esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-High-Performance Concrete (UHPC) Piles and Splicing Options
## 4 UHPC Pile Splices
### Other PPCP Cycles Applicable to UHPC Piles
#### 4.2.3 Post-Tensioned Splices  
Post-tensioning ducts can be used to form a connection between two pile segments. Splice is achieved by running post-tensioning strands through this duct across the two-pile segment. The strands are then tensioned and stressed to form connections. Post-tensioned splices can increase the tensile capacity near the ends of the prestressed pile and the splice region [52] and provide good bending and tensile strengths [17]. [PERSON] and [PERSON] [14] developed a post-tensioned splice detail where embedded anchors are provided in the ends of the piles (dually embedded anchors), as shown in Figure 15[14].  
Figure 14: Epoxy-bonded dowel splices: (**a**) drilled holes at the pile segment; (**b**) rebar dowels with epoxy [53].  
Figure 13: NU chuck splice system [17; 51].  
For this detail, strands were inserted into the anchors on each side, see Figure 16a[14], and then post-tensioned from the end of the top pile, see Figure 16b[14]. The post-tensioned strands were grouted after tensioning.  
Some drawbacks associated with this splice option usage are that it is susceptible to corrosion, and this limits its implementation in marine environments, the cost of materials and the technicality involved makes it an expensive option, and it will be difficult to use in unplanned situations [14; 17; 55].
# Study of the Targeted Regulation of Rice Drying Characteristics and Quality
## 2 Materials and Methods
### Response Surface Methodology  
The orthogonal experiment design scheme of five factors and five levels was constructed by RSM through Design-Expert-v.13 software. Compared with the previous research scheme, this scheme responds to quality indicators more comprehensively. The results were fitted with the second-order model to determine the interaction between the studied variables. The central composite design (CCD) was selected as the experimental design of RSM, including 59 experimental groups, 32 factor designs, 10 axis points, and 17 center points. Different levels of drying temperature (T, 35-55 \({}^{\circ}\)C), air humidity (RH, 30-50%), initial moisture content (MC, 20-28%), air speed (V, 0.36-0.84 m/s), and recovery ratio (TR, 1-4) were selected. The factor levels are shown in Table 2. The effects of five factors on the response parameters after drying included drying characteristics (net drying time), processing indicators (burst waist increase rate, milled rice rate), and nutritional quality (fatty acid value, resistant starch concentration, germination rate, change in protein content, change in fat content). The experimental scheme and results of the response surface methodology are shown in Figure 2.  
According to the test results in Table 3, the independent variable is fitted with the quadratic polynomial equation of the response according to Formula (8), and table in Section 3.1 is obtained.  
\[Y=\beta_{0}+\sum_{i}^{5}\beta_{i}x_{i}+\sum_{i}^{5}\beta_{ii}x_{ii}^{2}+\sum_{ i\
eq j=1}^{5}\beta_{ji}x_{i}x_{j}+\varepsilon \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Symbol**} & \multicolumn{5}{c}{**Encoded and Unencoded Values**} \\ \cline{3-6}  & & \(-\alpha=2.378\) & \(-1\) & \(0\) & \(1\) & \(\alpha=2.378\) \\ \hline dry medium temperature (\({}^{\circ}\)C) & T & 35 & 40.8 & 45 & 49.2 & 55 \\ relative medium humidity (\%) & RH & 30 & 35.8 & 40 & 44.2 & 50 \\ initial water content (\%) & MC & 20 & 22.3 & 24 & 25.7 & 28 \\ hot air speed (m/s) & V & 0.36 & 0.5 & 0.6 & 0.7 & 0.84 \\ slow sophomore & TR & 1 & 1.9 & 2.5 & 3.1 & 4 \\ \hline \hline \end{tabular} Note: The recovery ratio is the ratio of a single recovery time to the drying time.  
\end{table}
Table 2: Response surface test factor level.  
where \(Y\) is the response calculated by the model, \(\beta\)0, \(\beta\)i, \(\beta\)ii, and \(\beta\)ij are regression coefficients for the intercept, linear, quadratic, and interaction terms, respectively, and \(\varepsilon\) is the random error.  
Figure 2: Contribution diagram of factor response indexes. Note: T represents drying temperature, RH represents hot air humidity, MC represents initial moisture content, V represents air speed, TR represents hysteresis ratio.
## 4 Experimental Setup
### Social Robot Mini
Regarding the real-time performance of the RJAS pipeline, we measured the CPU usage of the perceptual stack when executed on the robot's on-board processor (Intel i7-1165G7) without graphical display output and with one user in front of the robot. Average CPU loads for each detector were as follows: face detector (4%), head pose detector (4.5%), body pose detector (3.5%), and hand gesture detector (7%). The RGB-D camera acquisition process consumed approximately 2%. The VAM and BBM, which are responsible for stimulus prioritization and motor control, respectively, together accounted for less than 2% of the CPU load. These values suggest that RJAS is lightweight enough to operate in real time within a tabletop scenario using a single-user setup, even on moderate consumer-grade CPUs.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 3 Results and Discussion
### PFCs Removal Efficiency Through Pilot Test
#### 3.4.2 Removal Variation by Ozone Oxidation  
The ozone contact test results in Figure 4 show that, in all compounds from PFOA to PFhxA, PFNA, PFFeA, and PFhXS, the removal was 5% or lower. Thus, regardless of the change in the ozone  
Figure 3: Variations in PFCs concentrations with Alum dosages.  
dose, the removal was negligible. Previous research showed that the PFOA removal was between 10% and 20%, but the removal by ozone oxidization was difficult [9].  
Due to the highly stable structure, PFCs are difficult to remove through existing biochemical or oxidization processes. We also determined that PFCs removal by ozone oxidization would be difficult. Therefore, to remove them effectively, other additional treatment methods are required.
## 5 Performance Analysis and Simulation
### Security Performance Analysis
#### 5.1.3 Comparison of Security Performance  
(1) The Case of Not Replacing the Phantom Node  
Suppose that the NRSPS scheme, the NRDPS scheme, the EPUSBRF and the RPBMP use the same network topology. The PNs are all represented as \(\rm p_{c}\), and the path of the data packets transmitted from \(\rm p_{c}\) to the SN does not pass through the visible area of the source node. The time that the attacker traces back one hop along the routing path is T. Since the purpose of using the PN is to hide the source node so that the attacker can only trace back to the PN while he cannot trace back to the real source node. Here, only the security performance is comparatively analyzed.  
The time required for the attacker to trace to the PN in the EPUSBRF scheme is:  
\[\rm T_{EPUSBRFt}=(\rm Hop_{pc,sink}) \tag{21}\]Suppose that the same-hop routing threshold in RPBMP is \(\mathrm{h_{RPBMP}}\), the time required for the attacker to trace to the PN is:  
\[\mathrm{T_{RPBMPt}}=(\mathrm{Hop_{pc,sink}}+\mathrm{h_{RPBMP}}) \tag{22}\]  
The time required for the attacker to trace to the PN in the NRSPS scheme is:  
\[\mathrm{T_{NRSPSt}}=(\mathrm{Hop_{pc,sink}})*\mathrm{T} \tag{23}\]  
The time required for the attacker to trace to the PN in the NRDPS scheme is:  
\[\mathrm{T_{NRDPSt}}=(\mathrm{Hop^{\prime}_{pc,sink}})*\mathrm{T} \tag{24}\]  
where \(\mathrm{Hop^{\prime}_{pc,sink}}=\mathrm{Hop_{ps,pr}}+\mathrm{Hop_{pr,sink}}\).  
Therefore, in the above assumption, \(\mathrm{T_{NRDPSt}}>\mathrm{T_{RPBMPt}}\geq\mathrm{T_{EPUSBRFt}}=\mathrm{T_{ NRSPSt}}\), and the equal sign is established when \(\mathrm{h_{RPBMP}}=0\).  
(2) The Case of Replacing the Phantom Node  
In this paper, \(M\) PNs are used in total. After the PN is replaced, the data packet transmission path will also change, and the attacker cannot eavesdrop the data packet and will return to the SN along the original path. The time required for the attacker to trace to the last PN in the RSPS scheme is:  
\[\mathrm{T_{RSPSt}}=(\mathrm{Hop_{pc,sink}})*\mathrm{T}*\mathrm{M}+\mathrm{T_{ Esum}}, \tag{25}\]  
where \(\mathrm{T_{Esum}}\) represents the total time taken by the attacker to return to the SN when he cannot overhear the data packets. The time required for the attacker to trace to the last PN in the RDPS scheme is:  
\[\mathrm{T_{RDPSt}}=(\mathrm{Hop^{\prime}_{pc,sink}})*\mathrm{T}*\mathrm{M}+ \mathrm{T_{Esum}}, \tag{26}\]  
where \(\mathrm{Hop^{\prime}_{pc,sink}}=\mathrm{Hop_{ps,pr}}+\mathrm{Hop_{pr,sink}}\).  
In summary, we can obtain:  
\[\mathrm{T_{RDPSt}}>\mathrm{T_{RSPSt}}>\mathrm{T_{NRDPSt}}>\mathrm{T_{RPBMPt}} \geq\mathrm{T_{EPUSBRF}}=\mathrm{T_{NRSPSt}}\gg\mathrm{T_{Shortest}}. \tag{27}\]  
Therefore, the security performance of our proposed RDPS scheme is superior to the RSPS scheme and better than the RPBMP, EPUSBRF and the shortest path algorithms.
## 1 Introduction
### Motivation and Solutions  
This paper proposes a CH-selection algorithm for nature-inspired supervised clustering to provide an innovative approach to mobile WSN applications. This enables the most adaptive CH selection in clustering. We also recommend an energy and transmission boundary range-aware routing protocol that aims to find the best mobile trajectories. The set of CHs that was obtained in the first algorithm is used in the routing algorithm. Figure 1 depicts our proposed combine scheme to the readers.  
The proposed scheme and state-of-art studies have been conducted on performance analysis for many control variables and metrics. The performance results clearly emphasize that the proposed methodology has very high performance in terms of network life and energy consumption over improved ant colony algorithm (IACO) in the study [36], particle swarm optimization (PSO) based  
Figure 1: Our proposal design scheme.  
study that is presented in [37], novel chemical reaction optimization based unequal clustering and routing algorithm (nCRO-UCRA) that is proposed in [38], backbone integrated discrete firefly algorithm based mobile data transporter (BI-DFAMDT) that is presented in [31], and the starfish algorithm proposed in [33] study based bio-inspired and swarm optimization methods.  
The major contributions of our study can be summarized, as follows:  
* we construct a heterogeneous WSN with mobile sink in order to minimize energy consumption and maximize the network lifetime;
* we develop a CH selection algorithm based on bacterial foraging paradigm. CHs are dynamically selected from the advanced nodes according to the proposed algorithm. These method differs from the existing studies. In this way, we obtain more shorter mobile sink trajectory sets when compared to other works;
* unlike other works, we combine the CH selection algorithm with proposed energy and transmission boundary range-aware routing algorithm developing the mathematically formulations;
* the energy model of our study consists of listening channel and sensing energy consumptions consideration as well as transmission and receiving costs; and,
* in this paper, we have implemented comprehensive simulation analyzes by measuring lots of performance metrics, including average advanced energy usage that we introduced.  
The rest of the paper is listed as follows. Section 2 presents the literature survey with state of the art studies. Preliminaries with energy and network model subsections are ordered in Section 3. In Section 4, the proposed algorithms with their methodologies are explained in detail. Section 5 discusses performance analysis results and, consequently, the paper is concluded in Section 6.
## 2 Sensors
### Light Detection and Ranging (LiDAR)
#### 2.4.1 Geometric Calibration of LiDARs  
Three calibration schemes could be considered for a LiDAR system including internal calibration of laser components, relative calibration of LiDAR with respect to other sensors, and time synchronization. Usually, the first calibration scheme is performed by the sensor manufacturer; however, the second and third schemes seem absolutely necessary for MMS developers to integrate and employ LiDARs.
## 4 Experiment Verification  
The experimental setup of the regenerative air road sweeper dust control system is shown in Figure 5. The prototype of the pickup head, particle suction port, and structure of the inlet ducts providing the secondary circulation of the airflow in the system were designed based on the calculation and experimental results. The function of the airflowsplitter in the system is to direct a portion of the secondary airflow to the pickup head inlet ducts for recirculation in the system and to distribute the remainder to the environment.  
The experimental phase consists of the following:  
1. The centrifugal fan (CY200H) with a rotation speed of 2900 r/min was regulated by a frequency converter (SQ580-011G/015P4) to produce an airflow velocity of the system 5-35 m/s and create a pressure drop of 1200 to 2400 Pa.
2. The conveyor belt speed was regulated by the control panel (ADLEEPOWER AS2-107) to produce the 4-16 km/h.
3. The dust feeder equipment was developed considering the uniform distribution of dust particles during conveyor belt movement. This device was controlled by a speed controller motor (US-52) and had a speed of 90-1400 r/min.
4. The flowmeter (Longlv LL-DC DN100 PTFE) instrument was used to measure the flow rate at each experimental point in the system.
5. An air pressure sensor (PTL 516) was used to measure the air pressure around the pickup suction port and the air pressure in the particle dust collector hopper.
6. An air control valve (Q911F-10S) was used to ensure that the secondary airflow was recirculated in the system. It was possible to change the airflow from the electronic control valve to the environment by 0-100%.
7. The particle counter (MKS800) used to measure the number of particles released into the atmosphere allowed observation and analysis of the number of particles in the secondary air.  
Figure 5: (**a**) Experimental setup of a regenerative air vacuum sweeper. (**b**) Close-up of the pickup head in the experimental setup. 1: Dust feeder; 2: conveyor belt; 3: conveyor belt speed regulator; 4: motor; 5: dust feeder controller; 6: electronic control valve; 7: air separation box; 8: dust particle box; 9: frequency converter 380 V; 10: particle counter CLJ-H3016; 11: data acquisition system; 12: centrifugal fan; 13: centrifugal fan inlet tube; 14: centrifugal fan outlet tube; 15: dust collector hopper; 16: dust collector hopper filter system; 17: pressure gauge; 18: pickup head; 19: blowing inlet duct; 20: suction mouth; 21: mechanical control valve; 22: lifting devices; EE660; 23: flowmeter DN100 PTFE; 24: frame base; 25: dynamic differential pressure sensors (PCM300H).  
8. The mass of the particles used in the test was measured and recorded. At the end of the experiment, the weight of the particles in the particle receiving hopper and the weight of the nonabsorbed particles were determined, which allowed us to determine the exact amount of suction efficiency.
9. The results obtained were reanalyzed and the effectiveness was evaluated.
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
## 1 Introduction
### The 5G Wireless Realm  
5G is the next leap in the evolution of wireless communication which introduces many improvements to the existing telecommunications industry, but also comes with various challenges. This emerging technology provides low latency, ultra-high-speed massive connectivity between devices leading to cross-industry transformations, pervasive processing in an ecosystem, where all devices are interconnected [4]. Organizations like The European Conference of Postal and TelecommunicationsAdministrations (CEPT) [5] and Federal Communications Commission (FCC) [6] allocate 5G frequency bands in Europe and USA accordingly. The 5G band licensing per geographical area is presented in Figure 1[7].  
Frequency band allocations in USA, Europe and Asia (only China and Japan are included) can be divided into three regions: low frequency (600-700 MHz), high frequency (2.5-7 GHz) cells as well as millimeter wave cells (above 24 GHz). Low frequency bands (below 1 GHz) are intended to be used for traditional local coverage applications, Internet of Things (IoT), vehicle-to-everything (V2X) and transport infrastructure. High frequency (up to 7 GHz) bands can be used for higher throughput data transfer, whereas millimeter wave bands will allow for wireless hotspots to emerge and mm-wave sensors to be included in V2X concept [8]. Other 5G specifications include user experienced data rates in the region of 100 Mbit/s to 1 Gbit/s; connection density of 1 million connections per km\({}^{2}\); end-to-end latency in the millisecond level; and mobility up to 500 km/h [5].  
Advanced CMOS radio frequency PA architectures for mobile applications in the low- and high-frequency ranges are the main topic of discussion in this article. Millimeter wave PA architectures, as will be mentioned in Section 2 of this paper, are usually kept as simple as possible (close to the classic arrangement) with only a handful of papers presenting results with more complex arrangements.
# Off-Axis Tension Behaviour of Unidirectional PEEK/AS4 Thermoplastic Composites
## 2 Materials and Methods
### DIC System  
To apply the DIC measurement, all specimens were painted with matt white spray paint. When dried, black spray paint was carefully applied onto the surface to create a randomly distributed speckle pattern.  
A GOM ARAMIS 4M 2D DIC system, manufactured by GOM in Braunschweig, Germany, is used to investigate the specimen deformation and strain variation during the tests. The system has a 4 Megapixel CMOS camera with a 35 mm lens. The image size was 2352 \(\times\) 1728 pixels2. Inspection of the speckle patterns using the DIC system is conducted to ensure the pattern density of approximately 50%. The measured average black pattern size is about 3.2 pixels. Images collected were analysed using the GOM ARAMIS 6.3.0 software provided by GOM mbH from Braunschweig, Germany to evaluate the displacement fields.  
Figure 4: Stress contour plots of 15\({}^{\circ}\) off-axis tension specimens: (**a**) \(\sigma_{y}\) of specimen with regular end-tabs; (**b**) \(\sigma_{y}\) of specimen with oblique end-tabs; (**c**) \(\tau_{xy}\) of specimen with regular end-tabs; (**d**) \(\tau_{xy}\) of specimen with oblique end-tabs.  
Figure 3: Deformation plots of 15\({}^{\circ}\) off-axis tension specimens: (**a**) 15\({}^{\circ}\) off-axis tension specimen with rectangle end-tabs; (**b**) 15\({}^{\circ}\) off-axis tension specimen with oblique end-tabs.  
Suggestions were made by the iDICs [30] on the user-defined DIC parameters. According to the guide, the subset size should include at least three transitions from dark to white patterns and the step size ought to be 1/3 to 1/2 of the subset size. Hence, in this study, a subset size of 17 \(\times\) 17 pixels2 and a step size of 7 pixels were selected.  
Engineering strain was calculated based on the virtual strain gauge (VSG) method. The strain computed using this method in the DIC analysis system is the average of all the strain data calculated within the pre-selected VSG region. The VSG size was set at 45 pixels in this work.
## 3 Results
### Description of Results and Meta-Analysis
#### 3.4.3 BoP (Figures 2 and 3)  
Four cross-sectional studies presented data on BoP. Three studies [25; 26; 27] were included in the meta-analysis (Figure 2). The study of [PERSON] et al. [24] was excluded because S.D. was not given.  
In total, 375 and 434 implants with KM \(\geq\) 2 mm and KM \(<\) 2 mm, respectively, were compared. There was no evidence of a difference in BoP% between groups (mean difference = \(-\)1.94%; 95% CI = \(-\)6.58; \(-\)92.71, \(p\) = 0.41). High heterogeneity in effects was detected among the studies (I\({}^{2}\) = 92%; \(p\) < 0.00001).  
Sensitivity analysis including only low-risk studies showed no evidence of any effect and did not change the results substantially (mean difference = 1.91%; CI = \(-\)3.02; \(-\)6.84, \(p\) = 0.45). Heterogeneity decreased but not significantly (I\({}^{2}\) = 82% \(p\) = 0.02) (Figure 3).  
Figure 2: Cross-sectional studies reporting on BoP included in the meta-analysis [25; 26; 27].  
Both cohort prospective clinical studies [31; 32] reported data on BoP and found a statistical significant difference in favor of KM \(\geq\) 2 mm (\(p\) < 0.05). In the RCT of [PERSON] et al. [30], a statistical significant difference in favor of KM \(\geq\) 2 mm was reported.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 3 System Design and Methodology
### Methodology  
#### 3.2.1 GNSS Barrage Jamming  
GNU Radio was used to develop scripts for jamming GNSS signals, with the first being barrage jamming (Figure 3). Barrage jamming involves transmitting continuous high-power noise across the GNSS frequency spectrum, overwhelming the weak GNSS signals, like GPS. This raises the noise floor, drowning out legitimate satellite signals and causing UAV GNSS receivers to lose positional accuracy or even complete functionality.  
The jamming script uses a sampling rate of 10 MHz and begins with a Noise Source block generating Gaussian noise. An amplitude of 4 (unitless) ensures a strong noise signal, while a seed value of 0 allows for random noise generation with a fixed seed for repeatability. A Low-Pass Filter (LPF) is essential for filtering the noise before transmission, limiting the bandwidth to 3.333 MHz and targeting the GPS L1 band. This cutoff frequency is one-third of the sample rate.  
Figure 3: GNSS barrage jamming Script using GNU radio.  
The PlutoSDR Source block receives signals at the GPS L1 frequency, while the PlutoSDR Sink block transmits the filtered noise at the same frequency and sample rate. The receiver is used for spectrum monitoring.