esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Computational Principle and Numerical Model
### Numerical Model
#### 3.2.3 Model Parameters  
For the selection of physical and mechanical parameters of the soil and pile in the site, they are determined mainly based on engineering data and combined with relevant research findings. The ontological model of the anti-slide pile is set as a Mohr-Coulomb plastic model when calculating the slope safety factor. Cohesion \(c\) and the friction angle \(\varphi\) can be calculated according to Equations (16) and (17).  
\[tan\varphi=\frac{\sigma_{c}-\sigma_{t}}{2\sqrt{\sigma_{c}\sigma_{t}}} \tag{16}\]  
\[c=\frac{1}{2}\sqrt{\sigma_{c}\sigma_{t}} \tag{17}\]  
We estimate the parameters of the anti-slide pile with reference to C30 concrete strength. It is known that its standard value of compressive strength is \(\sigma_{c}=20.1\) N/mm\({}^{2}\) and the standard value of tensile strength is \(\sigma_{t}=2.01\) N/mm\({}^{2}\). Inserting these strength values into Equations (16) and (17) can allow us to calculate the cohesive force \(c\) and friction angle \(\varphi\).  
The shear normal stiffness of the contact surface is approximately the same and can be calculated according to Equations (18)-(20), where \(E\) is the modulus of elasticity, \(\mu\) is Poisson's ratio, and \(K\) and  
Figure 6: Dimensional drawing of model.  
\(G\) are the shear modulus and bulk modulus of the abandoned dreg body around the anti-slide pile. \(\Delta z_{min}\) is the minimum size of the mesh in the normal direction of the contact surface. The contact surface cohesion and friction angle are taken as 80% of the surrounding soil body.  
\[K=E/3(1-2\mu) \tag{18}\]  
\[G=E/2(1+\mu) \tag{19}\]  
\[K_{n}{=K_{s}}=10\ \mathrm{max}\left[\frac{K+\frac{4}{3}G}{\Delta z_{min}}\right] \tag{20}\]  
The final selected physical-mechanical parameters of each part are shown in Table 1.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 3 O-RAN in Smart IoT Systems
### Smart Transportation Systems
\begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference, Year** & **Objective** & **Gap** & **Contribution** & **Remarks** \\ \hline Ref. [63; 2021] & \(X_{1},Y_{2}\) & Computation-intensive solutions are not suitable for delay-sensitive Internet of vehicle (IoV) networks & Leveraged O-RAN concept to improve IoV’s edge intelligence & The solution was technically presented and supported with a prototype but calls for the open-source community’s support to be standardised \\ \hline Ref. [74], 2022 & \(X_{1},Y_{2}\) & Traditional vehicular networks cannot facilitate cooperation among diverging agents in a resource constraint environment & Adopts O-RAN concept to implement _\"centralized training distributed execution (CTDE)\"_ which promotes cooperation between diverging agents in a given environment \\ \hline Ref. [64], 2022 & \(X_{1},Y_{2}\) & Balancing between computation and communication in a network of multiple radios and edges is expensive in delay-sensitive systems & Utilizing the O-RAN concept to create a cooperation space in a network consisting of multiple radios and edges to improve autonomous vehicles’ edge intelligence \\ \hline Ref. [76], 2022 & \(X_{1},Y_{1}\) & Multi-hop routing protocols do not consider the changing dynamics of the network participants & Proposed a fuzzy-based routing protocol by the RIC to accommodate the changing dynamics of multi-hop peer-to-peer communication \\ \hline Ref.[60], 2023 & \(X_{1},Y_{1}\) & Traditional RANs cannot support the emerging dynamic V2X communication in V2X & Presented O-RAN as a viable candidate for supporting dynamic control in V2X & Despite the idea’s novelty, the solution extends the system’s complexity by increasing the processing overhead in today’s delay-sensitive smart transportation systems. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of O-RAN in smart transportation systems.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 4 QoS-Oriented Extended Functions
### Caching
#### 4.2.2 Performance Evaluation  
To assess the performance of the caching function, we consider the 6 LoWPAN network deployment of Section 4.1.2, and we let the Proxy implement the proposed cache-management scheme.  
We model the arrival of LWM2M requests as a Poisson process with cumulative rate \(\lambda=1/10\) s\({}^{-1}\), and we compare the case in which the Proxy does not implement the cache against the case in which the Proxy implements the cache. For this latter case we consider two different refresh window values: (i) W = 60 s, i.e., the window value is equal to the sampling period; and (ii) W = 240 s. Each scenario is run for 250,000 s. To evaluate the performance of the caching function we consider the following metrics: (i) the number of exchanged messages in the sensor network and (ii) the AoI of the data at the Server.  
Figure 9 shows the number of exchanged messages, while Figure 10 shows the cumulative distribution function of the AoI. It is possible to notice that the case of no cache and the case of a cache with a window value equal to the sampling period guarantee the same AoI at the application, but the cache can reduce the number of exchanged messages with the Client. Moreover, results show the trade-off between the number of exchanged messages and the AoI of the data: when W increases, the number of exchanged messages decreases because the probability that a request gets a hit in the cache is higher, but the AoI increases. Minimizing the number of exchanged messages and minimizing the
## 3 Near Field Models of Reflectarray and Transmitarray Antennas
### Change of Coordinates  
The near field obtained with either (11) or (23) is referenced to the ACS. Moreover, to compute the near field following the equations derived in the previous subsections, the point in space \(\vec{r}=(x,y,z)\) must also be expressed in the ACS. However, the goal is to obtain the near field in the NFCS, since the array may be rotated with regard to the desired local coordinate system (for instance, if the reflectarray is placed on a wall, and it is desired to provide indoor coverage on a table).  
In general, the coordinates of the points where the near field is to be computed will be initially given in the NFCS. Thus, in order to compute the near field with (11) and (23), these coordinates need to be transformed from the NFCS to the ACS as detailed in Appendix A. Then, after the near field is computed in the ACS, it needs to be transformed to the NFCS as follows:  
\[\vec{E}_{\mathrm{NF}}(\vec{r})\Big{|}_{\mathrm{NFCS}} =T\left.\vec{E}_{\mathrm{NF}}(\vec{r})\right|_{\mathrm{ACS}}, \tag{32a}\] \[\vec{H}_{\mathrm{NF}}(\vec{r})\Big{|}_{\mathrm{NFCS}} =T\left.\vec{H}_{\mathrm{NF}}(\vec{r})\right|_{\mathrm{ACS}}, \tag{32b}\]  
where \(T\) is defined in Appendix A, and \(\left.\vec{E}_{\mathrm{NF}}(\vec{r})\right|_{\mathrm{ACS}}\) and \(\left.\vec{H}_{\mathrm{NF}}(\vec{r})\right|_{\mathrm{ACS}}\) are the same as in (11) and (23).
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 3 Methods
### Terrain Feature Extraction  
#### 3.2.1 Point Cloud to Grid Dimensionality Reduction Mapping  
In the preprocessing phase, the original point cloud data, which include a set of points each with coordinates, intensity, and environmental label, are denoted as \(\mathbf{P}=\left\{\mathbf{p}_{i}\right\}\), where \(\mathbf{p}_{i}=[x_{i},y_{i},z_{i},l_{i},l_{i}]\) and \(i=1,\ldots,N\). Here, \(N\) is the total number of points in the point cloud, \(l_{i}\) represents the intensity, and \(l_{i}\) is the label of the \(i\)-th point.  
Filtering based on the label \(l\) retains only the points associated with the target environment, resulting in a reduced point cloud \(\mathbf{P}^{\prime}=\left\{\mathbf{p}^{\prime}_{i}\right\}\), where \(\mathbf{p}^{\prime}_{i}=[x^{\prime}_{i},y^{\prime}_{i},z^{\prime}_{i}]\) and \(i^{\prime}=1,\ldots,N^{\prime}\). Here, \(N^{\prime}\) is the count of points in the filtered point cloud.  
The grid map parameters are computed as follows:  
\[\text{grid\_width}=\left\lfloor\frac{W}{\Delta x}\right\rfloor,\quad\text{ grid\_height}=\left\lfloor\frac{H}{\Delta y}\right\rfloor \tag{8}\]  
where \(W\) and \(H\) are the width and height of the scene, respectively, and \(\Delta x\) and \(\Delta y\) are the grid resolutions in the respective dimensions. An empty grid map \(\mathbf{G}\) is initialized with zero values:  
\[\mathbf{G}=\mathbf{0}_{\text{grid\_height}\times\text{grid\_width}} \tag{9}\]  
For each point \(\mathbf{p}^{\prime}_{i}\) within the specified range, the corresponding grid indices \((i_{x},i_{y})\) are calculated by:  
\[i_{x}=\left\lfloor\frac{x^{\prime}_{i}-x_{\text{min}}}{\Delta x}\right\rfloor,\quad i_{y}=\left\lfloor\frac{y^{\prime}_{i}-y_{\text{min}}}{\Delta y}\right\rfloor \tag{10}\]  
where \(x_{\text{min}}\) and \(y_{\text{min}}\) are the minimum coordinates of the grid. The z-coordinate is mapped to the grid if it falls within the specified range \([z_{\text{min}},z_{\text{max}}]\):\[\mathbf{G}(i_{y},i_{x})=\begin{cases}z_{i}^{\prime}&\text{if $z_{\text{min}}\leq z_{i}^{ \prime}\leq z_{\text{max}}$}\\ 0&\text{otherwise}\end{cases} \tag{11}\]  
The grid \(\mathbf{G}\) is then reshaped into a vector for efficient storage and processing:  
\[\mathbf{g}=\text{reshape}(\mathbf{G},\text{grid\_height}\cdot\text{grid\_width}) \tag{12}\]  
For visualization and further analysis, the grid map is normalized and converted into a pseudo-image \(\mathbf{I}_{\text{grid}}\):  
\[\mathbf{I}_{\text{grid}}=\frac{\mathbf{G}-\text{min}(\mathbf{G})}{\text{max} (\mathbf{G})-\text{min}(\mathbf{G})} \tag{13}\]  
This pseudo-image is then scaled to the range [0, 1] to standardize the data for subsequent processing steps:  
\[\mathbf{I}_{\text{grid}}=\frac{\mathbf{I}_{\text{grid}}}{255} \tag{14}\]  
The normalized grid map \(\mathbf{I}_{\text{grid}}\) is now ready for feature extraction and environmental perception tasks, providing a consistent input for machine learning algorithms.
## Appendix A Proof of Proposition 3
### Model Simplification  
According to Equation (10), we can obtain the following equations  
\[\begin{split}\theta_{i}&=\arctan\biggl{(}\frac{ \dot{X}_{i}\cos\psi_{i}+\dot{Y}_{i}\sin\psi_{i}}{\dot{Z}_{i}+g}\biggr{)}\\ \phi_{i}&=\arctan\biggl{(}\frac{\dot{X}_{i}\sin \psi_{i}-\dot{Y}_{i}\cos\psi_{i}}{\dot{Z}_{i}+g}\cos\biggl{(}\arctan\biggl{(} \frac{\dot{X}_{i}\cos\psi_{i}+\dot{Y}_{i}\sin\psi_{i}}{\dot{Z}_{i}+g}\biggr{)} \biggr{)}\biggr{)}\end{split} \tag{13}\]  
Without loss of generality, we assume that the yaw angle is controlled at origin, and the altitude is stabilized at some constant value, then,  
\[\theta_{i}=\arctan\bigl{(}\ddot{X}_{i}/g\bigr{)}\quad\text{and}\quad\phi_{i} =\arctan\bigl{(}-(\ddot{Y}_{i}/g)\cos\theta_{i}\bigr{)} \tag{14}\]  
Furthermore, considering Assumption 1, we obtain that  
\[\begin{split}\ddot{X}_{i}&=\theta_{i}g+\Delta_{X_{i }}\\ \ddot{Y}_{i}&=-\phi_{i}g+\Delta_{Y_{i}}\end{split} \tag{15}\]  
where the nonlinearities \(\Delta_{X_{i}}\) and \(\Delta_{Y_{i}}\) are bounded since the absolute values of \(\theta_{i}\) and \(\phi_{i}\) are bounded within \(30^{\circ}\) by assumption 1. Then, we know that (15) is Lyapunov stable, if the simplified system (16) is asymptotically stable.
## 4 Discussion
### Effectiveness of IoU-Prior Cascade Matching  
By incorporating IoU-prior cascade matching, the complete MFACNet further reduces a small number of IDSWs and improves MTs, which illustrates its ability to recover lost tracklets through multi-source information and delicate logic design. IoU-prior cascade matching utilizes the high overlap characteristics of objects in video frames to filter out the same targets using an IoU threshold in the first step. In the final association step, ReID information is also leveraged to associate the remaining detections with unmatched tracklets. This alleviates, to some extent, the issues of erroneous matching and trajectory interruptions caused by relying solely on localization information and using the greedy order algorithm to associate the detections with tracklets.
###### Abstract  
The primary goal in this paper is to verify the possibility of combining a quantum channel into a single optical fiber with other classical channels by using the so-called attenuation method. Since the quantum channel is very weak in terms of power, combining it into a single fiber with much more powerful classical channels is challenging. Thus, sufficiently high-quality filtering is important to avoid possible crosstalk. A second and more difficult problem to address is the interference caused by Raman noise, which increases with the fiber length and is also dependent on the input power of the classical channel. Thus, in this paper the focus is on the possibility of suppressing the Raman noise effect, both in advance by means of wavelength positioning and by means of installed optical components. Such phenomena must be considered in the route design, as the quantum channel must be placed at a suitable wavelength with respect to the classical channels. The influence of other nonlinear phenomena has been neglected. In this paper, a practical experiment aimed at building a fully functional multiplexed quantum key distribution link is also described.  
attenuation; [PERSON] (2012); coherent one-way protocol; quantum key distribution; wavelength-division multiplexing +
Footnote †: journal: _Photonics_  
0
Footnote 0: _E-mail:_ p.m.  
clarity, similar methods are referred to in the paper only as quantum multiplexing (QMUX). The service channels are emitted in opposite directions and jam the quantum channel with its Raman noise [2]. The basis of the entire setup is a research version of IDQ's Clavis3 device with a key rate of 1400 b/s at a 14 dB attenuation. To make this setup work, the so-called attenuation method was chosen. In contrast to currently used techniques, the main advantage is the small spacing between quantum and classical channels. The techniques used are further elaborated in Section 3.  
Footnote 3: The authors are grateful to the anonymous referee for their comments.
# A Study of Precision Current Efficiency Curve Measurement with a Casing-Type Anode
## 4 Experiments  
The experimental system is shown in Figure 13. It consists of electrodes, a fixture, a feed device, a power supply, an electrolyte circulating system, and a current monitoring system. As shown in Figure 14, the chucks used in the two experiments were specially designed. In the conventional method, the workpiece was a rod with a 16 mm \(\times\) 16 mm cross-sectional area that was fixed in the chuck. The conductor was made of metal, so the rod was conductive to the pole. This was different from the workpiece used in the conventional method. The anode material used in the proposed method was designed into two parts: the mandril and the casing. The casing was a rod similar to the rod used in the conventional method, but there was a circular hole with a diameter of 11.5 mm in the central region. It was fixed in: conductor 1, where the mandril was made of a cylindrical rod with a diameter of 11.3 mm; and conductor 2, where the relative position between conductor 1 and conductor 2 was fixed, and a gap of 0.1 mm was maintained between the mandril and the casing. The presence of an insulating plate allowed conductor 1 and conductor 2 to be kept insulated from each other. The mandril and the casing were connected to the positive pole of the power supply through the different poles, so the machining current of the mandril could be measured by using a separate loop. In this study, the testing of both methods was performed under the same conditions. Every experiment was repeated three times, and the average value is used in the analysis. Detailed experimental conditions are listed in Table 3.  
Figure 12: The current efficiency curves of 304 SS were obtained by conventional and proposed method simulations.
## 4 Simulation and Comparison of FMCW Laser Target Characteristics
### Echo Characteristic Simulation Results Under Smoke Particle Characteristic Variation
Under high-visibility conditions, the effects of particle size and position changes on the peak amplitude of the laser echo signal were relatively greater. The average rates of change in amplitude were about 49.6% and 49.1%, while the number characteristic was only about 6.7%. Therefore, it is still necessary to focus on the effect of positional variations on the echo characteristics. When the characteristics of the smoke particles were unchanged, the beat signal spectrum peak with a visibility of 15 m corresponded to a frequency of 12 kHz and it was the same as the beat frequency corresponding to the setting of a 3 m target, based on Figures 10-12. However, the situation was the opposite at a visibility of 8 m, when the probability of collision was relatively larger. In this case, the type of collision at low visibility was dominated by the simultaneous collision of photons with multiple smoke particles, while at high visibility, the collision of photons with a single smoke particle was shown. For a single-channel laser, false and missed alarms are affected by different smoke particle characteristics and their variations. The SNRs of a beat signal can be calculated using the ratio of the particle number in the target echo to the smoke echo, which can be expressed using the collision probability as  
\[SNR_{LowVisibility}=10 log_{10}\frac{1-p^{\prime}_{cN}}{p^{\prime}_{cN}}=10 log_{ 10}\frac{n^{(Low)}_{photon}}{N_{photon}-n^{(Low)}_{photon}}>0 \tag{25}\]  
\[SNR_{HighVisibility}=10 log_{10}\frac{1-p^{\prime}_{c1}}{p^{\prime}_{c1}}=10 log_{ 10}\frac{n^{(High)}_{photon}}{N_{photon}-n^{(High)}_{photon}}<0\]  
where \(SNR_{LowVisibility}\) and \(SNR_{HighVisibility}\) are the SNR for low and high visibility, respectively. \(p^{\prime}_{cN}\) and \(p^{\prime}_{c1}\) are the collision probability after a change in smoke particle characteristics, which can be expressed as  
\[p^{\prime}_{cN}=\sum_{i=1}^{N_{photon}}\frac{n_{photon}}{N_{photon}}\times \sigma_{sc}(r_{s1},\cdots,r_{sn})\times\Delta v_{ij}\Delta t \tag{26}\]  
\[p^{\prime}_{c1}=\sum_{i=1}^{N_{photon}}\frac{n_{photon}}{N_{photon}}\times \pi(r^{\prime}_{s}+r_{p})\times\Delta v_{ij}\Delta t\]  
The uncertainty in the collision probability, which was disturbed by variations in the number of photons emitted, caused significant errors in the SNR results and led to a failure to accurately detect the target. Therefore, it was necessary to increase the collision probability in low visibility and to decrease it in high visibility. On this basis, a reasonable layout of the multi-channel laser emission position is extremely important, which can enhance the anti-smoke interference ability of the laser detector.
## 3 Test Results  
### Failure Modes  
The failure modes of the joints are shown in Figure 6. The joints were in the elastic status before yielding, and there was no obvious deformation of the chords and braces. With the increase of load, the failure modes of the joints were different after yielding. After the K-CC and K-RC joints yield, micro-cracks appeared at the intersection of overlapped braces and the chords. With an increase in the cyclic loading, the unreinforced joint entered the yielding stage. Due to the differing stress state of the through support and the lap joint, the concave and convex deformation occurred on the chord surface, and the plastic deformation of the K-RC joint was more obvious. For the strengthened joints K-CCC1, K-CCC2, K-RCC1 and K-RCC2, the failure modes were similar. After yielding, micro-cracks appeared in the heat-affected zone of the weld seam on the through braces. Then the cracks gradually expanded with an increase in the load, but the plastic deformation of the chord surface was not obvious. Ultimately, the toe weld of the brace was ruptured by tension, and the compressed brace had slight local buckling.  
Figure 5: Layout of measuring points. (**a**) Front view of the K-joint; (**b**) top view of the K-joint; (**c**)the side view of the K-joint.  
In summary, the main failure mode for the joints without grouting concrete was plastic failure on the surface of the chords; the failure mode of the joints strengthened with concrete in the chord was tension crack failure of the toe weld of the brace, and the compressive brace had slight buckling; the strengthened joints with the different concrete (ordinary concrete and fly ash concrete) in chords had the same failure modes. In general, great changes have taken place in the failure modes of the strengthened joints with grouting concrete in chords. The strengthened measure of grouting concrete in chords effectively improved the mechanical properties of the joints, restrained the plastic deformation of the chord, and avoided the large stress concentration of the chord in the area where the braces and chords intersected.
## 2 Materials and Methods
### FR Calculation from Site Index
#### 2.3.3 Calculate Relationship between FR and Site Index  
After developing a relationship between stand volume and SI, an expression for FR based on SI was derived. SI of loblolly pine in the southeastern United States can approximately range from 10.7 m to 30.5 m [45, 46, 47]. Therefore, based on this range of site indices reported for loblolly pine in the southeastern United States, we assumed that SI of 10.7 m corresponded to FR = 0 and SI of 30.5 m corresponded to FR = 1. Then the volume at SI values between the maximum and minimum site indices was used to derive the relationship between SI and FR. We fit a sigmoidal equation: FR = \(\frac{\beta_{0}}{1+e^{(\beta_{1}+\beta_{2}\mathrm{SI})}}\), where \(\beta_{0}\), \(\beta_{1}\), and \(\beta_{2}\) are coefficients derived from data. We used nonlinear least square method to fit the sigmoidal equation using 'nls' procedure in \"R version 3.1.1\". To ensure the solution is global and not local, different initial values for the parameters were provided for the fits.
# Compact MIMO System Performances in Metallic Enclosures
###### Abstract  
In this work, we present a 2 \(\times\) 2 near-field multi-input multiple-output (MIMO) prototype for bit-error-rate (BER) and error vector magnitude (EVM) measurements in a metal enclosure. The near-field MIMO prototype was developed using software-defined-radios (SDRs) for over-the-air transmission of QPSK modulated baseband waveforms. We checked the near-field MIMO BER and EVM measurements in three different scenarios in a highly reflecting metal enclosure environment. In the first scenario, the line-of-sight (LOS) communication link was investigated when the mode stirrer was stationary. In the stationary channel conditions, near-field MIMO BER and EVM measurements are performed. In the second scenario, BER and EVM measurements were performed in dynamic channel conditions when the mode stirrer was set to move continuously. In the third scenario, LOS communication near-field MIMO BER and EVM measurements were performed in stationary channel conditions but now in the presence of MIMO interference. In three different scenarios, near-field MIMO BER and EVM measurements were investigated at different Tx USRP gain values and in the presence of varying levels of MIMO interference.  
software-defined-radio; USRP; near-field; rich scattering; BER measurement; EVM measurement +
Footnote †: journal: