esa-sceva/satcom-chunk-collection huggingface dataset:
# HVdc Circuit Breakers: Prospects and Challenges
## 6 More Recent Activities and HVdc Breaker Related Technologies
### Fault Current Limiters  
Most of the concepts used to accomplish the basic requirements of dc breakers, as discussed earlier, are applicable to fault current limiters (FCL) in both dc and ac systems. FCLs are deployed to limit the fault current in power systems, as the name infers. Thus, FCL would increase the impedance of the system either self-triggered or externally triggered. Further, FCL has to handle a large amount of dissipated energy during the limitation. Additionally, some FCL can interrupt the fault currents. However, sometimes, a load break switch in series of FCL is placed to interrupt the fault current if alone FCL cannot do so. Interested readers are recommended to see the literature on FCL in [121; 122], as no detailed discussion on FCL is repeated in this article.  
Solid-state current limiters (SSFLs) [123; 124], and hybrid FCL using fast mechanical switches [125] are two important topologies but both circuits have not been realized for high voltages so far. However, medium voltage FCLs (fuses) are self-triggered with interruption ability but are one-shot devices, need to replace manually, and widely available up to 10-20 kV [126; 127]. Superconducting FCLs of resistive type use an intrinsic physical property of superconductors to lose its zero resistance above a critical current density. Such FCLs have low conduction losses, resettable and fast, but extensive cooling is required to acquire a superconducting state.  
Mostly, FCLs are designed and deployed on distribution levels (36 kV). However, only resonance links and limiting reactors with capacitors are being used for high voltage applications; transmission and sub-transmission lines [122]. Researchers are working todevelop high voltage FCLS [128; 129]. Any progress in this field would also benefit the HVdc breaker activities.
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 2 Technical Background
### Lll_FP Algorithm
This procedure is often called in the LLL_FP algorithm to maintain the orthogonal vector space structure, to support subsequent truncation and swapping operations, and finally, to correct the error by Kahan summation. ```
1:functionCompategGS(**B**, **B\({}_{1}\)**, **mu**, **b**, **c**, \(k\), _bound_, _st_, **buf**)
2: Set number of columns \(n\) of B
3:if\(st<k\)then
4: Initializebuf
5:endif
6:for each row \(j=st\) to \(k-1\)do
7: InnerProduct s
8:if\(\textbf{b}[k]\) far greater than \(\textbf{b}[j]\)then
9:if\(s\) is not satisfies precision then
10: Precisely compute \(s\)
11:else
12:\(s\)
13:endif
14:else
15:\(s\)
16:endif
17:for summing terms do
18: Update \(t1\)
19:endfor
20: Compute \(\textbf{mu}[k][j]\)
21:endfor
22:if low precision then
23: Use Kahan summation for \(c[k]\)
24:else
25: Ordinary summation for \(c[k]\)
26:endif
27:endfunction
```  
**Algorithm 1** Calculation of Gram-Schmidt coefficient. The parameters are the large integer-type lattice basis matrix B, the floating-point-type matrix B1, the floating-point-type array mu for the Gram-Schmidt orthogonalizationcoefficients, the squared norm matrix b for the rows of B1, the squared norm matrix c for the Gram-Schmidt orthogonal vectors, the intermediate variable matrix buf, and t1 as an intermediate variable. By combining the above steps, the flowchart of LLL_FP algorithm in Figure 1 and Algorithm 2 are obtained. In the algorithm flowchart, the steps of the algorithm start with the initialization of the variables, followed by the calculation of the Gram-Schmidt coefficient, reduction, deep insertion, and finally, line exchange and advancement. If the condition is met, it proceeds to the next iteration of the loop; if the condition is not met, the algorithm terminates. ```
1:functionLLL_FP
2: Initialize loop parameters init_k and rst
3:for\(k=\) init_k...m do
4: Calculation of Gram-Schmidt coefficient
5:for\(j=\) rst\(-\)1...1 do
6:if the condition (6) is not satisfied then
7: Row transformation
8:endif
9:endfor
10:if deep insertion condition is met then
11: Perform deep insertion
12:endif
13:if condition (7) is not satisfied then
14: Perform row swap
15:endif
16:endfor
17:return\(m\)
18:endfunction
```  
**Algorithm 2** LLL_FP Algorithm  
In Algorithm 2, the process starts with initializing the loop parameters init_k and rst in line 2. Gram-Schmidt coefficients are computed in a loop at line 4 for subsequent size reduction operations. This is followed by size reduction at lines 5 to 8, aiming to make each vector of the matrix smaller and orthogonal. Deep insertion occurs at lines 10 to 12. If it is found that the length of the current row is too large or does not meet the required orthogonality or normalization conditions, the row needs to be inserted into a more appropriate position. Finally, line exchange and advancement are carried out. These operations continue until the conditions fail to be satisfied, at which point the algorithm terminates. Figure 1: LLL_FP algorithm flow.
## 8 Query/Data Protection Mechanism  
Queries and data are encrypted and integrity-protected using the session keys established through the key agreement protocol. To prevent replay or injection of messages, we use both a _session number_ (\(SN\)) and a _message sequence number_ in query and data messages. The session number is randomly changed whenever the keys are updated (if the master key has expired, the session number is re-generated by the sending mote as part of re-running the entire key agreement protocol; if only the session keys are being updated, the session number is updated via the nonce exchange procedure). The base station needs to keep a list of recently used session numbers in order to detect replay attacks. This list can be stored efficiently using a bloom filter. The base station and the mote also increase their message sequence numbers for each new query or data message. Received messages with an unexpected session number or sequence number are discarded. Note that it is difficult for an outside attacker to guess a session number in the first place.  
To save code space, we use the same symmetric cipher and MAC function in the query/data protection mechanism as in the key agreement protocol discussed previously (RC5 in ciphertext stealing mode and HMAC-SHA-1, respectively).
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 3 System Design and Algorithm
### GLAN Algorithm
The overhead is reduced by allowing the same cluster to send and receive data by connecting to the IP through location information, without the need for a routing table. In addition, the reliability of broadcast data that anyone can access is secured through the UAV swarm ad hoc key.
## 5 Modified MLE MMM Model Based on D-S Theory
### D-S Information Fusion  
(1) Two Reliability Functions Fusion  
\(Bel_{1}\) and \(Bel_{2}\) are two reliability functions of the same target framework set, \(m_{1}\) and \(m_{2}\) are the basic reliability allocation of function \(Bel_{1}\) and \(Bel_{2}\), respectively. MLE probabilities of the tangential and normal MMM characteristic value are selected as the \(Bel_{1}\) and \(Bel_{2}\). \(C\) and \(D\) represent the defect levels of tangential and normal signals, respectively.  
\[\sum_{C_{i}\cap D_{j}=\varphi}m_{1}(C_{i})m_{2}(D_{j})<1 \tag{21}\]  
Then, the basic reliability allocation is:  
\[m(C)=\left\{\begin{array}{cc}0&C=\varphi\\ \frac{\sum_{C_{i}\cap D_{j}=C}m_{1}(C_{i})m_{2}(D_{j})}{1-k}&C\
eq\varphi\\ \end{array}\right. \tag{22}\]  
\[k=\left(\sum_{C_{i}\cap D_{j}=\varphi}m_{1}(C_{i})m_{2}\big{(}D_{j}\big{)}\right) \tag{23}\]  
where \(k\) represents the inconsistency factor. \(i,j=1\), 2, 3 represent the level of defect levels, which are: 1 is normal state, 2 is hidden crack state and 3 is macroscopic crack state.  
(2) Decision rules of D-S theory  
\[m(C_{1})=\max(m(C_{i}),C\subset\Theta) \tag{24}\]  
\[m(C_{2})=\max(m(C_{i}),C\subset\Theta,C_{i}\
eq C_{1}) \tag{25}\]  
\[\left\{\begin{array}{c}m(C_{1})-m(C_{2})>\varepsilon_{1}\\ m(\Theta)<\varepsilon_{2}\\ m(C_{1})>m(\Theta)\end{array}\right. \tag{26}\]  
The recognition result is C1 when meeting the conditions of Formulas (24) to (26), where \(m(\Theta)\) is the uncertainty degree, and \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are set as threshold value.
# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 2 Basics of Superpixel Clustering and Sparse Representation (SC-SR) Algorithm
### Simple Linear Iterative Clustering Algorithm for Segmentation  
this paper, a simple linear iterative clustering (SLIC) algorithm was selected to generate compact and nearly uniform superpixels, since it has better performance in running speed, superpixel compactness and contour preservation in comparison with other methods [11]. SLIC generates superpixels by clustering pixels based on their similarity in color and proximity in the image plane [12]. This method seamlessly applies to color as well as grayscale images via replacing color similarity with gray information similarity. In this paper, experiments were conducted on grayscale images.  
SLIC is essentially a local k-means clustering method. Given the total number of image pixels \(N_{p}\) and the desired number of superpixels \(N_{s}\), cluster centers are sampled at a regular grid \(S=\sqrt{N_{p}/N_{s}}\). To speed up the generation of superpixels, SLIC assigns each pixel \(p_{i}\) to the nearest cluster centers within the local region of the pixel \(p_{i}\) rather than the whole image plane. The size of the local region is \(2S\times 2S\) that takes the pixel \(p_{i}\) as the center.  
For grayscale images, gray and space information are taken into consideration for describing a pixel as a vector. Instead of using a simple Euclidean norm, the distance measure \(D_{s}\) is defined as follows:  
\[D_{s}=d_{g}+\frac{m}{S}\times d_{xy}, \tag{1}\]  
where \(d_{g}\) is the gray distance, and \(d_{xy}\) is the plane distance normalized by the grid interval \(S\). A variable \(m\) is introduced to control the compactness of a superpixel. The greater the value of \(m\), the more spatial is emphasized and the more compact the cluster, and we empirically set \(m\) to 10 [12]. The values of \(d_{g}\) and \(d_{xy}\) are obtained as follows:  
\[d_{g}=\sqrt{\left(g_{i}-g_{j}\right)^{2}}, \tag{2}\]  
\[d_{xy}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}}, \tag{3}\]  
where \(g_{i}\) and \(g_{j}\) are the gray values of pixel \(i\) and \(j\); \(x_{i}\) and \(x_{j}\) are the \(x\)-coordinate values of pixel \(i\) and \(j\); and \(y_{i}\) and \(y_{j}\) are the \(y\)-coordinate values of pixel \(i\) and \(j\).  
Given the desired number of superpixels \(N_{s}\), SLIC begins by sampling \(M\) regularly spaced cluster centers, which are denoted by feature vectors \(\left\{\mathbf{C_{k}}=\left[g_{k},x_{k},y_{k}\right]^{T}\right\}_{k=1}^{M}\). To avoid placing a cluster center at an edge pixel or a noisy pixel, the cluster center is moved to the lowest gradient position in a \(3\times 3\) neighborhood. Next, each pixel in the image is associated with the nearest cluster center within the local area of the pixel by adopting \(k\)-means clustering method.  
The segmentation results of SLIC on some benchmark test images are displayed in Figure 1. As shown, SLIC generates compact and nearly uniform superpixels and achieves a good description of the image edges.
# Effects of Central Cut Width on the Dynamical Characteristics of Box Assembly Structure
###### Abstract  
An investigation into the Box Assembly with Removable Component (BARC) structure is conducted by utilizing computational simulations and experimental structural testing in order to determine the complex dynamical responses instigated by the central cut of the system. Because the dynamics of the BARC system is complex, this study focuses primarily on analyzing the behavior of the box assembly (BA) system. The investigation explores the dynamics of the BA system by varying the central cut widths, ranging from a cut as wide as 0.5\" cut to a 0.25\" cut system, as well as a 0.1\" cut and a system with no cut at all. Experimental testing is performed on each system including a free vibration test using an impact hammer to excite and identify the dominant frequencies of each structure. This testing is followed by pseudo-random vibration tests and swept sinusoidal excitation tests to determine the nonlinear aspects of these systems, such as the possible existence of nonlinear softening, hardening, and/or damping. The results show that nonlinear softening and nonlinear damping are present in each system. The no-cut system demonstrated the highest peak frequencies throughout all the tests, being the most rigid structure. The 0.25\" cut system was shown to have the highest peak frequencies among all the cut systems in both the finite elemenet analysis (FEA) and impact testing. This trend did not continue, though, in the random and harmonic testing, possibly due to the added stiffness of the test setup with the slip table and stinger. The results show the importance of accurately measuring the central cut width and how possible geometric uncertainties change the overall dynamical behaviors of complex systems, such as natural characteristics, nonlinear responses, coupling of modes, and oscillating amplitudes.  
BARC; cut width; FEA; experimental structural dynamics; nonlinear dynamics +
Footnote †: journal: Applied sciences
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 3 Results and Discussion
### MIMO Performance Parameters  
In this section, we will discuss the MIMO performance matrices, the envelope correlation coefficient, the total active reflection coefficient, the mean effective gain, and the channel capacity loss.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### Fuzzy Logic Technique  
This section describes the fuzzy logic technique to determine the best network. The fuzzy rules and attributes used in fuzzy logic are convenient to modify. Users can easily modify the fuzzy member function as well as the fuzzy rules to obtain the most suitable output for the considered network environment.  
The basic steps of the fuzzy logic technique are:  
Figure 3: Representation of Block Diagram for Selection of best Network.  
* The burst inputs are taken from chosen attributes and degree to which inputs of info have a place with each appropriate fuzzy set is resolved. This process is called fuzzification.
* At that point, this fuzzified input is taken and applied to the predecessor of the fuzzy principle and applied to ensuing part work.
* Aggregation is the step in which all the inputs are considered and merged.
* The output of the joined fuzzy set might be taken as input for defuzzification, and a single burst number is acquired as yield.  
To start with, the fuzzy logic engine analyzes routing layer to determine the best network. The information is collected and is stored in the LSPs packet in the format shown in Table 3: Network Condition.
## 5 Performance Evaluation  
To evaluate the performance of the proposed event-triggered CLAFS scheme, this section conducts simulation studies for the case given in Section 2 using Matlab along with the TrueTime toolbox [12]. Consider the following two scenarios:  
* Scenario I: The controller and the process are close to each other, WLAN operates at 11 Mbps, there is no interfering signals, \(\delta=0.03\), \(K_{0}=0.018\);
* Scenario II: Due to increased distance between the controller and the process, the transmission rate drops to 5.5 Mbps, the interfering transmitter sends a data packet of 1 KB to the corresponding receiver every 10 ms, \(\delta=0.06\), \(K_{0}=0.008\).  
It can be seen that different \(\delta\) values have been used in these two scenarios. This is because: 1) the DMR setpoints for different transmission rates are different, 2) this makes it convenient to compare the event-triggered feedback scheduling and time-triggered feedback scheduling, see Subsection 5.2.  
Some parameters used in the simulations are as follows: the nominal sampling period \(h_{0}=15\) ms, \(h_{max}=50\) ms, \(T_{ED}=500\) ms, \(\Delta\rho^{+}=0.1\), and \(\Delta\rho^{-}=0.08\). It is worth mentioning that completely identical results cannot be guaranteed for each run of the simulation even with the same system setup. This is a natural consequence of the inherent stochastic feature of communications over WLAN. All results given below are the only representative ones among many obtained from a variety of simulation runs.
## 5 Results and Discussion
### Results for the IM Operating at 20 Hz
This acquisition time is required because, otherwise, the failure harmonics are not visible in the spectrum. As a disadvantage of the proposed methodology, it can be mentioned that it is the one that requires the highest computational burden even though it only works when 10 s of the signal is in steady state. This is because the GA is an iterative technique, and in this case, a total of 300 iterations are required, i.e., the 10 s of the signal are analyzed a total of 300 times. The NF-MCSA methodology also works with 10 s of the signal. The computational cost is a little higher than in the case of the conventional MCSA and it is lower than the one presented by the proposed methodology. However, the enhancement in the results worth the computational effort; moreover, since the length of the signal under analysis is not very high, the computational time remains in a tolerable range. At this point, it must be said that the GA-MCSA methodology proved to be an effective tool for enhancing the identification of BRB on IM operating at steady state. Notwithstanding, these results cannot ensure that this methodology would lead to the same outcome in a transient analysis. To use the proposed methodology with transient signals, it must be applied in a piecewise sense. The GA cannot perform a parameter estimation with only one point, it requires a set of points in order to perform a proper estimation. Thus, if the signal frequency changes at a rate higher than the one allowed by the algorithm, the results would be erroneous. In this sense, it is possible that a different methodology is required to perform a transient analysis and ensure the estimation of instantaneous parameters.
## 2 Materials and Methods
### Primer Design and PCR Amplification  
Over 500 nucleotide and protein sequences of the hydroxylamine oxidoreductase (HAO) and nitrite reductase (NirS/NirK) genes were retrieved from the NCBI database ([[https://www.ncbi.nlm.nih.gov/protein/](https://www.ncbi.nlm.nih.gov/protein/)]([https://www.ncbi.nlm.nih.gov/protein/](https://www.ncbi.nlm.nih.gov/protein/))) with an identity \(>\)30% and size \(>\)500 b.p. Then, all the collected protein sequences were performed to multiple sequence alignment analysis and phylogenetic tree construction using the online tool Clustal Omega ([[https://www.ebi.ac.uk/Tools/msa/clustalo/](https://www.ebi.ac.uk/Tools/msa/clustalo/)]([https://www.ebi.ac.uk/Tools/msa/clustalo/](https://www.ebi.ac.uk/Tools/msa/clustalo/))). Then, some representative sequences from the sub-branch were selected to design gene-specific degenerate primers and phylogenetic analysis. Primers JBHAO-170F (GTATGGVGCGYTGINAAGCGYTA), JBHAO-939R (TGGAACTGGRAHGTHCVCTCAAG), JBNirS-1001F (CGGTGGGAAAYTAYTGGCCKCC), JBNirS-1242R (CAYGAYGGGHGGATC), JBNirK-526F (CACGACCGTCACGGNATGTAYGG), and JBNirK-1002R (GTGCGCGACARCGCRTGKTCNAC) were designed according to the sequence alignments of the typical candidate _hao_ (12), _nirS_ (20), and _nirK_ (19) nucleotide sequences, respectively.  
Metagenomic DNA was used for the PCR amplification of 16S rRNA genes (prokaryote specific primers 27F (AGAGTTTGATCCTGGCTCAG) and 1492R (TACGGTACCTTGTTACGACTT)), _nirS_, and _hao_ genes. PCR products were separated by agarose gel electrophoresis and cloned into the pMD19-T vector (TakRaRa, Bio Inc., Shiga, Japan) according to the manufacturer's instructions. Randomly selected clones from each sample were sequenced by Shanghai Sangon Biological Engineering Technology & Services Co. Ltd., China.
## 2 Data and Method  
### Data  
#### 2.1.1 Nighttime Light Imagery and Cloud Condition Data  
The VIIRS provides comprehensive global data across both the visible and the infrared spectra [25]. Specifically, the VIIRS Day/Night Band (DNB) offers daily global measurements of nighttime illumination in these spectra, making it particularly well suited for tracking fishing vessels that utilize lights. In addition to the DNB, VIIRS includes long-wave infrared channels, which are crucial for assessing sea surface temperature and distinguishing cloud formations. In our study, we employed a Level 3 VIIRS product from the Suomi NPP (VNP46A1), a daily radiance product that captured nighttime brightness at the sensor level, referred to as the VIIRS/NPP Daily Gridded Day Night Band 15 arc-second Linear Lat/Lon Grid Night [26]. This product, available since January 2012, was derived from VIIRS sensors and was processed daily within 3-5 h post-acquisition, supporting both near-real-time applications and long-term monitoring. For our research, we utilized Google Earth Engine (GEE) to filter and obtain VNP46A1 data for the North Pacific central-east stock during the fishing seasons (June-July) from 2012 to 2023 [27]. In addition to using DNB radiance as nighttime light imagery for detecting fishing vessels using lights, we also incorporated the M16 band brightness temperature to account for cloud reflections in our analysis.
## 4 Advanced Machine Health Diagnosis Framework
### Machine Learning-Based Prediction  
Extracted features are then input into a trained machine learning model, with long short-term memory (LSTM) networks serving as the core prediction engine. The LSTM captures the temporal evolution of spectral patterns and forecasts future degradation behavior [19]. This step enables proactive fault anticipation rather than mere detection.
## 2 Related Work
### Multicast Architectures
#### 2.1.1 SDN-Based Multicast  
In contrast to traditional IP multicast, SDN-based multicast can enable service providers in a more manageable and flexible manner. Therefore, lots of SDN-based multicast schemes are springing up in recent years [31] with objectives to achieve reliability [32; 33], load balance [34], controllability [35] and so on.  
[PERSON] et al. [32] proposed a reliable multicast routing for SDN based on recover-aware steiner tree which is a NP-hard problem, and they designed an approximate algorithm called Recover Aware Edge Reduction Algorithm (RAERA) to compute the multicast tree. The introduction of recovery nodes can reduce number of total retransmitted packets and the overall latency that users would experience, which is beneficial for stream providers at the cost of increment on the states in routers. To balance the trade-off between state and bandwidth, [PERSON] et al. [33] proposed an Adaptive Software Defined Multicast (ASDM) scheme which assists ISPs to dynamically adjust the trade-off between bandwidth and state. Their results showed that ASDM can reduce up to 30% bandwidth consumption compared to unicast while only a seventh of network state of multicast is used. [PERSON] et al. [34] proposed the Dynamic Software-Defined Multicast (DYNSDM) which introduces a network-layer multi-tree approach to distribute traffic and provides the mechanism to support dynamic group and fast reactions. In this way, the DYNSDM can balance the load and accommodate for changing client populations. Besides, to provide multicast member control, [PERSON] et al. [35] proposed a scheme in which multicast source has the full control over group members, and they designed an algorithms for calculating multicast forwarding trees based on Branch-Aware Modification and Early Branching algorithms together to achieve the maximal reusing unicast flow table entries and reduce the utilization of memory.  
SDN multicast is based on control and forwarding separation mechanism which is easy to compute the multicast tree in a central way. However, the security and mobility support are not easy to solve.