esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Case Study
\begin{tabular}{c c c} \hline
**Physical Element** & **Standard Description to Achieve Accessibility** & **Yes/No** \\ \hline Platform edge & The edge of the platform will be equipped with a non-slip longitudinal strip and a contrasting color paint, 5 cm wide, designed for people with severe vision impairments. The edge piece will be made of a solid material that prevents chipping and breakage, such as prefabricated concrete or natural stone, and will demarcate a safety zone no less than 30 cm wide. & Yes \\ \hline Intercom & Emergency intercom at a height no greater than 1.2 m. & No \\ \hline Elevator & Elevator with capacity to transport people with reduced mobility. & Not applicable \\ \hline Elevator & Elevator buttons at a height of 0.9–1.2 m with tactile relief, contrasting color, and a Braille system. & Not applicable \\ \hline Handrail inside the elevator. & Not applicable \\ \hline Audible signal of the stop on each floor. & Not applicable \\ \hline Height difference between the elevator and floor of no more than 1 cm. & Not applicable \\ \hline Benches & The benches should not obstruct the boarding and alighting area, nor the accessible route. & Yes \\ \hline The benches must have backrests. & No \\ \hline The benches must have armrests. & No \\ \hline The surface of the benches must be slip-resistant. & Yes \\ \hline The surface of the benches must be horizontal. & Yes \\ \hline Ischial supports & The ischial supports must not obstruct the boarding and alighting area, nor the accessible route. & Not applicable \\ \hline The ischial supports must have a backrest. & Not applicable \\ \hline The surface of the ischial supports must be slip-resistant. & Not applicable \\ \hline The surface of the ischial supports must be horizontal. & Not applicable \\ \hline Shelters and Waiting Areas & The shelter and the waiting area should not be transparent; if they are, horizontal bands of contrasting color should be incorporated. & No \\ \hline The ground-level support of the pillar of a shelter or waiting area & No \\ \hline If a shelter or waiting area is incorporated, its width must be & Yes \\ \hline sufficient to accommodate a wheelchair user. & Yes \\ \hline \end{tabular}   
In the case of boarding and alighting, the most important accessibility standard is the need for a special waiting area and flow markings on the platform. This is because it potentially reduces passenger interaction and improves the LOS, where passengers must wait on the platform beside the train doors to allow passengers to disembark before boarding, which is further studied through the experimentation at the laboratory.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 5 Results
### The Effect of Mixing Encoder Layer \(k\)  
For large-scale pre-trained models, the entire encoder is devoted to feature extraction. Therefore, we experiment with mixing the features from different layers \(k\) of the encoder on Mixer and Mixer-C, and the final experimental results are shown in Figures 5 and 6. When \(k\) is 0, it indicates that the input spectrum is being mixed. It can be observed that the results of mixing intermediate layers are not as good as those of mixing at the input, which may be due to the fact that the feature representations in intermediate layers are focused on different aspects, which confuses the model's learning. However, mixing deeper layer features can also achieve good results, because the later layers, compared to intermediate layers, can encode more acoustic information. Moreover, the deeper features are also closer to the model's decoding output, which can strengthen the connection between the linear enhancement relationships. Compared to Mixer, Mixer-C shows a significant fluctuation in performance across different layers, indicating that the mixing of channel information is more influenced by the form of feature representation. If the feature is not well suited for representing channel information, it may greatly affect the performance of Mixer-C.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \(\alpha\) & 0.2 & 0.5 & 1 & 2 & 5 & 8 \\ \hline Mixer & 39.50 & 37.45 & 37.93 & **36.07** & 37.61 & 38.94 \\ \hline Mixer-C & 35.72 & 36.77 & 36.20 & **35.27** & 37.43 & 36.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: WER (%) of Mixer/Mixer-C under different hyperparameter \(\alpha\).
# A 5G V2X Ecosystem Providing Internet of Vehicles +
Footnote †: This paper is an extended version of our paper published in IEEE Latin-American Conference on Communications (LATINCOM 2018).  
[PERSON]  
1 Department of Electro-Electronics and Computing, Federal Center for Technological Education of Minas Gerais, Contagen 32146-054, Brazil  
2 Department of Computer Science, Pontifical Catholic University of Minas Gerais,  
Belo Horizonte 30535-610, Brazil; [EMAIL_ADDRESS]  
2  
[PERSON]  
1 Department of Electro-Electronics and Computing, Federal Center for Technological Education of Minas Gerais, Contagen 32146-054, Brazil  
2 Department of Computer Science, Pontifical Catholic University of Minas Gerais,  
Belo Horizonte 30535-610, Brazil; [EMAIL_ADDRESS]  
2  
Footnote 1: email: {drenato.duarte-figueiredo.duarte-figueiredo.  
Among the IoV premises, there is a need for efficient management, control, and operationability, besides high capacity and assurance. In the literature, the OpenFlow (OF) protocol was indicated for the Software-Defined Internet-of-Vehicles (SDIoV) architecture as the communication protocol between the control plane and the data plane.  
This paper presents an SDN-based 5G network to support IoV communications. Nowadays, with the accelerating and fostering goals of the 5G development all around the world, new architecture design, testing, and evaluation are real and urgent needs. The high complexity expected from a 5G V2X network architecture together with the imposed challenges for its development and evaluation is this work motivation. The multimedia service delivery, that is a typical non-safety eV2X application, is analyzed. The onboard entertainment services can be managed through an SDN controller and the processing can be performed on a V2X-Server. Then, an evaluation is performed of the packages' delivery rate based on 5G V2V communication scenarios regarding the safety eV2X applications; for example, a collision avoidance system through IoV. A set of vehicle communication simulations with the 5G network, using millimeter wave links, is performed using the ns-3 simulator. As simulation results, the system throughput, the transmission delay, and packet delivery rate are analyzed with different vehicle densities.  
The main contributions of this paper are the following:  
* The 5G SDN-based ecosystem as a basic IoV infrastructure providing multimedia service.
* The simulation of rural and urban scenarios.
* Results comparing urban and rural scenarios.
* Results showing the proposal effectiveness.
* Results comparing this proposal with others.  
The remainder of this paper is organized as follows: Section 2 presents the related works; Section 3 describes relevant definitions and explanations about and for the 5G ecosystem; Section 4 describes the proposed 5G V2X ecosystem; Section 5 presents the adopted methodology; Section 6 discusses the results; and Section 7 concludes the paper.  
Figure 1: IoV through cellular network.
# A Node Density Control Learning Method for the Internet of Things
## 2 Overview of Node Density Control  
In this section we give an overview of node density control. In general, the number of nodes in WSNs is large and the distribution of those nodes is very dense. The nodes are prone to failure when they are affected by environmental control or run out of available battery life. Environmental interference and node failure can easily cause changes in the network topology from its initial deployment. In this case, multiple nodes work at the same time to reduce the working load on any single node. In order to extend the life of a single node, a rotation cycle is usually implemented to reduce energy consumption and balance the energy of nodes. Therefore node density within the network itself needs to be controlled. Therefore, density control is allowing some nodes into a low-power sleep state without sacrificing system performance, while keeping only some nodes as active working nodes. In this way, the density of active nodes in the network can be reduced. A by product of effective node density control is that the redundancy of perceived data can be reduced and wireless communication conflicts and interference can be reduced as well. We also observe a reduction of energy consumption of the whole system. When a work node fails due to a dead battery or other reasons, neighbouring sleep nodes can resume work to replace the failed node and continue to maintain the normal workload of the network. Therefore, a well schemed density control system can effectively prolong the lifetime of a WSN. Its study and effective strategies are crucial to effective WSN and IoT as a whole.
# Systematic Analysis of Wind Resources for Eolic Potential in Bangladesh
## 4 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models and** & **Kutubdia** & **Kutubdia** & **Kutubdia** & **Chittagong** & **Chittagong** \\
**MAPE (\%)** & **(Site 1)** & **(Site 2)** & **(Site 3)** & **Data: [6]** & **Data: ERA5** \\ \hline Exponential & 0.897 & 0.927 & 0.932 & 0.97 & 0.589 \\ Polynomial & 0.881 & 0.892 & 0.904 & 0.974 & 0.597 \\ Physical & 0.899 & 0.936 & 0.938 & 0.973 & 0.606 \\ Exponential & 32.10 & 34.22 & 33.89 & 10.26 & 56.65 \\ Polynomial & 23.31 & 26.15 & 25.75 & 7.690 & 52.05 \\ Physical & 70.30 & 70.43 & 70.43 & 54.09 & 82.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SPPM correlation and predictability for four locations in Bangladesh. Figure 8: SPPM Forecasts: Polynomial (dashed line), Exponential (solid line), and Physical (thick solid line) compared with observation (open circle) valid for observed data in Kutubdia from [10]. The Annual Energy Production (AEP) is carried out utilizing SPPM models. From ERA5, the descriptive statistics for hourly wind speed in 2018 is given in Table 5. Since the data have unimodal characteristics, average wind speed is considered for AEP calculations. Figure 10 elucidates that the Barishal region is very suitable for wind power production and implies nearly 2200 megawatt (MW) of energy per year. Despite the coastal regions, Rajshahi is also promising for energy harvest, i.e., around 1400 MW. The average AEP for the southern and northern regions is 1748 and 1070 MW, respectively, except Sylhet. The AEP for both regions are reliable, since the MAPE mostly for the PN model is found below 10%. For all models, the MAPE again is calculated with GWA and illustrated in Table 6. Therefore, the best performance for AEP and power forecast is again obtained by the PN model. Further validations and comparisons using secondary data from [6,10] signify a model compatibility of around 85 to 90%, which is higher than 50% accuracy in [14]. To note, wind direction is an important influence on the wake of the wind generator, which can significantly affect the power output [14,20]. The above results from SPPM provide a future platform for incorporating more variables such as wind directions and temperature to improve the model robustness. Several notable previous papers established forecasting with machine learning (ML) algorithms, artificial neural networks, and statistical modeling and found similar predictabilities to the SPPM. For instance, one study compares various ML techniques such as one dimensional (1D) Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Random Forest (RF), concluding model accuracies reaching up to 93.9% and 94.7%, up to 92.8% and 93.8%, and up to 88.7% and 89.3% for speed and direction, respectively [28,29]. Therefore, the SPPM will reserve its future works incorporating more observed variables and compare its performances to other data-driven models like ML techniques. Figure 9: Same as in Figure 8, but valid for observation from GWA power data.
## 4 Results and Discussion
### Scenario I: Balanced and Distorted Source Voltage  
In Scenario I, the applied source voltage is balanced in terms of magnitude and contains an equal amount of harmonic distortion across each operating phase (refer to Figure 6a). The simulation findings obtained from this category of testing are presented in Figures 7-11. Meanwhile, to better observe the demonstrated performance, important findings are tabulated in a comparative manner, as in Table 2. First, referring to Figure 7, the enhanced-ADALINE algorithm is revealed to effectively extract the magnitude (mean value) of fundamental load current. Note that only the mean value is presented. The mean value is obtained by first adding the magnitude of fundamental load current in each operating phase, and then divides the total summation by three. As illustrated in Figure 4, the mean value is the key component needed for generating reference current, thus must be accurate. From Figure 7, without integrating the averaging function, a significant peak-to-peak oscillation (ripples) can be observed at the extracted mean value (approximately 1 A for Load A and 0.8 A for Load B). Meanwhile, by integrating the averaging function, the extracted mean value does not exhibit any significant ripples, which indicates a more accurate result. Next, from Figures 8 and 10, the findings revealed that both enhanced-ADALINE and STF-dq0 algorithms are effective in directing their respective SAPF in mitigating the harmonics generated by Loads A and B under Scenario I. For both unbalanced load conditions, the highly distorted source currents which are mitigated by both algorithms have recovered the desired sinusoidal wave-shape with THD values (refer to Table 2) below the 5% harmonic limit. Nevertheless, as indicated in Table 2, the THD values demonstrated by the enhanced-ADALINE algorithm (1.01-2.59%) are lower than that demonstrated by the STF-dq0 algorithm (1.50-3.41%). Moreover, it is also clear from the findings that the high neutral currents that resulted from unbalanced connection of single-phase loads (both Loads A and B) were minimized. In other words, both enhanced-ADALINE and STF-dq0 algorithms are effective in removing excessive neutral currents. Figure 6: Non-ideal source voltages considered in this work: (**a**) balanced and distorted (Scenario I), (**b**) unbalanced and sinusoidal (Scenario II), and (**c**) unbalanced and distorted (Scenario III). Furthermore, for both unbalanced load conditions, the large phase differences between the source voltage and current were also reduced by both algorithms. This directly indicates that both algorithms are able to synchronize operation of the SAPF with the connected power system under Scenario I. Nevertheless, the enhanced-ADALINE algorithm demonstrated a better synchronization performance by providing a lower phase difference value. As a result, in-phase operation of the source current and voltage can be achieved, and this provides a near to unity power factor (PF) of 0.999. Therefore, as an overall, the enhanced-ADALINE algorithm provides a better mitigation performance in comparison to the STF-dq0 algorithm.
## 2 System Model and Transmission Process
### Transmission Process  
In underlay CR-NOMA networks, the primary and secondary networks transmit messages simultaneously within the same spectrum. In the primary network, PT sends a signal, \(x_{p}\), to PR with the transmit power, \(P_{T}\), where \(E\Big{\{}\left\{x_{p}|^{2}\right\}\!\!=1\). In the secondary network, the transmission process consists of direct transmission stage and cooperative transmission stage, which are completed in two time slots as follows.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 4 Signal Identification
### Random Forest Classifier  
After the feature vector is generated, it is fed into the classification model to determine the class to which the signal belongs. The model selection should take into consideration both accuracy and complexity. The random forest classifier has been reported to be one of the most effective off-the-shelf methods in machine learning, working well for a wide range of problems [34].  
This method consists of building an ensemble (forest) of decision trees. Each tree provides a classification result and the forest chooses the class that has the highest votes as the overall output [35]. Random Forest increases the diversity of the trees by making them grow from different training data subsets created through bootstrap aggregating (bagging) [36]. The implementation steps of a random forest classifier can thus be summarized as follows [23]:  
* Building the individual trees of the forest using algorithms such as C4.5 or CART.
* Sampling randomly the original training dataset without deletion of the selected data in order to create an in-bag subset for each tree.
* Selecting randomly a set of features to construct the nodes and leaves of each tree.
* Selecting the root node of the tree, which represents the attribute (feature) with the highest Information Gain (IG).
* Splitting the training data at the root node into subsets for every possible value of the attribute. Then, at each node, the splitting is conducted if the IG is positive; otherwise the node becomes a leaf node. The information gain of splitting the training dataset (\(\gamma\)) into subsets (\(Y_{i}\)) is given by: \[IG=-\sum_{i}\frac{size(Y_{i})}{size(Y)}E(Y_{i})\quad;\quad E(Y_{i})=-\sum_{j=1 }^{J}p_{j}\log_{2}(p_{j}),\] (17) where \(J\) is the number of signal classes and \(p_{j}\) the proportion of the class \(j\) in the subset \(Y_{i}\).
* Repeating this process of tree growing at each node using the subset that reaches the branch and the remaining attributes until all attributes are selected. The most occurring signal class that reached that node is the classification output of the tree.  
It is worth mentioning here that injecting randomness in both bagging and feature selection strategies increases the stability and the accuracy of classification, decreases the sensitivity to noise in the data, and minimizes the correlation among features [35].
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## 2 Proposed Range Model for CSSAR
### Proposed Range Model
The parameters of the target are: \(R_{c}\) = 20 km, \(a_{x}\) = 0.3 m/s\({}^{2}\), and \(a_{y}\) = 0.2 m/s\({}^{2}\). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 10 GHz \\ \hline Range bandwidth & 150 MHz \\ \hline Sampling frequency & 180 MHz \\ \hline Pulse repetition frequency & 1500 Hz \\ \hline Azimuth resolution & 1 m \\ \hline Illumination time & 1.69 s \\ \hline Flying radius & 2.3 km \\ \hline Platform altitude & 8 km \\ \hline Platform velocity & 125 m/s \\ \hline Ground range of scene center & 16 km \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of airborne CSSAR for simulation.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Types of Cyber-Physical Attacks
#### 3.2.2 Eavesdropping Attacks  
Attackers can obtain confidential and sensitive information by eavesdropping on communication channels that are known to be used by individuals or institutions to communicate such information. Eavesdropping can take a number of forms, such as tapping into telephone lines, phishing and monitoring network traffic. Should any information that contains sensitive content relating to how a certain system or production process operates be shared on an insecure communication channel, it could be of great value to an attacker to eavesdrop on such communications in order to plan an attack in advance.
## 1 Introduction  
Worm attacks against sensor motes are realistic in sensor networks, as demonstrated by [1; 2] In particular, they show that a worm could be propagated by exploiting code injection vulnerabilities in target sensor motes. Wide-spread worm dissemination makes it possible for an attacker to rapidly gain control over the network.  
To defend against a worm, it is very important to understand how a worm is propagated. In the sense that epidemic model [3] is good at modeling the spread of epidemic disease, researchers adapted it to worm propagation modeling in the internet and static sensor network [4, 5, 6, 7]. However, these previous works do not consider node mobility which can greatly affect the worm infection capability and thus they are not appropriate for worm propagation in a mobile sensor network.  
To mitigate this problem, we discuss a hop-by-hop worm propagation strategy in a mobile sensor network. In this strategy, worm infection is incurred only by neighbor-to-neighbor contact and thus node mobility will have little effect on worm propagation. Furthermore, we propose a carryover epidemic model in which the infection quota deficit in the current time slot is carried over to the next time slot. This model is adequate for the scenario where infection quota deficit occurs due to the frequent change of network topology and hence it is effective in the modeling of mobile sensor worm propagation. We analyze the cumulative number of infected nodes in the carryover epidemic model and derive Chernoff bounds on the number of time slots required for the whole network infection. Furthermore, we use an ns-2 simulator to evaluate the hop-by-hop worm propagation with carryover epidemic mode. The simulation results show that a worm infection quota deficit rarely occurs under the situation where a node's maximum speed is at least 20 m/s and thus worm infection is not delayed but completed in time. This indicates that node mobility makes it possible for a mobile node to have enough neighbors, leading to expedition of worm infection.  
The rest of paper is organized as follows. In Section 2, we describe the related work. In Section 4, we describe hop-by-hop worm propagation strategy and the carryover epidemic model in a mobile sensor network. In Section 5, we analyze the hop-by-hop worm propagation with carryover epidemic model. In Section 6, we simulate hop-by-hop worm propagation with carryover epidemic model in mobile sensor network and present the simulation results. In Section 7, we conclude the paper.