esa-sceva/satcom-chunk-collection huggingface dataset:
# Extending the Coverage of IEEE 802.15.4z HRP UWB Ranging
## 2 UWB Local Regulations and UWB Pulse Characteristics
As shown in Figure 2b, when the number of pulses increases, the peak power becomes smaller than the value of local regulation 2. Instead, by using a large number of pulses, additional processing gain can be obtained through the spread spectrum. However, since the number of pulses cannot be increased indefinitely, it must be limited so as to not exceed the average power suggested in local regulation 1. In other words, in order to increase the processing gain, the more pulses  
Figure 1: Local regulation for output power of UWB transmitter (**a**) in the case of one pulse and (**b**) in the case of multiple pulses. the better, but the number of pulses cannot exceed the local regulation for the average power. Since UWB communication must maintain \(-14.3\) dBm according to the average power under local regulation 1, HRP UWB communication becomes a system whose performance is limited only by the average power regardless of the peak power.
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 4 Conclusions  
This research paper highlights the single-label and multi-label classification and the need for multi-label classification for documents. The document classification community is dominated with the content-based approaches. Of course, these approaches have richness in features and produce promising results. To make these schemes applicable the content of the documents is a vital requirement but most of the digital libraries are subscription-based, such as ACM, IEEE, and Springer, etc. There is a need for some alternative method to categorize documents when the content is not available. Such an alternative is available in the form of metadata such as authors, title, keywords, etc. There are very few document classifications approaches that exploit the metadata of research articles and perform single-label classification. However, exploiting metadata for the multi-label classification is also a challenging task for the researchers, by using freely available metadata in the best possible way to perform multi-label classification and to evaluate to what extent metadata-based features can perform in the same way as content-based approaches. Furthermore, it can be gleaned from the extensive literature review that there is still a significant gap in the investigation of deep learning models for the metadata-based approaches in a multi-label documents' classification paradigm. In this regard, other variants of deep learning must be investigated such as transfer learning, ensemble approaches, and the fused models, in particular, when more than one pre-trained model is used to classify the document based on a joint consensus.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Primary Stability (PS) depends on different factors, such as bone structure (quality and density), implant characteristics (macro and micro), and the relationship between thread shape and implant insertion hole size. PS is crucial for the prognosis and success of dental implants. The use of healing chambers, which create empty spaces between the implant and peri-implant bone, helps reduce the risk of compressive bone necrosis. High Insertion Torque (IT) values could potentially lead to the deterioration of the implant-abutment connection. However, the optimal implant macro-structure and IT values remain unknown. In this study, implants with healing chambers (Test) and without (Control) were inserted into polyurethane blocks with densities of 10, 20, 30, and 40 pounds per cubic foot (PCF). In blocks with densities of 30 and 40 PCF, Control implants had significantly higher IT values than Test implants. Additionally, Control implants exhibited significantly higher Removal Torque (RT) values than Test implants, but only in the 30 PCF density block (\(p<0.0001\)). However, no differences were found between the implants in the 10, 20, and 40 PCF density blocks. Similarly, no significant differences were observed in the Implant Stability Quotient (ISQ) values between the Test and Control implants. The results of the present study confirm that adding healing chambers to the macro-structure of dental implants can significantly reduce IT values without affecting ISQ values. This suggests the potential for avoiding compression and damage to peri-implant bone while maintaining consistent levels of PS.  
dental implants; healing chambers; implant primary stability; insertion torque; polyurethane; removal torque 2023
## 2 Related Works
### Feature Extraction  
#### 2.1.1 Hand-Crafted Feature Representation  
Traditional handcrafted features are manually designed to extract specific image features, which are usually divided into two categories: local and global descriptors. Taking advantage of compact representation and computational efficiency, the methods implemented with global features can describe the image appearance using a single vector, e.g., BRIEF [7] and SURF [8]. Histogram statistics can also be used for global description, e.g., HOG [9]. In addition, BoW enables the holistic description of incoming images by aggregating quantized local features, known as visual words [10]. The work VLAD [11] combines local image descriptors into a vector representation. In addition, several classical unsupervised algorithms transform the original features into binary codes, such as ITQ [12], CBE [13], SGH [14], and UBEF [15].  
However, there are some well-established methods for extracting local features, such as SIFT [16] and ORB [17]. LPM [18] aims to preserve the local neighborhood structures of these potential true matches. These handcrafted-based methods neglect latent geometric and structural information, making it difficult to cope with challenging situations such as intense illumination changes and dynamic environments.
## 3 Methodology  
Soil erosion, the most significant land degradation activity in the Shatt Al-Arab basin, is described as a leveling action in which soil and rock fragments are transported downslope by gravity. The RUSLE, a predictive empirical model, is used in the present study to calculate the annual soil loss in the Shatt Al-Arab basin. The RUSLE is the most extensively used erosion method to forecast the average annual soil loss through computing the soil erosion parameters [48]. It is a refined form of the USLE model [63], which has been thoroughly examined and applied for decades. The RUSLE calculates the average annual soil loss in tons per hectare based on five parameters:  
\[\mathrm{A=R\times K\times LS\times C\times P} \tag{1}\]  
where A represents the average annual soil loss (ton ha\({}^{-1}\)year\({}^{-1}\)); R denotes the rainfall erosivity parameter (MJ mm ha\({}^{-1}\) h\({}^{-1}\) year\({}^{-1}\)); K represents the soil erodibility parameter (ton h MJ\({}^{-1}\) mm\({}^{-1}\)); L denotes the slope length parameter; S refers to the slope steepness parameter; C represents the cover and management parameter; and P denotes the conservation support practices parameter. The LS, C, and P are dimensionless parameters [64]. After calculating the RUSLE parameters and preparing a raster format for each parameter, the soil erosion map was prepared by using \"Raster Calculator\", which is a tool under \"Spatial Analyst Tools\" in ArcGIS. The erosion rate of a region pertains to an interaction between climate, topography, soil, and land use (Figure 2). The RUSLE model of predicting soil erosion in the Shatt Al-Arab basin was performed by incorporating all the factors that determine the erosion rates (ton ha\({}^{-1}\) year\({}^{-1}\)).  
Figure 1: Study site. Note. From “GIS-based Multi-Criteria Analysis for Flood Prone Areas Mapping in the Trans-boundary Shatt Al-Arab Basin, Iraq–Iran” [62].
## 5 Performance Evaluation
### Scaling to Networks with High Density or Hop-Distance  
The localization algorithm works based on the RSSI measurements, thereby being spotted in an area lacking enough anchors to retrieve this information is fatal for the filter. On the other hand, there may be scenarios with too many anchors or the anchors are so far that the RSSI reports do not reach the controller in time.  
SDMob's anchor nodes inherit RPL's design choices when it comes to handling high data rate traffic. Once an MN enters an area with a large number of anchors, it will congest the network and hence the local links will measure a higher ETX. Most standard-compliant implementations of RPL reset the DIO interval when there is a significant change  
Figure 9: Two examples of ARMOR algorithm, (**a**) when mobile node \(M\) wisely selects \(S2\) as its parent since it provides the most long-lasting connection. RPL would have chosen \(S1\) by only considering the short-term link quality, and (**b**) depicts a scenario, where ARMOR is less effective as \(M\) ignores the stable (fixed) parents (\(S1\), \(S3\)) and sticks to \(S2\).  
in the rank value assuming that it has been caused by a lossy link. This leads to more frequent transmissions of DIO packets and further congesting the network. To avoid the aforementioned scenario, we have employed the congestion timer. The simulated set of scenarios consists of one MN moving in a linear trajectory and the anchors are manually positioned to keep the number of neighbors constant over time as depicted in Figure 12. Figure 13 shows the performance of SDMob in networks with an increasing number of neighbors. For up to 5 neighbors, we can see an increasing trend in the PDR but for 6 neighbors the network gets so congested that a sharp drop in the PDR is noticed both for control and data traffic. Localization and E2E delay also follow the same pattern.  
Figure 11: Communication overhead (in bytes) for increasing number of MNs with different solutions.  
Figure 12: Simulation scenarios when increasing number of neighbors of a MN: (**a**) with 2 neighbors, (**b**) with 3 neighbors, and (**c**) with 4 neighbors.  
Figure 10: Simulation results for increasing number of MNs with different solutions—(**a**) PDR; (**b**) localization error; and (**c**) end-to-end delay.  
Another parameter that changes in scale is the hop distance from the MN to the controller. To examine the performance of SDMob in this regard, we manually placed the anchors to keep the hop distance of MN constant over time as illustrated in Figure 14 and experimented with different distances. As illustrated in Figure 15, the PDR and localization are more stable with increasing hop distance. However, there is a continual growth in E2E delay and handoff delay.
## 4 Empirical Evaluation
### Data Collection and Analysis Process  
To answer the six research questions specified above, we analyzed the reviews included in our dataset and, for each analyzed review abstract, we populated the question-driven data extraction form that Table 1 reports. Similar to [PERSON] et al. [15], we tested our question-based data extraction form with pilot data coming from three systematic reviews. This allows ensuring the usability of the data extraction form and helped solve potential disagreements on its understanding. We also included data of the pilot test in the final analysis. Two researchers then extracted data from the abstracts of the reviews and assessed them. More specifically, one researcher iteratively extracted all the data from the reviews included in the dataset by populating the question-driven data extraction forms (see Table 1) and specifying the individual confidence values for each answer to a given question. A more experienced researcher then validated the extracted data by double-checking the content of the question-driven data extraction forms. After the validation of the extracted data, one researcher then used Equation (1) to compute the overall confidence in the systematicity of each review's abstract.  
To further answer RQ1, RQ2, RQ3, and RQ4, we created Python scripts that allowed aggregating the overall confidence values obtained for all the abstracts of the reviews included in the dataset. This allowed us to create the charts used to answer these four research questions.  
To answer RQ5, we used Google Scholar to retrieve the number of citations of each review as of July 2022. We then computed the Spearman correlation between the systematicity of the review abstracts and their number of Google citations.  
To answer RQ6, we relied on the answers to questions 11 that we collected from the analyzed review abstracts (when applicable). Recall from above that question 11 focused on identifying the various keywords that the systematic reviewers often use to structure their review abstracts. This allowed us to generate a word cloud depicting the most frequent keywords used to structure review abstracts.
## 5 Results
### Impact of Attention-Based LSTM  
The attention-based LSTM variant 3 DCNN(MBSTAL) has more impact as compared to the MBSTRU variant. In Table 5, we consider the BJTaxi and NYCBike datasets with variations due to attention-based LSTM. The RMSE is 11.13 and 3.88. Technically, LSTM is an excellent neural network for processing the time series problem. The way to tackle critical information is attention mechanism mimics the way. We fuse the spatial-temporal features from the historical traffic flow. We combine attention mechanisms with LSTM. In Tables 3 and 4 we present the detailed inflow and outflow of traffic flow, the RMSE of inflow and outflow on BJTaxi and NYCBike datasets. LSTM considers the spatial and temporal correlation. The attention mechanism is to assign the weights--setting the weights to represent the new representation of historical data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **BJTaxi (RMSE)** & **NYCBike (RMSE)** \\ \hline
3 DCNN(MBSTRU) & 11.25 & 4.15 \\
3 DCNN(MBSTAL) & 11.13 & 3.88 \\
3 DCNN(MBSTALRU) & 11.07 & 3.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of residual fusion and attention-based LSTM.  
Figure 7: RMSE of inflow and outflow comparison with baseline methods on the NYCBike dataset.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Spike-Encoding Methods
#### 2.4.2 Temporal Encoding  
Commonly used temporal coding can be divided into several subcategories: a global reference scheme, an ISI encoding scheme, and an associated synchronization scheme.
* Global Reference Scheme The most basic temporal encoding scheme is **Time-to-First-Spike (TTFS)**, where information is encoded as the time delay between stimulus onset and the neuron's first spike. In the most basic model, this time delay can be expressed as the reciprocal \(\Delta t=1/a\) of the normalized signal amplitude \(a\), or as a linear relationship \(\Delta t=1-a\). In biology, studies by [PERSON] and [PERSON] elucidate the phenomenon of TTFS encoding in the human tactile system. In a discrete fingertip event, the time difference between the onset of contact and the generation of the first spike contains information about the direction and magnitude of the force [11].  
**Phase-encoding** theory encodes information as the time difference between a spike and a reference oscillation [19; 20]. [PERSON], [PERSON], [PERSON], and [PERSON] identified the existence of this coding theory in their studies of neurons in the cat visual cortex and identified the relationship between spike production and reference oscillations [21].  
**Ranked-order coding (ROC)** theory encodes information as the order of firing among neuronal clusters relative to a global reference [22; 23]. Compared with TTFS, the ROCscheme avoids considering the precise time difference between the spike and the reference, and its working mode is closer to a discrete normalization filter. However, the absolute amplitude information of the stimulus is lost in ROC coding; therefore, the original signal with consistent amplitude cannot be reconstructed.
* ISI Encoding Scheme In **inter-spike interval (ISI)** encoding (some papers call it \"Latency Encoding\"), information is defined by the time difference between spikes produced by different groups of neurons; this encoding scheme is observed in the neural activity of pyramidal cells [24]. [PERSON] and [PERSON] show that in this encoding theory, longer periods of silence can contain more information than periods of higher spike activity [25].
* Associated Synchronization Scheme  
**Correlation and synchrony encoding** uses the spikes produced by groups of neurons to encode information, and the stimulation of a group of neurons will be converted into a set of spatiotemporal spike matrices for expression. Within this matrix, clusters of spikes with relatively short **ISIs** represent specific input patterns [17], information encoded as which neurons fire simultaneously. **Sparse Distributed Representations (SDRs)**[26; 27], which are often used in compressed sensing theory, also belong to this encoding theory. Biological studies have identified this encoding scheme in the activity of cells in the somatosensory cortex of monkeys [28] and the visual cortex of cats [21; 29].
## 4 Experimental Evaluation
### Hardware, Software and Databases  
For this work, a server from the cluster of the Laboratory of Technological Research in Pattern Recognition (LITRP) of the Universidad Catolica del Maule was used. The server was equipped with two CPUs, an Intel Xeon Gold 6238 252 CPU @ 2.20-4.00 GHz (56 physical cores), 126 GB RAM and a GPU NVIDIA Titan RTX. The algorithms were implemented in the MATLAB R2020a programming language.  
To examine the performance of Algorithm 1, we used the CIFAR10, UMist, MNIST, NORB, Stanford Cars and Caltech256 databases, which were obtained from different free online repositories. These datasets are briefly described below.  
1. MNIST. This dataset contains 70,000 images of handwritten digits from zero to nine. These are grayscale images of size 28 \(\times\) 28 pixels which generate a vector of 784 components.
2. NORB. This consists of 48,600 grayscale stereo images of 50 toys from five generic classes: cars, four-legged animals, human figures, airplanes, and trucks. The size of the images is 96 \(\times\) 96 pixels, and the output is a vector of 96 \(\times\) 96 \(\times\) 2 = 18,432 components.
3. CIFAR10. This dataset consists of 60,000 color images distributed into 10 classes: airplanes, birds, automobiles, cats, dogs, frogs, deer, ships, horses, and trucks. The number of images per class is 6000, with 5000 used for training and the remainder for testing. In this experiment, the color images were converted to grayscale and had a size of 32 \(\times\) 32 pixels.
4. UMist. This contains 575 grayscale images of 20 people, showing individuals in different positions. The images are rectangular, with a size of 92 \(\times\) 112 pixels.
5. Caltech256. This dataset contains 30,607 color images grouped into 257 categories, each of which contain at least 80 images. For the purposes of this study, the images were converted to grayscale and had a size of 100 \(\times\) 100 pixels.
6. Stanford Cars (SCars). The Stanford Cars dataset contains 16,185 images of 196 categories of automobiles. For training, the color images were converted to grayscale images of size 96 \(\times\) 97 pixels.  
```
0: training set \(\{x_{i}\}_{i=1}^{N}\), maximum number of iterations \(I\), stopping criterion \(P\), number of neurons \(L\), regularization parameter \(\lambda\).
1: Random and orthogonal assignment of weights \(W\) and biases \(b\) in the hidden layer.
2: Calculation of the matrix \(H\) evaluating \(g\) in the terms \((x_{i},W,b)\).
3:while\(P\) does not occur do
4: Calculate the optimal value of (4) using the G-FISTA or LC-FISTA algorithms for \(f(\beta)=\|H\beta-X\|_{2}^{2}\) and \(\phi(\beta)=\lambda|\beta|_{1}\).
5:endwhile
```  
**Algorithm 1** Training of the ELM-AE with FISTA, G-FISTA, and LC-FISTA.  
Table 1 shows the numbers of training and test data, the numbers of categories, and the sizes of the input vectors for CIFAR10, UMist, MNIST, NORB, Stanford Cars, and Caltech256.
## 2 Wave Model and Wave Force Calculation
### Random Wave Model  
In fact, the wave situation is random, and the direction, size and period of the wave are irregular. The power spectrum of the wave is developed to reflect the energy distribution characteristics of random waves at different frequencies, and is an important means of describing random waves. At present, the commonly used wave spectrum includes Pierson Moskowitz (P-M) spectra, JONSWAP spectra [23; 28; 29]. They use semi-theoretical and semi-empirical formulas to describe the power spectral characteristics of random waves based on the observation of an actual wave. The one-sided DNV (Det Norske Veritas) version of the P-M spectra [28] is used in this paper.  
\[S_{\eta}=\frac{{H_{s}}^{2}T_{z}}{8\pi^{2}}(T_{z}f)^{-5}\exp[-\frac{1}{\pi}(T_{ z}f)^{-4}], \tag{2}\]  
where \(f\) is the wave frequency in cycles per second; \(H_{s}\) is the significant wave height, and \(T_{z}\) is the zero up crossing period; \(S_{\eta}\) is the single-sided P-M sea surface elevation spectrum.  
The interval between two adjacent upward zero-crossing points recorded by the wave surface is usually defined as a period. From the P-M spectrum, the relationship between the peak spectrum period and zero up crossing period satisfies [30]  
\[T_{p}=\left(\frac{5\pi}{4}\right)^{\frac{1}{4}}T_{z}\approx 1.41T_{z}, \tag{3}\]  
where \(T_{p}\) is the peak spectral period.