esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Calorimetry
The temperature of the core thermistors was remotely monitored via instrumentation controlled by a LabVIEW program (LabVIEW software version 2010, National Instruments, www.ni.com/; accessed 18 September 2023) running on a computer located within the control area. The energy delivered by the laser system during the sequence of 5 shots was 556.0, 592.0, 582.4, 620.9 and 617.7 J. The laser was operated at full power with \(\sim\)500 fs duration pulses focused onto 15 \(\upmu\)m gold targets (with the exception of shot 2, in which 1 \(\upmu\)m CH plastic was used) [38]. The calorimeter operates in adiabatic mode, which allows the temperature of the calorimeter components to drift so energy deposited through impinging radiation will translate to an induced temperature rise in the graphite components. It was expected that a significant electromagnetic pulse (EMP) would be generated through the laser-target interaction, a typical feature of high-power laser systems [12]. As a result, precautions, such as (i) strategic placement of the instrumentation, (ii) protection of the  
Figure 4: Irradiated film which was attached to the front surface of the SPGC. The dose is integrated over a region corresponding to that of the total core area (highlighted in dashed yellow), giving a measurement of the average dose deposited over the entirety of the core. The red marked regions indicate the rough relative positions of the calorimeter circumference (**left**). Reference dose and spatial scales are included in the analysed image (**right**). Figure 3: **(a). ** The NPL small portable graphite calorimeter mounted in a custom-made holder alongside the associated electronics. This was attached to a specially made carriage so that it could be fitted onto the rail system and slide along the pipe to the irradiation point. **(b). ** The SPGC being attached to the rail and inserted within the re-entrance pipe. electronics through implementation of electronic circuitry consisting of discrete components that were designed to sit externally alongside the current DC Wheatstone bridge connection, and (iii) operation of the SPGC in a full adiabatic mode, aimed to limit such effects. Out of the 5 shots, 4 adopted the previously described setup (Figure 2b), with the SPGC placed at the irradiation point. The system was retracted from the beam path in the remaining shot, allowing for a simple assessment of the EMP-induced effects by monitoring the temperature drift in the absence of incident particles. The temperature drift data are analysed by observing a 30 s pre- and post-irradiation window, with a quadratic fit applied to best extrapolate the temperature data to mid-run. This interval allows the absorbed dose to be calculated whilst minimising the impact from changes in the environment surrounding the calorimeter. The rise in temperature at the mid-run position is then calculated and converted to the absorbed dose to the core through prior knowledge of the specific heat capacity of the absorber material.
## 4 Evaluation
### Evaluation Metrics  
This paper uses accuracy (ACC), detection rate (DR), precision(P), and \(F_{1}\) as evaluation indicators [34]. The accuracy rate is an indicator that describes the correctness of the intrusion detection algorithm to detect whether there is an intrusion. The detection rate is used to measure the detection performance of the intrusion detection system. The precision refers to the ratio of the number of positive samples that are actually predicted to positive samples to the number of positive samples predicted by the intrusion detection system. The F1 is the harmonic mean of the precision and detection rates. The formulas are as follows. The meanings of True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN) [35; 36] are shown in Table 4.  
\[Accuracy(ACC)=\frac{TP+TN}{TP+FP+FN+TN}, \tag{14}\]  
\[DetectionRate(DR)=\frac{TP}{TP+FN}, \tag{15}\]  
\[Precision(P)=\frac{TP}{TP+FP}, \tag{16}\]  
\[F_{1}=2\cdot\frac{\text{P }\cdot\text{ DR}}{\text{P }+\text{ DR}}. \tag{17}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Date** & **Data Description** & **Data Size** \\ \hline
11 June & Normal & 16.1 GB \\
12 June & Normal, Brute Force SSH & 4.22 GB \\
13 June & Normal, Infiltrating & 3.95 GB \\
14 June & Normal, HttpDoS & 23.4 GB \\
15 June & Normal, DDOS & 23.4 GB \\
16 June & Normal & 17.6 GB \\
17 June & Normal, Brute Force SSH & 12.3 GB \\ \hline \end{tabular}
\end{table}
Table 1: Data Description of the ISCX 2012 Dataset [6].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ISCX2012** & \multicolumn{2}{c}{**Train Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-5}  & **Count** & **Percentage** & **Count** & **Percentage** \\ \hline Normal & 890,726 & 97.27\% & 593,811 & 97.27\% \\ \hline Brute Force SSH & 4197 & 0.46\% & 2785 & 0.46\% \\ \hline Infiltrating & 6027 & 0.66\% & 4017 & 0.66\% \\ \hline Http DoS & 2090 & 0.23\% & 1392 & 0.23\% \\ \hline DDOS & 12,673 & 1.38\% & 8448 & 1.38\% \\ \hline Total & \multicolumn{2}{c}{915,695} & \multicolumn{2}{c}{610,453} \\ \hline \end{tabular}
\end{table}
Table 2: ISCX 2012 Dataset [27].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & \multicolumn{2}{c}{**Train Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-5}  & **Count** & **Percentage** & **Count** & **Percentage** \\ \hline Normal & 136,653 & 20.0\% & 45,564 & 20.0\% \\ \hline Brute Force SSH & 136,653 & 20.0\% & 45,564 & 20.0\% \\ \hline Infiltrating & 136,653 & 20.0\% & 45,564 & 20.0\% \\ \hline Http DoS & 136,653 & 20.0\% & 45,564 & 20.0\% \\ \hline Total & 683,265 & 100.0\% & 227,820 & 100.0\% \\ \hline \end{tabular}
\end{table}
Table 3: Statistics after ISCX2012 Balanced Processing.
## 3 Materials and Methods
### Model Performance Metrics  
To evaluate the models' performances, ten percent of the entire dataset was reserved for testing. To mitigate overfitting during training, early stopping was implemented with a patience threshold of three epochs. This approach allowed the models to stop training if no improvement in validation performance was observed over three consecutive epochs, ensuring a more generalized learning process. Once the training phase was completed, the reserved test set was used to evaluate the effectiveness of the models. The final models' performances were evaluated using key performance metrics, including a confusion matrix, accuracy, precision, recall, and F1-score [45], to provide a comprehensive analysis of the predictive capabilities of the models.
## 2 Materials and Methods  
### CHRIS Study and Phenotype Construction  
The Cooperative Health Research in South Tyrol (CHRIS) study is a population-based study whose baseline assessment was carried out in Vinschgau/Val Venosta (Bozen/Bolzano, Italy) between 2011 and 2018 [12]. Participants were invited to the study centre located in the central town of the valley where they underwent biospecimen collection [13], interviews, and physical examinations including electrocardiographic (ECG) analysis using the PC-ECG-System Custo 200 (Customed) workstation with a sampling rate of 1000 Hz (Custo med GmbH, Ottobrunn, Germany). The 12-lead ECG at rest was registered as a conventional 10 s and a longer 20 min ECG recording, as described previously [14]. The values of P wave duration in milliseconds (ms) for each 10 s ECG were extracted and stored digitally. Collected blood samples were aliquoted, and serum and plasma samples were obtained for posterior biobarking. Additionally, information on medication use during the seven days prior to the study centre visit was collected by scanning the barcodes of drug boxes brought by the participants to the interview and classified using the Anatomical Therapeutic Chemical (ATC) coding system. Administration mode, frequency, and duration of therapy were registered for each scanned drug. Additional information pertinent to common risk factors for AF was collected. A description of data collection procedures is available in Supplementary Table S1.  
We evaluated the collected data (CHRIS study data release 3.0) which include phenotype information from 10,518 study participants. The 10 s ECG recording was available for 10,438 (99.24%) individuals. Using clinical guidelines for the management of AF [2; 3], we initially selected, as possible AF cases, participants who responded positively to the question \"Do you have atrial fibrillation diagnosed by a doctor?\" or who had a P wave duration of 0 ms at the 10 s ECG recording. To strengthen the selection, we further refined cases to those for whom we identified the use of cardiovascular drugs corresponding to the ATC classification group \"C\", with specific dosage and information on administration mode, and consulted a clinical cardiologist for the experience-based identification of possible therapy for AF (Supplementary Table S2). In order to objective the method and minimize the misclassification of AF cases, 51 individuals who responded positively to the question \"Do you have atrial fibrillation diagnosed by doctor?\" but did not take the above-specified medications and had a P wave \(\
eq\) 0 ms were excluded as cases. All remaining participants not fulfilling the above phenotype classification criteria were defined as controls. Finally,nine participants were assigned as missing for the AF phenotype, since no information was available both for the 10 s ECG and for medication. This approach might lead to some false negatives in the large size control group; this, however, ensures having fewer false positives among AF cases.
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Multi-Scale FAPAR Truth Data
#### 3.4.2 Coarse-Resolution FAPAR  
ASTER satellite data were geometrically registered using a SPOT image at 10 m resolution. Coarse-resolution (MODIS) FAPAR products were then registered to match the ASTER data.  
Based on a careful geometric registration between ASTER and coarse-resolution FAPAR products, fine-resolution FAPAR data were aggregated to coarse resolutions to form the truth reference FAPAR data. The MODIS FAPAR product and the coarse-resolution reference FAPAR data were compared pixel by pixel.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
To further examine the changes in blueberry aroma during storage and simulated marketing, the concentration results for 23 selected key odorants were subjected to PCA. The PCA score plot shown in Figure 1A was responsible for an overall cumulative variance of 78.85%. The score plot was able to clearly separate and cluster SC from S1 based primarily on the concentrations of ethyl 3-methylbutyrate, 3-methylbutyric acid, linalool, and hexanal (Figure 1B). This loss of aroma compounds in S1 agreed with the R-index results (Table 4). In addition, S3 clearly separated from SC and S1 due to the large accumulation of (_E,Z_)-2,6-nonadienal, 1-octen-3-ol, guaicol, (_E_)-cinnamaldehyde, decalactone, geraniol, nerol, citronello1, 4-vinylguaiacol, and (_Z_)-3-hexenal that occurred as the blueberries were warmed to 20 \({}^{\circ}\)C (Figure 1B).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Storage Treatments** & **R-Index JB****a** \\ \hline S1 & 81 * \\ S2 & 57 \\ S3 & 62 \\ \hline \end{tabular} \({}^{*}\)Calculated using [PERSON]’s computation against the SC blueberry sample (control) expressed as a percent. There were 5 females and 16 males (18–61 years old). Critical R-index value = 66.44 using a two-tailed test. * Significantly different from the control (SC) at \(\alpha\) = 0.05.  
\end{table}
Table 4: R-index values based on the degree of similarity ranking versus the control (SC) of the overall aroma of blueberry powders prepared from blueberries stored under different conditions.  
Figure 1: Effect of storage condition on 20 significant key odorants from the analysis using blueberries immediately after harvest (SC) and after 3 weeks of storage at 1 \({}^{\circ}\)C (S1), followed by a further 1 week at 10 \({}^{\circ}\)C (S2) and then 2 d at 20 \({}^{\circ}\)C (S3). (**A**) PCA score plot; (**B**) PCA biplot. Numbers within the graph are 1: SC, 2: SI, 3: S2, and 4: S3.
# Voice Simulation: The Next Generation
## 3 Results  
### Low-Dimensional Airflow and Intraglottal Pressure Modeling  
With relatively low Strouhal, Reynolds, and Mach numbers, the glottal airflow may be considered quasi-steady, inviscid, and incompressible. The exception is in the brief periods near glottal closure, in which both the air particle velocity \(v\) and the vocal fold separation \(h\) decrease, becoming zero at full glottal closure. Then, the Reynolds number decreases and the fluid becomes viscous [5]. Further, the Strouhal number increases and the assumption of a quasi-steady flow is not valid. Nevertheless, those periods are brief compared to the full glottal cycle and an empirical correction may be possible. Another relevant parameter is the ratio of vocal fold separation to vocal fold length, \(h/L\ll 1\), which implies that the flow within the glottal channel may be considered two-dimensional. However, this reduced dimensionality applies only to a simple mode of vocal fold vibration that offers a single flow channel. When islands of partial vocal fold contact occur, the airflow becomes highly complex and multi-directional, as illustrated later.  
Under the above assumptions and simplifications, single-channel glottal airflow is predictable by a modified [PERSON]'s equation that includes flow separation from the glottal walls and vortex formation for a divergent glottis. An expression for the mean glottal pressure \(p_{g}\) can then be written in the form  
\[p_{g}=p_{e}+\frac{p_{s}-p_{e}}{k_{t}}\bigg{(}1-\frac{a_{2}}{a_{1}}\bigg{)}, \tag{6}\]  
where \(p_{e}\) is the supraglottal pressure (at the epiglottis), \(p_{s}\) is the subglottal pressure (at the upper end of the trachea), \(k_{t}\) is a transglottal pressure coefficient that includes the effects of flow separation and vorticity. The area \(a_{1}\) is the glottal entry area and \(a_{2}\) is the flow separation area for a divergent glottis. Generally, \(a_{2}\) is assumed to be the exit area for a convergent glottis [6]. The supraglottal pressure \(p_{e}\) can further be approximated as  
\[p_{e}=I\,dU/dt, \tag{7}\]  
where \(I\) is the vocal tract acoustic inertance and \(dU/dt\) is the derivative of the glottal exit volume flow. The above analytic equations contain two independent mechanisms of driving pressure on the vocal folds for self-sustained vocal fold oscillation, one which is independent of the airway (second term in Equation (6)), and one which is critically dependent on the airway (first term in Equations (6) and (7)). The relative contributions of these two mechanisms, labeled the mucosal wave (MW) mechanism and the supraglottal inertance (SI) mechanism, have recently been reported [7].
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## System Design
### Azure Cloud Integration
#### 3.3.1 Azure IoT Hub  
The Azure IoT Hub plays a central role. It acts as a bridge between the Azure Sphere devices and the Azure cloud services. On one hand, not only the device-to-cloud messages are collected by Azure IoT Hub to understand the real-time state of the MT3620 devices, but also the cloud-to-device commands and notifications are sent reliably to update the policies of sensor data collecting that are stored in the MT3620 device. On the other hand, a zero-touch, just-in-time provisioning is achieved with the help of Device Provisioning Service, which means that millions of devices can be provisioned in a secure and scalable way with no human intervention.
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 2 System Model  
We consider an indoor VLC system that consists of a single LED transmitter located on the ceiling of a room. This LED is used for illumination and communication simultaneously to serve \(M\) users. We apply OOK modulation technique to modulate the light intensity based on the data received through a power line communications (PLC) network. Moreover, each user has a single photodetector (PD) facing the ceiling.
# Semantic Description of Explainable Machine Learning Workflows for Improving Trust
## 2 Ontology Concepts
### Ontology Specification
#### 2.1.1 Ontology Purpose and Requirements  
The purpose of developing our domain-specific ontology is to represent the entire ML process and post-hoc explanation process, enabling data scientists to have a holistic view and a better understanding of those processes, aiming to complement and leverage the post-hoc explanations. The ontology captures the concepts of the domain and makes them machine-interpretable, making it possible to keep track of the steps from the processes and retrieve information from them.  
The ontology must comply with functional and non-functional requirements. The functional requirements are related to the knowledge or content of the ontology; therefore, they can be stated as competency questions (CQs) that the ontology should be able to answer. The CQs we defined for our ontology are related to the ML and explanation process components:  
* CQ1. Which data were used to train the model?
* CQ2. How privacy-sensitive was the dataset?
* CQ3. How balanced are the data?
* CQ4. How were the data preprocessed?
* CQ5. What are the correlations of the input datasets?
* CQ6. What are the characteristics of the ML algorithm?
* CQ7. What is the logic behind the ML model?
* CQ8. Why did the model generate this output?
* CQ9. How was the ML model evaluated? What is the meaning of those metrics?
* CQ10. How were the explanations generated? How are the explanations presented to the user? How faithful are the explanations?
* CQ11. How general are the explanations? Do they apply to all instances?  
The non-functional requirements are related to characteristics, qualities, and general aspects not related to the content [13]. We define the non-functional requirements of our ontology as follows:  
* REQ1. Guarantee usability to data scientists and developers who want to understand the adequacy of the ML model and improve product efficiency, research, and new functionalities, helping understand the whole ML process and explanation process, possibly identifying where to make adaptations in the process;
* REQ2. Guarantee extensibility by defining a generic ML ontology that represents ML processes that tackle different problems besides classification and can be further adapted or specialized;
* REQ3. The ontology should be implemented using standard languages and tools;
* REQ4. Guarantee interoperability with already existing ontologies by grounding them into a foundational ontology.  
In order to comply with REQ2 and considering the complexity of the ontology, we decided to structure the ontology into three modules: (1) a general module that represents general ML processes independently of the task or the learning type performed; (2) a specific module for supervised classification; and (3) an explanation module, which represents the post-hoc explanation process. Splitting the ontology into smaller parts allows the modeling problems to be tackled one at a time [13].
## 2 UAV-Enabled ITS Framework
### Second Layer: Communication Networks for Traffic Monitoring
#### 2.2.2 Advances in Traffic Monitoring  
Proper monitoring of the transportation system is a prerequisite for the proactive management and effective control of ITSs. In UAV-based traffic monitoring, a level of human interaction is involved in collecting traffic flow parameters and conducting further analysis to assess traffic behavior and the performance of road geometries. Different traffic behaviors (congestion, shock waves, lane changes, gap acceptance, and accidents) and road geometries (roundabouts and signalized and non-signalized intersections) are monitored using UAVs. A summary of the research on traffic monitoring using UAVs can be found in Table 3.  
With technological advancements, most research work includes using the latest wireless technologies to ensure uninterrupted communications between UAVs and GTs. [PERSON] et al. [48] studied various UAV-aided traffic monitoring systems that many researchers have proposed over the years with a detailed step-by-step framework. A review of the literature on UAV-aided traffic monitoring is presented in Ref. [49]. An extensive data-based system for traffic congestion analysis in large-scale transportation networks is proposed by [PERSON] and [PERSON] [50]. Using a swarm of 10 drones, the authors recorded traffic streams from an urban area for five days and analyzed the data to extract traffic behaviors at both microscopic and macroscopic levels. [PERSON] et al. [12] proposed a multiple UAV-aided real-time traffic monitoring system. In this system, UAVs adaptively adjust their trajectories, collect vehicle information from city roads, and send them to a data processing center to help make traffic management decisions. The proposed system performed better than the traditional fixed trajectory UAV-based traffic monitoring systems, considering the metrics of vehicle coverage and event detection rates.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Carthamus tinctorius L
#### 3.1.6 Anticancer Properties  
Flavonoids in safflower can induce anticancer effects in many human organs. HSYA, one of the flavonoids derived and isolated from safflower, poses antitumor activity. HSYA may reduce the proliferation of the Skov3 ovarian cancer cell line and enhance its sensitivity to chemotherapeutic agents by downregulating WSB1 (WD repeat and SOCS box-containing protein 1) expression [38]. HSYA shows potential as a candidate for preventing and treating hepatocellular carcinoma. Research on H22 (hepatoma cell line) tumor-bearing mice demonstrated that HSYA can significantly inhibit tumor progress by lessening the secretion of angiogenesis factors. [PERSON] et al. [39] suggest that HSYA can induce apoptosis in cancer cells without causing cytotoxic effects on normal liver cells. In the case of hepatocellular carcinoma, the anticancer effect of safflower yellow (SY) has also been tested. Safflower yellow presented a potent effect on the development of hepatocellular carcinoma by amplifying liver immune infiltration, promoting collagen degradation, and modulating the gut microbiota, which may contribute to the immune microenvironment [40]. The literature data suggest that safflower can be developed as a breast anticancer agent. The ethanol extract of safflower inhibits cell proliferation and induces apoptosis in breast cancer cell lines T47D [41]. Treatment with hydroxysafflor yellow B (HSYB), an isomer of HSYA, decreased survival and proliferation of human breast cancer MCF-7 cells in a dose-dependent manner. Moreover, HSYB restricted the MCF-7 cell cycle at the S phase [42]. Safflower polysaccharide (SPS) is an active fraction purified from _Carthamus titorius_ L petals. Safflower polysaccharide significantly inhibited the proliferation of the MCF-7 human breast cancer cell line. These inhibitory effects are enhanced in a dose- and time-dependent manner [43]. SPS demonstrates cytotoxic activity against diverse types of cancer cells. SPS could significantly inhibit azoxymethane (AOM)/ dextran sodium sulfate (DSS)-induced mice colorectal cancer through the regulation of macrophage polarization [44]. Furthermore, SPS induces cervical cancer cell apoptosis via inhibition of the PI3K/AKT pathway [45], and inhibits the growth of tongue squamous cell carcinoma [46].