esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Packet Delivery Ratio of 2n-Rm  
In 3H-RM, there is no mechanism introduced for the acknowledgment process when step size is less than 3 and data packet has been reached at the sink node. Similarly, in 2H-ACK, when the second node becomes sink, it has no choice for taking acknowledgment from other receivers and send it to the source node. Source node waits 60 s for the acknowledgment, after that it will flood the data packet again towards the next node. Consequently, a cyclic process starts between sink and source node that will increase the energy consumption, duplication of data at the sink node, and end to end delay of the network. 2N-RM consists of two nodes reliability models. Its hello packet and acknowledgment packet format is lighter than these two models. When data reached at sink node, there is a zero probability of duplication data packet at sink node due to one to one relationship.  
Packet Delivery Ratio  
The PDR of all these models is presented in Figure 12. PDR is increased as the number of nodes increases. 3H-RM and 2H-ACK have almost same PDR due to their same procedure and participation of more than two nodes for the reliability measurement. Whereas in 2N-RM only two nodes ensure the reliability by taking the consideration of ETX value. Consequently, 2N-RM has higher PDR as compared to 3H-RM and 2H-ACK.
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 3 Information Fusion Based on Extended Information Filtering  
In the process of cooperative target tracking for UAVs, each UAV needs to make a precise estimation of the state of the moving target in order to calculate the control input in real-time. Because of the non-linearity of the observation model, the traditional Kalman filter cannot be qualified for the filtering process, so other filtering methods are needed. Generally speaking, the extended Kalman filter [22], unscented Kalman filter [12; 22], and particle filter [23] are widely used to solve the nonlinearity of the system model problem [3]. In this paper, extended Kalman filter was used.  
Time update process of extended Kalman filter:  
\[\hat{X}_{k}^{-}=F_{cv}\hat{X}_{k} \tag{18}\]  
\[P_{k}^{-}=F_{cv}P_{i}P_{cv}^{T}+Q_{k}. \tag{19}\]  
Measurement update process of extended Kalman filter:  
\[K_{k}=P_{k}^{-}H_{k}^{T}(\hat{X}_{k}^{-})[H_{k}(\hat{X}_{k}^{-})P_{k}^{-}H_{k}^ {T}(\hat{X}_{k}^{-})+R_{k}]^{-1} \tag{20}\]  
\[\hat{X}_{k}=\hat{X}_{k}^{-}+K_{k}[z_{k}-h(\hat{X}_{k}^{-})] \tag{21}\]  
\[P_{k}=P_{k}^{-}-K_{k}H_{k}(\hat{X}_{k}^{-})P_{k}^{-} \tag{22}\]  
where \(h(\cdot)\) is the corresponding function of the observation Equation (4), \(H_{k}(\cdot)\) the Jacobian matrix of \(h(\cdot)\), \(F_{cv}\) the transition matrix of the target, \(\hat{X}_{k}^{-}\) the predicted target state at time \(k\), \(\hat{X}_{k}\) the estimated target state at time \(k\), \(Q_{k}\) the covariance matrix of the process noise at time \(k\), \(R_{k}\) the covariance matrix of the observation noise at time \(k\), \(P_{k}^{-}\) the covariance matrix of the predicted target state, \(P_{k}\) the covariance matrix of the estimated target state, and \(K_{k}\) the gain matrix of Kalman filter.
# Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes
## 2 Theoretical Background
### Nonparametric Control Chart Based on Mood Statistic  
NP-Mood was derived for monitoring dispersion. The principle of this NPCC is well described in [40].  
Suppose that we have a reference sample \(X\) from a statistically stable process with \(m\) values and an arbitrary test sample \(Y\) of \(n\) values. \(R_{1}<\cdots<R_{n}\) are the combined-sample ranks of the \(X\) value arranged in increasing order of magnitude. The statistics recorded into this NPCC are computed using Formula (15):  
\[M_{m,n}=\sum_{i=1}^{m}\left(R_{i}-\frac{N+1}{2}\right)^{2},\text{ where }N=m+n \tag{15}\]  
The control limits are then computed as follows:  
\[UCL=E(M_{-}(m,n))+c\sqrt{(var(M_{-}(m,n)))} \tag{16}\]  
\[LCL=E(M_{-}(m,n))-c\sqrt{(var(M_{-}(m,n)))} \tag{17}\]  
where \(E(M_{-}(m,n))\) is the mean, and \(\sqrt{(var(M_{-}(m,n)))}\) is the variance of the statistic \((M_{-}(m,n))\); it is recommended to compute them using Formulas (18) and (19) [40], p. 758.  
\[E(M_{m,n})=\frac{m\big{(}N^{2}-1\big{)}}{12} \tag{18}\]  
\[var(M_{m,n})=\frac{mn(N+1)\big{(}N^{2}-4\big{)}}{180} \tag{19}\]  
The value of \(c\) is set to correspond to the required value of ARL(0). For instance, for ARL(0) = 370.4, \(c\) = 2.782177 [40], p. 761.
# Strong Ground Motion Sensor Network for Civil Protection Rapid Decision Support Systems
## 2 Research Area, Sensors, and Data
The earthquake parameters i.e., magnitude, epicenter, hypocenter and origin time, in this work, accrue from the manual revised solution announced by the National Observatory of Athens seismic network (NOA) [20]. The investigating time period for the present work is from 2017 up to the first days of February 2021. All the earthquakes that recorded from the available strong ground motion sensors during the aforementioned time period have been examined and the events that gave rise to pronounced acceleration values are presented as study cases (Table 2). Among these examples are the distant, shallow, strong earthquake with magnitude M\({}_{\rm L}\) = 6.7 that occurred in 2020/10/30 offshore of Samos Island and the intermediate-depth, strong earthquake, M\({}_{\rm L}\) = 6.1, that occurred in 2019/11/27 with epicenter just a few tens of kilometers west of Chania. Smaller magnitude events that generated similar spectral acceleration values, due to their short epicentral distance from the urban area of Chania city, are also presented for fruitful comparison reasons. \begin{table}
\begin{tabular}{c c c c c} \hline
**Station** & **Latitude** & **Longitude** & **Sensor** & **Geology** \\ \hline ABEA & 35.5162 & 24.0113 & 130 SM & Marles with sandstones \\ ATEI & 35.5193 & 24.0431 & 130 SM & Neogene limestone \\ DEYA & 35.4825 & 24.0140 & ANSS & Quaternary deposits \\ GOLD & 35.4955 & 24.0306 & 130 SM & Thick Quaternary deposits \\ LENT & 35.5074 & 24.0403 & 130 SM & Neogene limestone \\ MULT & 35.5091 & 24.0118 & ANSS & Neogene marles-Quaternary deposits \\ NERO & 35.4739 & 24.0423 & 130 SM & Neogene limestone \\ PASK & 35.4996 & 24.0121 & 130 SM & Thick Quaternary deposits \\ PERI & 35.4825 & 23.9928 & ANSS & Quaternary deposits \\ POLY & 35.5226 & 24.0602 & 130 SM & Neogene limestone \\ SOUD & 35.4878 & 24.0678 & 130 SM & Loose Quaternary deposits \\ \hline \end{tabular}
\end{table} 
Table 1: Strong ground motion sensors’ location, type and underlying subsoil.
Even though metallic materials are not superior to other material combinations because of the increased number of failure cases demanding revision procedures, they are still utilised in some developing countries due to their reasonable cost and availability [6]. Composite scaffolds of bioceramics with polymers have been extensively used as materials for bone repair studies. The natural bone matrix is a combination of organic/inorganic composites; thus, the composite scaffolds for bone regeneration are designed by combining the advantages of both components. The most common composite scaffolds for bone substitution are bioceramic and polymer scaffolds [4; 5]. Bioactive molecules such as bone morphogenetic proteins (BMPPs) promote bone healing, usually in combination with a scaffold as a carrier. Several commercial products are based on recombinant human bone morphogenetic proteins (rhBMIPs) and are used for alveolar bone augmentation, sinus lift procedures, and periodontal defects [7]. The direct administration of growth factors into the bone defect is considered an excellent strategy for bone tissue regeneration. The critical point when using bioactive molecules is selecting a suitable scaffold carrier system to tailor the localized and sustained release of these molecules [8]. Stem cells (SCs) therapy is an up-and-coming technique for BTE. In BTE, SCs have been used due to their ability to produce and differentiate into osteoblasts. SCs have several advantages, including the ability to differentiate directly into osteoblasts, modulate immune responses, promote angiogenesis, and exhibit plasticity. [9]. Various scaffolds are used to mimic native ECM for SCs seeding, providing a conducive framework for the attachment and growth of cells [10]. The common sources from which SCs can be obtained are bone marrow, dental pulp, embryo, and adipose tissue [10].
###### Abstract  
Rapid and nondestructive determination of quality attributes in fresh and dry _Chrysanthemum morifolium_ is of great importance for quality sorting and monitoring during harvest and trade. Near-infrared hyperspectral imaging covering the spectral range of 874-1734 nm was used to detect chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content in _Chrysanthemum morifolium_. Fresh and dry _Chrysanthemum morifolium_ flowers were studied for harvest and trade. Pixelwise spectra were preprocessed by wavelet transform (WT) and area normalization, and calculated as average spectrum. Successive projections algorithm (SPA) was used to select optimal wavelengths. Partial least squares (PLS), extreme learning machine (ELM), and least-squares support vector machine (LS-SVM) were used to build calibration models based on full spectra and optimal wavelengths. Calibration models of fresh and dry flowers obtained good results. Calibration models for chlorogenic acid in fresh flowers obtained best performances, with coefficient of determination (R\({}^{2}\)) over 0.85 and residual predictive deviation (RPD) over 2.50. Visualization maps of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid in single fresh and dry flowers were obtained. The overall results showed that hyperspectral imaging was feasible to determine chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid. Much more work should be done in the future to improve the prediction performance.  
near-infrared hyperspectral imaging; chlorogenic acid; luteolin-7-O-glucose; 3,5-O-dicaffeoylquinic acid; _Chrysanthemum morifolium_; prediction maps
## 2 Experimental Procedure  
### Experimental Materials  
The schematic diagram of the experimental setup is shown in Figure 1a. The experimental testing was carried out on an electrical discharge machine (CNC EDM 430, Aristech, Taichung City, Taiwan). The work material was the grinding plate of plastic mold steel AISI P20 material, which is a material used for pre-quenching of the produced injection plastic mold, shown in Table 1. A workpiece setup in the form of a couple of steel plates with a thickness of 10 mm was mounted in the working tank of the machine by a vise. The alignment of the workpiece along the y-axis of the machine was checked by the dial test indicator. The electrode was mounted on the head of the machine, then the alignments in the \(x\)-axis and \(y\)-axis along the \(z\)-axis were controlled by \(\pm\)0.005 mm for the moving distance in the \(z\)-axis of 50 mm. The drilling positions were located on the parting line of the workpiece which used an electrode touching on both sides of the edge of the workpiece then moving to the center of drilling hole. The depth of the drilling hole was limited to 50 mm as shown in Figure 1b. Distance and alignment were controlled by a Mitutoyo dial test indicator (Resolution 0.001 mm, Accuracy \(\pm\)0.003 mm, Nitutoyp Corporation, Kanagawa, Japan). The machining time and depth of the drilling hole were directly recorded to calculate the MRR. The EWR was evaluated at the end of the machining tests. The quality of the machined surface was measured by athematic roughness (Ra) using a roughness tester (MarSurf PS1, MAHR, Gottingen, Germany), and the gap clearance along the depth of the drilled hole was measured using an optical microscope (STM6, OLYMPUS, Tokyo, Japan).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Tensile strength (MPa) & 862 \\ Ultimate strength (MPa) & 932 \\ Compressive strength (MPa) & 826 \\ Elongation at break (\%) & 20 \\ Charpy impact (J) & 34 \\ Hardness (HRC) & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the plastic mold steel AISI P20.  
Figure 1: Experimental setup and schematic of the machining operation. (**a**) Experimental setup; (**b**) schematic of machining operation.
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
###### Abstract  
Street lighting characterizes many smart city initiatives around the world. In fact, significant savings can be achieved by not only replacing traditional luminaires with low-power LEDs, but also providing streetlights with smart light controllers and network connectivity, allowing the introduction of a sensible light intensity management and reduction of maintenance costs. Moreover, if designed with a far-looking view, smart lighting infrastructure could also support city-wide Internet of Things services, becoming key enablers of the smart city revolution, also in the 5G perspective. In this paper, we provide a thorough discussion on network architectures and communication technologies that could be adopted for smart public lighting applications, showing their benefits and downsides. Starting with significant activity on research, implementation and in-field testing, we also outline the steps required for the deployment of a smart public lighting infrastructure, each discussed in accordance with the network topology considered. Finally, we introduce some additional services that a smart public lighting infrastructure could support and discuss the benefits that would arise from integration with the upcoming 5G cellular network.  
smart lighting; street lighting; smart city; Internet of Things; 5G networks +
Footnote †: journal: Applied Sciences
## 4 Experiment and Discuss
### Model Parameter Setup  
In this paper, some state-of-the-art methods containing probability neural network (PNN) [35; 36], SVM, ELM, and kernel ELM (KELM) [37; 38; 39] are used to construct diagnostic models for comparison.  
To obtain the optimal decision threshold for identifying the various modes, an independent determination threshold set Dthresholding containing both single failure and multiple faults is utilized to optimize an appropriate discriminative threshold \(\epsilon^{*}\) within the interval [0, 1] by using PSO [40; 41]. The inertial weight of the PSO algorithm which is used to balance the ability of global search and local search is set to 0.9, the learning factors are set to 2, the iteration number is set to 1000, and the population size is 100.  
For the diagnostic model based on SVM, the regularization parameter C is set to 1.0, and the sigmoid kernel function is utilized in which the parameter \(\gamma\) is set to 1.0. The diagnostic model based on KELM uses the same parameter sets C and \(\gamma\) with a default value of 1.0. For the diagnostic model based on ELM, the optimal number of hidden nodes is searched in the range of 10 and 300 with the increment of 10.  
PNN is a typical probability classifier, and the parameter spread is greatly related to the performance. With a smaller spread value, the function fits smoother and the error becomes larger. Meanwhile, the calculation cost of the network is more, and vice versa.
###### Abstract
To date, the heterogeneous uptake of gaseous chemical species by aerosols, cloud droplets and ocean spray has been reported [16]. There has been a report of oxidation reaction of mercury in liquid phase with ozone [17], but none of the reports have studied the uptake of GEM by acid water. This is because mercury is less reactive than hydronium ions in the activity series, and the oxidation reactions are often slow. This is also because the equilibrium assumption between the atmospheric GEM and water (clouds, fog, rain droplets and sea spray), that being the solubility of GEM, may deliver a reasonable estimation for the amount of mercury directly dissolved in atmospheric water. Contradictory to the mercury reaction with acids, it is known that some inorganic acids, such as sulfuric acid and a mixture of nitric and hydrochloric acids, oxidize and dissolve metal mercury [18; 19; 20]. To the best of our knowledge, the acidity of atmospheric water has not been considered in the calculation of the mercury cycle in the natural environment yet. It is known that, like acid rain, atmospheric water becomes acidic when secondary pollutants, such as sulfuric acid nitric acids, are absorbed. In the East Asian region, a low pH, such a pH of 2.9 in the cloud water [21] and a pH between 3 and 4 in the rainwater [22; 23], has been frequently observed due to the large anthropogenic emissions of precursor gases for sulfuric and nitric acids. When such acidic droplets meet with GEM, it is possible that the uptake of GEM can be enhanced. The observed high mercury concentrations in the low pH (<4) cloud water collected at Mt. Bamboo in Taiwan may be due to the oxidation of GEM in the acidic cloud water. In this research, we conducted laboratory experiments for the heterogeneous uptake of GEM on bulk solutions under room lighting and near the standard temperature (293-299 K) and pressure (1.0-1.6 Pa above the room's air pressure) conditions to evaluate if the heterogeneous uptake was significant. Approximately 100-150 ng m\({}^{-3}\) of a GEM standard gas was introduced into a 2.5 L rectangular glass vessel, at the bottom of which a 500 mL uptake solution of pure water, acidified water (with sulfuric or nitric acid, typical anthropogenic acidic pollutants) or natural seawater rested. After exposure to the standard gas for a given period of time, the solutions--sampled and dissolved mercury species, dissolved elemental mercury (Hg\({}^{0}\)\({}_{\text{aq}}\)) and dissolved oxidized mercury (Hg\({}^{2+}\)\({}_{\text{aq}}\))--were separately extracted using the conventional purging and trapping method with only an inertial gas or together with a spiking SnCl\({}_{2}\) solution. Then, they were quantitatively analyzed by a fluorescent spectrometer. The acquired data were then evaluated to see if this process was significant in a real atmosphere.
###### Abstract  
The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USVs swarms using a lead-follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller's thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull's movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.  
unmanned surface vehicles (USVs); recurrent neural network (RNN); long short-term memory models (LSTM); swarm control +
Footnote †: journal: Medical Imaging  
1 Maritime ICT & Mobility Research Department, Korea Institute of Ocean Science and Technology,  
Busan 4911, Republic of Korea; [EMAIL_ADDRESS] (S.K.J.; [EMAIL_ADDRESS] (M.K.K.); [EMAIL_ADDRESS] (M.K.K.K.); [EMAIL_ADDRESS] (M.Y.F.); [EMAIL_ADDRESS] (Y.C.K.)
## 1 Introduction  
Quantum key distribution (QKD) can provide an information theoretic security to share keys between two distant parties [1; 2; 3]. Since its initial proposal, the QKD is believed to be one of the technologies in quantum information science to reach the applications of the quantum network. The rapid development of the ground-based QKD scheme and implementation has resulted in the communication range reaching as far as 511 km with modern technology in the optical fiber channel [4]. Scaling quantum communication protocols over long distances is very challenging due to the losses experienced during the propagation inside the optical fibers. Attempting to overcome the limits imposed by losses, there has been increasing interest [5; 6; 7; 8; 9] in implementing QKD through free-space channels.  
At present, the polarization-encoded photons from a weak coherent pulse (WCP) source have been experimentally demonstrated to be more suitable than the phase-encoding scheme in free-space QKD [10]. However, in some previous theoretical studies [11; 12; 13], the fluctuation properties of quantum polarization through a turbulent atmosphere have been ignored when estimating the performance of polarization-encoding QKD, where the misalignment error is only assumed as a constant result (i.e., 1.5%) from the experiment [9], which is not theoretically rigorous.  
However, in a free-space polarization-encoded QKD, the spatially-dependent polarization effects resulting from the use of curved optics in an off-axis configuration will lead to decoherence of the qubits and rotation of the polarization angle [14; 15; 16], which will directly increase the misalignment error and further influence the performance of the key rate in the polarization-encoded QKD. Specifically, when estimating the key rate of the QKD system, the gain and the error rate of single photon states are required to be estimated in the decoy-state QKD method [17; 18; 19], where the standard fiber-based channel model [20] uses some assumed channel parameters. In this paper, we propose a method based on the cross-spectral density matrix (CSDM) [21] to show a more practical free-space QKD key rate estimation. Compared with other methods, our proposal exploits the unified theory of coherence and polarization to model the spatially-dependent polarization effects on the properties of quantum signals in propagation. Moreover, the fluctuation properties of quantum polarization through turbulent atmosphere can also be characterized by our method.