esa-sceva/satcom-chunk-collection huggingface dataset:
## The Ocean
### SARAL/AltiKa a Data Into Operational Systems
#### 2.3.3 Assimilation of SWH Data Into the Meteo-France Operational Weather Forecast System  
SARAL/AltiKa provides a very accurate SWH to improve the operational wave forecasting of Meteo-France. During the last three years of operational use of SARAL/AltiKa, it has been demonstrated that the operational wave forecast is significantly improved for the global and regional scales. In addition, SARAL/AltiKa plays an important role in the correction of sea state in coastal zones. Figure 9 shows a snapshot of SWH from the operational Meteo-France wave model (MFWAM) of a resolution of 10 km and dedicated to European seas during the storm happened in March 2016. The MFWAM model is routinely using SARAL/AltiKa, Jason-2 and CryoSat-2 altimeters wave data. The Jason-2 track overlaid on the snapshot indicates the accurate forecast at the peak of the event on 27 March 2016 at 21:00 UTC. The SARAL/AltiKa wave data were also used efficiently in the validation of high resolution coastal wave model of 200 m of resolution implemented at Meteo-France. Figure 10 indicates a snapshot of SWH during the the storm of the 8 February 2016 which generated high waves in the English channel near the French coasts at Cherbourg. The SARAL/AltiKa track passing over the English channel and close to the French coast reveals the accurate forecast of the coastal model. Moreover with SARAL/AltiKa the assimilation of small wave heights (less than 1 m) in regional models MFWAM has been significantly improved for closed seas and lakes.  
Figure 8: Normalized Mean Square forecast Error difference in percentage between the experiments assimilating three and four altimeter data sets under CryoSat-2, Jason-2 and HY-2A tracks with the 1/12\({}^{\circ}\) global system: (innov 3 alt)\({}^{2}-\) (innov 4 alt)\({}^{2}\) / (innov 3 alt)\({}^{2}-\) SARAL/AltiKa being the fourth altimeter.  
Figure 10: SWH (in m) in the English channel from the operational coastal model WaveWatch3 (WW3) with unstructured grid of a resolution of 200 m during the event of 8 February 2016 at 06:00 UTC. The model is driven by the wind forcing from the model ARPEGE. The SARAL/AltiKa track is overlaid and indicates SWH (in m) at the peak of the event near the coast.  
Figure 9: SWH (in m) from the regional wave model MFWAM dedicated to European seas on 27 March 2016 at 21:00 (UTC). The model MFWAM is driven by wind forcing from the atmospheric model of Méteo-France ARPEGE (Action de Recherche pour Petite Echelle Grande Echelle) with a resolution of 10 km. The Jason-2 track in the gulf of Biscay indicates SWH (in m) during the peak of the event. SWH of more than 9 m was recorded off shore of the Brittany coast.
###### Abstract  
This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air aid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20-30, who had not previously heard an air aid siren. A 70 dB \"S-40 electric sign\" (400-450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min \"no-stress\" phase, followed by 3 min \"stress\" phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the \"stress \"and \"no-stress\" states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress.  
gularic skin response; photoplethysmography; stress; machine learning Article
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 2 Two-Way Ranging (TWR) Overview  
In this section, we briefly define a timing and propagation model and outline some rudimentary time-of-arrival (ToA) and time-of-flight (ToF) estimation techniques.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 3 Experiments
### Hybrid Loss Function  
In previous research, only cross-entropy loss was applied for training models, which is defined as:  
\[L_{ce}=-\frac{1}{N}\sum_{i=0}^{N}\left(y\log y^{\prime}+(1-y)\log\bigl{(}1-y^{ \prime}\bigr{)}\right) \tag{4}\]  
Here, \(N\) means categories. \(y\) and \(y\prime\) indicate the label and prediction vectors, respectively. The hybrid loss function can be defined as:  
\[Loss=L2_{loss}+\sum_{i=1}^{N}BEC_{loss}(P_{i},GT_{i})+lambda\times\sum_{i=1}^ {N}|P_{i}-GT_{i}| \tag{5}\]  
Here, \(L2_{loss}\) represents \(L2\) regularization loss in the improved network. \(\sum_{i=1}^{N}BEC_{loss}(P_{i},GT_{i})\) indicates binary cross-entropy loss with \(N\) training images. \(P\) represents the prediction result, while \(GT\) is the label image. In addition, \(\sum_{i=1}^{N}|P_{i}-GT_{i}|\) shows the difference between prediction and label images on the pixel level. \(lambda\) indicates the weight (set to approximately 0.7) in terms of total loss.  
Figure 8: The construction of D-Block.
# A Local Discrete Feature Histogram for Point Cloud Feature Representation
## 4 Experimental Results
### Performance Evaluation of the LDFH Descriptor
#### 4.3.2 Compactness  
A descriptor's compactness is an important property, directly affecting its computational and storage efficiency. According to [16], we calculate compactness by dividing the mean value of AUCpr by the descriptor's length, as shown in Equation (8). Table 3 presents the AUCpr results of all RPC curves in Section 3.3, where GN represents Gaussian noise and MD stands for mesh decimation in the B3R dataset. The bold text indicates the best results. Table 2 lists the lengths of all descriptors. The compactness of each descriptor is shown in Figure 6. LDFH, LDFH-X, and LDFH-AZ achieve the best compactness. FPFH comes next due to its very low dimensionality (33). TOLDI, on the other hand, has the lowest compactness, because of its high dimensionality (1200).  
\[\text{compactness}=\frac{\text{The mean value of AUCpr}}{\text{The length of descriptor}}. \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Descriptor** & **0.3 mr GN** & **0.5 mr GN** & **1/4 MD** & **1/8 MD** & \begin{tabular}{c} **1/4 MD** \\ **0.3 mr GN** \\ \end{tabular} & \
\begin{tabular}{c} **1/8 MD** \\ **0.5 mr GN** \\ \end{tabular} &
\begin{tabular}{c} **1/8 MD** \\ **Kinect** \\ \end{tabular} \\ \hline FPFH & 0.2250 & 0.1154 & 0.1424 & 0.0749 & 0.0898 & 0.0488 & 0.0889 \\ SHOT & 0.6945 & 0.6560 & 0.5716 & 0.2395 & 0.4510 & 0.1818 & **0.3030** \\ TOLDI & 0.9002 & 0.8401 & 0.5930 & 0.3206 & 0.4915 & 0.1990 & 0.1936 \\ SDASS & 0.9689 & 0.9349 & 0.8790 & 0.5959 & 0.8415 & 0.4630 & 0.1612 \\ LDFH-AZ & 0.9687 & 0.9386 & 0.9101 & 0.7022 & 0.8641 & 0.5321 & 0.2550 \\ LDFH-X & 0.9676 & 0.9392 & 0.9136 & 0.7097 & 0.8685 & 0.5483 & 0.2661 \\ LDFH & **0.9731** & **0.9528** & **0.9308** & **0.7198** & **0.8872** & **0.5532** & 0.2929 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The AUCpr results of selected descriptors on B3R dataset (with Gaussian noise and mesh decimation) and Kinect dataset. GN represents Gaussian noise and MD stands for mesh decimation in the B3R dataset. The bold text indicates the best results.  
Figure 5: The RPC results on the Kinect dataset.
# Blending Sea Surface Winds from the HY-2 Satellite Scatterometers Based on a 2D-Var Method
## 4 Results
### General Validation  
The 2D-Var analysis results are further verified using the collocated buoy winds. The total amount of collocations is about 50 k over the above-mentioned period. Figure 6 illustrates the blended winds versus the buoy measurements for the GSF and EBEC experiments, respectively. Both results show good consistency with respect to the buoy references. Compared with the GSF experiment, the blended winds derived from EBEC are closer to both buoy measurements and ERA5 winds (see below Figure 7), which again demonstrates the advantage of the proposed EBEC in the 2D-Var wind blending.  
Figure 5: (**a**) HSCATs wind fields on 15 January 2022 at UTC 00:00 \(\pm\) 3 h; (**b**) ERA5 background wind field on 15 January 2022 at UTC 00:00; (**c**) the 2D-Var analysis wind field based on the GSF; (**e**) the wind speed difference between (**b,c**); (**d,f**) are similar to (**c,e**), but for the 2D-Var analysis with EBEC. Color bars indicate the wind speed.  
To further validate the proposed method, Figure 7 shows the normalized histograms of wind \(u\) (a) and \(v\) (b) differences for ERA5 versus buoys (black), GSF-derived results versus buoys (red), and EBEC-derived results versus buoys (blue), respectively. The spread of \(u_{\text{ERA5}}-u_{\text{buoy}}\) (\(v_{\text{ERA5}}-v_{\text{buoy}}\)) shows a smaller deviation than that of \(u_{\text{GSF}}-u_{\text{buoy}}\) or \(u_{\text{EBEC}}\)-\(u_{\text{buoy}}\) (\(v_{\text{GSF}}-v_{\text{buoy}}\) or \(v_{\text{EBEC}}-v_{\text{buoy}}\)), which indicates assimilating HSCAT retrieved winds actually leads to \"degraded\" winds with respect to the background winds. Figure 7c,d are the same as the upper panels, but they used HSCAT winds as a reference. The spread of \(u_{\text{GSF}}-u_{\text{HSCAT}}\) or \(u_{\text{EBEC}}-u_{\text{HSCAT}}\) is much smaller than that of \(u_{\text{ERA5}}-u_{\text{HSCAT}}\). This implies that bias may persist in the input data sets (e.g., the HSCAT wind retrievals), and, as such, bias correction should be carried out to further improve the blending of HSCAT winds with ERA5 background data [23].  
As shown in Figure 1, about 30% of sea surface area is actually not observed by the HSCATs during the six-hourly 2D-Var analysis window. Therefore, a more comprehensive study is needed to better understand the characteristics of the blended wind fields. Practically, the blended winds are separated into two categories according to the corresponding inputs, i.e., the one with HSCATs observation(s) (denoted as 'Obs') and the other without any observation (denoted as 'no Obs'), and then verified against buoy measurements separately. The statistical scores are summarized in Table 5.  
Figure 6: Blended winds versus buoy measurements: GSF-derived wind speed (**a**) and direction (**b**); EBEC-derived wind speed (**c**) and direction (**d**).
## 3 Architecture  
Our framework makes use of three main innovations to implement unique properties, which we rely upon to address the limitations of existing indoor location systems. The architecture encompasses these as modules such that it allows interoperability between them to achieve an additive effect of their unique properties. In our implementation, we design a unique cost-effective passive indoor location system that relies on off-the-shelf sensors embedded in an additional layer between the tiling described in more detail in Section 3.2. At the local level, the sensors form a WSN which reduces the complexity of the large-scale implementations. The security and network anonymity [45] concerns are addressed by a specially designed computational model that relies on onion-routing [46] messages for network anonymity, and a general-purpose obfuscated computing model. By using multi-layer encryption and onion routing, nodes are able to collaborate in federated and distributed computations without ever revealing what the global computation is, nor the origin of the computation; further details can be found in Section 4. In the third module, we further improve the security and reliability of the solution by decentralizing the system to introduce much-needed fault tolerance, and secure the entire solution against a single point of failure (SPOF). By using blockchain, we are able to replace sink nodes with smart contracts. We implement an access control module that protects the underlying WSN against unauthorized queries, further detailed in Section 5.  
In our vision, different deployments of indoor location systems have different requirements, ranging from personal home deployments (smart home) to health providers (hospitals, homes for older adults, clinics, nursing homes, etc.), and public buildings (muxiicipalities, government buildings, etc.) illustrated in Figure 1. Using a global blockchain network, which stakeholders can participate in, we can inherit the same security level on all of the underlying sensor deployments.  
Figure 1: High level view of the presented architecture.
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 5 Experiments and Results
### Model Comparison and Analysis  
In an effort to substantiate the enhanced model's efficacy in determining the ages of red giants, this investigation juxtaposed it with a spectrum of prevalent regression methodologies. These included an array of deep-learning approaches, namely Long Short-Term Memory (LSTM) [60] and the traditional machine-learning regression algorithm Random Forest Regression (RF) [10], Backpropagation Neural Networks (BPNN) [61], Convolutional Neural Networks (CNN) [47], Recurrent Neural Networks (RNN) [62], and Temporal Convolutional Networks (TCN) [63]. These algorithms were utilized on the same dataset for this study. The optimal hyperparameter configurations for different algorithms are delineated in Table 4.  
The results of these comparative analyses are delineated in Figure 10 and Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Settings** \\ \hline LSTM & Learning rate = 0.001, batch size = 32, epochs = 50, optimizer = [PERSON], \\  & activation functions = tanh \\  & n\_estimators = 100, max, depth = 3 \\  & Learning rate = 0.001, batch size = 64, epochs = 50, optimizer = [PERSON], \\  & activation functions = ReLU \\ CNN & Learning rate = 0.001, batch size = 64, epochs = 50, optimizer = [PERSON], \\  & activation functions = ReLU \\ RNN & Learning rate = 0.001, batch size = 32, epochs = 50, optimizer = [PERSON], \\  & activation functions = tanh \\  & Learning rate = 0.001, batch size = 64, epochs = 50, optimizer = [PERSON], \\  & activation functions = ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 4: The optimal hyperparameter configurations for different algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline LSTM & 0.860 & 1.146 & 0.802 \\ RF & 0.749 & 0.981 & 0.848 \\ BPNN & 0.697 & 0.913 & 0.856 \\ CNN & 0.672 & 0.882 & 0.871 \\ RNN & 0.681 & 0.876 & 0.878 \\ TCN & 0.593 & 0.796 & 0.901 \\ FFNN–TabNet & 0.535 & 0.711 & 0.946 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of determination using different algorithms.  
Experimental results show that FFNN-TabNet outperforms these current popular regression methods in determining stellar ages, effectively enhancing the performance of the determination.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
###### Abstract  
This paper proposed a \"Probabilistic and Deterministic Tree-based Routing for WSNs (PDTR)\". The PDTR builds a tree from the leaves to the head (sink), according to the best elements in the initial probabilistic routing table, measured by the product of hops-count distribution, and transmission distance distribution, to select the best tree-paths. Each sender node forwards the received data to the next hop via the deterministic built tree. After that, when any node loses of its energy, PDTR updates the tree at that node. This update links probabilistically one of that node's children to a new parent, according to the updated probabilistic routing table, measured by the product of the updated: Hops-count distribution, transmission distance distribution, and residual energy distribution at the loss of \(\ell_{e}\) energy. By implementing the control parameters in each distribution, PDTR shows the impact of each distribution in the routing path. These control parameters are oriented by the user for different performances. The simulation results prove that selecting the initial best paths to root the packets via unicast, then improving the tree at the node with loss of energy by rooting the packets via anycast, leads to better performance in terms of energy consumption and network lifetime.  
wireless sensor networks; routing; probabilistic routing table; hops-count; energy efficiency; network lifetime; transmission distance; residual energy; probability distribution +
Footnote †: journal: Sensors  
_Article_  
PDTR: Probabilistic and Deterministic Tree-based Routing for WSNs  
[PERSON]  
1 College of Computer Science and Electronic Engineering, Hunan University, Changsha 410006, China; [PERSON]DRESS] (R.G.); [EMAIL_ADDRESS] (J.H.)  
2 Faculty of medicine [PERSON], University of Algiers 1, Algiers 16000, Algeria; [EMAIL_ADDRESS]  
3 State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS]  
4 School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea  
5 Correspondence: [EMAIL_ADDRESS]
# Dynamic Analysis of CRTS II Ballastless Track-Vehicle System Based on Moving Unit Method
## 2 Vehicle and Ballastless Track Models  
### Basic Model Assumptions  
High-speed railway tracks primarily consist of two types: ballast track and ballastless track. Ballast track refers to the track with a stone-granular substructure under the rail, while ballastless track refers to the track structure that replaces the loose crushed stone bed with integral foundations such as concrete and asphalt mixtures. The CRTS II ballastless track system is an improved ballastless track system. CRTS II ballastless track consists of steel rail, fasteners, type II plate, mortar adjustment layer, sliding layer, lateral block, etc.  
The high-speed rail vehicle-ballastless track system established in this article is made up of two parts: the upper vehicle model, consisting of the vehicle body, bogie, and wheelsets, and the lower ballastless track model. The system model is built on the following premise assumptions: (1) only the vertical dynamic response of the vehicle-track coupled system is studied; (2) as the vehicle-track coupled system is symmetrical in the longitudinal direction about the center line, only half of the track is taken for the modeling study; (3) the track system is based on CRTS II-type slab ballastless track; (4) the track slab and concrete foundation are regarded as elastic beams supported by continuous cement asphalt(CA) mortar and roadbed; (5) for CA mortar and roadbed, only their elastic stiffness and damping properties are considered; (6) the track is regarded as an infinitely long elastic beam supported by discrete blocks of pads; (7) and the vehicle model is built based on locomotive units with primary and secondary suspension systems, and the vertical and pitch motions of the car and bogie are considered.
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 2 Test Methodology
### Instrumentation  
On both sides (ring and pinion gears), the torques and rotation angles are measured with the same sensor types and frequencies. Furthermore, for condition monitoring accelerometers are placed on both sides of the transmission. The sensors are shown in Figure 5. One accelerometer (IPE1) is on the ring gear side, radially oriented to the shaft of the gear. On the pinion gear side, the accelerometer (IEPE2) is positioned axially to the drive shaft. These orientations were selected to evaluate the acceleration in the direction of the main reaction forces from the gear contact. Both sensors are fixed by magnets. A summary of the used sensors and references can be found in the Table 1.  
Figure 4: Test bed structure and control.  
Figure 5: Additional sensors for CM.  
Additionally, as shown in Figure 4, the CMS receives the torques, temperatures, and speeds at the gears \((1)\) via CAN-bus from the test bed control, while the rotational angle, measured at the gears, is connected directly to the CMS \((4)\), \((5)\). The accelerometers \((2)\), \((3)\) are also connected directly to the CMS. Furthermore, to stop testing in case of damage, the CMS sends an analogue signal to the test bed control \((1)\). Load cells are also used in determining the gear contact forces and allow a feedback loop with the tower movement to account for assembly stiffness. These load cells, which measure loads in the E, P, G and \(\alpha\) directions, are not used for condition monitoring but are crucial to correct the positioning of the samples.  
The CMS consists of the ibaBM-CAN Bus monitor and the iba Modular System for the directly connected sensors. As a head-station for the iba Modular system, the ibaPADU-S-CM is used with an ibaMS8 xIEPE for the IEPE sensors, an ibaMS4 xADIO for analog and digital in- and outputs and an ibaMS4 xUCO for incremental encoders connected via backplane bus. The hardware modules are connected via fiber optics with an industrial PC, where the signals are processed and recorded with the data-aquisition software ibaPDA. All calculations of indicators described in the following sections were carried out with the software tools ibaPDA and ibaAnalyzer, including the add-on ibaInSpectra.
# Intelligent Environment Monitoring System for University Laboratories
## 1 Introduction
It combines wireless communication protocol, system control instruction and a sensor manual to design the module function of our proposed system. The proposed system, with characteristics including user-friendly control, high performance and reliable operation, is an application in Internet of things. Compared to a traditional environmental monitoring mode, our system is more suitable for environmental monitoring and extensive usage. For instance, it can be used as a subsystem [17] which is able to connect the public network through the specific interface. The platform then analyzes and processes the collected information. This process can be controlled by different terminals. In addition, our system can also speed up the information construction of colleges and universities. The real experiments show that front-end data acquisition is effective, data transmission is reliable, and the alarm message is received in time. The main contributions of this paper are summarized below. * The proposed system consists of a single chip microcomputer, GPRS wireless communication and a sensor function module realizing data monitoring and short message warning. Therefore, three features, including front-end data acquisition, wireless communication and security alarm, are achieved by the proposed system. * The system mainly uses Keil-uVision4, Proteus, Protel99 SE and a firing machine as the development tools to design the module function of system. * The real experiments show that the function of front-end data acquisition is effective, data transmission is reliable, and the alarm message is received in time. The rest of the paper is organized as follows. In Section 2, the system architecture, including the data acquisition module and control and the communication module, is proposed. Section 3 illustrates the system software design idea. In Section 4, the proposed system is tested under real conditions and an analysis of some debugging results is described. Finally, conclusions are shown in Section 5.