esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theory of Proposed Method
### Filtering Outliers by Weight Estimation
#### 3.1.2 Iteration between Weight Estimation and Source Location  
Preliminary weights can be obtained after the initial location with equal weights, while they may not be the optimal. First, the location with equal weights is likely to be inaccurate or even incorrect, and the standard deviation \(\sigma\) of the residuals is always large, especially when outliers exists. In this case, it is difficult to distinguish the outliers and minor residuals from all of the observations, because both the thresholds \(\lambda_{m}\) and \(\lambda_{L}\) are high. Second, the residual \(\triangle_{i}\) does not reflect the deviation of the real model; it only describes the deviation of the fitting model. The residuals and the estimated weights will be both incorrect, if the location result of the fitting model deviates from the true source greatly. Therefore, further iterations between weight estimations and source locations are required [54].  
In the process of iterations, the source location at each step needs to solve a weighted least square solution:  
\[\theta=\arg\min\sum_{i=1}^{n}w_{i}(b_{i}-A_{i}\theta)^{2}=\arg\min\sum_{i=1}^ {n}w_{i}{\Delta_{i}}^{2}=\left(A^{\text{T}}WA\right)^{-1}A^{\text{T}}Wb \tag{14}\]  
where \(W\) is the diagonal matrix of weights, and all of the elements of \(w_{i}\) are updated according to Equation (7).  
After several iterations, the optimal weight estimation is obtained, and all of the outliers are identified and filtered.
# SDN-Based Routing for Backhauling in Ultra-Dense Networks
## 8 Conclusions  
This paper dealt with backhaul routing in 5G UDN.  
UDN paradigm is considered a pillar of future 5G networks but its deployment on large scale is challenging. In particular, UDN deployment could lead to a massive traffic forwarding on the backhaul network that may not be able to support all access-point traffic demands.  
This paper investigated the use of the SDN paradigm to have efficient and flexible routing policies. A problem specific for UDNs backhauling has been presented, and a new solution based on a modified Dijkstra algorithm has been proposed with the aim of reducing the traffic demand that the backhaul network is not able to support. The solution is defined for a centralized implementation and for splitting control and data planes following the SDN-approach. In particular, first, the control-flow routes in the backhaul network are identified using a bandwidth-constrained Dijkstra algorithm. The control flows must be always satisfied by the network. The second step allows finding of the routes for the data flows. The algorithm searches successively for each cell for the route that has the minimum cost and that satisfies the data-flow request. If this route does not exist the algorithm looks for the path that minimizes the weighted UTD, i.e., the amount of traffic that the backhaul network is not able to support due to the congestion of one or more links. The UTD is weighted to achieve  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mean Number of Cells** & **FUDM** & **HC** & **MNH** \\ \hline
20 & 88 μs & 100 μs & 105 μs \\
30 & 93 μs & 109 μs & 116 μs \\
40 & 101 μs & 120 μs & 126 μs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average transmission delay of the network for data traffic.  
Figure 7: Jain index of the percentage of UTD of each flow respect its data traffic demand.  
a fair distribution of the unsatisfied demand among different data flows that are allocated on the congested link.  
Presented numerical results have shown the effectiveness of the proposed scheme in case of backhaul network congestion in comparison with traditional shortest-path routing algorithm.  
This work showed the effectiveness of a centralized routing protocol that follows the SDN principles. However, the work should be extended to evaluate the network reactivity, meaning that the reaction time to changing conditions should be evaluated taking into account also the overhead introduced by the SDN message flow.  
Conceptualization, D.M. and R.F.; Methodology, D.M.; Software, L.S.; Investigation, D.M.; Data Curation, L.S.; Writing-Original Draft Preparation, D.M.; Writing-Review & Editing, R.F.; Supervision, D.M.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Image Acquisition and Calibration  
The hyperspectral images of broiler breast fillets were acquired in the National Poultry Research Center, USDA-ARS in Athens, GA. The fillets were scanned by an HSI system in reflectance mode in the VNIR spectral region (400-1000 nm). The hardware of the HSI system consisted of a line-scan hyperspectral camera, a pair of 50 W halogen MR16 lamps (Solux, EiKo, Shawnee, Kans), power supplies, a computer, and a translation stage. The VNIR hyperspectral camera consisted of a spectrograph (ImSpector V10E, Specim, Oulu, Finland), a 12-bit charge coupled device (CCD) sensor (SensiCam QE SVGA, Cooke Corp., Auburn Hills, MI, USA) with optical sensitivity from 400 to 1000 nm (473 spectral bands) and 688 \(\times\) 500-pixel resolution, and a C-mount focusing lens (XNP 1.4/17-0303, Schneider Optics, Hauppage, NY, USA). The nominal spectral resolution of the imaging spectrograph was 2.8 nm, and the field of view was 115 mm (width) \(\times\) 93 mm (height) corresponding to 688 lines \(\times\) 500 pixels. Fillets were put on the motorized translation stage under the hyperspectral camera so that image acquisition would proceed by successive line scanning.  
To remove the dark current effect of the camera and to obtain the relative reflectance from each image, image calibration was performed for the raw images. A white reference image was acquired from a white reference panel (\(\sim\)99.9% reflectance) and a dark reference image (\(\sim\)0% reflectance) was obtained by covering the camera with its own opaque cap [39]. A relative reflectance image was calculated using the following equation:  
\[\text{R}_{\text{c}}=\frac{\text{R}_{\text{o}}-\text{D}}{\text{W}-\text{D}} \times 100\% \tag{2}\]  
where R\({}_{\text{c}}\) is the resulting calibrated image and R\({}_{\text{o}}\) is original acquired images. D is the dark current image and W is the white reference image.
## 3 A Brief Overview of AHL Systems in _Burkholderia_
### Plant-Beneficial and Environmental (PBE) Group  
This group of the _Burkholderia_ genus currently includes 29 non-pathogenic species which are most often associated with plants [63]. The AHL QS systems have been identified only in some members of this group. One system related to the LasI/R system of _P. aeruginosa_ is BraI/R, and is highly conserved among all species of this group. The _braR_ and _braI_ genes are transcribed in the same direction and are under strict negative regulation by the RsaL repressor that is present between the _braR_ and _braI_ genes [16] (Figure 7). This arrangement is termed the L1 topology (\(\vec{R}\,\vec{L}\vec{I}\) ).  
Bral produces multiple AHLs, including 3-oxo-C6-HSL, 3-oxo-C8-HSL, 3-oxo-C10-HSL, and 3-oxo-C12-HSL and 3-oxo-C14-HSL. BraR responds best to 3-oxo-C14-HSL meaning that is likely to be the cognate AHL for BraI/R systems [63].  
Some members of the PBE group (mostly from the _B. xenovorans_, _B. graminis_ and _B. phytofirmans_ subclade) have an additional AHL system that resembles LuxI/R pairs in other _Burkholderia_ species. This additional AHL system is named as XenI2/R2 and produces and responds to 3-hydroxy-C8-HSL (Table 6).  
In members of the PBE group, _braI/R_ genes are always present on chromosome 2. The additional QS system XenI2/R2 is present on chromosome 1 and chromosome 3 in _B. rastroiformis_ and _B. xenovorans_, respectively. The position of QS genes in the PBE group within the chromosomes was conserved with respect to the _oriC_ (Figure 8).  
In contrast to members of the BCC group, there is apparently no hierarchical connection between the BraI/R and XenI2/R2 systems. However, an orphan LuxR homolog called BxeR is present in _B. xenovorans_ LB400 and other strains from this cluster (_B. graminis_ C4D1M, _B. rtericola_ LMG30594, _B. phytofirmans_ PsJN and _B. phenoliruptrix_ AC1100) and negatively regulates _xenI2/R2_ [63] (Figure 9).  
Figure 6: Schematic representation of the complex regulatory circuit in _B. pseudomallei_ K92643 [61]. Dashed lines indicate partial regulation.  
Figure 7: Regulatory circuit of BraR/I system in the plant-beneficial _Burkholderia_ group (for example _B. xenovorans_ LB400) [63].
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 4 Evaluation
Since \(r\) of GIFT-64/128 block cipher is 1, 2 instances are required, therefore the number of gates is two times the Table 6 result. This is why GIFT-64/128 block cipher in Table 7 requires more qubits than GIFT-128/128 block cipher. For parallel search, 2(key length)\(\cdot(r-1)\) CNOT gates are additionally used. In Table 7, quantum resources to apply PRESENT and GIFT algorithms to the oracle of [PERSON]'s search are shown. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quantum Circuit** & **Qubits** & **Toffoli Gates** & **CNOT Gates** & **X Gates** & **Circuit Depth** \\ \hline PRESENT-64/80 (This work) & 144 & 2108 & 4683 & 1118 & 311 \\ PRESENT-64/128 (This work) & 192 & 2232 & 4838 & 1164 & 311 \\ GIFT-64/128 (This work) & 192 & 1792 & 1792 & 3261 & 308 \\ GIFT-128/128 (This work) & 256 & 6144 & 6144 & 10,953 & 528 \\ SIMON-64/128 [9] & 192 & 1408 & 7396 & 1216 & 2643 \\ SIMON-128/128 [9] & 256 & 4352 & 17,152 & 4224 & 8427 \\ SPECK-64/128 [12] & 193 & 3286 & 9238 & 57 & - \\ SPECK-128/128 [12] & 257 & 7942 & 22,086 & 75 & - \\ CHAM-64/128 [12] & 196 & 2400 & 12,285 & 240 & - \\ CHAM-128/128 [12] & 268 & 4960 & 26,885 & 240 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of quantum resources to implement PRESENT, GIFT, and other block ciphers.
## 4 Simulation Results
### End-to-End Delay Results  
Based on the cluster heads and the best route selected by the proposed algorithm, we compared LDQSP with non-LDQSP in the simulation of end-to-end delay, in which non-LDQSP is a queuing strategy following the principle of first-come-first-served, and the route is found by the improved ACO algorithm.  
Figure 5a shows the relationship between end-to-end delay and arrival rate. Since the arrival rate represents the number of packets arriving per time unit, the network becomes more congested with the increase in arrival rate. It can be seen from the figure that as the network becomes congested, the end-to-end delay of all services increases and the delay of LDQSP is lower than that of non-LDQSP. Figure 5b shows the error bars of the mean of end-to-end delay. The bars in Figure 5b represent the mean values of end-to-end delay when the arrival rate gradually increases, and the vertical line at the top of the bars represents the standard deviation of end-to-end delay when the arrival rate gradually increases. It can be seen that LDQSP has less delay than non-LDQSP, especially for strict security safety, which is decreased by about 50% compared with non-LDQSP. The results confirm that our low-delay queuing strategy can significantly guarantee the high-priority services.  
Figure 6 depicts the variation of end-to-end delay with driving distance. Similar to the packet loss rate results, the end-to-end delay also has an increasing trend, which is due to the increase in communication distance and hop numbers as the train moves away from the station. The result of LDQSP is lower than non-LDQSP, which is consistent with the results in Figure 5. Due to the optimization for different services in LDQSP, the delay has less fluctuation than that of non-LDQSP, which ensures the data packets can be delivered to the receiving end in time.  
Figure 5: The statistical end-to-end delay: (**a**) End-to-end delay of different arrival rates; (**b**) The error bars of the mean value of end-to-end delay.  
Figure 6: End-to-end delay at different positions.
## 1 Introduction  
Organic aerosols (OA) represent an important share of particulate matter (PM) concentrations ranging from 20 to 90% [1]. Chemical transport models (CTM) tend to underestimate mostly the secondary organic aerosols (SOA) [2]. Originally OA schemes in CTM assumed that the primary organic aerosols (POA) are non-volatile and chemically inert. This is the case for the secondary organic aerosol processor (SOAP) scheme [3], which accounts for the formation of the secondary organic aerosols (SOA) species that exist in equilibrium with condensable gasses produced by volatile organic compounds (VOC) oxidation. Photolytic loss of SOA is included in the SOAP scheme.  
However, the POA was found to be mostly semi-volatile, and the vapor phase can undergo oxidation [4]. For this reason, the volatility basis set (VBS) approach was developed and implemented in CTM [5]. VBS provides a unified framework for gas-aerosol partitioning and chemical aging of both POA and SOA. It uses a set of semi-volatile OA species with volatility equally spaced in a logarithmic scale. The VBS species can react further in the atmosphere changing volatility.  
The impact of using different organic aerosol schemes in the performance of CTM is still under investigation, depending on, among other things, the characteristics of the study area and the time period of the year simulated, in addition to the parameterizations applied in the schemes (mostly for VBS). According to a CTM evaluation study for Italy, the SOAP scheme shows a better performance than VBS, although VBS allows a better repartition of POA and SOA than the SOAP scheme [6]. A considerable improvement in the modeled OA mass, compared to previous model applications, has been found in Europe when implementing a modified VBS scheme [7]. The use of the VBS approach to simulate aerosols over Europe (May 2008) has led to improved OA predictions [8].  
In this study, the model CAMx (v7.2) [9] was applied in very high spatial resolution for the largest urban center of Greece, Athens, and its performance on PM\({}_{10}\), PM\({}_{2.5}\), and OA simulation was compared when using two different OA gas-aerosol partitioning and oxidation schemes.
# Effects of Blade Extension on Power Production and Ultimate Loads of Wind Turbines
## 2 Evaluation Method and Transformation Scheme  
### Evaluation Method  
The aerodynamical-mechanical-electrical control integrated dynamic simulation is the basis of load evaluation for wind turbines. Currently, many software packages for load assessment of wind turbines, such as FOCUS, GH-Bladed, FAST, etc. have been developed. For comparison, GH-Bladed 3.82 software is used in this study to keep consistency with the software version adopted in the original design.  
GH-Bladed is an integrated simulation software package developed by Garrad-Hassan company. As an authoritative software for wind turbine design, it was widely used in performance analysis and load evaluation of wind turbines. The aerodynamic loads are calculated based on the blade element momentum theory (BEM), which is the main method for the fast assessment of wind turbine blades. In the momentum theory, the rotor is simplified as an \"actuator disk\" while in the blade element theory, the blades are divided into a number of blade elements. The aerodynamic loads can be quickly calculated by coupling the momentum theory and the blade element theory. The structure of the blades and tower are assumed to be flexible beams and dynamic analysis is performed based on the model analysis [16]. The empirical models and theoretical methods incorporated in GH-Bladed have been extensively validated against monitored data from a wide range of turbines with different sizes and configurations [17; 18]. By now, the software has been widely used by wind turbine design [19; 20] and manufacturing enterprises in the world, such as Goldwind, Envision, Mingyang, GE, etc.  
The ultimate loads and fatigue loads can be obtained by time series analysis of the load responses. The probability distribution of the 50-year load of wind turbines can be estimated resorting to the statistic extrapolation methods based on the 10 min load samples, and such a method was also used and well verified by [PERSON] et al. [21], [PERSON] et al. [22], etc.  
Design load cases are set according to the IEC 61400-1 edition 3 standard [23], which covers all the wind conditions including the normal turbulence model (NTM), extreme turbulence model (ETM), extreme operating gust (EOG), extreme coherent gust with direction change (ECD), extreme wind shear (EWS), extreme wind model (EWM), etc., and the operating conditions including the power production, power production plus occurrence of fault, normal shut down, emergency shut down, idling, etc. The design load cases are shown in Table 1. In this study, the ultimate loads of blade root, hub and tower bottom are mainly focused on. The coordinate systems for different loads are shown in Figure 1.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.3 Impact of Dataset Size on Model Performance  
Beyond examining how the model performance varies across different tasks, it is equally important to consider how external variables, such as the dataset size, contribute to these performance differences. This study also examines how the dataset size influences the relative performance of different machine learning models for time series prediction. To ensure a balanced analysis, dataset ranges were selected to contain an equal number of model comparisons within each range. The analysis considers TBML models and DL models, as well as their top-performing subclasses: SPTB models and RNNs. Figures 10 and 11 present the performance of these model classes across the dataset ranges using FPA and WRA metrics, respectively.  
In the smallest dataset range (0-2173 samples), TBML models perform comparably to DL models overall, but RNNs significantly outperform SPTB models, achieving an FPA advantage of 50% and a WRA advantage of 0.425. In the second range (2173-7800 samples),  
Figure 8: FPA scores for each model. Blue bars represent the overall model types, while orange bars highlight the best-performing model classes.  
Figure 9: WRA scores for each model. Blue bars represent the overall model types, while orange bars highlight the best-performing model classes.  
DL models demonstrate a slight edge over TBML models, with FPA and WRA gains of 12.12% and 0.0972, respectively. Within this range, RNNs outperform SPTB models with a WRA gain of 0.1296, while the FPA scores for both subclasses are equal.  
For mid-sized datasets (7800-35,712 samples), TBML models begin to show a slight advantage over DL models, outperforming them by 8.33% in FPA and 0.0417 in WRA. However, RNN models continue to outperform SPTB models, achieving an FPA gain of 5% and a WRA gain of 0.0542. In the second-largest dataset range (35,712-206,573 samples), TBML models maintain a modest advantage over DL models, with FPA and WRA improvements of 4.55% and 0.0462, respectively. Similarly, RNNs outperform SPTB models  
Figure 11: WRA percentage for each model based on dataset size. Blue bars represent the overall model types, while orange bars indicate the best-performing class of models. The dataset size range is exclusive on the lower bound and inclusive on the upper bound.  
Figure 10: FPA percentage for each model based on dataset size. Blue bars represent the overall model types, while orange bars indicate the best-performing class of models. The dataset size range is exclusive on the lower bound and inclusive on the upper bound.  
by 0.0556 in FPA, while both achieve equivalent WRA scores. The largest dataset range (206,573-11,275,200 samples) reveals a more definitive trend. In this range, TBML models outperform DL models by 4.55% in FPA and 15.15 in WRA. Moreover, SPTB models achieve significant gains over RNN models, with an FPA advantage of 16.67% and a WRA advantage of 0.3833.
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 1: What Is the Current State of the Usage Scenarios for Blockchain in Education?
#### 4.1.1 Certificate Issue and Management of Academic Results  
In 21 out of 28 articles (75%), the focus of the publication is a system to issue, manage, share among institutions and verify academic certificates or transcripts.  
Today, most of the certificates are paper-based or electronic and are difficult to preserve, find and use when required (e.g., a professional opportunity, a new educational endeavor, etc.), and authenticity is hard to check, as fake documents can be very realistic. Since there is no central organization that keeps a copy to validate each document, its originality has to be verified against the issuer institution (if still available), and this can be time- and resource-consuming, and involves several bureaucratic tasks.  
To solve this, the main idea behind these contributions is to store the information on a blockchain system, to make it tamper-proof and decentralized, so that data could be retrieved even if the institution or the whole educational system is discontinued. In a nutshell, students could share their curricula trustfully signed by an institution and have their credentials verified by potential employers or other academic institutions, improving their mobility [13], and reducing diploma and credentials fraud, while maintaining the learner's privacy.  
The works reviewed combine several blockchain solutions with other technologies. For example, in two cases, the resulting proposal is compatible with Open Badges [16; 34].
###### Abstract  
Unmanned aerial vehicles (UAVs) are utilized for wireless communication support of Internet of Intelligent Vehicles (IoVs). Intelligent vehicles (IVs) need vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) wireless communication for real-time perception knowledge exchange and dynamic environment modeling for safe autonomous driving and mission accomplishment. UAVs autonomously navigate through dense urban areas to provide aerial line-of-sight (LoS) communication links for IoVs. Real-time UAV trajectory design is required for minimum energy consumption and maximum channel performance. However, this is multidisciplinary research including (1) dynamic-aware kinematic (kinodynamic) planning by considering UAVs' motion and nonholonomic constraints; (2) channel modeling and channel performance improvement in future wireless networks (i.e., beyond 5G and 6G) that are limited to beamforming to LoS links with the aid of reconfigurable intelligent surfaces (RISs); and (3) real-time obstacle-free crash avoidance 3D trajectory optimization in dense urban areas by modeling obstacles and LoS paths in convex programming. Modeling and solving this multilateral problem in real-time are computationally prohibitive unless extensive computational and overhead processing costs are imposed. To pave the path for computationally efficient yet feasible real-time trajectory optimization, this paper presents UAV kinodynamic modeling. Then, it proposes a convex trajectory optimization problem with the developed linear kinodynamic models. The optimality and smoothness of the trajectory optimization problem are improved by utilizing model predictive control and quadratic state feedback control. Simulation results are provided to validate the methodology.  
autonomous navigation; Internet of Things (IoTs); intelligent vehicles (IVs); Internet of Vehicles (IoVs); kinematic-aware path planning; reconfigurable intelligent surface (RIS); sensor networks; trajectory optimization; wireless communication; unmanned aerial vehicles (UAVs) +
Footnote †: journal: Advances in Neural Information Processing Systems