esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 6 Experimental Simulation
### I-DEEC Simulation Results
#### 6.2.1 Comparison of Changes in the Number of Dead Nodes with Time  
Figure 11 shows that, in DEEC and IEE-LEACH, the first dead node appears in the first 1000 rounds and 1600 rounds, respectively. However, in the proposed I-DEEC, this condition takes place in the 1800 rounds. It also can be noticed that, when all sensor nodes die, the rounds of the network operation by the DEEC and IEE-LEACH are 3000 and 3600, while that by our algorithm is 4300. The reason behind this phenomenon is that our algorithm takes the residual energy and the distance to the sink into consideration. When compared with two mentioned protocols, I-DEEC increases the network lifetime by about 19% to 43%. Hence, the performance of I-DEEC on the network is better than others.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numerical Value** \\ \hline \(E_{0}\) & 0.5 J \\ \(E_{fs}\) & 10 pJ/(bit \(\cdot\) m\({}^{2}\)) \\ \(E_{mp}\) & 0.0013 pJ/(bit \(\cdot\) m\({}^{4}\)) \\ \(E_{DA}\) & 5 nJ/bit \\ Weighting coefficient & \(\alpha\)\(=\) 0.5, \(\beta\)\(=\) 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of simulation experiment parameters.  
Figure 10: Comparison of the number of activated sensor nodes for different numbers of monitored target points (MTPs).
## 2 Background
### Statistical Clock Models  
Estimating clock errors involves solving stochastic differential equations whose coefficients are derived from the Allan variance [40] characteristics of the oscillators that drive them [41]. EKF-based clock synchronization is well-studied in this context [42, 43]. We draw insights from [44, 45] to actualize the joint distributed coherence and ToF tracking using an extended Kalman filter. We do not consider flicker noise while modeling the clocks, and we direct the reader to [46, 47, 48] for more information on the subject. Furthermore, we do not consider applications in which the line of sight (LoS) between platforms is obstructed, but this subject is discussed in greater detail in [16, 49, 50].
## 3 Results and Discussion
### Removal of Basic Blue Properties
#### 3.2.2 Effect of WB Mass  
The dosage variation (used solid mass) provides an idea of the effectiveness of an adsorbent and the ability of a dye to be removed with a minimum dosage, so as to identify the ability of a dye from an economical point of view [58]. The effect of the WB mass on the removal of BB-41 was carried at 25 \({}^{\circ}\)C with 200 mg/L of BB-41 solution. The WB dosage ranged from 0.010 g to 1 g. Figure 6 presents the effect of the WB dosage on the removal efficiency of BB-41 dye. The removal percentage (%) of BB-41 by the WB material was improved drastically, from 16% to 87%, when the amount of Med-WB added increased from 0.25 g to 0.5 g. Furthermore, the increase of used solid waste improved up to 0.7 g led to a maximum removal percentage of 99%. This increase could be related to the increase in the removal sites, which make the removal of BB-41 easy until equilibrium. Removal of BB-41 cations remained constant for further increases of WB amounts above 0.7 g. Similar behavior was observed using the Jed-WB, however, by adding higher amounts of Jed-WB, to 1 g, the maximum of removal percentage was achieved and it was still lower than those obtained for Med-WB. These data confirmed that the aggregation or the overlapping of the removal sites could occur [59; 60].
###### Abstract  
In this paper, we propose a low-complexity peak-to-average power ratio (PAPR) reduction scheme that combines both multi-band (MB)-Hadamard precoding and clipping for the optical orthogonal frequency division multiplexing (OFDM) systems. Approximations of PAPR distribution for baseband OFDM signals are analyzed and the effective signal-to-noise ratio (SNR) of the whole transmission link considering both the clipping and quantization noise are presented. After that, the MB-Hadamard precoding is adopted to compresses the peak signals, minimizing the contaminating influence of signal distortions in subsequent clipping operations. In addition, the received SNRs and bit error rate (BER) are calculated theoretically for each split sub-band. The 50-m step-index polymer optical fiber (POF) transmission is adopted as a special case to both evaluate the system performance and then compare the proposed scheme with other well-known PAPR reduction techniques. With this scheme, the PAPR is reduced effectively and the system's BER performance is improved significantly. The results show that the proposed scheme with appropriate number of sub-bands precoding provides favorable trade-offs among PAPR reduction, power spectral density, transmission rate, BER, and computational complexity, which demonstrates its feasibility and validity.  
Article
# Application Characteristics of Bioethanol as an Oxygened Fuel Additive in Diesel Engines
## 4 Conclusions  
In this study, to explore the application characteristics of ethanol in a CRDI diesel engine, ethanol was directly blended with diesel fuel without any additives according to the volume ratios of 0:100, 5:95, 10:90 and 15:85. These test fuels were recorded as E0, E5, E10 and E15, respectively. The engine performance, combustion and emission characteristics of the CRDI diesel engine were compared and analyzed as the engine load changes. The main experimental results are as follows:  
1. For the engine performance: Only under low (40 Nm) and medium (60 Nm) engine load conditions, the brake-specific fuel consumption (BSFC) of the CRDI engine was increased with the addition of ethanol. However, under the high load of 80 Nm, the BSFC was reduced with the addition of ethanol.
2. For the combustion characteristics: Under the low engine load of 40 Nm, the start of combustion (SOC) and maximum in-cylinder pressure of all test fuels were almost the same, while under the load of 60 Nm and 80 Nm, the maximum in-cylinder pressure gradually decreased with the addition of ethanol. Additionally, the maximum heat release rate (HRR) of most blended fuels was higher than that of diesel.
3. For the emission characteristics: The addition of ethanol was beneficial to the reduction in carbon monoxide (CO) only under the medium and high loads of 60 Nm and 80 Nm, but under most test conditions, the addition of ethanol led to the increase in hydrocarbons (HCs) in varying degrees compared with diesel only. It is relevant to mention that nitrogen oxides (NOx) and smoke emissions were simultaneously reduced with the addition of ethanol.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# Towards a Real-Time Description of the Ionosphere:
## 7 Validation Results for _Inm_F2 Based on Ground-Based Ionosonde Observations
### Diurnal, Seasonal, and Solar Activity Statistics Variations for Different Zonal Sectors  
The same criteria described in Section 5.2 for the selection of the ionospheric stations and the validation of the results based on ground-based _fo_F2 ionosonde observations, were also adopted to validate the results of IRI and IRTAM models for _hm_F2. Therefore, as done in Section 5.2, the statistical grids for the same five ionospheric stations previously selected are shown in Figures 25-39 for _hm_F2.
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## 3 Results and Discussion  
### Material Characterization  
To confirm whether the EDTA salt was successfully modified to sodium alginate, we determined the IR spectra of sodium alginate, EDTA salt, Alg-EDTA, and Alg-EDTA loaded with Cd\({}^{2+}\). As shown in Figure 2, the absorption peaks near 2930 cm\({}^{-1}\) belong to the aliphatic C-H bond stretching vibration, and the absorption peaks at approximately 1570 and 1310 cm\({}^{-1}\) can be assigned to the C=O and C-O bond stretching vibrations, respectively [9; 10]. The absorption peak of COO\({}^{-}\) at 1390 cm\({}^{-1}\) in the Alg-EDTA was significantly reduced with respect to sodium alginate owing to the condensation reaction of excess amino groups with the COO\({}^{-}\) groups [11]. In addition, the absorption peak intensity of C-O bond and C-N bond increases relatively when Alg-EDTA adsorbs Cd\({}^{2+}\), which probably results from the coordination of N and O in Alg-EDTA with Cd\({}^{2+}\) ions.  
The morphology of Alg-EDTA has been characterized and illustrated in Figure 3. Similar to one of the lunar surfaces, the surface of Alg-EDTA is rough and porous, which increases the contact area of the adsorbent with the solution and provides more binding sites. In addition, the pore properties of Alg-EDTA were also studied by N\({}_{2}\) adsorption-desorption isotherm (Figure 4). The results showed that the BET surface area, pore volume, and average pore size of Alg-EDTA are 94.63 m\({}^{2}\)/g, 53.30 cm\({}^{3}\)/g,  
Figure 1: Synthesis route of the calcium alginate-disodium ethylenediaminetetraacetate dihydrate hybrid aerogel (Alg-EDTA).  
and 22.53 nm, respectively. The rough surface and large pore size are beneficial to the rapid adsorption of the target metal ions and the increase in the metal ion adsorption capacity.
## 2 Methods
### Performance Evaluation  
Four metrics were used to evaluate the performance of the neural networks: pixel-wise accuracy, Dice coefficient, sensitivity, and specificity. Pixel-wise accuracy measures all correctly identified pixels in the image:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
where \(TP\) is true positives, \(TN\) is true negatives, \(FP\) is false positives, and \(FN\) is false negative. Sensitivity describes how much atrophy is correctly labeled compared to the total amount of atrophy:  
\[Sensitivity=\frac{TP}{TP+FN}\]  
Specificity describes how much non-atrophied tissue is correctly labeled compared to the total amount of non-atrophied tissues:  
\[Specificity=\frac{TN}{TN+FP}\]  
The Dice coefficient of two sets \(A\) and \(B\) describes the spatial overlap between the two sets:  
\[Dice=\frac{2(A\cap B)}{A+B}\]
## 4 Results and Discussion
### Assessment of SAR SSW Direction and Speed Uncertainty as Proxy of Accuracy
(b) 1 m/s-binned scatterplot of the SAR SSW speed uncertainty, \(\Delta\Psi_{\text{max}}\) (m/s), (\(y\) axis) vs. the absolute difference between the SAR SSW speed and the ECMWF wind speed (\(x\) axis), for the same ROIs and for the applied GMF CMOD5.N. Cyan dashed line shows the regression line with a slope A = 0.31. Both black dashed lines represent the reference line with a slope of 1. Note: For each bin, the number of samples in the bin (N) and the aggregate statistics, m (mean) and s (standard deviation), are provided (unless N is too small, i.e., less than 1/100 of N_ROI). * NRCS value and its uncertainty are assumed to be given by the mean and standard deviation of the NRCS in the WVC. It has been shown that the distribution of NRCS values is an important factor which can influence the accuracy of SAR wind and that should be taken into account [48]. Nevertheless, the sigma nonught signal in the cell may depend on several factors other than the sea surface wind vector, which may be regarded as the dominant environmental factor responsible for the sea surface roughness. In fact, the effect of sea currents, wave motion, linear and non-linear waves in deep water and, to a lesser degree, bathymetry of shallow water, can represent other possible reasons for modification of the SAR backscattering [49]. Finally, error components derived from the calibration of the SAR signal might occur [12; 43]. * GMFs applied represent a semi-empirical model whose coefficients are experimentally derived by tuning on a large amount of wind data, such as those from in situ stations, scatterometers and NWP models. Such tuning may determine further errors in the methodology results. Last three bullets in the previous list represent general limits of application of the proposed methodology. With these detailed observations, the resulting good correlation (Tables 4 and 5) however suggests that SAR-derived SSW direction and speed uncertainty, obtained from the MS LG-Mod and the proposed methodology, respectively, may be considered as proxies for the determination of the wind speed and direction accuracies. In principle, SAR SSW field maps and accuracy distribution maps may be supplied to models that require such wind information as input.
## 3 System Model
### Network and Energy Model Assumptions  
In EDACP-CS, we use the simplified energy model proposed in [33]. According to the radio energy dissipation model illustrated in Figure 3, in order to achieve an acceptable Signal-to-Noise Ratio (SNR) in transmitting an \(L-\)bit message over a distance \(d\), the energy expended by the radio is given by:  
\[E_{Tx}(l,d)=\begin{cases}L.E_{elec}+L.\epsilon_{fs}.d^{2}&\text{ if }d\leq d_{0},\\ L.E_{elec}+L.\epsilon_{mp}.d^{4}&\text{ if }d>d_{0},\end{cases} \tag{5}\]  
where \(E_{elec}\) is the energy dissipated per bit to run the transmitter or the receiver circuit, \(\epsilon_{fs}\) and \(\epsilon_{mp}\) depend on the transmitter amplifier model we use, and \(d\) is the distance between the sender and the receiver. By equating the two expressions at \(d=d_{0}\), we have \(d_{0}=\sqrt{\frac{\epsilon_{fs}}{\epsilon_{mp}}}\). To receive an \(L-\)bit message the radio expends \(E_{Rx}=L.E_{elec}\).  
We make some assumptions about the sensor nodes and underlying network model as follows:  
1. All sensor nodes are stationary and uniformly distributed within a square field,
2. Communication among sensor nodes is based on single-hop approach,
3. Networked data vector is sparse or highly compressible in Distributed Wavelet Transform (DWT) domain, _i.e._, it contains \(K\) largest coefficients. Setting the rest coefficients zero will not cause much information loss,
4. A WSN consists of heterogeneous nodes in terms of node energy and transmission range.
## 3 Results
### Development of the Extraction Method
#### 3.1.2 Evaluation of the Best Process Sequence  
Since hydrophilicity of phenolic and flavonoid compounds is known to vary substantially from species to species the next part of our work was designed to optimize the extraction sequence after the enzymatic treatment. A series of extraction steps were applied to the biomass after enzymatic treatment, monitoring the extraction yield after each step.  
The graphs in Figure 3 show that the enzymatic treatment (referred as to EH(H\({}_{2}\)O)) of _V. nigrum_, followed by hydroalcoholic extraction (referred as to HA (EtOH + H\({}_{2}\)O)) and one cycle of alcoholic extraction (referred as to EE I (EtOH)), which allowed a recovery of phenolic compounds (Figure 3A) and flavonoids (Figure 3C) equal to 24 mg/g DM and 25 mg/g DM, respectively. Indeed, additional alcoholic extraction steps (referred as to EE II and EE III) did not result in substantial increase in extraction yields, as further shown in Figure 3B,D. Therefore, all subsequent experiments used this identified optimal sequence for recovery of active compounds: enzymatic treatment followed by hydroalcoholic extraction and subsequent alcoholic extraction.  
Figure 3: TPC and TFC analyzed by colorimetric assays at each extraction step (**A**,**C**), and contribution of the single step (**B**,**D**). EH: enzymatic treatment; HA: hydroalcoholic extraction, followed by hydroalcoholic extraction; EE I (EtOH): one cycle of alcoholic extraction; EE II and EE III: additional alcoholic extraction steps.
## 7 Case Studies of Color-Independent Visual-MIMO
### V2X Communication  
The communication between a vehicle and other devices is another application of visual-MIMO systems. This approach has been described in our research work [23]. Figure 14 shows an example of V2X communication. We proposed two methods for V2X communication using visual-MIMO networking: multipath transmission and multinode (multiple access) communication. Multipath transmission can be used when the distance between the source and destination is far. Information transmitted to the destination node can be relayed via other nodes. For example, a relay vehicle can be used to transfer data. This feature of the network allows multipath transmission, similar to cooperative RF communication, but with lower coordination overhead [23]. We may also consider another multiple access communication in which a destination node can receive the information from multiple source nodes simultaneously. If the light sources of multiple nodes do not overlap within the field of view of the camera during V2X communication, it is possible to receive each light source by separating multiple sources through image processing techniques. To avoid the overlap of the multiple light sources, a divided region of interest (ROI) method by dividing communication area has been proposed [23]. Then, we may achieve the simultaneous multiple access communication without interchannel (inter-node) interferences.
## 2 Materials and Methods
### Preparation of Drug-Loaded PLGA-CS-PEG Microspheres  
Drug-loaded blend of PLGA-CS-PEG microspheres were prepared using the water-oil-water (W/O/W) emulsions method with slight modifications [33; 34]. Briefly, 100 mg of PLGA polymer was dissolved in 3 mL of organic solvent (dichloromethane). This was followed by adding 5 mg/mL or 8 mg/mL of PGS or PTX, respectively, to form primary emulsions. Chitosan solution (4% \(w/w\)) was prepared in 1% glacial acetic acid, filtered, and then added to the aqueous PVA solution. PEG (5%, \(w/w\)) with a molecular weight of 8 kD was added to the aqueous PVA and chitosan solution before emulsification to produce PLGA-CS-PEG microparticles.  
The emulsification was done in an aqueous PVA solution (12 mL, 2% \(w/v\)) to form an oil-in-water (o/w) emulsion using an Ultra Turrax T10 basic homogenizer (Wilmington, NC, USA), set at a speed of 30,000 rpm for 3 min over an ice bath. The emulsion was kept in a magnetic stirrer that was operated overnight at 1000 revolutions per minute (rpm) for the evaporation of the organic solvent. Next, the emulsifier and nonincorporated drugs were rinsed off. A VirTis BenchTop Pro freeze dryer (VirTis SP Scientific, Stone Ridge, NY, USA) was used to lyophilize the recovered microparticles for 48 h. The nonloaded PLGA-CS-PEG microparticles were also prepared as described above without incorporating the drug.  
Material Characterization of Drug-Loaded Micorparticles  
SEM (JEOL 7000F, JEOL Inc., Peabody, MA, USA) was used to characterize the structure of the microparticles. Prior to the SEM session, the freeze-dried drug-encapsulated PLGA-CS-PEG microparticles samples were mounted on double-sided copper tape, with the other end affixed to an aluminum stub. This was followed by sputter-coating the resulting microparticles with a 5 nm gold-palladium layer. The ImageJ software package (National Institutes of Health, Bethesda, MD, USA) was then used to analyze the mean diameter of the microparticles.  
The physicochemical properties of the drug-loaded PLGA-CS-PEG microparticles were analyzed using Fourier transform infrared spectroscopy (FTIR) (IRSpirit, Shimadzu Corporation, Tokyo, Japan). Using the IR solution software package (ver.1.10) (IRSpirit, Shimadzu Corporation, Tokyo, Japan), the lyophilized drug-encapsulated PLGA-CS-PEG microparticles were scanned at 4 mm/s at a resolution of 2 cm\({}^{-1}\) from 400 to 3500 cm\({}^{-1}\).
## 5 Conclusions  
No differences in the clinical leg parameters between the involved and non-involved legs of hip OA patients could be detected using the EOS system. This could suggest that in our study collective, the leg alignment per se was not the crucial factor for the development of hip OA or that the progression of hip OA did not depend on leg alignment. The last was supported by the present results, as no correlations between the hip adduction moments and leg alignment were found. The present study confirmed that patients with unilateral hip OA walked with a deviating gait pattern and showed lower adduction moments at the involved knee and higher adduction moments at the non-involved hip joint. The current results also confirmed that the vurus alignment of the knee (HKA), as well as the femoral parameters (especially the FO), had an influence on the knee adduction moments in unilateral hip OA patients. These two clinical leg parameters explained 61% of the variance in KAM1, and the FO alone explained 50% of the variance of KAM2. A combination of static alignment and gait kinematics explained the knee adduction moments during gait even better in unilateral hip OA patients: adding the pelvic drop of the contralateral side increased the model with the FO and the HKA and explained 78% of KAM1. The hip adduction moments were best explained by the hip kinematics and not by the leg alignment.  
Conceptualization, S.v.D., F.S., and A.M.; methodology, S.v.D. and F.S.; validation, S.v.D. and F.S.; formal analysis, S.v.D., H.K., B.F., T.T., and A.A.; investigation, S.v.D., H.K., A.A., and F.S.; resources, B.F. and T.T.; data curation, S.v.D. and H.K.; writing--original draft preparation, S.v.D. and H.K.;writing--review and editing, S.v.D., H.K., B.F., T.T., A.A., F.S., and A.M.; visualization, S.v.D.; supervision, A.M.; project administration, S.v.D. and H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest. EOS Imaging (Paris, France) provided support for this study. EOS Imaging had no role in the design, execution, interpretation, or writing of the study.
## 4 Optimal Frame Generation Time Threshold Calculation
### Overall Structure
#### 4.1.3 Reward Function  
During each training step, the agent receives the current reward value \(r_{t}\) based on the composite function \(R(s_{t},a_{t})\), which indicates the reward for the action \(a_{t}\) taken at state \(s_{t}\) of the current slot \(t\). The reward function value \(r_{t}\) is obtained by feedback after setting thethreshold value. Moreover, \(r_{t}\) provides an effective signal for the agent to guide its decision making in the next training step. The reward function defined in this paper is  
\[R(s_{t},a_{t})=\begin{cases}\alpha L_{f}-\beta T_{f}\cdots\gamma T_{h}\leq T_{f} \leq\delta T_{h}\\ \beta L_{f}-\alpha T_{f}\cdots\cdots\cdots otherwise\end{cases} \tag{15}\]  
where \(L_{f}\) is the normalized average effective frame length (excluding idle packets), \(T_{f}\) is the normalized average frame generation time, and \(\alpha\) and \(\beta\) are the adjustable weights for the average effective frame length and average frame generation time, respectively, with \(\alpha+\beta=1\).  
The reward function in Equation (15) is specifically formulated in this work, combining delay and frame utilization in a custom composite formulation to reflect the QoS needs of real-time services in the AOS.