esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis
### Impact of Content Popularity in Caching  
The end-users access the popular contents frequently. If a popular content is accessed by an end-user at any place, it inherits the value of the content popularity. Therefore, the popular content is easily replaced by a non-popular content at the edge of the core and the access network devices. Moreover, the popularity of the content increases continuously based on every cache hit. We considered the same topology and simulated the same topology by adding two more content consumers. They were the proximity devices of each content consumer. The newly added content consumer node numbers were thirty-three and thirty-four. The node number thirty-two was requesting different contents from the six different content providers that were located at the one-hop to six-hop distances from the requested content consumers. The node number thirty-three is the neighbor node of thirty-two, also requesting for the same content after a while. Similarly, the node thirty-four was also requesting for the same content  
the nearby devices. Figure 12 shows the simulation result, which displays how the efficient caching mechanism affects the content access time. Node thirty-two was accessing the contents from different content providers but node thirty-three was accessing the contents from node thirty-two, and node thirty-four was accessing the contents from node thirty-three. Therefore, Tier 2, 3, and 4 devices were free from all kinds of network traffic and bandwidth consumptions due to the content accessing from the nearby cached memory. Our cluster-based network always ensured that the content is accessible from the nearby devices and manage the cache memory of each node efficiently based on our proposed popularity-driven content replacement mechanism. Our mechanism also ensures low latency communication in every scenario of downstream content flow.
# Quadrupedal Robots' Gaits Identification via Contact Forces Optimization
## 4 Results
### Gaits Analysis for Different Speed and Cycle Duration \(T\)  
The optimization solutions are tested for different speeds and cycle period \(T\).  
Figure 16 depicts the trend of the walking gait representing \(E_{d}\), following Equation (30), as dependent on the cycle duration, for different speeds between 0.7 and 2 m/s. This map permits finding natural associations between convenient gait period and body velocity, to obtain gaits with a reasonably limited energy cost and smooth forces.  
We notice that for \(T<0.5\) s the total energy is very large, because the swing phase has a very limited duration, needing very high energy to relocate the foot at its initial position. On the other hand, at high values for \(T\), again the energy cost increases. Therefore, the map suggests that for any desired speed, there is an intermediate region for the gait period that allows a low energy consumption. Minimum energy expenditures are identified at \(T=1.25\) s for speed 1.35 m/s, at \(T=1\) s for speed 2 m/s, and \(T=1.66\) s for speed 0.7 m/s (blue square, green triangle, and red diamond, respectively).  
Figure 16: Walk gait energy \(E_{d}\) for different speeds and time cycles T.  
Figure 15: Mechanical energy comparison between experimental data and the optimal solution.  
In Figure 17, the trend of the trot gait is depicted. We notice the same behavior of low energy cost for the walking gait in the middle region, with an optimal association between periods and speed: \(T=1.25\) s for 1.35 m/s, \(T=1\) s for 2 m/s, and \(T=1.66\) s for 0.7 m/s.  
Then, taking the minimum points for each gait and comparing them with the energy cost and jerk performances (Figure 18), we see how the transition between walking and trotting takes place at a speed between 0.7 m/s and 1.35 m/s, by using a Pareto criterion. In fact, for the lowest speed, 0.7 m/s, the walk is optimal, in a Pareto sense, since energy cost is lower, and jerk substantially similar. Increasing the speed at 1.35 m/s, a trot performs much better, since both energy and jerk indexes are lower with respect to the walking gait. For higher speeds, the trend of the two curves shows the trot gait is better than walking.  
Figure 17: Trot gait energy \(E_{d}\) for different speeds and time cycles T.  
Figure 18: Gait transition from walk to trot based on [PERSON]’s frontier analysis.
# Analysis of Confinement in Dual Spherical-Tapered Ended Fiber Optical Trap
## 1 Introduction  
Since [PERSON] first proposed the optical trap in 1970 [1], it has become a versatile tool for the micromanipulation of biological objects [2, 3] without physical contact. The optical trap has been widely used from basic research on light to manipulation of microspheres [4, 5]. The dual-fiber optical trap relies on two laser beams transmitting toward each other to realize the powerful control of the microsphere. In 1993, [PERSON] et al. first used two counter-propagating beams emanating from two cleaved fibers to \"clamp\" particles [6]. It captured particles at a low light intensity, and could effectively reduce damage to the captured particles, especially biological cells. At the same time, compared with the single-beam optical trap, the dual-fiber optical trap has a longer working distance and a wider lateral range, and has applications, such as optical wrench [7] and optical stretching [8]. However, axial trapping efficiencies in the dual-beam optical fiber were low, and the phenomenon of multiple captured stable positions was frequently encountered [9]. Attempts to improve axial stability have been realized by the use of a spherical-tapered ended fiber to focus the beams to a spot [10, 11, 12].  
In this paper, the dual-fiber optical trap system was integrated into a chip. Two spherical-tapered ended fibers were designed to replace traditional flat ended fibers. The spherical-tapered ended fiber is more conducive to beam focusing. The use of spherical-tapered ended fiber in a dual-fiber optical trap provides greater accessibility to improve the stability of the optical trap. It was proved by simulation and experiment that the dual spherical-tapered ended fiber optical trap had higher optical trap stiffness than the dual flat ended fiber optical trap. Furthermore, the integrated chip-scale platform makes this system stable and more convenient to debug and reuse. This structure can be effectively applied in physics, biology [13], chemistry [14], and medical research [15], such as optical wrench [16], optical orbital rotation [17], cell screening [18], and optical stretching [19]. This research on optical trapping by the dual spherical-tapered ended fiber optical trap shows the great potential of micro-structured optical fibers that can be used for optical trapping and micro-manipulation.
## 3 Results
### Manufacturing of the Micro Structured Encoder Discs  
Because of the miniaturized dimensions of the optical encoder, small encoder discs were necessary. The encoder discs were fabricated using precision injection molding of polycarbonate (Makrolon\({}^{\circledR}\) OD2015). Therefore, an injection mold was constructed. Figure 17 shows the design concept of the injection mold. The cavity of the encoder disc was located at the inner position of the mold and the marked stamper carrying the grating was necessary to form the micro structures of the blazed grating into the surface of the disc. The commercially available blazed grating used for the first demonstrator for proof of principle of the encoder was used to fabricate nickel stampers with a thickness of 0.3 mm using the galvanic reproduction technique. The nickel stamper was clamped inside the cavity of the injection mold. This way, encoder discs were fabricated, while this process allows for a scalable production at low manufacturing costs.  
Figure 16: Lens mounted on top of the opto-ASIC.  
Figure 17: Design of the injection mold for encoder discs.  
As the blazed grating was a continuous shape, an alignment of the stamper inside the cavity was not required. Figure 18 shows the molded encoder disc directly after its fabrication. The picture shows the encoder disc with the sprue still present to the left and the right of the encoder disc. To create a reflective surface, the discs were coated with gold using a PVD process. The quality of the gold depended on the sputtering target. The used target had a purity level of 99.999%. That means that the gold layer had a purity level slightly lower, because, during the sputtering process, there is the possibility that foreign atoms are incorporated into the gold crystal lattice. The gold layer had a thickness of 50 nm.  
With a total diameter of 4.2 mm, a height of 1.2 mm, and a grating constant of 1.7 um, the encoder discs can be duplicated economically. The discs were assembled to the shaft of the encoder using a suitable adhesive. The size of the disc toward smaller diameters is only limited for practical reasons such as handling. The challenge is probably to find a feasible handling concept for very small discs. However, bigger discs are also possible with this concept, such as hollow shaft encoders.
## 5 Discussion and Conclusions  
The findings of the research outlined in this paper explain how the combination of blockchain and tracking technologies can contribute to improvements in supply chain transparency and traceability, by feeding key data from IoT devices directly into the blockchain. The proposed simple taxonomy of physical tracking technologies provides guidance for understanding the different application areas, requirements, and limitations of physical trackers. It turns out that different physical trackers can be of use depending on the application and material, the processing stage, and other particularities of the supply chain. In this context, blockchain technology can help not only to secure data entered manually or automatically from IoT devices, but to merge the data generated by the variety of trackers along the supply chain in a tamper-proof manner. Blockchain technology can also be designed in such a way as to balance the needs for transparency with the need for confidentiality of the different stakeholders along the supply chain. Although this paper illustrates how these transformative technologies render raw material supply chains more transparent, some questions still remain. The limitation of this research paper resides in its inability to address the throughput, latency, and scalability issues inherent in blockchain technology, which pose a significant issue as trillions of data points from IoT devices will need to feed into the blockchain annually. Furthermore, this paper does not deal with artificial intelligence to assess, organize, and extrapolate further results from datasets [33]. However, this paper does illustrate how these transformative technologies can be used in
## 2 Materials and Methods  
### VOCs GC-MS Analyses  
Following the previously optimized protocols [49], VOCs were collected in stainless steel TD tubes, which are able to retain C\({}_{4}\)-C\({}_{30}\) compounds (_Markes International_, Lantrisant,UK; inert biomonitoring sorbent tubes), were desorbed with a thermal desorber (Unity-xr, _Markes International_), directly connected to the gas chromatograph with a heated transfer line. The tube was heated for 10 min at 220 \({}^{\circ}\)C and the desorbed VOCs were directly transferred in the gas chromatograph injector at 200 \({}^{\circ}\)C, operating in split mode (50% in and 50% out), utilizing helium as a carrier gas, at a linear velocity of 0.5 cm\(\cdot\)s\({}^{-1}\). The separation and quantification of the desorbed VOCs were performed with a gas chromatograph (Clarus 680, _PerkinElmer_, Boston, MA, USA), coupled with a quadrupole mass spectrometer (Clarus SQ 8T, _PerkinElmer_, Boston, MA, USA). A 60 m \(\times\) 0.25 mm i.d., 1.4 \(\upmu\)m film thickness, capillary column Rtx(r)-VMS (_Restek_, Bellefonte, PA, USA) was utilized with the following oven temperature program: 50 \({}^{\circ}\)C for 5 min, then increased by 10 \({}^{\circ}\)C-min\({}^{-1}\) to 160 \({}^{\circ}\)C, 5 min at 160 \({}^{\circ}\)C, increased by 10 \({}^{\circ}\)C-min\({}^{-1}\) to 220 \({}^{\circ}\)C, and 5 min at 220 \({}^{\circ}\)C. The temperatures of the transfer line and the ion source of the quadrupole were 280 \({}^{\circ}\)C and 220 \({}^{\circ}\)C, respectively. The MS was performed at 70 eV electron impact ionization energy, in full-scan mode (SCAN) with a scan range of 40-250 amu. SCAN monitoring mode was used for compound identification and quantification in the case of acetonitrile. Quantification of the other selected analytes was made from extracted ion chromatogram (XIC) and obtained in SCAN mode. The Clarus SQ8 GC-MS software (_PerkinElmer_) allowed the acquisition and elaboration of data.  
To prevent memory effects, after each analysis, two empty TD tubes (without the adsorbent phase) were analyzed to remove any eventual residues of the previous sample from the thermal desorber and analysis apparatus.  
After each use, the TD tubes were conditioned at 340 \({}^{\circ}\)C for 3 h, as recommended by the producer, capped, sealed with parafilm, and stored at 8 \({}^{\circ}\)C.
# Assessment of the Quality of Video Sequences Performed by Viewers at Home and in the Laboratory
## 3 Results and Discussion
### Objective Quality Assessment of Video Encoded Using H.264, H.265, and VP9 Standards
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & \multicolumn{8}{c}{**QoS vs. QoE Correlations**} \\ \cline{3-10}  & & \multicolumn{4}{c}{**Lab**} & \multicolumn{4}{c}{**Home**} \\ \cline{3-10}  & & **360p** & **480p** & **720p** & **1080p** & **360p** & **480p** & **720p** & **1080p** \\ \hline PSNR & 360p & 0.997 & 0.998 & 0.999 & 0.998 & 0.994 & 0.990 & 0.996 & 0.993 \\  & 480p & 0.990 & 0.996 & 0.997 & 0.997 & 0.997 & 0.997 & 0.999 & 0.999 \\  & 720p & 0.980 & 0.989 & 0.990 & 0.991 & 0.995 & 0.999 & 0.997 & 0.999 \\  & 1080p & 0.951 & 0.966 & 0.968 & 0.972 & 0.978 & 0.989 & 0.984 & 0.987 \\ \hline SSIM & 360p & 0.998 & 0.997 & 0.997 & 0.995 & 0.988 & 0.981 & 0.989 & 0.985 \\  & 480p & 0.998 & 0.998 & 0.998 & 0.996 & 0.991 & 0.984 & 0.991 & 0.988 \\  & 720p & 0.998 & 0.998 & 0.998 & 0.997 & 0.991 & 0.986 & 0.993 & 0.990 \\  & 1080p & 0.988 & 0.991 & 0.992 & 0.995 & 0.990 & 0.994 & 0.999 & 0.995 \\ \hline VMAF & 360p & 0.995 & 0.996 & 0.997 & 0.998 & 0.993 & 0.993 & 0.998 & 0.995 \\  & 480p & 0.994 & 0.995 & 0.996 & 0.998 & 0.992 & 0.993 & 0.998 & 0.996 \\  & 720p & 0.994 & 0.994 & 0.995 & 0.997 & 0.989 & 0.990 & 0.997 & 0.993 \\  & 1080p & 0.993 & 0.988 & 0.987 & 0.991 & 0.978 & 0.980 & 0.991 & 0.983 \\ \hline \end{tabular}
\end{table} 
Table 12: Correlations between QoS and QoE values (in the lab and home) for VP9 encoded video.
## 5 Results  
### Optical Response  
The optical response of plasmonic nanoparticles was studied by the backscattered signal from the center of gold nanoparticles, by comparing the modulation of the backscattering cross-section when the probe wavelength is changed. This was performed by measuring 150, 200 and 250 nm gold nanoparticles immersed in water because their optical response shows different behaviours unlike air surrounding media (Figure 2e). The range of the probe wavelength is from 720 to 800 nm, which is sufficient to cover the region of special interest, the drop in the optical response at \(\sim\)750 nm wavelength for a 200 nm gold particle.  
The acquisition data procedure for each probe wavelength is the same to avoid any phenomena that may affect the particle backscattering. The input laser powers are set to the same value. The overlapping of both lasers are checked on a thin gold film coated on the same sample cover-slip. The region of interest is realigned to compensate any movements of the particle over time caused by changes in the laboratory conditions. The error bars are the standard deviation of the optical response of a gold nanoparticle surrounded by water for a fixed probe laser wavelength following the same scan procedure several times. The optical response of the three sizes, shown in Figure 3, matches the theoretical modulation depth (optical sensitivity over the radar backscattering cross-section) given by Mie theory. The optical response of a 150 nm particle increases a factor of \(\sim\)1.3 when the probe wavelength is tuned from 720 to 800 nm (Figure 3a,d). On the other hand, the same tunable range produces a decrease in a factor of \(\sim\)3 in the optical response of a 250 nm particle (Figure 3c,f). Finally, the maximum enhancement in our experimental data, a factor of \(\sim\)9, is given by a 200 nm particle when the probe wavelength is tuned from 745 to 770 nm (Figure 3b,e).  
Figure 3: Measured amplitude (blue) and theoretical modulation depth (red) for: (**a**) 150 nm; (**b**) 200 nm and (**c**) 250 nm gold nanoparticle in water tuning the probe laser wavelength from 720 to 800 nm measuring the reflected light; (**d**–**f**) show the oscillation amplitudes (small box figure), their frequency spectra and the degree of enhancement for the circle and squares from a–c.  
This enhanced response provides a way to improve the detection of the different main vibrational modes by choosing the right probe wavelength based on Mie theory without increasing the laser power. Moreover, it might be possible to suppress the signal from certain particles by careful choice of the wavelength. The discrepancies between the experimental data and the theory might be related to the extra reflections produced by the cover-slip or the effect of the electrostatic layer-by-layer self-assembly process, which bonds the particle to the substrate.
## 4 Design of Proposed Scheme: FAPR
### Reactive Part
#### 4.3.3 Failure Detection Module  
The failure detection module connects the switch to the controller, and its role is to perceive link failures in the network, standardize the information returned by the data collection module, and pass to subsequent modules for processing. This module is in real-time monitoring status, which can promise to respond to network events in a timely manner upon a link failure, and invoke the data collection module to send data retrieval requests to the corresponding switches. After receiving the requests, switches will return detailed information about the faulty link. In addition, this module can also retrieve other data, such as the original path of the flow. Note that the flow analysis module is posterior to the failure detection module, and the former can obtain switch IDs of the faulty link and the flow entries in these switches from the standardized information and other data mentioned above.
## 4 Discussion
### Effect of Midsole Hardness on Landing Biomechanics  
The second hypothesis of the present study was that more attenuating landing strategies would be adopted in response to harder midsoles, and this hypothesis is partly supported by the results. Collectively, small changes in midsole hardness within a commercially relevant range showed subtle effects on lower limb biomechanics in landing. With a softer midsole, participants showed increased rectus femoris and tibialis anterior muscle activation during the preparation phase to landing of DVJs. This change in neuromuscular response, however, did not result in any change in the kinematics or eccentric energy absorption of the lower limb joints, thus it may be attributed to increased efforts of joint stabilization, deemed necessary in response to softer midsole hardness. Midsole hardness significantly affected peak force at the forefoot and the rearfoot, as well as the peak flexion moment in the ankle and hip, though the magnitude of change was much smaller than that due to drop height (Figure 2). Nevertheless, the comparatively higher forefoot peak forces and comparatively lower rearfoot peak forces in shoes with softer midsoles indicate a subtle load shift to the forefoot region, observed during landings from the medium and the highest drop height, inducing larger input energy to be controlled. It can be assumed that the forefoot with its more complex anatomical structure is better able to counteract potential lower stability coming along with softer midsole hardness of the experimental basketball footwear than the more rigid anatomical structure of the rearfoot.  
The consistent landing kinematics between shoes with different midsole hardness in the present study were in agreement with previous works [3; 11]. For example, [PERSON] et al. [3] also found no differences in joint angles in the lower limbs between shoes with different cushioning properties. As the knee joint extensors contribute to the energy dissipation during landing [39], the lack of differences in total eccentric joint work can explain the consistent landing strategies between shoe conditions. Flat-footed landings were occasionally adopted by some participants when dropping from the lowest platform. The switch in flat-foot and forefoot landing techniques may have accounted for the interaction effect between midsole hardness and drop height that was observed for rearfoot peak vGRF.
## 2 Materials and Methods
### Methods
#### 2.3.3 Dual-Type Randomized Extraction Algorithm  
The dual-type randomized extraction algorithm is primarily designed for datasets comprising two distinct data types. It employs a random sampling strategy to extract rectangular vectors of irrigated and non-irrigated cropland within small regions. Within each of these rectangular vector ranges, all other environmental variables are assumed to remain constant. Based on this assumption, the algorithm enables the accurate quantification of irrigation's impact on cropland CWUE across different regions. This approach supports the optimal use of carbon and water resources, helps maintain ecological balance, enhance agricultural productivity, and provide a scientific foundation for agricultural decision-making and sustainable development. For more details, refer to [PERSON] et al. (2018). In this study, the dual-type randomized extraction algorithm was used to generate random geographic coordinate points across Henan Province. Around each point, a 20 km \(\times\) 20 km rectangular vector cell (referred to as a study subregion) was constructed. Each randomly generated cell was required to include a sufficient number of irrigated and non-irrigated cropland pixels with stable spatial locations. Specifically, each cell had to contain (1) at least 40 pixels of non-irrigated cropland and (2) at least 40 pixels of irrigated cropland, both of which remained unchanged throughout the study period. Only cells that met both thresholds were selected for further analysis. Ultimately, 100 eligible rectangular cells were identified and extracted as representative study areas.