esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems
## 2 System Model
### Deployment  
We consider a scenario where a single BS with circular coverage provides service to the users in a crowded environment in urban conditions (Figure 1). The radius of the coverage area of the cell BS \(r_{BS}\) depends on the antennas used, both on the UE and BS, as well as their height difference and targeted outage probability \(P_{0}\), i.e., the probability of signal loss that is deemed acceptable and is computed using the propagation model presented below. Since we consider an urban scenario, the signal that the BS sends to the UE can be blocked by both large static buildings and other small mobile user bodies. The heights of the BS and UE are assumed to be constant and given by \(h_{BS}\) and \(h_{UE}\), respectively. In order to enhance the coverage area, we consider and compare two cases of using relays, located at height \(h_{Ter}\) in the case of a terrestrial relay, or \(h_{ULAV}\) in the case of an aerial mobile relay located on the UAV (Figure 1). In both these cases, the signal from the BS is first sent to the relay node and then forwarded by the relay to the UE. Thus, the coverage areas are calculated for both the BS and relay nodes. The resulting coverage consists of the sum of both coverages from BS to the relay and from the relay to the UE. We consider that the BS uses a single output antenna, while, in the case of direct connection, the UE uses a MIMO antenna array with \(k\) elements. In scenarios with relays, the UE has a fixed \(4\times 4\) MIMO antenna, while the relays have MIMO antenna arrays with \(k\) elements. This assumption is made in order to investigate the impact the number of elements has on the considered metrics by varying the parameter \(k\). Since we mainly focus on the signal received by the UE, we can safely ignore the number of transmitting antenna elements on the UE and only vary the number of transmitting and receiving antenna elements on the relay and the number of receiving antenna elements on the UE. We have to consider a number of transmitting and receiving antenna elements on the relay since it receives the signal from the BS and forwards it to the UE. However, to keep the plots two-dimensional, we only vary the number of receiving antenna elements on the UE in the case of direct connection with the BS, or the number of receiving antenna elements on the relay, while fixing all other numbers of antenna elements as constants.
# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 3 The Proposed Method
### General Formula of Similarity Measure Based on Meta Structure
#### 3.3.1 Basic Idea  
To explore the complicated semantics between objects, we propose a novel meta-structure-based similarity measure called StructSim. The basic idea is that two objects are more similar if the probability of expansion from the source node to the target node along a given meta structure is greater.  
Different from random walk based on the meta path, subgraph expansion based on the meta structure involves a more complex structure than the path. Moreover, we introduce the subgraph expansion with bias to make full use of leveraging the structure information of the network in this study, which has an essential difference from the existing subgraph expansion.  
Specifically, StructSim claims that the expansion probability from the same node to different nodes with the same type is different in each step when the subgraph is expanded along the meta structure. That is, the probability of expanding to some nodes is high, while the probability of expanding to other nodes may be low. Here, to embody the bias of subgraph expansion, StructSim, meanwhile, considers the node information at both ends of the expansion, including the out-neighbor information on the current node, and the in-neighbor information on the next-hop node to be expanded. In a word, the expansion is biased and StructSim models the probability of subgraph expansion with bias.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 4 Analysis Model
### Connection Failure Probability  
Connection failure probability is defined as the probability that a device fails to acquire connectivity by failing the RA process \(N_{max}\) times. Let \(\mathbb{P}_{CS,T=t|N,Z=k}\) and \(\mathbb{P}_{CF,T=t|N,Z=k}\) denote the conditional connection success and connection failure probabilities, respectively, of a device with a trial number \(t\) for given _zone_\(k\) and total arrival \(N\), respectively. Therefore, \(\mathbb{P}_{CS,T=t|N,Z=k}\) is equal to the probability that the device fails at RA \((t-1)\) times, and finally succeeds in the \(t^{th}\) RA trial. Then,  
\[\mathbb{P}_{CS,T=t|N,Z=k}=\begin{cases}\mathbb{P}_{S|N,R=r}\prod_{i=1}^{t-1}(1- \mathbb{P}_{S|N,R=i}),&\text{if }t\leq k,\\ \mathbb{P}_{S|N,R=k}(1-\mathbb{P}_{S|N,R=k})^{t-k}\prod_{i=1}^{k-1}(1-\mathbb{ P}_{S|N,R=i}),&\text{if, }t>k,\end{cases} \tag{23}\]  
Figure 3: Summary for the iterative derivation of the steady state RA throughput.  
Figure 2: Markov chain model of the devices. State \(<t,k>\) indicates that the devices with the trial number \(T_{u}=t\) and \(Z_{u}=k\). Note that state transitions occur when (**a**) random access (RA) failure, (**b**) RA success, and (**c**) connection failure occurs, respectively.  
where \(\mathbb{P}_{S|N,R=r}\) is the simple notation of \(\mathbb{P}(S_{u}|N,R_{u}=r)\), as defined in Equation (20). Then, the connection failure event is same as the complementary event of the connection success cases. Thus, \(\mathbb{P}_{CF|N,Z=k}\) can be derived as follows:  
\[\mathbb{P}_{CF|N,Z=k} =1-\sum_{t=1}^{N_{max}}\mathbb{P}_{CS,T=t|N,Z=k} \tag{24}\] \[=1-\sum_{r=1}^{k}\mathbb{P}_{S|N,R=r}\prod_{i=1}^{r-1}(1-\mathbb{ P}_{S|N,R=i})-\mathbb{P}_{S|N,R=k}\prod_{i=1}^{k-1}(1-\mathbb{P}_{S|N,R=i}) \sum_{t=k+1}^{N_{max}}(1-\mathbb{P}_{S|N,R=k})^{t-k}.\]  
Finally, the connection failure probability, \(\mathbb{P}_{CF|N}\), can be derived using Equation (8) as follows:  
\[\mathbb{P}_{CF|N}=\sum_{k=1}^{N_{max}}\mathbb{P}_{CF|N,Z=k}\mathbb{P}(Z_{u}=k). \tag{25}\]
# Characterization of a Mechanical Antenna Based on Rotating Permanent Magnets
## 4 Results
### Experimental Results up to 100 mm
#### 4.2.1 Mechanical Antenna with CRRM  
Figure 13 presents the experimental results for the mechanical antenna with CRRM.  
Figure 12: (**a**) The relative errors obtained after comparing the values obtained numerically and those obtained experimentally; (**b**) the root mean square deviation.  
Figure 13: Magnetic field values in static and dynamic (70 Hz) regimes obtained for the CRRM between 2 and 100 mm.  
A very good agreement between the values measured in static regime and the one measured in the dynamic case is observed, with the maximum difference being less than 3.16% up to 90 mm.
## 3 Algorithms
### Coupling Matrix  
The Coupling Matrix (CM) in (5) is employed to characterize the normalized coupling level through the coupling coefficients \(M_{i,j}\).  
\[M=\left[\begin{array}{cccccc}0&M_{S1}&0&0&0&0\\ M_{15}&0&M_{12}&0&\pm M_{14}&0\\ 0&M_{21}&0&M_{23}&0&0\\ 0&0&M_{32}&0&M_{34}&0\\ 0&\pm M_{41}&0&M_{43}&0&M_{4L}\\ 0&0&0&0&M_{L4}&0\end{array}\right] \tag{5}\]  
where \(S\) indicates the Source port, \(L\) represents the Load port, \(M_{i,j}\) accounts for self-resonance effects, and \(M_{i,j}\) quantifies the inter-resonator coupling between elements \(i\) and \(j\).  
This coupling matrix describes a fourth-order filter network comprising four internal resonators, with the non-zero \(M_{41}\) parameter specifically characterizing the cross-coupling mechanism that generates either transmission zeros or equalization zeros, determined by the sign of the matrix element.  
For this passive, symmetric two-port network, where \([S]=[S]^{T}\) and \(S_{11}=S_{22}\), the reciprocity condition implies \(M_{41}=M_{14}\), under ideal circuit assumptions, as described in (5).
## 8 Conclusions  
Nonlinear reduced-attitude controllers for fixed-wing UAVs have been proposed, using geometric methods on the unit two-sphere. The attitude representation is singularity-free, independent of the yaw/heading angle and allows the UAV to perform banked-turn maneuvers while simultaneously tracking a turn rate satisfying the coordinated-turn equation. Using an aerodynamic model of the rotational dynamics, almost global asymptotic stability is established for the proposed controllers.  
The suitability of the presented approach has been verified using Matlab-simulations as well as more realistic SITL simulations, where the control law shows that it successfully completes the defined control objectives in the presence of uncertain aerodynamics and  
Figure 14: SITL: Control input. Control surface deflections are normalized to \([-1,1]\) and the throttle to \([0,1]\).  
Figure 13: SITL: Reduced-attitude vector \(\eta\) (red) vs. reference attitude (blue).  
reference velocities, and integrates into a state-of-the-art open-source autopilot for fixed-wing UAVs. The next step is to validate the controllers in flight experiments using a physical fixed-wing UAV.  
The simulation results show some deterioration of tracking performance when the flow angles are not slowly varying. Therefore, future work could consider different robust and/or adaptive control techniques to better compensate for a wider class of exogenous disturbances, including harsh wind conditions. Also, the coordinated-turn equation, Equation (20), is only an approximation of the relationship between roll angle and turn rate. Future work could seek to relax the assumptions used to derive this relation, e.g., by considering the equations derived in [41], and do a comparative study of different turn coordination methods for fixed-wing UAVs. Another topic for future work is to investigate if a tailor-made guidance scheme can be designed that directly produces a reduced-attitude vector reference.  
Conceptualization, E.M.C.; methodology, E.M.C.; software, E.M.C.; writing--original draft preparation, E.M.C.; writing--review and editing, E.M.C. and T.I.F.; supervision, T.I.F.; project administration, T.I.F.; funding acquisition, T.I.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Council of Norway through the Centres of Excellence funding scheme, grant number 223254 NTNU AMOS, and grant number 261791 Autoffly.  
Not applicable.  
The simulated data presented in this study are available on request from the first author. No external data sets were used.  
The first author wishes to thank [PERSON] for discussions regarding the topic of this manuscript.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract  
This novel study investigated the loading of muiprocin nanoparticles into a hydrogel which was expected to enhance the antibacterial activity of muiprocin. The inhibition of isoleucyl-tRNA synthetase and global gene expression in methicillin-resistant _Staphylococcus aureus_ (MRSA) by the muiprocin nanoparticle-loaded hydrogel (MLH) and by pure muiprocin was compared. MLH and muiprocin rapidly inhibited the growth of bacterial populations after 1 h of treatment. At 12 h, muiprocin and MLH inhibited isoleucyl-tRNA synthetase in MRSA. Transcriptome profiles of MRSA showed that gene expression alterations after treatment with muiprocin were similar to those after treatment with MLH at MICs. These alterations included changes to DNA transcription, translation, and replication pathways, and the fold changes in these genes decreased more rapidly with MLH than with muiprocin only after 1 h of treatment. MLH released the muiprocin from the nanoparticles and hydrogel systems and then the drug permeated the cell wall and bound to bacterial isoleucyl-tRNA synthetase. The research also showed that MLH could be further developed for use in clinics for infected wounds.  
Keywords:MRSA; muiprocin; nanoparticle; gene expression +
Footnote †: journal: Appl. Microbiol.
## 5 Conclusions  
In conclusion, our investigation demonstrates the successful development of a compact intracavity DFG mid-infrared laser with considerable potential for GHG detection and free-space optical communication applications. The optimization of the overlap efficiency has led to a black-box efficiency of 3.4% W\({}^{-1}\) and a maximum output power of 63 mW, achieved with a pump power of 4.7 W and a signal power of 380 mW. The design incorporates a PPLN crystal that enhances wavelength tunability, enabling a tuning bandwidth of about 50 nm in the mid-IR through various PPLN lengths. Furthermore, the stability of our system, assessed at 0.91% over 2 min and 1.08% over 2 h, confirms its reliability for precise measurements in applications such as greenhouse gas detection. Given these advancements, further gains in DFG output power should be possible through scaling the input signal power appropriately. The main limitation on output DFG power will likely be the increased thermal load on the PPLN as a result of this increased signal power. An interesting direction for future work would be detailed modeling of the thermal behavior of the PPLN at high power, perhaps through finite difference modeling. With these optimizations in mind, the current device is promising for enabling long-distance free-space links in the mid-infrared spectrum, fulfilling critical needs in environmental monitoring and advanced communication technologies.
# Cosmeceutical Potentials of _Equisetum debile_ Roxb. ex Vaucher Extracts
## 2 Materials and Methods
### Determination of Total Phenolic Content by Folin-Ciocalteu Assay  
Total phenolic contents of _E. debile_ extracts were investigated using the Folin-Ciocalteu method. Gallic acid was used as a standard for a quantification of total phenolic content. The gallic acid was dissolved in 95% ethanol, and the solutions were prepared in various concentrations to construct a standard curve. A volume of 300 uL of gallic acid at each concentration was mixed with 1.5 mL of Folin-Ciocalteu reagent (10% \(\upsilon/\upsilon\)) and kept at room temperature in the dark for three minutes, which was followed by an addition of 1.2 mL Na\({}_{2}\)CO\({}_{3}\) (75 mg/mL). Incubation of the reactions continued for 30 min. The sample absorbance was measured at 731 nm by using a spectrophotometer (Beckman Coulter DTX880, Fullerton, CA, USA). The experiment was performed in triplicate for each gallic acid concentration. For plant samples, the procedure was also carried out similarly to gallic acid. All dried extracts were re-suspended by 95% ethanol prior to mixing with Folin-Ciocalteu reagent. Total phenolic content of plant samples was expressed as gallic acid equivalents (GAE) by extrapolating its corrected absorbance against the gallic acid calibration curve.
## 4 Conclusions  
Laser-induced plasma-mediated cavitation within a finite confined liquid, bounded by elastic walls, gives rise to the generation of a vibrational rarefaction wave series within the liquid, exerting a substantial influence on bubble dynamics. This study delves into the impact of cuvette walls and liquid geometry on the formation of vibrational rarefaction waves via monitoring the dynamics of both the laser bubble and secondary bubble clouds, employing high-speed photography and acoustic measurements.  
Our findings reveal that, with increases in either liquid height or cuvette wall inner size, both the inception and the peak-to-peak interval of the secondary bubble cloud series experience augmentation. However, the modulation period of \(R_{\text{max2}}\) (\(T_{\text{osc1}}\)) remains almost constant with varying liquid height and increases with alterations in cuvette inner size. These observations solidify the notion that the vibrational rarefaction wave series is directly induced by wall vibrations, which are intimately connected to laser bubble oscillation and its induced liquid-mass oscillations.  
The characteristics of the vibrational pressure series, including its onset time, period, and time intervals, are determined by both liquid-mass oscillations and wall vibrations, as well as laser bubbles. This complex oscillatory system with multiple degrees of freedom offers various means for changing the eigen-oscillations of walls and liquids, including adjustments to their geometry, material properties, and even the location of laser-induced breakdown. This flexibility provides a versatile method for controlling the generation of extra rarefaction waves within the liquid and, in turn, achieving control over the dynamics of laser-induced plasma-mediated cavitation in liquid. Such control holds significant promise in improving efficiency and mitigating side effects in the treatment of cardiovascular laser plaque.  
Conceptualization, L.F. and J.W.; methodology, L.F.; software, L.F., and X.D.; validation, L.F. and Z.P.; formal analysis, L.F., X.D., and C.Y.; investigation, L.F. and Z.P.; resources, Z.Z., J.W., and C.Y.; data curation, L.F.; writing--original draft preparation, L.F.; writing--review and editing, C.Y.; visualization, L.F. and Z.P.; supervision, C.Y.; project administration, Z.Z. and C.Y.; funding acquisition, L.F., J.W., and C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62175198, 82272048, U22A2092, 62005210); Sino-German Mobility Programme (M-0063); the Fundamental Research Funds for the Central Universities (xtr062022002).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors thank [PERSON] and [PERSON] from the University of Luebeck, Germany for their help on this work.  
The authors declare no conflict of interest.
# Revisiting the High-Performance Reconfigurable Computing for Future Datacenters
## 4 Revisiting the FPGA Virtualization
### Multi-Node Level Virtualization  
The primary job is to distribute an acceleration job among multiple FPGAs, while abstracting the complex details from the user. The architecture of virtualization largely depends on how the multiple FPGAs are connected, there ways are depicted in Figure 6. This is not a standard, but the works so far have exhibited these formations. The direct model where FPGAs directly communicate with other FPGAs, where link represent the physical connection or virtualized I/O interface. The slave model where FPGAs are connected to the CPUs through PCIe or other links and CPUs are connected to the network, so if FPGA wants to send data to another FPGA, it goes through CPUs and network. The standalone model where FPGAs and CPUs are accessible though the network as standalone node. The designer can also combine them to form a hybrid model to meet the certain objectives.  
Before discussing the sub classes, the salient features of some representative works of multiple FPGAs are to be discussed. [PERSON] et al. [75] focused on the minimum virtualization overhead of medium scale datacenter providing commercial cloud services. They achieved significant performance when compared to regular virtual machines along with reduced iteration time for design. [PERSON] et al. [78] focused on maximizing the utilization of high-end FPGAs through paravirtualization and provided homogeneous virtualized FPGA regions for the clients. This flexible multi tenancy approach enables the individual resources to adopt the user requirements. [PERSON] et al. [79] developed an operating system to share single FPGA chip among different users at run-time with an improved resource manager. However, these mentioned works have not discussed the FPGA to FPGA or CPU connectivity in detail and the interfaces are not clearly described, except an indication of PCIe. [PERSON] et al. [41] presented a different approach, FPGA as a standalone connected to datacenter network. The decoupled approach can utilize FPGA as an equal processing resource, especially in hyperscale datacenters. They chalked out a detailed system architecture with an outlook analysis on resource estimation and scaling perspectives.  
A relatively recent trend is the emergence of tightly coupled CPU-FPGA platforms. Examples include Heterogeneous Architecture Research Platform (HARP) by the Intel and power chip combined with Coherent Accelerator Processor Interface (CAPI) by IBM. Academics responded to the call for proposals by Intel and several works have been published in last four years, some recent examples are [113; 114].
# Bus Travel Time Prediction Based on the Similarity in Drivers' Driving Styles
## 4 Case Study  
### Data Collection and Processing  
In this section, experiments were conducted using the Shenyang City 239 bus route as an example to verify the effectiveness of the proposed travel time prediction method described in the last section. The 239 bus route map is shown in Figure 4. The 239 bus line starts from Kang li Automobile Company in the west to the Quan Yuan community in the east. There are 26 stops in the entire journey with a total length of 13.9 km. The operating time period of this bus line is from 06:00 am to 20:30 pm. All buses in the 239 bus line are equipped with GPS positioning devices to collect the location data of each bus every 5 s and transmit them to the data center in real time. The data recorded are listed in Table 1. The serial number of the vehicle-mounted device on each bus is unique and corresponds to a fixed driver. To maintain consistency, only the driving data from the west to the east were used in this study.  
Figure 4: The map of bus route no. 239.  
Perhaps due to the influence of GPS signal strength, some road sections have incomplete data. Therefore, the data from the fourth stop to the thirteenth stop were collected and used as the research object in this study (see Figure 4). The corresponding road section consists of nine adjacent stops. For example, road section 7 represents the road section between the sixth stop and the seventh stop, road section 13 represents the road section between the twelfth stop and the thirteenth stop, and so on.  
In order to verify the influence of the drivers' driving styles on the travel time, the interference of other factors has to be considered. The time period selected in this study was sunny, and the traffic conditions were normal (no traffic accidents). The buses to be predicted in our model were roughly the same in terms of vehicle type and vehicle performance. Therefore, it can be considered that the main factor affecting the bus travel time was the driving styles of individual drivers.  
The data in this study were collected over 11 working days from 4 January 2016 to 18 January 2016. A total of 3204 valid history records of bus travel times were obtained. The data of the first 10 days (4 January 2016 to 15 January 2016) were used as the training data, and the data of the remaining day (18 January 2016) were used as the prediction data.