esa-sceva/satcom-chunk-collection huggingface dataset:
# Microwave Staring Correlated Imaging Based on Unsteady Aerostat Platform
## 2 MSCI Method Based on Unsteady Aerostat Platform
### Influence of Platform Motion on Beam Coverage  
The aerostat platform instability not only causes the antenna motion, but also changes the beam coverage in the overall imaging region \(S\). All echo data contains the information of all beam covered areas, therefore as the union of all beam coverages, the overall imaging region \(S\) is considered in imaging. The selection matrix of beam coverage is introduced to indicate the dynamically illuminated area of each pulse within the overall imaging region.  
The beam coverage of the \(l\)-th pulse is denoted as \(S_{l}\), and the coordinate \(\left(x_{l}^{c},y_{l}^{c}\right)\) is the beam coverage center on the \(O_{l}X_{l}Y_{l}\) plane of the \(l\)-th pulse:  
\[x_{l}^{c} =x_{l}^{s}-(tan\varphi_{l}cos\varphi_{l}/cos\theta_{l}-sin\varphi_ {l}tan\theta_{l})z_{l}^{s}, \tag{9}\] \[y_{l}^{c} =y_{l}^{s}+(tan\varphi_{l}sin\varphi_{l}/cos\theta_{l}+cos\varphi _{l}tan\theta_{l})z_{l}^{s}, \tag{10}\]  
where \(\left(x_{l}^{s},y_{l}^{s},z_{l}^{s}\right)\), \(\left(\theta_{l},\varphi_{l},\phi_{l}\right)\) are its center position coordinate and its attitude angles of the antenna array at the start time of the \(l\)-th pulse.  
The overall imaging region \(S\) is the union of all beam covered areas during imaging, i.e., \(S=S_{1}\bigcup S_{2}\bigcup...\bigcup S_{L}\). The size of the beam covered area of a single pulse is denoted as \(w_{x}\times w_{y}\), where \(w_{x},w_{y}\) are the side length. The size of the overall imaging region is  
\[W_{x}\times W_{y}=\left(x_{max}^{c}-x_{min}^{c}+w_{x}\right)\times\left(y_{max }^{c}-y_{min}^{c}+w_{y}\right), \tag{11}\]  
where \(W_{x},W_{y}\) are the side length of \(S\). \(x_{\max}^{c}\) and \(x_{min}^{c}\) are the maximum value and the minimum value of \(x_{l}^{c},l=1,2,\cdots,L\). \(y_{\max}^{c}\) and \(y_{min}^{c}\) are the maximum value and the minimum value of \(y_{l}^{c},l=1,2,\cdots,L\).  
The overall imaging region will be discretized into \(M=P\times Q\) discrete grids, where \(P\) is the row number of azimuth resolution cells, and \(Q\) is the column number of range resolution cells. In \(O_{l}X_{l}Y_{l}Z_{l}\), the position vectors of the \(m\)-th grid is denoted as \(\mathbf{r}_{m}\), \(m=1,2,\cdots,M\).  
Selection matrix of beam coverage is as below  
\[\mathbf{D}=\begin{bmatrix}D_{1}\left(1\right)&D_{1}\left(2\right)&\cdots&D_{ 1}\left(M\right)\\ D_{2}\left(1\right)&D_{2}\left(2\right)&\cdots&D_{2}\left(M\right)\\ \vdots&\vdots&\vdots&\vdots\\ D_{L}\left(1\right)&D_{L}\left(2\right)&\cdots&D_{L}\left(M\right)\\ \end{bmatrix}. \tag{12}\]  
The element \(D_{l}\left(m\right)\) indicates whether the \(m\)-th grid is illuminated by the \(l\)-th pulse beam:  
\[D_{l}\left(m\right)=\begin{cases}1&if\left(\mathbf{r}_{m}\in S_{l}\right)\\ 0&if\left(\mathbf{r}_{m}\
otin S_{l}\right)\\ \end{cases} \tag{13}\]
## 3 Case Study Application
### Data and Characteristics Used within the Vulnerability Conceptual Framework  
The analysis conducted in this study used the QGIS software, chosen due to its free and open-source nature as a geographic information system program. QGIS serves as a Geographic Information System (GIS) software, providing users with the capability to analyze and manipulate spatial data, while also enabling the creation and export of graphical maps. It seamlessly integrates with other open-source GIS packages, such as GRASS GIS and MapServer, facilitating collaborative and comprehensive geospatial analyses.  
Multiple layers were created and incorporated into the project. For the case study in Guimaraes historical center, relevant information was sourced from the municipality's online services [28] and an open-source project provided by Lisbon's university [29]. Additionally, data from previous studies, including the Fire Risk Index (FRI) obtained through the simplified Arica methodology, were considered [19; 20]. Further layers were obtained using the QuickOSM plugin, enabling easy querying of various information specific to Guimaraes. Finally, the project incorporated Quick Map services from Bing, offering satellite and map views.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 1 Introduction
The proposed hybrid network architecture implements a cellular network where both radiofrequency and optical wireless technologies are integrated into the IoT ecosystem. For that, three different models of communication under the low-consumption premise are used: Zigbee, LoRaWAN (employing the LoRa physical layer), and VLC. ZigBee and VLC networks are used to provide both sensors and actuators interconnection, and LoRa is used as the backbone of the system providing long-range communications. The rest of this paper is organized as follows. Section 2 carries out a comprehensive state-of-the-art analysis in heterogeneous networks related to this work. Section 3 describes the proposed network architecture. In Section 4, the system design is explained, while the hardware implementation is described in Section 5. The experimental results are shown and discussed in Section 6. Finally, the conclusions are shown in Section 7.
## 1 Introduction
[15] proposed a shallow model for wind speed prediction (WSF) based on artificial neural networks, which is more accurate than physical or traditional statistical methods. The hybrid method integrates the physical and statistical models to improve forecast performance by preserving the advantages of each approach [16; 17; 18], but the hybrid models may not have the capability to achieve stable prediction, as their complex learning architecture may cause low efficiency in training and even underfitting [9]. With the development of deep learning techniques in recent years, and because wind power prediction possesses a natural time-series feature, some deep neural network (DNN)-based time series forecast methods have been developed and used for wind power estimation, such as the methods based on recurrent neural networks (RNNs) [19], long short-term memory (LSTM) [20], Transformer [21], temporal convolutional networks (TCNs) [22], sample convolution and interaction networks (SCINets) [23], etc. These form the deep learning-based method for wind power forecast. It is promising in terms of new model development for time series forecasting; however, none of the existing methods can claim to be perfect in time series prediction, which depends on the available data and data quality. SCINet is a novel framework proposed by [PERSON] et al. [23] very recently that has been applied to time series forecasting problems. It performs sample convolution and interaction at multiple resolutions for time-series modeling. Although good prediction results can be achieved by SCINet models, the SCINet framework has some shortcomings that affect the prediction performance, i.e., the prediction accuracy. One of the shortcomings is the strict binary tree structure taken in SCINet causing information blockage as the number of network levels increases. To address this issue, this present paper proposes a novel framework with a shuffle-and-fusion interaction network, named SFINet, to avoid the information blocking of SCINet sequence channels and develops an improved algorithm for wind power forecasting. The developed models based on SFINet have been proven effective to achieve the economic dispatch of energy production and reliable operation of the power system, providing an opportunity for reducing the operation costs of wind farms. The main contributions are as follows: (1) considering the sequence interaction modeling of time series tasks, we introduced the shuffle operation to increase the dependence between sequences; (2) in order to further promote interactive learning, we proposed feature fusion based on the time series attention mechanism; and (3) the developed models are applied to wind power forecasting using real wind power production data collected from a wind farm in China, verifying the outperformance of our models by comparing them with other baseline approaches.
## 5 Discussion
### Contributions of Multiple Variables to the GPP Simulation
Keeping the land cover in its initial state had little effect on the global GPP simulation, with the NI-LUE\({}_{\text{LCO}}\) GPP increasing at a rate of 0.54 Pg C/yr. The contribution of land cover changes to GPP from 2001 to 2018 was only \(-\)1.88 PgC. Figure 16d showed that the contribution of land cover was concentrated near the equator. In East South America, Central Africa, Central Europe, and Southeast Asia, land cover made a significant negative contribution. Figure 16: _Cont._  
Figure 15: Interannual variation of the global annual total GPP (NI-LUE\({}_{i0}\)) with variable \(i\) fixed at the initial state. CI\({}_{\text{green}}\), an index to characterize the canopy N concentrations (NI) in the NI-LUE model, was used to calculate dynamic \(\varepsilon_{max}\), which had a significant impact on global GPP  
Figure 16: Spatial patterns of cumulative contributions of various variables to global annual GPP from 2001 to 2018. (**a**) Climate variables; (**b**) atmospheric CO\({}_{2}\) concentrations; (**c**) Leaf area index (LAI); (**d**) Land cover (LC); (**e**) Nitrogen index (NI). simulations. The seasonal variability of the \(\varepsilon_{max}\) in the LUE model could make the model more suitable because the foliage responds adaptively to the seasonal fluctuations in the environmental conditions [47]. In this study, we used satellite reflectance to calculate NI, and although N itself may not be the only variable driving the observed pattern, leaf N concentrations influence leaf traits related to photosynthetic capacity that affect reflectance [53]. Canopy N concentrations may explain the temporal and spatial variation of the \(\varepsilon_{max}\)[46]. In fact, the \(\varepsilon_{max}\) is a large source of uncertainty. Even for the same vegetation, the \(\varepsilon_{max}\) may be different [83]. Fixing NI at the initial state in 2001, the increasing rate of the global NI-LUEN10 GPP, from 2001 to 2018, was significantly lower than that of the original NI-LUE GPP, at 0.19 Pg C/yr. Moreover, NI contributed 62.55 Pg C cumulatively to global GPP simulations from 2001 to 2018. The cumulative contribution of NI was concentrated in northern South America, central Africa, southeastern China, and Southeast Asia.
## 1 Introduction
Likely attack vectors in the maritime sector include all of the same threats to land-based businesses--ransomware, phishing, Business Email Compromise (BEC)--and several more that are unique to the sector; for example, malware capable of disabling a ship's navigational interfaces or critical OT systems such as steering and engines, and/or AIS/GPS spoofing or jamming causing confusion and potential for collisions at sea. With regard to the convergence of cyber and physical systems, a key finding of the research is that Cyber/online/IT/software systems cannot and must not be considered in isolation, rather they must be analysed and managed in the context of the physical/OT systems to which they are connected--hence this article's emphasis on hybrid threats rather than purely cybersecurity or cyber threats. Furthermore, regional threat actors in Southeast Asia have in the past specifically targeted and sought to disrupt shipping and a number of key terrorist and criminal attacks on regional shipping--some of which have spanned the cyber-physical divide--are examined. Regional threat actors show a greater proficiency in the use of digital technology to achieve their goals. However, whereas some incidents of cyber-physical interference on ships' critical systems have been recorded, to date there has not been a catastrophic cyber-physical attack on shipping, but such attacks are theoretically proven to be possible and have been simulated in laboratory tests. Finally, some international governance measures are underway to mitigate hybrid threats to shipping, but they are in the early stages of development and need to be further consolidated through key bodies like the IMO and leading national governments such as the UK, US, and EU--some recommendations are made in this regard. In the absence of a universal framework for maritime cybersecurity, the article recommends SOMS littoral states rapidly build upon existing solid multi-lateral regional cooperation initiatives such as the Malacca Straits Patrols (MSP), the Regional Cooperation Agreement on Combating Piracy and Armed Robbery against Ships in Asia (ReCAAP), and frameworks under the auspices of the Association of Southeast Asian Nations (ASEAN) such as the ASEAN Coastguard Forum (ACF), ASEAN Maritime Outlook (AMO), ASEAN Defence Ministers Meeting (ADMM), and others. This rapid, regional-focussed approach may be relevant for consideration to other global shipping chokepoints mentioned above.
## 4 Discussion
The cell viability was greater than 100%, which is not unusual in the MTT assay [40], where utilizing a low concentration of treatment can lead to a higher cell viability, as observed in film formulations that contain low concentrations. The safety result implies that the fabricated biofilm is biocompatible. The hydrogel formulations were fabricated in order to delay the microbial growth on the surface of the urinary devices; hence, it was crucial to determine the zone of inhibition over a prolonged period of time. The antimicrobial activity for p(HEMA) and drugs-loaded p(HEMA) persisted for 14 days. Loading p(HEMA) with a higher ratio of LVX alone or using the highest ratio of LVX (0.5% of LVX and loading p(HEMA) with 0.125%:0.375% (_w/w_) AMP: LVX (F6)) significantly increased antimicrobial activity. This was reflected in the spectrum of activity of LVX [34]. There are many microorganism species that can be involved in CAUTIs. Gram-positive bacteria such as _S. aureus_ and Gram-negative bacteria such as _E. coli_ and _P. aeruginosa_ can adhere and form biofilms on catheter device surfaces. The biofilm is a complicated structure population of microbes that produces an extracellular polymeric matrix that is adherent on a surface [1]. Urinary biofilms are documented to be of mixed microbial species [2], which makes their treatment and management challenging. To detect the ability of p(HEMA) and drug-loaded p(HEMA) to withstand the conditions and resist biofilm formation, a test using an MTT kit was conducted to determine the metabolic activity of the adhered cells. The films were exposed to a mixed strain of common causative of CAUTIs for 3 h, then treated properly to calculate the viable cells of the bacteria. The results showed that p(HEMA) alone without drug loading led to an enormous growth of microorganisms (Figure 5), which highlights the necessity of employing microbicides. Moreover, the use of LVX alone to load p(HEMA) in F3 and the use of a combination of LVX to AMP with high ratio of LVX in F6 were more capable of lowering the potential for biofilm formation. Employing antimicrobial agents to inhibit bacterial growth and biofilm-formation ability is a reasonable approach to both the prevention and treatment of infections [2]. p(HEMA) is a hydrophilic coating polymer with excellent antifouling capability due to the presence of the O-H group in its structure [16]. Both Gram-positive and Gram-negative bacteria carry a negative charge on their surfaces, which led to initiating the repulsion between negatively charged bacteria and the negative charge of the p(HEMA) surface. The interaction between the bacteria and p(HEMA) surface decreased the biofilm-forming ability after loading p(HEMA) with antimicrobial agents due to the antimicrobial action against bacteria. Both AMP and LVX act against Gram-positive and Gram-negative bacteria [12]. AMP acts as an irreversible inhibitor of the enzyme transpeptidase and thus inhibits cell wall synthesis and leads to cell lysis [41]. LVX inhibits bacterial deoxyribonucleic acid DNA gyrase, a type II topoisomerase [10].
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
## 4 The PM-QKD with Discrete Phase Modulation of Coherent State Sources
### The Yield Difference between Continuous and Discrete Phase Randomization  
To compare the yield difference of continuous phase randomization and discrete phase randomization, the density matrix of the continuous phase randomization can be written as  
\[\begin{split}\rho_{\rm{AB}}&{=}\frac{1}{2\pi}\int_ {0}^{2\pi}\left|\sqrt{\alpha_{\rm{A}}}e^{i(\pi\kappa_{\rm{A}}+\varphi_{\rm{A}}) }\right\rangle_{\rm{A}}\left\langle\sqrt{\alpha_{\rm{A}}}e^{-i(\pi\kappa_{\rm{ A}}+\varphi_{\rm{A}})}\right|\\ &\otimes\left|\sqrt{\alpha_{\rm{B}}}e^{i(\pi\kappa_{\rm{B}}+ \varphi_{\rm{B}})}\right\rangle_{\rm{B}}\left\langle\sqrt{\alpha_{\rm{B}}}e^{ -i(\pi\kappa_{\rm{B}}+\varphi_{\rm{B}})}\right|\\ &=\sum_{j=0}^{\infty}P_{j|\alpha}|j\rangle_{\rm{AB}}\langle j| \end{split} \tag{22}\]  
where the general Poisson distribution \(P_{j|\alpha}\) is given by Equation (1), with \(|j\rangle_{\rm{AB}}=\frac{1}{\sqrt{2/j}!}\)\((a^{\dagger}\pm b^{\dagger})/|00\rangle_{\rm{AB}}\).  
In the ideal case, \(D\rightarrow\infty\), the fidelity \(F_{j|\alpha}^{C,D}\) between \(|j\rangle_{\rm{AB}}\) and \(\left|\lambda_{j|\alpha}^{D}\right\rangle_{\rm{AB}}\) should be the same. In the security analysis, the fidelity \(F_{j|\alpha}^{C,D}\) between \(|j\rangle_{\rm{AB}}\) and \(\left|\lambda_{j|\alpha}^{D}\right\rangle_{\rm{AB}}\) is bounded by  
\[\begin{split} F_{j|\alpha}^{C,D}&=F\Big{(}|j\rangle_ {\rm{AB}^{\prime}}\left|\lambda_{j|\alpha}^{D}\right\rangle_{\rm{AB}}\Big{)} =\frac{\left|\left\langle j\mid\lambda_{j|\alpha}^{D}\right\rangle_{\rm{AB}} \right|}{\sqrt{\left\langle j\mid j\right\rangle_{\rm{AB}}\left\langle\lambda_ {j|\alpha}^{D}\mid\lambda_{j|\alpha}^{D}\right\rangle_{\rm{AB}}}}\\ &=1\left/\frac{e^{-\alpha/2}}{\sqrt{P_{j|\alpha}^{D}}}\sum_{l=0}^{ \infty}\frac{\left(\sqrt{\alpha}\right)^{ID+j}}{\sqrt{(ID+j)!}}\right.\end{split} \tag{23}\]  
which is related to the intensity \(\alpha\), photon number \(j\) and discrete phase numbers \(D\).  
Therefore, the yield difference is bounded by  
\[\left|Y_{j|\alpha}-Y_{j|\alpha}^{D}\right|\leq\sqrt{1-F_{j|\alpha}^{C,D}}= \sqrt{1-1\Bigg{/}\frac{e^{-\alpha/2}}{\sqrt{P_{j|\alpha}^{D}}}\sum_{l=0}^{ \infty}\frac{\left(\sqrt{\alpha}\right)^{ID+j}}{\sqrt{(ID+j)!}}} \tag{24}\]
# Single-Cell Patterning Based on Immunocapture and a Surface Modified Substrate
## 3 Single Cell Patterning
### Experiments
#### 3.2.4 HL-60 Cells Seeding  
Before the experiment, the cells were cultured with the cell culture medium, which is prepared with RPMI-1640 culture medium containing HEPES, 10% of newborn bovine serum and 1% of Penicillin-Streptomycin Solution. HEPES is a biocompatible buffer and is harmless to cells. The culture medium containing HEPES can be helpful for the longtime maintenance of a constant PH range. It can also effectively prevent the influence of PH fluctuation on cell growth.  
For the generation of HL-60 cells array, HL-60 cells were collected and centrifuged at 500 g for 4.5 min and resuspended in PBS, through which a cell suspension was prepared with the concentration of 5 \(\times\) 10\({}^{6}\) cells/mL. The adequate amount of the suspension was loaded to the surface of the substrate until full coverage. After an incubation of 30 min, the liquid residues were removed using a pipette, and the substrate was immersed and cleaned in PBS twice. The prepared substrate was removed and placed into a Petri dish for observation under a fluorescence microscope.  
The affinity between antibodies and antigens is relatively weak, and the bond is easily broken under external forces. However, the cells are affected by various factors in the patterning process. The cell seeding and rinsing process should be meticulously performed to avoid excessive external forces. The following processing parameters, including the time and the cell concentration in seeding, will affect the quality of single-cell patterning.
## 7 Discussion
Therefore, it is unknown how the method would generalize to real-world binaural music recordings or to excerpts synthesized with \"reverberant\" binaural room impulse responses (BRIRs). Second, the boundary shape of the ensembles studied in this work was only circular. Extending the method by incorporating BRIRs and ensembles of arbitrary boundary shapes are left for future work. Third, the modified occlusion sensitive technique adopted in this study may potentially yield oversimplified results (since the method quantifies the influence of obscuring one frequency band at a time, it does not take into account any interactions between the frequency bands).
# Precise Point Positioning Using Triple GNSS Constellations in Various Modes
The developed PPP model employs GPS L1/L2, Galileo E1/E5a, and BeiDou B1/B2 signals in dual-frequency ionosphere-free linear combinations. Sequential least-squares estimation technique is used to obtain the best estimates, in the least-squares sense, for the unknown parameters. It is shown that the convergence time of the un-differenced post-processed GNSS PPP solution is improved by about 25%, in comparison with the GPS-only counterpart. On the other hand, the use of BSSD linear combinations in post-processing mode improves the precision of the estimated positioning parameters by about 25% in comparison with the GPS-only counterpart. Additionally, the solution convergence time is reduced to 10 min BSSD model is used, which represents about 50% improvement in comparison with the GPS-only PPP solution. Moreover, the GNSS RT-PPP solution shows a similar convergence time and positioning precision to the GPS-only counterpart. In all cases, the inter-system bias parameter is found to be essentially constant over the observation time span (one hour) and is receiver-dependent.
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 1 Introduction
As the complexity of the vehicle path problem increased, researchers began to continuously improve the genetic algorithm by combining ant colony algorithms [27], particle swarm algorithms [28], and simulated annealing algorithms [29; 30], and by developing various improved genetic algorithms such as non-dominated genetic algorithms [31], greedy genetic algorithms [32], and other improved genetic algorithms [33; 34]. These enhanced genetic algorithms have significantly increased the efficiency and adaptability of solving VRP instances, thereby offering more comprehensive path optimization solutions for companies, further enhancing their practical value. Although research on vehicle path optimization has yielded certain results, several limitations persist: (1) for green vehicle path optimization, the current research is less likely to consider the effects of the three key factors of time window, carbon emission, and vehicle load simultaneously; (2) despite the relative maturity of genetic algorithms in solving the vehicle routing problem (VRP), issues of slow convergence and susceptibility to local optima remain. In light of these challenges, the objective of this paper is to develop a low-carbon logistics and distribution path model that accounts for time windows, carbon emissions, and vehicle weight, and to design an improved genetic algorithm to effectively simulate this model, addressing the aforementioned shortcomings of existing genetic algorithms. This approach aims to provide innovative solutions and technical support to further promote the sustainable development of low-carbon logistics systems.