esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Hence, to ascertain whether the greater ability of the mixed radiation field associated with the BNCT is ascribable to such LET-dependent increase in CA complexity, we used two Fluorescence In Situ Hybridization (FISH) techniques--Whole Chromosome Painting (WCP) and multicolor(m)-FISH. The former paints two pairs of homologous chromosomes and allows rapid quantification of the overall damage burden induced; the latter, instead, adds valuable information specifically on the degree of complexity within the detected spectrum of the aberration types [34; 35; 36]. Boronophenylalanine (BPA), an amino acid derivative actively incorporated by cancer cells [8], was used as a boron carrier. The normal immortalized breast epithelial MCF-10A cell line, frequently used in radiobiology to study normal-cell radiation-induced damage and particularly in CA studies due to its stable genome, was the in vitro model system of choice so as to avoid the confoundingly high baseline frequency of inherently genomic unstable cancer cell lines. Using WCP, we found that the presence of BPA enhanced the frequency of CAs in samples exposed to thermal neutrons compared to those observed in cells exposed to X-rays at the same doses. Moreover, m-FISH-based karyotype examination revealed a much higher frequency of complex CAs following BNCT compared to X-ray exposure; in particular, the number of chromosomes and that of breaks involved per complex CA was markedly greater in BNCT-treated samples. We therefore showed for the first time that DNA damage complexity is the key factor for the radiobiological effectiveness of BNCT in keeping with the high-LET components of the radiation field elicited by the neutron capture reaction.
# Aerodynamic Optimization and Characterization of a Ducted Tail for a Box-Launched Aircraft
## 4 Aerodynamic Optimization
### Approximate Model Selection
Compared with the PSO-BP approximation model, the prediction errors of the SSA-BP approximation model for the lift coefficient were reduced by 47.87% for MAE, 77.54% for MAPE, and 52.39% for RMSE; in the prediction errors for the drag coefficient, the MAE was reduced by 58.82%, 70.83% for MAPE, and 65.03% for RMSE. Compared with the BP approximation model, the SSA-BP approximation model reduced the prediction errors of the lift coefficient by 67.70% for MAE, 90.13% for MAPE, and 68.47% for RMSE; and in the prediction errors of the drag coefficient, the MAE was reduced by 48.26%, 88.00% for MAPE, and 77.62% for RMSE. In the comprehensive comparison, the SSA-BP approximate model has the smallest MAE, MAPE, and RMSE, and the SSA-BP approximate model has the best approximation of the lift-drag characteristics under the comprehensive error evaluation criterion. In summary, SSA-BP is selected as the approximate model in this paper. Figure 7: Comparison of the combined errors. (**a**) Combined error of lift coefficient; (**b**) combined error of drag coefficient.
# Wireless Data Acquisition System with Feedback Function
## 3 Results and Discussion
* Powering the sensors from autonomous sources that are not galvanically connected to power circuits, as well as the wireless method of transmitting information, makes it possible to avoid a \"web\" of wires near the power circuits of the SWPP and thus minimizes their influence on the measuring circuits and recording equipment. * The use of autonomous power supplies for sensors significantly expands the freedom of choice of their installation locations in the power circuits of the SWPP, which are at different potentials in relation to the common wire, which is especially important, for example, for series-connected SWPP batteries. * Connecting the voltage measuring circuits of the sensors to the batteries only for the duration of the measurement automatically eliminates the problem of preventing damage to the SWPP batteries due to their deep discharge during a long-term shortage of solar and wind energy or during temporary conservation of the SWPP. This is particularly relevant for GEPPs with low-capacity batteries. The resource of need relays allows us in our case to carry out such switching for several decades [45]. * The hardware anti-freeze protection built into the sensors and the DAS receiver prevents disruptions to the monitoring processes and operation of the ACS and PDAOC due to interference that occurs during switching in the power circuits of the SWPP or in the supply network. * The developed sensors contain tools for rapid diagnoses of their serviceability and control of the main parameters of their settings. The diagnostic process can be initiated at almost any time using the hand-held IR remote control. This does not require disconnecting the sensors from the SWPP circuits and connecting additional wires to them. An analysis of the current level of technology developed for remote intervention in the operation of control systems led the authors to the conclusion that reliable protection against harmful influences should be ensured by developing equipment whose operation is hardware-independent of the Internet. In the system proposed by the authors, the process of remote data collection is protected from outside interference not only in software, but also in hardware due to the refusal to exchange critical information, from the point of view of hacking, over radio channels. The transmission of requests to sensors and control actions on the power equipment is carried out exclusively via the IR channel. With this approach, even deliberately clogging the radio channel with powerful interference will only pause the monitoring process, but will not lead to the issuance of false control actions on the power equipment of the GEPP. For clarity, the significant differences of the proposed system are summarized in Table 1.
## 1 Introduction
Several studies have already analysed uncertainty in the flood hazard and risk analysis, how this uncertainty affects the results obtained, and consequently, the effect it has on flood risk management. So, the goal of this study is not to reinforce these past findings, but to try to quantify the effect of the coupled peak flow return period data in urban areas, especially when multiple cultural heritage elements are located in them. In order to achieve these objectives, the information recorded at two gauging stations was used to characterise (in a first phase) the peak return period ratio for the Douro River in Zamora. In a second phase, Monte Carlo techniques were applied to analyse the uncertainty of the peak flow/return period ratio, caused by the limited length of the recorded data series and, therefore, a partial knowledge of the general behaviour of this ratio. From the Monte Carlo method results, the peak flow return period ratio and its associated uncertainty were delimited through the estimation of the confidence intervals of this ratio. Using a 2D hydrodynamic model, flood hazard scenarios were then analysed for different elements exposed in the city of Zamora. All those results allow for a more consistent representation of flood-prone areas, as well as for hazard level classification. These flood hazard scenarios offer us, and may offer flood risk managers in the future, the possibility of an estimation of direct economic damage and its related uncertainty. On the other hand, changes in the distribution of pedestrian hazard stability for flooded urban areas [5; 30] were also analysed as have those related to car stability, with the results showing a significant increase (up to 20%) of high level hazard areas from the benchmark scenario (those represented by the results associated with the estimated peak flows, without considering its uncertainty as confidence intervals). Finally,all these results were integrated and mapped to analyse changes in flood risk management associated with uncertainty in the estimation of flood hazard.
## 2 Principle of the CMUDPD Technique
### Group A: CMUPD of PAs Operating in Class AB  
In this group, the PAs are working in Class AB with a constant bias voltage \(V_{\text{CG}}\) (\(-2.9\) V) and varied supply voltage \(V_{\text{DD}}\) (\(28\), \(20\), and \(12\) V), as shown in Figure 8. The \(1\) dB compression point (indicated by arrows) of these three gain curves was selected as the input power of each mode. At first, the PAs containing operation modes a1, a2, and a3 were simulated by ADS, and the output signal before pre-distortion was obtained. By injecting these output signals into MATLAB, the coefficients \(a_{mk}\) of CMUPD were obtained through the memory polynomial (\(M=7\), \(K=5\)). Then, the pre-distortion signal obtained by inverting the MPM was loaded into the CMUPD block. Finally, the PSDs with and without CMUPD1, CMUPD2, and CMUPD3 are shown in Figure 9a-c, respectively, where CMUPD1, CMUPD2, and CMUPD3 represent the pre-distortion results when the outputs of mode a1, a2, and a3 are used as the feedback paths, respectively.  
Figure 9a shows that the pre-distortion signals generated by mode a1 can successfully linearize modes a1, a2, and a3. Similarly, the pre-distortion signals from modes a2 and a3 can also be used to linearize all these three modes, which can be concluded from Figure 9b,c. It can be obtained from Table 2 that, in group A, the mean square errors \(\varepsilon\) of the three operation modes are comparable. The memory polynomial (\(M=7\), \(K=5\)) can fit any of the three modes, and any of the three modes can be used as the feedback mode to extract the pre-distortion input for the whole group of modes. The best pre-distortion performance for the three operation modes in Group A can be obtained when mode a2 is used as the feedback mode. This can be explained by the \(\varepsilon\) value of mode a2 (\(-53.82\) dB) being in between these values of modes a1 (\(-52.40\) dB) and a3 (\(-54.84\) dB).  
Figure 8: Nonlinear characteristics of Class AB. (**a**) Gain curves versus input power at Class AB. (**b**) AM/PM characteristic of Class AB.
###### Abstract  
The surface plasmon resonance sensor was used to detect and measure low concentrations of sugar. A polypyrrole-chitosan-BaFe\({}_{2}\)O\({}_{4}\) nanocomposite layer was prepared to improve the surface of the gold layer for the detection of glucose, fructose, and sucrose using the surface plasmon resonance technique. The polypyrrole-chitosan-BaFe\({}_{2}\)O\({}_{4}\) was synthesized using the electrodeposition method in different thicknesses. The functional group, crystal structure, and morphology of the layer were investigated with Fourier transform infrared spectroscopy, X-ray diffraction technique, and field emission electron microscopy. Consequently, the BaFe\({}_{2}\)O\({}_{4}\) was scattered on the surface of the polymer, and the affinity of polypyrrole-chitosan-BaFe\({}_{2}\)O\({}_{4}\) to bond with glucose is higher than that for the other sugars. The sensor limit was 0.005 ppm.  
surface plasmon resonance; BaFe\({}_{2}\)O\({}_{4}\); polypyrrole; chitosan; sugar
## 3 Results
### Choice of Evaluation Criteria  
In order to objectively evaluate the temperature prediction capability of the model proposed in this paper, the root mean square error (\(RMSE\)), mean absolute error (\(MAE\)), and coefficient of determination (\(R^{2}\)) were adopted as performance measurement metrics. The expressions for RMSE, MAE, and \(R^{2}\) are as follows:  
\[MSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-h_{i})^{2}}{n}}, \tag{19}\]  
\[MAE=\frac{\sum_{i=1}^{n}|y_{i}-h_{i}|}{n}, \tag{20}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-h_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\overline {y})^{2}}, \tag{21}\]  
where \(y_{i}\) is the true value, \(\overline{y}\) is the mean value of the time series, \(h_{i}\) is the predicted value, and \(n\) is the length of the sequence.
## 5 Analysis of Prominent Clustering Routing Protocols in WSNs
### Data-Transmission Based Clustering Routing Protocols
#### 5.2.5 Ccs  
The Concentric Clustering Scheme (CCS) has been proposed in [30] by [PERSON] _et al_. to reduce the energy consumption loopholes in PEGASIS. The main idea of CCS is to consider the location of the BS to enhance its performance and to prolong the lifetime of the network.  
In CCS, the network is divided into a variety of concentric circular tracks which represent different clusters and each circular track is assigned with a level. The track nearest to the BS is assigned with level-1 and the level number increases with the increase of the distance to the BS. Thus, each node in the network is assigned with its own level. Besides, chains are constructed within the track as that in PEGASIS. One of the nodes on the chain at each level area is selected as a CH. A CH in level \(L\) is selected with node number obtained by calculating \(i\) mod \(M_{L}\), where \(M_{L}\) represents the number of nodes that have the same level in \(i\) round. Data transmission in CCS is based on the process of PEGASIS protocol. After CH selection, each CH transmits the data of its own location to both the upper and lower level CH in one grade. In the process of the data transmission, all nodes in each level transmit the data to the nearest node from themselves along the chain. The node receives the data and fuses its own data and transmits these data to the next node. Therefore, the CH receives at most two data messages. Subsequently, the CH in each level transmits the data to the lower CH. At last, level 1 CH transmits these data to the BS. The data transmission scheme in CCS is shown in Figure 11.  
Compared to PEGASIS, CCS embodies the advantaged as follows: (1) The distance over which the data can be transmitted to the BS from the CH is reduced in CCS. Hence, a considerable amount of energy is saved on account of the reduction of transmission distance in CCS [76]; (2) The network is divided into a series of concentric clusters, and the reverse data flow from the BS is also reduced. Thus, a considerable amount of energy is also conserved during data transmission.  
However, there are some disadvantages to be considered as follows: (1) Node distribution in each level is unbalanced, thus the levels with small number of nodes will deplete their energy first, in that the probability of election to be a CH is high; (2) Residual energy is not considered for CH election, which may lead to unbalanced energy consumption among all nodes; (3) Chain-based protocols, such as PEGASIS and CCS, enable nodes to communicate with their closest neighbor by using low radio  
Figure 11: Illumination of the Data Transmission Scheme in CCS.  
power, but the long chain would cause large delay [77]; (4) The CH selection for next hop is based on the location rather than the residual energy of nodes, thus energy of CH may dissipates quickly on the path among CHs, and even energy hole will appear in the network.
## 1 Introduction
In a fully digital system, we investigated a secure beam precoding scheme based on GSVD, providing the optimal digital precoder and its corresponding combiner design. As a comparison, an SVD-based precoder design aimed at optimizing spectral efficiency was also presented. The GSVD-based scheme, at the cost of sacrificing some transmission efficiency, can effectively prevent eavesdropping even at a low SNR. In contrast, the SVD-based scheme fails to offer any eavesdropping protection. 3. In a hybrid precoding system, the GSVD-Sparsity algorithm is proposed to achieve secure beams. Peak search was performed in the beamspace channel matrix to identify several optimal propagation paths, and their array response vectors were used to construct the RF precoder. The baseband precoder was then obtained using a simple minimum mean square error (MMSE) method. The low-complexity GSVD-Sparsityalgorithm ensures secure communication while approximating the transmission efficiency of the optimal digital precoder. The rest of the paper is organized as follows. Section 2 introduces the mmWave massive MIMO system model and channel sparsity, providing a general expression for the secrecy capacity in the eavesdropping channel model. Section 3 explores the GSVD-based secure beam digital precoding scheme and proposes the GSVD-Sparsity mmWave secure beam hybrid precoding algorithm. Simulation evaluations of the security performance and transmission efficiency of the GSVD-based digital precoding scheme and the GSVD-Sparsity algorithm are conducted in Section 4. Section 5 concludes the paper.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 4 Merging Pollution Data over a Grid on the City Map
### Kriging for Spatial Interpolation
In our case, we have used the _Matern_ model [23], shown in Equation (5), because it has more versatility:  
\[\gamma(h)=\sigma^{2}\frac{2^{1-\
u}}{\Gamma(\
u)}\left(\frac{\sqrt{2\
u}h}{ \rho}\right)^{\
u}K_{\
u}\left(\frac{\sqrt{2\
u}h}{\rho}\right) \tag{5}\]  
where \(\sigma^{2}\) is the variance parameter representing the _sill_, which is the limit of the variogram as distance \(h\to\infty\), \(\
u\) is the smoothness parameter, which determines the rate of decay of the variogram, \(\rho\) is the range parameter which represents the distance at which the variogram reaches the sill value, \(\Gamma(\
u)\) is the gamma function, and \(K_{\
u}\) is the modified Bessel function with order \(\
u\). In this model, if \(\
u=0.5\), the model becomes exponential. If \(\
u>0.5\), it allows discontinuities in the variogram at the origin, and if \(\
u\to\infty\), it becomes a Gaussian model [24].
## 3 Results
### Clinical Findings and Periodontal Outcomes  
Basic periodontal treatment significantly improved periodontal parameters (\(p\) = 0.0001), except for BoP:  
* HI decreased from 30.14% \(\pm\) 32.71% to 13.10% \(\pm\) 17.28%.
* Mean PD decreased from 2.46 \(\pm\) 0.67 mm to 1.88 \(\pm\) 0.53 mm.
* CAL improved from 3.26 \(\pm\) 1.19 mm to 2.51 \(\pm\) 0.74 mm.  
These results align with clinical practice guidelines for stage I-III periodontitis treatment [16]. The lack of BoP improvement may be due to patients brushing more thoroughly only on the day of re-evaluation, masking true gingival inflammation [37]. Additionally, a one-month re-evaluation period may be insufficient for complete periodontal tissue repair, which can take up to eight weeks [38].  
Masticatory function showed significant subjective improvements (\(p\) = 0.045), with total QMFQ scores decreasing from 25.06 \(\pm\) 18.44 to 22.63 \(\pm\) 14.26. These results align with findings by [PERSON] et al. [39], who attributed subjective improvements to reduced inflammation, pain relief, and potential mechanoreceptor regeneration. However, HueCheck Gum test values remained unchanged before (0.59 \(\pm\) 0.09) and after treatment (0.61 \(\pm\) 0.11), indicating no objective improvement in masticatory efficiency. Similar to our findings, [PERSON] et al. [33] noted that subjective chewing perception can be influenced by psychological adaptability. In contrast, [PERSON] et al. [27] found a significant correlation between objective masticatory efficiency and QMFQ scores (\(p\) = 0.037), particularly in stage IV periodontitis patients.  
A possible \"placebo effect\" may have contributed to the perceived improvement, as patients undergoing treatment might expect positive changes in their condition. Althoughno prior studies have objectively evaluated masticatory efficacy post-periodantal treatment, [PERSON] et al. [27] and [PERSON] et al. [24] established associations between periodontal status and masticatory performance.
## 2 Architectural Framework of IBSN and Its Components
### Data Handling Strategies  
Bio-sensors are often affected by noise due to bio-fouling, motion artifact, and interference. For example, ECG (Electrocardiogram) sensors are highly sensitive to motion artifact, which can hinder its ubiquitous use. To improve the sensor reliability, multi-sensor or sensor array approaches are commonly used [20]. Sensor fusion techniques can then be applied to fuse information from these sensors. In practice, feature selection techniques can be employed to identify relevant vital signatures from an optimum set of physiological data. However, a large amount of raw data is often required for processing. Transmission of this large amount of data will reduce the battery-life significantly when it is done continuously. On-node signal processing or pre-processing of the data within the capability of the sensor node can reduce the active duty-cycle of the wireless communication and enable the transmission of only the critical data to the base station. These on-node processing activities will reduce the amount of data transmitted and result in a significant reduction of power consumption. Distributed processing of data will improve the total network lifetime. They will also enable faster recognition of medical anomalies, improving the accuracy of medical therapies. Special attention should be given to maintaining a low false-positive anomaly detection. However, prolonged and heavy computational processing can cause over-heating problems for the sensor nodes, which can cause fatal damages to the tissues surrounding the implant. For example, in [21], authors theoretically calculated that, 2 \({}^{\circ}\)C temperature rise in 1 cm\({}^{2}\) of tissue, when a neural amplifier was operating at 40 mW power, is valid for most of the tissues in the human body. This variation in temperature was observed for 7 weeks, which resulted in the formation of a fibrous capsule with increased capillary density around the implant. Authors also demonstrated in [21], that the thermal over-load can be minimized by distributing the processing functionality among different devices. It has to be noted that the poor data handling strategies will significantly reduce the reliability of the system. Algorithms which enable not only low-power and less complex operation but also ensure reliability of the system are yet to be designed.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Network Parameter**} & \multicolumn{5}{c}{**Requirement of Implantable Medical Devices**} \\ \cline{2-7}  & **Paze-Maker** & **Neural Stimations** & **Drop-Delivery Systems** & **Retinal Implants** & **Coellular Implants** & **Endoscopy Capss** & **Active Prostheses** \\ \hline Theong/patient & up to 100 K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K2K22K2K22K22K22K22K222K222K
## 3 Research Results and Their Analysis
Conversely, higher pH values promote the precipitation of inorganic salts, including calcium phosphate, calcium carbonate, and magnesium hydroxides, which enhance floc formation through charge neutralization. This multi-step process--comprising charge neutralization, metal hydroxide precipitation, and sweep flocculation--efficiently removes suspended cells and improves flocculation and sedimentation in algal suspensions. Optimization of Flokor 1.2A concentrations (0.01-0.36 g/L) identified the lowest effective dose for achieving maximum algae harvesting efficiency at pH 6.6. These findings establish Flokor 1.2A as a highly effective algae harvesting agent, surpassing natural and conventional alternatives at lower concentrations. Its high efficiency and reduced dosage requirements position Flokor 1.2A as a cost-effective solution for _Chlorella vulgaris_ BA-167 recovery, with significant potential for industrial biomass production and water treatment applications.