esa-sceva/satcom-chunk-collection huggingface dataset:
# Determining the Safety Level of State Roads: An Italian Case Study
## 5 Case Study
### Calibration  
#### 5.3.1 Allocation of Observed Crashes (\(N_{obs}\)) to Homogeneous Segments  
To calibrate the predictive HSM model, it is necessary to collect data on crashes that occurred on the homogeneous segments identified above. In particular, the number of observed crashes is reported for each year of the study period (from 2015 to 2017). Table 4 synthetizes the total number of observed crashes for all investigated segments, but it is worth noting that each crash should be localized to the specific segment where it occurred. This step is essential to carrying out the calibration, which is based on a segment-by-segment comparison between the number of observed crashes and the number of non-calibrated predicted crashes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Observed Crashes with Injuries and Fatalities 1**} \\
**Year** & **Crashes (\(N_{obs}\))** & **Injuries** & **Fatalities** \\ \hline
2015 & 94 & 166 & 5 \\
2016 & 75 & 129 & 1 \\
2017 & 107 & 173 & 2 \\ \hline Total (3 years) & 276 & 468 & 8 \\ Average (1 year) & 92 & 156 & 3 \\ \hline \hline \end{tabular}  
* Total number of observed crashes, injuries, and fatalities calculated for 285 segments.  
\end{table}
Table 4: Summary statistics of crash data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Severity Level** & \(SPF\)**—Safety Performance Function** & \(N_{SPF}\)**1** [Crashes/Year]** & \(N_{pred,n.c.}\)1** [Crashes/Year]** \\ \hline All & \(N_{SPF}=AADT\)-\(L\) 365-\(10^{-6}\) \(e^{(-0.312)}\) & 375.38 & 500.73 \\ \hline KABC & \(N_{SPF}=exp[-9.006+0.977\cdot ln(AADT)+ln(L)]\) & 137.78 & 183.85 \\ \hline KAB & \(N_{SPF}=exp[-8.499+0.852\cdot ln(AADT)+ln(L)]\) & 67.88 & 90.71 \\ \hline \hline \end{tabular}  
* Total number of predicted crashes calculated for 285 segments.  
\end{table}
Table 3: \(SPF_{s}\) considered in the study and relative value of predicted crashes in ‘base’ conditions and non-calibrated predicted crashes.  
By comparing the average number of observed crashes per year (92 crashes/year) with the number of non-calibrated predicted crashes obtained for the different \(SPF_{\text{s}}\) considered in Table 3, it is clear that the most appropriate \(SPF\) is KAB (90.71 crashes/year). In fact, a closer value of the non-calibrated predicted crashes to the observed ones allows for a calibration factor closer to one, which makes a model more adherent to reality. Therefore, in the following section, only KAB \(SPF\) is accounted for the calibration step. In addition, by excluding the \"C\" severity level, which refers to possible injury, the crash prediction is more consistent with the Italian accident record system (only accounting for crashes with injuries and fatalities).
## 3 Hardware Architecture
### Mobile Receiver Unit
#### 3.3.1 Modified DVB-T USB Dongle  
Since the beacons transmit signals at different frequencies, a power density spectrum of the received signal has to be computed to determine the RSS values and to identify the corresponding beacons. In order to compute the spectrum, a low-cost DVB-T USB dongle capable of software-defined radio (SDR) is used. This is the main contribution to bring the system to the \(\mu\)AUV domain. In the original work [3; 4; 5; 6; 7], a full-fledged spectrum analyzer is deployed. Instead, the NooElec(tm) NESDR Mini DVB-T device (approximately USD 25) is chosen in this work and is depicted in Figure 12. It processes signal sequences received through an antenna within a range of 24 to 1700 MHz. After demodulation and analog digital conversion (ADC), it transmits them via USB interface to the SBC. The core elements of the DVB-T dongle are the tuner and the demodulator. The integrated circuit tuner used in this work is an R820 chip. It receives analog EM signals, amplifies them and performs bandpass filtering. Afterwards, it down-converts the signal to a lower intermediate frequency (sub-sampling). This allows the subsequent eight-bit-ADC to sample at much lower sampling rates than the carrier frequency of the incoming analog RF signal. The demodulator, a RTL2832U chip, contains the ADC and encodes the signal to I/Q samples via coded orthogonal frequency-division multiplexing (COFDM). The I/Q samples are then processed by the SBC for spectrum analysis and RSS estimation. The maximum sample rate of the demodulator amounts to 3.2 MS/s. However, in order to avoid sample dropping and due to USB 2.0 data transfer restrictions, the sample rate is set to 2.4 MS/s.
###### Abstract  
The transportation sector is sharply shifting towards electric vehicles (EVs) to reduce environmental issues and the energy crisis. To enhance the driving range and performance of EVs, the integral parts of EVs are being developed with higher energy densities and compact structures. Traction inverters are one of the important parts of EVs which are continuously updating to higher power densities with smaller sizes. This has led to issues of high heat generation, which causes the performance degradation and failure of traction inverters. Therefore, an efficient cooling strategy needs to be proposed for the effective thermal management of traction inverters in EVs. In the present work, the magnetohydrodynamics (MHD) pump-based cooling system is developed for the thermal management of traction inverter for EVs. The cooling performance of traction inverters is investigated using a MHD pump-based cooling system with water and ferrofluid as coolants. The outlet velocity, inverter maximum temperature, and Nusselt number are numerically simulated as the cooling performance characteristics for various operating conditions of inlet velocity, magnetic field intensity, voltage, and volume fraction of ferrofluid. The coupled numerical model is developed using COMSOL Multiphysics commercial software to simulate the cooling performance of a traction inverter with an MHD pump-based cooling system under various conditions. The MHD pump improves the cooling performance of a traction inverter for ferrofluid cooling over water cooling. The cooling performance of the traction inverter improves with an increase in inlet velocity for both water and ferrofluid cooling. However, with an increase in voltage, magnetic field intensity, and volume fraction, the cooling performance of the traction inverter improves only for ferrofluid cooling. The outlet velocity, inverter maximum temperature and Nusselt number in the case of water cooling are 4.03 mm/s and 7.02 mm/s, 49.65 \({}^{\circ}\)C, respectively, whereas that in the case of ferrofluid cooling are 40.96 \({}^{\circ}\)C, 15.35, and 18.49, respectively. Further, the cooling performance improves for ferrofluid cooling at a magnetic field intensity of 0.4 T and volume fraction of 10% with outlet velocity, inverter maximum temperature, and Nusselt number approach to 12.08 mm/s, 32 \({}^{\circ}\)C and 21.43, respectively.  
cooling system; cooling performance; electric vehicle; MHD pump; traction inverter +
Footnote †: journal: Applied
# Recent Advances in Brillouin Optical Correlation-Domain Reflectometry
## 3 Various Configurations of BOCDR  
Since the proposal of BOCDR, numerous special configurations have been developed to enhance its performance. For instance, in order to mitigate the trade-off relationship between the spatial resolution and the measurement range, a temporal gating scheme [42] and a double modulation scheme [43] were developed. In the temporal gating scheme (Figure 4a), the measurement range was extended while maintaining the spatial resolution by exploiting relatively long optical pulses generated using intensity modulators (IMs). Then, we can arbitrarily select a single correlation peak from multiple correlation peaks located in the FUT. In contrast, in the double modulation scheme (Figure 4b), by modulating the optical frequency at two different frequencies simultaneously, the spatial resolution is given by the higher frequency while the measurement range is given by the lower frequency. These techniques can be simultaneously employed.  
Regarding the polarization, BOCDR was found to have an ability to measure the beat length distribution [44], while the polarization-dependent fluctuations of the Brillouin signals were suppressed by polarization scrambling [45; 46]. Some specialty fibers, such as tellurite fibers [38; 40], bismuth-oxide fibers [47], and polymer fibers [48], were used in BOCDR to make the most of their special features (high Brillouin gain coefficients, high flexibility, etc.). The influence of a ghost correlation peak was investigated and its suppression was shown to be extremely important [49]. Some simple configurations of BOCDR were developed to further reduce the system cost [50; 51; 52]. An apodization scheme based on synchronous intensity modulation [53] and a lock-in detection scheme [54] were also performed to suppress the noise floor and to enhance the strain dynamic range, etc. Finally, to achieve high-speed operation (the sampling rate was limited to <20 Hz in  
Figure 4: Special BOCDR configurations with extended measurement ranges. (**a**) Temporal gating scheme and (**b**) double modulation scheme. AC, alternating current; DAQ, data acquisition; DC, direct current; DFB-LD, distributed-feedback laser diode; EDFA, erbium-doped fiber amplifier; ESA, electrical spectrum analyzer; FUT, fiber under test; GPIB, general-purpose interface bus; IM, intensity modulator; PC, polarization controller; PD, photodiode; PSCR, polarization scrambler. [42; 43]. Copyright (2009,2010) OSA.  
Figure 3: Comparison of the operating principles of BOCDA and BOCDR.  
the standard configuration [55]), phase-detected BOCDR [56; 57; 58] and slope-assisted BOCDR were developed. In both schemes, the idea for high-speed operation is the same; namely, the omission of the frequency-scanning process for acquiring the BGS. In phase-detected BOCDR, the BGS is converted from a frequency domain into a time domain and approximated by a one-period sinusoidal waveform, the phase of which is detected at a high speed. The slope-assisted BOCDR is the main focus of this paper and will be detailed in the following section.
## 4 Methodology and Testing Process
### Baseline: Pix2 pixHD  
To evaluate whether the SD model-based method offers advantages over the GAN model-based method, this paper conducted a comparative experiment. The pix2 pixHD model, a widely used and effective GAN-based model, is capable of generating images with a maximum resolution of 2048 \(\times\) 1024. To ensure a fair comparison, this experiment used the same small sample dataset for training the pix2 pixHD model.  
The test data was derived from real floor plans. We selected the best results from pix2 pixHD training for comparison. Table 6 shows five results generated by the SD model and the pix2 pixHD model, along with their corresponding real floor plans. The qualitative evaluation of the results is based on the following three aspects:  
1. Reasonableness of Functional Layout: Both models can generate relatively reasonable functional layouts. Pix2 pixHD tends to conform closely to the training dataset, with few instances of layouts not present in the training data. The SD model, however, can control parameters such as iteration steps, denoising strength, and random seeds to generate reasonable layouts.
2. Generality of Functional Layout: Since pix2 pixHD does not have extensive parameter inputs for generation, its generality is mainly reflected in generating different layouts under varying boundary conditions. The SD model, on the other hand, can generate desirable layouts under the same boundary conditions by controlling parameters and can produce a wide variety of derivative floor plans based on the existing dataset by controlling the random seed, offering more options.
3. Image Quality of Generated Floor Plans: This metric mainly reflects the clarity of the generated image boundaries and the accuracy of the color values for each functional block. If both values are ideal, the generated result can facilitate operations such as region recognition. From the generated results, the pix2 pixHD model trained under  
Figure 13: Image-to-Image + ControlNet, change the seed.  
the same conditions as the SD model cannot produce very clear and straight functional block boundaries, although the color values for each block are generally correct. The SD model, however, can produce results with both clear functional block boundaries and accurate color values, making the generated images more suitable for subsequent processing and analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & \multicolumn{1}{c}{**Output**} \\ \hline
**SD + LoRA** & **Pix2 PixHD (GAN)** & **Dataset Simple** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test two deep learning models using a real-world planar validation set.  
By comparing these aspects, it can be inferred that the SD model-based method shows certain advantages over the GAN model-based method, particularly in the clarity and accuracy of the generated images and the versatility in generating diverse layouts.
# The Equivalence between Successive Approximations and
## 6 Numerical Validation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Proc. Time (ms)** & **Iterations** & **Losses (p.u)** \\ \hline \multicolumn{4}{c}{**Test system composed of 33 buses**} \\ \hline GS & 441.973960 & 2313 & 2.109785 \\ AG (\(\alpha=1.82\)) & 38.55403 & 227 & 2.109785 \\ NR & 10.751203 & 5 & 2.109785 \\ LM & 10.881656 & 5 & 2.109785 \\ MBF & 1.322962 & 10 & 2.109785 \\
**SA** & 0.518957 & 10 & 2.109785 \\ \hline \hline \multicolumn{4}{c}{**Test system composed of 69 buses**} \\ \hline GS & 31107.756292 & 49031 & 2.421523 \\ AG (\(\alpha=1.92\)) & 1662.690792 & 2455 & 2.421523 \\ NR & 38.303088 & 5 & 2.421523 \\ LM & 42.719055 & 5 & 2.421523 \\ MBF & 5.369374 & 10 & 2.421523 \\ SA & 2.488095 & 10 & 2.421523 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Load flow results for both test feeders. Successive approximations (SA); Newton–Raphson (NR); Gauss–Seidel (GS); matricial backward/forward (MBF); and Levenberg–Marquardt (LM). Note that the behavior of the convergence error in the 69-node test feeder listed in Table 2 confirms that the SA and MBF are completely equivalent load flow methods, as their recursive formulas have exactly the same numerical performance. Finally, Figure 2 presents the voltage profiles of both test feeders when the load flow is solved with the SA load flow method. These are completely equal for each one of the comparative methods listed in Table 1, as the convergence error of \(1\times 10^{-10}\) is appropriate for each of them.
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 3 DSLMS Adaptive Filter
### Algorithmic Principles
\(d_{j}\) denotes the actual signal, the superposition of the desired output signal and the noise signal. \(y_{j}\) is the filter's actual output signal. \(s(n)\) denotes the desired output signal. \(v_{0}(n)\) is the noise signal. \(v_{1}(n)\) represents the reference signal. \(w^{T}(n)\) is a transposition of the filter weight vector. \[w(n+1)=w(n)+2\mu\mathrm{e}\gamma\mathrm{e}\gamma \tag{11}\]  
where \(w(n+1)\) denotes the weight vector at the next moment. \(w(n)\) is the weight vector at the current moment. \(\mu\) is the step factor, a positive constant that controls the speed of weight adjustment. \[\mu(n)= \begin{cases}p,&n\leq N_{0}\\ \beta\Bigg{(}\frac{1}{\alpha E(n)e(n-1)^{2}+1}\Bigg{)}+\gamma,n>N_{0}\end{cases}  \tag{12}\]
## 6 Conclusions  
This work focused on hierarchical routing in WSNs through the use of a new enhanced reactive protocol, named SHPER, which aims at the achievement of power conservation through energy efficient routing.  
Contrary to other popular routing protocols, SHPER performs the cluster leadership in a non randomized way, but by taking into account the residual energy of nodes. Thus, energy balance is achieved and the power depletion among the nodes is performed in a more even way.  
Moreover, the data routing is based on a route selection policy which takes into consideration both the energy reserves of the nodes and the communication cost associated with the potential paths. A consequence of this is that optimal routes are selected and significant power conservation is achieved.  
Furthermore, many other protocols are based on single hop routing. This kind of routing is impractical in networks deployed over wide regions. In contradiction to this, SHPER adopts both multihop routing and hierarchical architecture. In this way, the network scalability is retained.  
An extended set of simulation tests were performed, comparing SHPER against TEEN, which is considered to be one of the most advanced hierarchical WSN protocols. The simulation comparison of these two protocols took place over several network configurations for various locations of the base station both with and without uniform distribution of node initial energy. Simulation tests compared the two protocols with reference to the number of nodes remaining alive over time, the first node depletion time, the last node depletion time and the average consumption.  
Simulation results showed that SHPER outperforms TEEN concerning all of the energy performance metrics. Moreover, it was shown that the beneficial results offered by the use of SHPER are further increased when a non uniform initial node energy distribution is adopted. This makes SHPER even more preferable in real life applications where imbalance in energy distribution is the common case.
# A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations
## 2 Adaptive Cartesian Grid Generation Method
### Assignment of Newly Generated Grids  
As grid refinement and coarsening progress, newly generated grids are continuously produced. Figure 5 presents two types of these grids (for both refinement and coarsening scenarios). Unlike the geometric adaptivity prior to computation, which only requires assigning location information, topological relationships, and intersection relations to the newly generated grids, the flow-based adaptivity also involves processing the flow field values of the new grids, such as basic flow variables and turbulence quantities. The flow field information of the new grids must first ensure conservation, for example, by employing a simple assignment method. For new grids generated by coarsening, the calculation of the flow field value \(U\) is as follows:  
\[U_{K^{\prime}}=\frac{1}{n}\sum_{i=1}^{n}U_{K_{i}} \tag{7}\]  
Figure 4: Common cases of unreasonable grid distribution in Cartesian grids. (**a**) Hole elements; (**b**) grid level discrepancy.  
In the formula, \(n\) denotes the number of sub-cells. Specifically, within the isotropic Cartesian grid described in this study, \(n\) equals 4 in two dimensions and 8 in three dimensions. Additionally, for newly refined grids, the method to calculate the flow field values is as follows:  
\[U_{K_{i}^{\prime}}=U_{K} \tag{8}\]  
To ensure accuracy and local conservation, [PERSON] and [PERSON] [32] proposed an \(L_{2}\) projection method for determining the flow field information of newly emerged grid cells. Their research provides a detailed derivation process and analyzes its conservation and accuracy. Here, the final expression with second-order accuracy is given; for example, in two dimensions, for calculating the flow field value \(U\) of sub-cells in the refinement scenario, the formula is as follows:  
\[\begin{array}{l}U_{K_{i}^{\prime}}=U_{K}+a_{i}U_{K}^{(1)}+b_{i}U_{K}^{(2)}\\ U_{K}^{(1)}=\left.\frac{\Delta x}{4}\left.\frac{\partial U}{\partial x}\right| _{K^{\prime}}U_{K}^{(2)}=\left.\frac{\Delta y}{4}\left.\frac{\partial U}{ \partial y}\right|_{K}\right.\end{array} \tag{9}\]  
wherein \((a_{i},\,b_{i})\in\{(-1,-1),\,(1,-1),\,(1,1),\,(-1,1)\}\), for \(i=1,\,2,\,3,\,4\), represent the quadrants of the four sub-cells \(K_{1}^{\prime}\) to \(K_{4}^{\prime}\). \(\Delta x\) and \(\Delta y\) represent the size of cell \(K\); while \(\left.\frac{\partial U}{\partial x}\right|_{K}\) and \(\left.\frac{\partial U}{\partial y}\right|_{K}\) signify the gradients of the flow field variable \(U\) at point \(K\). It is apparent that this formulation is easily extendable to three-dimensional scenarios. For the coarsening scenario, the formula for calculating the flow field values of the new grid remains as in Equation (7).
## 6 Experimental Evaluation
### Experimental Setup
#### 6.1.2 Model Architecture  
Each dataset required specific model configurations to achieve optimal performance. Below, we outline the architectures used for each dataset.  
* For MNIST and Fashion-MNIST: a simpler convolutional neural network (CNN) architecture was used:
* Input layer: \(28\times 28\) pixels for MNIST, \(32\times 32\) for Fashion-MNIST.
* Convolutional layers:
* First convolutional layer with 32 filters of size \(3\times 3\) and ReLU activation;
* Second convolutional layer with 64 filters of size \(3\times 3\) and ReLU activation.
* Fully connected layer: dense layer with 128 units and ReLU activation.
* Output layer: 10 units (corresponding to 10 classes) with softmax activation.
* For EMNIST: given the increase in data complexity and the inclusion of both digits and letters, a slightly deeper model with additional convolutional layers was used:
* Input Layer: 28 \(\times\) 28 pixels.
* Convolutional layers:
* First convolutional layer with 32 filters of size 3 \(\times\) 3 and ReLU activation;
* Second convolutional layer with 64 filters of size 3 \(\times\) 3 and ReLU activation;
* Third convolutional layer with 128 filters of size 3 \(\times\) 3 and ReLU activation.
* Fully connected layer: dense layer with 256 units and ReLU activation.
* Output layer: 62 units (corresponding to 62 classes) with softmax activation.
* For CIFAR-10: a deeper CNN architecture was used to handle the complexity of the CIFAR-10 dataset:
* Input Layer: 32 \(\times\) 32 pixels.
* Convolutional layers:
* First convolutional layer with 32 filters of size 3 \(\times\) 3 and ReLU activation;
* Second convolutional layer with 64 filters of size 3 \(\times\) 3 and ReLU activation;
* Third convolutional layer with 128 filters of size 3 \(\times\) 3 and ReLU activation.
* Fully connected layer: dense layer with 512 units and ReLU activation.
* Output layer: 10 units (corresponding to 10 classes) with softmax activation.