esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Machine Learning-Based Evaluation Methods for Urban Tunnel Landscapes  
Previous studies have highlighted the limitations of traditional methods in evaluating urban tunnel landscapes, primarily due to their inherent subjectivity and potential for subjective bias. To surmount these challenges, the research community has pivoted toward leveraging machine learning technologies for objective, precise, and reproducible assessments of tunnel landscapes.  
One innovative approach involves the use of image processing and machine learning to assess tunnel landscapes. This method entails the collection of a vast array of tunnel landscape images, followed by the application of computer vision techniques to extract features such as color, texture, and shape. Machine learning algorithms, including Support Vector Machines (SVM) and Deep Learning models, are then utilized to automatically classify and analyze these features, facilitating a nuanced assessment of tunnel landscapes.  
Another avenue of research employs sensor data for the evaluation of tunnel landscapes. In this methodology, a network of sensors is deployed to capture data pertaining to air quality, noise levels, and lighting conditions within tunnels. Machine learning algorithms, such as cluster analysis and regression models, are applied to process and model these data. This approach enables the evaluation of the quality and comfort levels of the tunnel landscape based on objective sensor data.  
Furthermore, some studies have sought to enrich machine learning analyses by integrating them with user survey data for a holistic assessment of tunnel landscapes. This involves gathering user evaluations of tunnel landscapes via questionnaires and feedback, which are subsequently combined with machine learning algorithms. This integrated approach facilitates the development of predictive models that assess the quality of tunnel landscapes and gauge user satisfaction effectively.  
In essence, machine-learning-based methodologies offer promising avenues for the assessment of urban tunnel landscapes. By harnessing diverse data sources, including image analysis, sensor data, and user feedback, in conjunction with sophisticated machine learning algorithms, researchers can achieve objective, accurate, and comprehensive evaluations of tunnel landscapes.
## 5 Experimental Implementation  
The field-oriented control will be used to implement three different velocity controllers, a Fourier Series Learning Controller (FSLC), a traditional PI controller of the FOC and an Artificial Neural Network (ANN). The performance of the three controllers will be compared. In Figure 1, the block diagram of the FOC for the velocity control of a PMSM with three different master controllers is shown.  
Figure 1: Block diagram of the FOC (Field Oriented Control) for the velocity control of a PMSM (Permanent Magnet Synchronous Motor) with three different master controllers, the FSLC (Fourier Series Learning Controller), a traditional PI (Proportional Integral) and an ANN (Artificial Neural Network).  
The reference value of the flux control \(I_{dRef}\) is zero for PMSMs, as is shown in the block diagram [29]. To estimate the angular velocity of PMSM \(\theta\), a filter of position proposed by [30] is used, which can reduce the noise on the velocity signal compared with the analytical derivative of the angular position. The gain of the filter was \(A=5.0\).  
The proposed controller is compared with a classical PI control and with an ANN. The architecture and all details of the ANN are those proposed by [31]. We use three neurons in the intermediate layer and three inputs: \(e^{k}\), \(e^{k-1}\) and \(e^{k-2}\), which are the error and error in two previous instants and one neuron for the output layer.  
The controllers will be tested to control the velocity of the EMJ-04 APB22 PMSM, which has four poles and the following parameters: 200 (V), 2.7 (A), 400 (W) maximum power, 3000 (rpm) rated speed, 4.7 (\(\omega\)) stator resistance, 0.014 (\(H\)) stator inductance and 2500 (\(PPR\)) incremental encoder attached.  
Figure 2 shows the interconnection diagram of the control system. The control algorithms were programed on a computer Aspire V5-561-7455 (acer, New Taipei, Xizhi, Taiwan). The control output values, calculated by the controller, are sent to an acquisition card, which is based on a microcontroller dsPIC33 FJ12 MC202 (MICROCHIP, Chandler, AZ, USA). For the communication of the microcontroller with the computer, a device FT232 RL (FTDI, Seaward Place, Glasgow, UK), was used. The acquisition card has three tasks, to acquire data from the PMSM encoder (Anaheim Automation, Anaheim, CA, USA) and the current sensors, to send data to the computer and to apply three PWM signals to the power stage. The currents of each phase of PMSM are measured from the ACS712 sensors (Allegro, Worcester, MA, USA). The power stage is based on H bridges LMD18200 (Texas Instruments, Dallas, TX, USA), which generate the AC voltages for each phase of the PMSM.  
Figure 2: Interconnections diagram of the control system.
## 4 Methodology
### Measurements  
The measurements were made from April, 2021 to May, 2022. The data are publicly available in a form of comma-separated values (CSV) files [46]. We used R environment for statistical computing [47] to create a M&V report and calculate energy efficiency (1), as well as underheating (2) and overheating (3) indices for different optimization algorithms.  
The control loop of the legacy local controller is presented in a simplified form as Algorithm 1. In an infinite loop, the outdoor temperature and the temperature of central heating circuit supply water are used to derive the set point of central heating circuit supply water (line 3). In the legacy system, it is immediately followed by the execution of control logic code responsible for setting valves and pumps in order to reach that set point (as shown in line 5). However, in the proposed solution, before execution, the value of the set point could be modified by a selected optimization algorithm (in line 4). From the software engineering point of view, it resembles a _Strategy_ behavioral design pattern [48]. It simplifies the definition of a family of algorithms and makes them interchangeable. This way, the complexity and details of the optimization function are separated from the legacy system.  
The performance of the new implementation was tested with different optimization algorithms, e.g., the function call in line 4 of Algorithm 1 was referring to various algorithms that were altering the set point of TCR08.  
```
1:TRC07\(\triangleright\) Outdoor temperature
2:TCR08\(\triangleright\) Present value of temperature of central heating circuit supply water
3:while true do
4: Update values of TCR07 and TCR08\(\triangleright\) Communicates with sensors to read present values
5:spTCR08\(\leftarrow\)Compute(TCR07, TCR08)\(\triangleright\) Computes the set point using the legacy algorithm
6:spTCR08\(\leftarrow\)Optimize(spTCR08)\(\triangleright\) Adjusts set point of temperature of central heating circuit supply water
7:Executes control logic to set valves and pumps in order to reach spTCR08.
8:endwhile
```  
**Algorithm 1** Central heating control loop  
Optimization algorithms do not control any specific actuators shown in Figure 1. They modify only the set point of TCR08--the temperature of central heating circuit supply water.  
For that reason, we were able to measure differences in their performance and directly compare them. Low-level control mechanisms, including control over all the valves and pumps, were preserved as the source code of the local controller and were left unchanged.
# Bottle Aging Affected Aromatic and Phenolic Wine Composition More than Yeast Starter Strains
## 2 Materials and Methods  
### Winemaking and Aging in Bottles  
The trial was carried out with around 450 kg of Tempranillo grapes, in perfect sanitary conditions, harvested manually at their optimum maturation stage. At the cell, the clusters were destemmed and crushed. The resulting paste and must were homogeneously distributed in eight tanks of 50 L, due to the trials with the four yeast strains being performed in duplicate, and 3 g/hL of total SO\({}_{2}\) was added on each tank. They were kept for 4 days in cold storage, in a chamber at 8 \({}^{\circ}\)C. Subsequently, they were tempered (20 \({}^{\circ}\)C) and inoculated, at 20 g/hL, with the 4 strains of _Saccharomyces cerevisiae_ yeasts: two tanks were inoculated with Maurivin AWRI 796 (AB Mauri-ABBiotek, Toouoomba, Australia), called as Sc1; two tanks were inoculated with Safoeno SC22 (Fermentis, Marcq-en-Barecul, France), called as Sc2; two tanks were inoculated with Pinnacle Fruit Red (ABBiotek), called as Sc3, and two tanks were inoculated with Pinnacle Fructo Select (ABBiotek), called as Sc4.  
The alcoholic fermentation (AF) was developed at 20 \({}^{\circ}\)C, with the density and temperature being measured daily. Furthermore, glucose/fructose was measured at the end of AF (the coefficients of variation in glucose/fructose ratio between the two tanks of each treatment (%) were: 0.00-2.67). During the fermentation process, the cap was punched down, one a day, with the purpose of improving the contact between the marc and the must, and to extract the compounds found in the skins. Once the AF was completed (whenglucose/fructose concentrations were not detected), wines were drawn off and pressed. Then, the wines were transferred to 25 L stainless steel tanks, each of the batches was treated separately, where lactic acid bacteria _Oeonococcus oeni_ Pinnacle MaloSafe (ABBiotek) were inoculated, at 1 g/hL, to carry out the malolactic fermentation (MLF). These fermentations were carried out at 20 \({}^{\circ}\)C. MLF was monitored by malic and lactic acids determination (the coefficients of variation (%) between the two tanks of each treatment were: malic acid (0.22-6.09) and lactic acid (2.08-8.23)). Once finished (when malic acid was not detected), the wines were cold stabilized (10 \({}^{\circ}\)C, 1 month) and bottled (in 15 green glass bottles of 75 cL worked with cork stopper), remaining in the cellar bottle rack at controlled temperature and humidity (16 \({}^{\circ}\)C, 50-60%).  
In the initial must, in the wines at the end of MLF and after 6 and 9 months of aging in bottles, the classical parameters were analyzed (Section 2.2). Moreover, two aliquots of each wine, from the two tanks and from two bottles of each repetition, at these three moments (end of MLF and after 6 and 9 months of aging in bottles) were taken and frozen at \(-20\)\({}^{\circ}\)C for the determination of their volatile composition (Section 2.3) and their phenolic composition (Section 3.4).
## 3 Results and Discussion
### Comparison of Results between Different European Countries  
The measured values of the electric field for each country are summarized and presented in Figure 27. From the boxplots, we can see that the distributions of the \(E_{total}\) in the car's cabin during traveling on different roads in different countries are skewed. We see that the lower quartile (Q1 = 0.18 V/m) and median (Q2 = 0.24 V/m) are the same for \(E_{total}\) distributions in Croatia, Hungary, Italy, and the Republic of Serbia. Moreover, \(E_{total}\) distribution in Austria and the Republic of Serbia has more variability than in other countries. In addition, the highest value of 17.4 V/m was obtained in Austria on the Vienna Ring Road. We supposed that these results are due to the fact that two of the road sectors pass through Vienna and Belgrade, the capitals of Austria and the Republic of Serbia. These are the very densely populated urban areas in which there are a large number of sources of EMF, such as the antennas of cellular network base stations close to roadways. The total electric field in the car cabin during travel varied insignificantly across the different countries. The median of \(E_{total}\) is 0.23 V/m in Bulgaria, 0.28 V/m in Slovenia, and 0.31 V/m in Austria.  
Figure 26: Comparison of the total electric field (\(E_{total}\)) on (**a**) Motorways and main roads passing through large cities or covering ring roads of large cities; (**b**) Motorways and main roads that do not pass through populated areas.  
Moreover, the data show that the exposure of car drivers and passengers during travel on European roads is below the ICNIRP reference level [17].  
The obtained data are helpful for adequate and accurate exposure assessment of both car drivers and passengers during short or long travel. Moreover, the results of this work can be used to study human exposure to new 5G-V2X communication scenarios, such as vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-network wireless communications. The obtained results of this measurement campaign will have far-reaching applications, including electromagnetic compatibility (EMC), the development of new absorbers of EMF in cars, and the development of new flexible rectennas for EMF energy harvesting in vehicles. EMC testing for new vehicle components (sensors, ABS, airbags, etc.) is required by law in order to avoid an accident caused by electromagnetic influences on the onboard electronics. Another application where the results of E-field measurements in a car are desired for the development of new materials is for the car interior, where excellent electromagnetic wave absorption properties [39] can lead to reducing passenger exposure and electromagnetic interference. The results will also apply to EMF energy harvesting by helping to more precisely determine the locations of the rectennas so that more EMF energy can be collected in the vehicle during travel.
## 3 Results
### Detection Abilities of Diverse Precipitation Intensities  
Figure 7 depicts the Probability density function estimated from the GPPs and gauges throughout the entire period in different seasons over JRB for seven classes of daily precipitation frequency distribution as described in Section 2.4. All GPPs and gauges exhibited a higher percentage of low-intensity precipitation events between 0 and 1 precipitation classes. In contrast, the high intensity was recorded in the class where precipitation was greater than 50 mm/day in this region. Overall, the ratio of lowest precipitation occurrence (<1 mm/day) is greater than heavy precipitation events (>20 mm/day). ERA-5 showed underestimation of light (\(\leq\)1 mm/day) and overestimation of moderate (1-20 mm/day) and heavy (>20 mm/day) precipitation events for the entire period as well as in the winter and pre-monsoon seasons (Figure 7a-c). However, it was much closer to reference data with slightly overestimating moderate precipitation events in the winter (Figure 7d). Furthermore, PGMFD behaved differently as it overestimated the light but underestimated the moderate precipitation events and overestimated the heavy precipitation events (Figure 7a-c). Notably, MSWEP and APHRO have a higher degree of similarity with the reference data during the entire winter season and pre-monsoon. Still, APHRO, in comparison with MSWEP, was less accurate in showing such resemblance with the reference data for moderate and heavy precipitation intensities in the monsoon season. The CHIRPS product had feebleness in detecting precipitation events. It could not capture the moderate and heavy precipitation intensities, particularly during the monsoon season (Figure 7d); that's why it has higher FAR values than other GPPs (Table 4). All other GPPs performed well in capturing the precipitation classes from 1 to 50 mm/day.  
Figure 7: The probability density function (PDF) of GPPs with diverse intensities during (**a**) entire study period, (**b**) winter, (**c**) pre-monsoon, and (**d**) monsoon.
# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 3 System Design and Model
### Algorithm for the R-DEAR Strategy  
The proposed R-DEAR strategy, in terms of pseudo code, is elaborated in Algorithm 1. The algorithm takes input as SWs. Each workflow is stored in a queue referred to as W\({}_{\text{Q}}\). The workflow then transforms them into tasks for execution and stores them in a queue referred to as T\({}_{\text{Q}}\). Resource information is obtained from the WRIP and stored in a queue referred to as R\({}_{\text{Q}}\).  
The power utilization of each task is obtained, and then all the tasks of a workflow are sorted in ascending order per their power utilization. At this stage, the replica of each node is also generated. Resources with minimum power consumption are found for each task and assigned to the tasks. The tasks are executed through the workflow engine. If a task's execution fails during execution, a replica of the task is provided, and it is re-executed. However, if the node on which the task is executed has failed, the replicas of tasks executed on that node are provided to an alternative node for execution. The resources are monitored during execution, and if power utilization of any resource is null, it is powered off. Finally, the results are obtained and returned to the user.  
The proposed R-DEAR strategy is a resource- and service-provisioning strategy that incorporates a replication-based fault-tolerant and load-balancing mechanism. The proposed R-DEAR strategy is a way to schedule the tasks of a scientific workflow using a replication-based fault-tolerant mechanism. The proposed R-DEAR strategy manages the power consumption of IaaS cloud resources dynamically through a load-sharing process.
## 1 Introduction
### The Spinal Cord  
The spinal cord is the main communication link between the brain and body via the central nervous system (CNS). It can also independently process reflexes and repetitive motor tasks through central pattern generators (CPGs). It begins at the medulla oblongata, passes through the foramen magnum [3], and runs from cervical vertebrae C1-C8, through thoracic T1-T12, to lumbar L1-L2 [4], ending at the conus medullaris and cauda equina [3]. Each vertebral level connects to a spinal nerve that controls a myotome (muscles) and a dermatome (skin) [3]. Sensory (afferent) neurons enter via dorsal roots containing dorsal root ganglia, while motor (efferent) neurons exit via ventral roots [5]. These roots join to form spinal nerve pathways throughout the spinal cord as shown in Figure 1[3]. Cervical nerves control the head, diaphragm, arms, and hands; thoracic nerves handle the chest and abdomen; lumbar nerves control the legs; sacral nerves manage bowel, bladder, and sexual functions; and the cocygeal nerve serves the skin over the tailbone. Protected by vertebrae and meninges, the spinal cord is cushioned by cerebrospinal fluid and connective tissue. It contains central grey matter (neuron bodies) and surrounding white matter (myelinated axons) [3]. Ascending tracts carry sensory data to the brain, while descending tracts send motor commands to the body [6].  
Figure 1: Cross-section of a spinal cord, showing the distinction between the grey and white matter and the various neural root pathways surrounding the spinal cord. Arrows on cross section indicate direction of blood flow [3].
# A New Approach to Production Process Capability Assessment for Non-Normal Data
## 4 Statistical Background of the Study
### Quality Capability Indicators for Unilateral Specification Limit  
Let \(\left\{x_{i}\right\}_{1\leq i\leq n}\) be the sample drawn from a population with normal distribution \(N\big{(}m,\sigma^{2}\big{)}\), and an unilateral specification limit defined as \(LSL\) or \(USL\) be assumed. In the case of a unilateral specification limit, the probability of obtaining good products:  
\[P(LSL\ \leq X\leq USL)=P(X\leq USL)-P(X\leq LSL)\]can be determined by numerically determining the integral (10) or (11) depending on whether the tolerance is specified as _LSL_ or _USL_.  
\[P(X\leq LSL)=\int_{-\infty}^{LSL}\frac{1}{\sqrt{2\pi}\sigma}e^{\frac{-(x-m)^{2}} {2\sigma^{2}}}dx \tag{10}\]  
\[P(X\leq USL)=\int_{-\infty}^{UISL}\frac{1}{\sqrt{2\pi}\sigma}e^{\frac{-(x-m)^{2 }}{2\sigma^{2}}}dx \tag{11}\]  
This probability can also be determined using the properties of the distribution of the standard normal distribution \(N(0,1)\)--(12) or (13) for _LSL_ or _USL_, respectively:  
\[P(X\leq LSL)=\mathrm{F}_{N(0,1)}\left(\frac{LSL-\mathrm{m}}{\sigma}\right) \tag{12}\]  
\[P(X\leq USL)=\mathrm{F}_{N(0,1)}\left(\frac{USL-\mathrm{m}}{\sigma}\right) \tag{13}\]  
where \(\mathrm{F}_{N(0,1)}(z)=\int_{-\infty}^{z}\frac{1}{\sqrt{2\pi}\sigma}e^{\frac{ -z^{2}}{2}}dx\). The method of determining the probability and values of \(C_{p}\) and \(C_{pk}\) indicators should include the following steps:  
1. Determination of the mean value from the collected sample \(\overline{x}\) and standard deviation \(\sigma\).
2. Calculation and determination of the absolute value from (14) or (15) depending on whether the specification limit is specified as _LSL_ or _USL_.  
\[\mathrm{F}_{N(0,1)}\left(\frac{LSL-\overline{x}}{\sigma}\right) \tag{14}\]  
\[\mathrm{F}_{N(0,1)}\left(\frac{USL-\overline{x}}{\sigma}\right) \tag{15}\]
3. Determine the probability of obtaining of goods products  
\[P(LSL\ \leq X\leq USL)=\mathrm{F}_{N(0,1)}\left(\frac{USL-\overline{x}}{ \sigma}\right)-\mathrm{F}_{N(0,1)}\left(\frac{LSL-\overline{x}}{\sigma}\right)\]
4. Read the \(C_{p}\) value from the graph (Figure 1) corresponding to the determined probability value.  
Figure 1: The Cp value for the value of the distribution function.  
The \(C_{pk}\) value should be determined from Equation (2) depending on whether the specification limit is specified as _LSL_ or _USL_. The value of this indicator should be referred to the value in Table 1.