esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Monitoring critical temperatures in permanent magnet synchronous motors is crucial for improving working reliability. Aiming at resolving the difficulty in online temperature estimation, an accurate and simple five-node lumped parameter thermal network (LPTN) is proposed and the mathematical model of the LPTN is built. Both radial and axial heat transfer paths inside the motor are considered to model the complete thermal circuit. In addition, an innovative parameter identification method based on multiple linear regression is applied to identify the parameters of the LPTN model. The parameters in the state equation are identified instead of the data of the motor, which are strongly dependent on the material and geometrical parameters. Finally, an open-loop estimation scheme based on the state equation and Kalman filter algorithm is adopted to predict the motor temperature online. The model performances are validated by extensive experiments under varying speed and torque conditions in terms of the accuracy and robustness. The results indicate that the temperature estimation error is within the range of \(\pm 5\)\({}^{\circ}\)C in most cases and the proposed model can quickly follow the load variation. Besides, the online temperature estimation scheme and parameter identification method are easy and convenient to implement in an embedded system, which is feasible in automobile applications.  
Keywords:permanent magnet synchronous motor; lumped parameter thermal network; multiple linear regression; Kalman filter +
Footnote †: journal: Applied
## 2 Related Work
### Literature Review
There are papers applying machine learning (ML) to improve performance in NTN. In [10], the ML techniques applied to each situation in NTN are summarized. For example, Reinforcement Learning (RL) Q-Learning is used to improve moving cell connectivity, and trajectory optimization [11], and Mobile Edge Computing (MEC) and RL are used to efficiently provide services to unserved areas. Application example, RL example for throughput increase, RL for secondary link backup, Bidirectional forwarding detection (BFD), Long term-short memory (LSTM) and Neural Network (NN) application example, multi-layer for disaster relief Perceptron (MLP)--Examples of applying LSTM and Deep Q-Learning for Broadcasting/Multicasting are summarized. Most of the problems given in NTN showed an approach to solve with RL by defining the target to be optimized as an object function [26; 27; 28; 29]. In addition, examples and architecture of MEC offloading for user data traffic in NTN were shown [29; 30].
# Preamble-Based Noncoherent Synchronization in Molecular Communication: A Machine Learning Approach
## 4 Proposed Machine Learning Model
For multi-class classification, Categorical Cross Entropy is used to further refine the model's performance. ML training was conducted using TensorFlow and Keras, with reference to relevant code sources to ensure accurate implementation [39,40]. The parameters used in the simulation are detailed in Table 1.
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 2 Background
### NDN Based Big Science  
In [7], the authors studied federated distributed catalog systems that store and manage NDN names to support climate data discovery in multiple domains using Named Data Networking. The authors in [8] use a VIP forwarding and caching mechanism [24] and designed an NDN-based Large Hadron Collider (LHC) network. This work shows that NDN with VIP forwarding and caching can achieve a large reduction in the average delay per chunk compared to a no-caching case in the simulated LHC network [8]. The work of [9] uses NDN-based primitives to present a design of the deadline-based data transfer protocol for reserved bandwidth data transfers. However, these studies focused on the metadata management of huge scientific data or complex scientific workflow, rarely involved the collected data persistence storage.  
Combined with the existing work above, we propose the use of in-network storage to support high-speed scientific data collection, retrieval, and sharing via ICN architecture. We designed the structure of an in-network storage service node, and cooperatively store the data based on an ID protocol.
# Small-Scale Face Detection Based on Improved R-FCN
###### Abstract  
Face detection is an important basic technique for face-related applications, such as face analysis, recognition, and reconstruction. Images in unconstrained scenes may contain many small-scale faces. The features that the detector can extract from small-scale faces are limited, which will cause missed detection and greatly reduce the precision of face detection. Therefore, this study proposes a novel method to detect small-scale faces based on region-based fully convolutional network (R-FCN). First, we propose a novel R-FCN framework with the ability of feature fusion and receptive field adaptation. Second, a bottom-up feature fusion branch is established to enrich the local information of high-layer features. Third, a receptive field adaptation block (RFAB) is proposed to ensure that the receptive field can be adaptively selected to strengthen the expression ability of features. Finally, we improve the anchor setting method and adopt soft non-maximum suppression (SoftNMS) as the selection method of candidate boxes. Experimental results show that average precision for small-scale face detection of R-FCN with feature fusion branch and RFAB (RFAB-f-R-FCN) is improved by 0.8%, 2.9%, and 11% on three subsets of Wider Face compared with that of R-FCN.  
small-scale face detection; R-FCN; feature fusion; receptive field adaptation +
Footnote †: journal: Applied Sciences
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 3 Research and Current Status of Energy Management Strategies for HEVs
### Learning Based Energy Management Strategies  
With the development of artificial intelligence, energy management problems are becoming increasingly integrated with computer technology [157; 158; 159]. Among them, a learning-based EMS is a kind of energy management strategy with highly adaptive learning capability [160; 161]. It is well suited to the control of highly non-linear systems due to its good adaptability, robustness, and other characteristics, and has also gained the attention of many industries in recent years [162; 163; 164; 165]. As for HEVs, learning-based EMS is mostly  
Figure 12: Overall schematic of the proposed energy management.  
used for optimizing rule-based control, predicting vehicle states, and learning optimal control behavior [166; 167; 168].  
In [169], [PERSON] et al. introduced an EMS for a series-parallel PHEV that utilizes deep deterministic policy gradients. This algorithm is an actor-critic, model-free reinforcement learning approach that can determine the optimal energy distribution for a vehicle over continuous spaces. The authors used traffic-simulation-generated travel cycles to train and evaluate the intelligence and performed flexible power allocation to the PHEV. [PERSON] et al. integrated computer vision and deep reinforcement learning to enhance the financial efficiency of HEVs [170]. The approach was able to learn the optimal control strategy autonomously from the visual input. State-of-the-art convolutional neural-network-based target inspection methods are used to extract available visual information from the camera. The detected visual information was taken as state input to output an EMS. In [171], a study was presented by [PERSON] et al. for pattern recognition. The 2-D visualization of training data was achieved by the \(t\)-distributed stochastic neighbor embedding algorithm.  
Table 5 provides a comprehensive overview and comparison of various EMS used in HEVs. The table summarizes the features of each EMS topology in a clear and concise manner. The EMS topologies covered in the table include rule-based, optimization-based, and learning-based strategies. The advantages and disadvantages of each strategy are also listed to help readers make informed decisions based on their needs and preferences. The table provides valuable information for researchers and engineers working in the field of HEVs, as it offers a quick reference guide to help them choose the most appropriate EMS for their specific application.
# Characterization of GNSS Signals Tracked by the iGMAS Network Considering Recent BDS-3 Satellites
## 3 Methodology and Results
### Inter-Frequency Clock Bias
\begin{table}
\begin{tabular}{l l l} \hline \hline
**GNSS System** & **Signals** & **Combination** \\ \hline GPS & (L1, L2, L5) & +0.285 L1 \(-\) 1.546 L2 + 1.261 L5 \\ \hline Galileo & (E1, E5b, E5a) & +0.161 E1 \(-\) 1.422 E5b + 1.261 E5a \\ \hline BDS-3 & (B1I, B3I, B2b) & +0.457 B1I \(-\) 1.944 B3I + 1.487 B2b \\  & (B1C, B2b, B2a) & +0.161 B1C \(-\) 1.422 B2b + 1.261 B2a \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Triple-frequency carrier phase combinations for analysis of carrier phase noise and multipath errors, as well as the inter-frequency clock bias (IFCB) variations. Figure 8: Variations of triple-frequency carrier phase combinations and elevation for G08 and E07 satellites at station WHU1 on 28 May 2018. For BDS-3, the carrier phase observations on five frequencies from four selected satellites are used to form (B1I, B3I, B2b) and (B1C, B2b, B2a) triple-frequency combinations, as plotted in Figure 9. We can see that the time series of triple-frequency carrier phase combinations vary within \(\pm\)2 cm for all the satellites. For C20 and C22 satellites manufactured by CAST, the (B1I, B3I, B2b) combination shows a good consistency and the series are dominated by the carrier phase multipath and noise with the dependence on elevation angles, while the (B1C, B2b, B2a) combination shows small IFCB variations with peak amplitudes of about 1 cm. For C27 and C29 satellites manufactured by CAS, the IFCB variations are observed in both (B1I, B3I, B2b) and (B1C, B2b, B2a) combinations and are generally confined to peak amplitudes of less than about 1 cm.
## 3 Historical Review to State-of-the-Art  
### Early Satellite Analyses  
The importance of accounting for variations in reflected shortwave radiation with angle has been appreciated since early in the ERB satellite era. [PERSON] [28] and [PERSON] [29] noted that, for constant surface and meteorological conditions, reflectance could change by a factor of 1.5 between noon (when the sun is close to overhead) and later in the afternoon when the sun is 30\({}^{\circ}\) above the horizon. This effect had been neglected in earlier satellite analyses [30; 31], but should be accounted for when calculating time-averages of reflected shortwave radiation taken at different times in the diurnal cycle. The first models to account for this change in reflectance with solar zenith angle were derived from TIROS IV and TIROS VII satellite observations in highly inclined orbits that lacked global coverage. This was followed by analysis of observations made from the sun-synchronous Nimbus II satellite [32], which provided the first global model (Figure 3a).  
A subsequent analysis of Nimbus III observations [6] was the first to account for angular variations of different scene types (Figure 3b). This included three fixed scene types of ocean, snow, and a land-cloud combination that were generated from \"gross-empirical\" models that used a combination of aircraft, balloons, and early satellite data. This analysis, together with the earlier seminal work of [PERSON] and [PERSON] [14], had profound implications for our understanding of the climate system, including the discovery that Earth was significantly darker than most pre-satellite estimates, and that low latitudes absorbed more energy than previously thought, implying a stronger poleward heat transport by approximately 40%.  
A further development came with analysis of Nimbus VII observations [3], which used 4 scenes: ocean, land, snow-ice combination, and cloud. Importantly, given the distinct anisotropy presented by clouds, the Nimbus VII analysis was the first to attempt dynamic cloud identification [33]. Observations from Nimbus VII were later used in the development of shortwave ADMs applied during the Earth Radiation Budget Experiment  
Figure 3: Variation in normalized reflectance (r) with cosine of solar zenith angle determined from early ERB satellite observations. (**a**) Mean variation estimated from TIROS IV, TIROS VII, and Nimbus II (reproduced and adapted from [PERSON] and [PERSON] [32] © American Meteorological Society. Used with permission.). (**b**) Variation estimated from Nimbus III as a function of three scene types (reproduced and adapted from [PERSON] et al. [6] © American Meteorological Society. Used with permission.).  
(ERBE) mission, as described in the next section. A summary of the early satellite analyses presented in this section is provided in Table 1.
## 4 Discussion
### Contributions  
The contributions of this study are as follows:  
A better understanding of the traffic situations recreated through simulations, i.e., situations where vehicles traveling at different speeds interact with each other.  
The developed procedures (and models within) for modeling and explaining the observed data from simulations. The best procedures in terms of a lower error were identified.  
The patterns of \(T\) and \(V\) for each \(sp_{1}\) case and \(p_{2}\) range were identified. \(T\) exhibited an identifiable pattern for all \(sp_{1}\) cases and \(p_{2}\) ranges. For all \(sp_{1}\) cases and the range \(p_{2}=10\%\) to 40%, \(V\) shows a linear pattern. For \(sp_{1}\)[60,65,75,80,90] cases and the range \(p_{2}=50\%\) to 80%, \(V\) shows an unidentifiable pattern. For \(sp_{1}\)[70] case and the range \(p_{2}=50\%\) to 80%, \(V\) shows an inverted U-shaped pattern. For \(sp_{1}\)[85] case and the range \(p_{2}=50\%\) to 80%, \(V\) shows a U-shaped pattern.  
The findings of this work help to better understand heterogeneous traffic scenarios. The procedures can be used to describe the traffic of real locations and, with this information, authorities may decide which actions are required to alleviate traffic issues. Moreover, the traffic outcome of possible future scenarios can help to better plan public infrastructure that avoids congestions, low speeds, high noise levels, pollutions, and other problems that arise as a result of traffic problems.
## 4 Empirical Assessment Using Real-World Data
### Experimental Design
#### 4.2.3 Full Model Assessment and Interpretability  
In the full model assessment stage of our analysis, we synthesized the outcomes of the classification and regression stages from our proposed two-stage model to obtain a final prediction of crash frequencies. This integration is crucial for providing a more holistic view of crash risk on Korean roads.  
To validate the efficacy and accuracy of our proposed model, we compared it against the negative binomial hurdle (NBH) model and the single-stage CatBoost model. The NBH model is a compound model, combining a binary outcome model for predicting whether crashes occur with a count model for the number of crashes, conditioned on at least one crash occurring. It handles excess zeros and overdispersion, making it well suited for crash frequency data. We also compared our proposed model's performance against a single-stage CatBoost model that directly predicts the crash frequency. This comparison helps us understand the benefit of the two-stage modeling approach in capturing the complex distribution of crash data. The assessment involved comparing the RMSE and MAE between the predicted crash frequencies from our model and those from the benchmark models. By doing this, we can discern the model that provides the closest approximation to the real-world data, and thus, the most reliable for practical use in road safety analysis and intervention planning.  
Model interpretability is essential to ensure that the actionable insights derived from machine learning models are transparent and trustworthy, especially in critical fields such as road safety analysis. In this study, we implemented SHAP (SHapley Additive exPlanations) values to interpret the predictions of our model at both the classification and regression stages. The SHAP summary plot and SHAP partial dependence plots are shown and explained below. The SHAP summary plot provides a global view of the impact and importance of each feature across all predictions. In essence, it visualizes the magnitude and direction (positive or negative impact) of each feature on the model output. For our classification stage, the SHAP summary plot illustrates how each predictor contributes to the likelihood of a crash occurring on a road segment. For the regression stage, it displays how the predictors affect the expected count of crashes, given that at least one crash has occurred. SHAP partial dependence plots take this a step further by showing the effect of a single feature on the prediction outcome while averaging out the effects of all other features. These plots help us to understand complex interactions and non-linear relationships between features and the target variable.  
For both the classification and regression stages, examining SHAP values allows us to decompose the model output into the sum of effects from each feature, providing a powerful tool for explanation. This approach aligns with the analysis method used in traditional hurdle models, where the two processes are analyzed separately.
## 4 Results and Discussion
### Performance Assessment of Heat Transfer Models  
In this study, the heat transfer correlations (Woschni and proposed) are assessed under similar combustion settings to evaluate the potential of the proposed heat transfer model. To meet the objectives of the current study, the combustion duration is set to 45 CAD, and the start of combustion at 11 CAD before TDC, while maintaining a constant air-to-fuel ratio of 14.7. Along with the combustion parameters, the peak of the heat transfer coefficient during combustion (Woschni and proposed) and scavenging in the proposed heat transfer model is set to the value of 2100 \(W/m^{2}k\), as depicted in Figure 6A. This has been done by calibrating the coefficient b in Equation (16). Moreover, the engine speed and exhaust configuration are the same as in the previous study.  
As from the previous study, it is well established that a higher heat transfer coefficient leads to a lower in-cylinder fluid temperature. Similar results on lower fluid temperature using the proposed heat transfer coefficient over the Woschni correlation can be observed from Figure 6B. This led to higher trapped mass using the proposed heat transfer model, as depicted in Figure 6C. Two quantifiable results can be seen in this evaluation: one during scavenging, i.e., temperature profiles and their impact on the trapped mass; the other is the effect on the combustion pressure due to higher trapped mass, which can be seen in Figure 6D. Quantifying results during scavenging, the results on the instantaneous heat transfer coefficient during scavenging show that the Woschni heat transfer coefficient is as low as 200 \(W/m^{2}k\) (data captured at 210 CAD). While for the same piston position, the proposed heat transfer model provides a peak coefficient of 2100 \(W/m^{2}k\), leading to 80 \({}^{\circ}\)C lower temperature, as depicted in the zoom area 'a' in Figure 6B. Due to this lower fluid temperature, a peak trapped mass of 0.68 g can be seen using the proposed heat transfer correlation. On the other hand, the Woschni correlation depicts a peak trapped mass of 0.62 g, which is approximately 9% lower in trapped mass.  
Due to the above phenomena, the trapped mass at the exhaust port closing is higher in the proposed model. A trapped mass of 0.48 g using the proposed model can be seen, while the state-of-the-art model depicts 0.42 g. Due to this additional trapped mass, and under similar AFR ratio and combustion settings, an increased pressure during combustion can be observed. To quantify the enhanced performance of the proposed model, the Indicated Mean Effect Pressure (_IMEP_) is used, given by:  
\[IMEP=\frac{1}{V_{disp}}\int p\ dV, \tag{20}\]  
where \(V_{disp}\) is the displacement volume of the cylinder, \(p\) is the pressure, and \(V\) is the volume of the cylinder. Using Equation (20), an _IMEP_ of 3.49 and 3.92 bar can be observed for Woschni and the proposed heat transfer model, respectively. In other words, approximately a 12% difference in engine performance can be detected when using different heat transfer models.
# Seamless Indoor-Outdoor Localization Through Transition Detection
## 2 Related Works  
Recent research on indoor-outdoor seamless tracking has focused on integrating various sensors and positioning systems such as GNSS, Bluetooth, WiFi, and IMU using advanced fusion algorithms such as federated filters and machine learning. Key trends include smooth transitions at indoor-outdoor boundaries, high accuracy, real-time performance, and minimal infrastructure requirements [9, 10, 11, 12, 13].  
Ref. [14] proposes a method to automatically detect and distinguish indoor, outdoor, and transition zones by analyzing the time series of GNSS error statistics. This helps decision-making process between visual-inertial odometry and GNSS-based trajectory estimation. Ref. [15] presents a hybrid positioning system that fuses Bluetooth Angle of Arrival and GNSS signals, which utilizes commercial tracking devices to simultaneously transmit Bluetooth and receive GNSS signals.  
The hidden Markov model and Bayesian inference-based indoor-outdoor transition tracking in [16] overcome the limitations of threshold-based sensor switching by employing hidden Markov models (HMMs) and Bayesian inference for seamless indoor-outdoor tracking. This algorithm takes advantage of the continuous-time nature of HMMs for smooth and accurate localization during transitions.  
The adaptive federated filter-based approach in [17] integrates IMU, GNSS, and indoor positioning systems using an adaptive federated filter. It enables multi-target indoor tracking and identification without additional hardware as well as provides stable and smooth tracking throughout the entire indoor-outdoor trajectory.  
A navigation system [3] was designed to provide seamless and ubiquitous positioning for users as they move between indoor and outdoor environments. It enhances environmental awareness by integrating multiple sensing technologies, such as GPS, WiFi, and inertial sensors, to accurately detect whether the user is indoors or outdoors. The IODetector in [2] detects whether a mobile device is indoors, semi-outdoor, or outdoors. It combines data from multiple sensors such as GPS, WiFi, Cellular RSS, IMU, proximity, light, and magnetic field sensors. To enable light and magnetism detectors, certain conditions must be met. For instance, the tracked device should be free from the interference caused by external magnetic sources, and the difference in lighting between indoor and outdoor environments should be significant.  
Most related works on seamless indoor-outdoor tracking have focused on handover strategies within the transition zones between environments. Unlike other works, this paper proposes unsupervised data collection to detect the transition zone using a softmax activation function as a machine learning approach. Fused data also include multi-sensor as well as previously estimated position information.