esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The monitoring photodetector was thermoelectric cooled to maintain the etalon characteristics in the detector. They reported ground-based CO\({}_{2}\) absorption measurements over a 1 km long horizontal path with 4 ppm fluctuations after the system was improved [18]. With the more accurate absorption line information (2% to 5% uncertainty) in the HITRAN database and the development of photodetectors and frequency-locked narrow linewidth lasers, these parameters (i.e., absorption line information, detector noise, wavelength accuracy, stability and spectral purity) are not the main factors limiting the near-ground horizontal path measurement accuracy of a 1.57 \(\upmu\)m IPDA lidar any more [19; 20]. However, some potential sources of uncertainty remain, which may arise from the temperature sensitivity of the optical components in an optical fiber transmission system without calibration [18]. In addition, speckle appears when a laser is scattered by heterogeneities at the scale of its wavelength, such as variations in the surface roughness, or in the refractive index caused by atmospheric turbulence [21]. Speckle fluctuation leads to a large uncertainty in detected powers unless the signal is averaged over many independent speckle patterns. [PERSON] et al. have revealed the temperature-dependent characteristics of optical components and the speckle effect, which have an impact on the measurement accuracy in their IM-CW IPDA lidar. These phenomena also appeared in our experiments, indicating that they are ubiquitous in IM-CW IPDA fiber transmission lidar systems. In a previous study, we developed a 1.57 \(\upmu\)m IM-CW IPDA lidar system for CO\({}_{2}\) sensing, with which the transmitted optical signals are backscattered from a random topographic target [20]. In this paper, we verified the effectiveness of the lidar calibration process reported by [PERSON] et al. on our system and presented the improvement of speckle statistics. An optical fiber link test (OFLT) unit is designed to locate the temperature-dependent optical components that cause the bias drift of our lidar system, and the calibration of the OFLT is completed. Then, we demonstrate, using theoretical and modeling techniques, that there is a substantial advantage (in terms of the variance of the ratio of return powers) for a highly correlated speckle. A theoretical prediction is that placing a vibrating motor on the telescope to perturb the emitted beam and reduce the wavelength separation between the on and off wavelengths will increase the correlation of speckle. The results of laboratory experiments are consistent with the theoretical prediction. This paper is structured as follows: Section 2 introduces the principle of IPDA lidar and the preliminary instrument setup. The process of system calibration and the theoretical analysis of speckle statistics are discussed in Section 3. In Section 4, the verification experiment of improved speckle statistics is introduced and the results of the CO\({}_{2}\) concentration measurement using the improved lidar system are shown. Section 5 summarizes the most important findings and describes the limitations of this study.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 2 Aspects of Variability in Voice
### Variability over the Voice Range  
In the vocological literature we often encounter mathematical models that serve to represent some aspect of how the voice behaves as its driving conditions are varied. Suchmodels are valuable and necessary, and, if well formulated, they can provide important insights and predictions [35]. However, they tend also to impose a mind-set according to which every voice can be expected to follow similar rules, on the whole. This is of course true, but the complete set of rules and constraints operating on any given voice is as yet unknown, and--as any professional voice _user_ will attest--it is more complex and diverse than is often appreciated or covered by any particular model. Therefore, it is important to observe reality as it is, and try to describe it without immediately imposing a model. Voice maps are a useful tool for doing so.  
In numerous studies, first on voice range profiles [4], or phonetograms, and more recently on voice maps in general [5; 13; 18; 19], it has been demonstrated that the variability within and between individual voices is surprisingly large. Presenting the full extent of this variability here would require a prohibitive amount of space. We strongly encourage the reader instead to browse the extensive albums of voice maps that are contained in data sets supplementary to articles already published. Direct links are provided with the references [13; 18; 19] at the end of this article.
## 3 Methods  
### Fire Rate Detection and Analysis  
To map fire events, we used the geospatial fire database created in the Sukachev Institute of Forest, SB RAS. This database uses satellite fire detections obtained from the NOAA/AVHRR, TERRA/AQUA/MODIS, and SNPP/NOAA-20/VIIRS platforms. This database has been maintained since 1996. Fire radiative power (FRP) was estimated using MODIS thematic thermal anomaly product (MOD14/MYD14) [33]. The product contains data on coordinates, the detection time of a thermal anomaly, and FRP values with a spatial resolution of ~1000 m. Thermal anomaly products were downloaded using the LAADS (Level-1 Atmosphere Archive and Distribution System) service ([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)); accessed on 11 May 2023). We used settlement data from Open Street Map ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/))) to exclude thermal anomalies of anthropogenic origin, i.e., we excluded \"fire\" pixels located within settlement boundaries from further analysis. We also excluded thermal anomalies related to gas flares in oil and gas fields using high-resolution data from Google Earth. These gas flares were mainly located in the northwestern part of the study area.
# Experimental Research on the Properties of Rock-Filled Concrete
## 2 Design for Mix Proportion of RFC and Construction Technology
### Mix Proportion of SCC and Materials  
Except for the impermeable facing slab on the upstream of dam and the overflow surface, other parts of dam are cast by RFC with C15. Before the casting process, the quality of self-compacting concrete (SCC) must be strictly tested. According to the design requirements, the mix proportion of special self-compacting concrete should satisfy the requirements of C15W6. The grade of coarse aggregate in SCC is continuously distributed from 5 mm to 20 mm. The theoretical mix proportion of SCC is cement 209 kg, fly ash 345 kg, water 214 kg, sand 857 kg, stone 605 kg, admixture I (R218A4) 8.94 kg, admixture II (N) 0.42 kg, admixture III (HSI) 0.85 kg, the air 2.9% in per cubic meter. According to the working performance requirements of SCC, the slump of SCC is set around 250-280 mm, slump flow (SF) is set around 630-750 mm, V funnel (VF) time is around 7-25 s The rock block must be fresh and hard, which size should only range from 30 cm to 100 cm, silt content less than 0.5%, compressive strength should be more than 40 Mpa, density should range from 2.5 g/cm\({}^{3}\) to 2.76 g/cm\({}^{3}\)The content of rock block should be 55%. General type RFC construction technology is employed, and the thickness of each lift of RFC is 1.8-2.0 m.
## 2 Materials and Methods
### Analysis of Fecal SCFAs and Lactic Acid  
Fatty acid purification and extraction from feces was performed as described by [PERSON] and [PERSON] [35], with slight modifications. Acidification and homogenization (Snijders Vortex 34524, Snijders Scientific, Tilburg, Holland) of feces (0.20-0.25 g) in 1 mL H\({}_{2}\)SO\({}_{4}\) (0.15 mM) was performed, followed by centrifugation (9000\(\times\)_g_, 4 \({}^{\circ}\)C, 20 min) (K241R Medium Prime Centrifuge, Centurion Scientific, Chichester, UK). The supernatant was treated with trichloroacetic acid (85%) at a concentration of 75 \(\upmu\)L/2 mL and incubated on wet ice for 45 min. Protein precipitation was completed after centrifugation (17,000\(\times\)_g_, 20 \({}^{\circ}\)C, 20 min), and the supernatant was filtered twice in a nylon filter 0.22 \(\upmu\)m. The samples were stored at \(-\)20 \({}^{\circ}\)C until analysis.  
SCFAs (acetic, propionic, butyric, isobutyric, valeric and isovaleric acids), as well as lactic acid concentrations, were determined by HPLC, using a Shimadzu chromatography system (Shimadzu Corp., Duisburg, Germany) equipped with a Nucleogel ION 300 OA column (Macherey-Nagel, Duren, Germany), a DGU- 20A5R degassing unit, a LC-20 AD pump, a CTO-20 AC oven at 85 \({}^{\circ}\)C and a RID-10A refractive index detector, as previously described [36], with some modifications. In brief, a solution of 0.049 g/L H\({}_{2}\)SO\({}_{4}\) was used as the mobile phase at 0.4 mL/min. A total of 20 \(\upmu\)L of each sample were injected directly into the column, and the detector cell temperature was set at 60 \({}^{\circ}\)C. SCFAs and lactic acid concentrations were calculated using standard curves prepared by standard solutions (R\({}^{2}\geq\) 0.99).  
Fecal SCFAs and lactate concentrations were expressed as mean \(\upmu\)mol per gram of feces, using the following equation, as described by [PERSON] et al. [37]: SCFAs/lactate (\(\upmu\)mol/g) = [organic acid in fecal contents (mmol/mL) * V\({}_{\mathrm{d}}\) (mL) * 1000]/weight of feces (g), where: V\({}_{\mathrm{d}}\) = Total Volume of Dilution.
## 3 Materials and Methods
### User Satisfaction Modeling
#### 4.2.2 Kano Model  
We employed the Kano model, developed by [PERSON] et al. [31], which is a two-dimensional model. Kano model is a well-known model of user satisfaction. This model categorizes product features into different classes including must-be, performance, excitement, indifferent, and reverse. These features values are associated with user satisfaction [14]. Details of each feature are described as follows:  
1. Must-be: These features are essential customer' requirements and expectations and are taken for granted. These features must be fulfilled, otherwise the product customer becomes dissatisfied.
2. One-dimensional (Performance): These features are related to product quality promised by the product service provider. These features have a direct impact on customer satisfaction when fulfilled.
3. Attractive (Excitement): These features give satisfaction when filled, but have no effect on customer dissatisfaction.
4. Indifferent: These product features neither influence on user satisfaction nor dissatisfaction.
5. Reverse: These features reveal a more significant degree of achievement, causing more customer dissatisfaction.  
Based on the rules defined by [14], we also mapped the UXDs on the Kano model. We mapped the \(\widehat{w}_{i}^{pos}\) and \(\widehat{w}_{i}^{neg}\) on the Kano model's five categories for modeling the user satisfaction. The details of these rules are the following:  
1. If \(\widehat{w}_{i}^{pos}\leq 0\) and \(\widehat{w}_{i}^{neg}<0\) then \(UXD_{i}\) is a must-be.
2. If \(\widehat{w}_{i}^{pos}\leq 0\) and \(\widehat{w}_{i}^{neg}\geq 0\) then \(UXD_{i}\) is a reverse.
3. If \(\widehat{w}_{i}^{pos}>0\) and \(\widehat{w}_{i}^{neg}<0\) then \(UXD_{i}\) is a performance.
4. If \(\widehat{w}_{i}^{pos}>0\) and \(\widehat{w}_{i}^{neg}\geq 0\) then \(UXD_{i}\) is an excitement.  
Figure 10 depicts the mapping on the Kano model based on the rules above.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Online Reviews**} & \multicolumn{6}{c}{**UX Dimensions (UXDs)**} \\  & \multicolumn{2}{c}{**\(D_{1}\)**} & \multicolumn{3}{c}{**\(D_{1}\)**} & \multicolumn{3}{c}{**\(D_{n}\)**} \\  & **Pos** & **Neg** & **Pos** & **Neg** & **...** & **Pos** & **Neg** \\ \hline \(r_{1}\) & \(1\) & \(0\) & \(0\) & \(1\) &... & \(0\) & \(0\) \\ \(r_{2}\) & & & & & &... & \(0\) & \(1\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \(r_{n}\) & \(0\) & \(1\) &
## 2 Framework and Methodologies
### Feature Extraction  
The original CSI data have high dimensionality and contain a large number of redundant components as well as some valid information. Its direct use for localization can consume excessive computational resources. Furthermore, this approach may affect the localization accuracy. Although PCA is a popular dimensionality reduction method, it is not suitable for complex nonlinear data. In this paper, an autoencoder is used for feature extraction. An autoencoder, an unsupervised neural network model, aims to compress input information, extracting most of the original data content while reducing its dimensionality [41]. A simple autoencoder structure consists of an encoder and a decoder. As shown in Figure 2.  
Assuming an input vector \(\mathbf{x}\in\mathbb{R}^{d}\), an encoded vector \(\mathbf{z}\in\mathbb{R}^{d}\), and an output vector \(\mathbf{x}t\in\mathbb{R}^{d}\), the encoding and decoding processes of the autoencoder can be described as follows:  
\[\mathbf{z}=\psi(\mathbf{w}_{encoder}\times\mathbf{x}+\mathbf{b}_{encoder}) \tag{4}\]  
\[\mathbf{x}t=\psi(\mathbf{w}_{decoder}\times\mathbf{z}+\mathbf{b}_{decoder}) \tag{5}\]  
Figure 2: Autoencoder structure.  
Let \(\psi\) denote a nonlinear activation function. In this paper, we employ the hyperbolic tangent function as the activation function for both input and output layers with the following expression:  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{6}\]  
The terms \(\mathbf{w}_{encoder}\in\mathbb{R}^{d\times d}\) and \(\mathbf{b}_{encoder}\in\mathbb{R}^{d\prime}\) signify the weights and biases of the encoding layer, respectively. Within the decoding layer, the network endeavors to reconstruct the \(dt\)-dimensional data back into \(d\)-dimensional form. Here, \(\mathbf{w}_{decoder}\in\mathbb{R}^{d\times d\prime}\) and \(\mathbf{b}_{decoder}\in\mathbb{R}^{d}\) stand for the weights and biases of the decoding layer. The autoencoder utilizes the conventional backpropagation algorithm for weight updates, With the objective of minimizing the root-mean-square error between the input and output, i.e.,  
\[L(\mathbf{x}-\mathbf{x}\prime)=\left\|\mathbf{x}-\mathbf{x}\prime\right\|^{2} \tag{7}\]  
When the total losses are minimized, i.e.,  
\[L=\frac{1}{n}\sum_{i}^{n}\left(\left\|\mathbf{x}-\mathbf{x}\prime\right\|^{2}\right) \tag{8}\]  
where \(n\) is the number of samples, at which point \(\mathbf{z}\) emerges as the optimal feature representation of the input data.
## 2 Methods
### BFA Test Validation  
During our deployment, we implemented the BioFire FilmArray(r) BioThreat-E test (BioFire Diagnostics, Salt Lake City, UT, USA) for EVD diagnosis for the first time under field-laboratory conditions. As previously stated, the BFA had never been validated for EVD testing prior to the Ebola outbreak and our deployment in N'Zerekore. The B-LiFE team intended to use it for the first time as an indicator of a team's ability to quickly learn a new emerging technology during an ongoing mission, and to teach trainees how to useit through a JiTT. The primary objective was to familiarise laboratory staff and trainees with the BFA test's analytical steps. Prior to validating its use for EVD diagnosis, the preparatory work considered the constraints and requirements of the harsh field conditions and focused on the biosafety procedures associated with the BFA testing. We analysed a series of nasal swabs collected from B-LiFE volunteers using the BFA respiratory panel kit, which did not require any specific biosafety procedure. During this learning phase, we used a tent-environment exposed swab which serves as negative control. This negative control may not appear to be an optimal quality-control method, but it provided the B-LiFE staff and trainees with hands-on experience with the system and better prepared them for BFA testing of Ebola samples. The following step entailed validating the use of BFA for EVD diagnosis. There was enough material for BFA testing in 16 archived samples (11 plasma and 5 urine samples) that were stored at \(-20\)\({}^{\circ}\)C. The obtained EVD diagnostic results were compared with previously acquired qPCR results for the same samples. Each sample was tested in triplicate.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 4 Results
### Detection of Frozen/Unfrozen Plots
#### 4.2.2 Freezing Detection Results over Site B
For the ascending (afternoon) acquisitions in the VH polarization, the highest percentage of frozen plots were observed on 21 January and 24 January 2019 for all the land cover classes, with a reported temperature of 1.5 \({}^{\circ}\)C. On 21 January 2019, the percentage of frozen plots was 91% for both LC1 and LC2, and 94.5% for LC3. Alternatively, on 24 January 2019, the percentage of frozen plots was less than the previous date, with, respectively, 75%, 32.5%, and 51.4% for LC1, LC2, and LC3. Using VH polarization, the detected anomalies for LC1, LC2, and LC3 were very few during the freezing period (November to mid-February) and for both descending (morning), and ascending (afternoon) acquisitions. In addition, the anomalies during this period, when detected, did not exceed 5% of the total number of plots for the three classes. However, as with site A, the detected anomalies  
Figure 10: Percentage of plots detected as frozen in site B for each S1 acquisition date, using VH and VV polarizations. (**a**) VH-LC1, (**b**) VH-LC2, (**c**) VH-LC3, (**d**) VV-LC1, (**e**) VV-LC2, (**f**) VV-LC3. Precipitation (P) and temperature (t) are given in the graph at the top. mostly occurred in October due to three heavy rainfall episodes happening in September, and starting from mid-February onwards, and these are due to a combination of rainfall, irrigation, tillage and/or vegetation growth. The freezing detection results across the three land cover classes using VV polarization (Figure 10d-f) show mostly the same detection capabilities as those obtained using VH polarization for the period between November and mid-February, and for both descending (morning), and ascending (afternoon) acquisitions. However, due to the lower threshold values used for VV, our algorithm detected more frozen plots on 13 March 2019 in comparison to VH, and also a lot more anomalies starting from mid-February onwards.
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
###### Abstract  
How to control deformation and avoid resonance is the key to ensuring the safety of the super-long pipeline when it is floating in the sea. Based on the deformation warning value of pipeline prototype composite material obtained from laboratory tests, the raw water pipeline project in Tong'an Xiamen adopts wireless communication equipment to transmit data, supplemented by aerial photography technology to monitor and feedback the strain and vibration during the dynamic construction of long-distance pipeline floating transportation. Combined with dynamic construction, this monitoring method avoids excessive deformation and resonance of the steel pipeline during floating transportation, and prevents the destruction of the anticorrosive coating. The airtightness test after completion shows that the whole pipeline meets the acceptance requirements. The monitoring results show that the strain at the bent position of the pipeline is large in the process of floating transportation, and the jacking speed and position of the tugboats have an important influence on the deformation of the pipeline. The same type of project should focus on these aspects and timely feedback monitoring data. At the same time, the study also provides detailed strain, modal analysis and effective monitoring technology for the safety of offshore steel pipeline floating transportation.  
marine floating transport; field monitoring; dynamic construction; steel pipeline strain; vibration mode +
Footnote †: journal: Applied Sciences  
11 September 2021  
Accepted: 27 October 2021  
Subject: 30 October 2021
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 2 Dataset
### Weather Radar  
We use two different networks. The NOAA network is mainly used to document the impact of rain on NRCS and to develop the detection method. We rely on data from the JMA network for independent validation. The location and coverage of the coastal radars from NOAA and JMA are shown in Figure 1.  
The NOAA network is called Next Generation Weather Radar (NEXRAD) network, which consists of 159 S-band high-resolution weather radars throughout the United States and several overseas locations. A total of 34 stations are distributed in the coastal area, as shown in Figure 1a. NEXRAD provides different precipitation products, including base reflectivity (1 km/5 min), base velocity (1 km/5 min), one-hour precipitation (1 km/1 h) and hydrometeor classification (0.25 km/5 min). In this paper, we rely on the base reflectivity instead of one-hour precipitation for rain intensity reference because of its high temporal resolution and its larger coverage. The base reflectivity is an indicator of rain intensity, which is usually used to derive rain rate by classical Marshall-Palmer conversion [27]. It is a radar station centered product with a radius of 460 km acquired with a 0.5\({}^{\circ}\) elevation angle. Please note that the radar beam altitude increases with the distance from radar station to reach more than 10 km at the edge of radar coverage.  
The rain rate provided by JMA has 1 km resolution every 10 min on a single grid and covers the full Japanese archipelago. The product is based on constant altitude plan position indicator (CAPPI) reflectivity data at about 2 km altitude from multiple JMA radars.  
Rainfall rate (mm/h) is estimated from reflectivity values (dBZ) using a reflectivity-rainfall (Z-R) relationship of Marshall and Palmer [27].