esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Phase Deep Learning-Based EDoS Detection System
## 3 Background Knowledge
### Basics of Artificial Neural Network  
In this study, an ANN model is used in the first phase of the proposed two-phase deep learning-based EDoS detection mechanism. The ANN module collects the traffic information during a time period and detects whether an attack is occurring during this time period. An ANN is a biologically inspired form of distributed computation [22] and is composed of simple processing units and connections between them. In this study, we employ classic feed-forward neural networks trained using a back-propagation algorithm.  
A feed-forward neural network has an input layer and an output layer, with one or more hidden layers in between the input and output layers. The ANN functions as follows: each node \(i\) in the input layer has a signal \(x_{i}\) as the network input, multiplied by a weight value \(w_{ij}\) between the input layer and the hidden layer. Each node \(j\) in the hidden layer receives the signal \(aj\) according to this formula:  
\[a(j)=\theta_{j}+\sum_{j=1}^{n}x_{i}w_{ij} \tag{1}\]  
The output \(a_{j}\) of each node \(j\) of the hidden layer is then broadcast to the output layer:  
\[y_{k}=\theta_{k}+\sum_{j=1}^{m}a_{j}w_{ij} \tag{2}\]  
where \(\theta_{j}\), \(\theta_{k}\) are the biases in the hidden layer and output layer, respectively; \(n\) and \(m\) are the numbers of nodes in the hidden layer and output layer. The output of the output layer is passed through a function. The activation could be a sigmoid function for a binary classification problem or a softmax function for a multiple classification. The output obtained from the activation function will be compared with the target. In this study, we used the mean square error as the error function:  
\[E_{m}=\frac{1}{n}\sum_{i=1}^{n}(T_{i}-Y_{i})^{2} \tag{3}\]  
where \(T_{i}\) and \(Y_{i}\) are the target value and output value, respectively. The training process is the process where the ANN model is updated by parameters \(w_{ij}\) to make the error function \(E_{m}\) go toward zero.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle
### Planning Phase  
The planning phase aims to give a first approach to the number and position of the required SRs before deploying the system in the real scenario. For every fingerprinting-based ILS, it is desired to optimize these parameters to achieve better accuracy with the lowest cost possible. This is a task that is being pursued by several authors, but its analysis is not easy because of the complexity of radio propagation indoors and the number of variables to take into account (antenna radiation pattern, scenario model, antenna orientation and position, _etc._). Only a few have dared to give some approaches based on genetic algorithms, but considering simple assumptions and propagation models [56; 57].  
In order to perform the planning phase, a simulation tool and a set of qualitative criteria or helping \"tips\" have been considered. The following planning criteria for the initial positioning of the SRs are listed below:  
Figure 5: Multilayer perceptron: Proposed architecture.  
* Consider valid locations to place the SRs; the antennas usually will be located next to walls/roofs or places where it is possible to fix their position without hindering the normal movement of people.
* A trade-off between accuracy and cost must be taken into account; the more antennas, the greater the accuracy [15; 16; 17; 29; 30], but also, a higher cost is incurred.
* The whole scenario should be connected by at least one SR with 90% (connectivity criterion). Omnidirectional antennas are useful to report wide coverage regions, while directional antennas can be added to give coverage to more specific zones.
* In order to strengthen the accuracy in a specific room/area, directional antennas can be positioned, oriented toward that direction. Thus, a stronger RSS should be captured by that directional antenna when the tag is located in the pointed area, generating more robust RSS fingerprints.
* Antennas must be separated enough to avoid similar RSS measurements.  
These criteria have been extracted empirically from the experimental tests conducted and also from results obtained in previous works [24; 25; 26; 27; 28; 29; 30]. Finally, note that the planning process will be iteratively repeated until the system achieves a suitable performance in the whole scenario under consideration of all of the defined rooms.
# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
## 4 Experiments and Results
### Results
#### 4.2.3 Comparisons between Different SNRs  
In our previous experiments, we set the SNR a 2.5 dB, which meant that the amplitude spectrogram of interference speech was 0.75 times that of the target speech. To evaluate the speech extraction performance under different circumstances, we set SNRs from \(-\)5 dB to 5 dB to test the validation sets and compare speech extraction performance.  
From Table 6, it can be seen that the improvements increased when the noise increased, meaning that the EGG signals performed better for speech extraction in noisy environments. The SDRi and SISDRi values decreased by about 1.5 dB while the SNR value increased by 2.5 dB, which showed that speech extraction performance was better in environments with low SNRs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Training Dataset** & **SDRi (dB)** & **SISDRi (dB)** \\ \hline SpeakerBeam & EMODB & 2.28 & 0.99 \\ Pre\_EGG\_Aux & EMODB & 3.69 & 2.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison between the SDRi and SISDRi on the EMO-DB dataset. The parameters are the average of all test results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FF** & **MM** & **FM** & **MF** \\ \hline SpeakerBeam & 3.12 & 2.46 & 3.55 & 4.58 \\ Pre\_EGG\_Aux & 5.12 & 3.46 & 5.37 & 4.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The SISDRi (dB) on the CDESD. The results are divided into four groups according to the gender of the target speaker and the gender of the interference speaker. The left letter represents the gender of the target and the right letter represents the gender of the interference speaker.  
Figure 11: A column chart of the SDRi for different target speakers using the two methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**SNR (dB)** & \(\mathbf{-5}\) & \(\mathbf{-2.5}\) & \(\mathbf{0}\) & \(\mathbf{2.5}\) & \(\mathbf{5}\) \\ \hline SDR (Mixed Speech) & \(-3.99\) & \(-1.41\) & \(1.07\) & \(3.10\) & \(6.11\) \\ SDR (Prediction) & \(5.98\) & \(6.74\) & \(6.95\) & \(8.87\) & \(11.35\) \\ SISDR (Mixed Speech) & \(-4.00\) & \(-1.41\) & \(1.07\) & \(4.08\) & \(6.11\) \\ SISDR (Prediction) & \(4.70\) & \(6.74\) & \(7.35\) & \(9.45\) & \(11.08\) \\ SDRi & \(9.97\) & \(8.15\) & \(6.95\) & \(5.77\) & \(5.25\) \\ SISDRi & \(8.70\) & \(7.15\) & \(6.29\) & \(5.37\) & \(4.97\) \\ \hline \end{tabular}
\end{table}
Table 6: The SDRi and SISDRi under different SNRs on the CDESD using our Pre_EGG_Aux network.  
Figure 10: A box-plot of the SDRi and SISDRi results for different gender groups using the pre_EGG_Aux method.  
The figures above show the speech extraction results under different SNRs for target speakers and interference speakers of different genders. It can be seen that the Pre_EGG_Aux algorithm performed better under the different SNR circumstances, meaning that our speech extraction method for performed better in different environments.
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 3 Energy-Efficient Reliable Multipath Routing Protocol Using Opportunistic Routing  
In this section, we describe energy-efficient and reliable multipath routing using opportunistic routing to satisfy the required packet delivery ratio in long-hop WSNs. The proposed scheme can be largely categorized as a bifurcation decision, bifurcation transmission, and exceptional handling process.
## 3 Results and Discussion
### Surface Functionalization of Nanostructured Porous Silicon
A strong absorption band appeared in the FTIR spectrum at 1040 cm\({}^{-1}\) assigned to the Si-O-Si asymmetric stretching surface groups, demonstrating the absence of interactions with chitosan NH\({}_{3}\)\({}^{+}\) groups [23]. Furthermore, two absorption peaks were observed at 790 cm\({}^{-1}\) and 1636 cm\({}^{-1}\) corresponding to the N-H stretching band and NH\({}_{3}\)\({}^{+}\) symmetric deformation, respectively. Additionally, a weak peak emerges at 3321 cm\({}^{-1}\) due to NH\({}_{2}\) stretching vibrations superposed on the wide band associated with the O-H groups [23]. Figure 3: Nanostructured porous silicon (nPSi) synthesis and functionalization process. (**A**) nPSi synthesis and oxidation reaction (nPSi-Ox); (**B**) nPSi/Undecylenic acid reaction (nPSi-UAc); (**C**) nPSi-Ox/APTS reaction (nPSi-APTS); (**D**) nPSi-Ox Chitosan reaction (nPSi-Chi) and (**E**) nPSi-ox/\(\beta\)CD reaction (nPSi-\(\beta\)CD). Finally, nPSi layers were also functionalized by a polymerization process using chitosan and \(\beta\)-cyclodextrinrin. The representation of the final structure is shown in Figure 3E. The FTIR spectrum presented a peak at 1721 cm\({}^{-1}\) whose absorption was associated with the C=O stretching vibration of ester and carboxyl groups, which was attributed to the polymerization between \(\beta\)CD and citric acid [23]. In addition, strong absorption bands also appear at 1036 cm\({}^{-1}\) and 1150 cm\({}^{-1}\), in this case attributed to C-OH stretching vibration and C-O-C stretching, respectively [23]. Moreover, a band located at 2930 cm\({}^{-1}\) was described as CH\({}_{2}\) asymmetric stretching vibration band, and the one found at 3293 cm\({}^{-1}\) was associated with O-H stretching groups [23]. The absorption peak located at a shorter wavenumber (790 cm\({}^{-1}\)) is associated with N-H stretching band vibration, as in the nPSi-Chi sample, since this stage is preliminar to the synthesis of nPSi-\(\beta\)CD. Finally, the peak observed at 1630 cm\({}^{-1}\) could represent the molecular water (H\({}_{2}\)O) deformation bands [34]. Hence, these results indicate that the chemical engineering functionalization processes to obtain each nPSi structure with different surface chemistry were successful.
# Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction
## 4 Noble Metal-Based Heterogeneous Catalysts
### Interface Engineering  
In addition to the alloying strategy, interface engineering can modify the geometric structure of catalytic materials, reasonably control the atomic arrangement of the surface or interface, optimize the adsorption capacity of reactants, intermediates, and products and effectively improve the efficiency of electrocatalysis. Based on noble metal nanomaterials, this part will be discussed according to the interfacial geometry of the catalyst. Firstly, the coated core-shell nanostructured precious metal particles are introduced, then some typical 2D structure-supported precious metals are introduced, then 1D/3D structure-supported noble metals are introduced, and finally, special atomic interface materials such as single atoms are introduced. A comparison of the HER properties of different interfacial electrocatalysts listed in this paper is summarized in Table 2. The above hybrid nanostructures can be constructed from different materials, such as metal-metal, metal-oxide/hydroxide, metal-sulfide, a metal-metal organic framework, metal-MXene and metal-carbon.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Catalyst**} & \multirow{2}{*}{**Electrolyte**} & \multicolumn{2}{c}{**Overpotential**} & **Tafel Slope** & \multirow{2}{*}{**Ref.**} \\  & & **(\(\eta_{\rm 10}\)/mV)** & & **(mV-dec\({}^{-1}\))** & \\ \hline Pd@PdPt & 0.5 M H\({}_{2}\)SO\({}_{4}\) & 39 & 38 & [59] \\ PtRu & 0.5 M H\({}_{2}\)SO\({}_{4}\) & 19.7 & 27.2 & [60] \\ H-AgPt NCs a  & 0.5 M H\({}_{2}\)SO\({}_{4}\) & 51 & 40 & [62] \\ PdNi & 0.5 M H\({}_{2}\)SO\({}_{4}\) & 55 & 57 & [72] \\ RInCo ANAs a  & 0.5 M H\({}_{2}\)SO\({}_{4}\) & 12.4 & 30.7 & [77] \\ RuB & 0.5 M H\({}_{2}\)SO\({}_{4}\) & 22 & 30.7 & [82] \\ Pt\({}_{3}\)Ti & 0.1 M HClO\({}_{4}\) & 32.7 & 32.3 & [67] \\ Co-RuIr & 0.1 M HClO\({}_{4}\) & 14 & 31.1 & [76] \\ Au\({}_{33}\)Pt\({}_{67}\) & 0.1 M KOH & 171 & 81 & [61] \\ PtCo & 0.1 M KOH & \(\eta_{20}=76.2\) & 76 & [64] \\ O-Pt\({}_{3}\)Co-NWs a  & 0.1 M KOH & 56.1 & 30 & [81] \\ Pt\({}_{24}\)Cu\({}_{76}\) NFs a  & 0.5 M KOH & 18 & 52 & [66] \\ CoFe-Pt\({}_{1}\)V & 1 M KOH & 18 & 29 & [63] \\ Pd\({}_{3}\)Ru & 1 M KOH & 42 & - & [70] \\ IrFe@NC a  & 1 M KOH & \(\eta_{1000}=850\) & 30 & [73] \\ RuNi & 1 M KOH & 15 & 28 & [78] \\ \hline \end{tabular}
\end{table}
Table 1: The HER Performance of Various Alloyed Electrocatalysts under Different Reaction Conditions.
## 4 Discussion
### Mapping Approach and Uncertainties  
The combination of new methods developed here, including improved stream maps, moderate resolution impervious area maps, and non-parametric modeling allowed for the successful mapping of burial probability for every stream reach within the PRB, overcoming obstacles presented by previous mapping attempts requiring intensive, on-the-ground investigations [3]. There are several instances, however, where error may have been introduced into the burial classification. For example, the improved stream maps of [PERSON] et al. [35] were modeled using training data gathered from forested watersheds as a reference, and using digital elevation models to place streams where they should occur naturally, based on the topographic flow of water across the landscape. These methods may be less accurate in heavily urbanized landscapes, where land cover and topographic relief have been highly altered, and streams may have never existed or been diverted significantly from their original course. In many of these areas, there may be no maps to confirm the pre-development status and location of streams, thereby preventing confirmation of their previous existence, or lack thereof.  
The methods for calculating the probability of burial for stream reaches cannot account for slight variations in stream path caused by natural shift of channels over time, or other co-registration errors between the stream and impervious cover data layers, potentially leading to inaccuracies in the calculation of values for the average level of impervious cover in the neighborhood of each stream pixel, which was used to classify burial potential. Additionally, while creating the training data for classifying burial potential, only burial related to impervious cover was considered. In reality, other forms of burial can also be prevalent across a watershed, including streams routed into pipes, and buried under residential lawns, in agricultural fields [52], or inundated by man-made impoundments. As we were primarily interested in urban-related stream burial, these cases were not classified as being buried, leading to the probability that stream burial is underestimated. Despite these uncertainties, we see robust, repeated patterns in the data.
## 4 Discussion
### Discussion on Influencing Factors of Subspace Morphology  
In the context of mountainous settlements, the natural terrain and topography significantly determine the direction, development model, and overall framework of settlement growth [39]. Taking Xingdi Village as an example, the mountainous areas to the northeast directly influence its development towards the southwest. The demand for relatively flat terrain in new village construction necessitates a site-selection approach for new buildings. Additionally, the orientation of mountain ranges affects the layout of roads within the settlement, thereby impacting its overall spatial structure. This study aims to reveal how topography influences internal subspace morphology in Xingdi Village. The regression re sults indicate that in most areas of Xingdi Village, flatter plots with smaller slopes positively influence the complexity and density of the planar distribution of settlement buildings, with an even greater impact in mountainous regions. Settlement construction and development exhibit a certain reliance on flat areas; modern practices in building, developing, renewing, and preserving settlements are largely dependent on level terrain. Flat land facilitates transportation convenience, ease of construction, and accessibility for utilities--all factors that contribute to enhancing spatial morphology within settlements. In steep terrains, building layouts exhibit higher sensitivity. Flat plots in such areas are relatively scarce and hold significant importance, making building arrangements highly dependent on them. The abundance or scarcity of flat plots can substantially alter the complexity of building layouts. In contrast, in gentler terrains, the widespread availability of flat plots reduces their constraints on building distribution, resulting in a relatively weaker influence on subspace complexity and density. Moreover, the boundary effects of flat plots are more pronounced in steep terrains, where flat plots often exhibit irregular shapes, exerting a greater impact on the spatial organization patterns of building layouts. The limited availability of flat terrains also constrains planning and construction activities, encouraging more rational considerations. The shape of flat plots directly affects the morphology of building layouts, significantly influencing their complexity and spatial density. Regarding slope orientation, traditional units in Jiexiu District predominantly consist of residential structures designed in courtyard styles that benefit from four-directional exposure; thus, north-south slopes have minimal impact on settlement spatial morphology. For elevation, the regression results indicate that in most areas of Xingdi Village, the average elevation of subregions has a positive impact on the complexity and density of building layouts, with a stronger effect in both higher and lower elevation areas. In high-elevation and low-elevation regions, building layouts are more sensitive to changes in elevation, primarily due to transportation and natural factors. In contrast, transitional areas are more influenced by social factors.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Case Studies
#### 4.2.1 Data Sources  
The traffic flow experimental data used in this article came from the PeMS system of the California Department of Transportation, which has used advanced induction coil detection technology to record traffic data for more than 20 years, since 2000, using more than 45,000 sensors in some sections of California. These advanced sensors collect data every 30 s. The authors selected the traffic flow data on a total of 5 days on the main road of a city recorded by 308,512 sensors from 7 October 2019 to 11 October 2019, with a data recording interval of 5 min and a total of 1440 traffic flow data (1152 data from the first 4 days as the training set, and 288 data on the last day as the test set). The authors only studied traffic flow, so some of the data after the redundant information were omitted are shown in Table 1.
# _Sci_
## 4 Discussion
### Civil and Commercial Sector Adoption
In development by the public-private partnership of the European space agency and Airbus is the space-data highway, an \"optical fiber network in the sky\" using geosynchronous orbiting satellites linked to ground stations and transmitting at 1.8 giga-bits per second (that is an uncompressed six gigabyte high-definition movie in twenty-eight seconds, not one truncated for streaming). The Airbus and European space agency's space-data highway has completed in excess of twenty-thousand laser connections in last two or more years, downloading more than a petabyte of data, with a reliability rate exceeding ninety-nine percent [100]. Considering the reliability of the components used, including the source, optical pump, and external modulators and so on, a reliability of 0.9998 over ten years of operation without degradation in space has been achieved [51]. A proposed laser communication between the Moon and Earth system would consist of eight solid lasers, each being one-hundred-twenty-five milliwatts, at eight-hundred-and-ten nanometers with five-hundred to fifteen-hundred microradian divergence. Four of the communication lasers would form one six-hundred mega-bits per second channel, transmitting with right-hand circular polarization, and the other four would form the other channel, transmitting with left-hand circular polarization. The total of 1.2 giga-bits per second would be transmitted [59]. Research and development efforts to bring the proposed idea to fruition will depend strongly on six key factors for optimization of the optical link operating at ten gigabits per second duplex at forty-thousand kilometers distance with a bit error rate of one nano bit error per second [51]. \({}^{13}\)A five-to-eighteen-meter large dish (and associated facility) radio frequency ground segment system costs more than USD one million. A laser transmitter and steering system cost USD fifteen-thousand (e.g., Massachusetts institute of technology's nanosatellite optical downlink experiment, a thirty-centimeter astronomy telescope costing USD forty-thousand [136]. The European space agency's secure and laser communication technology supports the research, development, and evolution of laser communications technologies, and provides flight opportunities for their in-orbit verification [101].