esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Real-Time Performance Evaluation and Model Size  
In Table 8, we summarize some of the most important characteristics of our RetinaNet-based implementation, contrasted with other two models in the recent literature. As can be observed from the table, the memory footprint is relatively low (125.5 MB), which enables the model to attain a low inference time (500 ms), while achieving a very competitive precision (mAP). These factors make our model one of the most amenable for implementation in constrained devices in the literature. As we mentioned in Section 2, the inference time of the detector depends largely on the choice of the backbone or feature extraction layers (various configurations of ResNet or VGG), which can range from 200 to 500 milliseconds in our experiments. The configuration used here makes use of VGG19, which represents a good compromise in terms of memory footprint and accuracy, at the cost of a slightly larger inference time. However, the acquisition process is done at 40 km/h (11 m/s) and, given the large line of sight, 500 milliseconds is more than sufficient to acquire the samples. It must be noted that the obtained inferences using GPU are much lower, ranging from 20 to 60 milliseconds, but their use is simply not possible in this context (but it could work in an ADAS context).
# Personality and Reputation: A Complex Relationship in Virtual Environments
## 3 Results
### Effects of Socio-Demographic Characteristics on Operative Variables  
In this subsection, we explore the relationship between the socio-demographic characteristics (i.e., age and gender), and the operative variables of the experiment. Given the unbalanced sub-samples determined by gender, we adopted a generalized linear mixed model approach to assess the relationship between age, gender and operative variables (i.e., Donation, Acceptance, and Rating behaviors). As shown in Table 3, the socio-demographic characteristics seem to be not related to the \"Acceptance\" and \"Rating\" behaviors in our experiment. Only the Gender variable appears to be associated with the \"Donation\" behavior.  
In Table 3 is the effect of Gender on Donation behavior. Under our experimental conditions, namely with a reputational system included in the game, males appear to be significantly more generous than Females (3.73 euros vs. 3.22 euros). On average, they offer the 13-15% more than females. Furthermore, within our sample, the subjects' ages are not significantly related to the donation behavior. Nevertheless, given the narrow range of variation of such dimension within our sample (i.e., 14-19 years old), we consider such result just as a control measure, and not interesting for general considerations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Mean (S.D.)** & **Skewness** & **Kurtosis** & **Min** & **Max** \\ \hline Age & 15.81 (1.29) & 0.001 & \(-\)1.068 & 14 & 19 \\ Openness & 7.21 (1.72) & \(-\)0.387 & \(-\)0.068 & 2 & 10 \\ Extraversion & 6.28 (2.23) & \(-\)0.103 & \(-\)0.890 & 2 & 10 \\ Neuroticism & 6.39 (1.92) & \(-\)0.251 & \(-\)0.362 & 2 & 10 \\ Conscientiousness & 7.14 (1.85) & \(-\)0.302 & \(-\)0.370 & 2 & 10 \\ Agreeableness & 7.53 (1.73) & \(-\)0.518 & \(-\)0.033 & 2 & 10 \\ State Anxiety & 47.76 (10.78) & 0.490 & \(-\)0.408 & 25 & 76 \\ Social Dominance Orientation & 34.72 (9.52) & 0.529 & 1.050 & 16 & 73 \\ Offer & 3.30 (1.17) & 0.235 & \(-\)0.280 & 0 & 10 \\ Average Acceptance Rate 1 & 68\% (17\%) & 0.006 & \(-\)0.027 & 6\% & 100\% \\ Average Rating Rate 1 & 58\% (18\%) & \(-\)0.460 & 0.880 & 0\% & 100\% \\ \hline \hline \end{tabular} \(1\) = Normalised frequency.  
\end{table}
Table 2: Descriptive statistics of Socio-demographic Characteristics and Operative Variables. In the table are reported the psychometric features of the Socio-demographic Characteristics, the Psychological Traits, and the Game’s Operative Variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GLMM Best Model for Gender Effect on Donation Behavior** & **F** & **Df-1 (2)** \\ \hline Best Model & 13266.651 & 35.737 *** & 4 (3222) \\ \hline \hline \multicolumn{4}{c}{**Fixed effects**} \\ \hline
**Factor** & **F** & **Df-1 (2)** & **Coefficient (\(\beta\))** & **Student \(t\)** \\ \hline Gender (Male) & 35.737 *** & 1 (3222) & 0.507 & 5.978 *** \\ \hline \hline \multicolumn{4}{c}{*** = \(p\) < 0.001.} \\ \end{tabular}
\end{table}
Table 3: Gender effects on Donation behavior.
# Demodulation Technique Based on Laser Interference for Weak Photo-Acoustic Signals on Water Surface
## 2 Materials and Methods
### Characteristic Ratio Demodulation Algorithm  
The spatial phase modulation and time phase modulation provided by water surface waves for incident laser are analogous. Spatial modulation depth can be estimated from the light intensity distribution of diffraction. For laser interference detection, we can also estimate the laser time phase modulation depth caused by water surface waves from the spectrum of the interference signals [14]. To achieve this, Bessel function was used to expand interference signals, and \(P(\omega)\) was used to represent the amplitude of the frequency of \(\omega\) in the spectrum. The characteristic ratio \(R\) was then defined as:  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Amplitude/nm** & **Frequency/Hz** & **Initial Phases/rad** \\ \hline \multirow{4}{*}{Disturbances (superimposition)} & 1500 & 3 & 0.8\(\pi\) \\ \cline{2-3}  & 3000 & 4 & 0.2\(\pi\) \\ \cline{2-3}  & 1000 & 5 & 1.2\(\pi\) \\ \hline WSAW & 30 & 1000 & 0.5\(\pi\) \\ \hline Laser wavelength \(\lambda/\)nm & & 632.8 & \\ \hline System gain of the signals \(A/\)V & & 1 \\ \hline The fixed system phase \(\Omega/\)rad & & 10,000 \\ \hline SNR of system/dB & & 20 \\ \hline \end{tabular} WSAW = water surface acoustic wave; SNR = signal to noise ratio.  
\end{table}
Table 1: Simulation parameter settings.  
Figure 1: Spectral distribution of 1 kHz simulated interference signals.  
Figure 2: Principle diagram of demodulation algorithm for underwater acoustic source detection.  
\[R=\sqrt{\frac{\sum_{m}P[(2m+1)\omega_{i}]}{\sum_{m}P[(2m+1)\omega_{i}+\omega_{s}]} \cdot\frac{\sum_{m}P(2m\omega_{i})}{\sum_{m}P(2m\omega_{i}+\omega_{s})}}=\frac{J _{0}(\frac{2\pi}{\lambda}A_{s})}{J_{1}(\frac{2\pi}{\lambda}A_{s})} \tag{2}\]  
where \((2m+1)\omega_{i}\) and \(2m\omega_{i}\) were odd and even frequency doubling of low frequency components; \(J_{0}\), \(J_{1}\) are Bessel functions. Since the amplitude \(A_{i}\) of water surface disturbance waves was significantly higher than WSAW amplitude \(A_{s}\) caused by the underwater acoustic source, according to the nature of Bessel function of the first kind, \(R\) can be rewritten as:  
\[R=\frac{\sum_{m}P(m\omega_{i})}{\sum_{m}P(m\omega_{i}+\omega_{s})} \tag{3}\]  
It can be seen from the above equation that without aliasing of the low-frequency bands with the signals under measurement, the characteristic ratio \(R\) can be determined by calculating the ratio of the sum of low-frequency band amplitudes to that of the frequency component amplitudes after a frequency shift by \(\omega_{s}\). With the characteristic ratio \(R\) thus obtained, WSAW amplitude \(A_{s}\) can be resolved with the Bessel function. Since the analytic solution of \(A_{s}\) is hard to derive in practice, we estimate the numerical solution of \(A_{s}\) from the look-up table which is shown in Figure 3.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 4 Combination of Optical and Thermal Remote Sensing  
As has been stated in the previous sections, both the vegetation indices (VI) from optical remote sensing and the land surface temperature (LST) from thermal remote sensing are closely related to vegetation growth conditions and soil moisture. VI can reflect vegetation growth status and leaf water content underwater stress through the various combinations of VIS, NIR, or SWIR reflectance spectra of green plants [193], while LST can be used to indirectly reflect soil moisture conditions under the same vegetation cover conditions [194]. On the other hand, despite the difference in internal mechanisms (optical remote sensing relies on the reflectance of solar radiation, while thermal remote sensing measures the emission of the land surface itself), optical and thermal remote sensing often share similar spatiotemporal resolutions and data availability, for many satellite sensors have optical and thermal detecting capabilities at the same time, e.g., AVHRR [182; 195], MODIS [196; 197], and the Visible/Infrared Imager Radiometer Suite (VIIRS) [198; 199]. These two respects form the mechanical and practical foundations for the combination of optical and thermal remote sensing in agricultural drought monitoring [200; 201].
## Content Sharing Graphs for Deduplication-Enabled Storage Systems
The file-to-chunk mappings are either kept internally as part of the per-file metadata [1; 2; 3; 4] or externally as database tables [5; 6]. To the best of our knowledge, this paper is the first in the open literature to leverage the deduplication metadata (the file-to-chunk mappings), to adapt them to graphs, and to explore graph-based algorithms to solve real-world file management problems on deduplication-enabled storage systems. The key contribution of this paper is the introduction and showcase of the concept of a content sharing graph as a primitive to solve problems of storage management in deduplicated storage systems. Our graph structures illustrate the sharing of content between storage objects (e.g., files). The power of content sharing graphs lies in the fact that they reveal the hidden connectivity of individual storage objects, and enable graph-based algorithms to exploit the content sharing among storage objects. For example, in deduplicated storage systems, it is interesting to find out how many files share content with others; or what is the storage footprint of a set of deduplicated files. Thus, by modeling content sharing as graphs, we can leverage existing graph research results to efficiently solve practical problems in the management of deduplicated storage systems. Among other management problems, in this paper, we solve two storage management problems using graph-based algorithms: (1) to partition a deduplication domain into sub-domains with minimal deduplication loss; and (2) to quickly find out the deduplicated size of a user-defined subset of files. The rest of the paper is organized as follows. Section 2 defines content sharing graphs (referred to as CSGs) and presents two graph models to represent the same set of files in deduplicated storage systems. Section 3 provides two real-world problems that can be efficiently solved with graph-based algorithms, and a trace-driven analysis of these graph-based algorithms. Section 4 discusses the two proposed graph models. Section 5 presents related work in the field of storage management on deduplicated storage systems. Finally, Section 6 presents a summary of our results and concludes the paper.
# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 4 Experiments and Discussion
### URC Data Set Classification Experiment  
Since the UCR public data set is widely used in time series processing problems, including time series data sets taken from different fields, some of the data sets are selected to test the performance of the proposed time series classification model based on IRRNN.  
The experiment selected seven sequence data sets in the UCR for classification experiments. Four algorithms were used for comparison experiments, including the most basic feedforward neural networks (FNNs), traditional RNNs model, LSTM, and IRRNN proposed in this paper. The experiment uses the cross-validation method to determine the optimal network parameter values to prevent over-fitting phenomena that may occur during training. In addition, we performed 50 Monte Carlo simulations for each sample set, taking the average of all results as the final result. The accuracy of the classification for each data set by the four algorithms is shown in Table 2, and the algorithm with the highest classification accuracy for each data set is in bold.  
As show in Table 2, it can be seen that the IRRNN model obtains the highest classification accuracy on the six data sets and is slightly lower than the LSTM model only on the \"SyntheticControl\" data set. In general, the model classification performance based on RNNs is better than the basic feedforward neural network model, indicating that the recurrent structure of the neural network in time is very beneficial to the classification task of sequences. In the UCR data set classification task, the classification performance of LSTM is better than the RNNs model. However, the proposed IRRNN is more advantageous than the LSTM in the classification task. The independent structure solves the problem of gradient vanishing and explosion and can quickly converge to the optimal solution. The addition of random weighted history information makes the data tend to be easy to classify, and the network can be generalized. The validity and robustness of the IRRNN algorithm is tested by experiments.
## 3 Matching Parameters  
### Summary of Matching Parameters  
This article argues that the matching parameters of brake discs for high-speed trains can be summarised from three aspects: the feasibility of installation between brake discs and the train, the characteristics of the brake discs during high-speed braking, and the integral braking efficiency of the train. Thus, they can be translated into three dimensions: the brake discs' assembly interface, the brake discs' physical characteristics, and the trains' braking performance. Based on previous research, the parameters that characterise the matching performance of carbon-ceramic brake discs have been grouped according to these three dimensions as follows:  
(a) The assembly interface dimension includes three categories of parameters: size, mass (or density), and structure of the brake discs. Analysis of these parameters helps to check whether the carbon-ceramic brake disc meets the changeover requirements of the train.  
The size category includes the inner diameter, the outer diameter, and the thickness of the brake disc and the diameter of the blots located at the connection of the brake discs. The structural category includes the pattern of the heat sink distribution.  
(b) The physical characteristics dimension includes five categories of parameters: mechanical effect, friction coefficient, temperature, wear, and the dust effect on the brake discs. Analysis of these parameters helps to investigate the braking capacity as well as the safety of carbon-ceramic brake discs during the braking process.  
The mechanical effect category includes the elastic force parameters, including elastic modulus, yield strength, and hardness; the thermodynamic parameters include specific heat capacity, thermal conductivity, and the coefficient of linear thermal expansion of the carbon-ceramic material. The friction coefficient category includes the average frictional coefficient of the brake discs. The temperature category includes the peak temperature on the disc's surface and thermal fatigue. The \"dust effect\" category includes the value of the adhesion coefficient of the rail due to carbon dust contamination.  
(c) The braking performance dimension includes three categories of parameters: emergency braking distance, braking deceleration, and braking impulse, which can be used to evaluate the efficiency of high-speed trains' braking system and thus reflect the reliability of the braking discs. The category of brake deceleration includes the average braking deceleration, and the braking impulse category includes the maximum common braking impulse.
## 2 Technical Backgrounds
### 5g  
Many industries and academia have extensively researched 5G for more advanced wireless technologies such as innovative speed, high bandwidth, and high availability [16]. 5G must meet many requirements, and to achieve this, integration of various technologies, rather than improved existing cellular communications, must be achieved. The integration of various technologies, such as edge computing to meet low latency, and high-density base station deployment for high availability, creates the complexity of 5G networks. As a result, 5G can efficiently manage complex networks through Software Defined Networking (SDN) and Network Function Virtualization (NFV), which are critical technologies in 5G configurations [17].
## 5 Results of Health Monitoring
### Internal Force Monitoring Results
#### 5.3.2 Transverse Distributions of Girder Stress Increments  
The measured variations of girder longitudinal stresses are shown in Figure 9. As shown in Figure 9a,b, the compressive stresses of side-span girder generally increased during the first half year affected by the concrete shrinkage and elastic compression of girder under the huge horizontal cable forces. Then, the compressive stresses of side-span girder top plate decreased slightly after the first year, which was caused by the rise of side-span girder along with the cables that were affected by the movements of cable anchoring positions towards the mid-span. As for the side-span girder bottom plate, the compressive stresses of the measuring points including SD2, SD3, SD6 and SD7, which are close to the hanger points, also decreased slightly after the first year along with the decreased horizontal cable forces. However, the stresses of SD1, SD4, SD5 and SD8, which are far away from the hanger points, increased continually because the influences brought by the cable force changes were weak. The uneven transverse distributions of stress changes reflect the significant spatial effect of the extra-wide girder.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Location of** & \multicolumn{3}{c}{**Measured Cable Force (kN)**} & \multicolumn{3}{c}{**Change Percentage (\%)**} \\ \cline{2-5}
**Anchoring Span** & **15 April 2015** & **15 October 2015** & **15 April 2016** & **First Half Year Period** & **One-Year Period** \\ \hline SC1-8 (Southwest) & 53,758 & 53,545 & 53,612 & \(-\)0.40 & \(-\)0.27 \\ SC1-N(Northwest) & 51,763 & 51,328 & 51,312 & \(-\)0.84 & \(-\)0.87 \\ SC2-5 (Southeast) & 50,868 & 50,848 & 50,782 & \(-\)0.04 & \(-\)0.17 \\ SC2-N (Northeast) & 49,870 & 49,812 & 49,469 & \(-\)0.12 & \(-\)0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measured cable force and change percentage.  
Figure 9: Measured changes of longitudinal girder stresses: (**a**) Section C2 of west side-span; (**b**) Section C12 of east side-span; (**c**) Section C5 of mid-span; and (**d**) Section C7 of mid-span.  
The stress changes of mid-span girder are shown in Figure 9c,d. The compressive stresses of top plate increased continually along with the girder deflection, and the bottom plate stresses changed insignificantly during the first half year and began to decrease after the first year affected by concrete S&C.
## 3 Materials and Methods  
In this section, we detail the research methodology adopted to develop and validate the proposed advanced intelligent frame generation (AIFG) algorithm. The complete process is structured into several key steps, as illustrated in Figure 2.  
* Queuing System Modeling: We establish the system model for the AOS environment, integrating heterogeneous data sources. The system architecture includes service classification (delay-sensitive and non-delay-sensitive), queuing structures, and differentiated framing processes, as described in Section 2.
* Problem Formulation: The framing problem for delay-sensitive services is formulated as a Markov decision process, where the agent selects optimal frame generation times to minimize queuing delay and maximize the transmission efficiency. Key mathematical models include the Poisson arrival model (Equation (1)), average frame generation time and packet delay equations (Equations (2)-(6)), and the self-similar traffic model formula (Equations (7)-(11)). The above content is displayed in Section 2.
* Algorithm Design: First, a model algorithm for Optimal Frame Generation Time Threshold Calculation was developed, based on which the RTFG was subsequently derived. A real-time framing algorithm (RTFG) is designed for delay-sensitive queues to satisfy strict latency constraints. A high-efficiency framing algorithm (HEFG) is applied to non-delay-sensitive queues for maximizing frame utilization. Then, an advanced intelligent frame generation algorithm (AIFG) is composed of the RTFG and HEFG. Reinforcement learning-based adaptive framing using the PPO algorithm optimizes frame generation thresholds dynamically based on real-time traffic conditions. The above content is displayed in Sections 2 and 4.
* Simulation Setup: We implement the AIFG algorithm using NS-3 and PyTorch. The simulation experiments are conducted under varying traffic intensities and service mixes. The key simulation parameters include a fixed frame capacity, delay threshold for real-time services, and maximum tolerable delay for non-delay-sensitive services. More than 300-round simulation runs are performed to ensure statistical reliability, and average values along with standard deviations are reported. The above content is shown in Section 5.
* Performance Evaluation: The performance metrics used for evaluation include the following: average queuing delay, frame multiplexing efficiency and system throughput. Comparative analyses are performed against conventional isochronous framing and high-efficiency framing baselines. Additionally, the proposed AIFG algorithm is evaluated alongside a DQN+HEFG algorithm to further demonstrate its advantages in learning stability, convergence efficiency, and QoS adaptability.  
The complete research process is summarized in Figure 2, showing the sequence from system modeling, problem formulation, algorithm design, and simulation implementation to the performance evaluation.
## 8 The Effect of Alloying Elements on Electrical Steel
### Effect of Nickel-Iron (Ni Fe)  
Typically, electrical machines utilize Ni-Fe alloys with a nickel content ranging from 40% to 50%. Higher nickel content leads to increased permeability in these alloys. However, it is essential to consider that higher nickel content also results in higher electrical conductivity and, consequently, greater eddy current losses [83].  
To achieve optimal magnetic properties in NiFe alloys, a high-temperature annealing process exceeding 1100 \({}^{\circ}\)C is required along with the introduction of a surface oxide layer to develop desirable magnetic characteristics.  
NiFe alloys containing approximately 45% to 50% nickel are commonly used in electrical machines for which low losses are prioritized, such as dental and surgical instruments operating in precisely controlled thermal environments. These alloys also find applications in aerospace devices for low-loss and non-driving components, such as analysers [84]. Figure 12 provides a comparison of the initial BH magnetization curves between typical lamination alloys (with a thickness of 0.35 mm) and alloys with 50% NiFe and 50% CoFe.  
The advantages of high permeability and large resistivity in Ni-Fe alloys are further demonstrated in Figure 13, which presents iron losses for the same materials at a frequency of 400 Hz.