esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Experimental Setup
#### 3.1.3 Evaluation Metrics and Comparison Models  
The following quantitative metrics were used to evaluate the performance of the BBCN model:  
1. Global Positioning Error (GPE): The average Euclidean distance between the robot's actual position and the target position.
2. Path Efficiency (PEF): The ratio of the actual path length to the ideal optimal path length, reflecting the optimality of the path planning.
3. Obstacle Avoidance Success Rate (OASR): The percentage of successful obstacle avoidance in dynamic environments.
4. System Latency (SL): The time taken for sensor data processing and control command generation, reflecting the real-time performance of the system.  
The following common models were compared:  
1. Single Beidou Navigation Model (GNSS): Only relies on Beidou navigation for global path planning, with no local perception capability.
2. Vision-based Local Navigation Model (Vision-SLAM): Uses visual SLAM for positioning and path planning.
3. Beidou and Vision Fusion Model (GNSS-Vision): Uses the Extended Kalman Filter (EKF) to fuse Beidou and vision data for navigation and obstacle avoidance.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 5 Parametric Study
### Influence of Cut Width  
Figure 17 displays the impact of notch width on the pull-out performance of notched T-perfobond connectors. In this study, the notched ratio \((c_{w}/l_{f})\) was varied from 0 to 0.50 across the specimens. The conventional T-perfobond connectors featured a notched width of 0 mm, corresponding to a notched ratio of 0. The notched T-perfobond connectors had notched widths of 8 mm, 16 mm, 24 mm, and 40 mm, with notched ratios of 0.10, 0.20, 0.30, and 0.50, respectively. The findings indicate that an increase in notch width has a  
Figure 16: Influence of flange width. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
Figure 15: Influence of flange thickness. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
minor influence on the pull-out resistance of notched T-perfobond connectors. When the notch width is less than 16 mm, corresponding to a notch ratio below 0.20, the reduction in pull-out resistance does not exceed 4.50%. Beyond a notch width of 16 mm up to 40 mm, the ultimate pull-out resistance of the specimens remains relatively stable. This constancy could be due to the flange providing the necessary pull-out resistance to counteract the effects of the notch.
# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 2 Key Concepts
### Blockchain Oracles  
Blockchain oracles, illustrated in Figure 1, act as intermediaries between a blockchain and external data sources. They allow smart contracts to access real-world information. Since blockchains are inherently isolated from external data to preserve their security and decentralization, oracles provide a bridge that introduces necessary external information to execute smart contracts [18]. These oracles are not data sources but mechanisms that query, verify, and authenticate external data before transmitting it to the blockchain.  
Figure 1: The Figure shows the representation of a coffee futures contract, specifically for Colombian coffee. It indicates the electronic symbol KC, used on the Intercontinental Exchange (ICE) to refer to Arabica coffee futures. Additionally, it details the contract size, which corresponds to 37,500 pounds, and the type of coffee, identified as washed Arabica.  
Oracles can provide various data types, such as market prices, event outcomes, weather conditions, and more, significantly expanding the range of smart contract applications. However, their use also introduces security and trust challenges, known as the \"oracle problem\", which refers to the need to ensure the integrity and accuracy of the data provided in a decentralized environment [19].
## 2 System Model
### Directional Modulation Model  
Figure 5 illustrates the model of a uniform linear antenna array. Let \(N\) represent the number of elements in the array. The spacing between the array elements \(d\) is set to half of the carrier wavelength, and the azimuth angle is denoted by \(\theta\). So, the excitation vector is as follows:  
\[S\ =\ APU \tag{9}\]  
The channel line-of-sight vector, which is fundamentally determined by the antenna array's geometry, is given by the array factor (also known as the steering vector) \(H(\theta)\):  
\[H(\theta)=\left[e^{j\pi[1-(\frac{N+1}{2})\cos\theta]}\ e^{j\pi[2-(\frac{N+1}{2}) \cos\theta]}\ \ldots\ e^{j0}\ \ldots\ e^{j\pi[N-1-(\frac{N+1}{2})\cos\theta]}\ e^{j\pi[N-(\frac{N+1}{2}) \cos\theta]}\right]^{T} \tag{10}\]  
The array factor \(H(\theta)\) describes the collective phase response of the \(N\)-element uniform linear array at a given azimuth angle \(\theta\). From this, the normalized array factor \(P=\frac{H(\theta)}{\|H(\theta)\|}\), amplitude \(A=\frac{1}{\sqrt{N}}\), and \(u\) represents the symbols sent by the transmitter.  
Then the signal received by a legal receiver on an azimuth of \(\theta\) is:  
\[y_{B}=H(\theta)^{H}S+n_{B} \tag{11}\]  
where \(n_{B}\) denotes Gaussian white noise. When the azimuth angle of the eavesdropper is not equal to the azimuth angle of the correct signal, the eavesdropper cannot demodulate the signal properly.
## 3 Background and Problem Statement
### Prediction of Free Calcium Oxide (f-CaO) Content
More formally, the prediction problem of the f-CaO content in cement clinker can be formulated as follows. We are given a set of control data records continuously measured and collected in the DCS system up to time point \(T\), denoted as \(D_{T}=\{\mathbf{x}_{i}\ =\ \left\langle x_{i,1},x_{i,2},\ldots,\right.\)\(\left.x_{i,j},\ldots,x_{i,N}\right\rangle\), \(y_{i}\}\), where \(i=1,2,\ldots,T\) signifies the time points when DCS monitoring sensors collect status data of the cement production plants and \(j=1,2,\ldots,N\) is the number of the data attributes (i.e., features) in the dataset; our ultimate goal is to predict the near future f-CaO content \(y_{t^{\prime}}\) in cement clinker at time \(t^{\prime}>T\), based on \(D_{T}\). The attributes in the feature vector \(\mathbf{x}_{i}\) have different ranges of possible values in a parameter space of multiple dimensions of different magnitudes, and the DCS measurement dataset \(D_{T}\) can be viewed as samples from this space. These samples are used to generalize to the whole feature set and the manufacturing process for prediction purposes, and we use machine learning methods to train predictive models based on historical DCS control and measurement data. More specifically, we utilize the time-series sensor measurements exported from the DCS system of a cement plant as a training dataset \(D_{T}\) that consists of \(N\) laboratory test results of f-CaO content, i.e., \(D_{T}=\{(\mathbf{x}_{1},y_{1}),(\mathbf{x}_{2},y_{2}),\ldots,(\mathbf{x}_{N}, y_{N})\}\), where \(\mathbf{x}_{i},i=1,2,\ldots,N\) is an instance of \(\mathbf{x}\) that collectively determines the corresponding f-CaO content \(y_{i}\) at time point \(i\). We propose employing machine learning-based methods to estimate \(y_{t^{\prime}}\) based on \(D_{T},t^{\prime}>T\), such that the predicted (estimated) value \(\hat{y}_{t^{\prime}}\) is close enough to the ground truth \(y_{t^{\prime}}\) for all testing samples \((\mathbf{x}_{t},y_{t}),t\leq T\), and can be applied to arbitrary future cases (i.e., when \(t^{\prime}>T\)) with high accuracy based on \(D_{T}\).
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### Result Categories
#### 6.3.2 Impact of Operator Requirements on the Outcome Probabilities  
From an operator's perspective, it is expected to have better results in relaxed network scenarios, which in this case is the higher TDL (see Table 9). Figure 18 shows the share of outcomes under each category for the three scenarios listed in Table 8. Here, S1 and S2 correspond to scenarios where an operator has a throughput degradation limit (TDL) of 0 and 20 percent, respectively, while in S3, the operator switches between the two extreme TDLs of 0 and 20 percent over 24 h. In an environment consisting of stationary users, having a more relaxed TDL increased the probability of fulfilling both the KPIs by 16 percent (i.e., 78.1 percent to 94.5 percent). However, the average energy savings for 0 and 20 percent TDL over 24 h stood at 15.1 and 14.3 percent, respectively. This indicates that similar network optimization actions taken in more relaxed scenarios (i.e., with higher TDL) could lead to favorable outcomes especially when we considered a static environment.  
From a TowerCo's perspective, we found that stringent requirements by all the MNOs (S1) reduced the probability of fulfilling all requirements for all the MNOs simultaneously. This is seen in Figure 19, where the probability of a \(TC_{1}\) outcome is 20 and 45 percent lower in the case of an S3 and S1 scenario in comparison to S2. Furthermore, the probability of a \(TC_{1}\) outcome is 39 percent lower as compared to the same category at the MNO level (i.e., 78 percent). Now, given that all the MNOs optimized their share of resources independently and the probability of \(MNO_{1}\) in S1 and S2 were comparable, a lower probability of \(TC_{1}\) outcomes in S1 indicated a possibility for the TowerCo to improve in terms of resource allocation and (or) transferring optimization policies to the MNOs. Two ways to improve that could be through:  
Figure 18: Outcome probabilities for an operator under varying throughput degradation limits.  
* A dynamic allocation of resources to MNOs based on their traffic demand and user distribution;
* Allowing the TowerCo to optimize the network for all the MNOs in a centralized manner.  
These approaches require sharing additional information by the MNOs compared to the current implementation. The latter could involve migrating from RANaaS to RAN services and relinquishing control and responsibility of resource management by the MNOs to the TowerCo service provider. In RANaaS, the analysis from a TowerCo's perspective boils down to studying the energy-saving features activated by the operators at different times and how often their actions overlap.
## 2 Multi-Robot Cooperative Formation Control Based on Group Formation
### Graph Theory Description
In order to explain the relationship between the directed graph and adjacency matrix, between the diagonal matrix and Laplace matrix, a directed graph of a multi-robot system is assumed as shown in Figure 3. So, its adjoint matrix \(\mathbf{A}\), diagonal matrix \(\mathbf{D}\) and Laplacian matrix \(\mathbf{L}\) can be shown in Equation (3). In Figure 3, the red circle represents the leader robot. The blue circle represents the following robot. The number represents the serial number of the robot. The arrow  
Figure 3: Directed graph of multi-robot system. represents the directed edge. The corresponding matrixes of the directed graph are shown as follows in Equation (3). Then  
\[\mathbf{A}=\left( \begin{array}{cccccc}0&0&0&0&0&0\\ 1&0&0&0&0&1\\ 1&1&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&1&0&0&0\\ 0&0&0&1&1&0\end{array} \right),\ \mathbf{D}=\left( \begin{array}{cccccc}0&0&0&0&0 &0\\ 0&2&0&0&0&0\\ 0&0&2&0&0&0\\ 0&0&0&1&0&0\\ 0&0&0&0&1&0\\ 0&0&0&0&2\end{array} \right),\ \mathbf{L}=\left( \begin{array}{cccccc}0&0&0&0&0&0 \\ -1&2&0&0&0&-1\\ -1&-1&2&0&0&0\\ 0&-1&0&1&0&0\\ 0&0&-1&0&1&0\\ 0&0&0&-1&0&2\end{array} \right), \tag{3}\]  
The characteristic structure of the graph Laplacian matrix \(\mathbf{L}\) plays a key role in the dynamic system analysis on the graph. The eigenvalue of \(\mathbf{L}\) is analyzed below, and the approximate form of the graph Laplacian matrix is defined as follows in Equation (4). Then  
\[\mathbf{L}\]  
=MjM-1,  
In Equation (4), the Jordan matrix \(\mathbf{J}\) and the transformation matrix \(\mathbf{M}\) are shown as Equation (5). Then  
\[\left\{ \begin{array}{cccc}\mathbf{M}=\left(\begin{array}{cccc}V_{1}&V_{2}& \cdots&V_{N}\end{array}\right)\\ \mathbf{J}=\left(\begin{array}{cccc}\lambda_{1}&1&&\\ &\lambda_{2}&1&\\ &&\cdots&1&\\ &&&\lambda_{N}\end{array}\right)\end{array} \right., \tag{5}\]  
In Equation (5), the eigenvalue \(\lambda_{i}\) and the right eigenvectors \(r_{i}\) of \(\mathbf{M}\) satisfy Equation (6). Then  
\[(\lambda_{i}\mathbf{I}-\mathbf{L})r_{i}=0, \tag{6}\]  
where \(\mathbf{I}\) is the identity matrix. The left eigenvector \(\omega_{i}\) of matrix \(\mathbf{M}\) satisfies Equation (7). Then  
\[\left( \begin{array}{cc}\omega_{i}&0\\ 0&\omega_{i}^{\rm T}\end{array} \right)\left( \begin{array}{cc}\lambda_{i} \mathbf{I}-\mathbf{L}\\ r_{i}\end{array} \right)=\left( \begin{array}{c}0\\ 1\end{array} \right). \tag{7}\]  
where \(\omega_{i}^{\rm T}\) is the transpose vector of the eigenvector \(\omega_{i}\). Since the sum of all row elements of matrix \(\mathbf{L}\) is 0, if and only if \(\mathbf{L}\) has a unique eigenvector \(c\) such that \(\mathbf{L}c=0\) and its eigenvalue is 0. The rank of \(\mathbf{L}\) is \((N-1)\). Graph \(\mathbf{G}\) has a spanning tree, which plays an important role in forming the stability of formation.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 4 Problem Solving
### Formulate the Problem as the MDP
#### 4.1.3 Reward Function  
There are two types of rewards in this enemy avoidance problem, denoted by \(r_{1}\) and \(r_{2}\), respectively. The first type of reward \(r_{1}\) is the usual reward given at each decision making step, which aims to encourage the agent to reach the goal point in as few steps as possible. The other reward \(r_{2}\) is the reward given at the end of the episode for different ending reasons. There are four situations that will end an episode. Denote these four situations by \(S_{T}=\{s_{T1},s_{T2},s_{T3},s_{T4}\}\), which are defined as follows.  
* \(s_{T1}\): The decision making UAV has collided with the enemy UAV.
* \(s_{T2}\): The decision making UAV has reached the goal point.
* \(s_{T3}\): The decision making UAV has attacked the enemy UAV successfully.
* \(s_{T4}\): The decision making UAV has been out of the region.  
Since the mission of the decision making UAV is to reach the goal point successfully, the agent will be rewarded with a very big value when the mission is fulfilled. Cases that the decision making  
Figure 3: The denotations for the state variables.  
UAV needs to avoid, such as colliding with the enemy UAV or out of the region, should be punished. Successfully attacking the enemy UAV can guarantee the fulfillment of the mission for the decision making UAV, therefore it should be rewarded. However, since the attacking action is costly and may fail, it will be better to limit the use of the attacking action. Considering this, a small punishment is given when attacking action is used. Therefore, we define the reward \(r=r_{1}+r_{2}\), where \(r_{1}=-1\), and \(r_{2}\) is defined as below:  
\[r_{2}=\begin{cases}-20&a=a_{5};\\ -100&s=s_{T1};\\ -100&s=s_{T4};\\ 2&s=s_{T3};\\ 500&s=s_{T2}.\end{cases} \tag{5}\]
## 2 System Model
### OFDM Systems  
Orthogonal frequency-division multiplexing (OFDM) is a multi-carrier modulation technology with the architecture shown in Figure 1. First, the data need to undergo quadrature amplitude modulation (QAM), pilot information insertion, and serial-to-parallel (S/P) conversion. Then, the OFDM signal is obtained through the \(N\)-point inverse fast Fourier transform (IFFT), which can be described as follows [33]:  
\[x_{\text{OFDM}}[n]=\sum_{k=0}^{N-1}X[k]\cdot e^{\frac{(2-\text{min}}{N}},\;0\leq n \leq(N-1) \tag{1}\]  
where \(X[k]\) is the QAM symbol signal and \(N\) is number of subcarriers.  
Afterward, the cyclic prefix (CP) is introduced into the OFDM signal to make it resistant to the adverse effects of inter-symbol interference (ISI); the length of CP is usually 25 % of the number of subcarriers [44]. The signal with added CP is still a baseband signal after parallel-to-serial (P/S) conversion, and needs to be up-converted to radio frequency (RF) before transmitting.  
At the receiving end, the signal passes through a series of actions, including down-conversion into a baseband signal, S/P processing, CP removal, and \(N\)-point fast Fourier transformation (FFT). Then, it is divided into data and pilot parts. The pilot part plays an important role in channel estimation and assumes that the estimated channel matrix is denoted as \(\hat{\mathbf{H}}_{LS}\). Afterward, the data part can be estimated using \(\hat{\mathbf{H}}_{LS}\), and is indicated as \(\hat{\mathbf{x}}\). Finally, the signal undergoes P/S processing and QAM demodulation to recover the transmitted data.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 5 Discussion and Conclusions
The encumbrance of the sensors also limits the possibility to take both measurements exactly at the same point. For the NIRS signal, this is not a big issue, as the investigated muscle is the whole volume under the probe, while the recorded EMG is strongly affected by electrode positioning and should be placed consistently with anatomical positioning sites. Another limitation may be the fact that, with the combination of NIRS and EMG, it is possible to have only a muscular evaluation of the patient and not a description of other related domains--such as the activity of the motor cortex--during movement. In order to overcome this limitation, it should be possible to employ multi-channel devices for the NIRS signal, positioning some optical channels also on the brain and then coupling them with traditional techniques, such as EEG. Of course, this configuration increases the cost of the device, which is, instead, limited for the muscle-only application. Furthermore, the use of multiple techniques increases the time for the subject preparation, which is compliant with clinical sessions only in the case of application of NIRS and EMG sensors on muscle. The joint use of the two techniques might impact several fields of medicine and rehabilitation. In fact, while several neurological or orthopedical pathologies are commonly assessed only in the domain of one of the measures, the coupling of the two may strengthen the assessment and produce multi-factorial and reliable results. Looking at the applications, we have found, the main field where these two techniques are promising seems to be that of diagnostics and rehabilitation. In our opinion, joint NIRS-EMG assessment will provide insights into clinical cases where a muscle impairment needs to be assessed in order to plan a physiotherapeutic program, following the progress of the patient. The EMG can give information about the muscular fibers, whether they are recruited in a higher number or in a different way during the rehabilitation process, while NIRS can contribute to the characterization of the muscle from a metabolic point of view, assessing the dynamic balance between O\({}_{2}\) utilization and O\({}_{2}\) delivery. **Author Contributions:** conceptualization, R.R. and A.S.; methodology, R.R. and A.S.; resources, I.P., M.Z., R.R., and A.S.; writing--original draft preparation, I.P., M.Z., R.R., and A.S.; writing--review and editing, D.C., L.S., A.T., R.R., and A.S.; supervision, R.R. ; funding acquisition, A.T. and L.S. **Funding:** This research was partially supported by Regione Lombardia and Fondazione Cariplo in the framework of the project EMPATIA@Lecco--EMpowerment del PAzienTe In cAsa--Rif. 2016-1428. **Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Investigation of the MIMO-Antenna Diversity Performance
### Channel Capacity Loss (CCL)  
The CCL is another significant diversity performance parameter used to examine the MIMO-antenna performance. For an efficient MIMO-antenna performance, it can be defined as the rate of data reliably transmitted over a specific channel in a fading environment [8; 39]. For a better performance, the CCL must be less than 0.4 bits/Hz/s [36; 37]. It is calculated using the S parameters using Equations (6)-(8) [36; 39; 41]:  
\[CLL=-\left|\psi^{R}\right| \tag{6}\]  
where \(\psi^{R}\) is the correlation matrix of the receiving antenna for the case of a two-element MIMO antenna, which is obtained as follows:  
\[\psi^{R}=\begin{bmatrix}\rho_{11}&\rho_{12}\\ \rho_{21}&\rho_{22}\end{bmatrix} \tag{7}\]  
Figure 15: Total active reflection coefficients of proposed antenna.  
\[\rho_{ii}=1-\left(\left|S_{ii}\right|^{2}+\left|S_{ji}\right|^{2}\right)\text{; }\rho_{ij}=S_{ii}^{\ast}S_{ij}-S_{ji}^{\ast}S_{jj} \tag{8}\]  
Figure 16 illustrates the simulated and measured CCLs. Less than 0.25 bits/Hz/s CLL values were obtained over the entire FFC frequency span. This lower CCL meets the good diversity standard for the MIMO system.
## 1 Introduction
### Evaluation of Wind Turbine Performance Utilizing Wind Power Curves  
The realm of wind turbine performance assessment currently employs methodologies grounded in wind power curve analysis and artificial intelligence-driven evaluations [14; 15; 16; 17; 18; 19; 20; 21]. The former approach, notable for its ease of modeling and quantification capabilities, is exemplified by a method proposed in [22]. This method enhances the precision in defining the evaluation parameters by calculating the limits of automatic power curves, thus facilitating a robust assessment of turbine health through a comprehensive analysis of deviations between actual and ideal static power curves, alongside efficiency metrics.  
To summarize, the operational health of turbines is contingent upon a multitude of factors, including environmental conditions, hardware integrity, and control mechanisms. Reference [23] posits that wind energy is a renewable resource, with wind power generation harnessing this energy for electricity production. Wind speed serves as an indicator of the magnitude of wind energy, while wind speed-power data directly reflect the efficiency of wind energy capture by wind turbines. Among these data, the most significant correlation is observed between wind speed and power. The establishment of a wind power curve model that accurately mirrors real-time turbine operation enables the substantive assessment of turbine health. In light of this, this study introduces a refined polynomial curve fitting (PCF) algorithm tailored for wind power curve modeling. This novel approach, coupled with a method to ascertain turbine health regions via data increment inflection points, aims to elevate the precision in turbine performance evaluation. This paper's novel contributions are outlined as follows:  
1. The incorporation of a regularization term within the least squares framework propels an enhanced segmented modeling optimization technique for the wind power curve via the refined PCF algorithm. This innovation addresses the challenges of curve-function disparities and the extensive duration required for model training, potentially affected by local optima, thereby paving new avenues in health performance curve modeling for turbines.
2. Leveraging the proposed optimization algorithm for wind power curve modeling, this study devises an optimized approach to defining health regions grounded in data increment inflection points. This delineation fosters a solid foundation for the subsequent evaluation of turbine health performance.  
This study pioneers the integration of wind power curves with parameters such as rotor speed stability, consistency in power characteristics, and efficiency metrics, facilitating a comprehensive assessment of turbine health. This methodology underscores the efficacy of the proposed turbine performance evaluation framework.