esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Disease Spreading Behavior Oriented to Three-Layer Complex Networks
## 3 Construction of Three-Layer Complex Networks
### The Three-Layer EEE Network Modeling  
The algorithm for the construction of the three-layer EEE network model is as follows.  
Step 1: At the initial moment, there are \(N_{\alpha}\) nodes in the network layer \(\alpha\), the intra-layer connectivity probability is \(P_{\alpha}\) and the adjacency matrix is \(A^{\alpha}\). There are \(N_{\beta}\), \(N_{\gamma}\) nodes in the network layer \(\beta\),\(\gamma\); the intra-layer connectivity probability is \(P_{\beta}\),\(P_{\gamma}\); and the adjacency matrix is \(A^{\beta}\), \(A^{\gamma}\). The inter-layer connectivity probability between \(\alpha\) and \(\beta\) layers is \(P_{\alpha\beta}\), and the adjacency matrix is \(A^{\alpha\beta}\). The inter-layer connectivity probability between \(\beta\) and \(\gamma\) layers is \(P_{\beta\gamma}\), the adjacency matrix is \(A^{\beta\gamma}\), the total number of nodes in the three-layer network is \(N_{\alpha}+N_{\beta}+N_{\gamma}\), and the global network adjacency matrix is \(A\).  
Step 2: The ER random network construction method is used in the intra-layer. In the network layer \(\alpha\), the connected edges between nodes \(i^{\alpha}\) and \(j^{\alpha}\) are traversed and each connected edge generates a random probability \(P_{1}\). If \(P_{\alpha}>P_{1}\), then \(a(i,j)=1\) in the adjacency matrix \(A^{\alpha}\), otherwise \(a(i,j)=0\); the traversal of the intra-layer nodes is completed. The network layers \(\beta\) and \(\gamma\) are constructed in the same way for the network layer \(\alpha\).  
Step 3: The inter-layer link uses a random link method, traversing the nodes \(i^{\alpha}\) in the layer \(\alpha\) and the node \(i^{\beta}\) in the layer \(\beta\), generating a random probability \(P_{2}\), if \(P_{\beta}>P_{2}\), then \(a(i,j)=1\) in the adjacency matrix \(A^{\alpha\beta}\), otherwise \(a(i,j)=0\); the nodes in the layer \(\alpha\) are traversed to finish. The inter-layer connectivity algorithm for the layers \(\beta\) and \(\gamma\) is the same as described above. There is no directly connected edges between the network layers \(\alpha\) and \(\gamma\).  
The construction of the network model is completed according to the above three-layer EEE network construction method. Figure 1a shows the schematic diagram of the three-layer EEE network model with the following parameter settings: \(N_{\alpha}=25\), \(N_{\beta}=20\), \(N_{\gamma}=17\), \(P_{\alpha}=0.2\), \(P_{\beta}=0.2\), \(P_{\gamma}=0.2\), \(P_{\alpha\beta}=0.2\), and \(P_{\beta\gamma}=0.2\).
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 4 Experimental Results and Analysis
### System Environment  
We used the computer hardware and software configurations listed in Table 3 to gather sensor stream data in practical work environments with the aim of validating the performance achieved by Kafka and ActiveMQ in the real-time air pressure stream data monitoring scheme.
## 2 Materials and Methods
### Cytotoxic Properties  
The MCF-7 adenocarcinoma, cell line was obtained from the American Type Culture Collection (ATCC) cat. no. HTB-22. The T47D ductal, epithelial cancer cell line was obtained from the European Collection of Authenticated Cell Cultures (ECACC) no. 85102201. These two human breast cancer cell lines were grown at standard conditions (37 \({}^{\circ}\)C, 5% CO\({}_{2}\), 95% humidity) in RPMI 1640 medium (Sigma) supplemented with 10% fetal bovine serum (FBS, Sigma), 100 U/mL penicillin (Polfa, Poland) and 100 \(\mu\)g/mL streptomycin (Polfa, Warsaw, Poland). For MCF-7 cells 50 \(\mu\)g/mL of bovine insulin (Sigma) was also added. Cytotoxicity effect of tested WSP was measured with a quantitative colorimetric toxicity MTT assay based on the transformation of yellow, soluble tetrazolium salts (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolzolium bromide) to purple-blue insoluble formazan by mitochondrial succinate dehydrogenase of living, metabolically active cells [30; 31].  
T47D and MCF-7 cells (100 \(\mu\)L/well) were seeded on 96-well plates at concentrations of \(3\times 10^{5}\) cells/mL and \(2\times 10^{5}\) cells/mL, respectively. After 24 h incubation at standard conditions, when cells in each well reached 75-80% confluence, the growth medium was replaced with WSP dilutions in RPMI 1640 with 2% FBS. The concentrations ranged from 25 \(\mu\)g/mL to 250 \(\mu\)g/mL. Cells were incubated with polysaccharides for 24 h, followed by addition of 25 \(\mu\)L of MTT (Sigma) solution (5 mg/mL in PBS) per well. After 3 h incubation at 37 \({}^{\circ}\)C formazan crystals were solubilized with 100 \(\mu\)L of lysis buffer (10% SDS in 0.01 M HCl) per well. Plates were incubated overnight at standard conditions. The absorbance was read at 540 nm with a plate reader (680 XR, Bio-Rad), and the mean value for each concentrations was calculated. The percentage of viable cells were calculated from the absorbance.
# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 4 Simulation
### Evaluation Results
#### 4.2.3 Performance Analysis According to Random Values of Two Rayleigh Ratio Parameters  
To determine the optimal parameters, we randomly generated values by adjusting the CDF parameter for the ratio of the two Rayleigh distributions and assessed their performance. The parameters that govern the PDF of the two Rayleigh ratios are denoted as \(\rho_{1}\) and \(\rho_{2}\) as in (11). We constrained the randomly generated values within the range of 0.5 to 5 to prevent them from being excessively large or small.  
For each experiment, we increased the data to 4800 for classes with a count of less than or equal to 4000 in each fold of the development dataset, as described in Section 4.1 Evaluation Setup. The results displayed in Figure 9 are those for the test data in the development dataset. As a result of the experiment, by adjusting the variables, we were able to confirm that we had the highest performance when sigma and lambda were 1.  
In these results, the Rayleigh parameters \(\rho_{1}\) and \(\rho_{2}\) showed overall better performance when they had identical values; this may be because, in real room environments, the statistical properties of the RIRs are more likely to be similar rather than significantly different, so having identical values might better simulate real-world conditions. Additionally, when \(\rho_{1}\) was larger than \(\rho_{2}\), the scores tended to be lower overall; however, the small number of experiments makes it difficult to generalize this result. Consequently, it is preferable to set the Rayleigh parameters \(\rho_{1}\) and \(\rho_{2}\) to the same value or to avoid cases where \(\rho_{1}\) is larger than \(\rho_{2}\) if they are set differently.
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 5 Security Analysis
### Informal Analysis
#### 5.1.6. Replay and Man-In-The-Middle Attacks  
An adversary can learn about transmitted messages over open wireless channels according to Section 3.2. However, in our proposed scheme, the adversary cannot conduct replay and man-in-the-middle attacks because every transmitted message contains timestamp or random nonce. Timestamps or random nonces \(T_{1}\), \(T_{2}\), \(T_{3}\), \(R_{cs}\), and \(r_{A}\) are generated by \(DU_{j}\) or \(CS\) and included in the message \(MA_{j}=h_{1}(Verif_{j}||HD_{j}||T_{1})\), \(AUTH_{cs}=h_{1}(MA_{j}||Verif_{j}||R_{cs})\), \(R_{A}=PK_{a}^{r_{A}}\), \(L_{AB}=h_{1}(V_{A}||T_{2})\), and \(L_{BA}=h_{1}(V_{A}||HGID_{1}||T_{3})\). Therefore, the proposed scheme can successfully prevent against replay and man-in-the-middle attacks.
## 2 Proposed System Methodology
### System Overview  
Figure 2 provides a detailed overview of the proposed haptic-driven bilateral TeleCaring system, aligned with the IEEE P1918.1 Tactile Internet standard architecture [25]. The robots on the slave side care for elder people in various environments, transmitting activity and localization data to the master side over the communication network and receiving  
haptic feedback. The proposed system core network consists of SDN-based technology, allowing for the separation of the control and data plane and supporting next-generation emerging applications. The SDN switch and controller communicate using OpenFlow protocol. The solid lines (black color) represent the transmission link weights in ms while the dotted lines (yellow color) represent the link between SDN-enabled switches and the controller. The SDN framework minimizes network complexity and allows for dynamic network resource sharing. Furthermore, it offers flexibility to the network by allowing operators to define virtual slices according to use cases while ensuring QoS.  
Active Queue Management techniques are employed in the proposed system that prevent buffer overflow by discarding or marking before buffer overflow, helping to deal with congestion challenges [26, 27, 28]. Moreover, we design a tactile support engine known as an ECI engine near the master domain following the Tactile Internet standard architecture (IEEE P1918.1), where dual-trained LSTM models are deployed for activity recognition and localization for remote-side caring robots. The edge server on the master side is responsible for activity and localization prediction of caring robots and displays the performance to the human operator via a haptic display. By processing data at the edge, critical decisions can be made in real time, significantly reducing the communication latency and enhancing the overall system's performance with a focus on QoS and QoE provisioning for telecaring services. A human operator can monitor multiple robots simultaneously and provide feedback in case of anomalies, which is then communicated back to the slave domain over the network.
## 2 Materials and Methods
### Proposed Method  
In order to reduce control delay and signaling overhead, we deploy a fully distributed MARL algorithm at the edge of the network (each BS) to obtain D-TDD configuration. Each BS learns D-TDD configuration by itself based on local observations under the condition of lacking global information (buffer length, D-TDD configurations, and rates of other BS). This leads to two problems:  
* Non-stationary learning environment: The lack of other BS's buffer length and D-TDD configurations makes the learning environment of each BS non-stationarity. The non-stationarity environment leads to the reward fluctuating greatly under the same local observation and action, which increases the difficulty of learning.
* Relative over-generalization: The lack of other BS rates means that BS prefer to select a sub-optimal action in a joint action space. This results in that a certain BS selects a sub-optimal D-TDD configuration which increases its own data rate but increases interference to other BS. At last, the overall system rate is decreased.  
Refer to the experience of applying lenient learning to genetic algorithm, we apply LC-MARL to solve those problem.  
In this section, we introduce the proposed LC-MARL based decentralized D-TDD configuration method in three parts. First, we introduce reinforcement model. Second, we explain the principle of LC-MARL. Last, the detail of proposed method is illustrated.
## 1 Introduction
### Pilot Projects of Polymer Flooding
The combined oil recovery from pre-flush and polymer flooding increased by 14% (pilot one) and 11.6% (pilot two) compared to water flooding. * [PERSON] et al. [41] presented a single-well chemical tracer test (SWCT) performed in one Wara producer of the Greater Burgan Field (Kuwait). The reported connate water salinity was 160 g/L total dissolved solids (TDS). Low-salinity brine (diluted SWCT chemicals) flooding was performed as SWCT Test #2, and low-salinity polymer injection was performed as SWCT Test #3. The associated reduction of residual oil saturation (\(S_{\sigma}\)) was 7% from low-salinity pre-flush and low-salinity polymer flooding. * Polymer flooding in the Adorf Field (Germany) was applied after a low-salinity pre-flush [71] with formation brine with an initial salinity of 250 g/L. In this case, polymer flooding did not contribute to the expected additional oil recovery. The main reason for the poor performance was a reaction between the injected low-salinity brine and the rock minerals. This reaction significantly increased the brine hardness, hence limiting the viscosity control and performance of the polymer flooding. This paper focuses on variables paying particular attention to temperature, salinity, and mechanical shear to influence polymer stability and performance evaluations in porous media. Figure 1 summarizes the objectives for this investigation, focusing on rheological behavior before and after core flooding experiments. In each case, rheological behavior was assessed via elongational viscosity (using a microfluidic device) and rotational rheometry (steady shear viscosity, normal stress differences, and oscillatory). In addition, micromodel flooding experiments were performed to observe the role of polymer solutions on oil recovery when mixed at variated levels of salinity and hardness. Figure 1: Workflow adopted in this study, to investigate change in polymer viscoelastic properties through a Bentheimer core plug/micromodel.
## 2 IIr-Ofdm
### IIR-OFDM Receiver  
Suppose \(\mathbf{F}\) and \(\mathbf{F}^{\mathrm{H}}\) are the DFT and the IDFT matrices, respectively. After inserting the guard interval, the signal can conform to the cyclic convolution, and the signal matrix can be represented as  
\[\mathbf{\hat{s}}_{v}=\mathbf{A}_{c}\mathbf{r}_{v}=\mathbf{F}^{\mathrm{H}} \mathbf{d}_{v}+\mathbf{A}_{c}\mathbf{w}, \tag{20}\]  
where \(\mathbf{w}\) represents the additive white Gaussian noise (AWGN) vector whose entries have complex-valued Gaussian distributions of \(\mathcal{CN}(0,\sigma_{w}^{2})\). \(\mathbf{A}_{c}\) represents the IIR channel matrix that satisfies the \(N\)-point circular convolution, which can be written as  
\[\mathbf{A}_{c}=\sum_{i=0}^{l_{g}}a(i)\Gamma^{i}=\begin{bmatrix}a(0)&&&a(l_{g}) &\cdots&a(1)\\ \vdots&\ddots&&&\ddots&\vdots\\ a(l_{g}-1)&\cdots&a(0)&&&a(l_{g})\\ &a(l_{g})&\cdots&a(0)&&\\ &&&\ddots&&\ddots&\\ &&&a(l_{g})&\cdots&a(0)\end{bmatrix}. \tag{21}\]  
The received signal can be recovered by low-order tap equalization, which is similar to classical OFDM. The recovery signal can be expressed as  
\[\mathbf{\hat{d}}_{v}=\mathbf{FA}_{c}\mathbf{r}_{v}=\mathbf{d}_{v}+\mathbf{FA} _{c}\mathbf{w}, \tag{22}\]  
where \(\mathbf{\hat{d}}_{v}\) is the matrix of the recovered signal after the equalization, which is only affected by AWGN.  
IIR-OFDM has a more complex guard interval than classical OFDM. Therefore, IIR-OFDM has performance requirements for the transmitter. However, at the receiver, the equalization process is consistent with classical OFDM. As a result, when deploying IIR-OFDM, people should pay attention to whether the performance of the transmitter can achieve the construction and transmission of complex guard intervals.
## 5 Conclusions and Future Research
### Future Research  
In future research, further studies should be conducted based on the findings of the tests listed above.  
1. The effect of the optical fibers on the mechanical strength of the TCP will be determined in future study.
2. The integration of OFs into TCPs with compound parabolic concentrators [48; 49], including parametric analysis by building numerical models using commercial software, should be explored.
3. The multi-physical analysis of the TCPs should be conducted considering thermal, light transmission and mechanical actions. Furthermore, numerical analysis and physical testing approaches should be developed to comprehensively evaluate the overall performance of the building facade.  
Conceptualization, B.H.; methodology, B.H.; software, B.H.; writing--original draft preparation, B.H.; writing--review and editing, W.L.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by China National Science Foundation under award numbers 51608381, 51078290, and 51578405, the International Joint Research Laboratory of Earthquake (ILEE) through grant number ILE-IJRP-P2-P3-2017, and China Ministration of Science and Technology Major Project through grant number 2016 YFE0105600.  
The Solar Energy Research Institute of Singapore (SERIS) provided facilities for the tests conducted in this report, and [PERSON] gave technical help in the SERIS lab. [PERSON] of the National University of Singapore provided facilities for the construction and mechanical testing of the TCPs. The presented research was partially funded by the Republic of Singapore's National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. BEARS has been established by the University of California, Berkeley as a center for intellectual excellence in research and education in Singapore.  
The authors declare no conflict of interest.
## 2 Background  
### Multi-Objective Optimization  
Multi-objective optimization (MOPs) consists of finding solutions which simultaneously consider two or more conflicting objectives to be minimized or maximized. Thus, the search aims to find a set of solutions, each one reflecting a trade-off between the objectives. MOPs are tackled today using Evolutionary Algorithms by engineers, computer scientists, biologists, and operations researchers alike [46]. These algorithms are heuristic techniques that allow a flexible representation of the solutions and do not impose continuity conditions on the functions to be optimized. Moreover, MOEAs are extensions of EAs for multi-objective problems that usually apply the concepts of Pareto dominance [47].  
MOEAs have been applied to solve MOPs from different areas, from logistic problems as the Ridesharing problem [48], Software Engineering problems as architecture optimization [49], machine learning problems such as feature selection [50], and by optimizing antibiotic treatments [51].  
Besides EAs, there are other nature-based algorithms that have been successfully applied to solve optimization problems. This is the case of the Ant Colony Optimization [52] and related improved versions such as [53], and Particle Swarm Optimization [54; 55]. This last one with several different applications such as a vehicle routing problem [56; 57; 58] and engineering problems such as the design of near-field time delay equalizer metasurface [59], the Artificial Magnetic Conductor Surface [60], and for the design of a dielectric phase-correcting structure for antennas [61].  
There are several MOEAs proposed in the literature, some of them are based on genetic algorithms and differ from each other on their replacement strategies; others are based on differential evolution. All of them use a population of current solutions \(P\) to generate offspring solutions \(O\), combine them in \(P\cup O\), and then employ a replacement strategy to generate a new population of solutions \(P^{\prime}\). In the sequence, we describe five MOEAs used in this work.
## 3 Results and Discussion
### Monitoring the Effectiveness of the HRP Retardant on Wood Samples  
All samples exposed to flame for 30 s were ignited. The resulting flame reached a maximum height of 8.82 cm for spruce (Figure 4). The monitoring of mass loss was conducted by observing the mass change of the sample at the beginning and the end of the experiment. The experiments were short in time (30 s), with corresponding mass loss values (Figure 4). The assumptions of 100% efficiency and complete protection could not be confirmed. The mass changes were symbolic (Table 3) and smaller compared to the mass change of untreated samples. At the same time, the generated flames caused degradation of the sample surface which resulted in surface charring, as reported in [53; 68; 69]. Samples of different wood species demonstrated the variation of flame spread values (Table 4 and Figure 4). These values were confirmed by one-way ANOVA, which determined a significant dependence of flame spread on wood species (Table 3).  
Figure 4: Dependence of mass change after application of a small flame initiator for 30 s and comparison of flame height for each wood sample.  
HolzProf producers [53; 69] present the efficiency of HolzProf (HRP) fire retardant as very high. Their tests with video, performed in internationally well-known accredited laboratories and certification centres, meet the classifications of fireproof efficiency Euro-class B-s1-d0, (UK class 0) on solid wood, according to EN 13501-1 [63]. However, the effect of other coatings has not been investigated. HRP was used as a protective coating on insulating panels made of recycled technical textiles. [PERSON] et al. [70] also determined that the fire-technical properties complied with EN ISO 11925-2 [66]. The results obtained present a mass loss of more than 10% and place the material in reaction to fire class E [63]. [PERSON] et al. [71] conducted research on the effect of a selected concentration of diammonium phosphate (\(\geq\)98% purity) on wood. Based on the results of their study, wood samples impregnated with 30%wt of DAP can be classified as self-extinguishing materials and could meet the requirement for nonflammability in construction. However, their results were based on heat loading.
# Optimization of Optical Trapping and Laser Interferometry in Biological Cells
## 4 Experimental Results and Discussions
### LI Sensitivity Depending on Radial and Axial Offsets
Considering that the optical trapping force is derived from the change of the momentum of photons, the conversion of the sign is elusive and not observed in experiments. Lorenz-Mie theory has been frequently used for investigating the scattering of the plane light wave by a spherical object [19]. In order to predict the LI sensitivity for the optically trapped particle, it might be necessary to extend classical Lorenz-Mie theory for a strongly focused Gaussian laser. Regardless of the exact physical origin, the focal-offset dependence of the LI sensitivity clarifies that the probe heights compared to the laser focus must be maintained for the best LI sensitivity during LI experiments in cells. The feedback tracking is indispensable for that purpose and the stability must be optimized independently.