esa-sceva/satcom-chunk-collection huggingface dataset:
# Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach
###### Abstract  
The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.  
multi-cloud; edge servers; internet of things (IoT); data replication; partial replication; system soundness; formal methods; regression; machine learning +
Footnote †: journal: Sensors
## 5 Frequency Manipulation and Communication Feasibility
### ISL Communication Feasibility
The obtained link budget showed that the ISL network has a system link margin of \(-9.2\) dB and \(-10.4\) dB at \(400.15\) MHz and \(460.00\) MHz, respectively. In order to achieve mission success, ways of improving this link budget are explained in detail in the discussion section. Table 4 shows the link budget estimation for this mission.
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
###### Abstract  
The advent of renewable energy resources and distributed energy systems herald a new set of challenges of power quality, efficient distribution, and stability in the power system. Furthermore, the power electronic converters integration has been increased in interfacing alternate energy systems and industries with the transmission and distribution grids. Owing to the intermittency of renewable energy resources and the application of power electronic converters the power distribution faces peculiar challenges. The dead-time effects are among the main challenges, which leads to the distortion of third harmonics, phase angle, torque pulsation, and induction motor current, causing severe quality problems for power delivery. To tackle these problems, this paper proposes a novel dead time compensation technique for improving the power quality parameters and improving the efficiency of power converters. The proposed model is simulated in MATLAB and the parametric equations are plotted against the corresponding parametric values. Furthermore, by implementing the proposed strategy, significant improvements are attained in the torque pulsation, speed, and total harmonic distortion of the induction motor. The comparisons are drawn between with and without dead time compensation technique, the former shows significant improvements in all aspects of the power quality parameters and power converters efficiency.  
industrial microgrid; dead-time compensation; power quality; variable frequency drive; third harmonic distortion; induction motor +
Footnote †: journal: Applied Sciences
## 3 Experimental Program
### FRP Reinforcement
#### 3.2.3 Specimen S5  
As introduced in Section 1 and described in more detail in Section 6.1, the anchored U-wrap on S1B failed unexpectedly and undesirably by anchor pullout at around 3% drift. In light of the subpar performance of the traditional embedded anchors and the lack of space in the flange to lengthen the anchor dowels, external shear strengthening for specimen S5 was provided in the form of fully closed CFRP strips, as shown in Figure 6. This scheme was designed to overcome an analytically predicted 30% shear overstress at the end-yielding region and to mitigate buckling of the top longitudinal reinforcement by providing additional confinement. The FRP reinforcement consisted of two-layer U-strips made of 1.0 mm (0.04 in)-thick, 16.5 cm (6.5 in)-wide CFRP laminate, spaced at 27.9 cm (11 in) on center along a length from the fixed end of 1.5 times the effective depth of the beam. Each strip was developed with two 1.3 cm (0.5 in)-diameter CFRP splice anchors passing through predrilled holes across the flanges and connecting the ends of the U-wrap, thus providing complete continuity of the FRP around the perimeter of the beam. The over-the-top anchors were embedded into the slab along recessed grooves for a flat surface finish, as this was a requirement in the in-situ application to avoid tripping hazards. The anchor splays were detailed to fully cover the width of the FRP strip and have a length of 30.5 cm (12 in), resulting in a 16\({}^{\circ}\) fan angle. Anchor splays were sandwiched between the two layers of FRP. Beam web corners and anchor hole edges were rounded to a minimum radius of 1.27 cm (0.5 in) to reduce stress concentrations in the FRP. The CFRP anchors were designed for a maximum strain of 0.004. The FRP reinforcement was supplemented with 1.27 cm (0.5 in)-deep intermediate saw-cut grooves in between the FRP strips and along the beam's soffit, sides, and slab, intended to act as crack-control joints to force an even distribution of thinner flexural cracks away from the FRP strips.
## 2 Material and Methods
### Auxiliary Measurements  
The LI-7200 was installed at 2.05 m height with a northward, eastward and vertical sensor separation of \(-\)5.5 cm, +5.0 cm and 0 cm from the CSAT3, respectively. The north off set was 55\({}^{\circ}\). A net radiometer (CNR4, Kipp&Zonan, Delft, the Netherlands), photosynthetically active radiation (PAR) sensor (PQS 1, Kipp&Zonan) were installed at 2.90 m height, whereas, the air temperature (AT) probe (Pt 100, Rotornic, Bassersdorf, Switzerland) and relative humidity (RH) probe (Hygro Clip S3, Rotornic) were installed at 2.0 m above the soil surface. Rainfall was measured with a tipping bucket rain gauge(TE-525, Texas Instruments, Dallas, TX, USA). Data of these slow sensors were averaged over a 30-minute period. The system received its power from four 12 V, 100 AH DC batteries and was also supported by an AC connection in parallel on foggy and cloudy days. Vapor pressure deficit (VPD) was estimated from the vapor pressure monitored by the LI-7200. The leaf area index (LAI) was periodically measured by a canopy analyzer LI 2200 (LICOR, Lincoln, NE, USA) at the vegetative, reproductive and maturity stages. Half-hourly measurements of soil moisture (SM), heat flux (SHF) and temperature (ST) were taken at 5 cm soil depth using a water content reflectometer (CS 616-L), soil heat flux sensor (HFT3 transducer) and soil and water temperature probe (107 B), respectively.
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 4 System Model
### Cache Model  
Assume that the set of locally installed NFs at the cache of the fog nodes in the F-RAN is denoted by \({\bf S}=[s]\), i.e., \(S\subset{\bf F}_{r}\) with a processing and memory resource of \(q_{prc}(s)\) and \(q_{me}(s)\), respectively. Here, the cache status of a NF is represented by a binary variable,  
\[\Upsilon_{t,i}\ =\ \left\{\begin{array}{ll}1,&\mbox{if NF $f_{t}$ is cached on $n_{i}$,}\\ 0,&\mbox{otherwise.}\end{array}\right. \tag{7}\]  
Here, the set of cached NFs is predetermined by content popularity and most requested NFs as per the study in [32] as depicted in Figure 2 (retrieved from the study in [32]). It shows the most popular NFs in the network, where \(f_{1}\) and \(f_{2}\) account for call-in and call-out, \(f_{3}\) and \(f_{4}\) account for sms-in and sms-out, and \(f_{5}\) represents internet-access traffic, respectively. Note that the set of popular traffic varies according to the geographic areas and nature of the traffic. Here, the same algorithm can be applied to other types of NFs and content popularity as well. Along with this, the cached NFs demanded by the MS terminal \(m\) can be directly retrieved from the local fog node, whereas uncached content needs to be traversed to the cloud core for processing, i.e., conveyed to the cloud BBU via the fronthaul links.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 4 DSP Techniques on Compensation of MDL Effects in FMF Links
### Interference Cancellation (IC)  
Linear MIMO equalizers are also attractive to mitigate MDL effect because of its relative low complexity, adaptive schemes and high compatibility. The performance of these equalizers could be further enhanced by introducing more prior information of the channels which indicates that the SNR of each mode can be considered to utilize. Inspired by this idea, the interference from higher-SNR channels to the lower-SNR ones can be  
Figure 8: (**a**) OSNR penalty vs. distance and (**b**) normalized histogram of the OSNR penalty (320 km) for weakly and strongly coupled modes, MMSE equalizer and ML detection, 200 channel realizations.  
Figure 9: Number of complex multiplications per bit as a function of the number of tributaries NT for the MMSE equalizer, IRSML and ML detector for different modulation formats.  
cancelled by equalizing stronger modes in advance. Each channel is equalized one by one from the highest- to the lowest-SNR modes by estimating the interference from other channels, which equalizer is called successive interference cancellation (SIC) schemes.  
In order to realize longer-distance SDM transmission in [58; 59], an unreplicated successive interference cancelling (U-SIC) equalizer that mitigates MDL-induced intermodal interference were proposed and successfully demonstrated. Through the corresponding experiments, an MDL tolerance improvement of 3.3 dB and an OSNR gain of >4.5 dB were demonstrated even under high DMGD up to 39 ns. As shown in Figure 10, as compared to the conventional SIC algorithm, channel estimation is no more required for the proposed U-SIC.  
Figure 11 shows the advantages of U-SIC scheme on the improvement of Q factor and lower requirement of OSNR to achieve hard-decision (HD) forward error correction (FEC) (FEC) limit for bit error ratio (BER) in comparison with conventional linear MIMO algorithms. What's more, in [60] the combination of U-SIC technique and mode scramblers enable the demonstration of the 2500-km long-haul MDM transmission with more than 2-dB Q-factor gain.  
For higher performance in MDL-impaired MDM systems, a novel unreplicated parallel interference cancellation (U-PIC) scheme was proposed in [61] by increasing the number of iterations compared with U-SIC. By iterative utilization of the soft information of transmitted symbols from decoders, the U-PIC equalizer successfully cancel the MDL-induced intermodal interference among the channels with similar SNR. Experiment demonstrated that the U-PIC algorithm could achieve better performance than both the conventional linear MIMO equalizers and the above U-SIC algorithm.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 5 Multiple Input Multiple Output Technology
### Transmit Diversity  
Transmit diversity is the reverse of the receive diversity. In transmit diversity, the transmitter is equipped with multiple antennas at the transmitter to introduce diversity by  
Figure 14: Receive diversity in MIMO.  
Figure 13: MIMO classification.  
transmitting redundant versions of the same signal. The Alamouti technique is a popular algorithm among the transmit diversity techniques [138]. There are various versions of transmit diversity, such as space-time block coding (STBC) and space-frequency block coding (SFBC). With STBC [139; 140], signals are mapped as space and time to exploit the diversity present in the time domain. SFBC is operationally different from STBC because it exploits the antenna spacing and frequency domains encoding in the antenna [141; 142]. Just like in receive diversity, transmit diversity does not increase UAV tracking antenna data rates. However, the scheme can enhance link reliability which is the primary goal of the UAV tracking antenna system. Both STBC and SFBC signals can be received using appropriate finite filter design.
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 1 Introduction
In the literature, some works try to reduce the tag lost ratio in mobile systems with either mathematic modeling or group scheduling strategies. For example, [PERSON] et al. analyzed the tag lost ratio with a Markov chain model [12]. [PERSON] et al modeled a dynamic RFID system for an optimal configuration [13]. In [14; 15], some two-phase identification protocols are proposed. However, the tag lost ratio of existing works is large, especially when the tag's moving speed increases. In this work, we improve the identification efficiency of mobile RFID systems in two aspects, i.e., controlling the participating tags and accelerating the recognition process in each reading round. In general, we propose a novel admission-control-based dynamic query tree (ACDQT) protocol for mobile RFID tag identification. The main contributions are as follows,  
* The identification process in mobile systems is analyzed, and a new multi-round admission control (MRAC) algorithm is developed to optimize the number of tags admitted in each reading round. With MRAC, the tag lost ratio is guaranteed, and the identification efficiency in each round is effectively improved;
* A dynamic query tree recognition (DQTR) algorithm is proposed to reduce the identification delay in each reading round. With DQTR, colliding tags are divided into more subsets based on the number of consecutive colliding bits; thus, the tag recognition efficiency is improved;
* Theoretic analysis is conducted to analyze the performance of DQTR in a single reading round. Moreover, numerous simulation results are presented to demonstrate the effectiveness of the proposed algorithm. Compared with existing benchmark works, the proposed ACDQT is proven to support higher tag flow rate with a lower tag lost ratio. The remainder of this work is organized as follows: Section 2 introduces some representative tag identification protocols in both static and mobile systems. In Section 3, a fast-moving RFID tag identification system model is exhibited, and the frame structure and commands are described. Next, in Section 4, the proposed ACDQT protocol including the admission control and recognition strategies is described in detail. Then, theoretical analysis is conducted to obtain the identification delay of a single reading round in Section 5. The simulation results are illustrated in Section 6. Finally, Section 7 gives some conclusion remarks.
## 5 Simulation Results--BER Performance
### Rayleigh Channel
Thus, the following conclusions can be made for proposed processing algorithm (9) with decision feedback. First, it is possible to estimate resulting energy losses from an increase in the bit rate at a fixed error probability (\(BER=10^{-4}\)) using the developed methodology. Second, the energy loss in relation to potential BER increases by approximately 2 dB with an increase in the bit rate speed by 1.5 times (above the Nyquist barrier). Third, an increase in the bit rate by 1.5 times leads to an increase in the energy loss from 1.2 dB to 3.7 dB for a fixed \(BER=10^{-4}\) with a fixed computational complexity (which is often the main criterion for the practical implementation of a digital demodulator). Finally, an increase in the bit rate by 1.5 times leads to an increase in computational complexity from \(0.8\times 10^{5}\) to \(3.3\times 10^{5}\) numbers of operations for acceptable increases in energy loss equal to 2 dB. Summarizing this conclusion, we can estimate how much additional increases in the  
Figure 8: Energy efficiency vs. number of operations for algorithm (9). number of computational operations will cost a gain in energy efficiency, for example, 1 dB. For the proposed processing algorithm with decision feedback, each decibel is achieved by increasing the number of computational operations by \(1.5\times 10^{5}\).
## 5 Performance Assessment of BDS-3 Satellite Clocks  
Based on the information of satellite table [32; 33], Table 5 lists the pseudo-random noise (PRN) and the corresponding clock type of GNSS satellites. The processing period is from DOY 190 to 238 in 2019, which is consistent with the assessment period of ODTS noise. During this period, the BDS-2 included 5 GEO, 7 IGSO, and 3 MEO satellites, with the clock type being RAFS. The BDS-3 basic system included 10 MEO satellites with RAFS, 8 MEO satellites with PHM, and 1 GEO satellite with RAFS, where the GEO satellite was excluded due to the testing status during this period. We only chose the GPS block IIF satellites with RAFS and Galileo IOV and FOC satellites with PHM. Hence, there were 10 GPS satellites and 21 Galileo satellites.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatment** & **C** & **L1** & **L2** & \(p\)**-Value** \\ \hline
**Animals, n.** & **36** & **36** & 36 & \\
**Tail lesions severity (\%)** & & & & \\ _Beginning_ & & & & \\ _Class 0 (intact)_ & 50 & 64 & 58 & \\ _Class 1 (mild or moderate)_ & 44 & 33 & 36 & 0.800 \\ _Class 2 (severe)_ & 6 & 3 & 6 & \\ _End_ & & & & \\ _Class 0 (intact)_ & 75 & 97 & 72 & \\ _Class 1 (mild or moderate)_ & 25 & A & 0 & B & 22 A & 0.011 \\ Class 2 (severe) & 0 & 3 & 6 & \\
**Overall body lesions severity** & & & & & \\ _Beginning_ & & & & & \\ _Class 0 (low presence)_ & 65 & 77 & 81 & \\ _Class 1 (moderate presence)_ & 35 & 23 & 19 & 0.283 \\ _Class 2 (high presence)_ & 0 & 0 & 0 & 0 & \\ _End_ & & & & & \\ _Class 0 (low presence)_ & 89 & & 78 & 49 & \\ _Class 1 (moderate presence)_ & 11 & B & 22 & B & 51 A & 0.0003 \\ Class 2 (high presence)_ & 0 & & & 0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Skin and tail classification (expressed as a percentage) of the animals belonging to the three experimental groups (C = control, L1 = lavender nebulization once a day, L2 = lavender nebulization twice a day) assessed at the beginning and at the end of the trial. Different letters indicate significant differences in the class distribution between groups (A,B \(p<0.01\)). Similarly, as concerns lesion severity classes, no significant difference across groups was observed in class distribution at the beginning of the trial. At the end of the trial, the increased number of lesions described above resulted in a significantly higher presence of mildly damaged animals in group L2 compared to both L1 (\(p<0.01\)) and C (\(p<0.05\)). With respect to tail lesions, L1 showed the best class distribution (less animals whose tail was classified as 1, i.e., moderately damaged) compared to both C and L2 (\(p<0.01\)). The lesions on the carcasses are summarized in Table 3. No statistically significant difference was observed among groups, and only a tendential difference (\(p<0.1\)) was found in the shoulder region, with the L1 group having tendentially more lesions than C.  
Table 4 shows slaughtering and carcass quality parameters (including the main cuts yield), and Table 5 summarizes meat quality parameters (pH, color, water holding capacity and tenderness). No differences were observed among groups with respect to carcass parameters, whereas minor differences were observed in meat quality. In particular, meat from L1 animals showed lower pH values compared to group C, and animals subjected to the laverader treatment (both L1 and L2) showed higher drip loss (i.e., lower WHC, Water Holding Capacity) than C animals (\(p<0.05\) for all differences). Table 6 shows the analytical results of stress indicators in blood. The three groups did not show statistically significant differences. With respect to LEO residuals in the sampled tissues, all the samples of subcutaneous fat and muscle from the treated animals did not exceed the LOD of linalool (limit area of 5.84 for the fat and 8.62 for the lean tissue, <0.25 nL/g).
## 4 Semantic Understanding of Object Layer
### Spatial Structure Semantics
#### 4.3.1 Pixel-Neighborhood-Window-Based Method  
Taking a pixel as the basic processing unit, a window is defined for each pixel (also called the central pixel) in the image, which describes the spatial distribution pattern of the pixel values in the window area. The spatial structure features of the pixels in the window area are used as the spatial structure features of the central pixel [75]. This method describes the spatial structure of pixel neighborhoods. It can make up for the lack of spectral information of the central pixel by using the information of neighboring pixels, but it is important and uncertain for the reasonable selection of window size [76].  
Among the existing methods, two kinds of neighborhood structure patterns are common. One is the interactive mode between the central pixel and its neighbor pixels, which is a \"one-to-multiple\" relationship. This relationship is represented using methods such as random fields, local spatial autocorrelation statistics and data fields [77, 78]. The other is the spatial structure relationship of multiple pixels in the neighborhood window. The method equates the center pixel with its neighbor pixel, and is a \"multiple-to-multiple\" relationship, which is represented using methods such as the gray level co-occurrence matrix, global spatial autocorrelation statistic and the spatial semi-variogram function [79, 80].