esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Computational Experiments
### Test Approaches  
The three approaches considered in our computational experiments are described as follows:  
* FIFO: In this approach, the list of tasks to be completed is iterated in a first-in, first-out (FIFO) manner without taking into account the estimated delays for each of the circuits as well. Once the list of tasks is iterated, the actual delays associated with the FIFO permutation of tasks are computed.
* Non-Reactive: In this approach, the permutation of tasks is created using the non-reactive heuristic for dynamic delays presented in a previous section. Thus, the task with the lowest expected delay when traversing its circuit is added to the permutation of tasks at each iteration. Subsequently, the actual delays associated with the permutation of tasks are computed.
* Reactive: Regarding the last approach, the permutation of tasks is created using the reactive heuristic algorithm described in Section 4. Thus, a new permutation of remaining tasks along with their respective actual delays are computed, and the system is updated with the actual time it takes to traverse each of the circuits.  
In our numerical experiments, we employ white noise [32] in order to emulate the behavior of the actual delay values that the AGV will encounter when traversing eachcircuit. In particular, each sample generated from white noise has a normal distribution with zero mean, which is added to the estimated delays to emulate the actual delay values. In other words, the actual delays are simulated by adding a white Gaussian noise sample to the estimated delays. During the experiments, the samples are generated with mean \(\mu=0\) and standard deviation \(\sigma\). The standard deviation \(\sigma\) allows us to consider different levels of dynamism in our experiments, as lower values of \(\sigma\) indicate a lower dynamism and higher values of \(\sigma\) produce a high level of dynamism. In our experiments, we have set \(\sigma\) to the values 100, 200, and 300 for a low, medium, and high level of dynamism, respectively.
# A Study of Current Controlled Discharge in a Nitrogen Filled Tube
## 3 Results
### Modelling of Laboratory Experiments
#### 3.3.2 Discharge Plasma Evolution According to Two-Dimensional Code ZSTAR  
The ZSTAR code allows for obtaining plasma parameters as well as the electrical and magnetic fields in the whole space of the tube and electrodes. Using two-dimensional code ZSTAR and Tecplot [25] for the same experimental conditions, we obtain spatial distributions of basic plasma quantities inside the tube for any selected time during the discharge. Representative spatial distributions of electron density \(N_{e}\) and temperature \(T_{e}\) for initial pressure of \(100\,\mathrm{Pa}\) at \(t=5.6\,\upmu\)s are shown in Figure 6.  
The peak value of the electron density \(N_{e}\) in the central part of the tube is \(2.4\times 10^{16}\,\mathrm{cm}^{
###### Abstract  
Soil improvement is one of the fundamental practices in civil engineering, with a long-standing history. In today's context, the rapidly increasing demand for construction driven by urbanization has further emphasized the necessity and significance of soil stabilization techniques. This study aims to determine the optimum parameters for improving sandy soils by incorporating sodium alginate (SA) as a biopolymer additive into the microbial calcium carbonate precipitation (MICP) process. Sand types S1, S2, and S3, each with distinct particle size distributions, were selected, and the specimens were prepared at medium relative density. Three distinct approaches, MICP, SA, and MICP + SA, were tested for comparison. Additionally, two different improvement methods, injection and mixing, were applied to investigate their effects on the geotechnical properties of the soils. In this context, hydraulic conductivity, unconfined compressive strength (UCS), and calcite content tests, as well as scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) analyses, were performed to assess the changes in soil behavior. SA contributed positively to the overall efficiency of the MICP process. The study highlights SA-assisted MICP as an alternative that enhances the microstructural integrity of treated soils and responds to the environmental limitations of conventional methods through sustainable innovation.  
microbialy induced calcite precipitation (MICP); sodium alginate (SA); soil improvement; injection method; mixing method Article
# Real-Time Prediction of Operating Parameter of TBM during Tunneling
## 2 Methodology  
### Arimax  
An autoregressive integrated moving average (ARIMA) developed from an ARMA that considers the time series characteristics of output parameters was developed [16]. ARMA is a model that combines the autoregression (AR) proposed by [PERSON] [17] and the moving average (MA) proposed by [PERSON] [18] (Equation (1)).  
\[Y_{t}=c+\varepsilon_{t}+\sum\
olimits_{i=1}^{p}\phi_{i}Y_{t-i}+\sum\
olimits_{ j=1}^{q}\theta_{j}\varepsilon_{t-j} \tag{1}\]  
where \(c\) is constant, \(\varepsilon\) is error, and \(p\) and \(q\) are the order of AR and MA, respectively. ARMA expresses the current time series using the previous time series of the output parameter and error. This model is mainly applicable to stationary conditions where the mean and variance of the time series are constant.  
In social science, however, time series such as statistics, exchange rates, and prices often take non-stationary patterns that are not constant in the mean and variance. If the output parameter is non-stationary, ARIMA taking into account differences is generally used (Equation (2)).  
\[\Delta Y_{t}=c+\varepsilon_{t}+\sum\
olimits_{i=1}^{p}\phi_{i}\Delta Y_{t-i}+ \sum\
olimits_{j=1}^{q}\theta_{j}\varepsilon_{t-j} \tag{2}\]  
where \(\Delta Y\) is the differentiated output parameter. ARIMA considers not only the characteristics of the ARMA, but also the trends and momentum of the previous time series. In general, ARIMA is mainly used for short-term prediction problems.  
However, ARIMA only handles previous time series parameters, and it cannot consider the influence of exogenous parameters. ARIMAX can improve ARIMA by combining exogenous parameters with ARIMA (Equation (3)):  
\[\Delta Y_{t}=c+\varepsilon_{t}+\sum\
olimits_{i=1}^{p}\phi_{i}\Delta Y_{t-i}+ \sum\
olimits_{j=1}^{q}\theta_{j}\varepsilon_{t-j}+\sum\
olimits_{k=1}^{r} \tau_{k}X_{t-k} \tag{3}\]  
where \(X\) is the exogenous parameters, and \(r\) is the lagged order of the exogenous parameters.  
Operating parameters such as thrust are dependent on time-varying and non-stationary features. For this reason, it is desirable to use ARIMAX to capture the prediction accuracy. In addition, geological parameters affecting the thrust values [19,20] can be considered as inputs, which are the exogenous parameters of ARIMAX.
## 5 Conclusions  
The research presented here demonstrates that a non-parametric supervised land cover classification technique performs well in this heterogenous savanna landscape. Using a SVM classification and time-series change trajectories, we characterized woody and non-woody conversion in PCNP from 1975 to 2019 and found a relatively stable landscape. Our land cover classification revealed that some portions of PCNP that are susceptible to change tend to be in areas where the dominant vegetation cover is transitioning. These areas overlap with changes in elevation and/or riparian zones, indicating the importance of slight variations in drainage and nutrient availability. Consequently, our analysis suggests that topo-edaphic factors play a key role in controlling woody biomass in PCNP, and future studies should explore this further. We attribute the other areas with similarly grouped conversions to be disturbance or human induced, such as fire, hurricane-induced vegetation damage, or logging. Unlike many other savanna landscapes, woody encroachment is not widespread in PCNP.  
The findings from this study have important implications for savanna scientists and land managers in Belize, such as a better understanding of fine-scale savanna vegetation drivers and areas susceptible to vegetation conversion. Adding to the knowledge of how this landscape functions will allow land managers to improve their efforts of protecting the pine savanna and its associated wildlife. Future work should improve the vegetation classification with field validation, quantitatively relate topo-edaphic factors to vegetation conversion, and compare these findings to other Central American savannas.  
Conceptualization, L.B. and H.V.H; methodology, L.B. and H.V.H; software, L.B.; formal analysis, L.B.; investigation, L.B.; resources, L.B.; data curation, L.B. and H.V.H; writing--original draft preparation, L.B.; writing--review and editing, L.B. and H.V.H; visualization, L.B.; supervision, H.V.H.; project administration, H.V.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We would like to thank [PERSON] for introducing us to this landscape and [PERSON] for providing insight about the study area.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiments
### Visualization and Analysis
#### 4.5.2 Class Activation Visualization  
To further investigate whether the CNN networks have learned discriminative features in complex context, we employ Grad-CAM [60] to visualize class activation of different networks. Grad-CAM, standing for gradient-weighted class activation mapping, is a technique devised to provide visual explanations from models in computer vision, particularly convolutional neural networks (CNNs). Through Grad-CAM, it is feasible to produce a heatmap for a specific class over the input image, highlighting regions critical for the model's prediction.  
As shown in Figure 9, these selected scene images are sampled from the NWPU-RESISC45 validation set. These samples are very difficult to distinguish and the baseline model cannot classify them correctly. Specifically, we compared the prediction labels and confidence scores obtained using a GFSC-trained network (WRN-50+GFSC) with those of the corresponding baseline model (WRN-50) based on ImageNet pretraining. As Figure 9 shows, the networks trained under GFSC have stronger abilities of feature extraction. It is clear that GFSC-trained network better captures the details representing semantic features in complex contextual scenes. Compared with the baseline model, it not only classifies correctly but also obtains higher confidence in the classification results for some difficult scenes.  
Figure 8: Visualizations of image representations learned by WRN-50 with ImageNet pretraining, WRN-50 with SSL pretraining, WRN-50 with ImageNet pretraining followed by fine-tuning and WRN-50 trained with GFSC on the AID dataset with a training ratio of 20%, generated using t-SNE [59]. Each image is visualized as one point, and the colors represent different classes. (**a**) ImageNet pretraining; (**b**) SSL pretraining; (**c**) previous paradigm; (**d**) GFSC.
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
###### Abstract  
Standing wave field distributions in three classic types of graded-index antireflection coatings are studied. These graded-index antireflection coatings are designed at wavelengths from 200 nm to 1200 nm, which is the working wavelength range of high energy laser system for inertial-fusion research. The standing wave field distributions in these coatings are obtained by the numerical calculation of electromagnetic wave equation. We find that standing wave field distributions in these three graded-index anti-reflection coatings are quite different. For the coating with linear index distribution, intensity of standing wave field decreases periodically from surface to substrate with narrow oscillation range and the period is proportional to the incident wavelength. For the coating with exponential index distribution, intensity of standing wave field decreases periodically from surface to substrate with large oscillation range and the period is also proportional to the incident wavelength. Finally, for the coating with polynomial index, intensity of standing wave field is quickly falling down from surface to substrate without an obvious oscillation. We find that the intensity of standing wave field in the interface between coating and substrate for linear index, exponential index and polynomial index are about 0.7, 0.9 and 0.7, respectively. Our results indicate that the distributions of standing wave field in linear index coating and polynomial index coating are better than that in exponential index coating for the application in high energy laser system. Moreover, we find that the transmittance of linear index coating and polynomial index coating are also better than exponential index coating at the designed wavelength range. Present simulation results are useful for the design and application of graded-index antireflection coating in high energy laser system.  
graded-index antireflection coating; standing wave field distribution; laser induced damage of optical coating; high energy laser system +
Footnote †: journal: Applied Physics Letters
## 3 Methodology  
In this section, the methodology to automatically identify the proper use of PPE is detailed, which follows the subsequent steps:  
1. For each image captured by an on-site surveillance camera, individual(s) are detected, together with their keypoints coordinates, using an individual detection model.
2. PPE(s) are recognized and localized using an object detection model.
3. The proper use of PPE identification is performed by analyzing the geometric relationships of the individual's keypoints and the detected PPE(s).
# Influence of Burner Nozzle Parameters Analysis on the Aluminium Melting Process
## 2 Experimental Research
### Modification of Burner  
The experimental measurements were carried out using a burner with 13.5 kW input power for the natural gas combustion. Due to the shortening of the flame front at higher concentrations of oxygen in the oxidation mixture, the outlet diameter of the burner was modified during the experimental research, according to oxygen concentrations in the oxidizer at the constant fuel flow. The burner orifice was modified (according to the Equation (1)), so that the flame length was the same for each examined level of oxygen enrichment from the oxidizer [6; 17]:  
\[L_{fl}=r\sqrt{\left(\frac{w_{m}}{u_{n}}\right)^{2}-1}, \tag{1}\]  
where \(L_{fl}\) is the flame length (m), \(r\) is the semi-diameter of the burner orifice (m), \(w_{m}\) is the outlet rate of the mixture (m s\({}^{-1}\)), and \(u_{n}\) is the normal combustion rate of the oxidizer and gas mixture (m s\({}^{-1}\)).  
On the basis of Equation (1), the flame length in the air fuel combustion was set to 42 cm. The increase of oxygen concentration results in the reduction of the combustion mixture amount, and hence, the outlet rate of the mixture \(w_{m}\) decreases. As already noted, the increase of the oxygen concentration in the combustion mixture results in the increase of the combustion rate \(u_{n}\). On the basis of available literature data [16], the parameter \(u_{n}\) for natural gas with the increased oxidizer oxygen concentration can be expressed using the following mathematical function:  
\[u_{n}=-8.4\times 10^{-7}O_{2}^{3}-1.0898\times 10^{-4}O_{2}^{2}+0.06228767O_{2}- 0.94666177, \tag{2}\]  
where \(O_{2}\) is the oxidizer oxygen concentration (vol. %)  
Thus, the flame length is simultaneously reduced by both parameters \(w_{m}\) and \(u_{n}\). Therefore, in order to achieve a constant flame length, it is necessary to modify the diameter of the burner orifice.  
For the purpose of studying the impact of the increased oxygen concentration in the air on the combustion process, an upstream burner was selected. The burner consists of a closed gas tube with a diameter of 8 mm, with four holes with diameters of 2.5 mm (Figure 3a) transversally drilled along the perimeter to ensure a better mixing of fuel and oxidizer. Construction modifications of the burner consisted of producing an optimised air nozzle for each examined level of oxygen enrichment of the oxidizer. The increase of the oxygen concentration was ensured by injecting oxygen into the combustion air inlet.  
Figure 2: A schematic diagram of the experimental device.
# Context-Aware Caching Distribution and UAV Deployment: A Game-Theoretic Approach
## 3 UAV-Assisted Caching Game
### Game Model  
It is well known that game theory is efficient for solving the challenge of resource competition for example the anti-jamming transmission problem [25; 26], the opportunistic spectrum access problem [27], the distributed channel-slot selection optimizing problem [28], even the challenging UAVrelay selection problem [29], and the ultra-dense small cell network problem [30]. Similarly, we propose a UAV-assisted caching game in this paper from a game-theoretic perspective. The proposed game considers not only the UAVs' service locations, but also the caching contents. Moreover, UAVs will cooperate with their neighbors and share the caching contents, which reflects cooperation. Therefore, when one UAV selects its action, it will consider the influence that its action has upon its neighbors.  
Let \(G=\{\mathcal{N},A_{n},J_{n},U_{n}\}\) denotes the UAV-assisted caching game, where \(\mathcal{N}\) denotes the set of players (UAVs), \(A_{n}\) is the strategy space of UAV \(n\), \(J_{n}\) is the neighbors of UAV \(n\), \(U_{n}\) indicates the utility function of player \(n\) (UAV \(n\)).  
Considering the influence of marginal contribution of UAVs to the system service, we designed the utility function of UAVs as follows:  
\[U_{n}(a_{n},a_{J_{n}})=R(a_{n},a_{-n})-R(a_{n}=0,a_{-n}), \tag{16}\]  
where \(a_{n}\) indicates the current action of UAV \(n\), \(a_{J_{n}}\) is the joint action profile of UAV \(n\)'s neighbors, \(a_{-n}=(a_{1},...,a_{n-1},a_{n+1},...,a_{N})\) denotes the joint action profile of UAVs except UAV \(n\), \(a_{n}=0\) reflects the fact that the current UAV \(n\) takes no action, \(R(a_{n},a_{-n})=\sum\limits_{n\in\mathcal{N}}u_{n}\left(a_{1},a_{2},...,a_{N}\right)\) is the performance evaluation function of all UAVs, which is the same as Equation (14).  
The utility function shows the individual contribution of each UAV to the UAV-assisted network performance, which is influenced by the caching contents and service locations of UAVs. Therefore, when UAVs take proper arrangement of caching contents and service locations, each individual contribution will increase. Therefore, the optimization object of the UAV-assisted caching game is given by:  
\[\begin{array}{c}\mathcal{G}:\max\limits_{a_{n}\in A_{n}}U_{n}(a_{n},a_{J_{n}} ),\forall n\in\mathcal{N},\\ s.t.\quad c1\text{ and }c2.\end{array} \tag{17}\]
## 3 Related Work  
Eleven review papers related to DTN were identified. Even though this paper is an SLR, it was considered relevant to include not only SLRs but also survey and review papers as they provide important elements and perspectives for a comprehensive understanding of this technology. Al-Fagih et al. present a survey to classify and organize routing schemes in DTN and map them with applications [3]. Chahal et al. center their SLR on routing, focusing on different algorithms and approaches [4]. Madni et al. performed an extensive survey on DNT and non-DTN routing for CubeSat communications [5]. Tornell et al. developed a survey on vehicular DTNs, focusing also on routing schemes and applications [6]. Xia et al. present a survey that relates social aware networking (SAN) with mobility patterns of mobile devices and the application of these concepts to DTN routing [7]. Kumar et al. focus their survey on the architecture of DNT and routing techniques, especially for natural disaster scenarios [8]. Wei et al. present a survey to classify technologies that support maritime communications, including hybrid satellite and terrestrial networks, and here, DTN plays an important role [9]. Sakthivel et al. study geographical routing for multi-hop wireless networks (MWNs), including MANET and DTN [10]. It focuses on mobility models as a major factor that influences the performance of geographical routing. Perumal et al. present an SLR about DTN for implementing internet connections in rural areas in Malaysia [11]. More et al. discuss issues that affect DNT performance, including routing protocols. The study also includes applications of DNTs [12]. Makawana et al. developed an SLR that mostly focused on paper objectives, citations, and paper types to provide a perception of this area of research [13].  
Different from this work, most of these review papers were focused on routing. Perumal's work is somehow closer to the aim of this SLR; however, it is applied to a specific geographical region. Finally, Makawana's bibliometric study is related mostly to the interests of researchers behind the topic of DNTs. Our work is devoted to collecting and analyzing DTN works focused on terrestrial applications, to understand the main aspects and constraints to implement DTN, especially for services that use TCP/IP. Our work also aims to find the trends in DTN applications, as well as the main concerns of researchers around this protocol.
# A Stable and High-Precision Downward Continuation Method of Magnetic Data
###### Abstract  
Downward continuation is an effective technique that can be used to transform the magnetic data measured on one surface to the data that would be measured on another arbitrary lower surface. However, it suffers from amplitude attenuation and is susceptible to noise, especially when the continuation distance is large. To solve these problems, we present a stable and high-precision downward continuation method combining the ideas of equivalent source technique, Tikhonov regularization, radial logarithmic power spectrum analysis, and constrained strategy. To implement this method, the observed data is used to construct the equivalent source in the study area, and the small amount of measured magnetic data at the lower surface (relative to the original observation surface) is employed to constrain the calculation procedure simultaneously. Then the magnetic data at the target surface can be obtained by using a forward calculation procedure instead of the risky downward continuation procedure. The proposed method is tested on both synthetic model data and real magnetic data collected in the South China sea. Various obtained results demonstrate that the method reported in this study has higher accuracy and better noise resistance than the traditional downward continuation methods.  
constrained downward continuation; equivalent source; Tikhonov regularization; magnetic data +
Footnote †: journal: Applied Sciences  
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