esa-sceva/satcom-chunk-collection huggingface dataset:
# Modal Properties of Photonic Crystal Cavities and Applications to Lasers
## 3 Microcavity Lasers for Energy-Efficient Communications
Therefore, the energy cost increases as the data rate grows. Overall, Figure 5c emphasizes that an intimate relationship exists among size, speed and energy efficiency [62]. Compact lasers are necessary to minimize the energy cost. In fact, the energy cost benefits twofold from a shorter cavity length, i.e., a smaller active volume. Firstly, via reduction of the threshold current. Secondly, via the enhanced modulation bandwidth at a given current in excess to threshold (i.e., a larger \(\sigma\)). The low threshold current and small active volume equip PhC lasers with reduced energy cost, around 1-10 f/bit [54; 63]. This makes PhC lasers promising light sources for chip-scale optical interconnects [69]. In practice, though, the impact of quantum noise on the laser performance should be also considered [14; 70]. For reliable encoding of information, microcavity lasers should be operated well above threshold [71], with a potential increase in the energy cost. The required bit-error rate (BER), depending on the specific application, may also restrain the achievable data rate and further degrade the energy cost [72].
## 4 Empirical Results
### Alliance Selection Decision
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DMU** & **RP** & **RA** & **Comparison** \\ \hline \multicolumn{4}{c}{**Effective**} \\ \hline DMU1 + DMU3 & 31 & 10 & 21 \\ DMU1 + DMU18 & 36 & 19 & 17 \\ DMU1 + DMU5 & 38 & 26 & 12 \\ DMU1 + DMU4 & 39 & 28 & 11 \\ DMU1 + DMU8 & 25 & 14 & 11 \\ DMU1 + DMU9 & 35 & 24 & 11 \\ DMU1 + DMU6 & 37 & 27 & 10 \\ DMU1 + DMU7 & 33 & 23 & 10 \\ DMU1 + DMU15 & 32 & 22 & 10 \\ DMU1 + DMU20 & 30 & 20 & 10 \\ DMU1 + DMU19 & 29 & 21 & 8 \\ \hline \multicolumn{4}{c}{**Ineffective**} \\ \hline DMU1 + DMU2 & 7 & 9 & \(-2\) \\ DMU1 + DMU10 & 6 & 15 & \(-9\) \\ DMU1 + DMU11 & 1 & 11 & \(-10\) \\ DMU1 + DMU12 & 8 & 18 & \(-10\) \\ DMU1 + DMU14 & 2 & 12 & \(-10\) \\ DMU1 + DMU17 & 3 & 13 & \(-10\) \\ DMU1 + DMU13 & 5 & 16 & \(-11\) \\ DMU1 + DMU16 & 4 & 17 & \(-13\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The effective and ineffective alliance partnerships. DMU1 + DMU8, DMU1 + DMU9, DMU1 + DMU6, DMU1 + DMU7, DMU1 + DMU15, DMU1 + DMU20, and DMU1 + DMU19. The green point of these alliances was closer to the center than the yellow point in the radar chart. The alliances in the \"ineffective\" group were DMU1 + DMU2, DMU1 + DMU10, DMU1 + DMU11, DMU1 + DMU12, DMU1 + DMU14, DMU1 + DMU17, DMU1 + DMU13, and DMU1 + DMU16. The yellow point was closer to the center than the green point in the radar chart. Obviously, the efficiency of both the target and partners improved at the same time. The previous studies usually required a combination of different methods--for example, the combination of DEA and gray models. The gray prediction model parameters are estimated by the least squares method, which will produce biased estimates when the system is disturbed. Even though the gray model can achieve a good prediction effect on less data and information, the shortcoming in the gray model is the background value, which is not sufficient. Gray prediction does not take the characteristics of data into account. However, resampling improves the accuracy of forecasting as the resampling method considers the characteristics of the data. Previous research focuses on the performance evaluation of the target company rather than partners. The Super-SBM model is suitable for more detailed efficiency rankings. The resampling past-present-future model extends the past-present (Super-SBM) model. Therefore, this study uses resampling to predict and establish alliances, which is an alternative method. On the basis of the above information, this research extended the basis of previous studies. Thus far, there is no research to establish strategic alliances with resampling prediction models. This study proposed a new comparative evaluation model for strategic alliances, not only from the perspective of the target company but also from the perspective of the partner company. Companies can compare the efficiency of both sides simultaneously. In short, the results and findings of the research also lead to new suggestions for strategic alliance.
## 3 Dynamic Model and Integrated Controller of FWIDSV
### Following Control Layer  
The ideal \(a_{X}\) and \(a_{Y}\) in the global coordinate system can be obtained from the RVF in the previous discussion. To obtain the total longitudinal and lateral forces of the vehicle in vehicle coordinates, the coordinate system is converted by Equation (18).  
\[\begin{split} a_{xd}&=a_{X}\cos\varphi+a_{Y}\sin \varphi\\ a_{yd}&=a_{Y}\cos\varphi-a_{X}\sin\varphi\end{split} \tag{18}\]  
where \(\varphi\) denotes the yaw angle of the vehicle; \(a_{xd}\) and \(a_{yd}\) are desired longitudinal acceleration and lateral acceleration in vehicle coordinate system.  
When controlling the yaw moment of the vehicle, it is necessary to consider the tracking error of the yaw angle. In this paper, the ideal yaw moment is obtained by SMC. The non-singular interrupted sliding surface is adapted to ensure that the tracking error can converge in the effective time on the interrupted sliding surface and it has fast and high-precision response characteristics [18]. The structural sliding surface \(s\) is as shown in the (19).  
\[s=\alpha(\varphi-\varphi_{d})+\beta\big{(}\dot{\varphi}-\dot{\varphi}_{d} \big{)}^{p/q} \tag{19}\]  
where \(\varphi_{d}=\frac{y}{L}\). \(\alpha\) and \(\beta\) denote the positive constant; \(p\) and \(q\) are the positive odd number, which satisfies that \(1<p/q<2\).  
The calculation method of \(M_{z}\) is as shown in the (20), in which \(\eta\) determines the approach rate of the system state on the sliding surface; \(\Phi\) can smooth the chattering phenomenon in the control process. The controller parameters are shown in the Table 1.  
\[M_{z}=-\eta sat(s/\phi),sat(s/\phi)=\left\{\begin{array}{ll}s/\phi&if|s|<\ \phi\\ \mathrm{sgn}(s/\phi)&if|s|<\ \phi\end{array}\right. \tag{20}\]
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 4 Experiments  
### Implementation Details  
The algorithm in this paper is implemented based on the Python programming language in the Pytorch framework on the computer hardware platform of Intel Broadwell 2.4 GHz GPU (Tesla V100) and Intel(R)Core(TM) i7-10700 [EMAIL_ADDRESS] GHz. Our method is divided into two training parts. The optical flow estimation network is trained offline on the ILSVRC-2015 [34] video dataset, and the pre-trained FlowNet2.0 is used as the backbone network of the optical flow estimation network. Set the number of epochs to 20, and the learning rate declines linearly from 0.01 to 0.001. The tracking network is trained on the ILSVRC-2015 and YouTube-BB [35] datasets. ILSVRC-2015 has approximately 1.3 million frames and approximately 2 million tracked objects with terrestrial real bounding boxes. YouTube-BB consist of more than 100,000 videos annotated once in every 30 frames. The pre-trained AlexNet [26] network is used as the backbone network of the tracking network, randomly selecting a pair of images from the images, cropping out the central region z, and the other one Figure crop out x. The network parameters were optimized by Stochastic Gradient Descent (SGD), the number of epochs was set to 50, and the learning rate decreased from 0.01 to 0.001.
## 5 Further Discussion and Prospects for the Future
### Algorithms of Hearing Aids  
Since the goal of the self-fitting algorithm is to replace the audiologists to fit hearing aids, large amounts of professional knowledge must be integrated into algorithms of hearing aids. Related algorithms consist of several aspects.  
1. Judgment of hearing loss type. Many studies have shown that there is little difference between traditional audiometer and online audiometer [39]. However, a hearing aid is not applicable for every patient, such as a patient with asymmetrical hearing loss, conductive hearing loss or mixed hearing loss. So, the patient's hearing loss type should be determined before a hearing aid is used. From the algorithm, the way to detect these hearing loss types is the major constraint for self-fitting hearing aids for the effective detection algorithm has not yet been found. Certainly, the currently safest method is that the hearing loss type is determined by an audiologist.
2. Multi-parameter hybrid optimization strategy. With the increasing complexity of hearing aid algorithms, there are more and more parameters need to be adjusted, such as compression threshold, compression ratio, noise suppression, frequency shift, microphone mode, etc. Therefore, the joint optimization of different algorithms parameters is facing greater challenges. Although there is no similar study, parameter optimization of self-fitting hearing aids is the similar to multi-source information fusion whose decision-making is implemented with different source data. Therefore, multi-source information fusion theory can be applied to multi-parameter hybrid optimization of self-fitting hearing aids. In addition, with the development of network technology and the explosive growth of information, algorithms of distributed information fusion and multi-modal heterogeneous information fusion can be applied to parameters optimization of self-fitting hearing aids, even web server [40] and loud computing should be studied.
## 3 Materials and Methods
### Waveform Prediction
#### 3.5.1 Processing  
Ten seconds of the PPG waveform are used as the input to the network and one second (\(t=9\) s \(\to t=10\) s) of the ABP waveform is used as the output. With the data's sampling frequency of \(F_{s}=125\) Hz, this corresponds to 1250 input points and 125 output points. To reduce the number of outputs of the neural network, the output data was down-sampled from 125 Hz to 25 Hz, resulting in 25 output points. The ABP signal has a bandwidth of approximately \(F_{b}=8\) Hz, so the down-sampled frequency of 25 Hz is well above the [PERSON] criterion of \(F_{s}\geq 2F_{b}=16\) Hz. To implement feature scaling, the average mean and average range for both PPG and ABP datasets was calculated. Each signal was then feature scaled as per Equation (4), where \(E_{s}[\overline{PPG}]\) refers to the expected value (or mean) across the data sample's means and the denominator refers to the expected value (or mean) of the whole data sample's ranges. The same computation applies to the ABP signals.  
\[PPG_{scaled}=\frac{PPG-E_{s}[\overline{PPG}]}{E_{s}[max(PPG)-min(PPG)]} \tag{4}\]  
Data observations consisting of the 1250-point PPG signals and the 25-point ABP signals were generated from the data segments, resulting in multiple observations from one segment and a total of over 300,000 observations.
## 3 Methodology
### Compression of Convolutional Neural Networks Using Tensor Decomposition Methods
#### 3.2.4 SVD Convolutional Layer  
Singular Value Decomposition (SVD) is applied to compress convolutional layers with a kernel size of \(1\times 1\). Let the weight tensor of such a layer be \(W(T,S,1,1)\).  
SVD decomposition of \(W\) with rank \(R\) is expressed as follows:  
\[W(t,s,1,1)=\sum_{r=1}^{R}w_{1}(t,r,1,1)w_{2}(r,s,1,1) \tag{10}\]  
where \(w_{1}\in\mathbb{R}^{S\times R}\) and \(w_{2}\in\mathbb{R}^{R\times T}\).  
This allows the original convolutional layer to be replaced with two sequential convolutional layers:  
1. A convolutional layer with \(S\) input channels, \(R\) output channels, and a kernel size of \(1\times 1\).  
2. A convolutional layer with \(R\) input channels, \(T\) output channels, and a kernel size of \(1\times 1\).  
A detailed explanation of the layer shapes is provided in Appendix D.  
By factorizing the weight tensor into two smaller components, the SVD Convolutional Layer reduces the number of parameters and computations while maintaining the functional equivalence of the original layer.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 1 Introduction
### Phenological Monitoring
#### 1.3.3 State of the Art: UAVs for Phenological Monitoring  
An alternative and more novel approach for monitoring phenology involves the use of Uncrewed Aerial Vehicles (UAVs). UAVs equipped with RGB, multispectral, or hyperspectral sensors can be used to collect data across a range of spatial and temporal scales, and have much potential in studies examining the heterogeneity of peatland environments and their phenology [55; 56; 57]. UAV data can be spectrally calibrated and geo-referenced over a defined survey area, which facilitates their comparison with measurements at the plot- and ecosystem scale (100-10\({}^{6}\) m\({}^{2}\)), as well as with satellite data. Furthermore, UAV platforms can collect data at an ultra-high spatial resolution, with ground sampling distances (GSDs, i.e., the distance on the ground covered by each image pixel) in the order of centimetres. As such, UAVs have become a valuable tool for tracking fine-scale heterogeneity in greening, e.g., of individual tree crowns [56; 58; 59] and agricultural crops [60]. However, detailed time-series of UAV data in peatland environments are rare due to their structural complexity and pronounced spatial heterogeneity. In a recent study of a Finnish peatland, [PERSON] et al. [61] examined a combination of hyperspectral UAV data and ground-based measurements for detecting patterns in vegetation. The authors demonstrated the potential of UAVs for detecting seasonal trends and fine-scale spatial variability in structural characteristics such as leaf area and biomass. [PERSON] et al. [62] used a multispectral UAV platform to examine changes in greenness in a tundra environment, finding that a GSD of 0.5 m was able to capture the variability in dominant vegetation types. By spatially aggregating UAV image pixels (5-cm GSD), the authors reported that as much as 60% of seasonal variability in greenness was lost at the spatial resolution of the employed satellite datasets (e.g., Landsat, 30 m; Sentinel-2, 10 m), highlighting the benefits of UAV-based monitoring over satellite data.  
Inter-comparison studies employing data across multiple spatial and temporal scales are key to improving our understanding of vegetation phenology and its impact on CO\({}_{2}\) exchange [56; 63; 64; 65]. In boreal peatland ecosystems, for example, [PERSON] et al. [53], found a strong agreement between phenocam- and satellite-derived VIs. The authors found that while seasonal changes in greenness were closely correlated with peatland GPP, short-term variability in GPP could only be explained by phenocam-derived greenness during certain periods. Multi-scale comparison studies incorporating UAV data are rare, although a study by [PERSON] et al. [56] compared ground-, UAV-, and satellite-based observations of phenology at a mixed woodland. They found that fine-scale UAV data were able to track canopy phenology of individual trees, whereas satellite-derived datasets were related to phenological dynamics at the ecosystem scale.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 3 Proposed Machine Learning Models
### Ensemble Methods
#### 3.5.3 Stacking  
Stacking regression combines multiple regression models via a meta-regressor, using the out-of-fold prediction concept [70]. The stacking method used in this work splits the dataset into k folds, in which the k-1 folds are used to train the first-level regressors in k successive rounds. In each round, the first-level regressors are used to predict based on the remaining 1 subset. After that, the prediction results are used and stacked as the input data to the second-level regressors to form a final set of predictions [71]. The schematic diagram of the stacking model is shown in Figure 8. In this study, one stacking-based ensemble model (abbreviated as Stack_LR) was proposed based on the two-level scheme. SVR, BPNN, and CART were used as regression models in the first level to obtain the prediction results, and LR was used as the meta-regressor in the second level to combine and generate the final prediction results.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 5 Evaluation
### Experimental Results
#### 5.2.3 Farneback Optical Flow  
The Farneback optical flow algorithm showed its performance overhead from launching many kernels. Therefore, the kernel merge technique can improve the Farneback algorithm's overhead. Section 4.2 dealt with the kernel merge technique and described candidate kernels in the Farneback algorithm for merging.  
Figure 12 shows the performance improvement of the optical flow algorithm after applying the kernel merge technique. Please note that the operation of the kernel KF is absorbed into the kernel resizeLN, and two kernels updateFlow and updateMatrics are merged into a new kernel updateFM. As described in Section 4.2, decreasing the cost of API calls and data transfer causes performance improvement. The _kernel merge_ scheme achieved average performance improvement with 10% and 6-8% in the APU and d-GPU environments as compared to the baseline.  
The analysis of the OpenCL API calls and the kernels of the Farneback algorithm can identify factors leading to performance improvement. Table 9 shows the profiling results of the OpenCL API calls while running the Farneback optical flow algorithm. The table includes the execution time of each API. The total execution time of API calls is reduced by 210 ms from the baseline. Most of the execution time reduction came from APIs related to the buffer memory. By merging two consecutive kernels, the overheads associated with data transfer from one to the next can be saved. Hence, it is unnecessary to allocate a buffer, establish address mapping, and read/write from/to a buffer, so the overheads can be eliminated between the two kernels.  
Table 10 shows the execution time and the number of kernel calls before and after applying the kernel merge technique. The table includes the number of calls and the total execution time of the kernels of the algorithm. First, the number of calls and the execution time of KF are eliminated because the kernel operation is merged into resizeLN. Second, two third of the execution of the two kernels updateFlow and updateMatrices are replaced to the unified kernel updateFM. Accordingly, _merge_ saved the time to invoke the kernels and the buffer management overhead associated with the kernels.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Baseline** & **Occup** \\ \hline GlobalWorkSize & 1536/16/1 & 1536/16/1 \\ WorkGroupSize & 16/16/1 & 16/16/1 \\ Time & 3220 & 26.76 \\ LocalMemSize & 0 & 128 \\ VGPRs & 67 & 64 \\ Wavefronts & 384 & 384 \\ FetchSize & 80,516.25 & 110,256.56 \\ VALUInsts & 374,354.74 & 329,929.70 \\ SALUInsts & 17,289.77 & 15,461.60 \\ LDSInsts & 0 & 203.53 \\ VALUBusy & 75.78 & 79.89 \\ SALUBusy & 3.26 & 3.48 \\ MemUnitBusy & 91.31 & 91.06 \\ FlatVMemInsts & 36,950 & 32,672 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hardware performance counter results of runLBPClassifierStumpSimple.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 3 Identity-Based Encryption Scheme  
The efficiency of MIBFHE schemes is largely determined by the underlying IBE scheme. To construct a compact and streamlined MIBFHE scheme, the design of the underlying IBE scheme is crucial. The two most mainstream IBE schemes are CHKP10 and ABB10. However, the size of the public key and the length of the identity ID in CHKP10 grow proportionally and linearly, which limits its storage space and computational efficiency. In this section, we incorporate the preimage sampling algorithm based on the YJW23 trapdoor into the ABB10 scheme, constructing a compact IBE scheme. The parameters of the improved scheme are more concise, and we have verified its correctness and security. Compared to similar IBE schemes, our compact IBE scheme achieves significant optimizations in its main parameters.
# Development of a New Test-Rig for Wheel-Rail Contact Experiments under Large Slip Conditions
## 1 Introduction
### Experimental Investigations  
Related evidence of adhesion recovery in large slip conditions first attracted attention in line tests in France back in 1982 ([PERSON], 2017). Two peak points were noticed in the creep curve during braking, and the corresponding creepage of the second peak is between 5% and 25%. Later, more experimental investigations were conducted utilizing various test-rigs. [PERSON] (2018) of China investigated the effects of creepage, speed and axle load on adhesion recovery using a full-scale bogie roller. [PERSON] (2019) of Czechia used a full-scale tram wheel on a roller to investigate adhesion recovery and a quantitative formula of adhesion recovery was established. [PERSON] (2020) of Japan used a twin-disc rig and a water spray device to conduct wheel-rail contact experiments and the decrease in the fluid layer thickness caused by the rising temperature of the contact interface was considered as the mechanism of adhesion recovery. [PERSON] (2019) of Italy investigated the mutual interaction of adhesion among axles known as rail adhesion recovery utilizing a four-wheel roller-rig. However, the test-rigs used in the studies above are classified as wheel-on-roller test-rigs, and the fluid layer distribution on the contact interface is greatly different from the line test.  
Figure 1: Adhesion recovery during braking with large creepage.
## 4 Blockchain
#### Integration of Federated Learning and Blockchain in Vehicular Networks
Vehicle clients also have computation and communication resources and storage [127]. The vehicle clients train their local ML models with the help of their local data and forward the updated models to a central server after receiving requests from a centralized server. The following are some of the issues that FL and blockchain integration can solve in VANET:  
**Security and privacy:** FL's primary responsibility is the preservation of data privacy. However, several weaknesses exist in FL from the point of view of privacy and security, such as that the attacker can access confidential information of the end device through their model updates already available on the central server. Furthermore, the malicious end device can also modify the updated local model. The attackers can also launch an injection attack within the central server to send an incorrect model update to attached end devices in the process of the FL algorithm. Because of all of the above reasons, FL security and privacy are most important. **Scalability:** Local model constraints are significant for FL because they are used in the process of global model updation. Consequently, if the number of devices increases in each learning round, then the performance of FL will also increase. The process of increasing the number of devices in FL is called scalability. Scalability can be accomplished in FL through different methods, such as picking end devices based on computation power and resource optimization. **Quantization:** The process of reducing local trained model data size during the updating process. With the help of this process, we can increase the throughput time, which will eventually increase the conjunction time of FL. **Robustness:** The capability of the central system to provide services under attack or the failure of the central server is called the robustness of the central system. **Sparsification:** The process of the collection of appropriate devices from the available set of devices involved in FL. Throughout the training rounds, only the best suitable set of end devices are allowed in the FL method. Several parameters are involved in selecting the end device, such as unique parameters in available data, size of the selected dataset, computation power, and noisiness in the selected dataset.