esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 3 Background  
### Road Network Elements and Anomaly Definitions  
**Definition 1**.: _Road network: A road network is represented as a directed graph \(G=(V,E)\) where \(V\) is a set of vertices representing the points of connection between two edges and \(E\) is a set of edges of the graph representing the road segments. Every edge \(e_{i}\in E\) from a graph \(G\) represents a road network segment with the starting vertex \(v_{i}\) and the ending vertex \(v_{j}\)._  
**Definition 2**.: _Transition: A transition is a movement of one vehicle between two consecutive road network segments \(e_{i}\) and \(e_{i+1}\). Where origin edge of transition is \(e_{i}\) and destination edge is \(e_{i+1}\)._  
**Definition 3**.: _Speed transition: A speed transition is a change in obtained speed when a vehicle is traveling through one transition. Then, speed on the origin edge \(e_{i}\) is named origin speed \(s_{O}\) and speed on the destination edge \(e_{i+1}\) is named destination speed \(s_{D}\). Both speeds are computed as harmonic mean speeds of all obtained speed values on the origin and destination edges._  
**Definition 4**.: _Traffic anomaly: This method is focused on two types of road traffic anomalies: The first one is sudden braking in transition which can be described as a bottleneck start, and the second type is intense acceleration in transition where vehicles are achieving unexpectedly high speeds when leaving the congested area. The anomaly is defined as a distance-based approach by computing the distance between the CoM and the closest point at the diagonal of the STM, \(d_{CoM}\). The main goal is to find the anomaly distance \(d_{A}\), which is used as a threshold value for the anomaly detection. Then, if distance from the observed CoM from the diagonal \(d_{CoM}\) is larger or equal than \(d_{A}\) anomaly is detected. In Figure 1 two types of anomalies are represented: (a) Sudden breaks and (b) intense accelerations._
# First Report of Hepatitisis E Virus in Shellfish in Southeast Italy
###### Abstract  
Hepatitis E virus (HEV) represents one of the principal causative agents of hepatitis globally. Among the five HEV genotypes affecting humans, genotypes 3 and 4 are zoonotic and are the main source of hepatitis E in developed countries. HEV has been detected in several foods. The present work investigated the presence of this virus in shellfish sold at retail in the Apulia region of Italy. The presence of HEV RNA was assessed by real-time RT-PCR in 225 shellfish samples collected during 2018. Overall, two (0.89%) of these samples tested positive for HEV RNA. To our knowledge, this is the first notification of the detection of HEV in mussels sold at retail in the Apulia region. These data highlight the potential role of shellfish as a vehicle for the transmission of viral pathogens.  
hepatitis E virus; shellfish; food-borne; risk assessment +
Footnote †: journal: Applied Sciences
## 4 Numerical Simulation
### Gaussian Noise  
The measurement noise \(\mathbf{r}_{k}\) satisfies the Gaussian distribution with zero mean, that is, \(\mathbf{r}_{k}\sim N(\mathbf{0},\mathbf{R}_{k})\). The initial value of \(\mathbf{R}_{k}\) is a one-dimensional constant value of 0.001 rad. Before comparing the proposed algorithms, the influence of Gaussian kernel bandwidth \(\sigma\) on the algorithm RSRCD-MCCKF is discussed. The \(\text{RMSE}_{\text{pos}}\) and \(\text{RMSE}_{\text{vel}}\) of the algorithm at the final time instant are shown in Table 1.  
As shown in Table 1, when the Gaussian kernel bandwidth \(\sigma\) changes, the algorithm shows different performance. When the value of \(\sigma\) is too small or too large, the filtering accuracy will be reduced. Among them, -- refers to the filtering as diverged. When the value of \(\sigma\) increases gradually, its filtering performance has approached the SRCD-CKF algorithm, which is in line with the previous theoretical derivation. But under the premise of \(\sigma=50\), its filtering accuracy is still higher than the SRCD-CKF algorithm. In other words, as long as the proper value of \(\sigma\) is selected, the filtering accuracy of the RSRCD-MCCKF algorithm is higher than the traditional algorithm.  
On this basis, the Gaussian kernel bandwidth \(\sigma=5\) is selected, and we can see the \(\text{RMSE}_{\text{pos}}\) and \(\text{RMSE}_{\text{vel}}\) comparison of each algorithm in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & \(\text{RMSE}_{\text{pos}}\) [km] & \(\text{RMSE}_{\text{vel}}\) [km-\(\sigma\)-1] \\ \hline RSRCD-MCCKF (\(\sigma=0.1\)) & — & — \\ RSRCD-MCCKF (\(\sigma=1\)) & 0.0528 & 0.000205 \\ RSRCD-MCCKF (\(\sigma=2\)) & 0.0449 & 0.000188 \\ RSRCD-MCCKF (\(\sigma=5\)) & 0.0482 & 0.000179 \\ RSRCD-MCCKF (\(\sigma=10\)) & 0.0645 & 0.000211 \\ RSRCD-MCCKF (\(\sigma=50\)) & 0.0771 & 0.000239 \\ SRCD-CKF & 0.0784 & 0.000247 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Algorithm accuracy under Gaussian noise when kernel bandwidth is different.  
Figure 1: The trajectory of target and observer.  
As shown in Figure 2, when the measurement noise is Gaussian noise, the previous trends of each algorithm are roughly the same, and they all tend to converge as the time increases. Among them, HRSRCD-CKF and MRSRCD-CKF have small fluctuations compared with the SRCD-CKF algorithm, and the accuracy is basically the same under normal measurement conditions. The accuracy of the VBSRCD-CKF algorithm is slightly higher than that of HRSRCD-CKF and MRSRCD-CKF; this reflects the higher accuracy of the variational Bayesian method in approximate estimation. The RSRCD-MCCKF has higher accuracy than the above algorithms, because the maximum correntropy criterion can utilize higher-order information of measurements, applying the Gaussian kernel to each element of the estimation error matrix, which has a better combination with SRCD-CKF.
## 3 Results and Discussion
### Comprehensive Summary of Results
Explanations: MC—crumb moisture content; BV—specific volume of bread; BCH—breadcrumb hardness; L*—lightness of crumb; a*—color parameter a* of crumb; b*—color parameter b* of crumb; C—chroma of crumb; AA—acrylamide content of bread; P—protein content; A—ash content; F—fat content; TDE—total dietary fiber content; IN-DF—insoluble dietary fiber content; S-DF—soluble dietary fiber content; CC—carbohydrate content; EV—energy value; TPC—total polyphenol content; DPPH—antioxidant activity of bread; WB—wheat bread (control sample); 1 umB, 5 umB, 10 umB—bread samples with the addition of unmolasses SBP, respectively, in the amount of 1, 5 and 10%; 1 mB, 5 mB, 10 mB—bread samples with the addition of molasses SBP, respectively, in the amount of 1, 5 and 10%. characterized by a higher antioxidant potential, which resulted from the addition of SBP with molasses (mSBP) in the recipe. Moreover, bread samples supplemented with 5% and 10% SBP, without and with molasses, were located in the positive part of PC2 at a considerable distance from each other. As shown in Figure 3c, the use of different levels of supplementation of wheat bread with sugar beet pulp (SBP), without and with molasses, allowed the division of the obtained data by separating the WB, 1 umB, and 1 mB samples, and the 5 umB, and 5 mB samples with similar properties and suitability, as well as the 10 umB and 10 mB samples with significantly different properties. The type and level of SBP addition to bread baking were found to be an important factor in the clustering of the data (Figure 3d).
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 1 Introduction
However, in the previous work, the locations (no matter real coordinates or dummy coordinates used for protecting location privacy) of all of the user's friends must be sent to the location server for distance calculation and comparison regardless of the user's query distance. In fact, some locations may be far beyond the query distance of the user, so the distance calculation is not required. In addition, most of the previous works only focus on the basic security requirements in location-sharing systems such as protecting users' identities and locations. However, users may have privacy-preserving requirements for some sensitive areas or specific geofences such as workplace. Therefore, how to design user-defined access control policies to meet users' flexible privacy-preserving requirements is also an urgent issue. In this paper, we propose a search efficient privacy-preserving location-sharing scheme (i.e., SELS) to solve the issues mentioned above. The main contributions of this paper are as follows. 1. We create a new approach named associated grids to improve the efficiency of location-sharing systems. Based on the grid structure and a user's query distance, we can find out the smallest set of grid cells (i.e., the associated grids) that cover the user's query distance. By using the approach, our scheme can filter out the locations of the user's friends that are not in the associated grids, which can reduce the burden of distance calculation and comparison on the location server. 2. We propose a new user-defined access control policy to meet users' flexible privacy-preserving requirements. For some specific geofences or sensitive areas that users do not want to share with their friends, users can use the access control policy to prevent the leakage of privacy in social networks. Compared with paper [10], our solution can provide a more flexible privacy-preserving way, rather than simply preventing users from sharing locations with their friends. 3. Extensive simulations are implemented to explore the relationship between different solutions and parameters, as well as the performance of our scheme (i.e., SELS). The remainder of this paper is organized as follows. After discussing the related work in Section 2, the notations and techniques are introduced in Section 3. Subsequently, we show our system architecture and security model in Section 4. Then, the detailed scheme description and discussion about security and efficiency are given in Sections 5 and 6, respectively. Finally, we show the experiment results and our conclusions in Sections 7 and 8.
## 6 Discussion
### Resource Usage Metrics  
Despite the advantages of the proposed LSTM-GA framework for GPS spoofing detection, its practical deployment in UAVs necessitates a thorough examination of real-world resource consumption factors such as inference time, power usage, and memory footprint. These considerations are vital in ensuring the model's reliability in constrained environments. However, the dataset employed in the present study comprises only GPS- and signal-based features (e.g., carrier-to-noise ratio, Doppler shifts, correlator outputs) and therefore does not incorporate measurements pertaining to onboard system resources.
# Characterization of Invar Syntactic Foams Obtained by Spark Plasma Sintering
###### Abstract  
This study presents the synthesis of sintered composite foams based on the Invar alloy (64 Fe-36 Ni), using hollow spherical particles from a nickel superalloy (NiCrSiB) in order to generate porosity. The Invar powder was obtained by mechanical alloying (MA), and the NiCrSiB hollow spherical particles were incorporated into the composite at 20 vol %. The sintering was realized using the spark plasma sintering (SPS) process in an argon atmosphere at 600 \({}^{\circ}\)C and 5 MPa, with 10 s holding time. The porous structures were structurally characterized by optical microscopy (OM), scanning electron microscopy (SEM) and X-ray diffraction (XRD). The coefficient of linear thermal expansion (CTE) of the Invar/NiCrSiB syntactic foams was found to be 2.52 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\) in the 25-150 \({}^{\circ}\)C temperature range and 19.68 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\) in the 150-400 \({}^{\circ}\)C range.  
Invar syntactic foams; spark plasma sintering; coefficient of linear thermal expansion Article
# Shear Behavior of Superposed Perfobond Connectors Considering Lateral Constraints
## 3 Experimental Results
### Test Parameter Analysis
#### 3.5.3 Shear Stiffness  
Based on Table 4 and Figures 14-16, the crucial factors affecting the shear stiffness of the perfobond connector are found to be the concrete's strength and bearing area during the initial loading stage. The shear stiffness of the C60 series specimens is 29.7% higher than that of the C40 series ones. The shear stiffnesses of Specimens P-5 and HP-1 (featuring larger holes) in the vertical loading direction are 8.7% and 11.8% higher than that of P-1, respectively. This is because, at loading onset, the external load is first transmitted to the upper concrete dowel through the perfobond rib, which is associated with the gradual compression of the concrete between the perfobond rib and perforating rebar. Then, the load is gradually transmitted to the rebar. Thus, the initial shear stiffness is determined by the concrete strength and the bearing area of the concrete dowel.
# Evaluation of AEP Predictions for Commercial Wind Farms in Sweden
## 3 Methods
### Wind Index Correction and Comparison with Predicted P50  
Due to the interannual variations in wind speed, the typically short-term wind measurement data available when calculating the predicted annual electricity yield of a proposed wind turbine must be correlated using a long-term wind dataset [16]. For a sufficiently long time series, the insecurity of the correlation may surpass that of the confirmation between the data and a normal wind year [17]. However, when comparing a vast number of wind turbines, they must have data for the same sufficiently long period. If using different periods for different wind turbines, as in this study, the production data must be individually correlated before comparing.  
The normalization was performed by extracting the monthly correlation indices for 77 sites spread across Sweden. For the location of these index sites, see Figure 1. The correlation indices were calculated using the ERA5T reanalysis, which was shown to perform well for the often complex terrain sites of Swedish wind turbines in [18]. For each site, they correspond to the wind energy at 100 m in height for the actual month, compared to the same month for a normal wind year. The mean distance from a wind turbine to its closest index site was 30 km and the maximal distance was 70 km. The yearly mean of all 77 indices together is listed in Figure 2. For an individual wind turbine, all monthly production data normalized for availability (\(E_{n}\)) were plotted against the correlation index for the same month with the index site closest to the specific wind turbine (\(I_{n}\)). This is illustrated in Figure 3 for one of the wind turbines. From a linear regression line, the wind-index-normalized production per month (\(E_{n}\)) was attained for \(I_{n}=1\). The wind-index-corrected annual production (WCP) was calculated as follows:  
\[WCP=12\cdot E_{n} \tag{3}\]  
The error between the production-based WCP and the predicted P50 (AEP) from the project developing phase can then calculated as:  
\[error=\frac{P50-WCP}{WCP} \tag{4}\]  
A positive error means that the P50 value was an overestimation of the actual AEP, whereas a negative error indicates an underestimation. To evaluate the magnitude rather than the placing of the error, the absolute error could instead have been used. However, because the predicted AEP reported to Vindstat for some wind turbines is presented as a fraction of the AEP for an entire wind farm, the mean absolute error is enlarged; therefore, it was not used in this study.  
Figure 2: Yearly mean of the ERA5T correlation indices for all 77 sites.
## 2 Methodology
### Preparation of Niosome Emulgel  
The schematic representation of prepared gels and emulgels of insulin are presented in Figure 1. The compositions of various formulations are summarized in Table 1. Niosomes were primarily prepared according to the method reported in our earlier study [23] with certain modifications. Accurately weighed amounts of Span 60 were mixed with a fixed ratio of cholesterol (1:1) in chloroform in a round bottom flask to obtain a final concentration of 100 \(\upmu\)mol, total lipid. The previous mixture was evaporated in vacuum rotary evaporator (Cole-Parmer T-1602-21, Japan), for 15-min until a dry thin film was formed. The thin lipid film was hydrated with phosphate buffer (pH 7.4) containing insulin (1.4 mg/mL), then the flask was securely closed and heated in a thermostatic water bath (55-60 \({}^{\circ}\)C) for 5 min until to form niosome suspension. Subsequently, the resultant niosomes were sonicated at 150 W (probe sonicator, Qsonica, LLC, Misonic, Farmingdale, NY, USA) for about 15 min at 20 \({}^{\circ}\)C in cycles of 3 min sonication followed by 2 min stop of sonication to prevent heating of the niosome-insulin suspension. The gels were formulated by sprinkling adequate quantity of gelling agent evenly over the surface of the water containing insulin and blending for 10 min until a homogenous gel was obtained. In case of emulgel, insulin was dissolved in aqueous phase containing the surfactant (Tween 80), and then the oily phase (paraffin oil) was added gradually. The mixture was homogenized at 12,000 rpm for 5 min. The required amount of sodium carboxymethyl cellulose was dusted gradually over the surface of the drug-loaded emulsion and homogenized at the same rate for 10 min until a homogenous emulgel was obtained [6]. In case of niosome gel or emulgel combinations, an equivalent amount of insulin solution was replaced by insulin-encapsulated niosome preparations and the formulations was developed as mentioned before.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Insulin** & **Insulin** & **Insulin** & **Insulin** & **Insulin Niosome** \\  & **Gel** & **Niosome** & **Niosome Gel** & **Emulgel** & **Emulgel** \\ \hline Insulin (mL) \({}^{\star}\) & 4 & 0 & 0 & 4 & 0 \\ Sodium carboxymethyl cellulose (g) & 0.4 & – & 0.4 & 0.4 & 0.4 \\ Liquid paraffin (mL) & 0 & 0 & 0 & 1.25 & 1.25 \\ Tween 80 (mL) & 0 & 0 & 0.125 & 0.125 \\ Niosomes-loaded insulin (mL) \({}^{\star}\) & 0 & 4 & 4 & 0 & 4 \\ Buffer pH 7.4 Q.S. to (g) & 10 & 10 & 10 & 10 & 10 \\ \hline \hline \end{tabular}  
* Each mL contained 1.4 mg of insulin.  
\end{table}
Table 1: Composition of prepared gels and emulgels of insulin.  
Figure 1: Schematic representation of prepared gels and emulgels of insulin.
# A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems
## 3 Analysis of the Data-Storage and Information-Discovery Schemes Proposed for WSANs
### Schemes Based on the Query-Driven Model
Although the authors in [42] have considered many scenarios to make the data-storage and information-discovery schemes as perfect as possible and they even have designed four analysis models to compute the optimal number of replicas corresponding to the four application profiles (Figure 1), there is something more they need to do because they did not consider the update of the replicas. Generally speaking, the loads of the replicas are much heavier than other normal sensor nodes. If the replicas cannot exchange roles with the normal sensor nodes, they will die much faster and the network lifetime will be shrunk greatly. Moreover, if the replicas never have been changed, the data stored on the replicas will not last long, and they will be overwritten within a short time period because of the limited storage capacity of the replicas. In 2014, to support long-term storage as well as prolonging the lifetime of WSANs, [PERSON] et al. proposed a novel data-centric storage framework [44], in which the rendezvous nodes are updated periodically based on periods of fixed duration called epochs so that it is possible to perform temporal queries to previous rendezvous nodes in order to discovery information from the past [44]. The significant contribution in [44] is that it presents a model to compute the optimal number of replicas that can maximize the data availability. Specifically, suppose \(r\) sensor nodes out of \(N\) nodes are selected out as the replicas, then the optimal value of \(r\) is the one to minimize the probability \(P(A_{i}(0,t]>S,\,\forall i=1,\,2,\,\ldots,\,r)\), which is shown in Equation (1) [44], assuming that \(N>>r\):  
\[P(A_{i}(0,t]>S,\,\,\forall i=1,2,\ldots)=\left(1-\sum_{i=1}^{S}\left( \begin{array} []{c}t\\ i\end{array} \right)\left(\frac{r}{N}\right)^{i}\left(1-\frac{r}{N}\right)^{t-i} \right)^{r}. \tag{1}\]  
In Label (1), \(A_{i}(0,t]\) denotes the times that the \(i^{th}\) node is selected as a replica after epoch 0 and before epoch \(t\), and \(S\) symbolizes the ratio of the number of events for which a replica can store in its storage space to the number of events a sensor node needs to store in an epoch. From our point of view, the schemes mentioned above are generally unsuitable for WSANs because they are similar with those proposed for WSNs and can be seen as a straightforward extension of WSNs to WSANs. Those schemes cannot effectively utilize the rich resources of the actuators, and they do not consider the mobility feature of the actuators either. In fact, because of the mobility of the actuators, it is challenging to take actuators as the rendezvous nodes traditional data-centric storage schemes should not be straightforwardly applied. Moreover, using such a data-centric storage model can hardly achieve real-time information discovery because event data are not sent directly to the actuators.
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 3 BRR-Based UEP Algorithm and Scalable Video Layer-Switching
### Proposed Adaptive Scalable Video Layer-Switching  
The proposed SVC layer-switching method selects the best layer(s) for streaming automatically. The switching is the easiest natural bandwidth adaptation method in the middle of streaming and has very low computational complexity without any transcoding that requires very high computing power. Thus, these SVC features enable the following working scenarios; a VSP server sends whole SVC layers to an individual home gateway, and the switching is performed in the gateway according to the link-quality feedback from the client. For example, if a user stays in an area that has a good channel quality, the gateway switches its service to a higher level with multiple ELs. In this system, the channel condition is measured using signal strength indicator (RSSI)-based feedback, packet loss ratio (PLR), and available bandwidth.  
In the implemented system, network abstraction layer (NAL) packets are analyzed and extracted the real-time layer-switching, as shown in Figure 7. In addition, the instant decoder refresh (IDR) information of SVC is the starting point of the proposed layer-switching method.  
Figure 7: Layer importance-based unequal error protection (UEP) method.