esa-sceva/satcom-chunk-collection huggingface dataset:
# A High Latitude Model for the E Layer Dominated Ionosphere
## 3 Modeling Approach
### Model Coefficient Computation
#### 3.2.2 ELDI Occurrence Probability Model Coefficients  
As a basis for computing the coefficients of the \(\%ELDI\) model equation, we used the full modeling dataset of profiles without any form of prior outlier filtering.  
To estimate the internal coefficients, we proceeded analogously to their computation for the E layer observables. In this manner, we first set up a cumulative histogram of the used model driver values, subsequently dividing it into 20 bins and then computing the median of the driver values for each bin. Unlike for the E layer observables, for each bin we computed a \(\%ELDI\) value by relating the number of ELDI profiles to the total number of profiles. Subsequently, we fit the model term to the resulting 20 datapoints.  
To compute the external coefficients, we first divided the value range of each model driver into intervals containing approximately the same number of profiles. We refer to a particular combination of these intervals as an interval key. Thereafter, we assigned each profile to such a key, depending on the intervals into which its associated model driver values fell. Below, we give a simple example to demonstrate the assignment of profiles to interval keys. Here, we consider only the three model drivers \(LT\), \(DOY\) and \(F10.7\), whose value ranges have been divided into two, three and three intervals, respectively (Table 1).  
Assuming that, for a given profile, the model drivers have an associated value assignment of \(LT=15.3\), \(DOY=250\) and \(F10.7=85.0\), the profile will be assigned to the interval key \"2-3-1\" based on the indices of the matching intervals.  
After assigning the profiles to the interval keys, we computed the \(\%ELDI\) value for each key as the ratio of the ELDI profile number to the total profile number and also the median value for each driver. Subsequently, we fit the entire equation to the \(\%ELDI\)-model driver combinations of the keys, while keeping the internal coefficients fixed to the previously determined values. This process resulted in all the external coefficients of the submodel.
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 5 Mel-Cepstral Distortion as an Objective Measure  
We use Mel-Cepstral Distortion (MCD) to evaluate the quality of the transformed speech, and to be able to compare our work to other work in the literature. MCD has been used as an objective error measure for evaluating the quality of synthetic voice [10]. It is a measure of the difference between two sequences of mel-cepstra. It is computed as  
\[MCD=(10/\ln 10)\sqrt{2\sum_{i=1}^{25}(mc_{i}^{(t)}-mc_{i}^{(w)})^{2}} \tag{6}\]  
where \(mc_{i}^{(t)}\) and \(mc_{i}^{(w)}\) represent the mel-cepstrum of the target speech and warped speech, respectively.  
We used MCD both as a way of evaluating the performance of different NN architectures, as well as a tool for comparing with previous VT research which reports MCD scores.  
Figure 9: Un-interpolated and interpolated paths. (a) \(\mathbf{a}\) and \(\tilde{\mathbf{a}}\). (b) \(\mathbf{b}\) and \(\tilde{\mathbf{b}}\).
# Contactless Fingerprint Recognition Using Deep Learning--A Systematic Review
## 3 Contactless Fingerprint Capturing Methods
### 3D Contactless Fingerprint Capturing Methods  
Researchers have used lab-developed prototypes of the 3D fingerprint capture approach, which requires (i) photometric stereo techniques, (ii) structured light scanning, and (iii) stereo vision [51]. The photometric stereo-based 3D fingerprint method captures multiple 2D images under heterogeneous illumination using a high-speed camera. Time-of-flight (ToF) is the main principle of this technique, where surface reflectance is measured from fingerprint to light source [52]. This system is low-cost because it uses only one high-speed camera and multiple LEDs. Figure 6 shows the capturing method with camera and finger position.  
The structured light-scanning method consists of several high-speed cameras and a digital light-processing projector [53, 54]. During the capture process, multiple 2D fingerprint images are captured under pattern illuminations, and 3D depth information is calculated through triangulation according to the point correspondences between images [55]. This method can recover ridge-valley details and achieve relatively accurate 3D depth information. However, the hardware system is expensive and bulky [39].  
The stereo-vision-based 3D contactless fingerprint method is usually comprised of two or more cameras [51, 56, 57]. During the capture process, 2D fingerprint images are captured from different views. The 3D fingerprints are reconstructed by calculating 3D depth information between corresponding points according to the triangulation principle. The advantage is that the systems are simple, low-cost, and relatively compact. However, current methods are usually time-consuming because of the extensive computation of the correspondences between pixel points [58]. Table 2 shows the 2D and 3D capturing devices and their approximate cost, with light environment, etc.  
Figure 6: Acquisition of 3D fingerprint using photometric stereo techniques [39].
## 4 Different Landslide Prediction Methods
### Physically Based Models
The TRISHAL landslide-susceptibility algorithm and a real-time tracking system were integrated in this study to create an alert index for emergency preparedness and response by showing the consistency of regional slopes in real time and providing an alert level under rainstorm situations for emergency preparedness and response, respectively. The TRIGRS (transient rainfall infiltration and grid-based regional slope-stability) model and an RGA (real-coded genetic algorithm) backward study were used to evaluate the global hydro-geological variables. Slope stability was studied in relation to rainfall infiltration by TRIGRS, which used real rainfall data to determine the durability of regional slopes. Real-time global slope landslide susceptibility study could not be performed using the TRIGRS tool obtained from the USGS website since it did not include an automated rainfall setting mechanism, as shown in Figure 13. The bulk of the spatial-temporal hydrological parameters (e.g., intrusion, evapotranspiration, subsurface dynamics, and moisture in the soil levels) can be simulated using a triangulated irregular network (TIN)-based real-time integrated basin model (TRIBS) [42; 45]. This model incorporates regional uncertainty in rainfall fields, soil characteristics, and associated moisture to account for the environmental impacts of varied and anisotropic soils [46]. Figure 12: DEM is used to observe the landslides [40].
# 60 Years of Studies into the Initiation of Chromosome Replication in Bacteria
## 3 The DnaA Protein
### Mechanisms Regulating Initiation
#### 3.4.3 DARS1 and DARS2  
Since DnaA-ADP is unable to open _oriC_, the factors that replace the ADP by ATP on DnaA may participate in initiation control. [PERSON]'s group found two loci, termed DnaA-Reactivating Sequences, on the _E. coli_ genome that catalyze this replacement in vitro: DARS1 (a 103 bp sequence) and DARS2 (a 455 bp sequence) [51; 63]. DARS1 and DARS2 contain three and six DnaA boxes, respectively. The presence of either of these sequences on a low copy number plasmid decreases the initiation mass [64]. Finally, a very recent publication demonstrates that the deletion of all four elements (Hda, DAtA, DARS1, and DARS2) does not cause major problems to either replication or division, indicating either that these changes are of limited importance to the cell cycle or that _E. coli_ is able to adapt to their absence [65]. That said, under fast growth conditions, this mutant did have more asynchronous initiations compared with the parental strain.
## 2 Results and Discussion
### Exploring the Volatile Compounds Associated with Baking
#### 2.2.1 Furans and Furanones  
Furan and furanone derivatives are among the most common volatiles of heated foods [20; 66], and give burnt, savory, sweet and caramel aroma to foods [9; 15; 20]. The major furans included 2-pentylfuran (F20) and 2-butylfuran (F14), which have been previously detected in _broas_[27]. 2-Pentyfuran (F20) was particularly abundant, and it is a potent odorant [48], giving floral-fruity notes [60], which may significantly contribute to their aroma. It has been reported as the most common aroma-active furan in wheat bread crumb [60] and a likely contributor to the total aroma and flavor of maize tortilla chips [43] and popcorn [33].  
Several furans with oxygenated substituents, such as furfurals and furanones, were also identified in _broas_. Among all the compounds described in the present work, the highest peak areas were obtained for 2-furanmethanol (F13). Although it is not a very strong odorant [32; 48], this compound may also be relevant for the aroma of _broas_ due to its abundance. It is usually associated with pleasant, creamy and caramel notes [41] and may be relevant to the aroma of popcorn [33]. Other oxygenated furans include 5-methyl-2-furanmethanol (F19), 3-furfural (F10), and isomaltol (F22), which had not been previously reported in _broas_[27]. Among these, 5-methyl-2-furanmethanol showed a relatively low OT and was confirmed as a key aroma compound of Chinese white bread [49]. It was not possible to confirm the OTs of 3-furfural and isomaltol, but they have been considered important compounds to the aroma of popcorn [33] and wheat bread [5; 21], respectively.  
Only two out of the twenty-six furanones detected in the present work have been previously detected in _broas_, namely \(N\)-caprolactone (Fo19) and \(\gamma\)-nonalactone (Fo26). Both are possible contributors to _broa's_ sweet aroma [5; 41] since they were present in high amounts in _broas_ and are potent odorants [44; 47]. \(\gamma\)-Octalactone (Fo23), detected in _broas_ for the first time, may also contribute to their aroma, considering its abundance and low OT.
## 4 Risk Analysis for Current Protocols
### Automated Identification System (AIS)
#### 4.4.2 Spoofing  
We further posit that spoofing attacks for AIS can be rather creative. For example, a spoofing against a ship can be performed by creating a fake \"ship\" with assigned static information, such as vessel's name, identifier, and type of ship [37].
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 10 Conclusions  
We studied protocols for secure bootstrapping of resource-constrained smart home and automation devices. In our study, we looked at secure bootstrapping processes that are defined in standards specifications as well as currently available off-the-shelf devices. We observed that several standards, such as Z-Wave, now realize the need for identifying and configuring individual devices rather than relying on network-wide shared secrets. We then proposed a bootstrapping method that relies on a cloud-based AAA server and EAP. We verified the feasibility of EAP-NOOB as a bootstrapping mechanism by implementing it on resource-constrained devices with an embedded OS. Our implementation also developed a novel OOB channel that relies on a tiny LED bulb and can transfer data at approximately 80 bps, even on an embedded platform. Although secure bootstrapping is still an open research topic, our paper discusses important design principles that can help protocol engineers of forthcoming standards and devices.  
Conceptualization, A.P., E.I, S.L., M.S. and T.A.; Data curation, M.S.; Investigation, A.P.; Methodology, A.P., E.I., S.L. and D.G.-C.; Project administration, T.A.; Resources, E.I., M.S. and T.A.; Software, A.P., E.I, S.L. and D.G.-C.; Supervision, M.S. and T.A.; Validation, A.P. and M.S.; Visualization, E.I, S.L., D.G.-C. and M.S.; Writing--original draft, A.P., E.I, S.L., D.G.-C. and M.S.; Writing--review and editing, A.P. and M.S. All authors made significant contributions to the entire article. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Academy of Finland (grant number 296693), the European Commission through the IoTCrawler project (contract 779852), and the Spanish Ministry of Economy and Competitiveness through the Industrial PhD grant DI-16-08432.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
## 3 Results and Discussions
### KDE, NMEM, NAPS(KDE) and NAPS(NMEM) Comparisons
The MPE accuracy from NMEM, NAPS(KDE) and NAPS(NMEM) are listed in Table 1 for all fourteen distributions considered in test sets 1 and 2 for three sample sizes. Sometimes NMEM does better than NAPS(KDE) and vice versa. We devise a relative error ratio (RER) that divides the MPE of NAPS(NMEM) by the minimum MPE between NMEM and NAPS(KDE). Almost always RER is less than 1, indicating NAPS(NMEM) is overall the best method. Nevertheless, there are a few cases where RER is greater than 1. This can be seen in Figure 14.  
Figure 14: Mean percent error comparisons: Following Figure 8, the MPE for the NAPS(KDE) and NAPS(NMEM) estimators are compared using box and whisker plots for 100 trials: (**a**) normal(5, 1) (**b**) uniform(4, 8) (**c**) trimodal normal (**d**) Beta(2, 0.5) (**e**) Beta(0.5, 1.5) (**f**) Beta(0.5, 0.5) (**g**) generalized-Pareto(2, 1, 0) (**h**) and stable(0.5, 0.5, 1, 4). To avoid overlaps in data, NAPS(KDE) and NAPS(NMEM) sample sizes are slightly displaced about the size being plotted. occur when the minimum error from NMEM or NAPS(KDE) is very low. In these cases, the overall accuracy of NAPS(NMEM) can be very good, despite having RER \(>1\). These results suggest that NAPS(NMEM) is generally the most accurate method. Yet Figure 8 shows that for the normal distribution (and possibly other simple distributions), NMEM has greater accuracy than NAPS(NMEM). This occurs because a Gaussian distribution requires three Lagrange multipliers. This global knowledge is lost when the data is split up into small blocks. Special circumstances puts NAPS(NMEM) at a disadvantage.
# Nitrite: From Application to Detection and Development
## 3 Optical-Based Detection Methods
### Detection Methods Based on Colorimetric Methods
It is worth mentioning that researchers have made the first full-wavelength-coverage visible light stimulated dual-channel colorimetric sensor for visual detection [57]. For on-site quantification, the Griess-reagent-doped hydrogel kit combined with a smartphone makes it easier and more environmentally friendly to quantify nitrate and nitrite on-site [58]. A similar method has the advantage of being environmentally friendly [59]. In addition to the kits, there is also research on portable spectrophotometers, which do not differ from commercial kit measurements. Most importantly, they are noteworthy for their low price and simplicity of operation, making them a low-cost alternative to commercial kit testing equipment [60]. Other investigators have also demonstrated an affordable, smartphone-based field sensing tool that provides results for nitrite that are in good agreement with laboratory-grade spectrophotometers [61]. Colorimetric detection is a very widely used method that relies on the shade of the reaction color between the sample and the reagent for quantification [37]. In some of the existing cutting-edge research work, it has been learned that the use of nanomaterials, smartphones, and combination with a variety of modalities is possible. Therefore, it might play an important role in the future of the colorimetric detection of nitrite.
# Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems +
## 2 Model-Based Attack Graph Implementation
### Attack Scenarios Implementation  
In this section, we present the Attack Graphs resulted by running the JKind model checker for the encoded AADL \(CN_{1}\) and \(CN_{2}\) descriptions, respectively, against the security property \(\varphi\)[20]. JKind is an infinite state model checker for checking safety properties of synchronous systems [38], which are expressed in Lustre, a formally defined, declarative, and synchronous dataflow programming language for programming reactive systems [39]. The Verification is based on k-induction and property directed reachability using a back-end Satisfiability Modulo Theories (SMT) solver. A verified property is determined to be true for all runs of the system. A property violation is reported with an explicit Counter-Example (CE), which is given here as an attack scenario (a sequence of attack and recovery actions resulting in system disruption).  
In our work, the \(CN_{1}\) and \(CN_{2}\) descriptive models included entities and their interfaces and connections, which were encoded using Architecture Analysis and Design Language (AADL), within the open-source integrated development environment (Osate2). The _AADL_ models were embedded with the AGREE Annex plug-in [17] that is used to specify the component models and system-level security properties. AGREE also translates the AADL+Annex models and properties to Lustre language, which JKind can verify against a security property of concern and delivers the result as a CE, if it exists [40].  
Figures 2 and 3 show the Attack graphs for \(CN_{1}\) and \(CN_{2}\), respectively [20], capturing the state evolution of the two networks dynamical variables given in the earlier formal description under attack instances. These graphs are visualized using _Unity_ tool that supports two-dimensional (2D) and three-dimensional (3D) graphics, drag-and-drop functionality, and scripting using C# [41].  
From the obtained graphs it can be seen that both networks have six attack scenarios, resulting in networks loss of stability as determined by the unbounded traffic loss over time [42]. Each attack is a sequence of faults and recovery actions occurring sequentially as follow:  
S\({}_{1}\): \(a_{13}\to a_{23}\)[13]  
S\({}_{2}\): \(a_{13}\to a_{12}\)[13]  
S\({}_{3}\): \(a_{12}\to a_{23}\)[12]  
S\({}_{4}\): \(a_{23}\to a_{13}\)[23]  
S\({}_{5}\): \(a_{23}\to a_{12}\)[23]  
S\({}_{6}\): \(a_{12}\to a_{13}\)[12]  
Figure 2: Network \(CN_{1}\) Attack Graph.
###### Abstract  
Thiswork addresses the trajectory-tracking-control problem for a quadrotor unmanned aerial vehicle with external disturbances and parameter uncertainties. A novel adaptive-dynamic-programming-based robust control method is proposed to eliminate the effects of lumped uncertainties (including external disturbances and parameter uncertainties) and to ensure the approximate optimal control performance. Its novelty lies in that two radial basis function neural network observers with fixed-time convergence properties were first established to reconstruct the lumped uncertainties. Notably, they tune only the scalar parameters online and have low computational complexities. Subsequently, two actor-critic neural networks were designed to approximate the optimal cost functions and control policies for the nominal system. In this design, two new actor-critic neural network weight update laws are proposed to eliminate the persistent excitation condition. Then, two adaptive-dynamic-programming-based robust control laws were obtained by integrating the observer reconstruction information and the nominal control policies. The uniformly ultimately bounded stability of the closed-loop tracking control systems was ensured using the Lyapunov methodology. Finally, numerical results are shown to verify the effectiveness and superiority of the proposed control scheme.  
adaptive dynamic programming; fixed-time neural-network-based observer; actor-critic neural networks; quadrotor unmanned aerial vehicle; trajectory tracking control +
Footnote †: journal:
## 5 Conclusions  
This paper systematically addresses the problem of coordinated optimization for electric vehicle charging station siting and capacity determination using a bi-level programming model. At the theoretical level, we innovatively construct an upper-level user equilibrium traffic flow distribution model and a lower-level generalized minimum cost model. The upper-level model aims to minimize total road network travel time. The traffic flow allocation of the electric vehicle is solved as an input to the underlying model. The lower-level model minimizes the generalized cost--integrating driving time, charging waiting time, charging duration, and electricity expenses. The location and capacity of the electric vehicle charging station are obtained by solving. Global optimization is achieved through bidirectional iteration between traffic flow distribution and user cost optimization.  
In the empirical application phase, using the road network of a holiday resort in Hefei as a case study and employing GA, GWO, and PSO algorithms, the optimized solution demonstrates a 91.8% reduction in generalized travel cost, a 37% decrease in charging pile idle rate, and a reduction in average user waiting time to 7.2 min, significantly outperforming the current layout.  
During sensitivity testing, we changed traffic flow by \(\pm\)60%. When traffic flow stayed above 80%, all charging stations remained the same. When traffic flow was near 60%, station B replaced station E to save money. When traffic flow dropped to 40%, station G replaced station F, making the network smaller. But central station D always kept 7 to 10 charging piles in every situation.  
The study demonstrates that this model provides urban EV charging infrastructure planning with an efficient and robust decision-making tool. In the future, the conditions of power grid load will be fully considered, the power grid load balancing model and user behavior preference model will be integrated, and interdisciplinary research on the synergy of \"transportation-energy-behavior\" will be deepened so as to promote the evolution of charging networks from single-point optimization to systemic resilience.  
M.M.: Writing--original draft preparation, Conceptualization, Data curation, Methodology, Writing--reviewing and editing. F.W.: Supervision, Writing--reviewing and editing. L.X.: Methodology, Writing--reviewing and editing. Y.W.: Software and Writing--reviewing and editing. W.L.: Supervision, Methodology, Validation, Software, Writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Hubei Provincial Natural Science Foundation Project-Xiangyang innovation and development joint fund (2025 AFD064).  
The data that support the findings of this study are available from the authors on reasonable request.  
The authors are grateful for the useful contributions made by their project partners.  
The authors declare no conflicts of interest.