esa-sceva/satcom-chunk-collection huggingface dataset:
# Physicochemical Characterization of Hyaluronic Acid and Chitosan Liposome Coatings
## 1 Introduction
The molecular weight greatly influences the viscosity of polysaccharide aqueous solutions. HA presents a high solubility in aqueous systems and plays a fundamental role in the inflammatory process and other physiological processes. HA shows high biocompatibility and has many uses in the biomedical field [18]. The use of HA as a polymer coating for liposomes is encouraged by its properties of being a protector against vesicle leakage [19], stealth enhancer [20] and lyoprotectant [21]. In this paper, chitosan and hyaluronic acid were used as coatings for DOPC/DOPE zwitterionic liposomes. Liposomes that were coated and purified were characterized through the study of their physical properties, such as the hydrodynamic diameter, \(\zeta\)-potential and polydispersity index (PDI), which also provide useful information on its stability. Subsequently, the chemical characterization of the coated liposomal systems was performed by nuclear magnetic resonance (NMR), surface infrared (FTIR-ATR) spectroscopy and time-of-flight secondary ion mass spectrometry (ToF-SIMS) analysis to provide fundamental information for understanding and optimizing the coating processes. The final aim of the present study was to synthesize and fully characterize coated liposomes to obtain vesicles with increased chemical properties for new drug delivery systems. In particular, the use of hyaluronic acid as a coating system for vesicles could help to develop carriers with biological properties typical of HA, such as regenerative, moisturizing and repairing properties.
## 2 Materials and Methods
### Orbiting Carbon Observatory-2 (OCO-2) Sif
Instantaneous SIF (\(\text{SIF}_{\text{inst}}\)) was calculated using the average of instantaneous SIF measured at 757 nm and instantaneous SIF at 771 nm times a factor of 1.7 based on the OCO-2 Data Product User's Guide and previous literature [15; 28; 126; 129; 130] as follows:  
\[\text{SIF}_{\text{inst}}\ =\ \frac{\text{SIF}_{\text{257 nm}}+1.7\text{SIF}_{ \text{21 nm}}}{2} \tag{1}\]  
\(\text{SIF}_{\text{inst}}\) was normalized for differences in sun angle and daily changes by application of the daily mean correction factor (DCF), which is a time averaged first order approximation of a daily average SIF\({}_{\text{d}}\)[14; 126; 130; 131]. The DCF varies with time and latitude because these relate to the variation of the solar zenith angle (SZA) and length of the day, thus having a seasonal cycle. Although this cycle is weaker in the tropics than higher latitudes, applying the DCF to SIF is recommended for examination of seasonal cycles, comparing SIF across large spatial scales, and when examining OCO-2 based SIF to other products with different collection times [15; 27; 131; 132].The DCF approximately accounts for the length of day and variability of the solar zenith angle (sza) using pure geometric incoming light scaling for the location in ten-minute time steps. \(\text{SIF}_{\text{d}}\) is calculated with the following equation  
\[\text{SIF}_{\text{d}}\ =\ \frac{\text{SIF}_{\text{257~{}out}}\ \text{SIF}_{ \text{11}}}{\cos(\text{Sza}(\text{t}_{\text{m}}))}\int_{\text{t}\ =\ \text{t}_{\text{m}}-12\text{h}}^{\text{t} =\ \text{t}_{\text{m}}+12\text{h}}\cos(\text{sza}(\text{t}))\text{dt} \tag{2}\]  
where sza is the solar zenith angle from the OCO-2 dataset and t\({}_{\text{m}}\) is the time of measurement [14; 126; 130]  
The programming for downloading and processing of OCO-2 data was completed using the interactive data language (IDL; Harris Geospatial, Inc., Boulder, Co.).
# Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network
## 4 Performance Evaluation  
We have used the open source component-based C++ simulation tool, OMNeT++ [72]. By employing Castalia [72; 73], we have used nine sensor points (nodes) in our simulation model and to analyze the performance of our proposed routing protocol. Our approach uses the concept of sink node which is static and this node transmits data coming from various sensor nodes to a remote location such as a server process. The proposed protocol follows three assumptions, i.e., all nodes are homogeneous in all respects of their computation, temperature reading etc., reading must not beyond the sink point and the minimum packet size of 37 bytes is adequate to exchange the header information and drop out the payload. Based on these assumptions, we have compared the performance with the MRRP [47]. We have used an identified node as a relay agent and sampled its temperature readings. The simulation setup parameters along with the initial temperature values are illustrated in the Table 2. The results against the following metrics are calculated to evaluate the proposed study. A brief definition of each metric is delineated below.  
* Temperature rise: This metric will measure the rise in the sensor nodes while they transmit data from one node to another.
* Average Temperature rise: This value indicates the average rise in temperature of all the nodes participating in the simulation.
* Maximum temperature rise: Maximum temperature rise is the highest temperature rise inside the whole network.
* Average Throughput: It is the rate in which a success is measured against the number of packets transmitted through the network in each unit of time. These packets are sent by the sensor nodes that are participating in our simulation model.
## 2 Related Work  
### \(\ell_{1}\) and \(\ell_{2}\) Regularization  
[PERSON] et al. [14] presented efficient algorithms for group sparse optimization with mixed \(\ell_{2,1}\) regularization for the estimation and reconstruction of signals. Their technique is rooted in a variable splitting strategy and ADMM. [PERSON] and [PERSON] [8] suggested that an elastic net, a generalization of the lasso, is a linear combination of both \(\ell_{1}\) and \(\ell_{2}\) norm. It contributes to sparsity without permitting a coefficient to become too large. However, [PERSON] and [PERSON] [15] introduced a new estimator called the Dantzig selector for a linear model when the parameters are larger than the number of observations for which they established optimal \(\ell_{2}\) rate properties under a sparsity. [PERSON] et al. [16] enforced sparse embedding to ridge regression, obtaining solutions \(\hat{x}\) with \(\|\hat{x}-x^{*}\|_{2}\leq\epsilon\|x^{*}\|\) small, where \(x^{*}\) is optimal, and also did this in \(\mathcal{O}(nnz(A)+n^{3}/\epsilon^{2})\) time, where nnz(A) is the number of nonzero entries of A. Recently, [PERSON] et al. [5] have proposed an ordered \(\ell_{1}\)-regularization technique inspired from a statistical viewpoint, in particular, by a focus on controlling the false discovery rate (FDR) for variable selection in linear regressions. Our proposed method is similar but focused on parameter estimation based on the ordered \(\ell_{2}\) regularization and ADMM. Several methods have been proposed based on Reference [5] and similar ideas. For example, [PERSON] et al. [2] introduced a new model-fitting strategy called Sorted L-One Penalized Estimation (SLOPE).which regularizes least-squares estimates with rank-dependent penalty coefficients. [PERSON] and [PERSON] [17] proposed DWSL1 as a generalization of octagonal shrinkage and clustering algorithm (OSCAR) that aims to promote feature grouping without previous knowledge of the group structure. [PERSON] et al. [6] have introduced an image restoration method based on a random projection and a partial sorted \(\ell_{p}\) norm. In this method, an input signal is decomposed into two components: a low-rank component and a sparse component. The low-rank component is approximated by random projection, and the sparse one is recovered by the partial sorted \(\ell_{p}\) norm. Our method can potentially be used in various other domains such as cyber security [18] and recommendation [19].
## 7 Experimental Results
### Experiment on AU Weighting Multi-Layer Fusion Expression Recognition Performance Analysis  
To test the performance of our fusion expression recognition approach with or without the learned weights of AU, we did comprehensive experiments to classify six expressions (angry, disgust, fear, happy, sad, surprise), which will ensure the comprehensiveness of final recognition results. Several different metrics are used for evaluating algorithm performance, which are the recognition rate, false positive rate and receiver operating characteristic curve (ROC) area for each expression. Figures 10 and 11 show the results of the multi-layer model with or without AU optimal weight respectively. From top to bottom, performance comparison on the CK and JAFFE datasets is shown in terms of (a) recognition rate; (b) false positive rate (FTP) and (c) ROC area(ROCA) for six basic expressions.  
Experimental results in Figures 9 and 10 show that the proposed multi-layer face recognition method with the AU weighted strategy has a significantly increased recognition rate. For six expressions, the mean accuracy achieved is 96.62% for CK and 94.63% for JAFFE, which exceed, by 2.02% and 2.23%, that without AU weight tuning. The average FTP of the AU optimal weight is 0.103 in CK and 0.147 in JAFFE, much lower than un-tuning AU weighted. The average of ROCA is 0.94 for CK and 0.91 for JAFFE, which shows the capability of classifier recognition: the higher the value, the stronger the performance. We can obviously conclude that our proposed AU optimal weighted fusion is effective and accurate for facial expression recognition.  
Figure 8: Confusion Matrix. (**a**) Linear SVM; (**b**) Polynomial kernel SVM; (**c**) Radial basis function Kernel SVM.  
Figure 9: Happy and fear expression examples.  
The computational complexity of this approach is much less than for any kind of deep learning method; input image is 355 for CK and 187 for JAFFE. The classifiers on average took about 0.35 s (CK) and 0.21 s (JAFFE) to complete classification in a 10-fold experiment. The recognition results using the highest performance classifiers respectively on CK and JAFFE are shown in Table 10.
## 4 Functional Model of the Framework
### Technologies Used at Each Level
#### 4.5.2 Second Level--Main Streets  
The level of congestion discovered through several different methods on roads is used to alert commuters to choose other routes. These methods include:  
* Drones (model number 4 drc F11 Pro Com Camera 4k Ptz) that capture and send images to the nearest station to be analyzed for the detection of any traffic congestion, shown as object 4 in Figure 4. The algorithm for processing these images has been published in our previous work [65].
* The proposed algorithm for analyzing tweets, shown as object 5 in Figure 4.  
Figure 4: The levels at which technologies are applied in the functional model.  
* APIs of service providers such as Google Maps or OpenStreetMap that return information about the traffic state of a specific location, shown as object 6 in Figure 4.
* A mobile application developed to enable users to collaborate by sending and confirming warnings about the traffic state on streets, shown as object 7 in Figure 4.
# A Numerical Aerodynamic Analysis on the Effect of Rear Underbody Diffusers on Road Cars
## 3 Results and Discussion  
Table 3 shows the results of the RANS simulations performed with the four configurations mentioned earlier in the manuscript.  
The \(C_{L}\) of the model had a massive range among the examined configurations: from an insignificant \(-0.047\) for the baseline configuration to a considerable \(-0.887\) for the highest-downforce setup. The baseline model alone barely produced any downforce, a similar result to the one reported in [4], but the addition of the rear diffuser truly made a huge difference. Surprisingly, the addition of the diagonal skirts proved to be more effective than the incorporation of the small frontal diffusers in terms of downforce generation. This could be explained because helping to channelise the airflow towards the big rear diffuser has a higher effect on the creation of aerodynamic loads, as the size of the latter is remarkably greater than that the two geometrical frontal slots. From a manufacturing point of view, it is also a great advantage as the design and implementation of diagonal skirts is significantly easier than sculpting a proper frontal slot close to the tire region.  
In regard to the drag coefficient \(C_{D}\), it can be noted that the addition of the diffuser inherently entails a small increase in the air resistance, from a baseline value of 0.320 up to a 0.332 in the most draggy configuration, just as observed in [4]. However, as observed in the works of [PERSON] et al. [6], the performance achieved with underbody aerodynamics, as opposed to the one obtained with conventional wings, was cleaner regarding wake disturbances and downwash phenomena.  
Similarly, the aerodynamic efficiency \(E\) experienced an increasing trend when the rear underbody was modified. It can be seen that the most efficient setup is the one involving the venturi skirts, although it involves a higher drag coefficient. A strict evaluation of compromise was performed to consider the trade-off of maximising downforce at the cost of higher flow resistance.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 3 Results
### Simulation Results
#### 3.1.2 Load-Displacement Curve  
Normal use limit (36 mm): Figure 6a and Table 1 shows that the result of the numerical simulation is basically identical to that of the theoretical calculation. The unloading section of the characteristic curve obtained by the software has a hysteresis phenomenon. The characteristic curve obtained by the software is more consistent with the actual test results. Under normal loading and unloading cycles, the ring spring can be divided into three kinds of stiffness (loading stiffness K\({}_{1}\), unloading stiffness K\({}_{2}\) and unloading stiffness K\({}_{3}\)). At the displacement of about 1.4 mm when the spring just starts to unload, the unloading stiffness is K\({}_{2}\) = 236.872 kN/mm.  
Figure 5: Mises stress contour of finite element model for different capacity limits: (**a**) 36 mm; (**b**) 48 mm.  
Bearing capacity limit (48 mm): Figure 6b and Table 1 shows that the spring is in an elastic state when the spring is loaded. The software results are consistent with the theoretical results. After the spring is loaded to the normal service limit of 36 mm, the spring stiffness begins to decrease slowly. The spring stiffness rapidly increases as it approaches 48 mm. The final bearing capacity is 3147.296 kN. From the normal use limit to the loading capacity limit, the ring spring tightens gradually. The spring can be regarded as a hollow cylinder if the structural significance of the spring is lost. The large stiffness and bearing capacity help protect the spring and serve as a safety guarantee for the gravity of the superstructure. The spring shows a small amount of plastic deformation during the unloading process, which causes the unloading curves not to overlap. If the impact is not large, the spring can still work.
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 6 Conclusions  
This research successfully develops and tests a model named the Vehicle Routing Problem for Relief Item Distribution under Flood Uncertainty (VRP-RIDFU), which employs Genetic Algorithms (GA) as the principal mechanism for generating and enhancing the quality of routing plans. A subset of the GA population, enhanced population, is specifically crafted to handle uncertainties in flooding-related road conditions. This achievement is realized by utilizing expected outcomes based on waiting times and flood risk assessments to select routes, effectively increasing resilience to unpredictable circumstances. The enhanced population's proportion significantly influences the routes' effectiveness when applied to various disaster-affected population distributions. The Population Sizing Module (PSM) is introduced to accommodate these distributions with grid generation techniques and standard deviation assessments for node dispersion. Moreover, the Complete Subtour Order Crossover (CSOX) is selected as the crossover operator because it produces high-quality routes, facilitates quick solution convergence, and reduces operational times. Testing demonstrates that the developed model efficiently plans vehicle routes that reduce unpredictable waiting times caused by flooding uncertainties compared to the shortest routes from standard CVRP problems. Furthermore, the developed model requires less time to generate these routing plans. Implementing the VRP-RIDFU model for planning vehicle routes for disaster relief significantly decreases the waiting time for aid, enhancing both the route creation efficiency and the delivery's effectiveness amidst flood uncertainties. Communities benefit from quicker relief and the expedited restoration of their quality of life. However, a limitation of this model is its dependency on accurate flood risk data, which, if precise, dramatically enhance the model's effectiveness in producing efficient vehicle routes. Such data can be sourced from relevant governmental and private sector information repositories. Additionally, the current PSM only allows the enhanced population proportion to be set in uniform and clustered distributions. Future developments in PSM could enable finer adjustments to accommodate various dispersion patterns, further refining the model's applicability and performance.  
Conceptualization, T.T. and P.C.; methodology, T.T. and P.C.; validation, T.T. and P.C.; formal analysis, T.T.; investigation, T.T.; resources, T.T. and P.C.; data curation, T.T.; writing--original draft preparation, T.T.; writing--review and editing, P.C.; visualization, T.T. and P.C.; supervision, P.C.; project administration, P.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Intelligent Reflective Surfaces for Wireless Networks:
## 2 Theory and Design of IRSs  
The hardware implementation of the IRSs is based on the concept of meta-surface, i.e., a controllable two-dimentional meta-material [12; 17]. Specifically, the meta-surface consists of a planar array with a large number of meta-atoms with a sub-wavelength electrical thickness (as shown in Figure 1), according to the operating frequency. The meta-atoms are metals or dielectrics able to transform the impinging electromagnetic waves. The properties of these elements and the structural arrangement in the array determine the transformations on the incident waves. The specific physical structure of the meta-surface defines the electromagnetic properties and thus the purpose at the specific frequency [18; 19]. The tunable chips inserted in the meta-surface to interact with the scattering element and communicate with an IRS controller are implemented through positive-intrinsic negative (PIN) diodes [20; 21], ferro-electric devices, or varactor diodes [22; 23; 24; 25].  
Generally, the approaches in the literature related to the design of meta-surfaces are based on [PERSON]'s law of reflection: the strongest scattered signal is obtained in the specular direction, i.e., being \(\theta_{\mathrm{i}}\) the angle of the incident wave, the strongest reflected signal is obtained for an angular direction \(\theta_{\mathrm{s}}\), such that \(\theta_{\mathrm{s}}=\theta_{\mathrm{i}}\) (as shown in Figure 2). There are a few papers that mainly focus on the theoretical basis, physics and classification of the meta-surfaces [26; 27]. In [26], the authors discuss the theoretical basis to characterize meta-surfaces and comment on their application as well as how meta-surfaces are distinguished from conventional frequency-selective surfaces. In [27], the physical properties and future applications of the meta-surfaces are analyzed.  
Figure 1: Meta-surface with composed by an array of passive reflective elements.  
Figure 2: [PERSON]’s law, the scattered wave by the \(a\times b\) metal plate is strongest for the specular direction, i.e., \(\theta_{\mathrm{i}}=\theta_{\mathrm{s}}\).  
The IRS's controller is devoted to receiving and communicating the reconfiguration requests, and then distributing the phase shift decisions to all the tunable IRS elements. This controller might be implemented by employing a field-programmable gate array (FPGA) [20; 28], a direct current (DC) source [29], or a microcontroller [23; 30]. In [31], a meta-surface composed of reconfigurable meta-material strips arranged in a grid has been designed. Specifically, a set of four meta-material strips is configured via a controller switch. The work in [32] also considers a reduced number of controller chips to serve the full IRS array.
## 3 Acquisition and Processing of GPR Data
If \(s^{\prime}\) and \(G_{u}\) are vectors containing all values of \(s_{i}^{\prime}\) and \(G_{ui}\), then \(G_{u}\) can be estimated by (Step 2):  
\[G_{u}=\frac{s^{\prime}-\text{min}(s^{\prime})}{\text{max}[s^{\prime}-\text{ min}(s^{\prime})]} \tag{2}\]  
The function \(Gu\), based on Equation (2), is considered the vector containing the weights, one for each velocity value, with which each constant velocity migrated section contributes to the final stack implemented by:  
\[B_{w,x,t}=\frac{1}{\sum\limits_{i=1}^{N}G_{ui}}\sum\limits_{i=1}^{N}\left[A_ {x,t,u_{i}}G_{ui}\right] \tag{3}\]  
where \(A_{x,t,u}\) is the migrated GPR section using a constant velocity value, \(u\). [PERSON] et al. [19] observed that the \(G_{u}\) function has a Gaussian-like shape, but it is still not a Gaussian. This means that after a minimum weight value at low-velocity values, followed by a maximum, a minimum occurs again as the weights become very low. After this second minimum, the weights start gaining higher values again. This is because over-migration, after a point, tends to transform the local slopes closer to zero, thus decreasing the std values. Therefore, an assessment of the weights must take place before multipath summation in order to choose the optimum velocity range, avoiding, at the same time, the use of all of the velocity values utilized, which would somewhat blur the result. An example for this is given below in the description of Figure 3. Within the weighted multipath summation section, \(B_{w,x,t}\), the moving hyperbola branches, are expected to introduce lower energy in comparison to the apices. This concludes on processing Step 3. The final processing step involves time-varying amplitude spectral shaping, which is implemented in the t-f domain, using S-transform [29; 30]. The main idea is to construct a reference amplitude spectrum, based on which the spectral shaping transforms the trace time-gated spectra to higher dominant frequency harmonics. More details on the implementation of the multipath summation methodology can be retrieved from [19].