esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor
## 4 Simulation and Experimental Results
### Experiment Based on FPGA and a CAPS
The programming language is python3.6 and the CPU model is 17-7700K of Intel Corporation, clocked at 4.2 GHz. Since both schemes are iterative algorithms, comparing the execution time of one iteration is beneficial to distinguish the efficiency of the two schemes (the total execution time of the iterative algorithm is affected by the parameters and the actual processed data). Each iteration completes an update of the parameters. In the experiment, the time to complete an iteration is 40 ns at a frequency of 25 MHz. Since the calculation is completed in one clock cycle and the pipeline operation guarantees the data throughput rate, one iteration time is the length of one clock cycle. However, the highest frequency of the circuit design is also affected by the delay of the critical path in the circuit. When using PrimeTime for timing analysis, the maximum \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Identification Values** \\ \hline \(a_{1}\) & 0.6085 \\ \(a_{2}\) & 0.6221 \\ \(b_{1}\) & 0.1323 \\ \(b_{2}\) & 0.1834 \\ \(\beta\) & 3.0734 \\ \hline \end{tabular}
\end{table} 
Table 10: Parameter identification values obtained based on technology detailed in [9]. \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Device** & **Total Execution Time** & **Execution Time for One Iteration** \\ \hline Proposed method & Cyclone V FPGA [29] & 197.83 s & 40 ns \\ Method in [9] & Laptop & 46.42 s & 0.46 ms \\ \hline \end{tabular}
\end{table} 
Table 11: Results about execution time in experiments. frequency of the system is expected to be around 70 MHz. For the execution speed in [9], the total time of 100,000 iterations is calculated and averaged, and the time of a single iteration is 0.464 ms. From this result, the circuit execution speed is faster, but the algorithm in the circuit has low utilization of data, and the parameters are not optimal. These reasons make the total execution time in the experiment longer than that in [9]. In addition to the velocity analysis, the Fourier analysis of the signal was also carried out. The result is shown in Figure 19. The signal contains harmonic components, and the fundamental frequency value is 0.051 Hz, which deviates from the set value. In addition, the error curve in the experiment has a certain periodicity, which may be caused by the speed fluctuation and the harmonic component of the signal. It is beyond the capability of the coprocessor and needs to be processed by a more efficient algorithm running in the processor.
## 2 Mathematical Background  
Let \(GF(q)\) be the finite field of \(q\) elements, \(q\) being a prime, and \(GF[q,x]\) the set of all polynomials with coefficients in \(GF(q)\). Equivalently, let \(GF(q^{n})\) be the finite field of \(q^{n}\) elements, and \(GF[q^{n},x]\) the set of all polynomials with coefficients from \(GF(q^{n})\). A generator of the cyclic group of a finite field is called a primitive element of that field. Hence, a polynomial \(p(x)\in GF[q,x]\) of degree \(m\geq 1\) is called primitive over \(GF(q)\) if it is the minimal polynomial over \(GF(q)\) of a primitive element \(\alpha\in GF(q^{n})\). A primitive polynomial \(h(x)\) of degree \(n\) allows one to construct \(GF(q^{n})\) in such a way that:  
\[GF(q^{n})\approx GF[q,x]/h(x) \tag{1}\]  
The addition and multiplication in \(GF(q^{n})\) are the ones in \(GF[q,x]\), but performing the module \(h(x)\) reduction, as all the elements in \(GF(q^{n})\) can be represented as polynomials of degree less than \(n\) with coefficients in \(GF(q)\). On the other hand, any element \(a\in GF(q^{n})\) can be expressed in terms of a basis \(\{a^{n-1},\ldots,a^{2},\alpha,1\}\), \(\alpha\) being a root of \(h(x)\) in \(GF(q^{n})\). Consequently, any element \(a\in GF(q^{n})\) can be written as the vector \((a_{n-1},\ldots,a_{1},a_{0})\) where:  
\[a=a_{n-1}\alpha^{n-1}+\cdots+a_{1}\alpha+a_{0}1. \tag{2}\]  
For \(q=2\), it is very common to use the hexadecimal notation as a compact representation of the elements in \(GF(q^{n})\). Thus, if we use \(h(x)=x^{8}+x^{4}+x^{3}+x^{2}+1\) to construct \(GF(2^{8})\), \(\alpha\) being a root of \(h(x)\), as any element in \(GF(2^{8})\) can be represented as a power of \(\alpha\), we can write the element \(\alpha^{3}\) as \((0,0,0,0,1,0,0,0)\) or \(0x08\) and the element \(\alpha^{10}=\alpha^{6}+\alpha^{5}+\alpha^{4}+\alpha^{2}\) as \((0,1,1,1,0,1,0,0)\) or \(0x74\). Note that the powers of \(\alpha\) correspond to the vector components in descending order, beginning from the left, to facilitate the conversion to and from hexadecimal values.
## 3 SSI System versus Fixed-Base Oscillator
### Shift in System Frequency  
Figure 4 shows a spectrum correspondence between the structure response \(\left|u_{\rm b}\right|\)(the line style follows Figure 3) and the incident wave. Due to the shift in peak frequency, the SSI system demonstrates different seismic performances compared to the fixed-base oscillator. When the predominant frequency of the incident wave matches the peak frequency of the SSI system (acceleration record 1 in Figure 4), the dynamic responses of the SSI system are probably larger than the fixed-base oscillator, which probably has detrimental SSI effects on the seismic performances of a structure, so the seismic designs on the assumption of a fixed-base oscillator are probably unsafe. Whereas, for a case in which the predominant frequency matches that of the fixed-base oscillator (acceleration record 2 in Figure 4), the responses of the former are probably smaller than the latter, which probably has beneficial SSI effects, so the seismic designs on the assumption are probably too conservative. Certainly, for a case in which the predominant frequency of the incident wave is not obvious, it also probably results in beneficial SSI effects, since the peak amplitude of the SSI system is always smaller than that of the corresponding fixed-base oscillator. In summary, a smaller peak amplitude of SSI system in the frequency domain does not necessarily mean that the dynamic responses of SSI in the time domain is always smaller than the fixed-base structure. SSI effects are sometimes detrimental to the seismic performances of structures, depending on the dynamic properties of both the structure itself and the soil.  
Figure 5 presents the ratio \(\widetilde{\eta}_{\rm b}/\eta_{\rm b}\) (system frequency/fixed-base frequency) as that which is shown in Figure 3b, but for different structure and site parameters. The structure mass is for three values \(M_{\rm b}/M_{0}\) = 5, 10, 15, and two kinds of half-space are concerned, \(\beta_{\rm R}/\beta_{j}\) = 1 and 2, with the other parameters the same as Figure 3. \(\beta_{\rm R}/\beta_{j}\) = 1 corresponds to a uniform half-space. The shift in system frequency in uniform half-space is larger than that in layered half-space, since the bedrock adds rigidity to the site. Also, the shift in system frequency for a high structure is larger than that for a short structure, since a high structure is more flexible according to our parameters.  
Figure 4: Peak frequency of the structure response and predominant frequency of excitations.
## 5 Simulation Results  
In this section, we evaluate the effects of these two types of data integrity and availability attacks-DoS and FDI attacks-on the test LFC system through simulations. As shown in Figure 1, the two-area system is interconnected with AC/HVDC transmission lines and equipped with bulk ESS for inertia emulation. The parameters of the two-area system and also associated control loops, i.e., the parameters appeared in Equations (1)-(12), are referred to Table 1 in Section 2.4. Then, the matrices involved in the original state-space model of (13) for the two-area LFC system can be obtained through (15) to (17). In particular, we are interested in the difference between effects of these two types of data integrity and availability attacks on the LFC models of the following studied systems in this article:  
* Normal AC system.
* AC/DC interconnected system.
* AC/DC interconnected system with virtual inertia.  
From Section 2, we have seen that the LFC model in the system interconnected by AC/HVDC lines and equipped with ESS has more controllable devices, comparing with the one in the normal AC system. Intuitively, an attacker can manipulate more vulnerable measurements as it can attack frequencies of both areas and also power flows on both AC and HVDC transmission lines. Furthermore, the DoS and FDI attacks on all of these measurements would affect not only the supervisory AGC loop but also the SPMC for power modulation in control center. Thus, in this section, we perform a comparison study through simulations to explore the difference of attack impact on these three LFC system models.
## 4 TCO Evaluation Model of Intelligent Driving
### Hardware Cost  
With the expansion of the production scale and the iteration of technology (such as the optimization of integration and structure), the hardware cost gradually decreases. The change of hardware cost is assumed to follow the law of learning curve [35]. The total hardware cost of intelligent driving can be expressed as (1):  
\[C_{H}=\sum[\ C_{i,single,base}\times(1-\alpha)^{(T-2021)}\times N_{i}\ ]. \tag{1}\]  
The average market price of component \(i\) in 2021 is selected as the benchmark \(C_{i,single,base}\), and the original data comes from 3 OEMs cooperating with the research team. The cost reduction rate \(\alpha\) is revised by taking the change rate of market price in previous years as a reference and integrating the current technical development trend and production characteristics of technical components. The parameter \(T\), corresponding to the three levels of low, medium and high, are 2021, 2025 and 2030, respectively. \(N_{i}\) is the number of components \(i\) in the technical solution. Table 4 shows the unit cost of each technical component in different years.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Components** & **2021** & **2025** & **2030** \\ \hline Camera (2 MP) & \$33, 10 W & \$31, 8 W & \$30, 6 W \\ Camera (8 MP) & \$93, 15 W & \$86, 12 W & \$78, 10 W \\ Camera (12 MP) & \$148, 20 W & \$120, 16 W & \$93, 12 W \\ MMW (short-range) & \$76, 5 W & \$73, 4 W & \$69, 3 W \\ MMW (long-range) & \$140, 12 W & \$130, 10 W & \$117, 8 W \\ MMW (4D-imaging) & \$312, 20 W & \$204, 18 W & \$121, 16 W \\ Lidar (mechanical) & \$2836, 45 W & \$1866, 40 W & \$1104, 36 W \\ Lidar (hybrid-solid) & \$933, 40 W & \$760, 35 W & \$588, 32 W \\ Lidar (solid-state) & \$560, 35 W & \$367, 32 W & \$216, 30 W \\ MEMS & \$3, 1 W & \$2.8, 1 W & \$2.7, 1 W \\ Ultrasonic radar & \$24, 12 W & \$23, 11 W & \$22, 10 W \\ HD map (m) & \$343, 1 W & \$279, 1 W & \$216, 1 W \\ HD map (dm) & \$1104, 2 W & \$957, 2 W & \$740, 2 W \\ HD map (cm) & \$2015, 4 W & \$1858, 4 W & \$1433, 3 W \\ T-Box (4G-V2X) & \$21, 4 W & \$17, 4 W & \$13, 4 W \\ T-Box (5G-V2X) & \$93, 8 W & \$61, 8 W & \$36, 8 W \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cost and power consumption of various technical components. All costs are shown in USD.
## 2 Materials and Methods  
### Fractional Order Calculus  
Fractional calculus is a generalization of ordinary calculus by extending the integration and differentiation order to the non-integer order. It has been developed for a long time as a field of mathematics and only applied for control engineering in the last two decades. It presented a fractional operator \({}_{a}D_{t}^{o}\) where \(a\) and \(t\) are the limits and \(v\) is the fractional order (\(v\in R\)). There are several definitions for fractional operator but the most commonly used one was proposed by [PERSON] and [PERSON] [3]. It is defined as follow:  
\[{}_{a}D_{t}^{v}f(t)=\frac{1}{\Gamma(n-v)}\frac{d}{dt^{n}}\int\limits_{a}^{t} \frac{f(\tau)}{(t-\tau)^{n-n+1}}dt,\ n-1<v<n \tag{1}\]  
where \(\Gamma(\bullet)\) represents the Euler's gamma function; with a positive \(v\), the fractional operator denotes fractional derivative, and a negative \(v\) represents fractional integral.  
The Laplace transformation is used for Equation (1) under the assumption that all initial conditions are zero. Its result has the form given in (2):  
\[L\Big{\{}_{a}D_{t}^{v}f(t)\Big{\}}=s^{v}F(s) \tag{2}\]  
Consider a SISO, linear time invariant fractional order system described by a typical fractional order differential equation (FODE) as follow:  
\[\sum_{i=0}^{n}a_{i}D_{0}^{v_{i}}y(t)= \sum_{i=0}^{m}b_{i}D_{0}^{\lambda_{i}}u(t) \tag{3}\]  
where \(y(t)\) and \(u(t)\) are the output and input respectively; \(a_{i}\), \(b_{i}\) are constant coefficients of the system; \(v_{i}\), \(\lambda_{i}\) are the orders of fractional operator.  
In order to obtain the transfer function of the system, the Laplace transform for Equation (3) is applied and combined with the result from Equation (2). As a result, the SISO system can be described in Laplace domain by the following transfer function:  
\[G(s)=\frac{Y(s)}{U(s)}=\frac{b_{m}s^{\lambda_{m}}+b_{m-1}s^{\lambda_{m-1}}+ \ldots+b_{o}s^{\lambda_{o}}}{a_{n}s^{\
u_{n}}+a_{n-1}s^{\
u_{n-1}}+\ldots+a_{o} s^{\
u_{o}}} \tag{4}\]  
It is obvious that the fractional order of \(s\) in Equation (4) makes it difficult to simulate or implement a fractional order system. Therefore, it should be approximated to integer order transfer function with a similar response. The Oustaloup recursive algorithm with finite numbers of poles and zeros is employed for most applications [3; 8; 9; 10; 12]. Within the specific range of frequency \([\omega_{b}\), \(\omega_{h}]\), the approximation of the FO operators\(s^{v}\), can be obtained by the following equation:  
\[s^{v}\cong s^{v}_{[\omega_{b},\omega_{h}]}\approx K\sum_{k=-N}^{N}\frac{s+ \omega^{\prime}{}_{k}}{s+\omega_{k}} \tag{5}\]  
where the zero, pole and gain can be calculated respectively from:  
\[K=\omega_{h}^{v} \tag{6}\]  
\[\omega^{\prime}{}_{k}=\omega_{b}\bigg{(}\frac{\omega_{h}}{\omega_{b}}\bigg{)} ^{(k+N+0.5-0.5v)/(2N+1)} \tag{7}\]  
\[\omega_{k}=\omega_{b}\bigg{(}\frac{\omega_{h}}{\omega_{b}}\bigg{)}^{(k+N+0.5+ 0.5v)/(2N+1)} \tag{8}\]
## 3 Study of the Floor Heave in Gob-Side Entry Retaining
### Simulation Discussions  
Several conclusions can be drawn according to the above analyses:  
(1) The main reason for dramatic floor heave in gob-side entry retaining is the vertical loading of the filling wall by the weight of the roof that has lost its support after coal extraction in the longwall face. The high pressure of the roof leads to the indentation of the filling wall body into the immediate floor and the formation of a zone of increased vertical stresses underneath it. These stresses are higher than those on the side of the coal seam body, which is the main reason for the asymmetry of the gateroad deformation. Further redistribution of stresses in the immediate floor leads to an increase in horizontal stresses, which have a key role in floor heave.  
(2) High vertical stresses underneath the filling wall and coal seam body lead to dilatancy expansion of rocks in the horizontal direction. High horizontal stresses in the immediate floor under the side wall provoke the formation of a post-peak horizontal compression zone under the entry span. In this zone, the dilatancy and lateral expansion of rocks in the vertical direction cause delamination of the immediate floor and uplift of discontinuous rocks.  
(3) A zone of high shear occurs between the zone of rock uplift under the entry span and the zone of rock subsidence underneath the filling wall body. A zone of high shear stress and strain is also formed under the steel arch leg on the solid coal side. The rock failure in these zones complicates the floor heave mechanism described above. In shear strain zones, vertical stresses are transformed into horizontal ones.  
(4) Numerical simulation results are valid only for geostress, geological and mechanical parameters of rock mass, filling wall setting, and support elements that were applied. However, the mechanism of floor heave is typical for similar engineering conditions.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### Phase Synchronization and Stability  
To demonstrate WISCANet's phase-stable behavior between cycles, a sinusoidal tone is transmitted at a fixed carrier frequency into a power divider, which then feeds through phase-matched, equal length cables to each channel of a WISCANet configured phase-synchronized receiver. A block diagram of this experiment is depicted in Figure 5. The incoming phases of the reception at each channel of the receiver are compared. Results of these comparisons [27] are shown in Figures 6 and 7. In Figure 6, we observe that throughout each system transmit/receive cycle, stable phase is maintained. At the cycle boundary some number of \(2\pi\) jumps occur. The same comparison plot is performed in Figure 7, for a 4 channel receiver, between the 6 channel pairs to observe the same synchronized behavior. Throught a cycle the relative phase stays stable, and the relative phase stability is maintained across cycles modulo \(2\pi\).  
Initially, there will be a random phase shift between the two channels, in accordance with the manufacturer specification [70]. Algorithms running on the system can calibrate their processing to remove this random shift. This feature is implemented through a common 10 MHz clock reference and PPS is shared between multiple chained radios, and care is taken to not reset the synthesizer's phase on transmission/reception start and stop--including when switching between transmit and receive modes. Phase stability is only available to a group of radios connected to a single edge node. Due to the behavior of the 10 MHz reference clock buffer, there are random shifts of multiples of \(2\pi\) each cycle. To verify phase-coherence on transmit, a similar experiment was done in the reverse configuration. The nominal measured phase variance was 0.106 rad = 6.073\({}^{\circ}\).  
Figure 5: Phase Stability Testing Configuration: Multiple USRP, Shared GPSDO, Sinusoid Generator. The 4 channel receiver configuration captures the transmitted reference tone and then compares the received signals phase between the 4 channels.
## 2 Analysis of Rockburst Manifestation Characteristics
### Manifestation Law of Rockburst on Both Sides of the Roadway
#### 4.3.2 Analysis of Vertical Displacement Evolution of Both Sidewalls Under Different Schemes  
Vertical displacement serves as a direct indicator of surrounding rock softening and pressure relief efficacy, and it is also an important metric for evaluating potential rockburst intensity. Simulated vertical displacement curves for both the coal pillar side and the working face side under various schemes are presented in Figure 12, and the maximum subsidence values are summarized in Table 4.  
Figure 11: The surrounding rock’s vertical stress distribution on the roadway’s two sides after hydraulic fracturing pressure relief among roofs.  
As shown in Figure 12 and Table 4, under the no-relief condition, the maximum subsidence on the coal pillar and the operating face reaches 1.14 cm and 1.18 cm, respectively, with a symmetric displacement pattern. After hydraulic fracturing, deformation is mitigated to varying degrees across schemes. Scheme 9 yields the lowest maximum subsidence values--0.67 cm and 0.63 cm--representing a reduction of approximately 44% relative to the initial state, which further validates the efficacy of hydraulic fracturing in redistributing the surrounding rock stress.  
Combining the vertical stress distribution and displacement evolution characteristics, hydraulic fracturing of the roof effectively forms a decompression zone on both the coal pillar and working face sides, releasing stored bending elastic energy in the surrounding rock. Notably, the 45\({}^{\circ}\) borehole inclination facilitates crack propagation along bedding and structural weak planes, achieving optimal pressure relief pathways. This method not only reduces the concentration of high stress in the roadway's surrounding rock but also offers a potential solution to issues posed by thick, hard, and difficult-to-collapse roofs, thereby holding significant engineering value for improving rockburst disaster prevention.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Max Subsidence on Coal Pillar Side (cm)** & **Max Subsidence on Working Face Side (cm)** \\ \hline No Relief & 1.14 & 1.18 \\ Scheme 1 & 0.98 & 0.96 \\ Scheme 2 & 1.01 & 0.79 \\ Scheme 3 & 1.03 & 0.61 \\ Scheme 4 & 0.81 & 0.99 \\ Scheme 5 & 0.83 & 0.81 \\ Scheme 6 & 0.84 & 0.62 \\ Scheme 7 & 0.65 & 1.02 \\ Scheme 8 & 0.66 & 0.82 \\ Scheme 9 & 0.67 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum subsidence on the roadway’s two sides following hydraulic fracturing pressure relief in the roof.  
Figure 12: The surrounding rock’s vertical displacement distribution on the roadway’s two sides after hydraulic fracturing pressure relief in the roof.
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 4 Our Proposed Approach
### Dynamic Costing of Cloud Resources  
There are several different costing models used by cloud service providers to charge their customers. In [7], the author identifies three costing models typical of the cloud computing market: pay-as-you-go (PAYG), on-demand (OD), and spot market (SM).  
As per [7], the PAYG model uses a combination of a fixed fee and a variable cost per use of the cloud resource [7]. Under the PAYG model, the fixed fee component can be considered a down payment to ensure that the cloud resource will be available when required [7]. Equation (4) shows the total cost of a PAYG cloud resource, where \(f\) represents the fixed component, \(v\) represents the variable component, and \(\tau\) represents the usage time.  
\[C_{PAYG}(\tau)=f+v\tau \tag{4}\]  
In contrast, the OD model, which is still based on a per-use charge, does not guarantee the availability of a resource [7]. Thus, it is suitable for tasks which are delay-tolerant [7]. Equation (5) shows the total cost of an OD resource.  
\[C_{OD}(\tau)=v\tau \tag{5}\]  
The complement to the OD model is the SM model, in which the cloud provider sells off access to idle resources that are not demanded by OD customers at a particular time [7]. SM resources are sold at significant discounts, but executing tasks may be interrupted at any time and the resource reallocated to an OD customer if demanded [7].  
The total cost of a spot market resource is determined by the nature of the market pricing, i.e., whether the price per unit of time is set at the time of purchase, or whether it is variable, recomputed at each time slot used over the course of the task in accordance with the market fluctuations. In our model, we propose the latter, more general case, giving the total cost of an SM resource as in (6), where \(S\) is a set of time slots in which the SM resource was used, and \(\Phi(t)\) refers to the spot market price of the resource during the time slot \(t\).  
\[C_{SM}(S)=\sum_{i}^{|S|}\Phi(t_{i}),\ t_{i}eS \tag{6}\]  
Thus, in contrast with the PAYG and OD models, the cost of resources in an SM model is dynamic, driven by various market forces including scarcity, demand, and global system congestion [7].