esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Precise position information available from smartphones can play an important role in developing new location-based service (LBS) applications. Starting from 2016, and after the release of Nougat version (Version 7) by Google, developers have had access to the GNSS raw measurements through the new application programming interface (API), namely android.location (API level 24). However, the new API does not provide the typical GNSS observations directly (e.g., pseudorange, carrier-phase and Doppler observations) which have to be generated by the users themselves. Although several Apps have been developed for the GNSS observations generation, various data analyses indicate quality concerns, from biases to observation inconsistency in the generated GNSS observations output from those Apps. The quality concerns would subsequently affect GNSS data processing such as cycle slip detection, code smoothing and ultimately positioning performance. In this study, we first investigate algorithms for GNSS observations generation from the android.location API output. We then evaluate the performances of two widely used Apps (Geo++RINEX logger and GnssLogger Apps), as well as our newly developed one (namely UofC CSV2 RINEX tool) which converts the CSV file to a Receiver INdependent Exchange (RINEX) file. Positioning performance analysis is also provided which indicates improved positioning accuracy using our newly developed tool. Future work finding out the potential reasons for the identified misbehavior in the generated GNSS observations is recommended; it will require a joint effort with the App developers.  
smartphone positioning; precise point positioning (PPP); android location API; smartphone GNSS logging apps; GnssLogger app; Geo++ RINEX logger app; UofC CSV2 RINEX tool
###### Abstract
One main task is the preparation of the data and the associated selection of relevant features [19]. In the DED process, a variety of sensors can be used for process monitoring. Most of them are optical systems that detect emissions in a specific wavelength range. They are comparatively easy to handle and are inexpensive [20]. Essential information for the evaluation and control of a build-up process is often obtained from the temperature and its distribution, as well as the melt pool geometry. Generated measurement data consist of dynamic curves. To train an ANN, static features must be extracted from those curves. This procedure is known as feature extraction and can be realized in different ways [21]. In addition to manually selecting common and simple mathematically and physically explainable features, automated feature extraction carried out, for example, by using the python package tsfresh, has become increasingly popular in recent years [22]. Until now, there have been few studies in the literature on the consideration of measurement data by ANNs for quality assurance in the DED process. [PERSON] et al. [23] show, in their work on the aluminum alloy Al7075, how promising this approach is. They use an Artificial Intelligence (AI) technique based on the process data from emission spectroscopy to predict the component quality. In this context, the importance of data preparation and appropriate feature extraction is demonstrated. However, it is also shown that there is no standardized procedure. This study investigates the influence of measurement data on the accuracy of ANNs in the DED process. In addition to welding parameters, various measurement data from different sensors are used as a data basis. A combination of different sensors offers the possibility to create a large information base for different quality characteristics. It is necessary to identify significant features from dynamic measurement series to make them usable in the ANN application. The preparation of data, especially measurement data, is an important and critical step in data analysis for ANNs, and has a significant impact on ANN accuracy. In the context of this work, focus is placed on feature analysis, as methods for the practical application case have not been sufficiently represented in the state of the art so far. Furthermore, based on the evaluated features, ANNs are trained and validated for three different geometries. The results are compared with an ANN based on welding parameters alone. Finally, the added value of measurement data in ANNs for the DED process is evaluated.
## 4 Data Analysis and Results
### Structural Model  
The theoretical model and hypothesized relationships were estimated by using the bootstrap approach, with a sample size of 500, to generate t-values and standard errors for determining the significance of paths in the structural model. Figure 2a summarizes the results of the structural model test. As hypothesized, receiving online support and providing online support exert positive impacts on subjective well-being (\(\beta\) = 0.37, 0.30; \(t\) = 4.56, 3.73, respectively). These results support H1 and H2. Furthermore, the results also indicate that social capital significantly impacts receiving and providing online support (\(\beta\) = 0.71, 0.64; \(t\) = 21.16, 16.06, respectively), supporting H3 and H4.  
Figure 2: SEM analysis of the research model. **(a)** results of full sample; **(b)** results of low-experience group; **(c)** results of high-experience group. \({}^{*}p<0.1\).  
We used the subgroup analysis method to test the moderation effect of experience [67]. We first dichotomized use experience into two subgroups (high and low), using the mean splitting method, following [68]. We then estimated structural models for each subgroup and employed the formula implemented by [69], to test the moderating effects of experience, following [70] and [71]. Figure 2b,c and Table 4 summarize the results of the moderating-effect tests. The results show that the relationship between receiving online support and subjective well-being is stronger for users with high levels of experience, while providing online support has a stronger influence on subjective well-being for users with low levels of experience (\(t\) = 28.79, 40.39, respectively). Thus, H5 and H6 are supported.
# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
###### Abstract  
Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers' interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed.  
distributed visual crowdsensing framework; resource constrained environments +
Footnote †: journal: Sensors  
doi: 10.3390/s22155467
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 5 Experimental Results
### TPC Execution  
In this section, we evaluate the performance of proposed TPC. We take the wind-powered sensor node serving as a sender. An unlimited power supply sink is responsible for receiving messages and returning the RSSI data every minute. The programmable transmission power level ranges from 0 to +20 dBm with a step size of 1 dBm and the corresponding power consumption \(P_{tran}\) comes from the hardware measurement in the existing literature [29]. The noise power \(P_{noise}\) is considered Gaussian White Noise with a mean of \(-105\) dBm and a standard deviation of 1.5 dBm. The transmission rate is 9.6 kbps and the data packet size is 32 bytes. The energy per useful bit \(E_{u}\) is used for evaluating.  
Figure 9: Error comparison result between Weather-Conditioned Selective Additive Decomposition (WCSAD) and WCMA.  
We validate the effectiveness of our TPC module by using two comparisons. The existing RSS-based transmission power controller [17] is implemented as a reference, which is referred as a fixed-RSS scheme. The fixed transmission power scheme is also implemented, which is referred to as a fixed-TX scheme. The wind-harvesting IoT device and the sink are placed in outdoor park surroundings.  
Figure 10 demonstrates the average transmission energy of 10,080 packets (one packet per minute during 7 days). We can see that the proposed method achieves the lowest energy consumption compared with the other TPC strategies no matter the distance. Compared with the fixed-TX scheme, our proposed module achieves energy reduction around 30%. Compared with the fixed-RSS scheme, our proposed module lowers the transmission energy consumption around 15%. The wireless path loss and the background noise are always time varying, a fixed RSSI value or transmission power definitely cannot guarantee the optimal transmission energy efficiency. The proposed TPC module is effective in dynamically selecting the optimal transmission power along with the lowest energy cost \(E_{u}\).
# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
### Contributions in Automatic Thai Vowels Pronunciation Recognition
This research proposes a method for Thai vowel speech recognition based on a convolutional neural network (CNN), which is one of the most well-known deep neural networks (DNNs). The CNN model is applied to automatic speech recognition on the automatic CAPT for Thai vowels. The optimal CNN model is utilized to learn the spectral characteristics of 18 Thai vowel classes. 3. This research generates two different acoustic feature inputs for CNN and long short-term memory (LSTM) models. They are MS and MFCC acoustic features from the raw speech waveform to learn deep multimodal features. 4. This research proposes automatic CAPT for Thai vowel speech that can display the learners' vowel pronunciation results. If the pronunciation is incorrect, then it will suggest the correct practice with text, real video, and 3D video. The automatic CAPT for Thai vowel speech uses a deep learning structure. It is a new system that develops computer techniques integrated with linguistic theory. The system can be used to guide learners such as the voice impaired, nonnative learners, and nonstandard Thai speakers. This system, therefore, allows learners to practice vowel pronunciation in real time, similar to having an expert, Thai teachers, and linguists provide assistance on the correctness of pronouncing vowels. The outcome of this work benefits the innovation of advanced systems, for example, the classification of tones, words, phrases, and sentences to facilitate learning standard Thai pronunciation. The remainder of the paper is organized as follows. In Section 2, the background is described, related works are presented in Section 3, and materials and methods are shown in Section 4. Section 5 describes the results of the experiments, and conclusions are displayed in Section 6. In Section 7, the discussion is presented. Finally, the automatic computer-assisted pronunciation training for Thai vowels is presented. The definitions of variables and acronyms are shown in Appendix A (Table A1) after the last section.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 2 Mission and Payload Operations
### Operations Architecture  
The mission architecture is divided into a space segment and a ground segment. The ground segment is comprised of the mission operations center and related software tools, with the main Ground Station (GS) located at NTNU in Trondheim. Additionally, the KSAT Svalbard ground station has been used to expand the ground station coverage and to increase downlink capacity at times when the satellite is not in range of the main GS. However, due to the orbit and the geographical proximity of Svalbard and Trondheim, the overall capacity gain of using both locations in succession during nominal operations is small. The orbit of HYPSO-1 gives an average of 10 consecutive communication passes per day over the main GS, from about 07:00 UTC to 22:30 UTC.  
At the time of writing, the processing pipeline consists of a CCSDS-123v1 lossless compressing algorithm [27] typically run on the on-board Field Programmable Gate Array (FPGA). More processing steps will be added to the pipeline later via software updates. After the onboard processing, the data are transferred to the payload controller to await downlinking.
## 4 Discussions
Both numerical models and satellite products require ground-based data for calibration and validation. However, the observational data (chiefly from flux towers) are very limited, which results in inaccurate, poorly constrained estimates of energy, water, and carbon fluxes in the dryland to study its ecosystem's structural and functional dynamics. In Figures 12 and 13, the proposed SSM/ET products by using machine-learning models demonstrate an appreciable improvement over the LDAS model. The previous studies applying similar automatic methodologies have been discussed [35; 40; 93], highlighting the importance of balancing the performance and explainability of the machine learning models for scientific studies. The machine learning models, along with numerical models based on physical processes (e.g., FLDAS), are the most important sources for hydrological studies in the dryland countries due to their limited scientific data and technological ability. For example, countries in the Nile River Basin (shared by Egypt, Sudan, Tanzania, Ethiopia, etc.) are undergoing accelerated population growth and urbanization [39]. The ongoing construction and filling of Ethiopia's Grand Ethiopian Renaissance Dam have brought up intensifying tensions between Ethiopia and downstream dryland countries including Egypt and Sudan. However, scientific studies are restricted to support the decision making and collaborations due to limited on-site observations, while the number of functional hydrometeorological gauging stations declined by 88% between the early 1960s and 2014 [94]. It is noted that our approach to the correction of the process-based hydrological models is also known as a process of hydrological post-processing to obtain \"point\" predictions. By contrast, new data-driven practices [95; 96] of delivering \"probabilistic\" hydrological predictions (e.g., quantile regression [97]) were introduced recently with their potential in expressing complex hydrological processes considering predictive uncertainties [98]. Fortunately, new and emerging advances leverage significant opportunities for overcoming previous challenges in dryland remote sensing and thus revolutionize our ability to contextualize arid and semi-arid regions within the broader Earth system. Accordingly, the following strategies are recommended [99]: (1) exploring novel fusions of sensors and techniques across different spatiotemporal scales (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) to understand dryland structural and functional dynamics; (2) capturing instant responses of dryland ecosystems to diurnal variation in water stress by utilizing near-continuous observations from geostationary satellites; (3) building up new ground observational networks for validation and calibration to better represent the heterogeneity of dryland system; (4) developing algorithms specifically for dryland ecosystems; (5) coupling physical process-based models with remote sensing observations using data assimilation to improve ecological forecasts and long-term projections.
## 3 Results
### Areas of Application: An Overview  
The range of the IRIDE missions will cover a tremendous application area, from observation of the coasts to monitoring the atmosphere and the land ecosystems, and from the quality of the water and the water system to the movements of the land and large infrastructures. These are all capabilities that can be translated into security, prevention, and emergency management services or into applications to support agriculture, forestry management, and services already available within the Copernicus and Landsat programs,  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Questions** \\ \hline
1 & Count the number of words that mentioned IRIDE space program; \\ \hline
2 & Sum up main issues arisen by users; \\ \hline
3 & Detect and count main strengths or perspective underlined by users; \\ \hline
4 & Detect and count main problems or weakness underlined by users; \\ \hline
5 & Upon the previous questions filter by Earth observation platform, download data, willing perspectives; \\ \hline
6 & Count the entire feedback and, with respect to the previous questions, compute the statistical percentage. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Questions to aggregate data and perform scraping into RCS.  
for example, but with a unique temporal and spatial resolution. Table 3, lists the main services according to 8 IRIDE macro-services and relative topics.  
The constellation will play a pivotal role in assisting the Department of Civil Protection and other administrations in addressing challenges related to hydrogeological instability and fires. Additionally, it will contribute to safeguarding coastlines and overseeing critical infrastructure, air quality, and meteorological conditions. This comprehensive system enables multi-sensor and multi-temporal monitoring, facilitating swift mapping for various emergency scenarios, including droughts, fires, water conservation in agriculture, and the supervision of waste disposal and polluting discharges. Specifically, as summarized in Table 3, IRIDE will offer eight macro services encompassing marine and coastal monitoring, air quality assessment, monitoring of land movements, land cover analysis, agriculture and forestry monitoring, hydro-weather climate observation, assessment of water resources, emergency management, and safety. These services are strategically planned to cover both terrestrial and marine components, demonstrating the versatility of the system in addressing a wide array of environmental and emergency-related concerns.
## 3 Data Pre-Processing and Feature Analysis
### Evaluation Indicators  
To evaluate the performance of the models and the prediction errors, three commonly used metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE), were employed. The error indicators are defined as below:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}, \tag{24}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i}), \tag{25}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}|(y_{i}-\hat{y}_{i})/y_{i}|, \tag{26}\]  
where \(y_{i}\) is the actual value, \(\hat{y}_{i}\) is the prediction value, and \(N\) is the total amount of data.  
Figure 5: XGB feature importance.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 4 Experimental Results
### Experiments with Simulated Data
#### 4.2.1 Comparative Experiments on Chikusei Data
The SAM index of PC and GLP surpasses that of other methods, indicating that PC and GLP methods exhibit significant spectrum distortion in comparison to other approaches. Based on the four indicators of RMSE, ERGAS, PSNR, and RASE, it has been determined that the overall fusion results of the FHP approach are superior to those of the FHM method. The fusion results of HMPCNMF and IFNPMH, two FHMP methods, exhibit superior quantitative performance compared to the fusion results of FHP and FHM methods. Out of the methods mentioned, IFNPMH, which is based on deep learning, outperforms the HMPCNMF approach in terms of all quantitative metrics. Additionally, IFNPMH demonstrates the highest performance across all indicators on the Chikusei dataset, further confirming the effectiveness of IFNPMH.
# Progress on the Application of Nanomaterial Expansion in Oil Displacement
## 3 Nanomaterial Swelling Oil Displacement Mechanism
### Nano-Silica Oil Droplets Enhanced Expansion
The promoting effect of nanoparticles on oil droplet expansion is due to their unique interface behavior. Nanoparticles change the distribution of surface-active substances by adsorbing at the oil-water interface. This leads to the movement of the active material inside the oil droplets, which increases the volume of the oil droplets and causes the oil droplets to expand. Further studies have shown that amphiphilic Janus nanomaterials can also expand oil droplets and exhibit better stability than ordinary nanofluids. The characteristics of nanofluids make it possible to promote the expansion of crude oil, and the study of nanofluid expansion oil displacement technology is of great significance for improving the efficiency of crude oil exploitation in practical engineering.
## 3 Numerical Model and Validation
### Track-Soil-Building Finite Element Model
The minimum element size was set to be less than one-sixth of the minimum shear wavelength of the soil of interest in order to ensure accurate calculations for frequencies below 80 Hz [35]. The upper surface of the rails should specifically bear the wheel-rail force, with an element size set to 0.025 m. The appropriate element size for the surface soil is determined as 0.285 m. Considering the train load on the surface and computational speed requirements, the distribution of the soil elements transitions from dense to sparse as the depth increases. For over-track buildings and pile foundations, a uniform grid cell size of 0.5 m was employed. The full-scale track-soil-building finite element model comprises a total of 3,742,568 elements and 3,990,731 nodes.