esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cross-Property Relationships
#### 3.5.4 Thermal Properties and Electrical Conductivity  
As Figure 10 shows, electrical conductivity, like open porosity, was inversely related to thermal conductivity and directly to specific heat capacity with determination coefficients R\({}^{2}\) for linearity above 0.830. The explanation for these findings lies in the greater electrical conductivity of the recycled materials mixes than the reference mix, resulting from the greater volume of saturated pores in the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter/Differential**} & \multicolumn{3}{c}{**NA mix**} & \multicolumn{3}{c}{**MRA mix**} \\ \cline{2-7}  & **NAC** & **N10/0** & **N25/0** & **R0/50** & **R10/50** & **R25/50** \\ \hline \(\uplambda\)s (W/m-K) 1 ** & 2.345 & 2.303 & 2.223 & 2.310 & 2.256 & 2.186 \\ \(\uplambda\) with GRC (\%) & - & \(-1.8\) & \(-5.2\) & - & \(-2.4\) & \(-5.4\) \\ \(\uplambda\) with MRA (\%) & & & \(-1.5\) & \(-2.0\) & \(-1.7\) \\ \(\uplambda\) with GRC (\%) \(\times\) MRA & & & & \(-3.8\) & \(-6.8\) \\ (\%) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Thermal conductivity of the concrete matrices.  
Figure 9: Open porosity/thermal conductivity and open porosity/specific heat capacity relationships for all concrete mixes.  
former (see item 3.3.3). Earlier authors also mentioned a rise in electrical conductivity (reported as a decline in electrical resistivity) with the higher porosity attributed to using MRA coarse aggregate [50], RCA coarse aggregate [62], RCA fines [57] or RCA coarse and fine aggregate jointly [63].
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 2 Millimeter-Wave Antenna Array for 5G Communication
### Millimeter-Wave Phased Arrays for 5G Access Terminals
#### 2.2.1 Millimeter-Wave PCB Phased Arrays
Figure 17: _Cont._  
In [15], mm-wave massive MIMO with digital beamforming (DBF) architecture operating at a 28-GHz frequency with an operating bandwidth of 500 MHz has been proposed. The proposed transceiver has a (\(16\times 4\)) 64-element antenna array configuration. A printed Yagi-Uda antenna element combined with a microstrip balun structure was chosen as the radiating element in the transceiver. Owing to the compact size, ease of fabrication, high gain, and low-cost factors, the proposed Yagi-Uda antenna element exhibits the best option to be considered for mm-wave MIMO transceivers [51]. Figure 18a depicts the design overview of the printed Yagi-Uda antenna element. The experimental reflection coefficient of the designed array is lower than \(-14\) dB at a 28-GHz operating frequency with a bandwidth of more than 3 GHz. The wide azimuthal angular beam-scanning achieved by the proposed DBF-based phased-array system is \(\pm 67^{\circ}\), as shown in Figure 18b. The experimental Tx and Rx peak gains are 29 dBi and 27 dBi, respectively. Figure 17: **(a)** Side view of seven-element antenna array combined with EM lens. **(b)** Top view of the seven-element SIW fed by a stacked patch antenna array. **(c)** Photograph of the lens integrated array antenna prototype. **(d)** Experimental and simulated 10-dB impedance bandwidth. **(e)** Measured scan beams of the proposed lens-fed phased-array antenna (Redrawn from [39]). Figure 18: **(a)** Design overview of proposed Yagi-Uda antenna element with its 3D far-field radiation pattern. **(b)** Radiation patterns of 16-element DBF-based phased array in azimuth plane. **(c)** Photograph of the proposed DBF-based massive MIMO transceiver system with 64-antenna elements. (Redrawn from [15]).
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Photoplethysmography (PPG)
#### 3.2.2 Non-Wearable PPG Sensors  
In DMS, non-wearable PPG sensors are usually integrated into the steering wheel (Figure 3d). In 2010, the integration of a PPG sensor for HR monitoring in the steering wheel of a test car was demonstrated by [PERSON] et al. [32]. [PERSON] et al. [31] demonstrated SpO2 measurement from the steering wheel of a demonstration cockpit, in addition to contact ECG monitoring in the steering wheel and capacitive ECG monitoring in the seat. Also, in 2010, [PERSON] et al. [29] presented a steering wheel design incorporating conductive fabrics for ECG recording and a PPG sensor using infrared light with a wavelength of 940 nm.  
In 2014, [PERSON] et al. [52] developed a sensor system for cuffless BP measurement using ECG and PPG sensors embedded in the steering wheel, correlating the two signals to measure the PTT and PAT values. In 2016, [PERSON] et al. [53] also presented a  
Figure 3: PPG and IRT methods and relevant techniques and sensors for acquisition of the driver’s PPG signal.  
driving simulator using the same method with ECG and PPG sensors embedded in the steering wheel. This technique has the same disadvantages as those considered for the ECG measurement: it requires a two-handed grip on the steering wheel and its constant movement introduces interference.  
A common drawback of the steering-wheel-integrated PPG sensor approach is that 100% sensor coverage cannot be guaranteed, as the driver must consciously cover it with their hand. Therefore, this technique can be useful for opt-in applications, but continuous monitoring requires driver involvement.  
[PERSON] et al. [54] in 2025 developed and validated a reflective PPG sensor integrated into the backrest of a car seat, which allows continuous HR monitoring through clothing (Figure 3e). They used an LED with a wavelength of 657 nm (red light) and a high current value, which are optimal for penetrating through clothing and illuminating the skin underneath. In real-road driving with 12 participants, the results demonstrated high accuracy, achieving mean absolute error of 2.51 bpm. The authors pointed out the sensor's sensitivity to driver movements, such as body shifts, stretching, or adjusting posture, which can introduce motion artefacts and affect signal quality, as a significant limitation for continuous monitoring.
###### Abstract  
The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the very-high-frequency (VHF) data exchange system (VDES). While the VDES increases bandwidth and data rates, ensuring the stable transmission of maritime IoT (MIoT) application data in congested coastal areas remains a challenge due to frequent collisions of AIS messages. This paper presents a slot occupancy-based collision avoidance algorithm (SOCA) for a VDES network in the MIoT. SOCA is designed to mitigate the impact of interference caused by transmissions of AIS messages on transmissions of VDE-Terrestrial (VDE-TER) data in coastal areas. To this end, SOCA provides four steps: (1) construction of the neighbor information table (NIT) and VDES frame maps, (2) construction of the candidate slot list, (3) TDMA channel selection, and (4) slot selection for collision avoidance. SOCA operates by constructing the NIT based on AIS messages to estimate the transmission intervals of AIS messages and updating VDES frame maps upon receiving VDES messages to monitor slot usage dynamically. After that, it generates a candidate slot list for VDE-TER channels, classifying the slots into interference and non-interference categories. SOCA then selects a TDMA channel that minimizes AIS interference and allocates slots with low expected occupancy probabilities to avoid collisions. To evaluate the performance of SOCA, we conducted experimental simulations under static and dynamic ship scenarios. In the static ship scenario, SOCA outperforms the existing VDES, achieving improvements of 13.58% in aggregate throughput, 11.50% in average latency, 33.60% in collision ratio, and 22.64% in packet delivery ratio. Similarly, in the dynamic ship scenario, SOCA demonstrates improvements of 7.30%, 11.99%, 39.27%, and 11.82% in the same metrics, respectively.  
collision avoidance; maritime Internet of Things; slot occupancy; VDES; VDE-TER
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 3 Methods
### Tasks  
#### 3.6.1 NARMA  
We analyzed multi-cluster ESNs and DDNs, which were optimized for the nonlinear autoregressive moving average (NARMA) system approximation task [35]. This task is commonly used as a benchmark for RC systems, and requires both non-linear processing and time-specific memory. The NARMA system is defined by the following equation.  
\[y(t+1)=c_{1}\cdot y(t)+c_{2}\cdot y(t)\sum_{i=0}^{p}y(t-i)+c_{3}\cdot u(t-p)u( t)+c_{4} \tag{10}\]  
Here, \(c_{1}\) to \(c_{4}\) are task parameters, and \(p\) is the NARMA order. \(y(t)\) represents the state of the NARMA system at time \(t\), while \(u(t)\) is the system input at time \(t\). \(u(t)\) is uniformly sampled between 0 and 0.5. Given the serially presented previous inputs of the NARMA system, the task objective is to forecast the subsequent state (so-called one step ahead prediction). The reservoirs we used were optimized for NARMA-30, with \(p=29,c_{1}=0.2\), \(c_{2}=0.04\), \(c_{3}=1.5\), and \(c_{4}=0.001\), using a training set of 8000 samples and a validation set of 4000 samples.  
An important practical benefit of using the NARMA benchmark in our experiments was that the task capacity could be easily computed and was upper bounded to 1 if the inputs were rescaled to the input distribution corresponding to the IPC polynomials (i.e., in this case, a uniform distribution over the interval \([-1,1]\)).
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 4 Implementation of the OLSR-HBP
### Analysis of the OLSR-HBP Implementation
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values Used at Simulation** \\ \hline Node deployment & Static Grid 4 \(\times\) 4 \\ Surface/deployment area & 120 \(\times\) 120 m \\ Number of nodes & 16 \\ Confidence & 0 bits (no confidence) \\ Prediction algorithm & Last Value \\ Data traffic & 1 KByte periodic pings \\ Ping periods & no pings, 10 s, 1 s \\ \hline NS-3 Parameter & Values used at simulation \\ \hline wifiChannel & YansWifiChannelHelper \\ wifiChannel PropagationLoss & FriisPropagationLossModel \\ wifiPhy TxGain & \(-\)10 \\ wifiPhy EnergyDetectionThreshold & \(-\)75.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: OLSR-HBP simulation parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**OLSR**} & \multicolumn{2}{c}{**OLSR-HBP**} \\
**Packet Generation Period** & **10 s** & **1 s** & **10 s** & **1 s** \\ \hline \%TP msgs predicted @dest & - & - & 99.9\% & 99.9\% \\ Avg. \% of pings received & 100\% & 44.3\% & +0\% & +3.4\% \\ Avg. RT (ms) & 11.6 & 50.7 & \(-\)4.5\% & \(-\)9.6\% \\ Avg. Energy/node (J) & 8.6 & 32.5 & \(-\)11.5\% & +4.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of OLSR and OLSR-HBP in static scenarios.
## 2 Numerical Aspects and Experimental Designs
### Settings of the Model Experiments
BND and REF are the same as GTS but assimilate additional RO bending angle and refractivity, respectively, while BND_RP and BND_RV are the same as BND but are combined with the DVI with P-match and V-match, respectively. It is similar for REF_RP and REF_RV, as well as GTS_RP and GTS_RV. For comparison with the results at 60-15 km resolution, the higher-resolution experiments with the 60-15-3 km grid mesh are also conducted for BND with and without the DVI. The grid mesh of 60-15-3 km can be seen in [PERSON] et al. ([22], Figure 3). For these sensitivity tests, the resolution of 60-15-3 km is also applied in the cycled DA and the MPAS main forecast of 5 days. Note that in each DA cycle, the high-resolution of 60-15 or 60-15-3 km is used in the 6 h forecast after 3 DVAR analysis, while GSI uses the analysis at the coarser resolution of 120 km for 6 h integration of each member. As the 60-15-3 km resolution is used for the sensitivity tests, the scale-aware problem may be associated with application of cumulus parameterization. When the horizontal resolution is highly increased at some regions, specific cumulus parameterization may not be scale-aware without any adjustment causing the misrepresentation of convection. This would not be critical for the previous simulations with the coarser 60-15 km resolution; even the new Tiedtke scheme applied in the current \"mesoscale reference physics suites\" of MPAS 5.2 is not a scale-aware scheme. We tested the scale-aware scheme (Grell-Freitas) available in MPAS 5.2. The simulation results with the Grell-Freitas scheme, however, have no particular improvement but with a more southward track deviation at earlier stages compared to that with the new Tiedtke scheme. When more scale-aware cumulus parameterizations are available in MPAS, it is worth conducting further sensitivity tests to illustrate the impacts of the scale-aware convection on the typhoon forecasts. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**CASE** & **Observation** & **DVI** & **Resolution (km)** \\ \hline NoDA & X & X & 60-15 \\ GTS & RS+OA & X & 60-15 \\ REF & RS+OA+RO & X & 60-15 \\ BND & RS+OA+RO & X & 60-15 \\ \hline GTS\_RP & RS+OA & P-match & 60-15 \\ GTS\_RV & RS+OA & V-match & 60-15 \\ REF\_RP & RS+OA+RO & P-match & 60-15 \\ REF\_RV & RS+OA+RO & V-match & 60-15 \\ BND\_RP & RS+OA+RO & P-match & 60-15 \\ BND\_RV & RS+OA+RO & V-match & 60-15 \\ \hline BND\_H & RS+OA+RO & X & 60-15-3 \\ BND\_RPH & RS+OA+RO & P-match & 60-15-3 \\ BND\_RVH & RS+OA+RO & V-match & 60-15-3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical experiments for Typhoon Nepartak (2016). For the experiments, GTS includes the conventional radiosonde soundings (RS), while BND and REF also apply RO bending angle and refractivity soundings, respectively, in addition to the data used in GTS. Note that GTS, BND, and REF used the other observations (OA) (including satellite radiances of AMSU-A and AISR) routinely available from the CWB. NoDA is the experiment without performing DA (thus, no observations are used).
## 4 Experiments
### Overfitting Problem  
The ability to alleviate the overfitting problem of CVNN has been discussed earlier, and this section will give an explanation in detail. Figure 4 illustrates two training curves of RVNN and CVNN, respectively, in which the loss of RVNN decreases monotonically until convergence as the epoch grows, whereas CVNN endures a higher loss with an unsteady learning process. However, CVNN learns a better generalization feature in spite of the difficulty of training, which can be demonstrated by the qualitative results on two hard samples within the testing set. As shown in Figure 4, when peppers are surrounded by dense green leaves, CVNN still enables an acceptable location, while one could not recognize where peppers are at the first glance of the results from RVNN. This is because RVNN has a high degrees of freedom, so it tends to choose features that cater to the training dataset as much as possible but may not fit on the testing dataset. From this perspective, CVNN shows its huge advantage in preventing overfitting.
## 2 Materials and Methods
### Support Vector Machine for Regression (SVR)  
Support vector regression (SVR) is a nonlinear regression method based on support vector machines (SVM) [33; 34]. It aims to construct a hyperplane that reduces the prediction errors and maximizes the margin between the hyperplane and the closest data points. An epsilon tube is used to define acceptable error boundaries. The algorithm applies a kernel function to project the input data into a higher-dimensional feature space, allowing for the creation of the hyperplane and the minimization of errors in this new space. The structure of SVM regression is illustrated in Figure 2.  
SVR can be expressed as a convex optimization problem according to Equation (3). Convex optimization ensures that any local minimum is a global minimum, simplifying the task of determining the optimal hyperplane. The optimization problem aims to minimize the error within a specified margin of tolerance, often incorporating a regularization term to prevent overfitting.  
\[\text{Minimize}:\frac{1}{2}W^{2}+C\sum_{i=1}^{1}\left(\xi_{i}+\xi_{i}^{*}\right)\]  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{**Machine: Sainsmart Genmitsu 3018-PROVer CNC (Sainsmart, Las Vegas, NV, USA)**} \\ \hline \multicolumn{4}{c}{**Cutting Tool: HSS 118\({}^{\circ}\)**} \\ \hline \multirow{4}{*}{Process parameter} & Spindle speed (rpm) & Feed rate (mm/min) & Drill bit diameter (mm) \\  & 1000 & 20 & 2 \\  & 1500 & 30 & 3 \\  & 2000 & 40 & 4 \\ \hline \end{tabular}
\end{table}
Table 1: Process parameters used in the experiment.  
\[Constraints:\left\{\begin{aligned} Y_{i}-\langle W,X_{i}\rangle-b \leq\varepsilon+\xi_{i},\\ \langle W,X_{i}\rangle+b-Y_{i}\leq\varepsilon+\xi_{i}^{*},\\ \xi_{i},\xi_{i}^{*}\geq 0,\end{aligned}\right. \tag{3}\]  
where \(Y_{i}\) represents the classes, and \(X_{i}\) denotes a set of features. The parameter \(b\) corresponds to the width of the hyperplane, and \(W\) signifies the margin. To handle situations where the constraints become infeasible, slack variables \(\xi_{i}\) and \(\xi_{i}^{*}\) are introduced. The parameter \(C\) is used to achieve a trade-off between the width of the margin and the degree of misclassification, effectively controlling the balance between achieving a wider margin and allowing some errors in classification. The process of building an SVM can be visualized through the flow chart presented in Figure 3, which outlines the steps involved in training and predicting the model.  
Figure 3: Flow chart for the implementation process of a support vector machine.  
Figure 2: The framework of support vector machine regression.  
SVR finds application in various fields requiring continuous value predictions, such as financial forecasting, time-series prediction, and other domains necessitating robust regression analysis. Understanding SVR requires a solid grasp of the basic concepts of machine learning, optimization, and linear algebra. This technique is powerful because of its ability to handle high-dimensional data and provide accurate predictions.
## 3 Extended State Observer Design  
Designing a high-performance model-based controller usually requires a full-state feedback. However, for the electric oil-filled joint actuator, only the joint position sensor is installed in the joint. Therefore, the task of the ESO observer is to estimate the unmeasured system state \(x_{2}\) and the lumped uncertainty \(d\) for the later controller design.  
ESO has many advantages than other observers. Besides fast convergence and high robustness characteristics, the most important thing to appreciate is that it can estimate both system states and the lumped uncertainties simultaneously with a boundedness of the estimation errors. Therefore, ESO is becoming an effective tool in the control of dynamic systems.  
According to the structure of the system model (5), extend the lumped uncertainty \(d\) as state \(x_{3}\), and \(f(t)\) represent the time derivative of \(d\), then the dynamic Equation (5) can be rewritten as  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=\theta_{\text{1n}}u+\theta_{\text{2n}}x_{2}+x_{3}\\ \dot{x}_{3}=f(t)\end{array}\right. \tag{14}\]  
**Assumption 1**: \(f(t)\) _is bounded, i.e., \(|f(t)|\leq\overline{f}\), where \(\overline{f}>0\) is a constant._  
According to Equation (14), the ESO is designed as  
\[\left\{\begin{array}{l}\dot{\hat{x}}_{1}=\hat{x}_{2}+l_{1}\omega_{0}\widetilde {x}_{1}\\ \dot{\hat{x}}_{2}=\theta_{\text{1n}}u+\theta_{\text{2n}}\hat{x}_{2}+\hat{x}_{ 3}+l_{2}\omega_{0}^{2}\widetilde{x}_{1}\\ \dot{\hat{x}}_{3}=l_{3}\omega_{0}^{3}\widetilde{x}_{1}\end{array}\right. \tag{15}\]  
where \(\hat{x}=[\hat{x}_{1},\,\hat{x}_{2},\,\hat{x}_{3}]^{T}\) is the estimated state vector, \(\widetilde{x}=x-\hat{x}=[\widetilde{x}_{1},\,\widetilde{x}_{2},\,\widetilde{ x}_{3}]^{T}\) is the estimation error vector, and \(\omega_{0}>0\) can be treated as the bandwidth of the ESO.  
In order to guarantee the stability of the ESO, the gains of the observer are designed to satisfy the following Hurwitz polynomial [48; 49]:  
\[L(\mathfrak{s})=s^{3}+l_{1}\omega_{0}s^{2}+l_{2}\omega_{0}^{2}\mathfrak{s}+l _{3}\omega_{0}^{3}=(s+5\omega_{0})^{3} \tag{16}\]  
According to Equations (14) and (15), the dynamic equation of the estimation errors is represented as  
\[\left\{\begin{array}{l}\dot{\widetilde{x}}_{1}=\widetilde{x}_{2}-l_{1}\omega _{0}\widetilde{x}_{1}\\ \dot{\widetilde{x}}_{2}=\theta_{\text{2n}}\widetilde{x}_{2}+\hat{x}_{3}-l_{2} \omega_{0}^{2}\widetilde{x}_{1}\\ \dot{\hat{x}}_{3}=f(t)-l_{3}\omega_{0}^{3}\widetilde{x}_{1}\end{array}\right. \tag{17}\]  
**Theorem 1**[17; 48]: _Under Assumption 1, the estimated states are always bounded and there exist a constant \(\sigma_{i}>0\), and a finite time \(t_{1}>0\) such that_  
\[|\widetilde{x}_{i}|\leq\sigma_{i},\ \sigma_{i}=O\bigg{(}\frac{1}{\omega_{0}^{c}} \bigg{)}.\ i=1,\ 2,\ 3,\ \ \forall t>t_{1} \tag{18}\]  
_for some positive integer \(c\)._  
From Theorem 1, we can see that the estimation errors \(\widetilde{x}_{i}\) are bounded and will converge to an arbitrarily small range if the parameter \(\omega_{0}\) increases largely enough, i.e., \(\lim\limits_{\omega_{0}\rightarrow\infty}\hat{x}_{i}=x_{i},\ i=1,\ 2,\ 3\).
## 1 Introduction  
Recently, numerous methods have been used to modify implant surfaces to enhance the speed of bone healing [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. It was found that osseointegration could be effectively promoted by surface modification [1; 2; 4; 11; 12]. The osseointegration time is a crucial factor in clinical cases, for example, patients with submerged implants and devices. However, invasive detection is not suitable for sensing stability on the osseointegration of submerged implants. The voltage-driven full Wheatstone bridge configuration is a traditional instrumentation system, which is applied to quantify strain gauges. Whilethere is a disadvantage of low-power for submerged implantology applications [13]. Accordingly, it is very important to fabricate bioactive surfaces on implants and monitoring osseointegration for biomedical engineering.  
According to previous studies [11; 14], aluminum nitride (AIN), as a biosensing film, performed effective sensing for fibroblast cell (NIH3T3) and for the biophysical detection of implants. However, the biological reaction of bone cells is a critical factor in bone remolding and enhancing osseointegration. Furthermore, the effects of molecules and cells on the osseointegration of bone implants include factors affecting bone formation [15] and bone adsorption [16]. Numerous studies have shown that growth factors, matrix molecules, or stem cells can be used to facilitate bone healing and regeneration in implants [17; 18; 19; 20]. Predictable bone regeneration after a pathological bone loss in implants, a solution to reduce bone loss, and accelerated healing at the site of traditionally poor prognosis as well as simultaneous enlargement and implant placement, may become clinical realities. Biologic approaches such as molecular imaging define a research method. Nevertheless, these strategies are intrinsically linked to biomechanical factors.  
As stated above, developing a new method to realize the reaction of bone cells is important. Therefore, the study was to fabricate a promising biosensing approach to detect bone-cell integration such as (osteoblast-like MG-63) with the piezoelectric AlN/Ta (PAT) thin film, and the reaction between them. The measurement of stress variation was utilized to analyze the reaction between the bone cells and the PAT thin film.