esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Algorithms
#### 3.4.2 gNB Matching Functions  
At the gNB, two OpenFlow tables _Tg(0)_ and _Tg(1)_, process vehicular data using matching rule functions _Rg(0)_ (Algorithm 3) and _Rg(1)_ (Algorithm 4). The input ports are _Pg(in)_ = {1, 2, \(\ldots\), n}, and the output ports are Pcam, Plid, and Prad for Camera, LiDAR, and RADAR data, respectively. The gNB matching functions verify the integrity of the received vehicular data and dynamically offload to edge application servers using the vehicle-assigned Slice ID.  
```
input:\(D\)//Vehicular Data \(\textit{Pv(in)}\)//Input port \(\textit{d}\)//Data type (Camera, LiDAR, RADAR) \(D\) with assigned Slice ID forwarded to _Pv(out)_ = 13 //Step 1: Match input data to appropriate class 1 if _Pv(in)_ {1, 2, 3, 4, 5} and \(d\) = \"Camera\" then 2 Slice ID \(\gets\) 1 3 else if _Pv(in)_ {6, 7, 8} and \(d\) = \"LiDAR\" then 4 Slice ID \(\gets\) 2 5 else if _Pv(in)_ {9, 10} and \(d\) = \"RADAR\" then 6 Slice ID \(\gets\) 3 7 end if //Step 2: Forward to output port \(\textit{8}\) forward \(D\) with Slice ID to _Pv(out)_
```  
**Algorithm 3** gNB Matching Rule Function _Rg(0)_ on Table _Tg(0)_  
The proposed SDN Controller functions presented above apply different SDN principles in the reception, processing, and offloading of vehicular data to edge application servers. On SDVs, NFV was applied to create an IoT network connected to a local SDN gateway via VNFs. The SDN Controller's matching functions enable the real-time management of vehicular data in queues via OpenFlow tables. Real-time data analytics, traffic engineering, and dynamic SDN forwarding principles were applied in the data plane (Underlay network) of the SDN architecture. Lastly, the SDN Controller matching functions at the SDN gateway and gNB enable end-to-end network slicing for service differentiation and the dynamic offloading of critical vehicular data to edge application servers.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 5 Results
### Scenario 1: IoT Sensors for EU-EU and EU-U.S. Communication  
Upon comparing the two experiments in Scenario 1, hlthe 600 satellite constellation resulted in nearly continuous curves with distinct periodicity and more compressed RTT ranges (see Figure 4). In addition, a lower range and mean RTT, as well as decreased or even eliminated ping losses for every Sensor were observed within the 600 satellite experiment (refer to Figure 5). Specifically, every sensor besides Sensor[0] in the 360 satellite constellation provided ping loss in the range of 0-4.89%, whereas the 600 satellite constellation resulted in nearly eliminated loss (0-0.69%). Even for Sensor[0], in which the received pings were limited, likely due to collision issues arising from simultaneous transmissions, the ping loss was reduced from 70.48% to 69.09%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline MCC & **Location** & **Coordinates** & **Name** \\ \hline MCC[5] & USA & 50.334241, \(-\)74.0060 & **-** \\ \hline MCC[8] & Athens/Greece & 37.970833, 23.725110 & NETMODE \\ \hline MCC[9] & Xanthi/Greece & 41.130036, 24.886490 & **-** \\ \hline MCC[13] & Barcelona/Spain & 41.346176, 2.168365 & OFELIA \\ \hline MCC[14] & Malaga/Spain & 36.719444, \(-\)4.420000 & Triangle \\ \hline MCC[15] & Antwerp/Belgium & 51.260197, 4.402771 & CityLab \\ \hline MCC[16] & Smart Santander/ Spain & 43.462776, \(-\)3.805000 & smartSantander \\ \hline MCC[17] & Dublin/Ireland & 53.342686, \(-\)6.267118 & IRIS \\ \hline MCC[18] & Amsterdam/Netherlands & 52.377956, 4.897070 & ExoGENI \\ \hline MCC[19] & Volos/Greece & 39.366669, 22.933332 & NITOS \\ \hline MCC[20] & Geneva/Switzerland & 46.204391, 6.143158 & IoT Lab \\ \hline MCC[21] & Ljubljiana/Slovenia & 46.056947, 14.505751 & LOG-a-TEC \\ \hline MCC[22] & Warsaw/Poland & 52.237049, 21.017532 & PL-LAB \\ \hline MCC[23] & Paris/France & 48.864716, 2.349014 & EdgeNET\&Grid’5000 \\ \hline MCC[24] & Monaco/France & 43.6155, 7.0550 & R2 lab \\ \hline \hline \end{tabular}
\end{table}
Table 6: MCC testbeds and features of Scenario 2.  
Figure 3: Simulation snapshots from the 360 (**left**) and 600 (**right**) satellite constellation in Scenario 2.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 4 SNDR Analysis
### Different Amplification Coefficients  
In the case of different amplification coefficients, we have \(y=\alpha s+\beta e+d\). The exact definitions of the two amplification coefficients \(\alpha\) and \(\beta\) are given by  
\[\alpha=\frac{E[s^{*}(y-\beta e)]}{E\left[\left|s\right|^{2}\right]}, \tag{29}\]  
\[\beta=\frac{E[e^{*}(y-\alpha s)]}{E\left[\left|e\right|^{2}\right]}, \tag{30}\]  
respectively. We can see that according to the above definitions, the two coefficients are mutually coupled and very difficult to calculate. Fortunately, in the SWIPT scenario, we can simplify the calculation with proper approximations. Exploiting the fact that the information signal \(s\) is small compared with the energy signal \(e,y-\alpha s\) can be approximately treated as equal to \(y\) when we calculate \(\beta\). It follows that we have \(y\approx\beta e+d\). The amplification coefficient of the energy signal is then given by  
\[\beta=\frac{E[e^{*}y]}{E\left[\left|e\right|^{2}\right]} \tag{31}\]  
where  
\[E[e^{*}y]=\int_{0}^{\infty}\int_{0}^{\infty}\left|e|g(r)\exp(j(\phi_{x}-\phi_ {e}+f(r)))\cdot f_{cy}(e,y)\right. \tag{32}\]  
and \(f_{cy}(e,y)\) is the joint PDF of energy signal \(e\) and output signal \(y\). Although function \(f_{cy}(e,y)\) cannot be expressed in closed form, it can be numerically evaluated.  
The power of the distortion term can be written as  
\[\varepsilon_{d}=E\left[\left|d\right|^{2}\right]=E\left[\left|y\right|^{2} \right]-\left|\alpha\right|^{2}E\left[\left|s\right|^{2}\right]-\left|\beta \right|^{2}E\left[\left|e\right|^{2}\right]. \tag{33}\]  
Finally, the SNDR can be expressed as  
\[\text{SNDR}=\frac{\left|\alpha\right|^{2}E\left[\left|s\right|^{2}\right]}{ \varepsilon_{d}+\sigma_{v}^{2}}=\frac{\left|\alpha\right|^{2}E\left[\left|s \right|^{2}\right]}{E\left[\left|y\right|^{2}\right]-\left|\alpha\right|^{2}E \left[\left|s\right|^{2}\right]-\left|\beta\right|^{2}E\left[\left|e\right|^{2 }\right]+\sigma_{v}^{2}}. \tag{34}\]
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 3 Smart Chatter Suppression in Wheel Reprofiling  
### Selection of the Adaptative Chatter Suppression Techniques for the Smart System  
In Section 1, several chatter suppression techniques were mentioned. However, not all solutions are applicable in every scenario. [PERSON] et al. [10] studied the different chatter suppression techniques and discussed the most suitable application for each chatter problem. The number of waves per revolution or lobe order (\(k\)) was defined as one of the key factors in selecting the optimal solution. In the present case of structural vibrations arising in wheel reprofiling turning operations, the ratio between the chatter frequency and the cutting frequency \(k\) was very high (\(k>200\)), due to the low rotating frequency of the machined wheel. This located the process in the A zone of the stability diagram (Figure 5), or the so-called process damping zone [10].  
In this zone, according to the domain experts [10], two techniques are especially effective, the above-mentioned process damping maximization, and CSSV [10]. Although active damping could be also a suitable solution for chatter removal in this zone, the high damping of the critical mode at 55 Hz discouraged its use for the chatter suppression smart system [10; 23; 24].
# Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control
## 4 MiFREN Adaptive Control and Closed-Loop Analysis  
### MiFREN Adaptive Controller  
Another MiFREN is established for the design of the controller. The purpose of the controller is to generate the control effort \(u(k)\) that forces the system to follow the desired trajectory \(r(k+1)\). For the class of discrete-time systems given by (1) and (2), some essential assumptions are stated, as follows:  
**Assumption 2**.: _There exists an idea control effort \(u^{*}(k)\) for the controlled plant (1) and (2), such that \(r(k+1)=f(w(k),u^{*}(k))\) and and \(r(k+1)\) are known in advance._  
**Assumption 3**.: _There exists an idea weight parameter \(\beta^{*}\in\mathbb{R}^{F}\) for MiFREN, where \(F\) denotes the number of IF-THEN rules for the network structure, such that_  
\[u^{*}(k)={\beta^{*}}^{T}\varphi(k)+\varepsilon_{u}(k), \tag{26}\]  
_where \(\varphi^{T}(k)\in\mathbb{R}^{F}\) is the regressive vector from the rule interface of MiFREN and \(\varepsilon_{u}(k)\) is the residue error, where \(|\varepsilon_{u}(k)|\leq\varepsilon_{M}\)._  
**Remark 1**.: _Assumption 2 is very general for the design of adaptive controllers, and Assumption 3 is the universal function approximation of MiFREN [18]._  
For the negative feedback of the output \(q(k)\), the tracking error \(e(k)\) is defined by  
\[e(k)=r(k)-q(k). \tag{27}\]  
The practical control effort \(u(k)\) is utilized by MiFREN as  
\[u(k)=\beta^{T}(k)\varphi(k), \tag{28}\]  
where \(\beta(k)\in\mathbb{R}^{F}\) denotes the weight parameters and \(\varphi(k)\) is a regressive vector for the IF-THEN rules of the tracking error and the desired trajectory. In general, the vector \(\varphi(k)\) can be expressed as  
\[\varphi(k)=[\mu_{e1}(e_{k})\mu_{r1}(r_{k+1}),\cdots,\mu_{ei}(e_{k})\mu_{ri}(r_ {k+1}),\cdots,\mu_{eF}(e_{k})\mu_{rF}(r_{k+1})]^{T}, \tag{29}\]  
where \(\mu_{ei}(-)\) and \(\mu_{ri}(-)\) are the membership functions of \(e\) and \(r\) for the \(i\)th rule, respectively.  
The IF-THEN rules are established by the relation between the tracking error and the desired trajectory, such that:  
_IF \(e(k)\) is Positive Large AND \(r(k+1)\) is Positive Large THEN \(u(k)\) should be Positive Large._  
Each of the inputs \(e(k)\) and \(r(k+1)\) have five membership functions: Positive Large (PL), Positive Small (PS), Almost Zero (AZ), Negative Small (NS), and Negative Large (NL). This leads to 25 IF-THEN rules. Figure 3 represents the network architecture of MiFREN for the controller design.
# Research on Optimal Control of Treadmill Shock Absorption Based on Ground Reaction Force Constraint
## 4 Experimental Data Collection  
### Foot Pressure Testing System  
#### 4.1.1 Dataset Creation  
Participants: A total of 27 subjects of different genders, ages, and weights were selected for this experiment. This article focuses on a single male participant in the testing. The subject is 170 cm tall, weighs 71 kg, is in good health, has experience using a treadmill, possesses good athletic ability and running habits, does not have flat feet or high arches, and has had no history of lower limb injuries in the year prior to the experiment (Figure 3a).  
Methods: The participant wore athletic clothing and running shoes equipped with foot pressure sensor insoles (Figure 3b) and performed an 8 min warm-up jog before running. Since the typical treadmill running speeds range from 4 km/h to 11 km/h, the testing speeds were set to 4 km/h, 5 km/h, 6 km/h, 7 km/h, 8 km/h, 9 km/h, 10 km/h, and 11 km/h (with 4 km/h corresponding to a brisk walk and the other speeds corresponding to running). The foot pressure sensor was zeroed before each test. After running for 1 min, the runner reached a stable state, and data collection began for 1 min. There was a 10 min rest period between each run to ensure the participant had time to recover. During each pace run, adjustments were continuously made to the air pressure in the airbags, allowing  
Figure 2: Block diagram of the overall design.  
multiple sets of different pressure values to be tested at each speed to determine the optimal pressure value for different speeds. The peak foot pressure values collected within 1 min were averaged, and then the average foot pressure values at different air pressure settings in the airbags were compared at a fixed speed. The air pressure value corresponding to the minimum average foot pressure was identified as the optimal pressure value for that specific pace. This process was repeated to determine the optimal pressure values for different paces. Subsequently, the average peak foot pressure values were also collected for a standard rubber shock-absorbing treadmill at different paces.
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 3 Results and Discussion
### Effect of Static Factors on Impedance and ECG
#### 3.1.3 Effect of Electrode Shape  
According to the equivalent circuit model shown in Figure 4, for electrodes with different shapes but the same areas, the equivalent resistance \(R_{el}\), the equivalent capacitance \(C_{el}\), the electron transfer resistance \(R_{es}\), and the equivalent capacitance \(C_{es}\) between the electrode and skin should be the same. Therefore, for electrodes with different shapes but the same areas, the skin-electrode impedance should be the same and have little effect on ECG.  
Figure 7 shows the experimental results of three electrodes of the same area with different shapes. From Figure 7a, it could be seen that the skin-electrode impedance for different shapes of PEDOT:PSS electrodes was similar, which was consistent with the equivalent circuit model analysis. From the spectrum in Figure 7b and the ECG signal in Figure 7c, it can be seen that PEDOT electrodes with different shapes but the same area have no significant impact on the frequency amplitude and ECG waveform.  
The SNRs were 9.2649 \(\pm\) 0.6326 dB, 9.2471 \(\pm\) 0.6806 dB, and 9.1514 \(\pm\) 0.6875 dB for the circular electrode, square electrode and triangular electrode, respectively. These results show that the electrode shape of the substrate-free PEDOT:PSS electrode had little effect on the skin-electrode impedance and ECG. Unlike the anisotropic fabric electrodes whose shape had a great impact on skin-electrode impedance and ECG quality [31], the performance of the substrate-free PEDOT:PSS electrodes was independent of the electrode shape and was better than fabric electrodes.  
Figure 6: Results for different electrode sizes (**a**) Bode plot of skin–electrode impedance; (**b**) Spectrogram of different electrode sizes; (**c**) ECG waveforms.
## 4 Damping Force Characteristics
### Position Control of the SBP  
The SBP is determined whether the bypass orifice is opened or closed. The SBP change depending on the current applied to the bypass orifice control part \(I_{B}\) is shown in Figure 14. At this time, the current  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Piston area & \(A_{p}\) & \(10.7521\times 10^{-4}\) & m\({}^{2}\) \\ Rod area & \(A_{r}\) & \(7.8540\times 10^{-5}\) & m\({}^{2}\) \\ Total orifice length of the main orifice & \(L_{t,M}\) & 40 & mm \\ Maximum effective pole length of the main orifice & \(L_{p,max}\) & 10 & mm \\ Minimum effective pole length of the main orifice & \(x_{m}\) & 1 & mm \\ Gap size of the main orifice & \(h_{M}\) & 0.6 & mm \\ Effective pole length of the bypass orifice & \(L_{p,B}\) & 15 & mm \\ Width of the bypass orifice & \(b_{B}\) & \(3\times 10\) & mm \\ Gap size of the bypass orifice & \(h_{B}\) & 0.9 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of MRD-PSF.  
Figure 13: Damping force shaping by damping coefficient control.  
applied in main orifice control part \(I_{M}\) is set to 3 A so that it makes the effect of main orifice constant. When \(I_{B}\) are 2, 1.5, 1, and 0.5 A, the point (damping force) where the bypass orifice is open is evaluated by 1326, 1063, 736, and 369 N. It is observed that the dramatic change in the damping coefficient occurs before and after these points. It is also seen that the stroke velocity at the SBP increases as the current applied to the bypass orifice \(I_{B}\) increases.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability  
The network coverage problem for BackCom and its solution is highlighted in addition to security and reliability of data. Various techniques are used to extend the communication range of BackCom. These include multi-antennas and MIMO technology, followed by relaying and D2D communications. Moreover, techniques for reliable communications and security for BackCom are also explained.
###### Abstract  
Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories.  
autonomous and adaptive manufacturing process; smart factory; Big Data; NoSQL; subsequence pattern +
Footnote †: journal: Applied Sciences
## 2 Data Establishment
### Data Initial Preparation  
Conventional statistical analysis approaches often fall short of fully capturing the distribution characteristics of rockburst data. Therefore, a scatter plot can be used as a data visualization tool to clearly understand the properties of rockbursts. However, it is difficult to directly visualize the dataset consisting of six dimensions. To overcome this challenge, two effective approaches, T-SNE (t-distributed stochastic neighbor embedding) [61] and PCA (principal component analysis) [62], were used and compared in this study. As illustrated in Figure 3, T-SNE demonstrated superior performance compared to PCA and was therefore selected for dimensionality reduction and data visualization. T-SNE is particularly adept at reducing global distortions while preserving the local structure and relationships of the original data. For nonlinear structures and local clustering phenomena, T-SNE can map the high-dimensional data to a lower-dimensional space and maintain the commonalities between data points successfully.  
In Figure 3b, it is difficult to distinguish between level IV and other levels since the rockburst data is imbalanced, and there are outliers. Therefore, SMOTE [63] was used for data balancing. SMOTE is an effective oversampling method that generates synthetic samples based on the minority class samples and integrates them into the dataset. This process helps in balancing the dataset and improving the model's ability to learn from all classes, particularly the minority class. Figure 4 is a schematic of SMOTE. The detailed steps of SMOTE are as follows:  
Figure 3: Scatterplot of the original dataset mapped onto the two-dimension space: (**a**) PCA and (**b**) T-SNE.  
Step 1: In the minority class, calculate the distance between one sample and all samples, then find the k closest minority samples to it.  
Step 2: Pick a random sample from the closest minority samples, denoting it as S.  
Step 3: Choose a random point between S and any one of the k randomly chosen minority samples. The difference between them for each feature is calculated to create the synthetic sample.  
Step 4: The feature value of the new synthetic sample is determined by multiplying a random number between zero and one with the difference and adding the equivalent feature value of S.  
Step 5: Repeat steps 2-4 to generate synthetic samples until the number of minority samples reaches a predetermined proportion.  
By using the SMOTE method, the number of samples under levels I, II, and IV was increased to match the number of samples under level III, respectively. As a result, there were 122 samples under each level, indicating a balance among the four rockburst levels. The distribution of the dataset after preprocessing is shown in Figure 5. The preprocessed dataset was dimensionally reduced and visualized by the T-SNE technique, as shown in Figure 6. It can be seen that the distribution of the preprocessed dataset is more balanced, and the separability has improved significantly.  
Figure 4: Schematic of SMOTE algorithm.  
Figure 5: The distribution of the dataset after preprocessing.
## 5 Results and Discussion
### Critical Discussion
#### 5.8.3 Guaranteeing Security under Personalized Customization  
To guarantee the security concerns under personalized customization, the proposed framework has an inherent capacity for adaption and scalability. Whether the manufacturing industry wishes to scale up, add more devices, or adjust the roles of existing ones, the proposed framework's modular nature allows for such adaptability. However, the underlying principles remain consistent, especially the employment of the AODV routing protocol and NTRUEncrypt cryptography. This ensures that even under various customization, the security of the system under consideration remains robust.
## 2 Theoretical Background
### Sound Absorption
#### 2.1.2 Acoustic Resonators  
A Helmholtz resonator consists of a cavity with one or more holes and necks, as represented in Figure 3b. The air inside the neck behaves like an oscillating piston (mass) while the air in the cavity behaves like an elastic element (spring). When the resonance frequency of the mass-spring system is equal to the frequency of the incident wave, the resonator express its maximum absorption. For these systems, the resonance frequency \(f_{0}\) is defined as:  
\[f_{0}=\frac{c_{0}}{2\pi}\sqrt{\frac{r^{2}}{V(l+\frac{\pi}{2r})}} \tag{7}\]  
where \(c_{0}\) is the speed of sound in air, \(r\) is the radius of the hole, \(V\) is the volume of the cavity and \(l\) is the length of the neck. However, Helmholtz resonators do not express any sound absorption outside the resonance frequency region. The transmission loss \(TL\) and the absorption coefficient of a Helmholtz resonator are defined as:  
\[TL=-10 log\left|\frac{p_{t}}{p_{i}}\right|^{2}=20 log\left|1+\frac{1}{2}\frac{S_ {0}\rho c_{0}}{SZ}\right| \tag{8}\]  
\[\alpha=1-\left|\frac{p_{t}}{p_{i}}\right|^{2}=\frac{4c_{0}\rho_{0}\frac{S}{S_ {0}}Z_{Re}}{(\frac{S}{S_{0}}Z_{Re}+\rho_{0}c_{0})^{2}+\frac{S^{2}}{S_{0}^{2}} Z_{Im}} \tag{9}\]  
where \(S\) is the cross-sectional area of the neck, \(S_{0}\) is the total areas of all necks, \(Z\) is the acoustic impedance of the resonator and represent the ratio between pressure amplitude and the particle velocity at the interface of the resonator [28].