esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we constructed a fingerprint database using ray-traced simulation and model-based interpolation and conducted numerical analyses to optimize the flight path of UAV sensors by evaluating the localization error via PSO for two scenarios, a circular orbit and a free-path orbit. Comparison with fixed sensors installed on the ground showed that UAV sensors can be used for low-cost and highly accurate outdoor localization. Our numerical results revealed the superiority of the proposed optimized routes, especially when the UAV can fly freely in the case of non-circular orbit. Specifically, our numerical results reveal the improved localization estimation error performance of our proposed approach. When evaluating at the 90 th percentile of the error's cumulative distribution function (CDF), the proposed approach can reach an error of 28.59 m with a circular orbit and 12.91 m with a free-path orbit, as compared to the conventional fixed sensor case whose localization estimation error is 55.02 m.  
Since the presented mechanism is general and not only for illegal emitters, the extension of the proposed localization method to authorized (non-illegal) radios is straightforward [4]. However, deploying a UAV sensor for localization purposes in practical environments is expected to face a lot of challenges, including power constraints, energy-efficient route planning, UAV self-localization issues, and security aspects [22, 23]. Such practical considerations will be investigated in our future works. Furthermore, our future prospects also include the development of advanced optimization algorithms and the location estimation of moving radio sources to realize a system capable of tracking and policing illegal radio stations and experimental validations of the proposed technology in realistic environments.  
Figure 13: CDF of localization error (non-circular orbit).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Average [m]** & **CDF 90\% [m]** \\ \hline Fixed & 19.69 & 55.02 \\ \hline Circular orbit & 13.09 & 28.59 \\ \hline Non-circular orbit & 9.66 & 12.91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of localization error (non-circular orbit).  
**Author Contributions:** Conceptualization, G.K.T., T.K., and S.T.; methodology, T.K. and G.K.T.; software, T.K.; validation, T.K. and G.K.T.; formal analysis, T.K.; investigation, T.K. and G.K.T.; resources, G.K.T. and S.T.; data curation, T.K.; writing--original draft preparation, T.K. and G.K.T.; writing--review and editing, G.K.T.; visualization, T.K.; supervision, G.K.T. and S.T.; project administration, G.K.T. and S.T.; funding acquisition, G.K.T. and S.T. All authors have read and agreed to the published version of this manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions to improve the quality of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Introduction to Adsorption Separation Technology
### Polarity Selective Adsorbent  
Polar selective adsorption materials are widely used in practical production because of their simple and convenient synthesis method, recyclability, and high economic value. The main representatives of these materials are activated carbon, alumina, molecular sieve, etc., which are characterized by ultra-high pore size, specific surface area, and surface degree points [23]. Thus, they are widely used in waste gas treatment, wastewater treatment, and other fields. Taking the common activated carbon as an example, it has a large number of pore structures, including V-shaped pores, and uniformly fractured pores [24], and the rich pores provide a large number of reactive active sites for the activated carbon. At the same time, the performance of activated carbon can be further improved by loading functional groups on its surface. [PERSON] [25] studied the adsorption principle and process of nitrogen and sulfur heterocycles in liquid hydrocarbons by introducing organic groups on the carbon surface.  
In the absence of oxygen-containing functional groups on the surface, the adsorption of such materials mainly depends on \(\pi\)-\(\pi\) stacking interaction, and the affinity for adsorbates depends on the size of the \(\pi\) system. Therefore, the selectivity and capacity for hydrocarbon molecules will be greatly improved. For materials with a large number of oxygen-containing functional groups on the surface, the adsorption depends on the acidic sites on the surface and \(\pi\)-H interaction, and its adsorption performance will change with the introduction of different functional groups. [PERSON] et al. [26]. reported that a water-soluble conjugated polyelectrolyte (PBCSO\({}_{3}\)Na) was used to prepare the PBCSO\({}_{3}\)Na/graphene composite sensor. The conjugated polyelectrolyte can also be used as an organic intermediate layer to improve electron transfer. Because the polar groups attached to the main chain can form dipoles at the electrode interface, thus minimizing the interface energy barrier [27], which is conducive to the adsorption of aromatic hydrocarbons by multilayer graphene to monitor the content of aromatic hydrocarbons with high sensitivity.  
The adsorption principle of molecular sieves is to use the acidic sites on the surface. The tri-coordinated Al in the framework is used as the Lewis acid acceptable electron pair, while the aromatic hydrocarbon in the hydrocarbon molecule is the \(\pi\) electron donor. As a Lewis base, it can interact with the Lewis acid active sites in the adsorbent to form coordination bonds, which can be attached to the adsorbent surface through chemical adsorption. [PERSON] et al. [20] used molecular sieves rich in Lewis acid active sites to carry out adsorption tests on different kinds of polycyclic aromatic hydrocarbons and proved that Lewis acid active sites have a good adsorption effect on \(\pi\) electrons.
## 4 Experiment and Result Analysis
### Experimental Settings  
#### 4.1.1 Datasets  
In this paper, we verify the effectiveness of EINET on three publicly available datasets, namely Conll04, SciERC, and ADE.  
**Conll04**: The Conll04 [29] dataset consists of sentences containing entities and relations extracted from news articles. There are four entity types in total, namely _Location_, _Organization_, _People_, and _Other_. There are five relationship types in total, namely _Work-For_, _Kill_, _Organization-based-in_, _Live-In_, and _Location-In_. We choose 1153 sentences as the training set, 288 sentences as the test set, and 20% of the training set as the validation set, which is consistent with [PERSON] et al [30].  
**SciERC**: The SciERC [17] dataset is derived from 500 abstracts of papers in artificial intelligence-related fields. It contains a total of six scientific entity types, namely _Task_, _Method_, _Metric_, _Material_, _Other-Scientific-Term_, and _Generic_, and seven relation types, namely _Compare_, _Conjunction_, _Evaluate-For_, _Used-For_, _Feature-Of_, _Part-Of_, and _Hyponym-Of_. We use the same train (1861 sentences), validation (275 sentences) and test (551 sentences) split as in [17].  
**ADE**: The ADE [31] dataset comes from medical reports describing adverse drug reactions, and it contains two entity types, _Adverse-Effect_ and _Drug_, and one relationship type, _Adverse-Effects_.
## 1 Introduction
These adaptations include a dynamic key reconciliation mechanism within the BB84 protocol that adapts to varying atmospheric conditions, such as fog and rain, optimizing the key generation rates for UAV communication scenarios; a decentralized key management framework utilizing quantum entanglement-based synchronization, enabling secure, scalable multi-UAV networks that do not rely on centralized nodes, which is a significant advancement over traditional QKD implementations; an energy-optimized QKD hardware architecture tailored for UAVs, incorporating compact, low-power single-photon detectors and polarization modulators to address the power and payload limitations unique to UAV platforms; a hybrid encryption framework with adaptive error correction using Low-Density Parity-Check (LDPC) codes, which reduces the computational complexity by up to 25% in dynamic communication environments; and a critical factor for real-time agrotechnical data transmission and extensive simulation and security stress testing under realistic UAV flight conditions, including scenarios such as packet injection, man-in-the-middle attacks, and quantum channel disruption, to empirically validate the robustness and efficiency of the proposed system.
# Piecewise Function Hysteretic Model for Cold-Formed Steel Shear Walls with Reinforced End Studs
## 2 Construction and Shear Behavior of CFS Shear Wall with Reinforced End Studs  
As shown in Figure 1, a typical CFS shear wall with reinforced end studs consists of a steel frame, sheathings, and screw connections. The steel frame is framed with continuous CFRST columns, C-section interior studs, and U-section tracks; the CFRST column is welded face-to-face along the vertical direction to create a rectangular tube using double CFS lipped channel section studs that are conveniently filled with fine aggregate concrete as soon as the wallboards have been fixed on the frame; a sheathing combination of gypsum wallboards (GWB) and bolivian magnesium boards (BMB), which exhibits good fire performance, is adopted with a staggered configuration; sheathings are attached to the frame using self-drilling screws with a 4.8 mm diameter, and all fastener spacings are 150 mm. In addition, hold downs are placed at the bottom and top of the wall to further strengthen the connection between the CFRST columns and tracks.  
An experimental investigation of full-scale CFS shear walls with reinforced end studs, as shown in Figure 1, subjected to cyclic loading was conducted by [PERSON] and [PERSON] [21]. The experimental results indicate that, for screw connections in the perimeter of the walls, the failure modes were either screws being sheared off or screws being pulled through wallboards, both of which were the main reasons for causing the walls' failure in shear; whereas, no obvious deformation was observed in the screw connections of interior studs. In addition, when the wall became damaged, there were a large number of cross inclined cracks in the base-layer wallboards. It is worth mentioning that the experimental results of CFS shear walls sheathed with ribbed steel plates in shear that were demonstrated by [PERSON] et al. [23] also show that the ribbed steel plates had obvious oblique shear buckling. It can be concluded that \"tension fields\" that were similar to those of the steel plate shear wall were formed in the wallboard when the wall with reinforced end studs was in shear.  
Figure 2 describes a typical load-displacement hysteretic curve of the wall with reinforced end studs subjected to cyclic loading. It is observed that the curve of the wall with reinforced end studs has similar pinching characteristic with that of a traditional CFS shear wall with coupled C-section end studs. Screw connections play a governing role in the shear behavior of the wall; once maximum historical displacement of screw connections are exceeded, the screws will squeeze and frictionate the wallboard in the vicinity of the screw holes, resulting in stiffness deterioration of the wall; looseness of those screw holes during cyclic loading generates the slipping and pinching characteristics of the hysteresis curve.  
Figure 1: Construction of a cold-formed steel (CFS) shear wall with reinforced end studs. CFRST, concrete-filled rectangular steel tube.  
Figure 2: Typical load-displacement hysteretic curve. Reprinted from [21]. Copyright 2015, with permission from Elsevier.
## 2 Materials and Methods
### DAP Measurement with Single-Layer Method  
A plane-parallel ionization chamber (Markus chamber type SN001790 TM23343, PTW-Freiburg) was positioned at depth 2 cm in the water tank. The Markus chamber was connected to a Unidos wehline (Series No. T10022-002056, PTW-Freiburg). The front window of the water tank was aligned to the ISO-center. A 10 \(\times\) 10 cm\({}^{2}\) square field with uniform lateral dose profile was scanned with 32 energies ranging from 70.6 MeV to 235 MeV. The spot spacing was 2.5 mm at ISO-center. The Markus chamber was positioned in the center of the square field (Figure 1). Therefore, spot spacing at water depth 2 cm can be computed employing a similar triangle equation. Pressure and temperature correction factor, electrometer calibration factor, polarity correction factor and the recombination correction factor were measured according to the procedure of IAEA TRS-398 [2]. All the measurements were conducted with the fixed beam.  
DAP measured with MC in a uniform field, \(DAP_{w}^{A(Markus)}\) is calculated by:  
\[DAP_{w}^{A(Markus)}=M_{Markus}\times N_{D,w,Markus}\times k_{Q,Q_{0}}\times\Delta x \times\Delta y\times FSCF \tag{1}\]  
where \(M_{Markus}\) is the readout of the ionization chamber. \(N_{D,w,Markus}\) is the calibration coefficient in terms of absorbed dose to water. \(k_{Q,Q_{0}}\) denotes the beam quality correction factor. \(\Delta x\) and \(\Delta y\) are the spot spacing at water depth 2 cm. \(FSCF\) is the field size correction factor (FSCF).
## 3 Public Datasets for Scene Understanding Methods
Each point in the scene point cloud is annotated with one of the 13 semantic classes shown in Figure 5. Besides five furniture classes, S3 DIS contains seven classes (ceiling, floor, wall, column, beam, window, and door) relevant to the construction industry [59]. Because S3 DIS is the only real-world dataset of this size and holds a reasonable number of classes to classify structures of buildings, we adopted the S3 DIS dataset as our reference dataset in the following sections. Figure 3: Distribution of scene-type variety and sensor hardware used to capture 52 famous 3D datasets for scene understanding from Table 1. Declaration: static sensor (S) position, dynamic sensor (D) position from a vehicle-mounted device, aerial laser scanning (ALS), mobile laser scanning (MLS), terrestrial laser scanning (TLS). In the presented datasets, no content of the scene type industrial can be found and only four datasets include scenes showing infrastructure objects. A few publications exist about deep learning approaches for semantic segmentation of bridge components and industrial environments [20, 23, 24], but at the time of this publication, only RC Bridges [86], which contains ten reinforced concrete bridges in the area around Cambridge, is freely available. This dataset is intended to cluster point clouds into the listed bridge parts _slab, pier, pier cap_, and _girder_, but [PERSON] et al. [86] did not publish ground truth semantic annotations. The only two sources featuring semantically annotated point clouds of infrastructure content have low quality, representing urban areas captured from an aerial view with sparse point density and low Level of Detail (LoD) [19, 78]. Object detection on 3D data is becoming increasingly relevant for the construction industry in terms of detecting building parts (e.g., doors, railings, pipes, rebars, and built-in parts) in the context of BIM but is also crucial for autonomous robots on construction sites to detect interactive objects and obstacles. The available datasets predominantly feature models of furniture and decoration from residential buildings [70, 73]. ShapeNet, the most famous 3D-object dataset, holds a collection of human-made objects divided into 270 categories, including vehicles, clothing, weapons, and household equipment, but only a few construction elements, like towers [51]. Figure 4: Examples of poor registration in 3D reconstruction from depth frames in the SceneNN dataset [56]. (**a**) Misalignment of the ceiling in two point clouds, highlighted by the red circle. (**b**) Deviation from the ground plane and large parts missing from the wall behind the desk. Highlighted by the two red lines and the opening angle, the left part of the room tilts, and the floor is not level. Figure 5: Illustrative class–instance distribution in the S3 DIS semantics dataset considering all six areas [59]. The pictured distribution is representative of institutional, educational, and office areas. Other indoor datasets may deviate depending on the use of space.
# Development of a Datalogger for Submarine Glider: Integration of Fault-Tolerant Software Layers
Our novel design, comprised of different layers of software integrated into a datalogger, uses a NAND flash memory chip, which is widely used due to its high speed in writing, reading, and erasing operations, and for its high storage density [38; 39; 40]. However, NAND flash memory chips suffer wear and bit-flipping because their cells are highly dense [41]. The writing/erasing operations degrade the NAND flash memory chip and increase the probability of bit-flipping. Error correction codes (ECC) are used to improve the reliability of NAND flash memory chips, reducing the probability of errors during their lifetime [42; 43; 44]. In this paper, the ECC used is a BCH, which is one of the most recently used in flash memory and digital communications systems [45; 46]. The BCH code used corrects 4 bad bits per 255-bit code. An indicator of memory chip degradation is the decoding time, it means that, an increase in the reading time of the information is a sign that the memory chip is beginning to wear. This article describes the hardware and the different layers of software of the datalogger for orderly and correct storage. The characteristics that are described are as follows:  
* Collection and storage of calibration variables and external signals monitored in different sections of the memory, and for different operating states of the underwater glider. * Method to choose and integrate the BCH module to the storage system and detection of bad blocks. * Integration of a failure protocol in the event of loss of electrical power. * Integration of NAND flash operations: read, write, and erase. Section 2 describes the datalogger system in detail in terms of hardware design, software, choice of ECC, and read and write algorithms on the NAND flash memory chip. Section 3 presents the obtained results for write, read, and ECC operations. The results confirm that the proposed design presents a good performance. In Section 4, the discussion of this project is presented, and in Section 5, the general conclusion is given.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 3 Self-Optimizing Traffic Steering
### Use Cases
#### 3.3.2 Joint Intra-Network and Inter-Network Resource Allocation  
The joint optimization of intra-network and inter-network resource allocation offers more opportunities to enhance resource utilization and thus can further improve system performance. According to the different spectrum types, there are mainly two kinds of inter-network resource allocation in the proposed HetNet. First, licensed resources can be allocated among LTE, LTE-LAA, and mmWave networks to guarantee the control signal transmission. Secondly, unlicensed sub-6 GHz resources can be allocated to WiFi and LTE-LAA networks for resource sharing. On the other hand, intra-network resource allocation can be performed in the same network, where resources are reconfigured among different SBSs, WiFi APs, or mmWave APs, according to their specific requirements. Here, advanced optimization theories can be implemented to devise optimal resource allocation algorithms based on the traffic status and system environment.
# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 1 Introduction
### Present Study  
Recent advancements in ET technology have greatly improved the understanding of driver behavior and distraction, especially on-road, where there are many diverse environmental variables. Although traditional methods can provide valuable insights, they may not fully address the complexities of real-world driving due to reliability issues, and the inability to adapt to the three-dimensional nature of a driver's Field of View (FOV). This gap in the literature underscores a pressing need for innovative solutions capable of overcoming these aggravating factors. Specifically, there exists a critical demand for methodologies that can accurately define and detect AOIs within the cabin space of passenger vehicles, where the windshield's tilt and dashboard architecture introduce unique spatial considerations. Conventional flat-surface marker-based identification systems do not suffice due to potential obstructions, such as the steering wheel or the driver's hands, and external factors like changing light conditions and glare, which compromise detection efficiency. To address these challenges, our study presents a novel approach that utilizes a mathematical model implemented in Matlab. This model is designed to detect markers with exceptional efficiency, accurately identifying complex areas of interest (AOIs) within the driver's operational environment. Our method stands out due to its ability to consider the spatial dynamics of in-vehicle interfaces and the external environmental factors that affect visibility and detection accuracy.  
This research aims to bridge the gap in AOI detection methodologies by providing a robust tool that enhances the reliability and applicability of eye-tracking studies in CDS. Our contribution is expected to have a significant impact on the field by offering a practical solution to one of the most pressing challenges in understanding driver distraction and cognitive load in real-world conditions.
# Movement-in-a-Video Detection Scheme for Sign Language Gesture Recognition Using Neural Network
## 2 Sign Language Recognition Method
### Sign Language Dataset
#### 2.1.1 Argentinian Sign Language (LSA64)  
LSA64 is an Argentinian sign language dataset containing the most common vocabularies in the LSA lexicon. It contains videos of 10 non-expert gesturers performing sign language gestures for 64 vocabularies (including verbs and nouns). Each gesturer performs each sign language gesture five times. The dataset comprises two sets, which differ in terms of the nature of the recording. This is intended to provide differences in illumination between signs. The first and second sets were recorded outdoors (natural lighting) and indoors (artificial lighting), respectively. The second set is divided further into two: one-handed and two-handed gestures. Figure 2a-c show a few examples of the two sets of the LSA64 dataset. Figure 2a is an image captured from a video recorded outdoors (natural lighting), Figure 2b is an image captured from a video of a one-handed gesture recorded indoors (artificial lighting), and Figure 2c is a snippet of video of a two-handed gesture recorded indoors (artificial lighting). Among the datasets used in this study, only the LSA64 dataset includes information on lighting in the videos.