esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2010**, _10_, 9529-9540; doi:10.3390/s101009529  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Feedback-Based Secure Path Approach for Wireless Sensor Network Data Collection**  
**[PERSON]* and [PERSON]**  
School of Computer and Information Engineering, Zhejiang Gongshang University, Xuezheng Street NO. 18, Hangzhou 310018, Zhejiang, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-571-288008286; Fax: +86-571-28008303.  
_Received: 16 August 2010; in revised form: 13 September 2010 / Accepted: 19 October 2010 / Published: 22 October 2010_  
**Abstract:** The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose.  
**Keywords:** data collection; feedback; secure path; wireless sensor network
# An Event-Driven Agent-Based Simulation Model for Industrial Processes
## 1 Introduction
A MAS approach for modeling and self-optimizing the production of flat steel products was presented in reference [16], where the decentralized optimization of the production scheduling is carried out by intelligent agents exploiting product and process knowledge. An exemplar application of MAS in the chemical sector for production scheduling optimization is provided in reference [17]. An interesting review on the application of MAS for complex networks management in the context of process systems engineering is provided in reference [18]. This paper proposes an event-driven agent-based model for simulating the operations of an industrial process subject to unexpected and uncertain events, such as faults, off-spec productions, and overloading of the production lines. In order to handle unforeseen events, a combination of reactive agents and a communication mechanism for timely information dissemination were adopted. Differently from most of existing models, the proposed tool targets the specific needs of the process industry and combines the potential and the flexibility of the agent-based approach with usability and practicality in order to facilitate its deployment and customization to different production chains which are typically found e.g., in the metallurgical sector. The paper is organized as follows: Section 2 describes a generic industrial process used as prototype of the application. Section 3 presents the agent-based model with the main actors involved in the system. A summary of the interactions among agents and the used communication mechanism are also presented here. The software architecture is presented in Section 4, while simulation results and discussions are presented in Section 5. Finally, the conclusions are given in Section 6.
# Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision
This kind of parking space detection method is effective but still has shortcomings: the judgment of the parking space direction after locating the key point is too complicated, and it is difficult to take into account the accuracy and real-time. Therefore, in view of the existing parking space detection methods, this paper divided the parking space detection into two stages. In the first stage, the key point detection was carried out by using Yolov11 on complex global images. Yolov11, an enhanced deep learning-based object detection model, enables rapid and accurate identification of critical points within images. In the second stage, the rotation identification of parking space was carried out by using traditional computer vision methods combined with the convolution kernel of size \(p\times p\) proposed in this paper. After obtaining the key point information of the parking space and the rotation of the parking space, the position of the real parking space in the global image can be inferred according to the known information. After testing, it is found that the two-stage parking space detection system has high robustness and greatly reduces the time required for parking space inference. The experimental results demonstrate that this two-stage parking space detection system is highly robust and significantly reduces inference time. This method is particularly effective in automated parking environments, where it expedites the detection process, optimizing vehicle scheduling and minimizing overall parking time.
# Control of Flow around an Oscillating Plate for Lift Enhancement by Plasma Actuators
## 4 Results and Discussion
### Effects of Driving Conditions  
#### 4.3.1 Effects of Driving Ratio  
Figure 17 exhibits the effects of the driving ratio, \(R\), on the lift coefficient for the time offset of \(t_{0}/T=0\) for the control only on the top surface. As mentioned above, the case of \(R=50\%\) produced the highest time-averaged lift force in all the present conditions. In addition, the increase of the lift for the condition of \(R=25\%\) is comparable to the condition of \(R=50\%\), which was discussed in the previous section. In the case of \(25\%\), the lift coefficient increased particularly in the duration of \(t/T=0.3\)-\(0.5\), when the actuator is not driven. Figure 18 shows the vortices and the contours of the pressure around the oscillating plate at \(t/T=0.35\) for \(R=25\), \(75\) and \(100\%\). Figure 18a presents that the vortex shedding from the trailing edge on the bottom surface is suppressed along with the suppression of the flow separation on the top surface while the actuator is driven, from \(t/T=0.0\)-\(0.25\), for \(R=25\%\). This mechanism for the increase in lift is similar to that for \(R=50\%\) as aforementioned (Section 4.2).  
Figure 16: Time-averaged velocity profile at the trailing edge along the vertical position from the bottom surface, \(y_{\mathrm{w}}\), during the up-stroke motion.  
Figure 17: Effects of the driving ratio on the lift coefficient (\(t_{0}/T=0\)) for the control only on the top surface. (**a**) Time-averaged lift coefficient; (**b**) Time fluctuations of lift coefficient.  
As shown in Figure 17b, the fluctuations of the lift coefficient become smoother, particularly in the time range of \(t/T=0.0\)-0.5 by the control with \(R=100\%\). This is because the vortex shedding from the trailing edge becomes weaker due to the suppression of the flow separation on the top surface throughout the oscillation period, as illustrated in Figure 18c. The increases in the time-averaged lift for \(R=75,100\%\) are quite small, as exhibited in Figure 17a. This can be explained by the decrease of the lift between \(t/T=0.75\)-1.0 in comparison with the baseline flow. As shown in Figure 18b,c, the separation vortex from the leading edge on the upper surface, which contributes to the lift generation in the baseline case, is also suppressed during the down-stroke motion (\(t/T=0.75\)). This suggests that the control in the down-stroke motion negatively affects lift. Accordingly, the control with the driving ratio of \(R=50\%\) is more effective in increasing lift.
# Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering
## 2 Nanomaterials
### Nanoparticle Synthesis and Processing Methods  
Currently, two main approaches are used to synthesise nanoparticles, the 'bottom up' approach and the 'top down' approach. Like natural biological systems, the bottom-up approach refers to when materials are built from atoms or molecules and are assembled into conformation. Products generated from this approach are usually smaller in size and more cost-effective. The top-down approach refers to the production of nanoparticles by breaking down bulk materials into the particles that are within the nanoscale size [22,23].
## 2 Materials and Methods
Chemical analyses of major and minor elements (Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Cu, Sn, Sb, and Pb) were performed using a CAMECA-CAMEBAX equipped with four scanning Wavelength Dispersive Spectrometers (WDS). A beam current of 2 nA, an acceleration voltage of 20 kV, and a spot size of 5 \(\upmu\)m were used for Na, K, Si, and Al; for all other elements, a beam current of 20 nA, an acceleration voltage of 20 kV, and a spot size of 1 \(\upmu\)m were used. Synthetic pure oxides were used as standards for Al, Cr, Fe, and Sn, synthetic MnTiO\({}_{3}\) for Mn and Ti, wollastonite for Si and Ca, albite for Na, periclase for Mg, PbS for Pb, orthoclase for K, apatite for P, sphalerite for S, Sb\({}_{2}\)S for Sb, and pure elements for Co, Cu, and Ni. SMITHSONIAN GLASS A standard [10] was also employed as a reference sample. Ten points were analysed on each sample, and the mean values were calculated. The measured accuracy for the analysed elements was better than 3%. The standard deviations among the analysed points resulted in between 1% and 3% and 3% and 5% for major and minor constituents, respectively. The detection limit for the minor elements was between 0.01 wt% and 0.04 wt%. The correction program was based on the PAP method [11] and was used to process the results for matrix effects. Laser Ablation-Inductively Plasma Coupled-Mass Spectrometry (LA-ICP-MS) was carried out to determine the concentration of 37 trace elements. Analyses were performed by a Thermo Fisher X-Series II quadrupole based ICP-MS coupled with a New Wave ablation system with a frequency quintupled (\(\uplambda=213\) nm) Nd:YAG laser. Laser repetition rate and laser energy density on the sample surface were fixed at 20 Hz and \(\sim\)18 J/cm\({}^{2}\)respectively. Analyses were carried out using a laser spot diameter of 100 \(\upmu\)m on the same polished samples used for EPMA after carbon-coating removal. Due to the highly heterogeneous microstructure of the tesserae, six points were analysed on each sample and the mean values were then calculated. External calibration was performed using NIST 610 and 614 glass as external standards; NIST 612 was also used as a secondary reference sample to check precision and accuracy ([PERSON] et al., 1997). \({}^{29}\)Si was employed as an internal standard, whose concentration was determined by EPMA following the method proposed by [PERSON] and colleagues (2012). The distribution of REE and of the other trace elements was analysed by normalising the data to the upper continental crust ([PERSON], 2012).
## 2 Materials and Methods
### Extracted Building Characteristics and Accuracy Assessment
#### 2.3.2 Building Heights  
Building heights were assigned to the building polygons using relative heights obtained through differencing digital surface models (DSMs) output from Metashape and digital terrain models (DTMs) (i.e., with surface features such as vegetation and buildings removed) generated using LAStools (v.200509) [70]. To generate DTMs, we used the 'last ground_new' tool to with a 50 m step size ('-metro'), the 'ultra_fine' flag and 0.5 m 'bulge' to remove buildings and vegetation. Relative heights were obtained by subtracting DTMs from DSMs. We assigned heights to building polygons using a mean of all intersectingpixels where the following criteria were met: (1) the building height was \(\geq\)2 m; (2) \(\geq\)80% of the building polygon featured valid height pixels; and (3) the polygons did not intersect with the occurrence of water obtained from [PERSON] et al. (2017). These criteria were designed to remove misclassifications. Furthermore, we applied internal buffers of \(-\)1 m, \(-\)2 m, and \(-\)3 m to the building polygons and used the same criteria to assign building heights. Internal buffers removed edge effects around building polygons caused by irregularities in the extracted footprint or vegetation overlapping with the building roof that could bias the extracted building height. The number of building stories was estimated using a sample of 300 buildings where we counted vertical window occurrence in Google Street View imagery (dated 2015). A linear relationship was derived between the building height and number of stories, which incorporated variation due to variable roof heights (Figure S3).
## 2 Methods
### SSA-LSTM Land Subsidence Prediction Model  
The workflow of the SSA-LSTM model consists of two parts: the left side represents the LSTM neural network, while the right side depicts the SSA, as shown in Figure 2. The SSA algorithm optimizes the neural network's learning rate, hidden layer nodes, and regularization coefficient. The global optimal position found by the algorithm is used as the initial structural parameters for the LSTM network.  
Figure 1: LSTM structural framework.
# Glass Ceramic with Improved Structure and Properties Made with Wastes from FeNi Production
## 3 Materials and Methods
For these measurements, an Ar displacement pycnometer (AccyPyc 1330) was used. The percentage of crystal phase, % C\({}_{\rm P}\), was calculated by the relationship:  
\[\%\;\rm C_{P}=100\;\times(1\rho_{g}-1\rho_{gc})/K \tag{1}\]  
where \(\rho_{g}\) is the parent glass density and \(\rho_{gc}\) is the glass-ceramic density. Here, the calibration coefficient K depends upon the density difference between the crystal and the corresponding amorphous structures:  
\[K=1\rho_{(g^{\star})}-1\rho_{\rm cr} \tag{2}\]  
where \(\rho_{\rm cr}\) is the crystal phase density and \(\rho_{(g^{\star})}\) is the density of a glass with a composition identical to that of the crystal phase. This method is suitable when phases with a high density-difference between the amorphous and crystal structures are formed. In the case of pyroxene and spinel (which are the crystal phases formed in the studied glass-ceramics) the differences between \(\rho_{\rm cr}\) and \(\rho_{(g^{\star})}\) are so high that the ratio \(\rho_{(g^{\star})}/\rho_{\rm cr}\) for pyroxene crystallization is 0.84 \(\pm\) 0.1 and for precipitation of the spinel phase is 0.82 \(\pm\) 0.2. Considering that the experimental error in density measurements was evaluated as \(\pm\)0.005 g/cm\({}^{3}\), the experimental error is at approximately \(\pm\)2% in the crystal phase. The phase formation in samples crystallized in different regimes was investigated by SEM-EDS (JEOL JSM 6390 coupled with an Oxford INCA energy dispersive x-ray spectrometer). The specimens were grinded, polished, and etched for 3 s in 2% HF solution, and then coated with Au. Additionally, the structure of the final glass-ceramic and the element mapping were studied by FESEM (Zeiss Merlin with high efficiency HE-SE2 detector). The formed-crystal phases were identified by XRD (PANalytical Empyrean). Certain powder samples (<54 \(\upmu\)m) were observed by HRTEM (JEOL JEM 2100 at 200 kV accelerating voltage) after dispersion in distilled water, sonication in ultrasonic bath and fixation on a 3-D Cu grid. The mechanical properties of the final product were determined by a universal strength testing set (SHIMADZU AGS-X at 10 kN load, connected with TRAPEZIUM X supporting software) [21]. For evaluation of the bending strength, 5 \(\times\) 5 \(\times\) 50 mm samples were used, while for the compressive strength--5 \(\times\) 5 \(\times\) 5 mm specimens. The Vickers micro-hardness was also evaluated (AVERY 6460 with optical microscope). The coefficient of linear thermal expansion in the interval 20-400 \({}^{\circ}\)C was measured at 5 \({}^{\circ}\)C/min by optical dilatometer (Misura HOD 1400). The leaching of the parent glass and that of the final glass-ceramic also were evaluated by TCLP test at conditions identical to those for the used wastes.
# A 40 Mb/s VLC System Reusing an Existing Large LED Panel in an Indoor Office Environment
## 2 Experimental Setup  
Figure 1 shows the proposed system block diagram and a photo of the experimental setup. The lighting device used in our demonstration system is a 60 \(\times\) 60 cm\({}^{2}\) commercial LED panel (DFx 563-004-01), which has been installed in our laboratory ceiling. We use a bias-tee circuit to modulate the LED panel's intensity with information-carrying signals being generated by an arbitrary waveform generator (AWG) and place an optical receiver underneath that is connected to an oscilloscope (OSC) to capture the regenerated transmitted signals for offline MATLAB processing. Full characterisation of the panel was performed, followed by modulation circuit design, modulation scheme design, and experimental test, to upgrade the LED panel with the data transmission function.
## 3 Doppler Frequency Evaluation in the Absence of Noise  
The signal acquisition process is basically a two dimensional search in a grid plane (commonly referred to as _search space_), as shown in Figure 4, where \(\overline{\tau}\in(0,T_{p})\)(X-axis range), and \(\overline{f}_{d}\in(-f_{dmax},f_{dmax})\)(Y-axis range). The variables under test \(\overline{\tau}\) and \(\overline{f}_{d}\) are discretized with a step \(\tau_{sp}\) for the code delay, and a step \(f_{sp}\) for the Doppler frequency. The integration time is \(T_{d}=LT_{s}\) (where \(L\) is the total number of integrated samples). The number of trial points in the two axes are \(N_{\tau}=T_{d}/\tau_{sp}\), and \(N_{f}=2f_{dmax}/f_{sp}\). Therefore the grid plane contains \(N_{\tau}\times N_{f}\) cells, and each cell (marked by yellow color in Figure 4) corresponds to a parameter pair \(\left\langle\overline{\tau},\overline{f}_{d}\right\rangle\). Finally the decision variable for the acquisition is  
\[\overline{S}(\overline{\tau},\overline{f}_{d})_{\rm ml}=\left|S_{i}(\overline {\tau},\overline{f}_{d})_{\rm ml}\right| \tag{20}\]  
The purpose of a traditional acquisition system is to find the coordinates of the peak cell of the grid plane when the satellite we want to detect is visible. To improve the accuracy of the estimates, the steps \(\tau_{sp}\) and \(f_{sp}\) must be decreased, at the expenses of the computational complexity, since the number of points of the search space increases. The empirical value \(f_{sp}=2/(3 LT_{s})\) is a typical choice [8] for the Doppler frequency step.  
Figure 4: Two-dimensional search space.  
An example of search space is shown in Figure 4. The column (marked in green) crossing the peak cell (marked A) contains cells that share the same code delay. When the \(i\)-th satellite is visible, the function in this column is as shown in Figure 5, where the X-axis contains the variable Doppler frequency \(\overline{f}_{d}\), while the Y-axis represents the absolute value of the acquisition test statistic \(\overline{S}(\overline{\tau},\overline{f}_{d})_{\rm ml}\). As indicated in Figure 3, the width of the main lobe is \(2/T_{d}=2/(LT_{s})\). Since \(f_{sp}=2/(3 LT_{s})\), this guarantees that three adjacent points of the frequency domain CAF (A, B, C) are located in the main lobe, and then we can assume that Equation (19) is always valid in these points.  
The cells A, B and C are characterized by the triplets \((\overline{\tau}_{A},\overline{f}_{A},\overline{S}_{A})\), \((\overline{\tau}_{B},\overline{f}_{B},\overline{S}_{B})\), and \((\overline{\tau}_{C},\overline{f}_{C},\overline{S}_{C})\), where the code delay is the same for all the cells in the same column (\(\overline{\tau}_{A}=\overline{\tau}_{B}=\overline{\tau}_{C}\)) and we adopt the notation \(\overline{S}_{X}=\overline{S}(\overline{\tau}_{X},\overline{f}_{d_{X}})_{\rm ml },\,X=(A,B,C)\) for simplicity. In the case of no noise, these triplets can be used to find the true Doppler frequency \(f_{d}\), as it will be shown hereafter. This will be also the starting point of the estimation method proposed in this paper, when the measurements are affected by noise.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### Clustering
#### 4.3.2 Euclidean Space
These results were improved to an EPAS in [182], and also extended to doubling metrics [183]. Other metrics and \(k\)-Center.For the \(k\)-Center problem an EPAS exists when parametrizing by both \(k\) and the doubling dimension [184], and also for planar graphs there is an EPAS for parameter \(k\), which is implied by the EPTAS of Fox-Epstein et al. [185] (cf. [184]). There are also parameterized approximation schemes for metric spaces with bounded highway dimension [186; 187; 184] and various graph width parameters [108]. Capacitated clustering and other variants.Another example where the parameterization by \(k\) helps is Capacitated \(k\)-Median, where each possible center \(c\in F\) has a capacity \(u_{c}\in\mathbb{N}\) and can be assigned at most \(u_{c}\) points. It is not known whether there exists a constant-factor approximation algorithm, and known constant factor approximation algorithms either open \((1+\varepsilon)k\) centers [188] or violate capacity constraints by an \((1+\varepsilon)\) factor [189]. Adamczyk et al. [190] gave a \((7+\varepsilon)\)-approximation algorithm in \(f(k,\varepsilon)n^{O(1)}\) time, showing that a constant factor parameterized approximation algorithm is possible. The approximation ratio was soon improved to \((3+\varepsilon)\)[191]. For Capacitated Euclidean \(k\)-Means, [192] also gave a \((69+\varepsilon)\)-approximation algorithm for in \(f(k,\varepsilon)n^{O(1)}\) time. While the capacitated versions of clustering look much harder than their uncapacitated counterparts, there is no known theoretical separation between the capacitated version and the uncapacitated version in any clustering task. Since the power of parameterized algorithms for uncapacitated clustering is well understood, it is a natural question to understand the \"capacitated VS uncapacitated question\" in the FPT setting. Open Question 10._Does Capacitated \(k\)-Median admit an \((1+2/e)\)-approximation algorithm in FPT time with parameter \(k\)? Do Capacitated Euclidean \(k\)-Means/\(k\)-Median admit an EPAS with parameter \(k\) or \(d\)?Since clustering is a universal task, like capacitated versions, many variants of clustering tasks have been studied including \(k\)-Median\(/k\)-Means with Outliers[193] and Matroid/Knapsack Median[194]. While no variant is proved to harder than the basic versions, it would be interesting to see whether they all have the same parameterized approximability with the basic versions.