esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Parameters Used for Evaluation of Models  
We evaluate different models on the basis of various statistical parameters including the root-mean squared error (RMSE) of the fit to the flux tower-based GPP, the variance explained (\(r^{2}\)), and the bias.  
As in [PERSON] et al. (2010), we use the model efficiency factor (MEF), a measure of how well our model reproduces independent data as compared with the mean value of observations, defined as:  
\[\text{MEF}=1-\frac{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\overline {y})^{2}}, \tag{9}\]  
where \(y_{i}\) and \(x_{i}\) are the observed and fitted values, respectively, and \(\overline{y}\) is the mean of \(y_{i}\). Values of \(\text{MEF}>0\) indicate that the model used is better than the mean, and a value of unity would indicate a perfect model. We also use the metric \(\lambda\) developed by [PERSON] et al. (2013), defined as:  
\[\lambda=1-\frac{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(x_{i}-\overline {x})^{2}+\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}+n(\overline{x}-\overline{y})+\kappa} \tag{10}\]  
where \(\kappa=0\) if \(r\geq 0\) and \(\kappa=2|\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})|\) if \(r<0\). This index is equal to the correlation coefficient \(r\) when \(r\geq 0\) and there is no additive or multiplicative bias and will be less than \(r\) in the presence of a bias. \(\lambda\) has the advantage of summarizing the agreement in a single metric irrespective of whether any disagreement is due to lack of correlation or to a bias.
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Proposed Scheme 2: E-Ace  
In poor underwater environment (noise, water bubbles, fading, etc), high BER is introduced that make two retransmissions insufficient. Therefore, to increase reliability and throughput efficiency of the network, E-ACE is proposed. Unlike ACE, E-ACE allows \(m\) relays to perform cooperative retransmissions as shown in Figure 5b. Instead of using two cooperative nodes, as in ACE, for retransmissions, rest of the nodes, present in the cooperative region, are used. In this way energy balancing is achieved and node does not deplete its energy in performing multiple retransmissions. ACE is actually a special case of E-ACE with cooperative nodes limited to 2.  
Rest of the E-ACE protocol operation is similar to that of ACE, i.e., information exchange phase, path establishment phase, and data transmission phase. It is worth mentioning in E-ACE that all the nodes present in cooperative region are candidates for retransmitting the data. The nodes in cooperative region may vary for each S-D pair. More \(m\) results in increased retransmissions, thereby, reducing outage probability and vice versa.
# Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler
## 1 Introduction
It showed that the maximum E2E latency is lower than that of first-in-first-out (FIFO) and round-robin (RR) schedulers even in an environment where random flows are generated. In addition, we proposed two heuristic algorithms for comparison with the RL-based scheduler using the metadata described above. Unlike scheduling algorithms that rely on the number of hops, all of these algorithms avoid starvation and do not heavily rely on probabilities for service decisions, resulting in superior performance. In simulations, the proposed heuristic algorithms outperformed FIFO or RR, with one of them achieving similar performance to the RL-based scheduler. The structure of this paper is as follows. After the introduction in Section 1, we present the material and method in Section 2. We first review the RL algorithms used in this work. Afterwards, we describe the experimental settings, including an RL model for the simulations and the network topologies designed for the simulation environment. Additionally, we look at the heuristic algorithms proposed for comparison of the results. In Section 3, we show the results of the RL-based scheduler in the network topologies as a simulation environment we designed. We conclude the paper in Section 4 with a discussion and contribution drawn from the results.
## 2 The Uncertainty Models of Renewable Energy and Load Demand  
### The Model of Uncertainty for the Output of Photovoltaic Power Generation System  
The intensity of illumination can have a significant impact on the active power provided by a photovoltaic power generation system. By predicting the intensity of illumination in the environment where the photovoltaic system is located, the predicted value of the active power output \(P_{pv}\) of the photovoltaic system can be represented as:  
\[P_{pv}=HA\eta K \tag{1}\]  
where \(H\) is the predicted local light intensity value; \(A\) is the area of photovoltaic system; \(\eta\) is the conversion efficiency of photovoltaic modules; and \(K\) is the correction coefficient, including the operational attenuation coefficient, inverter efficiency, correction factor for photovoltaic system orientation, etc.  
Considerable empirical statistical evidence demonstrates that the illumination level during a specific time period conforms closely to the Beta distribution [17], and its corresponding probability density function can be approximated as follows:  
\[f(x;\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}x^{ \alpha-1}(1-x)^{\beta-1} \tag{2}\]  
where \(\alpha\) and \(\beta\) are the shape parameters of the Beta distribution, which can be calculated from the mean and variance of the intensity of illumination over a certain time period. \(\Gamma(\,)\) is the gamma function. \(x\) is the ratio of illumination intensity to the maximum illumination intensity, which is defined as:  
\[x=\frac{r}{r_{\max}} \tag{3}\]  
where \(r_{\max}\) denotes the maximum illumination intensity at a certain time period.  
After considering the uncertainty of photovoltaic power, the active power output of the photovoltaic system and the maximum deviation of the active power output can be obtained. Then, the active power output of the photovoltaic system can be represented as  
\[PU_{pv}=P_{pv}+PD_{pv}\varepsilon_{pv} \tag{4}\]where \(PU_{pv}\) is the active power output of photovoltaic system considering the uncertainty, \(P_{pv}\) is the predicted active power output of the photovoltaic system, \(PD_{pv}\) is the maximum deviation of the active power output of the photovoltaic system, and \(\epsilon_{pv}\) is the fluctuation degree of the photovoltaic output.
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 1 Introduction
The main contributions of the paper are as follows:  
(1) Train the weights of Mask-R-CNN based on COCO dataset [30] to create moving target recognition; (2) Based on the trained NN (Neural Network) model, recognize the objects with motions relative to the ground, and cover them with mask and preserve the background at the same time; (3) Use pyramid LK algorithm to deal with the processed images; (4) Add a mask on the moving object to enhance the similarity between background pixels and object pixels, so only the global motion is completely retained, and the error caused by the moving object can be reduced [31]. As the original image is processed by Mask-R-CNN, some information of the image is lost. Thus, singular optical flow values may appear when calculated by pyramid LK algorithm. Then, the K-Means method is used to cluster the optical flow values to get correct optical flow data, which are calculated based on global motion in the processed images. It is the basic principle to obtain precise vehicle velocity. The paper is organized as follows: the improved optical flow algorithm based on Mask-R-CNN and K-Means is given in Section 2. Section 3 gives the results of experiment and comparisons to show that the proposed algorithm can enhance the accuracy and reliability of vehicle velocity calculated by optical flow. The paper ends with a conclusion in Section 4.
## 3 Comparison and Verification
### Centrifuge Model Tests  
The results of the active earth pressure distribution and its standard values for \(c\)-\(\varphi\) soil of limited width were compared with the centrifuge model test results of [PERSON] and [PERSON] (1987) [6] and of [PERSON] and [PERSON] (2001) [7]. They were also compared with the theoretical results of multiple scholars, as shown in Figure 11. The parameters are the same as those used in [PERSON]'s (2001) [7] centrifuge model tests, i.e., \(\gamma=16.2\) kN/m\({}^{3}\), \(q=c=0\), \(\varphi=29^{\circ}\), \(\delta=23^{\circ}\), \(h=5.25\) m (equals to 140 mm on the centrifuge model scale), and \(B=0.525\) m, 1.4175 m, 2.625 m, and 6.825 m.  
Figure 10: Comparison of the active earth pressure distribution for (**a**) \(B/h=0.083\); (**b**) \(B/h=0.167\); (**c**) \(B/h=0.333\); (**d**) \(B/h=0.500\); and (**e**) \(B/h=0.667\).  
Figure 9: Displacement cloud map for \(c\)-\(\varphi\) soil (cohesive soil) of limited width of (**a**) 1 m; (**b**) 2 m; (**c**) 4 m; (**d**) 6 m; and (**e**) 8 m.  
The maximum width of limited soil is 1.421 m according to Equation (19). The soil width set by the centrifuge model tests exceed the maximum boundary of limited soil width in this study except for \(B=0.525\) m and 1.4175 m. However, \(B=1.4175\) m is like the max limited soil width 1.421 m. the active earth pressure distribution is shown in Figure 11a. The black, red, and blue lines in Figure 11a are almost overlapped because they represent the active earth pressure for \(B=6.825\) m, 2.625 m, and 1.4175 m.  
Figure 11b gives out the normalized active earth pressure distribution, which is obtained through standardizing the active earth pressure \(\sigma_{\rm h}\) with the vertical gravity stress \(\gamma z\) and standardizing the burial depth \(z\) with the limited soil width \(B\). By comparing the theoretical results with those of other scholars, it shows that the results obtained in this study are almost the same as the solution by [PERSON] (1985) [17] and [PERSON] (2017) [19] when the burial depth is small. However, [PERSON]'s (1985) [17] and [PERSON]'s (2017) [19] solution does not transit from top to bottom smoothly and [PERSON]'s (2017) [19] solution overrated \(\sigma_{\rm wh}\) while the burial depth is large. Although the soil cohesion is considered in [PERSON]'s (2016) [26] method, the influence of the limited width cannot be considered. Both soil cohesion and the limited width are considered in this study. Figure 11 shows that the results in this study are the closest to the results of centrifuge model tests by comparing with the results of other theoretical methods.
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
###### Abstract  
In this paper, a new transformerless high voltage gain dc-dc converter is proposed for low and medium power application. The proposed converter has high quadratic gain and utilizes only two inductors to achieve this gain. It has two switches that are operated simultaneously, making control of the converter easy. The proposed converter's output voltage gain is higher than the conventional quadratic boost converter and other recently proposed high gain quadratic converters. A voltage multiplier circuit (VMC) is integrated with the proposed converter, which significantly increases the converter's output voltage. Apart from a high output voltage, the proposed converter has low voltage stress across switches and capacitors, which is a major advantage of the proposed topology. A hardware prototype of 200 W of the proposed converter is developed in the laboratory to validate the converter's performance. The efficiency of the converter is obtained through PLECS software by incorporating the switching and conduction losses.  
energy storage system; high gain; voltage multiplier circuit (VMC); continuous current mode (CCM); efficiency Article
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 3 Test Methods and New Approach
### Test and Reference Signals  
The different resampling methods were tested with bandlimited noise and sine waves of 0.05 Hz \(<f_{L}<0.5\) Hz in steps of 0.01 Hz as inputs, which is equivalent to an OSR between 1 and 10. Sensor measurements were simulated for a time window from 0 to 200 s with \(T_{S}=1\) s. The reconstruction was compared with a reference signal, which was generated by the same sine function or noise process with a higher time resolution of 0.1 s. The bandlimited noise was generated by applying a sinc filter to a white Gaussian noise sequence.  
For the non-uniform case, the sampling time points \(t_{n}\) were varied by an evenly distributed offset between \(-0.24\ T_{AV}\leq\Delta t_{n}\leq 0.24\ T_{AV}\), complying with the Paley-Wiener-Levinson theorem (3). This timing sequence is called a 'random jitter' or simply 'jitter'.  
Figure 2: Spectrum of the impulse response for windowed WSI and Kriging.  
Additional timing sequences were generated to produce a higher variation \(\Delta t_{n}\), while still complying to the theorem. The second sequence 'alternate jitter' toggles the sign of random \(\Delta t_{n}\) with \(\,\mid\Delta t_{n}\mid\,\leq 0.24\ T_{AV}\). The third'max jitter' sequence also toggles the sign but with the maximum allowed value of constant \(\,\mid\Delta t_{n}\mid\,=0.24\ T_{AV}\).  
The case of missing samples in otherwise uniform sampling was also tested. Every 11 th sample was deleted. The prime factor 11 was selected to avoid multiples of \(f_{L}\). It should be noted that the missing sample case includes occasional \(\Delta T\) = 2\(T_{S}\) and thus violated the theorem (3). Furthermore, the average interval increases to \(T_{AV}\) = 1.1\(\cdot\)\(T_{S}\). The Kriging range was increased by the same factor.  
All inputs were scaled to 0 mean and variance of 1. In additional simulations, the stability of the reconstruction against noise and quantization caused by the ADC was tested.
# Triaxial Compression Performance Research of Steel Slag Concrete on the Unified Strength Theory
## 4 Conclusions  
* 1. The failure modes of SSC are plate-splitting and slant-shear. When the stress ratio \(\sigma_{2}\) : \(\sigma_{3}\) = 0.25: 1, the failure mode of SSC is plate-splitting. At other stress ratios, the failure mode is slant-shear.
* 2. Compared with uniaxial compressive strength, the strength of SSC under triaxial compression is greater at the same replacement ratio, and the influence of stress ratio on the triaxial compressive strength is obvious. When the stress ratio \(\sigma_{1}/\sigma_{3}\)  is constant, \(\sigma_{3}/f_{m}\) increases first and then decreases with the increase of \(\sigma_{2}/\sigma_{3}\). When the stress ratio \(\sigma_{2}/\sigma_{3}\) is constant, The value of \(\sigma_{3}/f_{m}\) increases with the increase of stress ratio \(\sigma_{1}/\sigma_{3}\).
* 3. The triaxial peak strain \(\varepsilon_{3}\) is larger than the corresponding uniaxial peak strain \(\epsilon_{m}\), and increases first and then decreases as \(\sigma_{2}/\sigma_{3}\) increases. The peak strain \(\epsilon_{2}\) increases with the increase of the stress ratio \(\sigma_{2}/\sigma_{3}\). The value of \(\epsilon_{1}\) increases and increases as \(\sigma_{2}/\sigma_{3}\) increases but decreases as \(\sigma_{1}/\sigma_{3}\) increases. \(\varepsilon_{3}\), \(\epsilon_{2}\) and \(\epsilon_{1}\) increased first and then decreased as the replacement ratio increases.
* 4. The unified theory established the failure criterion of SSC under triaxial compression on experimental data. It can accurately predict the envelope of concrete under different Lode angles.  
Conceptualization, Z.D.; methodology, B.C.; software, J.Z.; validation, Z.D., B.L.; investigation, B.C.; resources, Z.D.; writing--original draft preparation, J.Z.; writing--review and editing, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant Nos. 51478126, 51868005.  
Data Availability Statement: Data is contained within the article.  
The support from the program is gratefully acknowledged.  
The authors declare no conflict of interest.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 9 Smart Port Use Case
### Virtual Reality (VR) (eMBB)  
It allows the field operator to work efficiently by allowing them to monitor the port operations in real time via video surveillance, and also enables them to visit the port in a VR environment and to receive remote assistance. It requires higher throughput and a reliable connection, which can be achieved by deploying 5G network slicing.
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 5 Influence of Linear Movement within the Signal Bandwidth due to Doppler
### Bandwidth Paradox in Multi-Channel Processing with a High Instantaneous Bandwidth  
A similar but an additionally to be highlighted aspect is that the loss is worsen quadratically by every further increase of the integration time or bandwidth. If a constant effective velocity is considered, the loss added will be magnified by 6 dB for every doubling of the time-bandwidth product as illustrated in double log scale in Figure 10b. This will lead to the bandwidth paradox that shall be described in the following. Imagine two coherent transmit channels with the same bandwidth and equal spectral power density being located next to each other. Both channels shall be processed coherently (\(B_{\mathrm{eff}}\)\(+\)). Despite the matched filter gain is typically assumed to be independent of bandwidth (Section 2.1) and thereby the combination contains more spectrally distributed energy, it will show a negative dependence in this multi-channel configuration for a violated stationary boundary. If the coherently covered bandwidth is simply extended to process both channels so that \(B_{\mathrm{eff}}\) and \(f_{\mathrm{s}}\) are at least doubled, a loss of 6 dB will occur. The effective gain will then be \(-3\,\mathrm{dB}\) instead of a gain increased by 3 dB. Thus, \(G_{\mathrm{int}}\) will not be doubled but halved or, respectively, the detectable RCS will not be doubled but halved if no further compensation is performed. It should hereby be mentioned that for twice the bandwidth, the loss will at least double because \(f_{\rm s}\geq B_{\rm eff}\) as given in Equation (4). Solely if the same \(f_{\rm s}\) was used, the desired gain increase would vanish but the resolution would still be lower (Section 5.2).
## 4 Discussion
### Limitations of the Study  
This study presents some limitations. Firstly, the sample size is relatively small (54 participants) and includes individuals from various age groups, predominantly male, with different mobility, which may further limit the generalizability of the results. To address the reduced statistical power inherent in a limited sample, effect sizes for all associations were calculated and reported, providing an estimate of effect magnitude independent of \(p\)-values. Secondly, the use of an ad hoc questionnaire that, although developed by experts and informed by the existing literature, was not subjected to formal psychometric validation may limit the reliability of the results. Moreover, participants were recruited from various regions of Italy, but the geographical distribution is uneven, with some regions overrepresented and others underrepresented. This could affect the results related to the variability of PA based on geographical location as well as cultural and socio-economic differences. In addition, the geographical distribution of the sample, which came mainly from Northern Italy (59.4%), could influence the perception of structural barriers to accessing services that are strictly linked to the areas of origin. Finally, the high percentage of people who were physically active (as measured by the LTPA questionnaire) may reflect a bias in favor of positive attitudes towards PA.
## 3 Feature Extraction
### Time-Frequency-Domain Feature Extraction  
Commonly employed techniques for extracting time-frequency features include the Fourier transform (FT) and wavelet transform (WT). FT, owing to its inherent characteristics, exhibits inefficiency when processing non-stationary EMG signal data and offers only a single resolution, alongside other limitations [27]. Consequently, we opt for an analytical approach with increased resolution and adaptability to data length. In the spectral analysis of biological signals, WT proves more suited than FT. WT's proficiency in feature extraction can be attributed to the scale and translation properties of its mother wavelet, which enhances its ability to effectively process and analyze the amplitude, frequency, and time characteristics of the signal [29]. As an exemplar of the WT family, we select the marginal discrete wavelet transform (mDWT), which does not necessitate wavelet time instants. Instead, it relies on the energy accumulated within a signal segment, rendering it a superior discriminant criterion [29].  
Marginal Discrete Wavelet Transform (mDWT)  
Discrete Wavelet Transform (DWT) discretizes the scale and translation of the basic wavelet. The basis functions of any wavelet transform fundamentally involve scaling and translating the mother wavelet and father wavelet (also known as the scaling function). Here, \(x_{l}\) represents the muscle EMG signal segment at time \(t\), which undergoes transformation and decomposition using the wavelet basis \(\psi_{l,\tau}(t)\). This process separates the high-frequency and low-frequency components of the signal. After undergoing multilevel decomposition, both the frequency-domain and time-domain features of the signal are adequately represented.  
\[\hat{x}_{l}=\sum_{\tau=0}^{T/2^{l}-1}\sum_{t=1}^{T}x_{t}\psi_{l,\tau}(t) \tag{8}\]  
\[\psi_{l,\tau}(t)=2^{-\frac{1}{2}}\psi\left(2^{-l}t-\tau\right) \tag{9}\]  
where \(\psi_{l,\tau}(t)\) represents the wavelet basis. For this study, the Daubechies Wavelet basis is chosen for decomposing the EMG signal. Here, \(x\) signifies the variable, where \(\tau\) takes values from 0 to \(2^{l}-1\) inclusive. The parameter \(l\) denotes the scale factor, altering which scales the function values. \(\tau\) is the translation parameter, which when modified, shifts the function along the \(t\)-direction.
## Reduction in AOG Events  
The aviation industry has implemented advanced CAMO maintenance software solutions such as RAIDO, OASES, and ROAM to tackle AOG events by identifying delays and inconsistencies in maintenance and operational workflows. These systems work together to predict potential part failures, manage spare parts inventory, and streamline maintenance schedules.