esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Formulation of Vehicle Subsystems
The inner rotor encompasses a distributed three-phase winding and the outer rotor is equipped with permanent magnets. The EVT subsystem's inputs are the inner and the outer rotor's torque and speed. These operating points are considered and a set of five independent currents that minimize iron and copper losses are correspondingly used in different axes (\(d\) and \(q\)). In this regard, as illustrated in Figure 3, the stator current is in the d and q-axis, the outer rotor's current is in the d axis, and the inner rotor's current is in the \(d\)- and \(q\)-axes [52]. Finite element (FE) calculations validated on a prototype [53] were used to store the results of the corresponding fluxes (\(\Psi\)) in look-up tables used in the EVT subsystem. Knowing the flux and current, the corresponding torque on each component can be calculated as follows, where \(N_{p}\) is the number of pole pairs and subscripts 1-3 are related to the stator, the outer rotor, and the inner rotor, respectively. \[T_{1}=\frac{3}{2}N_{p}(\psi_{1q}I_{1d}-\psi_{1d}I_{1q}) \tag{10}\]  
\[T_{2}=-T_{1}-T_{3} \tag{11}\]  
\[T_{3}=\frac{3}{2}N_{p}(\psi_{3q}I_{3d}-\psi_{3d}I_{3q}) \tag{12}\]  
The individual subsystems mathematically explained in the current section were modeled and integrated in a Simulink(r) environment to form the whole vehicle model, as illustrated in Figure 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Quantity** \\ \hline Rated capacity & 14 Ah \\ Nominal voltage & 3.6 V \\ Max discharging current & 100 A \\ \(N_{Batts}\) & 60 \\ \(SoC_{initial}\) & 80\% \\ Min Voltage & 2.5 \\ Max Voltage & 4.15 \\ C\_rate charging limit & \(-3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: LiFePO4 battery cell specifications. Figure 3: Cross-sectional view of the permanent magnet (PM) EVT.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 3 Delay-Sensitive NOMA-HARQ
### Packet Delivery Delay Profile of D-NOMA-HARQ  
As with D-NOMA-HARQ there is no queuing, each user sends at most \(N+1\) packets when \(N\) packets are scheduled for the transmission. The delay profile of each user can be characterized as follow  
\[D_{i}^{\mathsf{D}}[d]=\sum_{u\in\mathcal{S}_{i}}p_{u}\delta[d-N]+(1-\sum_{u\in \mathcal{S}_{i}}p_{u})\delta[d-N-1], \tag{7}\]  
where \(\mathcal{S}_{1}=\{1,2,3\}\) and \(\mathcal{S}_{2}=\{1\}\) denote the states of user \(1\) and \(2\), respectively, when a packet is successful without any retransmission.
## 4 Main Result
### Numerical Study
#### 4.4.2 Simulations for Ideal Communication Channel  
The simulations were performed in the MATLAB/Simulink software environment. For the numerical solution of the differential equations the MATLAB variable-step routine ode45 with the relative tolerance \(10^{-3}\) was used. For hybrid (continuous-discrete systems), including the coding/decoding procedure model, the blocks from the standard Simulink/Discrete blockset were included. The simulation time was confined to \(t_{\rm fin}=54\),\(000\) s \(=15\) h.  
Firstly, consider the \"ideal\" case where the position information is transferred over the channel without sampling and level quantization. For the performance criterion, let as pick up instant \(T^{*}\) when the relative trajectory on the \((X,Z)\) plane reaches the circle with given radius \(Q\) and does not leave it in the future, i.e., \(T^{*}=\max\limits_{t}\left(\sqrt{x_{12}^{2}+z_{12}^{2}}>Q\right)\) (\(T^{*}\) is further called _the regulation time_). It is natural to assume that within this circle the regulation rule switches to the rule ensuring collision avoidance, which is beyond the scope of this paper.  
The simulation results for various initial conditions are depicted in Figures 2-5. Figures 2 and 3 are related to the case of \(x_{12}(0)=20\) m, \(\dot{x}_{12}(0)=0.05\) m/s, and other initial conditions are zero. Time histories of relative positions \(x_{12}(t)\), \(z_{12}(t)\) and control action \(u(t)\) are plotted in Figure 2. Symbol \"+\" marks regulation time \(T^{*}\). For the given initial conditions, \(T^{*}=3.27\) h is obtained. Figures 4 and 5 show similar plots for the case of \(x_{12}(0)=200\) m, \(\dot{x}_{12}(0)=0.025\) m/s, \(z_{12}(0)=-50\) m, and \(\dot{z}_{12}(0)=-0.025\) m/s. In this case, \(T^{*}=4.61\) h.  
The simulation results show that, despite of the control signal saturation at the beginning of the process, the targeting manifold is attained for both sets of the initial conditions with regulation time \(T^{*}=3.27\) h (Figures 2 and 3) and \(T^{*}=4.61\) h (Figures 4 and 5), respectively. Moreover, based on the harmonic balance method arguments [52; 53] the conclusion can be made that the closed-loop system is globally asymptotically stable.  
Figure 3: Satellites relative trajectories on the (XZ) plane for \(x_{12}(0)=20\) m, \(\dot{x}_{12}(0)=0.05\) m/s. The ideal channel case. \(T^{*}=3.27\) h.
## 3 Results and Discussion
### Effect of Lignin Concentration on Nanoparticle Synthesis
#### 3.2.2 Surface Charge  
Figure 2A-C shows that there were some aggregates of nanoparticles in a colloidal liquid. It can generally be explained that attractive interactions between condensates (SL nanoparticles) were stronger than repulsive interactions. Calculations using a Zetasizer gave more detailed values regarding the degree of electrostatic repulsion of SL nanoparticles. Figure 3A shows the stability of a lignin nanoparticle surface charge that was prepared at 1, 2, and 4 mg/mL. Zeta potential of the nanoparticle surface increased with increasing pre-dialysis concentration, from -26.3 mV for 1 mg/mL to -37.8 mV. Particle stability was achieved when the zeta potential value was more than 30 mV or less than -30 mV. SL nanoparticles prepared at 2 and 4 mg/mL were relatively low, under -30 mV, and had a stable zeta potential. Under this circumstance, the colloid will resist aggregation. Further study is necessary to determine the effect of time on particle stability.  
Figure 3: Zeta potential value of SL nanoparticles under different concentrations (**A**) and nanoparticles formed by each SL fractions (**B**).  
Figure 2: TEM images of (**A**) NP-C1, (**B**) NP-C2, (**C**) NP-C4, (**D**) NP-F1, (**E**) NP-F2, and (**F**) NP-F3 (NP-C_n_: lignin nanoparticle at a concentration \(n\) mg/mL, NP-F_n_: nanoparticles formed by lignin fraction _n_).
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 6 Performance Evaluation
### Simulation Scenarios and Settings  
We consider a shallow sea located in 26.5 N and 123.5 E, with an average depth of 124 m, 21 nodes (including a sink) randomly anchored in a \(7\times 3\) grid with a total region size of 1 km \(\times\) 2 km \(\times\) 124 m, and the sink located in the left of the area, and the average distance of each neighbor is about 400 m. Bellhop is used to calculate the path loss between each node in a given location. In addition, the \(\delta_{p}^{2}\) is set to 5 dB to represent a time-varying channel condition in this sea area. \(w\) and \(s\) are set to 6.5 m/s and to 0.5, respectively.  
We assume that each node is equipped with a WHOI micromod-2 [37; 62]. Additionally, uncoded BPSK modulation is chosen to obtain a higher data rate. We select a carrier frequency of 25.6 kHz with a bandwidth of 4 kHz. The data rate is set to 4000 bps, so the bandwidth efficiency is 1 bps/Hz. To model the energy consumption, we set the transmit/receive/idle power to 2.8/0.8/0.285 W and assume that FBR uses three levels of transmission power: 2.8 W, 8 W, and 35 W.  
In the simulation, the traffic is generated according to a Possion process with network traffic rates of {0.01, 0.02, 0.04, 0.08, 0.1, 0.12, 0.16, 0.2} packets/min. The payload of a data packet is set to {128, 256, 512} Bytes (B). The total size of a data packet is given by its payload plus the size of headers in different layers. As in [5], the physical header overhead changes with the data rate and is dominated by 10 ms for synchronization. The MAC header overhead of the handshake-based protocols and of Elfood are set to 4 B and to 3 B. The size of a HELLO packet is set to 6 B. We set the size of PONG/ACK in CARP to 7/6 B according to [5]. The size of CTF/ACK in CLOR are set to 8/7 B. Considering the length of control packets, we set the maximum size of PING/RTF to 20 bytes. In both CLOR and CARP, unless otherwise specified, the maximum burst size is set to 8. The maximum entry times of the handshake-based protocols are set to 3. The cone aperture \(\theta\) in FBR is set to \(2\pi/3\). When SL = 158 dB re uPa @ 1m source, the average hop count, the average of the maximum hop count and the average node degree are 2.30, 4.48, and 4.91, respectively. The simulation time of each run lasts 7200 s, and the average value of 50 runs in different scenarios with the 95% confidence interval is reported.  
The metrics used in the evaluation are defined as follows:  
Packet delivery ratio (PDR): The ratio of packets successfully received by the sink node to the packets sent by source nodes.  
End-to-end delay: The time of a packet reaching the sink plus the generating time of the corresponding packet.  
Goodput: The total bit successfully delivered to the sink divided by the simulation duration.  
Energy per bit: The energy consumption of the network for successfully delivering a bit of data to the sink.
## 5 Performance Evaluation
### Simulation Setup  
_Surveillance UAVs._ We consider a DJI Phantom4 Pro v2.0 drone (DJI, Shenzhen, China) equipped with a surveillance camera [73]. The built-in battery in the drone permits a maximum flight time of 30 min in windless conditions. The altitude of the UAVs is set to 100 m; thus, the shapes and areas of corresponding surveillance areas are identical. The initial energies of the UAV batteries are uniform-randomly selected within \([4000,5870]\) mAh with the output voltage of 17.4 V.  
_Monitoring Map._ We consider a well-known \(3\times 3\) Manhattan grid map [74]. As shown in Figure 2, we place four charging towers at the apexes of a virtual rectangle. We assume that each charging tower has four charging panels. In the middle of the map, one ground station controls the flight of the UAVs and has enough charging panels to charge all given UAVs. In order to easily calculate the overlapping areas of UAVs, we define the grid-shaped 1479 reference points every 25 m in the Manhattan map as shown in Figure 2. Therefore, we regard the overlapping area in this paper as the number of reference points between UAVs by calculating Jaccard similarity index of (13) by counting the numbers, spatially and temporally at the same. _Deployment Scenarios._ For the simulation study, we consider both fixed UAVs (F-UAVs) and moving UAVs (M-UAVs) that fly waypoints every unit period. We assume three different types of deployments for the F-UAVs as follows. 1. Scenario 1 (sparse lattice grid): 28 F-UAVs monitor the entire map without overlapping areas. 2. Scenario 2 (dense lattice grid): 45 F-UAVs monitor the entire map with overlapping areas that are fully covered without any blank space. 3. Scenario 3 (random): 20 F-UAVs are uniform-randomly distributed within the map. Scenarios 1 and 2 are both illustrated in Figure 2. Moreover, 10 M-UAVs fly along certain trajectories that consist of the sequences of waypoints. For realistic trajectories, we adopt the scan, oval, stay-at, eight, and waypoint movement based on the Paparazzi mobility model [75], as listed in Table 2. In each scenario, 5 or 10 M-UAVs are deployed. With the given topology, we simulate the surveillance systems for one or two hours while assuming each unit period repeats every 10 minutes. Note that the environmental setup parameters are summarized in Table 3.  
Baseline of ComparisonTo demonstrate the effectiveness of our proposed algorithm, the performance is evaluated and compared to the performance of the battery-based algorithm as the baseline, which is the most straightforward and advanced method [76; 77]. The operation of the battery-based algorithm is as follows:
# Adaptive Versioning in Transactional Memory Systems +
## 6 Experimental Evaluation
### Benchmarks  
We use both micro and complex benchmarks from the TM literature. A summary of some prominent properties of these benchmarks such as targeted applications, short description of the applications, the size of the read write (RW) set, and the amount of contention is given in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Benchmark** & **Application** & **Description** & **RW Set** & **Contention** \\ \hline _bank_ & banking & implements banking transactions & small & low \\ \hline _red black tree_ & BST & balances the nodes of binary tree & small & low \\ \hline _hash set_ & data structure & stores information using hashing & medium & low \\ \hline _linked list_ & data structure & maintains linear collection of data & medium & medium \\ \hline _skip list_ & data structure & maintains linked hierarchy of subsequences & medium & low \\ \hline _bayes_ & machine learning & learns a structure of a Bayesian network & large & high \\ \hline _genome_ & bioinformatics & performs gene sequencing & medium & low \\ \hline _intruder_ & security & detects network intrusions & medium & high \\ \hline _kmeans_ & data mining & implements \(k\)-means clustering & small & low \\ \hline _labyrinth_ & engineering & routes paths in maze & large & high \\ \hline _ssca2_ & scientific & creates efficient graph representation & small & low \\ \hline _vacation_ & OLTP & emulates travel reservation system & medium & low/medium \\ \hline _yada_ & scientific & refines a Delaunay mesh & large & medium \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of five micro-benchmarks and eight complex benchmarks in STAMP (and STAMPEDE) benchmark suite, including some of their properties.  
_Micro-Benchmarks:_ We use five micro-benchmarks, namely _bank_, _red black tree_, _hash set_, _linked list_, and _skip list_ used in several previous studies, e.g., [21; 22; 26; 27; 28]. These benchmarks simulate the basic concurrent execution scenario for transactions with (relatively) small read/write sets.  
_STAMP:_ STAMP is a well-known and widely-used benchmark. It consists of eight applications: _bayes_, _genome_, _intruder_, _kmeans_, _labyrinth_, _ssca2_, _vacation_, and _yada_ of varying complexity. These applications span a variety of computing domains as well as runtime transactional characteristics such as varying transaction lengths, read and write set sizes, and amounts of contention [23].  
_STAMPEDE:_ Recently, [PERSON] et al. [29] argued that the programming model and data structures used in STAMP benchmarks suffer from performance bottlenecks. They then modified the programming structure of these benchmarks in a way the bottlenecks can be removed. They finally provided a set of rewritten STAMP benchmarks calling them STAMPEDE benchmarks.
## 7 Discussions and Future Directions
### Mobility Challenge  
We assume the mobility control and implementation in our work but rather focus on the ideal location identification given the locations of the user and the stationary base station. Because the ideal location for the communication performance provides us with the flying base channel capacity limit which holds true regardless of the flying base station's location, we analyze and identify the ideal positioning/location. However, the mobility implementation incurs real-time costs of energy and time, i.e., moving requires time and energy consumption. Future research is needed to study such factors, costs, constraints, and the trade-off between moving vs. transmitting with greater power. The flying base station's mobility requirement also results in the limitations in energy resource (battery-operated as opposed to having a stable and constant power supply), which in turn requires energy-efficient designs, such as those for controlling sleeping/idle state [32], charging station assignments [33], and positioning [16; 34; 35].
# Integrated Replay Spoofing-Aware Text-Independent Speaker Verification
## 4 Experiments and Results  
### Dataset  
All experiments in this study were conducted using the ASVspoof2017-v2 dataset [23], because the official trials do not exist for integrated systems for the ASVspoof2019 dataset. Therefore, we evaluated the proposed integrated system by utilizing the official trials reported in [12] on the ASVspoof2017-v2 dataset. We used training and development sets to train all systems comprising 2267 bona fide and 2457 replay spoofed utterances from 18 speakers. To evaluate speaker verification and presentation attack detection performances, we measured the ZE-EER and the PAD-EER using the ASVspoof2017 joint PAD+SV evaluation trial. This trial comprised 1106 target, 18,624 ZE, and 10,878 replayed trials. We used target and ZE for ZE-EER and target and replayed for PAD-EER evaluations.
# Use of Lignin, Waste Tire Rubber, and Waste Glass for Soil Stabilization
## 4 Results and Discussion  
### Results of the Standard Compaction Tests  
Table 3 shows how different mixtures of additives affect the optimum moisture content (w\({}_{\rm opt}\)) and the maximum dry unit weight (\(\gamma_{\rm dmax}\)) of low-plasticity clay soil (CL). The pure clay soil (S) has a \(\gamma_{\rm dmax}\) of 1.47 g/cm\({}^{3}\) and a w\({}_{\rm opt}\) of 24.1%. With the addition of 5% glass powder (GP) and 5% lignin (LG) (S1), \(\gamma_{\rm dmax}\) increases to 1.57 g/cm\({}^{3}\) while w\({}_{\rm opt}\) decreases to 22.3%. If the proportions of GP and LG are increased to 10% (S2) and 15% (S3), respectively, \(\gamma_{\rm dmax}\) increases to 1.59 g/cm\({}^{3}\) and 1.63 g/cm\({}^{3}\) while w\({}_{\rm opt}\) falls to 19.6% and 16.1%. Similarly, adding 5% GP and 0.5% recycled tire rubber (RT) (S4) increases \(\gamma_{\rm dmax}\) to 1.56 g/cm\({}^{3}\) and reduces w\({}_{\rm opt}\) to 23.3%. If the GP and RT content is increased to 10% GP and 1% RT (S5), \(\gamma_{\rm dmax}\) increases to 1.59 g/cm\({}^{3}\) and w\({}_{\rm opt}\) drops to 22.1%. Further increases to 15% GP and 2% RT (S6), 20% GP and 3% RT (S7), and 25% GP and 4% RT (S8) lead to \(\gamma_{\rm dmax}\) values of 1.62, 1.65, and 1.66 g/cm\({}^{3}\), respectively, with corresponding w\({}_{\rm opt}\) values of 20.6%, 19.1%, and 17.8%. It was found that as a result of the homogeneous distribution of the grains in the stabilized clay soil, the microstructure of the soil changed positively so that the void ratio and optimum water content of the soil decreased while the maximum dry unit weight increased (see Figure 5).  
Since lignin has a water-reducing effect due to its chemical properties, the water content of the samples mixed with glass powder and lignin was low. It is assumed that the reason why the water content of the samples with glass powder + rubber tire additive is higher than that of the samples with glass powder + lignin additive is due to the shape of the rubber tire particles. The main reason why the maximum dry unit weight values of all samples are close to each other is probably the glass powder. Since the specific density of glass powder is higher than that of other waste materials, the water requirement decreases with increasing density [40; 61; 62; 63; 64].  
As can be seen from Table 3, the water content of the GP + LG soil mixtures is slightly lower than the water content of the GP + RT soil mixtures. This is due to the hydrophobic properties of lignin and the porous voids of the rubber tire. Since glass powder does not absorb water, the maximum dry unit volume weight increases with increasing glass powder content in the mixtures.
# Riboregulation in Nitrogen-Fixing Endosymbiotic Bacteria
## 4 Assigning Functions to Rhizbial _Trans_-sRNAs  
RNA-Seq and comparative genomics have delivered extensive repertoires of sRNAs in _S. meliloti_ and related \(\alpha\)-proteobacteria. The challenge now is to decipher the biological functions of thistranscriptional output, which can be still regarded as dark matter of rhizbial genomes. In prokaryotes, a large fraction of these transcripts are catalogued as _trans_-sRNA regulators, which is an intensively investigated class of sRNAs since the early studies of the non-coding transcriptome in classical model bacteria [13]. The biology of bacterial _trans_-sRNAs is essentially characterised by their differential accumulation in response to environmental signals and target mRNA regulation by protein-assisted base-pairing mechanisms (Figure 2). Therefore, primary studies to unravel _trans_-sRNA functions must necessarily address expression profiling, transcriptional regulation, gain- and loss-of-function phenotyping, identification of mRNA targets and targeting motifs, and characterization of the associated proteins (e.g., RNA chaperones or RNases). Experimental and in silico approaches to functionally characterize _trans_-sRNAs in rhizobia are described with detail elsewhere [16; 17; 47; 48]. Expression of _trans_-sRNAs can be tracked by classical molecular-genetics methods that include the use of promoter-reporter fusions and probing of total RNA on Northern blot membranes [47]. On the other hand, target identification typically relies on comparative genomics-based predictions of most probable conserved sRNA-mRNA base-pairing interactions (e.g., CopraRNA algorithm) followed by experimental validation of the candidates using a two-plasmid genetic reporter assay in vivo [17; 47; 49; 50]. The latter is based on the constitutive co-expression from compatible plasmids in the same cell of the sRNA of interest and a translational fusion of the putative mRNA target to GFP, so that fluorescence of the reporter strains is related to sRNA-mediated translational repression or activation of the mRNA [17; 47]. Co-expression of relevant sRNA and mRNA mutant variants in these assays enables the precise mapping of the interacting motifs in both molecules. Alternatively, _trans_-sRNA targetomes, i.e., the set of mRNA targets, can be characterized on a genome-wide scale by profiling of either transcriptome alterations upon pulse expression of the tested sRNA or the subset of mRNAs picked up by affinity chromatography using an aptamer-tagged (e.g., MS2) version of the sRNA as a bait [51; 52]. Remarkably, this latter approach is also suitable to capture in vivo assembled sRNA-protein complexes, which can be further characterized by mass spectrometry analyses [48].  
A combination of these strategies has been used to gain insights into the function and activity mechanisms of a handful of _trans_-sRNAs identified in _S. melilioti_. In the following paragraphs we describe how these sRNAs contribute to the regulation of cellular processes relevant to both the free-living and endosymbiotic rhizbial states.
# Thermal and Mechanical Behavior of Elastomers Incorporated with Thermoregulating Microcapsules
## 2 Materials and Methods
### Characterization Techniques
#### 2.2.6 Mechanical Tests  
The tensile mechanical properties of the developed elastomer composites were analyzed by means of an MTS Criterion Model 43 equipped with a 1 kN load cell. The used samples had a dog bone shape (type 1) with a thickness between 2 and 3 mm, a length of 115 mm and a width in the narrower section of 6.2 \(\pm\) 0.2 mm. The tensile tests were carried out at a crosshead speed of 50 mm/min until the sample broke, according to standard ISO 37:2013. The resilience tests were carried out by performing four tensile test cycles from zero load to a deformation of 14% and returning to zero load.  
The Shore A hardness of the elastomers was measured with a Sauter HDA100-1 tester. All the thermal and mechanical characterization tests were performed three times.
# An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks
## 5 Experiments  
Our proposed method is evaluated based on the following inquiries:  
* Comparing the accuracy of our method for multi-relational LP tasks with that of competitors.
* Assessing how our VEM-based inference method enhances the efficiency of MLN-based LP.
* Investigating the impact of parameters on the accuracy of the LP.