esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Improving the accuracy of runoff simulations is a significant focus of hydrological science for multiple purposes such as water resources management, flood and drought prediction, and water environment protection. However, the simulated runoff has limitations that cannot be eliminated. This paper proposes a method that integrates the hydrological and time series models to improve the reliability and accuracy of simulated runoffs. Specifically, the block-wise use of TOPMODEL (BTOP) is integrated with three time series models to improve the simulated runoff from a hydrological model of the Zhou River Basin, China. Unlike most previous research that has not addressed the influence of runoff patterns while correcting the runoff, this study manually adds the hydrologic cycle to the machine learning-based time series model. This also incorporates scenario-specific knowledge from the researcher's area of expertise into the prediction model. The results show that the improved Prophet model proposed in this study, that is, by adjusting its holiday function to a flow function, significantly improved the Nash-Sutcliffe efficiency (NSE) of the simulated runoff by 53.47% (highest) and 23.93% (average). The autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) improved the runoff but performed less well than the improved Prophet model. This paper presents an effective method to improve the runoff simulation by integrating the hydrological and time series models.  
runoff simulation correction; runoff pattern; BTOP model; improved Prophet model +
Footnote †: journal: Applied sciences
# A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem
## 4 MDP Framework for HFSP
### Exploration and Exploitation
#### 4.3.2 Boltzmann Exploration Policy  
Boltzmann exploration policy is to determine the probability that each action is selected with a random distribution function. Given random temperature coefficient \(T(>1)\), the probability of the \(i^{th}\) action being selected in time step \(t\) at state is:  
\[p(s_{t},a_{it})=\frac{e^{Q(s_{t},a_{it})/T}}{\sum\limits_{i}^{N}e^{Q(s_{t},a_{ it})/T}}, \tag{13}\]  
where \(N\) is the total number of actions.  
The temperature coefficient \(T\) is high and Q-value is relatively small at the beginning of the learning, resulting in all the action to be chosen with nearly equal probabilities, which is beneficial for the agent to explore the actions with non-optimal Q-value. As the learning progresses, the temperature coefficient \(T\) decreases gradually, and the probability changes with \(Q(s,a)\), the probability of adopting random actions decrease, which is instrumental in selecting the optimal action with the largest Q-value. In the later stage of learning, the temperature parameter \(T\) tends to 0, \(Q(s,a)\) is the largest, and the corresponding action is selected with the largest probability, and the action with the largest Q-value is selected each time, which means the policy change to greedy policy.  
The iteration of temperature coefficient is usually achieved by three policies:  
\[T=\lambda^{e}T_{0}, \tag{14}\]  
\[T=\frac{T_{0}}{\log(e+e_{0})}, \tag{15}\]  
\[T=\frac{T_{0}}{e+e_{0}}, \tag{16}\]  
where \(\lambda(\in(0,1))\) is the cooling parameter, often set as a decimal close to 1. \(e\) is the times of iteration and \(e_{0}\) is a positive constant. \(T_{0}\) is the initial value of temperature coefficient \(T\); if set too small, it will cause the local minimum value of the algorithm; on the contrary, the calculation of the algorithm will be increased with a large \(T_{0}\). Equation (14) is an equivalent cooling policy, Equation (15) is logarithmic cooling strategy, and Equation (16) is a fast cooling policy.
## 4 Materials and Methods
### Parameter Extraction
#### 4.3.4 Spectral Entropy (SpE)  
The SpE parameter is a special case of ShE wherein the calculation of the entropy is done using the distribution of the normalized power spectrum of the signal. Mathematically, it is represented by Equation (6).  
\[\text{SpE}=-\sum_{t=1}^{n}p(Y_{t})\cdot\ln\left(p(Y_{t})\right) \tag{6}\]  
where \(Y_{t}\) represents the possible amplitude values in each frequency band.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Benefits to IoT Application Areas
#### Vehicular Networks  
The application of CR-LPWAN in vehicular networks presents great potentials for improved performance. CR-LPWANs would allow moving vehicles to communicate over long single hops to a remote roadside infrastructure against short range technologies, which require multi-hops to reach an infrastructure [99]. Specifically, high density vehicular networks could arise in market places, cinemas and sporting arenas where large events are ongoing. This high-density condition typically breeds increased interference, which might grow spontaneously to cripple the entire network. CR-LPWAN systems are most suited to such cases by providing frequency hopping capacity to robust bands in order to relieve network congestion and improve performance.  
In the literature, for example, CR was used to increase communication bandwidth in vehicular ad hoc networks [100]. In particular, cooperative SS was deployed among neighboring vehicles along with a belief propagation model. This was applied to address the problem of distributed observations and to exploit redundancies in both space and time. Improved performance was recorded. A spectrum database assisted CR vehicular network design was proposed in [101], which adheres to the FCC specification for CR deployment, and it also provides a cost-revenue analysis based on parameters such as vehicular density, base station road coverage, and database assisted sensing. The findings in [101] will greatly improve the development of CR in vehicular networks in terms of cost reduction and long-range transmissions. There are several applications of CR in vehicular networks, which can be found in [28; 102], and CR-LPWAN can only add to this growing list of application benefits.
## 3 Modelling, Analysis Tools and Experimental Support for Stability Analysis
### Small-Signal Linearization of the Analytic Model  
Based on the previous (11) nonlinear state model, the aim of this section is to linearize the model in order to allow a simple characterization of the stability domain by means of the root locus of the powertrain. For that purpose, we have chosen here to derive a small-signal-state model linearized around steady-state operating points.
## 5 Virtual Inertia Adaptive Control Strategy Based on TDC Algorithm  
Section 3.2 highlights that \(J_{\omega}\) must be appropriately configured to improve the system's frequency support capability. The relationship between \(J_{\omega}\) and frequency is demonstrated in Equations (18) and (19) as follows:  
\[\frac{P_{\text{ref}}-P_{\text{out}}-(D_{\omega}\omega_{0}+K_{\omega}\omega_{0}) (\omega-\omega_{0})}{J_{\omega}\omega_{0}}=\frac{\text{d}\omega}{\text{d}t} \tag{18}\]  
\[\frac{P_{\text{ref}}-P_{\text{out}}-J_{\omega}\omega_{0}\frac{\text{d}\omega} {\text{d}t}}{D_{\omega}\omega_{0}+K_{\omega}\omega_{0}}=(\omega-\omega_{0}) \tag{19}\]  
From the preceding equation, when \(P_{\text{ref}}-P_{\text{out}}-(D_{\omega}\omega_{0}+K_{\omega}\omega_{0})( \omega-\omega_{0})\) remains constant, an increase in the value of \(J_{\omega}\) significantly enhances the inhibitory effect on \(|\,\text{d}\omega/\text{d}t|\,\). Similarly, when \(P_{\text{ref}}-P_{\text{out}}\) is held constant, larger \(J_{\omega}\) correlates with a more pronounced inhibitory effect on \(|\,\omega-\omega_{0}|\,\).  
To optimize the dynamics and the transient stability of the APL system in the TVSG, the TVSG adaptive control strategy [14] employs \(|\,\text{d}\omega/\text{d}t|\,\) to dynamically adjust the \(J_{\omega}\) in real time, and its specific expression is provided below:  
\[J_{\omega}=\left\{\begin{array}{ll}J_{0},&\Delta\omega\times\frac{\text{d} \omega}{\text{d}t}\leq 0\\ J_{0},&\left|\frac{\text{d}\omega}{\text{d}t}\right|\leq\text{T}_{\text{J}}\\ J_{0}+K_{\text{J}}\big{|}\frac{\text{d}\omega}{\text{d}t}\big{|},&\Delta \omega\times\frac{\text{d}\omega}{\text{d}t}>0\delta\epsilon\Big{|}\frac{\text{ d}\omega}{\text{d}t}\Big{|}>T_{\text{J}}\end{array}\right. \tag{20}\]  
\[D=\left\{\begin{array}{ll}D_{0},&|\Delta\omega|\leq T_{\text{D}}\\ D_{0}+K_{\text{d}}|\Delta\omega|,&|\Delta\omega|>T_{\text{D}}\end{array}\right. \tag{21}\]  
where \(J_{0}\) is the steady-state value of the virtual inertia; \(T_{\text{J}}\) is the threshold of inertia determination; \(K_{\text{J}}\) is the inertia compensation coefficient; \(T_{\text{D}}\) is the judgment threshold of the damping coefficient; and \(K_{\text{d}}\) is the compensation coefficient of the damping coefficient.
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Classification of Pesticide Concentrations in Fruits with Multivariate Analysis Methods  
This study also evaluated the ability of the E-nose and E-tongue to differentiate between different concentrations of pesticides existing in each fruit, based on the measurements made and the application of different data processing methods. Therefore, different concentrations were prepared between 0.2 and 3 ppm values in each pesticide to determine the discrimination and classification between organic fruit samples and those contaminated with pesticides.
## 2 Material and Algorithm
### DC-DC Converter and Algorithm  
For indoor light conditions, the output voltage is usually lower than what is required by digital electronics and batteries. Thus, a boost DC-DC converter is required, which can step up the PV cell's output voltage to recharge the battery. The \(I\)-\(V\) (current-voltage) curve of PVs is non-linear. MPP techniques are required to harvest maximum energy. A DC-DC converter with a low-voltage start-up circuit is presented in [PERSON] et al. [53; 54]. The energy accumulated by the DC-DC converter can be transferred to the load by activating the switch when the storage capacitor reaches its maximum capacity limit, as shown in Figure 1. The capacity of the intermediate buffer-capacitor can be chosen to have less terminal voltage fluctuation, and to increase the efficiency along with size restrictions [18].  
Figure 1: Overall procedure of the proposed methodology.  
The stop-and-go technique presented by [PERSON] et al. [18] can be used for achieving high efficiency in buffer-capacitor to smartphone battery charging. Currently, the presented system is composed of three converters: solar to electrical energy, an MPPT-based DC-DC converter for buffer-capacitor charging, and buffer-capacitor to the smart device's battery charger, or its internal circuit. The battery keeps its output voltage at a constant level but the buffer-capacitor terminal voltage will vary, so techniques such as the maximum power transfer tracking (MPTT) algorithm are required at the output converter. This will result in increased system efficiency, but also increases circuit complexity and the number of components.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 3 Ground Resource Scheduling Model
### Constraint Condition  
#### 3.3.1 Importance of Data Transmission Tasks  
During the hardware resource scheduling process at the ground station, priority should be given to the execution of data transmission tasks with a priority level of 2 or 3. That is, ground station hardware resources should be allocated in descending order of data transmission task priority, where the data transmission task priority levels include level 1, level 2, and level 3.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 6 Test Campaigns and Results
### SNAP Positioning  
A series of tests have been performed in order to assess the performance of the SNAP service in its three positioning modes: Standalone GNSS, standalone cellular, and hybrid GNSS + cellular.
## 3 Results
### Spatial Patterns in SMAP/CYGNSS \(\beta\) Values
#### 3.3.3 Relationship between Topographic Roughness and \(\beta\) Values  
SMAP emissivity and CYGNSS reflectivity are often poorly correlated in areas with high topographic roughness. Topographic roughness increases the uncertainty in both passive and active microwave remote sensing observations, due to increased scattering of the signal.  
The positive relationship between SMAP/CYGNSS \(\beta\) values and SRTM topographic roughness (described in Section 2.3) is shown in Figure 9d, with \(\beta\) values approaching zero as topographic roughness increases. Since both dry and densely vegetated areas have near-zero \(\beta\) values, regardless of topographic roughness, we did not include any 9 km grid cells with mean annual precipitation less than 0.25 m or mean NDVI greater than 0.8 in our topographic roughness analysis.  
Figure 9: The number of 9 km grid cells (n-values) used to calculate each boxplot are included above each subplot. (**a**) Relationship between SMAP/CYGNSS \(\beta\) values and annual SMAP NSSM variance. Each boxplot spans a NSSM variance range of 0.0025. (**b**) Relationship between SMAP/CYGNSS \(\beta\) values and FLDAS mean annual precipitation. Each boxplot spans a precipitation range of 0.25 m. (**c**) Relationship between SMAP/CYGNSS \(\beta\) values and MOIDIS/Terra mean NDVI. Each boxplot spans an NDVI range of 0.05. (**d**) Relationship between SMAP/CYGNSS \(\beta\) values and SRTM topographic roughness. Each boxplot spans a topographic roughness range of 50 m. (**e**) Relationship between SMAP/CYGNSS \(\beta\) values and MOIDIS IGBP landcover class.
# Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
## 6 Real-World Test
### Results from the Four-Post Test Rig
Metrics smaller than 1 represent a superior performance of the RL agent, and metrics greater than 1 correspond to a superior performance of the benchmark controller. (Remark: Due to a corrupted measurement, road type B is missing in subplot (**b**)). Since the control policy of the RL agent was obtained by a data-driven training process, it was not apparent why the RL agent performed well on some of the excitations and worse on others. Additionally, the semi-active vertical dynamics is a highly complex control problem for which an optimal solution is not trivial. This makes the reasoning of the learned RL policy intricate. In general, the comparison of the control signals of both controller variants on the sweep inputs showed that the trained controller applied higher damper currents, especially at higher frequencies. This way, the RL agent outperformed the benchmark controller in controlling the wheel movement and, therefore, improved road-holding for the sweep excitations with a zero-crossing velocity \(\geq 150\) mm/s. One explanation for the superior performance of the learned controller for higher frequencies lies in the choice of the RL _discount rate_\(\gamma=0.99\) together with the sample time \(T_{s}=1\) ms: in RL, the algorithm tries to optimize the _discounted future return_\(G\), defined as the discounted sum of future rewards \(R\) at timestep \(i\):  
\[G_{i}=\sum_{k=0}^{N}\gamma^{k}R_{i+k+1}. \tag{19}\]  
This means that the contribution of future rewards decreases exponentially with the discount rate \(\gamma\). For \(\gamma=0.99\) and a sample time \(T_{s}=1\) ms, this means that for 0.23 s the reward contribution is already decreased to less then 10%. This results in a relatively short horizon and might explain the difficulties of the agent to deal with the lower frequencies during the 1-30 Hz sweep excitation. During the training stage, we also performed trainings with \(\gamma=0.999\). However, the results did not meet the desired performance requirements during the agent assessment, described in Section 4.2.4. To summarize, the results of the four-post test rig evaluation of the trained controller, we can conclude that the trained RL-based controller is able to outperform the offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average.
## 4 Biological Applications of EUV/SXR Microscopes Based on a Double Stream Gas-Puff Target Source
### Imaging of Dried and Fixed Biological Samples Using Contact Microscopy  
#### 4.3.1 Non-Malignant Human Bladder HCV29 Cells  
The developed contact microscope has been used for preliminary experiments acquiring SXR images by the AFM scanning of the surface of 500 nm thick PMMA photoresist of non-malignant human bladder HCV29 cell lines, particularly interesting for their elongated shape and well known morphological features. The cells were prepared following the protocol reported in [85, 87]. The sample was then placed on top of a silicon wafer spin-coated PMMA. The imprint of cells on the PMMA photoresist was realized using a \(1.5\times 1.5\) mm\({}^{2}\), 100 nm thick Si\({}_{3}\)N\({}_{4}\) window that filtered out the longer wavelengths above the SXR region. The produced relief-map of the cellular structures was then observed using an atomic force microscope (AFM, NT-MDT Spectrum Instruments, Moscow, Russia). In the SXR imprint HCV29 cells (Figure 24a-e) and their internal structures can be observed, at various scales of magnification. The AFM images of HCV29 cells with 150 nm diameter gold beads were obtained after 150 SXR pulses (15 s exposure, Figure 24a-c), and 20 SXR pulses (2 s exposure, Figure 24d,e) [85, 87]. In the smallest scan of Figure 24e, 150 nm in diameter gold beads can also be seen. The KE test applied to those imprints suggests a spatial resolution of the order of 80 nm.  
Figure 23: (**a**) SEM image of the caffeine crystals, on the top of the tip-sample holder. (**b**) Shows some of the projections acquired in SXR, at different angles. (**c**) Three-dimensional (3D) reconstruction of the reconstructed image of the crystallized caffeine (electron density surface) obtained using the ImageJ software [93, 94]. (**d**) Crystals reconstruction using the total iterative constraint strategy. Figure modified, based on [59].
## 2 Materials and Methods
### SiC Sensors: Tecnology Features
#### 2.1.1 Novel SiC Detectors  
The device structures used for the realization of a SiC sensor, produced by SenSic GmbH [19], are based on PIN junctions, i.e., composed by a thin p+, highly doped layer (0.3 um, NA = 1 \(\times\) 10\({}^{19}\) cm\({}^{-3}\)) on top of a n- low doped layer (ranging from a minimum of 200 nm up to 100 \(\upmu\)m, ND = 8 \(\times\) 10\({}^{13}\) cm\({}^{-3}\)) on top of a n+ thick substrate (370 um, ND = 5 \(\times\) 10\({}^{18}\) cm\({}^{-3}\)); see Figure 1a. Sensors with different active areas, from 1 \(\times\) 1 mm\({}^{2}\) up to 10 \(\times\) 10 mm\({}^{2}\), and active thicknesses, from 0.2 \(\upmu\)m up to 100 \(\upmu\)m, have been realized. In particular, the results obtained for the 10 \(\upmu\)m thick and 1 \(\times\) 1 cm\({}^{2}\) active area will be shown in this work.  
Moreover, the SenSiC company is able to produce the so-called \"free-standing membrane\" by removing the 370 \(\upmu\)m n+ thick substrate using doping selective electrochemical etching as shown in Figure 1a [21; 22]. A picture of a developed SiC sensor mounted on a PCB is also shown in Figure 1b.  
The use of ultrathin, <20 \(\upmu\)m, membranes will reduce beam perturbation, thus being suitable for an in-transmission real-time monitoring of the UHDR beams.  
A first characterization of these free-standing membrane sensors, in terms of response as a function of the irradiation temperature and of the radiation damage, has been performed by using low energy proton microbeams (with 1.5 and 3.5 MeV) in the Division of Experimental Physics, Ruder Boskovic' Institute, showing promising radiation resistance tolerances [23].
# Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review
## 3 Advanced Technology of Saline-Alkaline Soil Management  
Traditional methods for addressing saline-alkaline soils have proven to be limited in their efficiency, scalability, and long-term environmental sustainability. As such, there is an increasing demand for innovative solutions that offer more effective and sustainable approaches. In response, modern technologies have emerged as valuable alternatives, providing new strategies to tackle the challenges associated with saline-alkaline soils. These methods include a wide variety of techniques, ranging from information technology to Emerging Chemical Treatments, nano technology, and wood vinegar.  
Physical and chemical remediation methods focus on techniques such as solidification, heat treatment, redox reaction, and electrodynamics, which directly address the soil to fix or remove harmful substances. Although technically effective, these methods face several challenges when applied on a large scale to remediation cropland soils, including high operational complexity, high costs and the potential risk of secondary environmental contamination [69]. In contrast, bioremediation technologies based on plant and microbial actions are preferred due to their lower cost and ease of operation. However, the effectiveness of bioremediation is often limited by the nature of the soil itself, and external environmental conditions, which can result in inadequate stability and predictability of remediation effects [70; 71]. To a certain degree, this constrained the wide application of bioremediation in complex environments or highly polluted soil. Therefore, developing an environmentally friendly and economically feasible soil remediation method. To achieve high-efficiency, low-consumption, and safe treatment of soil pollution, it is necessary to consider the effectiveness, cost-effectiveness, and long-term environmental impact of the technology to support the sustainable use of soil resources.  
The limitation of traditional single-treatment methods is evident in the complex soil environment under salt alkali stress, making it urgent to develop some new techniques that can adapt to the contemporary saline-alkaline and alkaline land treatment. As research advances, the technology of treating saline-alkaline and alkaline lands is being innovated and developed. New management techniques, including biotechnology, information technology, and salt-tolerant crop breeding, provide a new ideas and methods for saline-alkaline land management [72; 73; 74].