esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physiological Ability and Functionality Study  
All isolates show growth ability at pH of 2 to 9 and can survive between 2 and 15% (\(w/v\)) of the salt medium (Table 3). Salinity is a severe abiotic stress that restricts crop development and productivity. The soil's high alkalinity and exchangeable salt levels harm crop output and productivity. According to [PERSON] et al. [20], _Bacillus_ sp. CL3, _B. cereus_ CL7, and _Burkholderia thailandensis_ CL4 could tolerate salinity up to 5% NaCl, but _B. subtilis_ CL1, _Pseudomonas fluorescens_ CL12, and _P. putida_ CL9 had the highest salt tolerance (6% NaCl). [PERSON] et al. [20] reported that _P. fluorescens_, _P. putida_, and _B. subtilis_, had the highest salt tolerance, while _Agrobacteriumium tumificiens_ had the lowest (1% NaCl). [PERSON] et al. [54] found almost identical results for _A. tumificiens_ (0.50-1.00% NaCl), _P. fluorescens_ (4% NaCl), and _Bacillus_ sp. (3.50% NaCl), while [PERSON] et al. [55] found salinity tolerance in _Burkholderia_ sp. (2-5% NaCl). _Bacillus thailandensis_ was also found to withstand 5% NaCl [20]. [PERSON] et al. [56] reported that _Bacillus_ sp. isolated from peat environments had a high salinity level of 10% NaCl. As a result, all of the used isolates were equivalent to and within the salinity ranges described in various bacteria species, although salinity tolerance was species-specifically dependent.  
The rhizosphere is a critically important zone for microbial proliferations because of the abundance of root exudates. Most rhizobacteria observed belonged to one of three proteobacterial phyla, which colonized the Gram-negative microbial population [57].
# Correction of Directional Effects in VEGETATION NDVI Time-Series
## 3 Methodology
### BRDF Correction
#### 3.2.5 BRDF Model Inversion and Adjustment
Moreover, it allows the BRDF inversion procedure to find the kernel weights that could best represent the current surface conditions captured by the observations, albeit being constrained by prior information on BRDF surface. Then, Equation (8) is solved as:  
\[\mathbf{k}=\mathbf{C}(\mathbf{A}^{T}\mathbf{b}+\mathbf{C}_{prior}^{-1} \mathbf{k}_{prior}) \tag{11}\]  
where \(\mathbf{C}=(\mathbf{A}^{T}\mathbf{A}+\mathbf{C}_{prior}^{-1})^{-1}\) is the covariance matrix. Assuming uncorrelated errors, we consider the covariance matrix's diagonal elements as the variances associated with the wavelength-dependent \(\mathbf{k}\) parameters obtained via Equation (11). In other words, \(\sigma_{k_{0}}^{2}=\mathbf{C}_{11}\), \(\sigma_{k_{1}}^{2}=\mathbf{C}_{22}\) and \(\sigma_{k_{2}}^{2}=\mathbf{C}_{33}\). It is important to notice that the quality of the BRDF inversion attested by the uncertainties associated with the retrieved BRDF parameters is strongly correlated with the number and diversity of observations used in the inversion. Once we have approximated the surface anisotropy with a semi-empirical BRDF model [24], we use the wavelength-dependent models to estimate RED and NIR reflectances at a standard Sun-sensor geometry. Using the \(\mathbf{k}\) parameters obtained from solvingEquation (11), the BRDF-adjusted reflectances are calculated using Equation (4) for the solar zenith angle corresponding to 10:00 local time and viewing zenith angle at nadir.
## 2 Materials and Methods
### Investigated Classification Techniques
\begin{table}
\begin{tabular}{c l} \hline \hline
**Techniques** & **Applications** \\ \hline \multirow{4}{*}{\(k\)NN} & Modeling liver-related adverse effects of drugs [14]. \\  & Predicting drug side effects [15]. \\  & Personalized drug concentration predictions [16]. \\  & Detecting adverse drug events [17]. \\ \multirow{4}{*}{C4.5} & The analysis of adverse drug reactions [18]. \\  & Expert system for adverse drug reaction detection [19]. \\  & Antidiabetic drug failure prediction [20]. \\ \multirow{4}{*}{SVM} & Prediction models for drug-induced hepatotoxicity [21]. \\  & Prediction of drug-induced eosinophil adverse effect [22]. \\  & Prediction of cardiovascular outcomes [23]. \\ \multirow{4}{*}{RF} & Adverse drug reaction detection [19]. \\  & Prediction of drug-induced nephrotoxicity [24]. \\ \multirow{4}{*}{ANN} & Predicting the serum digoxin concentrations of infants [25]. \\  & Prediction of the serious adverse drug reactions [26]. \\ \multirow{4}{*}{LGR} & Clinical decision support system to toxicity in patients \\ \cline{1-1}  & treated with digoxin [27]. \\ \cline{1-1}  & Analysis of patients with digoxin serum concentration [28]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Applications of classification techniques in medical domain. Logistic regression (LGR) is a statistical learning method that uses the dataset to develop a mathematical model for predicting the DV. LGR is a nonlinear regression model that can be applied when the DV is binomial category data; it can find the relationships between the binomial DV and a group of IVs [38]. Specifically, it puts the values of IVs into the logistic function to get a probability value.
# Research on Cloud Task Scheduling Algorithm with Conflict Constraints Based on Branch-and-Price
## 6 Experimental Results and Analysis
### Experimental Setup  
To verify the correctness and effectiveness of BPTSC, we refer to the numerical experiment of the one-dimensional packing problem with conflict constraints in [11] for experimental design. The experimental settings are as follows:  
* The experiment is divided into three scales based on the number of tasks: small-scale (30 tasks), medium-scale (100 tasks), and large-scale (200 tasks).
* There are a total of 200 cloud computing VMs, each with three hardware resources: CPU, memory, and storage. The resource capacity of each VM is \(c=(100,300,3000)\).
* The CPU requirements for each task are randomly generated on a uniform distribution of [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64], memory requirements are randomly generated on a uniform distribution of [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], and storage requirements are randomly generated on a uniform distribution of [100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 210; 22; 23; 24; 25; 26; 27; 28], and storage requirements are randomly generated on a uniform distribution of [100; 1000].
* Generate a conflict graph \(G\) according to the settings in [11], and add the conflict relationship between tasks based on the density values \(\delta(\delta=0.1,0.2,\ldots,0.9)\) of the number of conflicting tasks.The specific method is to assign a feature value \(q_{j}\) that satisfies a uniform distribution on [0; 1; 2] to each task \(j\). If tasks \(j\) and \(j^{\prime}\) meet \((q_{j}+q_{j^{\prime}})\Big{/}2\leq\delta\), add a conflict relationship between tasks \(j\) and \(j^{\prime}\).
* Each scale is further divided into nine groups according to the increasing conflict density value \(\delta\). To ensure the universality of the experiment, each group includes 20 instances, and the experimental results are the average values simulated by 20 instances. The total number of the instances is 540.
# Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
## 2 Materials and Methods  
In this paper, a new procedure is presented by which to detect the OD properly. The proposed algorithm consists of 4 steps: (1) increasing the significance of the image in a preprocessing step, (2) eliminating the pixels below the average brightness level by subtracting by the average brightness level of the image, (3) implementing the modified robust rank order method for edge detection, and (4) detection of the circular OD via circular Hough transform.  
In the first phase, green channel extraction was performed on the retinal images to enhance the image contrast. Then, the clarity of the image was increased by using contrast-limited adaptive histogram equalization (CLAHE).  
In the second phase, the average brightness level of each image was calculated. The pixels that were below the average brightness level of the image were eliminated because the OD region is the brightest region of the retinal image [14]. Furthermore, due to the specific brightness level of each image, a dynamic calculation of the average is more accurate for OD detection.  
In the third phase of the algorithm, the modified robust rank order-based edge-detection algorithm was applied to the image, in which the non-bright pixels were eliminated.  
In the last phase, the circular Hough transform was applied to the edge-extracted image to locate the OD. Due to the fact that the OD has a circular structure and the OD diameter is about 80-100 pixels in each retinal image [1], the circular Hough transform is easy to implement.
# A Database for Tsunamis and Meteotsunamis in the Adriatic Sea
###### Abstract  
In the frame of the Interreg Italy-Croatia program, the EU has funded the PMO-GATE project, focusing on the prevention and mitigation of the socioeconomic impact of natural hazards in the Adriatic region. The Database of Adriatic Tsunamis and Meteotsunamis (DAMT) is one of the deliverables of this project. DAMT is a collection of data documenting both meteotsunami and tsunami effects along the Eastern and Western Adriatic coasts, and it was realized by starting from the available database and catalogues, with the inclusion of new data gained from recent studies, newspapers and websites. For each tsunami and meteotsunami, the database provides an overview of the event and a detailed description of the effects observed at each affected location and gives a picture of the geographical distribution of the effects. The database can be accessed through a GIS WebApp, which allows the user to visualize the georeferenced information on a map. The DAMT WebApp includes three layers: (1) Adriatic Tsunami Sources, (2) Adriatic Tsunami Observation Points and (3) Adriatic Meteotsunamis Observation Points. The database contains 57 observations of tsunami effects related to 27 tsunamis along the Italian, Croatian, Montenegrin and Albanian coasts and 102 observations of meteotsunami effects related to 33 meteotsunamis.  
Keywords:Adriatic Sea; database; tsunami; meteotsunami; ArcGs; WebApp +
Footnote †: journal: Physics Letters B
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 3 Vision and Objectives  
The previous section indicates a lack of therapy tools for USN that include multiple modalities, despite the consideration of USN as a multi-sensory deficit. MNT exploits these multiple modalities and is therefore considered to be a promising tool for the rehabilitation of USN patients [49]. The use of VR for the rehabilitation of USN offers the advantage of not limiting the therapy to visual stimuli due to the easy integration of auditory, tactile and motor stimuli. A VR environment facilitates the creation of an immersive experience that can be adapted to the needs of the patient. Moreover, the use of a digital tool, as opposed to manual assessment, provides the opportunity for more unbiased data collection during therapy. Information, such as head direction or responses to stimuli in the VR environment, can be tracked to evaluate progression over time.  
The objective of this research is to investigate whether MNT for USN can be successfully translated to an immersive VR environment and whether the data collected during the immersive training can be used for the assessment and thus evaluation of the progress of patients affected by USN. To achieve this, a digital tool has been designed that includes an immersive VR environment in which the patient has to perform several musical exercises, tracking and displaying session data on a therapist dashboard.  
To ensure that this tool achieves its objective, expert knowledge about the needs of the target audience and offered therapy is necessary. An iterative participatory design approach has been taken, involving experts and patients in the design process. This research has been established in close co-operation with specialists and therapists from the Rehabilitation Department of the Ghent University Hospital. To evaluate the resulting system, a pilot study, approved by the ethics committee, has been conducted involving several patients who were currently undergoing treatment for USN at the Rehabilitation Department of the Ghent University Hospital.  
Three research questions have been investigated:  
1. Can MNT be effectively translated into a VR environment?
2. Can the newly designed VR system be used for the treatment of adults with USN with an acquired brain injury?
3. Will the spatial performance of the patient with an acquired brain injury evolve positively after treatment with the VR system?
## 5 Experiments
### Data Preparation  
Most buoys have hourly readings while some have readings in every 30 or 6 minutes. In such cases, we have down sampled data by selecting only hourly readings and discarding the rest to generate maximum 24 readings for each day. If a buoy has no reading corresponding to an hour, we fill the missing reading with a data point which was read less than 30 min ago. If there was a missing reading for any of the attributes in the historical data, it was imputed by National Data Buoy Center using any of the following values depending on the attribute: 99.00, 999.0, or 9999.0. We have imputed these missing observations by using piecewise linear interpolation technique by putting a limit of 24 (one day of data) on a maximum number of consecutive missing values that could be filled. Even after interpolation, there is still huge number of missing attributes for some of buoys as can be seen in Figure 6.  
Figure 6: Sample data from buoys 42001, 42003, 42039, 42040, FWYF1, KYWF1, NPSF1, and SPGF1 after interpolation. Attributes shown: Water temperature (WTMP), Gust speed (GST), Barometric Pressure (BAR), and Wind speed (WSPD). The wind direction attribute is defined as \"the direction the wind is coming from in degrees clockwise from true N\" by National Data Buoy Center and it takes values between 0\({}^{\circ}\) and 360\({}^{\circ}\). In this study, we have divided the full 360\({}^{\circ}\) value into eight equally sized slices of 45\({}^{\circ}\) and label encoded each slice. Then, wind speed attribute of observations from buoys were one-hot encoded using these labels based on which slice direction its value falls in. Meanwhile, we have applied the min-max scaling technique on five attributes, which are water temperature (WTMP), air temperature (ATMP), wind speed (WSPD), gust speed (GST) and barometric pressure (BAR), to map all values to the range of \([0,\ 1]\). We consider all data readings starting from January 1 st, 2005 00:00 till August 31 st, 2005 23:59; separating data from January till the end of June as training set, data from July as validation set and data from August as test set. We need all selected attribute values available for all data points to train models. We have discarded any observations with missing attributes, resulting a total of 93,982 readings from 21 different buoys. In the final dataset, the total number of data points is different for each buoy as the number of missing attributes differs significantly. In the final dataset, the number of data readings from each buoy is shown in Table 1, in column \"# Observations\". Training is executed using overlapping sequences obtained with a sliding window algorithm with a window size \(T\) and step size of \(s\). The sliding window technique applied to the pre-processed buoy dataset to extract subsequences is given in Algorithm 1. The length of each subsequence is equal to the window size. Using sliding window technique, for a long sequence with length L, the number of
## 2 Prior Work  
Defining the best electromagnetic model and finding the best antenna placement in an autonomous vehicle can be a challenge, due to the variations in antenna behavior induced, for example, by roof materials or the positioning of one or a set of antennas [1; 2].  
Genetic algorithms were used to optimize the positioning of multiple antennas on a ship's platform in [3]. They reduced the amount of time needed to simulate a position and determined the superiority of one position at the expense of another by comparing the fitness functions. For the case of an antenna, characteristic models were applied to determine the position of a monopole antenna on a metal roof of a vehicle, which achieved the greatest gain in the horizontal plane [4].  
Using electromagnetic simulation tools with optimization algorithms has already been investigated. In [5], the position of a simple model of an antenna on a vehicle was examined using a genetic algorithm and with the XFdtd software and a finite difference time domain (FDTD) solver.  
Recent progress in development and use of machine learning in electromagnetic problems for antenna design, synthesis and characterization was shown in [6; 7; 8]. ML can be applied to reduce computational complexity, allowing faster, real-time operation, and helping to deal with complicated structures problems in cases where the problem requires accurate simulations.  
An investigation on the usage of machine learning in antenna design was done in [9]. It was seen that machine learning accelerated the antenna design process while maintaining high accuracy levels in the prediction of antenna behavior.  
A neural network method also can help avoid complexity in smart-antenna-array designs, where the behavior is nonlinear, by establishing a relation between the desired radiation patterns and feeding details, such as voltage and spacing in the real antenna array [10]; or it can be used to help with the design of metasurfaces [11].  
In [12], a deep neural network was designed to synthesize the radiation pattern of an antenna array. The authors showed that the model exhibits reasonable performance, and the radiation patterns from the outputs were quite similar to the input radiation pattern.  
The main contribution of this work is to provide a methodology that allows using an electromagnetic simulator to get radiation pattern as the output of a simplified vehicle model simulation, and then uses a machine learning model to infer the outputs on the remaining antenna placements but with the accuracy of a full model and in a faster way.  
The rest of the paper is organized as follows. Section 3 contains the vehicle model preparation. Section 4 contains the machine learning modeling and training, the U-Net configuration, evaluation metrics, and initial tests. Section 5 contains the optimization model. Section 6 contains the previous results, and Section 7 contains the conclusion.
## 5 System Tests on Site  
### Design Decisions  
The transmitters and receivers (Figure 16) have been designed around an Arduino Mega 2560 board with a GPS module and the XTend radio frequency module. The GPS board and the XTend board dialog with the 2560 microcontroller by two physical serial ports (RS232). An LCD screen connected in I2C was also installed in each of the cases (transmitter and receiver) to display results similar to the distance between the train and Hi-Rail pickup.  
To confirm that the specific antennas (Figure 17) chosen are tuned at the right frequency, we have used a network analyzer to analyze the antenna S11 return loss (Figure 18): the result is quite coherent because the antenna center frequency is 902.07 MHz, and the bandwidth is large enough to work at 915 MHz.  
Figure 16: In (**a**) the Receiver/Transceiver case; (**b**) the inside of the Receiver/Transceiver.  
Figure 15: Negative budget link between Emeril (Labrador) and Schefferville (Quebec) on the railway.  
The watch has been fully developed (Figure 19): this watch should be resistant in hard-working conditions and it must be open source if later, other functionalities (hardware and software) are helpful to implement. Therefore, it is not possible to use ready-made watches. The heart of the watch is a microcontroller 32u4 driving a 0.66\" TFT screen by SPI connection. There is also a buzzer and a very small Bluetooth board to receive the distance from the receiver located in the Hi-Rail pickup. The Bluetooth Low Energy (BLE) is the solution used in the connected watch: the battery life is important, and BLE gives this possibility to extend it. The battery is a lithium-ion 3.7 volts and 150 mAh: note that a technology such as Wifi is never used for the connected watch because of the high consumption of this technology. As the employees are working around the Hi-Rail pickup, the Bluetooth range is enough.  
Figure 17: Specific UHF railway antenna.  
Figure 18: Antenna S11 return loss measured with network analyzer.  
The following sequences were programmed and are displayed:  
* Bluetooth connection: when the watch is switched on, it looks for pairing with the receiver in the Hi-Rail pickup;
* No danger: meaning that the locomotive is outside a distance of 10 km;
* The distance and a slowly tonality when the locomotive is located at a distance between 7 and 10 km;
* The distance and a medium tonality when the locomotive is located at a distance between 3 and 7 km;
* The distance and a very high-pitched sound when the locomotive is inside 3 km and also a flashing display.
## 2 Proposed Method
### Degradation Trend Prediction  
After quantifying the differences between the data to be evaluated and the health model as the PDIs of the FTU, the degradation trend prediction task is converted to a time series forecasting problem. The TPA-LSTM introduced a temporal pattern attention mechanism based on the 1 DCNN to the traditional LSTM model. The TPA-LSTM effectively improves the accuracy and stability of time series prediction by learning the attention weights of previous hidden states [39]. The basic framework of the TPA-LSTM model is shown in Figure 5.  
The classic LSTM network is adopted to calculate the hidden state \(h\) with \(m\) dimensions according to the input data \(x\). Then, a one-dimensional convolution operation with \(k\) kernels is performed on the matrix constructed with previous hidden state vectors to gain various temporal patterns, expressed as:  
\[H_{i,j}^{c}=\sum_{n=1}^{w}h_{i,t-w-1+n}\times c_{j} \tag{23}\]  
where \(w\) is the window length of the input data, \(t\) represents the time stamp, and \(c_{j}\) is the \(j^{th}\) 1 DCNN kernel.  
The attention weights vector \(\alpha\) is calculated by the scoring function, which is essentially a fully connected layer.  
\[\alpha_{i}=sigmoid(W_{\alpha}h_{t}H_{i}^{c}) \tag{24}\]  
The context vector \(v_{t}\) and the final hidden state \(h_{t}^{\prime}\) are defined as:  
\[v_{t}=\sum_{n=1}^{m}\alpha_{i}H_{i}^{c} \tag{25}\]  
\[h_{t}^{\prime}=W_{h}h_{t}+W_{v}v_{t} \tag{26}\]