esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 3 Recent Technical Advancements
### I/Q Mismatch Mitigation  
Besides distortion in the conventional AC coupled receiver baseband, another source of distortion comes from the \(I/Q\) imbalance in quadrature radar receiver [59; 60]. The \(I/Q\) imbalance includes amplitude imbalance and phase imbalance, which are studied in details in [74]. With the advancement of integrated circuit (IC) technologies, many of nowadays' RF components have good \(I/Q\) balance performance. For example, the quadrature mixer used in [58], i.e., Skyworks73009, has a maximum \(I/Q\) amplitude imbalance of 0.3 dB and a typical phase error of only \(1^{\circ}\). However, lower quality radars at reasonable price are still popular for some applications, such as the automotive anti-collision system and the automotive doors. For instance, the radar module used in [60] has amplitude imbalance of \(1.25^{\circ}\) and phase imbalance of \(23^{\circ}\). Therefore, it is worthwhile exploring techniques to mitigate \(I/Q\) imbalance in inexpensive radar modules for high-accuracy displacement measurement.  
The Gram-Schmidt (GS) or ellipse correction method is commonly used in coherent optical communication systems to compensate the \(I/Q\) imbalance [59; 60]. This method uses a least square fitting algorithm to match the \(I/Q\) data to an ellipse, based on which the orthogonality of the \(I/Q\) signals are corrected by a known transformation [59]. A method using GS to estimate the amplitude and phase imbalances in Doppler radar was proposed in [59]. However, this method requires modifying the radar hardware by adding an external voltage controlled phase shifter between antenna and radar transceiver. Moreover, it requires more than one full circle of data in the \(I/Q\) plane, while most cases of radar displacement sensing only occupy a portion of the unit circle. A data-based method called \"ellipse fitting\" that requires no hardware modifications has been proposed in [59]. With a mechanical target moving linearly in front of the radar, this method estimates the imbalance parameters from the ellipse that the data forms in the \(I/Q\) plane. Since all the data points along the arc in \(I/Q\) plane contribute to the estimation, this method is more robust. To fit the ellipse parameters to the data, there are generally two methods: algebraic fitting or geometric fitting, the latter of which is known to be more accurate and robust. The method in [60] is based on algebraic ellipse-fitting. In [60], the performances of algebraic and geometric fitting methods are compared in different conditions in radar sensing and the \(I/Q\) imbalance estimation problem is studied in details. The Levenberg-Marquardt (LM) method is also proposed in [60] as an easy imbalance estimation approach to address the \(I/Q\) imbalance problem in radar sensing.
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 1 Introduction  
Stromboli is one of the Aeolian Islands that build the volcanic arc north of Sicily (Italy). The island contains an active volcano with the same name. Its Strombolian eruptions [1, 2] are often visible from the surrounding sea, which lead to the island's nickname \"Lighthouse of the Mediterranean\". The volcano's typical activity is characterized by regular explosions that eject particles \(\sim\)100 m above the crater terrace a couple of times per hour. Continuous effusive activity is uncommon for Stromboli. During the last decades, only three major effusive episodes were recorded: 1985/86 [3], 2002/03 [4, 5] and 2007 [6, 7, 8]. The effusive activity presents no direct hazard to the island's inhabitants. However, the effusive activity has often been followed by paroxysmal phases which might cause the partial collapse of the Sciara del Fuoco (part of the north slope of the volcano). Sciara del Fuoco could also collapse due to a submarine landslide at the start of an effusive eruption; this was the case during the 2002/2003 eruption, which also produced a tsunami [9, 10]. In other words, the effusive activity at Stromboli is an exceptional activity that can be a precursor of a major hazard in the broader area.  
A new effusive activity period at Stromboli started on 6 August 2014. This event had been preceded by two months of increased Strombolian activity and several lava overflows from its different craters [11, 12]. Lava flows extended down the Sciara del Fuoco from a fracture 650 m a.s.l.. The effusive period lasted until 13 November 2014. Thermal observations of this event allowed usto estimate the time averaged lava discharge rate. The thermal data were retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the small TET-1 satellite (German abbreviation for \"_Technologie-Erprobungstrager 1\"_ meaning Technology Experiment Carrier). This was the first time a small and low cost satellite provided a long term satellite monitoring of active lava flows at high spatial resolution (160 m) and a relatively high temporal resolution (~3 days, sometimes even two images per day).  
Prior to the presentation of the satellite and field data processing in Section 3, a brief review of techniques for thermal observations of active volcanoes is given in Section 2. Volcanic radiant power and time averaged lava discharge rates derived from satellite observations are presented in Section 4. These are followed by a discussion focusing on the comparison of TET-1 data to other satellite data and field observations.
## 4 Experimental Results  
To verify the accuracy of the established roadway model in reflecting the transmission characteristics of wireless signals, an experimental study was conducted. The Ganhe Huozhou Coal Mine has been in operation for 10 years and is a coal mine with low amounts of gas. In this test, the roadway is dark and humid, the inner wall is very rough, and the roadway is covered by cables, pipelines, ventilation and drainage pipes, and supporting metal mesh. Coal seams can be seen through the supporting mesh, and the roadway has a turning point of non-line-of-sight propagation; thus, a good waveguide cannot be formed. The roadway is approximately 1.1 km deep, and the maximum drop can reach 40 m. The locations of the transmitting and receiving ends in the roadway are shown in Figure 9. Table 1 lists the detected signal strengths.  
The simulation and measurement results of the wireless signal strength are shown in Figure 10. In the visible direction of the slope or flat line, the signal attenuation is gentle,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**700 HMz** & \begin{tabular}{c} **Downlink** \\ **Received Power** \\ \end{tabular} & \begin{tabular}{c} **Downlink Data** \\ **Throughput** \\ \end{tabular} & \
\begin{tabular}{c} **Uplink** \\ **Received Power** \\ \end{tabular} &
\begin{tabular}{c} **Upstream Data** \\ **Throughput** \\ \end{tabular} \\ \hline T1 & \(-\)49.1 dBm & 14.6 Mbps & \(-\)50.9 dBm & 14.0 bps \\ T2 & \(-\)69.5 dBm & 8.1 Mbps & \(-\)70.7 dBm & 8.1 bps \\ T3 & \(-\)86.0 dBm & 4.7 Mbps & \(-\)88.0 dBm & 4.8 bps \\ T4 & N/A & N/A & N/A & N/A \\ T5 & N/A & N/A & N/A & N/A \\ \hline \hline \end{tabular}  
* The signal could not be connected at T4 and T5. N/A means that the test project does not need to be completed.  
\end{table}
Table 1: Wireless signal strength test results 1.  
Figure 8: Rx receives power when Tx is close to the roadway wall; (**a**) line diagram of field strength near the roadway wall; (**b**) near the roadway wall field strong cloud map.  
Figure 9: Diagram of the distribution of transceivers in the roadway.  
while at the non-line-of-sight roadway turning point, a large amount of signal is absorbed, resulting in substantial attenuation. For a certain distance before the turning point, the wireless signal strength increases because a large number of wireless signal paths could not propagate downward at the turning point, resulting in an aggregation phenomenon, which enhances the signal strength. By fitting the simulation curve, the trend of the wireless signal is found to be basically consistent with the trend of the measured signal strength. The results show that the model created by the 3D ray-tracing method can predict the field strength distribution of wireless signals.
###### Abstract  
Parallel extraction of headspace volatiles from multiwell plates using sorbent sheets (HS-SPMESH) followed by direct analysis in real-time high-resolution mass spectrometry (DART-HRMS) can be used as a rapid alternative to solid-phase micro-extraction (SPME) gas-chromatography mass-spectrometry (GC-MS) for trace level volatile analyses. However, an earlier validation study of SPMESH-DART-MS using 3-isobutyl-2-methoxypyrazine (IBMP) in grape juice showed poor correlation between SPMESH-DART-MS and a gold standard SPME-GC-MS around the compound's odor detection threshold (<10 ng/kg) in grape juice, and lacked sufficient sensitivity to detect IBMP at this concentration in grape homogenate. In this work, we report on the development and validation of an improved SPMESH extraction approach that lowers the limit of detection (LOD < 0.5 ng/kg), and regulates crosstalk between wells (<0.5%) over a calibration range of 0.5-100 ng/kg. The optimized SPMESH-DART-MS method was validated using Cabernet Sauvignon and Merlot grape samples harvested from commercial vineyards in the central valley of California (\(n=302\)) and achieved good correlation and agreement with SPME-GC-MS (R\({}^{2}=0.84\)) over the native range of IBMP (<0.5-20 ng/kg). Coupling of SPMESH to a lower resolution triple quadrupole (QqQ)-MS via a new JumpShot-HTS DART source also achieved low ng/kg detection limits, and throughput was improved through positioning stage optimizations which reduced time spent on intra-well SPMESH areas.  
DART-MS; SPMESH; SPME; volatile analysis; GC-MS; HRMS; methoxypyrazines Article  
Optimized Solid-Phase Mesh-Enhanced Sorption from Headspace (SPMESH) for Rapid Sub-ng/kg Measurements of 3-Isobutyl-2-methoxypyrazine (IBMP) in Grapes  
[PERSON]  
1 Department of Food Science, Stocking Hall, Cornell University, Ithaca, NY 14853, USA 1  
[PERSON]  
1 Department of Food Science, Stocking Hall, Cornell University, Ithaca, NY 14853, USA 1  
[PERSON]  
2E&J Gallo Winery, Modesto, CA 95354, USA 2  
[PERSON]  
2E&J Gallo Winery, Modesto, CA 95354, USA 2
# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 3 Outdoor Activity Recognition System
### POI Identification Mechanism
#### 3.1.2 Second Iteration
Both algorithms can clusterize it correctly thanks to the underlying DBSCAN algorithm that both interactions use. However, the first-iteration erroneously clusterizes some other points that are not a real cluster but a change of trajectory of the user. Reducing the eps of the initial DBSCAN from 25 m to 9 m allows the second iteration to avoid making this mistake. Figure 7: Static-drifts resolution. Raw (**left**), first iteration (**center**), second iteration (**right**). In Figure 9, we also see an example previously shown. In this case, we see (left) an example of zig-zag shaped static GPS drift that the data cleaning algorithm of the first iteration (center) is unable to remove. These kinds of drifts are really cumbersome and they need to be processed differently from other drifts. The multi-clustering algorithm developed in the second-iteration (right) can cope with these drifts correctly. In the route that can be seen in Figure 10, we have a clear example of multi-clustering. At first, it seems it is a normal route, but after a thorough analysis we realize that the user has not really left the home and the rest all the points are a mix between GPS drifts and static-jumps. The first iteration (center) is able to deal with some of the drifts, but the static-jumps confuse this algorithm and it is unable to clear the route correctly. Thanks to the multi-clustering method of the second iteration, the route is perfectly cleaned and it marks just the point that identifies the user's home. Finally, another example of multi-clustering (left) is shown in Figure 11. This is the worst example of multi-clustering that we have detected in all the analyzed samples. Although you might think it is a route through the old town, it is actually a terrible mix of  
Figure 8: Normal clustering, raw (**left**), first iteration (**center**), second iteration (**right**). Figure 10: Multi-clustering. Raw (**left**), first iteration (**center**), second iteration (**right**). Figure 9: Zig-zag shaped static drift. Raw (**left**), first iteration (**center**), second iteration (**right**). drifts and static-jumps of the GPS while the user is working in his office. The algorithm of the first iteration (center) is able to do some cleaning in that point cloud (eliminating the main drifts) but the result is far from acceptable, erroneously detecting five POIs (the red dots). In contrast, the multi-clustering method of the second iteration (right) neatly clusters all points into one (red dot) which is the actual user's office.
# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
###### Abstract  
Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (_Zea mays_) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R\({}^{2}\) value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4-R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R\({}^{2}\) value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4-R6).  
Sentinel-2; precision agriculture; machine learning; vegetation indices; corn yield; within-field variability; digital farming 2019 11 2873 10.3390/rs11232873
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Unsupervised Methods
#### 3.2.1 Clustering Algorithms  
Clustering techniques are employed either as a classification model or as a method for data preprocessing. They are algorithms used to group samples with similar behaviors [17; 55; 56; 67; 71]. Applying K-Means and K-medoids, results similar to those obtained with supervised methods are achieved. The disadvantage of these methods is that it is not exactly known how many groups to generate and which group or groups to consider as positive--fraud [64].
## 7 Conclusions  
In this paper we presented a proposed PBNM framework for end-to-end QoS control in converged networks, CNQF (Converged Networks QoS Management Framework), together with its implementation and enhancements which have been evaluated on real testbed environments. CNQF is designed to provide homogenous unified QoS management and resource control over heterogeneous access technologies by leveraging PBNM paradigm which allows transport plane technology-independent solutions to be developed as we have demonstrated in this paper. The potential for PBNM to simplify the administration of complex network characteristics such as QoS, access control, security, resource allocation _etc._ has driven the standardization of architectures within the IETF, 3 GPP and TISPAN which all bear similarities. Whilst the standards preclude implementation details and experience of domain specific applications, several studies can be found proposing PBNM in different contexts such as cloud environments, _ad hoc_ networks, VPNs, _etc._ Beyond previously presented PBNM studies, in this paper we have proposed means for more intelligent and adaptive management of network resources in cost efficient manner by incorporating fuzzy logic system as a key underpinning enhancement to our CNQF policy based management framework.  
By leveraging fuzzy logic there is the potential to simplify network management as well as reduce monitoring load requirements as intelligent solutions can be derived through the use of imprecise data instead of crisp measurements. Furthermore, fuzzy rule based systems provide a convenient and flexible means to couple dynamic operational policies with resource management strategies in an extensible and easily modifiable manner without disrupting normal operation. Our implementation of fuzzy-rule-based driven resource allocation within the CNQF policy based admission control process demonstrated the efficacy of this approach. We further tested the performance of the fuzzy based system implementation compared to scenarios without the approach using real traffic flows on our testbed. The results of the experiments showed that not only is it possible to gain improved service availability and lower the blocking probability for high revenue bandwidth hungry services such as video, but also improved bandwidth resource utilization can also be obtained from the approach. Nevertheless, the flexibility in tuning the resource allocation without ongoing service disruption via fuzzy rule base and the allocation policies to potentially achieve even better performance is a promising prospect. Service classes can also be easily extended leading to a more scalable and cost-effective resource management solution for PBNM systems which are likely to be a dominant network management paradigm in next generation converged networks. In our future work, we would like to extend the fuzzy rule based system to support and enhance context-aware resource allocation and QoS control in the PBNM system and also evaluate the performance.
###### Abstract  
This paper presents a method to assess the load transfer efficiency (LTE) of concrete pavement joints using distributed optical vibration sensors. First, a theoretical analysis of concrete pavement vibration was conducted to investigate how to reflect LTE by spectral amplitude. Second, distributed optical vibration sensor (DOVS) was applied to measure vibration around joints distributedly. Third, the corresponding processing method for DOVS data was proposed to calculate the ratio of spectral amplitude from different slabs through power spectral density (PSD) analysis. Then, field tests were conducted on nine concrete pavement slabs with three different types of joints (dummy joint, rabbet joint, and dowel bars). The deflection-based method as well as the proposed vibration-based method were employed to assess the LTE of eleven joints on two different dates. The comparative analysis results indicate the deflection-based LTE (DLTE) and the ratio of PSD (RPSD) have a strong correlation (0.871) and a slight difference (<\(\pm\)0.03) overall. The correlation is robust in different dates and types of joints (0.844-0.88). These findings prove the accuracy and effectiveness of the proposed vibration-based method.  
Keywords:load transfer efficiency; concrete pavement; joint; distributed optical vibration; power spectral density +
Footnote †: journal:
## 3 Results and Discussion
### Features of the Obtaining Process of MgO from Serpentinite
Magnesium hydroxide is practically impossible to quantitatively precipitate with just ammonia from solutions containing excessive amounts of ammonium sulfate. We used the process of precipitation of a hydroxide-carbonate complex, similar to the analogous process in [22], according to reaction (9). \[ \begin{array}{l}3\ \text{Mg}(\text{NH}_{4})_{2}(\text{SO}_{4})_{2}+3\ \text{NH}_{4}\text{OH}+1.5\ (\text{NH}_{4})_{2}\text{CO}_{3}\to 1.5\ (\text{MgOH})_{2}\text{CO}_{3}\downarrow+6\ (\text{NH}_{4})_{2}\text{SO}_{4}\end{array}  \tag{9}\]Reactions (5), (7)-(9) give a total of 6.4 mol of ammonium sulfate. Removal in solid form by evaporation and subsequent heat treatment of ammonium sulfate leads to the production of all reagents necessary for the implementation of the cyclic process, reaction (10). \[6.4\ (\mathrm{NH_{4}})_{2}\mathrm{SO_{4}}\to 6.4\ \mathrm{NH_{4}HSO_{4}}+6.4\ \mathrm{NH_{3}} \tag{10}\]  
In particular, when this amount of ammonia is dissolved, 6.4 mol of its aqueous solution can be obtained, of which 3.4 mol isfor carrying out reactions (7)-(9), as well as 3 mol for obtaining 1.5 mol of (NH4)2 CO3 for reaction (9). A real technological process can also be closed in terms of carbon dioxide, which is released during the calcination of magnesium hydroxide-carbonate obtained in accordance with reaction (9). While carrying out sequentially repeated laboratory experiments, as can be seen from the description in Section 2.1, a closed process for ammonium bisulfite was carried out. To standardize the conditions for laboratory experiments, the collection of gas components and recuperation of ammonia solution on a small scale were not carried out. According to our estimates, the cost of the enlarged proposed process for the production of MgO from serpentinite should not exceed $400 per ton, which corresponds to the cost of Portland cement.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 4 Optimization Process
### Other Important Topics: Regularization Parameter, Artifacts, Local Minima, and AI
The proposed method reduced cross-talk artifacts, whereas the Gauss-Newton method reconstructed images contaminated by artifacts. [PERSON] et al. proposed image reconstruction using the long short-term memory (LSTM) deep learning method, which is an extension of the RNN that uses gates to selectively retain and forget information [156]. The deep learning scheme is composed of two LSTM layers and a dense layer to reconstruct the positions with changes in \(\mu_{a}\). The datasets generated using TD RTE were used to train the ANN. By comparing the DOT images reconstructed using the proposed method and the conventional nonlinear optimization method with DA, it was demonstrated that the proposed method reduced the blurriness of the region with a large \(\mu_{a}\). As shown in the above studies, the use of AI technology represented by deep learning can reduce computational costs in the forward and optimization processes using training data prior to practical image reconstruction. Forward calculation, particularly with RTE, which is repeatedly required in the nonlinear optimization process, requires massive computation, and becomes a bottleneck in the clinical application of DOT. AI can improve the quality of an image by confining the solution space of the inverse problem of DOT image reconstruction in accordance with appropriate prior information.