esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scenario 1 (S1)
#### 3.1.3 Nh\({}_{4}\)-N  
In the calculation of NH\({}_{4}\)-N environmental capacity, after the implementation of scheme B (HZ1), the EC reduced by 4.45% (Table 6). The influence was larger than the change in COD environmental capacity (1.48%), indicating that reclamation condition HZ1 had a slightly greater influence on the diffusion movement of NH\({}_{4}\)-N pollutants. The implementation of scheme C (HZ1+HZ2) caused the change of NH\({}_{4}\)-N environmental capacity to reach 21.43%. This indicated that the position of HZ2 as a working condition also had a large influence on the diffusion of NH\({}_{4}\)-N pollutants. The implementation of schemes D, E and F had the same effect on the EC of NH\({}_{4}\)-N as the change of COD environmental capacity.  
The feasibility evaluation results of NH\({}_{4}\)-N environmental capacity are shown in Table 5. The results demonstrated that the evaluation index R\({}_{2}\) of scheme B was 8, so scheme B was basically feasible. The evaluation index R\({}_{2}\) of schemes C, D, E and F was 0, so schemes C, D, E and F were not feasible, that is, the working conditions HZ2, HZ3, HZ4 and HZ5 were prohibited.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reclamation Scheme** & **NH\({}_{4}\)-N(t/a)** & **EC Change (\%)** & **R\({}_{2}\)** \\ \hline A & 3366 & - & - \\ B & 3217 & 4.45 & 8 \\ C & 2645 & 21.43 & 0 \\ D & 2564 & 23.83 & 0 \\ E & 2664 & 20.87 & 0 \\ F & 2831 & 15.90 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Feasibility evaluation in NH\({}_{4}\)-N environmental capacity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reclamation Scheme** & **COD (t/a)** & **EC Change (\%)** & **R\({}_{1}\)** \\ \hline A & 62,444 & - & - \\ B & 61,517 & 1.48 & 10 \\ C & 49,213 & 21.19 & 0 \\ D & 47,803 & 23.44 & 0 \\ E & 49,657 & 20.48 & 0 \\ F & 52,314 & 16.22 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Feasibility assessment in COD environmental capacity.
# Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution
###### Abstract  
The goal of continuous variable quantum key distribution (CVQKD) is to be diffusely used and adopted in diverse scenarios, so the adhibition of atmospheric channel will play a crucial part in constituting global secure quantum communications. Atmospheric channel transmittance is affected by many factors and does not vary linearly, leading to great changes in signal-to-noise ratio. It is crucial to choose the appropriate modulation variance under different turbulence intensities to acquire the optimal secret key rate. In this paper, the four-state protocol, back-propagation neural network (BPNN) algorithm was discussed in the proposed scheme. We employ BPNN to CVQKD, which could adjust the modulation variance to an optimum value for ensuring the system security and making the system performance optimal. The numerical results show that the proposed scheme is equipped to improve the secret key rate efficiently.  
continuous variable quantum key distribution; atmosphere turbulence; machine learning; back-propagation neural network Article
### Standard Procedure for Classifying PCG  
The standard approach followed by the reported research includes data acquisition and preprocessing, which is then segmented based on S1 and S2 peaks. Specific localized audio features are extracted, followed by feature selection. These features are then fed to a classification procedure to label the artifacts as normal or abnormal heart sounds or specifically identified ailments [5].These four stages are visually demonstrated in Figure 2. In the literature, among the various procedures, neural network-based classification procedures have outperformed various classic procedures such as SVM, Adaboost, etc. Various neural network architectures have been proposed, and each of these variations considers one or more parameters to introduce improvements to the basic ANN. Neural network-based algorithms, which learn incrementally based on the supervised, unsupervised, or reinforcement learning approach, can be scaled to add more connections without additional maintenance. Their intrinsic design efficiently executes the model over a massive parallel platform and performs massive calculations within the target time range [6]. In most cases, the training and test/validation datasets used are different. If the dataset is too small, the jack-knife technique [9; 10] is used.  
The key contribution of this research includes the following:  
* The selection of a balanced, usable dataset helps to avoid overfitting and improves the training of the ANN model.
* Denoising the heart sounds using DWT for signals clipped up to 600 Hz.
* When harmonic and percussive components are separated in PCG audio analysis, more accurate diagnoses are possible. Harmonic elements uncover periodic structures that are indicative of cardiac conditions. In contrast, percussive elements record  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sound Type** & **Frequency Range** \\ \hline S1 (Lub) & 10 to 200 Hz \\ S2 (dub) & 20 to 250 Hz \\ S3 (start of diastole, immediately after S2) & 25 to 70 Hz \\ S4 (end of diastole, immediately before S1) & 15 to 70 Hz \\ Innocent murmurs & 120 to 250 Hz \\ Systolic/diastolic murmurs & upto 600 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Types of heart sound [5].  
Figure 2: General approach of PCG classification.  
important non-periodic events such as murmurs or valve closures, greatly enhancing the assessment of cardiac health assessment.
* Outliers for each extracted harmonic and percussive feature are determined separately for both labels (i.e., normal and abnormal). This is performed to improve the model training and reduce overfitting.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 2 Disclose the Valuable Information
### Transformations
#### 2.1.3 S-Transform (ST)  
An S transform, derived from CWT, offers time-frequency demonstration of frequency-dependent resolution based on scalable and moving localized Gaussian window [31]. Local spectral characteristics can be obtained effectively from S-transform, useful in expediting transients [32]. ST calculations are stored in S-matrix and ST contours are plotted for two-dimensional visualization and then features are extracted. ST is used to overcome the shortcomings of the DWT such as; sensitive to noise and not able to exactly reveal the characteristics of harmonics [33; 34]. In [35], S-transform contour energy and standard deviation used to select faulty lines and sections, respectively. [PERSON] et.al. used ST to find amplitude, impedance and fault points [36].
## 4 Discussion
### Validity of Satellite-Derived Soil Moisture
The interplay between meteorological factors and vegetation indices illustrates the complex dynamics of crop growth and water availability, which can be captured using remote sensing tools [63]. However, discrepancies were noted between satellite-derived vegetation indices and field-measured soil moisture, particularly during transitions between wet and dry periods. Field measurements provided more granular insights into the spatial variability of soil moisture across the orchard, which the satellite data could not fully capture due to their 30 m resolution. Microclimatic factors such as soil texture, slope, and shading likely contributed to this variability, resulting in discrepancies between field data and satellite estimates. For example, areas with dense canopy coverage showed higher NDVI and SAVI values but experienced localized water stress due to limited irrigation or soil moisture retention variations. These observations highlight the importance of integrating satellite data with field measurements for a more accurate and reliable assessment of crop water status, particularly in areas with spatial variability that remote sensing alone cannot capture.
## 2 Materials and Methods
### Methods  
A commercially available FTO glass substrate with sheet resistance of 12-15 \(\Omega\)/sq was used. The FTO glass substrate was etched using zinc powder and hydrochloric acid (2 M) diluted in deionized (DI) water. This step was required to prevent short circuiting during the cell test. Then, substrates were cleaned with acetone, methanol, and isopropanol. Finally, Oxygen plasma was used for 10 minutes to clean substrate and improve the adhesion between the FTO and the compact TiO\({}_{2}\) layer. This is followed by the deposition of 70 nm of compact TiO\({}_{2}\) on the FTO glass substrate using DC-sputtering powered at 200 W [15]. The mesoporous TiO\({}_{2}\) was deposited using spinning coating at a speed of 4000 rpm with acceleration of 2000 rpm/s for 10 s by dissolving 150 mg of TiO\({}_{2}\) paste (30N-RD) in 1 mL ethanol and stirred at 60 \({}^{\circ}\)C for at least 3 h before use. The M-TiO\({}_{2}\) was doped with FK209 cobalt (2.5 mg of FK209 dissolved in 200 \(\mu\)L acetonitrile) and Lithium (2.5 mg of Li\({}^{+}\) dissolved in 200 \(\mu\)L acetonitrile). A 200 \(\mu\)L of the final stock (FK209 or Li) was added to the 1 mL of M-TiO\({}_{2}\) solution. To find the optimum concentration, the concentration of the FK209 cobalt was changed from 1.5 mg to 5 mg and dissolved in 200 \(\mu\)L of acetonitrile. A 150-nanometer thick M-TiO\({}_{2}\) was achieved using a spinner at 4000 rpm with 2000 rpm/s acceleration for 10 s. The M-TiO\({}_{2}\) layer was annealed for half an hour at 450 \({}^{\circ}\)C. The perovskite layer was prepared in a glovebox filled with N\({}_{2}\) circulated by two steps spinning. Triple cation perovskite was prepared using FAI (1 M), MABr (0.2), PbI\({}_{2}\) (1.1 M), PbBr\({}_{2}\) (0.2 M) in anhydrous dimethylformamide (DMF) and dimethyl sulfoxide (DMSO) were prepared by volume ratio of DMF: DMSO 4:1 (\(v\)\(v\)). The CsI was prepared by dissolving 1.5 M of CsI in DMSO. Following that, a 5% of the CsI was added to the total precursor mixture. The first step in this process was dispensing 100 \(\mu\)L of the triple cation perovskite on the substrates while spinning at a speed of 1000 rpm for 10 s. Then, a second step of spinning at 6000 rpm for 25 s followed by dispensing 230 \(\mu\)L chlorobenzene for 10 s at the end of the rotation and then annealing at 100 \({}^{\circ}\)C for 40 min. For the hole transport layer, a 72 mg of spiro-MeOTAD was first dissolved with 1 mL chlorobenzene. Then, a 29 \(\mu\)L of 4-tert-butylpyridine and 18 \(\mu\)L of Li\({}^{+}\) salt were added to the spiro-MeOTAD layer. This was accomplished by using 520 mg of bis(trifluoromethane)sulfonimide lithium salt dissolved in 1 mL of acetonitrile. Finally, 70 nm of gold was deposited as the top electrode using electron beam evaporation.
###### Abstract
Additional risks of land subsidence, which are not only limited to Yunlin, and include flooding and change of water flows. There have been many studies monitoring land subsidence in Yunlin. A study applying InSAR analysis on ERS and Envisat data shows that the mean land subsidence rate in Yunlin was about 30 mm/a over 1995-2001, rising to 50 mm/a from 2005 to 2008 [8]. [PERSON] and [PERSON] [10] showed that Tuku and Yuanchang Townships experienced the largest subsidence rates in Yunlin, reaching 78 mm/a along the line of sight (LOS) direction of ERS between 1996 and 1998. Using Envisat data, [PERSON] et al. [6] showed that the largest land subsidence rate from 2006 to 2008 was about 70 mm/a in Huwei and Tuku Townships. Finally, [PERSON] et al. [11] used ALOS/PALSAR to show that land subsidence rates in Huwei Township were over 100 mm/a between December 2006 and February 2011, the highest reported rates of subsidence in Yunlin. Most InSAR studies in Yunlin focused on long-term and large-scale monitoring of subsidence. Only few studies in Yunlin show how InSAR can detect small-scale subsidence that have not previously been identified by leveling and GPS measurements [6; 10; 11]. In addition, most subsidence monitoring results in Yunlin only show long-term (over few years) subsidence behaviors without detecting the ups and downs of land surface in response to seasonal groundwater fluctuations [12], which can be used for groundwater management to mitigate land subsidence in dry and wet seasons. Some parts of Yunlin are such highly responsive areas that information is needed regarding their seasonal surface displacements, particularly because of the concern over the THSR's structural safety during droughts. In April 2014, the European Space Agency (ESA) launched the Sentinel-1A SAR satellite mission. Sentinel-1A features a high revisiting frequency (12 days in the area of interest), high spatial resolution (approx. 5m x 20m along range and azimuth directions), wide swath (250 km), and free cost. In this study, we use SAR images from Sentinel-1A from April 2016 to April 2017, along with the SBAS approach, which is suit for monitoring the temporal evolution of surfaces in non-urban areas like Yunlin [13], to determine both the annual and seasonal displacements in Yunlin. We use data from GPS to calibrate the initial InSAR result to achieve a final InSAR velocity field, which was assessed by GPS and leveling. The calibration by GPS removes systematic errors in the InSAR measurements due to orbit errors, long-wavelength atmospheric effects, and other sources. Our results cover the entire Yunlin County and show how land surface fluctuates with rainfall. We also show spots of subsidence not identified by leveling, and advise the responsible agency to install a local leveling network to monitor detailed land displacements in these areas. Finally, we show how rainfall in dry seasons affect areas of significant land subsidence, and how a new reservoir in Yunlin may be a measure of subsidence prevention by supplying surface water to counter rainfall shortage.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 3 Proposed SS-FGSM Scheme  
In this section, details of the proposed SS-FGSM and deep learning detector will be presented. Firstly, the time-domain underwater acoustic channel impulse response is also briefly discussed. Then a review of the conventional FGSM scheme is presented. After that, SS-FGSM system model is explained in detail. Finally, the deep learning-based detector is discussed and presented.
## 2 Categorization and Classification of Quality of Service Routing Protocols  
Based on the research issues, we have classified QoS routing protocols into two categories, which are probabilistic and deterministic, which in turn include soft real time and hard real time QoS routing protocols. This will help researchers choose the best QoS routing protocol according to the requirements of the application in order to reduce energy consumption and obtain better throughput as given in Figure 1.  
In probabilistic routing protocols, the routing between sources and destinations depends on the probability of the last lower rebroadcasted rate. In the probabilistic approach, the sensor node transmits the message with a known probability [18]. The transmission probability involves different factors such as hop-distance from source to destination, the number of hops a packet has already traveled, time in which sensor node already forwarded the packets, number of neighbor nodes, _etc._ The probability- based protocols perform directed and controlled flooding. As a result, multiple packets are copied. In addition, probabilistic protocols use the knowledge of past history. Unlike probabilistic protocols, the deterministic protocols keep the complete information of node trajectories, encounter probability of nodes and the period in which a decision is forwarded [19]. Both probabilistic and deterministic routing protocols are also classified into soft and hard real time. Soft real time protocols can miss few data points that cannot affect the performance. If some bits are missed, performance is eventually degraded. On the other hand, hard real-time protocols such as those in nuclear systems, some medical applications, military applications, avionics, _etc_, must absolutely hit every deadline.  
Figure 1: Classification of real-time quality of service (QoS) routing protocols.
# Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection
## 2 Materials and Methods
### Network Design  
A network based on CNNs was built to extract features from parking slot images because CNNs are known to perform well in image processing. In addition, several structurally different networks whose classification parts are identical were designed to compare the characteristics of their extracted features. The networks were constructed to output same-size feature maps for direct comparison, because feature map comparisons are difficult if the sizes of their feature maps are different. The networks were based on ordinary convolutional neural networks, since vacant parking slot detection using ROI initialization is a simple task. The networks consisted of convolution, max-pooling, drop-out, and fully connected layers. The convolution layers extract valuable features from the input images based on convolution operations between the convolution filters and receptive fields of the input images. The max-pooling layers find the largest value in receptive fields and eliminate the rest to make the network faster and lighter by eliminating trivial information. The drop-out layers prevent networks from overfitting by randomly disconnecting propagation. The fully connected layers establish complete connections between successive layers. Our baseline network structure is shown in Figure 4.  
The baseline network is comprised of four convolution layers, four max-pooling layers, one flatten layer, and two dense layers. The third max-pooling layer is followed by one drop-out layer. The rectified linear unit activation function was used in all the layers except for one; the Softmax function was used in the last dense layer. The other networks in this study were derived from the baseline network. As mentioned before, all the networks were designed to output same-size feature maps for direct feature comparisons, as shown in Figure 5.  
Figure 4: Architecture of the baseline network.
## Chloride Ingress Modeling  
### Chloride Ingress for Uncracked Concrete  
The diffusion of chloride ions in saturated concretes is described by the second Fick's law [26] with the assumption that concrete is a homogeneous and isotropic material [31]:  
\[\frac{\partial C}{\partial t}=D\frac{\partial^{2}C}{\partial x^{2}} \tag{1}\]  
where C is the free chloride concentration, t is time, D is the diffusion coefficient of the chloride ions, and x is the depth of the concrete in the diffusion direction. With the following initial conditions: (1) the concentration is zero at the beginning of the exposure t\({}_{0}\), and (2) the chloride surface concentration is constant; the concentration of free chloride ions C(x,t) at depth x and at time t for a semi-infinite medium can be expressed by an analytical solution of Fick law using the error function:  
\[C(x,t)=C_{s}\left[1-\mathrm{erf}\left(\frac{x}{2\sqrt{D\cdot t}}\right)\right] \tag{2}\]  
where C\({}_{s}\) is the chloride concentration at the surface, and erf\((\cdot)\) is the error function. This solution makes possible to calculate chloride profiles at given times and depths as well as the corrosion initiation time t\({}_{ini}\). t\({}_{ini}\) is the time at which the chloride concentration at the surface of the reinforcements reaches a threshold value C\({}_{th}\). This threshold value represents the concentration of chlorides for which the passive layer of the steel is destroyed and the corrosion reaction starts. The corrosion initiation time is, therefore, estimated for the chloride concentration equal to the threshold value C\({}_{th}\) and x equal to the cover depth of the concrete c, according to the following equation:  
\[t_{ini}=\frac{c^{2}}{4D}\left[\mathrm{erf}^{-1}\left(1-\frac{C_{th}}{C_{s}} \right)\right]^{-2} \tag{3}\]  
[PERSON]'s model (Equation (2)) is only valid for saturated concrete exposed to a constant concentration of chloride on the surface. In reality, for a heterogeneous material, such as concrete, these conditions are rarely observed and the concentration of chlorides on the surface varies over time. Although this model does not take into account several important parameters such as the aging state of concrete and environmental factors (temperature, humidity) [32; 33], it will be used to identify the variables required because the beams of the tests are subjected to a constant concentration of chloride on the surface of the concrete. In addition, this solution (Equation (2)) remains complex enough to illustrate the proposed methodology. More elaborated models could be considered but they require additional experimental data to determine supplementary model parameters.
# Impacts of Heat and Drought on Gross Primary Productivity in China
## 4 Results and Analysis
### Impact of Heat on GPP  
A 2D joint distribution of the annual GPP anomaly and Tmax sequences was constructed utilizing the 2D copula function pixel by pixel, and the ENAG probability (dramatic decrease in GPP) under different heat intensities was calculated for each pixel. The results are shown in Figure 6.  
Figure 5: Boxplots of ENAG probabilities in each vegetation region under different drought levels. (**a**,**c**,**e**,**g**) represent extreme, severe, moderate, and mild drought classified in terms of the SPI, respectively; (**b**,**d**,**f**,**h**) represent extreme, severe, moderate, and mild drought classified in terms of the SPEI, respectively.  
Generally, the ENAG probabilities under high-temperature conditions were less than those under drought conditions. Under severe heat, the maximum ENAG probability was 66.6% (Figure 6a). Pixels with ENAG probabilities greater than 40% sporadically appeared in the temperate grassland region (R2) and the subtropical evergreen broad-leaved forest region (R4), and pixels with ENAG probabilities between 20% and 40% mainly occurred  
Figure 6: ENAG probabilities under different heat intensities. (**a**,**c**,**e**) represent the spatial distributions of the probabilities under severe, moderate, and mild heat, respectively; (**b**,**d**,**f**) represent the boxplots of ENAG probabilities in each vegetation region under severe, moderate, and mild heat, respectively (Note: the pixels displayed in white have the same meaning as Figure 4).  
in the temperate grassland region (R2), the subtropical evergreen broad-leaved forest region (R4), and the warm temperate deciduous broad-leaved forest region (R6). When the temperature intensity was reduced from high to moderate or mild (Figure 6c,e), ENAG probabilities greater than 40% only occurred in a few pixels, mainly located in the eastern part of the subtropical evergreen broad-leaved forest region (R4). In other areas, the ENAG probabilities were generally less than 20%.  
When heat intensities increased, the ENAG probabilities in each vegetation region increased, and the higher the heat intensity was, the greater the increase in the ENAG probability was (Figure 6b,d,f). In terms of the total average ENAG probability, the impact of heat on GPP in the different vegetation regions was calculated in order from highest to lowest as follows: the temperate grassland region (R2, 39.66%) > the warm temperate deciduous broad-leaved forest region (R6, 38.20%) > the subtropical evergreen broad-leaved forest region (R4, 37.28%) > the tropical monsoon forest and rainforest region (R5, 35.88%) > the cold temperate coniferous forest region (R8, 34.56%) > the temperate coniferous and deciduous forest mixed forest region (R7, 30.28%) > the temperate desert region (R1, 14.94%) > the alpine vegetation region on the Qinghai-Tibet Plateau (R3, 14.64%).