esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Performance Analyses of Hardware and Software Co-Design
This is reasonable, because in addition to the memory access time between source and destination addresses and the bus turn-around time between transfers of data blocks, the SG-DMA execution overheads for each burst transfer for a DBS additionally include the programming efforts of uP, such as descriptor configurations, interrupt services executions, and so on. To transfer 8192-byte data in the memory, the number of burst transfers of DBS:1024 is 8 times, which is less than 128 times of DBS:64. Accordingly, the SG-DMA execution overhead of DBS:1024 (with 8 times of transfers) is less than that of DBS:64 (with 128 times of transfers). Consequently, the throughput performance of DBS:1024 overcame that of DBS:64 by around 238% (=15.83/6.66 \(\times\) 100%). Apart from the performance comparisons among different DBSs of SG-DMA, we furthermore transferred the 8192-byte data using pure uP programming (i.e., without SG-DMA) from the source to the destination addresses that are identical to those of the SG-DMA experiments in the previous sub-section. As Figure 15b shows, the throughput performance of the memory access by pure uP is a small value of 0.51 MB/s, which is less than the average throughput of 11.81 MB/s of SG-DMA. That means that only 4.32% ([0.51 MB/s]/[11.81 MB/s]) processing time of pure software (Pure uP) is required for uP, with the help of the SG-DMA controller. Accordingly, this experimental result demonstrates and validates the fact that a hardware accelerator (i.e., SG-DMA) for memory  
Figure 15: Performance comparisons of SG-DMA controller (**a**) among different data block sizes (DBSs) and (**b**) between pure software (Pure uP) processing and a hardware and software co-design approach (uP + HW Accelerator). data movements is absolutely necessary for NB-IoT devices to generate the number of Transport Blocks (TBs) for accessing the 6G gNB. In summary, this sub-section illustrates the performance advantages gained through the utilization of the hardware and software co-design approach (i.e., \(\mu\)P + HW Accelerator) in the proposed OTP\({}^{3}\) secure mechanism. This enables the micro-processor (\(\mu\)P in Figure 8) to retrieve the key block generated by the \"KS-Generator\" controller, then performs a check on the generated key block using the REG-CAM controller (c.f. Section 4.3.1) to determine its repeating status. If the key block has not been used, it is stored for subsequent encryption with a plaintext data stream block. Finally, the micro-processor manages the SG-DMA controller (c.f. Section 4.3.2) to transfer the encrypted datagrams from the \"Input FIFO Buffer\" (c.f. Figure 6) to the external memory, preparing them for transmission to the Internet through the network controller. The system design architecture and execution flow are detailed in Sections 4.1 and 4.2, respectively.
## 2 Problem Formulation and Models  
### Privacy-Preserving Deep Network Inference  
In this work, we focus on the privacy-preserving outsourcing of deep neural network inferences from an IoT device to a nearby edge server. The deep neural network is pre-loaded on both the IoT device and the edge server. The IoT device holds the private data, which it encrypts and sends to the edge server. The edge performs the most resource-consuming linear operations and returns the results back to the IoT device in a timely manner. For convenience, we use a typical convolution neural network (CNN) structure such as AlexNet for the case study. The network consists of multiple _convolutional layers_, _non-linear layers_, and the final _fully connected layers_. The proposed scheme is applicable to networks that share similar structures.  
A convolutional layer takes as its input a tensor \(T\in\mathbb{R}^{\hat{n}\times\hat{n}\times D}\) with \(H\) kernels \(K_{h}\), each of size \(k\times k\times D\). The convolutional layer outputs a tensor \(T^{\prime}\) of size \(\hat{n}^{\prime}\times\hat{n}^{\prime}\times H\), where \(\hat{n}^{\prime}=\frac{\hat{n}-k+2p}{\hat{s}}+1\), and \(\hat{s}\), \(p\) indicate the stride and padding, respectively. The computation process of a convolutional layer can be viewed as multiplying each \(k\times k\) matrix of a \(D\)-channel kernel with the input \(\hat{n}\times\hat{n}\) matrix by sliding the former across the latter in a top-down and left-right manner with stride \(\hat{s}\). The process is repeated for all \(D\) channels, and the output tensor is a weighted sum. From a mathematical perspective, each element of channel \(h\in[1,H]\) of the output tensors can be represented as: \(T^{\prime}(i,j,h)=\Sigma_{t,t^{\prime}\in[k]\in[D]}T(i\hat{s}+t,j\hat{s}+t^{ \prime},c)K_{h}(t,t^{\prime},c)\). The input into the fully connected layer is an \(n\)-dimensional vector \(x\in\mathbb{R}^{\hat{n}}\), which goes through a linear transformation parameterized by a weight matrix \(\tilde{W}\in\mathbb{R}^{n\times m}\) and a bias vector \(b\in\mathbb{R}^{\hat{n}}\). The output is \(y=x\tilde{W}+b\), a vector of length \(\hat{m}\). For simplicity, we use scalar products to represent the convolution operations and the linear transformations throughout the paper.  
Non-linear layers consist of _activation functions_ and _pooling layers_. The activation functions aim at shrinking the output size by performing computation on each element of the input. One of the most common activation functions in deep neural networks is the ReLU function \(\text{ReLU}(x)=\max(0,x)\). Pooling layers consists of the widely used max-pooling function or average-pooling function. Throughout the paper, we will focus on the outsouncing of linear layers, since the non-linear function counts for only a small portion and can be efficiently computed on IoT devices.
## 6 Discussions
### Discussion for the Case Results  
In order to further discuss the case results, a comparison scheme has been designed and analyzed. The ticket price of the comparison scheme adopts the actual fixed ticket price of each OD section of the G19 train, and the number of ticket sales in the comparison scheme is the average ticket-purchasing demand of each OD section during the pre-sale period obtained by the above simulation. The joint optimization scheme of the ticket price and allocation proposed in this paper is compared with the comparison scheme. The ticket prices and number of ticket sales of each period during the pre-sale period in each OD section of the two schemes are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline OD & \multicolumn{4}{c}{**Ticket Price/CNY**} & \multicolumn{4}{c}{**Number of Ticket Sales/Tickets**} \\ \cline{2-9}
**Section** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 1** & **t = 2** & **t = 3** & **t = 4** \\ \hline (1,2) & 207 & 212 & 212 & 223 & 11 & 23 & 63 & 25 \\ (1,3) & 495 & 520 & 532 & 533 & 20 & 50 & 114 & 31 \\ (1,4) & 626 & 626 & 662 & 662 & 53 & 113 & 247 & 85 \\ (2,3) & 315 & 325 & 333 & 333 & 11 & 48 & 38 & 31 \\ (2,4) & 433 & 476 & 479 & 479 & 15 & 42 & 72 & 30 \\ (3,4) & 151 & 151 & 151 & 155 & 12 & 39 & 33 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model results.  
Figure 9: Ticket price change in different OD sections during pre-sale period.  
Compared with CNY 595,848 for the revenue of the comparison scheme, the revenue of the joint optimization scheme proposed in this paper is CNY 601,881, which is an increase in the revenue by 1.01% with a small adjustment of the price. In fact, the comparison scheme reflects the actual situation wherein the transportation enterprise operator decides the ticket allocation under the fixed ticket price, without considering the dynamic ticket-purchasing demand during the pre-sale period. Thus, it is clear that the joint optimization scheme proposed in this paper can not only meet the passengers' dynamic ticket demand during the pre-sale period but also provide effective strategies for enterprises to expand the market and increase profits.  
However, the revenue increase rate in the results' comparison is at a low level--only a 1% increase in revenue. The main reason was determined to be that during the modeling process, the average seat utilization rate of the train was constrained to be no lower than a certain limit to ensure that the average seat utilization rate did not be greatly affected.
## 2 Methodology
### Study Cases
#### 2.5.3 Simulation and Disturbance Modeling  
This system was simulated using MATLAB/Simulink to test its response to voltage disturbances under controlled conditions.  
The IEEE 33-bus test system is widely adopted for its radial structure, realistic voltage drops, and load diversity, making it a suitable benchmark for evaluating power quality event detection methods. It includes buses with varied electrical distances from the substation, enabling robust analysis of spatially distributed voltage anomalies.  
To evaluate the classifier's generalization across network conditions, voltage disturbances were injected at four representative buses: 4, 12, 18, and 30. These were selected to reflect a range of topological characteristics:  
* Bus 4 is located near the source, experiencing minimal voltage drop.
* Bus 12 lies in the middle of the feeder.
* Bus 18 and Bus 30 are at the feeder end, where voltage sag and transient sensitivity are more severe.  
Each bus underwent three disturbance durations (0.2 s, 0.4 s, 0.8 s) across all event types. This setup produced a diverse labeled dataset to train and validate the ANN classifier. Labels used for classification were previously listed in Table 1.  
The IEEE 33-bus system provides a reliable environment for studying the behavior of distribution networks under anomalous operating conditions. Its structure supports the development and validation of detection algorithms and facilitates dynamic analysis of power quality events. The system's topology is detailed in Figure 2, in which Bus 0 is referred to as the connection point to the grid.
## 2 Mathematical Model of Stepless Capacity Control System
### Computational Fluid Dynamics (CFD) Model of Reciprocating Compressor
#### 2.2.4 Dynamic Mesh Control Equation of Valve Plate  
In the process of opening and closing or rebounding, the flow of the valve plate is caused by the movement of the boundary of the basin and the shape of the basin changes with time, so the dynamic grid model must be adopted.  
(1) In the opening and closing process of valve plate, the velocity equation is:  
\[v_{n}=v_{n-1}+\frac{d^{2}h}{dt^{2}}dt \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Unit** & **Value** \\ \hline Rotational speed & n & r/min & 300 \\ Radius of the crankshaft & r & m & 0.09 \\ connecting rod length & l & m & 0.45 \\ Radius of the cylinder & r\({}_{\text{c}}\) & m & 100 \\ suction pressure & Pi & kPa & 100 \\ Exhaust Pressure & Pd & kPa & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compressor parameters.  
Figure 3: Outline diagram of reciprocating compressor and velocity cloud diagram during suction.  
where, \(v_{n}\), \(v_{n-1}\) represents the velocity of different time steps, \(h\) represents the valve displacement, \(dt\) represents the length of time step.  
(2) During the whole working process of the valve plate, the acceleration equation is:  
\[\frac{d^{2}h}{dt^{2}}=\frac{F_{g}+{F_{s}}^{\prime}+m_{v}g}{m_{v}} \tag{17}\]  
where, \(F_{g}\) represents the gas resultant force on each unit on the upper and lower surface of the valve plate, \({F_{s}}^{\prime}\) represents the spring resultant force of valve, \(m_{v}\) represents the valve quality.  
(3) During the collision process, the velocity is:  
\[v_{2}^{\prime}=-C_{R}v_{1}^{\prime} \tag{18}\]  
where, \(v_{1}^{\prime}\) represents the speed of valve plate before collision, \(v_{2}^{\prime}\) represents the speed of the valve plate after collision, \(C_{R}\) represents the rebound coefficient, the minus sign of the equation means the velocity is going in the opposite direction. The numerical value of the rebound coefficient is usually given by experience, For the plate valve, the coefficient is usually between 0.2 and 0.3 [13].
## 5 Cognitive Security in the Future IoT  
Data collection is one of the major energy-consuming tasks and a vulnerable state when they become susceptible to attacks from intruders. Thus, the cognitive security (CSC) group encompasses solutions that give nodes the ability to learn, understand and evolve using data to proactively predict, detect and prevent threats [74]. Apart from the ability to learn, CSC also aims to provide an improved incidence response time and reduces false positives during threat detection. There are two peculiarities of IoT nodes that make them susceptible to attacks [75]. Firstly, they cannot use advanced security algorithms and secondly, they have a wide attack vector due to their usually large-scale deployments [76; 77]. According to [PERSON] [78], the three (3) key elements for cognitive security are device and data protection, proactive threat intelligence, and cognitive risk management. The first element ensures that only authenticated and authorized users/applications have access to the network, device or data service provided by the IoT system. The second element describes the use of automated response tools to visualize and prioritize threats while the last element describes the use of cognitive tools to adaptively manage threats using information from acquired data.  
To achieve CSC, authors in ref. [74] proposed the use of cognitive firewalls and supervisors with embedded ML/AI. In this approach, a hardened cloud act as a proxy that handles data request, commands and responses between nodes to provide a scalable security system. The weakness of this system, however, is the trust assumption it makes on some system elements which might not always be realistic. In a related study, authors in ref. [79] proposed a three-layer CSC model comprising of knowledge, information, and cognitive domains. The model carries out five major objectives that include modeling mental maps, knowledge generation, data fusion, data security handling, automated threat handling. Other attempts at developing a CSC solution for IoT can be found in ref. [80].  
However, irrespective of the approach used to achieve CSC, a fact which remains immutable is that all viable solutions must collect, have access, and learn from security-related data. Table 4 summarizes the usage of AI-based techniques for IoT cognitive security. In the next section, the fourth group of CS solutions is presented.
## Appendix C Proof of Theorem 3  
In this section, the proof of Theorem 3 is given.  
Proof of Theorem 3.: Lyapunov function \(V(y_{x,K}(k))=\max_{i}\{(y_{x,K})_{i}(k)\}-\min_{i}\{(y_{x,K})_{i}(k)\}\) is considered. According to (A2) and Assumption 3, there exist an integer \(\hat{K}=\max\{K_{1},K_{2}\}\) such that  
\[|x_{i}(K)-x_{j}(K)|=\mathrm{O}(f(K))\] (A4)for any integer \(K\geq\hat{K}\). Furthermore, according to (A1), there exists a constant \(a\in(0,1)\) being related to \(M(\hat{K})\) and \(\tilde{c}\) such that  
\[|x_{i}(k)-x_{j}(k)|\leq V(y_{x,\hat{K}}(k))\leq\mathrm{O}\Big{(}a^{k}\Big{)}\] (A5)  
for all integers \(k\in[0,\hat{K}]\).  
From (A4) and (A5), it follows that  
\[|x_{i}(K)-x_{j}(K)|\to 0,K\to\infty.\] (A6)  
From (3), we have  
\[x_{1}(K)=x_{1}(K-1)+\Bigg{(}\sum_{s=0}^{M(K)}\sum_{i\in V}\Big{(}[\widehat{A} _{Ks}(K-1)]_{1i}x_{i}(K-1-s)\Big{)}-x_{1}(K-1)\Bigg{)}\] (A7)  
for all integers \(K\geq 1\). Because of  
\[\frac{\Big{(}1-\tilde{c}^{H_{M}(K)}\Big{)}^{\frac{K-1}{H_{M}(K)}}}{\big{(}1- \tilde{c}^{H_{M}(K)}\big{)}^{\frac{K}{H_{M}(K)}}}\to 1\;as\;K\to\infty,\]  
then  
\[\Big{(}1-\tilde{c}^{H_{M}(K)}\Big{)}^{\frac{K-1}{H_{M}(K)}}=\mathrm{O}\bigg{(} \Big{(}1-\tilde{c}^{H_{M}(K)}\Big{)}^{\frac{K}{H_{M}(K)}}\Bigg{)}\] (A8)  
for all integers \(K\in[\hat{K},\infty)\). From (A1), (6) shown in Assumption 3, and (A8), the function \(V(y_{x,K}(K-1))\) satisfies the following inequality that  
\[V(y_{x,K}(K-1))=\mathrm{O}(f(K))\] (A9)  
for all integer \(K\in[\hat{K},\infty)\).  
According to (A9), we have  
\[\max_{i\in V,K-M(K)-1\leq k\leq K-1}|x_{1}(K-1)-x_{i}(k)| \leq V(y_{x,K}(K-1))\] (A10) \[=\mathrm{O}(f(K))\]  
for all integer \(K\geq\hat{K}\).  
Then, combining (A7) and (A10), we have  
\[|x_{1}(K)-x_{1}(K-1)|\leq\mathrm{O}(f(K))\] (A11)  
for all integer \(K\geq\hat{K}\).  
According to (A11) and \(f(K)=\mathrm{o}\Big{(}\frac{1}{K}\Big{)}\) as \(K\to\infty\) shown in Assumption 3, by using the [PERSON]'s convergence test, we can get that there exists a constant \(x^{*}\) such that \(|x_{i}(k)-x^{*}|\to 0,k\to\infty\) for all \(i\in V\). Since there are no external inputs in system (1), we have \(x^{*}\in[\min_{i\in V}\{x_{i}(0)\},\max_{i\in V}\{x_{i}(0)\}]\). Therefore, system (1) satisfies Definition 1.
## 3 Experimental Verification on the Damping Performance of the Pipeline System  
To investigate the damping performance of the particle impact damper, a random excitation experiment was conducted. Random excitation is a kind of uncertain excitation; the application of random excitation on the test pipeline can better simulate the working conditions of the actual pipeline so that the test results are more practical. The schematic diagram of the vibration damping test system is shown in Figure 4.  
This paper refers to the vibration mode of the test pipeline (external diameter \(\phi=16\) mm, wall thickness \(t=1\) mm) with a length of 50 mm, where the measuring point of the sensor (PCB, model 352C22) was 12.5 cm away from the left end of the pipeline, and the particle impact damper was selected at the middle of the pipeline. The instruments required for the test and the test layout of the vibration table (ES-10-240, frequency range 2-5000 Hz) are shown in Figure 5.  
The vibration table can output a set random excitation signal. The frequency domain of random excitation is 10-2000 Hz. The load spectrum curve of random excitation is shown in Figure 6. Figure 6 represents the spectrum setting of random vibration excitation, which is the data that must be input for random excitation. After the random excitation signal is input, the vibration table starts to work and obtains the response signal of the pipeline through the acceleration sensor. The excitation conditions in Figure 6 are used for all tests.  
Figure 4: Schematic diagram of the vibration damping test system.  
Figure 5: Experimental setup of the vibration test.  
During the test, the vibration table is loaded along the X direction (horizontal and vertical to the pipeline). An acceleration sensor was placed 12.5 cm away from the left end of the pipeline to measure the vibration acceleration. Two experiments are conducted under each experimental working condition, and the average of the results of the two experiments is taken as the experimental results under each experimental working condition.  
The energy level of structural vibration is evaluated by the root-mean-square value of the damping effect of random excitation [28]; the expression is  
\[E=\sqrt{\frac{1}{N}\sum_{i=1}^{N}y_{i}^{2}} \tag{8}\]  
where \(N\) and \(y_{i}\) are the total numbers of discrete signals and vibrational signals. During the test, the formula of the damping effect of the particle impact damper is expressed as follows:  
\[\Delta=\frac{E_{a}-E_{b}}{E_{a}}\times 100\% \tag{9}\]  
where the subscripts '\(a\)' and '\(b\)' represent the RMS values of the bare pipeline and damped pipeline at a certain point, respectively.
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
## 2 Mathematical Model of the Rolling Element Bearing
### Equations of Motion  
In the derived mathematical model, the external forces can be subjected in two directions of the x-y plane. Taking into account proposed nonlinear features, the final solution of the mathematical model consists of the following set of differential equations (Equation (10)). The governing equations are obtained by the transformation of the Lagrange equation of 2 nd kind taking into consideration the mass of bearing-rotor system equal to 1 for the dimensionless model and they have the following form by defining the state vector [21]:  
\[\left\{\begin{array}{c}\frac{d\delta_{\text{xs}}}{d\xi}\\ \frac{d\delta_{\text{xs}}}{d\xi}\\ \frac{d\delta_{\text{xs}}}{d\xi}\\ \frac{d\delta_{\text{xs}}}{d\xi}\\ \frac{d\delta_{\text{xs}}}{d\xi}\end{array}\right\}=\left\{\begin{array}{c} \dot{\delta}_{\text{xs}}\\ \dot{\delta}_{\text{ys}}\\ F_{x}+ecc\;\omega_{\text{s}}^{2}\cos(\omega_{\text{s}}t)-K_{\text{xxs}}-c_{ \text{x}}\dot{\delta}_{\text{xs}}\\ F_{y}+ecc\;\omega_{\text{s}}^{2}\cos(\omega_{\text{s}}t)-K_{\text{yys}}-c_{y} \dot{\delta}_{\text{ys}}\end{array}\right\}, \tag{10}\]  
where (\(\cdot\)) denotes time derivative with respect to the dimensionless time, \(t\), such as \(t=\tau\Omega\) (\(\tau\) is the original time in seconds), \(\Omega\) is the characteristic frequency scaling the natural frequency related to the linearized continuous contact forces, \(K^{\prime}_{x}\) and \(K^{\prime}_{y}\): \(r_{c}{}^{\gamma-1}\)\(k^{\prime}_{b}\)\(m\)\(=r_{c}{}^{\gamma-1}\)\(k^{\prime}_{b}\)\(/m\)\(=\)\(\Omega^{2}\). Consequently, \(\omega_{\text{s}}=\omega^{\prime}_{\text{s}}\)\(/\Omega\). Furthermore, the characteristic lengths and displacements are scaled by the radial clearance \(r_{c}\) as the rotor eccentricity \(ecc=ecc^{\prime}/r_{c}\) and corresponding shaft displacements \(\delta_{\text{xs}}=\delta^{\prime}_{\text{xs}}\)\(/r_{c}\), \(\delta_{\text{ys}}=\delta^{\prime}_{\text{ys}}\)\(/r_{c}\) together with their corresponding time derivatives. Dimensionless load force components are \(F_{x}\)--external force subjected in the vertical direction, \(F_{y}\)--external force subjected in the horizontal direction (\(F_{x,y}=F^{\prime}_{x,y}/(m\;r_{c}\;\Omega^{2})\)). While dimensionless damping force components--(\(c_{x,y}\)\(\dot{\delta}_{\text{xs,ys}}\)) with dimensionless damping coefficients \(c_{x,y}=c^{\prime}_{x,y}/\Omega\).