esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### FEM Test Case
#### 3.1.5 Condition Four  
In this condition, it is verified that the magnitude achieved in the power spectrum of iteration is not more than the reference. The reference signal is in the perfect form of the excitation when it is normalised and not affected by any noise, while any the windowed iterations are subject to noise; hence, the maximum magnitude of the iteration cannot be more than the reference. Nonetheless, due to the finite resolution of the windows, a safety value is defined as \"_sMargin_\" to allow small differences between the two to be neglected.  
Keeping in mind that in the case of FEM signal, since source points were not placed linearly and variable transfer functions were used, the characteristics of the generated signals can be different. This is clearly illustrated in Figure 10, where the received has more than 10 cycles; hence, the maximum magnitude in the power spectrum of the iteration would be increased. The shown windowed signal is approximately located around the 4-metre region (second torsional wave), where the results of impact from backward leakage and the front defect are received from the forward direction. As can be seen, the condition will not hold in this case; however, when setting the threshold as 10% of the maximum magnitude (15) the result of the first detection is increased and this wave will also be detected. Changing this value to 15% increases the detection amplitude of the second signal and the pipe end even more, but the first defect signal remains constant. At 20%, the maximum values are achieved (while the overall detection result of the defect becomes less due to the increment of the associated value to the pipe end), and anything above does not change the generated results. Nonetheless, for the purpose of experimental tests, this value is only set as one which is less even than 1% of the maximum magnitude of the reference. This ensures that only calculation errors due to the limitation of the window size are neglected.  
Figure 9: Example of an outlier case detected using Condition Three (C3), where (**a**) shows the time domain of the iteration window and (**b**) is its respective power spectrum. The red lines (dotted, +) show the references achieved from excitation sequence and the black lines (solid, x) show the results from each iteration.
## 3 Results and Discussion
### Micomorphological Analysis  
SEM results showed that the surface morphology of WPC (Figure 7b and Figure 51b,c) was compact and smooth, while the surface of poplar (Figure 7a) was loose and rough. This could have resulted under the influence of relatively high temperature and pressure (180 \({}^{\circ}\)C and 65 MPa) applied, which subsequently squashed the cracks between these fibers. On the other hand, the smoothness and hydrophobicity of the material surfaces could be increased when the plastic in the viscous state [24] flowed through and covered the surface of the fiber or occupied the cracks. As a result, the diffusion of water into WPC was hindered. In addition, the surface of WF/ABS-8 was smoother than that of other WPCs, as shown in Figure 7b and Figure 51b,c, suggesting the complete merging of ABS and fiber [28], which was also illustrated by WPCs produced by other researchers [29]. This finding was corroborated by its excellent mechanical properties and hydrophobicity.  
As observed from the cross-section surface, the WPC (Figure 7d and Figure 51e-h) showed smaller pores and smoother fiber than those of poplar (Figure 7c). The cellulose, hemicellulose and lignin would have recombined under the influence of temperature and pressure to form more hydrogen bonds within the resulted product. Subsequently, the mechanical and hydrophobic properties of the materials were due to the reduction of hydroxyl groups that can bind with water molecules [30]. In addition, WF/ABS-8 showed the smoothest cross-section surface compared with those of the other four materials (Figure 7d and Figure 51e-h). In short, WF/ABS-8 represented the most suitable combination to produce desirable WPC due to its good combination between ABS and fiber, denser pressing, smoother surface and better mechanical and hydrophobic properties.  
Figure 6: (**a**,**b**) Thermogravimetric analysis of WPC and poplar.
## 4 Edge Computing  
Cloud computing began as a solution for the storage and processing of large-scale data. However, in recent years, the massive growth in smart and connected devices, coupled with technologies such as IoT, V2X, augmented reality (AR), etc., has presented challenges to centralized cloud computing. The requirements for ultra-low latency and real-time response, high bandwidth, mobility support, and context-awareness services are the focus of researchers in academia and industry.  
According to Gartner, it is estimated that 75% of data in 2025 will be processed outside the traditional data center [57]. Edge computing aims to bring cloud computing services closer to the end-user for a real-time, context-aware response. It comprises a continually expanding set of connected devices and systems that gather and analyze data close to the users. According to Intel predictions, an autonomous vehicle will generate about 4 TB of data in an hour (1 GB of data is generated every second). In the following, we review edge computing approaches including fog computing [5], cloudlets [6], and mobile edge computing (MEC) [58]. A detailed comparison of fog computing, cloudlets, and MEC is presented in [59]. Table 3 shows a comparison of different computing strategies. Various vendors, including Microsoft Azure [60], Cisco Edge Computing [61], and IBM [62] are already employing edge computing. Some approaches to edge computing described in the literature are:  
* Fog computing: Fog computing is proposed to address the issue of the large latency between mobile devices and the cloud. [PERSON] et al. [5] presented its purpose and key characteristics. It is a highly virtual platform to provide computing, storage, and networking services closer to end devices than the Cloud. In general, it offers the following benefits: quick response to delay-sensitive applications, such as emergency and multimedia applications, data aggregation, data protection and security, context-aware and location-aware service provisioning [63]. Details on fog computing can be found in [64].
* Cloudlets: Cloudlets are a project developed by CMU (Carnegie Mellon University) [6]. They provide the middle tier in a three-tier hierarchy: mobile device-cloudlet-cloud. Their main goal is to merge mobile computing and cloud computing by introducing a middle-tier forming a multi-hierarchical structure. They can be viewed as \"data centers in a box\", whose goal is to \"bring the cloud closer\" by means of low end-to-end latency and high bandwidth. Mobile end devices can offload computational tasks to a cloudlet.
* Mobile edge computing (MEC). Mobile edge computing is a European Telecommunications Standards Institute (ETSI) initiative to provide IT and cloud-computing capabilities within the RAN (radio area network) in close proximity to mobile subscribers. The RAN edge offers a service environment with ultra-low latency and high bandwidth, as well as direct access to real-time RAN information (subscriber location, cell load, channel load, etc.) and is well-suited to provide context-related services.
# Relationship between Total Antioxidant Capacity, Cannabinoids and Terpenoids in Hops and Cannabis
## 4 Materials and Methods
### Measuring Antioxidant Activity with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) Assay  
Antioxidant activities of hops and cannabis were determined using the DPPH assay introduced by [PERSON] et al. (1995) and used by [PERSON] et al. (2021) for cannabis, with some modifications. A calibration curve was generated using different serial dilutions of a 10 mM Trolox(r) standard (Sigma-Aldrich, Saint Louis, MI, USA) in HPLC-grade methanol (Thermo Fisher Scientific, Waltham, MA, USA). A stock solution of 0.1 mM DPPH ion (Sigma-Aldrich, Saint Louis, MA, USA) in HPLC-grade methanol was prepared fresh daily. Aliquots (100 \(\upmu\)L) of extracted samples or standards were placed in 15 mL Falcon tubes and 2900 \(\upmu\)L of DPPH ion stock solution was added. The mixture was subjected to vigorous vortexing (Thermo Scientific vortex, Waltham, MA, USA) for 30 sec then incubated for 30 min at room temperature in the dark. Absorbances were measured at 517 nm using the Uttropec 2100 pro ultraviolet/visible spectrophotometer (Biochrom Limited, Cambridge, England). A DPPH ion solution was used as a control and HPLC-grade methanol was used to zero the spectrophotometer. The average radical scavenging activity of the samples was calculated and the DPPH inhibition (%) was calculated using Equation (1). Concentration (M) of Trolox equivalent antioxidant activity (TEAC) using the calibration curve was calculated using Equation (2). Results are reported as the concentration (M) of Trolox equivalent antioxidant activity (TEAC) per gram dry matter sample using Equation (3). The experiment was carried out in triplicate.  
\[\%\ \text{DPPH inhibition}=\frac{\text{Absorbance}_{\text{control}}- \text{Absorbance}_{\text{sample}}}{\text{Absorbance}_{\text{control}}} \tag{1}\]\[\text{TEAC (M)}=\frac{\left(\frac{\%\text{DPPH inhibition}-8.7009}{36.361}\right)}{1000} \tag{2}\]  
\[\text{TEAC (M) dry matter (g)}^{-1}=\frac{\text{Extraction volume (0.001 L)}\times\text{TEAC (M)}}{\text{Analysis volume (0.0001 L)}\times\text{(sample mass }-(\%\text{ mc}\times\text{sample mass))}} \tag{3}\]
## 4 Discussion
### Listening in Noise  
Listening in noise is a clear challenge, which is not handled in the same way by CI users and NH people. Noise flattens the spectrum and the subsequent structures in the auditory system do not react identically [37]. The study of speech recognition in noise has become of great interest as it is present in daily listening conditions. Additionally, we can see the coding behaviour for different SNRs (floor and ceiling effect and intermediate situation).  
In this study, the CI user group was older on average than the NH group. In general, older people have lower speech perception scores in noise, even with normal or age-related hearing, compared to young people. However, the purpose of the study was to test both groups and see if a similar trend (between FC and CP coding schemes) could emerge and not to compare CI users with NH subjects.  
Another finding was the effect of noise on performances for each strategy, which makes this study interesting in that each manufacturer can set any coding scheme on their devices. Consequently, it is worthwhile to evaluate the results through different approaches. Our work may suggest that the strategy is noise-dependent.  
The number of channels needed to understand speech in noise or in quiet is an important issue. Studies have indicated that, when speech is processed in the same manner as in CIs and presented in quiet to NH listeners, sentence recognition scores are higher than 90% with as few as 4 channels [38; 39]. In the literature, results show that more channels are needed to understand speech in noise than in quiet [10] but selecting more than 12 channels may not yield significant improvements on the recognition performances [21]. These considerations orientated the choice of our parameters.  
In noise, performances of CI users reach a plateau as the number of channel increases; for NH subjects performances continue to increase (up to 100%), suggesting that, CI subjects could not fully utilize the spectral information provided by the number of electrodes, possibly because of the channel interaction [38]. As indicated above, trends are similar for NH and CI listeners but results are not interchangeable. It is sensible to say that more channels imply more information but this also implies more overlap between the electrodes. This conflict needs to be studied in the future; we can simulate channel interaction with NH subjects.  
The acoustical material (in our case the Fournier's lists and the cocktail party noise) seemed to be well adapted to the situation.
## 2 Materials and Methods
### System Description  
In this study, the technological system flow is shown in Figure 1. The HABR system was made up of six compartments. Each compartment had an open upside for wastewater sampling and a tap at the downside for sludge sampling. A baffle was set in each compartment to make the inflow first mix downwards with the fluid at a relatively high speed. At the bottom of the baffle, there was a 30\({}^{\circ}\) guide baffle to make sure that wastewater was transferred to the downside center of the room bottom in order to mix with the sludge adequately in the reactor room [29]. Then the wastewater experienced an upward flow before entering the next compartment. The cross-flow packing largely increased the chance of contact between organic pollutants and biofilms. Each compartment of the HABR had a cubic size of 1.5 m \(\times\) 0.6 m \(\times\) 3.6 m (length \(\times\) width \(\times\) height) and the effective working volume of the whole reactor was 18.0 m\({}^{3}\) with the final flow rate of 1.5 m\({}^{3}\)/h.  
Influent wastewater was pumped from the coagulation pretreatment sedimentation tank to the inflow tank. H2 SO4 was added by an acid pump to the inflow tank to regulate the influent pH, and then wastewater flowed to the HABR by gravity. Temperature and pH were continuously monitored by on-line equipment. The outflow was collected by a catchment tank before the following aerobic treatment.  
Figure 1: Flow chart of the hybrid anaerobic baffled reactor (HABR) reaction system. 1. coagulation sedimentation tank; 2. measuring pump; 3. inflow tank; 4. acid pump; 5. acid tank; 6. hybrid anaerobic baffled reactor; 7. cross-flow packing; 8. catchment tank.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Chemical**} & \multirow{2}{*}{**pH**} & **Total** & **Total** & \multirow{2}{*}{**Sulfates**} & **Suspended** & **Color** \\  & **oxygen demand** & & & **Nitrogen** & & **Phosphorus** & **solid (SS)** & **Color** \\  & **(COD)** & & & & & & \\ \hline Unit & mg/L & – & mg/L & mg/L & mg/L & mg/L & times \\ Value & 700–1300 & 9–11 & 40–45 & 0.5–3 & 800–1200 & 150–350 & 200–300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wastewater quality.
## 4 Results
### Surveys 3-4  
In 2009, twelve ERT profiles were carried out along Omirou Avenue with the aim of imaging the stratigraphy of the soil and to verify the presence of archaeological structures into the subsoil (ERT profiles 11-22 in Figure 9). As the planning of the survey was influenced by the necessity to ensure the traffic of cars, two main fragmented lines of investigation were conducted (blue and red lines in Figure 9). Profiles had variable lengths and the depth reached for all was 10 m. Figure 10 shows two tridimensional perspective views of the imaged ERT profiles. In general, a uniform stratigraphy is detected and characterized by materials with low resistivity values. In some cases, an increase of resistivity can be attested at about 8 m in depth. High resistivity features that can be associated to huge empty cavities were not detected, as well as it was not possible to distinguish small cavities filled up with loose conductive sediments.  
Figure 8: Solomon Square: location of ERT profiles 6–10 (**a**), data acquisition (**b**) and geophysical results (**c**–**f**).  
Figure 10: Omirou Avenue: 3D perspective views of ERT profiles 11–16 (**a**) and 17-22 (**b**).  
Figure 9: Omirou Avenue: location of ERT profiles 11–22. Blue and red lines indicate the two main fragmented lines of investigation.  
Resistivity features with well-defined shapes similar to the ones that were detected on the ERT profiles of surveys 1 and 2 are not attested here, such as to hypothesize the presence of archaeological remains under the road plane. However, isolated shallow high resistivity values were highlighted in each section (marked with black arrows in Figure 10) that can suggest the existence of nuclei with a non-vanishing probability to find ancient remains. Noteworthy are the two high resistive nuclei that repeat on the ERT profiles 13, 14, 19 and 20, which seem to be on the same route.  
In 2017, before the removal of the asphalt layer from the road, a GPR survey was performed. Figure 11a displays two examples of radargrams in which punctual targets, probable manholes, and horizontal discontinuities are marked with coloured arrows. The same marks are reported on the horizontal slice relative to the time window 10-15 ns, the one that better enhances the presence of targets into the subsoil (Figure 11b). The map shows the presence of different linear high amplitude values of the electromagnetic signal that probably refer to pipes buried into the shallow ground. Particularly evident is the presence of a high amplitude feature flanking the south side of the road attributable to the underground sewer pipes (with dotted lines in Figure 11b). Furthermore, the map clearly highlights the presence of manholes indicated with crosses in Figure 11b.
## 4 Results and Discussion
### Discriminant Analysis for Classifying Defects  
Ultrasonic GWs through the defects (numbered #1 to #5) were measured ten times for each defect at three measuring angles (0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\)), as illustrated in Figure 2b. Figure 6a,b show the results of the LDA in the S0 and A0 modes, respectively, obtained by the STFT between specimens with and without defects. The lines in Figure 6a,b are discriminant lines obtained by the LDA to classify the defects.  
Figure 5: Results of applying Equation (4) to the images resulting from the STFT from test specimens without and with defects.  
Figure 6: Results of LDA in (**a**) S0 and (**b**) A0 mode.  
In the two modes, the average discrimination rate by the LDA was 80.3% for S0 mode and 74.0% for A0 mode, as summarized in Table 2.  
Table 2 shows that the classification rates for defects in the S0 and A0 modes were 80.3% and 74.0%, respectively. We thought that the LDA using S0 mode would be useful for the classification of no-defect specimens. As a method to classify the defects using LDA, first, all specimens were classified in S0 mode. Second, misclassified specimens were re-classified in A0 mode. For this purpose, the experimental data were randomly divided into two groups. As mentioned above, for the calibration set, no- and with-defect data were classified using the discriminant line in S0 mode, as shown in Figure 7a. Then, misclassified defect data (defect data within the no-defect region in Figure 7a) were classified again using the discriminant line in A0 mode, as shown in Figure 7b. From Figure 7a and Table 3, 15 no-defect data and 18 with-defect data were misclassified.  
Figure 7b shows the classification results for misclassified data in the calibrating data set by LDA in A0 mode. The defect data misclassified by LDA in S0 mode were classified as defect data by LDA in A0 mode. From the results of Figure 7a,b, the classification rate for the calibrating set was 95.5%.  
To validate the two newly developed discriminant lines in Figure 7a, the validation data set was applied to them. Table 4 shows the validation results of the newly developed LDA models for classifying the defects. The final classification rate was 97% (97 of 100). Based on the above results, we determined that the artificial defects of the 304 SS specimen were successfully classified by the LDA model based on the difference in the number of pixels obtained by STFT between with-defect and no-defect specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S0 Mode** & **Classification Rate** & **A0 Mode** & **Classification Rate** \\ \hline No defects (150) & 83.4\% & No defects (150) & 80.7\% \\ With defects (150) & 77.4\% & With defects (150) & 67.3\% \\ Total (300) & 80.3\% & Total (300) & 74.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average classification rates for defects by LDA in S0 and A0 modes.  
Figure 7: Classification results of **(a)** calibration data set classified by LDA in S0 mode and **(b)** data in the calibration data set misclassified by LDA in A0 mode.
## 2 Materials and Methods  
### Participants  
Thirty patients with an ACL injury who were having acute reconstruction or planning were included in our research. Four of them were eliminated because they had suture surgery for an associated cartilage injury, their original muscle strength did not decline as a result of chronic damage, or they were elite athletes. A total of 26 patients were assigned to the general rehabilitation exercise group (GRE, \(n=13\)) and the blood flow restriction group (BFR, \(n=13\)) according to age, gender, height and weight. All participants underwent ACL reconstruction by an authorized knee orthopedic surgeon at M Hospital using the autograft transplantation method, and rehabilitation training was performed by a rehabilitation team trainer with a physical therapy certificate and rehabilitation degree at the same hospital. All evaluations were performed the same way before ACL reconstruction and after the 12-week rehabilitation exercise program. All participants were required to visit the hospital rehabilitation center for 12 weeks, three times a week. For a period of 12 weeks, all participants were forced to visit the hospital rehabilitation facility three times each week. One participant from each group who had poor participation rates were eliminated, and we analyzed the final 24 participants (age: 29.3 \(\pm\) 8.0 years, male: \(n\) = 18, female: \(n\) = 6) for the study. The physical characteristics of the participants and the CONSORT (Consolidated Standards of Reporting Trials) flowchart are shown in Table 1 and Figure 1, respectively.  
All participants received information about the purpose and process of the study, including possible side effects, and consent was obtained. As shown in Table 1, there were no significant differences in the participants' characteristics between the groups. All procedures were in accordance with the ethical standards of the responsible committee on human experimentation. The study was approved by the Institutional Review Board of Konkuk University (7001355-202210-E-180) in Korea and was conducted according to the Declaration of Helsinki.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **GRE (\(n\) = 12)** & **BFR (\(n\) = 12)** & \(p\)**-Value** \\ \hline Sex (men/women) & 9/3 & 9/3 & - \\ Age (yr) & 27.83 \(\pm\) 8.43 & 30.83 \(\pm\) 7.59 & 0.370 \\ Height (cm) & 170.27 \(\pm\) 8.80 & 172.58 \(\pm\) 4.76 & 0.431 \\ Weight (kg) & 78.68 \(\pm\) 18.63 & 71.98 \(\pm\) 10.05 & 0.288 \\ ACL leg (right/left) & 6/6 & 3/9 & - \\ \hline \end{tabular} Values are expressed as the mean \(\pm\) standard deviation. GRE, general rehabilitation exercise group; BFR, blood flow restriction group; ACL, anterior cruciate ligament.  
\end{table}
Table 1: Participants’ characteristics.  
Figure 1: CONSORT flowchart. GRE, general rehabilitation exercise group; BFR, blood flow restriction group.