esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Key Technologies of Data Processing Mechanisms in Ternary Optical Computer
## 3 Computation-Data Files  
### Principles of Computation-Data File Construction  
The computation-data file is the sole method of information exchange between users and the ternary optical computer when processing large volumes of data. The data entered by users must strictly adhere to the protocol requirements of the computation-data file to be recognized and computed by the TOC. The header and the data area are two important parts of the computation-data file. The header records the overall information of the file as well as all of the user's computational needs. And the TOC's task management system will allocate processor bit resources and reconfigure the TOP based on the information in the file header. The data section stores all the raw data input by the user and also places the computed results returned by the TOC to the user. In the latest version of the computation-data file, the file header occupies 26,920 bytes, and the data section ranges from 0 to (1T - 26,920) bytes. The principles for defining the format of the computation-data file are as follows:  
1. **File header:** The file header. It includes all the information needed to reconfigure the processor, as well as the user's priority, the number of computational units, and the total amount of data to be computed.
2. **Ease of parsing:** Because the task management system for parsing computation-data files runs on a secondary PC that typically processes binary data in 32- or 64-bit units, the design of the computation-data file header must be compatible with the current processing requirements of the PC.
3. **Reserved expansion bits:** As the TOC continues to evolve and the problems it can handle become increasingly complex, the parameters and functions contained in the computation-data file will also become more enriched and varied. To accommodate these changes, it is necessary to reserve some bits in the appropriate positions of the computation-data file header to facilitate future upgrades of the computation-data file version.
## 4 Case Study and Results
### Baseline Model Development  
Once the underlying behavior of the operation is understood from the Petri net and the dependency graph, and cycle times are calculated, a model is built using a discrete event simulation software named jStrobe (2016)([PERSON] and [PERSON], 2016). This is a construction-oriented discrete event simulation package that allows the user to model earthmoving operations as a chronological sequence of events with the help of a user-friendly graphical interface. jStrobe allows the user to model a variety of different operations using different levels of detail. Figure 12 shows the complete DES model. The relationship between activities is defined from the discovered process (i.e., Petri net) and the primary input to the model is the distribution of cycle times. The primary three components of the DES model are queue, activity, and link. Queues are nodes holding the resources until any activity draws them away and are denoted by circular shape. It can be in Figure 12 that the number of trucks (e.g., denoted as 'truck1' and 'truck2'), and excavators (e.g., denoted as 'exc1' and 'exc2') are modeled using queues. These four queues represent the four placeholders for these resources. Moreover, the soil stockpile, as well as the dumped soil, is also modeled using queues. Activities are nodes that represent the tasks to be performed in the model using the required resources. There are two types of activities, combi and normal. Combis are activities whose startup depends on certain conditions being met, denoted by rectangles with a beveled corner. On the other hand, Normals are activities that start starts as soon as the preceding activity is completed and are denoted by the rectangular symbol. In Figure 12, the loading with excavator 1 ('loadExc1') can only start when there are resources in the 'truck1' and 'exc1' queues. Each activity has a time duration that follows a distribution, and in this paper, a 'triangular' distribution is used for the cycle time of the activities. The third major component of the DES model is linked. The different nodes in the network are connected by links. The resources from one node to another are passed through the links by meeting certain conditions. For example, in Figure 13, when there is more than one truck  
Figure 12: Distribution of loading time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Activity**} & \multicolumn{3}{c}{**Duration (Minutes)**} \\ \cline{2-3}  & **Mean** & **Std. Dev.** \\ \hline Loading & 2.49 & 0.23 \\ \hline Hauling & 15.02 & 1.77 \\ \hline Dumping & 0.83 & 0.18 \\ \hline Returning & 15.55 & 2.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean and standard deviation of each activity.  
in the 'truck1' queue, the 'loadExc1' activity pulls one truck through the link. Once the baseline DES model is created, this is further updated using the Bayesian updating method as the project progresses.
###### Abstract
Due to the presence of oxygen, GONPs have greater solubility in various solvents, allowing an ideal adsorbent material for extraction of small molecules, bulky and aromatic compounds and large biomolecules [34; 35; 36]. A GONP/silica hybrid stationary phase aminated and functionalised with C18 ligands was one of the first instances in which GO was used in liquid chromatography; here the separations were dominated by \(\pi\)-\(\pi\) interactions. GONP-modified stationary phases resulted in changes of elution order of analytes including polyaromatic hydrocarbons (PAHs), anilines and phenols when compared to commercial C18 stationary phases [37]. Another GONP/silica hybrid stationary phase bonded with C18 ligands was modified with gold NPs and used in reverse-phase liquid chromatography (RP-HPLC) and hydrophilic interaction liquid chromatography (HILIC) for separation of isomeric dihydroxylbenzenes, alkylbenzenes, nucleosides and amino acids [38]. Additionally, the use of GONP-functionalised monoliths have also been demonstrated where GONPs were silanised and used as a crosslinker for the polymerisation of glycidyl methacrylate and ethylene glycol dimethacrylate [39]. The resulting monoliths were shown to separate mixtures of steroids as well as more polar analytes such as aromatic amines. In addition to utilising GO-modified stationary phases in liquid chromatography, the use of GO in stationary phases as conductive stationary phases is of increasing interest. Increased conductivity is generally achieved by the reduction of the GONPs with hydrazine hydrate, however the reduction is also possible using ascorbic acid. The enhanced conductive properties allow for interesting electrochemically functionalised separations. Such modifications have already been demonstrated in the separation of acid nitrophenol isomers, basic nitroaniline isomers, and neutral PAHs using open tubular capillary electrochromatography (OTEC) [40]. To date however, polyHIPE modifications with GONPs have not been explored. In this study GONPs measuring 250 nm in size were incorporated into polystyrene-divinylbenzene polymeric high internal phase emulsions (PS-_co_-DVB polyHIPEs) in an attempt to align the amphiphilic GONPs along the aqueous phase-organic phase interface while simultaneously retaining the open pore structure of the polyHIPE. A major limitation of polyHIPE materials is their low surface area. The addition of GONP was a hypothesised novel method to overcome this limitation. Resultingnovel GONP-modified polyHIPE chromatographic stationary phases were fabricated and successfully applied for the RPLC separation of alkylbenzenes. The GONP-modified columns were reduced in situ by flushing with ascorbic acid. Despite the modified materials having lower surface areas than the unmodified material, the GONP polyHIPEs showed comparable separation of alkylbenzenes to that of the higher surface area polyHIPE and superior separation to GONP/silica hybrid stationary phases in the literature [38].
## 2 Materials and Methods
### Orthogonal Arrays  
The sonography protocol settings for the common iliac artery assigned five factors: angle of probe, water depth, sonography preset frame rate, amplitude gain, and imaging compression ratio. Thus, a total set of 162 (\(2^{1}\times 3^{4}\)) combinations of 5 factors was analyzed (each factor was categorized into 2 or 3 possible levels). The arrangement of samples into only eighteen groups via [PERSON]'s analysis ensured the same confidence level of results as the conventional optimization of processes [15]. A typical Taguchi L\({}_{18}\) (\(2^{1}\times 3^{4}\)) orthogonal array is presented in Table 1, where the digits in each column indicate the levels (i.e., practical arrangements) of particular factors from A to E. Various factors are summarized in Table 2, and the importance of factors is described as follows: (A) angle of probe: the probe needs to be preset vertically or horizontally to suppress the uncertainty in theory while practical surveys always suggest the angle alignment to acquire better echo reflection; (B) water depth: short wavelengths can penetrate a deep water depth and still maintain the imaging quality; yet, under some conditions, short wavelengths imply high frequencies, which cause unexpected noise; (C) sonography preset frame rate: a high frame rate can provide a comparative long persistence of vision, yet, too high a frame rate may also mislead the instant distinguishing of the artifact; (D) amplitude gain (because it can intensify the received signal, but too a high gain may also increase the unwanted noise in reality); and (E) imaging compress ratio: a small compress ratio can provide contrast and sharp image for diagnosis, but too small a setting may ignore essential information and misinterpret the sonography images [16].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Group**} & \multicolumn{5}{c}{**Factor**} \\ \cline{2-6}  & **A** & **B** & **C** & **D** & **E** \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: The standard Taguchi’s L\({}_{18}\) (\(2^{1}\times 3^{4}\)) orthogonal array, where the numbers in each column, except the first column, indicate the specific factor (A–E) level or practical arrangement.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Factor**} & \multicolumn{3}{c}{**Levels**} \\ \cline{2-4}  & **1** & **2** & **3** \\ \hline (A) angle of probe (degree) & 0 & 30 & \\ (B) water depth (cm) & 5 & 6 & 7 \\ (C) frame rate (/sec) & 45 & 50 & 55 \\ (D) amplitude gain (\%) & 50 & 60 & 70 \\ (E) compress ratio (\%) & 45 & 50 & 55 \\ \hline \end{tabular}
\end{table}
Table 2: Five factors (each bearing two or three levels) used in the proposed sonography protocol, according to [PERSON]’s settings in Table 1.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 4 Experiments Demonstrating Opportunities and Challenges in Hybrid RF-Acoustic Positioning
### Conducted Experiments
#### 4.2.2 Ranging-RF
Another solution would be to position the dipole antenna on the perpendicular axis since it has an omnidirectional radiation pattern in this plane.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 4 Clinical Problems
### Does Mycobacterial Biofilm Exist In Vivo?  
The features of biofilm formed by mycobacteria have been described in detail; however, the existence of biofilm in vivo was verified only within the last few years. Researchers have illustrated several features of Mtb infections, such as prolonged treatment, immune escape, and a high recurrent rate, all of which resemble microbial biofilm infection [6]. In 2007, [PERSON] and colleagues infected guinea pigs with Mtb and found persistent bacteria at the acellular rim of primary granuloma shaped like microcolonies and clusters. They hypothesized that Mtb might have formed biofilm in vivo [64]. This hypothesis was further supported by the detection of mycolic acid and Mtb in the same area [65]. Recently, cellulose was used as a biomarker to identify the existence of biofilm in humans [66]. Cellulose is a crucial composition of Mtb biofilm but is absent in human cells [66]. [PERSON] and colleagues demonstrated Mtb biofilm formation in the lung tissues of infected mice and macaques using calcofluor white stain and cellulose-targeting fluorescence probe [6]. They further observed the existence of Mtb biofilm in the autopsied lung samples from humans infected with Mtb [6]. In addition, some mycobacterium strains isolated from patients can form biofilm in vitro [67; 68]. These demonstrate the presence of Mtb biofilm in humans to a large extent, from which a series of clinical problems ensue.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Cluster Head Election Strategy
#### 4.2.4 Weighted Metric Criterion  
Based the above \(D_{i}\), \(E_{i}\) and \(L_{i}\), the updated criteria of cluster head measurement weight is expressed as  
\[R=a_{1}D_{i}+a_{2}E_{i}+a_{3}\frac{1}{L_{i}}, \tag{19}\]  
where \(a_{1}\), \(a_{2}\), \(a_{3}\) are the weights of the three factors, and \(a_{1}+a_{2}+a_{3}=1\). Moreover, these weights are defined on the interval \([0,1]\). The specific proportion coefficient's value can be assigned according to task requirements. Through this criterion, the randomness of cluster head election and the defect of falling into local optimal solution can be avoided effectively.  
Based on the definition of the three factors, the cluster head selection is proposed, of which the pseudocode is listed in Algorithm 2.  
```
0: The optimal number of clusters \(n\), clusters set \(CL=\{CL_{1}\), \(CL_{2}\),.., \(CL_{n}\}\)
0: Every node gets information about its adaptive node degree and distance by HELLO message sent by the neighbor nodes
1: Every node \(i\) calculates the weight \(R_{i}\) by the Equation (19)
2: The node with the largest value of \(R\) in each cluster is set as the cluster head and broadcasts its message within the cluster
3:return\(CH_{1}\), \(CH_{2}\),..., \(CH_{n}\)
```  
**Algorithm 2** Cluster Head Selection Algorithm.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 3 Rosenbrock-Algorithm-Based Beam Training
### Rosenbrock Search  
Instead of calculating the derivative, only simple evaluations of the objective function are required. The function gradient is then approached by a two-step search (i.e., probes and pattern move).
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 4 Experimental Planning and Test Bed Design
### Specifications
#### 4.1.1 Hardware  
The materials necessary to perform the experimental measurements include a USRP N210 from Ettus Research, an antenna, etc. A brief description of each of these components is given below.  
* USRP N210: It is an SDR product that enables designing and implementing software radio systems. The USRP N210 presents high-dynamic range processing capability and high bandwidth [36]. It is compatible with a wide range of Radio Frequency (RF) modules called daughter-boards. In receiving mode, the samples acquired by the daughter card are scanned by the Analogue-to-Digital Converter (ADC), then converted to the desired intermediate frequency (IF). Finally, the sequence of samples is multiplied by a sine and a cosine to obtain respectively the two components I and Q. The motherboard is connected to a daughter-board allowing the transmission and reception of analogue RF signals. On its daughter-boards, the signal is filtered, amplified and put on a baseband frequency that depends on the IF band of the board and the frequency of the local oscillator. These cards also include basic Rx/Tx cards without conversion frequency or filtering. The two daughter-boards used in this paper are RFX 2400 and SBX. These cards can operate in duplex mode, integral for simultaneous transmission and reception. They offer a bandwidth up to 50 MHz and cover the 2.4-GHz band.
* Antenna: The antenna used is the _ANT_-2.4-CW-HW model by Link. It belongs to the HW series \(1/2\)_-wave_ center-fed dipole antennas. It is an omnidirectional antenna in wide cover rubber. It uses a horizontal polarization and is connected to the USRP via a SubMiniature Version A (SMA) connector. The characteristics of the antenna are among others a frequency range between 2300 and 2600 MHz, a gain of 3.2 dBi and a maximum power of 50 W.
* Generator bar: To generate the impulsive noise in the laboratory, we use the generator bar as described in Section 3.
### Major Contribution  
The major objective of this paper is to investigate the energy consumption profiles of commercial, industrial, and residential loads. A sophisticated DSM control technique is proposed, which reduces the power consumption during the peak hours. The characteristics of NB-PLC when it communicates over low voltage (LV) channels, MV channels, and transformer bridge are examined in depth for various types of loads. The influence of rise in temperature on power consumption profile of all three types of consumers is studied and optimally controlled with the DSM control technique, which reduces power consumption, especially during peak hours. A comprehensive comparison of NB-PLC performance on residential, commercial, and industrial sites will help to analyze the feasibility and characteristics of their corresponding power line channels for DSM services. The DSM control technique presented in this paper has been previously investigated in South Asian countries, such as in Pakistan, as per the authors' knowledge. This research work will facilitate the designers and researchers to evaluate the performance reduction due to mutual interference of coexisting NB-PLC channels. The SNR profiles will help to design the procedures that will improve forward error correction (FEC) when NB-PLC signals pass through transformer bridge and power line channels according to noise types in South Asian countries like Pakistan. The present example shows the improved performance of G3-PLC as compared to PRIME in the presence of transformer bridge and NB-PLC channel noises. However, power spectral density and dynamics of NB-PLC noise in South Asian countries are different from Europe, for which there is a need to improve the FEC codes. Moreover, SNR profiles will help designers to estimate the processing power required for additional codes.
# Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF
## 3 Hybrid Algorithm UMAP-RF
### UMAP-RF Hybrid Algorithm  
This paper proposes a hybrid algorithm UMAP-RF, reduces the dimensionality of UNWS-NB15 dataset by the UMAP algorithm and thus improves the speed and accuracy of network attack traffic detection. The random forest algorithm is improved based on parameter optimization, and the dimensionality reduction data are bifurcated with the improved random forest algorithm. The abnormal data distinguished after bifurcation are classified to complete the network attack traffic detection task.  
The UMAP algorithm is chosen as the dimensionality reduction method for the dataset in this paper mainly because it can preserve the data structure after the classification algorithm as much as possible. UMAP algorithm focuses more on the visualization of the classified data structure, and the UMAP visualization graph can intuitively represent the relationship between individual data features in the two-dimensional plane. For high-dimensional network-traffic-type datasets, this dimensionality reduction method is very effective for visualizing binary and classifying network attack traffic detection.  
Improving the random forest algorithm based on parameter optimization aims to prevent overfitting when classifying more features in the UNSW-NB15 dataset and further enhance the random forest algorithm classification performance. The optimized random forest algorithm can effectively distinguish between normal and abnormal data and classify abnormal data. The accuracy obtained after classification is much higher compared to traditional machine-learning algorithms. The Figure 1 and Table 1 are training steps of the UMAP-RF hybrid algorithm and UMAP-RF hybrid algorithm flow chart.  
Figure 1: UMAP hybrid algorithm.  
1. Divide the UNSW-NB15 dataset into 70% training set, 10% validation set (Set hyperparameters) and 20% test set;
2. Convert the non-numerical features to numerical values and remove non-numerical features, such as proto, service and state, from the dataset to obtain dataset \(\delta\);
3. The processed dataset \(\delta\) is dimensionally reduced by the UMAP algorithm to obtain dataset \(\theta\);
4. Optimize the parameters of the random forest algorithm with the number of base evaluators and the maximum number of leaf nodes. n_estimators is set to 1000, and max_leaf_nodes is set to 10 by manual parameter tuning;
5. Import the dataset \(\theta\) obtained after UMAP dimensionality reduction into the improved random forest algorithm for training. Classify normal data \(\alpha\) and abnormal data \(\beta\), and complete malicious traffic detection;
6. Import the detected abnormal data \(\beta\) into the improved random forest algorithm to classify the abnormal data \(\beta\), and obtain each specific type of network attack traffic.
# _electronics_
## 2 Satellite Network Model and Cluster
The distance \(d_{ij}\) between node \(i\) and node \(j\) is derived by:  
\[d_{ij}=\sqrt{R_{i}^{2}+R_{j}^{2}-2R_{i}R_{j}\cos\theta} \tag{3}\]  
where \(R_{i}\) and \(R_{j}\) are the orbital radii of \(S_{i}\) and \(S_{j}\), respectively. The process of clustering satellite nodes is as follows:  
* In the initial stage, the node calculates its degree of connection \(degree_{i}\) according to the formula, periodically broadcasts the hello package to exchange information with neighbor nodes, obtains the degree of connection \(degree_{j}\) of neighbor nodes and the distance \(d_{ij}\) between nodes, and adds the above information to the node's neighbor list; the list is sorted in the ascending order of the distance between nodes. * Each node compares the connection degree with its neighbor nodes; if it finds that its connection degree is the largest among the one-hop neighbors, and it is an unclustered node, it will become a cluster head node and broadcast a clustering message. * All nodes listen to the message. If the node is not the cluster head and receives the clustering message, it sends a joining request to the cluster head node with the highest connection degree. The cluster head node selects nodes to join the cluster from top to bottom according to the neighbor list nodes. The node that successfully joins the cluster broadcasts the joining message. If nodes with higher connection degrees among the neighboring nodes have broadcast the joining message, then the current node is designated as the cluster head node and proceeds to broadcast the clustering message. * The above process is iterated multiple times until all nodes are classified into the cluster structure. When a node enters a cluster, the head node of the cluster will randomly assign the identity of the node (Node ID) to this node. After clustering, we can use \(W=\{w_{1},w_{2},\cdots,w_{u}\}\) to represent the cluster of satellite nodes; \(u\) is the maximum cluster structure number, \(w_{u}=\{w_{u1},w_{u2},\cdots,w_{uw}\}\) represents the clustered collection of satellite nodes, and \(w_{uv}\) represents the satellite node \(v\) of the cluster \(u\).