esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials
### Sentinel-2 Imagery and Preprocessing  
A total of 1712 scenes of Sentinel-2 orthorectified images (Level-1C) acquired between 1 June and 31 October 2017, in 44 footprints were imported as optical imagery data sources in this research, and were included in the GEE platform as an image collection of the Sentinel-2 Multi-Spectral Instrument (MSI) Level-1C dataset. The total number of observations for each pixel was counted in the GEE platform, and is shown in Figure 3a. Sentinel-2 revisits each pixel 40.62 times on average and 12 times at minimum. Sentinel-2 had 20-30 observations in 44.1% of the total area and 50-60 observations in 34.4% of the entire study area. The frequent revisits in the bright green area (Figure 3a) were due to repeated observations, which can provide abundant information for metric composites and corn identification.  
The Sentinel-2 collection contains 13 MSI bands of top of atmosphere (TOA) reflectance scaled by 10,000. Invalid observations were removed according to the quality assessment band (QA60) that  
Figure 3: Total observation numbers of (**a**) Sentinel-2 and (**b**) Sentinel-1 images acquired between 1 June and 31 October 2017, in Hebei Province. The embedded histograms depict the distribution of the number of Sentinel images with different color bars, and the color is same as the map legends.  
Figure 2: Crop calendar for the dominant crops in Hebei Province.  
labeled opaque and cirrus clouds in the bitmask. Both of these cloud types were excluded to eliminate the effects of clouds. Six spectral bands, including blue, green, red, NIR (near infrared), SWIR1 (shortwave infrared), and SWIR2, were selected and investigated to participate in the classification task. The former four bands with 10-m spatial resolutions were normal bands that were used to distinguish vegetation, while the latter two bands with 20-m spatial resolution were tested in relation to crop water content and have the potential to recognize corn and soybean crops [53]. In addition, three familiar and useful vegetation indices were employed, including the NDVI [54], enhanced vegetation index (EVI) [55], and land surface water index (LSWI) [56], which is defined with the following formulas:  
\[\text{EVI}=2.5\times\frac{NIR-RED}{NIR+6\times RED-7.5\times BLUE+1} \tag{1}\]  
\[\text{LSWI}=\frac{NIR-SWIR1}{NIR+SWIR2} \tag{2}\]  
\[\text{NDVI}=\frac{NIR-RED}{NIR+RED} \tag{3}\]  
The NDVI is commonly used to determine the photosynthetic capacity of plants because healthy vegetation absorbs more red light than NIR light [57]. The EVI has been shown to be less sensitive to atmospheric conditions involving blue bands, and can reduce the effects of saturated NDVI data because of high leaf area biomass [55]. Introduced by [PERSON], the LSWI is an indicator that is designed to estimate canopy water thickness by including the SWIR band, which is sensitive to crop and soil water content [56].
## 6 Potential Future Technological Developments
### Virtual Reality and Augmented Reality Based Visualization Assessment Techniques  
Through the integration of virtual reality (VR) and augmented reality (AR) technologies, a digital three-dimensional model of the cut-off wall and its surrounding soil environment is constructed [153]. Real-time monitoring data, geological information, and pollution diffusion simulation results are seamlessly integrated into the model, creating an intuitive and dynamic visual interface for performance evaluation. Engineers and decision-makers, equipped with VR/AR devices, can view the internal structure, performance status, and pollution migration pathways of the cut-off wall in real time. This capability facilitates more efficient performance assessments and program optimization, thereby enhancing the scientific basis and accuracy of decision-making processes. Ultimately, this technological advancement promotes the remediation of heavy metal-contaminated sites towards intelligent and visualized approaches.
## 4 Results and Discussion
### Results of the Simplified Fire Model
#### 4.2.2 Comparison with Experiments  
The dependence of the frequency of the oscillating flow through the open vents was found to be well described by expressions of the form  
\[f=k({\rm HRR})^{n} \tag{7}\]  
(see [37] and references therein for a review of results available in the literature). From experimental and numerical results it was shown in [40; 41] that the flow oscillation frequency in a compartment fire  
Figure 5: (**a**) Mass flow rates through the west and east vents obtained with FDS for Case G-4 (\(T_{\rm in}=1500\) K). Triangle symbols: CO\({}_{2}\) mass flow rate through the west vent divided by \(C=7\times 10^{-3}\). (**b**) Comparison between FDS results for the mass flow rates through the west and east vents for cases G-1 and G-4 (\(T_{\rm in}=2436\) and 1500 K).  
with two open vents is proportional to \((\text{HRR})^{1/3}\). A similar problem but for a compartment with a single horizontal ceiling vent was studied numerically in [42], where the flow oscillation frequency was found to be proportional to \((\text{HRR})^{0.29}\) for heat release rates below a critical value. Figure 6a shows the numerical results for the oscillation frequency as a function of the heat release rate (in the interval 500 to 2500 kW) obtained in the present work using FDS and the simplified fire model, with \(T_{\text{in}}=1500\) K and \(L/\delta\approx 10\). The figure also shows the correlation  
\[f=1.562\times 10^{-4}\,(\text{HRR})^{0.345}, \tag{8}\]  
which fits the FDS results with \(R^{2}=0.9922\), along with correlations of the form of Equation (7), with the values of the exponent \(n\) proposed in [40; 41; 42]. Note that the results for the oscillation frequency obtained in the present work exhibit a dependence on the HRR that is very close to that of the experimental results of [40; 41].  
Figure 6b shows a comparison between the present numerical results and the experimental results of [40; 41], obtained in a cubic enclosure of side 0.6 m for thermal powers of 1 and 8 kW, for which the  
Figure 6: **(a)** Oscillation frequency of the fire-induced flow through the open vents as a function of heat release rate (HRR). Comparison of the present numerical results with correlations of the form of Equation (7) with values of the exponent \(n\) deduced from experimental and numerical results [40; 41; 42]. **(b)** Collapse of FDS and experimental results on the correlation \(fH^{3}=k(\text{HRR})^{1/3}\) with \(k=4.97\times 10^{-3}\).  
measured frequencies were 0.3 and 0.6 Hz, respectively. Although the experimental results available for comparison are scarce, it is interesting to point out that numerical and experimental results collapse very well after scaling on the curve \(fH^{3}=k(\text{HRR})^{1/3}\), despite the wide ranges of the compartment height, \(H\), and HRR values considered.
## 2 Models and Assumptions  
### Network Model  
We assume the sensor network is composed of \(N\) sensor nodes, randomly deployed in a square area. The communication range of each node is denoted by \(R\). There is a sink node which acts as the base station to collect data from the network. We assume that the minimum and the maximum moving speed are \(\mathrm{V}_{min}\) and \(\mathrm{V}_{max}\), respectively. The clocks of all nodes are assumed to be loosely synchronized. Assume that GPS systems or localization schemes proposed in [7; 8; 9] have been applied to the network such that all nodes know their own positions. Also, only some beacon nodes are equipped with GPS systems or other location support systems to be aware of their own locations. A distributed localization algorithm is executed in other common sensor nodes by using the location information of beacons to make them aware of their own location information.  
In similar literature focusing on defeating malicious attacks, the location error of GPS positioning is rarely discussed. Interested readers may review the method for error rate analysis of GPS receiver systems [10]. There are two major aspects that should be considered in the error rate analysis of GPS receiver systems: (1) finding error sources including deterministic error and non-deterministic error; and (2) establishing a mathematical error model to analyze data, like Gauss-Markov (GM) and auto-regressive (AR) models. We assume that the localization accuracy of the embedded GPS receivers or other positioning chips is acceptable for most of the applications.  
The sensor nodes have mobility and move according to the RWP model [11]. In this model, each node randomly chooses a destination point (waypoint) in the sensing field, and moves toward it with aspect randomly selected from a predefined interval. After reaching the destination, the node stays static for a random time and then moves again according to the same rule. In general, the models used in this paper are the same as the ones in most of the existing works.
## 3 Data Prediction for Energy Saving in WSNs
### Dual Prediction Scheme  
In a DPS, predictions are made in both sensor nodes and CHs or gateways. Both devices are able to produce the same results since the prediction model is shared between them, but sensors can check the accuracy of the predictions by comparing the measured data to avoid an unnecessary transmission. The sensor node is constantly comparing the current observation with the predicted value, and only when the difference falls outside a specified threshold, it transmits the measured value to the CH, which will substitute the prediction with the real value [8; 31; 32]. Then the substituted value will be used in the next prediction. Thus, sensors can reduce transmission and reduce energy consumption. Figure 11 shows the functioning of this scheme and the different strategies of model generation.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 1 Introduction  
The global population is projected to reach approximately 10 billion by 2050, and the demand for food is expected to increase by 60-70% [1]. In order to sustain this ever-growing population, agricultural technologies have continuously developed under the great efforts of scientists and scholars worldwide [2]. Among these advances, biotechnology has played a significant role in improving crop resistance to drought, plant diseases, and insect pests under global warming [3]. The past two decades have witnessed remarkable advancements in bio-agricultural science, with a strong focus on sustainable farming systems, improved molecular breeding, and advanced crop management [4]. However, the natural resources required to meet the food needs of the population, such as arable land and fresh water, are becoming increasingly scarce [5]. Therefore, the future challenges faced by the bio-agricultural sector will be more complex than ever before, threatening global food security [6]. Against this backdrop, biotechnologies have emerged as critical tools to address these issues [7].  
The articles reviewed in this editorial collectively present significant progress in areas such as irrigation technology, seed biotechnologies, intercropping systems, drought stress management, and renewable energy applications in agriculture. The diverse research topics underline the interdisciplinary nature of modern agricultural science, blending traditional agronomic practices with cutting-edge biotechnologies. This editorial synthesizes the key findings of the studies in this Special Issue and discusses future directions for sustainable agricultural development. It also captures the essence of advancements in biotechnologies, showcasing how scientific research and technological innovations are transforming the agricultural landscape, enabling the development of solutions to reduce water resource waste, conserve biodiversity, and guarantee food security. By summarizing irrigation technologies, crop management, seed biology, abiotic stress mitigation, renewable energy, and genetic diversity, this editorial will provide a comprehensive picture of the progress being made toward building a sustainable and resilient agricultural future. In the sections that follow, we delve into the key findings, implications, and future directions highlighted in these studies.
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 5 The Proposed Prediction Models
### Neighborhood Construction and QoS Prediction
#### 5.4.2 QoS Prediction and Service Recommendation  
User-Based QoS Prediction  
Now we have finished two tasks: similarity computation using user-based random walk model and similar neighbor selection \(N(u)\). Similar to the CF algorithm, the QoS value \(q_{ui}\) of user \(u\) invoking service \(i\) is predicted by  
\[q_{ui}\approx\frac{\sum_{v\in N(u)}sim^{*}(u,v)\times q_{vi}}{\sum_{v\in N(u)} sim^{*}(u,v)} \tag{18}\]  
We name this method as UL-RW (User Location-based Random Walk).  
Similar to the user-based prediction, in service-based QoS prediction we can also finish the similarity computation with service-based random walk model, and similar neighbor selection for target service \(i\). Let \(N(i)\)denote the neighbor set of target service \(i\). Also based on the CF algorithm, the QoS value is predicted by  
\[q_{ui}\approx\frac{\sum_{j\in N(i)}sim^{*}(i,j)\times q_{uj}}{\sum_{j\in N(i)} sim^{*}(i,j)} \tag{19}\]  
We name this method as SL-RW (Service Location-based Random Walk).  
Hybrid QoS Prediction  
To fully take advantage of the context information on both the user side and service side, we further propose a hybrid model, which combines the results of the UL-RW model and the SL-RW model. We name the model as HL-RW (Hybrid Location-based Random Walk), which is shown below.  
\[q_{ui}=\lambda\times q_{ui}^{UL-RW}+(1-\lambda)\times q_{ui}^{SL-RW} \tag{20}\]  
where \(q_{ui}^{UL-RW}\) and \(q_{ui}^{SL-RW}\) are the prediction values of UL-RW model and SL-RW model, respectively: The parameter \(\lambda\) is used to balance the weight of the two models in the final result. The sensitivity of our model to \(\lambda\) will be studied in the experiment section.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 1 Introduction
### Contribution and Organization  
The problem of joint resource allocation and user scheduling with carrier aggregation in current cellular networks were investigated in the literature [34; 35]. However, very few were discussed when facing the dense deployments of small cells in the HetNets. In this context, the focus is on the network capacity optimization problem through joint cell association and user scheduling in a HetNet with multiple carrier aggregation across different layers. In such complicated communication environment, the original joint maximization on the network utility is formulated to be NP-hard. The main contribution of this work is providing an upper bound for the proposed joint problem through a nontrivial reformulation based on multi-flow carrier aggregation across the HetNet, which enables the users could associate with BSs over multiple carrier bands. Hence the joint maximization problem could be solved by convex optimization solutions with reasonable complexity. Under different carrier aggregation deployment, the performance advantages have been proved by numerical analysis.  
The rest of this paper is organized as follows: Section 2 describes the system overview of the HetNet with both network and signal models, and formulates the problem of joint network capacity maximization. Section 3 presents the proposed problem reformulation and optimization solutions over multi-flow relaxation. Numerical simulation results are provided to validate the proposed joint cell association and user scheduling schemes under all kinds of carrier deployment in Section 4. Section 5 concludes the paper and discusses potential research interests.  
Throughout this paper the following notations are adopted: \((x(m,n))_{M\times N}\) denotes a \(M\times N\) matrix with element \(x(m,n)\) on row \(m\) column \(n\); \(\lceil x\rceil_{a}^{b}\) sets boundary on variable \(x\), which makes \(a\leq x\leq b\). Matrices and column vectors are demonstrated by bold upper and lower case letters, respectively.
## 5 Discussion
High levels of cooperation are required, as the success of treatment is closely linked to the regular and proper use of the device according to the orthodontist's instructions. Non-compliant patients may fail to wear the device for the prescribed amount of time, compromising the treatment's efficacy. The use of digital technology for treatment planning and impression-taking allows for personalized treatment, enhancing clinical outcomes and patient satisfaction [18]. Given these limitations, the orthodontist must conduct a comprehensive evaluation before recommending such therapies, providing appropriate counseling on realistic treatment expectations and potential alternatives. From the perspective of dentoalveolar transverse expansion, elastodontic devices have shown greater efficacy, particularly in actively growing patients, in enhancing the transverse dimension of the maxillary arch while minimizing the potential negative effects of expansion (Figure 4). Clear aligners, while offering numerous aesthetic and comfort advantages, have been less effective in directly influencing skeletal growth, being limited to dental movements rather than skeletal changes (Figure 5). Figure 4: Intra-oral photos before and after treatment in a patient treated with Eptamed device. However, both devices have demonstrated a valid capacity for dental alignment and improving dental architecture, yielding similar results in terms of aesthetic and functional occlusal improvements. No statistically significant differences were observed in the overall improvement of transverse discrepancies [20,21] between the two treatments, suggesting that, despite the different mechanisms of action, both may be beneficial for cases of minor or moderate dental discrepancies. In conclusion, while both therapeutic options have demonstrated efficacy in the treatment of dentoalveolar transverse discrepancies, the choice between elastodontic devices and clear aligners should be guided by specific clinical needs, patient preferences, and the therapeutic context. Treatment customization, patient compliance, and consideration of economic factors are key determinants for the long-term success of any orthodontic approach. However, the results of this study should be interpreted with caution. As a pilot study, it has several limitations. The sample size is relatively small; increasing the sample size would strengthen the findings. Furthermore, the follow-up period was limited to one year from the start of treatment. A longer follow-up period would provide more insight into the long-term effects. Future studies with a larger, more homogeneous cohort and extended follow-up are necessary to further validate these findings.
# Electrical Characteristics of Ultra-High-Performance Concrete Containing Carbon-Based Materials
## 2 Materials and Methods
### Alternating Current Measurements  
In the alternating current (AC) experiments, the impedance was measured by connecting an AC measuring device to a copper plate, as shown in Figure 2. The resistance of each specimen was determined by measuring the impedance (Z) in the frequency range of 20 Hz to 1 MHz.  
Impedance is a value that interferes with the current flow when a voltage is applied to a circuit. The impedance is expressed as a complex number and as a ratio of the voltage and current of an AC circuit. In a DC circuit, the impedance and the resistance are the same, which is a case where the phase angle of the impedance is zero. The impedance in AC experiments can be expressed as the sum of the impedance values of the resistor (\(R\)), inductor (\(L\)), and capacitor (\(C\)), as shown in Equation (1). If the inductor and capacitor impedances are expressed in the unit of resistance (\(\Omega\)), they can be correlated by Equation (2). These equations indicate that the resistance (\(R\)) is not affected by the frequency, the inductor (\(L\)) has a proportional relationship with the frequency, and the capacitor (\(C\)) has a fractional function, as shown in Figure 3. Consequently, the inductor impedance increases as the frequency increases, while the value of the capacitor impedance tends to decrease. The  
Figure 1: Specimens for the conductivity measurements.  
Figure 2: The setups for conductivity measurements (**Left**: direct current, **Right**: alternating current).  
impedance response of the concrete is represented by a semicircle. The point at which \(\omega\) becomes zero indicates the resistance of the entire conductor model [29]. Therefore, for AC measurements, the point where the arc converges with the real part (or the imaginary part becomes zero) indicates the resistance (\(R\)) of concrete [30]. This point indicates the resistance of the entire conductor model during the AC measurement.  
\[Z=R+L-C \tag{1}\]  
\[Z=R+\omega L-\frac{1}{\omega L} \tag{2}\]
###### Abstract
Recently, in Reference [21], the influence of a random modulated SiC-based buck converter on the G3-PLC channel performance is presented, where it was shown that the use of RPWM reduces the channel capacity compared to conventional PWM. However, in that work, only a specific random modulation technique was implemented (i.e., random frequency modulation), and other strategies (such as the random pulse position modulation considered in this work) were not considered. Therefore, further investigations are still needed in order to investigate the advantages/disadvantages of different RPWM in power grids involving PLC systems. Moreover, the works of References [18; 19; 20; 21] focus on experimental analysis only, and simulation models combining accurate modeling of both the power and communication parts of the systems still need to be developed. Indeed, although several models of each separate parts of the system are already available, there is a lack of simulation models in which both the power system and the communication system are modeled as a whole. This work is intended to investigate the effects of power converter modulation schemes on PLC system, to understand the conditions under which RPWM can be considered as an effective alternative to conventional modulation schemes. To this end, the PLC system is implemented for AMR application in SG, that is to measure the load voltage remotely. The proposed model combines accurate models of both the power part and communication part of the PLC system using simulation tools. Particularly, SIMULINK is used to implement the PLC modem and the power circuit, including the RPWM scheme. Furthermore, this work also investigates the detrimental effects that unsymmetrical AC wires play on the PLC system. The remaining part of the manuscript is organized as follows. Section 2 introduces the system under analysis. The implementation of the PLC system and Quadrature Phase ShiftKey (QPSK) modulation/demodulation is presented in Section 3. Simulation results are presented and discussed in Section 4. Finally, in Section 5, conclusions are drawn.