esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls
## 4 Power Consumption Comparison
### Considering Next Generation APs  
It is claimed that for new-generation systems, the total power consumption could be significantly lower than in the previous section due to lower fixed power consumption. This section assesses the impact of such reduced fixed power in the same scenario. Specifically, the front-haul power consumption and transmit power values are kept while for the computation of power consumed in each AP with (3), the following values are assumed: \(P_{m}^{\text{AP},\,\text{fix}}=0.75\) W, \(P_{m}^{\text{AP},\,\text{chain}}=0.02\) W, and \(P_{m}^{\text{tx}}=0.1\) W. This way, it can be ensured that the total power consumption in the \(m\)-th AP (\(P_{m}^{\text{AP}}\)) does not exceed 1 W. It is worth noting that by maintaining the same level of \(P_{m}^{\text{tx}}\), the performance of the communication system is not affected.  
In Figure 5, the new total power consumption values are compared with those obtained in Section 4.1. As observed, the deployment using next-generation APs has a considerable saving power compared to the deployment that uses conventional APs. This power saving, in turn, means that front-haul power has an even more representative impact on total power than what we noted in the previous section. Additionally, for this setup, the front-haul power becomes more relevant, representing 11.96% and 93.15% of the total power for fully serial and fully dedicated deployments, respectively, as shown in Figure 6.  
Figure 4: Percentage of total power consumed in front-haul considering conventional APs depending on the number of links used.
## 2 Materials and Methods
### Experimental System  
The prototype of the wide-range ESP and its optimized model (featuring non-smooth surface arrays on the impeller blades) were experimentally investigated in a closed-loop circulation system as illustrated in Figure 7. The optimized configuration was derived from computational simulations targeting hydraulic performance enhancement. The closed-loop test system comprises a drive motor, a test section, and a data acquisition unit. The drive motor operates at a rated speed of 3000 rpm and a rated power of 3.7 kW. The test subject is a three-stage impeller-diffuser assembly. Two groups of samples were evaluated: the baseline group with conventional smooth-surface impellers and the optimized group featuring dimple arrays (diameter: 0.9 mm) distributed on the pressure side trailing edge and suction side leading edge of the impellers. The data acquisition system includes the following:  
* Flow measurement: Electromagnetic flowmeter (accuracy: 0.5% FS).
* Pressure measurement: Two dynamic pressure sensors (range: 0-1 MPa, accuracy: 0.1%) installed at both the inlet and outlet.
* Speed monitoring: Optical encoder (resolution: 0.1\({}^{\circ}\)).  
The head (\(H\)) and efficiency (\(\eta\)) were calculated using Equations (7) and (8), respectively. Tests were conducted across a flow rate range of 20-200 m\({}^{3}\)/d, with varying operating conditions achieved through adjustments to the valve. Each operating condition was measured three times, and the arithmetic mean was calculated. Experimental unctainty was quantified using Type B evaluation, with head uncertainty \(\delta_{H}\) and efficiency uncertainty \(\delta_{\eta}\) calculated according to Equations (13) and (14), respectively [30].  
\[\delta_{H}=\sqrt{\left(0.1\%\times\Delta p\right)^{2}+\left(0.3\%\right)^{2}} \tag{13}\]  
where \(\Delta p\) represents the pump differential pressure (discharge minus suction pressure), 0.1% represents the pressure transducer accuracy, and 0.3% accounts for elevation measurement uncertainty.  
\[\delta_{\eta}=\sqrt{\delta_{H}^{2}+\left(0.5\%\right)^{2}} \tag{14}\]  
where 0.5% represents the flowmeter precision.
## 4 Case Study: The Kimberley Climate Adaptation Project (KCAP)  
Unlike the grassroots examples, participant responses have been scientifically evaluated in the KCAP case study [12]. Therefore, the KCAP allows a more detailed analysis of virtual globe potential to raise interest and awareness and provide a more representative 3D model than static landscape visualizations (Table 1). The KCAP case study was conducted by four of the authors from the Collaborative for Advanced Landscape Planning (CALP). The project included a strong visualization component, as well as policy development and recommendations, and immediate and longer-term scientific evaluation of the process and visualizations. It thus begins to answer, through evaluative research, questions about the response to virtual globes in public planning processes raised in the literature (Benefits 2 and 3 in Table 1) and the two previous introductory examples.  
Figure 2: The colored buildings are not proposed yet, but with the change of height regulations, such buildings will become possible; the color-coding refers to the City report and marks different possible developments ([PERSON] 2011; © 2010 Google, Image © 2011 DigitalGlobe, Image © 2011 Province of British Columbia, Gray Buildings © 2008 Sanborn, Image © 2011 IMTCAN)
## 2 Theoretical Model
Because of the amplification of the forward Raman amplifier, the signal power in the front part of the fiber is very high, and in the DBP the high-power region is at the end. As in the preceding analysis, most of the overcompensation parts are in the high-power region. Thus, the overcompensation parts will produce intense extra NSNI and impair the performance of the signal. Split-DBP can effectively reduce the extra NSNI caused by DBP. When split-DBP is applied in the system, as shown in Figure 2, the NSNI includes two parts. Part one is the same as the case of the receiver-side DBP, which is caused by the overcompensation of the receiver-side DBP. Part two occurs because the NSNI in the fiber near the transmitter side is not compensated. In the same way, we can determine the total power of NSNI at the end of the receiver-side DBP by the following:  
\[ \begin{array}{ll}P_{S-N}&=\int\limits_{0}^{l}3\gamma^{2}\eta(z,l)P_{s}^{2}( z)P_{spRs}(z)dz\\ &+\int\limits_{l}^{L}3\gamma^{2}\eta(2L-z,2L-l)P_{s}^{2}(z)P_{spRs}(z)dz\end{array} , \tag{8}\]  
where \(l\) is the pre-compensation length of the transmitter-side DBP. By substituting Equations (7), (8) to (1), the final SNR of the signal can be calculated. In the practical calculations, by dividing the fiber into several blocks, the integral in Equations (7) and (8) can be simplified to the summation of the divided fiber blocks.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Manaus—Federal-Level Buffer Zones**} \\ \hline
**Full Protection** & \multicolumn{2}{c}{**2018**} & \multicolumn{2}{c}{**2019**} & \multicolumn{2}{c}{**2020**} & \multicolumn{2}{c}{**2021**} & \multicolumn{2}{c}{**2022**} & \multicolumn{2}{c}{**2023**} & \multicolumn{2}{c}{**Change**} \\
**LULC Class** & **ha** & \%** & **ha** & \%** & **ha** & \%** & **ha** & \%** & **ha** & \%** & **ha** & \%** & **ha** \\ \hline Terra-Frime Forest & 12,208 & 38.7 & 12,203 & 38.7 & 12,830 & 40.7 & 10,509 & 33.3 & 12,846 & 40.8 & 11,232 & 35.6 & \(-\)976 \\ Floodplain Forest & 5843 & 18.5 & 4062 & 12.9 & 4763 & 15.1 & 4878 & 15.5 & 3455 & 11.0 & 4243 & 13.5 & \(-\)1600 \\ Secondary Forest & 3670 & 11.6 & 5825 & 18.5 & 4385 & 13.9 & 6490 & 20.6 & 5392 & 17.1 & 6514 & 20.7 & \(-\)2844 \\ Agriculture/Pastureland & 792 & 2.5 & 441 & 1.4 & 546 & 1.7 & 513 & 1.6 & 777 & 2.5 & 582 & 1.8 & \(-\)210 \\ Burned Areas & 12 & 0.0 & 21 & 0.1 & 54 & 0.2 & 38 & 0.1 & 17 & 0.1 & 83 & 0.3 & \(+\)71 \\ Barren Land & 30 & 0.1 & 5 & 0.0 & 13 & 0.0 & 11 & 0.0 & 8 & 0.0 & 61 & 0.2 & \(+\)31 \\ Development Areas & 12 & 0.0 & 2 & 0.0 & 5 & 0.0 & 3 & 0.0 & 2 & 0.0 & 8 & 0.0 & \(-\)4 \\ Water Bodies & 8950 & 28.4 & 8958 & 28.4 & 8920 & 28.3 & 9076 & 28.8 & 9019 & 28.6 & 8794 & 27.9 & \(-\)157 \\ \hline
**Sustainable Use** & **2018** & **2019** & **2020** & **2021** & **2022** & **2022** & **2022** & **2023** & **Change** \\
**LULC Class** & **ha** & \%** & **ha** & \%** & **ha** & \%** & **ha** & \%** & **ha** & \%** & **ha** & \%** & **ha** \\ \hline Terra-Frime Forest & 3800 & 75.5 & 4082 & 81.1 & 4129 & 82.0 & 3939 & 78.3 & 3988 & 79.2 & 4077 & 81.0 & 277 \\ Floodplain Forest & 30 & 0.6 & 50 & 1.0 & 52 & 1.0 & 68 & 1.4 & 17 & 0.3 & 9 & 0.2 & \(-\)21 \\ Secondary Forest & 1186 & 23.6 & 886 & 17.6 & 833 & 16.6 & 999 & 19.9 & 996 & 19.8 & 916 & 18.2 & \(-\)270 \\ Agriculture/Pastureland & 9 & 0.2 & 15 & 0.3 & 18 & 0.4 & 25 & 0.5 & 30 & 0.6 & 19 & 0.4 & 10 \\ Burned Areas & 6 & 0.1 & 1 & 0.0 & 1 & 0.0 & 2 & 0.0 & 2 & 0.0 & 11 & 0.2 & \(+\)5 \\ Barren Land & 2 & 0.0 & 0 & 0.0 & 0 & 0.0 & 1 & 0.0 & 1 & 0.0 & 1 & 0.0 & \(-\)1 \\ Development Areas & 0 & 0.0 & 0 & 0.0 & 0 & 0.0 & 0 & 0.0 & 0 & 0.0 & 0 & 0.0 & 0 \\ Water Bodies & 0 & 0.0 & 0 & 0.0 & 0 & 0.0 & 0 & 0.0 & 0 & 0.0 & 0 & 0.0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: LULC area distribution (in hectares and %) and total area changed (ha) inside Manaus’. State-Level buffer zones in all years.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 1 Introduction
In this manuscript, we present an Agent-based cloud computing load balancing approach that integrates load balancing and co-resident attack mitigation to address the challenges arising from (i) dynamic workloads in cloud computing, (ii) volatile VM resource utilization, and (iii) potential co-resident attacks on servers. Our Agent-based VM migration technique combines (i) real-time VM migration and (ii) the interaction and collaboration capabilities of Agents to achieve the distributed design objectives, such as load balancing and CRA mitigation. The load-balanced multi-agent protocol can balance workloads and steer clear of servers that are susceptible to co-resident attacks on a collection of heterogeneous hosts. This protocol defines roles for three distinct types of agents, including Balancer Agent, Server Agent, and Virtual Machine Agent, which dynamically cooperate using balancer heuristics, migration policies, acceptance policies, and migration heuristics to balance workloads. The balancer heuristics are responsible for the initial allocation of VMs. The distributed migration heuristics enables each Server Agent to decide which VM should be migrated when, while the Server Agent that needs to migrate a VM and the Server Agent that is willing to host a VM collaborate to determine the target host. Our contributions are as follows:  
(1) We propose an Agent-based cloud computing VM migration architecture that combines load balancing with co-resident attack mitigation and allows for the trade-off between load balancing and co-resident attack mitigation within the same framework. (2) We develop a cooperative dynamic VM migration Agent-based system that can withstand potential co-resident attacks on servers while considering two factors: (i) fluctuating workloads and (ii) irregular resource use of VMs. (3) We propose Agent-based VM migration mechanisms that enable the Agents to decide the initial allocation of VMs, the timing of VM migration, the migration destination, and whether to accept the migration requests. The rest of the paper is structured as follows: Section 2 provides a review of related work, Section 3 defines the Agent-based VM migration problem, Sections 4 and 5 provide a comprehensive description of the Agent-based VM migration mechanism, and Section 6 presents experiments and analyses. In Section 7, we provide concluding remarks and suggest some future research directions.
# Fuzzy Model Predictive Control for Unmanned Helicopter
## 3 Control Methodology  
The methodology used in the presented approach relies on the Parallel Distributed Compensation (PDC) technique, which corresponds to utilizing multiple regulators, tuned for different operating conditions [32]. In this method, signals computed by several algorithms are combined in order to generate final control signal. The high-level control system scheme is presented in Figure 2.  
The proposed system is a full-state feedback control loop, which as input signals employs the reference state space trajectory vector \(r_{p}\) and the measured state vector, which consists of the following:  
* \([X,Y,Z]\)--position of the unmanned rotorcraft in the inertial reference coordinate system.
* \([\Phi,\Theta,\Psi]\)--attitude (roll, pitch, yaw) angles of the rotorcraft with respect to the reference coordinate system.
* \([V_{x},V_{y},V_{z}]\)--linear velocities measured in the body-fixed coordinate system.
* \([P,Q,R]\)--angular velocities of the rotorcraft.  
The computed control vector \(u\) stores the information about  
* \(x_{a},\ x_{b}\)--cyclic pitch components of the main rotor;
* \(x_{c}\)--collective pitch of the main rotor;
* \(x_{r}\)--collective pitch of the tail rotor.  
In this instance, control laws are defined for different forward flight velocities (\(V_{x}^{ref}=[0,10,20]\ [\mathrm{m}/\mathrm{s}]\)). At each timestep of the system's operation, control signals from each local MPC regulator are computed. Based on the measured forward flight velocity value, \(V_{x}\), the system generates an output using the Takagi-Sugeno inference algorithm. The basic principles of the Model Predictive Control algorithm and the Takagi-Sugeno inference method are described below.
## 3 Experimental Section
### Isolation of Linalool Synthase (VvRiLin) Gene from Vitis vinifera Riesling Berries  
The gene models which were annotated as putative linalool synthase genes (_LIN_) were obtained from database of VitisNet [36] and FLAGdb++ [37] both based on grape genome [38]. The redundant sequences, together with imperfect models caused by insert/deletion mutation and frame shift, were dropped. The amino acid sequences encoded by the rest of putative full length _LIN_ genes were compared with these known plant LINs. Their subcellular localization was predicted through three web tools: ChloroP [39], TargetP [40] and SignalP [41]. The gene models that were predicted to contain a chloroplast-located signal peptide were selected as putative grape linalool synthase genes (in short, _VvLins_). A blast program was performed to screen DFCI Grape Gene Index database [42] to find EST fragments corresponding to putative _VvLins_. The _in silico_ expression profile of _VvLins_ was analyzed.  
Specific primers covering the open reading frame (ORF) of _VvLin_ genes were designed with the help of Prime 5.0 software package (Premier Biosoft International, Palo Alto, CA, USA) and were then used to amplify their nucleic acid fragments from grape berry cDNA through polymerase chain reaction (PCR).  
The PCR system of a total volume of 20 \(\mu\)L was composed of 1 \(\mu\)L cDNA template, 1 \(\mu\)L of each primer (20 \(\mu\)M), 10 \(\mu\)L 2\({}^{\times}\) Taq PCR plus master mix and 7 \(\mu\)L ddH\(\mathrm{\SIUnit{2O}}\) (Sinopharm Chemical Reagent Beijing Co., Ltd., Beijing, China) according to the protocol of the supplier with slight modification. The temperature gradient PCR was conducted under various conditions: pre-denaturation at 95 \({}^{\circ}\)C for 5 min, denaturation at 94 \({}^{\circ}\)C for 30 s, primer annealing at 55 \(\pm\) 10 \({}^{\circ}\)C for 1 min and extension at 72 \({}^{\circ}\)C for 1 min 30 s at 35 cycles, a final extension at 72 \({}^{\circ}\)C for 10 min, and then cooled to 10 \({}^{\circ}\)C in a thermal cycler (Techne TC-512 PCR System, Fisher Scientific UK Ltd., Leicestershire, UK). A band of about 1800 bp was gel-purified, subcloned into a pMD19-T Easy vector (Takara, Shiga, Japan) and sequenced using ABI3700 DNA sequencer (Applied Biosystems, Foster City, CA, USA).
_Article_  
**Congestion Risk-Averse Stochastic Unit**  
**Commitment with Transmission Reserves**  
**in Wind-Thermal Power Systems**  
**[PERSON] 1\({}^{\copyright}\), [PERSON] 1,\({}^{\star}\) and [PERSON] 2,\({}^{\star}\)**  
Received: 13 August 2018; Accepted: 18 September 2018; Published: 22 September 2018  
**Featured Application: We present a congestion risk-averse stochastic unit commitment model with transmission reserves to hedge against uncertainty and variability induced by wind generation; the transmission reserve requirements are quantified in a systematic manner with relatively small computational effort and high accuracy; the proposed method can flexibly manage the uncertainty-induced congestion to enhance the system reliability and wind power utilization.**  
**Abstract: The great proliferation of wind power generation has brought about great challenges to power system operations. To mitigate the ramifications of wind power uncertainty on operational reliability, predictive scheduling of generation and transmission resources is required in the day-ahead and real-time markets. In this regard, this paper presents a risk-averse stochastic unit commitment model that incorporates transmission reserves to flexibly manage uncertainty-induced congestion. In this two-settlement market framework, the key statistical features of line flows are extracted using a high-dimensional probabilistic collocation method in the real-time dispatch, for which the spatial correlation between wind farms is also considered. These features are then used to quantify transmission reserve requirements in the transmission constraints at the day-ahead stage. Comparative studies on the IEEE 57-bus system demonstrate that the proposed method outperforms the conventional unit commitment (UC) to enhance the system reliability with wind power integration while leading to more cost-effective operations.**  
**Keywords: stochastic programming; unit commitment; transmission reserves; wind power forecast; real-time dispatch; uncertainty quantification**
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Coupled CC
#### 7.2.4 wVegas  
LIA, OLIA and BALIA all use packet loss as a signal to indicate congestion. This means that traffic can shift away from congested paths after a loss event occurs. wVegas is a delay-based coupled CC for MPTCP based on TCP-Vegas [9].  
The algorithm is applied to the SS and CA phases of CC. When a path queueing delay is larger than a user-defined threshold, _cvmd_ is reduced and the queue begins to drain. _cvmd_ on other paths get an opportunity to increase based on their estimated capacity. This makes wVegas more responsive to network changes when compared to other coupled CC. Like all delay-based algorithms, wVegas will compete poorly against other loss-based CC algorithms [9].
## 4 Results and Discussion
### Experimental Validation  
A prototype of the compliant constant-force actuator was built and mounted on the robot ABB 1200 for the experiment as shown in Figure 14. The whole experimental platform mainly included an air compressor, a control cabinet, a robot, a computer and the constant-force actuator prototype. The DAQ board (Zhongtai USB7660 BD) in the control cabinet was used for signal input and output. The control and data acquisition program on the host computer was programmed through Labview, shown in Figure 15. The practical application of the BPNN-SP-PID controller in this system was completed by the joint compilation of Labview and MATLAB [31; 32]. In order to reduce the influence of the posture and the surface shape of the workpiece, the posture of the robot arm was kept vertical during the entire experiment, and a flat steel plate was selected as the workpiece.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 4 Results
### Paddy Rice Classification  
Based on yearly MODIS time series and our trained OCSVM model, we classified paddy rice areas in mainland China at 250-m MODIS resolution for the years 2014, 2010, 2005 and 2002 (Figure 5).  
Figure 4: Results of the parameter grid search. (**a**) The f1-scores for all \(\
u\) and \(\
u\) combinations from the grid search; (**b**) a subset of \(\
u\) and \(\
u\) combinations around the one with the highest overall f1-score.  
Figure 5: Paddy rice classifications for mainland China.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 4 Result and Analysis
### Encryption Efficiency
Figure 7b,d,f compares the encryption efficiency of AES and the stream encryption algorithm ChaCha20-Poly1305 [25], where AES New Instructions (AES-NI) denotes AES using hardware instruction, AES denotes AES without hardware instruction, and Cipher Block Chaining (CBC) is the block encryption method that is still widely used at present. ChaCha20-poly1305 is a widely-used stream encryption algorithm with 256 bit symmetric encryption strength, and the strength of other key sizes is not implemented in OpenSSL, so it could not be tested. Process performance is improved using AES hardware instructions, but not all machines support AES hardware instructions; these machines can only use software algorithms. Symmetric encryption is used to encrypt the transmitted data and is the most time consuming part of encryption for systems that need to transmit a large amount of data in a fixed period of time. Compared with the AES algorithm, which does not use hardware instruction, ChaCha20-Poly1305 has higher encryption efficiency on these processors. Therefore, the ChaCha20-Poly1305 algorithm can be used against the client when the client does not support AES hardware instructions. When the version of the client's SSL/TLS protocol is older, only the AES algorithm can be used at this point. For clients that do not support AES hardware, only AES software encryption can be used on three types of processors. However, for processors that support AES hardware instructions, the AES algorithm is the preferred option over ChaCha20-Poly1305. As shown in Figure 7, the use of AES hardware instructions can yield improved performance for different block algorithms. In practice, if the client  
Figure 7: Comparison of encryption efficiency of the asymmetric encryption and the symmetric encryption algorithm in these platforms: Xilinx Zynq UltraScale+, FT-2000+: FTC662, and Intel Core i7-6700. (**a**) RSA in Xilinx Zynq; (**b**) AES in Xilinx Zynq; (**c**) RSA in FT-2000+: FTC662; (**d**) AES in FT-2000+: FTC662; (**e**) RSA in Intel Core i7-6700; (**f**) AES in Intel Core i7-6700. supports the AES hardware instruction, different AES encryption strengths can be set according to the client's identity information.