esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures
###### Abstract  
NarrowBand IoT (NB-IoT) is emerging as a promising communication technology offering a reliable wireless connection to a large number of devices employed in pervasive monitoring scenarios, such as Smart City, Precision Agriculture, and Industry 4.0. Since most of the NB-IoT transmissions occur in the uplink, the random access channel (that is the primary interface between devices and the base station) may usually become the main bottleneck of the entire system. For this reason, analytical models and simulation tools able to investigate its behavior in different scenarios are of the utmost importance for driving current and future research activities. Unfortunately, scientific literature partially addresses the current open issues by means of simplified and, in many cases, not standard-compliant approaches. To provide a significant step forward in this direction, the contribution of this paper is three-folded. First, it presents a flexible, open-source, and 3 GPP-compliant implementation of the NB-IoT random access procedure. Second, it formulates an analytical model capturing both collision and success probabilities associated with the aforementioned procedure. Third, it presents the cross-validation of both the analytical model and the simulation tool, by taking into account reference applications scenarios of sensor networks enabling periodic reporting in monitoring infrastructures. Obtained results prove the remarkable accuracy, demonstrating a well-calibrated instrument, which will be also useful for future research activities.  
monitoring system; sensor network; NB-IoT; random access channel; simulation tool; analytical model +
Footnote †: journal: Sensors
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 2 Antenna Configuration  
### Single UWB Antenna Configuration  
A low-cost 20 mm \(\times\) 20 mm FR-4 substrate of thickness 1 mm, relative permittivity of 4.4 and dielectric loss tangent of 0.02 is used to design the single antenna. The antenna geometry has evolved from a conventional rectangular patch. The bottom corners of the radiator are chamfered, while semi-circles of radius 2.5 mm are subtracted from both sides of the patch. Rectangular slits are introduced at the top edge of each radiating patch. The resulting semi-elliptical shape provides good impedance matching in the band of interest. A microstrip feed line with chamfered top edge is used to excite antenna elements with matched impedance. All modification steps and their effect on the reflection coefficient is shown in Figure 1a.  
Figure 1: \(Cont\).
## 5 Structure and Function of Clostridial Binary Toxins
### Toxin Uptake and CDTa/Ia Translocation  
Following oligomerization and receptor binding, the N-terminal domain of CDTa (residues 1-217) associates with heptamerization domain I (HD1; residues 212-297) of heptameric CDTb [11; 12; 15]. Similarly, the N-terminal domain of Ia (residues 1-210) associates with domain 1\({}^{\prime}\) (D1\({}^{\prime}\); residues 216-295) of oligomerized Ib [14]. The Ca-edge (CDTb; residues 214-231, Ib; 216-224) is the first constriction point encountered by CDTa/Ia upon B subunit association (Figure 5) [14; 15]. This association between toxin and receptor is a major driving force for the endocytic uptake of CDT and iota toxin [49]. For iota toxin specifically, dynamin was also determined to be required for its endocytic uptake [9; 60]. The second constriction point, a short loop consisting of an arginine and two serine residues in CDTb (NSS-loop; residues 491-493), is also significant in CDTa binding (Figure 5A) [8; 15]. In Ib, this loop is composed of an arginine, a serine, and a glutamine residue (NSQ-loop; residues 490-492) (Figure 5B) [14]. This \"NSX-loop\" also appears in CSTb (as NSQ) and C2 II (as NSN). This is distinct, but akin to the function of the \(\alpha\)-clamp located in the funnel rim of the anthrax toxin PA, which binds to helical portions of LF and aids in LF translocation through the PA pore [8; 61]. In both CDTb and Ib, the third and final constriction site prior to the \(\beta\)-barrel is the \"TEG-loop\" (Figure 5), which is conserved across clostridial binary toxins (Figure 2) [14; 15].  
Figure 5: Atomic model of constriction sites and domain structures for activated _C. difficile_ CDTb and _C. perfringens_ Ib. These structures are illustrated to show the key regions of active CDTb and Ib required for host cell receptor binding, pore-forming activities, and catalytic subunit translocation. (**A**) The four constriction sites and the \(\varphi\)-clamp of the CDTb-pore: Ca-edge (214–231 aa), NSS-loop (491–493 aa), TEG-loop (498–500 aa), \(\varphi\)-clamp (F455), and His-gate (H314). Structure modified from PDB: 7 VNN [15]. (**B**) The four constriction sites and the \(\varphi\)-clamp of the Ib-pore: Ca-edge (216–224 aa), NSQ-loop (490–492 aa), TEG-loop (497–499 aa), \(\varphi\)-clamp (F454), and His-gate (H313). Structure modified from PDB: 6 KLX [14]. Domain structures and their corresponding domain maps are presented below the atomic models of Ib and CDTb. This figure was created with the program BioRender.
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 3 Results
### Processing and Validation of the Digital Elevation Model  
In this study, for hydraulic analysis, a DEM with a spatial resolution of 1 m, generated from LiDAR data through the programs named LAKI I and LAKI II, developed by ANCPI, was used according to the research methodology (Figure 2). For modeling, a section of the Cigher River, a tributary of the Crisul Alb River, was selected, located between the confluence with the Timercea River upstream and Valea Mare downstream. The area of interest was extracted from the DEM for the entire watershed using ArcGIS, with a 200-m buffer from the river course (Figure 3).  
To validate the accuracy and precision of the DEM data, control points were collected in the field within test zones using GPS equipment. The collected data were imported into AutoCAD 19.0 software using the Stereographic 1970 projection system, and cross-sections of the river valley were created, which were later used for validating the DEM data. It was found that the accuracy of the DEM data is compliant, and thus it can be used for hydraulic modeling.  
For each GPS point, we extracted the corresponding altitude value from the DEM and calculated the differences between the DEM values obtained from LiDAR data and the GPS measurements. We analyzed the error distribution and computed statistical indicators such as the mean error (ME), root mean square error (RMSE), and standard deviation to assess the accuracy of the DEM in relation to the field data.  
The results showed that the DEM data exhibit high accuracy, and the differences between the measured and modeled elevations were within acceptable limits for hydraulic modeling applications.  
Figure 3: The Digital Elevation Model of the analyzed river sector.  
The preprocessed Digital Elevation Model was imported into HEC-RAS 6.5 software and converted into the specific format (Figure 3).
## 5 Numerical Simulations  
CFD simulations are performed using OpenFOAM in order to analyze the flow dynamics behind wind turbines. The flow field is solved using the OffWindSolver, which is a solver under development by [PERSON] at SINTEF Industry [42]. Both RANS and LES simulations are used in this work. The RANS simulations use a RNG (Re-Normalization Group) \(k\)-\(\varepsilon\) turbulence model, based on the authors' previous experience of this model's suitability for solving wind turbine flows [29]. It models the effect of turbulence as an added viscosity, the effect of which is introduced into the NSEs. Separate transport equations for turbulent kinetic energy \(k\) and turbulent dissipation rate \(\varepsilon\) are solved to find the turbulent viscosity. The RNG \(k\)-\(\varepsilon\) model is an extension of the standard \(k\)-\(\varepsilon\) model, incorporating additional modifications that work to provide better accuracy in capturing the flow physics and reduce the dependency on user-defined constants. The LES simulations use the Smagorinsky turbulence model to close the NSE equations [43]. The Smagorinsky turbulence model is a method used in LES to approximate the effects of the unresolved small-scale turbulence on the simulated larger-scale flow. It is also an eddy viscosity model and therefore assumes the turbulent stresses are proportional to the local deformation of the flow. Their magnitude is determined using the Smagorinsky coefficient. Its value is usually based on empirical or theoretical considerations, and it affects the accuracy of the model.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 3 Proposed SS-FGSM Scheme
### Deep Learning-Based Detector  
In conventional FGSM [17; 20], the ML optimal detector is performing a joint detection of the transmit antenna index and transmitted symbol to get the upper bound of the system average BER  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Transmitted Bits** & **Antenna Combination** \\ \hline
**Data Bits** & **Codes Bits** & **Antennas Bits** & \\ \hline \(\mathbf{d}_{M-ary_{\text{\tiny{I}}}}^{T}\) & \(\mathbf{d}_{\text{\tiny{I}}}^{T}\mathbf{d}_{Q,\text{\tiny{I}}}^{T}\) & 000 & \(T_{\text{\tiny{I}}}\) \\ \(\mathbf{d}_{M-ary_{\text{\tiny{I}}}}^{T}\) & \(\mathbf{d}_{\text{\tiny{I}}}^{T}\mathbf{d}_{Q,\text{\tiny{I}}}^{T}\) & 001 & \(T(ABER). Unfortunately, the ML optimal detector has the highest computational complexity. This paper proposes a deep learning-based detector to relax the computation complexity without high degradation in the BER performance. DL [41] with deep neural networks (DNNs) has been applied to many different fields. Recently, it has been applied to an underwater communication system for the underwater acoustic channel estimation problem [42]. To the best of author's knowledge, this paper is the first one addressing the DL-based detector in SMTs techniques in general. The DL-based detector is proposed in this paper to reduce the receiver computational complexity, specifically for the FGSM scheme which uses an active antenna number as an additional dimension to carry more information bits. The DL-based detector is proposed as a sub-optimal detector significantly reducing the complexity with acceptable performance. Only two hidden fully-connected (FC) nonlinear layers are used to achieve satisfying performance in the proposed DNN structure. The number of nodes in the hidden layer can adaptively be modified as a trade-off between complexity and performance.  
Figure 1 shows the DL-based detector structure. In the receiver side, assuming the channel is fully known, the received signal in (16) is multiplied with \(h_{i\
eq j}^{-1}(t,\tau)\) as:  
\[r_{i}(t)=\ h_{i\
eq j}^{-1}(t,\tau)\ y(t),\ ((i=1,\ 2,\ \cdots,N_{t})\&(j=1,\ 2,\ \cdots,N_{r})) \tag{18}\]  
where \((.)^{-1}\) is the pseudo-inverse operation. That process will inverse the channel to avoid the effect of the UWA channel. The channel can be estimated and equalized using any other algorithm. Although any other algorithm can be utilized with better performance, in this paper, the Zero forcing (ZF) algorithm is employed for simplicity and better performance is still gained as shown later. The \(r_{i}(t)\), including its real and imaginary parts, is used as the DNN coarse input, where the real part and imaginary part are concatenated and adopted into the input layer.
## 3 Results
### Biases in Remote Sensing Data Products and Consequences on Global GPP Estimation
The global annual average GPP estimated by the LUE-NDWI, after input data correction, was about \(125.6\)Pg C yr\({}^{-1}\). Without data correction, the LUE-NDWI model would overestimate global GPP by \(18\%\) (Figure 5a,b). The corresponding global growth rate of GPP decreased from \(0.34\) to \(0.17\) Pg C yr\({}^{-1}\) after input data correction (Figure 5c). Figure 3: Comparison of (**a**) spatial PAR (photosynthetic active radiation) and site PAR, (**b**) tower GPP and LUE-EF GPP (uncorrected spatial data), (**c**) tower GPP and LUE-EF GPP (corrected spatial data), (**d**) LUE-EF GPP with uncorrected and LUE-EF GPP corrected spatial data, (**e**) spatial EVI (500 m resolution) and spatial EVI (10 km resolution), (**f**) tower GPP and LUE-NDWI GPP (uncorrected spatial data), (**g**) tower GPP and LUE-NDWI GPP (corrected spatial data), and (**h**) LUE-NDWI GPP with uncorrected and LUE-NDWI GPP corrected spatial data. All comparisons are based on site scale. Corrected spatial data were generated from the uncorrected ones after correcting their biases using orthogonality regression (see Section 2.5).
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 3 Classification of Attacks in VANETs
### Attacks on Electric Vehicles
Various ISO/IEC standards have been defined to provide security for the charging systems and the smart grids. All the attacks which may be launched against other vehicles may also be launched against electric vehicles. In addition to those attacks, many more attacks are possible against infrastructure used for vehicle charging and electricity distribution systems. So, additional security is required in the case of electric vehicles.
## 2 Methods  
A semi-automatic methodology was applied to a Pleaides satellite image of 2015 to derive building footprints and building archetypes. OBIA was used to delineate building geometries and to provide an initial classification of buildings into typologies. An elaborate post-processing and revision process was then applied. The 2015 dataset and aerial images of 2008-2009 were used to detect the change of the building stock for the respective period.
## 3 Results
### Online Platform  
#### 3.3.1 Case Study 5: \"The Hanging Gardens of Babylon\"  
AR companies, traditionally game developers, are exploring its educational uses. History education benefits from AR, making intangible events and locations more interactive. Time Passport Inc. specializes in historic AR applications, allowing users to explore historical sites virtually. Their first release, the Hanging Gardens of Babylon, brings this lost wonder to life, enabling global access (Figure 10). This project aims to demonstrate AR's potential for immersive educational experiences and to pave the way for future apps tied to specific historical locations.  
Figure 10: Time Passport’s The Hanging Gardens of Babylon.  
User Interactions  
AR and VR technologies enhance information retention through interactive stimuli like sound, sight, and touch, aiding learning, tourism, history, and cultural awareness. AR offers richer interactions than conventional interfaces, expanding the capabilities of devices like smartphones and wearables [101; 102]. Unlike VR, AR supplements rather than replaces reality, overlaying digital information onto the real world. This integration helps users better understand and absorb information, making AR a valuable tool for various educational and experiential applications.  
Evaluation Method and Results  
The evaluation aimed to collect data on the usefulness of the Hanging Gardens of Babylon AR app for learning retention, comparing it to a VR app. The study hypothesized higher retention with AR and VR compared to traditional tools, and higher retention with VR over AR [101; 102; 103; 104; 105; 106].  
Using an A/B test design, participants were divided into two groups: group A (AR) and group B (VR). Each group completed seven tasks and a comprehension quiz to assess memory retention. This method allowed for a direct comparison of learning retention between the AR and VR conditions.  
Conclusions of Case Study 5  
The study evaluated historical knowledge retention using the Hanging Gardens of Babylon app developed with Time Passport's AR and VR platforms. The results indicated better retention with AR compared to VR, contradicting prior research that favored VR for education. Both AR and VR showed significant learning retention, highlighting their advantages over traditional methods. AR's effectiveness in educational settings is supported by this and other studies [107]. The full results can be found in the student's HCI master's project report [108].
###### Abstract  
We present a study of the problem of measuring ultra-low absorption in quartz materials using the time-resolved photothermal common-path interferometry (TPCI) method, which we proposed and elaborated, with allowance for developed heat diffusion in the samples. This task is related to the implementation of one of the ways of increasing the sensitivity of measurements, namely, increasing the energy of the heating pulse by increasing its duration. Analytical formulas for the time dependence of the power of the time-varying component of the probe radiation are obtained in the Gaussian approximation for the laser beams. A correction factor that takes into account the heat diffusion effect is calculated theoretically and used during calibration. The rate at which the power of the time-varying component decreases when the sample is cooled after the end of the heating pulse is calculated. When measuring in crystalline quartz, quartz glass, and also in air, the calculated rate coincided with the experimental one, which is additional evidence for the reliability of the calculations of the correction factor for calibrating the measurements. When the duration of the heating pulse is increased to 5 ms, the calculated sensitivity of the scheme for measuring absorption in quartz glasses is \(2\times 10^{-9}\) cm\({}^{-1}\).  
time-resolved photothermal common-path interferometry; ultrapure quartz materials; absorption measurement; heat diffusion
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 1 Introduction
### VIIRS Design
There are also four additional aggregation modes that are not used in any of the aggregation zones in the Earth-view. These are sometimes referred to as super-aggregation modes because they aggregate many pixels. There are 64 aggregation zones in the Earth-view and 36 aggregation modes. In the cal sector the aggregation sequence numbers are repeated every 72 scans with each aggregation mode being used for two scans of cal sector data in order to allow both HAM sides to be calibrated.
## 3 Samples and Methods
### Methods
#### 3.2.3 Multivariate Mathematical Analysis Method  
Suppose the absolute content data of Y components of X mixed-source natural gas products are obtained, then A matrix M\({}_{x\times y}\), with X rows and y columns can be obtained. Then, the composition of each end-element natural gas should also have the absolute content of Y components. If there are A end-elements (A \(\leq\) X), then the composition of all end-element natural gas also forms a matrix N\({}_{A\times Y}\) with a rows and Y columns. Then the matrix representing the proportion of mixed-sources must be A matrix with X rows and a columns, denoted as P\({}_{X\times A}\). At this point, the following formula holds:  
\[\mathbf{M}_{X\times Y}=\mathbf{P}_{X\times A}\times\mathbf{N}_{A\times Y}+e \tag{1}\]where M\({}_{\rm x\times y}\) is a matrix of dimensions \(X\times Y\), where each element represents the absolute concentration of each of the \(Y\) components in each of the \(X\) mixture samples; P\({}_{\rm X\times A}\) is the proportion matrix (or mixing ratio matrix) of size \(X\times A\), representing the proportion (or contribution) of each of the \(A\) end-member sources in each of the \(X\) mixture samples; N\({}_{\rm A\times Y}\) is the composition matrix of end-members, where each row represents an end-member source and each element denotes the absolute concentration of the \(Y\) components in that source; and e is error matrix of size \(X\times Y\), representing the deviation between the modeled and observed component concentrations.  
Through the Alternating Least Squares algorithm in multivariate mathematical analysis, continuous estimation, assignment and fitting are carried out. Eventually, if e is minimized, the quantity, composition and proportion of the terminal energy are finally calculated.
## 5 Results
### Comparison and Analysis of Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \(\leq\)**5 mmHg** & \(\leq\)**10 mmHg** & \(\leq\)**15 mmHg** \\ \hline \multirow{3}{*}{BHS} & Grade A & 60\% & 85\% & 95\% \\  & Grade B & 50\% & 75\% & 90\% \\  & Grade C & 40\% & 65\% & 85\% \\ \hline TCN-CBAM & SBP Estimation error & 48.74\% & 73.19\% & 87.06\% \\ Model test results & DBP Estimation error & 38.62\% & 88.73\% & 97.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of TCN-CBAM model test results’ accuracy. Figure 8: Error distribution of estimation results from different models. The MAE, STD, and MAPE of all the aforementioned models on the test set are shown in Table 3.  
From the table, it is evident that the proposed TCN-CBAM model in this chapter yielded the best results in both SBP and DBP estimation tasks. For SBP estimation, the MAE, STD, and MAPE stood at 5.3482 mmHg, 8.3410 mmHg, and 3.34%, respectively. While these are respectable figures, they still fall slightly short of the AAMI standard of 5 \(\pm\) 8 mmHg. In DBP estimation, however, the TCN-CBAM model excelled with metrics of 2.1190 mmHg, 3.1795 mmHg, and 2.40%, meeting the stringent requirements set by the AAMI standard. Notably, the TCN_CBAM model's MAE, STD, and MAPE were notably lower than its counterparts. This underscores the model's high precision and stability in blood pressure estimation, effectively tackling intricate blood pressure data and pinpointing crucial features. Furthermore, deep learning models like CNN-LSTM, CNN-GRU, and TCN showcased commendable performance. These models are inherently adept at handling time-series data, readily capturing long-term dependencies and intricate patterns within the dataset. Consequently, they fared well in blood pressure estimation tasks. In contrast, while traditional machine learning models like XGBoost, RF, and KNN had a decent performance, they lagged behind their deep learning counterparts. This disparity could stem from the limitations of traditional models in navigating complex nonlinear relationships. Nonetheless, these models might still prove useful in specific contexts. The linear model, however, fared the worst in our experiment, likely due to its inability to grasp the intricate nonlinear relationships inherent in blood pressure data.