esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Early Warning System for Rock-Fall Risks Reduction
Several techniques are reported in the literature [24; 25; 26], where a terrestrial laser scanner was used to detect changes occurring on the surface as well as to detect individual rock events. To detect multiple rock-fall events, semi-automated identification methods were used, capable of extracting information from events of rock-falls, including block shapes, volumes, and their locations [27]. In addition, [28] proposed a semiautomatic method in which three-dimensional terrain was reconstructed to obtain the geometry of the unstable blocks and to define the location of rock-fall source areas. Although few camera-based rock-fall monitoring techniques were found in the literature, the camera has outperformed many technologies, even the terrestrial laser scanner, regarding its ability to track multiple rocks in real time. In [PERSON] et al. [29], an artificial intelligence camera prototype was used for monitoring and tracking the fallen rocks in real time. All the above methods are used to detect falling rocks; however, they are not sufficiently effective to warn cars of rock-fall hazards before their occurrence. These methods respond after the rock-fall occurrence has caused harm on the roads. In addition, they have weak capabilities in dealing with the high falling speed and the slow response of the vehicle's driver that increases the risk. A prediction of rock-fall events is required to address the shortcomings of previous models. Recently, machine learning technologies have been used in developing effective models to predict rock-fall hazards. Some studies were conducted on rock-fall prediction models based on machine learning technology [30; 31; 32]. The existing methods are unreliable in mitigating the rock-fall risk in real time because the model is generated from the historical data concerning a specific site. Moreover, few studies focused on increasing the reliability of the detection or the prediction models. Therefore, this study aimed to increase the reliability by combining the detection and the prediction models in a hybrid reliable early warning system that guarantees to overcome the limitations of existing technologies and make the roads safer. Therefore, the key contributions of this study are summarized as follows:  
1. We developed a prediction model-based machine learning technology to predict the possibility of rock-fall. 2. We developed a detection model-based computer vision algorithms to detect and track rock-fall events. 3. We combined the detection and the prediction models in a hybrid reliable risk reduction model to increase the model reliability. 4. We developed a hybrid early warning system to reduce the rock-fall risks. The rest of this study is organized as follows. The study area and problems are presented in Section 2. The system platform and the methodology are introduced in Section 3. The proposed system validation is presented in Section 4. Section 5 presents the discussion and the conclusion of the study.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### Hadoop
#### 4.1.1 Architecture  
The overall architecture of Hadoop consists of two major components Hadoop Distributed File System (HDFS) [29] and Yet Another Resource Negotiator (YARN) [31]. HDFS is Hadoop's very own distributed file system which provides high throughput access to application data across thousands of machines in a fault tolerant manner. While YARN is responsible for resource management and scheduling. Figure 5 illustrates the architecture of Apache Hadoop.  
In HDFS, data is stored in the form of files that are divided into data blocks. Each block is stored in one of the nodes in the Hadoop cluster. Thus, each node in the cluster stores a part of a file. Data blocks are replicated to ensure high availability and fault tolerance. HDFS is primarily based on two daemons called NameNode and DataNode. The HDFS cluster consists of one master node and several slave nodes. The master runs the NameNode daemon that is responsible for managing the file system and regulating file access to users. While slave runs DataNode daemon that stores data blocks and serves read/write requests from a user.  
YARN was introduced by Yahoo! and Hortonworks in 2012 [31], and it became part of Hadoop as Hadoop 2.x [30]. In Hadoop 2.x, YARN takes the role of distributed application manager and MapReduce stays as pure computational framework. It has two major components named Resource Manager (RM) and Node Manager (NM). Resource Manager is a master daemon that is responsible for scheduling and resource management, while, Node Manager is a slave daemon that is responsible for managing containers and monitoring resource usage on a single node. It periodically monitors the health status of the host node and reports the same to RM.  
Figure 5: Apache Hadoop architecture.
## 2 Experimental Program
### Study and Characterization of the SCC
#### 2.1.1 Material  
To produce the SCC, the following materials were selected: Portland cement (CEM I 42.5R) with density 3140 kg/m\({}^{3}\); fly ash with density 2380 kg/m\({}^{3}\); modified carboxylate-based superplasticizer supplied by Sika Portugal, SA with the commercial name Sika ViscoCrete 3005 having a density 1050 kg/m\({}^{3}\); fine-rolled natural sand (Sand 0/2) with density 2600 kg/m\({}^{3}\) and fineness modulus 2.104; rolled natural sand from river with medium grain size (Sand 0/4) with density 2640 kg/m\({}^{3}\) and fineness modulus 3.035; crushed granite aggregate (Gravel 3/6) with density 2710 kg/m\({}^{3}\) and fineness modulus 5.311; and crushed granite aggregate (Gravel 6/15) with density 2700 kg/m\({}^{3}\), fineness modulus 6.692 and maximum size 19.1 mm.  
The optimum proportions of fine aggregates to fit with the fine aggregate reference curve was obtained by combining, in absolute volume ratio, 50% of Sand 0/2 and 50% of Sand 0/4, resulting in a mixture with fineness modulus 2.569. The coarse aggregates were combined in absolute volume ratio of 65% Gravel 3/6 and 35% Gravel 6/15 to fit with the coarse aggregate reference curve, resulting in a mixture with fineness modulus 5.794.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 3 Navigation Signal Design Considerations
### Receiver Complexity  
The correlation result between the received signal and the local replica of the desired signal is used in the acquisition and tracking processes. Assuming that there is no Doppler effect and the spreading code length is infinite, the autocorrelation function of BPSK signals does not have a sidelobe peak. However, in the case of BOC signals, it has significant sidelobe peaks. These non-negligible sidelobe peaks can cause false detection and false locks in the low signal-to-noise ratio (SNR) [42]. The implementation complexity of algorithms proposed to avoid the false detection and lock problem depends on the shape of the autocorrelation function of the desired signal [46; 47; 48; 49]. The higher the sidelobe peaks, the more complex algorithms are required.  
The autocorrelation main peak-to-secondary peak ratio (AMSR) is the ratio of the second-highest peak value to the highest peak value of the autocorrelation function. The AMSR is defined as  
\[AMSR=\frac{max\{R^{2}(\tau)\}}{R^{2}(0)},\ \tau\
otin\text{{Mainlobe}} \tag{15}\]  
where \(R(\cdot)\) is the normalized autocorrelation function with a main peak value of 1 and \(\tau\) is the code offset between the received signal and the local replica.
# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
## 2 Materials and Methods
### The U-Net Architecture  
Three segmentation models were used in this study. The first was U-net, as implemented in the nnUNet framework (version 1.0) [24]. Because nnUNet is a self-configuring medical segmentation framework, the user does not need to select any network hyperparameters, as these are deduced by the framework in an automated method directly from the data. The other two segmentation models, UNETR and Swin UNETR, were imported from the MONAI library ([[https://monai.io/](https://monai.io/)]([https://monai.io/](https://monai.io/))) and used with their preset values of hyperparameters. Swin UNETR monai architecture is based on \"[PERSON] et al., Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images\" <[[https://arxiv.org/abs/2201.01266](https://arxiv.org/abs/2201.01266)]([https://arxiv.org/abs/2201.01266](https://arxiv.org/abs/2201.01266))> (accessed on 1 June 2024) [25]. This architecture was developed by the authors for brain tumor segmentation tasks in MRI. UNETR monai architecture is based on \"[PERSON] et al., UNETR: Transformers for 3D Medical Image Segmentation\" <[[https://arxiv.org/abs/2103.10504](https://arxiv.org/abs/2103.10504)]([https://arxiv.org/abs/2103.10504](https://arxiv.org/abs/2103.10504))> (accessed on 1 June 2024) [26]. This architecture was tested by the authors on multi-organ segmentation, brain tumor, and spleen segmentation tasks.  
In the case of these last two architectures, the nnUNet framework delivered a unified training and inference environment, assuring that these models are trained in the same way as U-net. All the models were trained from scratch and subsequently tested using the training and testing data described in detail below. The selection of the nnUNet framework as a universal medical segmentation environment was guided by our previous experience with benchmarks of deep segmentation algorithms [27]. In particular, it follows from our previous study that U-net, as implemented in the nnUNet framework, provides the best option in terms of both segmentation quality and speed of inference [27]. The CTR and TCD were then calculated directly from the segmentations of these organs based on the definitions of CTR and TCD. Manual segmentations for the lungs and heart (created by one of the radiologists, P.S.M.) were used as the reference data necessary to train the lungs and heart segmentation models for chest radiograph images. To assess the inter-reader variability of the manual segmentations and compare it with the performance of the automated segmentation method, two other sets of manual segmentations were prepared by two radiologists (I.K. and J.D.J.). The values of CTR and TCD obtained through the manual reading measurements made by radiologists were compared to those obtained from the manual and automated segmentations.
## 2 Materials and Methods
### Research Data  
1. Sentinel-2  
Sentinel-2, at an orbital height of 786 km, is equipped with a multi-spectral imager (MSI) with 13 spectral bands, an image width of 290 km, and spatial resolutions of 10, 20,  
Figure 1: Study area.  
and 60 m. The revisit time of each satellite is 10 d. Sentinel-2A and 2B are complementary; thus, the revisit time is shortened to 5 d. In addition, the revisit time of Sentinel-2 in the mid-latitudes can be shortened to 2-3 d. Sentinel-2 has high temporal and spatial resolutions, a relatively rich spectral band, and a wide imaging width. During 2016-2018, Sentinel-2 only included Level-1C, which must be processed using Sen2 Cor to obtain Level-2A surface reflectance (SR). Since 2019, SR can be obtained directly from the official ESA through Sen2 Cor. Their spectral response functions are very similar; thus, the two satellites can be regarded as the same (Figure 2).  
(2) Auxiliary data  
To better analyze spatio-temporal variations in external factors affecting long-term water quality, we performed this study in Guangzhou ([[http://swj.gz.gov.cn/](http://swj.gz.gov.cn/)]([http://swj.gz.gov.cn/](http://swj.gz.gov.cn/))) and Shenzhen ([[http://swj.sz.gov.cn/](http://swj.sz.gov.cn/)]([http://swj.sz.gov.cn/](http://swj.sz.gov.cn/))). Precipitation data for each administrative region from January to December of 2016-2021 were obtained from the Water Affairs Bureau website. Table 1 presents the precipitation stations in Guangzhou and Shenzhen used in this study. Wind speeds were obtained from the National Centers for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA) ([[https://www.nceei.noaa.gov/data/global-summary-of-the-day/archive/](https://www.nceei.noaa.gov/data/global-summary-of-the-day/archive/)]([https://www.nceei.noaa.gov/data/global-summary-of-the-day/archive/](https://www.nceei.noaa.gov/data/global-summary-of-the-day/archive/))). Statistics on the residents, tourism, and Gross Domestic Product (GDP) of Guangzhou and Shenzhen were obtained from the official website of the GBA ([[https://www.dsec.gov.mo/BayArea/zh-MO/#5](https://www.dsec.gov.mo/BayArea/zh-MO/#5)]([https://www.dsec.gov.mo/BayArea/zh-MO/#5](https://www.dsec.gov.mo/BayArea/zh-MO/#5))).  
which can remove sun glint and residual noise effects from SR data [24]. The correction formula is:  
\[R_{rs}(\lambda)=\frac{R_{rs}(\lambda)-\min(R_{rs}(\text{NIR}):R_{rs}(\text{SWIR} ))}{\pi} \tag{1}\]  
where min (\(R_{rs}\)(NIR): \(R_{rs}\)(SWIR)) represents the minimum value of both NIR and SWIR bands, and \(R_{rs}(\lambda)\) represents the raw \(R_{rs}\) of the visible bands \(R_{rs}\)(443), \(R_{rs}\)(490), \(R_{rs}\)(560), and \(R_{rs}\)(705) of Sentinel-2 data.
## 3 Results and Discussion
### Quality Factor  
The overall performance of a material is quantified by using the quality factor to relate the FE and breathability a material provides. Figure 7 shows the quality factor of tested materials at three face velocities for PM2.5. The quality factors for other particle sizes can be found in the Supplemental Information. The quality factor was calculated using three measurements' average FE and pressure drop readings. In terms of the quality factor, the top three performing materials in this study were those commonly worn as PPE: the N95 mask, the surgical mask, and the P100 3M filter. Even though the N95 and P100 3M filters are considered more difficult to breathe through, their high FE makes them considerably better materials when compared to non-traditional mask materials. These certified materials were found to have, at a minimum, a quality factor three times higher than the top non-traditional mask material. The critical differences are the utilization of electrostatically charged fibers and the hydrophobic layers that are not considered in the homemade masks. Natural fabrics are all found to have the lowest quality factor ranging from 0.01 to 0.02. The effect of multiple material layers on the quality factor was also analyzed and included in the supplemental material. The quality factor tended to decrease as additional layers were added. While materials, such as the surgical mask, provided higher FE when double layers were used, the additional pressure from the second layer caused the overall mask performance to decline, as shown in Figure S5 of the Supplemental Information.
## 3 Data and Methods
### Predictive Models for Deep Learning
#### 3.2.1 Long Sequence Time Series Forecasting  
In many practical applications, we need predictions for long series time series, such as sensor network monitoring [28], stock price trends [29], and water quality forecasting. As explored in this paper, such predictions can provide us with timely warning of pollution thresholds and thus maintain ecological balance, which is important for social and ecological development. LSTF is closer to realistic scenarios than short-term predictions because a large number of time series of past behaviors are needed to support in its predictions. This also reflects the need for LSTF models to have high computational predictive power, that is, to effectively capture more accurate long-range correlations between outputs and inputs. The results in Figure 2(a), similarly, shows that, as [PERSON] et al. [14] states, the LSTF can extend beyond the short-range prediction limit for predicted sequences after training, and longer than short-range time-series predictions.  
However, LSTF does not perform as well as we expected due to the limitations of the predictive model at this stage. Despite the fact that it has better coverage and can be better used as a forecasting tool for planning and implementation, few people choose to use it. We can see that Figure 2(b) shows the MSE scores of models such as RNN, MLP, LSTM, and Transformer. These models are usually used for short-term prediction, and when the prediction type changes from short-term to long-term, the MSE scores of these models increase with the growth of the prediction sequence, while the inference speed decreases significantly. Therefore, when the prediction type changes from short-term to long-term and the length of the input sequence reaches 48, the MSE loss of the corresponding model is no longer acceptable [14] and is not suitable for solving the problem of LSTF class with long sequence of inputs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & **Minimum** & **Average** & **Median** & **Standard Deviation** \\ \hline T & \({}^{\circ}\)C & 3.8 & 17.66 & 16.4 & \(\pm\)8 \\ PH & / & 4.09 & 7.71 & 7.68 & \(\pm\)2.96 \\ TP & mg/L & 0.005 & 0.091 & 0.78 & \(\pm\)2.21 \\ DO & mg/L & 0.01 & 8.41 & 8.69 & \(\pm\)14.35 \\ TN & mg/L & 0.05 & 2.59 & 1.97 & \(\pm\)18.61 \\ NO\({}_{4}\)-N & mg/L & 0.025 & 0.347 & 0.164 & \(\pm\)6.74 \\ TUR & / & 0.1 & 65.51 & 45.1 & \(\pm\)1813.35 \\ COD\({}_{\text{Mn}}\) & mg/L & 0.25 & 4.095 & 3.98 & \(\pm\)24.15 \\ WQI & / & 46.875 & 66.03 & 66.25 & \(\pm\)27.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Daily monitoring data of water quality variables in Chaohu Lake and descriptive statistics of WQI during 2019–2022.
# A Low-Cost Printed Log-Periodic Dipole Array for DVB-T2 Digital TV Applications
## 4 Conclusions  
A printed log-periodic dipole array for DVB-T2 Digital TV applications has been designed and analyzed using CST Studio Suite. The LPDA covers the band from Channel 21 to Channel 69 (470 MHz-860 MHz), it is designed on an FR4 dielectric substrate, and meandered radiating dipoles are used to reduce the antenna size, which has been constrained into an A4 sheet dimension (210 \(\times\) 297 mm) to strongly reduce the production cost. The proposed structure offers all the advantages of the printed technology, resulting in an easy-to-produce antenna with a compact size and a low production cost compared to already published similar wire and printed LPDAs, with a normalized area of only 0.26 \(\lambda^{2}\) (with \(\lambda\) being the free-space wavelength at the central frequency) and a similar average gain. The presented results show a very good input matching with a relative bandwidth greater than 70% and an end-fire radiation pattern with a flat gain around 6.5 dBi within the working frequency band, maintaining a radiation efficiency above 95%.  
Figure 8: (a) E-plane and (b) H-plane radiation pattern of the designed LPDA antenna.  
Figure 7: Directivity, gain, and radiation efficiency of the designed LPDA antenna.  
**Author Contributions:** Conceptualization, G.A.C.; methodology, G.A.C.; software, G.A.C. and P.M.; validation, G.M. ([PERSON]) and G.M. ([PERSON]); formal analysis, G.A.C.; investigation, G.A.C.; resources, G.A.C. and G.M. ([PERSON]).; data curation, G.A.C.; writing--original draft preparation, G.A.C.; writing--review and editing, G.M. ([PERSON]) and G.M. ([PERSON]); visualization, P.M.; supervision, G.A.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 2 Advances in Irrigation Technologies and Water Management  
Water is one of the most critical resources for agriculture, and its efficient management is essential for ensuring food security in the face of increasing water scarcity (Contribution 1). The study on micro-sprinkling irrigation systems for wheat crops presents a compelling case for adopting modern irrigation technologies that enhance WUE whilemaintaining or even increasing crop yields (Contribution 2). By improving water distribution and optimizing irrigation strategies, this technique obtains comparable crop yields to traditional strategies. These findings underscore the potential of micro-sprinkling systems to transform water management practices in agriculture. Irrigation is particularly important in regions where water scarcity threatens local food production [8]. This paper provides a meaningful example of how advanced irrigation technologies meet the dual goals of lower water consumption and higher grain yields.
## 3 Methods and Results
### Evaluation
An important drawback of this method would be the fact that this does not use the information from prior tests to select and decide on the next set, and it does not use a strategy either to predict the next trial [38]. Considering the details presented above, we have implemented in the following section both grid search and random search as hyperparameter tuning for the algorithms chosen for our text classification. Each model has been run again in order to observe the best parameters. After observing the best ones, they have also been implemented for training the model again. The last step of this process was to observe the newly obtained accuracy for both methods, for all our algorithms. According to the above-mentioned steps followed for each of our algorithms, we can see the final results in Table 6.  
Considering the above results, we would also like to mention that we tried to implement the same action plan for the SVM algorithm; however, even after around six hours (which is six times more than the time consumed by the most consuming algorithm before), the SVM with hyperparameter tuning did not provide any results. Therefore, even though this might have provided good results (considering that the initial training also provided a very good accuracy and F1 score), the resources consumed and the time to process are not optimal for our scenario, especially since Logistic Regression provided a very good performance and took around an hour for the time taken to train the same amount of data. Last, but not least, we can conclude that, after trying to improve our models using this last step of hyperparameter tuning, we managed to obtain similar performance values to the initial ones obtained in the first step. Moreover, we can also observe the fact that grid search provided better results, even if the difference is very little in comparison to random search. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Method** & **TN** & **FP** & **FN** & **TP** & **Best Accuracy** \\ \hline \multirow{3}{*}{Naive Bayes} & Grid Search & 7009 & 6797 & 1308 & 21,516 & 77.75\% \\ \cline{2-7}  & Random Search & 12,216 & 1590 & 3209 & 19,615 & 77.67\% \\ \hline \multirow{3}{*}{Logistic Regression} & Grid Search & 12,820 & 986 & 1384 & 21,440 & 93.44\% \\ \cline{2-7}  & Random Search & 12,526 & 1277 & 1452 & 21,372 & 92.51\% \\ \hline \multirow{3}{*}{K-NN} & Grid Search & 8503 & 5303 & 4596 & 18,228 & 72.89\% \\ \cline{2-7}  & Random Search & 7712 & 6094 & 4896 & 17,928 & 72.43\% \\ \hline \multirow{3}{*}{SVM} & Grid Search & N/A & N/A & N/A & N/A & N/A \\ \cline{2-7}  & Random Search & N/A & N/A & N/A & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of results after introducing hyperparameter tuning.