esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Classification of Wines by Geographical Origin Based on Volatile Compounds
PCA analysis of aroma profiles can, therefore, clearly distinguish between Cabernet Sauvignon wines from the three producing regions. Additionally, it can be seen from Figure 3d that in this study, region is the primary indicator affecting wine volatile compounds. Although vintage is an important indicator affecting wine quality, the distribution of samples was more concentrated in the region. This may benefit from the unique geographical environment of the producing region. The volatile compounds from the six consecutive Ningxia vintages (2013-2018) were analyzed using the HCA, to compare differences between the Ningxia sub-regions, Bordeaux, and California. The 19 volatile compounds were clustered into 3 groups in the vertical clustering tree, and the wines from Ningxia (all 6 sub-regions), Bordeaux, and California formed 3 groups in the longitudinal clustering tree (Figure 4). Ningxia wines were mainly characterized by citronello1, benzyl alcohol, ethyl caprate, p-cymene, 1-decanol, isoamyl alcohol, isoamyl acetate, and isobutanol. Bordeaux wines were mainly character  
Figure 3: PCA (principal components analysis) scores plot for wines based on volatile compounds of six sub-regions of Ningxia (**a**) and three wine-producing regions (**c**). PCA loadings plot of volatile compounds of six sub-regions of Ningxia (**b**) and three wine-producing regions (**d**). ized by isoamyl alcohol, phenylethyl alcohol, decanal, ethyl hexanoate, and ethyl caprylate. California wines were mainly characterized by furfural, acetoin, methionol, butanedioic acid, diethyl ester, nonanal, 1-hexanol, and benzaldehyde. In addition, the six Ningxia sub-regions were partially separated into sub-sets; Yinchuan and Yongning had very similar aroma profiles, as did Helan and Qingtongxia, and both pairs formed the closest subsets. Hongsipu had a distinct aroma profile from the other sub-regions and formed a separate sub-group. The differences between the Ningxia, California, and Bordeaux regions were very clear; their volatile profiles were markedly different. This undoubtedly benefits from the unique geographical environment of the production area, although there may be an impact of vintage on the volatile compounds of wine.
## 3 Results
### rVSV\(\Delta\)G-EBOV-GP and Authentic Filovirus Cannot Infect SH-SY5Y Cells  
To validate our results obtained with lentiviral pseudotypes, we infected the different cell lines with the replication-competent VSV virus bearing the EBOV GP (rVSV\(\Delta\)G-EBOV-GP) or with authentic filoviruses. We inoculated the susceptible HEK293T and SK-N-BE(2)-C cells as well as the resistant SK-N-MC and SH-SY5Y cells with EBOV (Mayinga) or MARV (Musoke) at a multiplicity of infection (MOI) of 0.1 pfu/cell for 1 h. At 72 h post infection (hpi), the cells were fixed for immunofluorescence microscopy (IF) studies (Figure 2A) or lysed for immunoblotting (Figure 2B), and probed with antibodies (pAbs) directed against the viral nucleoprotein (NP). The HEK293T and SK-N-BE(2)-C cells were susceptible to EBOV and MARV infection, while the SH-SY5Y cells were resistant to both (Figure 2A,B). Of note, tubulin was also degraded in the susceptible SK-N-BE(2)-C cells but not in the resistant SH-SY5Y upon inoculation, a sign of extensive infection previously observed in our lab. The SK-N-MC did not express any EBOV NP protein, but displayed sporadic MARV NP labelling in few cells. Therefore, we focused our further analyses on SH-SY5Y cells.  
Next, we inoculated HEK293T or SH-SY5Y cells with rVSV\(\Delta\)G-EBOVGP virus at a MOI of 1. We evaluated cell rounding and detachment, an established phenotype of EBOV GP-expressing cells [57], and measured the titers of released virus at 24 hpi. In line with our other experiments, the HEK293T cells had initiated cell rounding at 24 hpi (Figure S1), and completed it at 48 hpi (Figure 2C), while the morphology of the SH-SY5Y cells had not changed upon inoculation by 24 h (Figure S1) or 48 h (Figure 2C). Furthermore, viral titers had increased by 51-fold in HEK293T but decreased in SH-SY5Y by 24 hpi (Figure 2D). Altogether, these experiments demonstrated that SH-SY5Y cells were resistant to filovirus infection.
# Astronomical Investigation to Verify the Calendar Theory of the Nasca Lines
## 3 Astronomical Investigations in the Nasca Project
### Verification of [PERSON]'s Results  
As already mentioned, [PERSON] identified many lines, areas and figures with an astronomical orientation. Therefore, the first task in the Nasca project was to verify these results. Therefore, in the first step, relevant geoglyphs were identified from [PERSON]'s publications and documents and their astronomical orientation was checked. Some examples of striking lines and figures with their astronomically relevant celestial bodies are briefly presented below [24; 25; 26].  
Figure 10 shows several lines and stone heaps with an astronomical orientation in the main area of the Nasca Pompa, west of the Pan-American Highway, according to [PERSON] [24]. A bird figure, the so-called condor, can be seen in the lower left area. The two highlighted lines running through the figure are aligned with the sunset at the December solstice and to the June solstice. In the middle of the picture, northeast of the condor, there is an approximately 850 \(\times\) 110 m rectangle with three large piles of stones at the narrow sides. The alignment between these piles of stones shows the rise of the Pleiades around 610 AD, which [PERSON] also confirmed, and he therefore spoke of the \"Plaza de las Pleiades\".  
The figure of the spider is located north of the large rectangular area. There is a very long line that, in one line center, begins east of the spider and runs through the spider towards the west, pointing to the sinking of the star Rigel in Orion around 100 BC. This line is also the connection between the two line centers.  
Figure 11a shows the figure of the heron, which can be found in the east of the Pan-American Highway, approximately one kilometer northeast of the Pleiades area. The figure is approximately 300 m long and the 185 m long beak points exactly in the direction of the sunrise during June solstice. Much further north, in the Pampa of Palpa, there is a line about 3.9 km long and around 3 m wide, which [PERSON] called the \"Sirius Line\" (Figure 11b) because it pointed to the sinking of Sirius, the brightest star, around 222 AC. The Sirius Line was badly damaged by the construction of the Pan-American Highway and a chicken farm. Strictly speaking, the Sirius line is not a line but a narrow, very long area. The approximately 3 m-wide area begins on a small hill in the east and ends in a narrow, around 600 m-long triangular area west of the Pan-American Highway.  
Overall, it can be said that all of the checked geoglyphs identified by [PERSON] point to the celestial bodies indicated by her [26]. However, this does not yet prove that the Nasca Pampa was actually a calendar system. It only shows that there are astronomically relevant geoglyphs.  
Figure 11: Two other orientations were documented by [PERSON]: (**a**) the heron’s beak points to the sunrise at the June solstice; (**b**) the so-called Sirius line is oriented to the sinking of Sirius during the Nasca time, around 222 AC.  
Figure 10: Stone heaps and lines with an astronomical alignment according to [PERSON] [24].
# Laplacian Eigenmaps Dimensionality Reduction Based on Clustering-Adjusted Similarity
###### Abstract  
Euclidean distance between instances is widely used to capture the manifold structure of data and for graph-based dimensionality reduction. However, in some circumstances, the basic Euclidean distance cannot accurately capture the similarity between instances; some instances from different classes but close to the decision boundary may be close to each other, which may mislead the graph-based dimensionality reduction and compromise the performance. To mitigate this issue, in this paper, we proposed an approach called Laplacian Eigenmaps based on Clustering-Adjusted Similarity (LE-CAS). LE-CAS first performs clustering on all instances to explore the global structure and discrimination of instances, and quantifies the similarity between cluster centers. Then, it adjusts the similarity between pairwise instances by multiplying the similarity between centers of clusters, which these two instances respectively belong to. In this way, if two instances are from different clusters, the similarity between them is reduced; otherwise, it is unchanged. Finally, LE-CAS performs graph-based dimensionality reduction (via Laplacian Eigenmaps) based on the adjusted similarity. We conducted comprehensive empirical studies on UCI datasets and show that LE-CAS not only has a better performance than other relevant comparing methods, but also is more robust to input parameters.  
Keywords:Laplacian Eigenmaps; dimensionality reduction; clustering-adjusted similarity +
Footnote †: journal:
# Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control
## 2 Preliminaries
### Pid  
The PID controller is a three-term controller. The output of the PID controller depends on the error (\(e\)) which is the plant output minus the reference signal or set point (\(r\)) and controls the plant input. The PID control algorithm aims to minimize the error. The proportional gain (\(kp\)) is used in order to get the control signal u(t) to respond to the error immediately, so as to increase the system response speed and to reduce steady-state error. The integral gain (\(ki\)) is used to eliminate the offset error but causes system response overshoot. In order to reduce the system response overshoot, the derivative gain (\(kd\)) is used [21]. The block diagram of a PID controller can be seen in Figure 1 and its transfer function is:  
\[G_{PID}(s)=kp+ki\!\cdot\!\frac{1}{s}+kd\!\cdot\!s \tag{7}\]  
Figure 1: Block diagram of PID controller.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 3 System Overview and Problem Formulation
### System Overview
#### 3.1.3 Leadership  
If an arbitrary node could decide whether a new node can join or leave, i.e., insert or remove node into or from the global membership table, it is possible to cause problems, e.g., difficulties in consistency of membership or logical issue in application. For example, if an arbitrary node could decide so, this node can do the leaving action by directly removing itself from the its own member table and gossip this table to the cluster. However, before the member table is globally consistent, some other members may not notice this leaving action and keep communicating to the leaving node. This may cause further issues like logical confusion. To avoid these problems, a temporary leader is selected to deal with actions affecting cluster's size. For adding or removing a node to or from the cluster, the leader does this action gracefully with the following steps. First, instead of suddenly adding or removing a member from the cluster, the leader sets an intermediate working state to this  
Figure 1: System overview of decentralized clusters that use gossip consensus protocol to manage members. (**a**) shows the member states, leadership and gossip process when detects a failure; (**b**) shows the down process of a leader.  
member. Then the leader gossips its state to the cluster. After that, the leader waits until the member state is converged. Converged state means that the member state is consistent in the cluster so that all the members are conscious about the further action of this leaving or joining member. When the leader confirms the consistency of the intermediate state, it finally does the action of inserting or removing the member to or from the member table. The leader selection should be non-electoral to avoid being centralized. In Akka, the process is that a node considers a reachable node with the smallest unique identity to be de leader. The unique identity can be, for example, IP address and port. If the member states of all the members are consistent, it is expected that only one leader exists in the cluster at a time. And because the leader actions are only done when the leader obverses that the member state is converged, all the members will keep pace with the leader. In this way, the member state can always be easy to converge. Furthermore, the application can easily get the message of a member being joining or leaving. Thus, this approach is demonstrated to be a reliable way for changing cluster size.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 4 Experiments
### Implementation Details  
The network utilized in this study comprises ten layers, arranged into basic blocks to integrate spatial-temporal feature extraction. The channel dimension is set to 240, and the number of attention heads is set to 10.  
All experiments in this study are implemented using the PyTorch 2.0 deep learning framework. The maximum number of training epochs is set to 150, with a warmup strategy introduced during the initial five epochs to stabilize the early training phase. The optimizer employed is stochastic gradient descent (SGD) with Nesterov momentum, applying a weight decay of \(1\times 10^{-4}\). The initial learning rate is 0.025, decayed by a factor of 0.1 at epochs 100 and 120, respectively. Cross-entropy loss is used for model training via backpropagation.  
For skeleton sequences in both NTU RGB+D 60 and NTU RGB+D 120 datasets, each sample is typically standardized to 64 frames, with a batch size of 64. Sequences shorter than this length are padded with zeros or looped until reaching the desired length. Missing data for the second subject in two-person sequences is replaced with zero-padding. All experiments are conducted using three NVIDIA GeForce GTX 1080 Ti GPUs.
## 3 Numerical Simulation Method for Ice Shedding
### Calculation of Wind Loads Based on Aerodynamic Parameters  
In this paper, the lift and drag of iced conductors were solved through aerodynamic methods, which mainly included the following steps:  
Step 1: Elaborate modeling of the cross-section of the iced conductor is conducted. In this paper, it is assumed that ice formation is uniform, thus simplifying the two-dimensional airflow phenomenon on the plane.  
Step 2: Establish boundary conditions. In actual scenarios, incoming winds typically flow horizontally. However, due to the potential occurrence of eccentric twisting in iced conductors, setting inputs, outputs, and airflow vectors is necessary during the modeling phase.  
Step 3: Partition the domain into grids, and optimize grid construction. The quality of grid partitioning directly influences the calculation results of aerodynamic coefficients in numerical simulation.  
Step 4: Solving the mathematical-physical equations of fluid mechanics, for a two-dimensional flow field, only requires considering the fundamental fluid mechanics control equations at the cross-section of the ice-covered conductor. The momentum conservation equation and mass conservation equation are as follows:  
\[\left\{\begin{array}{rl}\frac{\partial(\rho v_{x})}{\partial t}+\text{div}( \rho v_{x}\overline{u})&=\mu\text{div}(\text{grad}v_{x})-\frac{\partial p}{ \partial x}\\ \frac{\partial(\rho v_{y})}{\partial t}+\text{div}(\rho v_{y}\overline{u})&= \mu\text{div}(\text{grad}v_{y})-\frac{\partial p}{\partial y}\end{array}\right. \tag{4}\]  
where \(\rho\) is the air fluid density, kg/m\({}^{3}\); \(v_{x}\) and \(v_{y}\) represent the velocity components in the horizontal and vertical directions, respectively, m/s; \(P\) denotes the fluid pressure on the cross-section, Pa; \(\mu\) is the dynamic viscosity, Pa-s; div signifies divergence; grad indicates gradient; \(\overline{u}\) is the velocity vector of the two-dimensional cross-section flow.  
Step 5: Due to the change in the incoming wind direction caused by rotation, the output results in the software should be in terms of axial force coefficients and normal force coefficients. Therefore, it is necessary to convert lift and drag coefficients into angle form.  
Figure 3: The flowchart of the simulation process for ice shedding on conductors.
## 3 Preliminaries
### Channel State Information (CSI)  
CSI value reveals the channel properties of the communication link. Since the wireless medium is unstable and channel conditions may vary from time-to-time, channel estimation keeps changing on a short-term basis. COTS Wi-Fi devices following 802.11n standards work with orthogonal frequency division multiplexing (OFDM), achieve increased data rates, improved capacity, and reduced bit error rate (BER) of the system. Also, devices starting from IEEE 802.11n support multiple input multiple output (MIMO) with the OFDM scheme, enabling them to send and receive information over multiple antennas, as shown in Figure 1. The OFDM extracts the channel frequency response in the format of CSI, which contains both amplitude and phase information of the signal in subcarrier level, enabling the sensing to be more accurate. The CSI values at the transmitter and the receiver end may vary, and the data acquisition depends on how rapidly the channel conditions change. Therefore, the channel conditions highly influence the data acquisition of CSI traces and the instantaneous values need to be estimated in the receiver end on a short-term basis.  
CSI contains information such as hardware timestamp, frame counter, number of receiving and transmitting antennas, received signal strength indicator (RSSI) of each antenna, noise, automatic gain control, amplitude, and phase information of the subcarriers in the form of a complex matrix. Among the information above, RSSI and CSI gain research interest in device free sensing methods. However, CSI is more preferred than RSSI, as RSSI does not provide the phase information. Equation (1) represents the received signal, which consists of signal information of the sender and the channel frequency response (CFR) with noise.  
\[R(f,t)=H(f,t)\;X\;T(f,t)+N, \tag{1}\]  
where _R(f, t)_ is the received signal strength of carrier frequency \(f\) measured at a time \(t\); _H(f, t)_ is the CSI in the form of CFR for carrier frequency \(f\) measured at a time \(t\); _T(f, t)_ is the transmitter signal strength for carrier frequency f measured at a time t and \(N\) is the noise. COTS Wi-Fi devices capture the varying signal characteristics of human reflections in the line of sight (LoS) or non-line of sight (NLoS) path between the COTS Wi-Fi device (Router) and AP's (Laptop with Intel 5300 NIC) in the format of CSI, as shown in Figure 2. Wi-Fi signals from the transmitter get reflected from the floor, side walls, the ceiling, and objects in the confined experimental space.  
Figure 1: CSI representation of MIMO.  
Figure 2: Wi-Fi signal propagation in an indoor environment.
## 2 Specimen, Experimental Setup, and Acoustic Emission Monitoring  
### Specimen Details  
At first, a cylindrical specimen of 50 mm in diameter and 125 mm in length was cored from a granite block containing a macroscopic fracture. The fracture could be checked visually but was not completely penetrated and contained some unfractured small patches. The cylindrical specimens were cut along a direction so that the fracture was centered in the sample and had an angle of 32 degrees with respect to the axis, which was the direction of the greatest principal stress under the experimental conditions (Figure 1a). The granite was coarse grained and had a relatively high density of pre-existing microcracks, as indicated by a relatively lower initial P-wave velocity of approximately 4.5 km/s under room conditions. Similar, but microcrack-free, granites had an initial P-wave velocity greater than 6.0 km/s [20].  
In total, 16 piezoelectric AE transducers (PZT) (diameter: 5 mm, resonant frequency: 2 MHz) were mounted using epoxy adhesive directly to the curvilinear surface of the sample for AE monitoring and velocity measurements (Figure 1b,e). The PZT transducers functioned not only as receivers of acoustic signals but also as detonators for measuring the P-wave velocity during the experiment. Six flat surfaces of a width of approximately 7 mm were prepared for better coupling between the flat transducers and the specimen (Figure 1a,b). In addition, seven pairs of cross-type strain gauges were placed on the curvilinear surface of the specimen in order to record local strains along the axial and circumferential directions. Then, the sample and two end pieces were coated with silicone sealant in order to prevent confining oil from leaking into the sample (Figure 1c).
## 4 Discussion
[43; 44] reported that light intensity was an important factor in the production and antioxidant activity of flavonoids and phenolic phytochemicals in _Labisia pumila Benth_, a plant with known antifungal, antibacterial, antioxidant, and anti-inflammatory properties [43; 44]. Importantly, soil composition has been indicated as an important factor in the metabolism of plants and, hence, the nature of phytochemical secondary metabolites produced in their plant parts. It is a widely accepted theory that various environmental factors, either separately or in combination, can have direct and indirect influences on the phytochemical composition of plants. Understanding how these factors affect a plant's phytochemical profile is useful in identifying bioactive constituents of natural origin intended to be studied for their potential medicinal and therapeutic applications [45]. This study [46] submitted that the genetic make-up, stage of development, and surrounding environmental factors frequently lead to spatial-temporal variations in the chemical profile of plants. The chemical composition of medicinal plant material also varies noticeably depending on harvesting methods, post-harvest processing, storage conditions, widespread pesticide usage, frequent adulteration, and microbial contamination. The authors further stated that for a complete set of quality assurance for medicinal plant material, DNA-based diagnosis, quantitative profiling of chemical markers, and traditional purity measures should be used. Therefore, it has become crucial to use an integrated strategy of DNA barcoding and quantitative metabolomics to evaluate the potency, purity, consistency, and safety of medicinal plants. In addition, controlling the quality and guaranteeing the stability of herbal medicines are significant problems for the herbal business because there are numerous factors that might impact the concentration of active compounds in herbal materials and products. It is now possible to produce homogeneous medicinal herbal products because of the rapid development of sophisticated analytical techniques and industrial technologies, including in vitro cell culture, plant breeding, plant cell bioreactor, and organ culture, among others. In vitro plant tissue cultivation guarantees independence from geographic circumstances by removing the need to rely on wild plants. Moreover, the utilization of plants for the synthesis of more designed substances, including vaccines and a variety of medications, is made possible through plant transformation. Using plant tissue culture,several phytochemicals like anthocyanins, paclitaxel, scopolamine, shikonin, coumarins, alkaloids, and others have been produced for medicinal purposes. Additionally, scientists contend that bioreactors are novel biotechnology setups that, in promoting the large-scale production of desired secondary metabolites, can contribute to biodiversity and reduce the man-to-equipment ratio requirement of conventional in vitro culture setups [47; 48; 49].