esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 3 Results
### Assessing Soil Salinity for Different Soil Moisture Levels  
The linear relationship between soil salinity and CRSI, NDVI, and AWEI was used to evaluate the performance of the models for different soil moisture levels (Table 3). The \(R^{2}\) values of the five soil salinity models that considered the spatial variations of the soil moisture are higher than 0.744, whereas that of the entire study area is 0.712. Therefore, using salinity models based on different soil moisture levels provides higher inversion accuracy than using a model of the entire study area.  
We also compared the soil salinity obtained from field measurements with the model results to demonstrate the influence of soil moisture on soil salinity inversion (Figure 5). In the no drought and slight drought areas, 68% and 80% of the samples, respectively, are underestimated by the soil salinity model. In the strong drought and extreme drought areas, 66% and 78% of the samples, respectively, are overestimated by the soil salinity model. The soil salinity inversion model underestimates soil salinity in moist areas and overestimates it in dry areas. The average accuracy of the inversion models of the five different soil moisture levels demonstrate that it is essential to understand the spatial characteristic of the surface soil moisture before assessing soil salinity using spectral vegetation indices from remote sensing imagery.  
Figure 5: Soil salinity obtained from field measurements and models. (**a**) Entire study area, (**b**) no drought, (**c**) slight drought, (**d**) moderate drought, (**e**) strong drought, and (**f**) extreme drought.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Drought Level** & \multicolumn{2}{c}{**Model Formula**} & \(\mathbf{R^{2}}\) & **RMSE** \\ \hline Entire study area & EC = 5098.246 \(\times\) CRSI + 10290.813 \(\times\) NDVI + 0.323 \(\times\) AWEI \(-\) 1019.632 & 0.712 & 0.472 \\ No drought & EC = 2347.511 \(\times\) CRSI + 8924.583 \(\times\) NDVI + 0.790 \(\times\) AWEI + 2810.863 & 0.921 & 0.505 \\ Slight drought & EC = 7283.681 \(\times\) CRSI + 2984.574 \(\times\) NDVI + 0.333 \(\times\) AWEI + 4778.410 & 0.901 & 0.551 \\ Moderate drought & EC = 1019.427 \(\times\) CRSI + 12226.786 \(\times\) NDVI + 0.371 \(\times\) AWEI + 1463.097 & 0.852 & 0.501 \\ Strong drought & EC = 6862.115 \(\times\) CRSI + 12120.566 \(\times\) NDVI + 0.106 \(\times\) AWEI \(-\) 2906.257 & 0.930 & 0.492 \\ Extreme drought & EC = 4266.514 \(\times\) CRSI + 10798.926 \(\times\) NDVI + 0.456 \(\times\) AWEI \(-\) 432.860 & 0.744 & 0.491 \\ \hline \hline \end{tabular} EC is the electrical conductivity, CRSI is the canopy response salinity index, NDVI is the normalized difference vegetation index, and AWEI is the automate water extraction index.  
\end{table}
Table 3: Linear relationship between soil salinity and CRSI, NDVI, and AWEI for different drought levels (soil relative humidity) and the entire study area.
## 3 Experimental Results
### Motion Scenarios
#### 3.2.1 Motion Scenario I  
Figure 15 shows the system tilt angle with and without the slider for motion from the initial coordinates (\(0.153,\,-0.149\)) to the final coordinates (\(0,\,-0.29\)) in the planar workspace.  
Figure 16 shows the system tilt angle at the coordinates (\(0,\,-0.29\)), with and without the slider.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scenario** & \(\mathbf{(x_{0}y_{0}y^{\dagger}\;(mm)}\) & \(\mathbf{(x_{0}y_{0}y^{\dagger}\;(mm)}\) & \(\mathbf{\Delta\theta_{r}\;(^{\circ})}\) & \(\mathbf{\theta_{s}^{\prime}\;(^{\circ})}\) \\ \hline I & \((153,\,149)\) & \((0,\,-290)\) & \(1.11\) & \(0.17\) \\ II & \((140,\,150)\) & \((280,\,-2)\) & \(28.89\) & \(-29.34\) \\ III & \((280,\,-2)\) & \((140,\,150)\) & \(12.56\) & \(-13.79\) \\ \hline \end{tabular}
\end{table}
Table 2: Motion scenario coordinates and tilt angle reductions.  
Figure 15: System tilt angle for a motion from coordinates (\(0.153,\,0.149\)) to (\(0,\,-0.29\)).
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 6 Performance Evaluations
### Performance Evaluation of Dynamic Traffic Flow Prediction via Unequal Interval Vasicek Approach  
According to the aforementioned process, a series of detected and predicted singular points divide the process into finite unequal intervals, which are used for dynamic traffic flow prediction via our proposed algorithm. The number of unequal intervals is decided by the value of the wavelet scale. Figure 8 shows the traffic flow prediction performance in different numbers of unequal intervals. Based on the performances shown in Figure 7, we select the Haar-6 wavelet and evaluate eight different wavelet scales. The Vasicek model predicts the traffic flow trend with the biggest scale, which has the worst performance in traffic flow prediction. When the wavelet scale reduces, the number of detected singular points and the number of reprediction intervals increase, and the prediction results of the traffic flow become close to the real-time observation data. The smallest three wavelet scales divide the process into 17, 46, and 90 unequal dynamic intervals, respectively. Additionally, their traffic flow prediction results are limited to the realistic observation value. The remaining five bigger wavelet scales miss sorts of important singular points, and their traffic flow prediction cannot reflect the realistic signal trend.  
The cost of a prediction performance improvement is caused by the wavelet scale decrement and the number of intervals' increment, which reflects on the computing time of the prediction algorithm. Therefore, Figure 8 shows the relation between the MRE and the normalized computing time of the traffic flow prediction in different wavelet frequencies. Wavelet frequency is inversely proportional to wavelet scale, which means that the highest wavelet frequency corresponds to the smallest wavelet scale that can detect  
Figure 7: The next singular point prediction MRE results in different types of wavelet.  
the most singular points. When wavelet frequency increases, the intervals for reproductions are increased, respectively, which can reflect the traffic condition in more detail and the MRE of prediction reduces from 27.1% at the beginning to 1.5%.  
Accordingly, with the increment of intervals for reprodiction, the normalized computing time increases from 0.01 to 0.90. Furthermore, since the wavelet frequency increases to 5, the speed of computing time rises rapidly. It is observed that the MRE of prediction reduces, but the computing time increases with the increment of the number of reproductions. The reason is that the recalculation of the Vasicek model rises once the unequal intervals increase. When the wavelet frequency is chosen as 5 or 6, the MRE of traffic flow prediction is less than 5.0%, and the computation time is relatively lower, shown as 0.12 and 0.17, which realizes a comprehensive traffic flow prediction performance and the trade-off between accuracy and computing time.
## 2 Materials and Methods
### Analysis of Micro- and Macro-Elements Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS)  
For macro- and micro-element analysis in lentil samples, an Agilent 7700x ICP-MS (Agilent Technologies, Tokyo, Japan) and a software Mass Hunter Work Station for inductively coupled plasma mass spectrometry (ICP-MS), version B.01.01 (Agilent Technologies, Japan) were used. The samples were milled and homogenised (final particle size \(\leq\) 150 \(\upmu\)m). For the analysis, the following chemicals were used: nitric acid (concentration \(\geq\) 69.0%) for trace analysis (Sigma-Aldrich, Paris, France); hydrogen peroxide, 30% \(w/w\), extra pure (Scharlau, Sentmenat, Spain); and multielement standard solution V for ICP-MS calibration (Sigma-Aldrich, France). An Agilent 7700x ICP-MS (Agilent Technologies, Tokyo, Japan), with the software Mass Hunter Work Station for ICP-MS, version B.01.01 (AgilentTechnologies, Japan), was used for analyses. The sample preparation for ICP-MS analysis was carried out as follows: in a microwave vessel, we accurately weighed 0.3 g of the homogenized sample, 2 mL of deionized water, 8 mL of concentrated nitric acid, and 2 mL of concentrated hydrogen peroxide, which were added together, and we waited for 2-8 h for reaction stabilization, until the formation of bubbles was finished. The vessel was sealed and heated in the microwave system. The following thermal conditions were applied: a 150 \({}^{\circ}\)C temperature was reached in approximately 20 min and remained for 30 min and then, 200 \({}^{\circ}\)C was reached in approximately 20 min and maintained for 30 min for the completion of specific reactions. After cooling (approximately 40 min), the prepared solution was filtered through the filter with a pore size 8-10 \(\upmu\)m-\(\phi\). The solution was transferred to a 50 mL volumetric flask and filled with deionized water to a 50 mL volume. The following operating conditions of the Agilent 7700x ICP-MS were used for the analysis of the samples: plasma mode--normal, robust; RF forward power 1300 W; sampling depth 8.00 mm; carrier gas-flow 0.6 L/min; dilution gas-flow 0.4 L/min; spray chamber temperature 2 \({}^{\circ}\)C; extraction lens 1 V; and kinetic energy discrimination 3 V.
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 3 Result  
The performance of the ANN model was evaluated using the average prediction error of the training and test data across 70 modeling cycles--the number at which the average model error converged--to obtain a conservative result. To comprehensively assess the model's predictive capability and identify sources of error, the distribution of predicted BCS values was compared with the actual BCS value distribution to reveal the cause of existing errors. Finally, the model's accuracy was validated using physical test data from 10 commercially sized box dimensions. The validation results demonstrate the model's reliability for industrial applications, as the validated error was significantly lower than the errors observed in both the training and testing phases.
## 4 D2D Group Communication Scheme with Bidirectional and InCrentental A-Star (BICA*) Routing
### Path Selection Algorithm  
The path selection algorithm discussed in this subsection is the core component of the proposed scheme. It answers the question of whether content sharing via D2D group communication is preferred over downloading content from the core network. It constructs a path connecting UE \(i\) and UE \(j\) with the maximal utility and reports the obtained utility value to the group header for its decision making. The topology of a UE group can be represented by an undirected graph with UEs as nodes and links as edges. Search algorithms, such as the Dijkstra algorithm, can then be employed to find the shortest path between two given nodes. However, an exhaustive examination of all nodes could be time-consuming. Therefore, we adopt a Bidirectional, inCremental A* (BICA*) algorithm in our routing scheme, as shown in Algorithm 1. Radio links would be, in most situations, symmetric, so it is feasible for the bidirectional A* algorithm to construct the search trees from both sides, with which the search speed can be doubled. The lifelong-planning A* algorithm is an incremental version of the A* algorithm. This feature makes it well-suited for dealing with topology change, which is quite often in mobile networking. It repeatedly finds the shortest paths from a given start node to a destination node, while the edge costs of a graph change or vertices are added or deleted. Its first search is the same as that of a standard A* that breaks ties in favor of nodes with smaller \(g\)-values. However, subsequent searches are potentially faster because they reuse those parts of the previous search tree that are not changed. The symbols used in Algorithm 1 are defined as follows:  
* \(S\): Set of all nodes. * \(v(s^{\prime},s)\): The utility from node \(s^{\prime}\) to node \(s\). * \(g(s)\): The actual utility from the start node to node \(s\). It can be evaluated according to \[g(s)=\left\{\max_{s^{\prime}\in\text{predecessor of $s$}}\begin{array}{l}0, \  
both forward and reverse searches reach the same node. If they do, it means that a path with maximal utility has been found. Lines 16 to 26 implement forward search. Whenever there is any topology change, Function 1, Node Information Update Function, is invoked to update the node's _rhs_ value. Those successors of the updated node should be removed from the priority queue. Their key values will be updated by Function 2, Key Evaluation Function, and then placed back in the priority queue. Line 22 initializes \(g\) values, and Function 1 is then called to assess \(g\) values by Lines 23-25. Lines 27 to 37 implement the reverse search.
## 2 Materials and Methods
### Trace Metal Determination  
The mussel samples were analyzed for Fe, Mn, Zn, and Cu contents according to the International Organization for Standardization method [32] by atomic absorption spectrometry using a flame atomic absorption spectrometer (Varian SPECTRA AA-10, Varian Techron Pty Limited, Mulgrave, VC, Australia), equipped with a flame furnace and operated with an air acetylene flame after mineralization by dry ashing. Dry ashing was conducted according to [PERSON] et al. [33]. Briefly, after drying, the sample was charred on a hot plate and then incinerated in a muffle furnace at 450 \({}^{\circ}\)C overnight. After ash was obtained, it was moistened, treated with 10 mL of HCl/diH2O (1:1, \(v/v\)), and evaporated to dryness. The ash was redissolved with 10 mL of HCl/diH2O (1:9, \(v/v\)), transferred to a 50 mL volumetric flask, and diluted to volume with diH2O. Heavy metal analyses of Pb and Cd were performed by atomic absorption spectrometry after dry ashing according to the EN method [34], while Hg was determined using a direct mercury analyzer, namely the AMA 254 Advanced mercury analyzer (Altec, Prague, Czech Republic), based on the principles of sample thermal decomposition. The obtained results of the investigated elements in mussels were expressed in \(\mu\)g/g of sample dry weight. Each obtained value is the mean of five determinations. Procedural blanks were analyzed once for every five samples. To avoid possible contamination, all employed glassware and equipment were acid-washed.
# Vibrational Model of Heat Conduction in a Fluid of Hard Spheres
## 1 Introduction  
Our understanding of dynamical and transport properties of liquids remains incomplete and fragmented even in the case of the simplest atomic systems. Despite considerable progress achieved over the years [1; 2; 3; 4; 5; 6], we still often need to apply various phenomenological approaches, semi-quantitative models, and scaling relations. Among the renowned examples are the Stokes-Einstein relation between the self-diffusion and the shear viscosity coefficients [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], the excess entropy scaling of reduced transport coefficients [18; 19; 20; 21; 22], and several variants of the freezing temperature scaling [23; 24; 25; 26; 27]. Recently, a freezing density scaling of reduced transport coefficients of the Lennard-Jones and related fluids has been discussed as a new useful addition to the existing approaches [28; 29; 30].  
Heat transfer in fluids (throughout this paper the term \"fluid\" is applied to liquids and supercritical fluids) is an important topic of contemporary research with diverse interdisciplinary applications [31; 32; 33; 34; 35; 36]. A vibrational model of heat transfer has been recently applied to fluids with soft pairwise interactions [37]. In this model, the thermal conductivity coefficient is proportional to the effective frequency of atomic vibrations around the local temporary equilibrium positions. The effective frequency is related to the properties of the liquid collective excitation spectrum. The model has been applied to quantify heat transfer in a strongly coupled one-component plasma fluid, dense Lennard-Jones liquid, and plasma-related screened Coulomb (Yukawa) fluid. An impressive agreement with the available results from numerical simulations has been documented [38; 39; 37]. The purpose of this work is to consider the applicability of the vibrational model to a new physical system--a fluid made of hard spheres (HS fluid). The vibrational picture of atomic dynamics is clearly not very appropriate for extremely anharmonic HS system. Nevertheless, collective excitations are supported in the dense HS fluid and averaging over collective modes can formallybe performed. The question is whether the results have any relation to reality. It will be shown below that a formal expression based on the vibrational model of heat transfer is in agreement with the results from molecular dynamics simulations in a moderately dense regime, but overestimates the numerical results near the freezing point. The accuracy of some other simplistic approaches will be also examined. In particular, the coefficient in the Bridgman's expression, appropriate for the HS fluid, will be identified. The excess entropy scaling of the thermal conductivity coefficient in the HS fluid will be briefly discussed and a new expression will be proposed. The results can be useful for simple rough theoretical estimates when more accurate data are unavailable.
## 8 Practical Recommendations
While it is evident that adding hybrid fibers improves impact and thermal performance, research should quantify how much each fiber type and proportion contributes to these improvements, thus guiding the design of UHPC for earthquake, blast, or fire-prone structures. 6. **Improve sustainability and reduce cost. ** Tackle the high production costs and environmental impact of UHPC by developing more sustainable manufacturing methods. UHPC are currently relying on expensive raw materials and energy-intensive processes, and the lack of standard design codes can further increase costs. Researchshould focus on reducing the carbon footprint and cost of UHPC through measures such as the use of locally available or recycled aggregates, the incorporation of cheaper fibers (or recycled fibers) with lower energy embodied, and the addition of supplementary cementitious materials (such as fly ash, slag, or silica fume) to reduce cement usage. Innovations in mix design that maintain performance while lowering CO2 emissions and production energy demand will make UHPC with hybrid fibers more economically viable for widespread use. * **Adopt advanced production techniques. ** Embrace automation and digital fabrication technologies to streamline UHPC production. Automated batching and fiber dispersion systems, along with rigorous quality control, can ensure consistency and reduce labor costs. Support for technologies such as 3D concrete printing is particularly promising-these allow the creation of complex or non-standard structural elements with UHPC, potentially unlocking new applications. By automating production and using digital design-to-fabrication methods, manufacturers can reduce waste and variability, thus lowering costs and accelerating the adoption of hybrid fiber UHPC in construction. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The careful review and constructive suggestions of anonymous reviewers are appreciated. The authors declare that they have no known competing financial interest or personal relationships that could appear to influence the work reported in this document.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 2 Materials and Methods
### Impact Assessment  
The skeleton of the impact assessment was derived from the United Nations Platform for Space-Based Information for Disaster Management and Emergency Response (UNSPIDER) recommended framework for flooding assessment using Sentinel 1 SAR data. This provided the basis for the flooding model. The increasing frequency of flooding events [2] in combination with the constantly increasing number of economic assets and dense population located in flood-prone sites have amplified the need to better use of Earth observation (EO)-based information for disaster management. EO-based mapping products can provide information on the flood extent and facilitates extensive spatial analysis of the flood event. A fast response to a flooding incident is vital to minimize the impact and potential damage of flooding. Decisionmakers and disaster management  
Figure 1: Ana tropical cyclone track, intensity and rainfall patterns over the Mozambique Channel [22].  
are often reliant on mapping products to make relief decisions. EO data can provide the relevant information with the spatial and temporal coverage to drive the decision-making in a limited time frame. UN-SPIDER recommends the use of optical imagery and radar at different spatial and temporal resolutions for flood mapping. Sentinel-based flood mapping carried out using the UN-SPIDER workflow is immune to weather conditions (as opposed to more commonly used optical data). Both data sources are established in both practical applications and in the scientific literature [25; 26; 27; 28; 29; 30; 31]. Hence, the UN-SPIDER Sentinel-1-based flood-mapping workflow was used (see Figure 2).  
The tool was expanded using additional datasets to include impact parameters such as population density, high resolution land cover type and forest degradation, particularly in mangrove forests. In addition, masks were constructed to remove co-founding factors such as permanent surface water and topography parameters such as slope. An overview of the datasets used for the flooding model and impact assessment can be found in Table 1 overleaf. All the data presented below was resampled to have a 1 km spatial resolution prior to analysis.
In summary, this work concentrates on the design of the DP-LDPC system over OD-ISI MR channels and improving the error performance of this system, which can enrich the analysis of JSCC systems over memory channels. The organization of this paper is as follows. Section 2 presents the system model. Section 3 proposes the M-JPEXIT algorithm for this system and a re-design scheme for channel codes of this system is also presented. Section 4 presents and discusses the simulation results. Section 5 concludes the paper.