esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Acquisition of the Fetal Heart Rate and Uterine Contraction Data  
The ultrasonic Doppler autocorrelation FHR meter and guard-ring type strain-gauge octodynamometer were used for fetal monitoring in this study. The electrical output of FHR and contraction signals from the monitor is introduced into the analogue-to-digital (AD) converter of the analyzing computer at a sampling rate of 4 Hz. The data are then averaged to a rate of 0.5 Hz. This sampling rate was chosen as the optimal rate to achieve minimal recorded FHR variation, compared with the original FHR tracing. In this way, a data set consisting of 150 recordings are processed for FHR and contraction signals. Analysis time was 5 minutes in order to detect FHR changes in the earliest stages of the monitoring.  
The analyzing computer determines the FHR baseline in 5 minutes by averaging the FHR data in the step of the most frequent data among 20 FHR steps from 0 to 200 beats per minutes (bpm). The averaged data-to-data variation in FHR within this 5 minute period was added to the baseline to achieve a reference line for detection of transient FHR acceleration. Similarly, this data was subtracted from the baseline data to achieve a reference line for detection of transient FHR deceleration; FHR deceleration starts when the FHR data repeatedly falls below the reference line and ends when it reaches the reference line. Deceleration is defined as a FHR decrease of 20 bpm or more for 20 seconds or more. The last 5 minutes of an unfinished deceleration period is transferred to the first 5 minutes of the next period. A transient increase is defined as a FHR increase of 15 bpm or more for 15 or more seconds. The down-hill amplitude in FHR increases is defined as the long-term variability in the baseline, except for acceleration and deceleration. The uterine contraction is detected by using a similar algorithm. The area under the contraction curve is determined to assess uterine hyperactivity [9; 16; 17].  
The FHR baseline, long-term variability, deceleration, acceleration, and uterine contraction are detected as described above and the data are analyzed by the computer system [17; 23]. The 8 FHR parameters used for computer training and diagnosis are transformed into 16 ranks (0 to 15) and input into the trained NNW computer, where each of the 8 FHR parameters in the three succeeding 5 minutes periods are input into the input layers. Thus, the hybrid system of the NNW computer has the capacity to analyze a data set consisting of 24 FHR recording over 15 minutes instead of the entire FHR data set.
# Measuring Identification and Quantification Errors in Spectral CT Material Decomposition
###### Abstract  
Material decomposition methods are used to identify and quantify multiple tissue components in spectral CT but there is no published method to quantify the misidentification of materials. This paper describes a new method for assessing misidentification and mis-quantification in spectral CT. We scanned a phantom containing gadolinium (1, 2, 4, 8 mg/mL), hydroxyapatite (54.3, 211.7, 808.5 mg/mL), water and vegetable oil using a MARS spectral scanner equipped with a poly-energetic X-ray source operated at 118 kVp and a CdTe Medipix3 RX camera. Two imaging protocols were used; both with and without 0.375 mm external brass filter. A proprietary material decomposition method identified voxels as gadolinium, hydroxyapatite, lipid or water. Sensitivity and specificity information was used to evaluate material misidentification. Biological samples were also scanned. There were marked differences in identification and quantification between the two protocols even though spectral and linear correlation of gadolinium and hydroxyapatite in the reconstructed images was high and no qualitative segmentation differences in the material decomposed images were observed. At 8 mg/mL, gadolinium was correctly identified for both protocols, but concentration was underestimated by over half for the unfiltered protocol. At 1 mg/mL, gadolinium was misidentified in 38% of voxels for the filtered protocol and 58% of voxels for the unfiltered protocol. Hydroxyapatite was correctly identified at the two higher concentrations for both protocols, but mis-quantified for the unfiltered protocol. Gadolinium concentration as measured in the biological specimen showed a two-fold difference between protocols. In future, this methodology could be used to compare and optimize scanning protocols, image reconstruction methods, and methods for material differentiation in spectral CT.  
Keywords:computed tomography (CT); spectral CT; multi-energy CT; material decomposition; photon counting X-ray detectors (PCXD); medipix; material misidentification; data acquisition concepts +
Footnote †: journal: Applied
# An Accelerating Approach for Blockchain Information Transmission Based on NDN
## 6 Conclusions  
Due to the architectural characteristics of the blockchain, when running blockchain programs on the traditional network, it will generate great broadcast overhead and transmission delay, which has a great impact on the subsequent development of the blockchain-based applications. In this paper, we mainly focused on deploying the blockchain-based applications on NDN and realizing the acceleration of information transmission. In order to speed up the information transmission speed of the blockchain system and reduce data overhead, we have selected some more important nodes in the network as secondary nodes, giving them more bandwidth and cache to speed up the transmission of information. We present a method to calculate the number of secondary nodes, and give the function to calculate the importance of each node, these methods are used to select appropriate nodes as secondary nodes in the network. We designed multiple simulation schemes to verify the performance of the AITS strategy under the NDN network. The simulation results show that in complex networks, our AITS method has superior performance in accelerating information transmission and reducing data overhead. In addition, the more secondary nodes in the network, the better the performance of the AITS strategy.  
In the future, we focus on the information acceleration transmission strategies in more complex blockchain scenarios, such as data transmission services between multiple blockchains.  
Conceptualization: Z.-P.Y. and L.H.; methodology: N.-J.G. and Z.-P.Y.; software: L.H. and R.H.; validation: [PERSON] and T.H.; formal analysis: N.-J.G.; investigation: Z.-P.Y.; data curation: L.H.; writing--original draft preparation: Z.-P.Y. and N.-J.G.; writing--review and editing: [PERSON]; supervision: [PERSON]; project administration: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The MIIT of China 2020 (Identification Resources Search System for Industrial Internet of Things) and The MIIT of China 2019 (Innovative Identification and Resolution System for Industrial Internet of Things).  
Not Applicable, the study does not report any data.  
The authors declare no conflict of interest.
###### Abstract  
This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (R\({}_{\text{pre}}\geq\) 0.93) and low standard error of prediction (SEP \(\leq\) 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.  
FT-NIR; PLSR; sugar content; nutrition facts label; breakfast cereal; handheld sensor +
Footnote †: journal: Applied Sci. 2020, 10, 8774; doi:10.3390/app10248774
## 2 Materials and Methods
### Experimental Materials and Nitrogen Treatments  
Three-year-old saplings with similar growth status were grown in pots, each with a volume of 14.9 L (26.0 cm height, 26.0 cm bottom diameter, 28 cm top diameter) and containing 14 L of soil used in the experiment. The initial height and stem base diameter of the saplings were 71.38 \(\pm\) 3.01 cm and 12.63 \(\pm\) 0.20 mm, respectively. The carbon (C) and nitrogen (N) of the soil were 1.19 \(\pm\) 0.30 and 0.12 \(\pm\) 0.03 g\(\cdot\)kg\({}^{-1}\) and C/N ratio being 8.79 \(\pm\) 0.43, available phosphorus being 0.47 \(\pm\) 0.04 g\(\cdot\)kg\({}^{-1}\), containing poor biological productivity. Based on the average nitrogen deposition in the area, we set four nitrogen addition levels, control (CK), 23(LN), 46(MN), and 69 kgN\(\cdot\)hm\({}^{-2}\cdot\)a\({}^{-1}\) (HN), which were applied as the nitrogen treatments. Each treatment had three replicates and were placed in chambers in rotation every ten days. Owing to the main nitrogen sources for plants in nature are ammonium and nitrate nitrogen, an NH\({}_{4}\)NO\({}_{3}\) solution was used to simulate nitrogen deposition [31]. In addition, the impact of atmospheric nitrogen deposition on the ecosystem is a slow and long-term accumulation process, one-time or only several times nitrogen addition may overestimate the effect of nitrogen [32]. Therefore, ammonium nitrate solutions were sprayed ten times into the pots, with a time interval of about ten days, with 100 mL per pot from June to September in 2019. Control trees were sprayed with the same volume of nitrogen-free water. All plots were in the chambers and exposed to the same temperature, light, precipitation, and other environmental factors. The top windows of chamber were closed, only with two side windows open, so the plants were hardly affected by atmospheric nitrogen deposition. Saplings were watered and weeded regularly during the growing seasons so that soil water content remained relatively high and the seedlings were not water limited.
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 4 Evaluation
### Radar Data  
The annotations for static and moving road users are represented by three-dimensional bounding boxes. In our work, we exclusively use 4D radar data. As mentioned in Section 3.1, the radar outputs a point cloud of shape \(N\times 6\) for each scan, where \(N\) represents the number of points and 6 refers to the recorded information per point. Each point in the dataset can be described by a set of parameters, denoted as \(p=[x,y,z,v_{r},v_{rc},RCS]\), where \((x,y,z)\) represent the spatial coordinates, \(v_{r}\) denotes the Doppler velocity, \(v_{rc}\) corresponds to the radial velocity compensated with proper motion, and \(RCS\) refers to the radar cross-section. The VoD dataset allows for the use of radar data accumulated over three or five scans.  
The VoD dataset annotates 13 classes across 8693 frames. Each object within the FOV of the camera and within 50 m of the recording vehicle is labeled with a bounding box that includes six degrees of freedom. In total, the VoD dataset contains 26,587 _pedestrian_, 10,800 _cyclist_, and 26,949 _car_ labels.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 2 Proposed Tunnel-Free DMM Support Protocol  
This section introduces a novel tunnel-free DMM support protocol. It removes the MIPv6 limitations by making use of the current advancements in mobile network architectures.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 3 Methods
### Feature Sets
#### 3.2.2 Coherence Features  
Coherence is a correlation coefficient that explains that small changes in the surface (vegetation, non-vegetation, rock, etc.) occurring during the time interval between two SAR acquisitions [21]. As the coherence size determines whether the corresponding pixel changes, the coherence coefficient ranges from 0 to 1. A large coherence coefficient indicates that the change is small, whereas a small coherence coefficient indicates that the change is large. In this paper, the coherence of the SAR images was calculated using SARscape 5.2 software and 10 coherence images were obtained using the following InSAR data pairs: (1) 22 November 2015 and 9 January 2016; (2) 9 January 2016 and 26 February 2016; (3) 26 February 2016 and 21 March 2016; (4) 21 March 2016 and 14 April 2016; and (5) 14 April 2016 and 8 May 2016.
## 4 Mechanical Design and Thermal Modelling of 6U CubeSat
### Geometrical and Thermal Mathematical Model  
The Geometrical Mathematical Model (GMM) is a mathematical representation of physical surfaces of the components. It is used to calculate radiation coupling between surfaces as well as heating rates due to environmental heat fluxes. The radiation interchange couplings and environmental heat fluxes calculated by the GMM are used as inputs to the Thermal Mathematical Model (TMM). The TMM consists of a resistor-capacitor network of the spacecraft to calculate heat flows and temperatures.  
A thermal model of the 6U satellite was developed using the Thermal Desktop (TD) tool, which performs both the GMM and TMM components of the calculation. All the satellite components such as outer metal plates of the satellite, internal components, payload, and avionics are modelled as solid blocks and solar panels are modelled as 2D surfaces to reduce computational time. Moreover, the variation in temperature across the thickness is insignificant. However, the mass of each elements in both satellite configurations are kept the same as provided in Table 4. In the process of developing thermal mathematical model, thermal couplings were introduced to simulate the physical contact between various satellite components.  
Convergence studies with different finite elements were carried out on the thermal model and confirmed that the change in temperature (\(\Delta T\)) is negligible(\(<\)1%). Thermal models for these satellites are built such that it reflects all the physical connections, thermal behaviour, and to have the same actual mass. The 6U satellite thermal model has a mass of 9 kg.  
Thermal contact conductance (TCC) properties are generally empirically derived. However, a table of standard values for bolted interfaces from the Spacecraft Thermal Control Handbook are used in this study. All the major contacts with TCC are listed in Table 6 [11].  
Figure 13: Beta angle variation—STK.
# Correlation between Biogenic Amines and Their Precursors in Stored Chicken Meat
## 4 Discussion  
The main objective of this study was to determine the development of changes in BA levels and their precursor amino acids in chicken breast and leg muscles during cold storage. Additionally, the study sought to understand the correlation between BAs and AAs. With the exception of spermine and spermidine, BAs increased as the storage time lengthened. Similar results were also obtained by other authors [9; 10; 11; 12]. This is due to progressive proteolysis and increasing levels of free amino acids, which act as precursors for individual BA formation. While correlations between individual precursor amino acids and BAs have not been studied extensively, [PERSON] et al. identified a trend in chicken meat, pointing to a correlation between BA and AA levels [11]. In their research on pickled vegetables, [PERSON] et al. demonstrated that elevated AA levels influenced the formation of BAs in a BA-specific manner [4]. The results of this study suggest a relationship between BAs and their precursor amino acids. It is possible that the mechanisms dictating the formation of specific BAs, in the context of changes in individual amino acid levels, might vary. Leg muscles, which have a higher fat content, a lower protein level, and a slightly varied amino acid composition compared to breast muscles, might influence the synthesis of individual BAs. The authors showed that BA levels in leg muscles were markedly higher than in breast muscles. The different chemical and amino acid compositions, as well as different rates of muscle processes, lead to different correlation values between the AA precursor-BA pairs [13; 14]. What is also possibly related to the different activity of the breast and leg muscles during the life of the birds, as well as the different structure of the muscle fibers, are the proteolytic processes taking place in them. In the future, it will be important to find out what factors influence the different rate of BA formation, which not only lowers the meat product itself but also the biological value of the meat protein.
# Bisector-Based Tracking of In Plane Subpixel Translations and Rotations
## 3 Methods
\[MSE_{x_{d},y_{d},\Phi}\ =\frac{1}{N_{1}+N_{2}}\sum_{k=1}^{N_{1}+N_{2}}\left( \mathbf{Y}^{\prime}_{k}-m^{\prime}\mathbf{X}^{\prime}_{k}\right)^{2}, \tag{6}\]  
where \(\mathbf{X}^{\prime}_{k}=\left\{\mathbf{X}^{\prime}_{1,j},\mathbf{X}^{\prime} _{2,j}\right\}\); \(\mathbf{Y}^{\prime}_{k}=\left\{-\mathbf{Y}^{\prime}_{1,j},\mathbf{Y}^{\prime} _{2,j}\right\}\) with \(k=1,\ldots,N_{1}+N_{2}\). At this point, we would like to underline that there is no preference in the choosing of the segment that is reflected or in the choosing of the bisector with respect to that segment is reflected. In fact, the choice is irrelevant when we track a moving object in a video sequence, provided that we do not change the choice during the process. Selecting any option only implies that we obtain the orientation of the bisector at the angle \(\beta\) or at the complementary one. Both cases provide the same information, so from here onward and for simplicity, we only consider the angle \(\beta\). We use the Nelder-Mead simplex algorithm [15] in order to minimize (6) and obtain the optimum parameters \((x_{c},y_{c},\)\(\beta)\). Basically, the algorithm repeatedly modifies the parameters of the transformation from an initial guess until it meets a stopping criterion. In our case, we made a blind search with no initial guess, i.e., \((x_{d},y_{d},\)\(\phi)=(0,\)\(0,\)\(0)\), and established a termination tolerance on the parameters of \(10^{-8}\) and a maximum number of function evaluations allowed of \(10^{8}\). An issue we must not forget is that the object is supposed to be in movement, so the defined masks should follow that movement in order to avoid a case where the segments fall out of them in any frame. What is done in order to not lose the object and to accelerate the calculation is to recalculate the coordinates of the masks every third frame. They are successively transformed according to the relative movement of the two previous frames. This way, we assume that the location variation frame to frame is similar during three frames of the sequence. In the case of a sudden steep change of movement, the segments would fall out of the masks, and the tracking would fail. This may be solved by increasing the frame rate of the camera in order to avoid a change from frame to frame that is sudden and steep. The algorithm finally provides \((x_{c},y_{c},\)\(\beta)_{t}\), being \(t=1,\ldots,T\), so relative translations and rotations between previous frames are:  
\[\Delta(x_{c},y_{c},\)\(\beta)_{t}=(x_{c},y_{c},\)\(\beta)_{t-1}-(x_{c},y_{c},\)\(\beta)_{t-2}. \tag{7}\]
## 3 Results
### Antibiofilm Activity of Levan  
The inhibition of biofilm formation by levan was investigated quantitatively by crystal violet assay with different concentrations (0.125-2 mg mL\({}^{-1}\)) against the pathogenic biofilm forming _P. aeruginosa_ strain MW846272. The obtained results revealed that the rate of biofilm formation was significantly (\(p<0.0001\)) decreased with increasing levan concentration, as shown in Figure 5. This was shown when using concentrations 2, 1, 0.5, 0.25, and 0.125 mg mL\({}^{-1}\), with the inhibition percentage found to be 50%, 29.4%, 29.4%, 26.5%, and 14.7%, respectively.  
Figure 4: 13C NMR spectrum of levan from _Bacillus subtilis_ strain MZ292983.1. The recorded spectrum was at 100 MHz for the sample in D\({}_{2}\)O.  
Figure 5: (**A**) Inhibition of _P. aeruginosa_ biofilm formation using levan polymer and (**B**) antibacterial activity of levan against _S. aureus_ (ATCC 33592).
## 2 Data Collection and Preprocessing
### Metrics and Validation
The chosen stratified variant of cross-validation allows the set to be partitioned in such a way that in each of the k subsets the distribution of classes is similar. This allows the class distribution to be more accurately reflected during testing in cases where the original dataset is class imbalanced. The principle of data splitting based on used 5-fold cross-validation is illustrated in Figure 3. Preprocessing also included encoding class labels associated with system faults and normal operation. In addition, the data has been standardized to ensure the correct operation of machine learning algorithms.