esa-sceva/satcom-chunk-collection huggingface dataset:
# FASTSET: A Fast Data Structure for the Representation of Sets of Integers
## 2 Implementation
### Insertion  
Each insertion happens at the end of the region of consecutive elements stored in \(\texttt{elem}[\texttt{ }]\). Since we have direct access to the last element through \(\texttt{elem}[0]\), the cost is \(O(1)\).  
void Insert( FASTSET* s, int newel ) {  if ( Belongs ( s, newel ) ) return // newel is already present in s  s->elem[0] := s->elem[0] + 1  s->elem[s->elem[0]] := newel  s->pos[newel] := s->elem[0]  } Please note that there is no need for a test of full-set condition, since there is enough space for the largest subset possible (namely the whole \(E\)), and no element can be repeated in this data structure. See Figure 1a-e for examples of insertions in a FASTSET and corresponding updates of the data structure.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Membership** & **Insertion** & **Deletion** & **List All** & \(\textbf{S_{3}}\)**=\(\textbf{S_{1}}\cap\textbf{S_{2}}\) & \(\textbf{S_{3}}\)**=\(\textbf{S_{1}}\cup\textbf{S_{2}}\) \\ \hline Bitmap / Opt. Hashtable & \(O(1)\) & \(O(1)\) & \(O(1)\) & \(O(N)\) & \(O(N)\) & \(O(N)\) \\ Bucket Hashtable & \(O(|S|)\) & \(O(|S|)\) & \(O(|S|)\) & \(O(b+Mm)\) & \(O(b+Mm)\) \\ Linked List / Unsorted PFA & \(O(|S|)\) & \(O(|S|)\) & \(O(|S|)\) & \(O(|S|)\) & \(O(Mm)\) & \(O(Mm)\) \\ Sorted PFA & \(O(\log|S|)\) & \(O(|S|)\) & \(O(|S|)\) & \(O(|S|)\) & \(O(s)\) & \(O(s)\) \\ AVL Tree & \(O(\log|S|)\) & \(O(\log|S|)\) & \(O(\log|S|)\) & \(O(|S|)\) & \(O(m\log M)\) & \(O(s\log s)\) \\ \hline FASTSET & \(O(1)\) & \(O(1)\) & \(O(1)\) & \(O(|S|)\) & \(O(m)\) & \(O(s)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of asymptotic worst-case time performance for the main set operations of the most used data structures. We let \(s=|S_{1}|+|S_{2}|\), \(m=\min\{|S_{1}|,|S_{2}|\}\) and \(M=\max\{|S_{1}|,|S_{2}|\}\). In the Bucket Hashtable row, \(b\) is the number of buckets.
## 4 Proposed Algorithm
### Proposed Enhanced Algorithm
#### 4.3.2 Hybrid Solution Crossover  
We consider a crossover method to hybridize solutions to enhance the exploration ability of our proposed algorithm. Specifically, the best solution for the population often contains the most information and can guide all individuals to explore in a better direction.  
In each iteration, we select some individuals and use the crossover with the best solution to improve them. The details of this method are as follows. As with the proposed hybrid solution initialization method, this crossover method operates on different classes of solutions separately.  
For the discrete part, we first find the discrete dimension which is different from the best solution and exchange information between the two. Subsequently, we judge whether the discrete part of the current individual satisfies the selected number of antennas. If the number is too large or too small, we invert the random selection solution from the individual until the number of antennas selected by the individual is appropriate.  
For the continuous part, we make individuals exchange information with the best solution with half the probability. The pseudocode for this method is shown in Algorithm 1.  
```
1 Define the related parameters: a solution \(X_{i}\), best solution \(X^{\prime}=[x^{\prime}_{1},x^{\prime}_{2},...,x^{\prime}_{N_{lim}}]\), dimension number \(N_{dim}\), etc.;
2 for\(n=1\) to \(N_{dim}/2\)do
3 if\(N_{rand}\)< 0.5 then
4 if\(x_{n}\)!=\(x^{\prime}_{n}\)then
5\(x_{n}\)=\(x^{\prime}_{n}\);
6
7 end if
8\(x_{n+N_{dim}/2}\)=\(x^{\prime}_{n+N_{dim}/2}\);
9
10 end if
11
12 end for
13 while\(sum\) (\([x_{1},x_{2},...,x_{N_{dim}/2}]\))== \(N_{sel}\)do
14 if\(sum\) (\([x_{1},x_{2},...,x_{N_{dim}/2}]\))> \(N_{sel}\)then
15 Select a dimension with value 1 and assign it to 0;
16
17 end if
18 if\(sum\) (\([x_{1},x_{2},...,x_{N_{dim}/2}]\))< \(N_{sel}\)then
19 Select a dimension with value 0 and assign it to 1;
20
21 end if
22
23 end for
24
25 end for Return \(X_{i}\);
```  
**Algorithm 1**Hybrid Solution Crossover.
# Textile-Based Flexible Coils for Wireless Inductive Power Transmission
###### Abstract  
Wireless inductive power transmission systems can potentially supply wearable devices. Power cables or batteries can be eliminated by implementing a wireless power transfer system, making the wearable devices less obtrusive to users. However, rigid coils can cause discomfort to users in wearable applications. The novel screen-printed flexible coils on textiles reported here are intended to be a low-cost and comfortable solution when integrated into clothing. A constant-width circular-spiral flat coil has been designed to minimize the detrimental effect of the low conductivity of the screen-printed flexible conductors on the efficiency of the wireless power transfer system. The coils are printed on 65/35 polyester/cotton textile with a screen-printed Fabink-UV-IF1 interface layer coating. The interface layer provides a relatively flat and smooth surface to prevent the permeation of the conductive paste into the textile and allows the printing of finer-profile coils. A 5 V 1.2 W DC output has been achieved by a wireless power transfer system using the printed flexible coils with Qi standard circuitry; a DC-DC efficiency of 37% has been measured.  
Keywords:inductive power transmission; coil design; smart textile; screen printing; resonant coupling +
Footnote †: journal: Applied Sciences
# Downregulation of Squalene Synthase Broadly Impacts Isoprenoid Biosynthesis in Guayule
## 4 Materials and Methods  
### Plant Transformation Plasmid Construction  
Plasmid _pPZP200_[59] provided the backbone for generating the pND9 and pND9_SQSi_ constructs used in this study. Plasmid pND9 consisted of a shortened potato polyubiquitin promoter (409-Ps) [60], driving the neomycin phosphotransferase II (_nptII_) gene for conferring kanamycin resistance [61], and an octopine synthase terminator (Ocs-T) [62], along with a MHP-P promoter [63] controlling \(\beta\)-glucuronidase plus (_GUSPlus_) gene [64] expression, and a ubiquitin 3 terminator (Ub3-T) [65]. The plasmid pND9_SQSi_ was constructed by replacing the _GUSPlus_ gene with an inverted repeat of the partial guayule _squalene synthase_ gene (_SQS_) (see below and Figure 1).  
The SQS gene sequence was obtained from the published sequence in the NCBI database (GenBank accession no. HQ131832.1). This sequence was used to blast the Guayule Genomic Resources ([[https://probes.pw.usda.gov/Guayule/](https://probes.pw.usda.gov/Guayule/)]([https://probes.pw.usda.gov/Guayule/](https://probes.pw.usda.gov/Guayule/)), accessed on 25 March 2022) to obtain the guayule SQS sequence (clc8 thAssembly_contig_273481). Forward primer 5\({}^{\prime}\)-AAAACAAAATCATCCGGTATTATCAGA-3\({}^{\prime}\) and reverse primer 5\({}^{\prime}\)-CTGAGGAATAGCACAAAAACCTG-3\({}^{\prime}\) were used to PCR the guayule cDNA template. The resulting 252 bp PCR product was subcloned into a pGEM-T vector (Promega, Madison, WI, USA) and sequenced to confirm its integrity. Subsequently, an inverted repeat was made by joining an Ncol-Bglll reverse fragment and a 552 bp BamHI-Spel BAR gene [66] and Spel-SacI forward fragment together, replacing the GUSplus gene in pND9. The plasmids pND9 and pND9_SQSi_ were used to transform _Agrobacteriumium_ EHA101 [67] competent cells. The transformed _Agrobacteriumium_ EHA101, harboring either pND9 or pND9_SQSi_, were used to transform guayule G7-11, a publicly available USDA germplasm line, distributed as AZ-2 [68].  
Plasmid _pND9-SQS_ (Figure 1A) was generated by replacing the _GUS_ gene with a full-length _SQS_ gene (GeneBank accession no. sT7 aPBC069F0650.ab1). Plasmid _pND9-SQSi_ was constructed by replacing the GUS gene with an inverted repeat (Figure 1A). The repeat contained a reverse complimentary 252 bp in its 5\({}^{\prime}\) end and the forward 252 bp in its 3 end. A BAR gene [66] of 552 bp was inserted into the middle of this inverted repeat. The plasmids _pND9_ and pND9-_SQSi_ were used to transform _Agrobacteriumium EHA101_ competent cells [67]. The transformed _Agrobacteriumium EHA101_, either harboring _pND9_ or _pND9-SQSi_, were used to transform into guayule _G7-11_. Wildtype and transgenic guayule lines were maintained in tissue culture, as previously described [12].
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 3 Mathematical Model
### Classification Model Based on LightGBM  
A model for predicting heart failure is built around the use of LightGBM. LightGBM is a fast and efficient gradient lifting framework, which is suitable for processing large-scale data sets and various types of machine learning tasks. It is used here to model the characteristic data related to heart disease to predict the disease situation.  
In order to obtain better model performance, several key parameters of LightGBM model are traversed and optimized. Different parameter ranges are defined, including the learning rate, number of leaf nodes, minimum sub-node weight, bagging ratio, and bootstrap sampling times. Through training and evaluation under different combinations of these parameters, we try to find the parameter setting that can make the model performance optimal.  
In this paper, KFold 50% cross-validation is used to evaluate the performance stability of the model on different data subsets. In the process of each cross-validation, the training set is further divided into small training sets and validation sets. By training the model on these small training sets and forecasting and evaluating the model on the validation set, we can obtain the performance indexes of the model under diverse data partitions. In this way, the generalization ability and stability of the model can be comprehensively evaluated.
# Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards
## 2 Background
### Object Detection  
Object detection is a computer vision task to determine an object's location by outputting a bounding box and, at the same time, classifying the object [22]. When multiple objects are detected on a particular image, this is referred to as multi-class object detection.  
Deep learning strategies are state-of-the-art for object detection problems [22]. These strategies can be split into two main classes: two-stage and one-stage methods. The two-stage methods, the R-CNN (region with convolutional neural network features) family, first generate region proposals and then classify each region. The one-stage method (YOLO model) executes the object detection task in a single forward through the neural network.  
YOLO stands for You Only Look Once, and it is a real-time object detection system. The general idea is that a single neural network divides the image into regions and then  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Recovered Elements for 1 kg of ICs**} \\ \hline \multirow{2}{*}{Ferrous metal} & \multirow{2}{*}{120 g} & Fe & 68.88 g \\ \cline{3-4}  & & Ni & 31.2 g \\ \hline Metallic & 390 g & Cu & 311.5 g \\ \hline Non-metallic & 490 g & Si & 411.6 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical processing of the discarded integrated circuit (IC) outcomes [20].  
predicts several bounding boxes for each region. Its probability selects those bounding boxes as a final result [16].  
This work used the third version of YOLO (YOLOv3), which has improved performance compared to earlier versions. Since the first version, YOLO's backbone for feature extraction and classification is a deep convolutional network. In YOLOv2, the backbone was Darknet-19, while in YOLOv3, it was replaced by Darknet-53, a deeper network. Both Darknet architectures are convolutional neural networks. YOLOv3 also replaced the final softmax layer used in YOLOv2 for independent logistic classifiers (with binary cross-entropy loss). This change made the model become a multilabel object detector.
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 3 Methods
### Data Preprocessing  
The aim is to explore the relationship between the human brain functional network and Alzheimer's disease using fMRI data. To achieve this, a series of preprocessing tasks was conducted on the original data. Initially, time difference correction [28] was performed, and time drift was corrected by resampling the original data so that data at all time points were aligned on the time scale. Next, head movement correction was carried out to eliminate the offset and deformation caused by head movement [29], resulting in a final image that more accurately reflected the brain activity area. Neuroimaging data from other sources were aligned to eliminate differences in their spatial positions. Finally, smoothing was performed to remove noise and extreme values by conducting spatial filtering on the image, rendering the data easier to analyze and interpret. Each preprocessed fMRI data point was then matched with the AAL standard template [30], resulting in data containing 116 brain regions. Ninety of these regions, belonging to the cerebrum, were selected for subsequent research. Pearson correlation coefficients were calculated between these brain regions, and the corresponding correlation coefficient matrices for the 90 brain regions were obtained, as shown in Figure 2's heatmap. To obtain the brain functional network, a soft thresholding method [31] was employed to binarize the correlation coefficient matrices, selecting the top five most strongly correlated brain regions for each region as edges in the brain functional network. Ultimately, 312 brain functional networks were obtained and stored as binary matrices. Six data features were then extracted from these brain functional networks, including degree centrality, betweenness centrality, clustering coefficient, eigenvector centrality, closeness centrality, and PageRank centrality. The continuous features were discretized using the equal-interval binning method, ensuring the stability of the GIN model and reducing the risk of overfitting. The brain functional network and corresponding node feature data obtained in the preprocessing part were used for subsequent experimental analysis.  
Furthermore, to better explore the relationship between human cognition and brain functional network, the AGE and PTGENDER fields from ADNI's clinical information were utilized as age and gender labels [32]. For the age labels, the data were normalized to fall within the [0, 1] range. For the gender labels, binary encoding was used, where 0 represents male and 1 represents female.  
Finally, the preprocessed data were expanded fivefold using the bootstrap method [33] to meet the data volume requirements of subsequent experiments.  
Figure 1: Framework diagram of DMT-GIN approach, including data processing part, GIN layer part, and FC layer part.
## 4 Discussion
While sandblasting and acid-etching techniques have limitations such as stochastic nature, risk of surface contamination, long manufacturing times, high costs, and the need for additional cleaning steps [33; 34], femtosecond laser treatment offers the advantage of creating uniform nanostructures with minimal contamination risk, making it a simple and predictable method. Based on these advantages, femtosecond laser-treated implants are expected to be easily mass-produced and effective in future dental implant processes. In this study, the evaluation was conducted on implants prior to functional loading, and rabbits, not humans, were used as experimental subjects. Consequently, this study does not provide insight into how femtosecond laser-treated titanium implants would perform in actual clinical settings. This limitation should be addressed in future studies.
## 1 Introduction
### Analysis Visualization  
Three-dimensional models of architectural facilities make displaying component information more intuitive and convenient. Users can directly provide feedback on the reliability assessment results of the BIM model, which is highly efficient. [PERSON] et al. [29] employed Dynamo to reflect the average hourly pollen concentrations around buildings of different heights using a variety of colors. Similarly, [PERSON] et al. [30] integrated environmental impact rating parameters as properties of each element within the BIM model. By mapping these parameter values to the corresponding properties of elements in the BIM model, they achieved a visualization of the environmental impact assessment of an infrastructure design project, which included several concrete bridges, a highway, and a large amount of steel sheet piles, using a color-coded scheme. [PERSON] et al. [31] utilized a color-coded scheme in Dynamo, assigning element IDs extracted during the information retrieval process to computational results and defining colors based on the reliability properties of each element to achieve a diversified visualization of the results.  
However, the aforementioned studies all visualized models by associating the properties of elements, such as materials, with different material colors according to varied computational results. This presupposed that the method is suitable for models composed of multiple components, such as pipes, beams, and columns. This approach has its limitations when dealing with structural entities like box culverts. During the modeling process of a box culvert, a culvert section is usually treated as a unified entity, and the aforementioned method would only allow for changes in a single color. This does not correspond with actual structural stress analysis, where different stress points on a structural surface correspond to different stress values. Therefore, a range of colors is necessary to accurately reflect the stress condition of a box culvert structure. To address this issue, researchers need to develop more sophisticated visualization techniques to represent the structural stress distribution more accurately within the BIM model.  
Based on the above findings, this paper explores the application of BIM technology to box culverts. For box culvert engineering, starting from model creation, a BIM parametric modeling method suitable for box culverts is proposed to improve modeling efficiency and to fully realize information interaction. On this basis, a method for converting box culvert BIM models to Midas Civil finite element models and a way to visualize internal force are proposed, which effectively extend the research and application of BIM technology in culvert engineering.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 1 Introduction
### Communication versus Computational Complexity  
If the communication speed is extremely low, then the time needed to run cryptographic algorithms is no longer a bottleneck. IoT processors are very capable in this respect (see, for example, Figures 2 and 3 in [4] for SHA256, SHA3-256, and AES performance on MAX32620, MSP432P401R, and STM32L073 RZT6 microcontrollers). Moreover, their functionality can be extended with a cryptographic coprore  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method of Message Authentication** & **Size of the Authentication Component** \\  & **(in bits)** \\ \hline ECDSA signature on a 256-bit curve & 512 \\ \hline FALCON 512 post-quantum signature & \\ (with parameters for NIST security level 1) & \(>\)5000 \\ \hline \(n\) authentication tags, each 20-bits long & \\ (a separate tag generated for each receiver, & \\ probability of a forgery of a single tag: 2\({}^{-20}\)) & \(n\cdot 20=100\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different message authentication options for a message multicast to \(n=5\) receivers.  
Apart from the time cost, there is also the issue of energy consumption. In the case of battery-operated devices deployed in the field (e.g., underwater sensors), recharging or replacing the batteries is difficult or even impossible. On the other hand, energy consumption for a given kind of a physical channel cannot be optimized very much due to the laws of physics. This results in the energy cost for sending a bit being significantly higher than for computing it, even if hard cryptographic functions are concerned. This can be clearly seen in the examples of two publications: in [4], the energy consumed for cryptographic computations was measured in \(\upmu\)) (in fact, the most demanding computations investigated in [4], that is, elliptic curve point multiplication, yield consumption on the level of mJ) or in \(\upmu\)/B, while in [8] the energy consumed for sending packets was on the order of joules. However, in this case, the energy consumed strongly depends on the distance and frequency of the signal. In [9], more factors impacting the energy consumed for communication are indicated, including temperature and pH value.
# Are Electromyography Data a Fingerprint for Patients with Cerebral Palsy (CP)?
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Variable**} & **Gait Quality** & **Gait Quality** & \multirow{3}{*}{**No.**} & \multirow{3}{*}{**Mean (SD)**} & \multirow{3}{*}{**Mean (SD)**} & \multirow{3}{*}{**Mean (SD)**} & \multirow{3}{*}{**Between**} & \(p\)**-Value** & \(p\)**-Value** & \(p\)**-Value** \\  & **Condition** & & & & & & **Between** & & **Between** & **Between** \\  & **Before** & **After** & & & & **Between** & & **and** & **and** \\  & **Surgery (E1)** & **Surgery (E2)** & & & & & **Allid_to_Good** & **Mild_to_Good** & **Mild_to_Good** & **Mild_to_Good** \\  & & & & & & & & **and** & **and** \\  & **Surgery (E1)** & **Surgery (E2)** & & & & & & **Allid_to_Mild** & **Mild_to_Poor** \\  & & & & & & & **in E1** & **in E1** & **in E1** \\ \hline \multirow{3}{*}{GDI} & Mild & Good & 15 & 55.99 (3.7) & 86.55 (4.9) & \multirow{3}{*}{**<0.001**} & \multirow{3}{*}{0.09} & \multirow{3}{*}{0.374} & \multirow{3}{*}{0.128} \\  & Mild & Mild & 21 & 54.41 (3.1) & 73.05 (3.4) & & & & **\\  & Mild & Poor & 21 & 55.39 (2.4) & 58.9 (7.2) & & & & \\ \hline \multirow{3}{*}{GPS} & Mild & Good & 15 & 14.56 (1.3) & 6.9 (0.8) & \multirow{3}{*}{**<0.001**} & \multirow{3}{*}{0.065} & \multirow{3}{*}{0.379} & \multirow{3}{*}{0.149} \\  & Mild & Mild & 21 & 15.19 (1.2) & 9.49 (0.8) & & & & \\  & Mild & Poor & 21 & 14.77 (0.9) & 13.72 (3.2) & & & & \\ \hline \multirow{3}{*}{EDI} & Mild & Good & 15 & 83.41 (4.4) & 84.55 (4.9) & \multirow{3}{*}{0.291} & \multirow{3}{*}{0.138} & \multirow{3}{*}{0.14} & \multirow{3}{*}{0.448} \\  & Mild & Mild & 21 & 81.69 (4.7) & 83.12 (5.8) & & & & \\  & Mild & Poor & 21 & 81.99 (4.0) & 83.42 (4.5) & & & & \\ \hline \multirow{3}{*}{EPS} & Mild & Good & 15 & 71.35 (8.9) & 69.64 (10.2) & 0.294 & \multirow{3}{*}{0.194} & \multirow{3}{*}{0.183} & \multirow{3}{*}{0.207} & \multirow{3}{*}{0.479} \\  & Mild & Mild & 21 & 74.82 (10.3) & 71.62 (12) & & & & \\ \cline{1-1}  & Mild & Poor & 21 & 73.78 (8.8) & 70.85 (8.9) & & & & \\ \hline \multirow{3}{*}{DMC} & Mild & Good & 15 & 81.91 (7.3) & 79.98 (7.5) & 0.252 & \multirow{3}{*}{0.112} & \multirow{3}{*}{0.293} & \multirow{3}{*}{0.225} \\  & Mild & Mild & 21 & 78.14 (10.3) & 78.32 (8.2) & & & & \\ \cline{1-1}  & Mild & Poor & 21 & 80.07 (8.5) & 80.5 (7.1) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of gait and EMG indices among participants with mild gait quality at baseline (E1) across responders according to their post-operation gait quality.