esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods Revisited  
### Normality Polynomial  
In this section the normality polynomial proposed in [11] is described. The mathematical form is given by  
\[z_{p}=\sum_{j=0}^{r}a_{j}\Big{(}y_{p}\Big{)}^{j} \tag{1}\]  
where \(z_{p}\) denotes \(p\)-fractile of a standard normal variable \(Z\) with probability density function (PDF), \(\Phi(z)\), and cumulative distribution function (CDF), \(\Phi(z);y_{p}\) denotes \(p\)-fractile of a random variable \(Y\) with PDF, \(f_{Y}(y)\), and CDF, \(F_{Y}(y);a_{i},j=1,2,\ldots,r\), are the coefficients of a \(r\)th-order polynomial determined by fractile fitting. The fractile fitting is based on considering the following fractile constraints from a set of independent random observations of \(Y\) (i.e., \(y_{1}\), \(y_{2}\),... \(y_{t}\),... \(y_{n}\)) arranged in ascending order  
\[(y_{i},\ F(y_{i}))=\Big{(}y_{i},\ \frac{i}{n+1}\Big{)},\ \ \ \ \ \ \ \ i=1,\ 2,\ \ldots,\ n \tag{2}\]  
These fractile constraints can be mapped into a normal space by using  
\[z_{i}=\Phi^{-1}(F(y_{i})),\ \ \ \ \ \ \ i=1,\ 2,\ \ldots,\ n \tag{3}\]  
where \(\Phi^{-1}(\bullet)\) denotes the inverse of the standardized normal distribution function. The \(r\)th-order polynomial (Equation (1)) with \(r+1<n\) is used to model the distribution of the transformed random variable \(Y\). By considering the constraints in Equation (2), the coefficients \(a_{j}\) in Equation (1) can be determined using the least square method by minimizing the error \(\varepsilon_{fit}\) given by  
\[\varepsilon_{fit}=\sum_{j=1}^{m}\!\!\left(z_{j}-\sum_{i=0}^{r}a_{i}\Big{(}y_{ j}\Big{)}^{i}\right)^{2} \tag{4}\]  
where \(m\) is the number of constraints. The probability \(P\) (\(Y\leq y_{0}\)) is given by the CDF, i.e., \(F(y_{0})\), and can be computed with  
\[F(y_{0})=\Phi\Big{(}z_{p}\Big{)} \tag{5}\]  
where \(z_{p}\) is obtained by substituting \(y_{0}\) instead of \(y_{p}\) in Equation (1). If \(Y\) is the resulting random variable of the LSF, the probability of failure, \(p_{f}\), is  
\[p_{f}=F(0)=\Phi(a_{0}) \tag{6}\]  
and the reliability index is [17]  
\[\beta=-\Phi^{-1}\Big{(}p_{f}\Big{)} \tag{7}\]  
From Equation (7), it can be inferred that the reliability index can be readily obtained once the coefficients \(a_{j}\) are determined (i.e., \(\beta=-a_{0}\)). This is so, because \(a_{0}\) has a similar meaning to the so-calledgeneralized reliability index [11]. It is noted that the roots of the polynomial are not required [11] (unlike in the method in [13]). In the applications shown later, a third-order normality polynomial is used [11].  
It is pointed out that although the mathematical background of the normality polynomials is not thoroughly described here, it is based on sound grounds [11], like the advance theory of statistics [15; 16] and the fact that fractile constraints hold by a combinatorial argument [18; 19].  
Before going to the applications, the second revisited approach in this study is described in the following section.
## 4 Proposed Sing-Ant Methodology  
In our method, we use the Impinj xArray Reader, where the antenna used is able to produce 52 beams, albeit sequentially, which means that only one antenna can be accessed at a time. Though the xArray gateway reader already has wide-area monitoring, there are some setbacks which need improvement. In this paper, with our proposed method and implementation, the following questions can be solved.  
1. The first issue of xArray is a lack of environmental adaptability. Because there are no reference tags, the existing model cannot factor in the effects of the environment. Thus, when we use the LANDMARC approach, there is no weather bias since all the tags are subject to the exact same weather conditions.
2. Due to the fluctuating nature of RFID tag reads, the current RFID positioning system is highly unstable; usually, the location of the target tags is changed with every antenna beam change. This is solved by having clusters and sectors of antennas, which are more accurate, and also reference tags are used for positioning, so there is less fluctuation.
3. In addition, the operation range can be extended depending on the degree of accuracy required by changing the number of neighbors and reference tags used. The current xArray reader has a fixed location sensing algorithm that cannot factor in the dynamism of different working environments without the use of reference tags.  
The proposed method has the following settings for the xArray reader: the antenna read pattern, unless manually configured, always reads in ascending order. The first four make up the core center of the reading range, with each beam occupying almost a quarter of the central sphere. The rest of the beams are divided into circular rings around these four core beams, and the ring of beams consists of 8 antenna beams. Each ring expands outwards beyond the circle, forming circular as well as linear sections inside the overall functioning area. The ring and central core will be labeled as a cluster, whilst the combinations of linear beams being displaced further from the core will be named sectors. The first four antennas make up the core. This combination of the core and its sectors and clusters, respectively, will be the one used to locate tags in free space. Various combinations of arrangement and weighing will lead to better results and higher accuracy, at the same time accommodating longer ranges without any additional overhead.
## 2 Optimization Model for Reference Trajectory in ARP  
### Fundamental Principles of ARP  
The parking behavior of automatically maneuvering vehicles that meets the parking eligibility criteria from the initiating region to the designated parking area is termed as ARP. Figure 1 illustrates the technical framework for this study.  
Figure 1: Technical framework for this study.  
In the form of ARP, the vehicle that meets the parking conditions in the garage is parked from the initiating region to the permitted parking region. The specific principal schematic principal diagram of ARP is as follows in Figure 2.  
In Figure 2, we assume that during the ARP process, the region by vehicle body coverage is systematically collected in a sequential order \(ns\) times, which is recorded as the set of fixed-point regions \((A_{1},\cdots,A_{is},\cdots,A_{ns})\). Obviously, \(A_{1}\) should be the initiating region, \(A_{ns}\) should be the parking region, and \(P_{1}\), \(P_{is}\), and \(P_{ns}\) are the center points of the fixed-point region, respectively. \(L_{b}\) is the line of garage bottom, \(L_{nn}\) and \(L_{fe}\) are the near and far boundary lines of the parking garage, respectively, and \(P_{nt}\), \(P_{ft}\), \(P_{nb}\), and \(P_{fp}\) are the near corner and far corner of the garage top, near corner and far corner of the garage bottom, respectively. \(T_{R}\) represents the reference trajectory for parking, while \(T_{C}\) denotes the actual trajectory achieved through tracking control [19].
## 4 Experiment and Results
For measurement of the fluorescent light with the detector, a longpass filter with the cut-on wavelength 550 nm (Thorlabs FEL0550) was placed between lens L1 and the upper beam splitter. Furthermore, a voltage amplifier (Femto DLCPA-200) and another photosensor (Hamatsu H10720-20) were necessary to improve the detection, filter the noise, and detect the fluorescent signal. The fluorescent beads are detected by performing lateral scans of the above introduced sample, the resulting lateral scans are shown in Figure 6b. The lateral scans were performed using a movable stage actuated by a Newport XPS system (for details see Section 2). Thereby, the y-axis was used as the slow axis, while the x-axis was used as the fast axis. An axial shift in the z-axis was only achieved due to tuning the voltage applied to the AL. The scans originate from different axial positions above and underneath the glass plate. Simultaneously, the lateral scans images from the illumination beam were captured. These images serve as input images for the trained CNN. The estimations made by the CNN for the in-focus particles are then subtracted to obtain the thickness of the plate in the sample. In this case, the difference of the estimations is 169 \(\upmu\)m, and the ground truth optical pathway is 170 \(\pm\) 5 \(\upmu\)m. So, the length through the glass plate with fluorescent particles on top of the mirror and the glass was estimated properly. Figure 5: Comparison of ground truth and estimation of the CNN, here the elements from the test datasets are marked in black: (**a**) Results after the first training on the first dataset; (**b**) Results before and after transfer learning. All results are obtained on the second dataset. Figure 6: (**a**) Scheme of the prepared sample consisting of a glass plate covered by fluorescent beads placed onto a mirror. (**b**) Lateral scans of fluorescent beads above and underneath the glass plate.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 4 3D NAND Reliability
### Cell Current Fluctuations  
Cell current fluctuations in 3D cells, related to capture and emission of single carriers, present some distinct features that differentiate them from what is usually seen in planar devices. These can be ascribed to the trap location, either at one of the oxide interfaces (tunnel oxide and filler) or within the polysilicon itself, because of the polycrystalline nature of the conductive channel, and to 3D percolation as well as the conduction mechanisms of electrons in the polysilicon. These factors are discussed hereafter.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 5 Proposed Secure and Efficient Authentication Protocol for GLOMONET
### Password Update Process  
In the proposed protocol, a \(M\)U can easily update their password. Figure 5 presents the password change process of the proposed protocol.  
**Step 1:**: The \(M\)U inputs \(ID_{M\text{U}}^{*}\), \(PW_{M\text{U}}^{*}\) and imprints biometrics \(BIO_{i}^{*}\). After that, \(M\)U computes \(\langle R_{i},P_{i}\rangle\)=\(Gen(BIO_{i}^{*})\), \(RPW_{i}^{*}=h(PW_{M\text{U}}||R_{i})\) and sends \(\{ID_{M\text{U}}^{*},RPW_{i}^{*}\}\) to the mobile device.
**Step 2:**: Upon reception of \(\{ID_{M\text{U}}^{*},RPW_{i}^{*}\}\), the mobile device computes \(RID_{i}^{*}=h(ID_{M\text{U}}^{*}||RPW_{i}^{*})\), \(X_{i}^{*}=h(RID_{i}^{*}||RPW_{i}^{*})\oplus A_{i}\), \(B_{i}^{*}=h(RPW_{i}^{*}||X_{i}^{*})\), and the mobile device checks whether \(B_{i}^{*}\stackrel{{?}}{{=}}B_{i}\). If it is correct, the mobile device sends the authentication response message to the \(M\)U.
**Step 3:**: Upon reception of the authentication response message, the \(M\)U inputs a new password \(PW_{i}^{new}\) and imprints a new biometrics \(BIO_{i}^{new}\). \(M\)U computes \(\langle R_{i}^{new},P_{i}^{new}\rangle\)=\(Gen(BIO_{i}^{new})\), \(RPW_{i}^{new}=h(PW_{i}^{new}||R_{i}^{new})\) and sends \(\{RPW_{i}^{new}\}\) to the mobile device.
**Step 4:**: Upon reception of \(\{RPW_{i}^{new}\}\), the mobile device computes \(A_{i}^{new}=X_{i}^{*}\oplus h(RID_{i}^{*}||RPW_{i}^{new})\), \(B_{i}^{new}=h(RPW_{i}^{new}||X_{i}^{*})\) and replaces \(\{A_{i},B_{i}\}\) with \(\{A_{i}^{new},B_{i}^{new}\}\).
## 3 Long-Term Durability of Sensor Coupling and Mounting and of the Measurement Equipment
### Issues of Long-Term Use of Software  
Specific software development for data acquisition, storage and processing instead of using or adapting commercial codes has the advantage of offering full control and flexibility and, hence, independence from software suppliers and their support and updates. Another issue in the long term may be data format and software compatibility if upgrades or new software versions change these. Compatibility with older data formats or data acquired with previous software versions cannot be taken for granted in all cases. However, cost for own developments may be considerable, especially for ensuring proper operation of the software by extensive validation tests and quality control measures.  
For software development in general also applicable to software for AE or GUW data acquisition, data processing and analysis systems, ref. [113] again provides some guidelines. Planning and organizing the software development with a view to high reliability with quality assurance and validation, as well as writing modular and well documented code, are basic recommendations. Potential problems caused, e.g., by inexperienced users and resulting operating errors or even misuse shall also be considered. Users may need a certain level of software support that has to be readily provided when required.  
Depending on the type of computer operating system used, commercial suppliers will stop development and support for \"older\" system operating software at some point in time. The question then is whether a system still operating well will be replaced. With any change or upgrade with implementation of new software for improved performance or elimination of identified bugs or problems, there is a risk to installing new software bugs. Often, older operating systems do not offer the level of \"security\" that will protect the system against interference from outside. If signal transmission and data storage rely on commercial communication and data storage systems, such as internet and cloud services, installation of upgrades or changing to new operating systems can hardly be avoided. Data processing and analysis software continuously developed or periodically upgraded by the AE or GUW monitoring service provider can and shall be updated, if necessary. Information from one service provider indicates that software and hardware upgrades are implemented fairly frequently, often on a yearly or biannual basis. In this case, software and hardware development is performed in house but using standard components as far as possible.
###### Abstract  
Urban vegetation provides ecological services that promote both the ecosystem integrity and human well-being of urban areas, and thus is critical to urban sustainability. As a key indicator of ecological health, net primary productivity (NPP) provides valuable information about the performance of urban ecosystem in response to the changes in urban climate and atmosphere in the 21 st century. In this study, a process-based urban ecosystem model, HPM-UEM (Hierarchical Patch Mosaic-Urban Ecosystem Model), was used to investigate spatiotemporal dynamics of urban ecosystem NPP in the Phoenix city, AZ under three representative concentration pathway (RCP2.6, RCP4.5 and RCP8.5) during the 21 st century. The results indicated that, by the end of the 21 st century, the urban ecosystem's NPP would increase by 14% (in RCP2.6), 51% (in RCP4.5) and 99% (in RCP8.5) relative to that in the late 2000s, respectively. Factorial analysis indicated that CO2 fertilization effect would be the major driver of NPP change, accounting for 56-61% of the NPP increase under the scenarios. Under the RCP2.6 scenario, the strongest NPP increase would be found in the agricultural lands located in the west and southeast of the city. Under the RCP4.5 and RCP8.5 scenarios, the strongest NPP increase would be found in the mesic residential areas that mainly located to the eastern, southern, and southwestern of the Phoenix Mountains Preserve. Although higher ecosystem NPP in the future implies improved ecosystem services that may help to alleviate the heat stress (by providing more shading) and air pollution in the city, this will be at the cost of higher irrigation water usage, probably leading to water shortage in the natural ecosystems in this arid region. Furthermore, this study indicated the rich (such as in mesic residential area) would enjoy more benefits from the improved urban ecosystem services than the poor (such as in xeric residential area).  
net primary productivity (NPP); urban ecosystem; HPM-UEM model; carbon fertilization effect; climate change +
Footnote †: journal: NPP
## 3 Techniques for Field Data Collection
### Contextual and Environmental Factor Data Collection
#### 3.2.2 Reference Recordings  
A soundscape index, or any investigation of the impact of the physical environment on the soundscape, requires consistent and accurate measurement of the environment, most importantly calibrated measurement and recording of the acoustic environment. For this protocol, this has been achieved through the use of separate calibrated binaural recordings and measurements made with a calibrated sound level meter (SLM).
## 1 Introduction
The integration of computer vision technologies in UAVs has precipitated advancements in autonomous positioning, aerial collision avoidance, and other intelligent applications [8]. This undertaking [9] presents an in-depth examination of the deployment of computer vision systems by UAVs, with a particular emphasis on its myriad applications, facilitated through data search, information storage, and chiefly, data processing and analysis. A machine vision system tailored for aerial surveillance [10] is capable of interpreting and processing data acquired by a UAV's on-board infrared camera, which is configured for automatic fire detection applications, wherein an alarm is triggered upon fire identification. The implementation of an open-loop control algorithm is demonstrated to precisely position the TVS laser ray within the UAV's Field of View (FOV), leveraging the theoretical concept of a continuous FOV [11]. In [12] the attitude, altitude, and motion of a UAV can be estimated using a camera mounted on the UAV, employing either catadioptric or fish-eye sensors. The machine vision component of a system devised to enable Unmanned Aerial Systems (UAS) to autonomously maneuver around civil aerodromes, relying solely on a monocular camera, is expounded upon [13]. The paper's organizational structure follows this outline: Section 2 provides an overview of UAV classification, followed by Section 3 which delves into the applications of AI, Deep Learning, and Computer Vision in UAV sensing. Section 4 comprehensively covers UAV power sources, including batteries, fuel combustion engines, solar power, hydrogen fuel cells, methanol fuel cells, supercapacitors, and laser charging technologies. Section 5 focuses on the energy management system, while Section 6 highlights various UAV applications. Lastly, Section 7 addresses current challenges and future trends.
###### Abstract  
With the advancement of new technologies, the number of connected devices, the amount of data generated, and the need to build an intelligently connected network of things to improve and enrich the human ecosystem open new doors to modifications and adaptations of current cellular network infrastructures. While more focus is given to low power wide area (LPWA) applications and devices, a significant challenge is the definition of Internet of Things (IoT) use cases and the value generation of applications on already existing IoT devices. Smartphones and related devices are currently manufactured with a wide range of smart sensors such as accelerometers, video sensors, compasses, gyros, proximity sensors, fingerprint sensors, temperature sensors, and biometric sensors used for various purposes. Many of these sensors can be automatically expanded to monitor a user's daily activities (e.g., fitness workouts), locations, movements, and real-time body temperatures. Mobile network operators (MNOs) play a substantial role in providing IoT communications platforms, as they manage traffic flow in the network. In this paper, we discuss the global concept of IoT and machine-type communication (MTC), and we conduct device performance analytics based on data traffic collected from a cellular network. The experiment equips service providers with a model and framework to monitor device performance in a network.  
Internet of Things (IoT); device performance; low power wide area (LPWA); data analytics; smart sensors; predictive analytics; mobile network operators (MNOs) +
Footnote †: journal: Computer Networks
# Economic Impact Analysis of the Application of Different
## 2 Deterioration Models and Model Evaluation
### Evaluation and Model Parameters  
The proprietary ISEH (Integrovany System Ekonomickeho Hodnotenia) software (Version 2.18, University of Zilina, Zilina, Slovakia, 2021) was developed at the University of Zilina. It was used as an LCCA and CBA tool of the assessment of three case study simulations; the results show the impact of performance models herein presented on economic indicators of a project. The software is currently used by Slovak Road Administration as a tool for evaluation of economic efficiency of investments in repairs and maintenance of roads, while the latest version of the software developed and launched is adapted for use by all administration of roads and highways in the Slovak Republic. All deterioration equations described in previous sections were implemented into ISEH software via Visual Studio 2019.  
LCCA and CBA were carried out following the Methodic handbook for preparation of cost-benefit analysis [30; 31] for review of transport projects investment in phase 2014-2020 and obtain parameters as its net present value (\(NPV\)) and internal rate of return (\(IRR\)).  
Net present value (\(NPV\)) is the difference between the present value of cash inflows and the present value of cash outflows over a period of time. \(NPV\) is used in capital budgeting and investment planning to analyze the profitability of a projected investment or project. \(NPV\) is the result of calculations used to find today's value of a future stream of payments. In our case, we used income in form of user cost savings and outcome in form of investment costs. User costs consist of operational costs (as fuel and spare part consumption), travel time loss (effect of speed reduction), and costs of accidents.  
\[NPV=\sum_{t=1}^{T}\frac{R_{t}}{\left(1+i\right)^{t}} \tag{6}\]where \(i\) is the discount rate (in our case 5%), \(R_{i}\) is net cash inflow-outflows during a single period of time (mainly user costs), \(T\) is the number of time periods (analysis of 10-year period), and \(t\) is the year of the analysis.  
Internal rate of return (\(IRR\)) is a metric used in financial analysis to estimate the profitability of potential investments. \(IRR\) is a discount rate that makes the \(NPV\) of all cash flows equal to zero in a discounted cash flow analysis.  
\[0=\ NPV=\sum_{t=1}^{T}\frac{C_{t}}{\left(1+IRR\right)^{t}}-C_{0} \tag{7}\]  
where \(C_{t}\) is net cash inflow during the period of time, \(C_{0}\) is total initial investment costs, \(IRR\) is the internal rate of return \(T\) is the number of time periods, and \(t\) is the year of the analysis.
# Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link
## 4 Proof of Concept Demonstration  
### Experimental Set-Up  
The experimental setup for a high-speed line-of-sight VLC link using a VCSEL (Thorlabs, L670 VH1) is illustrated in Figure 5. This experiment is performed under ordinary indoor lighting. Two-level pulse amplitude modulation (2-PAM), also known as on-off-keying (OOK), is generated by an arbitrary waveform generator (Tektronix, 70002B) connected to the control PC. Amplified by a wideband amplifier (Mini-Circuits, ZHL-4240+) with a frequency range from 10 to 4200 MHz, the generated communication signal is combined with a suitable DC level by a bias tee (Mini-Circuit, ZFBT-6 GW+), according to the measured L-I curve of the VCSEL. Then, the generated electrical signal is applied to the VCSEL, which is transformed into the optical signal. Optomechanical translation stages and appropriate optics for both the transmitter and receiver are used to couple the light into the fiber-pigtailed photoreceiver (New-Focus, model 1591). The distance between the transmitter and receiver is set to 2 m. An oscilloscope (Tektronix, DPO70404C) captures and samples the electrical signal from the photoreceiver and sends the data to the control PC for offline signal processing via MATLAB. Subsequently, the recovered signal and the demodulated bits are processed for the bit-error-rate (BER) performance test.