esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SDN-based Intelligent Transportation System
This results in a unified application hurrying to convey organizing strategies, board instruments, safety efforts, and so on, and each method arranges virtualization. It likewise gives information streamlining and greater adaptability, exactness, and consistency in the design when contrasted with the manual arrangement of systems administration gadgets for a customary system [32]. In addition, programming characterized organizational difficulties are still being looked at (i.e., to adapt to the highest versatility of vehicles shows additional difficulties while utilizing the SDN idea). There are other significant difficulties is the network among the SDN controllers and the vehicles [33]. Some of the time, it is likewise conceivable that the SDN controller is not reachable. The greater part of the exploration directed in earlier research did exclude such problems as they pursued the old style of the vehicular systems, for example, the specially ad-hoc on demand distance vector (AODV) [8] and global positioning system receiver (GPSR) [34]. Through an important association among SDN and the Internet of Vehicles (IoV), the idea of programming characterized SDN-IoV is developing. SDN-IoV gives an adaptable and proficient association, ensures administration QoS, and helps with different simultaneous clients [35]. SDN-IoV generally controls and manages vehicular correspondence in a unified way by amassing the system state data and choosing as indicated by the environment. Furthermore, the association among the SDN-based controllers and the vehicle requires a legitimate association that keeps up its network all through the correspondence. In such manner, a portion of past investigations have misused the long term evolution (LTE), 4G advanced systems [35]. However, these innovations do not bolster a high and effective transfer speed. The SDN-based ITS is shown in Figure 6.  
Figure 5: Integration of intelligent transportation system with SDNs and the other technologies.
## 4 Results
### Accuracy Assessment
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Experimental Data** & **Evaluation Index** & **Forest** & **Shrubland** & **Gardenland** & **Farmland** & **Water** & **Artificial Surfaces** & **Others** \\ \hline \multirow{2}{*}{Landsat OLI (cloudiness: 26.28\%)} & UA (\%) & 64.41 & 51.28 & 47.83 & 64.62 & 78.57 & 80.33 & 55.56 \\  & PA (\%) & 76.00 & 43.48 & 44.00 & 65.63 & 81.48 & 84.48 & 41.67 \\ \cline{2-8}  & OA & & & & **0.941** & & & \\  & Kappa & & & & & **0.585** & & \\ \hline \multirow{4}{*}{Landsat OLI (cloudiness: 8.43\%)} & UA (\%) & 81.36 & 79.49 & 73.91 & 78.46 & 89.29 & 91.80 & 88.89 \\  & PA (\%) & 90.57 & 70.45 & 56.67 & 78.46 & 89.29 & 91.53 & 80.00 \\ \cline{2-8}  & OA & & & & **0.827** & & & \\  & Kappa & & & & **0.796** & & & \\ \hline \multirow{4}{*}{ALOS-2} & UA (\%) & 81.36 & 71.79 & 82.61 & 78.46 & 96.43 & 93.44 & 88.89 \\  & PA (\%) & 87.27 & 68.29 & 63.33 & 82.26 & 90.00 & 94.35 & 88.89 \\ \cline{2-8}  & OA & & & & **0.838** & & & \\ \cline{2-8}  & Kappa & & & & **0.840** & & & \\ \hline \multirow{4}{*}{Features fusion image} & UA (\%) & 83.05 & 76.92 & 82.61 & 84.62 & 96.43 & 96.72 & 88.89 \\  & PA (\%) & 92.45 & 71.43 & 67.86 & 84.62 & 96.43 & 96.72 & 100.00 \\ \cline{1-1} \cline{2-8}  & OA & & & & **0.870** & & & \\ \cline{1-1} \cline{2-8}  & Kappa & & & & **0.845** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification accuracies for different land types. Figure 16: Comparison of user accuracy (UA) of land cover types. Figure 17: Comparison of producer accuracy (PA) of various land-cover types. Figure 15: Comparison of the overall accuracies based on LULC classification.
## 2 Objective and Methods  
This research article aims to establish a practical and accurate mathematical-based model for estimating the RSI value of asphalt mixes using a hybrid machine learning approach, including the ANN and GEP techniques. The model will consider four input variables that significantly influence the moisture susceptibility of asphalt mixes, including the moisture absorption percentage of aggregates (Ab%), asphalt content (AC%), air void content (Va%), and percentage of mineral filler (F%) passing through a 0.075 mm sieve.  
To develop the model, different asphalt mixes with an appropriate variety in the input variables were selected for sample preparations. Sufficient numbers of compacted samples were then prepared and tested following the Marshall standard method. The moisture susceptibility of those mixes was evaluated based on the measured RSI values. Because of the limited number of measured RSI points, a robust ANN model was developed and utilized to generate a new dataset of RSI. The GEP technique was then used to establish a predictive model (equation) employing the combined datasets of the newly generated and experimental (raw) RSI data points. Sensitivity analyses were then performed to validate the accuracy of the proposed GEP model. The methodology of this study is outlined in Figure 1 and discussed in detail in the following sections.
# Plasmonics Induced Multifunction Optical Device via Hoof-Shaped Subwavelength Structure
## 1 Introduction  
Surface plasmons (SP) include surface plasmon polaritons (SPPs) and local surface plasmons (LSP), which are surface electromagnetic waves formed by the collective oscillation of free electrons in a metal and the incident light field [1; 2; 3]. In recent years, the enhanced optical transmission (EOT) effect brought by metal-based surface plasmons has been applied in different wavelengths, including visible light, near-infrared and infrared ranges. Correspondingly, a large number of micro/nano structures have been proposed, such as: circle holes [4], stripe grating hole [5], single/double aperture hole [6], composite structure [7], cross-shaped hole [8], triangular hole [9]; Also, a large number of devices and applications have been constructed, for example: A absorber device that realizes broadband multifunctional properties by introducing vanadium dioxide into a metamaterial [10]; A filter device that realizes multiple channels working simultaneously by changing the number of concentric apertures [11]; A color sensor was realized using interference effects in the metal-insulator-metal Fabry-Perot (FP) cavity of polydimethylsiloxane (PDMS) as the dielectric layer [12]; The function of displaying different colors in the visible light range is realized by changing the material characteristics and the geometric parametersof the structure, in order to achieve the application of the structure color [13; 14; 15; 16; 17; 18; 19]. However, the devices proposed by researchers have the defects of single function, complex structure and expensive materials; they cannot achieve better performance when the devices are integrated.  
In this paper, the photoelectric effect of the polarization direction of the incident light on the periodic hoof-shaped subwavelength structure array is studied by numerical simulation. The results show that as the polarization angle of incident light changes, the transmission peak splits in the near-infrared range, and a new transmission peak appears in the mid-infrared range. By comparing the EOT phenomenon of three structures, it is found that in the hoof-shaped structure, the LSP resonance modes excited by the U-shaped and rectangle portions, respectively, are different from those when they exist alone.  
Finally, we propose a multifunctional device work on different wave ranges with adjustable performance. By changing the polarization angle of the incident light, a mid-infrared range plasmonic optical switch with a contrast ratio exceeding 13.2 dB can be realized. Not only that, the device implements a plasmonic optical splitter in the near-infrared range, which can achieve optical splitting functions with different intensities.
# Bluetooth Low Energy Mesh Networks: A Survey
## 4 BLE Mesh Networks: Academic Solutions
### Routing-Based Solutions
#### MHTS  
The first BLE mesh solution, called MultiHop Transfer Service (MHTS), was published in 2013 [13]. MHTS was designed over Bluetooth 4.0, based on next-hop, on-demand routing over the GATT layer. MHTS consists of two phases. The first one handles neighbor discovery, connection establishment, and route discovery. The second one comprises data storing and forwarding over the end-to-end path.  
During the first phase, neighbor discovery and connection establishment are performed by using common BLE mechanisms. Route discovery is carried out when a packet is ready to be sent but a route to the intended destination is not available in the sender routing table. To initiate route discovery, the sender transmits an advertisement, which carries the target destination, the maximum number of hops between sender and destination (as set by the sender), and the maximum time for the route discovery process. If a neighbor that receives the advertising packet does not know a route to the destination, the neighbor proceeds like the sender and transmits advertising packets requesting for a route towards the destination. This process is repeated until a node that knows a route to the destination is found, which then establishes a Link Layer connection with its precursor neighbor. The latter creates a routing table entry that indicates the neighbor as the next hop towards the destination, the neighbor performs the same operation with its own precursor neighbor, and the procedure is repeated until the source node updates its routing table with the discovered route.  
In the second phase, every node in an end-to-end path transmits data as a slave in the Link Layer connection with its next hop, while the latter plays the role of a master in that connection. Since in Bluetooth 4.0 only a star topology is allowed, the above scheme is achieved by transferring the whole data unit (e.g., a large file) in a per-hop basis, as follows. In the first link of the end-to-end path, a connection is established between the sender and the receiver; then the whole data unit is transferred from the sender to the receiver, and the connection is then released. The same procedure is carried out in subsequent links, until the (possibly large) data unit reaches the destination. Note that, in this solution, a node never participates simultaneously in two connections. However, end-to-end transmission is limited by the available memory of BLE devices. With the resources of the CC2640 chip, MHTS can transmit packets over up to five hops for a file size of 1 kB [43]. For networks of greater diameter, devices need a larger amount of memory.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 3 Single-Carrier Transmission Schemes with Dimming Control
### Variable On-Off Keying (VOOK)--Digital Dimming  
VOOK is a modulation technique that combines OOK and PWM to achieve a specific dimming level. The LEDs are driven by digitally modulated pulses that result in different dimming levels through changes in the duty cycle [21]. The average transmitted optical power for VOOK must be the same as other modulation techniques, namely \(\overline{P}_{\text{TX}}\), while the peak power of VOOK pulses remains at \(2\overline{P}_{\text{TX},\text{max}}\) due to digital dimming. The symbol period for VOOK is divided into \(m\) bits consisting of data (\(d\)) bits and filler (\(0\)) bits. A desired dimming target is achieved by varying the data duty cycle, \(\delta_{d}\), which is the ratio of the time the data pulse is on to the symbol duration, \(T_{s}\). The inactive portions of the bits are filled with zeros according to the dimming factor, \(\gamma\). Table 1 shows VOOK codewords of interest, where \(\gamma=0\%\) means that no information can be transmitted by VOOK. For VOOK, the highest transmitted optical power is achieved when \(\gamma=100\%\), resulting in a traditional OOK signal.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 5 Design of \(Less\) Protocol
### Discussion  
**Compatibility of LESS.** Note that LESS is a supplementary technique and can be easily integrated into existing link quality estimation methods. Considering the fact that existing arts like 4-bit [2] entirely scan the links to obtain an accurate link quality estimation, LESS can be used to decrease the communication overhead while maintaining the accuracy.  
**Overhead of LESS.** The overhead of LESS is mainly in three aspects: (1) the delivery of the schedules; (2) the collection of the sampled information; and (3) routing path update. For the first one, the schedules can be delivered using existing dissemination protocols such as [59]. Furthermore, the update process is only invoked when the sampling rate is out of date. As for the second aspect, the link quality information can be embedded into data packets and the overhead can be minimized with coding methods such as [60] and we omit the details in this paper. For the last one, the update command is only sent when a route changes for a node. As mentioned above, the overhead of update commands for routing paths can be resolved by using compression methods shown in Figure 8.
## 2 Materials and Methods
### Participant Recruitment and Data Collection  
Thirty-three sonographers were recruited from a large private medical imaging practice in Western Australia through convenience sampling. Each participant attended a face-to-face demonstration of normal, marginal, and velamentous cord insertions utilising 2D ultrasound images, ultrasound videos, and our 3 DPMs. As far as we are aware, our 3 DPMs are the first printed models demonstrating the PCI, and the participants had no prior exposure to our models. As such, we believe there to be no cause for participant bias towards the models. The sonographers were initially asked to complete a questionnaire (Supplementary File S1) establishing the following:  
1. Informed consent;
2. Demographics (years of experience as a sonographer);
3. Confidence in assessing the PCI with ultrasound and ability to spatially visualise the placenta and PCI utilising a five-level Likert-scale rating (with 1 indicating extremely poor and 5 indicating excellent).  
Ultrasound images and videos of the three classifications of cord insertion were then presented in the form of a Power Point presentation (Supplementary File S2), followed by a hands-on demonstration of the 3 DPM. Participants were then asked to complete a subsequent questionnaire (Supplementary File S3). A one-to-three ranking scale was utilised to establish which of the three methods demonstrating the PCI (2D ultrasound images, 2D ultrasound videos, and 3 DPM), if any, best enhanced the participants' ability to spatially visualise the relationship between the placenta and the PCI. Five-level Likert-scale questions (with 1 indicating not at all and 5 indicating very much) were used to determine whether the 3 DPMs had the following characteristics:  
Figure 1: The completed 3D-printed models representing the **(a)** NCI, **(b)** MCI, **(c)**, VCI and a **(d)** close-up of the VCI inserting into the foetal membranes. 3D: three-dimensional, NCI: normal cord insertion, MCI: marginal cord insertion, VCI: velamentous cord insertion.  
Increased confidence in identifying the NCI, MCI, and VCI with ultrasound;  
Improved the understanding of the structural relationship between the placenta and the PCI;  
Enhanced the ability to spatially visualise the placenta and the PCI.  
Participants' opinions on the usefulness of the 3 DPMs in ultrasound education and their likelihood of recommending the 3 DPMs as an educational device were assessed using five-level Likert-scale questions (with 1 indicating not useful/not likely and 5 indicating extremely useful/likely).  
Participants were given the option of providing additional comments at the end of the second questionnaire. The demonstration, inclusive of questionnaire completion, took approximately 15 min for each participant to complete.
## 2 Preliminaries
### D-SCLF Decoder  
To further improve the performance of list flip decoders, the D-SCLF decoder [24] applies a new flip metric to approximate the probability of \(\epsilon^{(i)}\) occurring in a CA-SCL decoding process. \(\epsilon^{(i)}=\{u_{1}^{i-1}\in\big{\rfloor}_{best}^{(i-1)},u_{1}^{i}\
otin \big{\rfloor}_{best}^{(i)}\}\) represents the event where the first mistake happened in the \(i\)th bit. Based on the new flip metric, D-SCLF extends this flip metric to approximate the probability of high-order errors and utilizes a dynamic list of flip sets \(\mathcal{S}\) to reserve the potential location of high-order errors. The extended flip metric in [24] can be achieved by using the following calculation:  
\[\begin{split} M_{\beta}^{(i)}&=-\frac{1}{\beta}ln(P (\epsilon^{(i)}|y,S_{t})\\ &=-\frac{1}{\beta}ln(P_{\epsilon}^{(i)})-\frac{1}{\beta}\sum_{k \in S_{t}}ln(P_{\epsilon}^{(k)})-\frac{1}{\beta}\sum_{k<i,k\in\{\mathcal{A} \setminus\mathcal{A}^{\prime}\}\setminus S_{t}}ln(1-P_{\epsilon}^{(k)})\\ &=E_{1}^{(i)}+f_{\beta}(E_{1}^{(i)})+\sum_{k\in S_{t}}E_{1}^{(k) }+\sum_{k\in S_{t}}f_{\beta}(E_{1}^{(k)})+\sum_{k<i,k\in\{\mathcal{A}\setminus \mathcal{A}^{\prime}\}\setminus S_{t}}f_{\beta}(E_{1}^{(k)})\\ &=E_{1}^{(i)}+\sum_{k\in S_{t}}E_{1}^{(k)}+\sum_{k<=i,k\in \mathcal{A}\setminus\mathcal{A}^{\prime}}f_{\beta}(E_{1}^{(k)}),\end{split} \tag{4}\]  
where \(\beta\) is also a compensated coefficient similar to \(\alpha\), \(S_{t}\) is a flip set that records all flip indices for the \(t\)th additional decoding attempt, \(f_{\beta}(x)=\frac{1}{\beta}ln(1+e^{-\beta x})\) and \(E_{1}^{(i)}=E_{\alpha=1}^{(i)}\). It is worth noting that \(S_{t}\) is a subset of \(\mathcal{S}\), and \(\mathcal{S}=S_{1},S_{2},\ldots,S_{T}\) has a constant size of \(T\) but is updated after a failure in decoding attempts. \(P(\epsilon^{(i)}|y,S_{t})\) refers to the probability that \(\epsilon^{(i)}\) occurs with the flip set \(S_{t}\)[24], and it satisfies the following equation:  
\[P(\epsilon^{(i)}|y,S_{t})=P_{\epsilon}^{(i)}\cdot\prod_{k\in S_{t}}P_{ \epsilon}^{(k)}\cdot\prod_{k<i,k\in\{\mathcal{A}\setminus\mathcal{A}^{\prime }\}\setminus S_{t}}(1-P_{\epsilon}^{(k)}), \tag{5}\]  
where \(P_{\epsilon}^{(i)}=\frac{1}{1+\epsilon^{\mathcal{E}_{1}^{(i)}}}\).  
Similar to the SCL-Flip decoder, the D-SCLF decoder prioritizes flipping information bits set with a lower \(M_{\beta}\) value computed by (4). However, compared to the flip metric of the SCL-Flip decoder, this flip metric \(M_{\beta}\) introduces many logarithmic and exponential operations, especially when the D-SCLF decoder is applied to higher-order error correction. This will limit the application value of this decoder.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 6 Learning Paradigms
### Multiple Input Networks  
As discussed before, most ASC algorithms use a convolutional front-end to learn characteristic patterns in multi-dimensional feature representations. As a general difference from image processing, the time and frequency axes in spectrogram based feature representations do not carry the same semantic meaning. In order to train networks to detect spectral patterns, which are characteristic for certain frequency regions, several authors split a spectrogram into two [70] or multiple [71] sub-bands and used networks with multiple input branches. Using the same idea of distributed feature learning, other ASC systems individually process the left/right or mid/side channels [8] or filtered signal variants, which are obtained using harmonic/percussive separation (HPSS) [38] or nearest neighbor filtering (NNF) [37]. Instead of feeding multiple signal representations to the network as individual input branches, [PERSON] et al. proposed to concatenate both MFCC and log Mel spectrogram features along the frequency axis as input features [72]. The majority of state-of-the-art ASC algorithms process spectrogram based input data using network architectures with a single input and output branch.
## 2 Study Area and Materials
### Artificial Neural Network (ANN)  
The ANN is an abstract mathematical model based on the knowledge of the human brain and its activities. The scope of possible applications of ANN is practically unlimited in fields such as pattern recognition (also known as classification), decision making, automatic control systems, and many others. Thus, ANN can be applied to the classification of landslide susceptibility by solving the non-linear relationship between landslides and their spatial environmental factors [47].  
A feedforward ANN model called a multilayer perceptron (MLP) maps a set of input values onto a set of suitable outputs. The MLP comprises of an input and an output with one or more hidden layers of nonlinearly-activating nodes. Each node in one layer is connected with a certain weight to every node in the next layer. MLP utilizes a backpropagation algorithm for training the network. The algorithm trains the network until some goal minimal error is reached between the anticipated and actual output values of the network. At the end of this training step, the neural network produces a model that should be able to calculate a target value from a given input value [48].  
It is important to select training data, such as landslide- and non-landslide locations, to be used as input to the ANN's learning algorithm [49]. In this study, areas with zero slope value were assigned as areas not prone to landslides, and areas with known landslides were assigned as areas prone to landslide in the training set. Both groups had 41 datasets. The values of the 18 landslide-relatedenvironmental factors were normalized to a range of 0.1-0.9 as input data. The backpropagation algorithm, as one of the most popular training algorithms, was used in this study. The three layered feed-forward network based on the framework provided by [50] was applied using the MATLAB software package as 18 (input layer) \(\times\) 36 (hidden layer) \(\times\) 2 (output layer). A log-sigmoid transfer function was used in the hidden layer and the output layer.  
The flow of data processing was as follows. First, feedforward sent the input data to the neural network, and then the cost function with weight and bias were calculated. Many iterations of training satisfactorily minimized errors in updating optimized weight and bias for the training data. The relative influence indexes of the variables were calculated as the maximum repetitive number before reaching the targeted error of 2000, the learning rate of 0.01, and root mean square error (RMSE) of 0.001 using MATLAB. If the RMSE value of 0.001 was not achieved, then the maximum number of iterations was terminated at 2000 epochs. When the latter case occurred, then the maximum RMSE value was <0.1. As the calculated weights were granted to each factor (Table 2), landslide susceptibility for the whole study area was classified.
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
## 3 Results and Discussion
### Mechanical Tests
#### 3.2.2 Influence of Geopolymer Dosage to Compressive Strength, Elastic Modulus, and Tensile Strength  
Effects of cementitious material dosage on compressive strength, elastic modulus, and tensile strength of slag/fly-ash-based geopolymer stabilized macadam (28 d) is shown in Figure 2a-c, respectively. It can be seen that the dosage of cementitious material has a serious influence on the mechanical properties of the test piece. The compressive strength, elastic modulus, and tensile strength of slag/fly-ash-based geopolymer-stabilized macadam increase linearly with the dosage of cementitious material, which is consistent with the change of the compressive strength of polypropylene fiber-cement-stabilized macadam with cement dosage [26]. For example, when the dosage of cementitious material increases from 2% to 5%, the compressive strength increases by 69.5% (from 9.85 MPa to 16.70 MPa), with an increase by 49.4% for elastic modulus (from 2845 MPa to 4250 MPa), and the tensile strength increased by 89.2% (from 0.93 MPa to 1.76 MPa).