esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Meta-Analyses  
Regarding cardiorespiratory fitness, a significant improvement in VO\({}_{2}\)max in participants who trained using HRV-g (MD = 2.84, 95% CI 1.41, 4.27; \(p\) < 0.0001) and PT (MD = 1.80, 95% CI 0.24, 3.36; \(p\) = 0.02) was found after training (Figure 4a). Thus, HRV-g and PT led to a significant increase in maximum aerobic power or speed (HRV-g: SMD = 0.66, 95% CI 0.33, 0.98; \(p\) < 0.0001; PT: SMD = 0.48, 95% CI 0.12, 0.83; \(p\) = 0.009) (Figure 5a) after training. Moreover, aerobic performance increased after HRV-g (SMD = 0.71, 95% CI 0.16, 1.25; \(p\) = 0.01) and PT programs (SMD = 0.47, 95% CI 0.07, 0.86; \(p\) = 0.02) (Figure 6a); however, no significant differences in training were observed for VO\({}_{2}\)max (MD = 0.96, 95% CI -1.11, 3.03; \(p\) = 0.36), maximum aerobic power or speed (SMD=0.06, 95% CI -0.26, 0.38; \(p\) = 0.72), or aerobic performance (SMD = 0.14, 95% CI -0.22, 0.51; \(p\) = 0.45) (Figure 4b, Figure 5b, and Figure 6b, respectively).  
Concerning power or speed at VT1 and VT2, performance at VT1 increased significantly after HRV-g programs (SMD = 0.62, 95% CI 0.04, 1.20; \(p\) = 0.04) but not after PT (SMD = 0.17, 95% CI -0.25, 0.59; \(p\) = 0.42) (Figure 7a). In addition, performance at VT2 improved significantly after HRV-g (SMD = 0.81, 95% CI 0.41, 1.22; \(p\) < 0.0001) and PT (SMD = 0.53, 95% CI 0.13, 0.93; \(p\) = 0.009) training programs (Figure 8a). Nevertheless, no significant differences were observed between both training methods in performance at VT1 (SMD = 0.27, 95% CI -0.16, 0.70; \(p\) = 0.22) and VT2 (SMD = 0.18, 95% CI -0.20, 0.57; \(p\) = 0.35) (Figures 7b and 8b, respectively).  
Figure 2: Risk of bias in the included randomized controlled trials.  
Figure 3: Test for funnel plot asymmetry. (**a**) VO\({}_{2}\)max PT; (**b**) VO\({}_{2}\)max HRV-g; (**c**) WMax PT; (**d**) WMax HRV-g; (**e**) Performance PT; (**f**) Performance HRV-g; (**g**) WVT1 PT; (**h**) WVT1 HRV-g; (**i**) WVT2 PT; (**j**) WVT2 HRV-g.  
Figure 4: (a) Effects of endurance training on VO\({}_{2}\)max pre-training vs. post-training; (b) effects of endurance training on VO\({}_{2}\)max HRV-g vs. PT.  
Figure 5: (a) Effects of endurance training on WMax pre-training vs. post-training; (b) effects of endurance training on WMax HRV-g vs. PT.  
Figure 6: (a) Effects of endurance training on aerobic performance pre-training vs. post-training; (b) effects of endurance training on aerobic performance HRV-g vs. PT.  
Figure 7: (a) Effects of endurance training on WVT1 pre-training vs. post-training; (b) effects of endurance training on WVT1 HRV-g vs. PT.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 5 Outlook on Key Technologies for Lunar Constellation Navigation and Orbit Determination
### Moon Reference Frames and Time Scales
\begin{array}{l}TDB=TT+0.001658\times\sin g+0.000014s\times\sin(2g)\\ g=357.53^{\circ}+0.98560028^{\circ}\times(JD-2451545.0)\end{array}  \tag{17}\]  
where \(g\) is the Earth's mean anomaly. With the increase in global lunar exploration activities in the future, the current lunar surface timing method is unsustainable. How to unify the lunar area time and unify the lunar time scale becomes a problem. Therefore, it is necessary to set a time benchmark on the Moon that can be used by all parties to solve the problem of timekeeping on the lunar surface and the time difference between space projects of various countries. What is more, for distances up to the Moon, the time scales play a major role and relativistic effects should be considered accurately. First order approximations are not enough. Such considerations apply to the time scales, GNSS-based orbit determination and use of Doppler measurements. For the orbit determination of lunar satellites, the first-order approximation is not sufficient, mainly because at long distances and high speeds (relativeto the Earth's orbit), relativistic effects become significant, which will affect the precision and accuracy of the measurements. First, gravitational time dilation. According to general relativity, clocks run slower in strong gravitational fields (such as near the Earth's surface) than in weak gravitational fields (such as the orbit of the Moon). This gravitational time dilation effect needs to be taken into account in more precise orbit determinations. In long-distance communications and measurements, relativistic effects cause time and frequency shifts. The signal round-trip time between the ground station and the lunar orbiter is longer, and any small errors will accumulate, causing the overall error to increase.
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 2 Materials and Methods
### Feature Selection Methods  
Feature selection is an essential step in the analysis of large datasets that allows for reducing the dimensionality of data by removing redundant features and selecting the most important ones [15]. This study utilized three common feature selection methods for gene expression analysis, LASSO, IG, and ReliefF, which are briefly described below.
## 5 Discussion
### Model Applicability  
Examining the possibility of mutual applicability by testing the models obtained based on the datasets DS1 and DS2 on the datasets DS2 and DS1, it was concluded that the n-gram analysis gives the best average predictions compared to other data analyzing methods. When testing models based on the DS2 on the DS1 data, the n-gram analysis even achieves an increase in the average accuracy of all models compared to training and testing the models on the DS1 data (7.77%). The LIWC analysis, on the other hand, shows the least applicability to other datasets. As a result of the stated claims, the analysis by combining n-grams and LIWC achieves less favorable applicability than the analysis with n-grams, but it is still better than the analysis with LIWC. It can be concluded that n-grams are the most robust analysis method that allows the greatest flexibility when applying models trained on one dataset to another.
## 5 Key 6G Technologies
### Improved Wireless Interface
#### 5.2.7 Orbital Angular Momentum (OAM)  
OAM, an inherent property of electromagnetic (EM) waves, introduces a new modulation dimension in wireless communication, characterized by vortex EM waves with an angular momentum phase wavefront. Orthogonal OAM modes use distinct antennas to convey distinct data, enabling the coexistence of several modes and simultaneous data transmission over a single communication channel. This orthogonal characteristic enhances spectrum efficiency, increasing channel capacity without requiring additional frequency bands [33; 157; 158]. OAM technology, initially discovered in optical fields, has extended its applications to radio and acoustic domains, demonstrating potential in mmWave and THz regions [159].  
OAM holds significant promise for providing high data rate services in free-space optical, optical fiber, radio, and acoustic communication systems [160; 161]. The combination of OAM and MIMO (Multiple Input-Multiple Output) communication is explored to achieve higher capacity and spectrum efficiency [157]. Two types of OAM-based MIMO systems are proposed [162], demonstrating improved throughput and capacity in various scenarios. Challenges, such as beam divergence and misalignment, remain to be addressed for practical implementation, especially in non-line-of-sight (NLOS) scenarios. Despite challenges, OAM technology has showcased its potential for radar applications and microwave-sensing systems [163], introducing new avenues for innovation in 6G communication.  
The exploration of OAM technology brings forth exciting possibilities, and overcoming challenges in beam control, system analysis, and commercialization processes is crucial for its integration into 6G communication systems [164; 165].
## 2 Theoretical Background
### Communication Layer
#### 2.5.1 Denial of Service Attack Simulation  
A denial-of-service (DoS) attack is a type of cyber-attack in which the perpetrator tries to prevent intended users from accessing a node or network by temporarily or permanently disrupting the services of a host connected to the network. A DoS attack is regularly carried out in an attempt to overwhelm systems and prevent some or all real requests from being handled by flooding the targeted computer or resource with unnecessary requests or packets (e.g., TCP, UDP, SYN, etc.). Communication between nodes is disrupted during a DoS attack because a huge number of service requests are delivered to the target node, depleting all of the node's resources. A spike in network traffic to the victim (i.e., arrival rate) is created as a result of such an attack. As a result, large queues are formed, resultingin higher wait times and transmission delays [29]. Therefore, we record interarrival times (IAT) and transmission delays (TD) in our DoS attack datasets.  
To simulate the effects of a DoS attack on our network traffic SimPy and component toolkit SimComponents [7] are used at the communication layer to simulate network traffic in the smart grid layer. SimPy is a discrete-event simulation framework based on Python. Active components such as packets, packet generators, packet sinks, switch ports, and port monitors may all be simulated using it. To define and replicate these components and their functionality, the SimComponents toolkit is employed. To achieve synchronization between the communication layer, smart grid layer, and machine learning model, we append the start time and index to the sample measurements. We generate data for one day's worth of measurements and create datasets to reflect DoS attack scenarios. DoS Algorithm 1 illustrates our pseudo-code and shows the overview of our attack data generation. Figure 2 shows a histogram of malicious traffic sink interarrival times for DoS attacks. When the victim node is attacked, an influx of packets is sent to it, causing the frequency of packets received to increase.  
```
1:for One day worth of measurements do
2: Create arrays for the \(IAT\) & \(TD\) values
3: Append Index & Current Time
4:if An attack sample is detected then
5: Set error equal to 1
6: Create Attack Bus List
7: Extend Attack Bus List with attack bus number
8:else
9: Set error equal to 0
10:endif
11:for Each smart grid measurement do:
12:if error = 0 then
13: Simulate normal traffic
14: Append \(IAT\) & \(TD\) values to arrays
15:else
16:if a from bus is in the attack bus list then
17: Simulate malicious DoS traffic
18: Append \(IAT\) & \(TD\) values to arrays
19:else
20: Simulate normal traffic
21: Append \(IAT\) & \(TD\) values to arrays
22:endif
23:endif
24: Update IAT CSV File
25: Update TD CSV File
26:endfor
27:endfor
```  
**Algorithm 1** Denial of Service Attack
## 2 Related Works
### Non-Cooperative and Cooperative Opportunity Navigation  
Opportunity navigation utilizes existing radio signals, such as cellular communication signals and television broadcast signals, to provide navigation services [45]. Opportunity navigation can be categorized into two paradigms: cooperative and non-cooperative. Non-cooperative navigation typically operates without base station assistance, and the navigation receiver does not subscribe to base stations. Thereby, non-cooperative navigation offers enhanced privacy preservation. However, non-cooperative navigation suffers from several limitations. (1) The transmitter positions are either unknown or low-accuracy, resulting in degraded navigation accuracy. For terrestrial communication base stations, estimating transmitters' locations requires sophisticated systems and the estimated results often remain unsatisfactory [46; 47]. Although third parties may provide LEO satellite ephemerides, their accuracy is low [48; 49]. (2) Unauthorized users cannot precisely acquire signal structures [44; 50]. (3) Communication signals are not consistently active.  
These challenges in non-cooperative navigation can be effectively addressed through the cooperative method [8; 25]. For cooperative receivers, precise orbital data and signal structures can be readily obtained from the transmitter, and transmitting signals can be activated on demand according to receiver requirements. These features enhance navigation accuracy, simplify receiver processing, and ensure continuous navigation services [51]. Hence, cooperative opportunity navigation is adopted in this paper.
## 3 Digital Twin and Physical Twin Integration
### Implementation of Stable Grasping Actions  
Although the contact interaction among all the virtual objects in the environment is always present to mimic the sense of realism, the grasping of the objects must be enforced using a more stable connection. In fact, rigid bodies whose kinematics is driven by external sources (such as the arm and the forearm) may produce excessive contact impulses that make it difficult to reach a stable grasping due to the rapid motion increments. For this reason, the grasping condition is enhanced using the grasping active feature/object active feature GAF/OAF methodology [37]. According to this methodology, a series of reference systems is assigned to the hand (GAF) and the bodies (OAF) that can be grasped (Figure 7). These reference systems work as communicators. The main steps of the methodology are:  
* when the user expresses the intention of grasping using a specific pose, the corresponding GAF is activated;
* the relative position and attitude between the activated GAF and all the OAFs on the different objects in the scene are checked;
* if the check produces a positive match (GAF and OAF are close and aligned), the grasping is confirmed, a constraint condition between the two reference systems is enforced, and the vector \(\{\psi\}\) is updated accordingly;
* if the check produces a negative match (GAF and OAF are far and/or misaligned), the grasping is cancelled and the integration goes on without modifying the equations.  
Further specific details of the methodology can be found in the seminal paper [37].
## 5 Main Results
### Asymptotic Expansion of the Stackelberg Optimal Values of the Cost Functionals in the Game (9)-(11)  
Consider the values  
\[\bar{J}_{u,0}=\frac{1}{2}\int_{0}^{t_{f}}\big{[}\bar{z}_{0}^{T}(t )D_{u}(t)\bar{z}_{0}(t)+\bar{\lambda}^{T}_{v,0}(t)S_{u,v}(t)\bar{\lambda}_{v,0 }(t)\big{]}dt,\] \[\bar{J}_{v,0}=\frac{1}{2}\int_{0}^{t_{f}}\big{[}\bar{z}_{0}^{T}(t )D_{v}(t)\bar{z}_{0}(t)+\bar{\lambda}^{T}_{v,0}(t)S_{v}(t)\bar{\lambda}_{v,0}( t)\big{]}dt, \tag{114}\]  
where \(\bar{z}_{0}(t)=\left(\begin{array}{c}\bar{z}_{1,0}(t)\\ \bar{z}_{2,0}(t)\end{array}\right)\), \(\bar{z}_{1,0}(t)\), and \(\bar{\lambda}_{v,0}(t)\) are the corresponding components of the solution to the boundary-value Problems (39) and (37), and \(\bar{z}_{2,0}(t)\) is given by Equation (38).  
**Theorem 2**.: _Let Assumptions (A1)-(A7) be valid. Then, for all \(\varepsilon\in(0,\varepsilon_{0}]\), the Stackelberg optimal values of the cost functionals \(J^{*}_{u}(\varepsilon)\) and \(J^{*}_{v}(\varepsilon)\) in the game (9)-(11) satisfy the inequalities_  
\[\big{|}J^{*}_{u}(\varepsilon)-\bar{J}_{u,0}\big{|} \leq b\varepsilon,\] \[\big{|}J^{*}_{v}(\varepsilon)-\bar{J}_{v,0}\big{|} \leq b\varepsilon, \tag{115}\]  
_where \(b>0\) is some constant independent of \(\varepsilon\)._  
Proof.: Let us start with the proof of the first inequality in (115).  
Substituting the expressions for the \(u^{*}(t,\varepsilon)\), \(v^{*}(t,\varepsilon)\) (see Equations (19) and (113)) into Equation (10) and using Equations (14), (18), and (25), Proposition 2 and the equivalence ofthe boundary-value Problems (17), (26), and (27), we obtain, after a routine rearrangement, the following expression for \(J_{u}^{*}(\varepsilon)\):  
\[J_{u}^{*}(\varepsilon)=J_{u}\big{(}u^{*}(t,\varepsilon),v^{*}(t, \varepsilon)\big{)}\] \[=\frac{1}{2}\int_{0}^{t_{f}}\big{[}z_{1}^{T}(t,\varepsilon)D_{u,1 }(t)z_{1}(t,\varepsilon)+z_{2}^{T}(t,\varepsilon)D_{u,2}(t)z_{2}(t,\varepsilon)\] \[+\lambda_{u2}^{T}(t,\varepsilon)\lambda_{u2}(t,\varepsilon)+ \lambda_{v}^{T}(t,\varepsilon)S_{u,v}(t)\lambda_{v}(t,\varepsilon)\big{]}dt, \varepsilon\in(0,\varepsilon_{0}], \tag{116}\]  
where \(z_{1}(t,\varepsilon)\), \(z_{2}(t,\varepsilon)\), \(\lambda_{u2}(t,\varepsilon)\), and \(\lambda_{v}(t,\varepsilon)\) are the corresponding components of the solution to boundary-value Problems (26) and (27).  
Now, using Equation (116), as well as Lemma 1, the inequalities in (61), (66), (91), and (101), and the first equation in (114), immediately yields the first inequality in (115).  
The second inequality in (115) is proven quite similarly to the proof of the first inequality. This completes the proof of the theorem.  
**Remark 10**: _Using Proposition 3, Remark 8, and the first equation in (114), we can conclude that \(J_{u,0}=J_{u}^{*}\)._
## 4 Single Pixel Optimization
### dc Bias
Excellent uniformity has been demonstrated with a kilo-pixels array of TES X-ray calorimeters under development for X-IFU (Section 6.1). The detector design is based on the small, \(50\times 50\) um\({}^{2}\), Mo/Au bare TES described above. A combined energy resolution of \(2.16\pm 0.01\) eV at \(5.9\) keV, obtained with more than 200 pixels, in a \(8\times 32\) multiplexing configuration, has been reported [115]. Small pitch (\(50\) um) devices based on a similar design, fabricated in an array of \(60\times 60\) sensors, with \(1\) u thick, \(46\times 46\) um\({}^{2}\), gold absorbers have been developed at NASA-GSFC for the low energy band (0.2-0.75 keV) of the Ultra High Resolution (UHR) array for the future X-ray space mission Lynx Section 6.2. They measured 2.31 eV FWHM at \(1.49\) keV, limited by the broadening of the intrinsic linewidth of the Al source, and \(0.25\) eV FWHM at \(3\) eV using optical photons, from a blue laser diode, delivered through an optical fiber [69]. The latter energy resolution result is consistent with the estimated performance based on the signal size and noise [116].
# Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method
## 2 Related Work
### Neural Network Approach for Underwater Localization  
Various signal processing methods have been used for signal processing for underwater localization. It is divided into two main directions. First, building a dipole source flow field model and using different constraint methods to resolve the model [7; 42; 43; 44]. Second, a neural network modeling method was built to mimic the fish in a complex environment by training over a long period to complete various activities and behaviors, building neural networks to process the received signals. [PERSON] et al. used a multilayer perceptron network (MLP) to extract pressure amplitudes as features for localization [26]. [PERSON] et al. used generalized regression networks for underwater localization experiments [41]. [PERSON] et al. used artificial neural networks (ANNs) to identify the coordinates, frequency, and amplitude of vibration sources and achieved an accuracy of 93% [45]. [PERSON] et al. achieved dipole source localization in 2D and 3D using MLP networks [39]. [PERSON] et al. used the long short-term memory (LSTM) model to process pressure-varying time series data. They were able to effectively improve the robustness of the network using the memory function of the LSTM model [46].  
Previous research has shown that using artificial neural networks for underwater localization is an effective method. However, collecting data based on experimental platforms causes insufficient data volume, which makes the neural network lack sufficient training and causes overfitting. In addition, most of the studies focus on the localization of a single dipole source; in fact, most situations underwater have multiple objects moving at the same time, and the research on how to perform accurate localization under the conditions of interference from various things needs to be further studied.
## 2 Materials and Methods
### Image Pre-Processing  
In industrial and natural environment acquisition, there are many factors that can lead to poor image quality, such as interference with industrial lighting, strong exposure due to strong light, and low contrast due to cloudy and rainy days. Therefore, image processing is needed to improve the image quality [8]. Therefore, Multi-Scale Retinex with Color Restoration (_MSRCR_) was introduced to pre-process the image to enhance the differentiation between the marker points and the background, which is implemented as shown in Equation (1) [9; 10].  
\[R_{MSRCR_{i}}(x,y)=C_{i}(x,y)R_{MSR_{i}}(x,y) \tag{1}\]  
\(R_{MSRCR_{i}}(x,y)\) represents the recovered image of the \(i\)th channel after using this algorithm; _MSRCR_ adds a color recovery factor \(C\) as shown in Equation (2), which is used to adjust the color distortion due to contrast enhancement in local areas of the image, where \(\alpha\) is the adjustment factor, \(\beta\) is the gain constant, and \(I(x,y)\) represents the original image.  
\[C_{i}(x,y)=\beta\Big{\{}log[\alpha\cdot I_{i}(x,y)]-log\Big{[}\sum_{i=1}^{N}I_{ i}(x,y)\Big{]}\Big{\}} \tag{2}\]  
\[R_{MSR_{i}}(x,y)=\sum_{n=1}^{m}\lambda_{n}\{logI(x,y)-log[G_{n}(x,y)\cdot I(x,y )]\} \tag{3}\]  
In Equation (3), \(R_{MSR_{i}}(x,y)\) represents the recovered image of the \(i\)th channel after using multi-scale filtering, \(G_{n}(x,y)\) is a single-scale Gaussian filter, \(\lambda\) represents the weight, and \(m\) is the number of scales [11; 12; 13]. A comparison of the images before and after processing is shown in Figure 4.  
Figure 4: Comparison images: (**a**,**c**) for the original image, (**b**,**d**) for the processed image.  
From the results in Figure 4, it can be seen that after pre-processing, the environmental factors such as strong light and darkness, which cause unclear marking points, have been solved.
# IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
## 2 IoT Technologies and Architecture
### Transportation/Network Layer
Sigfox is a narrowband communication system for transmitting data over long ranges (up to 40 km [27]). Although it employs narrowband signals, Sigfox can be suitable for many kinds of applications, such as geolocation services and control messages. Finally, 5G is the fifth generation of cellular networks. It is a very low-latency (less than 1 ms) and high-bandwidth (10 Gb/s) protocol [28], making it a strong enabler for smart cities and allowing the interconnectivity of a large number of IoT devices, as well as being suitable for real-time processing. The last part of the network layer stack is in charge of suitably formatting data for their presentation. The client/server architecture, represented by the subscription mechanism between IoT devices and brokers, presents different modalities for sending/receiving messages/data, i.e., push and pull. Typically, pull protocols are REST call, web services, FTP and HTTP/HTTPS. On the other hand, the most common push protocols to receive data via data-driven subscriptions are: WebSocket (WS), Constrained Application Protocol (CoAP), Message Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP) and FIWARE NGSI and NGSI-V2 [29]. Furthermore, in order to allow data exchange among multiple devices and applications, distributed publish-subscribe messaging systems should be supported (such as Apache Kafka, RabbitMQ, Orion Broker of FIWARE) for handling multiple data streams in an efficient and scalable way [13]. In this context, in order to increase fault tolerance when handling data that should not be lost in case of failure, the data persistence of queues is an important feature (e.g., Apache Kafka).