and chronic diseases. Furthermore, due to ICH Cancer Center, patients with malignancies, who during the first wave of COVID-19 had been shielding inside their homes, have recently been accessing hospital care at an increasing rate, which allows for the continuity of treatment. This can partially explain the above-mentioned phenomenon. [PERSON] rightly emphasized the urgent need for a major reorganization of health care systems, in Italy and in other parts of the world, to allow the management of already anticipated waves as well as the continuity of the vaccination campaign from a medical and social point of view [35]. In line with this aim, the tactical build of COVID-19 facilities such as EH19 perfectly fits the aforementioned necessities. Additionally, strategic operational algorithms are mandatory, today and in the future, to optimize and further improve the quality of care of both COVID-19 and non-COVID-19 patients. The Lean model was adopted by our hospital, mainly because of its recognized high reliability, feasibility, and effectiveness [7; 15; 16; 36; 37; 38; 39]. It was reported as _\"an integrated socio-technical system whose main objective is to eliminate waste by concurrently reducing or minimizing supplier, customer, and internal variability\"_ [40]. The motivating principles of this operative model make it an optimal choice for the management of the COVID-19 outbreak. It allowed for the successful screening of a large number of patients and for their proper allocation on Emergency Department arrival. There are clear limitations of this study due to its retrospective nature and the relatively small sample size. All of these limitations are common to other studies and are also caused by the exceptional situation generated by the SARS-CoV-2 pandemic.
# Hourglass 3D CNN for Stereo Disparity Estimation for Mobile Robots
## 2 Related Work  
[PERSON] et al. originally introduced a CNN-based stereo-matching technique [17] whereby the similarity metric of tiny patch pairings was learned using a convolutional neural network. GCNet [9] was one of the first methods incorporating 4D cost volume, using the soft argmin operation in the disparity regression steps to obtain the best matching disparity. PSMNet [10] introduced a spatial pyramid pooling module and 3D stacked hourglass networks and yielded promising results. The authors of [18] proposed GwcNet, which is a modified 3D stacked hourglass architecture, and a combined 3D cost volume based on group-wise correlation. GA-Net [11] includes a semiglobal aggregation layer and a local guided aggregation layer to replace several 3D convolution layers. To replace the 3D architecture, AANet [19] includes an intrascale and cross-scale cost aggregation algorithm, which can reduce inference time and maintain equivalent accuracy. On the other hand, DeepPruner [20], a coarse-to-fine approach, includes a differentiable PathMatch-based module to estimate the pruned search range of each pixel. Although 4D cost volume-based methods have achieved promising results, they operate at high computational cost and do not accommodate real-time operation on low-budget devices.  
Therefore, some recent studies have focused on lightweight stereo networks based on 4D cost volumes to achieve real-time performance while maintaining competitive accuracy. These methods typically construct and aggregate 3D cost volume at low resolution to significantly reduce computational cost. For instance, StereoNet [13] is an edge-preserving refinement network that utilizes the left images as guidance to recover high-frequency details. [PERSON] et al. [21] proposed a cascade cost volume, which consists of two stages. Cost volume at the early stage is built on a low-resolution feature map. Then, the later stage used the estimated disparity maps from the earlier stage to construct new cost volumes to apply better semantic features. This leads to a remarkable improvement in GPU memory consumption and computation time. AAFS [12] constructs a 4D cost volume by adopting a distance metric (height \(\times\) width \(\times\) disparity \(\times\) 1). A disparity map is then computed at the lowest resolution, and disparity residuals are computed in later stages. However, its 3D CNN cannot exploit the contextual information for cost volume regularization, resulting in a limitation in estimation accuracy.
# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 10 Raman Microspectroscopy in Plant Disease Diagnosis
We believe that further estimations can be made related to these crucial points using Raman microspectroscopy: (i) the health of plant tissue and (ii) identifying through particular molecular characterization early walnut blight disease as caused by _X. arboricola_ pv. _juglands_. We propose using Raman detection to identify specific plant molecules such as carotenoids or antioxidant enzymes like catalase or peroxidase that are linked to plant  
Figure 3: Flow graphs showing the plant infection and the establishment of fungi (plant disease progress). (**a**) Methods of plant disease identification; (**b**) Raman spectroscopy for plant disease diagnosis; (**c**) Raman spectroscopy for plant disease diagnosis; (**d**) understanding mechanisms of pathogenesis. (**a**) aims to explain the plant disease progress as follows: 1. Fungus conidia germination; 2. First step of plant tissue infection; 3. Conidia hyphae invading the plant tissue and multiplying; 4. Initial infection is limited in the plant without visual symptoms; 5. Fungus mycelial network growing between plant cells; 6. Visual disease symptoms observed on leaf tissue; 7. Fungus sporulation occurring across the damaged leaf tissue. (**b**) aims to explain reliable diagnostic tools for the detection of plant diseases: microscope (A1), PCR (B1), ELISA (B2), fluorescence microscopy (B3), Raman spectroscopy (C1). (**c**) aims to explain Raman (micro)spectroscopy as a tool for identifying microbes with a Raman spectrum. Moreover, (**d**) shows a Raman chemical fingerprint that identifies molecules of the invading fungus. Figure 3d displays the benefits of Raman (micro)spectroscopy, using a chemical Raman fingerprint to identify molecules of the invading fungus, such as the species _Alternaria alternata_. All scientific figures and icons were created using BioRender scientific software (Version 04) ([[https://www.biorender.com/](https://www.biorender.com/)]([https://www.biorender.com/](https://www.biorender.com/))). responses to pathogens, such as _X. arboricola_ pv. _juglandis_[63]. By using Raman microspectroscopy to observe these biochemical reactions, we believe researchers can assess the early stages of the disease by detecting the accumulation of these compounds in the infected plant tissue. Our goal is to identify the early stages of walnut blight disease using Raman microspectroscopy, even in particularly damaging wet years or in early-leafing walnut cultivars. Further, by using Raman spectra, it is possible to analyze plant tissues in real time to detect early plant diseases.
## 3 Data Processing and Analysis for Smartphone Real-Time Kinematic (RTK) Positioning
### Data Processing for Smartphone RTK Positioning
#### 3.1.1 Stochastic Model and Cycle-Slip Detection
Thus, a larger cycle-slip threshold should be set (15 m in this study) to detect pseudo-range gross errors simultaneously. Moreover, we set the C/N0 threshold and elevation cut-off value to eliminate the observations which are severely affected by multipath interference and cycle slip. The specifications for RTK positioning are listed in Table 2.
## 5 Experimental Results
### Research Highlights  
* The network environment is constructed as an unequal Sierpinski cluster-tree topology (USCT) in order to eliminate the hotspot problem by increasing the energy efficiency of the network.  
Figure 14: Number of received packets (after coverage hole repair) vs. simulation time.  
Figure 15: Number of control packets (after coverage hole repair) vs. simulation time.  
* The cluster head is elected in an optimal manner by implementing multi-objective black widow optimization (MoBWo) in order to facilitate effective transmission of data in the network.
* The energy efficiency of the nodes in the network is further improved by scheduling the sensor nodes between active and sleep mode using Tsallis entropy enabled Bayesianprobability (TE2 BP) based on several significant factors.
* The coverage holes in the network are detected by executing virtual sector-based hole detection (ViSHD) in which the holes are detected by the CH both within the cluster and among the clusters.
* The QoSensing of the network is improved by performing a recovery process in which the base station repairs the coverage holes by implementing multi-agent SARSA (MA-SARSA) based on important parameters.
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs
### RNN Methodology
#### 7.2.3 Outputs of the RNN  
The output \(\mathbf{o}(t)\) represents the optimized positions of the mirrors, as follows:  
\[\mathbf{o}(t)=f_{o}(W_{o}\mathbf{h}(t)+\mathbf{b}_{o}), \tag{47}\]  
where \(W_{o}\) is the weight matrix for hidden-state-to-output transformation, \(\mathbf{b}_{o}\) is the Bias term, and \(f_{o}\) is the activation function (e.g., linear or sigmoid).