esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
DTs can improve design, production, operation, maintenance, training, and end-of-life administration over the whole lifecycle of a component or system, and [23] provides a comprehensive list of the applications of the digital twin classification throughout the lifecycle. This paper demonstrates that most digital twin applications and use cases belong to \"development\" or \"operations\" categories. The second is the main application area, and the function of the digital twin for both cycles received almost no consideration to present DevOps. Considering this gap, the current work is positioned precisely in this area because it includes the VC area after the creation of the virtual model of the robot. Applications in mechatronic systems represent an important category in scientific papers which present VC and DTs. Combining mechatronic components, control software, and digital technologies such as VC and DTs can help enterprises optimise their operations [24; 25; 26].  
The application of DTs and VC in robotics provides significant benefits in terms of cost savings, improved performance, and streamlined development processes. These advantages have led to the rapid adoption of DTs and VC technologies in the robotics field, making it a top area of interest for both industries and researchers [27; 28; 29; 30].  
Regarding software platforms and tools available for DT implementation in different architectures, industry and academia have different choices due to the distinct needs and goals [31]. While academia favours more adaptable and configurable tools to serve a wide range of research topics (Matlab, LabView, Automation Studio, Cires, Ansys, Unity, V-REP, Gazebo), the industry often looks for comprehensive, scalable solutions that can be quickly integrated into existing systems (Siemens PLM Software, FastSuite-2, Dassault's 3D experience, PTC's ThingWorx, AVEVA's digital twin, GE's Predix, RoboDK, CoppeliaSim, VisualComponents, Emulate3D) [1; 10; 23; 28; 32].  
The huge volume of data around DTs can be difficult for non-expert users to evaluate and analyse. In order to increase the accessibility and usability of the data produced by DTs,  
Figure 2: Synergy between DT and other components of Industry 4.0.  
XR technologies are used to bridge the gap between the virtual and real worlds. The potential of combining the strengths of DT and XR (specifically MR, [33]) to create more efficient and effective approaches for various applications, such as design, operation, maintenance, and training, was recognised by industries and researchers, which led to the beginning of the strong connection between these technologies. The benefits of leveraging mixed reality as an effective front-end for digital twin models in a more intuitive, immersive, and interactive manner are presented in [34; 35; 36; 37; 38].  
In the specialised literature, there appears to be a lack of research exploring the combined approach of implementing digital twins using both Automation Studio and Unity.
## 4 Supply and Demand
### Aplications
The Gulf War of 1990-1991, often referred to as the \"first space war\", demonstrated the strategic advantage reached due to using GPS [20; 37; 57; 58]. The tragic downning of a South Korean aircraft over the USSR in 1983, caused by a pilot's navigational error, led to the decision to make GPS available for civilian use [59]. In 2000, President [PERSON] authorized a policy directive ensuring that civilian users would have access to the same location accuracy once reserved for the U.S. military. This change significantly improved navigation accuracy, leading to the impressive growth in GPS adoption accompanied by the smartphones' spread [60]. Over half of all usage lies in phone-based location services; GNSS data also underpins aviation, maritime navigation, autonomous vehicles, environmental monitoring, precision agriculture and the precise timing required by banking and internet networks [25; 60]. Remote sensing, or Earth observation (EO), is a technique for identifying objects and their properties by analyzing data obtained remotely. It can be carried out using various platforms: balloons, aircraft, or satellites. A major milestone was achieved in 1946 with the first photograph taken from space when a V-2 rocket recorded an image. The Cold War further accelerated the development of reconnaissance satellites. The 1960s saw the expansion of remote sensing technologies into radar imaging, opening new possibilities for Earth monitoring, particularly under cloud cover or at night when optical photography was ineffective. Since then, EO has evolved rapidly, covering an ever-growing range of applications, from monitoring ecosystem changes to supporting crisis management and disaster response [25; 60; 61].
## 4 Discussion
The high response frequency of SIF implies that crops are more sensitive to flash droughts and cannot tolerate a rapid decline in SM to a drought condition that lasts for several weeks. Although flash droughts do not affect the vegetation in humid regions as severely as in dry regions due to the longer duration of water stress in the arid zone, these events can cause irreversible damage to crop yields, even in the SM recovery phase. The relationship between fluorescence and photosynthesis is altered under enhanced water stress due to environmental changes [37]. This complex relationship needs to be further explored to better understand the response characteristics of SIF to the evolution of flash drought. A unique perspective of this study is the use of high-resolution SM and SIF datasets. SMCI 1.0 provides daily high-resolution SM data with 10 cm intervals up to 100 cm (10 layers), incorporating a large number of in situ SM and reanalysis data using a machine-learning approach [55]. We applied the top five layers (0-50 cm) of SM to identify flash droughts. This can reasonably reflect the impact on crops, since most crops grown in southern China have their major root systems within 50 cm [74; 75]. CSIF and GOSIF are derived by fusing MODIS and carbon satellite observations using a deep learning approach [36; 38], and can robustly reflect the impacts of flash droughts on crop growth. To maintain consistency with the spatial resolution of SM, the SIF was resampled to 0.1\({}^{\circ}\), although this may increase the uncertainty of the impacts of flash droughts on SIF.
### Pathological and Physiological Functions of DUX4  
DUX4 plays an important role in both physiology and disease. DUX4 is a transcription factor that is highly conserved in primates such as orangutans and humans. DUX4 participates in important regulatory roles during early development of the human embryo [9; 10] (Figure 2), and thereafter is repressed in all tissues except for testis and thymus [9; 11] (Figure 2). The expression of DUX4 in skeletal muscle is similar to the transcriptional procedure in the embryonic stage, but it has a damaging effect on skeletal muscle. Studies have found that a large amount of DUX4 is distributed irregularly in the myoblasts of FSHD patients; with the development of myoblasts, the expression of DUX4 is further increased, resulting in the degeneration and necrosis of muscle cells. When abnormally activated, DUX4 can induce a variety of genetic processes, such as inflammation [12], oxidative stress [13; 14], and termination of muscle development. In addition, DUX4 is also associated with a variety of diseases, including viral infections [15], acute lymphoblastic leukemia [16], and tumors [17].  
Figure 1: Overview of molecular mechanism of 2 types of FSHD.
## 1 Introduction
Regarding human body transmission, since cells will change their composition based on the configuration of the forces they detect in the human body, the vibration stimulus is measured in the legs [17; 20; 29], the hip and lower back [30], and the head [16; 23; 31] for muscle activation, bone remodelling, and safety aspects, respectively. Furthermore, as forces can change their configuration in their transmission to the human body, the transmission studies look for the feasibility of vibration stimulus (positive effects and safety application). However, for technical reasons, the vibration transmitted to the human body has to be recorded with different parameters. Thus, to report the vibration transmission, the investigations have added human body accelerations (\(a_{\text{out}}\)) [17; 32], the transmissibility (\(T\)) [16; 18; 20; 21; 31], and the signal purity (\(P_{\text{out}}\)) [22; 24; 25]. Overall, studies have reported \(a_{\text{out}}\) and \(T\) amplifications of the vibration stimulus at the ankle [17; 20; 21] and attenuations over the knee, especially with bent-knee postures [17; 20; 21; 23] and as the frequencies increase [17; 20; 33]. Regarding \(P_{\text{out}}\), in general investigations this has shown good retention [22; 24; 25], however, depending on the posture adopted, caution should be exercised because in some joints it may not be satisfactory. Therefore, determining the feasibility of the vibration platform inputs and their transmission outputs through consistent reporting parameters may help to improve issues of non-replicability and inconsistency of the current literature. This becomes especially relevant when a new platform design is introduced due to the described variability be tween commercial vibration platforms [27]. As such, this study aims to explore vibration transmission from a new large vibration platform (LVP) by examining its \(f_{\text{in}}\), \(P_{\text{in}}\), \(D_{\text{in}}\), and \(a_{\text{in}}\) throughout the human body outputs of \(T\), \(P_{\text{out}}\), and \(a_{\text{out}}\) in different postures.
## 3 Experimental Setup  
### HES-SO VS Hydraulic Testing Infrastructure  
The universal hydraulic test rig of the HES-SO Valais//Wallis-Switzerland (see Figure 3) dedicated to small-power turbomachines has been employed to perform the hydraulic performance measurements. Its configuration, instrumentation and operation follow the IEC 60193 [21] standard recommendations on hydraulic model testing. Three recirculating multistage centrifugal pumps connected in parallel supply the closed-loop circuit of the test rig. The variable-speed pumps (2\(\times\) 18.5 kW and respectively 1\(\times\) 5.5 kW) can deliver a maximum discharge of about 100 m\({}^{3}\)/h and a maximum pressure of 160 mWC. The testing variable-speed model is placed in the upper part of the circuit, upstream a free-surface pressurized reservoir. The latest allows simulating a given setting level of the model, either positive or negative, and thus investigating also the cavitation performances. The operation of the test rig is ensured by an automatic system. Its customized  
Figure 2: Employed methodology to reconstruct the full hydraulic efficiency hill-chart of a double-regulated machine using the alternative dynamic measurements method: (**a**) First step: measurements of several efficiency hill-charts at constant rotational speed N\({}_{\text{p1,2,3}}\) of the test rig recirculating pumps and constant speed ratio between the two runners, \(\alpha_{i}\); (**b**) Second step: calculation of discharge-efficiency and head-efficiency envelopes for all testing recirculating pumps’ speed values; (**c**) Third step: construction of the final full 3D hill-chart of the machine by surface interpolation using the previously obtained envelopes.  
LabVIEW(r) interface allows for real-time measurements and displays the instantaneous values of pumps' speed, discharge, testing head, water temperature, Thoma number etc. The autonomous regulation system can keep constant the value of the pumps' speeds, the testing head or the discharge. Finally, the wireless communication architecture between the hydraulic test rig and the measurement/monitoring systems (e.g., testing model control system) ensures safe centralization of data, storage and sharing, [PERSON] et al. [12].
## 4 Results and Discussion  
In this section, the experimental results for the metrics (accuracy, CM, precision, recall, F-score, FPR, FNR, classification error, and training and testing times), defined in the previous section, are presented, as well as evaluation of the significance of achieved results by dual-flow models in comparison with vanilla single-flow models. Additionally, the number of epochs needed to reach the saturation and CMs are provided and discussed.
# An Accelerated Slicing Algorithm for Frep Models
## 2 Materials and Methods
### R-Trees  
An R-tree is a height-balanced tree [31] similar to a binary search tree. This type of B-tree handles minimal axis-aligned bounding boxes (AABB) in n-dimensional space. The R-trees support spatial queries such as obtaining the nearest neighbourhood, checking for the intersection/containment of bounding boxes, inserting elements, and removing elements. An R-tree provides the equal depth for each leaf node and maintains a certain number of elements between the minimum and maximum at each node (excluding the root node) of the tree. Each leaf node contains an AABB of a single object. The construction of an R-tree is usually performed with the help of the insert operation. The tree construction strategy is based on maintaining the height of the tree branches and minimizing the overlap between the bounding boxes and the empty space. It includes a choice between a subtree insertion point and the division of overflowing nodes. These two criteria influence the performance of an R-tree. They were improved in the R+-tree for avoiding overlapping bounding rectangles [32], and in the R*-tree, where the overlapping of nodal regions of the tree was minimized, as shown in [33; 34].  
K-d trees and R-trees are similar data structures, they both split nodes with respect to locations in space determined by some heuristic. It should be noted that R-trees partition scenes with respect to the objects using their minimal AABBs, whereas K-d trees partition the underlying space. However, the resulting partitions in the R-tree are not necessarily strictly binary partitions of the underlying space, as is the case with K-d trees. In addition, unlike the K-d tree, the R-tree is height-balanced and can be constructed incrementally by sequentially inserting data.
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 2 Materials and Methods
### Design Methodology  
This work is based on the following three steps. Initially the Delta encoding algorithm was selected as the one that requires the least complex processing, which contributes significantly to the system's power dissipation. A preliminary evaluation of the proposed encoding is offered, which also depicts the data formation. In the second step, the encoding system is designed. In this work design details for the buffer used to store the encoded information is given. The buffer is dynamically accessed from the processing unit for storing data and by the BLE unit for transmission of data. In the final step a simple programming and control of the system's functioning is offered, combining the data format for the buffer and controlling it for transmission.
## 4 Experiments
### Experimental Design
#### 4.4.1 Comparing Different Negative Sampling Methods  
In this section, we compare different negative sampling methods, including Bernoulli sampling, Kbgan sampling, and NSCcahing sampling as well as our proposed CCS sampling.  
Bernoulli [21]: Bernoulli is a randomized experiment conducted repeatedly and independently under the same conditions, with only two possible outcomes for each test. Specifically, it samples \((h^{\prime},r,t)\) or \((h,r,t^{\prime})\) under a predefined Bernoulli distribution.  
Kbgan [41]: Kbgan first samples the negative sample set \(Neg\) from the whole entity set, and then uses the entity in \(Neg\) to replace the head or tail entity to form a negative triplet \((h^{\prime},r,t)\) or \((h,r,t^{\prime})\).  
NSCcahing [38]: It generates two negative sample candidate sets from the entity set--the head entity candidate set cache and the tail entity candidate set cache. A negative triplet \((h^{\prime},r,t)\) or \((h,r,t^{\prime})\) is formed by replacing the head entity or tail entity, with one uniformly sampled from head cache or tail cache.  
CCS (Ours): As in Section 3.3, we extract entities of the same type from the clusters of the replaced entities to cache them as candidate triplets. High-quality head or tail entities are sampled from the corresponding cache to form a negative triplet \((h^{\prime},r,t)\) or \((h,r,t^{\prime})\). We use \(N=30\) and iterate s times re-cluster the entities once.  
Table 5 compares the link prediction performance of the translation models. Kbgan, NSCcahing and CCS all significantly outperform the baseline Bernoulli scheme. These methods effectively optimize the negative sampling in the training process, which verifies that the use of high-quality negative triplets can better improve the performance of the KGE models. We can also observe that the CCS negative sampling method outperforms the other advanced models.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **Score Function** \\ \hline TransE & \(\|h+r-t\|_{1/2}\) \\ TransH & \(\left\|(\mathbf{h}-w_{r}^{\prime}\mathbf{h}w_{r})+\mathbf{r}-(\mathbf{t}-w_{r}^{\prime}\mathbf{ t}w_{r})\right\|_{1/2}\) \\ TransR & \(\|hM_{r}+r-tM_{r}\|_{1/2}\) \\ TransD & \(\|hM_{rh}+r-tM_{rl}\|_{1/2}\) \\ \hline \end{tabular}
\end{table}
Table 4: Definition of scoring functions for TransE, TransH, TransR, TransD.  
Figure 8: Experimental structure diagram.
# Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments
## 3 Procedures, Material and Methods
### Monitoring Landforms  
#### 3.2.1 Solutions Based on UAVs  
To monitor changes in landform surfaces, several approaches can be used. One of the most important procedures for monitoring landforms is the SfM. According to [PERSON] et al. [3], conventional photogrammetric methods need the 3D position and position of the camera, or the 3-D position of ground control points to enable scene triangulation and reconstruction. SfM photogrammetry diverges from conventional photogrammetric methods, by setting internal automatic camera geometry, orientation and position, without a predefined set of ground control, evident points at known three-dimensional positions [53]. The SfM approach unravels the camera position and scene geometry instantaneously, using a highly redundant bundle correction for corresponding elements in several overlapping, offset images [3]. To apply this methodology, the use of UAVs is very common, due to the reduced costs and the possibility of covering large areas with high-precision outputs.
# Protective Effects Induced by a Hydroalcoholic _Allium sativum_ Extract in Isolated Mouse Heart
## 3 Results and Discussion  
### GC-MS Analysis of VOC Fraction  
#### 3.1.1 Garlic Powder  
The presence of specific compounds in garlic samples was found to be mainly related to the mode of garlic preparation and extraction [31]. SPME fibers have been compared through the extraction efficiency, which is well represented by the total chromatographic areas obtained for \(\mathrm{t_{eq}}\) = 20 min and \(\mathrm{t_{sa}}\) = 15 min in the 50-80 \({}^{\circ}\)C temperature range (Figure 1A-C; in Figure S1, the results obtained for \(\mathrm{t_{eq}}\) = 20 min and \(\mathrm{t_{sa}}\) = 35 min in the same temperature range are reported). The primary and secondary axes (Figure 1A-C and Figure S1A-C) indicate the total chromatographic area and the number of chromatographic peaks, respectively. The significant sample darkening attained at \(\mathrm{t_{eq}}\) = 90 \({}^{\circ}\)C, and the independence from \(\mathrm{t_{eq}}\) in the 20-40 min range, suggested us to focus the investigation to the 50-80 \({}^{\circ}\)C range with \(\mathrm{t_{eq}}\) = 20 min. Finally, the polar PA cartridge is no longer considered in the following discussion, due to the very poor extraction efficiency (the results are not reported). The best efficiency, in parallel to the higher number of detected compounds, was obtained with the DVB-CAR-PDMS fiber (\(\mathrm{T_{eq/sa}}\) = 80 \({}^{\circ}\)C, \(\mathrm{t_{eq}}\) = 20 min and \(\mathrm{t_{sa}}\) = 15 min; Figure 1A), which extracted three prevailing classes of compounds (Table 2), namely, alcohols, sulfur-containing compounds (SCC), and fatty acid esters (FAE), whose relative abundance crucially changes with the HS-SPME conditions (Figure 2 and Figure S2). The fiber selectivity presents remarkable differences; the DVB-CAR-PDMS cartridge has a similar selectivity for FAE as well as for SCC (39.4% and 40.3%, respectively), and a very low affinity for alcohols (3.6%). This pattern is reversed when the CAR-PDMS results are considered; indeed, alcohols definitely represent the principal class of compounds absorbed by this phase (61.2%). The selectivity of the PDMS-DVB cartridge is pronouncedly oriented toward SCC (52.1%), and significantly less toward FAE (30.4%). In the most efficient extractions performed in a different set of HS-SPME parameters (\(\mathrm{t_{eq}}\) = 20 min and \(\mathrm{t_{sa}}\) = 35 min), PDMS-DVB and CAR-PDMS fibers show a similar pattern to the result obtained for \(\mathrm{t_{sa}}\) = 15 min (Figure 2 and Figure S2). On the contrary, the DVB-CAR-PDMS one extracted FAE very efficiently (69.1% in Figure S2). Figure 1: _Cont._  
Figure 1: Optimization of HS-SPME conditions using (**A**) DVB–CAR–PDMS, (**B**) PDMS–DVB, (**C**) CAR–PDMS fibers with the following settings: \(\text{te}_{\text{eq}}=20\) min, \(\text{t}_{\text{sa}}=15\) min. Total peaks area (primary axes) and number of detected peaks (secondary axes) are plotted vs. equilibration/sampling temperature (in \({}^{\circ}\)C).
# Trademark Image Similarity Detection Using Convolutional Neural Network
## 3 Materials and Methods
### Features Reduction  
Many problems arise when working on high-dimensional features, i.e., adding too many features worsens recognition performance; this problem is known as the curse of dimensionality [22]. Feature reduction refers to the techniques used to reduce the size of the vectors. As mentioned above, the dimensions of the features produced by ResNet-50 and VGG-16 are too large; therefore, reducing the features is essential to overcome future issues.  
PCA is an unsupervised technique used in machine learning for reducing dimensionality [22]. It aims to reduce the data dimensions from \(n\) variables to \(m\) components, where \(m<n\). This means representing the data using \(m\) components (instead of \(n\)), accounting for the most variance [23], i.e., whenever the correlation between features is high, the PCA will combine these correlated features within a smaller number of uncorrelated features.  
In this study, PCA was used to reduce the features extracted by two models to improve the recognition/detection performance. PCA works better if a linear structure in the data and variables are scaled in the same unit. Since the features were already normalized, features scaling was unnecessary. Moreover, the features extracted by the ResNet-50 and VGG-16 models were represented by 1450 and 410 components, respectively. These components explained more than 95% of the variance.