esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The present study aimed to compare the HRV in healthy subjects during and immediately after riding a horse and riding a horse gait simulator. The main finding was that the HRV was significantly lower while riding on horseback than during the activity in the simulator. Conversely, the acute effects on the HRV of these two tasks did not significantly differ. Given that the HRV may be sensitive to exercise intensity in the following minutes after exercise performance [18], we hypothesize that both tasks caused similar physical demands. The results show that the sympathetic tone is higher when riding a real horse compared with the protocol on the horse simulator, which is supported by previous studies [9; 10]. However, the protocol for these two works takes longer duration (30 min in the study by [PERSON] et al. [9] and 11 min plus eight jumps in the study by [PERSON] et al. [10]. Therefore, differences in autonomic modulation during a short single bout of five minutes had never been detected. This is interesting not only in healthy people, but also in special populations where sometimes it is extremely difficult (above all at the beginning of intervention programs) to perform long horseback riding sessions. The sympathetic response in the heart rate is mediated by the release of epinephrine and norepinephrine [19; 20]. However, not only can exercise induce the release of these substances [21], but emotions can also influence this process [22]. In this regard, the HRV is also affected by emotions [23]. Accordingly, the differences observed are probably the result of emotions rather than the physical \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Horse Mean (SD)** & **Simulator Mean (SD)** & \(p\)**-Value** & **Cohen’s D** \\ \hline MeanHR & 95.20 (14.51) & 83.19 (11.74) & \(<\)0.001 & 0.874 \\ RR & 650.75 (99.8) & 739.95 (105.83) & \(<\)0.001 & \(-\)0.863 \\ SDNN & 34.39 (14.40) & 44.71 (12,40) & \(<\)0.001 & \(-\)0.764 \\ pNN50 & 7.29 (8.01) & 12.69 (10.46) & 0.001 & \(-\)0.548 \\ RMSSD & 25.18 (13.39) & 33.32 (14.07) & \(<\)0.001 & \(-\)0.591 \\ HF & 22.00 (10.59) & 21.95 (11.22) & 0.984 & 0.004 \\ LF & 77.91 (10.64) & 77.97 (11.25) & 0.981 & \(-\)0.005 \\ LF/HF & 4.73 (2.87) & 4.74 (2.78) & 0.982 & \(-\)0.005 \\ Total Power & 1483.11 (1219) & 2224.82 (1315) & 0.005 & \(-\)0.583 \\ ApEn & 1.11 (0.98) & 1.10 (0.81) & 0.573 & 0.113 \\ SampEn & 1.35 (0.25) & 1.48 (0.23) & 0.029 & \(-\)0.549 \\ \hline \hline \end{tabular}  
* HR: Heart Rate; RR: Time between R-R intervals; SDNN: Standard deviation of all normal to normal RR intervals; pNN50: Intervals percentage \(>\)50 ms different from preceding interval; RMSSD: The square root of the squares mean of successive RR interval differences; LF/HF: Ratio of Low Frequency (LF) (ms\({}^{2}\)) / High Frequency (HF) (ms\({}^{2}\)); Total Power: The sum of all spectra; ApEn: Approximate Entropy; SampEn: Sample Entropy.  
\end{table} 
Table 1: Heart rate variability values during the horse riding and the horse simulator riding.
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 2 Materials and Methods
### Algorithm Design  
The experiment assesses horse activity levels by analyzing their gaits and proposes a method for threshold analysis of combined acceleration [21; 22; 23]. This method includes three key stages: firstly, segmenting data through an interval counting approach to pinpoint specific gait patterns [24]; secondly, employing a variance analysis technique to examine the variability in acceleration across various datasets [25]; and finally, utilizing a variance-based proximity classification method for classification. Predicted horse activity levels are then compared with those recorded by observers to measure the accuracy of the assessment techniques.
## 3 Model Construction
### Establish Model Objective Functions and Constraints  
The MSIMPLTS-MLOO model is mainly divided into three layers: the superstructure of the MSIMPLTS-MLOO model is the task benefit objective function, and the constraints include satellite power-on time constraints, the maximum number of satellite observations constraints, and target task uniqueness constraints; the mesostructure of the MSIMPLTS-MLOO model objective function is the task allocation rate, and the constraints considered include side-swing angle transition time constraints, task urgency constraints, and task visible time window constraints; and the understructure of the MSIMPLTS-MLOO model objective function is the task response time, considering constraints including the neighboring task observation time window, observation duration, and task execution order constraints.
# Progress on the Studies of the Key Enzymes of Ginsnoside Biosynthesis
## 3 Farnesyl Pyrophosphate Synthase (FPS)  
Farnesyl pyrophosphate synthase (FPS) catalyzes the conversion of geranyl diphosphate (GPP) to farnesyl diphosphate (FPP). It is a key branch-point enzyme in MVA pathway. [PERSON] et al. [15] cloned the PgFPS gene encoding FPS from _P. ginseng_ roots. The deduced amino acid sequence of PgFPS had 77%, 84%, 87% and 95% identity with FPS from _Arabidopsis thaliana_, _Heeva brasiliensis_, _Artemisia annua_ and _Herba Centellae_, respectively. Southern blot analysis showed that _P. ginseng_ had more than two genes encoding FPS. When the PgFPS gene was expressed in _Escherichia coli_, the recombinant enzyme was found to possess FPS activity. Treatment of hairy roots with methyl jasmonate could increase the mRNA level of PgFPS. The accumulation of ginsengides in root suspension cells of _P. ginseng_ could also be induced by methyl jasmonate, which was related to the increased expression level of PgFPS [16]. [PERSON] et al. [17] expressed PgFPS of _P. ginseng_ in hairy roots of _Centella asiatica_ and found that levels of CaDDS (_C. asiatica_ dammarenediol synthase) and CaCYS (_C. asiatica_ cycloartenol synthase) mRNAs were significantly improved. In particular, the upregulation of CaDDS transcripts suggests that FPS may result in alterations in triterpene biosynthesis capacity. To evaluate the contribution of FPS to triterpene biosynthesis, they treated hairy roots expressing PgFFS with methyl jasmonate and found that the content of mac accessoside and asiaticaoside was transiently increased by 1.15-fold. [PERSON] et al. [18] reported that overexpression of PgFPS caused an approximate 2.4-fold increase of ginseng content in transgenic ginseng hairy roots. Furthermore, overexpression of PgFPS caused the upregulation of genes of ginseng biosynthesis, indicating a critical role of PgFPS in ginseng synthesis in _P. ginseng_. [PERSON] et al. [19] cloned cDNA of FPS from _P. notoginseng_ root. The FPS contains two conservative core fragments, DDIMD and QVQDDYLD, and shows 99% and 95% identity to _P. ginseng_ and _C. asiatica_, respectively, with the triterpenoid structures among the above plants being very similar. These results indicate that FPS plays an important role in the triterpene biosynthesis.
## 3 Results
### Antioxidant Potential of the KET18 Extract  
The antioxidant activity of the KET18 extract was evaluated through their ability to inhibit hydroxyl and DPPH free radicals. Regarding the hydroxyl radical, scavenging activity was observed at the concentration of 200 \(\upmu\)g/mL (39.7 \(\pm\) 2.6%) (Figure 3a). When the extract concentration doubled, the activity raised sharply and reached the highest percentage of 78.3 \(\pm\) 2.9% at 600 \(\upmu\)g/mL. Notably, the scavenging activity fell slightly when the extract concentration continued to raise. The DPPH-scavenging activity of the crude extract reached 69.06 \(\pm\) 2.4% at a concentration of 50 \(\upmu\)g/mL (Figure 3b). An increase in concentration to 800 \(\upmu\)g/mL led to the maximal percentage inhibition of 91.36 \(\pm\) 3.2%,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Crude Extract**} & \multicolumn{2}{c}{**Cytotoxic Activity (IC\({}_{50}\); \(\upmu\)g/mL)**} \\ \cline{2-3}  & **A549** & **MCF7** \\ \hline KET1 & 92.0 \({}^{\dagger}\)\(\pm\) 5.6 & \textgreater{}100 \\ KET2 & 38.8 \({}^{\text{c}}\)\(\pm\) 3.7 & 53.6 \({}^{\text{d}}\)\(\pm\) 7.8 \\ KET3 & 44.2 \({}^{\text{d}}\)\(\pm\) 6.5 & 35.2 \({}^{\text{c}}\)\(\pm\) 2.7 \\ KET6 & 47.8 \({}^{\text{e}}\)\(\pm\) 5.1 & 31.4 \({}^{\text{b}}\)\(\pm\) 3.3 \\ KET9 & 30.4 \({}^{\text{c}}\)\(\pm\) 2.6 & 29.2 \({}^{\text{b}}\)\(\pm\) 2.5 \\ KET18 & 18.8 \({}^{\text{b}}\)\(\pm\) 3.1 & 24.1 \({}^{\text{b}}\)\(\pm\) 2.5 \\ KET23 & \textgreater{}100 & \textgreater{}100 \\ KET24 & 85.8 \({}^{\text{f}}\)\(\pm\) 8.9 & \textgreater{}100 \\ Elliptic & 0.46 \({}^{\text{a}}\)\(\pm\) 0.05 & 0.42 \({}^{\text{a}}\)\(\pm\) 0.03 \\ \hline \hline \end{tabular} Notes: Letters in the same column statistically indicate significant deviation as assessed by LSD (\(p<0.05\)). Elliptic was used as a positive control.  
\end{table}
Table 1: Cytotoxic activities of 8 fungal crude extracts.  
Figure 2: A heatmap revealing the antimicrobial property of endophytic fungi isolated from _K. evelumina._ (1) _Candida albicans_ ATCC 10231; (2) MRSE ATCC 35984; (3) _Bacillus cereus_ ATCC 11778; (4) MRSA ATCC 33591; (5) _Pseudomonas aeruginosa_ ATCC 9027; (6) _Escherichia coli_ ATCC 11105; and (7) _Enterococcus faecalis_ ATCC 29212. Erythromycin and nystatin were used as positive controls.  
confirming the dose-dependent activity of the KET18 extract. Briefly, the DPPH-scavenging activity of the KET18 extract at the concentration ranging from 100 to 800 \(\upmu\)g/mL were consistently close to that of ascorbic acid. In support of the antioxidant activity, polyphenols (65.91 \(\pm\) 0.7 \(\upmu\)gAE/mg) and flavonoids (81.25 \(\pm\) 1.9 \(\upmu\)gQE/mg) were present in the KET18 extract.
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 2 Communication Protocols GOOSE and R-GOOSE and Disadvantages of the IEC61850 Standard
### Goose  
GOOSE (Generic Object-Oriented Substation Events) allows bay-level equipment (IEDs) to exchange information with each other if they are within the same network through a publisher-subscriber configuration using a multicast system, where a selected group of IEDs (subscribers) receive all the information simultaneously and act if required. This information comes from another IED called a publisher. GOOSE carries out the exchange of information between the process level and the bay level since if a failure occurs the IEDs must send GOOSE messages to the elements that must clear the failure. Due to the low transmission latency required by the GOOSE messages of 3 ms maximum, they are mapped directly to the link layer of the OSI model, which avoids the overhead of higher levels of network abstraction [12].  
Figure 1: Mapping of the communication protocols of the IEC61850 standard.
# The Sun and Space Weather
## 7 Space Weather Events and Spacecraft Anomalies
The impact is in the form of a geomagnetically induced current (GIC) caused by rapid variation in ionospheric currents during a shock compression of the magnetosphere (SSC), a substorm, or other fast processes [174; 175; 176]. GICs affect any large-scale conducting system at the surface of Earth such as railroads, telephone lines, pipelines, and electric power grids. Hazardous GICs have been found to be mainly associated with CMEs rather than CIRs [163; 177]. GICs at high latitudes have been extensively studied (see [178; 179; 180] and references therein). GICs can be significant at mid and low latitudes as well [181; 182]. SSCs are followed by geomagnetic storms. Sudden impulses represent the arrival of CME-driven shocks at the magnetosphere but are not followed by geomagnetic storms. Even during sudden impulses, GICs can increase significantly [182].
# Wireless Covert Communication with Polarization Dirty Constellation
## 4 Wireless Covert Channels with Polarized Dirty Constellation
### Fill and Rotation  
The size of the covert information block and the carrier information block are incompatible, the covert information needs to be filled with all 0 or all 1 bits as shown in Figure 4. For reducing the statistical relevance of covert information, our scheme will rotate the covert constellation point. If we modulate the covert information with BPSK, we see from Figure 4 the position of initial covert constellation points and carrier constellation points.  
Figure 3: The schematic diagram of wireless covert communication with polarization dirty constellation.  
To improve the undetectability, we rotate the covert constellation point with \(n\theta\) degree with \(n\in[1,\frac{\pi}{\theta}]\). Let us assume the coordinate of carrier constellation point is \((m_{c,r},m_{c,i})\). The coordinate of covert constellation point is \((m_{c,r}+m_{s,r},m_{c,r}+m_{s,i})\). When we rotate the covert constellation point, we can obtain  
\[\begin{cases}\tilde{m}_{s,r}=\cos(n\theta)\cdot m_{s,r}-\sin(n\theta)\cdot m_{ s,i}\\ \tilde{m}_{s,i}=\cos(n\theta)\cdot m_{s,i}+\sin(n\theta)\cdot m_{s,r}\end{cases} \tag{23}\]  
As we know, covert information is modulated with BPSK. Thus, we can express the covert constellation point as  
\[\begin{cases}\tilde{m}_{s,r}[i]=\cos((i-\left\lfloor\frac{i}{n}\right\rfloor \cdot n)\theta)\cdot m_{s,r}[i]\\ \tilde{m}_{s,i}[i]=\sin((i-\left\lfloor\frac{i}{n}\right\rfloor\cdot n)\theta )\cdot m_{s,r}[i]\end{cases} \tag{24}\]  
When we receive the subcarrier signal, the received carrier information should be modulated again to obtain the residual signal. By reversing the rotation of the residual signal, we can obtain the covert signal. The covert information can be obtained via Log-Likehood Ratio(LLR) calculation.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 4 Implementation of the Indoor Channel
### Transmitter  
This block represents the transmitter, defined by six input parameters: the position \(r_{s}\), the normal vector \(n_{s}\), Lambert's number for the LED radiation \(m\), the central wavelengths \(\lambda_{n}\) for each color LED, \(fwhm\) for each color LED, the specification of symbol constellation \(S_{i}\), and the luminous flux emitted by each symbol \(L_{S}\). The transmitter block provides functions that plot the spatial power distribution of the LED. Additionally, it computes the inverse luminous efficiency of the LEDs, that is, the average radiometric power emitted by each color LED according to the modulation. The encapsulation of the transmitter's parameters is represented by \(\mathbf{T}_{e}\) in Figure 4.
# Wireless Covert Communication with Polarization Dirty Constellation
## 2 Background  
### System Model  
The Alice-Bob model, as depicted in Figure 1, involves a transmitter, a receiver, and a detector commonly referred to as [PERSON], [PERSON], and [PERSON]. The objective is to transmit information covertly between [PERSON] and [PERSON] while evading detection by [PERSON], who observes the wireless channel and determines whether covert communication is taking place or not. [PERSON]'s probability of detection error comprises two components: the probability of missed detection \(PF\) and the probability of false alarm \(PM\). However, due to the practicality of detection, [PERSON] can only detect a finite number of samples. Our numerical analysis reveals that in the presence of noise uncertainty at Willie, there may exist an optimal number of samples that maximizes the communication rate subject to \(\xi\geq 1-\epsilon\), where \(\xi\) denotes the sum of \(PF\) (i.e., false positive rate) and \(PM\) (i.e., miss detection rate) at [PERSON]. We consider an arbitrarily small number \(\epsilon\) to be the maximum acceptable detection rate for Willie.  
Figure 1: The framework of wireless covert communication.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 3 BackCom: An Effective Solution
### Different Types of BackCom
#### 3.3.5 Hybrid BackCom  
In traditional BackCom, the tags first harvest energy and then transmit data. This process creates some delay in communications. To overcome the delay, ambient and dedicated sources can be used together. This combination is known as \"Hybrid BackCom.\" Figure 11 depicts a hybrid BackCom scenario, which includes various types of links and devices. Figure 11a demonstrates short-range ambient communications in the macro zone between ambient devices and hybrid devices connected via Hybrid-AP. Figure 11b exhibits a scenario for hybrid communications employing ambient as well as bistatic types of BackCom. Long-range BiS is shown in Figures 11c,d, showing short range Amb-BackCom with its dual mode of operations. In dual mode, tags can operate in the individual mode of Amb-BackCom, dedicated BiS, or hybrid (both together) mode [52].  
Figure 10: Monostatic BackCom.  
Figure 9: Bistatic Scatter Communication.  
**Discussion:** We can summarize the above section by emphasizing various types of BackCom, based on different categorization as shown in Table 3. This table is divided into two categories, based on the dedication/non-dedication of the carrier signal source and based on the arrangement of the source and the reader. The main points are as follows.  
1. The first category based on the usage of the dedicated source and reader, is known as \"dedicated BackCom (Ded-BackCom)\" [35]. This type is better suited for applications that need a dedicated power source for tags.
2. Another type in the first category is \"ambient BackCom (Amb-BackCom)\" [12]. It does not require any dedicated source but utilizes radio signals available in the vicinity. Since ambient radio waves (such as TV signals, WiFi signals, cellular signals, etc.) are present everywhere, Amb-BackCom can revolutionize wireless communications by promoting battery-free communications.
3. The second categorization of BackCom is based on the arrangement of source and reader. In BiS, the signal generator is dislocated from the reader to reduce the path loss [50]. This type of BackCom sends backscattered signals to a wide range compared to Ded-BackCom. This is because the carrier emitter approaches the tag. Moreover, signals received by the tag face less round trip path loss, and the backscattered signals travel far in comparison to dedicated or Amb-BackCom.
4. Another type in the second category is based on the antenna arrangement of the source and the reader and is referred to as \"monostatic BackCom (Mon-BackCom)\" [39]. This is useful for tiny devices that cannot afford space for separate antennas in the transmitter or the receiver. However, Mon-BackCom can share the single antenna for both transmission and reception operations.
5. The last type in the second category highlights \"hybrid BackCom,\" which was introduced to take advantage of both Amb-BackCom and Ded-BackCom [52].  
Figure 11: Hybrid BackCom.
### The Motivation  
#### 1.1.1 Mapping Data into Quantum States to Run on Quantum Computers  
Using parameterized quantum circuits (so-called variational quantum circuits, VQC), one can generate a matrix \(U(\theta)\) whose transformation depends on the parameters given by the vector \(\theta\): i.e., the parameters define the specifics for the quantum gates, such as the angle values. In general, because of the training and computational difficulties, the design structure of \(U(\theta)\) is fixed by using quantum gates, which guarantees the entanglement of the qubits and certain computational prowess.  
In many recent quantum machine learning models, various types of variational quantum circuits are used as a replacement to a model of a neural network. Given different inputs as the initial state of the quantum circuit, training in these models is generally performed by classically optimizing the parameters of the circuits. In other words, the learning task aims to find a circuit \(U(\theta)\) so that for an input \(\left|\psi\right\rangle\) representing a data vector, \(U(\theta)\left|\psi\right\rangle\)outputs a state from which the expected result can be obtained through quantum measurements with high probability.  
In this learning model, one of the fundamental tasks is to find a way to map a data vector \(\mathbf{x}\) into a quantum state \(\left|\psi\right\rangle\) without losing any information. This can be performed in two different ways [17; 18; 23; 24]: (i) as performed in neural networks, we use one qubit for each feature \(x_{i}\). Because of the similarities to classical neural networks, this mapping provides a natural way to perform quantum machine learning with variational quantum circuits. However, the difficulty is that the required number of qubits may be very high and is very likely beyond the capabilities of current (may also be near future) quantum computers. (ii) We can consider \(\left|\psi\right\rangle=\mathbf{x}/\|\mathbf{x}\|\). Although the learning task may become more difficult, the number of qubits becomes exponentially small in comparison to the dimension of the data vector; we will use \(N=2^{n}\). However, generating a general \(\mathbf{x}\) requires \(O(N)\) quantum gates. This complicates the simplicity of the variational quantum circuits and impedes the applications to the problems with high dimensional data vectors. As a result, as in the classical data analysis, for quantum computers, we need to find methods similar to the singular value decomposition [25] to reduce the dimensions of data sets in a way so that we can reduce the required number of qubits and the number of quantum gates.
# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
###### Abstract
Secondly, based on obtained hyperspectral data of different winter wheat cultivars, this study compared different data dimensionality reduction and classification methods to get better classification accuracy. Finally, this study also attempted to present the potential of deep learning in classifying hyperspectral imagery by using the U-Net-based ENVINet5 model in data processing. This user-friendly model can probably be popularized by both users and researchers. It can provide a reference for the accurate management and classification of different winter wheat, and it can give support for cultivar identification in related crop research.