esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Antibiofilm Therapeutics Strategies to Overcome Antimicrobial Drug Resistance
## 5 Emerging Antibiofilm Agents
### Antimicrobial Peptides  
AMPs are emerging as attractive antibofilm agents owing to various properties that they display, such as a broad-spectrum of antimicrobial activity, decreased resistance and synergistic effects shown with few antibiotics. Indeed, these properties mean that AMPs could become the next generation of antimicrobials to curb the biofilm related resistance shown by current antibiotics.  
[PERSON] et al. recently explored the antib biofilm effect of TAT-RasGAP317-326, an AMP made of a TAT HIV 48-57 sequence which gives it cell permeability, and a sequence of ten amino acids obtained from the Src homology domain of p120 RasGAP on biofilms of _P. aeruginosa, A. baumannii_ and _S. aureus_. It was observed that TAT-RasGAP317-326 attenuated biofilm formation at concentrations similar to or twice the MIC value obtained for planktonic cells. Additionally, TAT-RasGAP317-326 curbed the growth and spread of _P. aeruginosa_ and _A. baumannii_ preformed biofilms at twice the concentration. This study proclaims TAT-RasGAP317-326 as a propitious antibiofilm AMP [167].  
[PERSON] et al. assessed the effect of AMPs LL-37 (also known as cathelcidin) and human lactoferrin (LfcinH) on the growth of planktonic cells and biofilm formation in anaerobes associated with oral pathogenesis. Suspensions of multi-species of facultative anaerobic bacteria (FAB) including _Actinomyces naselundii, Streptococcus mutans_ and _Streptococcus sanguinis_ or obligate anaerobic bacteria (OAB) including _Parximonas micra, Veillonella parvula_ and _Fusobacterium nucleatum_ were subjected to concentration ranges of LL-37 and LfcinH. Compared to LfcinH, prominent inhibitory threshold concentrations of LL-37 were noticed (\(p<0.0001\)) but the biofilm mass was also decreased better by LL-37 compared to LfcinH, highlighting the scope of LL-37 as a better AMP [168; 169].  
[PERSON] et al. investigated the synergistic action of AMPs citropin 1.1, temperin A, Pal-KGK-NH\({}_{2}\) and CA(1-7)M(2-9)NH\({}_{2}\) towards methicillin-resistant _S.s aureus_ (MRSA) biofilms formed on a polystyrene surface and a central venous catheter. The combination evidently inhibited biofilm formation, although, disruption of preformed biofilms was tedious and achieved after 24 h of contact [170; 171].  
[PERSON] et al. recently worked on the AMP 1018-K6 as an antibiofilm agent against MRSA and enterotoxic _S. aureus_ isolated from cheese. This peptide exhibited remarkable eradication of preformed Staphylococcus within 15 min. Moreover, it prevented further formation of biofilms and displayed bactericidal action against the planktonic cells [172]. In a continuation of the work on 1018-K6, [PERSON] et al. worked on the antibiofilm ability of the food pathogen _Salmonella enterica._ Of the 42 strains included in the study, 1018-K6 profoundly decreased the biofilm formation in several _S. enterica_ strains at subinhibitory concentrations [173].
# Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
## 5 Conclusions  
To enhance the efficacy of cable planning and layout search, as well as improve wiring quality in digital design, this paper proposes an optimized RRT algorithm. The proposed strategy utilizes a bidirectional extended random tree to achieve fast global search between the start and target points. To further optimize the shortest distance calculation between path nodes, a Dijkstra path is introduced, which eliminates redundant nodes in the initial path. Additionally, the path buffer offset is implemented in order to correct inconsistencies caused by random sampling, effectively obtaining the optimal path. Furthermore, a Python static simulation experiment is conducted to validate the efficacy of two strategies; two-way extended sampling and path caching. In comparison with alternative approaches, the underwater robotic path-planning method proposed in this study exhibits superior planning efficacy within simulated underwater environments. This accomplishment not only establishes a benchmark for practical applications in underwater robotic path planning but also underscores its potential value. It is noteworthy that while the distribution of obstacles has been considered in this study, attributes such as material composition and volume of objects in underwater path planning were not accounted for in the assessment of planning effectiveness. In future endeavors, additional factors, including the motion states of underwater devices and the presence of mobile obstacles, will be incorporated into algorithmic design to enhance the comprehensiveness of the analysis.  
Conceptualization, J.G. and Y.Z.; funding acquisition, X.G.; investigation, J.W.; resources, Y.Z.; supervision, Y.Z.; writing--original draft, J.G.; writing--review and editing, Y.Z. and X.G. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Innovation project of Hainan Province of China (NO. Qhyb2022-82), Innovation project of Hainan Province of China (NO. Qhys2022-154), Key research and development planned project of China (NO. 2018 YFB1700501) and Key Development Project of Hainan Province of China (No. ZDYF2019024).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 2 Materials and Methods
### Deep Learning Network  
In this study, a GPP-merged product conforming to the distribution characteristics of the GLASS GPP dataset is developed by merging the simulated data of CMIP6 models. A neural network named Ynet [51] is adopted in the data merging part, which combines data merging and image super-resolution technology. The deep learning model establishes relationships between the reference and the CMIP6 GPP data in the historical period and is then applied in future projections. Compared with the multi-model ensemble mean, the merged data have higher accuracy and spatial resolution over 1850-2100 in China. The multi-model ensemble mean is a simple average of the simulation results of the 23 CMIP6 models. It uses the simplest strategy of assuming that all 23 model data have the same uncertainty; thus, the multi-model ensemble mean is a simple average of each model. The merged data in this study are generated based on the deep learning method, which compensates for this deficiency. The novelty of the deep learning method used in this study is that it combines data merging and image super-resolution technology, which combines the advantages of 23 CMIP6 GPP datasets without relying on any prior knowledge. The model architecture is shown in Figure 1. This structure consists of three parts. The first part is a symmetric encoder-decoder structure with a skip connection. Similar to the residual encoder-decoder network, this structure solves the problem of gradient disappearance when the error calculated by the loss function in the model training process is propagated back. This structure captures the abstract features of low-resolution images with noise and outputs a \"cleaner\" image, which can be regarded as a feature extractor. In addition to the 30 convolutional layers, this part also contains 15 deconvolution layers. It may introduce \"checkerboard artefacts\", resulting in lower image output quality [55].
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 2 Background
### Time-Frequency Analysis  
Time-frequency analysis is effective for analyzing non-stationary signals and exploring their time-varying characteristics. One common technique is the Hilbert-Huang transform, which is a two step transform proposed by [PERSON] et al. in 1998 [22]. The first step is called Empirical Mode Decomposition (EMD), and it transfers any complex signal \(x(t)\) into the linear superposition of \(K\) Intrinsic Mode Function (IMF) components \(c_{i}(t)(i=1,...,K)\), which contain the local characteristics of the original signal at different time scales. Therefore, the signal \(x(t)\) can be written as [23]:\[x(t)=\sum_{i=1}^{K}c_{i}(t)+r_{K}(t), \tag{7}\]  
where \(r_{K}(t)\) is the residue and represents the average trend of the signal.  
The second step consists of applying the Hilbert transform to the IMF component \(c_{i}(t)\) and constructing the analytic signal \(z_{i}(t)\) defined as:  
\[z_{i}(t)=c_{i}(t)+j\tilde{c_{i}}(t)=a_{i}(t)e^{\mathrm{i}\phi_{i}(t)}, \tag{8}\]  
where \(\tilde{c_{i}}(t)\) is the Hilbert transform of \(c_{i}(t)\) and is expressed as:  
\[\tilde{c_{i}}(t)=\frac{1}{\pi}\int_{-\infty}^{+\infty}\frac{c_{i}(t)}{t-\tau}d\tau. \tag{9}\]  
Thus, a non-stationary complex signal \(x(t)\) can be expressed by a time-dependent function \(Z(\omega,t)\) as follows [14]:  
\[Z(\omega,t)=\mathrm{Re}\sum_{i=1}^{K}a_{i}(t)\mathrm{exp}\Big{(}j\int\omega_{ i}(t)dt\Big{)}, \tag{10}\]  
where  
\[a_{i}(t)=\sqrt{c_{i}^{2}(t)+\tilde{c_{i}}^{2}(t)} \tag{11}\]  
is the instantaneous amplitude and  
\[\omega_{i}(t)=\frac{d\phi_{i}(t)}{dt}\quad;\quad\phi_{i}(t)=\tan^{-1}\bigg{(} \frac{\tilde{c_{i}}(t)}{\tilde{c_{i}}(t)}\bigg{)} \tag{12}\]  
are the instantaneous frequency and phase.
## 2 Research Methods and Principles
### Graph Learning Layer & Graph Convolution Module  
The core mechanism of the graph learning layer is the dynamic generation of adjacency matrices, which is designed to enhance the model's ability to represent complex spatio-temporal features by effectively capturing potential patterns of association between variables through adaptive graph structure learning. A non-assignment graph can be represented by the adjacency matrix \(A=\left(a_{ij}\right)_{N\times N}\). The calculation of the graph learning layer is represented as Equation (1).  
\[\left\{\begin{array}{l}M_{1}=\tanh(\alpha E_{1}\theta_{1})\\ M_{2}=\tanh(\alpha E_{2}\theta_{2})\\ A=\text{ReLU}(\tanh(\alpha(M_{1}M_{2}^{T}-M_{2}M_{1}^{T})))\\ for\quad i=1,2,\cdots,N\\ idx=argtopk(A[i,:])\\ A[i,-idx]=0\end{array}\right. \tag{1}\]  
where \(E_{1}\) and \(E_{2}\) are the initialized node embeddings; \(\theta_{1}\) and \(\theta_{2}\) are learnable model parameters; \(\alpha\) is the saturation rate that controls the activation function; \(argtopk\) denotes the number of nearest nodes of the selected node to be predicted.  
The graph convolution module is used to integrate the information from nodes and neighboring nodes, and a two-branch mix-hop propagation layer is used to process the input and output information from each node. The mix-hop propagation layer is calculated as shown in Equation (2).  
\[\left\{\begin{array}{l}H^{(k)}=\beta H_{in}+(1-\beta)\widetilde{A}H\\ H_{out}=\sum\limits_{i=0}^{K}H^{(k)}W^{(k)}\end{array}\right. \tag{2}\]  
where \(\beta\) is a hyperparameter to keep the proportion of original node information; \(k\) denotes the depth of the propagation layer; \(H_{in}\) denotes the inputs of the current layer (outputs of the previous layer); \(H_{out}\) denotes the outputs of the current layer; and \(\widetilde{A}=\widetilde{D}^{-1}(A+I)\) where \(\widetilde{D}\) is a diagonal matrix and \(\widetilde{D}_{ii}=1+\sum\limits_{j}A_{ij}\); \(W^{(k)}\) denotes the parameter matrix of the \(k\)th layer.  
The structure of the graph convolution module is shown in Figure 3.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 4 Network Coding Based P2P File Sharing Systems
### Sparse Network Coding
However, its influence on the content availability is worse than Pairecoding  
\[P_{(i,j)}=C_{i}X_{i}+C_{j}X_{j} \tag{10}\]  
where \(i\) is an odd number, \(1\leq i\leq n-1\) and \(j=i+1\). [PERSON] et al. [18] conducted an in-depth analysis of Avalanche and Pairecoding, and proposed a new fixed coding approach that is very similar to fixed-Pairecoding. Furthermore, the rarest-first scheduling policy is also considered. The proposed approach depends on computing the importance of the encoded pieces according to their underlying original pieces: those encoded pieces that consist of the original pieces that are less distributed among neighbors will be assigned with a higher priority to be requested. In contrast to [60], the authors claim that fixed Pairecoding could promote the availability and diversity of the pieces. To evaluate the performance, the proposed approach is tested and compared against: (1) BitTorrent, (2) adaptive neighbor selection BitTorrent [19], and (3) BitTorrent using FEC [43]. The results show that the proposed approach outperforms the other implementations with regard to throughput with a slight increase in the control overhead. In addition, it has been shown that the duration of the period during which no seeder is available while content is available, is greatly prolonged and only a few peers are sufficient to render the content available. Other variations of Pairecoding are Treecoding and Tree network coding [58; 59; 60]. In these schemes, the encoded pieces are defined by a complete binary tree, where the leaves contain the original pieces such that each leaf contains an original piece multiplied by a coefficient. For the upper levels, each piece is generated by XORing its children pieces. If the coefficients' vector is known, then any encoded piece in the tree can be decoded simply by XORing its two encoded children pieces, or from its encoded parent piece and its sibling. The main difference between Tree coding and Tree network coding is that the former only allows encoding at the seeders, whereas the latter allows recoding within the other peers. The main feature of Treecoding is that an encoded piece may contain information up to all \(n\) original pieces when compared with Pairecoding whose encoded piece covers only two original pieces at most. However, the static encoding, namely allowing encoding only at the seeder, yields a reduction in the availability of pieces. Figure 5 shows an example of Treecoding. In conclusion, sparse network coding could be considered practical and valid for implementation for real world P2P file sharing systems. We re-evaluate the decoding rate of Paircoding and, as depicted in Figure 6, the results show that Paircoding is outperformed only for the initial rounds, then it significantly outperforms BitTorrent. Table 3 summarizes the most important studies related to sparse network coding. Figure 5: (**a**) File \(X\) with \(n=8\), (**b**) Tree coding of file \(X\). Figure 6: Paircoding vs. BitTorrent decoding Rates.
# From Lean Production to Lean 4.0: A Systematic Literature Review with a Historical Perspective
## 3 Results
### Answering RQ3. Historical Evolution of the Term \"Lean\"
#### 3.2.4 Lean Management (1994)  
The origins of the term \"lean management\" (LMg) are unclear. The first reference in the English language academic literature was introduced by [PERSON] et al. in 1994: _Business Process Re-Engineering as an Enabling Factor for Lean Management_[65]. Nevertheless, the German literature has used this term since 1992. It seems to be a first attempt at shifting towards a more managerial concept in a similar way as the later emergence of lean enterprise or lean thinking.  
In any case, it was not until 2008 that the literature showed consistent interest in the topic.  
In 2014, [PERSON] et al. [66] presented the term in association with organizational sustainability. More recently in 2019, [PERSON] et al. [17] considered lean management to be an extension \"into an inter-disciplinary subject with linkages to operations management, organizational behaviour, and strategic management\".  
In 2016, the first paper on LMg and I4.0 was published: _Industry 4.0. The End Lean Management?_[67], concluding at the time that the correlations between both concepts were low.  
As a conclusion, lean management can be considered as a transfer to a more managerial approach. It refers to adopting lean principles in order to manage an entire organization. Although quite neglected until 2008, it has generated growing interest in the past decade, at least, up until 2019 (Figure 8).  
Figure 7: Lean logistics and lean supply chain evolution.
## 4 Results
### Pore Development Characteristics
#### 4.2.2 Microscopic Pore Characteristics  
In this study, samples from the designated study area underwent argon ion polishing before being observed using FE-SEM. The pores were categorized into intra-, intergranular, and organic based on their location and origin. In the study area, predominant pore types included intra- and intergranular dissolved pores, whereas organic pores were less prevalent.  
Figure 3: Core map of each lithology. (**a**) Felsic shale, 4691.21 m; (**b**) dolomitic felsic shale, 4648.65 m; (**c**) clay-bearing felsic shale, 4587.25 m; and (**d**) siltstone interlayers, 4623.91 m.  
As shown in Figure 4, on the whole, the pores of felsic shale are well-developed, most of which are intergranular pores, with a small number of intra-granular pores. Most of them are triangular, circular, and slit types. The width of the slit pores is 10-500 nm and the length is basically 100-2000 nm, while the size of the triangular and circular pores is smaller, ranging from 5 to 500 nm. On the whole, the pores of dolomitic felsic shale are well-developed, and the pore types are mainly intergranular pores and intra-granular pores, among which the intergranular pores are mostly triangular or polygonal, with scales ranging from 50 to 500 nm, while the intra-granular pores are mostly round or oval, with scales varying from 20 to 200 nm. On the whole, the pore development of clay-bearing felsic shale is worse than that of felsic shale and dolomitic felsic shale. Among them, the pore width of slits is only 5-100 nm and the length is 10-300 nm. Most of them are intergranular pores of clay minerals, and there are also intragranular dissolved pores, which are oval and round in shape and vary in diameter, from 50 to 350 nm. On the whole, the pore development of siltstone interlayers is obviously better than that of the other three lithology samples. The pores are mainly intergranular pores, and their morphology varies according to the different mineral particle edges, such as triangles and polygons. The scale span is large, and the side length is 100-4000 nm, which has the best reservoir ability.  
Figure 4: Pores in SEM of each lithology. (**a**) Felsic shale, 4755.67 m; (**b**) felsic shale (Part); (**c**) dolomitic felsic shale, 4857.57 m; (**d**) dolomitic felsic shale (Part); (**e**) clay-bearing felsic shale, 4587.92 m; (**f**) clay-bearing felsic shale (Part); (**g**) siltstone interlayers, 4635.30 m; and (**h**) siltstone interlayers (Part).
# Deep Learning-Based Sound Source Localization: A Review
## 3 General Principles of Deep Learning-Based Sound Source Localization  
### Feature Extraction  
Multi-channel input signals recorded by a microphone array are processed through a feature extraction module to generate input features. These features are then fed into various neural networks for direction-of-arrival DOA estimation. Signals are typically converted into time-frequency representations via generalized cross-correlation or short-time Fourier transform STFT, or by extracting features such as phase differences and cepstral differences.  
\[X(t,f)=\sum_{n=0}^{N-1}x(n)w(n-t/\Delta t)e^{-\frac{2\pi fn}{N}} \tag{1}\]  
\(X(t,f)\) denotes the STFT result, representing the signal intensity at time t, and frequency. \(X(n)\) is the discrete time-domain signal, defined as a complex exponential function which converts the time-domain signal to the frequency domain. \(w(n-t/\Delta t)\) is a window function. The core idea of STFT is to divide the signal into short segments, apply the Fourier transform to each segment, and thereby capture local time-frequency characteristics, commonly serving as input features for deep learning models.  
\[R_{ij}(\tau)=\sum_{f}\frac{X_{i}(f)X_{j}^{*}(f)}{\left|X_{i}(f)X_{j}^{*}(f) \right|}e^{j2\pi f/\tau} \tag{2}\]  
\(R_{ij}(\tau)\) quantifies the cross-correlation between signals \(xi(t)\) and \(xi(t)\) at time delay \(\tau\). \(X_{i}(f)\) and \(X_{j}(f)\) are their frequency-domain representations, with \(X_{j}^{*}(f)\) as the complex conjugate. The phase term \(e^{j2\pi f/\tau}\) models the time delay effect in the frequency domain. \(\Delta f=f_{s}/N=1/(N\Delta t)\). \(\sum_{f}\) sums over discrete frequency bins to ensure accurate correlation estimation, where \(f_{s}\) is the sampling rate and \(N\) is the FFT length.
# Survey of Smart Parking Systems
## 7 Discussion and Open Problems
### Electrical Vehicles  
EVs present one of the fastest-growing futures in ITS. Green transport is a reality that is becoming more and more established in cities around the world. Achieving a higher percentage of circulation of this type of vehicle entails various challenges, and many of these are linked to SPS.  
The investment in infrastructure needed to set up parking spaces with charging stations for plug-in EVs is expensive and complex. This involves planning the location of the parking spaces, their interconnection to the electricity grid or through solar energy, and the implementation of an EVPS that allows users to have information about the available spaces and the charge states of the stations.  
On the other hand, EVs currently comprise a very small percentage of circulation with respect to vehicles with other types of energy (fossil, gas, biodiesel); this means that there is less competition for parking spaces, and EVPSs have a better way of planning routes and optimizing resources. The systems and algorithms developed today must be prepared for growth in the percentage of EV and in the number of charging stations that can be placed in parking spaces, as well as the different types of these stations. The EVPS must be able to operate and provide information to users by stations that are either coupled to the electric grid or powered by solar panels, for example.  
Finally, the EVPS can be coupled to other types of SPS, allowing users to make a reservation for the charge of a vehicle in a parking lot, as in a PRS, guiding drivers with real-time information, as in a PGIS, or by sensing the availability of the car parks through the users themselves, as in a CITS.