esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Discussion and Future Work
### Draubacks and Constraints  
Our methodology, while innovative, encounters the challenge of balancing the addition of complex techniques like ascending feature selection and layer-static-weighted attention with the need for model interpretability. These advanced techniques may be useful, but the process becomes more robotic and complicated, and at the same time, they make the models more complicated and less transparent. This complication increases suspicion of the model's acceptance in clinical environments. Therefore, understanding and trusting the mechanism of the model's prediction is mandatory.  
In addition, the existence of bias in the FAERS dataset, which covers a wide range of patients notwithstanding, results in the limitation that the model may fail to detect the true range of patient demographics and conditions, which will tremendously compromise its generalization. It is therefore crucial to develop a comprehensive approach that considers these factors while maintaining the balance between the use of complex and interpretable models. An approach with diverse data sources, including algorithms, can reduce the built-in bias, which will make the model more relevant and embraced in the current pharmacovigilance practice. The goal is still to refine the strategies well enough to find their place in drug safety monitoring, and that is why we are constantly working on new ideas and developing the existing approaches to remain valid contributors to an already complex work of clinicians and researchers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Applied Mechanism** & **Performance** \\ \hline
[9] & Information component with pharmacological network model (PNM) & AUROC = 0.82 \\
[10] & Pharmacological network model (PNM) with similarity measure & AUROC = 0.824 (primary), 0.821 (secondary) \\
[16] & Machine learning regression models & Mean square error = 152.72 \(\pm\) 11.17 \\
[18] & Weighted score for ADE association selection & Performance not available (NA) \\
[19] & Multi-task multi-attribute with optimization technique & AUC = 0.9247, \(\mathrm{AUPR}\) = 0.7515 \\
[20] & Principle component analysis (PCA) & Relative risk (ROR) with a significant \(p\)-value (0.05) \\ Proposed & Layer-static-weighted attention and ascending feature selection (NLP + demographics) & Test accuracy = 74.56%, \(\mathrm{CVROC}\) Score = 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of performance in FAERS dataset.
## 3 Results
### Mangrove Extent and Age Estimation
#### 3.3.2 Accuracy Assessment of [PERSON]'s Mangrove Classification  
Four confusion matrices of L7, L8, and S1/2 based classification show high accuracy when compared with the UAV ground reference data. The reference data was subdivided into 70% fraction of the total sample points (cf. Section 2.3.3) for training purposes, equal to 808 points for all classes. The rest (30% of the sample points) or 347 points in total were used for validation samples. The values of Overall Accuracy (OA), Kappa Coefficient, Producer Accuracy (PA), and Consumer Accuracy (CA) of all the classifications were all above 98%. L7 or L8 alone are already excellent sources for the classification of the one-month composite, but the combination of S1 and S2 is even superior to those. The trained OA, Kappa, PA, and CA of S1-S2 classification were all 100%. Due to the SLC error, the trained L7 classification generated from a six-month mean composite also resulted in 100% OA, Kappa, PA, and CA values. The L8-trained OA, Kappa, PA, and CA were 99.88%, 0.99, 99.65%, and 99.62%, respectively.  
Figure 9: The detected mangrove trees height frequency histogram of (**a**) the north delta and (**b**) the south delta.  
The validation accuracies also showed consistently excellent values similar to those of the training. The combination of S1-S2 has a value of 100% for all confusion matrices. The L8-validated OA, Kappa, PA, and CA were 99.39%, 0.99, 97.92%, and 98.39%, respectively. The L7-validated OA, Kappa, PA, and CA were 99.54%, 0.99, 97.83%, and 98.18%, respectively.
## 3 Results and Discussion
### Bed Efficiency and Contaminant Saturation  
Figures 8 and 9 illustrate the calculated \(\beta\) and \(\theta\) using Equations (13) and (14) for Figure 2, Figure 5a, and Figure 7b. The value of \(\beta\) almost linearly increased as \(r_{c}\) increased because of the retardation of contaminant transport, while \(\theta\) decreased as \(r_{c}\) increased, caused by the increase in retained colloidal particles. Forty percent of available adsorption sites (\(\theta\sim 0.6\)) in the bed at \(r_{c}=2\)\(\upmu\)m after 30 PV of contaminant injection implies that the increase in adsorption sites affects the long-term  
Figure 5: (a) Simulated breakthrough curves of contaminant and (b) the retention profiles of particles at \(\mu_{c}=-1.72\) and \(\sigma_{c}=0\), 0.1, 0.2, 1.0, 1.2, and 1.5.  
Figure 6: Particle size distributions for colloidal particles (\(\mu_{c}=-1.72\)) used in the simulation.  
Figure 7: (a) Simulated breakthrough curves of contaminant and (b) the retention profiles of particles at \(\mu_{c}=-0.60\) and \(\sigma_{c}=0\), 0.1, 0.2, 1.0, 1.2, and 1.5.  
transport behavior of contaminants as well as the retardation of transport. In addition, the evaluation of \(\beta\) provides the threshold \(r_{c}\) at which the contaminant is not detected at the outlet after the given PVs. For instance, the extrapolation of \(\beta\) values presented in Figure 8 indicates that the contaminant would not be detected during 30 PV of injection at \(r_{c}>2.3\) um (i.e., \(\beta=1\) at \(r_{c}\sim 2.3\) um).  
In contrast, the increase in \(\sigma_{c}\) led to an increase in \(\beta\) and \(\theta\) at \(\mu_{c}=-1.72\) and \(-0.6\). However, while \(\beta\) increased significantly as \(\sigma_{c}\) increased, a slight increase in \(\theta\) was observed at \(\mu_{c}=-0.6\). This implies the exponential increase in the adsorption capacity of the bed as \(\sigma_{c}\) increases when \(\mu_{c}\) is relatively high. In other words, substantial adsorption sites were still available at high \(\sigma_{c}\) after the significant retardation of contaminant transport. As stated above, the calculation of \(\beta\) and \(\theta\) provides the quantitative description of observed/simulated breakthrough curves as well as the prediction of the breakthrough point.  
Figure 8: Calculated \(\beta\) and \(\theta\) for Figure 2.
## 3 Case Study
This study also applied the proposed HVAC system optimization control strategy involving FCU temperature control to a single chilled water system with variable frequency from June-August 2017. This strategy was used to calculate the heat demand of the building to correct the supply of chilled water flow. Compared with the HVAC system operated at full load, the HVAC system operated at optimized settings provided by the proposed strategy achieved maximum energy conservation in August, saving 54.9% of energy. The maximum efficiency (in kW/RT) of the chilled water system was 15.6%. Tables 2-4 present comparisons of the performance of the HVAC system operated at full load with that of the system operated at the optimized load in terms of power consumption, RT produced, and chilled water system efficiency in summer (i.e., from June to August). \begin{table}
\begin{tabular}{c c c c} \hline
**Full-Load Operation** & **June** & **July** & **August** \\ \hline RT & 176,550.7 & 378,983.8 & 413,532.6 \\ kW & 154,125 & 331,933.6 & 289,985.9 \\ kW/RT & 0.872979 & 0.875852 & 0.701241 \\ \hline \end{tabular}
\end{table} 
Table 2: Data of HVAC system operated at full load. Figure 8: Energy consumption regression model of the chilled water system. \begin{table}
\begin{tabular}{c c c c} \hline
**Optimal Control Operation** & **June** & **July** & **August** \\ \hline RT & 253,698.9 & 247,613.1 & 191,023.2 \\ kW & 170,202.6 & 166,792.5 & 130,912.7 \\ kW/RT & 0.670885 & 0.673601 & 0.685324 \\ \hline \end{tabular}
\end{table} 
Table 3: System optimization control strategy involving FCU temperature information. Figure 9 shows a comparison of outdoor temperatures in 2016 with those in 2017. To understand the energy conservation engendered by the proposed strategy, the outdoor temperatures in 2017 were substituted into Equation (9)--the regression model for calculating the power consumption of a chilled water system--to determine the energy conservation effectiveness associated with various outdoor temperatures. The actual power consumption engendered by the proposed strategy was compared with the baseline power consumption calculated using the regression equation. The comparison revealed a maximum energy conservation of 65.6% (Figure 10); the energy conservation increased when outdoor temperature increased. Therefore, the proposed strategy can achieve energy conservation.
## 3 Numerical Study
### Parametric Analysis  
To investigate the influence of variations in soil friction angle, pile material Young's modulus, and pile length on the lateral bearing characteristics of the GPF, this section conducts a parametric analysis based on the calibrated numerical model in Section 3.1. The ranges of variation for each parameter are presented in Table 3.  
Figure 12: Grid pile foundation stress diagram.  
Figure 13: Soil stress diagram.  
In Table 3, the experimental parameters include a soil friction angle of 22\({}^{\circ}\), a pile material Young's modulus of 2000 MPa, and a pile length of 0.9 m. To enhance the credibility of the analysis results, during the parameter variations, the remaining parameters are kept the same as the experimental parameters, with only one parameter being modified for comparative analysis and calculation.  
To visually demonstrate the influence of parameter variations on the lateral bearing performance of the GPF, this study utilizes characteristic values of lateral bearing capacity to represent the effects of parameter changes. According to the \"Technical Specification for Building Foundation Piles\" (JGJ94-2008), for reinforced concrete precast piles, steel piles, and cast-in-place piles with a reinforcement ratio not less than 0.65%, the characteristic value of the lateral bearing capacity for a single pile can be obtained by taking 75% of the load corresponding to a lateral displacement of 10 mm at ground level (or 6 mm for buildings sensitive to lateral displacement) from the static load test results. The numerical model is a scaled-down model based on the experimental cellular raft foundation, with a scale ratio of 1:30. Therefore, according to the specification, taking the load value at 1/3 mm and multiplying it by 75% will yield the characteristic value of the lateral bearing capacity for the numerical model of the GPF under different parameter variations [38].
## 4 Methodology
### 3-Parameter Transformation
#### 4.1.1 Transformation Parameters  
In the 3D case, three observation equations are needed for the transformation of a single point. Observation equations are linear as follows:  
\[\begin{split} v_{x}^{i}&=t_{x}+(X_{i}^{i}-X_{o}^{i})\\ v_{y}^{i}&=t_{y}+(Y_{i}^{i}-Y_{o}^{i})\\ v_{z}^{i}&=t_{z}+(Z_{i}^{i}-Z_{o}^{i})\end{split} \tag{3}\]  
where exponent \(i\) denotes ordinal number of the point (\(i\) = 1, 2, 3,..., n).  
Design block matrix and observation vector, following Equation (3), are  
\[\mathbf{A}=\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&1\\ \vdots&\vdots&\vdots\\ 1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix}\]  
\[\mathbf{1}=\begin{bmatrix}X_{o}^{i}-X_{i}^{i}\\ Y_{o}^{i}-Y_{i}^{i}\\ Z_{o}^{i}-Z_{i}^{i}\\ \vdots\\ X_{o}^{n}-X_{i}^{n}\\ Y_{o}^{n}-Y_{i}^{n}\\ Z_{o}^{n}-Z_{i}^{n}\end{bmatrix} \tag{4}\]  
Figure 2: The 3-parameter transformation.  
Least squares adjustment method using Equation (4) gives  
\[\mathbf{N}=\mathbf{A}^{T}\mathbf{P}\mathbf{A}\qquad\mathbf{n}=\mathbf{A}^{T} \mathbf{P}\mathbf{I}\qquad\mathbf{x}=\mathbf{N}^{-1}\mathbf{n} \tag{5}\]  
where \(\mathbf{P}\) is a unit weight matrix.  
The solution of Equation (5) gives three translation parameters that correspond to the arithmetic mean of all coordinate differences of identical points in both systems:  
\[t_{x} =\frac{\sum_{j=1}^{k}(X_{o}^{j}-X_{i}^{j})}{k} \tag{6}\] \[t_{y} =\frac{\sum_{j=1}^{k}(Y_{o}^{j}-Y_{i}^{j})}{k}\] \[t_{z} =\frac{\sum_{j=1}^{k}(Z_{o}^{j}-Z_{i}^{j})}{k}\]  
where \(X_{o}\), \(Y_{o}\), \(Z_{o}\) are coordinates in the output coordinate system, \(X_{i}\), \(Y_{i}\), \(Z_{i}\) are coordinates in the input coordinate system, and \(k\) is the number of points.
###### Abstract  
In this study, we explore the development of controlled PLGA-CS-PEG microspheres, which are used to encapsulate model anticancer drugs (prodigiosin (PGS) or paclitaxel (PTX)) for controlled breast cancer treatment. The PLGA microspheres are blended with hydrophilic polymers (chitosan and polyethylene glycol) in the presence of polyvinyl alcohol (PVA) that were synthesized via a water-oil-water (W/O/W) solvent evaporation technique. Chitosan (CS) and polyethylene glycol (PEG) were used as surface-modifying additives to improve the biocompatibility and reduce the adsorption of plasma proteins onto the microsphere surfaces. These PLGA-CS-PEG microspheres are loaded with varying concentrations (5 and 8 mg/mL) of PGS or PTX, respectively. Scanning electron microscopy (SEM) revealed the morphological properties while Fourier transform infrared spectroscopy (FTIR) was used to elucidate the functional groups of drug-loaded PLGA-CS-PEG microparticles. A thirty-day, in vitro, encapsulated drug (PGS or PTX) release was carried out at 37 \({}^{\circ}\)C, which corresponds to human body temperature, and at 41 \({}^{\circ}\)C and 44 \({}^{\circ}\)C, which correspond to hyperthermic temperatures. The thermodynamics and kinetics of in vitro drug release were also elucidated using a combination of mathematical models and the experimental results. The exponents of the Korsmeyer-Peppas model showed that the kinetics of drug release was well characterized by anomalous non-Fickian drug release. Endothermic and nonsportaneous processes are also associated with the thermodynamics of drug release. Finally, the controlled in vitro release of cancer drugs (PGS and PTX) is shown to decrease the viability of MDA-MB-231 cells. The implications of the results are discussed for the development of drug-encapsulated PLGA-CS-PEG microparticles for the controlled release of cancer drugs in treatment of triple negative breast cancer.  
thermodynamics; kinetics; drug delivery; prodigiosin; paclitaxel and polymeric microspheres +
Footnote †: journal: Applied Sci. 2021, 11, 7112. [[https://doi.org/10.3390/app11157112](https://doi.org/10.3390/app11157112)]([https://doi.org/10.3390/app11157112](https://doi.org/10.3390/app11157112))
# On the Use of the Multi-Agent Environment for Mobility Applications
## 2 Environment as a Spatio-Temporal Reference
### Space-Time Model of the Environment  
Let a transport network \(G=(V,E)\), with a set of nodes \(V=\{(v_{i})\},i=0,...,N\) and a set of arcs \(E=\{(v_{i},v_{j})|v_{i}\in V,v_{j}\in V,v_{i}\
eq v_{j}\}\). Let two matrices \(D=\{(d_{ij})\}\) and \(T=\{(t_{ij})\}\) of costs, of dimensions \(N\times N\) (the arc \((v_{i},v_{j})\) has a distance of \(d_{ij}\) and a travel time of \(t_{ij}\)). The representation of the multi-agent environment is made of a duplication of \(G\), \(H\) times, with \(H\) the maximum allowed time of the considered application: \(G(t)=(N(t),E(t))\), with \(N(t)\) a set of nodes at time \(t\) and \(E(t)\) a set of directed links at time \(t\), and \(0\leq t\leq H\). The temporal copies of \(G\) are not necessarily identical (cf. Figure 2). Indeed, we can have different travel times between two copies of \(G\) to reflect the traffic state. Some nodes may be present in one copy while absent in another, to reflect the expansion of a crisis situation for instance [9]. Arcs may also be absent to reflect vehicle schedules in public transportation as in the application described in the next section. The costs on the edges usually refer to travel times.  
This representation of the multi-agent environment through time can be either a \"passive\" knowledge representation used by the agent to plan their future actions or to store historical states of the environment. It can also be used more profitably as an \"active\" entity, which is materialised as an explicit data structure, may have its dynamics, be accessed by all the agents and have a behaviour that can influence the behaviours of the agents.  
So-called time-expanded graphs have long been used in the literature of transportation science, especially for transit networks, to compute shortest paths (e.g., [11; 12]). The difference with the model presented in this paper is that the space-time network is active. It can act and react in the system and influence the behaviours of the agents. We describe  
Figure 2: Space–time network.  
this concept of active space-time environment with two mobility applications described in the following sections.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 8 Conclusions  
Single-hop has become the de facto topology to transmit data in current LPWANs mainly due to the need for network simplicity and robustness, and the fear of consuming too much energy in processing tasks and/or maintaining complex routing mechanisms. However, the HARE protocol stack presented in the current article proves the suitability of alternative uplink multi-hop communication approaches. Distributed among three OSI layers (link, network and transport), the multiple mechanisms contained in HARE ensure network reliability and resilience against failures in uplink transmissions while keeping low energy consumption.  
Results from a real testbed show uplink PDR values above 95% for all considered configurations, with faster achievement of this level when using multi-hop topologies with multiple transmission windows. In addition, multi-hop topologies outperform single-hop ones in terms of energy consumption in the considered non error-prone scenarios, with up to 15% improvement (which could even be much higher in larger networks) and values as low as 50 mJ/bit when employing an  
Figure 23: Temporal evolution of STAs’ transmission power level in a single-hop setting.  
effective RDC sublayer. Similarly, network auto-configuration and resilience have been successfully put to the test after forcing the shutdown of some network STAs.  
In the near future, LPWANs are foreseen to occupy a central role in applications requiring to interconnect low-bandwidth devices, focusing on range and power efficiency. While range coverage is mostly an issue from the physical layer, future challenges regarding power efficiency will surely encompass the coordination of different layers and even the inclusion of novel cross-layer mechanisms.  
This work was partially supported by the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant agreement No. 605073 (ENTOMATIC FP7-SME-2013 EC project). It also received funding from the Catalan government through the project SGR-2014-1173, and from the Spanish government under the project TEC2016-79510-P.  
[PERSON] conceived and designed the presented protocol stack; [PERSON] and [PERSON] developed the implementation in real hardware; [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; and all of the authors wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Optimization of Optical Trapping and Laser Interferometry in Biological Cells
## 2 Laser Interferometry
The sensitivity is estimated for monochromatic Gaussian laser beam as [6]  
\[\frac{I_{+}-I_{-}}{I_{+}+I_{-}}\cdot\frac{1}{x}\propto\frac{n(n_{\text{p}}^{2}- n^{2})}{n_{\text{p}}^{2}+2n^{2}}\frac{a^{3}}{\lambda w_{0}^{3}}.\qquad(\text{ Rayleigh}) \tag{2}\]  
In these two limiting situations, the LI sensitivity can be expressed by separating variables as  
\[S(n,n_{\text{p}},a,w_{0},\lambda)\ \ \sim\ f(n,n_{\text{p}})G(a,w_{0},\lambda), \tag{3}\]  
where \(f(n,n_{\text{p}})\) describes how sample refractive index \(n\) affects the sensitivity and offers useful insights on how the heterogeneity of the refractive index in cells provides errors in LI experiments. For brevity, we choose to define  
\[f_{\text{G}}\ \equiv\ n(n_{\text{p}}-n)/n_{\text{p}} \tag{4}\]  
\[f_{\text{R}}\ \equiv\ n(n_{\text{p}}^{2}-n^{2})/(n_{\text{p}}^{2}+2n^{2}). \tag{5}\]  
Here and hereafter, G and R in subscripts indicate geometrical optics and Rayleigh regimes, respectively. On the other hand, \(G\) (\(a,w_{0}\), \(\lambda\)) in Equation (3) includes all the other effects of the optical conditions of OT and LI. Although \(a,w_{0}\), and \(\lambda\) are raised as a typical example of parameters, many other conditions (e.g., alignment of optics and aberrations of laser lights) could also affect \(G\) though they are usually hard to control. Thus, in order to systematically investigate LI sensitivities, the effect of the uncontrollable factor \(G\) must be carefully removed by conducting additional experiments in a reference material, as we will explain later. At the condition \(a\sim\lambda\), the electric field of the laser interacts with the probe in a more complicated manner than the two extreme optical regimes [19]. Therefore, it is not obvious whether the description approximated as Equation (3) is applicable for this condition. However, as we will see later, our experimental results show that separation of variables in Equation (3) also holds beyond the range of the approximations (i.e., for \(a\sim\lambda\)). From the simplified theoretical expressions, several caveats are obtained for conducting LI. (1) The probe laser should be tightly focused to have as small a \(w_{0}\) as possible, especially when using small probe particles with \(a\ll\lambda\) (or in a broader condition \(a<w_{0}\)). (2) The larger difference of the refractive index between the probe particle and surrounding media (larger difference of \(n_{\text{p}}\) and \(n\)) is preferred as long as the non-conservative scattering force does not prevent optical trapping [22]. (3) The position sensitivity of LI takes its maximum for \(a\sim\lambda\sim w_{0}\). Therefore, \(a\sim\lambda\sim w_{0}\sim 1\)\(\upmu\)m is chosen for OT and LI in living cells [15].
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 3 Simulation Results and Analysis
### Hold Power Comparison  
Hold power refers to the static power consumed by the SRAM cell when it remains idle while retaining its stored data ('0' or '1'). This power drain comes mainly from leakage currents (like subthreshold and gate leakage) flowing through the transistors even when they are technically \"off\". Since memory arrays often spend most of their time in this standby state, minimizing hold power is vital for energy efficiency, battery life in portable gadgets, and heat management in dense memory chips. In this area, the proposed RMLP-18T performs exceptionally well. It shows the lowest hold power consumption among all compared cells, just 2.72 nW (Figure 10). This is slightly better than the next lowest, SIMR18T (3.07 nW), and much lower than many others like SERSC16T (66 nW) or QCCS12T (51.11 nW). This extremely low static power is likely attributed to the transistor stacking effect inherent in the cell's 18T structure. By utilizing a larger number of transistors (18T), multiple series-connected \"off\" transistors are formed within potential leakage paths during the hold mode. This stacking significantly suppresses subthreshold leakage current, a primary contributor to static power. Consequently, the observed low hold power is a significant advantage, making RMLP-18T very attractive for applications where power is limited.