esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mixed Traffic Flow Car-Following Model
\(k_{1}\) and \(k_{2}\) are control coefficients, set to 0.23-2 and 0.07-2, respectively, based on the calibration results from real-vehicle experiments for the CACC model. The parameters \(l\), \(v\) and\(\Delta v\) have the same meanings as in Equation (1). (3) HDV as the following vehicle The Intelligent Driver Model (IDM), proposed by [PERSON] [29] et al., has the advantages of fewer parameters and clear physical meaning, better reflecting the car-following characteristics of HDV. Considering the varying levels of trust that human drivers have towards CAV, drivers in 'Scenario (4)' in Figure 1 are categorized into three car-following styles: hesitant, steady, and trustful. Based on this, we introduce a maximum acceleration sensitivity coefficient \(\lambda_{n}\) and a safe headway sensitivity coefficient \(\omega_{n}\) into the IDM model. In contrast, 'Scenario (3)' in Figure 1 represents a human-driven vehicle following another human-driven vehicle. The car-following style in this scenario is defined as 'normal', which is not affected by the two sensitivity coefficients, both of which are set to a value of 1. The modified IDM model considering reaction time and driver characteristics is expressed as: \[a_{h}=\lambda_{n}a_{\max}\left\{1-\left(\frac{v}{v_{f}}\right)^{4}-\left[\frac {s_{h}+v(\omega_{n}t_{h}+\tau_{h})+v\Delta v\left(2\sqrt{a_{\max}b}\right)^{-1 }}{x_{h}-l}\right]^{2}\right\}\] (3) In the above equation, \(a_{h}\) is the acceleration of the HDV. \(a_{\max}\) is the maximum acceleration, set to 1 m/s\({}^{2}\); \(v_{f}\) is the free-flow velocity, set to 11.1 m/s; \(s_{h}\) is minimum safe distance for HDV, set to 2 m; \(t_{h}\) is the desired time headway for HDV, set to 1.5 s; \(\tau_{h}\) is the reaction time for DCAV, set to 0.4 s [30]; \(b\) is the desired comfortable deceleration, set to 2.8 m/s\({}^{2}\); \(x_{h}\) is the headway of HDV. The parameters \(l\), \(v\) and \(\Delta v\) have the same meanings as in Equation (1). The values of the sensitivity \(\lambda_{n}\) and \(\omega_{n}\) coefficients [31] are shown in Table 1:
## 3 Results and Discussion
### Insole Hardness Effect  
Insole hardness is a key determinant of shock absorption and force distribution during dynamic sports movements [36]. While midsole and outside stiffness have been widely studied in relation to energy return and propulsion efficiency, the role of insole hardness in optimizing elastic energy storage and return remains less understood. One included study [20] investigated the effects of different insole hardness distributions on energy return during a badminton lunge and found no systematic alterations in elastic energy recovery, suggesting that insole properties alone may not be the primary factor influencing energy return in such activities.  
Further analysis showed that the soleus exhibited a substantially greater work contribution than the gastrocnemius, indicating that elastic energy storage primarily occurs within the soleus muscle-tendon unit (MTU) during maximal badminton lunges. This aligns with the findings of [PERSON] et al. [37], who demonstrated that the soleus tendon, due to its higher strain tolerance, stores greater elastic potential energy during the stretching phase and releases it as kinetic energy during propulsion. These observations highlight the critical role of the soleus in energy management during explosive lower-limb movements, suggesting that shoe design modifications should focus on optimizing areas that support this key muscle rather than insole hardness alone.  
The lack of significant modulation by insole hardness in this study suggests that current insole designs may not yet precisely align with the biomechanical loading characteristics of the soleus, possibly due to variability in foot strike mechanics and muscle activation strategies among athletes. Additionally, while insole hardness distribution did not exhibit systematic effects on energy return, individual differences in movement techniques indicate that personalized footwear customization may be necessary to optimize comfort, force distribution, and energy efficiency in badminton-specific footwork.
# Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems
## 5 Simulation Results
### The Performance of the OFDM/QPSK System  
The BER performance of the OFDM/QPSK system over Rayleigh fading channels with different Doppler spread is presented in Figure 6. The OFDM/QPSK system adopts comb-type pilots insertion for perfect channel estimation. At the target BER of \(10^{-3}\), the \(E_{\text{b}}/N_{0}\) gaps of the LS method under Doppler spread of 20, 40, 60, and 80 Hz are about 8.5, 2.0, and 0.5 dB. Moreover, the \(E_{\text{b}}/N_{0}\) gaps between the BER performance curves of ICE method with Doppler spread of 20, 40, 60, and 80 Hz are about 7.0, 2.2, and 0.8 dB at the target BER of \(10^{-4}\).  
Figure 4: Bit error rate (BER) performance of the OFDM/OQPSK system over Rayleigh fading channels with Doppler spread of 20, 40, 60, and 80 Hz. ICE: ideal channel estimation.  
Figure 5: Normalized minimum square error (NMSE) performance of the OFDM/OQPSK system over Rayleigh fading channels with Doppler spread of 20, 40, 60, and 80 Hz.  
Figure 6: BER performance of the OFDM/QPSK system over Rayleigh fading channels with Doppler spread of 20, 40, 60, and 80 Hz. LS: least square.  
Figure 7 presents the NMSE performance of the OFDM/QPSK system over Rayleigh fading channels with different Doppler spread, which can be seen is that the LS method with \(f_{\rm d}=20\)Hz outperforms the LS method with \(f_{\rm d}=40\)Hz, \(f_{\rm d}=60\)Hz, and \(f_{\rm d}=80\)Hz by about 4.0, 6.0, and 6.1 dB \(E_{\rm b}/N_{0}\) gains at the NMSE of 0.1. As shown in Figure 7, the LS method has very low NMSE value with the increase of \(E_{\rm b}/N_{0}\). It can suppress the AWGN impact and improve the accuracy of the estimated CIR, thereby it can increase the accuracy of channel estimation.
# Acoustic Emission for Evaluating the Reinforcement Effectiveness in Steel Fiber Reinforced Concrete
## 2 Experimental Details
### Three-Point Bending  
Three concrete specimens with 0%, 0.5% and 1% volumetric fraction of fiber content were prepared. The dimensions of the prismatic concrete specimens were 100 \(\times\) 100 \(\times\) 600 mm. The water to cement ratio by mass (w/c) of the mix was 0.6 and the cement was Cimentas CEM I 42.5R type. In addition, crushed limestone aggregates were of three classes: 0-3 mm, 5-15 mm and 15-25 mm. The mix proportions of the concrete were: cement 295 kg/m\({}^{3}\), water 177 kg/m\({}^{3}\), 0-3 mm aggregate 960 kg/m\({}^{3}\), 5-15 mm aggregate 394 kg/m\({}^{3}\), 15-25 mm aggregate 591 kg/m\({}^{3}\), plasticizer 3 kg/m\({}^{3}\), steel fiber 39 kg/m\({}^{3}\) for 0.5% and 78 kg/m\({}^{3}\) for 1%. The bulk density of the concrete was 2378 kg/m\({}^{3}\). The slump of the concrete mixture was measured as 8.2 mm. Fibers used in concrete were BetonFiber HE 0735 type hooked steel fibers. The fibers type was cold-drawn with a length of 35 mm, diameter of 0.7 mm and tensile strength of 1400 MPa. After casting, the concrete specimens were cured in the standard conditions for 28 days (20 \(\pm\) 2 \({}^{\circ}\)C and 95% relative humidity). The distribution and orientation of fibers are known to affect the final properties of the beam [23]. The fibers were added to the dry mixture to enable a good distribution. The volumetric distribution of fibers in the beam was not verified after casting. However, after the tests, when the fracture surfaces were examined, a relatively good distribution of the fibers throughout the cross-section could be seen.  
In the experimental study, flexure tests were conducted on each specimen in accordance with the TS EN 12390-5 standard [24] with a net span of 500 mm. Load was applied with a speed of 0.2 mm/min at the center of the mid-span by Shimadzu AG-IS 100 kN Universal Testing Machine (Figure 3). Simultaneously AE data were recorded during the loading. AE activities were recorded by an 8-channel AE system with six R15a AE sensors by Mistras Group (resonant at 150 kHz). Received AE activities were amplified with preamplifiers having 40 dB gain and the threshold was set as 40 dB to eliminate ambient noise. The sensors were hot-glued on the concrete surface to eliminate displacement, especially on the vertical surfaces. Hsu-Nielsen calibration (pencil lead breaks) was applied to ensure the acoustic coupling of the sensors. These experiments were conducted in Ege University, Izmir, Turkey.  
Figure 3: Three-point flexure test (**a**) drawing of the geometry and sensor placement, (**b**) photograph during test.
# Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots
## 2 Biologically-Inspired Kalman Filter Based RBFNN Control
### Classical RBFNN  
Figure 2 presents the structure of a classical RBFNN. This feed forward multilayer neural network has three layers, comprising the input layer, hidden layer and output layer. As shown in Figure 2, the inputs of the hidden layer in the RBFNN structure are the linear combinations of the weights and the input vector \([x_{1}\ x_{2},\ x_{3},\ldots\ \ldots,\ x_{n}]^{T}\). These vectors are then mapped by means of a radial basis functions in each node. Finally, the output layer of RBFNN generates a vector \(y_{p}\) for \(m\) outputs by linear combination of the outputs of the hidden nodes. This kind of artificial neural network has been regarded as a powerful tool that can approximate any continuous function with satisfactory accuracy [11]. In Figure 2, the output of the RBFNN is expressed by:  
\[y_{m}=\sum_{j=1}^{m}w_{j}h_{j} \tag{3}\]  
where \(h_{j}\) is the radial basis vector. This vector is described by using the following Gaussian function:  
\[h_{j}=\exp(\frac{-\|X-C_{j}\|^{2}}{2b_{j}^{2}}),j=1,2,\ldots,m \tag{4}\]  
where \(\|\bullet\|\) is the Euclidean norm operation, \(C_{j}=[c_{j1},c_{j2},\ \ldots,\ c_{jm}]^{T}\) is the center vector of the \(j\)th node, \(B=[b_{1},b_{2},\ \ldots,\ b_{m}]^{T}\) is the basis width vector. \(W=[w_{1},w_{2},\ \ldots,\ w_{m}]^{T}\) is the weight vector in the RBFNN. Typically, this neural network is initialized with a randomly determined of RBFNN parameters, including \(C_{j}\), \(B\), and \(W\).  
Gradient descent is an effective method for training RBFNN networks compared with other conventional training approaches [9; 10]. In gradient descent training with one neuron in the output layer, the weights are updated at each time step by using the following rules:  
\[w_{j}(k+1)=w_{j}(k)+\eta e(k)h_{j}(k), \tag{5}\]  
\[C_{ji}(k+1)=C_{ji}(k)+\eta e(k)w_{j}h_{j}\frac{x_{i}(k)-C_{ji}(k)}{b_{j}^{2}(k )}, \tag{6}\]  
Figure 2: Structure of the classical RBFNN (Radial Basis Function Neural Network).  
\[b_{j}(k+1)=b_{j}(k)+\eta e(k)w_{j}h_{j}\frac{\left\|X(k)-C_{j}(k)\right\|^{2}}{b_ {j}^{3}(k)}, \tag{7}\]  
where \(e(k)\) represents the error at the \(k\)th time step and \(\eta\) denotes the learning rate. Since the initial parameters for an RBFNN using the gradient descent method are determined either by a trial-and-error approach or randomly set, convergence to a local optimum solution is inevitable [10]. To improve the learning performance of the RBFNN, this paper has developed a Kalman filter to train the RBFNN network structure based on a gradient descent approach. The proposed KF-RBFNN is applied to the self-learning control of networked mobile robots.
# Solution-Processed CdTe Thin-Film Solar Cells Using ZnSe Nanocrystal as a Buffer Layer
## 3 Results and Discussion
It is clear that the EDT treatment device shows a higher EQE response in nearly the whole wavelength than the EQE response in a device without EDT treatment. From the EQE spectrum, one can see that both devices show lower EQE response in the whole wavelength when compared to CdTe NC solar cells with CdS or CdSe NC as buffer layer. We anticipate that the lattice mismatch between CdTe and ZnSe is larger than that of CdTe/CdS or CdTe/CdSe, which will result in low junction quality. Therefore, nonradiative recombination is serious in the interface of CdTe/ZnSe, which will lead to large leakage current and low output short circuit current and device performance. To further increase the performance of CdTe NC/ZnSe solar cells, much work should be done such as optimizing EDT treatment conditions (with different EDT concentration, spin-casting \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**ZnSe NCs**} & & & & & \\ \multicolumn{2}{c}{**(nanocrystals)**} & & & & & \\ \multicolumn{2}{c}{**Thickness (CdTe NCs**} & & & & & \\ \multicolumn{2}{c}{**Annealing at 350 \({}^{\circ}\)C**} & & & & & \\ \hline
25 nm & 3.58 & 0.40 & 30.17 & 0.38 & 67.56 & 148.18 \\
60 nm & 4.09 & 0.37 & 29.96 & 0.45 & 64.67 & 132.51 \\
80 nm & 6.03 & 0.44 & 38.04 & 1.02 & 35.84 & 219.07 \\
110 nm & 10.35 & 0.56 & 33.30 & 1.93 & 26.08 & 120.76 \\
125 nm & 5.61 & 0.39 & 37.83 & 0.84 & 33.27 & 250.40 \\ \hline \multicolumn{2}{c}{CdTe NCs annealing temperature (ZnSe thickness 110 nm)} & & & & & \\ \hline CdTe 300 \({}^{\circ}\)C & 7.07 & 0.41 & 33.89 & 0.99 & 32.9 & 115.80 \\ CdTe 330 \({}^{\circ}\)C & 10.35 & 0.51 & 33.3 & 1.70 & 12.55 & 106.72 \\ CdTe 370 \({}^{\circ}\)C & 11.94 & 0.63 & 40.79 & 3.07 & 23.50 & 246.15 \\ CdTe 400 \({}^{\circ}\)C & 11.23 & 0.53 & 30.49 & 1.81 & 33.32 & 79.59 \\ \hline \multicolumn{2}{c}{1,2-ethanedithiol (EDT)} & & & & & \\ \multicolumn{2}{c}{treatment (CdTe NCs annealing at 370 \({}^{\circ}\)C} & & & & & \\ \multicolumn{2}{c}{with 110 nm ZnSe)} & & & & & \\ \hline W EDT & 13.55 & 0.64 & 47.33 & 3.58 & 19.48 & 251.00 \\ WO EDT & 11.94 & 0.63 & 40.79 & 3.07 & 23.50 & 246.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarized performance of ZnSe/CdTe NC solar cells (FF: fill factor; NCs: Nanocrystals; PCE: Power conversion efficiency, Figure 2e,f). ratio or using different ligands such as 2-mercaptoethanolpropionic acid), using different ZnSe fabricating technics (such as chemical bath deposition, sputtering or evaporation). Furthermore, introducing a very thin layer of CdS or CdSe between CdTe and ZnSe to form graded bandgap solar cells is beneficial for carrier diffusion and collecting. Similar work had been confirmed in the device with configuration of glass/FTO/n-ZnS/n-CdS/n-CdTe/Au [38].
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
###### Abstract  
Increasing attention has recently been devoted to underwater sensor networks (UWSNs) because of their capabilities in the ocean monitoring and resource discovery. UWSNs are faced with different challenges, the most notable of which is perhaps how to efficiently deliver packets taking into account all of the constraints of the available acoustic communication channel. The opportunistic routing provides a reliable solution with the aid of intermediate nodes' collaboration to relay a packet toward the destination. In this paper, we propose a new routing protocol, called opportunistic void avoidance routing (OVAR), to address the void problem and also the energy-reliability trade-off in the forwarding set selection. OVAR takes advantage of distributed beaconing, constructs the adjacency graph at each hop and selects a forwarding set that holds the best trade-off between reliability and energy efficiency. The unique features of OVAR in selecting the candidate nodes in the vicinity of each other leads to the resolution of the hidden node problem. OVAR is also able to select the forwarding set in any direction from the sender, which increases its flexibility to bypass any kind of void area with the minimum deviation from the optimal path. The results of our extensive simulation study show that OVAR outperforms other protocols in terms of the packet delivery ratio, energy consumption, end-to-end delay, hop count and traversed distance.  
routing protocol; void area; underwater sensor network +
Footnote †: journal: Sensors
# Compressive Sensing-Based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems
## 3 Sparse Adaptive Matching Pursuit Algorithm
### Sparse Multipath Channel Estimation
At higher signal-to-noise ratios, since the gain coefficient of the channel tap is higher than the noise amplitude, at each iteration of the algorithm, \(T\)s of the element amplitude produces a larger rate of change; then the element before this position has to carry channel information. Therefore, calculating the maximum backward difference between adjacent elements determines the number of elements selected in this iteration, and the elements before this position are selected for the support set because they may carry channel information. In order to further improve the accuracy of the selected elements, the regularization process based on convex optimization is adopted to ensure that the selected element energy is larger than the energy of the unselected elements [23], and the noise is filtered out to support the set. From the above analysis we can see that the sparseness estimation of BSAMP algorithm first estimates the sparsity upper limit by setting the threshold according to the actual physical characteristics of the channel, that is to say, the maximum sparsity will not exceed the number of pilots so as to ensure that the channel taps will not miss selection. Sparseness is further estimated by finding the maximum difference location in this range to distinguish channel taps and noise. Unlike other compression-aware algorithms, only the influential factors associated with Gaussian white noise are considered \(\mathbf{B}^{\mathrm{H}}\mathbf{n}\). The BSAMP algorithm not only considers \(\mathbf{B}^{\mathrm{H}}\mathbf{n}\) but also considers the energy dispersion due to the non-orthogonality of the observation matrix \((\mathbf{B}^{\mathrm{H}}\mathbf{B}-\mathbf{I}) \mathbf{w}\) impact algorithm performance. In addition, the BSAMP algorithm uses a regularization method to filter the elements in the support set for secondary screening, which improves the accuracy of the support set. Therefore, the BSAMP algorithm has better estimation performance than other algorithms. The massive MIMO system needs to recover the larger channel dimension, and the sub-channel presents joint sparseness. The BSAMP algorithm takes advantage of channel block sparse features, greatly reducing the number of iterations. In the meantime, there are multiple element-selected support sets in each iteration, and the problem that the traditional step-based adaptive algorithm relies on iterative step-size is avoided. Therefore, the BSAMP algorithm has lower computational complexity. The specific BSAMP algorithm process is as follows in Algorithm 1.
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
###### Abstract  
In order to accelerate the execution of streaming applications on multi-core systems, this article studies the scheduling problem of synchronous data flow graphs (SDFG) on homogeneous multi-core systems. To describe the data flow computation process, we propose the SDAG (Super DAG) computation model based on the DAG model combined with data-driven thoughts. Further, we analyze the current common SDFG scheduling algorithms and propose an improved SDFG scheduling algorithm, LSEFT (level-shortest-first-earliest-finish time). The LSEFT algorithm uses an inverse traversal algorithm to calculate the priority of tasks in the task-selection phase; the shortest-job-priority earliest-finish-time policy is used in the processor selection phase to replace the original long job priority policy. In the experimental part, we designed an SDFG random generator and generated 958 SDFGs with the help of the random generator as test cases to verify the scheduling algorithm. The experimental results show that our improved algorithm performs well for different numbers of processor cores, especially for 8 cores, where the speedup of our improved algorithm improves by 10.17% on average.  
homogeneous synchronous data flow graph; computational model; multi-core systems; scheduling algorithm
###### Abstract  
Advances in the functionalities and operating frequencies of integrated circuits (IC) have led to the necessity of measuring their electromagnetic Interference (EMI). Three-dimensional integrated circuit (3D-IC) represents the current advancements for multi-functionalities, high speed, high performance, and low-power IC technology. While the thermal challenges of 3D-IC have been studied extensively, the influence of EMI among the stacked dies has not been investigated. With the decreasing spacing between the stacked dies, this EMI can become more severe. This work demonstrates the potential of EMI within a 3D-IC numerically, and determines the minimum distance between stack dies to reduce the impact of EMI from one another before they are fabricated. The limitations of using near field measurement for the EMI study in stacked dies 3D-IC are also illustrated.  
3D-IC (three-dimensional integrated circuit); electromagnetic interference; near field measurement +
Footnote †: journal: Applied Sciences  
1
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## Appendix A Speed Accuracy  
The expected speed accuracy given by the manufacturer is less than 0.2 m\(\cdot\)s\({}^{-1}\) on \(V_{los}\) measurements [39]. To make sure that this value was not affected by low wind conditions, it was checked in the conditions of use of the Passy-2015 field experiment by applying the correlation method described in [PERSON] and Cornman [50]. This method decomposes the measured velocity \(\hat{v}(k)\) as \(\hat{v}(k)=v(k)+\epsilon(k)\), where \(v(k)\) corresponds to the \"real\" velocity and \(\epsilon(k)\) the error. For each range gate, two correlation coefficients are computed, namely :  
\[C(0)=\frac{1}{K}\sum_{k=0}^{K-1}\hat{v}^{2}(k)\hskip 28.452756 ptC(1)=\frac{1}{K-1 }\sum_{k=0}^{K-2}\hat{v}(k)\hat{v}(k+1). \tag{1}\]  
The temporal average of the difference \(C(1)-C(0)\) is then computed and associated with the corresponding mean CNR for the range gate. Assuming that \(v(k)\) and \(\epsilon(k)\) are uncorrelated, that \(\epsilon(k)\) is white noise and that the pulse repetition frequency is sufficiently large, the difference between the two correlation coefficients provides an estimation of the mean error \(\langle C(0)\rangle-\langle C(1)\rangle\simeq\langle\epsilon^{2}(k)\rangle\).  
The result is displayed in Figure 11. For \(CNR>-24\) dB, the mean error on \(V_{los}\) measures is around 0.12 m\(\cdot\)s\({}^{-1}\) (horizontal dotted line). Below \(-24\) dB, the mean error increases when CNR decreases. Based one this consideration, we could use \(-24\) dB as a threshold for the quality control of the data. However, the CNR distribution has a double Gaussian shape, with a first mode between \(-40\) and \(-30\) dB and a second one between \(-30\) dB and \(-5\) dB (not shown). The first mode corresponds to noise, whereas the other is associated with consistent signal data. As a result, the CNR threshold is fixed at \(-29\) dB (vertical dashed line) in order to keep these significant data (which increases our database by 17%). Moreover, this \(-29\) dB threshold is in agreement with the one derived from the heuristic semiempirical model proposed by [PERSON] [51] (not shown). In this way, we have more than 80% of the database associated with a mean error of 0.12 m\(\cdot\)s\({}^{-1}\) and the remaining 20% with a mean error of 0.27 m\(\cdot\)s\({}^{-1}\). Since interactions of the laser beam with the motionless mountain sides are associated with high CNR, an upper CNR threshold is applied and set to \(-7\) dB.