esa-sceva/satcom-chunk-collection huggingface dataset:
_Remote Sens._**2012**, \(4\), 3689-3720; doi:10.3390/rs4123689  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
A Vegetation Index to Estimate Terrestrial Gross Primary Production Capacity for the Global Change Observation Mission-Climate (GCOM-C)/Second-Generation Global Imager (SGLI) Satellite Sensor  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4, [PERSON] 5, [PERSON] 6, [PERSON] 5, [PERSON] 7, [PERSON] 8, [PERSON] 9 and [PERSON] 10**  
Footnote 1: Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 30 September 2012; in revised form: 14 November 2012 / Accepted: 16 November 2012 / Published: 23 November 2012_  
**Abstract:** To estimate global gross primary production (GPP), which is an important parameter for studies of vegetation productivity and the carbon cycle, satellite data are useful. In 2014, the Japan Aerospace Exploration Agency (JAXA) plans to launch the Global Change Observation Mission-Climate (GCOM-C) satellite carrying the second-generation global imager (SGLI). The data obtained will be used to estimate global GPP. The rate of photosynthesis depends on photosynthesis reduction and photosynthetic capacity, which is the maximum photosynthetic velocity at light saturation under adequate environmental conditions. Photosynthesis reduction is influenced by weather conditions, and photosynthetic capacity is influenced by chlorophyll and RuBisCo content. To develop the GPP estimation algorithm, we focus on photosynthetic capacity because chlorophyll content can be detected by optical sensors. We hypothesized that the maximum rate of low-stress GPP (called \"GPP capacity\") is mainly dependent on the chlorophyll content that can be detected by a vegetation index (VI). The objective of this study was to select an appropriate VI with which to estimate global GPP capacity with the GCOM-C/SGLI. We analyzed reflectance data to select the VI that has the best linear correlation with chlorophyll content at the leaf scale and with GPP capacity at canopy and satellite scales. At the satellite scale, flux data of seven dominant plant functional types and reflectance data obtained by the Moderate-resolution Imaging Spectroradiometer (MODIS) were used because SGLI data were not available. The results indicated that the green chlorophyll index, \(\text{CI}_{\text{green}}(\text{P}_{\text{NIR}}/\text{p}_{\text{green}}\text{-}1)\), had a strong linear correlation with chlorophyll content at the leaf scale (R\({}^{2}\) = 0.87, p \(<\) 0.001) and with GPP capacity at the canopy (R\({}^{2}\) = 0.78, p \(<\) 0.001) and satellite scales (R\({}^{2}\) = 0.72, p \(<\) 0.01). Therefore, \(\text{CI}_{\text{green}}\) is a robust and suitable vegetation index for estimating global GPP capacity.  
**Keywords:** GCOM-C/SGLI; Vegetation index; Gross primary production; Photosynthesis; Chlorophyll content; Light-response curve; FluxNet
## 3 Results
### Performance Evaluation  
Regarding the evaluation metrics, we utilized those suggested in the DCASE challenge (DCASE, 2020). These metrics include Area Under the ROC Curve (AUC) and partial AUC (pAUC). The formulas for calculating AUC and pAUC are provided as Equations (11) and (12), respectively,  
\[AUC=\frac{1}{N_{-}N_{+}}{\sum_{i=1}^{N_{-}}{\sum_{j=1}^{N_{+}}{H\Big{(}A_{ \theta}\Big{(}x_{j}^{+}\Big{)}-A_{\theta}\Big{(}x_{i}^{-}\Big{)}}\Big{)}}} \tag{11}\]  
\[pAUC=\frac{1}{\lfloor pN_{-}\rfloor}\,\,{N_{+}}{\sum_{i=1}^{\lfloor pN_{-} \rfloor}\,\sum_{j=1}^{N_{+}}{H\Big{(}A_{\theta}\Big{(}x_{j}^{+}\Big{)}-A_{ \theta}\Big{(}x_{i}^{-}\Big{)}}\Big{)}} \tag{12}\]  
where \(H()\) returns 1 when \(x>0\) and 0 otherwisem and \(A_{\theta}\) denotes the model output given the parameters \(\theta\). \(x^{-}\) and \(x^{+}\) are normal and anomalous samples. \(N_{-}\) and \(N_{+}\) are the number of normal and anomalous test samples, respectively. Based on this formula, the anomaly score of the audio samples serves as the threshold for determining if a sample is normal or anomalous, as opposed to relying on the decision results. The purpose of utilizing pAUC, as explained in [2], is to enhance the True Positive Rate (TPR) while maintaining a low False Positive Rate (FPR).
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 2 Materials and Methods
### Datasets
The database cannot be distributed, but the recordings and metadata are available online (Metadata search [[https://bdb.bertsozale.eus/en/web/bertsoa/bilaketa](https://bdb.bertsozale.eus/en/web/bertsoa/bilaketa)]([https://bdb.bertsozale.eus/en/web/bertsoa/bilaketa](https://bdb.bertsozale.eus/en/web/bertsoa/bilaketa))) (Examples of signals contained in the Bertsolaritza database can be accessed at [[https://bdb.bertsozale.eus/en/web/bertsoa/view/14](https://bdb.bertsozale.eus/en/web/bertsoa/view/14) sdg]([https://bdb.bertsozale.eus/en/web/bertsoa/view/14](https://bdb.bertsozale.eus/en/web/bertsoa/view/14) sdg)). In the Bertsolaritza database, there are no mixed segments, i.e., all the frames in each segment identified by the VAD belong to the same class. As stated above, in the NUS database, each segment contains frames of only one class also. As expected, singing speech segments have longer durations than spoken speech segments in both databases (5.03 \(\pm\) 2.67 and 2.30 \(\pm\) 1.74 s, respectively, in the Bertsolaritza database and 3.83 \(\pm\) 2.21 and 1.92 \(\pm\) 1.06 s in the NUS database). The distributions of segment durations in both databases are shown in Figure 1,b. In the NUS database, the linguistic content is the same in singing and speech segments. On the contrary, in the Bertsolaritza database, linguistic content is different and both classes are heavily unbalanced. Nevertheless, both databases exhibit the similar behaviour with respect to duration distribution.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Fault-Tolerance  
In order to avoid the loss of significant data from nodes, fault-tolerant CHs are usually required. Re-clustering is considered the most intuitive method to recover from a cluster failure. However, re-clustering is difficult in CR-WSNs, especially if only the CH has dedicated hardware. Deploying multiple CHs for backup is a viable solution for CH failure.
## 6 Present Situation and Development Trend of Rockburst Early Warning  
### Suggestions on Early Warning of Rockbursts  
During the construction of the diversion tunnel of the Jinping II Hydropower Station, the rockburst disaster is prominent, and extremely strong rockbursts occur from time to time. During the construction period, there was no distinguishing between the microseismic index method and the mechanical model method for the early warning of rockbursts. In recent years, with the application of microseismic monitoring technology and the continuous development of support technology, there has been considerable progress in rockburst prevention and control. Taking the construction site of the Hanjiang-to-Weihe River Diversion Project as an example, most of the rockbursts in the TBM construction occurred before the completion of the support; however, there has rarely been any occurrence of the above moderate rockburst strong enough to destroy the supporting structure. To better ensure construction safety, rockburst early warning systems should adapt to the new changes. Existing data pertaining to the early warning of rockbursts from the Hanjiang-to-Weihe River Diversion Project show that it is necessary to distinguish between the microseismic index method and mechanical model method, and different methods should be adopted for different rockburst warning purposes.  
For a rockburst in the excavation unloading stage, based on the microseismicity near the working face, the early warning method based on microseismic indices should be adopted to predict the risk and level of the rockburst and form a daily report, so as to provide a basis for the adjustment of support measures and tunneling speed. For rockbursts that may occur after the completion of the support, based on the microseismicity behind the working face, the rockburst warning method based on a mechanical model should be adopted to evaluate the support effect and predict the potential rockburst area and rockburst probability. Once it is observed that the rockburst is approaching, an emergency warning should be issued in time to guide the construction personnel to avoid the risk.  
Figure 9: Microseismic events and energy characteristics during the measurement of stress relief boreholes.
# Conceptual Design of the Combinable Legged Robot Bio-Inspired by Ants' Structure
## 6 Discussion  
The innovation of the combinable multi-legged modular robot inspired by ants' structure is disclosed. The propose mechanisms are designed to have a total weight of 13.8 kg. This includes a rectangular prism base frame, four identical 3-DoF sprawling-type articulated leg structures, and a pair of link-hook systems. There are a total of 12 teams of actuators installed, each at every joint of the leg.  
To reveal the performance of the proposed robot in combining and walking ability, two experiments were carried accordingly. Referring to Section 5, the basic locomotion for  
Figure 11: IMU results during the walking test of the combined status for (**a**) Robot 1 and (**b**) Robot 2.  
both the single mode and combined status mode is examined through the interpretation of the IMU data.  
In order to obtain the orientation result of the proposed robots as the solid proof for them to be able to perform regular walking for both cases in the experiments, the IMU sensor was installed in the center of their base frames. Therefore, both the encoder input signal and IMU data were collected appropriately.  
As expected, both cases of the walking experiment showed that the robot was able to perform a stable walk. Checking all the IMU results obtained, they fluctuated between \(-10^{\circ}\) and \(15^{\circ}\), which would prevent any overturning of the robot base. Surprisingly, the combination and walking motion brought more stability as the number of legs in stance increased from three to six, as compared to the single walking test. The evidence of the orientation for Robot 1 and Robot 2 (Figure 11) showed a smaller oscillation for both the combined robot bases' pitch and roll angles compared to the single robot walking result (Figure 8). Although the oscillation angle along the roll axis of Robot 2 was a slightly higher than the single walking robot, the combined robot was still able to have stable walking performance without falling over. The coupling mechanism was capable of holding the base frame of Robot 2 close to Robot 1 and prevented the overturning of Robot 2.  
The capability of the coupling mechanisms to hold the two robots in combination with each other was proven through the walking experiment of the merged status. During the process of the combination, the gripper that mimics the ants' claws together with the use of the tensile spring provided a straightforward combination process. Additionally, with the assistance of this coupling mechanism, the two individual robots connected to each other and formed an eight legged robot to perform walking without any deformation occurring to the structure (Figure 6). From this, the capability of the hook-link system that mimics ant claws was validated.  
The overall result from this study achieved the objective of examining both the design and performance of the developed robot that was inspired by ants' structure. The proposed robot with this newly invented conceptual design successfully expands the physical capabilities of a conventional robot.
## 3 Results and Discussion
### Scenario Simulation
#### 3.2.2 Scenario of Reduced Water Level Fluctuation
When the fluctuation range of the water level is reduced by 5 cm, the fluctuation ranges of nitrite nitrogen, nitrate nitrogen and ammonium nitrogen in the coarse sand medium are reduced by 28.07%, 29.55%, and 22.62%, respectively. In the medium sand medium, the average fluctuation range is reduced by 13.00%, 27.23%, and 3.85%, respectively. In the fine sand model, the fluctuation range is reduced by an average of 1.51%, 18.79%, and 7.86%, respectively. Table 7 shows that among the three media, water level fluctuations have the most significant impact on the fluctuations of nitrate nitrogen, have less impact on the fluctuations of nitrite nitrogen and ammonium nitrogen, and the difference is the most obvious in the fine sand medium, followed by medium sand. This difference is not great in the coarse sand.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 4 Model
### Parameter Space Mapping
The parameter reconstruction process was implemented using the following techniques: _Linear Regression_ (LR), a process by which a linear function is used to estimate latent variables; _Natural Neighbour Interpolation_ (NaNI), a method for interpolating between scattered data points using Voronoi tessellation, as used by [14] for a similar application; _Nearest Neighbour Interpolation_ (NeNI), an interpolation method where the query point takes the value of the nearest neighbour [39]; _Linear Interpolation_ (LI), an interpolation technique that assumes a linear relationship between the existing points in a dataset; _Support Vector Regression_ (SVR), a non-linear kernel-based regression technique (see [40]), for which we choose a Gaussian kernel function. Network Topology  
The autoencoder was built using the Theano Python library [43], where we observed an error of 0.086 using a single hidden layer with \(N\) (in this case \(N=2\)) units. To reduce the error, a mirrored \([13-9-2]\) architecture was selected empirically, resulting in an error measurement of 0.08. To improve reconstruction accuracy further, noise was introduced at each stage in the network, as demonstrated by [44]. Here, the first autoencoder was corrupted with 0.6 magnitude noise, and the second with 0.5. This approach is able to further reduce the reconstruction error to 0.0784. Additionally, we replace the previously-used stochastic gradient descent algorithm with an _RMSprop_ method [45] with a batch size of 10 as the pre-training and fine-tuning methods of optimization, and a learning rate of 0.01 and 0.001, respectively. This approach allows for faster optimization, as shown in [46]. For the weighted parameters, we found that a three-layer denoising autoencoder with an architecture of \([13-9-6-2]\) and noise of magnitude \((0.5,0.4,0.3)\) is able to outperform our two-layer denoising autoencoder model.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Choice of Learning Function
As the value of the index approaches +1, the stronger the positive association between the variables in question becomes: \[\text{R}=\frac{\sum_{i=1}^{n}(y_{i}-\bar{y})(\hat{y}_{i}-\bar{y})}{\sqrt{\sum_{ i=1}^{n}(y_{i}-\bar{y})^{2}\sum_{i=1}^{n}(\hat{y}_{i}-\bar{y})^{2}}}\] (5)
* The Maximum Prediction Error (MaxPE) represents the largest discrepancy between the observed and predicted values. It serves as a metric to gauge the maximum error in predictions: \[\text{MaxPE}=\max_{i}|y_{i}-\hat{y}_{i}|\] (6) where: \[\bullet\quad y_{i}\text{ is the observed value;}\] \[\bullet\quad\hat{y}_{i}\text{ is the predicted value;}\] \[\bullet\quad\bar{y}\text{ and }\bar{y}\text{ are the mean of the observed and predicted data points, respectively;}\] \[\bullet\quad n\text{ is the number of data points.}\] Following our assessment, we have opted for the Conjugate Gradient with Powell/Beale Restarts learning function for our model owing to its performance, as evidenced by its recorded lowest MAE, MSE, RMSE, MaxPE, and higher R values. To gain a visual understanding of how the various learning functions perform based on the aforementioned metrics, please consult Figure 7 provided below.
## 2 Materials and Methods
### Water Sampling and the Microbiological Water Assessment  
Water samples for testing were collected within the summer season at four different locations, namely:  
1. River waters flowing into the lake (the Marusha River directly before entering the lake);
2. A bathing area on the northern shore of the lake;
3. A bathing area on the east side of the lake;
4. Waters flowing out of the lake as the river (the Rudniczanka River directly after leaving the lake).  
The sampling season was selected due to the highest number of people using the bathing areas at the time to better reflect the potential health risks associated with the possible presence and transmission of antibiotic-resistant bacteria.  
Water samples were taken into 250 cm\({}^{3}\) sterile containers, submerging the container 20 cm below the water surface at the time of collection. The samples were transported to the laboratory within 24 h in cooling conditions and subjected to microbiological tests. Microbiological water assessment covered the quantitative analysis of the total number of mesophilic bacteria (bacteria capable of growing in a temperature close to the temperature of the human's body), taking into account the number of bacteria resistant to selected antibiotics: ciprofloxacin and impenem. The bacteria were cultivated on nutrient agar medium (Biocorp Ltd., Warsaw, Poland) to determine the total bacterial count and on nutrient agar with the addition of 16 mg/L of impenem or 8 mg/L of ciprofloxacin (Lab Empire S.C., Rzeszow, Poland). The concentrations of antibiotics in the cultivation medium were determined to be significantly higher than the MIC values reported by EUCAST (2021) to ensure that only antibiotic-resistant strains would be able to grow. To determine the total number of mesophilic bacteria, 0.1 cm\({}^{3}\) of the tested water was spread on the surface of nutrient agar medium in Petri dishes and incubated at 37 \({}^{\circ}\)C for 48. After this time, all colonies capable of growing at 37 \({}^{\circ}\)C were counted and presented as colony forming units (CFU) counts in 1 cm\({}^{3}\) of the tested water. The resulting value was taken as the total number of mesophilic bacteria in the tested water. An analogous procedure was used to test bacteria resistant to the antibiotics tested. Samples of the water were spread on nutrient agar with ciprofloxacin and nutrient agar with impenem, respectively, and the subsequent incubation of bacteria for 48 h at 37 \({}^{\circ}\)C was applied. The counts of resistant bacteria were reported as the number of CFU in 1 cm\({}^{3}\) of water. The spread plate method was applied in triplicates. For the quantitative analysis, the average values and standard deviations were calculated. The percentages of impenem-resistant and ciprofloxacin-resistant bacteria in mesophilic bacteria were determined based on the average CFU counts.
## 3 Results
### Efficacy Assessment  
#### 3.3.1 Cell Viability Assay  
As demonstrated in Figure 5, the cell viability evaluation revealed that cells and tissues exposed to urban dust and UV irradiation exhibited a significant decline in viability, reaching approximately 40% reduction. The treatment of cells with the test sample resulted in a viability reduction of approximately 20%. These findings demonstrate that the test sample is able to preserve both HaCat cells (Figure 5a) and BJ cells (Figure 5b) from mortality induced by pollution and UV.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Sample**} & **\% Cell Viability (Mean Value)** & \multirow{2}{*}{**Results**} \\  & \(\pm\)**ST.DEV** & \\ \hline Negative Control & 100\% & \\ \hline Positive Control & & \\ (SDS 5\%) & 7.23\% \(\pm\) 3.24\% & Irritating \\ \hline Tested Product & & \\ (PLG–GRAPE) & 87.53\% \(\pm\) 4.33\% & Non-Irritating \\ \hline \end{tabular}
\end{table}
Table 2: Results of cell viability (%) measurement in the negative control, positive control, and tested product-treated tissues.  
Figure 4: Results of %RFI of CD86 and CD54 measured for each experimental group. Results are expressed as mean \(\pm\) st. dev, based on three replicates.
# A Phase Bias Compensation Method for Anti-Interference Antenna Arrays in RTK Positioning
## 3 Results
### The Performance of Anti-Interference Algorithm with Phase Bias Compensation  
Assume the incident signal from (\(30^{\circ}\), \(60^{\circ}\)). The RF is 1268.52 MHz, intermediate frequency (IF) is 46.52 MHz, and sampling frequency is 62 MHz. The carrier-to noise ratio (CNR) is 45 dB. A broadband interference signal is from (\(50^{\circ}\), \(90^{\circ}\)), and the interference-to-noise ratio is 75 dB.  
Figure 7: The phase bias distributions of SAP PI algorithm: (**a**) three-dimension; (**b**) two-dimension.  
The anti-interference algorithm with phase bias compensation creates a beam null in the direction of the interference, effectively suppressing the interference signals. However, it results in an approximately 10-12 dB attenuation of the useful signals, leading to a decrease in the output signal-to-noise ratio compared to the algorithm without bias compensation. Nevertheless, while the algorithm suppresses interference, it does so at the cost of signal-to-noise ratio loss without affecting the phase of the output signal, demonstrating the effectiveness of this algorithm.  
Figure 9: The phase bias distributions of STAP MVDR algorithm: (**a**) three-dimension; (**b**) two-dimension.
###### Abstract  
Presently, we are observing an explosion of data that need to be stored and processed over the Internet, and characterized by large volume, velocity and variety. For this reason, software developers have begun to look at NoSQL solutions for data storage. However, operations that are trivial in traditional Relational DataBase Management Systems (DBMSs) can become very complex in NoSQL DBMSs. This is the case of the join operation to establish a connection between two or more DB structures, whose construct is not explicitly available in many NoSQL databases. As a consequence, the data model has to be changed or a set of operations have to be performed to address particular queries on data. Thus, open questions are: how do NoSQL solutions work when they have to perform join operations on data that are not natively supported? What is the quality of NoSQL solutions in such cases? In this paper, we deal with such issues specifically considering one of the major NoSQL document oriented DB available on the market: MongoDB. In particular, we discuss an approach to perform join operations at application layer in MongoDB that allows us to preserve data models. We analyse performance of the proposes approach discussing the introduced overhead in comparison with SQL-like DBs.  
Keywords:future internet; big data; NoSQL; MongoDB; join +
Footnote †: journal:
## 5 Application to the Satellite Module Layout Problem  
In this section, the two-stage algorithm is applied to the multi-container SMLP. The upper and lower stages settings are first configured and their performances are shown and analyzed. The global results and performance are also shown and compared with existing results from the literature.