esa-sceva/satcom-chunk-collection huggingface dataset:
# Factorial Numbers and Their Practical Applications
## 3 Theory of Factorial Numbers
### Enumeration of Factorial Numbers with an Interval Equal to 1  
The enumeration of \(n\)-digit factorial numbers in the range \(P\) in ascending order contains the following steps:  
1. Starting from the first digit, all the digits of the factorial number \(F\) are looked through until a digit \(x_{i}\) appears that is different from the value of the digit \(i=1\), \(2\), \(3\), \(\ldots\), \(n-1\), in which it is located. This digit is increased by one, and the digits of the all low-order digits, starting with digit \(i-1\), are set to \(0\). The value of the zero digit is equal to \(0\).
2. If, when viewing all the digits of the number \(F\) in the order specified in step 1, the number \(x_{i}\), different from \(i=1\), \(2\), \(3\), \(\ldots\), \(n-1\), does not appear in them, then \(F=Fmax\). End.
3. Step 1 is repeated until each digit \(x_{i}\) of the factorial number \(F\) is equal \(i\). In this case, \(F=Fmax\). End. For example, factorial number \(F=5\ 3\ 3\ 2\ 1\ 0=5\times 5!+3\times 4!+3\times 3!+2\times 2!+1\times 1!+0\ \times 0!=695\). When increasing, in accordance with the algorithm, the fourth digit of the specified number by one and setting all low-order digits as 0, it is converted into the number \(5\ 4\ 0\ 0\ 0\ 0=5\times 5!+4\times 4!+0\times 3!+0\times 2!+0\times 1!+0\times 0!=696\). After that, the algorithm generates the number \(5\ 4\ 0\ 0\ 1\ 0=697\), then the number \(5\ 4\ 0\ 1\ 0\ 0=698\), \(\ldots\), \(5\ 4\ 3\ 2\ 1\ 0=6!-1=719\). End.  
The proof of the correctness of the considered algorithm is given below. It uses Theorem 1, which proves that for \(i=0\), \(1\ldots\), \(n-1\), \(Fmax=(i+1)!-1\). Then, the appearance in the number \(F\) of the first digit on the right \(x_{i}\), \(0\leq x_{i}\leq i\), less than its corresponding value \(i=1\), \(2\), \(3\), \(\ldots\), \(n-1\), will lead to the fact that the corresponding sum of its products \(x_{i-1}\times i!+\ldots+x_{1}\times 1!+0\times 0!\) preceding digit \(i\) will be equal to \(i!-1\). In this case, adding one to the number \(x_{i}\) will lead to an increase in the value of the product \(x_{i}\times i!\) it forms to the value \((x_{i}+1)\times i!=x_{i}\times i!+i!\) and, accordingly, to an increase in the number \(F\) by the amount \(i!\). If we now reset all digits of the number \(F\), starting from \((i-1)\) digit, then since \(x_{i}!+i!-(i!-1)=x_{i}!+1\), a new ordinal number \(F\) will be obtained with its value increased by one, which is the task of the proposed algorithm. After that, a new cycle ofincreasing the number \(F\) by one begins, similar to the above cycle, with obtaining new ordinal numbers \(F\). Such cycles and new numbers \(F\) continue until all \(n\) digits of the factorial number are in the order \(n-1\), \(n-2\),..., \(1\), \(0\), corresponding to the factorial number \(Fmax\). This concludes the algorithm for enumerating factorial numbers.  
Below in Table 1, all \(4!=24\) four-bit factorial numbers of length \(n=4\) are presented, obtained in accordance with the given algorithm.
## 5 User Study
### Testbed Environment
It is worth noting that current methods for defect detection in infrastructure inspections have achieved accuracy rates of 89.95% [18] and 85.33% [19], but the data sets used in these studies came from still images and, given the use case of the present study, a slightly more conservative accuracy rate of 80% was chosen for the highly accurate condition to compensate for the noisy, and moving, environment faced by the drone and the operator. Additionally, similar signal detection studies used comparable cuing accuracy rates [20]. We used a virtual camera in Unity to simulate the drone-mounted, gimbaled camera and in turn, a video pass-through AR user experience (Figure 2). The drone followed a fixed flight path--see Figure 1 for a graphical representation of the flight path delineated with white arrows--that mimicked a typical flight path a drone might take as it captured video of a bridge surface for an inspection. As such, participants could not control the position or speed of the drone, but could control the orientation of the camera by scanning with their head. The linear movement of the drone and camera followed a winding pattern that weaved in between each bridge section and gave the participant a view of every bridge surface plane that could have targets on it. While our path did not mimic the exact flight path of a real drone in terms of a real drone's non-rigid turning behavior and flight dynamics, we chose to rigidize the flight path taken to help better isolate the head movements of the user and decrease the effect of reactionary and compensatory user movements that may be caused by unpredictable and random movements of the simulated drone. Figure 2: Simulated bridge environment as displayed to a participant during the experiment. The blue hemisphere in the upper left of the figure denotes an area where the participant selected and the black line in the bottom center of the shows the ray-casting marker, used to display where the user is pointing. User selections were categorized as hits when users correctly selected an actual target, as false alarms when users selected an area that did not contain a target, and as misses when users did not select an area that contained a target. Movement speed and flightpath were the same in every condition and the total flight time of each trial was approximately two minutes. With 12 total trials excluding the practice run, the total signal detection task time was 24 min. Participants, however, were given time in between trials to complete survey batteries so the total experiment lasted for roughly one hour.
# Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction
## 4 Experimental Setup
### Computational Setup and Data Preparation  
The proposed approach as well as the other compared methods used in our work are implemented in Python. To implement the custom NEAT algorithm, we used the neat-python package ([[https://github.com/CodeReclaimers/neat-python](https://github.com/CodeReclaimers/neat-python)]([https://github.com/CodeReclaimers/neat-python](https://github.com/CodeReclaimers/neat-python)) (accessed on 10 November 2021)) [40]. In addition, TensorFlow 2.4 was used to implement the BPNNs. All the experiments for those networks have been conducted on an NVIDIA TITAN Xp GPU, while we used an Intel XEON E5 CPU for the experiments with our proposed method.  
Since we employ the neural networks, each time series is normalized to \([-1,1]\) by a min-max normalization. The CNNs described in Section 4.2 require time-windowed data as an input to apply 1D convolution in the temporal direction. Therefore, all the given time series are sliced by a time window of length 30 with stride 1 for the CNN proposed in [13] and stride 10 for the CNN proposed in [39]. Then, the input size of each CNN is \(30\times 14\) and \(30\times 20\), respectively.  
In our method, the time-windowed data are transformed by the SFA introduced in Section 3.1 so that we can reduce the dimension of the data and generate an integer vector which is used as an input for the SN P systems. In other words, a single integer vector should be attained from the SFA of the multivariate time series. To do so, we apply the SFA to each time series independently; then, we concatenate the output representations obtained from the SFA on all the time series to generate a single vector. In detail, the input length \(n\) is the same as the window size 30. The number of coefficients \(w\) is set to 3 for CMAPSS and 1 for N-CMAPSS, and the number of bins is set to 26. Thus, the dimension of each input of our SN P systems is 42 for the former and 20 for the latter. We selected these numbers based on our preliminary experiments, in which we concluded that the SFA is advantageous for our SN P systems compared to a simple quantization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Description** \\ \hline MLP [39] & 4 hidden layers \\ CNN [39] & 3 convolutional layers and 1 fully connected layer \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compared RUL prediction methods for the N-CMAPSS dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Description** \\ \hline MLP [13] & 1 hidden layer \\ CNN [13] & 2 convolutional layers, 2 pooling layers, and 1 fully connected layer \\ LSTM [14] & 2 LSTM layers and 2 fully connected layer \\ SVM [38] & SVM classifier and Weibull distribution \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compared RUL prediction methods for the CMAPSS dataset.
# Dual-Band Frequency Selective Surface-Backed Reflectarray for High-Speed Ka-Band Satellites
###### Abstract  
A dual-band, dual-polarized frequency selective surface (FSS)-backed multilayer reflectarray antenna is designed for 5G high-speed satellites operating at Ka-band uplink and downlink frequencies (20/30 GHz). A reflectarray antenna system consists of two reflectarrays that are separated from each other by an FSS layer that behaves as a planar bandpass filter at Ka-band satellite uplink frequencies. Each reflectarray antenna is designed with dual-polarized unit cells. In order to achieve a uniform phase distribution across the reflectarray surface, physical dimensions and positions of the unit cells with a fixed periodicity are carefully chosen. The FSS conductor is etched to the bottom layer of the 30 GHz reflectarray substrate to save cost and weight. The reflectarray performance is analyzed by using CST Microwave Studio and array theory. A prototype is fabricated, and the results are experimentally verified. The gain of the reflectarray is measured as 21.13 dBi and 26.94 dBi at 20 and 30 GHz, respectively. A crosspol level of more than 35 dB is observed at both frequencies. The simulated and measured results show that the proposed reflectarray is suitable for high-speed Ka-band satellites.  
5G high speed satellite; dual band; FSS; Ka-band; multilayer; reflectarray antenna +
Footnote †: journal: Applied  
1
Footnote 1: email: {[[https://doi.org/10.3390/app14072928](https://doi.org/10.3390/app14072928)]([https://doi.org/10.3390/app14072928](https://doi.org/10.3390/app14072928))}
# Near-Nyquist-Limit Optical Communication and Ranging Method Based on Waveform Matched PPM
## 2 Deep-Space Optical Communication and Ranging Method Based on PPM
### Ranging Method
The time difference (\(\Delta t\)) is given by:  
\[\Delta t=\frac{\varphi}{2^{K}}\times\frac{1}{R_{s}}, \tag{9}\]  
where \(\varphi\) represents the starting phase of the frame obtained by the signal synchronization algorithm, which is the phase corresponding to \(\Delta t\). \(K\) denotes the phase quantization depth of a slot, and \(R_{s}\) indicates the slot rate. It is shown that the timing accuracy is determined by \(K\) and \(R_{s}\) with the same \(\varphi\). As shown in Figure 4, the sum of the starting phase \(\varphi\) and the left phase \(\varepsilon\) is the code numerically controlled oscillator (\(NCO\)), i.e., \(\varphi+\varepsilon=NCO\), where \(NCO\) is determined by the signal synchronization algorithm. The starting phase of the \(h\) th frame is expressed as:  
\[t_{\text{G2}}-t_{\text{S0}} =\tau_{\text{S},\text{tx}}^{d}+\tau_{d}+\tau_{\text{G},\text{rx}} ^{d}, \tag{3}\]0  
where \(h\in\mathbb{Z}\) and \(\mathbb{Z}\) denotes the set of integers. The left phase \(\varepsilon\) is given by:  
\[t_{\text{G2}}-t_{\text{S0}} =\tau_{\text{S},\text{tx}}^{d}+\tau_{d}+\tau_{\text{G},\text{rx}} ^{d}, \tag{3}\]1  
where \(L_{s}\) is the number of slots setting in a frame, and \(L_{p}\) is the number of sampling points in a frame calculated by \(L_{s}\) and oversampling factors.
# XCT and DLW: Synergies of Two Techniques at Sub-Micrometer Resolution
## 3 Results
### Evaluation of DLW Structures via XCT
Thus, the fabrication time can be reduced by 81% by reducing the number of lines written in the structure. Therefore, it is highly desirable to use structures with an infill in DLW geometries applicable. Consequently, non-polymerised material is intended to be trapped inside the pyramid's infill. However, multiple fabricated structures showed a missing tip of the pyramid. Even though the same laser power and beam velocity was used for 3 DPoli and Cura-sliced pyramids. XCT was utilised to investigate when the damage on the tip occurred, before or after the development of the sample. If the tip would have been broken off after the development of SZ2080, there should be material inside the pyramid. If the damage to the tip occurred before or while removing the pre-polymer, a clear density difference should be seen in the XCT between the infill structure (4 \(\upmu\)m repeated pattern) and the removed photoresist. This is the case, as shown in Figure 8.  
In Figure 8b, a horizontal and in Figure 8c, a vertical cross section of such a structure can be seen. The top of the fabricated pyramid shows a missing tip, see Figure 8d. The infill consists of individual lines (walls) that were produced as a single DLW-voxel line. Detection of internal (hidden) voxel-sized structures is possible to visualise with XCT. In addition to the missing tip, various internal defects were found by XCT (as highlighted in Figure 8b,c), for instance, bending of the infill and unintended polymerised regions. With the ability to individually slice an XCT-measured structure, the size and shape of such deformations can be quantified. We assume that the bending stems from the capillary  
Figure 7: Views of a DLW fabricated fractal pyramid (sample 1): (**a**) SEM image, (**b**) 3D rendering from XCT and (**c**) XCT slice view parallel to the Z-plane (perpendicular to the building direction). Unintended polymerised lines are highlighted in (**c**). A 3D rendering of the binary STL-file is shown in (**d**). forces that occurred during the development of the structure. The unintended polymerised region in the diagonal direction of the pyramid's base stems from misinterpretations of the used slicing code. With further tuning of the fabrication parameters and decreasing the beam velocity, it was possible to obtain consistent results with entirely fabricated tips. This detailed XCT analysis demonstrates that not only the beam velocity and laser power play a role in successful fabrication, but also the slicing software. XCT proved to be a valuable analysis technique for DLW-fabricated structures. XCT was able to individually determine the origin of different flaws in the fabrication procedure by displaying them, which enables the operator to find an individual solution for the missing tip and the misinterpreted lines in the slicing code. Therefore, XCT is a highly useful tool for the process feedback of DLW-produced structures on the micro- and nanometer scale.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 5 Key Challenges and Future Research Directions
### Big Data & Data Deluge  
With the increase in the number of sensors and wireless sensing networks for long-term and continuous monitoring of grid parameters as well as the need for substantial input data for employing machine learning and deep learning algorithms for processing the acquired raw grid parameter data, an enormous amount of data is being sensed by the input sensor nodes. This makes other functions such as data transmission, data storage, and data processing very challenging, leading to data deluge [2]. Furthermore, deep network structures with complex algorithms and large storage memory will be required to extrapolate the unstructured big data and maintain its quality. Big data also leads to an increase in hardware for storage and processing as well as the overall cost of the entire network, debarring the concept of system miniaturization, which is the need of the hour, especially while creating an entire IoT system support for grid computations. Ref. [3] suggests data preprocessing at the edge level to reduce the size of data near the source, thereby reducing the size and power required for data transportation, data storage, and data processing. This also suggests the use of distributed file systems, NoSQL databases, and data processing tools in power grid computations such as Hadoop, Storm, Spark, and Grid Gain in order to avoid the issue of data deluge.
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 3 Methods
### Single-Robot End-to-End Navigation  
To accomplish the final goal, we start at the single robot navigation case in this section. In the end-to-end mapless navigation tasks, the relationship between the perception inputs and the output control law can be very complex. In our work, we consider a number of modifications to the Deep Deterministic Policy Gradient (DDPG) and use it as the basic framework.
## 6 Discussion
### Future Improvements  
Most of the existing research focuses on the contextual modeling of textual modalities, ignoring the potential value of multimodal information such as phonology and word form. Future research can construct a joint multimodal embedding space to improve the accuracy of disambiguation by fusing features from the trinity of phonology, morphology, and semantics. For example, the phonological modality introduces an acoustic feature analysis module that captures vocal pitch differences using [PERSON]'s spectral graph convolutional network [23], and the morphological modality employs a stroke sequence encoder combined with a graph convolutional network to resolve the structural features of Chinese characters. The multimodal features are dynamically fused through a gated attention mechanism, thus effectively resolving the ambiguity of homophonic characters such as \"\(\top\)L\" and \"\(\bigtriangleup\)\".  
In order to break through the limitations of the traditional end-to-end neural model, a neural-symbolic joint reasoning framework can be designed that integrates structured knowledge such as semantic roles, collocation habits, and domain features to construct a 500,000-node knowledge graph of Chinese homophones. A graph attention routing network can be developed to realize the end-to-end fusion of knowledge retrieval and inference, and a differential representation of symbolic rules in the neural network hidden space can be established.  
To address the problem of existing pre-training models not paying enough attention to local semantics, a hierarchical pre-training task can be designed in future research, introducing masked phoneme prediction at the character level to enhance the learning of acoustic-phonetic associations; constructing a homophone confusion matrix at the word level to force the model to pay attention to discriminative features; and adopting a contrastive learning strategy at the chapter level to narrow the semantic distance between the correct usages of homophones.  
A dual-adversarial training framework can be used, constructing phonological confusion adversarial samples in the input space to enhance the robustness of the model and constructing decoupled adversarial networks in the feature space to separate the phonological features from the semantic representations. Moreover, a cognitive uncertainty estimation module can be introduced to trigger the human-machine cooperative calibration mechanism when the model confidence falls below a threshold.  
In future research, if we want to break through the limitations of traditional black-box models, we need to develop a disambiguation interpretation framework based on causal reasoning, generate comparative explanations through counterfactual reasoning to reveal the influence weights of contextual keywords on decision making, and design a dynamic decision tree visualization module to transform the implicit inference process of neural networks into interpretable rule paths.
## 4 Description of Different Deployed VPN Solutions
### SpentConnect (Aka Cisco Anyconnect) Road Warriors Scenario  
The OpenConnect protocol is designed to create a channel that makes use of UDP packets and secondarily over TCP if UDP traffic is not allowed. The protocols used by Openconnect are: TLS [RFC8446], Datagram TLS [RFC6347] and HTTP [RFC2616]. The session begins with an HTTP connection over TLS on a known port, after which the client authentication begins. Once this is done, the client initiates an HTTP CONNECT command to establish a VPN channel over TCP and, if possible, a secondary VPN channel over UDP, depending on the response from the server. Once these negotiations are complete, IP packets can travel over the VPN. Figure 10 shows the comparison of the average throughput and standard deviation in OpenConnect Road Warriors scenario, wired (on the left) and wireless (on the right). As seen from the figure, OpenWRT as an OpenConnect client turns out to be the best performing, followed by Windows 10, which uses a third-party application to allow the connection but with a ratio of almost 5:1 and finally from the Raspberry Pi 2, with a nearly 10:1 ratio. As for the wireless clients, the iPhones show inferior performance to Android.
## 2 Theoretical Analysis and Improvement Research on Finite Element Models  
### Finite Element Analysis  
For low-frequency electromagnetic field problems, three methods are commonly used for simulation calculations [11; 12; 13; 14]: the Difference Method (DM), Transmission Line Matrix Method (TLM), and Finite Element Method (FEM) [15]  
Among them, the Difference Method is highly regarded for its accurate calculation results. However, since it requires the calculation of every node in space, it can be quite time-consuming and thus demands high computer performance. This method can become quite complex when dealing with irregular boundaries.  
The Transmission Line Matrix Method [16] can be considered as a variant of [PERSON]'s equations under certain boundary conditions. It shows particular advantages when dealing with high-frequency electromagnetic field problems. However, for low-frequency electromagnetic fields, this method may produce large errors.  
Finite Element Analysis is the current focus of research, with various finite element software emerging. Its core idea is to discretize the continuous domain into a finite number of elements and to approximate the solution of the original differential equation by successive calculations for each element. This method focuses on extremum within discrete elements, demonstrating outstanding computational capability when dealing with models that have complex boundary conditions. In complex subsurface environments, the heterogeneous distribution of formation conductivity and complexity of boundary structures are common issues. The Finite Element Method can flexibly combine these boundary conditions, accurately calculating electric and magnetic fields at any position within the model and ensuring high precision in the solution.