esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The longitudinal wave propagation of the mechanical metamaterial is theoretically and experimentally studied. We have demonstrated the possibility of using cylinder and conical structures, and how combining these two shapes controls wave motion in periodic structures. For a periodically repeating unit, cylindrical and conical structures are exploited for longitudinal wave in the straight rod and the curved rod. As well as a straight bar with a periodic structure, analysis of the curved bar was also conducted. The unit of various shapes can adjust the impedance mismatch of the structure, thereby controlling longitudinal waves. Finite element modeling was used to predict the dynamic behavior due to the impact load of straight bar in the periodic structure of cylinder and conical shape and the structure where these two shapes are combined. These analysis and predictions are confirmed by the results of relatively simple experiments due to processing problems. Unlike the common bar where the wave has not passed band gap, experimental and theoretical results indicate that cylindrical and conical periodic structures generate a band gap. It is possible to increase the number of band gaps and move the main frequency band through utilization of the radius, height, distance, and number of each shape. As a result, it is possible to control an unnecessary frequency during wave propagation through the periodic structure, considering a specific shape and each parameter as tools for establishing a band gap. This demonstrates the performance of straight and curved structures with periodic structures of conical and cylinder shape, which suppresses the propagation of waves, without the need for external energy supply to drive active devices such as electronic devices. One of the most interesting features of the metamaterials in curved structure is that the bandgaps for the loads in the other directions except the axial direction also can be exploited. We can use this phenomenon to set the band gap of the desired frequency band, and can consider the part for practical use. It is expected that there will be various fields of application, such as that it can be applied not only to straight structures but also to curved structures, and this part will be explored through future research. In addition, the metamaterial structure for bending load should be researched in the future.  
Conceptualization, G.H.Y.; methodology, D.H.O.; validation, D.H.O. and G.H.Y.; data curation, D.H.O.; writing--original draft preparation, D.H.O.; writing--review and editing, G.H.Y.; visualization, D.H.O.; supervision, G.H.Y.; project administration, G.H.Y.; funding acquisition, G.H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (202020800030, Development of Smart Hybrid Envelope Systems for Zero Energy Buildings through Holistic Performance Test and Evaluation Methods and Fields Verifications).  
The authors declare no conflict of interest.
## 4 Use Case: Application Scenario
### SWIFTNET Design Illustration for a Wildfire Application
Sensing will continue at a low sample interval, as long as the signal is below the emergency threshold. On the other hand, if the air temperature increases above the threat level, we can detect any event of interest by using the adaptive prediction algorithm, which also amounts to additional energy savings compared with a traditional sense-and-transmit method. To verify the suitability of SWIFTNET to wildfire data-trace, we examine the compressibility of the signal shown is Figure 5. We perform a numerical experiment on the time-domain of the data trace. Suppose we represent as a vector \(f\in\mathbb{R}^{n}\), assume \(x\in\mathbb{F}^{n}\) is the Fourier transform of \(f\). We choose the \(k\) largest elements of \(x\) and zero all other \(n-k\) elements, and put them into vector \(y\). We then perform an inverse FFT on the vector \(y\) to get the resulting vector \(v\), which is a recovered version of the original signal. We can see that from Figure 5 almost all the energy of the time-domain signal is concentrated on a few largest Fourier coefficients of its interval-domain transform. From this figure, it is seen that the signals from the wildfire environment are compressible or sufficiently sparse. Based on this experiment, we can safely apply SWIFTNET to the wildfire application scenario, without losing much accuracy on the reconstructed signal. Figure 5: Compressibility of wildfire data-trace. Figure 6: A front view of a WISPES W24 TH node prototype.
## 2 Instrument Developments
### PAU-Synthetic Aperture
Moreover, the four central antennas plus three additional ones (seven in total) are used to create a steerable array for _PAU/GNSS-R_ to point to the specular reflection points (total = 31 dual-polarization antennas). Figure 8 shows the _PAU_-Synthetic Aperture topology, and two figures taken during the integration. Hardware and simulation details can be found in [16,17]. Figure 8: (a) _PAU_-Synthetic Aperture’s topology, (b) View of the whole instrument with one arm opened, and (c) Detail of the hub, including digital correlator unit (right hand side, bottom), base-band clock distribution (right-hand side, top), calibration unit, including 2-level noise source and Pseudo-Random Noise (PRN) generator (left-hand side, bottom), power supply (left-hand side, up).
## 3 Sample Preparation for Carotenoids Determination
### Sample Extraction Techniques
#### 3.1.2. Biobased Solvent Extraction  
Bio-based solvents are more environmentally friendly than conventional solvents derived from petrochemicals [60]. These solvents, such as plant materials, are produced from renewable biomass and are known for their lower toxicity, making them safer for human health [60]. They can be classified into four categories based on the biomass used for production: (I) lignocellulosic, (II) starch and sugar, (III) oil and protein-based, and (IV) other food and forestry waste [61]. One extensively studied green solvent for extracting carotenoids is ethyl lactate [62].  
Ethyl lactate is produced industrially through the continuous esterification of lactic acid and alcohol with an acid catalyst. Research indicates that the extraction efficiency of carotenoids using ethyl lactate is significantly higher, yielding 243 mg/kg, compared to conventional solvents like acetone (51.90 mg/kg), hexane (34.45 mg/kg), and ethanol (17.57 mg/kg) [63]. When combined with UAE, its efficiency increases up to 8.7 times compared to acetone, 7 times to ethanol, and 1.6 times to sunflower oil [64].  
Another effective bio-based solvent is 2-methyltetrahydroofuran (2-MeTHF), obtained by catalytic furfural hydrogenation derived from plant polysaccharides. It has no reported mutagenic or genotoxic effects and has been shown to extract up to 12.5 times more carotenoids from olive pomace than n-hexane [65]. Ionic liquids (ILs), composed of organic cations and inorganic anions, are non-flammable but vary in biodegradability and toxicity. While concerns exist about their environmental impact, their ability to be reused makes them attractive for green extraction processes. ILs have demonstrated greater efficiency than acetone for carotenoid extraction and outperform MeOH in lutein extraction [66; 67].  
Deep eutectic solvents (DESs), often called \"solvents of the 21 st century\", are formed by hydrogen bonds between two or three components [68]. Natural deep eutectic solvents (NADESs), prepared from plant-based ingredients, are considered safer alternatives. Studies confirm that DES and NADES outperform conventional solvents in carotenoid extraction [69; 70]. However, some DES formulations pose toxicity risks to aquatic and terrestrial ecosystems, making NADES with edible components the safest choice [70]. Vegetable oils have emerged as a sustainable alternative for carotenoid extraction, eliminating the need for conventional solvents while also protecting carotenoids from oxidation. Extracted carotenoids can be directly incorporated into food without additional purification. Commonly used oils include flaxseed, sunflower, corn, soy, olive, and peanut [70]. [PERSON] and [PERSON] (2021) found that corn oil extracted twice as many carotenoids from pumpkin peels as hexane/isopropyl alcohol, yielding 33.70 mg/g versus 16.20 mg/g [71]. Similarly, [PERSON] et al. (2020) reported that while n-hexane, ethanol, and acetone provided higher recovery (-87%) than flaxseed oil (77.48%), the ability to avoid conventional solvents remains a key advantage [72].
## 2 Materials and Methods
### Experimental Procedures  
Before formal experimentation, the weight and height of each subject were measured using a stadiometer and a calibrated weighing scale. Assigned shoes were provided to reduce measurement error. The experimental protocol was divided into three parts: pre-fatigue, fatigue, and post-fatigue. During the pre-fatigue and post-fatigue phases, the kinetics and kinematics of the lower limbs were captured synchronously. An eight-camera infrared Vicon motion capture system (Oxford metric Ltd., Oxford, UK) and a Kistler Force Platform (Model9281B, Winterthur, Switzerland) were used to collect the kinematics and kinetic data with sampling frequencies of 200 Hz and 1000 Hz, respectively. To track the motion of participants, twenty-one reflective markers, and six rigid marker clusters were attached with adhesive tape, which is illustrated in Figure 3[31]. Reflective markers were bilaterally placed on the lower limbs and pelvis, which included the anterior superior iliac spines, iliac crests, greater trochanters, lateral and medial femoral epicondyles, lateral and medial malleolus, distal interphalangeal joint of the second toe, the first and fifth metatarsal heads, and the final marker was placed on the joint space between the fifth lumbar and the first sacral spinous. In addition, six marker clusters were bilaterally placed on the mid-thigh, mid-shank, and heel counter of the shoes with a self-adhering bandage. All these preparations were done by the same experienced researcher. Before data collection, the force platform was zero-leveled, and the definition of contact on the force platform was a value  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Mean** & **SD** \\ \hline Age (year) & 23.93 & 0.80 \\ Weight (kg) & 73.93 & 4.76 \\ Height (cm) & 176.70 & 2.75 \\ BMI & 23.35 & 1.22 \\ Foot length (mm) & 253.2 & 1.82 \\ Leg length (cm) & 87.4 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive characteristics of participants.  
Figure 2: Flow chart of participant selection. Note: FPI-foot posture index.  
of vertical GRF surpassing 20 N. In both pre-fatigue and post-fatigue phases, all subjects were asked to perform three successive trials. While performing the countermovement jump, participants were informed to stand in a position facing the force platform. When the researcher gave the instruction to start, the participants used their right foot to step on the force platform with his left foot following. Subjects then performed the countermovement jump with the hands placed on the hip. When the subjects completed the jumps, they stepped off the force platform and maintained an upright position. If the left foot touched the force platform, the trial was not analyzed.
# Reliability of a Global Gait Symmetry Index Based on Linear Joint Displacements
## 4 Discussion
This was likely because the linear displacement of segment extremities is less sensitive to errors in landmark position determination than joint angles and confirms that this limitation of joint angles was contributing to the error of the GGA. Conversely, the SDC values of the LGGA (29.7%) were higher (worse) than those reported by [9] for the symmetry ratio in stroke patients (between 8% and 18%, as calculated based on the published data). However, the latervalues may be questionable, considering the width and variability of the time interval between visits (between 3 and 36 days) and the equation used to calculate the SEM (from which the MDC is calculated). Explicitly, the SEM should not be calculated using an ICC model that includes systematic error because this is not reflected in the pooled standard deviation. Furthermore, the calculation of SEM from the ICC is not recommended as it highly depends on the sample's heterogeneity [6]. In practical terms, this study shows that a change of \(\leq\)0.2 in the LGGA score in a healthy person should be neglected, as it is considered measurement error. Moreover, based on the 95% LOA and SDC values obtained in this study, it is expected that the LGGA score may change by 0.55 when the same healthy participant is measured repeatedly without undergoing any real change between measurements [39]. Thus, only changes above 0.54 should be considered as real changes [6]. As for the clinical relevance of such a change, future investigations are needed to determine the minimal clinically important difference (as well as the other measurement properties analysed in this study) of the LGGA in specific clinical populations. Although the LGGA is more reliable than the GGA, a score based on linear joint displacements is more difficult to interpret rather than one based on joint angles.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 3 Results
### WUE Variations in Different Vegetation Covers  
Different vegetation cover may also influence WUE, as forest species, grass, and shrubs may consume different amounts of water to produce per unit organic matter. Therefore, it is important to investigate the WUE characteristics in different vegetation types. As shown in Figure 9, broadleaf forest had the largest WUE (approximately 1.8 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)mm\({}^{-1}\) over the entire research time range), followed by mixed forests, needleleaf forests, shrublands, wetlands, croplands, grasslands, and barren forests (approximately 0.3 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)mm\({}^{-1}\)). During the research time period, vegetation lands mainly showed increasing trends of WUE, while barren land areas showed a decreasing trend. Forests are more effective in utilizing limited water supply to produce organic matters, suggesting that those regions with more forests have large WUE. The results in Figures 3 and 4 all agree with this. However, the growth of wooden trees needs abundant water supply for transpiration, which, therefore, limits the distribution of wooden trees in western parts. Therefore, only those regions with enough precipitation can afford the growth of wooden trees. For those more arid regions, only shrub or grasslands could grow; the WUE will be far less than forest regions, though it still showed slowly increasing trends.
# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## 1 Introduction
Addressing the aforementioned challenges, this paper introduces an enhanced version of the You Only Look Once v7 (YOLOv7) target-detection model. Section 2 provides the foundational knowledge necessary for understanding the context and scope of this work. In Section 3, we delve into the refined components of the benchmark model and elucidate the rationale underlying each enhancement. The subsequent Section 4 offers a comprehensive exploration of the experimental setup, design considerations, achieved outcomes, and real-world performance of the novel model. In conclusion, we recapitulate the entirety of this endeavor, identifying limitations and charting pathways for future advancements. This paper introduces significant contributions to the algorithmic landscape, enhancing the efficacy of target detection. Primarily, our contributions encompass the following key aspects. Initially, we synergize the concepts of feature separation and merging, culminating in the refinement of the Spatial Pyramid Pooling, Cross-Stage Partial Channel (SPPCSPC) module. Furthermore, we introduce an innovative module known as the Spatial Pyramid Pooling-Fast, Cross-Stage Partial Channel (SPPFCSPC) module. This new module represents a substantial leap forward, as it optimizes the preservation of the YOLOv7 model's sensing field while ensuring precise localization of diverse small targets, irrespective of their sizes. Consequently, this innovation amplifies the extraction of effective features, concurrently mitigating the issue of detection leakage. To mitigate the challenges of false positives and missed detections in small target detection, we introduce the Coordinate Attention for efficient mobile network design (CA) mechanism. This attention mechanism is a remarkable innovation capable of simultaneously incorporating inter-channel relationships and positional information across extensive distances. It adeptly allocates attention to multiple feature elements in parallel, resulting in reduced information loss during small target detection and superior achievement of feature optimization. In the context of small target detection, we introduce a novel convolutional kernel that dynamically adapts the convolutional size in response to changes in target size. This adaptation enhances the model's capacity for expression and contributes to elevated detector performance, while concurrently fortifying the network's robustness and responsiveness to varying target sizes.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 1 Introduction
They interact specifically with four structurally and functionally unrelated classes of proteins: (**A**) In resting cells, guanine nucleotide dissociation inhibitors (GDIs; 4 human members) sequester RHO in the cytoplasm, away from the membrane, by binding to the lipid anchor and thus creating an inactivated cytosolic pool; (**B**) in stimulated cells, different classes of membrane receptors activate guanine nucleotide exchange factors (GEFs; 85 human members: 74 DBL and 11 DOCK family proteins), which in turn activate RHO by accelerating the intrinsic exchange of GDP for GTP and switch ON signal transduction; (**C**) active GTP-bound RHO interacts through the GTPase-binding domain (GBD) with and activates downstream targets (effectors; >70 human members) to cause a variety of intracellular pathways, which control a multitude of biochemical processes involved in the regulation of different biological (dys)functions; (**D**) GTPase-activating proteins (GAPs; 66 human members) negatively regulate RHO by stimulating its slow intrinsic GTP hydrolysis activity and switch OFF signal transduction. Notably, all RHO-interacting proteins recognize and bind RHO at consensus-binding sites called switch I and II.
## 4 Results and Discussion
### Flow Simulations Based on Different Models  
Figure 7 shows the steady-state groundwater flow fields for Case I. Figure 7a,b compare results obtained from the HD and ECPM models, while Figure 7c,d present the head distributions only on the fractures. The results have shown the consistent solutions obtained from three different models. Specifically, the developed HD model enables the simulations of flow in fractures and rock matrix, which shows the advantage of the system of equations to be solved simultaneously. The concept proposed in the HD model can accurately capture the behavior of flow interactions between fractures and matrix (Figure 7a,b) and in the fractures network (Figure 7c,d). There is a difference of four orders of magnitude in hydraulic conductivity between the fractures and rock matrix in this case. The results of the HD model agree well with those obtained from the ECPM model in the entire domain region and the DFN model in the fracture network.  
Figure 8 presents the flow results based on the conditions specified in Case II. This study excluded the DFN model because the three fractures have no contact with the domain surfaces. The results in Figure 8 indicate that the solutions obtained from the HD model show slightly deviated from the ECPM model, especially near the corners away from the specified head boundary conditions. In general, the overall pattern of the flow was captured by the developed HD model (Figure 8a,b). The results in Figure 8c,d show different views of the head distribution profile in the simulation domain. The head  
Figure 6: The computational mesh of the test Case II for the HD model: (**a**) The geometry of fractures (in blue color) and DH object (in black color); (**b**) The zoom-in view of the mesh generated for connecting the matrix and the DH object; (**c**) The close view of the generated nodes and elements for the connections of fractures, matrix, and DH object. Note that parts of the meshes in the figures are blanked for presentation purposes.  
along the profile has demonstrated the considerable influence of the fractures on the head variation. The fractures provide fast pathways and control the local flow patterns to change with the fracture locations. Such behavior is significantly influenced by the vertical fracture near the top and bottom boundaries (see Figure 8c,d).
## 2 Materials and Methods
### Equipment  
The kinematic data were collected using a three-dimensional motion analysis system (T20S, VICON, Oxford, UK). This system is composed of eight infrared high-speed cameras with a capture frequency of 300 Hz, two 3-axis force plates with an area of 90 \(\times\) 60 cm\({}^{2}\) (9287, Kistler, Chennai, India; 5507, AMTI, Watertown, MA, USA), two 3-axis force plates with an area of 60 \(\times\) 40 cm\({}^{2}\) (9281, Kistler, Chennai, India), and four amplifiers to measure joint moments and ground reaction forces. The measurement frequency of the four 3-axis force plates was 1500 Hz. The muscle activation status was collected at a sampling rate of 1500 Hz using a wireless electromyography system (2400-G2, Noraxon, Scottsdale, AZ, USA), as described in the following subsection. The experimental setup is illustrated in Figure 1a.  
Figure 1: **(a)** The experimental set-up of spike jump; **(b-1)** take-off and **(b-2)** touchdown during spike jump.