esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Path Following Control Design
### 3 DOFALOS Guidance Law
#### 5.1.3 Stability Analysis  
Based on above guidance law, the _Lyapunov_ function is established as Equation (31):  
\[V_{y_{e}}=\frac{1}{2}y_{e}^{2}\geq 0. \tag{31}\]  
By differentiating Equation (31) with respect to time and using (29), the equation can be obtained as Equation (32):  
\[\dot{V}_{y_{e}}=y_{e}\dot{y}_{e}=-\frac{Uy_{e}^{2}}{\sqrt{\Delta^{2}+\left(y_{ e}+\chi\right)^{2}}}\leq 0. \tag{32}\]  
Consequently, a non-negative system known \(V_{y_{e}}\) is bounded by _Lyapunov_ stability, and the system of path following is stable.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 2 Signal Model and Performance Metrics
### Performance Metrics
#### 2.2.2 Throughput  
Throughput refers to the amount of data that are successfully transmitted per unit of time. The throughput of a transmission system is defined as  
\[\text{Throughput}=(1-\text{BLER})\times\text{TBS}/T, \tag{3}\]  
where BLER denotes the block error rate, TBS denotes the transport block size and \(T\) is the period of transmission data block.  
The throughput of OFDM systems depends on the modulation order, channel coding rate and the number of occupied sub-carriers. However, for future-oriented new scenarios, the transmission rate supported by OFDM is still not high enough. In order to provide good performance for complicated application scenarios, waveforms with low PAPR and high spectrum efficiency are required for high-frequency wireless transmission.
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 2 Background and Related Work
### Knowledge Graph Embedding  
The knowledge graph is a multi-relational graph composed of entities (nodes) and relations (edges), and each edge is in the form of a triple (head entity, relation, tail entity). The existing knowledge graphs include Freebase [1], DBpedia [34], Wikidata, etc. Knowledge graph embedding [35] involves embedding the entities and relations in the knowledge graph into a continuous vector space. In general, knowledge graph embedding methods can be divided into two groups: translational distance models and semantic matching models [36; 37; 38]. The former use distance-based scoring functions, and the latter similarity-based ones. Among translational distance models, TransE [39] is the most representative. The main idea is to give a triple \((h,r,t)\), the goal is \(\mathbf{h}+\mathbf{r}\approx\mathbf{t}\), where \(h,r,t\) are the head entity, relation, and tail entity, respectively, and \(\mathbf{h},\mathbf{r},\mathbf{t}\) are, respectively, vector representations.  
To solve the limitations of the TransE model in dealing with 1-to-N, N-to-1, and N-to-N complex relations, TransH [40] introduces relation-specific hyperplanes that allow an entity to have different representations under different relations. In order to further improve the representation ability, TransR [41] introduces relation-specific spaces, rather than hyperplanes. TransD [42] simplifies TransR by further decomposing the projection matrix into a product of two vectors. TransM [43] assigns specific relation weight to each triple \((h,r,t)\).  
There are also recent knowledge graph embedding methods with better performance. [PERSON] et al. [44] proposed the hierarchy-aware knowledge graph embedding model (HAKE), which maps entities into a polar coordinate system. PairRE [45] has paired vectors for each relation representation, which can adaptively adjust the margin in a loss function to fit for complex relations. Additionally, PairRE can encode three relation patterns: symmetry/antisymmetry, inverse, and composition. DualE [46] introduces dual quaternions into knowledge graph embedding, where a dual quaternion is similar to a \"complex quaternion\" with its real and imaginary part all being quaternar. DualE universally models relations as the combination of a series of translation and rotation operations. EIGAT [47] allows correct incorporation of global information into the graph attention network (GAT) family of models by using scaled entity importance, which is computed by an attention-based global random walk algorithm. In order to focus on the importance of the knowledge graph structure for the entity linking task, the knowledge graph embedding method used in this article is the most basic TransE model.
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 1 Introduction
In [24], the authors propose a channel assignment technique together with a load balancing proposal, and in [25] three different algorithms are proposed to assign channels to APs considering only the three orthogonal (non-overlapping) channels. Two other very interesting channel assignment techniques are [7, 26]. In [7], the authors propose a channel assignment technique based on choosing the channel with the least interference by using the Wi-Fi control frames. In [26] a channel assignment is proposed which attempts to avoid the same channel in neighboring APs while trying to use the least number of possible channels. Finally, in [27], the authors propose a technique, which we have called AIIM according to its authors' initials, to improve the performance of the AP that has the lowest performance without compromising the global performance and iterating this procedure. This is the approach we have taken as a basis for this paper, showing that enhancing AIIM with a cluster-based approach (we have called it CB-AIMM) can significantly improve the resulting channel assignments. Table 1 shows a summary of all the abovementioned channel assignment techniques, showing where they fall in the aforementioned categories (i.e., centralized vs. distributed and heuristic vs. optimal). Moreover, we also show whether the techniques make use of the STAs or not. The main contribution of this paper is to show that the effect of STAs in a channel assignment technique is not negligible. To show this behavior, we propose a heuristic channel assignment technique called CB-AIIM (cluster-based AIIM) that extends the proposal in [27] by including STAs, and show in the results that the performance greatly increases. The structure of the paper is as follows. Section 2 defines the Wi-Fi architecture used in the rest of the paper and defines the multilayer graph model we propose, including the propagation and interference models and the definition of the utility concept to evaluate the performance of the results. Section 3 describes the channel assignment technique proposed in this work, called CB-AIIM. Finally, Section 4 describes the evaluation scenarios and shows the results by comparing CB-AIIM with other proposals from the literature. Finally, Section 5 summarizes our contribution and sheds light on some future work.
# Energy Harvesting over Rician Fading Channel:
## 3 System Performance
### Asymptotic Analysis
#### 3.4.2 SER Analysis  
As \(P/N_{0}\) approaches infinity, the symbol-error-rate at node \(S_{1}\) becomes  
\[SER_{1}^{\infty}=\frac{\omega\sqrt{\theta}}{2\sqrt{\pi}}\int\limits_{0}^{ \infty}\frac{e^{-\theta x}}{\sqrt{x}}F_{\gamma_{1}}^{\infty}(x)dx, \tag{24}\]  
where \(F_{\gamma_{1}}^{\infty}(x)\) is the CDF of \(\gamma_{1}\) as \(P/N_{0}\) goes to infinity.  
The result of our asymptotic SER analysis is presented in Theorem 4.  
**Theorem 4** (SER--Asymptotic form).: _The asymptotic symbol error rate for node \(S_{1}\) of the proposed half-duplex bidirectional wireless sensor network using a time-switching energy harvesting strategy over a Rician fading channel can be provided as the following:_  
* _If_ \(\xi_{1}=\xi_{2}\)_:_ \[\begin{split} SER_{1}^{\infty}&=\frac{\omega}{2} \\ &-\frac{\omega\sqrt{\theta}}{2\sqrt{\pi}}\zeta_{1}e^{-2K}\sum_{l=0 }^{\infty}\sum_{k=0}^{\infty}\sum_{m=0}^{l}\sum_{v=0}^{k+1}\left(\begin{array} []{c}k+1\\ v\end{array}\right)\frac{K^{l+k}\xi_{1}^{k}\xi_{2}^{m}a^{m+v}(-1)^{v}\Gamma(m+ k+1)}{l!m!(k)^{2}\xi_{1}^{m+1}a^{m+v+\frac{1}{2}}}\gamma\left(m+v+\frac{1}{2}, \frac{\theta}{a}\right).\end{split}\] (25)
* _If_ \(\xi_{1}\
eq\xi_{2}\)_:_ \[\begin{split} SER_{1}^{\infty}&=\frac{\omega}{2}- \frac{\omega\sqrt{\theta}}{2\sqrt{\pi}}\zeta_{1}e^{-2K}\sum_{l=0}^{\infty} \sum_{k=0}^{\infty}\sum_{m=0}^{l}\sum_{v=0}^{k+1}\sum_{p=0}^{\infty}\left(m+ k+1\right)_{p}\left[(\xi_{1}-\xi_{2})\right]^{p}\left(\begin{array}{c}k+1\\ v\end{array}\right)\\ &\times\frac{K^{l+k}\xi_{2}^{m}a^{m+v+p}(-1)^{v}\Gamma(m+k+1)}{l!m!(k!)^{2} p!\xi_{1}^{m+p+1}a^{m+v+p+\frac{1}{2}}}\gamma\left(m+v+p+\frac{1}{2},\frac{ \theta}{a}\right),\end{split}\] (26)_where \(a=\kappa^{2}+\kappa_{r}^{2}(1+\kappa^{2}),b=\frac{(1+\kappa_{r}^{2})}{P/\sigma_{r}^{ 2}},c=\frac{(1+\kappa^{2})}{\tilde{\psi}^{P}/\sigma_{r}^{2}},\tilde{\zeta}_{1}= \frac{K+1}{\lambda_{1}},\tilde{\gamma}_{th}=\frac{\gamma_{th}}{1-\alpha\gamma_ {th}},\)\(\Gamma(\cdot)\) is the complete Gamma function, \(\gamma(\cdot,\cdot)\) is the incomplete Gamma function, and \((\cdot)_{p}\) is the Pochhammer symbol._  
Proof of Theorem 4.: See Appendix D.
## 2 Analysis of the Radial Air-Gap Flux Density in a Permanent Magnet Synchronous Motor (PMSM)  
### Radial Air-Gap Flux Density without Eccentricity  
In general terms, the air-gap flux density can be obtained by the superposition of a permanent magnet field and an armature interaction field [28]:  
\[B_{0}(\alpha,t)=B_{\rm PM}(\alpha,t)+B_{\rm arm}(\alpha,t) \tag{1}\]  
where \(\alpha\) is the space angle of the rotor, \(B_{\rm PM}(\alpha,t)\) is the permanent magnet field, and \(B_{\rm arm}(\alpha,t)\) is the armature interaction field. It is commonly accepted that the radial component of the flux density is the main cause of UMP and the tangential component is negligible [13; 14]. Therefore, only the radial component of the air-gap flux density in this study is considered. By considering the effect of stator slotting, the permanent magnet field and the armature interaction field can be expressed as follows [24]:  
\[B_{\rm PM}(\alpha,t)=\lambda(\alpha)\sum_{n=1,3,5...}^{\infty}B_{\rm mr}\cos(np \alpha-n\omega_{e}t) \tag{2}\]  
\[B_{\rm arm}(\alpha,t)=\lambda(\alpha)\sum_{v=1,2,3...}^{\infty}B_{\rm ar}\cos( pv\alpha\mp\omega_{e}t) \tag{3}\]  
where \(\lambda(\alpha)\) is the relative permeance, \(B_{\rm mr}\) is the amplitude of the \(n\)th permanent magnet field, \(B_{\rm ar}\) is the amplitude of the \(v\)th armature interaction field, \(\omega_{e}\) is the electric angular frequency, and \(p\) is the number of pole-pairs. For the motors with an integral slot, \(v=6k\pm 1\), and the sign before \(\omega_{e}\) is a minus when it takes a plus. The relative permeance can be written as [26]:  
\[\lambda(\alpha)=\Lambda_{0}+\sum_{\mu=1}^{\infty}\Lambda_{\mu}\cos\mu Z(\alpha +\pi/Z) \tag{4}\]  
where \(Z\) is the slot number, \(\Lambda_{0}\) is the fundamental amplitude of relative permeance, and \(\Lambda_{\mu}\) is the harmonic amplitude of relative permeance.  
Since the harmonic amplitude of relative permeance is very small and the effect on the rotor surface is negligible [26], the harmonic components can be neglected for the convenience of the latter calculation. Therefore, the radial air-gap flux density of a PMSM without eccentricity can be written as:  
\[B_{r0}(\alpha,t)=\Lambda_{0}\sum_{n=1,3,5...}^{\infty}B_{\rm mr}\cos(np\alpha -n\omega_{e}t)+\Lambda_{0}\sum_{v=1,2,3...}^{\infty}B_{\rm ar}\cos(pv\alpha \mp\omega_{e}t) \tag{5}\]
## 3 Results  
The number of SWD and non-SWD adults that emerged per fruit show variation among sites in both the degree of infestation and the proportion of infestation caused by SWD. In thirteen out of seventeen samples in 2019 and eight out of ten samples in 2020, the total number of _Drosophila_ flies was greater in overripe than in ripe fruit. On average, six, three, and fourteen _Drosophila_ flies emerged per ripe fruit, and ten, six, and fourteen flies emerged per overripe fruit in 2019 from the Oxnard, Santa Maria, and Monterey Bay areas, respectively. In 2020, the average number of _Drosophila_ flies emerging was one and thirteen per ripe fruit, and one and twenty-nine per overripe fruit collected from the Santa Maria and Monterey Bay areas, respectively. The proportions of SWD were significantly greater in ripe than in overripe fruit (_p_ < 0.05), except in OXN 2 (\(\chi^{2}=0\); \(p\) = 0.9635), OXN 4 (\(\chi^{2}=1.06\); \(p\) = 0.3023), OXN 1\({}^{\prime}\) (\(\chi^{2}=0\); \(p\) = 0.9637), SAT 3 (\(\chi^{2}=0.12\); \(p\) = 0.7341), and MON 3\({}^{\prime}\) (\(\chi^{2}=0\); \(p\) = 1) in 2019, and SAT 5 (\(\chi^{2}=0.62\); \(p\) = 0.4306), MON 4 (\(\chi^{2}=3.61\); \(p\) = 0.0574), and MON 5\({}^{\prime}\) (\(\chi^{2}=1.11\); \(p\) = 0.2931) in 2020 (Table 1). Compared to the species distribution observed in the Oxnard area, larvae that were extracted from fruits in the Santa Maria and Monterey Bay production areas were primarily SWD (Table 1). In 2019, the percentage of SWD per sample ranged from 0.4 to 73.1%, from 73.5 to 100%, and from 70.5 to 100% in ripe fruit collected from the Oxnard, Santa Maria, and Monterey Bay areas, respectively. For the overripe fruit, the percentage of SWD per sample ranged from 0 to 51.4%, from 18.9 to 98.1%, and from 49 to 100% in the Oxnard, Santa Maria, and Monterey Bay areas, respectively (Figure 1). In 2020, the percentage of SWD per sample ranged from 0 to 50%and from 22.2 to 100% in ripe fruit and from 14.3 to 40% and from 15.8 to 100% in overripe fruit collected from the Santa Maria and Monterey Bay areas, respectively (Figure 1). These results were compared to and confirmed by species identification of larvae found in fruits using the real-time PCR TaqMan assay (Figure 2). All samples from the first collection of the MON sites were identified as SWD since the obtained signals for those samples fell below the FAM RFU value for the standard generated from DNA extracted from five \(D\). _suzukii_ and one \(D\). _simulans_ larvae (702.87) and above the Cal Orange 560 RFU value for the standard with one \(D\). _suzukii_ and five \(D\). _simulans_ larvae (443.74). For the second collection, all samples showed FAM RFU values lower than the value for the standard with five \(D\). _suzukii_ and one \(D\). _simulans_ larvae (723.12), and the Cal Orange 560 RFU value was greater than the standard with one \(D\). _suzukii_ and five \(D\). _simulans_ larvae (422.95) for all samples except for an overripe sample from the MON 6 site (Figure 2).
# A 112 Gb/s DAC-Based Duo-Binary PAM4 Transmitter in 28 nm CMOS
## 3 Transmitter System Architecture
### Driver Circuit Design
The output voltage swing is  
\[V_{out}=\frac{R_{T}\|Z_{0}}{\frac{\prod\limits_{i=0}^{6}{\left(R_{MOS}+R_{E} \right)_{i}}}{\sum\limits_{i=0}^{6}{\left(R_{MOS}+R_{E}\right)_{i}}}+R_{T}\|Z_{0}} \tag{6}\]  
where \(V_{out}\) is the output voltage swing, \(R_{T}\) is the parallel resistance, \(Z_{0}\) is the channel resistance, \(R_{MOS}\) is the MOS transistor resistance, \(R_{E}\) is the series resistance, and \(V_{CC}\) is the supply voltage. The 7-bit SST driver can act as a DAC, and the output voltage signal can be linear with the input digital signal. The static error characteristic has a crucial influence on the static precision of the DAC, mainly including differential nonlinearity (DNL) and integral nonlinearity (INL) [19]. The static parameters of the 7-bit SST DAC designed in this paper are shown in Figures 15 and 16. The DNL is 0.38 LSB and the INL is 0.68 LSB.
# An Optimized Genetic Algorithm-Based Wavelet Image Fusion Technique for PCB Detection
###### Abstract  
This study proposes an optimized genetic algorithm-based wavelet image fusion technique for printed circuit board (PCB) detection, incorporating an improved Genetic Algorithm (GA) with the Elite Strategy and integrating it with discrete wavelet transform (DWT). The proposed method aims to enhance both the accuracy and efficiency of image fusion, which is crucial for defect detection in PCB inspection. A DWT is utilized to decompose images into multiple frequency components, where the low-frequency band preserves the structural integrity of the image, and the high-frequency band retains essential fine details such as edges and textures, which are critical for identifying defects. An improved genetic algorithm is applied to optimize the fusion process, incorporating the Elite Strategy to retain the best solutions in each evolutionary iteration. This strategy prevents the loss of optimal wavelet decomposition weights, and ensures steady convergence towards the global optimum. By maintaining superior solutions throughout the evolutionary process, the algorithm effectively enhances the fusion quality and computational efficiency. Experimental evaluations validate the effectiveness of the proposed approach, demonstrating superior performance over conventional fusion methods. The enhanced algorithm achieves significant improvements in key performance metrics, including relative standard deviation (RSD), peak signal-to-noise ratio (PSNR), image clarity, and processing efficiency. The team developed a prototype system and conducted simulations in a relatively realistic environment to validate the proposed method's potential for high-precision PCB detection. The results demonstrate that the approach offers a robust solution for automated defect detection and quality assessment.  
genetic algorithm; DWT; image fusion; industrial inspection 2020 IEEEexample:BSTcontrol
# On the Local and String Stability Analysis of Traffic Collision Risk
## 5 Simulation Verification
### Simulation Setup  
Consider a platoon of vehicles traveling along a straight road with a single lane in the simulation; at a certain point \(t_{0}\), a disturbance causes the acceleration of the leading vehicle. The disturbance is a sine function as \(Asin(\omega_{0}t)\), where \(A\) is the amplitude of the disturbance and \(\omega_{0}\) is the disturbance frequency. The velocity increases from \(v^{eq}\) to \(v^{eq}+\frac{2A}{\omega}\) in time \(t_{0}\) to \(t_{0}+\frac{\pi}{\omega}\) and decreases from \(v^{eq}+\frac{2A}{\omega}\) to \(v^{eq}\) in \(t_{0}+\frac{\pi}{\omega}\) to \(t_{0}+\frac{2\pi}{\omega}\). Figure 6 shows an example of the acceleration and velocity profiles of the leading vehicle.  
The Intelligent Driver Model (IDM) [41] is selected as the car-following model in the demonstration of Figure 7. The IDM acceleration \(a_{i}(t)\) for vehicle \(i\) at time \(t\) is formulated as  
\[\ddot{x}_{n}(t)=\alpha\left[1-(\frac{v_{n}(t)}{v_{0}})^{4}-\left(\frac{S_{0}+ \max\{0,v_{n}(t)T+\frac{v_{n}(t)\Delta v_{n}(t)}{2\sqrt{\alpha\beta}}\}}{S_{i }(t)}\right)^{2}\right] \tag{40}\]  
where \(\alpha\) and \(\beta\) denote the maximal acceleration and deceleration and \(S_{0}\) represents the minimum gap. The IDM parameter settings are the same as those in [PERSON] et al. [42]: \(v_{0}~{}=~{}120\) km/h, \(S_{0}~{}=~{}2\) m.  
The Taylor expansion coefficients as follows of the IDM model are calculated as follows  
\[f_{\Delta x}=\frac{2\alpha(s_{0}+v^{eq}T)}{(s^{eq})^{3}},f_{\Delta x}=\frac{ \sqrt{\alpha}v^{eq}(s_{0}+v^{eq}T)}{\sqrt{\beta}(s^{eq})^{2}},f_{x}=-\alpha \Big{[}\frac{4(v^{eq})^{3}}{v_{0}^{4}}+\frac{2T(s_{0}+v^{eq}T)}{(s^{eq})^{2}} \Big{]}\]  
where \(s^{eq}=\frac{s_{0}+v^{eq}T}{\sqrt{1-(\frac{v_{0}^{eq}}{2})^{4}}}\).  
Figure 7 shows the speed profile and the risk profile for three cases: (a) both traffic- and risk-string-stable; (b) risk-string-unstable but traffic-string-stable; (c) both traffic- and risk-string-unstable. It can be seen that for Figure 7a,c, the traffic string stability and risk string stability are the same (i.e., both stable or both unstable), while for Figure 7b, it is  
Figure 6: Demonstration of acceleration and velocity profiles of the leading vehicle (\(A\) = 0.1 m/s\({}^{2}\), \(\omega\) = 0.5 rad/s, \(v^{eq}\) = 20 m/s).  
shown that even the traffic is string-stable, the risk can be string-unstable, which further verifies the insight in Section 4.3.2.
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 2 Finite Element Simulation  
FEA performs numerical simulation and analysis of complex physical systems by discretizing a continuous physical system into a finite number of small units. The paper provides a comprehensive exposition on the structural composition and heat transfer principles of QFN packages. Additionally, the boundary conditions and parameters employed in the simulations are elucidated, followed by the subsequent construction of the geometric model and mesh generation. Furthermore, the material properties employed in the study are presented.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Performance of Proposed Approach Using Real-Time Dataset  
Additional experiments are performed to analyze the performance of the proposed approach using a real-time collected dataset.
## 10 Discussion
### Derivation of the Earth Pressure at Rest by the Active Earth Pressure Coefficient  
Actually, this has been foreseen in EM1110-2-2502 [71] with the application of a Strength Mobilization Factor \((\mathit{SMF})\) to \(c^{\prime}\) and \(\tan\varphi^{\prime}\). According to this Engineer Manual, an appropriate _SMF_ value allows calculation of greater-than-active earth pressures using Coulomb's active force equation. Assuming an average _SMF_ value equal to 2/3 along Coulomb's failure surface, it has been shown that for purely frictional soils the derived coefficient value of earth pressure matches quite well with the respective one derived from [PERSON]'s \(K_{o}=1-\sin\varphi^{\prime}\) equation.  
More accurately, the mobilized friction angle \(\varphi_{m}\) for purely frictional soils can be obtained by the following equation:  
\[1-\sin\varphi^{\prime}=\tan^{2}\!\left(45^{\circ}-\frac{\varphi_{m}}{2}\right), \tag{60}\]  
which gives,  
\[\varphi_{m}=90^{\circ}-2\tan^{-1}\sqrt{1-\sin\varphi^{\prime}}, \tag{61}\]  
(the effect of backslope and wall inclination, as well as, the effect of wall friction has been ignored for simplicity).  
The latter gives \(SMF=\tan\varphi_{m}/\tan\varphi^{\prime}\) values ranging from 0.5 for \(\varphi^{\prime}\to 0^{\circ}\) to 0.653 for \(\varphi^{\prime}=45^{\circ}\), that is, values very close to the assumed 2/3 value (some intermediate values are \(SMF=0.561\), 0.596, and 0.627 for \(\varphi^{\prime}=\)15\({}^{\circ}\), 25\({}^{\circ}\), and 35\({}^{\circ}\) respectively).  
The \"Strength Mobilization Factor\" in the present solution, is the \(1/f_{m}\) ratio and what has been foreseen by EM1110-2-2502 [71], it can be calculated exactly using the proposed solution. For example, for \(c^{\prime}=20\) kPa, \(\varphi^{\prime}=30^{\circ}\), \(\gamma=18\;kN/m^{3}\), \(k_{h}=k_{v}=0\), and \(z=2\;m\), for the state at rest \(K_{o}=0.211\), \(c_{m}=9.00\;kPa\) and \(\varphi_{m}=14.57^{\circ}\). Using this (\(c_{m}\), \(\varphi_{m}\)) pair of values in Equation (19) \(\left(K_{ae}^{c-\varphi}\right)\), the latter returns a coefficient of earth pressure equal to 0.211, that is, the coefficient of earth pressure at rest.  
The above leads to the interesting observation that, under static conditions, both the earth pressure at rest and the active earth pressure are exerted by the same (Rankine's) soil wedge.