esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To realize the societal need for greener, safer, and smarter mobility, ambitious technical challenges need to be addressed. With this aim, the H2020-EUSPA project ESRIUM investigates various aspects of highly accurate, reliable, and assured EGNSS localization information for road vehicles with a particular focus on automated vehicles. To analyze the achievable accuracy, reliability, and availability of multi-frequency and multi-GNSS mass-market receivers, we have conducted test drives under different GNSS reception conditions. In the tests, special focus was placed on using the Galileo Open Service Navigation Message Authentication (OSNMA) service, offering an additional feature for assured PVT (position, velocity, and time) information with respect to spoofing. We analyzed the performance of three Septentrio Mosaic-X5 receivers operated with different OSNMA settings. It could be shown that strict use of OSNMA provides very good positioning accuracy as long as sufficient suitable satellites are available. However, the overall performance suffers from a reduced satellite number and is therefore limited. The performance of a receiver using authenticated Galileo with GPS signals (final status of Galileo OSNMA) is very good for a mass-market receiver: 92.55% of the solutions had a 2D position error below 20 cm during 8.5 h of driving through different environments.  
EGNSS localization; navigation; Galileo Open Service Navigation Message Authentication (OSNMA); automated driving 2023
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Challenges and Future Directions in IoT Security  
#### 3.8.1 Challenges  
The security challenges of the IoT are indeed multifaceted, especially due to its wide range of application scenarios and diverse device types. Some of these challenges include resource constraints, device heterogeneity, and a lack of standardization. Meanwhile, in the face of these challenges, future research directions present many promising areas, including lightweight encryption, AI-driven security, blockchain-based security, and privacy-preserving technologies.  
* _Resource Constraints_. Many IoT devices have limited processing power, memory, and communication bandwidth, which limits the possibility of implementing robust security measures on these devices. Therefore, designing security solutions that adapt to resource-constrained environments is a challenge.
* _Heterogeneity of Devices_. There are various types of devices in the IoT that may have different operating systems, processor architectures, and communication protocols. Harmonizing security standards and mechanisms for these heterogeneous devices is a complex task.
* _Lack of Standardization_. The lack of unified IoT security standards and specifications makes it more difficult to develop comprehensive security policies and implementation strategies. The lack of standardization may lead device manufacturers and service providers to take different approaches to security.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 2 Network Based Systems  
There are many different wireless networks that can be deployed in an indoor environment. From the typical WiFi networks that are deployed in millions of buildings around the world for providing internet access, to the WSNs designed for the IoT or the popular Bluetooth beacons among other alternatives such as the UWB networks. Leaving aside the election of the wireless network, which obviously will determine the accuracy and precision of the IPS, we can classify the network based IPS systems according to the information obtained from the wireless signals into two groups: (i) range based methods and (ii) range free methods.  
Range based methods extract geometric information (distance or angle) from the signals of different anchor nodes in the wireless network and then combine the geometric constraints of each anchor to obtain the position of the user. Alternatively, the range free methods are based on the inter-node connectivity information or in the identification of signal features patterns that are location dependent.
## 3 Results and Discussions  
### Structural Properties  
The crystalline phase and orientation of the ZnO thin films was determined by XRD measurements using [PERSON]'s formula [28] to calculate the crystalline size:  
\[D=\frac{k\lambda}{\beta cos\theta}\]  
Figure 1: Schematic representation of the automated pulsed laser deposition system.  
where the constant \(k=0.94\) is the shape factor, \(\lambda\) is the wavelength of X-rays 1.5406 A for Cu K\({}_{\alpha}\), \(\theta\) is the Bragg's angle, and \(\beta\) is the full width at half maximum.  
From the diffraction patterns, a good crystalline behavior is revealed with a preferential growth of the thin films on the (002) plane, suggesting that there are no secondary phases, as seen from Figure 2. The inset of the figure depicts the crystalline phase and orientation of the Si substrate as measured by XRD. The position of the (002) is consistent with the values from literature: PDF card no 00-005-0664.  
Knowing the structure of the (002) diffraction peaks, the crystalline plane distance d, the lattice constant c, and the strain \(\varepsilon\) of the ZnO thin films are calculated. The crystalline distance d for the index (002) is given by the Bragg formula:  
\[\text{d}=\lambda/2\sin\theta \tag{1}\]  
and the lattice constant c is equal to 2 d [28]. The strain \(\varepsilon\) in the films along the c-axis is calculated by the formula:  
\[\varepsilon=(\text{c}-\text{c}_{0})/\text{c}_{0} \tag{2}\]  
where c\({}_{0}\) is the unstrained lattice constant and has a value of 0.5205 nm for bulk ZnO [29]. The calculated values are presented in Table 1. The positive value for the strain \(\varepsilon\) depicts a very low tensile strain of ZnO thin films approaching an almost stress-free ZnO. The intensity of the (002) diffraction peak increases as a function of substrate temperature and is revealing an improvement of the film's crystallinity. The film deposited at room temperature exhibits a lower crystallinity compared with those deposited at about 350 \({}^{\circ}\)C. The annealing of different layers induced changes in the crystalline lattice constant of ZnO, which changed the tensile stress in the ZnO thin films. A shift toward a high angle of the XRD peak is observed due to the narrowing of the distance d between the lattice planes. As a result, preferentially c-plane-oriented, highly crystalline ZnO thin films are produced.  
Figure 2: XRD pattern for ZnO thin films and Si substrate (inset).
## 3 Results  
### US-LA3 Classification  
Since we considered the classification based on the WordView-3 data to be close to the truth based on the field observations, we evaluated the accuracy of the HLS classification through a confusion matrix of the WorldView-3 classification and the HLS classification results (Table 1). According to the HLS NDVI timeseries classification, open water and _Spartina_ patches were recognized quite successfully (86.74% and 93.45%, respectively), while the _Juncus_ classification had an accuracy of only 55.66%.  
Using the NDVI-based method, the patch distribution for the years 2016 through 2023 were derived. Figure 4 shows the yearly classification for each of the three distinct patch-types in the wetland, and Table 2 summarizes the vegetation cover under the tower footprint based on the NDVI-based classification used as input for the ELM to derive the corresponding methane and carbon flux.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**HLS Classification Patch-Type, Number of Pixels**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**\% HLS Pixels**} \\ \cline{3-6}  & & **Open Water** & **Juncus** & **Spartina** & **(WorldView-3)** & **Medding** \\ \hline \multirow{2}{*}{**WorldView-3**} & **Open water** & 2178 & 4 & 329 & 2511 & 86.74\% \\ \cline{2-6}  & _Juncus_ & 0 & 123 & 98 & 221 & 55.66\% \\ \cline{2-6}  & _Spartina_ & 0 & 102 & 1456 & 1558 & 93.45\% \\ \cline{2-6}  & **Total (HLS)** & 2178 & 229 & 1883 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of the WorldView-3 classification and the HLS classification results for year 2018.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Vegetation** & **Pixel Count** & **Percent Cover** \\ \hline \multirow{2}{*}{**2016**} & _Juncus_ & 6 & 2.3 \\ \cline{2-5}  & _Spartina_ & 250 & _97.7_ \\ \hline \multirow{2}{*}{**2017**} & _Juncus_ & 32 & 12.5 \\ \cline{2-5}  & _Spartina_ & 224 & 87.5 \\ \hline \multirow{2}{*}{**2018**} & _Juncus_ & 84 & 32.8 \\ \cline{2-5}  & _Spartina_ & 172 & 67.2 \\ \hline \multirow{2}{*}{**2019**} & _Juncus_ & 117 & 45.7 \\ \cline{2-5}  & _Spartina_ & 139 & 54.3 \\ \hline \multirow{2}{*}{**2020**} & _Juncus_ & 108 & 42.2 \\ \cline{2-5}  & _Spartina_ & 148 & 57.8 \\ \hline \multirow{2}{*}{**2021**} & _Juncus_ & 59 & 23.0 \\ \cline{2-5}  & _Spartina_ & 197 & 77.0 \\ \hline \multirow{2}{*}{**2022**} & _Juncus_ & 91 & 35.5 \\ \cline{2-5}  & _Spartina_ & 165 & 64.5 \\ \hline \multirow{2}{*}{**2023**} & _Juncus_ & 109 & 42.6 \\ \cline{2-5}  & _Spartina_ & 147 & 57.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of vegetation cover within the EC tower footprint derived from NDVI-based classification.
# Three-Year Follow-Up Assessment of Anthropogenic Contamination in the Nichupe Lagoon
## Materials and Methods  
### Description of the Study Area  
The Nichupte Lagoon System (NLS) is a coastal system in the state of Quintana Roo northeast of the Yucatan Peninsula (Figure 1); at lat. 21\({}^{\circ}\)02\({}^{\prime}\) to 21\({}^{\circ}\)06\({}^{\prime}\) N and long. 86\({}^{\circ}\)46\({}^{\prime}\) to 86\({}^{\circ}\)50\({}^{\prime}\) W [16].  
The SLNB interacts with the Caribbean Sea through two openings: the Cancun Inlet located to the north of the complex and the inlet at Punta Nizuc in the south [17]. This system is a coastal lagoon complex consisting of five interconnected water bodies, with Laguna Nichupte being the largest, comprising approximately 46% of the area. Its four peripheral lagoons are Laguna de Bojorquez, located in the northwest of the system; Rio Ingles is situated at the southwestern end and two smaller lagoons, Somosaya and La Caleta are found in the central-western and southern extremes, respectively [11].  
The NLS is a low-energy system with little exchange of water with the open sea and a small (<16 cm) tidal range, with an estimated flushing time that takes from 1 to 3 years [11]. The NLS contains marine water, with salinity gradients of 24 to 30 PSU, as well as DO and pH values like the marine environment; sandy sediments covered by patches of grass and mangrove growths at the shores. The mangrove area that surrounds the Nichupte Lagoon has been declared a Natural Protected Area in terms of its flora and fauna. The climate of the region corresponds to subtype Aw1, warm subhumid, with an annual mean atmospheric temperature greater than 22 \({}^{\circ}\)C and a temperature of the coldest month greater than 18 \({}^{\circ}\)C. It experiences abundant rainfall in summer and scarce rainfall in winter, with the precipitation of the driest month being less than 60 mm. In the three-year follow-up sampling period in August 2018 (27 th and 28 th), 2019 (27 th and 28 th), and 2020 (25 th and 26 th) in rainy season, the mean water temperature went 31.9 \({}^{\circ}\)C in 2018, 31.5 \({}^{\circ}\)C in 2019, and 29.5 \({}^{\circ}\)C in 2020 and the monthly average rainfall for 2018 were 23.5 mm (23.4 to 23.6 mm), for 2019 were 54.1 mm (2.8 to 111 mm), and for 2020 19.8 mm (3.4 to 43.0 mm).  
Based on the morphological characteristics of the NLS and with the aim of improving the design and distribution of the sampling stations, for this study, an 'a priori' zoning was carried out, considering criteria such as water exchange constriction and limitation between different areas. This zoning resulted in five zones distributed as follows: Bojorquez (ZI); North Zone (ZII); Central Zone (ZIII); South Zone (ZIV), and Rio Ingles (ZV) (Figure 1).
## 4 Results and Discussion
### Detected Anomalies  
#### 4.5.1 Outliers  
Outliers indicate abnormal sensing conditions and their detection is a critical task in many safety-critical environments that sensors are applied. In our experiment, we observed that four links laid abnormally far away, clearly isolated and inconsistent with the pattern of the other links. Such outcomes could be due to the quality of sensed data that may have been affected by noise, error or inconsistent packet reception. Because outliers are one of the sources that greatly influence the quality of link for transmission, in this section, we provide an overview of the results of detected outliers. Table 2 gives a description of our findings.  
Figure 9: KLSVM: Sensor node observations with PRR greater than 50% have a high degree of negative correlation and a moderate degree of positive correlation for the rest of the observations. These observed links with PRR less than 50% show inconsistent pattern of their retransmission counts, thus the cause of a moderate degree of positive correlation.  
KLSVM approach prevented normal sensed data from being classified as outliers and kept the detection rate high and false alarm rate low. The technique identifies outliers from the sensed data measurements but not performed in real-time. The technique pays attention to spatial correlation of neighboring nodes, which makes the results of outliers accurate.
## 3 Results
### Doses Deposited in the Water Phantoms  
Figure 5 shows the simulated dose maps in the head equivalent and body-equivalent water phantoms using different scanning protocols; the dose was normalized to the maximum value when neither an attenuation filter nor TCM was used. As shown in Figure 5, using the FDB or SB obviously reduces the dose deposited in the whole head and body-equivalent water phantoms, especially for the surface layer of the phantom. Compared with SB, the dose reduction effect using FDB is more obvious. It is considered that the increase in the effective energy reduces the radiation dose deposited in the surface layer (Figure 4), and the FDB can generate a more constant fluence across the elliptical projections (Figure 3). However, the effect of TCM was less obvious.  
Figure 4: X-ray energy spectra adding different bowties at a tube voltage of 120 kVp.  
Figure 5: Dose map of (**a**) head and (**b**) body phantom using different scanning protocols.  
For quantitative comparison, the doses deposited in all voxels of the phantoms were extracted and averaged. Table 4 summarizes the relative average doses deposited in the head and body phantoms using the different scanning protocols. As shown in Table 4, compared to the NoAtt_tcm0, the doses received by both head and body-equivalent water phantoms were reduced by 57% when the FDB was used, and the doses were also reduced by 23% and 35% for the head and body phantoms using the SB, respectively. The results indicate that FDB can reduce the radiation dose more efficiently than SB.
## 2 Literature Review
### Autoregressive Integrated Moving Average (ARIMA) Model  
In ARIMA, a time series model reproduces the patterns of a variable's previous movements across time and uses this information to forecast its future movements [29]. Wind speed measurements obtained over time tend to be positively correlated. Many parametric time series models that consider the autoregressive (AR) process exist to account for this autocorrelation [30]. In an autoregressive model, we forecast the wind speed using a linear combination of past wind speed values.  
\[u_{t}=\gamma_{1}u_{t-1}+\gamma_{2}u_{t-2}+\cdots+\gamma_{p}u_{t-p}+\epsilon_{t} \tag{15}\]  
Equation (15) provides the AR model of order p, where \(\gamma\) is the autoregression coefficient and \(\epsilon_{t}\) is the noise in time \(t\). A moving average term is added in the autoregressive model, and the autoregressive moving average model is developed and described as follows:  
The autoregressive moving average (ARMA) model is a type of autoregressive model that also adapts the moving average model. It is a statistical model that could be used for time series prediction of future wind speed values using past values and lagged forecast error. A general ARMA is denoted by ARMA (\(p\), \(q\)) and can be expressed by the following expressions:  
\[u_{t}=\delta+\sum_{i=1}^{p}\gamma_{i}\;u_{t-i}+\sum_{j=1}^{q}\varnothing_{j}e_ {t-j}+\;e_{t} \tag{16}\]  
where the second term from the right in Equation (16) is the moving average (MA) part of the ARMA model, \(\delta\) is the constant, \(\varnothing_{j}\) is the \(j^{\text{th}}\) moving average coefficient, \(e_{t}\) is the error term at time period \(t\), and \(u_{t}\) is the value of wind speed predicted at time step \(t\). If the differencing is added to the ARMA model, the model is transformed into the ARIMA model. Therefore, the ARIMA model, introduced by [PERSON] and [PERSON], includes autoregression (AR), a moving average (MA), and differencing [31]. The non-seasonal model structure of ARIMA is expressed in the form of ARIMA (\(p\), \(d\), \(q\)), where d is the order of differencing (I) to make the model stationary. Hence, the seasonal time series is stationery in nature and becomes zero, and the ARIMA model is converted to the ARMA model [32].
## 4 Applications to Real Measured Responses  
Having demonstrated the effectiveness of the current approach in handling numerically simulated responses, we extend its application to process actual measured responses from a two-span cable-stayed bridge subjected to an earthquake. Additionally, we employed the approach in the analysis of nonlinear responses from a five-story steel frame under a shaking table test.
# BODIPY-Based Nanomaterials--Sensing and Biomedical Applications
## 1 Introduction
### Brief Characterization of Selected Nanoparticles
Materials exhibiting such photon upconversion are usually comprised of host ceramic lattices embedded with a transition metal, as well as actinide or lanthanide ions such as Yb\({}^{3+}\), Er\({}^{3+}\), and Tm\({}^{3+}\). Using near-infrared wavelengths for excitation of UCNs allows for deeper tissue penetration. The emitted shorter wavelengths can further excite other PSs attached to the lanthanide-doped nanoparticles [38]. Another compelling group of metallic nanomaterials are zinc oxide (ZnO) nanoparticles, which are nowadays used as light-emitting diodes, photocatalysts, and biosensors [39]. Like silver, ZnO also exhibits antibacterial activities and can be considered a safe and stable inorganic antimicrobial agent [40]. Chemically synthesized from zinc sulfate, ZnO nanoparticles can cause cell membrane damage via ROS production, which occurs on their surface. It was proved that ZnO nanoparticles could provide significant growth inhibition within a wide range of pathogenic bacteria after irradiation of visible light [40]. Recently, metal-organic frameworks (MOFs) have been proposed in nanomedicine as both promising drug delivery carriers and theranostic agents. These nanostructures are formed via the reticular synthesis of metal-containing subunits (inorganic clusters) with organic linkers (carboxylates, imidazolates, and phosphonates) to give open crystalline frameworks with high flexibility and porosity [41; 42]. A vast number of available metal ions and organic linkers accompanied by a diversity of structural motifs leads to countless combinations in the design of MOFs [43]. Postsynthetic modification of MOFs allows for conjugation with various biologically active molecules for various purposes in medicine. They are known for their large surface area and high porosity, which allows them to host guest structures [43]. MOFs have recently been considered potential gas and vapor-phase sensors regarding their porosity. In the last decade, they have also been connected with porphyrinoids and related compounds for chemical sensing and catalysis [44; 45]. To sum up:  
* Some nanoparticles, such as gold and TiO\({}_{2}\), can generate ROS species when irradiated with light at the proper wavelength. * Silver and zinc oxide nanoparticles reveal antibacterial activity. * The most interesting feature of quantum dots is their fluorescence activity. * The emission of light of shorter wavelength after low-energy light excitation is an attribute of upconverted nanoparticles. * MOFs can be used as potential drug delivery systems due to their large surface area and high porosity.
## 5 Case Study
### Implementation and Results
**Step 4:** Via Equation (19), the average probability vector \(\widetilde{P}\) is established as  
\[\widetilde{P}=\{\langle[s_{3,118},\ s_{4.113}],(0.696,0.695)\rangle\,,\ \langle[s_{3,050},\ s_{4.048}],(0.675,0.664)\rangle\,,\] \[\langle[s_{3,048},\ s_{3.975}],(0.682,0.688)\rangle\}.\]  
**Step 5:** With Equations (20) and (21), the positive distance matrix \(D^{+}=\left[d^{+}_{ij}\right]_{3\times 3}\) and the negative distance matrix \(D^{-}=\left[d^{-}_{ij}\right]_{3\times 3}\) of target child events to the average probability vector \(\widetilde{P}\) are determined as  
\[D^{+}=\left[ \begin{array}{ccc}0.248&0.324&0.332\\ 0.076&0.165&0.142\\ 0.030&0.056&0.067\end{array} \right],\ D^{-}=\left[ \begin{array}{ccc}0.248&0. 324&0.332\\ 0.076&0.165&0.142\\ 0.030&0.056&0.067\end{array} \right].\]  
**Step 6:** By using Equations (22)-(23), the weighted sums of SP and SN for each target child event \(SP_{i}\) (\(i=1\), \(2\),..., \(9\)) and \(SN_{i}\) (\(i=1\), \(2\),..., \(9\)) are determined as expressed in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline
**Target Child Events** & **Occurrence Probabilities** \\ \hline \(B_{11}\mid X_{43}X_{51}X_{62}X_{71}\) & \(<\)[s\({}_{1}\), s\({}_{2}\)], (0.18,0.82)\(>\) \\ \(B_{12}\mid X_{43}X_{51}X_{62}X_{71}\) & \(<\)[s\({}_{1}\), s\({}_{2}\)], (0.70,0.23)\(>\) \\ \(B_{13}\mid X_{43}X_{51}X  
**Step 7:** Via Equations (24) and (25), the normalized values of \(\overline{SP_{i}}\)(\(i=1\), \(2\),..., \(9\)) and \(\overline{SN_{i}}\)(\(i=1\), \(2\),..., \(9\)) for each target child event are computed and shown in Table 5.  
**Step 8:** Applying Equation (26), the appraisal scores for nine target child events are displayed in Table 5. According to the descending order of the appraisal scores \(AS_{i}\)(\(i=1,2,\ldots,9\)), the ranking of nine target child events is determined as shown in Table 5.  
Accordingly, the ranking order of target child events within the circumstance of \(X_{43}X_{51}X_{62}X_{71}\) is \(B_{31}>B_{21}>B_{32}>B_{12}>B_{23}>B_{11}>B_{22}>B_{33}>B_{13}\). Thus, we can know that event \(B_{31}\mid X_{43}X_{51}X_{62}X_{71}\) has the highest occurrence probability, indicating that \(B_{31}\) is the most likely cause event in the case of \(X_{43}X_{51}X_{62}X_{71}\). This result is consistent with the statistical data obtained from the actual production.