esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Application Potential of SIF for Drought Monitoring in Sugarcane  
In this study, we find that the correlation coefficient of SIF and sugarcane yield is higher than that of the EVI and sugarcane yield, and SIF satellite observations can better capture the interannual variation in sugarcane GPP than the EVI, demonstrating that SIF is a good proxy for sugarcane production or GPP. These results are consistent with previous findings. For example, [PERSON] [25] found that SIF-estimated GPP loss during drought was similar to crop yield loss, proving that SIF can be directly used to estimate yield loss induced by drought. SIF is a newly used satellite data source for drought monitoring and assessment of crops [1; 48; 77].  
As indicated by the anomalies of SPEI, precipitation, air temperature, LST, and soil moisture, a severe drought event was detected over the study area during the 2009 sugarcane critical growth period. During the early stage of the 2009 drought, the SIF had a quick negative response on DOY 241, but the EVI did not show a negative response until DOY 273 (Figure 8), and ET did not show negative anomalies until DOY 257 (Figure 6). This  
Figure 13: Monthly time series of the area percentage of standardized PAR (**a**), fPAR (**b**), SIFpar (**c**), and SIFyield (**d**) anomalies from the multiyear mean over the study area. \(\sigma\) indicates the standard deviation of the monthly variables during 2001–2020.  
may be because photosynthesis has been inhibited and fluorescence emission may decline immediately, but sugarcane canopy structure and chlorophyll content do not change during the early stage of water stress. Moreover, many previous studies proved that there is a lag time of 31 \(\pm\) 16 days in the response of different vegetation types to water or temperature stress based on VI monitoring [22; 25; 27; 78], which is consistent with our results. We also find that SIF can capture more pronounced responses of sugarcane to drought stress than the EVI. These results may be because chlorophyll fluorescence originates from the photosynthetic machinery, and satellite SIF products are less affected by clouds and the soil background [79]. Thus, SIF can capture the rapid change in sugarcane photosynthetic activities induced by drought stresses with more pronounced signals. Our results prove that satellite SIF has great potential to monitor drought stress on sugarcane in a timely manner at a large spatial scale.
## 1 Introduction
Considering the importance of city governance and administration, as well as collaboration between different stakeholders, to meet the optimum city performance, innovation, expansion, sustainable development, and liveability [17], we aim to investigate how smart governance affects smart city performance directly and also moderates the association between innovation and city performance. The main objective of our study is to examine the relationship between technology-oriented knowledge management, innovation, e-governance, and smart city performance with the help of knowledge management that is based on service science theory [18] and diffusion of innovation theory [19], as service science theory discusses the use of knowledge that is collected through citizen and artificial intelligence can help to improve and optimize city's service delivery. Diffusion of innovation theory refers to the procedure by which people espouse a new concept, product, practice, and ethos. Further, we will investigate the indirect mediating role of innovation in the relationship between technology-oriented KM and smart city performance and the indirect moderating role of e-governance in the relationship between innovation and smart city performance. Previous scholars examined the direct impact of knowledge management on innovation [20; 21], the impact of innovation on smart city performance [22], and the effect of e-governance on smart city performance [23; 24], but only a few explored the indirect relationship between these constructs [25]. Our study will contribute to the existing literature by investigating indirect associations to know-how innovation that mediates the relationship between the integration KM and city performance and how e-governance strengthens the relationship between innovation and smart city performance. The subsequent section briefly outlines the literature on KM, innovation, e-governance, and smart city performance. Next, Section 3 describes the research method that was used to find relevant outcomes for this study. Finally, Section 4 describes the research findings, while Section 5 discusses the recommended next steps for smart city research from the perspective of knowledge management. Section 6 concludes with policy implications and a conclusion.
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
## 3 Topological Structure Mesh Routing
### Routing  
The TSMR is based on a simple routing strategy in which the next route in the path is selected based on a hop count using the new addressing scheme. The source device calculates the distance from it to the destination using the row and column ID. After calculating the distance, it selects the neighboring router with the closest distance between the source and destination devices and transmits the data frame to the selected router. The same process is repeated until the data frame is delivered to the destination device. The detailed routing method is as follows:  
1. The source device or routers that receive the data frame subtract their own address [R1.C1.0] from the address of the destination device [R2.C2.z] and include the calculation results in the frame header --that is, D1 = [(R2 - R1),(C2 - C1).z].
2. The source device or router selects the neighboring router with the closest distance to the destination device and transmits the data frame to the selected router. For example, the distance between the current location and destination device is D1 = [(Rd1 = (R2-R1)).Cd1 = (C2 - C1).z], and the distance between the neighboring router and the destination device is Dn(Rdn.Cdn.0). The source device or router selects the neighboring router with the address [([Rdn]<=|Rd11),(|Cdn|<=|Cd1.n] and transmits the data frame to the selected router.
3. If there are two neighboring routers satisfying the condition [(|Rdn|<=|Rd11), (|Cdn|<=|Cd1.n|), the source device or router randomly selects the next router and transmits the data frame to the selected router.
4. Whenever the data frame moves one hop to the next router, the router decreases the row ID or column ID of D1 by one, based on the direction of movement.
5. Steps b-d are repeated until Rd1 = 0 and Cd1 = 0.  
The benefits of the TSMR protocol can be summarized as follows: (i) because a multipath route between the source device and the destination device exists, the link failure is not influenced in the entire network; (ii) because the control message for the routing protocol is unnecessary, the TSMR protocol can reduce its energy consumption and control overhead; and (iii) using Steps a and c, the traffic load in the network is distributed.
## 5 Pattern Recognition and Quantitative Diagnosis
### Pattern Recognition of RC Valve Leakage  
After extracting CIs, they need to be classified to identify different fault modes. SVM is a powerful classification technology that is based on statistical learning theory [47; 48]. SVM was originally introduced as a generalized linear classifier of binary classification. Its decision boundary is the maximum-margin hyperplane solved for learning samples, which is eventually transformed into a convex quadratic programming problem. Consider \(\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots(x_{m},y_{m})\}\) that \(x_{i}\in R^{n}\) are classification samples, and \(y_{i}\in\{-1,\ 1\}\) are classification labels. The optimization problem can be formulated, as follows:  
\[min\frac{1}{2}\|\omega\|^{2}+C\sum_{i=1}^{m}\xi_{i},\ y_{i}(\omega\phi(x_{i})+b )\geq 1-\xi_{i};\ \xi_{i}\geq 0;\forall i=1,\ldots,m, \tag{14}\]  
where \(\omega\) is a normal vector to the hyperplane and \(C\geq 0\) is a penalty factor. \(\xi_{i}\) are slack variables. Function \(\phi(x_{i})\) is a feature mapping rule, which is determined by the kernel function. The most  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**LLICs** & **ERF** & **EDRF** & **ETRF** & **LLICs** & **ERF** & **EDRF** & **ETRF** \\ \hline \multirow{6}{*}{0 mm} & 1.88 & 2.21 & 5.09 & & 2.79 & 5.11 & 6.75 \\  & 1.86 & 2.17 & 6.03 & & 2.83 & 5.26 & 6.91 \\  & 1.86 & 2.48 & 6.65 & & 2.86 & 5.09 & 6.48 \\  &... &... &... & 2 mm &... &... &... \\  & 2.23 & 3.03 & 6.41 & & 2.84 & 5.4 widely used radial basis function (RBF) is selected as the kernel function of the SVM algorithm in this study [47]. The expression of the function is shown as in Equation (15).  
\[K\Big{(}x_{j},x_{i}\Big{)}=exp\Bigg{(}-\frac{\|x_{j}-x_{i}\|^{2}}{g^{2}}\Bigg{)}. \tag{15}\]  
The one-against-all method was used to train the SVM. While classifying different fault modes with SVM, a \"three-regions and four-point sets\" fault diagnoser was constructed to realize the quantitative diagnosis of different severity of valve leakage.
## 5 Results and Discussion
### Wall Shear Stress (WSS)  
Figure 7 shows WSS variation for the rigid and FSI models during the systolic and diastolic periods of the cardiac cycle. It can be clearly observed that a significant increase in the WSS was found across 90% of the area of stenosis as compared to 70% and 80% of the area of stenosis. A slight increase in the WSS for the diastolic phase of the cardiac cycle was found across the bifurcation regions compared to the systolic phase of the cardiac cycle. The highest WSS at 90% of the area of stenosis was found in the range 40 to 50 Pa. The wall shear stress plays a substantial role in evaluating the strength of the artery, and it is believed that the higher wall shear stress may assist in a rupture of the artery wall. The overall WSS in the segmented left coronary artery shows a slight variation when comparing the CFD and FSI models. This can be described by the deformation of artery wall, which produces pressure waves that promulgate at a finite speed through the arteries, in contrast to the CFD model with rigid walls which produce instantaneous wave propagation [24].  
Figure 6: Wall pressure field in left coronary artery during the systolic and diastolic phase of cardiac cycle for (**a**,**c**) Rigid and (**b**,**d**) FSI models.
# A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
## 2 System Model  
The system model of the wavelet-OFDM system is presented in Figure 1[17].  
At the transmitter, the incoming binary information is first grouped and mapped according to a pre-specified modulation scheme-quadrature phase shift keying (QPSK). After inserting few pilots, the QPSK symbols are converted into parallel streams by serial-to-parallel block. Inverse fast Fourier transform (IFFT) block is used to transform the data sequence \(X\) with \(N\) rows into time-domain signal, where \(N\) is the number of subcarriers in frequency-domain. Then, the CP is inserted into the  
Figure 1: System model of the wavelet-orthogonal frequency division multiplexing (OFDM) system.  
time-domain OFDM symbols \(x\) in order to reduce the ICI and ISI. The transmitted signal will pass through the multipath fading channel with AWGN after parallel to serial block.  
At the receiver, the CP is removed after the serial to parallel conversion. Following the fast Fourier transform (FFT), the estimated channel state information is obtained in the channel estimation block. The AWGN existing at the estimated channel coefficients is further reduced in the wavelet de-noising block. The detail process of dotted line block in Figure 1 is described in Section 4. At last, the binary information data is obtained after the demodulation with QPSK demodulation scheme.
## 2 Wireless Communications with Energy Harvesting  
### System Model  
Figure 1 demonstrates the basic system model of WCwEH with three devices (T, S, and D). Such a WCwEH may represent direct (uplink/downlink) transmission in mobile communications networks. S is supposed to be power-constrained and therefore it needs to harvest energy from T. T plays a role as a dedicated power transmitter, e.g., television and radio broadcasting stations. In the proposed WCwEH, T supplies energy for S's operations in a time fraction \(\beta\) of a communications frame \(U\), namely Phase 1, and S transmits its information transmission to D in the rest of \(U\), namely Phase 2. To increase the quantity of harvested energy, eventually ameliorating communications reliability, T is assumed to be equipped with \(M\) antennas, which is feasible for T to be a high-transmission-power source. Indeed, S can harvest much more energy when T transfers energy through its higher number of antennas. Notwithstanding, since S and D may be mobile devices, the deployment of a single antenna on them is a better assumption. Furthermore, for realistic consideration, all devices are supposed to suffer from HWi.
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.4 Multi-Layer Perceptron (MLP)  
Multi-layer perceptron (MLP) is an artificial neural network (ANN) inspired by the structure and function of biological neural networks. MLP consists of interconnected nodes, or \"neurons\", organised into layers. Data are fed into the input layer and pass through one or more hidden layers before reaching the output layer, where the network makes predictions and has been chosen as one of the models in [17]'s experiment due to its capability of modelling complex nonlinear correlations. Their experiment utilised a single-output MLP with a feed-forward and a fully connected three-layer network. The primitive selector determined the inputs and relevant primitives, and the output corresponded to the quality of service (QoS). MLP can be trained with an arbitrary quality dataset, indicating the potential accuracy of model predictions. It excels in detecting and diagnosing faults or anomalies in a system and can take corrective actions to address them. By continuously training with new data, MLP can adapt to changing conditions and improve its accuracy and effectiveness in detecting and responding to faults. This adaptability makes MLP particularly suitable for self-healing systems. The main limitation of MLP is its computational expense, especially when dealing with large and complex systems. Training MLPs can be resource-intensive, but advancements in hardware and software, such as parallel processing and cloud computing, have made training more efficient and practical for self-healing systems.
## 6 The Proposed RAWC Scheme  
### Scheme Description  
In this section, a resource-adaptive routing scheme with wavelength conflicts (RAWC) is proposed. The RAWC scheme is applied in scenarios where wavelength resources on links and quantum keys in QKP are limited and the simultaneous arrival of service requests occurs. Additionally, we consider short-distance QKDON, which means that point-to-point QKD can be carried out between any two nodes that are directly connected. The two nodes on the link generate keys directly via QCh and PCh, so the impact of link length on service requests can be ignored.  
The RAWC scheme is divided into two specific cases from the perspective of routing allocation: 1 Only one QKD service request arrives at a certain moment. Considering the characteristics of QKDON fully, the real-time remaining number of wavelengths available for each link and the real-time remaining number of quantum keys available in QKP are introduced into the calculation of link weights. After the arrival of each QKD service request, the communication path is calculated with the Dijkstra algorithm dynamically based on the resource surplus status. This facilitates the preference of the path with more wavelengths and quantum keys remaining to reduce the waste of resources on the service-free transmission link. In this way, network load balancing and the efficient utilization of network resources are achieved. 2 Specifically, when \(m\) (\(m\geq 2\)) QKD service requests arrive at the same time, the K-shortest-path (KSP) algorithm is used to calculate the K shortest paths for each request. Then, the wavelength conflict degree of each of the K paths for the current request is calculated. The path with the lowest wavelength conflict degree is selected as the communication path, which is calculated according to the method described in Section 6.2.2. Considering the influence of other \((m-1)\) requests, the path with low wavelength conflicts is preferentially selected when assigning routing for the current request. Therefore, the service blocking caused by potential wavelength competition will be reduced, which improves the request success rate. Table 2 lists some of the notations and definitions used in this paper.  
Figure 5: An example of wavelength conflicts. (**a**) Occupancy of links and wavelength channels by multiple services; **(b**) Mapping of resource occupancy in the adjacency matrix; (c) Wavelength occupancy for shared link 2\(\rightarrow\)3.
# Delay Management in Regional Railway Transport
## 2 Materials and Methods
### Waiting Time Issue
#### 2.2.1 Typical Causes of Operational Disruptions  
Typical causes of operational disruptions that result in train delays or interruptions in operational processes, thereby thwarting the operation of timetables, often include operational failures [14] such as the following:  
* vehicle technical problems;
* fixed installations' technical or construction problems (e.g., railway switches or safety system failures);
* electric supply interruptions;
* staff absences or inabilities;
* passengers' influence (e.g., long dwell times due to unusual passenger frequency);
* outside influences (e.g., weather influences, lower adhesion, etc.).  
Operational failures, as well as technological mistakes in timetable construction, can lead to scheduling conflicts or clashes between the scheduling of two or more trains. Their real-time mutual path requirements may not be achievable, taking into consideration issues pertaining to infrastructure, vehicles, crews, sequence of trains, and connections. Occupancy conflicts, vehicle circulation conflicts, and transfer connection conflicts may be separate and distinct. Every conflict has two basic solutions, as set out in Table 1, which shows that the second solution causes delay. On the other hand, the first solution does not cause delay, but can cause difficulty with respect to resources or extending transportation times for transferring passengers.  
The following disposal measures are commonly used in rescheduling, to achieve stabilization of railway operation, i.e., for determining timetables [16; 17]:  
* exchange of train sequence;
* run time or dwell time extension;
* transfer connections' unbinding;
* alternative paths (run routes) utilization;
* crossing or overtaking relocation/cancellation;
* changes in train stopping patterns (i.e., additional stops or cancellations of stops);
* premature vehicle reversion;
* canceling services, creating additional services;
* substituting train sets or services (e.g., buses); and/or
* exclusion timetables.  
Delays on single-track railway line without sufficient buffer times (i.e., margin times) converge to the value of the most-delayed train on the line/route, until any of the relevant disposal measures mentioned above are applied. When delay differences of opposite-direction trains on single-track railway lines are small (considering the time distance of neighboring crossing stations), the scheme of the crossing is maintained. When there are larger differences in the delays of opposite-direction trains, it is appropriate to shift the crossing to another suitable station, particularly for the maintenance of transfer connections for the train with less delay; however, such a shift of crossing can lead to further delay, and increase the delay of the train with higher delay
## 3 Result  
### Total Se in Soil and Soil Physicochemical Properties  
The total Se concentration in cropland soils ranged from 0.25 to 1.60 mg/kg, with a mean value of 0.50 mg/kg and a coefficient of variation (CV) of 0.43 (Table 3). The average Se content exceeded both the Chinese soil background value of 0.29 mg/kg [35] and the global average of 0.40 mg/kg [45]. According to the classification standards for total Se in surface soils in China [12], Se levels are categorized into five classes: deficient (<0.125 mg/kg), marginal (0.125-0.175 mg/kg), moderate (0.175-0.400 mg/kg), sufficient (0.400-3.000 mg/kg), and excessive (>3.000 mg/kg). All samples in this study fell within the moderate or higher categories, with 64% classified as \"sufficient\", indicating that Zheng'an County can be considered a Se-rich region.  
The CV of 0.43 suggests considerable spatial variability in total Se distribution across the study area. The observed differences in Se concentrations among sampling sites may be attributed to variations in parent material, land use types, or their combined effects. Soil pH values ranged from 4.84 to 8.16, with an average of 6.05, indicating that most soils in the area are acidic (Table 3). SOM content varied between 14.5 and 50.9 g/kg, with an average of 27.59 g/kg, reflecting relatively high SOM levels in the region.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multirow{2}{*}{**Standard Error**} & \multicolumn{2}{c}{**Limestone (\(n=38\))**} & \multicolumn{2}{c}{**Sandstone (\(n=42\))**} \\ \cline{3-6}  & & **Paddy Fields (\(n=20\))** & **Drylands (\(n=18\))** & **Paddy Fields (\(n=20\))** & **Drylands (\(n=22\))** \\ \hline Total Se (mg/kg) & 0.03 & 0.41 & 0.71 & 0.49 & 0.48 \\ pH & 0.08 & 6.64 & 6.20 & 5.71 & 5.78 \\ SOM (g/kg) & 0.80 & 25.41 & 27.97 & 28.99 & 27.22 \\ V (mg/kg) & 2.98 & 95.92 & 124.05 & 111.55 & 111.61 \\ Cr (mg/kg) & 2.04 & 75.99 & 94.44 & 89.50 & 89.91 \\ Zn (mg/kg) & 1.61 & 86.86 & 94.56 & 99.82 & 101.21 \\ Elevation (m) & 25.27 & 765.20 & 930.82 & 997.35 & 1036.27 \\ Slope (\({}^{\circ}\)) & 1.04 & 17.82 & 17.14 & 12.02 & 16.01 \\ TWI & 210.04 & 5.00 & 5.01 & 5.71 & 5.12 \\ SPI & 267.67 & 2.42 & 2.30 & 2.35 & 2.44 \\ \hline \end{tabular} All values are presented as mean values (\(n=80\)).  
\end{table}
Table 3: Summary of mean values for total Se and influencing factors.  
Figure 2 illustrates the spatial distribution patterns of total Se, soil pH, and SOM, all of which exhibit pronounced spatial variability across the study area. As shown in the figure, total Se concentrations were relatively high in the eastern part of Banzhu Town and the central region of Xiaoya Town in Zheng'an County, with values exceeding 1.1 mg/kg. In contrast, lower Se concentrations formed a belt-like pattern extending from north to south, closely mirroring the spatial distribution of SOM. Alkaline soils were mainly concentrated in the northeastern part of Anchang Town, whereas the rest of the region was predominantly characterized by acidic soils.