esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 2 ChG Glasses and Fibers  
### Overview of ChG Glasses  
ChG glasses are a non-oxide class of glasses that are mainly composed by one or more chalcogen elements in group VIA of the periodic table, such as sulfur (S), selenium (Se), and tellurium (Te), in combination with other metalloid elements (arsenic (As), antimony (Sb), germanium (Ge), etc.) [21]. ChG glasses have been known for more than half a century as infrared (IR) optical materials.  
Table 1 lists the typical optical parameters of silica and several IR glasses [22]. Several significant features of ChG glasses have made them attractive in the MIR range. A highly important property of ChG glasses is their broad IR transparency range, which extends the long wavelength cut-off edge from 12 to 20 \(\upmu\)m depending on the mass of anionic elements present in the ChG glasses (viz. S, Se, and Te, as shown in Figure 1) [18] covering the third atmospheric transmitting window. The IR coverage of ChG glasses implies potential applications in remote or (bio)chemical sensors and optical communication systems with limitless capacity [7; 23]. ChG glasses possess a high third-order optical nonlinearity up to a thousand times greater than that of silica glasses [19; 24; 25], attributed to their high refractive indices (\(\sim\)2.9 for As\({}_{2}\)Se\({}_{3}\) and \(\sim\)2.5 for As\({}_{2}\)S\({}_{3}\)). Furthermore, low phonon energies, a long multiphoton absorption edge [26], and high power handling also make ChG glasses excellent candidates for MIR nonlinear optics.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Source and Destination Exclusion Using Clouds (Seclouid)  
The _Seclouid_[53] technique has three steps: First step, each node transmits a \"hello message\" to all nodes in the network using flooding routing protocol. The \"hello message\" includes a _TTL_ counter in trying to find the nearby nodes within a specific range \(h\). These nodes create a cloud; if a node of a cloud desires to transmit, it selects some of the cloud members to act as pseudo-sources. The basic idea of pseudo-sources is to generate fake packets using the same transmission rate as the real source. All fake packets will be dropped by the receivers. The sink has its cloud and uses the same procedure of ordinary nodes. In the second step, _SECLOUD_ provides the source node with multiple options. The first option is to communicate with the sink using a single path routing algorithm. The other option is to use delegate sources and delegate sinks. In the second option, the source node assigns some of its cloud members to be delegate sources, and the sink assigns some of its cloud members to be delegate sinks. Therefore, packets can travel between the source and sink through delegate nodes. In the last step, the fake sources and fake sinks that are created in the first step can be utilized to transmit dummy traffic between them. This step is essential to hide the real traffic, which confuses adversaries about the location of the real event. However, the utilized mechanism to create fake clouds is not explained in details. Moreover, the use of the cloud concept might increase the power consumption and overhead of the system.
###### Abstract  
In this study, a nested ensemble filtering (NEF) approach is advanced for uncertainty parameter estimation and uncertainty quantification of a traffic noise model. As an extension of the ensemble Kalman filter (EnKF) and particle filter methods, the proposed NEF method improves upon the ensemble Kalman filter (EnKF) method by incorporating the sample importance resampling (SIR) procedures into the EnKF update process. The NEF method can avoid the overshooting problem (abnormal value (e.g., outside the predefined ranges, complex values) in parameter or state samples) existing in the EnKF update process. The proposed NEF method is applied to the traffic noise prediction on the Trans-Canada Highway in the City of Regina to demonstrate its applicability. The results indicate that: (a) when determining parameters in the traffic noise prediction model, the NEF method provides accurate estimation; (b) the model parameters can be recursively corrected with the NEF method whenever a new measurement becomes available; (c) the uncertainty in the traffic noise model (should be the noise itself) can be well reduced and quantified through the proposed NEF approach.  
Keywords:traffic noise; prediction; ensemble Kalman filter; particle filter; uncertainty +
Footnote †: journal: Applied
## 1 Introduction
Therefore, it is very important to further explore the effect of displays on the accuracy of exocentric distance estimation for the following reasons: First, to date, there has been little agreement on exocentric distance judgment in VR displays, whether the HMD is better than the projection display types or if there no significant difference between them, for example: HMD is better in distance judgment [23], no significant difference between the display [26], and the estimates in the tracked HMD were significantly smaller than the other two displays (monitor and fixed HMD) [19]. Second, with the advancement of VR technologies, VR systems are becoming simpler and more interactive, so that the users not only look around and experience a 3D environment, but also are able to move around the space, interact with targets in the immersive environment and provide guidance. Besides, it has been reported that more natural interactions (direct interaction) improve performance [27] and can be used as a conceivable response method [28]. Despite the fact that interaction is as important as perception, interaction performance in virtual environments, especially with regards to a direct interaction impact on VR displays, has not so far been sufficiently studied when compared to visual perception. In addition, most of the previous studies attempted to measure the accuracy of perception by using verbal, perceptual, and other visually directed techniques--probably because of the technical difficulties in implementing direct reaching tasks [16]. In this manner, the findings are expected to clarify the impact of VR displays on direct interaction or reaching performance. Third, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [26] and [PERSON] et al. [29] have shown the need for assessments of various display technologies, since the accessibility and the overall advancement is promising. [PERSON] et al. [30] and [PERSON] et al. [31] claimed that recent HMDs have enhanced precision. Therefore, the results of this study will be noteworthy in elucidating how the recent improvements of the HMDs influence user exocentric distance estimation and how its accuracy can be compared to an SWD performance. Fourth, we endeavored to explore the impact of exocentric distance, target size, and target distance (egocentric distance) with the hope that all independent variables would influence the accuracy of exocentric distance judgment and position accuracy.
## 5 Discussion
### Effect of Mineral Composition on Shale Adsorption Capacity
There is no correlation between quartz and feldspar content with SSA and \(\mathrm{V_{L}}\). The main reason is that the brittle minerals provide a limited pore SSA, and the methane adsorption is weak. Therefore, the content of quartz and feldspar minerals does not have an obvious effect on the shale adsorption capacity. However, due to the high content of carbonate minerals in individual shale samples, as the carbonate mineral content increases, the proportion of clay minerals may decrease. This results in a reduction in both the SSA and methane adsorption volume of shale, therefore decreasing the shale adsorption capacity [59]. Figure 15: The relationship between brittle minerals, clay minerals, and \(\mathrm{V_{L}}\) for the Taiyuan Formation shale in the SNCB. (**a**) Clay minerals content and SSA; (**b**) Clay minerals content and \(\mathrm{V_{L}}\); (**c**) Brittle minerals content and SSA; (**d**) Brittle minerals content and \(\mathrm{V_{L}}\).
###### Abstract  
Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy. F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions.  
classification; slope stability; tree-based models; random forest; AdaBoost; decision tree +
Footnote †: journal: Applied Sciences
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack
### Design of Control Plane  
Based on the discussion above, the control plane has functions including resource maintenance, routing calculation and path building. In order to implement these functions, the control plane consists of three protocols: the routing protocol, signal protocol and link management protocol.
# NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation
## 1 Introduction  
Artificial Intelligence (AI) is a branch of computer science that is widely studied by scientists and engineers to build machines to mimic human intelligence. Driven by curiosity and demand, humans have been trying to infuse AI into modern machines built to perform labor-intensive tasks. Mainstream research fields related to AI include, but not limited to, computer vision and pattern recognition, robotics and automation, natural language processing and their current real-world applications include Amazon Rekognition, an automated image and video analysis tool, to [PERSON]'s Full Self-Driving software [1; 2].  
Deep Convolutional Neural Network (Deep CNN) is a subset of Deep Neural Network (DNN) popular in the field of AI and commonly used for designing innovative techniques and algorithms for computer vision systems to perform complex operations such as image classification and object detection using multiple layers of neurons to simulate natural vision perception of human beings. Practical applications such as autonomous vehicles and autonomous robots using edge devices require accuracy and inference times close to that of human visual system. Existing real-time object detectors which provide higher accuracy, do not operate in real-time and require multiple Graphics Processing Units (GPU) to train the network on large datasets [3], which has fueled a need to develop efficient algorithms and innovative techniques to detect objects and perceive the surrounding environment using computer vision.  
In this paper, we propose a modern object detector called NextDet, with a goal to improve overall object detection accuracy whilst maintaining computational efficiency ofCondenseNeXt [4], a light-weight CNN, which is designed to reduce number of Floating-Point Operations (FLOPs) and parameters. The work presented within this paper can be summarized into three points as follows:  
1. An efficient and a powerful object detector is being proposed which can be trained on a single GPU.
2. State-of-the-art algorithms and methodologies such as FPN [5], PAN [6], YOLO [7], GIoU_Loss [8], etc. have been modified and implemented into the design of the proposed object detector.
3. Extensive training and evaluation experiments have been performed on two large-scale multi-class datasets: Argoverse-HD [9] and Microsoft COCO [10].
4. The remainder of this paper has been partitioned as follows: Section 2 provides the reader a detailed information about object detection models in general and its related works. Section 3 defines the methodology of the proposed NextDet object detection network. Section 4 evaluates the NextDet object detector by benchmarking on different datasets and Section 5 concludes this paper.
## 4 Discussion
### Spatio-Temporal Modeling of NAIs  
Previously, since complex interactions existed among different factors, and plant photosynthesis was usually ignored, traditional regression analysis failed to detect the NAI dynamics in the specific study area [10; 17]. In this study, when traditional regression models were used, poor predictions of NAI were achieved when plant-photosynthesis-related factors were excluded (R\({}^{2}\) = 0.07). In this study, as an alternative to plant photosynthesis, SIF was innovatively incorporated into the NAI inversion model. Meanwhile, an RF algorithm capable of both classification and regression was adopted to realize a spatially generalized NAI inversion model. The spatial distribution of NAIs was successfully depicted with an R\({}^{2}\) of 0.82 and 0.88 in the model training and verification (Figure 5). Our results once again proved the advantages of RF algorithms in explaining NAI with complex causes [19; 24].  
However, there was still room for improvement in the interpretation of NAIs considering the 88% accuracy in the model verification (Figure 5). One of the possibilities could be attributed to the differences within the vegetation itself. For example, plants with sharp leaves (such as needle and lanceolate leaves) tend to produce more NAIs [20]. Furthermore, even though the remote-sensing-based SIF and other environmental factors well predicted the regional NAI, their vertical distribution was usually ignored, especially in forest land [33; 34], which led to the NAI uncertainties across the canopy profile. However, these uncertainties could not be captured dependent on either the remote sensing data or the single-point site measurements. Secondly, even though the RF model successfully predicted the NAI distribution, its strong dependence on the data that were used in the NAI inversion could lead to overfitting [35], which weakens the applicability of the inverse model. All in all, more studies are still required to improve the accuracy of NAI modeling.
## 3 Results  
### Modeling and Design of the Diffractive Optical Elements  
The stack envisaged consists of two off-axis lens elements, one designed for a blue target wavelength, and one designed for a green, both having a focal point of 5 cm from the plane of the photopolymer element.  
The design considerations are as follows:  
* Photonic structure: Each grating needs to have a tailored range of spatial frequency and slant to produce correct output angle (at that wavelength) for every point on the diffractive lens (Equations (1) and (2))
* Single diffracted beam: The design needs to ensure that each grating is functioning as a volume grating at all points (to avoid multiple diffracted beams) (Equation (4))
* Grating strength: Each grating must have a specific Refractive Index Modulation to produce high DE for its target wavelength (Equation (3))
* Avoiding crosstalk: Each grating must have a sufficiently narrow angular and spectral selectivity curve so that each grating will only re-direct light of the wavelength that it is designed for.  
Target wavelengths [25] 514 nm and 488 nm were chosen to mimic typical green and blue peaks in a white LED spectrum for which close laser wavelengths were available for holographic recording. The design is based on high efficiency with the assumptions that higher diffracted orders are negligible, the grating is uniform though the depth of the polymer layer, and the polymer faithfully records the interference pattern (zero dimensional changes during recording).  
As discussed above, each element consists of a range of grating spatial frequencies, which will change with the design wavelength, the off-axis angle and the aperture and focal length of the lens. In turn, the set of spatial frequencies recorded in the lens influences all of the above design considerations. On-axis elements are not suitable here because of well-understood issues around the limits of volume diffraction [2] that arise where the diffraction angle is very small (near zero spatial frequency). Off-axis elements are, therefore, used to focus light for these applications, so the first design step was to choose an offset angle, or central spatial frequency, that would avoid this. For transmission elements, high spatial frequencies (large offset angles) are less challenging to record so the limits of the low spatial frequency range are explored here.
## 5 Experimental Results
### The Datasets and the Evaluation Metrics
#### 5.1.2 The Analysis of the Domain Shift Factors  
Three factors are responsible for the domain shift between Potsdam (the source domain), and Vaihingen (the target domain): Sensor variation, the variation of class representation, and the variation of resolution. Beginning by the sensor variation factor, Potsdam images are captured using RGB (Red-Green-Blue) sensor while Vaihingen images are captured using IRRG (Infrared, Red and Green) sensor. For example, green color is transformed to red in Vaihingen images. This makes the model which is trained on Potsdam images to fail in recognizing classes normally associated with green color like trees and low vegetation. Concerning the variation of resolution, we note that Vaihingen images are captured by using 9 cm per pixel resolution while those of Potsdam are captured using 5 cm per pixel resolution. This variation affects the ability of the model to recognize classes that are trained on a specific scale of resolution (like cars for example). Passing to the third factor, which is the difference of class representation, it is the most delicate factor to treat. To illustrate this domain shift factor, we can take the case of low vegetation class. Low vegetation in Potsdam are mostly grass areas in the modern town style. In Vaihingen, low vegetation class has different patterns and representations. In fact, they correspond to agricultural zones containing different types of vegetation. This difference of patterns on the same class affects the model ability when passing from a domain to another. We made a careful analysis of the domain between Potsdam and Vaihingen images for the six classes and the results are summarized in Table 2. This table helps us in studying the effect of our algorithm on every domain shift factor.  
Figure 6: Image samples from Vaihingen and Potsdam ISPRS datasets.
## Objectives  
To identify the effect of a promotion of vocal health program on the voice quality of college professors.  
**Introduction**  
College professors use their voice as primary tool of work. Therefore, it is important they keep good vocal performance. Vocal health programs have been widely used to promote voice care and prevent the appearance of vocal problems due to overuse or misuse. In general, vocal health programs have included face to face sessions and practice at home to reinforce training. Currently, the use of technological tools can be used to support vocal health programs in the application of healthy behaviors.  
**Methods**  
Ten professors will participants in the promotion of vocal health program. The program consists of 4 sessions. The first and last session will be face to face and the second and third online. Acoustic measurements will be obtained pre- and post-intervention. In addition, the teachers will give a self-report of the vocal functioning and benefits of the program in each of its modalities (face-to-face + virtual).  
**Expected Results**  
Changes on acoustic voice parameters will be associated with a self-reported perception of better voice quality.  
**Conclusions**  
Currently, the use of technologies has allowed strengthening the daily practice of vocal exercises to improve vocal health. The interaction with virtual tools promotes the adoption of healthy practices in university teachers. In addition, the application of virtual sessions can be used as a strategy for teachers who have difficulty attending face-to-face sessions. However, it is important to highlight the work of the speech and language pathologist in the accompaniment and feedback of the process.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## System Model
### Network Area  
We assume that all sensor nodes are uniformly distributed throughout the network. It is considered that the area of the network is a circle with radius R. The network is divided into K adjacent coronas, rings or annuli. This division must guarantee the maximum energy equilibrium [41]. The sensors are deployed throughout these zones and the sink is located at the center of this area and coronas have the same width (r). Networks with these properties are called simple corona-based WSNs (SCWSNs) [15; 19]. However, in our model the coronas are divided into sectors acting as clusters with a constant angle (\(\theta\)) as shown in Figure 1 which is called a clustered corona-based WSN (CCWSN) [12; 25; 34; 42]. By applying the clustering routing technique in SCWSNs, network scalability and easier management will be guaranteed [38]. The nodes in each cluster send their data packets toward the local CHs. In each round, every CH receives and aggregates the packets from local member nodes and also from outer CHs located in the same sectors. Data packets aggregated by CHs will be sent to their inner CHs after compressing by a factor \(\beta\), similar to the model proposed in [25].Suppose this is a uniform deployment with '\(\theta\)' density, all sensor nodes have the same initial energy (\(\varepsilon\)0) and generate l bit data packet per second.