esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contribution  
An RF codebook design approach for hybrid precoding algorithms in downlink multi-user mmWave systems is presented, demonstrating efficiency and effectiveness in mobile user environments. The proposed method does not require CSI feedback but learns the downlink beam codebook from RSRP feedback and CSI estimates for uplink sounding reference signals (SRSs). By using uplink CSI from SRSs as fingerprints for specific user locations, only sub-band sounding is needed. This increases the SNR at the base station, facilitating cell edge UE recovery. The proposed system replaces traditional fixed codebook beams with learned beams that adapt to the environment in an online process. The goal of this proposed work is to create a robust beamforming codebook for the base station in downlink communication, accommodating uncertain user locations even in mobile scenarios. The contributions from the proposed work are summarized as follows:  
1. A multiuser hybrid mmWave MIMO system model designed as a fully cooperative stochastic game under the constraint of the quantized RF phase shifters is proposed. A novel algorithm is developed to realize this model. By employing MADDPG to train DDPG (Wolpertinger variant), this approach effectively minimizes interference among simultaneous users, preventing overlapping beams. Unlike previous methods such as [10], this work uniquely addresses and mitigates potential interference between nearby beams with arbitrary shapes, ensuring the unparalleled performance and reliability in beamforming for multiuser communication systems.
2. A reward function for the RL agent is proposed, considering the comprehensive performance of the end-to-end communication system. The reward for each agent is based on RSRP and binary ARQ status, indicating whether a particular sub-frame scheduled for a specific user is successfully received (ACK) or not (NACK). Thismethod allows the RL agent to maximize successful transmission rates by improving the beamforming gain and reducing the interference among simultaneous user groups. The cumulative reward function is optimized by MADDPG, enhancing the overall system efficiency and reliability.
3. The proposed system is rigorously evaluated through simulations using a realistic ray tracing channel model. This comprehensive testing spans various SNR and different codebook sizes. The results demonstrate the system's robustness and efficiency, highlighting its adaptability and performance across diverse conditions.  
Simulation results demonstrate that the proposed method can create optimized beam patterns without needing feedback from downlink CSI, relying instead on RSRP, binary ARQ status, and periodic channel estimates from a small sub-band within SRS sub-frames. This deep reinforcement learning-based method efficiently selects beams for the downlink RF codebook, requiring occasional updates, typically when there is a significant change in the operating environment or the base station's position. The following sections delve into detailed discussions of the proposed systems, methods, algorithms, and results.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 6 Results and Analysis
### Single Value Comparisons  
The PDF of the values in the upper right triangle of the correlation matrix \(\mathbf{C}_{\text{norm}}\) was calculated to determine the difference between the real and spoofed measurements. The left CDF is calculated for the real data (denoted by \"R\" in the legend) and the right CDF is calculated for the spoofed data (denoted by \"S\"). The CDFs with the mean \(\mu\) and the standard deviation \(\sigma\) for each distribution, as well as an optimal threshold \(\lambda\) with error probabilities are shown in Figure 7. For a full analysis of this detector method, refer to [22].  
Figure 6: Sky-plot of the open-sky recording. ©IEEE. Reprinted, with permission, from [22].  
As a comparison, the same data for a two-element array (i.e., only using two-elements from the six-element array.) is shown in Figure 8.  
The spoofed signals have lower mean \(\mu\) and standard deviation \(\sigma\) values in both cases. This is expected as the steering vectors for these signals are similar. In both cases, low error probabilities were achieved. Often, no errors were observed at all.  
The fraction of detections based upon the decision threshold of Figure 7 is shown in Figure 9. As there are few to no errors in Figure 7, these results are consequently sparse. However, when the error probabilities are higher, then a spread of data can be observed, as shown in Figure 10.  
In this simple case of comparing the fraction of detections, a system of detectors has shown benefits to improve overall performance. However, the performance of the fraction of detections is dependent on the detection performance of the previous stage. Further, such an approach requires the setup of two detector thresholds; hence, increasing the complexity of tuning and calibration for such an approach.  
Figure 8: CDF comparison for the steering vector angles between satellites for a two-element array.  
Figure 7: CDF comparison for the steering vector angles between satellites for a six-element array.
## 3 Results
### Experimental Protocol  
The experimental purpose of the study is an auxiliary diagnostic tool, so the final judgment result can only be obtained based on the analysis of the pathology report and the professional experience of the physician, so this study uses _RMSE_ and mean absolute error percentage (_MAPE_) is the accuracy evaluation index, and the calculation method is shown in Formulas (17)-(19). Therefore, the accuracy of the system can be obtained by observing the above two indexes in this study.  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}} \tag{17}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{|\hat{y_{i}}-y_{i}|}{y_{i}} \tag{18}\]  
\[R^{2}=1-\frac{\sum_{i}(\hat{y_{i}}-y_{i})^{2}}{\sum_{i}\left(\hat{y_{i}}-y_{i }\right)^{2}} \tag{19}\]  
where \(y_{i}\) is the actual value of the \(i\)-th sample, \(\hat{y_{i}}\) is the measured value of the \(i\)-th sample, and \(n\) is the total number of test samples.  
In order to analyze the accuracy of the system and the effect of ambient light on the system, this study measured the heart rate and HRV of the 20 subjects measured by the statistical system and the instrument against the calculated error. The performance of the system at the following two measurement distances was as shown in Table 1.  
The error of each parameter was recorded at a distance of 100 cm, as shown in Table 1. According to the above results, it could be seen that the measurement at 50 cm was more accurate than that at a distance of 100 cm. Therefore, the following measurements on the forehead and the forehead plus cheeks were measured from 50 cm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measuring Distance** & **Measurement Standard** & **RMSE** & **MAPE** & \(R^{2}\) \\ \hline \multirow{4}{*}{50 cm} & Heart Rate & 1.968 bpm & 2\% & 0.83594449 \\  & SDNN & 1.85 ms & 4.87\% & 0.87628321 \\  & LF & 1.77\% & 2.425\% & 0.92371321 \\  & HF & 1.77\% & 3.95\% & 0.92371321 \\ \hline \multirow{4}{*}{100 cm} & Heart Rate & 3.32 bpm & 3.875\% & 0.71267363 \\  & SDNN & 2.8 ms & 8.9\% & 0.68062499 \\ \cline{1-1}  & LF & 4\% & 6.375\% & 0.73977200 \\ \cline{1-1}  & HF & 4\% & 8.9\% & 0.73977200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of forehead and cheek performance between 50 cm and 100 cm.
## 4 Effects of POSS-Containing MQL Oil  
### Adhesion Evaluation of POSS-Containing MQL Oil  
The POSS-containing MQL oils were prepared by mixing the POSS-C8 (1)(0.1%), POSS-C2 (2) (0.4%), and POSS-C1 CF3 (3)(0).8%), synthesized in this study, with MQL-specific oil (1.6%). For the evaluation of adhesion, the modified oil was sprayed for 5 min onto the drill, at flow rates of 60 mL/h, 120 mL/h, 180 mL/h, and 240 mL/h. The weight of the oils in adherence with the drill were then measured, as shown in Figure 4[24]. (However, since the amount of adhered oil is minimal, it does not flow down.) The x- and y-axes in Figures 5-7 indicate that the concentrations of POSS-C8, POSS-C2, and POSS-C1 CF3 and the weight of the POSS-containing MQL oils effectively adhered to the drill, respectively. The concentration value of zero for the POSS-C8, POSS-C2, and POSS-C1 CF3 represents the conventional MQL-specific oil (to which the POSS is not added). As represented in the graphs, the amount of adhered oil increased with growing concentrations of POSS-C8, POSS-C2, and POSS-C1 CF3, and increased up to 60% compared with that of the conventional MQL-specific oil. A possible explanation for this result is that the molecular weight of POSS-containing MQL oil increased in conjunction with the POSS concentration while the amount of oil scattered in the air is reduced. Furthermore, additional bindings may occur between POSS from the oil and the metal surface, leading to the increase in adhesion.  
Figure 4: Experimental setup for adhesion test.  
Figure 5: Relationship between W\({}_{\text{a}}\) and POSS-C8 (1) concentration.
## 2 System Model and Analysis
### Dynamic Model
In terms of kinematics, the kinematics of the planar PPR underactuated robot are described by the homogeneous transformation matrix from the base coordinate system to the end-effector coordinate system. The trigonometric functions containing the joint angle in the homogeneous transformation matrix indicate that there is a nonlinear mapping relationship between the position and attitude of the end effector and the joint angle. 3. In terms of the relationship between control input and system response, the planar PPR underactuated robot has a non-minimum phase characteristic. This non-minimum phase characteristic leads to a complex nonlinear relationship between control input and system response. Furthermore, the actuators of underactuated robot systems usually have nonlinear characteristics, such as saturation and dead zones.
## 4 Heterogeneous Systems: Ozonation
### Tracer Tests  
As shown in Section 2, selection of the right model for estimation of the intrinsic kinetic constant depends on the fluid dynamics of the system, the characteristic diffusion time, and the reaction kinetics. In the case of a capillary microreactor, usually, \(Pe\ \gg\ 1\). However, according to the general indications provided in Section 2, in the above-mentioned capillary reactor, the aspect ratio \(L/d\) is exceptionally high (magnitude \(\sim\) 10\({}^{4}\)), meaning that \(Pe^{\prime}\ll Pe\) and that diffusion in the radial direction can be considered very effective. Tracer test results showed that the reactor is far from being modeled by a pure convection laminar model, and the residence time distribution is more similar to the one of a plug flow reactor, with some degree of axial dispersion. This behavior of microchannels in laminar flow was also observed by [PERSON] et al. [66]. In this case, the axial dispersion can be evaluated through the well-known [PERSON] theory, fitting experimental data with a Gaussian cumulative distribution, whose variance can be correlated to the dispersion \(D\) in the system. Correlations exist depending on the magnitude of axial dispersion:  
\[E(t)=\frac{1}{\tau}\sqrt{\frac{1}{4\pi N_{D}}}exp\left[-\frac{\left(t-\tau \right)^{2}}{4\tau^{2}N_{D}}\right]\ \ if\ (N_{D}<0.01) \tag{21}\]  
\[\left(\frac{\sigma}{\tau}\right)^{2}=2N_{D}-2N_{D}^{2}\left[1-exp\left(-\frac {1}{N_{D}}\right)\right]\ if\ (N_{D}>0.01) \tag{22}\]where \(E(t)\) is the Gaussian residence time distribution experimentally determined by tracing, \(t\) is the experimental time, \(\tau\) is the mean residence time, \(\sigma\) is the standard deviation of the residence time distribution, and \(N_{D}\) is defined as  
\[N_{D}=\frac{D}{uL} \tag{23}\]  
where \(u\) is the mean velocity. Some authors define the Peclet number as the reciprocal of \(N_{D}\) due to its similarity to the one defined by Equation (7). However, they are substantially different, since \(D\) represents dispersion and not molecular diffusion. According to the [PERSON] theory, a plug flow reactor model is accurate for \(N_{D}<0.01\). However, the cited paper and other studies [66] have demonstrated that a plug flow reactor (PFR) model also gives satisfactory results for values of \(N_{D}<0.05\). Future research should include axial dispersion in this models to compare the results and further validate the results. Also a thorough study, should address the problem of the limiting values to distinguish between different models for the most common geometries.  
[PERSON] et al. [57] have provided a similar study for a rectangular microchannel used for heterogeneous catalysis, tracing the liquid phase. In their study, they claim that observed kinetic constant changes with the flow rate even for \(N_{D}\sim 0.05\) and a PFR is not adequate. However, they also observe \(Da_{II}>1\), that most likely is the reason for apparent constants changes, because of the concentration gradient in proximity of the solid catalytic layer (Section 2).
## 4 Real-Life Application: AI in Judgment of Tires during Use
### RFID Technology and Tire Identification  
RFID (Radio-Frequency Identification) technology plays a key role in tire identification and tracking. Michelin [25] uses RFID tags embedded in tires, allowing each tire to be easily and accurately identified throughout its lifetime. By integrating AI-based systems, tire manufacturers and service providers can effectively track tire wear, monitor tireperformance, and ensure proper maintenance and replacement. RFID technology combined with AI improves tire management, reduces costs, and increaessafety.  
Continental [26] introduced ContiConnect 2.0, an artificial intelligence-based tire management solution. It uses intelligent sensors embedded in tires to collect real-time data on temperature, pressure, and other relevant parameters. AI algorithms analyze this data, enabling fleet operators to monitor tire conditions, detect anomalies, and proactively respond to maintenance needs. This AI-based tire management system improves safety, optimizes tire performance, and reduces downtime, ultimately increasing operational efficiency.  
Michelin [27] has developed Michelin Track Connect, an innovative solution based on AI for motorsport enthusiasts. The system uses sensors installed on the tires to collect data on tire pressure, temperature, and lap times during track sessions. AI algorithms analyze this data to provide real-time feedback, helping drivers optimize tire performance and make informed decisions to improve their racing experience. Michelin Track Connect shows how AI can improve tire ratings in high-performance applications.
## 4 Results and Discussion
### Analysis of Computational Time  
The computational time of the models is discussed in Table 4. The computational time of the implemented FP-SFOA-DQN-FL is compared with the federated object detection, FedCV, DRFL, and active learning methods, in which minimal computational time is required for the devised method.
## 3 Classification of Organic Matter in Shale and Its Morphological Characteristics
### Depositional Organic Matter
#### 3.1.3 In Situ Remnants from Post-Hydrocarbon Generation  
In situ remnants from post-hydrocarbon generation refer to bitumen, solid bitumen, or pyrobitumen that form and accumulate in place from depositional kerogen during the hydrocarbon generation process, as well as kerogen that remains in situ following hydrocarbon generation. Microscopically, the organic matter typically displays a range of distribution patterns, including granular or banded structures, with distinct boundaries separating the organic matter from adjacent minerals. Notably, these remnants exhibit no evidence of crystal growth, and the pores developed within the organic matter are clearly visible (Figure 6). The morphology of in situ remnants from post-hydrocarbon generation is primarily influenced by the pore structures defined by surrounding minerals.  
The key distinctions between in situ remnants from post-hydrocarbon generation and compacted kerogen lie in their thermal maturation and physical properties. In situ remnants primarily represent the residual organic matter that remains during and after the \"oil window\" phase, whereas compacted kerogen refers to kerogen that has undergone deformation and filled intergranular pores due to compaction prior to entering the \"oil window.\" Morphologically, compacted kerogen typically lacks porosity, whereas in situ remnants from post-hydrocarbon generation exhibit significant pore development.  
Figure 5: SEM image of compacted kerogen. (**a**) Compacted kerogen, Y206#, 3853.13 m; (**b**) Compacted kerogen, Y206#, 3859 m; (**c**) Compacted kerogen, Y206#, 3868.06 m; (**d**) Compacted kerogen, Y206#, 3876.75 m; (**e**) Compacted kerogen, Y206#, 3876.26 m; (**f**) Compacted kerogen, Y206#, 3880.17 m.
# An Optimal Sizing Design Approach of Hybrid Energy Sources for Various Electric Vehicles
###### Abstract  
In this paper, we present a discussion about green energy sources that have been widely utilized in electric vehicles (EVs). To achieve different requirements of various EVs, the correct sizing of energy sources is crucial so that the cost and output performance will be optimized. In this research, three energy sources, supercapacitors (SCs), lithium titanate oxide (LTO) batteries, and Nickel Manganese Cobalt (NCM) (or Li3) batteries, were considered for hybridization. An effective global search algorithm (GSA) was designed for optimal sizing of hybrid electric energy systems (HEESs). The GSA procedures were: (1) vehicle specification and performance requirements of energy sources, (2) determination of cost function and constraints, (3) GSA optimization with for-loops, (4) optimal results. Five examples of EVs, the electric sedan, long-distance electric bus, short-distance electric bus, electric forklift, and electric sports car, were analyzed for optimal hybrid energy combination under different criteria and specifications. The GSA effectively optimized the designs of energy sizing. The performance indices and vehicle requirements studied were the specific price, specific energy at a constant volume, specific energy at a constant mass, and specific power at a constant mass for three energy sources, SCs, LTO batteries, and Li batteries. The vehicle requirements including the maximum output power, vehicle acceleration, climbability, and maximum speed have been formulated as the design constraints. A numerical analysis of five types of EVs was analyzed for optimal sizing of the HEES and the optimal position of the DC/DC converter with the lowest cost function. The integrated system and control designs of the HESS using the GSA, more applications for green energy sources, and different types of EVs will be studied in the future.  
electric vehicle; optimal sizing; lithium titanate oxide (LTO); supercapacitor (SC); global search algorithm +
Footnote †: journal: Applied Sciences
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 3 Thermal Bridge Detection Method Using Neural Network
### Feature Extraction  
#### 3.2.1 Data Distribution Linearity  
Thermal bridges in building envelopes are caused by structural insulation loss and mainly occur at the joints between wall-slab or wall-wall structures. Therefore, thermal images of the outer structure of buildings show long thermal bridges in a specific direction, such as the cross-sectional shape of slabs and walls. This study selected the ratio of the main axis in which data are distributed and the data perpendicular to the main axis as the first feature to reflect the linear shape of thermal bridges. Even if the shape of the thermal bridge is not vertical or horizontal in the thermal image, the feature proposed in this study can be applied robustly, because it calculates the main axis according to the distribution of data.  
To extract the features of the main axis and data distribution of the clusters segmented above, a covariance matrix is calculated by using the 2D coordinates of the data included in each cluster. The covariance matrix is calculated as follows:  
\[\text{Covariance matrix}=\ \frac{1}{N}\sum_{i=1}^{N}\bigl{(}X_{i}-\overline{X} \bigr{)}\bigl{(}X_{i}-\overline{X}\bigr{)}^{T} \tag{1}\]  
where \(N\) is the number of points in each cluster, \(X_{i}\) is the coordinates of the \(i\)-th point, and \(\overline{X}\) is the average of the coordinates of all the points in the cluster.  
The eigenvector and eigenvalue of each cluster can be obtained through decomposition of the covariance matrix. This eigenvector is the principal component vector, which shows the direction in which the variance is large in the distribution of the data, and the corresponding eigenvalue indicates the size of the variance in the that direction. This study extracted the first feature value \(F_{1}\) by calculating the ratio of the eigenvalue corresponding to the first eigenvector and the eigenvalue corresponding to the second eigenvector. The first feature is calculated as follows:  
\[F_{1}=\ \frac{\lambda_{2}}{\lambda_{1}} \tag{2}\]  
where \(\lambda_{1}\) is the eigenvalue corresponding to the first eigenvector, and \(\lambda_{2}\) is the eigenvalue corresponding to the second eigenvector.
# High-Frequency Limits of Graphene Field-Effect Transistors with Velocity Saturation
## 4 Simple Model of GFETs
### Hot Electron Effects  
To accurately describe the behavior of our transistor, we have to take into account the thermal broadening that arises at large \(V_{ds}^{0}\). Indeed, for high biased samples, the electronic temperature can  
Figure 4: (**a**) Sketch of the doping profile across the graphene channel when \(V_{ds}\) is increased. Starting from n-doping, \(\Delta E_{F}(x)\) can be decreased down to neutrality at the drain side. This is the Dirac pinch-off. (**b**) Simulated \(E_{F}(x)\) along the channel for increasing in-plane electric field. (**c**) Color plot of the local carrier density in the channel as a function of the x-position along the channel and the gate voltage \(V_{g}\) (**d**) Color plot of the local resistivity \(r_{ds}(V_{g},x)\) at \(V_{ds}^{0}=0.5\) V. The total resistance of the device is the integration over x of this resistivity.  
rise up to 800 K [28], which significantly modifies the energy distribution. We take into account this thermal broadening by writing the carrier density with a residual term \(n_{0}\) that depends on \(V_{ds}\):  
\[n(x)=\sqrt{\left(\frac{\Delta E_{F}(x)^{2}}{\pi\hbar^{2}\
u_{F}^{2}}\right)^{2} +n_{0}(V_{ds}^{0})^{2}} \tag{6}\]  
where \(n_{0}(V_{ds}^{0})\) is the sum of an impurity term (\(n_{imp}=2\times 10^{11}\,\text{cm}^{-2}\) at zero bias) and a hot electron term: \(n_{th}(V_{ds}^{0})\sim\frac{\Delta(k_{B}T_{e})^{2}}{\pi\hbar^{2}\
u_{F}^{2}}\). Here, \(T_{e}\) is governed by the interaction between electrons and acoustic phonons of graphene. It has been shown in previous studies [27] that, at low doping and moderate impurity scattering, electron cooling is controlled by electron-phonon-impurity supercollisions and that for a given Joule power input one has \(V_{ds}^{2}/(RLW)=AT_{e}^{3}\) (In very high mobility h-BN supported/encapsulated samples, supercollisions are suppressed and the leading relaxation mechanism becomes black-body and luminescent \(\Omega_{II}\)-HPhP emission). In this equation we have \(A\propto n\) and \(R\propto 1/n\) so that \(AR\) is constant. Finally, the hot electron residual density reads as follows,  
\[n_{th}=\alpha(V_{ds}^{0})^{4/3}\, \tag{7}\]  
where \(\alpha\approx 9\times 10^{3}/(WL)^{2/3}\) cm\({}^{-2}\)V\({}^{-4/3}\) is a constant that depends on sample dimensions. This is used to calculate the local resistance \(r_{ds}(V_{g},x)\), which is plotted in Figure 4d. for \(V_{ds}^{0}=0.5\) V. As expected, the maximum resistance line follows the minimum carrier density line, with a width related to \(V_{ds}^{0}\).