esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Verification  
### Constant Radius Cornering Tests  
The validity of the full-vehicle model was investigated firstly by comparing the calculated value of the understeering coefficient, \(K_{us}\), during a simulated constant radius cornering test with the value determined experimentally. The test was performed at a low speed of 10 km/h with a constant steering wheel angle set in such a way as to achieve a turn radius of 30 m. The steering wheel angle, speed, and lateral acceleration were computed (simulation study) and measured (experimental study), and the understeering coefficients, \(K_{us}\), were then calculated using Method 3. The corresponding results are presented in Table 1. As shown, the experimental and simulated values of \(K_{us}\) were 4.68 and 5.15, respectively. The discrepancy between the two values was due most likely to the rigid-body assumption made in the simulation model for the cornering subsystem components, compared to the flexibility of the actual components in the real-world system. However, the simulated value of \(K_{us}\) deviated from the experimental value by less than 10%. Hence, the validity of the full-vehicle analysis model for evaluating the steady-state cornering response of the vehicle was confirmed.  
Figure 5: Schematic illustration of impulse steering test.  
Figure 6: Impulse steering test measurement and analysis process. (FFT: Fast Fourier Transform).  
The validated model was used in a further series of simulations to investigate the roll gradient and yaw rate gain of the vehicle when driving a circular path with a radius of 50 m at constant speeds of 10-80 km/h, corresponding to lateral accelerations in the range of 0.01 G to 0.4 G.
# Chirped-Pulse Amplification in an Echo-Enabled Harmonic-Generation Free-Electron Laser
## 2 Proposed Method
### The Double-Grating Compressor in OPM Geometry  
Figure 1a illustrates the fundamental setup of a grazing-incidence double-grating compressor in the OPM geometry, which comprises two identical plane gratings and mirrors. Figure 1b shows the reflection grating of the compressor in the OPM geometry. The grating equation can be written as [35]  
\[\sin\zeta(\sin\mu+\sin\
u)=m\lambda\sigma, \tag{4}\]  
where \(\sigma\) denotes the groove density, \(\zeta\) is the altitude angle of the incidence rays, and \(\mu\) and \(\
u\) denote the azimuth angles of the incidence and diffracted rays, respectively. The diffraction efficiency of the grating reaches its maximum when the blaze angle \(\delta\) equals \((\mu+\
u)/2\). In order to realize the symmetry of the configuration, the azimuth angles \(\mu\) and \(\
u\) should satisfy the conditions of \(\mu_{2}=\
u_{1}\) and \(\
u_{2}=\mu_{1}\), where the indices represent the first and second gratings, respectively.  
The commercially available reflection gratings in the OPM geometry have a proven peak diffraction efficiency up to 60% for EUV and soft X-rays, which is much higher than the gratings in the classical diffraction mount (CMD) geometry [36]. The total transmission efficiency guarantees the sufficiently high power of the compressed laser pulses.  
The definition of the group delay dispersion (GDD) is given by \(GDD(\omega)=\partial OP(\omega)/c\partial\omega\), where \(OP(\omega)\) denotes the optical path length at frequency \(\omega\). As the optical path increases with the wavelength of incident light in most cases, the introduced GDD in the OPM geometry is always negative. In the case of a narrowband pulse with \(\Delta\omega/\omega<10\%\), specifically in the context of FELs, the GDD is estimated as [35]  
\[GDD=-\frac{Lc}{\omega_{s}^{3}}\left(\frac{2\pi\sigma}{\cos\mu}\right)^{2}, \tag{5}\]  
Figure 1: (**a**) Schematic layout of the CPA-EEHG. G and M represent the gratings and mirrors in the compressor, respectively. (**b**) The reflection grating of the compressor in the OPM geometry.  
where \(L\) is the distance between two gratings and \(\omega_{c}=2\pi c/\lambda\) denotes the central pulse angular frequency.  
After the compressor, the pulse will have its different frequency components separated in the spatial distribution that is perpendicular to the propagation direction. This is called spatial chirp, which may broaden the spot size and degrade the quality of the compressed pulses. For a narrow bandwidth pulse in the FEL, the spatial chirp can be expressed as  
\[SC=\frac{L\sigma}{\cos^{2}\mu}\frac{2\pi c}{\omega_{c}^{2}}\Delta\omega. \tag{6}\]  
As different frequency components of the pulse are focused in different places, the spatial chirp will lead to an asymmetrical broadening of the spot size. Nonetheless, taking the significant geometric demagnification of the FEL beamline into consideration, the influence of this spatial chirp on the pulses is effectively negligible, as observed at FELs operated in EUV and X-rays [35].
## 4 Measurement Strategy and Equipment
### Equipment of the ANC Experiments  
The strategy proposed in Section 3.1 was tested with the experimental setup shown in Figure 7. With the measurement microphone positioned at one out of three investigated points, as shown in Figure 7b, the setup corresponds to the wire connection diagram for the experiments (Figure 3).  
Each monopole source, with outer dimensions of 20 cm \(\times\) 18.5 cm \(\times\) 18.5 cm, includes a 4\({}^{\prime\prime}\)(\(\diameter=101.6\) mm, 4 Ohm) Visaton (Haan, Germany) KT 100 V driver with low resonance frequency, \(f_{\rm S}=37\) Hz, and low force factor, \(Bl=3.43\) T\(\cdot\)m, in an optimal bass-reflex box design [25]. The developed lightweight subwoofers were arranged in two quadrupole configurations for noise control (Figure 8). A computer was used with the developed software to drive the compound source into a Thomann (Burgebrach, Germany) The t.amp TA 600 MK-X 2-channel audio power amplifier, with each channel dedicated to a single dipole. The signal is reversed by 180\({}^{\circ}\) before the input to the second monopole. An active subwoofer loudspeaker system Genelec (Iisalmi, Finland) 7360 APM was driven by a third channel to simulate the primary source. The omnidirectional NTI Audio (Schaan, Liechtenstein) M4260 measurement microphone was used, positioned at the head height of the listener, \(\rm z=1.2\) m. A four-channel, high-resolution USB audio interface, Tascam (Santa Fe Springs, CA, USA) US-4x4 HR, was used for connection between the sound equipment and software.  
Figure 7: The equipment for the ANC experiments: (**a**) noise source, audio amplifier, sound card, and computer; (**b**) compound source at control position (CP) 1, and microphone at measurement point (MP) 1.
## 3 Dataset and Methodology
### Input Parameters
#### 3.1.4 Sediment Transport Parameters  
Dionisos is based on diffusion process that is controlled by several parameters, including diffusion coefficient, water discharge, and slope [4; 7; 47; 48]. Among these, the diffusion coefficient is perhaps the most challenging parameter to determine, as it is calculated based on variants that are difficult to quantify, such as grain shape and size, and accommodation space [48]. The diffusion coefficients used in this model are similar to other models and are listed in Table 1[4; 48; 49; 28].
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Physician/Emergency Room Scheduling
#### 4.2.1 Constraints  
* Supply and Demand Constraints
* Every shift must be assigned a physician [14].
* During the overall planning period, every shift must be performed by exactly one physician [17].
* Workload/Availability Constraints
* A physician cannot work more than one shift per day [14].
* An evening shift can not precede a day shift [14].
* Night shift cannot be assigned alongside another shift [14].
* Vacations must be allowed and allotted [14].
* Preassignments, forbidden assignments, vacations, preferences should be accounted for [17].
* Limits on workload [17].
* Limits on the number of shifts of the same type [17].
* Distribution Constraints
* Physicians should work X hours a week [14].
* Different types of shifts should be fairly distributed amongst staff [14].
* Antagonistic shifts (day vs. night) should be fairly distributed amongst staff [14].
* Distribution of types of shifts [17].
* Ergonomic Constraints
* A limit on the number of shifts of a kind [14].
* A limit to successive workdays [14].
* A limit to the number of consecutive weekend shifts [14].
* A weekend shift cannot precede a Monday shift [14].
* Night shifts are recommended in groups of three consecutive shifts [14].
* A day shift followed by a day shift must then be followed by two days off before another day shift [14].
* Three consecutive night shifts should be followed by three days off [14].
* Length of work sequences [17].
* Patterns of sequences of shifts [17].
* Patterns of sequences of a given length [17].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Methodology** & **Data** \\ \hline [PERSON] et al., 2000 [14] & Multi-objective IP, Branch and Bound & Sacré-Ceur Hospital in Montreal \\ \hline [PERSON] et al., 2002 [1] & Cyclic Mathematical Programming, Tabu search & Montreal-based hospitals \\ \hline [PERSON], [PERSON], and [PERSON] 2002 [16] & Constraint programming, Local Search, Genetic Algorithm & Santa Cabrini Hospital, Côte-des-Neiges \\ \hline [PERSON] et al., 2007 [17] & Constraint programming, Column Generation, Tabu Search, Mathematical Programming & Jewish General Hospital, Charles-Lemoyne Hospital, Santa-Cabrinwe Hospital, Sacré-Ceur Hospital, Côte-des-Neiges \\ \hline [PERSON] et al., 2015 [24] & Mixed integer programming, Poisson Generalized Additive Model & Thunder Bay Regional Health Sciences Centre \\ \hline [PERSON] et al., 2019 [31] & Mixed integer programming & Sacré-Ceur Hospital in Montreal \\ \hline [PERSON], [PERSON], and [PERSON] 2020 [23] & Gaussian Process Regressions & The Hospital for Sick Children, Toronto \\ \hline [PERSON] et al., 2021 [32] & MIP, Column Generation & Ambulatory Cancer Treatment Polytclinic \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physician/Emergency Room Scheduling.
## 3 Comparison of Premiums
### Premium Underestimation for Major Distributions  
It is interesting to analyze if the underestimation condition, as ascertained through Table 2 or Equation (18), is met for the commonest distribution of losses. In particular, we consider the following distributions: Generalized Pareto (GPD), Lognormal, Gamma, Pareto. The main features of those distributions are shown in Table 3, while their skewness and kurtosis are reported in Table 4[29; 30; 31].  
We now examine the underestimation condition for each distribution.  
For the Generalized Pareto distribution, we see from Table 4 that both the skewness and the kurtosis are positive under the same conditions for the existence of finite skewness and kurtosis (the shape parameter must be lower than \(1/3\) and \(1/4\) respectively), so that we fall into the underestimation condition of Table 2.  
Similarly, for the lognormal distribution, we have positive skewness and kurtosis. In particular, the kurtosis is always larger than 3. The second-order premium is an underestimate again.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Skewness** & **Kurtosis** & **Underestimation** \\ \hline + & + & YES \\ - & + & Distribution dependent \\ + & - & Distribution dependent \\ - & - & NO \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conditions for underestimation of the premium.  
For the Gamma distribution, the positivity of the shape parameter \(a\) implies that both the skewness and the kurtosis are positive, ending up again under the underestimation case.  
Finally, for the Pareto distribution, the conditions for the existence of finite skewness and kurtosis guarantee the positive of both central moments and therefore the underestimation.  
We can then conclude that for all these 4 common distributions, stopping at the second order in the premium computation leads us to underestimate the actual premium.
## 7 Conclusions  
1. The reason the milling cutter posture changes from time to time is milling vibration affects the milling cutter trajectory and bias angle. A model for solving the instantaneous cutting boundary of milling cutter tooth edge under vibration was proposed, the results showed that the milling cutter was displaced due to the influence of milling vibration, besides, the instantaneous cutting boundary was not only affected by the milling vibration during the cutting stage of the current cutter, but also related to the instantaneous pose of the adjacent previous cutter tooth.
2. The energy consumption distribution function of instantaneous main cutting force of the cutter teeth was constructed. The analysis results of energy consumption distribution showed that, the variation diversity of the instantaneous cutting speed vector, instantaneous main cutting force vector and instantaneous cutting layer parameters were affected by cutter tooth error and milling vibration. The instantaneous main cutting force energy consumption of each cutter tooth showed obvious differences in the aspects of peak value and changing cycle.
3. A method for identifying the dynamics of the energy consumption of the main cutting force of the milling cutter was proposed. The identification results showed that, the instantaneous energy consumption of main cutting force show dynamic changes mainly in its root mean square, while the kurtosis and changing frequency does not change obviously.
4. The analysis results of the main cutting force energy consumption responses showed that the energy consumption of the instantaneous main cutting force of the milling cutter were sensitive to the process design variables such as the speed of the milling cutter, the feed per tooth and the tooth error. The validation results of the proposed energy consumption model showed that, the relative errors between the calculation and experimental results were less than 20%, which proved the accuracy of the proposed model.  
Conceptualization, B.J. and H.L.; methodology, B.J. and H.L.; software, H.L. and L.F.; validation, L.F. and P.Z.; investigation, H.L.; resources, P.Z.; data curation, H.L. and L.F.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and L.F. and P.Z.; supervision, B.J. and P.Z.; project administration, B.J.; funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript.  
Figure 17: Relative errors of time–frequency parameters of energy consumption distribution of main cutting force of the milling cutter, (**a**) relative errors of energy consumption distribution of scheme 1, (**b**) relative errors of energy consumption distribution of scheme 2.  
**Funding:** This research was funded by National Nature Science Foundation of China, 51875145 and Nature Science Foundation of Heilongjiang Province of China, ZD2020E008.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Two-Wire Power Line Microwave Communication Using Low-Loss Modes
## 1 Background
[21] did not provide a complete expression for the induced current (i.e., the SG mode and continuous spectrum). In a recent publication, an investigation of propagation on an open wire as related to microwave communication systems on power lines was reported [22]. This paper was written in order to clarify the relationship between the discrete SG modal current and the total current since it is the total current that is relevant to the performance of these systems. More specifically, a closed-form solution for the current induced on an isolated infinitely long lossy conductor by a voltage source in series with it or an electric dipole in close proximity to it was found. This model included contributions from the continuous spectrum of radiation modes as well as the low-loss discrete SG mode. While the relative magnitudes of these two components were shown to be sensitive to frequency and wire conductivity, the total current on a lossy conductor is nearly the same as that for a perfect conductor over distances comparable to power line communication repeater spacing. Hence, it was shown to be possible to use a simple expression for the total current from antenna theory to evaluate the performance of these power line communication systems. Further, for observation points not near the source, the current induced by the voltage and dipole sources differs only by a known constant. Dipole sources were considered since voltage sources cannot be inserted into power line conductors. Finally, the advantage of a single-conductor communication channel over a wireless channel using high-gain antennas was quantified. However, it was also noted in [22] that power line conductors are not isolated, and, given the fact that SG mode fields decay slowly in the transverse plane, the influence of parallel conductors could be important. Some recent work has appeared on the topic of the SG mode on a pair of wires, but it did not incorporate the full expression for current, which would include the continuous spectrum [23]. Hence, the impact of a parallel conductor is investigated in this paper. More specifically, a single additional conductor will be assumed parallel to the one that is discussed in [22]. The two-conductor case is considered since its solution can be decomposed into simple common and differential modes leading to additional insight into the solution.
# An Efficient BGV-type Encryption Scheme for IoT Systems
## 2 Background Knowledge
### Principle of BGV  
Let \(p\), \(q\) be two prime numbers and \(p<q\). \(R_{p}\) and \(R_{q}\) are groups formed by \(p\) and \(q\) according to algebra. For any number \(m{\in}R_{p}\), if \(0<m+\epsilon p<q\), we have \([(m+\epsilon p)\) mod \(q]\) mod \(p=m\). For example, let \(p=5\), \(q=19\), and \(m=3\). If \(\epsilon=3\), \(m+\epsilon p=18\). \(18\) mod \(19\) mod \(5=3\). Otherwise, if \(\epsilon=4\), \(m+\epsilon p=23.23\) mod \(19\) mod \(5=4\). If \(\epsilon=-1\), \(m+\epsilon p=-2\). \(-2\) mod \(19\) mod \(5=2\). Let \(m\) be the plaintext and \((m+\epsilon p)\) be the ciphertext. If the operations on the ciphertext do not make the intermediate result exceeding the moduli \(q\), all the errors can be eliminated by moding \(q\) and then moding \(p\). That is because the error term is multiplied by \(p\). In our opinion, the BGV and BGV type schemes construct cryptosystems based on this principle and try their best to control the error growth in computing.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Experimental Setup  
For the purpose of conducting the experiment in line with the requirement for predicting demand, a Python version 3.9 program is run on a Windows 10 operating system. The system used possesses a memory capacity of 8 GB.
# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 2 State of the Art
### Classical Methodologies  
Beyond the two trends mentioned above, other studies have explored statistical machine learning algorithms without employing neural networks. For instance, [PERSON] [37] conducted a comparative analysis of four feature extraction methods for fingerprint classification--orientation map (OM), Gabor feature, minutiae map (MM), and orientation collinearity (OC)--which were combined using a rank-based fusion scheme to improve performance. Using the NIST-4 dataset, the combination of OM with OC surpassed the performance of classifiers employing Gabor filters, achieving a classification accuracy of 97.761%. However, the training time was not reported.  
[PERSON] [38] proposed a hierarchical classifier consisting of five steps, utilizing SVM and K-NN. The process includes (1) distinguishing as many arch fingerprints as possible using complex filters, (2) identifying the largest subset of whorl fingerprints using central points and a ridge-line flow classifier, (3) applying K-NN to classify the two main categories based on OC features and responses from complex filters, (4) distinguishing loops from non-whorl classes using ridge-line flow, and (5) using an SVM for final classification. Tests conducted on the NIST-DB4 dataset reported an accuracy of 95.9%, with a classification time of 4.31 s.  
[PERSON] [1] proposed a rule-based fingerprint classification method using decision trees, relying on singular points and their counts for analysis. For illegible fingerprints, additional features such as Center-to-Delta Flow (CDF) and Balance Arm Flow (BAF) were introduced. The method achieved accuracies of 92.74% and 97.2%, respectively, in classifying four classes without rejection, using three databases from FVC versions (2000, 2002, and 2004). However, processing times were not reported.  
[PERSON] [39] introduced an algorithm for fingerprint classification that combines Curvelet Transform (CT) with Grey-Level Co-occurrence Matrices (GLCMs). First, the original image is divided into five scales to reduce noise. Subsequently, the curvelet transform and GLCMs are calculated at a larger scale, generating 16 texture features based on four GLCMs. A total of 49 features are then derived across the remaining four scales. Finally, the classification process is carried out using the K-NN algorithm, employing the NIST-4 dataset consisting of 4000 fingerprint images. This approach achieves an accuracy of 94.6% in 1.47 s.
# Multispectral Fusion Approach for Traffic Target Detection in Bad Weather
## 3 Methods  
In this part, we introduce the network structure of our proposed dual input faster RCNN (D-F Net). As stated in Figure 2, the D-F Net model is two-step approach model, the same as faster RCNN. The first part is the convolutional base for feature extraction based on ResNet and the RPN. It contains two branches for processing infrared and visible images, respectively. We connect the feature maps of infrared and visible images processed by the basic network on the channel dimension, and then feed this into the RPN to find a list of regions that could contain an object. The second part is an object detector model. The regions from the above step run through the object detector model and generate the class probabilities and offset coordinates for each region.