esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PAMPA Studies  
The PAMPA is an in vitro method used to estimate transcellular passive permeability. It is considered robust, reproducible and fast [43]. The assay could be applied not only in the preformulation studies of single molecules but also for complex matrices such as plant extracts [39] and compounds loaded into formulations, i.e., SLNs and microemulsions [15; 16; 33; 34; 44].  
Considering that most compounds reach the blood stream by passive mechanism, PAMPA could be a helpful complement to Caco-2 assay to predict the oral absorption of SLM.  
The effective permeability (\(P_{c}\)) value after 4 h of incubation was 12.1 \(\pm\) 1.4 \(\times\) 10\({}^{-6}\) for free SLM and 132 \(\pm\) 11 \(\times\) 10\({}^{-6}\) in the case of extract loaded NLCs. Passive permeation of SLM was improved considerably with respect to aqueous solution (11-fold higher) when formulated. The recovery was 100% for the SLM-NLCs and 95% for the saturated aqueous solution of SLM. A high recovery value, above 80% is a proof of an acceptable permeation prediction. Moreover, this result suggests that there was neither relevant extract membrane retention nor binding to the surface of the filter. The developed formulations represent a successful attempt to ameliorate passive permeation of SLM.
## 2 Methodology
### Regional Intensity Accumulation  
This study observes that the contrast calculation of TRC is highly sensitive to changes in bright targets but performs poorly in responding to some weaker targets. Additionally, since the calculation involves statistical values within the DT, the responses from other frames in the target domain can influence the detection of the current frame.  
In response, this study proposes a detection method based on temporal regional energy accumulation, specifically targeting dim and weak objects with a focus on centroid localization. By analyzing differences between the current frame and neighboring frames, target energy is estimated, and its accumulation enhances the visibility of dim and weak targets. The results demonstrate that energy accumulation provides a strong response to such targets, while using a single frame as the reference for differences enables more precise centroid estimation.  
Specifically, similar to Section II-A Equation (1), we analyze \(x(t)\) by selecting the preceding \(N\) frames and succeeding \(N\) frames, performing difference calculations with \(x_{t}\). To mitigate interference caused by phase shifts, negative values resulting from the difference are truncated. After the differences, the images are accumulated, causing the energy to converge. We name this method Regional Intensity Accumulation (RIA), which can be defined as follows:  
\[\begin{split}\text{RIA}(x)&=\sum_{n=1}^{N}\max\{x_ {t}-x_{t-N},0\}\\ &+\sum_{n=1}^{N}\max\{x_{t}-x_{t+N},0\},\end{split} \tag{5}\]  
where \(N\) is determined based on the estimated speed of the target's movement. RIA repeatedly accumulates target intensity, exhibiting high sensitivity to moving dim targets while effectively mitigating the impact of background motion. As shown in Figure 4, a comparison of TRC and RIA on dim and bright targets highlights RIA's superior enhancement through accumulation.
# Recent Progress and Challenges Regarding Magnetic-Based Nanoparticles for Targeted Drug Delivery
## 3.3. **Magnetite-Based Nanomaterials for Specific Cancer Treatment**
### Breast Cancer Treatment
GQDs are highly soluble, biocompatible, demonstrate high loading capacity and photoluminescence activity. The cancerous cell viability was reduced with increasing CUR concentration (until 80 \(\upmu\)g/mL) and decreased to 2%, but the cytotoxic effect was more efficient for MCF-7 than for MG-63 cells. In another approach, graphene oxide functionalized with polyvinylpyrrolidone (PVP) was used as a cover of magnetite to efficiently encapsulate quercetin (QUR) via noncovalentinteractions and consequently increase its effectiveness against breast cancer cells [68]. Graphene oxide demonstrates a strong ability to load organic molecules through hydrogen bonding, electrostatic, or \(\pi\)-\(\pi\) interactions, while PVP ensures biocompatibility and hydrophilicity. Therefore, the QUR-loaded nanocarfer demonstrated improved toxicity against MDA-MB 231 breast cancer cells compared to the free active agent. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Components of Magnetite-Based Delivery System** & **Cell Model** & **Characterization of Anticancer Properties** & **Reference** \\ \hline SBA-15, L-cysteine, PEI, CD, FA & MCF7 & Drug release, cell viability, cellular uptake, biocompatibility & [46] \\ \hline Tragacanth gum, FA & MCF7 & Drug release, cytotoxicity, cellular uptake & [47] \\ \hline Oxidized alginate, cystamine & MCF7 & Interaction with human serum albumin, serum albumin, cytocompatibility, cytotoxicity & [48] \\ \hline Carboxymethyl chitosan, aminated lignocellulosic & MCF7 & Drug release, cytotoxicity & [49] \\ \hline Cationic molecularly imprinted polymer & 4T1 & Drug release, cytotoxicity, hemolysis assay & [50] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recently described targeted drug delivery systems based on Fe\({}_{3}\)O\({}_{4}\) NPs for potential application in breast cancer treatment. Figure 5: Schematic representation of synthesis procedure of Fe\({}_{3}\)O\({}_{4}\)-\(\beta\)-CD-CTD-FA for co-delivery of DOX and CUR. Reprinted from [65], Copyright (2023), with permission from Elsevier.
# Interdisciplinary Urban Tunnel Control within Smart Cities
## 2 Systemic Approach to Urban Road Tunnel
### Urban Tunnel Integration into Smart City Concept  
In terms of syntax interface between any two systems, we must focus on all seven layers of the reference ISO/OSI model [8], with the physical layer physically transmitting signals across a communication medium; the data link layer transforming a stream of bits from the physical layer into an error-free data frame for the network layer; the network layer which controls the operation of a packet transmitted from one network to another, such as how to route a packet and others. Apart from that, we must, however, also ensure that the subsystems understand each other and not only that they can exchange packages. For this we must talk about a semantic interface [9].  
Figure 1: Sustainable development of a road tunnel: balance of safety, economy, and environment.  
A semantic interface assures that the knowledge contained in each subsystem will have an unambiguous meaning for the second subsystem and form knowledge will be close to the human cognitive thinking. Ontologies [10] are suggested as a tool to assure understanding among particular subsystems.  
The semantics is of a great importance, while it is often neglected. There are many different stakeholders and actors that plays a role when designing a city subsystem. Each of them has a certain perspective on the given problem. Transport engineers are interested in the traffic flow, traffic occupancy, and a number of accidents. An architect understands the city tunnel from the urbanist point of view and looks at the urbanist details. A sociologist observes many details about drivers' behavior. A business specialist looks at the operational cost, energy consumption, etc. Environmental experts study the effects of city tunnel operation on various pollution indicators.  
These are often conflicting strategies of the heterogeneous fields, although we are looking at a single object (i.e., the urban tunnel). Each of these fields has a historically developed control concept according to its criteria functions, such as tunnel ventilation in relation to safety parameters, traffic flow control using variable signs, electricity control in relation to tunnel lighting, etc.  
In our view, the \"system of systems control\" means the maximum use of the control strategies of each system (i.e., transport, energy, environment, etc.) and the creation of the best possible combination of them so that these strategies support each other and are not in conflict. This is supported for example by the work of [PERSON] and [PERSON] [11]. The authors use the existing Supervisory Control and Data Acquisition (SCADA) system in connection to traffic simulation model, simulation of environmental characteristics and ventilation system. This resulting model connecting all above mentioned subsystems is used as a training base for simulating the impact of traffic accidents jointly on all subsystems and allows testing various human-machine interfaces.
## 2 Thermal and Strength Analysis of Compression and Tension Steel Elements in Fire
### Thermal Analysis
#### 2.1.2 Insulated Element Temperature Increment  
When temperature distribution in the section is uniform, the temperature increment \(\Delta\theta_{a}\) of the insulated steel member in the time interval \(\Delta t\) is given by the following formula [30]:  
\[\Delta\theta_{a}=\frac{\lambda_{p}A_{p}/V\!\left(\theta_{g}-\theta_{a}\right)} {d_{p}c_{a}\rho_{a}(1+\phi/3)}\Delta t_{fi}-\left(e^{\phi/10}-1\right)\Delta \theta_{g}, \tag{3}\]  
and  
\[\phi=\frac{c_{p}\rho_{p}}{c_{a}\rho_{a}}d_{p}\ A_{p}/V \tag{4}\]  
where:  
\(A_{p}/V\)--section factor for steel members insulated by fire protection material, determined from Table 4.3 [30];  
\(c_{a}\)--temperature-dependent specific heat of steel;  
\(c_{p}\)--temperature-independent specific heat of fire protection material;  
\(d_{p}\)--thickness of fire protection material;  
\(\Delta t_{fi}\)--time interval (not greater than 30 s);  
\(\theta_{a}\)--temperature of steel members;  
\(\theta_{g}\)--temperature of fire gases;  
\(\Delta\theta_{g}\)--temperature increment of fire gases in the time interval \(\Delta t_{fi}\);  
\(\lambda_{p}\)--thermal conductivity of the fire protection material;  
\(\rho_{a}\)--unit mass of steel;\(\rho_{p}\)--unit mass of the fire protection material.
## 5 Experimental Results and Discussion
### Experiment 2
#### 5.2.1 Description of Experimental Data  
Figure 11 shows a schematic diagram of the experimental platform, where the motor is denoted as O, and A and B denote two deep groove ball bearings of type 63,701 \(\times\) 3. The sampling frequency of the system was 15.3 kHz, and the measuring point was in the vertical direction of B. The bearing was artificially machined into seven faults, including outer race damage, inner race damage, rolling element damage, inner and outer race damage, outer race and rolling element damage, inner race and rolling element damage, inner and outer race and rolling element damage; the time-domain signal for all the fault types is shown in Figure 12.  
The dataset was expanded by the data augment algorithm with the step length of 300. In the end, there were 2000 samples for each fault type, of which 1750 samples were randomly selected as training samples, and the rest were used as test samples. In addition, the robustness of the model was tested by adding the noise of different intensity to the original vibration signal.  
Figure 11: Schematic diagram of the experimental platform.  
Figure 12: The time-domain signal for different fault types.
# Fault Tolerant Spectral/Spatial Optical Code Division Multiple Access Passive Optical Network
## 5 Reliability Analysis
Therefore, it can be observed that the connection availability of the fiber deployed across the feeder level is 99.997% for the tree-based architecture, which is significantly lower than the acceptable threshold of five nine. The connection availability at the distribution level is observed in two steps. The first step observes the connection availability of the 2 km OF medium deployed among the adjacent RNs. The analysis indicates that a connection availability of 99.9994% is obtained for the optical fiber medium deployed between the adjacent RNs. This can be attributed to the fact that short-span OF media are deployed between adjacent RNs that results in the elevation of connection availability. Furthermore, it can be observed that each RN can be fed from both clockwise and counterclockwise directions. Therefore, a failure across the OF media deployed between RNs does not affect the connection availability for the ONTs. For further analysis, DF deployed between the RNs and ONT modules is also considered for both networks. This is performed by considering 3 km and 5 km OF media between RNs of the proposed ring and tree-based architectures, respectively. It can be observed that the distribution of the OF media between the RNs and ONT modules for the proposed ring-based architecture has reduced the overall span of the fiber leading to the ONT modules. Consequently, a connection availability of 99.9992% is obtained for the optimized architecture, which is above the acceptable threshold of five nine. On the contrary, a connection availability of 99.998% is obtained for the tree-based architecture that deploys a 5 km DF between RNs and their corresponding ONTs. Thus, it can be observed that optimization of the 2D spectral/spatial OCDMA architecture with ring-based topology has significantly elevated the connection availability at both the feeder and distribution levels.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation
### Microcolony Formation and Dispersion  
#### 4.2.1 Definition of Microcolony Formation  
One key aspect of biofilm formation is the early development of microcolonies, where approximately 100 cells cluster together without clear borders, overlapping with the initial attachment phase [10]. These microcolonies serve as the foundation for biofilm formation, which requires bacteria to group, either as a single or multiple species, to create a community that offers protection, nutrients, and optimal conditions for survival. Bacteria within biofilms sense their environment and adjust metabolic processes to utilize available resources efficiently. The limited oxygen diffusion within biofilms creates distinct microenvironments that accommodate aerobic, facultative anaerobic, and obligate anaerobic bacteria [34]. Within biofilms, bacteria exhibit cooperative and altruistic behaviors facilitated by a process known as quorum sensing [35].
## 3 Stability Analysis
Note that the stability of CDM is \(\omega h\leq 2\), and \(\omega h\leq 2\sqrt{2}\) for four-order explicit RKM, and the stability of the present method is \(\omega h\leq 2\sqrt{2}\). 3. DQTFEM is highly symplectic. If the Jacobi matrix \(\mathbf{J}\) of a method satisfies Equation (35) as follows \[\left\{ \begin{array}{l}\mathbf{J}_{1}=J_{11}J_{22}^{\mathsf{T}}-J_{12}J_{21}^{ \mathsf{T}}=\mathbf{I}\quad,\quad\mathbf{J}_{2}=\tilde{J}_{11}^{\mathsf{T}}J_{22}- \tilde{J}_{21}^{\mathsf{T}}J_{12}=\mathbf{I}\\ \mathbf{J}_{3}=\tilde{J}_{11}^{\mathsf{T}}J_{21}-\tilde{J}_{21}^{\mathsf{T}}J_{11 }=\mathbf{0}\quad,\quad\mathbf{J}_{4}=\tilde{J}_{12}^{\mathsf{T}}J_{22}-\tilde{J}_{22 }^{\mathsf{T}}J_{12}=\mathbf{0}\\ \mathbf{J}_{5}=\tilde{J}_{11}\tilde{J}_{12}^{\mathsf{T}}-\tilde{J}_{12}\tilde{J}_{1 1}^{\mathsf{T}}=\mathbf{0}\quad,\quad\mathbf{J}_{6}=J_{21}\tilde{J}_{22}^{\mathsf{T}}- \tilde{J}_{22}\tilde{J}_{21}^{\mathsf{T}}=\mathbf{0}\end{array} \right.\,\] (35) then the method is symplectic. For the present single DOF system, all variables in Equation (35) are numbers and Table 2 shows that the precision of Equation (35) (especially \(\mathbf{J}_{3}\)-\(\mathbf{J}_{6}\)) is satisfactory, which indicates that DQTFEM is a symplectic method. In fact, we have numerically verified that DQTFEM is symplectic even if it is not stable. For a stable symplectic method, the magnitudes of all eigenvalues of \(\mathbf{J}\) are equal to \(1\), therefore all eigenvalues of \(\mathbf{J}\) of the present method are equal to \(1\) when it is stable.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### Weighted Subspace Fitting Technique  
Subspace fitting techniques are suboptimal approximations of the aforementioned ML techniques. They are useful because they have less computational complexity than maximum likelihood techniques. Furthermore, for larger sample sizes, if certain conditions are met, they are asymptotically the same as ML techniques [31].  
The WSF technique is a larger sample approximation of the SML technique, whose localization function is presented as [31]  
\[P_{WSF}(\mathbf{\Omega})=\mathit{Tr}\Big{\{}\mathbf{P}\widetilde{\mathbf{Y}}(\mathbf{\Omega} )\mathbf{E}_{\mathbf{s}}\mathbf{W}\mathbf{E}_{\mathbf{s}}^{H}\Big{\}},\ \mathbf{W}=\hat{\lambda}^{2}\lambda_{\mathbf{s}}^{-1} \tag{56}\]  
where \(\hat{\lambda}=\big{(}\lambda_{s}-\hat{o}^{2}\mathbf{I}\big{)}\), \(\lambda_{s}\) denotes the signal eigenvalue matrix, and \(\hat{o}^{2}=\frac{1}{\left(N_{h}+1\right)^{2}}\sum\mathit{diag}\{\lambda_{n}\}\) defines the average of the \(\left(N_{h}+1\right)^{2}-U\) lowest eigenvalues. This weighting provides the least asymptotic error variance, as depicted in [31].
## 4 Performance Evaluation and Analysis
### Test Bed Based on the Proposed System  
This paper has established a test bed based on open-source software using the Raspberry Pi as a network device for the proposed system. Before we conducted the evaluation, we installed the required packages through the Internet for a total of five Raspberry Pis to set the mesh network installation method and Docker-based cluster method. In addition, to automatically set the network, we used one of the Raspberry Pis as the master node and the others as member nodes.  
Additionally, all Raspberry Pis were set in a Docker-based mobile cloud structure as explained in Section 3. In other words, we set the system to enable Docker swarm usage through separately set master and member nodes by setting an environment where a custom OS based on a modified Raspberry Pi OS can be used for Docker [23].  
Each node can be explained through matrix 2. First, each node was equipped with a BLE dongle to receive Bluetooth beacon packets and an interface card for wireless LAN was set to establish the mesh network. One additional interface was set for the master node, which had the host name of node-01 to run Hostpad through a demon to allow users to have access [24]. This wireless access point was used to communicate with the maser node to conduct the evaluation.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 2 System Model  
The research object considered in this paper was the uplink for a massive MIMO system consisting of a base station equipped with \(N\) antennas and \(K\) single-antenna users where \(N\gg K\), as shown in Figure 1. Let \(\mathbf{s_{c}}=[\mathbf{s_{1}},\mathbf{s_{2}},\dots,~{}\mathbf{s_{K}}]^{T}\) denote the \(K\times\mathbf{1}\) dimensional symbol vector sent by all users simultaneously, where \(\mathbf{s_{k}}\in\mathbf{\varepsilon}\) was the transmitted symbol from the \(k\mathbf{th}\) user, and \(\mathbf{\varepsilon}\) was the modulation symbol set.  
Let \(\mathbf{H_{c}}\in\mathbb{C}^{N\times K}\) represent the Rayleigh fading channel matrix; then, the \(~{}N\times\mathbf{1}\) dimensional signal vector received by the base station could be recorded as:  
\[\mathbf{y_{c}}=\mathbf{H_{c}}\mathbf{s_{c}}+\mathbf{n_{c}} \tag{1}\]where \(\mathbf{n_{c}}\) represented an additive white Gaussian noise (AWGN) vector with an \(\mathbf{N}\times\mathbf{1}\) dimensional mean of 0 and a covariance matrix of \(\mathbf{\sigma^{2}I_{N}}\). Converting the complex model of Equation (1) into an equivalent real model gave:  
\[\mathbf{y}=\mathbf{H}\mathbf{s}+\mathbf{n} \tag{2}\]  
Among these terms, \(\mathbf{s}\in\mathbb{R}^{2\mathbf{K}},\mathbf{H}\in\mathbb{R}^{2\mathbf{N}\times 2\mathbf{K}}\), \(\mathbf{y}\in\mathbb{R}^{2\mathbf{N}}\), and \(\mathbf{n}\in\mathbb{R}^{2\mathbf{N}}\), which were:  
\[\begin{split}\mathbf{H}=\left[\begin{array}{cc}\Re(\mathbf{H_{c}})&- \Im(\mathbf{H_{c}})\\ \Im(\mathbf{H_{c}})&\Re(\mathbf{H_{c}})\end{array}\right]\text{, }\mathbf{y}=\left[ \begin{array}{c}\Re(\mathbf{y_{c}})\\ \Im(\mathbf{y_{c}})\end{array}\right]\\ \mathbf{s}=\left[\begin{array}{c}\Re(\mathbf{s_{c}})\\ \Im(\mathbf{s_{c}})\end{array}\right]\text{, }\mathbf{n}=\left[\begin{array}{c}\Re(\mathbf{n_{c}})\\ \Im(\mathbf{n_{c}})\end{array}\right]\end{split} \tag{3}\]  
Among those, \(\Re(\cdot)\) and \(\Im(\cdot)\) indicated the real part and imaginary part, respectively.
# SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells
## 4 Prediction Result Analysis
### Series-Parallel Pack Analysis  
According to the above elaboration, the cells-prediction method could provide better SOH prediction results under the premise of sufficient information from cells. In order to verify the generalization ability, we provide an application example of a four-cell pack in this section. The structure of a tested pack is shown in Figure 15. Figure 16 shows the comparison of the prediction result.  
Figure 16: Comparison of the SOH prediction result for a series-parallel pack.  
Figure 14: Relative errors of the prediction results: (**a**) series; (**b**) parallel.  
Figure 15: Series-parallel pack measurement schematic diagram.  
Among the 420 cycles data acquired in the experiment, the first 379 cycles (90% of the data) were used to train and construct the model, and the final 42 cycles (10% of data) were used to compare the prediction performance. As shown in Figure 16, in the prediction data, the cells-prediction gave the closest result to the experimental data, for which there were obvious fluctuations in the pack-prediction. Through data analysis, it was realized that the reason for this was a strong dependence on the input data caused by input information being too limited. In other words, when the temperature data appeared to fluctuate, the prediction result of the pack-prediction was in an exaggerated fashion. To confirm this reason, the correlation analysis between the degradation rate and temperature fluctuation is shown in Figure 17.  
As shown in Figure 17, there was a strong correlation between the degradation rate and the temperature fluctuation. However, as seen in Figure 5, the environment temperature was not the only parameter that had an effect on the degradation process of the battery. Due to the lack of information from the cells, other uncertain and certain influences were unknown. In Figure 17, compared with pack-prediction, the cells-prediction provided efficient fluctuation information and avoided excessive dependency on the limited input. The pack prediction with adequate information from the cells performed better, while a lack of information brought an exaggerated effect from input variances.