esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
###### Abstract  
The Global Navigation Satellite System (GNSS) receiver is one of the many sensors embedded in smartphones. The early versions of the Android operating system could only access limited information from the GNSS, allowing the related Application Program Interface (API) to obtain only the location. With the development of the Android 7.0 (Nougat) operating system in May 2016, raw measurements from the internal GNSS sensor installed in the smartphone could be accessed. This work aims to show an initial analysis regarding the feasibility of Zenith Total Delay (ZTD) estimation by GNSS measurements extracted from smartphones, evaluating the accuracy of estimation to open a new window on troposphere local monitoring. Two different test sites have been considered, and two different types of software for data processing have been used. ZTDs have been estimated from both a dual-frequency and a multi-constellation receiver embedded in the smartphone, and from a GNSS Continuously Operating Reference Station (CORS). The results have shown interesting performances in terms of ZTD estimation from the smartphone in respect of the estimations obtained with a geodetic receiver.  
Zenith Total Delay (ZTD); smartphones; GNSS positioning; troposphere monitoring; low-cost mass-market devices +
Footnote †: journal: Remote Sensing  
13202134567[[https://doi.org/10.3390/rs13224567](https://doi.org/10.3390/rs13224567)]([https://doi.org/10.3390/rs13224567](https://doi.org/10.3390/rs13224567))
# A Parallel Algorithm Based on Regularized Lattice Boltzmann Method for Multi-Layer Grids
## 3 Parallel Strategy and Algorithm
The data used for evolution are from the same layer of grid connections, the outermost vertical space on the grid. The data used for interpolation are from the intersection of different layers of the grid. These data are used to complete the interpolation between coarse and fine grids. Both the data used for transfer and the data used for interpolation contain information about the neighboring grids and the values of the distribution function. Parallel processing can be carried out after data transfer of interpolated and transmitted data. In this section, Algorithm 2 gives an MPI-based parallel algorithm for RLBM on multi-layer grids. To prevent deadlocks from occurring, each grid performs separate collisions, streaming, and computation of macro-physical quantities within the computational domain for each process. Neighboring processes pass the information on the boundary grid at the grid junction and then compute. This optimizes communication efficiency. Each process first performs a local computation. Finally, the results of the computations in each region are restored to the results of the complete original problem. Figure 8: Schematic diagram of MPI data transfer method. ```
0: Grid files after division, total number of MPI processes \(np\), number of grid layers \(N\), Initial information of the flow field. 0: Evolution results. 1. Calculate the number of grid points in each layer of the multi-layer grids. 2. The multi-layer grids are uniformly divided into \(np\) copies of grid regions according to Algorithm 1. 3. Allocate a grid area to each process in the MPI. 4. From layer \(N\) to layer 1, each process executes the RLBM based on multi-layer grid. 5. Once the grid calculations are complete at each layer, a synchronisation operation is performed. 6. Determine whether the exit condition has been met, if not, continue with step 4 and step 5. 7. Output calculation results. ```  
**Algorithm 2** MPI-based parallel algorithm for RLBM on multi-layer grids
## 2 Related Work
ALARMS [28] is one of the message-scheduling approaches that uses message ferries to forward messages and achieve good QoS. This publication [29] gives a promising solution, namely, \"Cost-Effective Multimode Offloading\"(CEMMO), that offloads data to the best possible choice among the following three options to reduce the overall cost in terms of energy efficiency, financial settlement, and user satisfaction. [PERSON] et al. [30] introduced the Always Best Connected (ABC) concept for always providing the best connectivity to all the applications. The handover decision is being taken on regrouping criteria such as network, terminal, user, and services. Another network-selection mechanism [31] was used in combination with AHP and GRA to trade off network circumstances, services, and user priorities. AHP was used for weighing based upon user preferences and GRA was used for ranking network alternatives. [PERSON] et al. [32] introduced a user-oriented network-selection scheme, where five different modules are considered for network selection. One of them is an input which includes a utility function, and the other is a user-preference calculation using FAHP to calculate weighing of judgment. [PERSON] et al. [33] proposed network selection using multi-service multi-modal terminals. They also used utility functions for multi-services for user satisfaction, network attributes, and service characteristics. In our previous work [34; 35; 3], we have introduced the use of a public transport network and offloaded data onto buses along with other networks for energy efficiency. We extended our work in the proposed manuscript with network selection and appropriate vehicle selection to offload data for energy efficiency.
# Distributed Bragg Reflectors for GaN-Based Vertical-Cavity Surface-Emitting Lasers
## 5 Airgap and Porous DBRs--A New Approach to Epitaxial DBR
### GaN/Air Gap DBR  
GaN is known to be chemically inert with almost no known wet etchant at room temperature. After the discovery of photoelectrochemical (PEC) etching of GaN [98], this method was employed [99] to create a GaN/air gap DBR where narrow bandgap InGaN layers are used as the sacrificial layer. Microcavity LEDs were also reported using such a GaN/airgap DBR which produces a peak reflectance of around 70% [100]. Several limitations in PEC etching include the structural quality of InGaN sacrificial layers and the available size of the DBR region due to a limited etch rate. Taking advantage of the chemical disparity between GaN and AlInN, several groups [101; 102] reported the selective wet-etching of AlInN in an AlInN/GaN structures with hot nitric acid to create GaN/airgap DBRs. Reflectance as high as 99% can be reached with only 4 pairs of DBR. A conductivity-selective electrochemical (EC) etching of GaN was also demonstrated to prepare GaN/airgap DBRs using epitaxially-grown doping superlattices of n\({}^{+}\)-GaN/GaN structures, followed by selective EC etching [103]. Taking advantage of the large disparity in thermal stability between GaN and AlGaN, [PERSON] et al. reported the selective thermal desorption of GaN in an AlGaN/GaN structure to form AlGaN/airgap DBRs and reported microcavity with a Q-factor of 1600 (Figure 11) [104]. In spite of all the very innovative approaches and the results of broad-band and high-reflectance DBRs, the airgap DBR approach has been confined to optical investigations of cavity physics; challenges in current injection and thermal management have more or less inhibited the possibility of electrical devices.
# Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem
## 4 Genetic Programming Aided Optimization of Conceptual Aircraft Design
### Parameterized Finite Element Model  
A representative beam-frame wing structure and its finite element model for static aeroelasticity and flutter analyses are shown in Figure 5. The geometric configuration of this high-aspect-ratio wing model was decided by seven parameters in Figure 5a, which are the inboard/outboard taper ratios (\(\mathfrak{n}_{int},\mathfrak{n}_{out}\)), the inboard/outboard aspect ratios (\(\mathfrak{n}_{int},\mathfrak{n}_{out}\)), the sweep angle of the leading edge (\(\chi\)), the inboard/outboard dihedral angles (\(\mathfrak{y}_{int},\mathfrak{y}_{out}\)). There are three more parameters (\(\mathfrak{p}_{r},\mathfrak{p}_{k},\mathfrak{p}_{l}\)) for determining the elastic axis positions at the root, the kink and the tip, respectively. The wing root  
Figure 4: Flowchart of GP (Genetic Programming) methodology.  
length (\(c_{r}\)) was kept constant. The wing structural stiffness properties were modelled by a variable cross-section beam from the root to the tip and the structural mass was represented by lumped mass elements. Material properties of aluminum were assigned to the primary beam of the wing, and the components such as the body, tail and fin, were modelled using rigid elements. The wing lifting surface was modelled with 282 aerodynamic elements. A fixed maximum take-off weight of 84.5 tons and cruise Mach of 0.785 at a height of 11,200 m were applied to perform the analysis. The Reynolds number was 5.71 million based on the mean aerodynamic chord. The root chord remains a constant length with 6.17 m, and the reference area was varied with the geometric design variables.
## 6 Results
When \((C/N_{0})_{\mathit{eff}}\) decreases from 19 to 15 dB-Hz, tracking standard deviation increases from 11.07 m to 22.72 m. In this regard, B1-I self-interference could pose a potential hazard on pseudorange measurement for high-sensitivity receivers. However, it must be underlined that for most receivers (from mass market to medium-grade receiver), a \((C/N_{0})_{\mathit{eff}}\) value under 20 dB-Hz, though may be possible, is an exceptional case. When \((C/N_{0})_{\mathit{eff}}\) decreases from 24 to 20 dB-Hz, tracking standard deviation increases from 5 m to 9 m, which does not impose significant impact on final positioning accuracy for most low- to medium-grade receivers. Figure 12: Time evolution of equivalent noise level for PRN 6, 8, and 9 in current BDS constellation. 3. Around 240 s for PRN 8 and around 1200 s for PRN 9, the self-interference effect on the desired signal PRN 6 reaches its maximal value for each undesired signal respectively. This corresponds to the time when these two PRN's Doppler shift (modulo 1 kHz) are aligned (zero differential Doppler shift) with the desired signal, PRN 6. The same situation has also been found in GPS C/A-to-C/A self-interference [4]. In this regard, the proposed model could be used in B1 receiver as a code-tracking performance indicator, since code tracking standard deviation is directly determined by \((C_{s}/N_{0})_{off}\), the effective carrier-to-noise power density ratio, which is in turn, directly affected by self-interference effects, as indicated in Figure 13. In the simplest case, the common satellite selection step (e.g., depending on DOP, Dilution of Precision) should be complemented by a new step to narrow down the list of satellites participating in navigation solution, based on the result of self-interference calculation according to Equations (20) and (23), as suggested in Figure 14.  
In Figure 14, a typical receiver baseband (correlator + measurements) plus navigation module is shown, aided by a satellite selection module, whose action depends on a Signal Quality Monitoring (SQM) module [24] on top of its normal operations, monitoring the equivalent noise spectral density imposed by self-interference,. This may be an indication to receiver manufacturers for their design of strategies for satellite selection in Position, Velocity and Time (PVT) computation and signal acquisition in case of very low \(C/N_{0}\), which could be estimated stably and accurately through novel estimators [25].
## 6 Discussion and Findings
### Discussion of Experiments with Inventory Control Models  
The experiment in Section 5.2.1 shows the effect of people's decisions on the inventory of pharmaceuticals. Regardless of people's perception of the risk of disease, the quantity of inventory increases or decreases as the number of infected people increases or decreases, because more orders are placed when the number of infected people increases or decreases. Excess inventory is risky because medicines have expiration dates. Therefore, it is effective to spread awareness of the risk of the disease over a long period of time to reduce the number of infected people at the time of peak infection, and, at the same time, to manage the inventory of pharmaceuticals appropriately.  
The experiment in Section 5.2.2 shows the effect of the ordering policy on the inventory of pharmaceuticals. It shows how the parameters \((\alpha,\ \beta)\), which represent the ordering policy, affect the inventory. Future work is to optimize \((\alpha,\ \beta)\) and to study the optimal ordering policy.
## 6 Results and Discussion
### Model Validation  
The suitability of the model developed in this study for real construction sites was validated based on a survey that engaged 28 experts currently employed in the construction industry. The information gathered from the surveyed experts is presented in Table 7.  
A survey was conducted with experts using a 7-point Likert scale to assess the effects of the model developed in this study compared with the existing scheduling methods in terms of cost, onsite applicability, and task efficiency. In addition, the opinions of experts on the reasons for scoring the items were gathered based on supplementary survey questions. To ensure the validity of the expert survey results, a reliability analysis was conducted using the software IPM SPSS Statistics (version 29.0.1.0). A reliability analysis was performed on the Likert scale-based survey items, which yielded a Cronbach's alpha coefficient of 0.875, thus confirming the high reliability of the survey. The survey results are summarized below.
# Strontium Ferromolybdate-Based Magnetic Tunnel Junctions
## 3 Results and Discussion
### Estimation of the Dead Layer Thickness of SFMO Thin Films  
Unfortunately, most of the reports on SFMO thin film deposition are devoted to films with a thickness of about 100 nm [22]. To the best of our knowledge, there is only one report considering the thickness dependence of the saturation magnetization \(M_{\rm s}\) of SFMO thin films deposited by PLD onto STO (001) and (LaAlO\({}_{3}\))\({}_{0.3}\)(Sr\({}_{2}\)AlTaO\({}_{6}\))\({}_{0.7}\) (001) (LSAT)  
Figure 3: One-dimensional power spectral density of an SFMO thin film deposited by ceramic target magnetron sputtering and crystallized at 900 \({}^{\circ}\)C (dots) in comparison with the ABC model, Equation (8) (dotted line). AFM scans were carried out at one point using scanning areas of 5 \(\times\) 5 \(\upmu\)m\({}^{2}\) (light blue), 10 \(\times\) 10 \(\upmu\)m\({}^{2}\) (blue), 30 \(\times\) 30 \(\upmu\)m\({}^{2}\) (red) and 70 \(\times\) 70 \(\upmu\)m\({}^{2}\) (green).  
Figure 2: One-dimensional power spectral density of an SFMO thin film deposited by multi-target reactive sputtering at 600 \({}^{\circ}\)C (dots) in comparison with the ABC model, Equation (8) (dashed line). AFM scans were carried out at three different points with a scan area of 1.05 \(\times\) 1.05 \(\upmu\)m\({}^{2}\).  
substrates [76]. Applying Equation (11) to this data, we find a total DL thickness (we are not able to distinguish between interface and surface layer) of 11 to 14 nm (Figure 4). This is in reasonable agreement with the values of nonmagnetic interface layers, obtained in La\({}_{2/3}\)Ca\({}_{1/3}\)MnO\({}_{3}\) thin films deposited by RF sputtering [77] and in PLD deposited La\({}_{0.67}\)Sr\({}_{0.33}\)MnO\({}_{3}\) thin films [78], amounting to 5.3 \(\pm\) 0.9 nm and 4.6 \(\pm\) 0.9 nm, respectively.  
On the other hand, there is evidence that the STO/CoFe\({}_{2}\) interface considered below is less problematic with respect to DLs. The DL is practically absent at Co/Cu interfaces [79]. High-quality epitaxial CoFe\({}_{2}\)O\({}_{4}\) (CFO) with a magnetization near the bulk value can be prepared on STO despite the large lattice mismatch. Diffusion of the involved ions is not expected due to different ion sizes, crystallographic structures, and electron configurations [80]. In addition, the FM order in the DL at a CFO/Al\({}_{2}\)O\({}_{3}\) interface is partially restored by increasing the CFO thickness \(d\) from 1.4 to 2.3 nm [81]. This restoration likely originates from an interlayer coupling of the DL at \(d\) = 1.4 nm with an additional 0.9-nm-thick CFO layer having a stronger FM ordering. This leads to a large slope of the magnetization-vs.-thickness curve at least up to \(d\) = 4 nm. Note that CFO is an insulating material that exhibits a half-metal-like behavior only in combination with a nonmagnetic electrode in a double spin-filter junction [82].
# _Applied Sciences_
## 2 Related Work
### Contradiction Detection Methods  
* Linguistic features approaches  
The most common approaches to contradiction detection in texts use the linguistic features extracted from texts to build a classifier by training from the annotated examples, such as the works in [3; 5; 12; 13].  
Early research on contradiction detection within the field of natural language processing was reported by the authors of [12] whose work tackled contradictions by means of three types of linguistic information: negation, antonymy, and semantic and pragmatic information associated with discourse relations. After evaluation experiments, over 62% of accuracy was obtained due to the fact that there are more types of contradictions possible in texts.  
Linguistic evidences such as polarity, numbers, dates and times, antonymy, structure, reactivity, and modality features were used by the authors of [3] to detect contradiction.  
An approach for detecting three different types of contradiction (negation, antonyms, and numeric mismatch) was proposed in [5]. This approach deploys a Recurrent Neural Network (RNN) using long short-term memory (LSTM) and Global Vectors for Word Representation (GloVe) and included four linguistic features extracted from the text: (1) Jaccard Coefficient, (2) Negation, (3) IsAntonym, and (4) Overlap Coefficient.  
Simple text similarity metrics (cosine similarity, f1 score, and local alignment) were used as baseline in [13], obtaining good results for contradiction classification. This approach used two datasets built with examples of tweet pairs.  
* Semantic features approaches  
Other approaches created contradiction detection systems are based on semantic features [4; 14; 15; 16].  
A model to detect contradiction and the architecture that enables validation of the model was proposed by [4]. The model defined the extraction of semantic relations between a pair of sentences and verified some rules to detect contradictions. Furthermore, this author defined contradiction measures by considering the structure of relations extracted from texts and the level of uncertainty attached to them.  
Other authors [14] combined shallow semantic representations derived from semantic role labeling (SRL) with binary relations extracted from sentences in a rule-based framework, and the authors of [15] extended the analysis using background knowledge.  
A contradiction-specific word embedding (CWE) model and a large-scale corpus of contrasting pairs were proposed in [16]. This approach improved the results in contradiction detection in SemEval 2014 [17]. This research concluded that traditional word embedding learning algorithms have been highly successful in accomplishing the main NLP tasks but most of these algorithms are not powerful enough for the contradiction detection task [16].
## 2 Data and Study Area
### Population Density Map  
In order to qualitatively analyze the population exposure of heatwaves, a China population density grid map in 2010 (1 km \(\times\) 1 km) from the resource and environment data cloud platform of the Institute of Geographic Sciences and Natural Resources Research, CAS (DOI:10.12078/2017121101) was adopted. China's population density map is based on the demographic data of counties across China. It takes into account many factors such as land use type, nighttime light brightness, and residential density, which are closely related to population. Then, the population data of the administrative district is rasterized using multi-factor weight distribution method to realize the spatialization of the China population.
# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 3 Proposed DA-R3 DCNN Human Activity Recognition Framework
### Dual Channel-Spatial Attention Module
#### 3.3.2 Spatial Attention  
The spatial attention mechanism involves learning a weighting mechanism that assigns importance scores to different spatial locations within an image. These importance scores indicate the relevance or saliency of each location in relation to the task at hand. The mechanism typically consists of trainable parameters that are optimized during the training process. To highlight the salient object-specific regions in the feature maps, DA-R3 DCNN takes advantage of inter-spatial features and their relationship between channels. This allows for more accurate tracing of the target object in the feature maps. DA-R3 DCNN achieves this by computing the relation of inter-spatial features between channels through max pooling and average pooling applied to the input channel attention feature maps, resulting in max-pooled channel attention \(Att_{\text{C}-max}^{H\times W\times 1}\) and average-pooled channel attention \(Att_{\text{C}-avg}^{H\times W\times 1}\), respectively. The concatenated max-pooled channel attention \(Att_{\text{C}-max}^{H\times W\times 1}\) and average-pooled channel attention \(Att_{\text{C}-avg}^{H\times W\times 1}\) are passed through a \(3\times 3\) convolutional layer \(Conv^{3\times 3}\) to form single-channel convoluted feature maps. The resulting maps are then normalized by a sigmoid activation function to produce intermediate spatial attention features \(\mathcal{A}_{S}^{H\times W\times 1}\). These intermediate features are fused with the input channel attention feature maps \(Att_{\text{C}}^{H\times W\times C}\) using a residual skip connection through element-wise multiplication operations to obtain the final spatial attention feature maps \(Att_{S}^{H\times W\times C}\) as illustrated in Figure 4. Mathematically, spatial attention and its key components can be expressed as follows:  
\[Att_{\text{C}-max}^{H\times W\times 1}=maxpool(Att_{\text{C}}^{H\times W\times C }), \tag{9}\]  
Figure 4: Architecture of the dual channel-spatial attention module.  
\[Att^{H\times W\times 1}_{C-agg}=avgpool(Att^{H\times W\times C}_{C}), \tag{10}\]  
\[\mathcal{A}^{H\times W\times 1}_{S}=\sigma(Conv^{3\times 3}(Att^{H\times W \times 1}_{C-max}\biguplus Att^{H\times W\times 1}_{C-avg})), \tag{11}\]  
\[Att^{H\times W\times C}_{S}=\mathcal{A}^{H\times W\times 1}_{S}\otimes Att^{H \times W\times C}_{C}, \tag{12}\]  
In the above equations, \(\biguplus\) denotes the concatenation operation, fusing \(Att^{H\times W\times C}_{C}\) and \(Att^{H\times W\times C}_{S}\).
## H-V isolation  
The isolation between the H- and V ports of the antenna (Figure 8b) was measured for 1,200 MHz to 1,600 MHz with the spectrum analyzer featuring an internal tracking source. The isolation is relatively constant over the radiometer bandwidth (1,400 MHz-1,427 MHz) and has a value of -41.7 dB at the radiometer center frequency. The measurements were the same, for either choosing the H- or the V port as the source. For a brightness temperature \(T_{\rm B}{}^{p}=300\) K this implies that the distortion caused by polarization crosstalk is less than 0.025 K, and therefore negligible.