esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
### Experimental Instructions  
To verify the effectiveness of the proposed intelligent control strategies for vehicle departures in parking lots, this study took Jinding Tiandi super-large underground parking lot in Pudong New Area, Shanghai as an example. We simulated the regional operation of the parking lot exit with timing and inductive control strategies based on the SUMO software. As shown in Figure 4, Jinding Tiandi is located on the north side of Pudong New Area and contains nine plots. The east side is Shenlun Road, the north side is Keqiao Road, and the south side is Jufeng Road. Specifically, Jufeng Road is the secondary trunk road, and the west side of Shenjiang Road is the trunk road, which is an important road for travelling from the inner ring of the city center to the outer ring, with a peak flow of more than 1200 pcu/h. With a land area of about 274,000 square meters and a total construction area of about 715,000 square meters, it is an urban complex with spaces for research and development, offices, hotels, commercial properties, and a theater. Jinding Tiandi is expected to attract a large amount of commercial and leisure interest in the future.  
Figure 3: Detector arrangement for inductive control strategy.  
As shown in Figure 5a, the Jinding Tiandi parking lot is equipped with nearly 7000 parking spaces and has 21 traffic entrances and exits. As shown in Figure 5b, the average spacing between the entrances and exits of some vehicles in the parking lot is less than 70 m from the intersections. The spaces between the exits are narrow, and the entrances and exits are distributed on the branch roads with a cross-section width of no more than 24 m. Since the overall road capacity is low, it is easy to generate local congestion points at the entrances and exits of the parking lot. Moreover, it is expected that the traffic flow from the trunk and secondary trunk roads (Shenjiang Road and Jufeng Road) to the Jinding Tiandi branch roads during the peak hours further aggravates the traffic pressure inside the plots. In summary, after construction is complete, the Jinding Tiandi parking lot will have a large parking management burden. The entrances and exits of the parking lot, as the key nodes of the external dynamic traffic and the internal static traffic conversion, are breakthrough points in terms of solving the problem of regional traffic control.  
Figure 4: Roads in and around Jinding Tiandi.  
Figure 5: The parking spaces, exits, and entrances of the Jinding Tiandi parking lot: (**a**) number of parking spaces; (**b**) spaces between entrances and exits (m).
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 5 Proposed Contention Model for Defence Against Wi-Fi Sensing
### Empirical Validation
#### 5.3.2 Results  
The results of the experiment are illustrated below.  
In Figure 11, we present a plot of the CSI data rate that Rx was able to achieve with varying levels of contention in the channel. The x-axis represents the number of 802.11n stations sending ping packets, and the y-axis represents the average CSI data rate that Rx was able to achieve over the duration of 60 s. The blue line represents the analytical result from earlier, and the red line is the experimentally derived result.  
We observe a striking similarity in the shape of the empirical result with the analytical result. The CSI snapshot rate is initially 1200 Hz, and it decreases to less than 400 Hz with the addition of only a single interference device. Introducing subsequent devices causes further degradation in the achievable CSI snapshot rate to less than 200 Hz. Both curves have similar shapes, with the key difference being a vertical translation. This difference can be understood when considering the real-world limitations of 802.11 communications. Although the analytical result implies that a CSI data rate of 4400 Hz is achievable with a channel bit-rate of 72 Mbps, the actual bandwidth is limited by the computation power of the wireless devices and their Network Interface Cards (NICs). Overall, this result supports the theoretical model for contention-based defence against undesired Wi-Fi sensing attacks.
## 1 Introduction
However, the classic GWRs, using multiple explanatory variables, assume that all the explanatory variables have a spatially homogeneous process of determining the pattern of a dependent variable, or simply speaking, operate on a single spatial scale. Scale is a fundamental geographic concept, and the role it plays in different geographical contexts and processes has been documented extensively [19; 20]. Therefore, a new multiscale geographically weighted regression (MGWR) was proposed and developed in the innovative work [21]. The MGWR method allows for the conditional relationship between response (dependent) variable and the variation of explanatory variables at different spatial scales. That is to say, each variable changes on the surface of the parameter to allow different bandwidths to be represented. Technically, MGWR minimizes overfitting by eliminating the limitations of all relationships varying on the same spatial scale, reducing bias and collinearity in parameter estimation [21; 22; 23; 24]. Therefore, when using flow focused GWR (FGWR) to explore multi-scale spatial heterogeneity in flow patterns, MGWR has become the most ideal local model specification [25]. Compared with the classic GWRs, the MGWR parameter estimates are more accurate and more intuitive [21]. Since the MGWR method currently only supports point/polygon data-based modeling, the aim of this paper is to extend MGWR and apply it to the modeling of spatial interaction data. Methodologically, the specific spatial relationship between flows is measured by flow-oriented spatial weight based on the Euclidean distance between flows as directed lines. The multiscale flow-focused geographically weighted regression model (MFGWR) is an extension of spatial interaction modelling and multi-scale geographically weighted regression (by considering multi-scale effects in the spatial non-stationarity of flows). This paper reports the first empirical study of multi-scale flow modelling applied for regional transport flows across the Jiangsu province in China. The contribution of this innovative method will be critically evaluated as well. This paper is organized as follows. Section 2 describes the study area, data collection and potential determinants of flow variations. Section 3 explains the modelling methods of MFGWR. Section 4 presents the modelling results and interpret the empirical study. The final section draws general conclusions, discusses limitations and makes recommendations for future work.
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 5 Conclusions  
This paper proposes a coordinated control method of VPP to improve power system short-term transient frequency response. The proposed strategy is based on a coordinated control of DERs and ESSs in the VPP. A variety of control modes are compared and validated through Monte Carlo simulations. The impact of communication delays, stochastic processes as well as of the capacity of ESS on the overall transient behavior are also outlined. Based on the simulation results, the following conclusions can be drawn.  
1. The proposed coordinated control approach for ESS and DERs in VPP can significantly improve power system frequency stability. The proposed control approach performs better than either conventional VPPs that do not regulate the frequency, i.e., use a constant power set-point, and VPPs that regulate the frequency through the independent controllers of ESSs and DERs.
2. Communication delays have a significant impact on the proposed coordinated control approach. This had to be expected, as the proposed strategy works as a sort of  
Figure 9: Frequency of the COI following a three-phase fault occurs in the TG, where the measurements \(\Delta\omega_{\mathrm{VOC}}\) and \(p_{\mathrm{inj}}\) are transmitted through high/medium/low-speed communication networks, respectively.  
fast_ secondary frequency control. To reduce the negative impact of communication networks without increasing the bandwidth, a two-phase coordinated control is proposed. In this operating mode, the ESS acts first whereas DERs are included in the coordinated control in a second phase. This reduces the impact of the limited capacity of the ESS and, in turn, improves the transient stability.  
Future work will focus on further improving the short-term frequency control of DERs and ESS included in VPPs. For example, _ad hoc_ feedback transfer functions for each DER and ESS rather than a single common \(H(s)\) will be considered. Secondary frequency control of VPPs will be also considered, thus allowing taking into account the state of charge of ESSs and short-term weather forecast. The impact of cybersecurity in a communication network includes bad data will also be also considered.  
All authors equally contributed to this work. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Science Foundation Ireland, by funding W.Z. and F.M. under project ESIPP, Grant No. SFI/15/SPP/E3125; and M.A.A.M., M.L. and F.M. under project AMPSAS, Grant No. SFI/15/IA/3074; and J.C. and F.M. under project EdgeFLEX, Grant No. 883710.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Problem Formulation
### Objective Function  
A multi-objective optimization problem is considered here. Apart from network power losses, the total operation cost and the network voltage deviation are also considered. The multi-objective problem is converted to a single objective problem by the method of weighting aggregation.  
\[\min f=\sum_{s=1}^{N_{k}}\pi_{s}(\omega_{1}C_{loss,s}+\omega_{2}C_{\text{cost}, s}+\omega_{3}C_{\Delta U,s}), \tag{3}\]  
\[\text{s.t.}\ C_{\text{loss},s}=c_{\text{loss}}\sum_{t=1}^{T}\sum_{(i,j)\in \psi_{t}}r_{ij}(P_{ij,s,t}^{2}+Q_{ij,s,t}^{2})/U_{i,s,t}^{2}, \tag{4}\]  
\[C_{\text{cost},s}=\sum_{t=1}^{T}c_{Grid,t}P_{Grid,s,t}+c_{\text{DG}}\sum_{t=1}^ {T}P_{DG,s,t}+c_{\text{ESS}}\sum_{t=1}^{T}(P_{ch,s,t}+P_{dis,s,t}), \tag{5}\]  
\[C_{\Delta U,s}=c_{\Delta U}\sum_{t=1}^{T}\Delta U_{s,t}=c_{\Delta U}\sum_{t=1} ^{T}\sum_{i\in\psi_{\text{P}}}\left|1-(U_{i,s,t}/U_{N})^{2}\right|, \tag{6}\]  
\[\omega_{1}+\omega_{2}+\omega_{3}=1\, \tag{7}\]  
where \(\pi^{s}\) is the probability of scenario \(s\) and \(N_{s}\) is the total number of scenarios. Equation (4) is the active power losses of ADN; Equation (5) computes the ADN's operation cost, including buying the electricity payment from the main grid (including the network losses) and the operation cost of DG and ESS, respectively; Equation (6) calculates the voltage deviation of all the buses during a day; \(c_{\text{Grid}}^{l}\) is the electricity price at time \(t\); \(c_{\text{DG}}\) is the cost coefficient of DG; \(c_{\text{ESS}}\) represents the operation cost of battery charge and discharge considering the battery life; \(c_{\Delta U}\) is the penalty coefficient of voltage deviation and is set to be 10 in this paper; \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are weight coefficients and \(\omega_{3}=\omega_{3}=\omega_{3}=\)1/3.
# Analysis on Flow and Temperature Field of High-Power Magnetorheological Fluid Transmission Device
## 6 Experiments and Discussions
### Rotating Speed Characteristics  
The relationship between torque and the slip rotating speed of the transmission device is obtained, as shown in Figures 19 and 20, where the exciting coil current is 0 A  
Figure 17: Experiment system.  
Figure 18: No-field torque variation.  
and 0.5 A, the slip rotating speed is 100 r/min, 300 r/min, 500 r/min, 700 r/min and 900 r/min, respectively.  
It can be seen from Figures 19 and 20 that, in general, the transmittable torque of the transmission device increases with the enhancement of rotating speed. As there is no exciting current and the rotating speed is 100 r/min, 300 r/min, 500 r/min, 700 r/min and 900 r/min, and the viscous torque of the device reaches 50 N.m, 260 N.m, 390 N.m, 460 N.m and 540 N.m, respectively. With the exciting current applied, the torque transmitted by MR fluid is always higher than that without exciting current at different rotating speeds. At rotate speeds higher than 300 r/min, the transmittable torque increases by about 150 N.m with the exciting current applied. At lower speeds, the transmittable torque increases even more due to the enhanced hydraulic torque caused by the disc deflection. Besides, under the above five working conditions, the magneto-induced torque at 0.5 A is 50%, 30%, 27%, 25% and 23%, respectively. The percentage of magneto-induced torque decreases with the increase in rotation speed, which will weaken the torque controllability of MR transmission devices.  
As also can be seen from Figure 19, the transmittable torque is always constant at lower slip speeds as the transmittable torque fluctuates less when the temperature increases  
Figure 19: Torque variation in the MR fluid device under different rotation speeds (exciting current is 0.5 A).  
Figure 20: Relationship between torque and rotate speed.  
slowly at a lower slip power. However, the transmittable torque at a rotating speed higher than 500 r/min decreases obviously with the running time as the temperature rises quickly with the increase in slip power. The constant torque characteristics deteriorate, leading to a difficult control of transmittable power.
## Dynamic Response of Structures
### Displacement Response of Structures  
In order to understand the dynamic response characteristics of different precast frames, the direct integration method was used for dynamic time history analysis under rare earthquakes (PGA: 400 gal) and extremely rare earthquakes (PGA: 600 gal). If the story drift of a certain floor exceeds the rotation angle value of the aforementioned feature point C, the structure will be considered as the total collapse. In rare earthquakes, only the PCE-D frame collapsed under Wave 1, so the reduction of joint ductility will cause the most adverse effect on the structure. Figure 13a,c plot the average maximum floor displacement of each frame under Waves 2-8 in rare earthquakes. Compared with the RC frame, the deformation of upper floors of emulated frames increase significantly, while the deformation of each floor of non-emulated frames generally increases, and their peak displacement response are higher than that of emulated frames. Under extremely rare earthquakes, the PCE-D frame collapses under Waves 1-3, and the PCH-F frame collapses under Waves 1 and 2. All precast frames under Wave 1 exceed the code displacement limit of 1/50. Figure 13b,d show the average maximum floor displacement of each frame under Waves 4-8 in extremely rare earthquakes; the maximum story drift of each structure shows similar laws. Therefore, attention should be paid to the displacement response of the upper structure for emulated frames, and the damage of structural components and non-structural members under large floor deformation for the non-emulated frames.  
The displacement response characteristics of different precast frames are described by taking the top displacement time history of Wave 2 (PGA: 600 gal) as an example. As shown in Figure 14, the peak displacement of PCE frame is significantly higher than that of the RC frame. In addition to the increase of the peak response, the vibration amplitude of PCH frame also generally increases in the time range. This earthquake ended at 40 s; the RC frame and PCE frame became static immediately after the earthquake, but the PCH frame still underwent a period of small vibration due to insufficient energy dissipation capacity before being in static. Due to the self-centering feature of joints, the residual deformation of the PCH frame is small.  
Figure 13: The distribution of maximum floor displacement under rare and extremely rare earthquakes: (**a**) Emulated frames under rare earthquakes; (**b**) Emulated connection frames under extremely rare earthquakes; (**c**) Non-emulated frames under rare earthquakes; (**d**) Non-emulated frames under extremely rare earthquakes.
# Maximizing the Capacity of Edge Networks with Multicasting
## 6 Optimal Number of Copies  
As shown above, two factors impact on the transmission distance. The first is the number of cooperative SBSs and the second is the number of content copies. They are correlated since the number of copies relies on the multicast tree structure and the number of cooperative nodes is decided by the copy distribution method. This study investigates the first factor with the multicast tree structure, that is, the number of cooperating SBSs will not exceed the number of nodes in the tree assuming that we obtain the copy numbers. The second factor is now analyzed.
# Color Point Cloud Registration Algorithm Based on [PERSON]
## 3 Algorithm Implementation
### Continuation of Hue Distribution  
\(P\) is a point cloud including many points, \(p\) is one point of it, and \(H(p)\) represents the hue of \(p\). When optimizing the error function, the gradient is needed, so we have to convert \(H(p)\) into continuous forms from discrete. As for any point \(p\in P\), \(n_{p}\) is the normal of \(p\), and \(\mathcal{N}_{p}\) is the neighbourhood of \(p\). Each point in \(\mathcal{N}_{p}\) has its hue value and the hue distribution of the points is discrete. When \(\mathcal{N}_{p}\) is small enough, we can use the hue distribution on the tangent plane to approximate that in \(\mathcal{N}_{p}\). Let \(u\) be one of the vectors on the tangent plane, the origin is point \(p\). Because the vector \(n_{p}\) is vertical to the tangent plane, we have \(u\cdot n_{p}=0\). We assume that \(H_{p}(u)\) is continuous and represents the hue distribution at \(p\)'s tangent plane. The point \(p\) is on the tangent plane and the hue of it is known, and when the radius of \(\mathcal{N}_{p}\) is small, we can normally use first-order Taylor formula at \(p\) to approximate \(H_{p}(u)\):  
\[H_{p}(u)\approx H(p)+d_{p}^{T}u \tag{3}\]  
where \(d_{p}^{T}\) is the gradient of hue distribution at p and can be estimated through the least square method. As for point \(p\in\mathcal{N}_{p}\), \(f(s)\) is the function which project the point \(s\) to the tangent plane:  
\[f(s)=s-n_{p}(s-p)^{T}n_{p} \tag{4}\]  
We can project every point \(p^{\prime}\) in \(\mathcal{N}_{p}\) to the tangent plane of \(p\), the projection coordinates are \(f(p^{\prime})\), and we can subtract it with the origin \(p\) to transform it to the vector on the tangent plane. In order to approximate the hue distribution of \(\mathcal{N}_{p}\) with that on the tangent plane, we should minimize the hue difference at \(p^{\prime}\) and \(f(p^{\prime})\). The former is known as a constant and the latter can be calculated through Equation (3). \(d_{p}^{T}\) in the equation determines the continuous distribution but is unknown and we can minimize the hue difference to solve it. The hue difference can be calculated related to \(d_{p}^{T}\) as following:  
\[\begin{split} L(d_{p})&=\sum_{p^{\prime}\in \mathcal{N}_{p}}(H_{p}(f(p^{\prime})-p)-H(p^{\prime}))^{2}\\ &\approx\sum_{p^{\prime}\in\mathcal{N}_{p}}H(p)+d_{p}^{T}(p^{ \prime}-p)-H(p^{\prime}))^{2}\end{split} \tag{5}\]  
Figure 3: The algorithm process.  
Because \(d_{p}^{T}\) is on the tangent plane of \(p\), there is a constraint:  
\[d_{p}^{t}n_{p}=0 \tag{6}\]  
Ideally, \(L(d_{p})\approx 0\) and the goal of optimization is to minimize it. This is a linear constrained optimization problem and it can be solved by the Lagrange method.
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 3 Proposed Federated Learning Model
### Anomaly Detection Based on LSTM  
Figure 4 displays the architecture of the LSTM model to be applied to the proposed federated learning. In this study, we trained a single LSTM model with the following architecture: an embedding layer with dimensions of 50 \(\times\) 16, a single LSTM layer with 128 units, and a dense output layer with two units for normal or anomaly prediction tasks. Each input-encoded log depends on the dataset. Each LSTM cell has three gates: the input, forget, and output. First, preprocessed features are input into the embedding layer to be embedded and vectorized. Then, the values entered in the hidden layer comprise 128 hidden nodes circulated by each LSTM cell to calculate the weight of the information in the vectorized log sequence. Finally, after 1D mapping of the matrix input through the FC layer, all weights for the hidden layer are added, and the softmax layer is applied for classification output to perform classification on the anomaly or normal data. Table 2 lists the model parameter settings for each layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Output** \\ \hline Input: encoded log & - \\ Embedding & \(50\times 16\) \\ CNN1D layer: 128 & 10,368 \(\times\) 128 \\ Dense layer: 128 & 129 \(\times\) 128 \\ Max Pooling layer: 2 & 0 \\ Dropout layer(0.3) & 0 \\ FC: 2 & 258 \\ Softmax layer & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific parameters of the CNN1D model used in our study.  
Figure 4: LSTM-based log anomaly detection model architecture.  
Figure 3: CNN1D-based log anomaly detection model architecture.
# A Robust Asynchronous Sampled-Data Control Design for Nonlinear Systems with Actuator Failures
## 2 Actuator Fault Observer and Asynchronous Sampled-Data Control Designs
### Disturbance Actuator Fault Observer  
From the linear model to the system (1):  
\[\dot{x}=Ax+B(u-u_{a});\quad x\in R^{n},\quad u\in R,\quad B\in R^{n},\quad A\in R ^{n\times n}, \tag{2}\]  
conceive the next auxiliary dynamic system:  
\[\dot{\hat{x}}=Ax+B(u-\hat{u}_{a})+L(x-\hat{x}),\quad L\in R^{n\times n}, \tag{3}\]  
where \(L=L^{T}>0\). Then, by defining  
\[e(t)=x(t)-\hat{x}(t), \tag{4}\]  
and by using (2) and (3), we obtain  
\[\dot{e}=-Le+B\hat{u},\quad\hat{u}(t)=-u_{a}(t)+\hat{u}_{a}(t). \tag{5}\]Just to construct an actuator fault estimation system, let us **temporally** assume that \(u_{a}:=u_{a}(t)\) is constant. Hence, and by invoking the next Lyapunov function  
\[V(t)=\frac{1}{2}e^{T}e+\frac{1}{2\alpha}\hat{u}^{2};\quad\alpha\in R^{+}, \tag{6}\]  
we get  
\[\dot{V}(t)=-e^{T}Le+\hat{u}[e^{T}B+\frac{1}{\alpha}\dot{u}_{a}]. \tag{7}\]  
Then, by setting  
\[\dot{u}_{a}=-\alpha B^{T}e, \tag{8}\]  
we force  
\[\dot{V}(t)=-e^{T}Le\leq 0. \tag{9}\]  
The above means that all time signals involved in (6) are bounded. Hence, in resume, we have the next result (this result is similar to the ones obtained in [17; 19]).  
**Theorem 1**.: _Given the nonlinear system (1) and its linear model (2); an actuator fault observer is granted by (3), (4), and (8) being \(\dot{u}_{a}(t)\) an estimation of \(u_{a}(t)\). Moreover, the signal \(\ddot{u}(t)\) is bounded on time too._
## 4 Results and Discussion
### ECG Database  
We utilized the MIT-BIH Arrhythmia Database [45] as the source of data for this paper. The database consists of 48 two-channel ambulatory ECG recordings; each recording has a duration of 30 min and a sampling rate of 360 Hz with an 11-bit resolution over a 10 mV range. The database also provides computer-readable reference annotations for each beat in every ECG recording, which makes it suitable for the ECG classification task.  
Figure 13: The proposed SVM classifier architecture.