esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Model Performance and Evaluation
#### 4.3.3 Monolithic Concrete  
Deck age, deterioration, performance, average daily traffic, and latitude are the most influential factors in predicting maintenance for bridge decks with Monolithic Concrete. The distribution of each of the influential variables is shown in Figure 8 with respect to maintenance type: intervention (Yes) and non-intervention (No) group. Specifically, we observe that the median deck age (19) of the intervened bridges is younger than the older bridges' deck age (33). Median deterioration scores (\(-0.0869\)) are lower in intervened type (\(-0.076\)) than the median deterioration score in the non-intervened bridge deck, suggesting that the intervention group of the bridge deck is deteriorating rapidly compared to the non-intervention group. Similarly, the intervention distribution has a lower performance score than the non-intervention distribution.  
In understanding the interaction between the two most influential variables, PDP plot Figure 9 shows a complex interaction between the deck age and deterioration.  
Figure 8: Distribution of influential variables with respect to intervention and non-intervention groups in predicting bridge deck maintenance with Monolithic Concrete wearing surface. Deck age, Deterioration, and Performance are the most influential variables.  
Figure 9: Two-way Partial Dependency Plot (PDP) showing interactions between Deck age (\(x\)-axis) and Deterioration (\(y\)-axis) for a bridge deck with Monolithic Concrete surface. The left side illustrates the grid format, and the right side presents contours. Lighter shades depict interactions related to bridge deck intervention, while darker shades represent non-intervened conditions.  
The two-way PDP is a 3D surface plot where the x and y-axes represent the two variables of interest, and the z-axis (often represented as a color scale or contours in 2D plots) shows the average prediction of the model. In the two-way PDP, each point with respect to the deterioration score and deck age on the plot corresponds to a color or height that represents the maintenance decision for the bridge deck with respect to that of the deterioration score and deck age. The lighter shades or higher contour lines typically represent higher predicted values (Intervened). In Figure 9, the lightest shade or the highest contour line occurs at low deck age; this indicates an interaction between the deck age and deterioration score. The darker shades or lower contour lines represent a non-intervened bridge deck. In the PDP plot, these occur at higher deck ages and higher deterioration scores (slow deterioration rate), meaning bridges with higher deterioration scores and higher deck age are associated with no intervention of the bridge deck.
## 4 Results
### Multi-Temporal Detection of Vegetation Recovery on Previously Bare Landslide Masses  
In this study, vegetation recovery refers to the vegetation growth based on the previous year's hazard region. Figure 4 in Section 4.1 also illustrates the vegetation recovery in the 15 selected sites (the decreasing area from 2008 to 2011). For most sites, the recovery conditions were very positive, and vegetation began to grow on the previously bare landslide mass. Utilizing Equation (1), the vegetation recovery areas were calculated. The results are shown in Table 5. In addition, the vegetation recovery region of several sites was illustrated in Figure 6 using overlay analysis in ArcGIS platform. The fastest vegetation recovery period occurred in the first year after the earthquake, which is in line  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Newly Developed Debris Flow Area (ha)**} & \multicolumn{3}{c}{**Debris Flow Development Rate (per year)**} \\ \cline{2-7}  & **2008–2009** & **2009–2011** & **2008–2011** & **2008–2009** & **2009–2011** & **2008–2011** \\ \hline
1 & 23.46 & 49.22 & 58.45 & 0.09 & 0.11 & 0.07 \\
2 & 147.89 & 78.78 & 104.52 & 0.23 & 0.06 & 0.06 \\
3 & 15.64 & 29.19 & 30.42 & 0.13 & 0.12 & 0.08 \\
4 & 15.69 & 0.00 & 7.00 & 0.14 & 0.00 & 0.02 \\
5 & 24.01 & 7.98 & 14.29 & 0.16 & 0.03 & 0.03 \\
6 & 9.07 & 2.98 & 4.75 & 0.19 & 0.03 & 0.03 \\
7 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
8 & 35.39 & 0.00 & 17.63 & 0.06 & 0.00 & 0.01 \\
9 & 3.11 & 7.79 & 7.21 & 0.03 & 0.04 & 0.02 \\
10 & 84.75 & 45.93 & 63.13 & 0.17 & 0.04 & 0.04 \\
11 & 31.73 & 0.00 & 19.58 & 0.20 & 0.00 & 0.04 \\
12 & 0.00 & 7.26 & 0.00 & 0.00 & 0.02 & 0.00 \\
13 & 11.31 & 6.94 & 15.14 & 0.03 & 0.02 & 0.01 \\
14 & 20.24 (2008–2010) & 13.88 (2010–2011) & 25.37 (2008–2011) & 0.07 (2008–2010) & 0.09 (2010–2011) & 0.06 (2008–2011) \\
15 & 19.72 (2009–2010) & 12.43 (2010–2011) & 29.88 (2009–2011) & 0.02 (2009–2010) & 0.01 (2010–2011) & 0.02 (2009–2011) \\ \hline Total & 402.05 & 236.07 & 342.12 & 0.102 & 0.034 & 0.029 \\ \hline \end{tabular} Note: The ”Total” row is the accumulation of the first thirteen hazards sites and does not include the last two. The years in the bracket of site No. 14 and No. 15 refers to the start and end years of variation.  
\end{table}
Table 4: Each site’s newly developed debris flow areas and rates.  
with the development of debris flows, with a recovery rate of 21.9%. Subsequently, the vegetation recovery rate trended down. In terms of the overall condition from 2008 to 2011, the average rate was 15% to 20%. The vegetation on the landslide mass may require at least five to ten years to completely recover.
## 1 Introduction  
The world leaders of 193 countries committed to end poverty, and fight against climate change and injustice in the world at the UN summit in New York in September 2015. As part of the Agenda 2030 [1], 17 Sustainable Development Goals (SDGs) were adopted, which represent the new standard for the development of all people in the world. To achieve the defined objectives, it is necessary to deal with the issue of the reduction of natural disasters which have a negative impact on people's lives. Apart from these impacts on human health and life, natural disasters also have additional consequences such as physical damage to the property of citizens, economic loss [2; 3] and significant damage to the environment [4]. In December 1999 was created The United Nations Office for Disaster Risk Reduction (UNDRR) and it supports the implementation of the Sendai Framework for Disaster Risk Reduction 2015-2030 [5]. Attention should also be paid to the correct assessment of the level of risk, especially in urban and industrial zones with the attention of possibilities cascading events triggered by natural disasters [6] and also take account of the effects of spatial planning [7; 8].  
According to the United Nations International Strategy for Disaster Reduction (UNISDR) report, over the last 20 years [9] almost 90% of natural disasters were caused by floods, storms, and other weather-related disasters. Weather-related disasters, particularly floods and storms (hurricanes, cyclones, tornadoes), show an increasing trend. In the United States, the economic losses caused by floods are estimated at billions of dollars in 2016 [10]. In the period 2000-2012, flood damage and costs associated with floods (flood prevention and rescue work) reached the amount of 6961,000,000 [11].  
According to data in the Emergency Events Database (EM-DAT database) [12], the occurrence of natural disasters in Europe also shows an increasing trend. The occurrence of 1744 natural disasters was registered in Europe between 1902, and 1980, and a similar number again from 1980 to date.  
Regarding the occurrence of natural disasters, floods can be included among the most frequently occurring phenomena. According to report UNDRR and the Centre for Research on the Epidemiology of disasters between 1995 and 2015 the most frequently occurring natural disaster was flooded (43%) [9]. They are a cyclically recurring problem in many cases. Climate change is often considered to be one of the reasons for the increased occurrence of floods in Europe in recent decades, and in the foreseeable future as well [13; 14; 15; 16]. The cause of floods is a complicated process and a complex of several factors, including both natural and human activities [17]. One of the main causes of locally flooding are also immediately or intensively rainfall. As a rapid source of runoff can be served too by small ungauged watersheds and therefore is need to be know their characteristic [18].
## 3 Experiment Two: Verification of the Localisation Thresholds
### Results
This result indicates that the perceived location of the main channel signal would be more affected by vertical interchannel crosstalk when the height layer is delayed with respect to the main. However, despite this result it is clear that the difference was not always significant, with there being a notable overlap between notch edges between the 0-dB ICLD conditions and the main layer-only conditions. This is particularly noticeable for the guitar and quartet sources at 0 ms. Nevertheless, is clear that vertical interchannel crosstalk at the very least resulted in an increase in the median perceived elevation of the main channel signal, which was notably reduced when the localisation thresholds derived from Experiment One were applied. A further result of note can be seen with respect to the main and height layer-only conditions. Firstly, for the latter condition it would appear that perceived elevation judgments were generally accurate for all sources, excluding the oboe, with respect to the physical position of the height layer. Conversely, for the main layer-only condition the judgments were less accurate, with perceived source elevation being in the range of 5.8\({}^{\circ}\)-13.0\({}^{\circ}\) with respect to the main layer's physical location. This elevation of the sound source with respect to the main layer was also maintained for the conditions whereby a localisation threshold was applied to the height layer. The results of a Wilcoxon signed rank test, which compared the results for the main layer-only condition to the physical position of the main layer (0\({}^{\circ}\)), showed that each source was perceived to be significantly higher than the physical height from which the source was presented (\(p\) = 0.000 for all sources).
## 3 Results and Discussion  
### Optimal pH Determination  
Preliminary studies aimed to determine the optimal pH for flocculation; that is, the pH at which the formation of primary flocs was achieved with the lowest flocculant dosage. The results are in Table 3. It was also intended to select the optimal pH for subsequent optimization tests. The optimal pH corresponded to the pH at which the minimum flocculant dosage was required to start the formation of primary flocs.  
The optimal pH for the inorganic flocculants (aluminum sulfate and ferric sulfate) was determined to be 9. In another study, close to 100% biomass recovery efficiencies were achieved with ferrous sulfate at pH 8.47 [25]. [PERSON] and [PERSON] [26] also obtained higher harvesting efficiency of _Chlorella_ sp. with aluminum sulfate and ferric sulfate at a basic pH. At pH 9, some charge neutralization can occur; however, this is not the primary mechanism involved in flocculation, especially since negatively charged forms of alum and iron predominate [15]. Still, optimum flocculation is expected at this pH because of the main mechanism of sweep flocculation. Aluminum and iron ions form aluminum hydroxide and ferric hydroxide precipitates. They can also bind with EOM and phosphate ions (present in the culture medium as KH\({}_{2}\)PO\({}_{4}\)), forming other types of precipitates. These precipitates entrap microalgal cells and allow floc formation.  
Organic flocculant chitosan performed better at an acid pH, just as reported in other studies [27; 28]. The mechanisms involved in the flocculation process are charge neutralization, intercell bridging, and sweep flocculation. This flocculant is a cationic polymer with high charge density at the optimal pH determined; therefore, a strong attraction between the cells and the flocculant is expected [27]. Since the pH of _C. vulgaris_ cultures is normally basic, the optimal pH of 5 means that, when chitosan is used, more acid solution has to be added to the culture medium.
## 4 Computations and Results  
In the numerical model, the deformation and stress response of the tunnel lining and the distribution of the cracks are investigated under the conditions of fault dislocation. By referring to the design strategy of the Xianglushan tunnel, the magnitudes of the fault dislocation are considered as 0.2, 0.4, 0.6, 0.8, and 1.0 m in this model. It can be seen from the following plots that the stress and maximum deformation are mainly concentrated at the bottom of the tunnel lining. Therefore, the lining bottom is selected as the subject of investigation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Parallel Bond Group**} & \multicolumn{4}{c}{**Parallel Bond Contact Model**} \\ \hline Minimum particle radius (m) & Maximum particle radius (m) & Effective modulus (GPa) & Effective modulus (GPa) & Tensile strength (MPa) & Cohesion(MPa) \\
0.50 & 1.00 & 3.00 & 3.00 & 0.60 & 1.00 \\ \hline Ball density (kg:m\({}^{-3}\)) & Friction coefficient & Normal-to-shear stiffness ratio & Normal-to-shear stiffness ratio & Friction angle (\({}^{\circ}\)) & Normal critical damping ratio \\
2600 & 0.30 & 1.00 & 3: 5 & 0.00 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Micro mechanical parameters of discrete particles.  
Figure 4: Sketch of tunnel in faulting model. (a) Cross section diagram; (b) grid division.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Effective Modulus** & **Poisson’s** & **Cohesion** & **Friction** & **Compression** \\  & **(GPa)** & **Ratio** & **(MPa)** & **Angle (\({}^{\circ}\))** & **Strength (MPa)** \\ \hline Surrounding rock mass of Xianglushan tunnel & 6.7 & 0.25 & 0.8 & 40 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Macro mechanical parameters of the surrounding rock mass.
# A Low Phase Noise Crystal Oscillator with a Fast Start-Up Bandgap Reference for WLAN Applications
## 4 Discussion  
In the realm of modern electronic information system technology, crystal oscillators are popular choices as reference clock sources. The output phase noise of reference clock sources is now considered a limiting factor for circuit and system performance in several applications, such as satellite communications and radar systems [27; 28]. To achieve low phase noise in a crystal oscillator, several measures need to be taken to reduce noise contributions from various sources, such as the amplifier flicker noise, resonator flicker noise, resistor thermal noise, power supply noise, and nonlinear effects of the crystal [29; 30; 31]. In the future, researchers can focus on developing crystal oscillators with ultra-low phase noise [32; 33], minimal frequency and temperature drift [34; 35], fast start-up [36; 37], and ultra-low power consumption [38; 39], in order to cater to various application scenarios.  
Table 2 presents a summary of the performances of the proposed crystal oscillator and a comparison with previous works [22; 32; 40; 41]. A close analysis of the data in the table reveals that the phase noise of the crystal oscillator in this paper surpasses those found in similar research.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Data** & **Unit** \\ \hline Technology & 55 & nm \\ Output frequency & 40 & MHz \\ \hline Power loss & 0.444 & mW \\ \hline \multirow{3}{*}{Phase noise} & \(-150.45\) @10 kHz offset & dBc/Hz \\  & \(-164.36\) @100 kHz offset & dBc/Hz \\  & \(-166.75\) @1 MHz offset & dBc/Hz \\ \hline Start-up time & 0.718 & ms \\ \hline Chip area & 0.088 & mm\({}^{2}\) \\ \hline FoM [FOOTNOTE:]Footnote : FoM \(=|PN|+20\log_{10}(f_{0}/\Delta f)-10\log_{10}10(P_{DC}/1 mW)\).[ENDFOOTNOTE] & 209.93 & dBc/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of simulation and test results of crystal oscillator.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **VLSI [40]** & **JSSC [22]** & **RFIC [41]** & **JSSC [32]** & **This Work** \\ \hline Frequency & 39.00 & 39.25 & 38.40 & 54.00 & 40.00 \\ (MHz) & 40 & 65 & 65 & 65 & 55 \\ \hline Voltage (V) & 0.7 & 3.3 & 1.4 & N/A & 1.2 \\ \hline Phase noise (@100 kHz offset) & \(-151.50\) & \(-153.10\) & \(-145.00\) & \(-144.60\) & \(-164.36\) \\ (dBc/Hz) & & & & & \\ \hline Start-up time (ms) & 0.259 & 3.900 & 1.250 & N/A & 0.718 \\ \hline Power loss (\(\mu\)W) & 9.2 & 19 & 5234 & 8200 & 443.8 \\ \hline Chip area (mm\({}^{2}\)) & 0.605 & 0.088 & 0.090 & 0.150 & 0.088 \\ \hline FoM [FOOTNOTE:]Footnote : FoM \(=|PN|+20\log_{10}(f_{0}/\Delta f)-10\log_{10}10(P_{DC}/1 mW)\).[ENDFOOTNOTE] & 213.68 & 212.19 & 179.50 & 180.11 & 209.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with previous works.
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 3 Methodology
### APSIM Model  
Model simulations allow for the quantitative assessment of how different environmental conditions and management practices influence corn phosphorus concentration and aboveground biomass. The agricultural production systems simulation (APSIM) model is widely recognized for its flexibility and accuracy in simulating crop growth and nutrient cycling. Over the years, APSIM has been used to evaluate corn growth under various fertilization and irrigation strategies, with a particular focus on phosphorus uptake efficiency and its contribution to biomass accumulation [46]. In this study, APSIM was utilized to simulate two key agronomic indicators--plant phosphorus concentration and aboveground biomass--across critical growth stages: jointing, trumpet, tasseling, silking, grain formation, and milk ripening. The model incorporated variety-specific genetic parameters, with scenario and field trial settings remaining consistent throughout the experiment. Calibration and validation were conducted using experimental data, with parameter adjustments made through a \"trial-and-error\" approach.
## 4 Simulation Results
### Scenario Two: EVs Coordinated Charging  
The coordinated charging aims to manage/organize EV charging in order to decrease the peak power consumption of the building while fulfilling user requirements. Figure 7 shows the result of the coordinated charging scheme for 100 EVs. The PV system and the ESS were not considered as in scenario one. The proposed algorithm was able to manage the charging of EVs with minimum charging cost, however, power consumption showed two peaks during the periods of low charging cost. The first time period was between 9:00 a.m. and 11:00 a.m., and the second time period was at 1:00 p.m. The peak power consumption was about 192 kW at 10:15 a.m., which was lower than in scenario one.
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 3 Proposed Method  
As discussed above, CNN-based detection and segmentation models generally have a low computational burden. Therefore, in this work, we focus on CNN-based models for pointer meter dial reading. Figure 1 illustrates the proposed model, which consists of three main components: the meter dial location network, the reference point segmentation network, and the reading dial number module. The location network aims to detect the meter dial, which translates the task of meter dial location into a regression problem that spatially separates bounding boxes by an object detection network. After that, the segmentation network segments the reference points, including pointer and grid points. Once the reference points are obtained, we can read (i.e., recognize) the meter dial number by computing the relative position between the pointer and grid reference points. Even though the structure of each part of our model is very simple, experimental results on our collected dataset demonstrate its efficiency and effectiveness. In the following, we describe the three components of our model in detail.
## 1 Introduction
The twin-SSB application can almost double the SE for a direct detection system. In this paper, we propose and investigate a novel scheme based on a combination of multi-CAP and twin-SSB techniques, which we call multi-twin-SSB, for long-reach high-speed PON downstream. The structure of multi-CAP allows the sub-bands to be dynamically scheduled via a centralized SDN-controller. A pair of Hilbert transform filters are utilized to generate the twin-SSB signal by making full use of the bandwidth of DACs so that the SE is almost doubled. Thanks to the SSB modulation, CD-induced power fading can be eliminated and longer transmission distance can supported for long-reach PON. At the optical network unit (ONU) side, KK-receiver is adopted to mitigate the SSBI effects from the square-law detection due to its predominant SSBI mitigation capability and low computation complexity. Furthermore, the downlink signal can be separated in the digital domain with cost-effective digitally filters as a conventional multi-CAP signal.
## 1 Introduction  
The natural frequencies and natural modes unambiguously define the dynamic properties of the structure and, thus, describe its dynamic individuality. The knowledge of these characteristics is necessary for the analysis of the structure response to various dynamic loads. The dynamic response depends on the ratio between excitation and natural frequencies. The knowledge of natural frequencies makes it possible to predict the response at a known frequency composition of the load. For example, the bridge mentioned at the end of Chapter 5 oscillates with a dominant frequency corresponding to the first natural frequency when one vehicle is driven. When moving a continuous traffic flow, the dominant oscillation frequency corresponds to the second natural frequency. These characteristics can be obtained numerically or experimentally. The finite element method (FEM) is currently the most commonly used numerical method [1]. Different variants of experimental modal analysis are used in the experimental solution [2; 3]. It is an effort to create a computational model of the structure such that, in the spirit of the accepted criteria, it gives identical results with the results of the experiment. These procedures are collectively referred to as modal updating [4; 5; 6]. Frequency response functions (FRF) can also be used for the analysis of fundamental natural frequencies, as they fully characterize the dynamic properties of a linear system [7]. The frequency response functions can be calculated for the discrete model of a bridge and the continuously distributed mass model at force and kinematic excitation. [PERSON]'s energy method (REM) also provides a very good estimate of the first natural frequency [8]. The practice needs the results of numerical and experimental analyses. However, it calls for fast and reliable solutions. Not every workplace has the necessary software or experimental equipment. Therefore, the demand for practices with simpler alternative procedures is high. In bridge construction, this is all the more pronounced, as the need for the rapid reconstruction of a large number of bridges is growing. This contribution offers alternatives for just such a compromise. The method discussed here should be a connecting link between theory and practice, and should help to stimulate the interest of engineers in deeper mathematical analyses.