esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### K-M Model Analysis of Lane Departure Duration
#### 3.2.2 Modelling Secondary Task  
In order to investigate the impact of distraction state on the utility of LDW modes, a study similar to Section 3.2.1 was conducted. Figure 7 shows the survival curves and comparative results from the K-M model. It indicates that the difference in LDD is also significant for different levels of STs (\(p<0.05\)). For the no warning condition (\(p=0.002\)), as shown in Figure 7a, the time (LDD) for median survival were 2.192 s, 3.207 s, 4.665 s, and 5.286 s, respectively, for no-, low-, medium- and high-level STs. The LDD increased significantly with the distraction level of the ST, implying that the distraction state had a significant impact on the LDD. For the early warning condition (\(p=0.040\)), as shown in Figure 7b, the time (LDD) for median survival were 3.099 s, 2.097 s, 2.285 s, and 3.099 s, respectively, for the four levels of STs. It was obvious that the LDD under different levels of distraction tasks were significantly shortened. For the late warning condition (\(p=0.018\)), as depicted in Figure 7c, the time (LDD) for median survival were 1.664 s, 2.235 s, 3.177 s, and 3.809 s, respectively, for the four levels of STs. In comparison with the no warning condition, both the early and late warning could effectively shorten the LDD for all four levels of STs.  
Figure 6: Survivor function of LDD for various driving styles: (**a**) no warning; (**b**) early warning; (**c**) late warning.
## 3 Strength Characterization
### Effect of the Cement-Sand Ratio on the Strength of the Cement  
Under the same conditions of curing age and slurry concentration, the relationship between the cement-sand ratio of the filler and the UCS was investigated (Figure 5).  
Figure 5(a)-c show that the strength of the filler increases as an approximately exponential function of the increase in the cement-sand ratio for a given condition. An increase in the cement-sand ratio represents a greater amount of cement added per unit volume of slurry, which has a significant cementing effect and thus shows an increase in the strength of the backfill.  
Through the comparison of the relationship between the strength of the cemented backfill and the cement-sand ratio for a given slurry concentration at 3d, 7d, and 28d, we found an exponential fit between them, and the fitted coefficient (R2) value exceeded 0.98 in the Table 7,indicating that there was a good exponential relationship between the strength of the cemented backfill and the cement-sand ratio. Moreover, the strength of the material increased exponentially with the increase in the cement-sand ratio and this trend became more and more significant with the increase in the slurry concentration.  
Figure 5: Effect of the cement–sand ratio on the UCS. (**a**) 3 d UCS. (**b**) 7 d UCS. (**c**) 28 d UCS.
## 3 Results and Discussion
### Influence of Numerical Aperture on Molten Area Characteristics
In conclusion, it can be expected that absorbed energy in glass is higher and thus the reliable joining can be achieved, when the size of molten area increases with increasing the pulse energy with numerical aperture such as N.A. of 0.65. Figure 14: Top and cross sectional views of molten area for various numerical apertures. Figure 15: Influence of numerical aperture on size of molten area. Figure 13: Influence of numerical aperture on state of molten area. Figure 16 shows the optical micro-photographs of molten area in the scanning direction of laser beam. In this experiment, laser beam was irradiated 300 \(\upmu\)m below the top surface of glass by objective lenses of various numerical apertures with spherical aberration correction at the conditions of pulse repetition rate 1.0 MHz and scanning velocity 100 mm/s. It can be seen that numerical aperture affected the shape of molten area, which varied with numerical aperture especially at the beginning of laser scanning. Lower N.A. such as 0.45 resulted in remarkable spike shape at the bottom of molten area, because absorption point dropped down from the top edge to the bottom one of molten area, and laser energy was not absorbed during this time. In addition, the highest points of absorption point were almost constant, and it could keep similar height of molten area from the beginning as shown in Figure 11. However, remarkable spike shape might cause crack on molten area, which would result in low mechanical strength of welded glass. On the other hand, in the case of higher N.A. such as 0.65 and 0.85, continuous molten areas were formed. In these cases, it can also be seen that the location of absorption point gradually increased from the beginning of laser irradiation, and it resulted in a certain critical value. This phenomenon indicated that the absorption point was simultaneously formed and overlapped in the upper and lower parts of the molten area as shown in Figure 7, thus continuous molten area was formed. Therefore, it can be expected that N.A. of 0.65 has superior focusing characteristics and can form continuous molten area, which leads to achieving a stable welding process and high mechanical strength of welded glass material.
## 8 Simulation Results and Analysis
**Efficiency and Reliability:** Both DSR and AODV offer reliable performance with high PDF and low TDP, which is vital for the sustainability of communication in urban search and rescue missions. * **Simulation Scenario III Simulation Setup:*
* **Simulation Tool:*
* NS3 network simulator
* 1000 m \(\times\) 1000 m
* **Node Speeds:*
* Slow speed (1-10 m/s), moderate speed (16-20 m/s) and high speed (24-28 m/s)
* **Traffic Patterns:*
* Regular and irregular traffic patterns
* average energy consumption, throughput, NRL, PDF and TDP **Use Case:*
* In vehicular ad hoc networks (VANETs), vehicles move at different speeds, incurring the need for reliable and efficient communication to provide safety and traffic management. In this simulation scenario, we mainly need to figure out the impact of different node speeds and different traffic loads on the behavior of DSDV, DSR and AODV protocols when a fixed number of nodes in the network is considered. * **Scenario Goals:*
* Determine the energy consumption of each protocol at different node speeds
* Measure data delivery capability under different node speeds
* Analyze routing overhead induced by each protocol
* Evaluate reliability and efficiency of data delivery under different node speeds **Results and Findings**:
* **Energy Consumption:*
* **Regular Traffic:*
* Figure 21 highlights that DSDV provided the best performance and consistent energy consumption across different speeds due to periodic routing table updates. DSR consumed more energy at higher speeds as route caches became ineffective, incurring the need for frequent route discovery. * **Irregular Traffic:*
* Overall, all protocols in Figure 22, consumed comparably less energy than in regular traffic in Figure 21. In addition, DSR had the lowest performance due to frequent route discovery activations, which were caused by the \"ON/OFF\" nature of irregular traffic. Figure 21: Energy consumption vs. node speed (regular traffic).
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 2 Related Work
### Power Optimization Issues in 5G  
The effectiveness of a cognitive radio (CR) network can be gauged by several QoS metrics, including energy consumption, power improvement, network capacity, and the SER. However, in a CR network, the addition of RNs to the BS often results in a gradual increase in the power consumption. To mitigate this issue, several power allocation (PA) schemes can be utilized, such as equal PA, the particle swarm optimization (PSO) algorithm, and the GA-based optimization scheme [29; 30; 31].  
Power optimization is an important concern in designing and deploying 5G networks, as 5G systems are expected to consume significantly more power than previous generations of mobile networks. Some specific power optimization issues that may arise in 5G networks include:  
1. High power consumption of 5G access points: The implementation of 5G technology has already begun globally in a non-standalone setup along with Long-Term Evolution (LTE) macro-structures. However, LTE has shortcomings for long-term use, and 5G's electronic design aims to conserve energy in idle states through reduced transmissions in an always-on mode, compared to LTE's brief sleep time of less than 1 ms and limited fast activation components. In a standalone setup, the large projected device density of one-million per square kilometer would put a significant strain on the 5G base station in a cell sector, presenting a challenge in terms of reducing energy consumption and ensuring the network's sustainability. Two revised power consumption models [32] that precisely reflect the energy usage of a 5G base station in a standalone network have been proposed. A new routing protocol has also been introduced to distribute the load across base stations during inter-cellular communication evenly. The trade-off between latency and power consumption is considered to create a sustainable 5G network that meets the quality of service requirements, taking into account the energy-intensive nature of reducing power dissipation.
2. Power optimization of small cells: With the expected surge in 5G users, there will be a significant increase in power usage for transmitters and receivers. Researchers are looking for ways to optimize power consumption. One approach to optimizingthe algorithm design is the application of convex optimization. A mathematical method for deploying small cell access points [33] has been proposed to reduce energy consumption in massive MIMO and small cell environments.
3. Power optimization of user equipment: The increased number of 5G users is expected to result in higher power consumption by user equipment such as smartphones and tablets. It is essential to optimize the power consumption of user equipment in order to extend the battery life and decrease the overall energy consumption of the 5G network. Various approaches can be employed to tackle these power optimization challenges in 5G networks, including enhanced power management algorithms, advanced hardware design, and the implementation of energy-efficient components.
# An Overview of Key Technologies in Physical Layer Security
## 4 Challenges and Promising PLS Solutions
### Promising PLS Solutions
#### 4.2.3 Unmanned Aerial Vehicle (UAV)  
Drones or UAV-based communication technology has been thoroughly studied and adopted by the 3 GPP standard [105]. A study on UAV systems that highlighted and gave an overview of the latest advances and current state of research in the field of PLS was conducted [105]. UAV systems have been envisaged to form an integral part of future wireless communication applications due their dynamic, flexible, and flying nature. It was shown in [106] that, due to their ability to reach higher altitudes, they usually have dominant LOS channels with the ground nodes. This capability can be used to provide confidentiality to the legitimate receivers against the eavesdroppers. This can be done by deploying UAVs to launch more effective jamming signal attacks to terrestrial eavesdroppers, as shown in Figure 18. The conventional cooperative jamming schemes make an assumption that the locations of terrestrial jammers are fixed, which might compromise the secrecy of the system if the jammers are located far away from the eavesdroppers, and is also not practical as it makes an assumption of perfect CSI of the jammer to eavesdropper channel [107]. Authors in [107] deployed a UAV-based system as a jammer to improve the secrecy rate of a ground wiretap channel. [PERSON] et al. [15] considered a scenario in which a UAV is equipped with an air-to-ground jammer and a ground communication network comprising a legitimate transmitter-receiver pair and an eavesdropper. They proposed an iterative algorithm to maximize the achievable average secrecy rate of a wireless communication system.
# Finite Element Analysis on Block Shear Mechanism of Lean Duplex Stainless Steel Welded Connections
## 3 Experimental Investigation
### Experimental Results  
The measured geometric dimensions and the test results of the tested welded connection specimens failing in block shear are listed in Table 2. As demonstrated in the literature, the fillet welded connection has a constraint effect, which inhibits necking of the net tension plane at the weld toe. As a result, tensile fracture is always initiated far away from the toe of the fillet weld at the corner of the lap plate (end return weld), shown in Figure 3. The dominant block shear mechanism was the tensile rupture and shear yielding mechanism, which is evident from the load-deflection graphs shown in Figure 4. Shear yielding was observed after complete tensile fracture.  
Table 2 shows the ratios of ultimate test load \(P_{t}\) to predicted strength \(R_{n}\) (called the professional factors) of Equations (1)-(3) for the welded connection specimens. The averaged material properties in Table 1 and the measured values of the geometric dimensions are used for determining the nominal block shear capacity.  
It can be seen that the current design specification is conservative for all test specimens. Although the full shear strain hardening assumption, which was found to be accurate for the carbon steel bolted connections in the literature [10], is used, the ultimate block shear strength is underestimated by up to 13% (1/1.15 = 0.87).  
The possible explanation for such inaccurate results of the AISI equations is the neglect of the constraint effect that increases stress on the tension section. It is evident from the  
Figure 2: Schematic plot of double-lap fillet welded connection specimens.  
professional factor of Equation (3) that the ultimate block shear capacity of the tested welded connection can be predicted well when using tensile stress higher than ultimate tensile strength \(F_{u}\), although it shows a rather low level of accuracy for Specim L55W50, having the largest block length and block width among the test specimens.  
Figure 3: Block shear failure shapes of filled welded connections: (**a**) L25W30a; (**b**) L45W30a; (**c**) L35W40a; and (**d**) L55W40a.
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
###### Abstract  
The line between HPC and Cloud is getting blurry: Performance is still the main driver in HPC, while cloud storage systems are assumed to offer low latency, high throughput, high availability, and scalability. The Simple Storage Service S3 has emerged as the de facto storage API for object storage in the Cloud. This paper seeks to check if the S3 API is already a viable alternative for HPC access patterns in terms of performance or if further performance advancements are necessary. For this purpose: (**a**) We extend two common HPC I/O benchmarks--the IO500 and MD-Workbench--to quantify the performance of the S3 API. We perform the analysis on the Mistral supercomputer by launching the enhanced benchmarks against different S3 implementations: on-premises (Swift, MinIO) and in the Cloud (Google, IBM...). We find that these implementations do not yet meet the demanding performance and scalability expectations of HPC workloads. (**b**) We aim to identify the cause for the performance loss by systematically replacing parts of a popular S3 client library with lightweight replacements of lower stack components. The created S3 Embedded library is highly scalable and leverages the shared cluster file systems of HPC infrastructure to accommodate arbitrary S3 client applications. Another introduced library, S3 remote, uses TCP/IP for communication instead of HTTP; it provides a single local S3 gateway on each node. By broadening the scope of the IO500, this research enables the community to track the performance growth of S3 and encourage sharing best practices for performance optimization. The analysis also proves that there can be a performance convergence--at the storage level--between Cloud and HPC over time by using a high-performance S3 library like S3 Embedded.  
HPC; cloud; convergence; storage; AWS S3; S3 Embedded +
Footnote †: journal: Applied Sciences  
1
## 5 Game-Theoretical Routing Scheme for Video-Streaming in MANETs
### The Benefit of Using a Particular Path to Transmit the I+P Video Frames  
Before defining the player's utility of the game, we will define a parameter that evaluates the benefit of using a particular path. As we have mentioned before, we assume that we always have at least two available paths (best and medium-quality paths) to send packets. Each path will have its own benefit as we describe next.  
**Definition**: _The benefit of a \(path_{k}\) is:_  
\[\beta_{k}=\begin{cases}\phi_{k},&\text{if the path fulfills the QoS requirements (see Equation(1))}\\ 0,&\text{otherwise}\end{cases} \tag{12}\]where \(\phi_{k}\in\mathbb{R}^{*}\).  
Let us assign \(\phi_{k}=\phi_{b}\) as the benefit for the best path and \(\phi_{k}=\phi_{m}\) as the benefit for the medium-quality path, where \(\phi_{b}\) and \(\phi_{m}\in\mathbb{R}^{*}\). Later, we will relate \(\phi_{b}\) and \(\phi_{m}\) with the subjective QoS experienced by the users regarding the video frames received from each path (see Equation (23)). Strategy \(\alpha_{i}\) is defined as follows:  
\[\alpha_{i}=\begin{cases}\text{Transmit using the best path}.\\ \text{Transmit using the medium-quality path}.\end{cases} \tag{13}\]  
Probability \(p\) is the probability of sending (I+P) frames through the best path and probability \((1-p)\) is the probability of sending those frames through the medium-quality path.
# Mode Choice Effects on Bike Sharing Systems
## 4 Descriptive Statistics and Modelling Approach
### Model Formulation  
Discrete choice data, where respondents choose between a limited number of alternatives, are commonly analyzed by applying random utility maximization theory. The theory assumes rational behaviour in which respondents choose the alternative with the highest utility [29; 41; 42; 43]. Namely, an individual \(n\) faced with \(J\) alternatives in \(T\) choice tasks associates an indirect utility \(U_{n\!j}\) for an alternative \(j\) in a choice task \(t\) and chooses the alternative with the highest utility. The utility of an alternative \(j\) is therefore decomposed as  
\[U_{n\!jt}=V_{n\!jt}+\varepsilon_{n\!jt}=x^{\prime}{}_{n\!jt}\beta+\varepsilon_ {n\!jt} \tag{1}\]  
where \(U_{n\!jt}\) is not observed, but \(V_{n\!jt}\) is the deterministic utility of alternative \(j\), and \(\varepsilon_{n\!jt}\) is a random component not included in \(V_{n\!jt}\). The deterministic utility \(V_{n\!jt}\) can be specified by the term \(x^{\prime}{}_{n\!jt}\), where \(x\) is a vector of explanatory variables (e.g., attribute levels), and \(\beta\) is the corresponding coefficients to be estimated.  
For each alternative, a utility function (\(V_{n\!jt}\)) is specified, whereby the alternative-specific attributes, characteristics of the respondent or the choice situation are included as explanatory variables. When specifying the utility function, it is important to understand that only the differences in utility matter, while the scale of utility is arbitrary [29] (p. 19). Therefore, to capture the differences in the utility of the alternatives, \(J\)-1 alternative-specific constants (ASC) are specified, whereby the estimated ASCs are interpreted relative to the omitted alternative, which is normalized to zero [29; 43]. For the categorical attributes, street type, surface type, and utilized capacity, the \(L\) levels of each attribute were transformed into \(L-1\) dummy variables. This means, the utility for one level per attribute is normalized to zero and serves as a reference category, while the parameter estimates for the \(L-1\) dummy variables capture the utility differences to this reference category [28; 29; 43].
## 3 Results and Discussion
### Photovoltaic Performance  
The photovoltaic performance of the sandwich-type cells using an organic dye (DN-F05) as a photosensitizer ([TiO\({}_{2}\):DN-F05]:Ag/electrolyte [DMPII]/Pt counter electrode) was measured at 1 sun (100 mW cm\({}^{-2}\)) intensity. Figure 9 shows the J-V curves of the TiO\({}_{2}\) immersed in dye DN-F05 (TiO\({}_{2}\):DN-F05) and TiO\({}_{2}\) immersed in the dye hybridized with Ag (TiO\({}_{2}\):DN-F05:Ag) for 12 h.  
Figure 9 shows that the addition of the dihydrid Ag into the DN-F05 dye increases electron transport, as indicated by the increasing value of Jsc, thereby helping to increase energy harvesting. This proves that the conductivity of TiO\({}_{2}\) increased with the addition of Ag [16; 29]. Therefore, the addition of Ag will increase the efficiency value.  
Table 3 summarizes the photovoltaic parameters, such as Voc, Jsc, and FF, and the fabricated DSSC devices.  
Figure 8: Band gap energy surfaces of: (**a**) TiO\({}_{2}\); (**b**) TiO\({}_{2}\):DN-F05; TiO\({}_{2}\):DN-F05:Ag.
## 2 Materials and Methods
### Statistical Analysis  
The significance of the difference in the results of the same samples tested by both GPP and DFS in-house RT-PCR was assessed using McNemar analysis (\(\alpha\) = 0.05). Comparison of the two assays was summarized using two parameters: (1) positive percent agreement (PPA) calculated as 100% \(\times\) [(\(a\))/(\(a+c\))], where \(a\) = the number of samples tested positive by both assays and \(c\) = the number of samples that tested positive by DFS in-house RT-PCR and negative by GPP, and (2) negative percent agreement (NPA), calculated as 100% \(\times\) [(\(b\))/(\(b+d\))], where \(b\) = the number of samples that tested negative by both assays, and \(d\) = the number of samples tested negative by DFS in-house RT-PCR and positive by GPP [20]. The overall agreement of GPP and the in-house RT-PCR were analyzed with Cohen's Kappa. We defined kappa scores of 0.6 to 0.69 as moderate, 0.7 to 0.8 as good and above 0.8 as excellent agreement [21]. The Ct values of the DFS RT-PCR of the samples tested positive for _C. difficile_ between participants with acute gastroenteritis and those with no gastroenteritis, and between participants who were <2 years and those \(\geq\)2 years, as _C. difficile_ more often represents colonization (i.e., not infection) in young children [22] were compared using Mann-Whitney U test. [PERSON]'s exact test was used to evaluate whether the frequency of concordance varied by participant age using 2 years as the cut-off. Statistical significance was set at \(p\) < 0.05 and analyses were performed using RStudio (Version 1.3.1073, Boston, MA, USA).  
Figure 1: Flow diagram of testing algorithm using the GPP, DFS RT-PCR, and PE RT-PCR. Abbreviations: GPP, Luminex xTAG® Gastrointestinal Pathogen Panel; DFS, Direct-from-stool; PE, Post enrichment.