esa-sceva/satcom-chunk-collection huggingface dataset:
# Big Data Perspective and Challenges in Next Generation Networks
## 3 Data Analytics Perspective on Next Generation Networks
### Optimization of Next, Generation Cellular Networks  
Machine learning is a branch of artificial intelligence. Machine learning helps computers to make (and improve) decisions on the basis of input events. The input source of machine learning algorithms is data; these input data could be diverse and from different data sources. Thus, machine learning is helpful in determining future outcomes based on the input data. For more details, the reader is referred to [55]. The types of machine learning and the associated algorithms are shown in Figure 6.  
Mobile networks are already complex and it is expected that 5G networks will be much more complex compared to its predecessors. Besides being more complex, 5G networks will be smarter and intelligent in terms of network management, resource distribution, load balancing, cost efficiency, power efficiency, etc. These features can be realized for 5G with machine learning techniques. Thus, we can say that machine learning can perform a major role in deployment of 5G networks. For example, if we recall cellular networks in past decades, telecommunication engineers had to perform drive test of hundreds of miles after every software updates, or had to climb on 50-meter-high cell tower to adjust the tilt of antennas for best quality of coverage. However, recently, networks are getting intelligent and smarter, engineers can use advance software tools for remote drives test instead of doing tiring physical efforts and flying drones can be sent to inspect high rise sites.  
As in 5G, there will be seamless and limitless connectivity of anything to anything and from everywhere to everywhere [54]. According to Nokia technology vision 2020 [56], number of events will increase from 400 to 10,000 events per hour for a network serving 10 million users. It is impossible to handle such huge number of events manually, and thus the network must be intelligent and smarter. Previously, self-optimizing networking (SON) have been used for improving network efficiency. A SON works in reactive states. As 5G networks will be different from previous networks and will be more complex, so SON is not enough to fulfill 5G network requirements. Nokia Vision 2020 [56] has presented a new phenomenon for 5G which is called self-aware networks, which we discuss in the the next section.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 5 Turbulent-Flow Chromatography (TFC)  
Turbulent-flow chromatography (TFC) is a technique commonly used for the separation of a wide variety of molecules in biological matrices. TFC uses the properties of dynamic fluids in a turbulent flow to separate the molecules based on their weight, like proteins from low molecules, without prior sample preparation. Conventional liquid chromatography usually employs flow rates lower than 1.5 mL/min exhibiting a laminar flow behavior (parabolic velocity profile). As the flow rate increases (>1.5 mL/min), the profile flow becomes turbulent (plug-like profile) and more dependent on velocity [6]. Due to the necessary use of high flow rates (up to 1.5-5 mL/min) the separation column has to be packed with large particles (around 50-100 \(\upmu\)m of diameter) to avoid high back pressures and at the same time get turbulent conditions [77; 78].  
This technique uses different configurations, but due to high sample complexity, the most common one uses a column switching system on-line with a LC system coupled to either MS or tandem MS [79]. The standard configuration requires two six-port valves, a loop, an auxiliary pump and an LC-MS system (Figure 5). Extraction and analysis are carried out in three steps: Loading, elution, and conditioning. Briefly, in the valve extraction position, the sample is loaded from the injector to a TFC column under turbulent conditions. Under these conditions the small molecules (<2000 Da) diffuse more quickly between the porous particles, allowing chemical interaction with the stationary phase and separated by size exclusion from large molecules (e.g., proteins), which are discarded to the waste while the small molecules are retained in the extraction column. Then, the valve switches to elution position and the molecules retained are transferred from TFC column to an analytical column by the mobile phase, to be subsequently separated and detected. Once transferred, the valve switches and the TFC column is washed and conditioned for the next run [79].
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods
### ENVISAT MERIS Data  
MERIS is an imaging spectrometer with a medium-spectral resolution onboard the ENVISAT platform of the European Space Agency (ESA). The instrument can sample surface reflectance in fifteen spectral bands with a range of 415-900 nm and has a temporal revisit time of 2-3 days. The data represent 15 spectral bands in the visible, near-infrared, and shortwave infrared regions with a spatial resolution of 300 m. The detailed specifications of the MERIS sensor are shown in Table 3. In this study, full-resolution surface reflectance products (for 25 June-24 September 2004), were produced by seven-day temporal synthesis from data collected at the original 2-3-day revisit frequency. The MERIS surface-reflectance product provides 13 bands, with bands 11 and 15 removed.
# Tunable Fano Resonances in an Ultra-Small Gap
## 1 Introduction
Moreover, the distance between the NPs and the substrate can be easily controlled by varying the thickness of an intervening dielectric layer, especially when the gap is reduced below 10 nm, a strong local field will be formed in the gap of the NPoM structure. Since the NPoM structure consists of a metallic film, a dielectric layer, and single NPs, it usually does not require costly fabrication techniques [35; 36; 37; 38; 39; 40; 41]. Therefore, it is expected to observe the Fano resonance in NPoM structures. Here, a Fano resonance is observed in a silver-nanocube-on a-silver-mirror (NCoM) structure that was fabricated by a bottom-up process with precisely controlled sub-6-nm gaps. The Fano resonance position is modulated by changing the nanocube size and its gap distance from the silver film. To examine the origin of the Fano resonance, the electric field magnitude profiles and charge distribution of the NCoM structure are numerically simulated; a mode analysis then reveals that the scattering spectrum is a Fano resonance. The refractive index sensitivity of the Fano resonance spectrum in the NCoM structure is also investigated by varying the dielectric environment around the silver nanocube.
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 3 IS-MAC Protocol  
This study proposes a new MAC protocol based on dynamically changing the value of contention window by summarizing the disadvantages of backoff algorithms mentioned in Section 2. The new protocol is called the IS-MAC protocol. The IS-MAC protocol uses several methods including multiplicative increase and multiplicative/linear reduction to adjust the contention window. At the same time, the IS-MAC protocol dynamically adjusts the size of the contention window according to the load of the network.
###### Abstract  
Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic 'OR' operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.  
dependency model; sequential diagnosis; concurrent diagnosis; multiple fault diagnosis; fault prior probability +
Footnote †: journal: Applied Sciences
## 4 Experiments and Results
### Step Response Tests  
The performance of the proposed OAC-DES was evaluated by extensive empirical testing. For the non-linear robotic mechanism, we specified a sequence of desired signals (driving cycle), which act as velocity control references \(\omega_{r}\). In line with this, the non-linear robotic mechanism's reactions were recorded according to the actual velocity \(\omega_{a}\) on the oscilloscope. These reactions are known as velocity control responses. The closer the step response to velocity control reference, the better the controller.  
In our study, we were actually interested in the results of three step response tests:  
1. the reaction of the actual velocity \(\omega_{a}\) to the changing reference velocity \(\omega_{r}\) (velocity control response),
2. the difference between the actual and reference velocities \(\omega_{r}-\omega_{a}\) obtained by changing the output of the velocity controller,
3. the output from the particular velocity controller in the form of a reference current signal \(i_{r}\).  
We should mention that the first step response primarily estimates the performance of the controller.  
As such, two separate tests were conducted. The purpose of the first was to test robotic behavior on the control plant using the reference velocity \(\omega_{r}=10\) rad/s, while the second used the higher reference velocity \(\omega_{r}=30\) rad/s. During experimentation, the actual velocity control response, velocity error, and reference current (i.e., output of OAC-DES) were measured.  
Normally, the response of the feedback system to the step function consists of overshoot and consequential settling. Additionally, in our case, periodic disturbances were experienced by the system due to the load torque caused by spring. In control theory, both signals present so-called peak errors, which are divided into two types:  
* transient peaks: error peaks due to transient responses immediately after overshooting,
* disturbance peaks: steady state error peaks due to load torque perturbations.  
In the remainder of the paper, these step response tests are described in detail.
## 7 Performance Evaluation
### Environment for Performance Evaluation
\begin{table}
\begin{tabular}{l c l} \hline \hline
**Parameter** & **Value (B: Bytes)** & **Description** \\ \hline \(L_{\text{header}}\) & 23 B & the header size (PHY: 6 B, Medium Access Control: 11 B, Deluge: 6 B) \\ \(L_{\text{page}}\) & 1024 B & the default page size of Deluge in TinyOS \\ \(L_{\text{payload}}\) & variable & the payload size for DATAs \\  & & (the default payload size of Deluge in TinyOS: 22 B) \\ \(L_{\text{ADV}}\) & 29 B & the size of the ADV \\  & & (PHY header: 6 B, Medium Access Control header: 11 B, Deluge: 12 B) \\ \(L_{\text{REQ}}\) & 32 B & the size of the REQ \\  & & (PHY header: 6 B, Medium Access Control header: 11 B, Deluge: 15 B) \\ \(L_{\text{hash}}\) & 20 B & the hash size in SHA-1 \\ \(L_{\text{MAC}}\) & 20 B & the MAC size in HMAC \\ \(L_{\text{sig}}\) & 40 B & the digital signature size in ECDSA-160 \\ \(m/N\) & 92 & false positive of \(6.36\times 10^{-20}\) \\ \(k\) & 64 & (\(m/N\))ln 2 \\ code image size & 10/20/30 & unit: kB \\ \(V_{\text{sup}}\) & 3 V & the supply voltage in TelosB [28] \\ \(I_{\text{rx}}\) & 19.5 mA & the current when the MCU is on and the radio is TX in TelosB [28] \\ \(I_{\text{rx}}\) & 21.8 mA & the current when the MCU is on and the radio is RX in TelosB [28] \\ \(I_{\text{max}}\) & 1.8 mA & the current when the MCU is on and the radio is off in TelosB [28] \\ \(I_{\text{alle}}\) & 54.5 \(\mu\)A & the current when the MCU is idle and the radio is off in TelosB [28] \\ \(I_{\text{drop}}\) & 5.1 \(\mu\)A & the current when the MCU is standby and the radio is off in TelosB [28] \\ SO/BO & 6/7 & the parameters to determine SD and BI \\ \(T_{\text{SPS}}\) & 192 \(\mu\)s & the duration of SIFS (short interframe spacing) \\ \(T_{\text{LIFS}}\) & 640 \(\mu\)s & the duration of LIFS (long interframe spacing) \\ \(T_{\text{backoff}}\) & \(R\times 320\)\(\mu\)s & the backoff period. \(r\) is a random value between 0 and 3 \\ \(T_{\text{inactive}}\) & 983,040 \(\mu\)s & the duration of the inactive period when SO and BO is 6 and 7 \\ \(T_{\text{byte}}\) & 32 \(\mu\)s & the time for transmitting one byte in TelosB \\ \(T_{\text{long}}\) & variable & the long frame period over which a ADV/REQ/DATA is transmitted \\ \(T_{\text{hash}}\) & 0.35 ms & the hash computation time in TelosB [29] \\ \(T_{\text{MAC}}\) & 0.71 ms & the MAC computation time in TelosB [29] \\ \(T_{\text{sig}}\) & 1.02 s & the signature verification time in TelosB [30] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for Performance Evaluation.
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
## 4 Results
### Energy Assessment  
In order to find out how effective is the AFC approach employed for the maximum efficiency and maximum lift cases, the power per unit length required by the SJA (\(W_{j}\)) as well as the power saved after the actuation (\(W_{G}\)) have to be calculated. The power needed to drive the synthetic jet is given as:  
\[W_{j}=\frac{1}{2}\rho_{j}S_{j}\sin(\theta)\overline{u_{j}^{3}} \tag{24}\]  
where \(S_{j}=h*l\) defines the groove cross-sectional area, due to the fact that the airfoil length is equal to unity \(l=1\), the groove and the the jet width \(h\) are equivalent. The parameter \(\theta\) stands for the jet inclination angle measured versus the wing profile surface.  
The definition of the synthetic jet actuator time dependent velocity profile to the power three, \(\overline{u_{j}^{3}}\), was taken from [6, 39].  
\[\overline{u_{j}^{3}}=\frac{1}{T/2}\int_{0}^{T/2}U_{max}{}^{3}\sin^{3}(2\pi ft )dt=\frac{4}{3\pi}U_{max}{}^{3} \tag{25}\]  
where \(U_{max}\) characterizes the jet maximum velocity. The equation representing the power saved when AFC is applied and due to the drag force reduction, takes the form:  
\[W_{G}=U_{\infty}(D_{\text{baseline}}-D_{\text{actuated }})=\frac{\rho U_{\infty}{}^{3}C}{2}\big{(}C_{d_{\text{baseline}}}-C_{d_{ \text{actuated }}}\big{)} \tag{26}\]  
where the drag force and the drag coefficient are respectively given as \(D\) and \(C_{d}\). When AFC is applied, the parameter defining the power ratio \(P_{R}\) is represented as:  
\[P_{R}=\frac{W_{G}}{W_{j}} \tag{27}\]  
Energy saving exist for power ratio values higher than one.  
The set up parameters and resulting power ratios are summarised in Table 4. Both maximum efficiency and maximum lift configurations present energy savings, with a power gain two orders of magnitude higher than the power required by the SJA.  
This result confirms that the introduction of AFC in an airfoil with separated flow is capable of reducing the drag coefficient and increase the lift coefficient significantly, by keeping the boundary layer attached for a larger portion of the airfoil. Therefore, it can be stated that the implementation of the SJA is energetically efficient for the separated flow case presented.
## 5 Discussion and Conclusions
Furthermore, real-time positioning systems must carefully balance accuracy, computational efficiency and system cost, tailoring solutions based on the specific requirements of use cases, such as AGV tracking or indoor navigation, where low latency and high scalability are critical. **Author Contributions:** Conceptualization, J.Q., F.Y. and J.L. ; methodology, J.L. and G.H. ; validation, J.Q., F.Y., J.L. and G.H. ; formal analysis, J.Q. and F.Y. ; investigation, J.L. and G.H. ; resources, J.Q. and F.Y. ; data curation, J.L. and G.H. ; writing--original draft preparation, J.Q., F.Y. and J.L. ; writing--review and editing, G.H. ; supervision, W.Z. and M.L. ; project administration, J.Q. ; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by National Key Research Development Program of China with project, grant number 2023 YFB3906101; Natural Science Fund of China, grant number 42474060; China Southern Power Grid co. Limited Science and Technology Program, grant number GDKJXM20220188; Natural Science Fund of Hubei, grant number 2024 AFD403; Wuhan AI innovation research programme, grant number 2023010402040029; Shenzhen Science and Technology Program, grant number JCYJ20210324123611032; Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of Wuhan University. **Data Availability Statement:** All data generated or analyzed during this study are included in this published article. **Conflicts of Interest:** Author [PERSON] was employed by the company China Southern Power Grid Co., Ltd. Author [PERSON] was employed by the company Shaoguan Power Supply Bureau, Guangdong Power Grid Co., Ltd. Author [PERSON] was employed by the company Wuhan Geo-Detection Technology Co., Ltd. [PERSON] was employed by the company Shenzhen R&D Center of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Overview of the Test
### Helical Anchor Preparation  
The uplift bearing capacity, \(N_{c}\), effectively represents the uplift bearing capacity of helical anchors. Consequently, this study focused on investigating \(N_{c}\). Previous research primarily examined the effect of \(N_{c}\) on various depth-to-diameter ratios (\(H\)/\(D_{H}\)) [13,14]. This paper, however, explored the impact of the helical diameter ratio and diameter ratio on \(N_{c}\) through laboratory modeling tests.  
Diameter ratio (\(D_{S}\)/\(D_{H}\)): As the diameter of the anchor increases, it decreases the effective support area of the anchor helix, thereby reducing the pullout resistance of the helical anchor.  
Shaft-to-plate diameter ratio (\(d_{m}\)/\(D_{H}\)): When the pitch was reduced, the available support area and contact friction were increased, enhancing the interaction between the helical anchor and the soil, and thereby improving the pullout resistance of the helical anchor.  
This study's tests were structured around four different diameter ratios, with each ratio tested across five separate sets. In total, four diameter ratios were examined, with each ratio subjected to embedment depths ranging from 7 to 12. The design details are presented in Table 1.  
The test helical anchor, as shown in Figure 2, utilizes an assembled structure. A tension load cell was installed between the anchor helix and the anchor shaft, with the lower part of the load cell connected to the anchor helix and the upper part to the anchor shaft. Compared to previous tests, this study introduced the following innovations: (1) Assembled helical anchors, unlike traditional integral helical anchors, consist of multiple components, allowing for more flexible installation and use. (2) Traditional tests often utilized external loading to assess the pullout resistance of the anchor helix, but this method may not accurately simulate real-world conditions. In this study, a tension load cell was placed between the anchor helix and the anchor shaft to more accurately simulate actual usage conditions, thereby obtaining more reliable data on uplift bearing capacity. The tensile force sensor utilizes a miniature tensile pressure sensor, model DYMH-103 (Dayang Sensor System Engineering Co, Bengbu, Hefei, China), with a range of 500 kg and an accuracy of 0.01 kg.
## 2 Materials and Methods
### Antioxidant Capacity Determination
#### 2.6.3 Ferric-Reducing Antioxidant Power (FRAP)  
The FRAP method implemented by [PERSON] et al. [42], with some modifications, was applied. Briefly, 25 \(\upmu\)L of the extract, 25 \(\upmu\)L of 0.2 M phosphate buffer (pH 6.6), and 50 \(\upmu\)L of 1% (\(w/v\)) potassium ferricyanide were mixed. Next, this mixture was kept at 50 \({}^{\circ}\)C for 20 min in agitation (700 rpm). The reaction was stopped by adding 50 \(\upmu\)L of 10% (\(w/v\)) of TCA. Finally, 100 \(\upmu\)L of the above solution was mixed with 600 \(\upmu\)L of 0.08% (\(w/v\)) FeCl\({}_{3}\) and stood for 3 min. After that, the absorbance was measured at 700 nm in a microplate spectrophotometer reader (Multiskan Sky, ThermoFisher, Germing, Germany) and compared to the glutathione (GSH) calibration curve (0.125-0.870 mg/mL) prepared equally. GSH is a tripeptide that reacts directly with free radicals and pro-oxidants, reducing their levels in a cell to a safe minimum [43]. The total antioxidant capacity was expressed as mg GSH/g sample.  
Determination of NEPs' Molecular Weight from Mangosteen Peel Extracts by High-Performance Liquid Size-Exclusion Chromatography (HPLC-SEC)  
Estimation of the molecular weight range of NEPs extracted under the optimal extraction conditions from mangosteen peel extracts was carried out by SEC using an HPLC system 1100 series from Agilent (Agilent Technologies, Palo Alto, CA, USA) according to [PERSON] et al. [44]. The HPLC instrument had an online degasser, a quaternary solvent pump, an auto-sampler, a column heater compartment, and a diode array detector (DAD) with scanning capabilities. The instrument was controlled by ChemStation (Agilent) software. Separation was performed on a size-exclusion chromatography (SEC) column (PolySep-GFC-P2000, 300 \(\times\) 7.8 mm, Phenomenex, Torrance, CA, USA) with a fractionation range of 100 Da-10 kDa., using 100% water as the mobile phase. Elution was performed in isocratic mode at 0.3 mL/min for 60 min, and the column temperature was 25 \({}^{\circ}\)C. Twenty microliters of extract were injected. The detection wavelength employed was 280 nm. Different standards were used to calibrate the molecular weight of the SEC column: polyethylene glycol (8000 Da), polyethylene glycol (4000 Da), twin 20 (1228 Da), and ethylene glycol (62 Da). The void volume was determined with dextran (50,000 Da). The molecular weight of the extracted NEPs was determined using the calibration curve obtained with the four standards by plotting the logarithm of the molecular weight as a function of retention time (min). Responses obtained were expressed by a linear equation (y = \(-\)0.085 \(\times\) +5.164) with an R\({}^{2}\) determination coefficient value of 0.98572.