esa-sceva/satcom-chunk-collection huggingface dataset:
# Connectivity Analysis in VANETs with Dynamic Ranges
## 2 System Model
Since we do not consider overtaking scenarios, communicating vehicle, \(T_{1}\) will be slowed down by the big vehicles \(B_{1},B_{2}\) with speeds \(V_{2},V_{3}\) respectively. As \(R_{1}\) is not impeded by any vehicles ahead, it drives away faster where the trailing vehicles follow. As the trailing big vehicle on the adjacent lane approaches, the effect is that the link established earlier is broken by this big vehicle. Now, a new randomized communication range can be derived by coupling both the big vehicle shadowing and the small-scale channel effects and following the limiting traffic inter-vehicle distance distributions. Figure 1: System model for VANET in a two-lane one-direction road section, with safety distance \(s\). (**a**) Free-flow traffic model, no big vehicle shadowing. (**b**) Synchronised-flow traffic model, due to big vehicle shadowing.
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 6 Distributed Resource Allocation  
In the absence of a centralized controller, allocation decisions are made by each base station independently based only on the knowledge of its own cell. This type of allocation does not fully reap the benefits of multi-connectivity. We illustrate this with the following example. Consider a 2-cell system containing cells \(c_{1}\) and \(c_{2}\). There are two PRBs available for allocation in each cell. We denote these as \(P_{1}\) and \(P_{2}\). Cell \(c_{1}\) has four users, \(\{u_{1},u_{2},u_{3},u_{4}\}\) and cell \(c_{2}\) has two users \(\{u_{5},u_{6}\}\). All users are streaming the same multicast content. Assume that user \(u_{1}\) has a good channel only in \(P_{1}\) and can successfully receive content only on \(P_{1}\). Users \(u_{3}\), \(u_{4},u_{5}\), and \(u_{6}\) have a good channel only in \(P_{2}\) and can, therefore, successfully receive content only on \(P_{2}\). User \(u_{2}\) has a good channel in both the PRBs and would be served on either of them. Users \(u_{1},u_{3}\), and \(u_{4}\) are connected to both the cells and can receive content from either of them.  
Let us now look at the allocations that will be conducted by a distributed policy that maximizes the number of users served in each cell independently. Cell \(c_{1}\) considers the users connected to its base station and allocates PRB \(P_{2}\) to the stream because it serves the maximum number of users, namely \(u_{2}\), \(u_{3}\), and \(u_{4}\). Cell \(c_{2}\) also optimizes independently and allocates PRB \(P_{2}\) to the stream to serve users \(u_{3},u_{4},u_{5}\), and \(u_{6}\). Under this allocation, user \(u_{1}\) remains unserved even though it was multi-connected, since it could only receive the content over PRB \(P_{1}\). On the other hand, users \(u_{3}\) and \(u_{4}\) receive content from both the cells. In contrast, a centralized policy would take the users of both the cells under consideration and allocate PRB \(P_{2}\) to the multicast stream in \(c_{2}\) and PRB \(P_{1}\) to the stream in \(c_{1}\) and successfully serve all the users in the system.  
Any centralized allocation policy, even if it is sub-optimal, will always do better in terms of the number of users successfully served than a policy that allocates resources in a distributed manner. A centralized policy does not necessarily mean that the policy is optimizing over the entire system. Any form of centralization that looks beyond just the individual cell will reap better performances than a completely uncoordinated allocation. In the following, we propose a distributed resource allocation algorithm for a MC multicast system that can be used even in the absence of a central controller.
# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 1 Introduction
### Contribution  
We propose an algorithm based on the application of the discrete Fourier transform (DFT) technique that is able to predict the running time of queries for the index service of Web search engines. The proposed running time prediction algorithm is more efficient than alternative algorithms and is general purpose with respect to the document ranking algorithm used to calculate the query results. The advantage of our proposal is that the application of the DFT technique significantly reduces the number of descriptors needed to train the respective machine learning model. We demonstrate its effectiveness for industry standard document ranking strategies such as the TF-IDF and BM25 document score functions executed either under the WAND or the BM-WAND document ranking algorithms.  
A preliminary version of our work was presented in [11] where we described the application of the DFT technique to predict the running time of the BM-WAND algorithm. This provided evidence that our DFT based strategy was able to achieve promising results. In the present paper, we deepen into its design and assessment as follows. We study the expressiveness of the DFT descriptors by considering both WAND and BM-WAND under the BM25 and TF-IDF document scoring strategies. We also present a comprehensive evaluation study involving alternative machine learning methods and comparison against the state of the art strategy for solving the same problem. The outcome of this study defines the final tuning of the parameters and machine learning method required by our prediction algorithm. We also demonstrate its practical utility by considering a use-case in multi-threaded query processing.
## 4 Results and Discussion
### Water Purity Trials  
If slag was used as a slaked lime substitute, another factor must be evaluated: the level of toxins in the remaining water to be released into the environment. In fact, a successful reactant not only increases the pH by neutralizing the acidic content of the wastewater, but also absorbs many dissolved elements that can be harmful for the environment. The results from the ICP-MS tests on the clean waters derived from the second set of AIO, along with the chemical composition of the original neutralized wastewater, and the threshold allowed by the Swedish regulatory laws are presented in Table 4.  
It is clear from the data that the same purification effect is equally achievable with both slag and lime. Around 99% of the Cr, Ni, and Fe dissolved in the wastewater are absorbed both by lime and slag, resulting in final values lower than the permitted ones. Mo is absorbed less than the other elements. Its quantity present in the wastewater is not as high as to begin with, but the absorbing capacity of both reactants is reduced. Despite the fact that there is no threshold regulating the amount of F to be released, it is of interest for future research to evaluate how much of the element is trapped in the reactant. In fact, OTK recirculates their sediments as slag builders, and CaF\({}_{2}\) is a key compound  
Figure 7: Results of AIO performed with O1 (**a**) and S1 (**b**).  
to ensure their usage in the furnace because it decreases the viscosity of the slag and facilitates the reactions between it and the steel bath.  
In addition, it is worth noting how important it is for circularity and sustainable production to recirculate the sediments in the steelmaking process. Not only is waste prevented by finding an internal use of the materials previously discarded, but they also limit material losses by capturing precious alloying elements.
# A Novel SIW Leaky-Wave Antenna for Continuous Beam Scanning from Backward to Forward
###### Abstract
In this paper, a novel unit cell is proposed for open stopband-suppressing of the SIW periodic LWA based on the impedance matching technique. The proposed unit cell consists of the longitudinal slot and the rectangular-shaped H-plane discontinuity. The rectangular-shaped H-plane discontinuity is one of the most commonly utilized waveguide junctions in waveguide filters and multiplexers design, which exhibits inductive behavior. The inductive nature of the rectangular-shaped H-plane discontinuity is used to compensate for the capacitive effect of the longitude slot and provide proper impedance matching. The circuit equivalent and dispersion analysis are performed to verify the validity of the proposed unit cell in providing the impedance matching and suppressing the OSB. For the experimental verification, a prototype of the proposed LWA array is fabricated. The experimental results confirm that an adequate level of agreement exists between measurement and simulation. Results show that the proposed antenna provides continuous beam scanning capability between ranges of \(-\)60 and +57.5, involving broadside direction with a maximum gain of 16 dBi.
## 3 Results
### The Lateral Gradient Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**ESV of 1 km**} & \multicolumn{3}{c}{**ESV of 2 km**} & \multicolumn{3}{c}{**ESV of 3 km**} & \multicolumn{3}{c}{**ESV of 4 km**} & \multicolumn{3}{c}{**ESV of 6 km**} & \multicolumn{3}{c}{**ESV of 7 km**} & \multicolumn{3}{c}{**ESV of 8 km**} & \multicolumn{3}{c}{**ESV of 8 km**} & \multicolumn{3}{c}{**ESV of 8 km**} & \multicolumn{3}{c}{**ESV of 9 km**} & \multicolumn{3}{c}{**ESV of 10 km**} \\
**Distance/CNY** & **Distance/CNY** & **Distance/CNY** & **Distance/CNY** & **Distance/CNY** & **Distance/CNY** & **Distance/CNY** & **Distance/CNY** & **Distance/CNY** & **Gradient/CNY** \\
**107** & **10** & **10** & **10** & **10** & **10** & **10** & **10** & **10** & **10** & **10** & **10** \\ \hline
**Grassland ecosystems** & 4.7 & 4.4 & 6.0 & 6.1 & 7.2 & 7.0 & 9.4 & 5.1 & 6.3 & 5.5 \\
**Scrub ecosystems** & 9032.4 & 30,038.9 & 36,041.8 & 37,468.3 & 39,581.2 & 41,659.7 & 42,675.3 & 40,999.8 & 41,572.1 & 41,244.5 \\
**Broad-leaved forest** & 26,667.4 & 59,654.1 & 67,788.0 & 68,334.9 & 69,998.7 & 70,345.7 & 78,040.7 & 81,837.1 & 83,906.7 & 84,468.3 \\
**Artificial wetland** & 74,637.3 & 50,942.2 & 42,485.5 & 41,363.9 & 35,708.0 & 31,485.4 & 29,107.8 & 28,330.7 & 23,088.1 & 24,302.7 \\
**Natural wetland** & 42,016.4 & 1064.0 & 874.0 & 6451.1 & 664.1 & 742.0 & 72.0 & 507.7 & 47.18 & 541.0 \\
**Paddy field ecosystems** & 19,060.0 & 22,907.1 & 23,331.7 & 20,277.6 & 19,483.3 & 19,807.8 & 18,107.5 & 17,071.3 & 16,027.7 & 15,082.5 \\
**Water bodies ecosystems** & 785,207.2 & 11,577.8 & 4244.6 & 12,741.4 & 14,251.4 & 15,109.9 & 12,483.0 & 10,530.8 & 12,507.4 & 12,620.7 \\
**Ceniferous forest** & 7092.9 & 13,304.8 & 12,479.6 & 11,717.7 & 10,884.8 & 10,392.1 & 10,857.1 & 10,874.8 & 10,298.6 & 9031.6 \\
**Total** & 922,966.9 & 189,794.1 & 184,764.2 & 19,881.8 & 190,661.5 & 19,825.7 & 19,979.2 & 19,228.3 & 10,798.7 & 187,596.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The revised lateral gradient table of the ecosystem service value in the water-land ecotone of the Guangdong section of the Xijiang River. Unit: CNY 10\({}^{4}\).
## 3 Optimization Framework
### Formulation of the Optimization Problem
The aerodynamic efficiency, mass flow, pressure ratio, and the number of blades are normalized by Equation (8):  
\[ \begin{array}{l}\min:\quad\left\{1/\eta_{1}^{*},1/\eta_{2}^{*},n_{1}^{*},n_{2 }^{*}\right\}\\ find:\quad X_{s},G_{e},J_{h},Y_{z},R_{c}\\ \left\{\begin{array}{l}X_{l}^{l}\leq X_{s}\leq X_{u}^{u}\quad G_{l}^{l}\leq G _{e}\leq G_{e}^{u}\\ J_{h}^{l}\leq J_{h}\leq J_{h}^{u}\quad Y_{l}^{l}\leq Y_{z}\leq Y_{z}^{u}\\ R_{c}^{l}\leq R_{c}\leq R_{c}^{u}\quad 0\leq a\leq 15\\ 0.98m_{ref}^{*}\leq m_{1}^{*}\leq 1.03m_{ref}^{*}\\ 0.90m_{ref}^{*}\leq m_{2}^{*}\leq 0.95m_{ref}^{*}\\ 0.98m_{ref}^{*}\leq\tau_{1}^{*}\leq 1.02m_{ref}^{*}\\ 0.75m_{ref}^{*}\leq\tau_{2}^{*}\leq 0.79m_{ref}^{*}\\ \delta_{1}\geq 10\%\quad\delta_{2}\geq 10\%\\ 150\leq\sigma_{\text{blade}}\leq 200\\ 300\leq\sigma_{\text{disc}}\leq 331\end{array}\right.,\end{array}  \tag{7}\]  
\[ \begin{array}{l}{\eta_{1}}^{*}={\eta_{1}}/{\eta_{1}}^{(0)},\ {\eta_{2}}^{*}={\eta_{2}}/{\eta_{1}}^{(0)}\\ {m_{1}}^{*}={m_{1}}/{m_{1}}^{(0)},\ {m_{2}}^{*}={m_{2}}/{m_{1}}^{(0)}\\ {\pi_{1}}^{*}={\pi_{1}}/{\pi_{1}}^{(0)},\ {\pi_{2}}^{*}={\pi_{2}}/{\pi_{1}}^{(0)} \\ {n_{1}}^{*}={n_{1}}/{n_{1}}^{(0)},\ {n_{2}}^{*}={n_{2}}/{n_{2}}^{(0)}\end{array} , \tag{8}\]  
where \(\eta_{1}\) and \(\eta_{2}\) represent aerodynamic efficiency at maximum speed state and cruise state, respectively; \(n_{1}\) and \(n_{2}\) represent the number of blades of the impeller and radial diffuser, respectively; \(m_{1}\) and \(m_{2}\) represent the mass flow at maximum speed state and cruise state, respectively, kg/s; \(\pi_{1}\) and \(\pi_{2}\) represent the pressure ratio at the maximum speed state and cruise state, respectively; the superscript \(l\) denotes the lower limit, \(u\) denotes the upper limit; the superscript _ref_ denotes the reference value; the superscript (0) denotes the value before optimization; the superscript * denotes the normalized value; \(\alpha\) is exit lean angle; \(\delta_{1}\) and \(\delta_{2}\) represent the vibration margin at maximum speed state and cruise state, respectively; \(\sigma_{\text{blade}}\) and \(\sigma_{\text{disc}}\) represent the maximum von Mises of the impeller blade and disc, respectively, MPa.
## 4 Numerical Results
### Results and Discussion
#### 4.3.6 Evaluation of Multiple Secondary Users on Different Datasets  
To demonstrate the generalizability and effectiveness of the proposed distributed source camera identification method, it was essential to evaluate its performance across a variety of datasets. Each dataset represents different shooting conditions and camera models, reflecting the diversity encountered in real-world scenarios. By conducting experiments on original images from four distinct datasets (i.e., Dresden, Alaska, Forchheim, and Vision), we aimed to validate the robustness and adaptability of the method across different environments. Table 4 presents the experimental results comparing our proposed distributed source camera identification scheme with the method by [PERSON] et al. across six distinct datasets. The results demonstrate that our method significantly outperformed [PERSON] et al.'s approach on all datasets. Specifically, our approach achieved higher accuracy rates, with values of 0.9822 on the Dresden dataset, 0.9793 on the ALASKA dataset, 0.9886 on the SOCRatES dataset, 0.9925 on the Forchheim dataset, 0.9750 on the SIHDR dataset, and 0.9999 on the VISION dataset. In contrast, [PERSON] et al.'s method showed lower accuracy across these datasets, particularly on the SOCRatES and SIHDR datasets, where the accuracy values were 0.2614 and 0.45, respectively. The results in Table 4 illustrate the robustness and superior performance of our method across a diverse range of datasets and challenging image conditions.
## 2 Methodology
### Patient-Specific Method  
To enhance personalized medicine, this novel method identifies the optimal architecture for the LSTM model for each patient. The methodology explores various configurations of LSTM architectures for each patient through an iterative process. The patient-specific selection process is illustrated in Algorithm 1, summarizing the steps necessary to identify the best LSTM architecture for each of the 70 patients.  
```
1:Initialize bestMSE = \(\infty\)
2:Define numLayers = [2, 3]
3:Define numNeurons = [10, 50, 75, 100]
4:Define optimizers = ['adam','sgdm', 'rmsprop']
5:Define stateActivations = {'tanh','softsign'}
6:Define gateActivations = {'hard-sigmoid','sigmoid'}
7:Define minibatchSizes = [16, 32, 64]
8:for numLayer in numLayers do
9:for numNeuron in numNeurons do
10:for optimizer in optimizers do
11:for stateActivation in stateActivations do
12:for gateActivation in gateActivations do
13:for minibatchSize in minibatchSizes do
14:Build and train LSTM model with current hyperparameters
15:Evaluate model performance and calculate MSE
16:if MSE <bestMSE then
17: bestMSE = MSE
18: bestModel = current model architecture
19:endif
20:endfor
21:endfor
22:endfor
23:endfor
24:endfor
25:endfor
26:Output bestModel and bestMSE
```  
**Algorithm 1** Best LSTM architecture search  
The algorithm execution consists of the following loops: The loop on line 8 varies the number of LSTM layers, and the number of LSTM layers is set between 2 and 3 to prevent overfitting or underfitting of the model; the loop on line 9 adjusts the number of neurons within each layer; the loop on line 10 explores different optimizers, including adam, sgdm,and rmsprop; the loops on (lines 11 and 12 iterate over activation functions for the state and gate, respectively; the loop on line 13 varies the mini-batch sizes. Within each run of the loops, a model is built, trained, and evaluated by calculating the MSE. If the MSE of the current model is lower than the best MSE recorded so far, the model architecture is updated to the new best. The design of the loops reflects an intensive strategy, enabling the algorithm to evaluate a wide range of architectural configurations. This algorithm is optimum seeking but is computationally intensive, requiring 130 h and 20160 iterations to identify the best architecture for each of the 70 patients.
# Parallel Extreme Learning Machines Based on Frequency Multiplexing
## 1 Introduction
The hidden neurons are measured through a photodiode, which records intensities and thus introduces a quadratic readout nonlinearity (since the information was originally encoded in light amplitudes). The comb line intensities are read one by one and memorized on a computer, where optimal output weights are evaluated, and the output of the network is calculated. The system also allows for the hidden layer to be optically multiplied by output weights, applying the proper optical attenuation to each comb line. In our previous work [19], we already mentioned the parallelization potentialities of our frequency multiplexing ELM. The parallelizability is an interesting feature and is currently exploited both by software and hardware implementations of neural networks to address the need for more efficient computations (for example, see [23] about the parallelization of software ELMs and [24; 25] about the parallelization of photonic RCs). In the present work, we investigate these parallelization capabilities, presenting a photonic system in which multiple frequency combs copropagate. We describe two possible operating modes for such system: either each comb can be employed to perform a different computation, which corresponds to running multiple ELMs on the same substrate, or the combs can be interpreted as different parts of the same neuron layer, which corresponds to executing a single ELM with an increased number of neurons with respect to our previous implementation. In Section 2 we describe the experimental setup, provide a description of it in terms of electric fields, and describe how periodic phase modulation can generate frequency combs and make comb lines interfere. In Section 3 we describe the two ways in which we operate the experiment to execute a single ELM algorithm or multiple ELMs in parallel. In Section 4 we present experimental results on the system performances. In Section 5 we report our conclusions.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Pond Objects Filtering (Post-Processing)
#### 3.4.2 Filtering of Natural Water Bodies  
As a final post-processing step, we applied an overlap analysis between the pond aquaculture mapping result and natural water body dataset. The overlap analysis on the basis of the globally available OSM dataset allows for the removal of even compact lakes and rectangular river segments (e.g., river section in between two bridges) that cannot be detected via geometrical parameters. For all countries in the study region, we acquired and merged all OSM country datasets (accessed via [[http://download.geofabrik.de/](http://download.geofabrik.de/)]([http://download.geofabrik.de/](http://download.geofabrik.de/)), accessed on 8 May 2020) and filtered for all relevant water data (OSM keys \"natural = water\" and \"landuse = reservoir\", waterway = \"), which are available in polygon (water) and polyline (waterway) format. The resulting OSM overlap percentage with the aquaculture pond dataset was calculated, and all polygons with more than 20 percent coverage were excluded.
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 4 Experiments and Results
### Ablation Studies  
Many ablation experiments have been done in view of the innovation in this paper. The results are shown in Tables 2-4. In the baseline network, the BEV map had six channels, as seen in References [6; 7], where the feature extractor and refinement network adopted a traditional FPN network and fully connected network without loss weight masks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Runtime**} & \multicolumn{3}{c}{**AP\({}_{\text{3D}}\) (\%)**} & \multicolumn{3}{c}{**AP\({}_{\text{BEV}}\) (\%)**} & \multicolumn{3}{c}{**AHS (\%)**} \\ \cline{3-10}  & & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline MV3D & 0.36 s & 71.29 & 62.68 & 56.56 & 86.55 & 78.10 & 76.67 & 52.74 & 43.75 & 39.86 \\ F-PointNet & 0.17 s & 83.76 & 70.92 & 63.65 & 88.16 & 84.02 & 76.44 & - & - & - \\ AVOD-FPN & 0.1 s & 84.41 & 74.44 & 68.56 & 89.72 & 86.85 & 79.69 & 84.19 & 74.11 & 68.28 \\ Our FDCA & 0.11 s & 85.38 & 76.82 & 69.34 & 89.76 & 87.05 & 79.88 & 85.28 & 76.57 & 69.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance comparison of our method and other methods using the KITTI validation set. AHS: average heading similarity, AP: average precision, Mod.: moderate.