dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Data are expressed as mean ± standard deviation (SD) or median (upper quartile-lower quartile). Qualitative variables were described using number of occurrences. Significance of differences between group 1 and group 2 was performed using a Student t-test, or a Mann-Whitney test for non-normal distribution. Frequencies were compared using chi-square tests. The performance of each parameter was assessed using the receiver operating characteristic (ROC) curve method for each parameter. Statistical analysis was performed using GraphPad Prism software version 5 (GraphPad Software Inc. , La Jolla, CA, USA). Statistical significance was defined as P < 0. 05.
RESULTS Fifty-four eyes of 54 patients were included in the analysis. All raw data of the 24-hour IOP related fluctuations by the CLS were complete and available. Twenty-two eyes were allocated into group 1 and 32 eyes were allocated into group 2. Demographic and ophthalmological characteristics of the population sample are shown in Table  2. Of the 54 patients, 27 patients were women and 27 were men. The mean age was 68. 5 ± 6. 0 years and was not significantly different between the 2 groups. Mean IOP before the CLS placement was 15. 9 ± 3. 1 mm Hg and was not significantly different between the 2 groups. The mean number of visual field tests available before the inclusion was 7. 7 ± 2. 3 and was also not significantly different between the 2 groups. The mean rate of progression was significantly faster in group 1 than in group 2 (-1.
Instead of the removal of these outliers, the Triggerfish software uses the median of these measurements in the final analysis. Thus, we speculated that this processing method of data points could bias the measurement of change in corneal curvature or circumference during the 24-hour monitoring by overestimating the amplitude and length of peaks particularly during the diurnal period. Furthermore, these outliers could also bias the comparison of the output signal between the day and night periods. Second, as expected during a 24-hour monitoring, diurnal changes in corneal curvature or circumference should follow a circadian biorhythm. 24 Thus, as the difference between the beginning and the end of the monitoring may overestimate the area and the amplitude of the output signal pattern recorded during the night period, we chose to compensate this difference by applying a linear regression to the entire monitoring.
A sustainable yield prediction in agricultural fields is a very critical task that aims to help farmers have an idea about agricultural operations. Generally, we can find a variety of applications proposed for this purpose that include fruit counting. These applications are based on Artificial Intelligence, especially Deep Learning (DL) and Machine Learning (ML) approaches. These approaches give reliable counting accuracy, but the problem is the use of a large database to achieve the desired accuracy. That makes these approaches limited. For this reason, in this work, we propose a low-complexity algorithm that aims to count green and red apples based on our real dataset collected in the Moroccan region, Fes-Meknes. This algorithm allowed us to further increase sustainability in agricultural fields based on yield prediction.
Fruit counting in precision agriculture is the process of using technology to count the number of fruits on a tree or plant in order to optimize crop yields. This can be conducted using various techniques, including. Image processing Using cameras or other imaging devices, images of the fruits can be captured and analyzed using computer algorithms. This can be conducted either by taking images of the entire tree or plant and counting the fruits manually or by using image processing techniques such as object detection to identify and count the fruits automatically. Machine learning algorithms can be trained to recognize and count fruits in images. This can be carried out by providing the algorithm with a large dataset of images that have been labeled with the number of fruits in each image.
Computer vision techniques can be used to detect and count fruits in images automatically. This can be conducted by using techniques such as edge detection and blob analysis to identify the fruits in the image. Robotics technology can be used to count fruits by using cameras or other sensors to detect and count fruits on a tree or plant. These techniques can be used to count the number of fruits, which can then be used to optimize crop yields by making decisions such as when to harvest or how to best care for the crops. However, the problem with these proposed techniques is the high algorithmic complexity as well as the use of embedded architecture that can increase the reliability of the developed system. For this reason, we propose in this work a low-complexity and real-time counting algorithm that does not require any pre-collected data or training of DL or ML models.
In this work, we propose an algorithm based on image processing to count green and red apples. This algorithm is based on the color detection method and the geometrical shapes of the apples. Thus, we propose an implementation of the proposed algorithm in a low-cost system. Then a comparison was presented between the raspberry Pi B+ embedded architecture and a desktop. The proposed algorithm is based on RGB to HSV image conversion and Hough transform. The reason for using these algorithms is to avoid the DL and ML techniques that require a large database. As well as GPU boards that require large energy consumption. In addition, another constraint is based on the long time required for the data training. The proposed algorithm is based on C/C++ language and a low-cost embedded architecture. Our contribution is as follows: (1) Proposition of a fruit detection and counting algorithm based on intelligent vision approaches.
Material The agricultural product studied in this paper is based on red and green apples, and this agricultural field is located in the Fes-Meknes region near Lake Daiit Aoua. This region is characterized by the high production of apples in Morocco. This field is located at 33 • 38 41 N and 5 • 02 04 W with a surface of 44. 12 ha and a perimeter of 3. 13 KM. The data collection was based on an RGB camera with a resolution of 4608 × 3456 and 16 Mpx. The camera has a high resolution in order to obtain accurate results. The images used in this work were collected on Thursday, 3 September 2020, around 13. 30. The choice of this region is due to the high production of green and red apples. Figure  1  shows the location of the studied field. The proposed work is divided into four parts, the first part for the general introduction to the topic to be developed.
Material The agricultural product studied in this paper is based on red and green apples, and this agricultural field is located in the Fes-Meknes region near Lake Daiit Aoua. This region is characterized by the high production of apples in Morocco. This field is located at 33°38'41''N and 5°02'04''W with a surface of 44. 12 ha and a perimeter of 3. 13 KM. The data collection was based on an RGB camera with a resolution of 4608 × 3456 and 16 Mpx. The camera has a high resolution in order to obtain accurate results. The images used in this work were collected on Thursday, September 3, 2020, around 13. 30. The choice of this region is due to the high production of green and red apples. Figure  1  shows the location of the studied field. Counting fruits such as apples can help farmers have an idea about the yield of agricultural fields.
Algorithm 1 shows the different steps to follow for the counting of fruits. The algorithm proposed in our work is based on low algorithmic complexity. On the other hand, the algorithms based on ML or DL require a huge database to handle all the cases. As well as the training of the weights generated in the ML or DL algorithms also requires a permanent update which influences the database collection, especially when dealing with seasonal fruits. In this case, we must wait one year or more to collect the new database. The algorithm in our case does not require any database. It is based first on acquiring images emitted by the RGB camera. Then these images will be filtered to have clear images without noise that will facilitate the recognition later. The filter used in our case is based on the median filter.
It is an alternative representation of the RGB (Red, Green, Blue) color model, which is widely used in electronic displays and digital imaging. In the HSV model, a color is represented by three components: The algorithm proposed in our work is based on low algorithmic complexity. On the other hand, the algorithms based on ML or DL require a huge database to handle all the cases. As well as the training of the weights generated in the ML or DL algorithms also requires a permanent update which influences the database collection, especially when dealing with seasonal fruits. In this case, we must wait one year or more to collect the new database. The algorithm in our case does not require any database. It is based first on acquiring images emitted by the RGB camera. Then these images will be filtered to have clear images without noise that will facilitate the recognition later.
Experimental Results The evaluation of the results was based on the algorithm mentioned in Figure  3  to count the different fruits of the apples. We have added a temporal evaluation to the proposed algorithm to study the specific architecture. The embedded architecture used in this evaluation is the Raspberry Pi 3B+ embedded system with a Broadcom Cortex-A53 @ 1. 4 Ghz processor and 1 GB LPDDR2 RAM. Additionally, we added a comparison with a desktop in order to extract the strong points of using low-cost embedded architecture. The approach followed in the evaluation of this work is divided into two parts; the first part is dedicated to evaluating the real database collected in the agricultural field. The second part focuses on temporal and architectural studies. The language used in this study is C/C++ based on the OpenCV library.
This technique allows us to locate the part that consumes the majority of processing time in order to accelerate it later. Figure  8  shows the detection accuracy and processing time on the desktop. The system demonstrated exceptional accuracy, with a final score of 98. 45%. This falls within the desired range of 94. 98% to 98. 91% and is among the highest levels of accuracy ever recorded for this type of model. The results confirm that the model is able to make highly accurate predictions and can be trusted to perform well in real-world scenarios. Figure  9  shows the detection accuracy and processing time in Raspberry. The system's performance was evaluated using a dataset, and it was found to have an accuracy of 95. 04%. This falls within the expected range of 91. 05% to 98. 77%. The high accuracy of the system indicates that it is able to classify and predict outcomes with a high degree of reliability.
The results demonstrate that the model is well-suited for practical applications and can be trusted to produce accurate results. Figure  9  shows the detection accuracy and processing time in Raspberry. The system's performance was evaluated using a dataset, and it was found to have an accuracy of 95. 04%. This falls within the expected range of 91. 05% to 98. 77%. The high accuracy of the system indicates that it is able to classify and predict outcomes with a high degree of reliability. The results demonstrate that the model is well-suited for practical applications and can be trusted to produce accurate results. we evaluate each block proposed in the algorithm. This technique allows us to locate the part that consumes the majority of processing time in order to accelerate it later. Figure  8  shows the detection accuracy and processing time on the desktop.
The system demonstrated exceptional accuracy, with a final score of 98. 45%. This falls within the desired range of 94. 98% to 98. 91% and is among the highest levels of accuracy ever recorded for this type of model. The results confirm that the model is able to make highly accurate predictions and can be trusted to perform well in real-world scenarios. Figure  9  shows the detection accuracy and processing time in Raspberry. The system's performance was evaluated using a dataset, and it was found to have an accuracy of 95. 04%. This falls within the expected range of 91. 05% to 98. 77%. The high accuracy of the system indicates that it is able to classify and predict outcomes with a high degree of reliability. The results demonstrate that the model is well-suited for practical applications and can be trusted to produce accurate results.
Conclusions In this work, we proposed a green and red apple-counting algorithm based on selecting pixel colors and the Hough transform for fruit detection. Then we apply the processing of counting fruit to predict the yield in agricultural fields. The proposed algorithm is based on three blocks that aim to process the desired application. Thus, we have evaluated our algorithm in an embedded architecture type Raspberry Pi B3+ and a desktop. The results show that we can achieve 40 frames/s on the desktop and 15 frames/s on the Raspberry board. The datasets used in this work are based on an RGB camera and an agricultural field located in the Moroccan region, Fes-Meknes. In future work, we aim to accelerate this algorithm in a low-cost system type CPU-GPU or CPU-FPGA. This acceleration will help us ensure the real-time constraint in our processing.
The system achieved accuracy on the test dataset, falling within the desired range of 95. 04% to 97. 22%. This level of accuracy indicates that the model is able to identify and classify the majority of the data points within the dataset. The results demonstrate that the model can be used for reliable and accurate prediction in practical applications. As a future suggestion, we aim to use a combination of weather data and historical yield information to make accurate predictions using our developed approach. This includes data on temperature, precipitation, soil moisture and other environmental factors that can affect crop growth.
Data analysis 2. 4. 1. Center of pressure parameters The force plate signals were filtered digitally with a 10 Hz low-pass filter (second order Butterworth). The force plate provided three forces and moment components: The anteroposterior axis was positive in the anterior direction, the mediolateral axis was positive toward the right side, while the vertical axis was positive when directed downward (Figure  1 ). CoP area corresponded to the surface area of an ellipse that covered 95% of the CoP displacement  (Paillard and Noé, 2015). To account for differences in trial duration between individual participants, CoP area was normalized to each participant's trial duration in seconds (s). The CoP amplitude was calculated as the distance between the minimum and maximum displacement in both anteroposterior (CoP y) and mediolateral (CoP x) axes.
Electromyography To obtain a linear envelope for EMG amplitude measures  (Shiavi et al. , 1998) , EMG signals were further digitally band-pass filtered between 10 and 400 Hz, full-wave rectified, and low pass filtered at 9 Hz using 4th order, zero-lag Butterworth filters with custom MATLAB scripts  (Desmyttere et al. , 2018). The envelope was equivalent to the maximal duration of the balance tasks. Using the digitally filtered signal, we quantified EMG as the mean root mean squared (RMS) amplitude of the signal over the entire duration of the trial, and EMG of the individual muscles during the balance tasks were normalized to their maximal values over 1 s at the peak of the isometric plantar flexion (LG, MG, and SOL) and dorsiflexion (TA) MVCs. Coactivation ratio was calculated by dividing the normalized EMG of the TA by the sum of the normalized plantar flexors' EMG.
Intermuscular coherence was calculated in the time-frequency domain for agonist-agonist and agonist-antagonist muscle pairs using the hardware filtered EMG signal (Coulbourn; Allentown, Pennsylvania, PA, United States) before applying the digital processing in MATLAB. Pooled agonist-agonist coherence was considered for each participant as the mean coherence across LG-MG, LG-SOL, and MG-SOL muscle pairs, while pooled agonistantagonist coherence was considered as the mean coherence across MG-TA, LG-TA, and SOL-TA muscle pairs  (Desmyttere et al. , 2018). Coherence was analyzed in 1-s epochs across the entire trial duration. The auto-spectrum of each EMG signal and the crossspectrum between each EMG signal combination were first quantified using the WavCrossSpec software for wavelet coherence analysis  (Grinsted et al.
General Description of the Proposed Method As shown in Figure  2 , the methodology we propose is composed of the following stages. After collecting the important information about the building under study (see  in Figure  2 ), the methodology starts with the estimation of its energy demand (see  in Figure  2 ). Today's designers, engineers, and builders depend on energy sufficiency simulation software for completing a retrofitting project and managing any uncertainty or risk that may develop throughout their assessment  [26]. The energy demand and operational costs of the studied building were estimated using TEE-KENAK, a CAD software developed by the National Observatory of Athens and certified by the Technical Chamber of Greece in Athens, Greece, to be compliant with the European Energy Performance of Buildings Directive (EPBD).
Based on the applicable national requirements, European standards, and instructions of the technical committees, this software tool implements the necessary algorithms for the calculation of the energy performance of buildings in Greece in accordance with ISO standards  [27, 28]. The TEE-KENAK software has been selected as the cheapest program     and  and     on the market and at the same time certified for its reliable results. Once preliminary data such as geometric features and measurements of the building envelope, material properties or orientation are collected, they become inputs to the software in the form of separate features. Note at this point that the software includes separate "masks" or screens for each category of built materials (walls, openings, floors, rooves) where all the distinct geometries are classified according to the orientation criterion, calculated from their deviation from north.
Subsequently, as indicated in  of Figure  2 , it is necessary to design the shape of the building and its particularities. In order to visually design the shape of the building and determine its "weak points" in terms of orientation, sun penetration and shading requirements, a three-dimensional model was created in AutoCAD software. This software was chosen because it is one of the most popular in the computer-aided design industry, where basic three-dimensional CAD skills are needed to quickly create a model for shading simulations. Geometric shapes and dimensions of the building envelope are applied, using basic software commands to create the building model. Neighboring buildings are also modeled to calculate their shading effect on the building under study. Once the geographical location is defined (latitude and longitude of the building location), sunlight simulations were performed for selected periods.
Simulating the shading of the building facades can provide valuable information to determine the weakness of certain facades and help propose retrofit solutions in terms of building form and geometry. It should be noted that, according to  [30] [31] [32] , building geometry is not considered important for the thermal performance of older structures, due to the potential application of newer, higher quality building fabrics with increased thermal insulation and reduced air leakage when applying retrofit solutions. The methodology continues with the coupling of the results of the two software packages, namely TEE-KENAK for the energy efficiency part and AutoCAD for the mechanical CAD part. As shown in Figure  3 , the following five criteria were considered in assessing the percentage contribution of each of the two software packages used: 1.
The number of input data. 2. The ease of access for the acquisition of these data (similar for both software). 3. Ease of use and automation of data entry. Specifically, several data can be automatically inserted in both software programs: for TEE-KENAK, variables such as -value (thermal behavior coefficient) and solar reflectivity are automatically defined once the year of construction of the building is registered. For AutoCAD, once the geographical location is selected on the map, variables such as longitude and latitude that define the solar angles are automatically set. 4. The workload (in terms of working hours) required to complete the two models. 5. The contribution of each software output to the proposal and evaluation of the retrofit solutions. Once the energy efficiency class is calculated, a copy of the existing building can be created.
This copy of the building is a model that serves as a reference for developing retrofit scenarios. By changing the inputs of energy-intensive variables to more energyefficient ones, improvements are made. In addition, the building copy model contains a new field for financial values. The consideration of financial values is of great importance in describing the simulation process. To this end, we can classify the types of inputs into two units of measurement. The first unit, expressed in euros/m 2 , refers to renovation scenarios that are expressed per unit area (i. e. , insulation, windows and solar panels). In many studies, solar panels are discussed on the basis of their nominal power, but the TEE-KENAK software receives the data measured per area. We should note that the above cost includes transportation, materials and labor.
Description of the Case Study The audited building is the Limenas High School, which is located on the island of Thassos, at the northern end of the eastern Aegean Sea, in Greece. The construction of the building was completed in 1976, at a time when Greece was under a dictatorship, hence the lack of significant data on the building. At the time, the construction quality of the buildings was poor and little attention was paid to the energy efficiency of the building envelope, resulting in high energy consumption. Since the building studied here is very old, consuming 170. 2 kWh/m 2 and producing 64. 3 kg/m 2 of CO2 emissions, its energy performance diagnostic (EPD) label, G, is the lowest on the EPD scale. The Limenas High School has an area of 2166. 0 m 2 and a total volume of 7542. 1 m 3.
As for the heating system, it consists of uninsulated steel pipes and steel radiators (AKAN type steel radiator panels) powered by an old diesel boiler with a nominal capacity of 581. 5 kW. The existing lighting, shown in green in Figure  5 , consists primarily of fluorescent tube fixtures that produce a combined output of 24. 16 kW. Despite this relatively low combined power, these luminaires are very uncomfortable for users in many rooms of the building because their light output is largely insufficient. Finally, it is worth mentioning the orientation of the building (see Figure  6  and Table  1 ), which plays a major role in the calculations performed by the TEE-KENAK software. The use of CAD software is also implemented for the calculation of daylighting zones, which we will note in the following DZ.