esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying Moss Response to Metal Contaminant Exposure Using Laser-Induced Fluorescence
## 3 Results  
### Moss Cu Content  
The total Cu deposited per dry weight of moss is reported in Table 2. The Cu content of moss fromd samples ranged from 21 to 360 umol/g dry wt. depending on the dosage. Treatments of Cu were applied uniformly across the area of the moss, but variation was expected based on individual from distribution and sampling. To have a better sense of this variability, two fromds per tray from the center or for days 1, 3, and 10 across three random sites were analyzed from each moss mass on each day. The effectiveness of the Cu treatment could be assessed by comparing the measured and applied Cu concentrations with the caveat that the measured Cu was in individual froms while the applied Cu was per moss mass. Figure 3 shows an approximately linear relationship with reasonably good agreement between measured and applied cumulative doses (e.g., days 1 and 2, 3 and 4, etc.). If we assume that the two fromds represent about 1 cm\({}^{2}\) surface area, then recoveries between what was applied and measured in the fromds were 95 \(\pm\) 43%, indicating that our methods were successful in quantitatively depositing Cu on moss surfaces.  
Figure 3: Comparison of the cumulative Cu dose (μmol/cm\({}^{2}\)) applied throughout the experiment to moss masses against the Cu content found in moss fromds (μmol/g dry wt). Error bars represent 1 standard deviation of the average of 3 fond pairs; these are only available for samples collected on days 1, 3, and 10 and provide a measure of scatter of Cu application within the mass moss. For samples where an error bar is absent, a horizontal line may be visible (trial 2) in the center of the marker.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 3 Noise in PLC Transmission
### Noise Characteristics Verified from in situ Measurements  
_In situ_ measurements concerning the noise involved in MV PLC transmission have resulted in the following conclusions [12]:  
* Every substation gives rise to a unique noise distribution.
* The transformers that electrically separate the MV grid from the HV and LV ones prevent the transition either of noise or of harmonic distortion products.
* The noise PSD in MV power lines is found to be higher than in LV ones by 20-30 dB.
* The maximum and minimum noise PSD levels may differ by up to 25 dB, indicating that corona noise from HV power lines is induced into the MV grid through the air.
* The noise PSD is affected by load fluctuations, especially in the NB-PLC frequency range.
* The noise levels observed in phase-to-phase (WTW) coupling does not usually exceed the ones observed in the relevant two single-phase-to-ground (WTG) couplings.  
The two primary properties taken into account to model noise in NB-PLC systems are:  
* The noise cumulative density function (CDF) can be better approximated by the Nakagami distribution and not by the Gaussian distribution as commonly assumed in the standard analysis and design of communication systems.  
* The mean PSD of the total noise encountered in NB-PLC systems can be modeled by an exponentially decreasing function of frequency, that is \[\text{PSD}(f)=Ae^{Bf},\] (5) where \(A\) and \(B\) are quantities depending on the parameters of the substation [12; 14].
###### Abstract  
The position error of two parallel surfaces is generally constrained by parallelism. However, as a range of change, it cannot represent the positional relation between two parallel surfaces. Large-scale equipment such as machine tools are complex and difficult to move. It is also an engineering problem to perform field measurements on it. To this end, a method of describing the positional relation between two horizontal parallel surfaces and two vertical parallel surfaces on-site is proposed in this paper, which is a novel kind of position error, enriching the form of parallel surface position error, and solves the inconvenience problem of large equipment position error measurement. The measurement mechanisms are designed, and the measurement principle is given. Firstly, the combined projection waveform of the measured surface can be obtained by the geometric relationship between the measurement mechanism and the measured surface. Secondly, an algorithm is studied to process the obtained waveform, and the combined projection curve of the measured surface is acquired. Then, under the condition of considering the shape contours of the two surfaces, an algorithm is developed to acquire the calculated shape contour of the measured surface. According to the difference between the calculated surface shape contour and the known shape contour of the measured surface, the positional relation of the two surfaces can be determined. Meanwhile, the mathematical models of algorithms are established, and the measurement experiments are carried out, and the algorithms are verified by the mutual measurement method of the two surfaces. The results show that this method can accurately obtain the positional relation of two horizontal parallel surfaces and two vertical parallel surfaces.  
measurement mechanism; machine tool; surface shape contour; on-site measurement; positional relation +
Footnote †: journal: Applied Physics Letters
## 3 Results
### Antibacterial Activity  
For illustrative purposes, a representative inhibition zone observed in the agar overlay assay for WA Manuka honey 2 pre-gel solutions is shown in Figure 5. Few bubbles were observed, which might be owing to the production of CO\({}_{2}\) as a consequence of the metabolism of the glucose and fructose in honeys by _E. coli._ Figure 6 displays the zone of inhibition recorded for WA Jarrah honey and its respective formulations (i.e., pre-gel solution, wet sheet, dry sheet) against all tested bacterial strains. The comparative antibacterial activity of all samples against the eight bacterial strains that were tested as part of this study are presented as bar diagram in Figures 7 and 8. The inhibition zone exhibited by honey-loaded formulations are also shown in Supplementary Tables S1 and S2. In most of the pure honeys and honey-loaded formulations, the pure honey showed the highest activity followed by the dry sheet, pre-gel solution and wet sheet (Figures 7 and 8) against  
Figure 4: Chromatograms of Jarrah (JAR) honey and its corresponding formulations. Images taken at white light after derivatization with DPPH reagent; (PH-1–PH-3)—pure JAR honey, (PG-1–PG-3)—JAR honey pre-gel solution, (WS-1–WS-3)—JAR honey wet sheet, (DS-1–DS-3)—JAR honey dry sheet.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Honey**} & \multicolumn{2}{c}{**Rf of**} & \multicolumn{2}{c}{**Gallic Acid Equivalent (\(\mu\)g/g Sample)**} & \multicolumn{2}{c}{**Gallic Acid Equivalent (\(\mu\)g/Sheet)**} \\ \cline{3-8}  & **Antivadant** & & & & & **(\(\mu\)g/Sheet)** \\ \cline{3-8}  & **Compound** & **Pure Honey** & **Pre-Gel** & **Wet Sheet** & **Dry Sheet** & **Wet Sheet** & **Dry Sheet** \\ \hline WA Jarrah & 0.650 & 7.44 \(\pm\) 0.02 & 7.43 \(\pm\) 0.03 & 7.43 \(\pm\) 0.01 & 20.64 \(\pm\) 0.02 & 185.75 \(\pm\) 0.03 & 185.76 \(\pm\) 0.02 \\ \hline WA Manuka 1 & 0.630 & 16.70 \(\pm\) 0.01 & 16.69 \(\pm\) 0.01 & 16.68 \(\pm\) 0.02 & 46.34 \(\pm\) 0.02 & 417.00 \(\pm\) 0.02 & 417.06 \(\pm\) 0.01 \\ \hline WA Manuka 2 & 0.630 & 29.3 \(\pm\) 0.02 & 29.28 \(\pm\) 0.02 & 29.27 \(\pm\) 0.01 & 81.30 \(\pm\) 0.02 & 731.75 \(\pm\) 0.03 & 731.73 \(\pm\) 0.02 \\ \hline WA Coastal & 0.630 & 4.96 \(\pm\) 0.02 & 4.95 \(\pm\) 0.02 & 4.94 \(\pm\) 0.01 & 13.72 \(\pm\) 0.02 & 123.50 \(\pm\) 0.03 & 123.48 \(\pm\) 0.01 \\ Peppermit & & & & & & & \\ \hline NZ Manuka & 0.650 & 29.13 \(\pm\) 0.02 & 29.11 \(\pm\) 0.02 & 29.10 \(\pm\) 0.02 & 80.83 \(\pm\) 0.02 & 727.50 \(\pm\) 0.02 & 727.4 \(\pm\) 0.03 \\ \hline \end{tabular}
\end{table}
Table 3: Antioxidant band activity of pure honeys and their corresponding formulations (\(n=3\), data represents mean \(\pm\) SD).
## 3 Results and Discussion
### Propeller Slipstream Effect on the Flow Structure  
Flow structures around and downstream of the propeller are presented and discussed in this section. Figure 10 shows the asymmetric flow structure of the MAV propeller model for the propeller at two different azimuth angles, \(\varphi=0^{\lx@math@degree}\) and \(90^{\lx@math@degree}\), respectively, with \(\alpha=0^{\lx@math@degree}\). With the propeller slipstream, the wingtip vortex was reduced as compared with the MAV without the propeller slipstream, shown in the previous paper [11]. A clear asymmetric flow appeared, which followed the airfoil local inflow angle of attack evolution. Larger local angle of attack was formed on the upward-going blade side than that on the downward-going blade side, as shown in Figure 10e,g, respectively. Toward the wingtip, the local angles of attack on both sides were about the same. Details are shown in Figure 10f,h.  
At \(\varphi=0^{\lx@math@degree}\), the leading edge separation bubble formed on the upper wing surface and it was located near the center plane region, as shown in Figure 10i. The low pressure region, as marked with dark blue color, can be clearly seen on the upper surface, indicating the asymmetric flow due to the propeller slipstream effects. On the lower wing surface, two separated and asymmetric bubbles formed underneath the wing near the leading edge. The smaller bubble formed on the upward-going blade side and the larger one on the downward-going blade side. The shape of the bubble at different spanwise locations can be found in Figure 10e-h. The areas with relative larger pressure force were on the lower surface at the up-going blade side (Figure 10d). This was due to the propeller tangential flow impinging on the lower wing surface. However, on the down-going blade side, the tangential flow impinged on the upper surface instead.  
Figure 9: Instantaneous C\({}_{J}\), C\({}_{d}\) distribution at various propeller azimuth angles at \(\omega=555\) rad/s, (**a**–**d**) lift and drag coefficient distribution along the wing span with changing of propeller azimuth angle from 0�math@degree} to 135°.  
Figure 10: Instantaneous flow structure around the MAV at \(\alpha=0^{\circ},\ \varphi=0^{\circ}\), (**a–d**) flow field around MAV, (**e**,**f**) airfoil sectional flow structure with up-going blade at spanwise location of \(2z/b=0.25\) and \(0.5\), (**g**,**h**) airfoil sectional flow structure with down-going blade at spanwise location of \(2z/b=0.25\) and \(0.5\), (**i**) flow structure at propeller’s azimuth angle \(\varphi=0^{\circ}\).
## 4 Experimental Results
### Learning Parameters and Assessment Measures
After several trials, we found that a value equal to 5% was a good compromise. In all of the experiments, the number of classifiers in an ensemble varied from 25 to 150. With predictive accuracy being defined as the ratio of correctly classified samples on the total number of samples of a testing set, from left to right, the columns in the following tables designate:* Average predictive accuracy of the model (correctly classified samples in the testing set divided by the number of samples);
* Average fidelity on the testing set, which is the degree of matching between the generated rules and the model. Specifically, with \(P\) samples in the testing set and \(Q\) samples for which the classification of the rules corresponds to the classification of the model, the fidelity is \(Q/P\). * Average predictive accuracy of the rules;
* Average predictive accuracy of the rules when the rules and model agree. Specifically, it is the proportion of correctly classified samples among the \(Q\) samples defined above. * Average number of rules extracted;
* Average number of rule antecedents.
## 2 Site Investigations and Laboratory Tests  
### O. Ysand Test Site  
Several possible sites were screened for this study; those sites form part of the Norwegian Geotechnical Test Sites project [10], which characterize five different sites with soil compositions ranging from gravel to clay. The most suitable site was the \(\mathcal{O}\)ysand site, because of its soil composition and the presence of a natural slope.  
The \(\mathcal{O}\)ysand site is located 15 km south-west from Trondheim, Norway. The soil deposit consists of fluvial material, underlain by deltaic and marine soil. The meandering Gaula River, which flows into the Trondheimsjord, borders the site to the east, see Figure 1.  
Figure 1: _Cont._  
In general, the stratigraphy down to a depth of 10 m from the top of the slope can be divided into two main units: (a) A top 6 m of clayey silt and silty soils and (b) a lower unit of deposited gravelly sand and gravels. The level of the groundwater table was identified by plotting the hydrostatic pore pressure (kPa) versus the depth (m) of the piezometer readings. A correction was applied to the pressure measured by the piezometer, namely by subtracting the piezometer to the air-pressure obtained from a nearby installed weather station. The groundwater level from two piezometers is estimated to be about 2 m from the slope surface.  
Total sounding tests were also used to determine the soil stratification in addition to the CPTs. Results of OYSTS05, OYSTS07 performed at the slope top are compared against the results of OYSTS09 located at the bottom of the slope. A correction of the depth was also applied to OYSTS09. Similar deductions, as for the ones reached from CPTu, were obtained from the TS. Silty clayey material over sand and gravels were found across the slope. Note that hammering and flushing were required to advance through sections of the gravel layers, the applied forces exceeded 20 kN in the deeper gravel layers. The TS results, (a) rotary speed rate (b) rotary force shown in Figure 4, provides a good indication of the soil variability with depth. A comprehensive geotechnical soil investigation of the Oysand site is given in [PERSON] et al. (2012).  
Figure 4: Location of most relevant field tests for slope stability screening analysis. (**a**) rotary speed rate with depth; (**b**) rotary force with depth.  
Figure 3: Cone penetration tests (CPTu)s close to the slope at Øysand: (**a**) qc, (**b**) fs, (**c**) u2, and (**d**) location of CPTu.
###### Abstract  
Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things, represented as any items from our daily life that are enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems has been the main concern for the research community. In this paper, different security threats of Zigbee networks in the IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office/corporate environment. Our proposed SIF can predict and protect against various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting against several potential security attacks efficiently, enabling a more secure way of intelligent monitoring in the IoT platform.  
Keywords:real-time intelligent monitoring; Zigbee protocol; internet of things (IoT), office security system; security threats +
Footnote †: journal: Applied Sciences
## 2 Liquid CO\({}_{2}\) Phase-Transition Rock Fracturing
### Advantages of CO\({}_{2}\) Phase-Transition Fracturing  
At the present time and under general conditions, rock excavations are still mainly dependent on controlled blasting methods, which have the clear advantages of high efficiency, good rock breakage effects, and low cost; however, explosive blasting methods also have high safety risks and strict control requirements. Blasting excavations are strictly prohibited in many special environments, such as areas with dense populations or buildings, vibration-sensitive areas, and so on. The conventionally used non-explosive rock-breaking methods, such as mechanical rock-drilling, hydraulic splitting, and static breaking agents, have been found to have low excavation efficiency and difficulties meeting the needs of the low cost and time limitations in engineering projects. However, it has been found that liquid CO\({}_{2}\) phase-transition fracturing excavation methods have overcome the defects of explosive blasting methods, and have displayed the following major advantages:  
1. High safety and reliable operations The fracturing pipes which are used in liquid CO\({}_{2}\) phase-transition fracturing excavation methods are made of high-strength heat-resistant steel alloy, which can withstand high-pressure conditions without plastic deformations. Furthermore, since CO\({}_{2}\) is a non-explosive gas, accidental explosions and combustion can be inhibited. The process of CO\({}_{2}\) fracturing and blasting is a physical process that does not involve sparks or open flames. Further, there is less risk from dust and flying stones, and no destructive vibrations or harmful gases are produced.
2. Controllable fracturing energy When liquid CO\({}_{2}\) phase-transition fracturing excavation methods are used, the sizes and directions of the fracturing energy release actions can be controlled by shearing sheets and energy release heads. Medium-deep hole fracturing can be realized by a series of connections, and combined fracturing can be realized simultaneously by parallel connections.
3. Reusable main parts After a CO\({}_{2}\) fracturing device is put into operations, other parts can be reused thousands of times, with the exception of the heating devices, constant pressure shear pieces, and sealing gaskets. The sources of liquid CO\({}_{2}\) are widespread, and the prices are considered to be low; therefore, the overall economic benefits are considerable.  
(4)  
Relatively low safety control levels  
At the present time, there are no special regulations for the management and regulation of liquid CO2 phase-transition fracturing excavation methods, and the control requirements only refer to the fireworks safety approval and safety standards for trial implementation.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methodologies** & & & **Accuracy \% (Total Sample Size = 10,000)** & \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & **Scenario 1:** & **Scenario 2: Intruder Has** & **Scenario 3: Intruder Has Fully** & **Scenario 4: Intruder** \\  & **Instructor with** & **Partially Covered Face with** & **Covered Face with Some Type** & **Is Captured in the** \\  & **Face Not** & **Some Type of Transparent, Solid, Plastic, Leather Material (5\%)** & **of Transparent, Solid, Plastic, Leather Material (5\%)** & **Dark Night or in Bad** \\  & **Obscured** & **Plastic, Leather Material (5\%)** & **Leather Material (5\%)** & **Light Conditions (\%)** \\ \hline Modified Haar & \multirow{2}{*}{97.01} & \multirow{2}{*}{84.13} & \multirow{2}{*}{78.19} & \multirow{2}{*}{66.5} \\  & Cascade & & & \\ \hline Neural Networks & 95 & 80.8 & 56.70 & 44.01 \\ \hline Skin & \multirow{2}{*}{75.6} & \multirow{2}{*}{65.5} & \multirow{2}{*}{CD3} & \multirow{2}{*}{CD4} \\ Segmentation & & & & \\ Template & & & & \\ \hline Matching & \multirow{2}{*}{69} & \multirow{2}{*}{34.5} & \multirow{2}{*}{CD3} & \multirow{2}{*}{CD4} \\ Modified Haar & \multirow{2}{*}{69} & \multirow{2}{*}{34.5} & \multirow{2}{*}{CD3} & \multirow{2}{*}{CD4} \\ Cascade & & & & \\ \hline Two Step R-CNN & 95 & 75 & 69 & 55 \\ \hline Higher Order & \multirow{2}{*}{72} & \multirow{2}{*}{50} & \multirow{2}{*}{CD3} & \multirow{2}{*}{CD4} \\ Statistics & & & & \\ \hline Faceness-Net & 94 & 75 & & CD3 \\ \hline \hline \end{tabular} CD3—Can not detect intruder in Scenario 3, CD4—Can not detect intruder in Scenario 4.  
\end{table} 
Table 4: Face Detection Rate Accuracy Analysis of the modified Haar Cascade Classifier with existing methodologies in four scenarios: (**a**) face not obscured; (**b**) face partially covered; (**c**) face fully covered; and (**d**) captured in the dark by an analog camera.
## 3 Results
### User Behavior
#### 3.1.1 Everyday Life Situations  
The evaluation of the data on the acoustic environments from all study participants is shown in Figure 4. The analysis revealed that the \"speech in quiet\" scenario accounted for 39% of participants' daily situations. Similarly, acoustic environments such as \"speech in noise\" (24%) or \"noise\" (18%) occurred with comparable frequency. Conversely, situations such as \"music\", \"reverberant speech\", and \"in car\" collectively represented only about 19% of all observed everyday scenarios. However, an analysis of individual participant datasets, as shown in Figure 5, highlighted the highly personalized nature of everyday life situations. For example, a significant disparity was evident between participants MHH01 and MHH04: while MHH01 frequently chose or found themselves in quiet situations with speech, MHH04 more often selected or experienced environments with both speech and background noise.
# A Time Bank System Design on the Basis of Hyperledger Fabric Blockchain
## 4 The Proposed Time Bank System
### Phase 1: Service Posting and Matching
In other words, a lower grade denotes a lower priority and probability that he or she can get a match with others. For example, as shown in Figure 5, both [PERSON] and [PERSON] can provide the driving service for [PERSON]; however, since [PERSON] has a higher ProvideGrade, our matching scheme will pair up [PERSON]'s service to [PERSON]'s service rather than [PERSON]'s. 4. _The Interaction Constraint on Cross-Channel Chaincode:_ As mentioned before, each channel owns its individual ledger in Fabric. In other words, different channels are built on different blockchains, i.e., they stay in different world states and record different ledger data. The applications in the Fabric-based system allow users to access the same peer, connect to different channels, invoke different Chaincodes, and search data from different ledgers. However, there is a strict constraint on the Chaincodes' cross-channel communications. In the official document of the Hyperledger Fabric, there is a paragraph mentioning this constraint. As the document states, access to Chaincodes in channels other than the channel targeted by the first Chaincode in the invokement will be read-only. Thus, in cross-channel communications, users from other channels have rights only to query an on-chain ledger but not to change a world state data. Although the user service-related process is run in SC, the grade data are recorded on the ledger in GC, which must be considered in the matching step. To deal with this cross-channel referencing issue, a designated Fabric cross-channel communication function in SC is applied to query the grade data in GC. In the same way, the balance of the receiver's wallet can be queried while the wallet data are recorded in TC. Furthermore, the user in SC can check whether the receiver's balance is sufficient enough to pay the required provider's fee, once their services are matched. Due to the previously described constraint on cross-channel interaction, users in the original channel cannot change the world state or upload data to another channel. That is, the time credit cannot be directly transferred from receiver to provider at the same time when their services are matched. Users have to go back to the application layer and connect to TC for executing the transactions regarding the time credit.