esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation Criteria  
The whole set of pixels of the MRI record with index \(i\) is \(\Omega_{i}\), which is separated by the ground truth into two disjoint sets: \(\Gamma_{i}^{(\pi)}\) and \(\Gamma_{i}^{(v)}\), the set of positive and negative pixels, respectively. The final segmentation result provides another separation of \(\Omega_{i}\) into two disjoint sets denoted by \(\Lambda_{i}^{(\pi)}\) and \(\Lambda_{i}^{(v)}\), which represent the positive and negative pixels, respectively, according to the final decision (labeling) of the proposed procedure. Statistical accuracy indicators reflect in different ways how much the subsets \(\Gamma_{i}^{(\pi)}\) and \(\Lambda_{i}^{(\pi)}\) and their complementary subsets \(\Gamma_{i}^{(v)}\) and \(\Lambda_{i}^{(v)}\) overlap. The most important accuracy markers obtained for any record with index \(i\) (\(i=1\ldots n_{\rho}\)) are:  
1. Dice similarity coefficient (DSC) or Dice score: \[\text{DSC}_{i}=\frac{2\times|\Gamma_{i}^{(\pi)}\cap\Lambda_{i}^{(\pi)}|}{| \Gamma_{i}^{(\pi)}|+|\Lambda_{i}^{(\pi)}|}\,\] (15)Sensitivity or true positive rate (TPR): \[\text{TPR}_{i}=\frac{|\Gamma_{i}^{(\pi)}\cap\Lambda_{i}^{(\pi)}|}{|\Gamma_{i}^{ (\pi)}|}\enspace,\] (16) 3. Specificity or true negative rate (TNR): \[\text{TNR}_{i}=\frac{|\Gamma_{i}^{(\
u)}\cap\Lambda_{i}^{(\
u)}|}{|\Gamma_{i}^ {(\
u)}|}\enspace,\] (17) 4. Positive predictive value: \[\text{PPV}_{i}=\frac{|\Gamma_{i}^{(\pi)}\cap\Lambda_{i}^{(\pi)}|}{|\Lambda_{i} ^{(\pi)}|}\enspace,\] (18) 5. Accuracy or rate of correct decisions (ACC): \[\text{ACC}_{i}=\frac{|\Gamma_{i}^{(\pi)}\cap\Lambda_{i}^{(\pi)}|+|\Gamma_{i}^ {(\
u)}\cap\Lambda_{i}^{(\
u)}|}{|\Gamma_{i}^{(\pi)}|+|\Gamma_{i}^{(\
u)}|}\enspace.\] (19)  
To characterize the global accuracy, we may compute the average values of the above-defined indicators over a whole set of MRI records. We denote them by \(\overline{\text{DSC}}\), \(\overline{\text{TPR}}\), \(\overline{\text{TNR}}\), \(\overline{\text{PPV}}\), and \(\overline{\text{ACC}}\). The average Dice similarity coefficient is given by the formula:  
\[\overline{\text{DSC}}=\frac{1}{n_{\rho}}\sum_{i=1}^{n_{\rho}}\text{DSC}_{i}\enspace. \tag{20}\]  
The average values of other accuracy indicators are computed with analogous formulas.  
The overall Dice similarity coefficient (\(\widetilde{\text{DSC}}\)) is a single Dice score extracted from the set of all pixels from \(\Omega\), given by the formula:  
\[\widetilde{\text{DSC}}=\frac{2\times\left|\bigcup\limits_{i=1}^{n_{\rho}} \Gamma_{i}^{(\pi)}\cap\bigcup\limits_{i=1}^{n_{\rho}}\Lambda_{i}^{(\pi)} \right|}{\left|\bigcup\limits_{i=1}^{n_{\rho}}\Gamma_{i}^{(\pi)}\right|+\left| \bigcup\limits_{i=1}^{n_{\rho}}\Lambda_{i}^{(\pi)}\right|}\enspace. \tag{21}\]  
All accuracy indicators are defined in the \([0,1]\) interval. Perfect segmentation sets all indicators to the maximum value of 1.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 4 Simulation Results and Analysis  
### Path Planning with Safe Distance  
We define an experimental environment on a map with 400 \(\times\) 400 pixels. The black shapes are obstacles, and the white regions are free space. We set the start node at the upper left of the map and the goal node at the lower right of the map. In addition, we set the safe distance as 10 pixels. We assume that the USV only moves one pixel each time. Figure 7(a) shows the path planned by the original D* Lite. We find that the planned path is very close to the obstacles. Figure 7(b) shows a path planned with a safe distance based on Algorithm 1. It shows that there is at least a 10-pixel distance between obstacles and the path. Thus, we improve the safety for the USV to sail through obstacles.  
Figure 8: Path planned on 400 \(\times\) 400 map.  
Figure 7: Procedure of multi-goal path planning in unknown environments.
# Comparative Study of Fresh and Frozen Broiler Neck Skin Sampled for Process Hygiene Purposes
## 1 Introduction
This was proven by several authors for different foods, such as milk, deep-frozen poultry meat, pork, rustic crossbred beef, frozen vegetables, and other food products [21; 23; 24; 25; 26; 27; 28]. However, after thawing, bacteria can multiply again [24], and even after storage at low temperatures around \(-80\)\({}^{\circ}\)C, recovery rates of 98% were reported for cultures of Gram-negative and Gram-positive bacteria [29]. This phenomenon of growing after a freezing period is used for the storage of pure bacteria cultures in laboratories. For this purpose, storage at \(-80\)\({}^{\circ}\)C is usually used. The bacterial isolates are added to media that contain glycerol or other cryoprotective supplements such as dimethyl sulfoxide (DMSO). This procedure allows storage for up to ten years at \(-80\)\({}^{\circ}\)C without relevant negative consequences [30; 31]. Several authors investigated the effect of storing foods at different freezing temperatures on the bacteria present. They concluded that there was no change in the total bacterial counts in various foods, including vegetables, milk, raw sausages, or poultry, when foods were stored frozen for a few days or up to over twelve months at temperatures from \(-18\) to \(-25\)\({}^{\circ}\)C (mainly storage at \(-20\)\({}^{\circ}\)C was examined) [24; 25; 26; 23; 33]. For high-fat foods stored at \(-20\)\({}^{\circ}\)C for three days, [PERSON] et al. (2008) [34] could not find significant changes in microbial counts. They concluded that this practice, as allowed in special cases in DIN EN ISO 7218:2014-02 (Microbiology of food and animal feeding stuffs--General requirements and guidance for microbiological examinations) [35], can be used for the analysis of food samples. Our study deals with the question of whether frozen then thawed samples of broiler neck skin are as suitable for hygienic assessment as fresh samples of broiler neck skin for process controls using TVC or Enterobacteriaceaeaee counts. More precisely, is there a difference in the bacterial loads between fresh and frozen/thawed broiler neck skin samples, for those bacteria that represent the microbial status of the samples? To answer this question, we compared the TVC and EC of broiler neck skin samples before and after a freezing period of eight weeks. Such information may be used in scientific works to evaluate the microbial status of skin samples of broiler carcasses.
# A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams
## 2 Preliminaries  
### Hot and Cold Spots in Spatial Analysis  
A hot or cold spot in spatial analysis is an area in the research area where a high/low concentration of events defining a particular phenomenon is focused [38].  
A technique used to identify these areas is the detection of hot/cold spots. Each event is geometrically represented as a point on the map when a clustering algorithm is used to detect hot and cool regions; the set of events forms the input data points for the clustering algorithm; the resultant cluster prototypes are the hot/cold spots painted as polygons on the map.  
For the benefit of the algorithm's faster calculation, it is not always required to identify the precise geometry of hot spots in spatial analysis issues. For example, in [19; 28], cluster prototypes are represented by points on the map, and hot spots are made up of areas containing these points. In [34; 36], EFCM is executed to detect hot spots as circular areas.  
Additionally, to examine how the phenomenon has changed through time, it is necessary to determine how it evolves and where it persists over a period of time. In [36], the dataset is partitioned into subsets; each subset contains data points corresponding to the events that occurred in a given period. To evaluate the temporal evolution of the hotspots that identify disease strains, EFCM is run for each subgroup. In [35], a hot spot detection method based on a fuzzy entropy-based variation of EFCM is executed to analyze the temporal evolution of the intensity of criminal events in each urban agglomeration of the City of London. An urban agglomeration whose extension is significantly covered by clusters detected in several consecutive time frames represents a hot spot for the criminal phenomenon analyzed.  
The definition of a hot or a cold spot can be generalized to an area in which a high or low frequency of events is detected for the period of time analyzed.  
Let the study area be partitioned into n atomic subzones s\({}_{1}\), s\({}_{2}\),..., s\({}_{\text{n}}\). Suppose we want to analyze the evolution of the intensity of a phenomenon in a period of time broken down into T time frames having equal duration. The ith subzone can be labeled as a hot/cold spot if the frequency of events occurring in ith is greater or less than a specified threshold.
# Control of _Francisella tulareis_ Virulence at Gene Level: Network of Transcription Factors
## 3 Regulators Participating in the Oxidative and Nitrosative Stress Responses
### The RNA-Binding Protein Hfq  
The Hfq protein was found to be a necessary host factor required for the synthesis of bacteriophage Q\(\beta\) RNA in _E. coli_[84]. Hfq is not an ordinary transcription factor due to its preferences to bind RNA rather than DNA. Nevertheless, Hfq was identified as a bacterial nucleoid-associated protein, suggesting it has a role as a transcription factor [85]. An ability to directly bind DNA by its C-terminal part was demonstrated in _E. coli_[86]. Hfq is present in the nuclear, cytoplasmic, and membrane fractions in _E. coli_[87], however, its amount varies among the fractions. The largest amounts of Hfq are found in the cytoplasmic and membrane fractions with 30% and 50%, respectively, whereas only 10-20% is present in the nuclear fraction [87]. Its presence close to the DNA chain was also confirmed by electron microscopy [87]. It was suggested that Hfq is a sequence non-specific DNA-binding protein and that the number of binding sites increases with higher concentrations of Hfq [85; 86], although a putative Hfq-binding motif has been proposed [86]. Nevertheless, Hfq is predominantly referred to as an RNA-binding protein and plays a key role in the pathogenesis of virulent bacteria such as _Yersinia pseudotuberculosiss_[88], _Neisseria meningitidis_[89], and _Salmonella enterica_[90]. Hfq is also known for its role in promoting sRNA-mRNA interactions that could be important for the bacterial response to various environment conditions (iron limitation, oxidative stress, anaerobic conditions, glucose starvation) [91]. In support of this hypothesis, Hfq interacts with the sRNA of DsrA (dissimilatory sulfite reductase subunit A) and mRNA of RpoS (RNA polymerase sigma factor), thereby participating in the regulation of the stress response factor RpoS in _E. coli_[92].  
The Hfq protein is also indispensable in Francisella. Hfq is important for stress tolerance and full virulence in _F. tulareis_ subsp. _holarctica_, where it plays a role as a pleiotropic regulator [43] under control of sigma factor 70. It regulates the expression of a multitude of genes, including the FPI genes. Interestingly, Hfq regulates only one (_pdp_) of the two FPI operons (_pdp_ and _igl_). Hfq acts, in most cases, as a negative regulator, as suggested by a study at the transcriptomic level in _F. tulareis_. The expression of genes from the _pdp_ operon was upregulated at the transcriptomic level in a _Alfq_ mutant strain, suggesting Hfq is their negative regulator in the wild-type context [43]. A proteomic study revealed decreased levels of the FPI proteins due to an _liftq_ gene deletion, which explains why an _liftq_ mutant is attenuated [44]. The importance of Hfq in stress tolerance was observed also in _F. novicida_[45].
## 4 ANN-Based Prediction Model Development and Validation
### Prediction Model Validation  
The most important value of an ANN model is its generalization capability--the ability to forecast the unknown test data. Once the model is developed, it is mandatory to analyze and validate its performance rigorously by exposing it to the data present both in the input and test datasets. Model validation judges the generalization capability of the model for completely new data. In this work, model performance was validated by calculating the loss function. Predicted values \((\hat{Y_{i}})\) and actual output values \((\hat{Y_{i}})\) in the input and test datasets are compared to calculate the loss function. The minimum loss function is the maximum robustness and reliability of the model. The _Root Mean Square Error (RMSE)_ has been used as the loss function.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}-\hat{Y_{i}}\right)} \tag{5}\]
## 2 Materials and Methods
### Chromatographic Analysis of C. sativa Leaf: LC-PDA and RP-LC-QTOF-MS
#### 2.2.5 Cell Cytotoxicity and Anticancer Activity of C. _sativa_ Leaf Extract  
Cytotoxicity and anticancer activity were determined by analyzing the impact of C. _sativa_ leaf extract on the metabolic activity of three different human carcinoma cell lines, namely gastric adenocarcinoma (AGS, ATCC(r) CRL-1739), lung carcinoma (A549, ATCC(r) CCL-185), and skin melanoma (A375, ATCC(r) CRL-1619). Additionally, two healthy cell lines were employed, i.e., Vero (ATCC(r) CCL-81) and human skin fibroblasts (HFF, ATCC(r) SCRC-1041).  
Cell viability was determined via a MTT metabolic activity assay (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolzolium Bromide)) following the manufacturer's instructions. For this, cells (7000-10,000 cells/well depending on the cell line) were seeded on 96-well microplates with supplemented culture medium (10% FBS) and then incubated at 37 degC, in a 5% CO2' and humidified atmosphere (humidity above 90%) for 24 h. Next, the culture medium was extracted and replaced by a non-supplemented medium containing the C. _sativin_ leaf extract at concentrations ranging from 0.05 to 0.0004 mg/mL (serial dilutions were performed). Cells were incubated at 37 degC, in a 5% CO2 and humidified atmosphere for 24 and 72 h. After the incubation time, 10 mL of the MTT reagent (5 mg/mL) was added to each well, and the microplates were incubated for 2 h under the same conditions. Finally, supernatants were extracted and replaced by 100 mL of DMSO to dissolve formazan crystals. Absorbance was recorded at 595 nm in a microplate reader (Multiskan(tm) FC Microplate Photometer, ThermoFisher Scientific, Waltham, MA, USA).  
Cell viability was calculated using the following equation:  
\[Cell viability(\%)=100*\frac{Abs(C-)-Abs(sample)}{Abs(C-)}\]  
where _Abs_ (\(C-\)) corresponds to the absorbance of the negative control (non-supplemented medium) at 595 nm and _Abs_ (_sample_) corresponds to the absorbance of the sample at 595 nm. In addition, _Cytotoxicity_ (%) was calculated as 100 \(-\)_Cell viability_ (%).
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 4 Linearized Cadzow Iterations in the Signal-Estimation Problem
### Limit Property of the Composition of Projections
Simulations have shown that the control of matrix conditionality by the parameter \(\alpha\) affects the convergence rate of the Cadzow method and the estimation accuracy. Decreasing \(\alpha\) increases the set on which the matrix \(\mathbf{R}\) is matched; this allows us to obtain a weight matrix with smaller values of \(\rho(\mathbf{W},\mathbf{L}\ast\mathbf{R})\) and leads to improved accuracy of the signal estimate. However, decreasing \(\alpha\) also results in slower convergence of the iterations, which requires a theoretical explanation. A general theoretical dependence on \(\alpha\) could not be obtained, so we demonstrate the convergence rate when \(\alpha\) tends to zero. We know from Theorem 5 that the convergence rate of the methods is affected by the \((2r+1)\)-th eigenvalues of the matrix \(\mathbf{P}_{\mathbf{S}_{0},(\mathbf{L},\mathbf{R})}\). From Remark 2, we know that this eigenvalue is equal to the squared cosine of the \((2r+1)\)-th angle between the corresponding subspaces. The calculation of the cosine of the \((2r+1)\)-th principal angle between \(\mathcal{H}\) and \(\mathcal{N}(\mathbf{S}_{0})\) can be reduced to finding the cosine between the orthogonal (in the inner product \(\langle\cdot\cdot\rangle_{\mathbf{L},\mathbf{R}}\), given in (4)) complements to \(\mathcal{H}\cap\mathcal{N}(\mathbf{S}_{0})\) in \(\mathcal{H}\) and \(\mathcal{N}(\mathbf{S}_{0})\), respectively. Let us denote these complements \(\widehat{\mathcal{H}}(\mathbf{L},\mathbf{R})\) and \(\widehat{\mathcal{N}}(\mathbf{S}_{0},\mathbf{L},\mathbf{R})\). Note that by Theorem 3, \(\mathcal{H}\cap\mathcal{N}(\mathbf{S}_{0})=\mathcal{T}_{L}(\mathcal{Z}( \mathbf{a}_{0}^{2}))\), and its rank equals \(2r\). For brevity, we will omit \(\mathbf{S}_{0}\) in \(\widehat{\mathcal{N}}(\mathbf{S}_{0},\mathbf{L},\mathbf{R})\) and \(\mathcal{N}(\mathbf{S}_{0})\). The properties of the principal angles concerning Remark 1 lead directly to the following statement. Proposition 3: _The cosine of the \((2r+1)\)-th principal angle between the subspaces \(\mathcal{H}\) and \(\mathcal{N}\) is equal to \(\cos(\widehat{\mathcal{H}}(\mathbf{L},\mathbf{R}),\widehat{\mathcal{N}}( \mathbf{L},\mathbf{R}))\)._
## 3 Experiment and Results
### THz-TDS Setup  
All of the experimental investigations were performed with a THz-TDS system (Menlo TeraSmart, Martinsried, Germany). The centered wavelength of the femtosecond laser that was used was 780 nm and the repetition rate was 100 MHz. The frequency resolution of the THz-TDS system was 1.2 GHz with a signal to noise ratio (SNR) of about 80 dB. The beam was guided between the transmitter and detector by off-axis parabolic mirrors.
## 2 Materials and Methods
### Proposed Improved MDDS Method
#### 2.2.1 Improved MDDS  
We designed a Simulink block diagram to simulate chirp pulse generation and for the pre-configuration of the FPGA. Figure 2 illustrates the block diagram for the MDDS, including a reference clock for FPGA operation, trigger, reset, and chirp waveform generation blocks. We added the delay blocks of 0, 1, 2, and 3 to multiplex four different 1/4-length signals.  
The proposed improved MDDS method has four ports for sine and four ports for cosine. Sine and cosine waveform in the Simulink represent 12 bit resolution as follows:  
\[sin(t)=\left[\left(round\left[2^{11}\left(-sin\frac{2\pi(0:2^{12}-1)}{2^{12}} \right)\right]\right)\right]+2048, \tag{5}\]  
\[cos(t)=\left[\left(round\left[2^{11}\left(-\cos\frac{2\pi(0:2^{12}-1)}{2^{12}} \right)\right]\right)\right]+2048. \tag{6}\]  
The four signals are mixed (one signal per clock) by the multiplexer in the hardware and converted to an analog signal. Therefore, it is possible to output a four-times-faster signal in one clock. This method has the advantages of decreasing the load on the FPGA and reducing hardware costs.
# Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
## 3 The Effect of Seismic Anisotropy on Seismic Data
On the other hand, our study estimates \(Z_{P}\) and \(Z_{S}\) based on statistical modeling and MLFN rather than calculating them empirically. The advantage of ML algorithms is that they reduce the random error of partial-stack inversion and result in accurate impedances that best match the logs. Another contribution is that the facies model uses logistic regression to model lithologies and fluids and then combines them using the decision tree algorithm. The integration of two ML algorithms adds stability to the model and raises the chance of being applied to other fields. Moreover, the model includes the depth variable, which acts as a trend factor that highly affects the facies distribution.
## 2 Experimental Data  
The experimental data for this study were recorded at a meteorological weather station located on the roof of the Higher Polytechnic School building of Burgos University (42\({}^{\circ}\)21\({}^{\prime}\)04\({}^{\prime\prime}\) N, 3\({}^{\circ}\)41\({}^{\prime}\)20\({}^{\prime\prime}\) W, 856 m above mean sea level). The flat roof of this five-story building, in an area with no other buildings of comparable height, is ideal for recording measurements with no external obstructions or reflections from other surfaces. The experimental equipment is shown in Figure 1.  
The daylight measurements included the global, diffuse, and beam outdoor illuminance on a horizontal plane; the vertical global components facing the four cardinal orientations (north, east, south, and west); and the luminance distributions for the whole sky, recorded with seven lumeters model ML-020S-O luxmeters, and a commercial MS-321 LR sky scanner both from EKO Instruments (EKO Instruments Europe B.V. Den Haag, Netherlands). The diffuse luxmeter was obscured from direct sunlight by a shadow hat. The direct solar illuminance (L\({}_{\text{bh}}\)) sensor was installed on a sun tracker (model SunTracker-3000, Geonica, Madrid, Spain) facing the sun. The technical specifications of the sky scanner and luxmeter are shown in Tables 1 and 2, respectively.  
new scan every 15 min. The first and last measurement of the day (\(\alpha_{\rm s}\leq 5^{\circ}\)) were discarded, as well measurements higher than 50 kcd/m\({}^{2}\) and lower than 0.1 kcd/m\({}^{2}\), following the specifications of the equipment. Only half-hourly and hourly sky scanner measurements were used in this study, to match simultaneous records of illuminance data. If the illuminance data failed to pass the quality criteria, then all the simultaneous data sets were rejected.  
The measurement campaign ran from September 2016 up until January 2019. A total of 3991 datasets of illuminance (horizontal global, diffuse, and beam and vertical global in the four cardinal orientations) and sky scanner measurements passed the quality criteria.