esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The use of remote robotic systems for inspection and maintenance in hazardous environments is a priority for all tasks potentially dangerous for humans. However, currently available robotic systems lack that level of usability which would allow inexperienced operators to accomplish complex tasks. Moreover, the task's complexity increases drastically when a single operator is required to control multiple remote agents (for example, when picking up and transporting big objects). In this paper, a system allowing an operator to prepare and configure cooperative behaviours for multiple remote agents is presented. The system is part of a human-robot interface that was designed at CERN, the European Center for Nuclear Research, to perform remote interventions in its particle accelerator complex, as part of the CERNTAURO project. In this paper, the modalities of interaction with the remote robots are presented in detail. The multimodal user interface enables the user to activate assisted cooperative behaviours according to a mission plan. The multi-robot interface has been validated at CERN in its Large Hadron Collider (LHC) mockup using a team of two mobile robotic platforms, each one equipped with a robotic manipulator. Moreover, great similarities were identified between the CERNTAURO and the TWINBOT projects, which aim to create usable robotic systems for underwater manipulations. Therefore, the cooperative behaviours were validated within a multi-robot pipe transport scenario in a simulated underwater environment, experimenting more advanced vision techniques. The cooperative teleoperation can be coupled with additional assisted tools such as vision-based tracking and grasping determination of metallic objects, and communication protocols design. The results show that the cooperative behaviours enable a single user to face a robotic intervention with more than one robot in a safer way.  
human-robot interface; robot teams; cooperation; manipulation +
Footnote †: journal: Applied Sciences
###### Abstract  
In this paper, a simple and flexible method for increasing the lifetime of fixed or mobile wireless sensor networks is proposed. Based on past residual energy information reported by the sensor nodes, the sink node or another central node dynamically optimizes the communication activity levels of the sensor nodes to save energy without sacrificing the data throughput. The activity levels are defined to represent portions of time or time-frequency slots in a frame, during which the sensor nodes are scheduled to communicate with the sink node to report sensory measurements. Besides node mobility, it is considered that sensors' batteries may be recharged via a wireless power transmission or equivalent energy harvesting scheme, bringing to the optimization problem an even more dynamic character. We report large increased lifetimes over the non-optimized network and comparable or even larger lifetime improvements with respect to an idealized greedy algorithm that uses both the real-time channel state and the residual energy information.  
convc optimization; energy harvesting; greedy algorithm; lifetime optimization; mobile wireless sensor networks; wireless power transmission +
Footnote †: journal: Sensors
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 4 SNR Estimators
### \(M_{2}m_{4}\) Estimator  
The DA ML estimator requires the receiver and prior information sequences of the transmitter. However, obtaining prior information is impossible in some cases of practical application. This paper presents the M\({}_{2}\)M\({}_{4}\) estimation method without prior information sequence. The two-order moments and four-order moments of the received sequence in Formula (20) can be expressed as  
\[M_{2}=E[r_{k}r_{k}^{*}]=A^{2}E[|s_{k}|^{2}]+AE[s_{k}z_{k}^{*}]+AE[s_{k}^{*}z_{k }]+E[|z_{k}|^{2}] \tag{29}\]  
and:  
\[\begin{array}{l}M_{4}=E[(r_{k}r_{k}^{*})^{2}]=A^{4}E[|s_{k}|^{4}]+2A^{3}(E[|s_ {k}|^{2}s_{k}z_{k}^{*}]+E[|s_{k}|^{2}s_{k}^{*}z_{k}])+\\ A^{2}(E[(s_{k}z_{k}^{*})^{2}]+4E[|s_{k}|^{2}|z_{k}]^{2}]+E[(s_{k}^{*}z_{k})^{2} ])+\\ 2A(E[|s_{k}|^{2}s_{k}z_{k}^{*}]+E[|z_{k}|^{2}s_{k}^{*}z_{k}])+E[|z_{k}|^{4}] \end{array} \tag{30}\]  
where, the conjugate of \(r_{k}\), \(s_{k}\) and \(z_{k}\) are denoted \(r_{k}^{*}\), \(s_{k}^{*}\) and \(z_{k}^{*}\).  
Given that the signal and noise are independent of each other, and the mean is zero, Equations (29) and (30) can be simplified as follows  
\[M_{2}=A^{2}+\delta^{2} \tag{31}\]  
and  
\[M_{4}=A^{4}+4A^{2}\delta^{2}+2\delta^{4}. \tag{32}\]  
The estimated value of \(A\) and \(\delta^{2}\) can be expressed as  
\[\hat{A}_{MM}^{2}=\sqrt{2{(M_{2})}^{2}-M_{4}} \tag{33}\]  
and  
\[\delta_{MM}^{2}=M_{2}-\hat{A}_{MM}^{2}. \tag{34}\]  
The corresponding SNR estimator is  
\[SNR=10\log_{10}(\frac{\hat{A}_{MM}^{2}}{m_{1}^{2}\hat{\delta}_{MM}^{2}}). \tag{35}\]  
\(M_{2}\) and \(M_{4}\) can be obtained in practical applications through the average value of the receiving sequence and expressed respectively as  
\[M_{2}=\frac{1}{K}{\sum_{k=1}^{K}}{|r_{k}|}^{2} \tag{36}\]  
and  
\[M_{4}=\frac{1}{K}{\sum_{k=1}^{K}}{|r_{k}|}^{4}. \tag{37}\]
# Copper Price Prediction Using Support Vector Regression Technique
## 5 Methodology
### Step 2: Parameters Adjust and Training  
In the case of the prediction of time series with SVR, it is assumed that the actual value \(y_{t}\) is a function of its previous \(L\) values \(\vec{x}_{t}=[y_{t-1},\dots,y_{t-L}]\) and the hyperparameter of the SVR \(\vec{w}=[C,\varepsilon,\gamma]\). Hence, the model has four parameters: \(L\), the number of prior values to predict the actual value and the three hyperparameters of the SVR. The range of each one is shown in Table 1.  
The \(i\)-th model of the SVR (\(M_{i}\)) is defined by the set of parameters \(Q_{i}=\{L_{i},C_{i},\varepsilon_{i},\gamma_{i}\}\). To adjust these parameters, it is made into a grid search, according to the recommendation of [PERSON] et al. [31] and its computational design shows in Algorithm 1. For all combinations of parameters \(Q_{i}\), the model \(M_{i}\) is trained with the series \(S_{a}\) and is tested with the series \(S_{b}\), and vice versa. Then, for each training and testing, the set of parameters \(Q_{i}\) with the least mean squared error (MSE) between the predicted and the original data is selected. This process of training/testing is made using the balanced cross-validation method proposed by [PERSON] [32].  
```
Input :training time series (\(R\)); testing time series (\(T\)) Output :array of the best models with minimum testing error (\(BTM\)) FindBestTestingModels (\(R,T\)) foreach\(L\in\{1,2,\dots,10\}\)do foreach\(C\in\{2^{-8},2^{-7},\dots,2^{12}\};\varepsilon\in\{0.01,0.02,\dots,0.30\}; \gamma\in\{2^{-8},2^{-7},\dots,2^{12}\}\)do \(M_{i}\leftarrow\) Make the \(i\)-th model with parameters \(L,C,\varepsilon,\gamma\)  Train \(M_{i}\) with R \(\hat{T}\leftarrow\) Predict using the trained model \(M_{i}\) \(MSE_{i}\leftarrow\) MSE between the predicted (\(\hat{T}\)) and the original testing data (\(T\)) \(BTM_{L}\leftarrow\) add the best model \(M_{i}\) (i.e., the model with minimum \(MSE_{i}\)) return\(BTM\)
```  
**Algorithm 1**Algorithm design for grid search to find the best testing models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range** \\ \hline \(L\) & \(1,2,\dots,10\) \\ \(C\) & \(2^{-8},2^{-7},\dots,2^{12}\) \\ \(\varepsilon\) & \(0.01,0.02,\dots,0.30\) \\ \(\gamma\) & \(2^{-8},2^{-7},\dots,2^{12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ranges for the grid of the parameters \(L\), \(C\), \(\varepsilon\) and \(\gamma\) for the radial and linear kernel.
# Computational Modeling of U-Shaped Seismic Dampers for Structural Damage Mitigation
## 5 Future Research
### Toward Simplified Model of U-Shaped Damper Hysteresis  
This study provides a foundation for developing a simplified model performance of the U-shaped damper. A simplified force-displacement relationship model should be easily incorporated into larger structural models, thereby reducing computational demands but without compromising accuracy. This future research can benefit from exploring the simplified models discussed by [PERSON] and [PERSON] [41] and [PERSON] et al. [42], and their significance in the context of seismic applications. To express the hysteretic behavior using parametric equations involving the key geometric parameters, such as height, thickness, and width of the U-shaped damper, the validated numerical tool should be used. The obtained approach is expected to enable other researchers to easily adapt the damper configurations for diverse application requirements, including large-scale building structures, without the need for complex and computationally intensive numerical simulations.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### Power Measurements and Path Loss Modeling
#### 5.1.4 5G QPSK at 868 MHz  
Three link quality parameters, SS-RSRP, SS-RSRQ and SS-RINR, are used in 5G measurements. For each quality parameter, four values representing each transmitted beam are obtained. As the transmission is performed by a monopole antenna, the four beams are transmitted with the same antenna. Therefore, they will have the same value since they do not travel along different paths. This can be seen in Figure 10, where the value of all the measurements made is represented in the upper part, while the lower part represents the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Transmission** & **Zone** & \(\mathbf{L_{0}}\) **(dB)** & \(\mathbf{n}\) & \(\mathbf{\sigma}\) **(dB)** \\ \hline \multirow{2}{*}{CI} & \multirow{2}{*}{Narrowband} & NLOS-1 & 31.21 & 1.97 & 5.08 \\  & & NLOS-2 & 31.21 & 3.03 & 4.56 \\ \hline \multirow{2}{*}{CI} & \multirow{2}{*}{LoRa RSSI} & NLOS-1 & 31.21 & 1.96 & 4.47 \\  & & NLOS-2 & 31.21 & 3.01 & 2.33 \\ \hline \multirow{2}{*}{CI} & \multirow{2}{*}{Zigbee RSSI} & NLOS-1 & 31.21 & 1.98 & 3.31 \\  & & NLOS-2 & - & - & - \\ \hline \multirow{2}{*}{CI} & \multirow{2}{*}{5G SS-RSRP} & NLOS-1 & 31.21 & 1.91 & 4.76 \\  & & NLOS-2 & 31.21 & 3.15 & 0.01 \\ \hline \multirow{2}{*}{FI} & \multirow{2}{*}{Narrowband} & NLOS-1 & 31.79 & 1.94 & 5.1 \\  & & NLOS-2 & \(-\)45.94 & 7.26 & 3.89 \\ \hline \multirow{2}{*}{FI} & \multirow{2}{*}{LoRa RSSI} & NLOS-1 & 34.63 & 1.72 & 4.38 \\  & & NLOS-2 & 2.44 & 4.59 & 2.16 \\ \hline \multirow{2}{*}{FI} & \multirow{2}{*}{Zigbee RSSI} & NLOS-1 & 35.32 & 1.69 & 3.13 \\  & & NLOS-2 & - & - & - \\ \hline \multirow{2}{*}{FI} & \multirow{2}{*}{5G SS-RSRP} & NLOS-1 & 42.56 & 1.11 & 3.78 \\  & & NLOS-2 & \(-\)42.65 & 7.39 & 0.013 \\ \hline \hline \end{tabular}
\end{table}
Table 3: CI and FI models’ parameters for the two scenarios at 868 MHz.  
Figure 11: Experimental data of 5G SS-RSRQ and SS-RINR at 868 MHz.  
Figure 12: 5G SS-RSRP path loss models at 868 MHz and their dispersion.  
Figure 10: Experimental data of 5G measurements at 868 MHz.
## 5 Conclusions  
This paper proposes a fault identification method based on an average correlation signal based stochastic subspace identification (ACS-SSI) algorithm and verifies its application feasibility in fault diagnosis of dump truck suspension through a series of simulations and an experiment. The main conclusions can be made as follows:  
1. It can be concluded that the modal energy difference (MED) is sensitive to the leaf spring failure in both the bounce and pitch modes. Moreover, the position of the fault and the variation amplitude of stiffness can be determined by the combination of MED in bounce and pitch modes. The plus or minus sign of the MEDs can indicate the specific location of faults, and the magnitude of change in MED can characterize the severity of the fault.
2. The change in MED caused by damping failure is different from that caused by stiffness failure, but its location and fault degree can still be effectively identified by the combined analysis of MED.  
In conclusion, the front and rear MEDs in bounce and pitch modes can serve as a vector to indicate the location and severity of the fault that occurred in the dump truck suspension. It shows that the proposed average correlation signal based stochastic subspace identification (ACS-SSI) method can identify the changes in suspension modal parameters effectively with respect to different  
Figure 15: Changes of MED caused by different damper faults.  
spring stiffness and damping ratio conditions, which paves a promising way for the application of vibration-based online fault diagnosis to commercial vehicles operating in poor working environments and complex operating conditions.  
The contributions of the individual authors to this research work are as follows: [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] reviewed previous research work and wrote the paper.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 3 Results  
We start by presenting the performance metrics for each individual machine learning model. The analysis initially employs random sampling to generate training and test subsets, followed by stratified sampling to ensure consistent class distribution across these subsets. For random sampling, data points are randomly selected to form training and test subsets, giving each data point an equal probability of inclusion. In stratified sampling, subsets are created by maintaining equal population proportions based on maternal risk levels, ensuring consistent class distribution across the splits. Finally, we present the findings from the ensemble model, which was developed by combining the best-performing individual models identified during the analysis. This approach leverages the complementary strengths of these models to enhance accuracy and robustness. Table 2 below presents the performance of different machine learning models while using random sampling for predicting maternal health risks across high-, low-, and mid-risk categories. Logistic regression-Gaussian naive Bayes achieved an accuracy of 57.6%, with reasonable precision for high-risk cases (0.79) but notably poor recall for mid-risk cases (0.18), indicating its limitations in detecting this category. Support vector machines (SVM) improved overall accuracy to 59.6% and showed a slightly better recall for high-risk cases (0.60), though performance for mid-risk cases remained low (precision: 0.61, recall: 0.25). Gradient boosting significantly increased accuracy to 73.9%, improving precision and recall across all risk levels, particularly high-risk cases (F1 score: 0.81). Decision trees and random forests further improved accuracy to 80.3% and 81.3%, respectively, delivering robust performances across all risk levels, with F1 scores for high-risk cases reaching 0.86 and mid-risk cases reaching 0.78 and 0.79, respectively. XGBoost showed a notable improvement with an accuracy of 84.2%, achieving high precision (0.87) and recall (0.82) for low-risk cases and an F1 score of 0.83 for mid-risk cases. Finally, CatBoost emerged as the best-performing model with the highest accuracy of 84.7%, excelling in high-risk predictions with an F1 score of 0.91 and delivering robust performance for low-risk and mid-risk cases with F1 scores of 0.85 and 0.80, respectively. While using random sampling, CatBoost and XGBoost performed the best, with CatBoost demonstrating the most robust and reliable performance across all risk categories. Next, we present Table 3, which outlines the performance of various machine learning models using stratified sampling for predicting maternal health risks across high-, low-, and mid-risk categories. A comparison between the results from stratified sampling and random sampling reveals a consistent improvement in the predictive performance of all machine learning models.
## 3 Theoretical Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & **Unit Cell** & **Substrate** & **Operating** & **3 dB Axial** & **Dielectric** & **LTC** & **Type of** **Polarization** & **No of** **Bands** \\  & **p(mm)** & **Thickness** & **B(mm)** & **Bandwidth** & **Ratio (\%)** & **Layer** & **PCR (\%)** & **Conversion** & **Bands** \\ \hline Ref. [6] & 9.00 & 1.0/1.0 & \begin{tabular}{c} 9.05–9.65/ \\ 12.5–13.1 \\ \end{tabular} & \begin{tabular}{c} 6.4/ \\ 4.6 \\ \end{tabular} & Bilayer & N/A & Linear to circular & 2 \\ \hline Ref. [7] & 6 & 1.6 & 20.18–33.93 & 50.8 & Single layer & N/A & Linear to circular & 1 \\ \hline Ref. [8] & 8.00 & 3.00/4.30 & 5.8–20.4 & 112 & Bilayer & N/A & Linear to circular & 1 \\ \hline Ref. [10] & 9.2 & 3.1 & \begin{tabular}{c} 5.95–8.80/ \\ 12.68–15.58 \\ \end{tabular} & \begin{tabular}{c} 38.6/ \\ 20.5 \\ \end{tabular} & Single layer & N/A & Linear to circular & 2 \\ \hline Ref. [11] & 6.65 & 0.13 & \begin{tabular}{c} 19.4–21.8/ \\ 27.9–30.5 \\ \end{tabular} & \begin{tabular}{c} 12.5/ \\ 8.7 \\ \end{tabular} & Six layers & N/A & Linear to circular & 2 \\ \hline Ref. [16] & 9.5 & 3.5 & 6.1–12.6 & 69.0 & Bilayer & N/A & Linear to circular & 1 \\ \hline Ref. [28] & 10.0 & 2.80 & \begin{tabular}{c} 5.35–5.95/ \\ 8.88–12.03 \\ \end{tabular} & \begin{tabular}{c} 10.45/ \\ 30.12 \\ \end{tabular} & Single layer & N/A & Linear to circular & 2 \\ \hline This paper & 6 & 1.6 & \
\begin{tabular}{c} 16.49–23.54/ \\ 26.44–34.56 \\ \end{tabular} &
\begin{tabular}{c} 35.23/ \\ 26.62 \\ \end{tabular} & Single layer & 99 & Linear to circular & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative study of LTC polarization conversion of the proposed metasurface with the previously published works.
## 3 System Model
### Trust Model  
In cases where the distance of the adjacent node is less than the transmission range, the trust of the node needs to be determined. RSSI is determined based on distance.  
\[\text{Dist}_{s,i}=\text{RSSI}(N,G_{i}) \tag{1}\]  
where \(N\) refers to the input node, \(\text{Dist}_{s,i}\) refers to the signal strength between the source node \(S\) and the current node \(i\), and \(G_{i}\) refers to the \(i^{th}\) node in the graph.  
Trust can be calculated using direct as well as indirect trust values, as shown below:  
\[\text{Trust}_{xy}(i)=\omega\text{Trust}_{xy}^{DT}(i)+\gamma\text{Trust}_{xy}^{ IDT}(i) \tag{2}\]  
where \(\text{Trust}_{xy}^{DT}(i)\) refers to the degree of direct trust that exists within a node at time \(i\), \(\text{Trust}_{xy}^{IDT}(i)\) refers to the degree of indirect trust that exists within a neighboring node at time \(i\), and \(\omega\) and \(\gamma\) are the fitness factors that range between 0 and 1, such that \(\omega+\gamma=1\).  
The degree of DT can be estimated based on packets sent and received at a node.  
\[\text{Trust}_{xy}^{DT}(i)=\frac{T_{xy}^{RX}(i)}{T_{xy}^{SD}(i)} \tag{3}\]  
where \(T_{xy}^{RX}(i)\) denotes the number of packets received by the node \(x\) at time \(i\) and \(T_{xy}^{SD}(i)\) denotes the total number of packets sent by the node \(y\) at time \(i\). The degree of IDT is based on the transmission of the packet to neighboring nodes, as shown below.  
\[\text{Trust}_{xy}^{IDT}(i)=\frac{1}{N_{d}}\sum_{d=1}^{N_{d}}\text{Trust}_{dy}^{ DT}(i) \tag{4}\]  
where \(\text{Trust}_{dy}^{DT}(i)\) refers to the trust degree that exists in the neighbors at time \(i\) and \(N_{d}\) refers to the number of neighboring nodes.  
This factor improves the degree of trust by ignoring anomalous nodes and choosing nodes with increased trust for forwarding data packets with complete trust. In every iteration, the degree of trust is determined by using the moving average model as stated below:  
\[\text{Trust}_{xy}^{*}(i+1)=\tau\cdot\text{Trust}_{xy}(i)+(1-\tau)\cdot\text{ Trust}_{xy}(i+1) \tag{5}\]  
where \(\tau\) is the fitness factor utilized to stabilize the computations in the recent and the past iterations and is specified in the range \(0<\tau<1\), while \(\text{Trust}_{xy}^{*}(i+1)\) is the degree of trust computed at time \(i+1\). Trust values are recorded as a table that contains information related to the trust of every neighbor involved in data forwarding. The node detection level (\(NDL_{y}\)) is determined as follows:  
\[\text{NDL}_{y}=\frac{1}{\text{Trust}_{xy}^{*}(i)} \tag{6}\]  
\(NDL_{y}\) can be either normal or the attacker, as shown in the following conditions.  
1. If \(0\leq NDL_{y}\leq 0.7\), the node will be considered a normal node.
2. If \(0.8\leq NDL_{y}\leq 1\), the node will be considered an attacker node.
###### Abstract  
Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, _K_-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.  
solar PV power forecasting; weather classification; sample scale; SVM; KNN 20188286103390/app8010028
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
###### Abstract  
The Ocean Color--Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations to provide accurate remote sensing reflectances (\(R_{\mathrm{rs}}\) estimates), aerosol optical depths, and inherent optical properties. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and _a priori_ data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for \(R_{\mathrm{rs}}\) retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.  
uncertainty; neural networks; Bayesian inversion; remote sensing +
Footnote †: journal: _Algorithms_