esa-sceva/satcom-chunk-collection huggingface dataset:
Using the MFC characteristics in [20], the current study attempted to classify MFC into the following three sub-categories: (i) lower than normal (MFC < 1.5 cm), (ii) safe range (1.5 cm < MFC < 2 cm), and (iii) well above the safety requirement (MFC > 2 cm). The outcomes of this research might help to predict risky (low MFC) swing foot clearance following the toe-off event utilizing a single IMU. The objective of the current study was to establish machine learning models to classify MFC data into different risk categories. Once a preliminary model is successfully developed, its prediction accuracy can be improved and generalized by feeding further data. Actuation to prevent tripping is an important consideration for active wearables such as exoskeletons and footwear. Development of such assistive devices could be an effective solution for minimizing tripping if such risks (low MFC) could be detected earlier in the swing phase, i.e., following toe-off. Machine learning algorithms can extract and recognize features using mathematical relationships between different variables in the sample space [44]. A collection of mathematically related feature variables in a sample space forms the sample data for the development of training and testing algorithms. Machine learning applications in gait and neurological studies have created intrinsic understanding in previously underdiscovered areas, giving rise to wider applications involving neurological processes relating human gait from recognition of intention to physical locomotion [45; 46; 47]. In the current study, a range of supervised machine learning algorithms was used to build a classifier to make predictions on new unlabeled data points. The K-Nearest Neighbor (KNN) uses distance measures (e.g., Euclidean distance function) between every two data points to classify the points to find the K-Nearest Neighbor. Random Forest and XGBoost use the decision tree ensemble for model representation and inference, but XGBoost uses a different training algorithm. XGBoost training is more complex than Random Forest because different computational kernels require specific optimization techniques. For example, XGBoost uses gradient boosting to train decision trees and focuses on minimizing the loss function, while Random Forest uses bagging to train decision trees and focuses on optimizing the hyperparameters in the parameter space. XGBoost is also a more complex and flexible model, which requires more parameter tuning, while Random Forest is simpler and more interpretable [48]. Since low MFC is associated with tripping falls [20], our work in this sense is to use machine learning algorithms to classify MFC heights into lower than normal, safe range, and above safe range categories. In addition to striving for accurate measurements as seen in other studies, our approach incorporates multiple classification layers to categorize tripping risks, therefore enhancing our ability to identify and mitigate them effectively.
# RNase L Induces Expression of A Novel Serine/Threonine Protein Kinase, DRAK1, to Promote Apoptosis
## 2 Results
### Ectopic Expression of DRAK1 Induces Apoptosis by Activating JNK and Cleavage of Caspase-3
Mitochondrial pathway of apoptosis involves cleavage of caspase 3 along with release of cytochrome C from the mitochondria and loss of transmembrane potential [28] which can be measured by uptake of fluorescent dye, tetramethylrhodamine ethyl ester (TMRE). DRAK1 expressing PC3 cells showed progressive loss of mitochondrial membrane potential (less than 50% compared to control cells) suggesting that DRAK1 induces apoptosis by mitochondrial damage (Figure 3E). Taken together, these results suggest that ectopic expression of DRAK1 activates JNK that is required for cleavage of key apoptotic inducers, caspase 3 and PARP accompanied by loss of mitochondrial transmembrane potential resulting in apoptosis. Figure 3: DRAK1 overexpression activates JNK signaling and caspase 3 cleavage to promote apoptosis. (**A**) PC3 cells were transfected with DRAK1 and cell lysates from the indicated time points were immunoblotted with indicated antibodies. PC3 cells were treated with JNK inhibitor (SP600125, 25 \(\upmu\)M) or caspase 3 inhibitor (zVAD-FMK, 20 \(\upmu\)M) and transfected with DRAK1 for 24 h and (**B**) Cell lysates were probed with indicated antibodies and normalized to \(\beta\)-actin levels, (**C**) Cell viability analyzed by MTT assay. (**D**) PC3 cells were transfected with vector alone or DRAK1 in the presence or absence of caspase 3 inhibitor (zVAD-FMK, 20 \(\upmu\)M). Caspase 3/7 activity was measured in cell lysates after 24 h. (**E**) Cells expressing DRAK1 or vector alone were stained with TMRE at indicated times and fluorescence intensity was normalized to control cells. Values shown are mean \(\pm\) s.d of triplicate assays. * \(p<0.01\), ** \(p<0.001\), *** \(p<0.0001\), n.s. not significant.
# Biological Activities of Selected Medicinal and Edible Plants Aqueous Infusions
## 3 Results and Discussion  
### HPLC Analysis of Aqueous Herbal Infusions  
The main phenolic compounds from selected herbal infusions are shown in Table 1. The highest total phenolics values were found in _G. macrornhizum_ (1531.45 mg/L) and _P. axiculare_ (1254.72 mg/L), the highest total phenolic acids in _A. millefolium_ (648.13 mg/L), and the highest total flavonoids in _P. axiculare_ (1060.99 mg/L). The main phenolic acids detected in _A. millefolium_ were chlorogenic acid and its derivatives (337.38 mg/L) and ferulic acid and its derivatives (189.66 mg/L), while other infusions, especially _P. axiculare_ did not have a very high content of phenolic acids (17.59 mg/L). The most important flavonoids in the selected herbal infusions were: flavan-3-ols (_P. axiculare_ and _O. spinosa_), quercetin and its derivatives (_P. axiculare_ and _G. macrornhizum_), kaempfer and its derivatives (_P. axiculare_ and _G. macrornhizum_), luteolin and its derivatives (_G. macrornhizum_), and catechin and its derivatives (_P. axiculare_). A high content of procyanidins was found in the infusion of _G. macrornhizum_ (744.48 mg/L). There is no study comparing the phenolic composition from aqueous infusions prepared of the plants used in this study. Moreover, most studies on the plants used in this study are based on the phenolic composition of different extracts (ethanolic, methanolic, acetone, hexane, ethyl acetate) and not on aqueous infusions or aqueous extracts. It is known that the total phenolic content and the phenolic composition vary depending on the extraction method, the analytical method and the plant part as well as the origin of
## 2 Data and Sensor System  
### Data  
This paper uses three types of data with seven sensors obtained in observations from four satellites. The data types are solar wind speed, north-south component of IMF, and high-energy electron flux. The three of the four satellites observe the solar wind parameters of solar wind speed and north-south component of IMF. These three satellites monitor solar wind parameters at different positions in space. The last data are high-energy electron flux at GEO.  
We use one-hour averaged data calculated from observation data of the solar wind parameters and the high-energy electron flux at GEO. Two satellites, STEREO-A and STEREO-B, comprise the Solar Terrestrial Relations Observatory (STEREO) [18]. STEREO-A and B were launched in 2006 into orbits around the Sun that cause them, respectively, to pull further ahead of and fall gradually behind the Earth. The solar wind data observed by STEREO-A and STEREO-B are obtained from the Coordinated Data Analysis Web (CDAWeb) [19] in the National Space Science Data Center (NSSDC) of the National Aeronautics and Space Administration/Goddard Space Flight Center (NASA/GSFC).  
The solar wind data observed by the Advanced Composition Explorer (ACE) satellite are obtained from the OMNI-2 database [20] in the National Space Science Data Center (NSSDC) of the National Aeronautics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The electron flux data observed by the GOES satellite are obtained from the National Geophysical Data Center (NGDC) and the National Oceanic and Atmospheric Administration (NOAA/NGDC) [21]. We obtained all data during the period from 2 March 2007 to 7 December 2009, approximately two years in total.
## 4 Experiment Results and Discussion
### Evaluation Criteria for the Classification Algorithms  
To evaluate the performance of the proposed algorithm, four evaluation metrics were used, namely, accuracy, precision, recall, and F1-score, which are commonly used evaluation metrics to assess the robustness and efficiency of the algorithm. True Positive (_TP_) refers to the case where both the actual class value and the predicted class value are 1. True Negative (_TN_) indicates that both the actual class value and the predicted class value are 0. False Negative (_FN_) is the case where the actual class value is 1 and the predicted class value is 0, while False Positive (_FP_) is the case where the actual class value is 0 and the predicted class value is 1. Accuracy is the proportion of correctly predicted observations among the total observations, while _Recall_ is the proportion of actual class values that are classified as 1 among those that are actually 1. _Precision_ refers to the proportion of predicted class values that are actually 1 among those classified as 1, while F1-score is the harmonic mean of _Precision_ and _Recall_. F1-score is mainly used when the data are imbalanced. Accuracy, _Recall_, _Precision_ and F1 score can be calculated by the following formulas:  
\[\text{Accuracy}=(TP+TN)/(TP+TN+FP+FN), \tag{4}\]  
\[Recall=TP/(TP+FN), \tag{5}\]  
\[Precision=TP/(TP+FP), \tag{6}\]  
\[\text{F1}-\text{score}=2\times\{(Precision\times Recall)/(Precision+ Recall)\}, \tag{7}\]  
Figure 5: Experiment environment.
# Semantic Web and Knowledge Graphs for Industry 4.0
## 5 Semantic Web and Ontologies for Industry 4.0  
With the advancement of information technology, the mode of manufacturing is transforming from mechanization to intelligence and digitization. This transformation is influenced by the utilization of the internet of things, sensors, and CPS [36; 37]. All these devices and systems are vertically integrated in a smart factory [38].  
Ontologies appeared as a significant tool to represent the domain knowledge of I4.0 to support integration and interoperability [39; 40]. [PERSON] et al. in [41], define an ontology as the formal, explicit specification of a shared conceptualization. The basic elements of an ontology are a concept and the relation between it, and axioms. When the instances of concepts are populated into the ontology it becomes knowledge base also known as knowledge graph ([[https://web.stanford.edu/class/cs520/](https://web.stanford.edu/class/cs520/)]([https://web.stanford.edu/class/cs520/](https://web.stanford.edu/class/cs520/)), accessed on 1 May 2021). There are two key elements in Ontology: terminological components (Tbox) and assertional components (Abox) that specifies the concepts and its instances, respectively. Ontologies provide an automatic process known as reasoning to retrieve axioms that have not been explicitly incorporated in the knowledge graph. Based on the representation of conceptualizations the ontologies can be classified as general or core ontology and domain ontology [42]. Core ontology represent conceptualizations that are domain-independent and universal, i.e., time ontology [43], sensor ontology [44], etc., and can be utilized in different scenarios across many realms of knowledge. Domain ontology represents the knowledge of a specific domain such as ontology of the process specification language (PSL) [45], MAnufacturing Semantic ONtology (MASON) [46], etc.  
Ontologies have been widely used to model the concept of devices and it's capabilities, parameters, processes, etc., in I4.0. The knowledge representation with ontology has helped to solve various problems like interoperability (between standards, devices), modelling domain knowledge, integrating IoT, etc. Table 3. represents the research focus, and datasets of identified ontologies.
## 5 Discussion
An exhaustive assessment of past degradation would need to be based on a long-time series of satellite imagery, such as that which has been conducted in moist tropical forests [6]. Methods targeting the monitoring of forest degradation and forest disturbances [19; 68] could be used in combination with the forest cover maps that have been presented in this study in order to assess and map the forest degradation that has happened in the following years (i.e., post 2018). The advances in dry tropical forest mapping that are demonstrated in this study can be integrated into national monitoring systems. Open data from Copernicus were used in combination with publicly available tools such as Collect Earth and the Google Earth Engine platform. A unique expert-based training dataset that is suitable to dry forest monitoring applications with EO data was created for this specific study and is available to be re-used for other studies. The reference dataset of 4196 samples was created by the visual interpretation of VHR images on a spatial window of 0.25 ha. The spatial window allowed the capture of the canopy cover of the different forest classes. The reference dataset that was created is a multi-level legend and was used at different levels of complexity. The archive of satellite data that were made available in recent years represents an amount of data that cannot be handled with traditional techniques. The challenges of classifying the annual time series of EO data are commonly tackled by machine learning approaches while keeping a place for user interaction. Such algorithms require input layers that are prepared and selected accordingly. The user interaction takes place in the selection of those layers and the construction of the ad-hoc training and validation dataset. More advanced users can test the fitness of different algorithms to their mapping requirements. In the context of NFMS, national institutions favor flexible approaches that they can have full control over [29]. The methodology that was followed here can be easily adopted at different levels of complexity while ensuring a timely production of maps in the Eastern African region. From a capacity development perspective, such a study could lead to the development of toolkits for (i) facilitating the access to pre-processed S1 and S2 data time series (ii) producing meaningful layers for dry forest mapping and monitoring and (iii) allowing the incorporation of available national or local training datasets.
## 2 Materials and Methods
### Location and Data
The data shown in Figure 4 are divided by the date of acquisition into three categories, namely: pre-event, consists of data from November to December 2019, which represent conditions before the major flood occurred; co-event is data taken on or near the 2 January 2020 flood, and the rest is categorized as post-event data. In Figure 5, the SAR images are set into a dataset, which contains 39 cross-polarized and 39 co-polarized images from Sentinel-1a between November 2019 and October 2020, with co-event images designated as the target image. The dataset SAR was collected using Google Earth Engine and consisted of Sentinel-1a VV and VH images between November 2019 and December 2020 as RGB composite TIF images. All images are resized into 946 \(\times\) 681 pixels covering the Jakarta area and part of the Bekasi and Tangerang Regencies that flooded. The target image is further broken down as flood markers to make a 25 \(\times\) 25 pixels-sized kernel. The previous individual images shown in Figure 5 are combined into three-dimensional data with a 946 \(\times\) 681 \(\times\) 78 pixel dataset and 25 \(\times\) 25 \(\times\) 20 pixel kernel.
## 2 Related Work
Our methodology of designing the wireless sensor network monitoring for the environment using SSAIL technology focused on inexpensive deployment cost, rapid scaling up, and application in the production lines of factories. Since this system allows for the installation of any type of sensor, it can also be used for outdoor monitoring. Our process of designing a sensor node contributes:  
* The hardware modification of the communication module, providing the opportunity to adopt the RF-harvesting antenna circuit;
* The design of a fractal antenna for RF-harvesting applications, and the investigation of simulation vs. measurement antenna characteristics;
* The simulation of randomly distributed sensor nodes to find the position of the sensors according to the parameters of area length, width, expected sensor reading per minute, and size of the data packet. The detached nodes and the communication path are analysed;
* The sensor node architecture and its specific operational regime;
* The power consumption of the communication module and its mode of operation;
* A simulation of the improved LEACH protocol compared with the standard LEACH protocol, which is analysed for its lifetime of the sensor node, total energy dissipated, and the throughput;
* The estimation of the overall energy consumption per day of the analysed network consisting of 10 sensor nodes;
* The hosting of high-level PHP applications using the MySQL database and data visualisation in real-time charts on the web server.
## 2 Materials and Methods
### Analytical Instrumentation and Operating Conditions for GC-ECD  
The solid-phase micro-extraction (SPME) fiber used was DVB-PDMS (Supelco, Bellefonte, PA, USA). The fiber was conditioned before the first application by using a GC hot injector at 250 \({}^{\circ}\)C and detector at 300 \({}^{\circ}\)C for 1h. The injector, column and detector were cleaned to remove any contamination for 30 min at 260, 280 and 310 \({}^{\circ}\)C, respectively. A blank test was made by injecting 1-2 microliters of highly pure ethanol and running on GC, followed by checks for the presence of any possible contamination in the injector, column and detector of the GC-ECD. After clearance, water samples were analyzed on GC for the determination of various pesticides.  
All chromatographic analysis of chlorinated pesticides was carried out on an Agilent gas chromatograph (6890 N series, Agilent Technologies, Palo Alto, CA, USA) equipped with ECD (electron capture detector) and a split/split less injector. The injector was used in split less mode. Chromatographic separations were performed using HP-5 fused-silica capillary column, 30 m \(\times\) 0.32 mm id and 0.25 um film thickness (5% diphenyl-95% dimethyl siloxane coating) from Agilent. The operating GC conditions for the analysis of organochlorine pesticides in water samples were as follows: initial oven temperature was 80 \({}^{\circ}\)C (4 min) and then it was increased at 15 \({}^{\circ}\)C min\({}^{-1}\) to 215 \({}^{\circ}\)C (for 30 sec). Then it was raised again at 2 \({}^{\circ}\)C min\({}^{-1}\) to 230 \({}^{\circ}\)C and remained constant for 3 min at this temperature.  
After this it was increased at 10 \({}^{\circ}\)C min\({}^{-1}\) to 260 \({}^{\circ}\)C. The detector temperature was 300 \({}^{\circ}\)C and the injector temperature was 250 \({}^{\circ}\)C. A single GC run was completed in 28 min. N\({}_{2}\) gas (99.999% purity) was used as a make-up and carrier gas at a flow rate of 45 mL min\({}^{-1}\) and 1 ml min\({}^{-1}\), respectively. For fiber, an extraction time of 45 min, desorption time of 2 min and agitator speed of 250 rpm were selected. When using the CTC autosampler, the SPME fiber is fixed in the holder assembly of the autosampler. The autosampler immersed the SPME fiber in the 20 mL vials for 45 min with agitation. The SPME fiber then extracted the pesticides from water samples. After 45 min, SPME fiber was removed from the vial and directly injected to the GC injector, where hot flow of nitrogen gas at 250 \({}^{\circ}\)C desorbed the organics (pesticides) from the SPME fiber to the injector, and then to the GC column for separation.  
Figure 2 shows a typical chromatogram of 20 standard organochlorine pesticides using the above-mentioned GC conditions and using SPME fiber.
## 1 Introduction  
The Rapid Stain IDentification (RSID(tm)) Blood, Saliva, Semen and Urine Kits (Independent Forensics, Lombard, IL, USA) are lateral flow immunohistochemical strip tests designed for identifying one of four body fluids from biological residue on forensic evidence recovered from crime scenes [1; 2; 3; 4; 5]. Lateral flow tests, like the RSID(tm) series are widely used for screening forensic evidence. Like the majority of lateral flow strip tests, RSID(tm) tests are qualitative and results are recorded as either positive or negative based on the presence or absence of a visible single red or blue line at the \"Test\" position on the strip, 10, or 15 min following addition of the sample to the sample well (saliva, semen, blood and urine, respectively). The results are determined by visual inspection of the strip test and no image analysis or optical reader is required for scoring the test. For the majority of samples assayed by lateral flow methods, there is little or no ambiguity regarding the results of the analysis; either the Test band is visible or it is not. However, under field conditions, when testing samples at or near the limit of detection for the device, or in the hands of less experienced personnel, a handheld Reader capable of recording and scoring the tests can be a useful tool. The steadily increasing requirements for documenting all steps in forensic testing is another advantage to the Reader as the device records a time and date stamp and can download a recorded image of the test strip. The Reader is not required for correct or proper interpretation of strip test results, but may be useful in resolving potentially ambiguous (_i.e._, very faint bands) results.  
The Rapid Stain IDentification (RSID(tm)) Reader System (Independent Forensics, Lombard, IL, USA) is a ruggedized, hand-held strip test reader unit that allows analysis and documentation of RSID(tm) lateral flow strip tests using pre-configured settings. The unit features a touch screen based on Palm Pilot technology and an imaging system for analyzing RSID(tm) cassettes (Figure 1A).  
Figure 1: The Rapid Stain IDentification (RSID(tm)) Reader. (**A**) Ruggedized hand-held RSID(tm) Reader unit with touch screen. Cassettes are inserted into the slot on the upper right hand side, observation window facing down, cassette name first; (**B**) Screen capture image of the downloaded electronic data from Reader; sample name, time, date and results of individual tests are recorded by the software and shown in a spread sheet format.  
The test data, including reader ID, test name, sample ID, date and time of the test, results of the analysis and digital images of control and test lines of RSIDTM strips are automatically recorded by, and stored in, the RSIDTM Reader memory; these data can be downloaded through a USB cable to a computer and are then displayed in the format shown (Figure 1B).  
In this study we tested the accuracy and reproducibility of the RSIDTM Reader using image detection parameters and thresholds that were set during developmental validation of this device.