esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Material and Methods
### Antiproliferative and Cytotoxic Activities  
Human neuroblastoma cells (SH-SYSY, ATCC(r) CRL-2266), human adenocarcinoma breast cells (MCF7, ATCC(r) HTB-22 and MDA, ATCC(r) MB-231) and normal breast epithelial cells (MCF10A, ATCC(r) CRL-10317) were used to determine the antiproliferative and cytotoxic activity of the \"Carciofo Ortano\" leaf extracts.  
Neuroblastoma and adenocarcinoma cells were cultured in DMEM F-12 supplemented with 10% fetal bovine serum, 1% glutamine and 1% penicillin-streptomycin. The MCF10 Acells were maintained in DMEM F-12 medium supplemented with 100 ng/mL cholera toxin, 20 ng/mL epidermal growth factor (EGF), 0.01 mg/mL insulin, 500 ng/mL hydrocortisone, 5% horse serum (HS), 1% glutamine and 1% penicillin-streptomycin. Before being seeded for the viability assay, the culture medium of the normal cells was deprived of EGF and the HS was reduced to 2%.  
The cells were maintained at 37 \({}^{\circ}\)C and in 5% CO\({}_{2}\) until the logarithmic phase of growth was reached and then used for the analyses.  
The MTT assay was performed using the protocol described by [PERSON] and colleagues [79]. At 24 h after seeding, the cells were treated with ten two-fold concentrations, from 250 to 0.48 \(\upmu\)g/mL of extracts dissolved in dimethylsulphoxide (DMSO). The cells without treatment and treated with DMSO were used as the negative and solvent control, respectively.  
The antiproliferative activity of the samples was investigated in a dose- and time-dependent manner for 24 h, 48 h and 72 h while, to assess the cytotoxicity effect, the cells were treated for 72 h in dose-independent mode. After each incubation time, the treatments were removed and 100 \(\upmu\)L of MTT (0.5 mg/mL) was added and incubated for 3 h at 37 \({}^{\circ}\)C and 5% of CO\({}_{2}\) until formazane crystals were completely formed. The absorbance was read at 595 nm after the addition of 100 \(\upmu\)L of DMSO.  
The half-maximal concentration (EC\({}_{50}\)) for each biological replicate was determined using a logical response curve constructed using GraphPad Prism version 8.0.2 for Windows (GraphPad Software, San Diego, CA, USA). Three independent biological tests were used to calculate the overall EC\({}_{50}\).
# A Systematic Literature Review on Military Software Defined Networks
## 7 Discussion and Recommendations for Future Work
### Basic Evaluation  
Software-defined networking has the potential to offer significant advantages over conventional networks in military networks. However, there are still open issues that need to be evaluated before usage in a production environment:  
* Evaluation of the SDN control plane performance in large-scale heterogeneous networks, and its ability to respond to failures;
* Evaluation/investigation of the number and placement of controllers. A centralized management miles away from the forwarding devices in an operational scenario is challenging, and the number and placement of controllers is a research problem in military networks;
* Evaluation of extensions to OpenFlow that include features for fine-grained control of wireless access points in a similar manner to OpenRadio;
* Development of a comprehensive methodology to evaluate the performance of SDN load balancers.
## 3 Results and Discussion
### A 10-Gbaud Optical-Fiber Communication System
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equalizer** & **Per Symbol Computational Cost** & **Memory Storage 1** \\ \hline FFE[\(N\)] & \(N\) & \(N\) \\ DFE[\(N_{1}\),N\({}_{2}\)] & \(N_{1}+N_{2}\) & \(N_{1}+N_{2}\) \\ ANNE-AT/ET[\(N\),\(m\)] & \(mN\) & \(m(N+1)\) \\ MLSE[\(N\)] & \(2^{N}\) & \(2^{N}\) \\ \hline \hline \end{tabular}  
* Memory storage is given in terms of the required floating-point numbers to be stored.  
\end{table} 
Table 1: A comparison of the computational complexity and memory storage requirements during equalization. Figure 7: BER performance of the 10 Gbaud optical-fiber communication system versus received optical power with a noisy EDFA whose noise figure is 4.77 dB. The optical-fiber transmission distance is 140 km. The performance without equalizer, with ANNE-ET[7; 6], and with ANNE-AT[7; 6] are compared to the zero-dispersion transmission case. As can be seen from Figure 7, the overall system performance slightly deteriorates compared to the case of noise-free EDFA (see Figure 5a). The ANNE-ET obtains \(\text{BER}=10^{-3}\) and \(10^{-9}\) at received optical powers of 1.7 dBm and 5.0 dBm, respectively, as opposed to the 1.3 dBm and 4.4 dBm needed in the case of noise-free EDFA. In other words, the EDFA noise affects the performance obtained using the ANNE-ET by a 0.4 dB and 0.6 dB at BER of \(10^{-3}\) and \(10^{-9}\), respectively. As compared to the zero-dispersion performance, the ANNE-ET requires \(\sim\)3.1 dB and \(\sim\)1.5 dB power penalties to obtain the \(\text{BER}=10^{-3}\) and \(10^{-9}\), respectively. On the other hand, the ANNE-AT shows superior performance that is quite close to the zero-dispersion transmission case, owing to the inclusion of the system noise effect during its training process. Particularly, the presence of the EDFA optical noise reduces the BER performance achieved utilizing the ANNE-AT by 0.4 dB at both the \(\text{BER}=10^{-3}\) and \(10^{-9}\) levels (see Figures 5a and 7). Compared to the zero-dispersion transmission case, the ANNE-AT requires power penalties of as low as \(\sim\)1.1 dB and \(\sim\)0.2 dB to achieve \(\text{BER}=10^{-3}\) and \(10^{-9}\), respectively. Moreover, the proposed ANNE-AT outperforms the ANNE-ET by power benefits of \(\sim\)2 dB and \(\sim\)1.3 dB for achieving a BER of \(10^{-3}\) and \(10^{-9}\), respectively. Comparing this with the case of no optical noise (Figure 5a), we find that the inclusion of the EDFA noise effect does not change the average power benefit of the proposed ANNE-AT over the ANNE-ET.
## 3 Research Method
### Resource Quota Management  
This aspect covers features of CO frameworks that a cluster administrator must understand in order to organize the hardware resources of a cluster among different teams or organizations.  
_Concept for partitioning API objects into logically named user groups._ All container orchestration frameworks offer a concept for partitioning one or more types of API objects (e.g., services, volumes) into a logically named user group that corresponds with a specific organization or tenant that is able to contract resources from the cluster. The typical use case of user groups is to support multi-tenancy such that the resources and services of a cluster can be divided among different tenant of the cluster.  
Mesos does not offer the concept of user groups. Instead, it offers a similar concept, named framework roles [199], for dividing hardware resources across multiple scheduler frameworks.  
_Declaring a minimum guarantee and/or maximum limit on CPU, memory and disk quota per user group._ Kubernetes, Mesos and Aurora provide support for declaring a minimum and a maximum quota of CPU, memory and disk resources per user group. More specifically:  
* Kubernetes supports attaching to user groups minimal guarantees and maximum limits for CPU and memory quota [200] and maximum limits for disk quota per storage class [201].
* Mesos supports attaching to framework roles minimal guarantees [202] for CPU, mem and disk quota [203] for local volumes as well as weights [204] for dividing resources across roles.
* Apache Aurora allows attaching to user groups quota for memory and disk [205] via the aurora_admin set_quota command.  
_Declaring an object count quota limit for the number of API objects per user group._ Kubernetes and Mesos allow assigning to user groups/framework roles a maximum number of API objects such as the number of nodes, containers, services, etc. More specifically, in Kubernetes [206], object count quota can be declared by expressing a maximum quantity for different kinds of Kubernetes API objects. In Mesos [203], port ranges can be associated with framework roles.  
In Docker EE [207] high-level resources such as nodes [208], volumes [209] and services can be organized in collections, but there is no declaration of a maximum limit. The DC/OS distribution of Marathon [210] also allows organizing services into service groups without enforcing a limit on the number of services for a service group.  
_Reserving resources for the CO framework._ The available set of resources on a node is automatically computed via the operating system in all CO frameworks. Additionally, Kubernetes [211] and Marathon [212] can be configured to reserve a subset of the node resources for the framework's operation and local daemons.
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 3 Economy-Financial Analysis
### Main Calculation
#### 3.1.2 Internal Rate of Return--ROI (IRR)  
The internal rate of return defines the amount of profit gained by investing in an energy system. [8; 17; 35]. The internal rate of return measures the amount of profit relative to the cost of an investment. The return on an investment, in this case the savings accumulated over the life of the plant, is divided by the cost of the investment, in this case the initial investment. The results can be expressed as a ratio.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Category of Work** & **Pre-Tax Value** & **VAT Tax** & **Value after Tax** \\ \hline
1 & Compensation, support and & 5711 & 0 & 5711 \\  & resettlement expenses & 501,098 & 50,110 & 551,208 \\
2 & Construction cost & 594,622 & 59,462 & 654,084 \\
3 & Equipment cost & 10,747 & 0 & 10,747 \\
4 & Project management cost & 10,747 & 0 & 10,747 \\
5 & Construction investment & 32,356 & 3236 & 35,592 \\  & consulting cost & 70,966 & 3542 & 74,508 \\
6 & Other cost & 97,417 & 9610 & 107,027 \\
7 & Redundancy cost & **1,312,917** & **125,960** & **1,438,877** \\ \hline \end{tabular}
\end{table}
Table 5: Total investment amount (Unit: million VND).  
\[\text{ROI}=\frac{\text{Benefit}}{\text{Investment}\text{ cost}\text{t}} \tag{4}\]  
Solar plants will be profitable when IRR > Ik. Ik is the cost price determined by the loan interest rate or the required cost of the corresponding source of capital.
###### Abstract  
This study introduces a monopole \(4\times 4\) Ultra-Wide-Band (UWB) Multiple-Input Multiple-Output (MIMO) antenna system with a novel structure and outstanding performance. The proposed design has triple-notched characteristics due to CSRR etching and a C-shaped curve. The notching occurs in 4.5 GHz, 5.5 GHz, and 8.8 GHz frequencies in the C-band, WLAN band, and satellite network, respectively. Complementary Split-Ring Resonators (CSRR) are etched at the feed line and ground plane, and a C-shaped curve is used to reduce interference between the ultra-wide band and narrowband. The mutual coupling of CSRR enables the MIMO architecture to achieve high isolation and polarisation diversity. With prototype dimensions of (60.4 \(\times\) 60.4) mm\({}^{2}\), the proposed antenna design is small. The simulated and measured results show good agreement, indicating the effectiveness of the UWB-MIMO antenna for wireless communication and portable systems.  
UWB; MIMO; CSRR; C-shaped; MIMO diversity; high isolation; triple-notched band +
Footnote †: journal: _Sensors_**2023**, 23, 6985. [[https://doi.org/10.3390/s23156985](https://doi.org/10.3390/s23156985)]([https://doi.org/10.3390/s23156985](https://doi.org/10.3390/s23156985))
## 3 System Model
### Energy Model  
Considering the heterogeneity in UAV energy levels, we assume that each UAV \(U_{i}\) starts with an initial energy \(E_{initial}\), which follows a random distribution within a certain range. As the mission progresses, energy is continuously consumed, primarily due to flight and communication.  
Flight energy consumption is the primary contributor to overall UAV energy usage and is influenced by aerodynamic properties, thrust requirements, and velocity. It consists of two main components: hovering power consumption and motion power consumption.The power required to maintain a hovering state in the absence of wind resistance is given by  
\[P_{\text{hover},i}=\frac{mg}{\eta} \tag{3}\]  
where \(m\) is the UAV's mass and \(\eta\) represents the efficiency of the propulsion system, which quantifies the conversion between electrical power and mechanical thrust. During motion, energy consumption is largely determined by the UAV's velocity and aerodynamic drag, expressed as  
\[P_{\text{motion},i}=\frac{1}{2}C_{d}\rho Av_{i}^{3} \tag{4}\]  
where \(C_{d}\) is the drag coefficient, \(\rho\) is the air density, \(A\) is the UAV's frontal area, and \(v_{i}\) is its velocity.The total flight energy consumption over a time interval \(\Delta t\) is given by  
\[E_{\text{flight},i}=\int_{t}^{t+\Delta t}P_{\text{flight},i}dt=\int_{t}^{t+ \Delta t}P_{\text{hover},i}+P_{\text{motion},i}dt \tag{5}\]  
The energy consumed for communication depends on the power required for information exchange with neighboring UAVs. It consists of three main components: transmission power, reception power, and idle power when the wireless module remains active but is not transmitting or receiving data. Assuming the transmission and reception power remain constant over time, the total communication energy consumption over \(\Delta t\) is given by  
\[E_{\text{comm},i}=P_{\text{tx},i}T_{\text{tx}}+P_{\text{rx},i}T_{\text{rx}}+ P_{\text{idle},i}T_{\text{idle}} \tag{6}\]  
where \(P_{\text{tx},i}\) and \(P_{\text{rx},i}\) are the power required for transmission and reception, respectively, and \(T_{\text{tx}}T_{\text{rx}}T_{\text{idle}}\) represent the time spent in each corresponding state.  
At any given time \(t\), the remaining energy of UAV \(U_{i}\) is  
\[E_{\text{res},i}(t)=E_{\text{initial},i}-(E_{\text{flight},i}+E_{\text{comm},i}) \tag{7}\]  
If this energy level drops below the predefined threshold \(E_{\text{threshold}}\), the UAV is considered to have insufficient power to continue its mission and must disengage from the network to return to base.
## 2 Quality Assessment of Water Level through Calibration and Validation
### Global Validation of SARAL/AltiKa Sea Surface Height over Ocean
Provided the application of a fine editing (for 20 Hz measurements: based on the Sea Level Anomaly coherence of consecutives measurements) and a correction [31] dedicated to reduce correlated noise between altimeter range and Significant Wave Height (SWH), it does even better than delay-doppler altimetry missions such as Sentinel-3A. Smaller ocean scales are therefore observable notably around 35 km where the spectral \"bump\" [32] is clearly reduced compared to \"standard\" use of the data (see Figure 1 in [3] for comparison). In conclusion, the SARAL/AltiKa mission performance remains excellent, compared to Jason-2, Jason-3 and Sentinel-3A, from fine scale ocean dynamics to long-term stability. And its quality will keep on improving, notably, thanks to several updates of geophysical corrections which are getting ready for the future reprocessing (GDR-E (Geophysical Data Record)) planned for 2018. The benefits of SARAL/AltiKa data to access the fine scale ocean dynamics are highlighted in our companion paper [3]. Figure 13: SARAL/AltiKa’s SLA power spectrum (green) compared to Jason-2 (blue) and Sentinel-3A (red), with adapted editing and [31] correction for the three missions.
# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Supervisory Control  
Supervisory control is when the operator asks the follower robot to perform autonomous high-level tasks under the supervision of the operator who can interrupt them. Functions provided by supervisory control include planning, teaching, monitoring, repairing, and learning from experience [368]. This approach derives from contexts with very high transmission delays and low bandwidth, such as in space and submarine applications [369], which could result in the inability of the operator to provide the appropriate commands on the fly (due to the affected causality) and could result in instability and divergence. It has been reused repetitively for swarm robotics where the followers are numerous so not directly controllable by the operator [370].  
Supervisory control is now used to get performance on \"routine tasks\" [368], such as autonomous parking functions in autonomous cars. In the surgical domain, this is a very active topic of research and the corresponding research is accelerating. There are several technical challenges to be addressed including tissue deformations and mobility, unpredictable scene changes resulting from cutting, suturing, or auterizing operations. There are some legal structure which should be also implemented for autonomous surgery before its wide deployment [371].  
In 2016, [PERSON] et al., demonstrated [372] that supervised control performed on the smart tissue autonomous robot (STAR) was _\"not only feasible but also, by some metrics, surpassed the performance of accepted surgical techniques, including robot-assisted surgeries (RAS), laparoscopy, and manual surgery\"_. In 2020, [PERSON] et al. report the use of supervisory control in orthopedic operations with a RoboDoc (THINK) robot [373]. Operations are performed in two stages: surgical planning by the surgeon considering pre- or intraoperative patient information from CT-MRI scans or 2D/3D fluoroscopy, and then robotic autonomous execution. More examples of surgical autonomy can be found in [372; 374; 375; 376] and references therein.
# Study About the Influence of Diatoms on the Durability of Monumental Limestone
## 2 Materials and Methods
### Methodology
#### 2.2.1 Porosity Test  
The porosity tests performed to study the effect of diatoms include:  
* Open or communicated porosity, also called efficient or effective.
* Closed porosity.
* Total porosity (sum of the previous two).  
The study of the open porosity of the samples was performed following the UNE EN 1936:2007 [25] standard that describes the test method to determine the real and apparent density and the open and total porosity of natural stone. To ascertain the real density, each specimen should be ground separately, so this part was left aside to be able to use the samples in subsequent tests. Six cubic samples with 50 mm edges were used: three with diatoms (D1, D2, and D3) and the other three as control samples (C1, C2, and C3). Figure 4 shows the samples used for the open-porosity test painted and tagged as control or diatom.  
All specimens were waterproofed with fiberglass paint on the face opposite to the surface under analysis. This measure was taken as this side did not show the presence of diatom biodeposition.  
Once the samples were painted and after drying the paint in the air for 1 day, they were placed in a ventilated oven at 68 \({}^{\circ}\)C for another 4 consecutive days to achieve maximum drying and constant masses. The samples were dried and weighed one by one, and the value of their mass was noted as dry mass (m\({}_{\text{d}}\)); then, they were allowed to cool in a desiccator until reaching room temperature. The next step of the test consists in obtaining the water-saturated mass of the samples (m\({}_{\text{s}}\)). Note that, even when the saturation of the sample is reached, the smaller the difference between two consecutive weighings, the greater the amount of air in the open pores has been removed. The same sequence was followed with each sample: the sample is removed from the water, quickly dried with a damp cloth to remove only the water from the surface, weighed and reimmersed, as shown in Figure 5a. When saturation is reached in all samples, the next step is to obtain the value of the mass of the test cube submerged in water (m\({}_{\text{h}}\)). A hydrostatic weighing was used, as shown in Figure 5b; the sample was placed in a basket under water attached to the hydrostatic balance, and its mass value was tared. At this point, values of the mass of the dry test piece, the mass of the sample saturated in water, the mass of the test piece submerged in water, and the density of water in kg/m\({}^{3}\) at 20 \({}^{\circ}\)C (998 kg/m\({}^{3}\)) were known.  
Figure 4: Samples used for the open-porosity test.
## 2 Results and Discussion
### Principal Component Analysis (PCA)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **F1** & **F1** & **F1** & **F1** & **F1** & **F1** \\ \hline ethyl acetate & 0.5989 & \(-\)0.0668 & 0.1046 & \(-\)0.1006 & 0.3285 & 0.2665 & 0.0473 \\ cyclotrisiloxane & \(-\)0.20479 & 0.5122 & 0.0398 & \(-\)0.1300 & 0.0020 & \(-\)0.0686 & 0.0354 \\
1R-alpha-pinene & 0.5844 & \(-\)0.0557 & \(-\)0.4895 & 0.1708 & \(-\)0.5931 & \(-\)0.0475 & \(-\)0.0058 \\
2,6,6-Trimethyl & \(-\)0.4089 & 0.6959 & 0.1444 & 0.0552 & 0.0072 & \(-\)0.2591 & \(-\)0.2182 \\ camphene & \(-\)0.5717 & \(-\)0.6812 & \(-\)0.0450 & \(-\)0.1776 & 0.0228 & \(-\)0.2657 & 0.2247 \\ beta-pinene & \(-\)0.5727 & \(-\)0.5703 & \(-\)0.0208 & \(-\)0.1859 & 0.0216 & \(-\)0.3885 & \(-\)0.0783 \\ beta-myrcene & **0.7863** & \(-\)0.1486 & 0.1526 & 0.0816 & \(-\)0.175 & \(-\)0.0822 & \(-\)0.0206 \\ hexanoic acid & 0.4131 & \(-\)0.0892 & \(-\)0.2776 & 0.2624 & \(-\)0.5664 & \(-\)0.2539 & \(-\)0.047 \\ (+)-3-carene & \(-\)0.5509 & 0.4894 & 0.1065 & \(-\)0.0780 & 0.0180 & \(-\)0.3210 & \(-\)0.4591 \\
3-heptene & \(-\)0.2235 & 0.5403 & 0.0822 & \(-\)0.0053 & 0.0053 & \(-\)0.1772 & 0.4339 \\ (+)-4-carene & \(-\)0.5356 & \(-\)0.6359 & 0.0472 & 0.1751 & \(-\)0.0037 & \(-\)0.1551 & 0.2726 \\
1,3-cyclohexadiene & \(-\)0.6668 & 0.0028 & 0.2105 & 0.5431 & \(-\)0.0093 & 0.0828 & \(-\)0.0138 \\ \(\phi\)-cymene & \(-\)0.6223 & \(-\)0.0869 & 0.2047 & 0.5145 & \(-\)0.0273 & \(-\)0.1676 & 0.3267 \\ _trans-beta_-ocimene & 0.4162 & \(-\)0.1325 & **0.8085** & \(-\)0.2094 & \(-\)0.2824 & 0.0615 & 0.0085 \\ beta-ocimene & 0.4108 & \(-\)0.1333 & **0.8144** & \(-\)0.2078 & \(-\)0.2764 & 0.058 & 0.0078 \\ gamma-terpinene & \(-\)0.445 & \(-\)0.1911 & 0.1321 & 0.4781 & \(-\)0.0441 & 0.5204 & \(-\)0.3493 \\ undecane & \(-\)0.4981 & \(-\)0.5488 & \(-\)0.0366 & \(-\)0.0694 & 0.0025 & 0.3551 & \(-\)0.1115 \\ _allo_-ocimene & **0.8647** & \(-\)0.1314 & \(-\)0.2506 & 0.2139 & \(-\)0.1572 & \(-\)0.1157 & \(-\)0.0287 \\ octanoic acid & 0.5698 & \(-\)0.1241 & \(-\)0.0111 & 0.2726 & 0.54 & \(-\)0.2734 & \(-\)0.062 \\ ethyl (\(E\)-2-ocetanoate & 0.5551 & \(-\)0.0501 & \(-\)0.3193 & 0.0568 & 0.1525 & 0.2036 & 0.0299 \\ decanoic acid & _0.6249_ & \(-\)0.1302 & \(-\)0.0043 & 0.215 & 0.6032 & \(-\)0.0902 & \(-\)0.0292 \\ _gamma_-murricane & \(-\)0.3906 & 0.1662 & 0.0649 & 0.048 & 0.0043 & 0.105 & 0.5499 \\ germrace D & _0.7344_ & \(-\)0.1102 & 0.2001 & 0.2503 & 0.1101 & \(-\)0.2713 & \(-\)0.0247 \\ henecosane & **0.8052** & \(-\)0.1997 & 0.4348 & 0.1317 & 0.0326 & \(-\)0.2029 & \(-\)0.0434 \\ Eicosane & \(-\)0.609 & \(-\)0.1034 & 0.2084 & 0.5369 & \(-\)0.0353 & \(-\)0.1027 & \(-\)0.2779 \\ \hline eigenvalue & 8.3184 & 3.265 & 2.2706 & 1.8592 & 1.6844 & 1.3503 & 1.3106 \\ variability (\%) & 31.9939 & 12.5577 & 8.733 & 7.1509 & 6.4786 & 5.1935 & 5.0408 \\ cumulative (\%) & 31.9939 & 44.5515 & 53.2845 & 60.4354 & 66.9141 & 72.1076 & 77.1484 \\ \hline \hline \end{tabular} Note: Strong loadings (\(>\)0.75) are shown in bold and moderate loading (0.5–0.75) in italic.  
\end{table} 
Table 3: Loadings of volatile organic compounds (VOCs) for Harumanis mango samples from point sources.