esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 2 Error Control Scheme (ECS)
### Channel-Adaptive Techniques  
The wireless channel is time-varying in nature, and this is valid for WSN too. Channel-adaptive techniques are introduced to combat issues with time varying channels [40]. In the presence of multi-path fading and interference, channel adaptation is one way to fully utilise the channel capacity. In this technique, the system adapts system parameters (such as transmission power, code rate, modulation order, coding scheme or any combination of them) according to channel conditions [41].  
The channel-adaptive technique provides flexibility and utilises full channel capacity, but it requires lots of extra work. The additional work includes prediction of channel (channel estimation); a return channel to send channel side information (CSI) from receiver to transmitter; update of system parameter at both transmitter and receiver; and flexible and fast re-configurable transmitter and receiver to support adaptive nature.  
In the channel-adaptive approach, the system parameter varies according to channel condition. For example, when modulation order is an adaptive parameter, the order of modulation is changed according to the condition of the communication link. When the communication channel quality is better (i.e., better SNR), higher order modulation is used, whereas, when the communication link quality is poor, the modulation order is decreased in order to achieve the same BER [42]. Similarly, when the transmission power is an adaptive parameter, the transmission power at transmitter is changed according to channel condition. When the received SNR is good, lower transmission power is used, whereas, when the received SNR is bad, higher transmission power is used in order to achieve the same reliability. Similarly, we can adapt other parameters according to channel condition. Figure 6 illustrates the channel adaptive model where modulation order, code rate and transmission power can be adapted.
## 2 Numerical Model
### Lubrication Force Correction  
Since the fictitious domain method is based on a fixed Cartesian grid, the short-range lubrication force cannot be resolved when the gap distance between the particle and the cylinder is smaller than one mesh. Thus, the lubrication force correction is needed. We denote the particle radius and the cylinder radius as \(a\) and \(R\), respectively, and define the size ratio as \(\beta=R/a\), the gap distance between the particle and cylinder surfaces as \(\delta\), and the normalized gap distance \(\varepsilon=\delta/a\).  
The lubrication force correction has the following form:  
\[\mathbf{F}_{\text{lub}}=-6\pi\mu\mathbf{a}\mathbf{u}_{n}[\lambda(\epsilon)-\lambda( \epsilon_{c})], \tag{27}\]  
where \(\mathbf{u}_{n}\) is the particle normal relative velocity with respect to the cylinder. \(\epsilon_{c}\) is the critical gap below which the lubrication correction is activated, and in the present study we set \(\epsilon_{c}=h/a\), where \(h\) is the mesh size. \(\lambda(\epsilon)\) is a function of the normalized gap distance \(\epsilon\). Since we cannot find the formulation of the lubrication force between a particle and a cylinder, we adopt the formulation for two spherical particles [38]:  
\[\lambda(\epsilon)=\frac{\beta^{2}}{(1+\beta)^{2}}\frac{1}{\epsilon} \tag{28}\]  
Note that the lubrication correction is kept constant for \(\delta<h_{s}\) to account for the lubrication force saturation due to the effect of the surface roughness. The lubrication force is solved implicitly. It should be noted that the lubrication force for two spherical particles is weaker than that for the particle-cylinder at the same gap distance, thus we consider our computation of the particle motion reasonably accurate, not highly accurate.
## 1 Introduction
With the SePCS, the requests of higher type can be executed immediately so as to increase the performance of the system. The key contributions of this study are as follows:  
(1) In the SePCS, user requests are served by grid resources geographically dispersed in different areas as a wireless distributed system to speed up the corresponding services and exploit wireless convenience. (2) Our algorithm is a semi-preemptive approach in which, when available resources are insufficient to service requests, the resource broker will preempt suitable resources that are serving low priority requests so that a high priority request can be served as soon as possible. (3) The SePCS can help vehicular passengers to access local and/or remote resources for their computation needs. The rest of this article is organized as follows: Chapter 2 briefly describes the background and related research of this study. Chapters 3 and 4 introduce the proposed system architecture and the resource-brokering algorithm, respectively. Experimental results are presented and discussed in Chapter 5. Chapter 6 concludes this paper and addresses our future work.
## Materials and Methods
### Conventional Transmit Delay Calcuation  
Figure 4 is a geometrical model of the transmit delay calculation. To excite a plane-wave from a curved array transducer, the transmit time delay for each array element can be calculated by the following:  
\[\tau_{TX}(\theta_{PW},n)=R\cdot\cos\bigl{(}\theta_{PW}-\theta_{de,n}\bigr{)}/c \ (n=1,2,\ldots,N_{ele}) \tag{1}\]  
where \(R\) is the radius of the transducer, \(\theta_{PW}\) is the steering angle of the plane-wave, \(\theta_{ele,n}\) is the angle between the direction vector of the \(n^{\text{th}}\) element and the \(z\)-axis, \(N_{ele}\) is the number of array elements, and \(c\) is the speed of sound [20].  
As indicated in Equation (1), the number of cosine operations is proportional to the number of elements and the number of steering angles. In addition, since curved array imaging has a larger ROI than linear array imaging, a wider range of steering angles is required to interrogate the entire sector-shaped ROI. Therefore, more plane-waves are required to suppress artifacts resulting from grating lobes or side lobes, leading to an increase in the number of calculations, as shown in Figure 5.  
Figure 5a,b show the difference in the number of artifacts between two images acquired using nine plane-waves and 65 plane-waves, respectively, when the scan angle of the ROI is 75.54 degrees.  
Figure 4: Geometrical model of delay calculation for plane-wave transmission in a curved array transducer, where \(R\) is the radius of the transducer, \(\theta_{PW}\) is the steering angle of the plane-wave, and \(\theta_{ele,n}\) is the angle between the direction vector of the \(n^{\text{th}}\) element and the \(z\)-axis.
## 3 Results and Discussion
### Cost, Portability, and Ease of Use  
As part of this study, a user-based study was carried out and administered to the users of the scanners. It included several questions regarding the set-up procedure, ease of use, and accessibility. The users were asked to rate the set-up procedure, ease of use, and accessibility. The users were requested to rate the scanners on the scale of 1 to 10, with 1 pointing at low value, while 10 represented higher value as shown in Figure 18. Notice that the students and researchers who had undergone at least eight hours of training on the scanners or had used scanners in some of their projects were eligible for this study. A total of ten users, based on their experience, rated the different scanners. Once all the ratings were obtained, an average was computed to achieve the final ratings. Based on the study, the qualitative comparison of two scanners was obtained. It was found that the white light scanner was easy to move from one location to another as compared to the laser scanner. The reason can be attributed to its lower weight, as well as lower set up time (or dismantle time. Moreover, white light scanner was easy to use in contrast to the laser scanner. According to the majority of users, the white light scanner was easy to understand; it also had higher accessibility on the part.
### [PERSON] * and [PERSON]  
Laboratoire des Sciences du Climat et l'Environnement, Commissariat a l'Energie Atomique et aux Energies Alternatives--Centre National de la Recherche Scientifique--Universite de Versailles Saint-Quentin-en-Yvelines, Gif sur Yvette Cedex 91191, France; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-1-6908-7889; Fax: +33-1-6908-7716.  
Academic Editors: [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
_Received: 11 February 2015 / Accepted: 20 April 2015 / Published: 27 April 2015_  
**Abstract:** In order to study forests at the global scale, a detailed link budget for a lidar system onboard satellite is presented. It is based on an original approach coupling airborne lidar observations and an end-to-end simulator. The simulator is initialized by airborne lidar measurements performed over temperate and tropical forests on the French territory, representing a wide range of forests ecosystems. Considering two complementary wavelengths of 355 and 1064 nm, the end-to-end simulator computes the performance of spaceborne lidar systems for different orbits. The analysis is based on forest structural (tree top height, quadratic mean canopy height) and optical (forest optical thickness) parameters. Although an ultraviolet lidar appears to be a good candidate for airborne measurements, our results show that the limited energy is not favorable for spaceborne missions with such a wavelength. A near infrared wavelength at 1064 nm is preferable, requiring \(\sim\)100 mJ laser emitted energy, which is in agreement with current and future spaceborne missions involving a lidar. We find that the signal-to-noise ratio at the ground level to extract both the structural and optical parameters of forests must be larger than 10. Hence, considering the presence of clouds and aerosols in the atmosphere and assuming a stationary forest, a good detection probability of 99% can be reached when 4 or 5 satellite revisits are considered for a lidar system onboard the ISS or ICESat, respectively. This concerns \(\sim\)90% of forest covers observed from the lidar, which have an optical thickness less than 3.  
keywords:link budget; spaceborne; canopy; lidar; simulation
# The Role of Microorganisms and Their Antibacterial Compounds in Food Biopreservation
## 3 Antimicrobial Compounds Produced by Bacteria
### Bacteriocins
They have a broad spectrum of antibacterial activity against Gram-positive (inhibits _Listeria monocytogenes_ and _S. aureus coli growth, _Clostridium_ spp. spores and vegetative cells) and Gram-negative bacteria (_S. aureus_, _E. coli_), yeasts, and fungi, and are effective against bacterial spores. Reuterin are water soluble, thermostable, and active in a broad range of pH [80,81]. Lacticins are produced by _L. lactis_, and exhibit antimicrobial activity against _Listeria_, _Bacillus_, and _Clostridium tyrobutyricum_. They were applied in milk and dairy products as bioppreservatives [13,80].
## 2 Background
Furthermore, AHB has found that cellulosic ethanol could achieve carbon neutrality depending on the process details [12]. Long term, however, as electric cars become more common, renewable hydrocarbon fuels will move to a dominant position servicing those sectors-aviation, long-haul trucking, and marine shipping--that are challenging to power with batteries or ethanol [13]. But as of 2017, Ethanol Producer Magazine reported that while commercial-scale, cellulosic ethanol plants had completed construction and were in the final stages of commissioning, none had produced fuels in commercial quantities [14]. Since the magazine's assessment, several refineries have curtailed production or shut down. Accordingly, four propositions have been brought forward to revitalize the business of cellulosic biofuels: (1) Promotion of biofuel's social and environmental attributes, (2) investment in more-cost effective conversion technologies, (3) initiation of the industry using feedstocks with established supply chains, and (4) progression from research and demonstration to commercialization in a step-wise manner [15]. Along the lines of the first three of these propositions, it is widely acknowledged that hybrid poplar has much to offer. First, the environmental impact of poplar biomass production should be an improvement over the first-generation energy crops and second-generation herbaceous perennial ones especially as coppice cycles are lengthened [15; 16; 17]. Reductions in greenhouse gas emissions from hybrid poplar relative to perennial herbaceous biomass crops have been reported to be improved by 26% to 38% [15; 18] and roughly equivalent to those of successional herbaceous plants on marginal lands [19]. Related is the projected annual value of environmental benefits owing from cropland conversion to hybrid poplar biomass production (water quality enhancement and carbon sequestration) that has been estimated between $6 and $50 ha\({}^{-1}\)[20]. Second is the long history of research and development of hybrid poplar specifically as a biomass species beginning with the U.S. Department of Energy's Short Rotation Woody Crops Program that began, in 1990, with yield projections of 9.0 Mg ha\({}^{-1}\) yr\({}^{-1}\) to 16 Mg ha\({}^{-1}\) yr\({}^{-1}\) following a successive six-year rotations and delivered cost of $40 Mg\({}^{-1}\) to $105 Mg\({}^{-1}\)[21; 22]. More recently, yields up to 20 Mg ha\({}^{-1}\) yr\({}^{-1}\) over four consecutive three-year coppice rotations have been measured [23]. Today, research in feedstock genomics and conversion technology at the Department of Energy's Center for Bioenergy Innovation (CBI) features hybrid poplar as one of the project's main feedstocks. Third, hybrid poplar is differentiated among all other purpose-grown, cellulosic feedstock candidates as the only one with a history of commercial use and a proven supply chain. The past reliance on hybrid poplar by the pulp and paper and veneer industries will accelerate the design of similarly efficient logistics for the biorefining industry, encompassing local sourcing and satellite preprocessing [24].
# Student's t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter for GPS Navigation
## 4 Results and Discussion
### Environment 1: Effect of Outliers on Data with Changing Standard Deviation over Time
Smaller kernel bandwidths correspond to the filter's response to outliers, highlighting its ability to adjust dynamically for robust performance under diverse noise conditions. This improvement can be attributed to the use of the Student's t-distribution with a fixed degree of freedom (\(v=3\)) and an adaptive kernel bandwidth, which dynamically adjusts to changing noise conditions. The adaptive mechanism allows STMCCEKF to respond flexibly to varying levels of measurement uncertainty, thereby enhancing its resilience to both noise and outliers. This adaptive strategy significantly improves the filter's ability to suppress measurement anomalies under non-Gaussian conditions. These configurations show that the estimation performance is highly influenced by the choice of kernel mechanism and the ability to adapt to dynamic noise environments. While STMCCEKF filters with fixed kernel bandwidth may suffer from parameter rigidity, STMCCEKF-AKB achieves better overall robustness by combining heavy-tailed modeling with adaptive kernel tuning.
## 4 Case Study 2: Chatbot
### Chatbot Adoption of AI Technologies
#### 4.1.3 Gesture Recognition and Chatbots  
Gesture recognition technology integration into chatbots is another researched combination that is still not widely implemented, but is mentioned in many research papers. Most of these papers also include voice and speech recognition. The idea to use gesture recognition for human-computer interaction is not new (e.g., [65]) and especially for chatbots [66]; however, the actual case studies and pioneering implementations of this technology came much later. [PERSON] et al. [62] report integrating gesture recognition capabilities with a chatbot using capabilities of two external systems: (1) IBM Watson and (2) cleverbot. An advanced chatbot with the abilities of face recognition and hand gesture recognition is described in [PERSON] et al. [67].  
Several recent papers suggested integrating sign language and chatbots for serving people with hearing disabilities. [PERSON] et al. [68] report their experience of integrating American sign language based on fingers and hand gestures. The system they devised has also a full audio interface. [PERSON], [PERSON] and [PERSON] [69] propose a new design of a chat bot system for people with Hearing or Speech Disorder (PHSD) with Leap Motion gesture recognition. Fledgling use of Gesture recognition in chatbots was mentioned in a medical chatbot applications [70] and in a Human-Resource (HR) management application [71].
## 4 Experiments and Results
### Training and Testing Preparation  
A four-class motor imagery (001-2014) dataset can be retrieved from [[https://bnci-horizon-20.eu/database/data-sets](https://bnci-horizon-20.eu/database/data-sets)]([https://bnci-horizon-20.eu/database/data-sets](https://bnci-horizon-20.eu/database/data-sets)) (accessed on 17 September 2024); there are 18 data files for 9 subjects, so 1 subject has 1 training data file ending with T (A01T, A02T, A03T,...) and 1 testing data file ending with E (A01E, A02E, A03E,...). Before training, there are 2 different data preparation procedures:  
* Without a bandpass filter, data are loaded from Matlab files, and for each trial, \(T=1000\), which is equivalent to 4 s from the cue section to the end of the motor imagery section.
* With a bandpass filter, data are exactly the same as above; then the data are processed by a bandpass filter step with the order of the filter \(N\) and the minimum attenuation required in the stop band \(rs\).  
The data preparation without a bandpass filter is utilised for training and evaluating models with different hyperparameters; then the data preparation with a bandpass filter is utilised for evaluating the influence of different bandpass filters on models' performance. After the data preparation stage, the processed data are trained with 3 models, EEG-TCNet, EEF-Conformer, and EEG-TCNTransformer. For each model, there are different configurations, as follows:  
* Regarding EEG-TCNet, each subject is trained with 1000 epochs with a learning rate of 0.001, the criterion is categorical cross-entropy, the optimiser is [PERSON], and the activation function is ELU.
* With regard to EEG-Conformer, each subject is trained with 2000 epochs with a learning rate of 0.0002, the criterion is cross-entropy, the optimiser is [PERSON], the activation function in the convolution module and classification module is ELU, and the activation function in self-attention blocks is GELU.
* With respect to EEG-TCNTransformer, each subject is trained 5000 epochs with a learning rate of 0.0002, the criterion is cross-entropy, the optimiser is [PERSON], the activation function in EEGNet module, TCN module and classification module is ELU, and the activation function in self-attention blocks is GELU.  
Figure 8: Architecture of EEG-TCNTransformer.  
The accuracy metric is utilised to measure the performance.  
\[Accuracy=\frac{Correct\ predictions}{All\ predictions} \tag{12}\]
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 4 Experiment and Analysis
### Experiments and Analysis of Results
#### 4.3.2 Comparative Experiments on Single-Predictive Models
This method results in a reduction of 53.96% in MSE and 31.21% in MAE compared to the original Informer model. The InformerStack model, which is a stacked version of the Informer model, possesses additional layer structures for module stacking; the proposed method demonstrates a reduction of 40.38% in MSE and 36.58% in MAE when compared to the InformerStack model. Overall, it is evident that the proposed Enhanced_Informer model provides higher accuracy in supply volume predictions in the context of single-prediction model comparisons.
###### Abstract  
Complex networks have encouraged scholars to develop an effective method for abstracting and optimizing aviation networks. However, researchers often overlook the aviation network's temporal attribute and treat it as a static network. Aviation networks have strong temporal characteristics and the dynamic connection cannot be realistically described by a static network. It is necessary to more accurately and realistically represent these connections during the operation of an aviation network. This study explored temporal structures of the Chinese aviation temporal network (CATN) based on flight schedules and actual operational time data. Temporal networks based on time windows were represented to analyze the temporal topology features and robustness of the CATN. The results demonstrated the following: (1) based on the spatial-temporal aviation network, there is a morning departure peak (7:00-8:00) and an evening arrival peak at the airline hub (20:00-21:00); (2) examining the centrality of each airport in the CATN at various time intervals exposed fluctuations in their rankings, which could not be identified by a static network, and (3) the robustness of the CATN was found to be unaffected by time windows, but it displayed poor resilience against deliberate attacks, particularly when subjected to betweenness and closeness attacks, which target the network's shortest paths. For obtaining a greater understanding of the operating situation of civil aviation, displaying the topological features and robustness of the temporal network is of great importance.  
Chinese aviation temporal network; temporal network; topological structure; complex networks, robustness +
Footnote †: journal:  
Article  
The Chinese Aviation Network: An Empirical Temporal Analysis on Its Structural Properties and Robustness.
# Enhancing Cybersecurity Through Comprehensive Investigation of Data Flow-Based Attack Scenarios
## 3 Proposed Approach
### Methodology
#### 3.2.1 Threat Analysis Using Extended STRIDE  
This is a crucial step in the proposed approach to identify attack scenarios, drawing on previous studies [13,14] and applying the STRIDE framework. It serves as the first step in our proposed process, and provides valuable insights and results that are utilized in the subsequent steps. We use the STRIDE framework, which relies on the Microsoft Threat Modeling tool and prior knowledge of the system design and requirements. We create a DFD using the tool, which automatically generates a list of potential threats. The outcomes of this step are important inputs for subsequent steps in our approach. These outcomes include the DFD, which shows a visual representation of the system's architecture, helping to identify potential attack surfaces. The other outcome is the threat list, which categorizes threats into Spoofing, Tampering, Repudiation, Information disclosure, Denial of Service, and Elevation of Privileges. This threat list is important for creating the attack impact enumeration, while both the DFD and the threat list are essential in generating the attack profile.