esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Establishment and Evaluation of the Models  
#### 2.4.1 Data Preprocessing and Models Development  
Prior to the modeling process, several correction preprocessing methods as well as their combinations were separately used in order to remove or reduce physical biases such as scattering effects in the spectra. The methods of standard normal variate (SNV), multiplicative scatter correction (MSC), detrend, and the first and second order derivatives (Der1 and Der2) were included in our study. For detailed information about the effect and parameter settings of the different preprocessing methods, please refer to [PERSON] et al. [21]. Performances of all the developed models based on spectra with or without (raw) preprocessing were compared. For further evaluation, only the optimal preprocessing technique was retained. Aforementioned spectral pretreatments were all performed using Unscrambler software (X10.1, CAMO, Trondheim, Norway).  
PLSR is a linear and supervised chemometric algorithm for constructing models in spectral analysis [23]. In this study, the PLSR was adopted to establish the quantitative models between adulteration levels and the extracted spectra of the prepared samples. A leave-one-out cross-validation (LOOCV) approach was also used to minimize the risk of over-fitting. Cross-validation calculates the predictive ability of potential models to optimize the number of latent variables (LVs) in PLSR models. The models' development was all conducted with the help of Matlab software (R2013b, The MathWorks Inc., Massachusetts, USA). Accordingly, three spectral calibration sets with different units, i.e., R, A, and KM, were separately used to establish PLSR models. The established models were indicated as R-PLSR, A-PLSR, and KM-PLSR, respectively.
## 5 The Problems of Crowd Sourced Audio-Visual Evidence
### Collecting and Managing Crowd Sourced Audio-Visual Evidence  
An important problem with crowd sourced evidence relates to the process of receiving, imaging and managing what may become significant numbers of evidence items from numerous sources.  
In a normal investigation, this involves attending a \"crime scene\", seizing, bagging/tagging and then imaging the evidence. In crowd sourced audio-visual evidence, the evidence sources are remotely located and just as evidence from a crime scene must be protected from the likelihood of being tainted or corrupted, so must evidence supplied remotely. This implies that the evidence must be received over a secure channel using protocols such as SSL/HTTPS. This does not of course prevent the tainting and corruption of evidence at either end of the transmission.  
This process must not only be secure, but also be seen to be secure; in other words, users must have confidence in the security as otherwise anxious users may feel uncomfortable in submitting potentially incriminating evidence. This implies that the whole system of encouraging citizens to submit evidence through to the receipt and management of evidence must contain visual and verbal cues that instil confidence in those submitting evidence.  
Added to this--and learning from the VPD experience, we have the problem of concurrency and load management/balancing. The receiving servers must be designed to withstand large and numerous concurrent data uploads in potentially sort periods of time.
## 4 Packet Scheduling Schemes
### Proposed Scheduler (CAQDB)  
Future networks like 5G will serve not only a mixed traffic environment, but also different use cases. These different use cases and traffic have different requirements. It is therefore important to have schedulers that will be able to dynamically fulfil the requirements in terms of throughput and QoS of these different use cases without compromising spectral efficiency. It is on this premise that Channel Aware Queue and Delay Based (CAQDB) scheduler is proposed. The CAQDB scheduler is developed to provide an acceptable trade-off among the system throughput, QoS (delay) and fairness irrespective of the use case. The proposed algorithm uses a cross-layer approach that allows interactions between different layers. From the physical layer the scheduler uses the data rate selected according to the CQI received from the UE/IoRT device. The scheduler does not only use the delay of the Head of Line packet but also instantaneous queue length from the MAC layer. The scheduler also uses the packet's set delay deadline depending on the traffic type to make scheduling decisions. The CAQDB scheduler implements its metric as follows:  
\[m_{i,j}=\frac{R_{i,j}(t)}{T_{i}(t)}\log\left(\frac{Q_{i}(t)}{\tau_{i}-D_{HOL,i} (t)}\right) \tag{7}\]  
Other definitions are the same as previously used and \(Q_{i}(t)\) is the instantaneous queue size of UE/IoRT device \(i\) at TTI \(t\). The CAQDB considers not only the delay but also the queue status in making scheduling decision, this ensures IoRT devices, that are not delay sensitive, are not starved and are given fair allocation of the available resources.
# Dombi Aggregation Operators of Neutrosophic Cubic Sets for Multiple Attribute Decision-Making
## 1 Introduction
Hence, the paper extends the Dombi operations to NCSs and proposes a neutrosophic cubic Dombi weighted arithmetic average (NCDWAA) operator and a neutrosophic cubic Dombi weighted geometric average (NCDWGA) operator. In order to solve MADM problems with neutrosophic cubic information, a MADM method based on the NCDWAA and NCDWGA operators is proposed in this paper. The remainder of the paper is arranged as follows. Section 2 briefly introduces some concepts of NCSs to be used for the following study. Some Dombi operations of NCSs are introduced in Section 3. Section 4 presents the NCDWAA and NCDWGA operators and discusses their properties. In Section 5, we establish a MADM method based on the NCDWAA and NCDWGA operators. Section 6 presents two illustrative examples to demonstrate the effectiveness and feasibility of the proposed method. Conclusions and future research are given in Section 7.
## 3 Results and Discussion
### Effect of Time and Temperature on Adsorption  
Contact time between adsorbents and pollutants is very important for finding the equilibrium time during adsorption experiments. In the present work, the amount of Cr(VI) and Ni(II) adsorbed as a function of time on Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composites were determined as shown in the Figure 7a. All the experiments were carried out by adding 0.035-0.036 g of each Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composites to Cr(VI) and Ni(II) ion solutions. The pH of the Cr(VI) solution was adjusted at 3-4 while that of Ni(II) was adjusted to 7.5. The results show that initially the rate of adsorption was very high on all the adsorbent materials due to the presence of a large number of active sites. With the passage of time, these active sites were filled with Cr(VI) and Ni(II) ions which repelled further adsorption. As a result adsorption slowed down. By close observation of Figure 7a, it is clear that point of equilibrium was reached within 60-90 min for adsorbent materials [73]. Moreover, adsorption capacity of hybrid materials was higher than pure Fe\({}_{3}\)O\({}_{4}\) and PANI. Figure 7b shows that adsorption was high at low temperatures and goes on to decrease as the temperature increased. This behavior showed the spontaneous and exothermic nature of adsorption and strongly correlated with the results shown by the Tempkin adsorption isotherm [74].  
Figure 6: pH effect on removal of (**a**) Cr(VI), (**b**) Ni(II) through Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composites.
## 2 Results and Discussion
### Antileishminal Activity
major_ promastigotes, showing greater potency than bisdemethoxycurcumin (IC\({}_{50}\) = 21.5 \(\pm\) 6.9 \(\upmu\)g/mL) but lower potency than curcumin (IC\({}_{50}\) = 7.8 \(\pm\) 3.3 \(\upmu\)g/mL) [52]. The genus _Leishman_ includes more than 20 pathogenic species [1] and numerous strains with varying sensitivity to antileishman agents, which could partly explain the variability in treatment effectiveness [53]. Both species tested in this study were sensitive to compound **1**. Previous studies have also demonstrated the activity of this compound against promastigotes of _L. amazonensis_[54] and _L. infantum_[55]. For intracellular amastigotes of _L. amazonensis_, photodynamic therapy using nanoparticle-formulated curcumin reduced parasite viability after 24 h of treatment [25]. Intriguingly, despite several reports of curcumin's antileishmanial activity, there is a lack of its activity against intracellular amastigotes of _L. infantum_, the clinically relevant biological form of the parasite. Based on promising results, **1** activity was evaluated against intracellular _L. infantum_ amastigotes, resulting in an IC\({}_{50}\) of 13.6 \(\pm\) 0.7 \(\upmu\)g/mL and an IS of 5.13, with a dose-dependent effect. The reference drug control PAT showed an IC\({}_{50}\) of 18.9 \(\pm\) 4.06 and an IS of 1.63 (Supplementary Material, Figure S6). Both treatments significantly reduced the number of amastigotes per 100 infected macrophages at a concentration of 20 \(\upmu\)g/mL (Figure 4). Figure 4: Effect of curcumin and PAT in intracellular amastigotes amounts after 48 h of treatment in _L. infantum_-infected RAW 264.7 cells. Data are expressed as the mean of two independent experiments, each performed in duplicate. Statistical analysis was conducted using One-Way ANOVA followed by [PERSON]’s post-test, comparing each treatment with untreated cells (control), where (*) = \(p<0.05\).
# Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
## 2 Methods
### Proposed Method  
In this work, we propose an approach to remove the impulse noise with fast adaptive dictionary learning to provide sparsity of images and formulate the denoising problems with the L\({}_{0}\) norms regularizations.  
The flowchart of the whole scheme is summarized in Figure 1. First, a reference image is initially obtained from a noised image using the AMF. Then, the geometric directions are learnt from the reference image to get the adaptive sparse transform is via fast dictionary training. Next, a denoised image is reconstructed using the proposed L\({}_{0}\) norms regularization model.  
In the following, the essential part of the approach will be given in more details.
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
###### Abstract  
Extensive use of unmanned aerial vehicles (commonly referred to as a \"drone\") has posed security and safety challenges. To mitigate security threats caused by flights of unauthorized drones, we present a framework called SENTINEL (**Sec**ure and **E**fficient **a**u**h**NT**cation for **u**N**manned **a**E**rial **v**hic**L**es) under the Internet of Drones (IoD) infrastructure. SENTINEL is specifically designed to minimize the computational and traffic overheads caused by certificate exchanges and asymmetric cryptography computations that are typically required for authentication protocols. SENTINEL initially generates a flight session key for a drone having a flight plan and registers the flight session key and its flight plan into a centralized database that can be accessed by ground stations. The registered flight session key is then used as the message authentication code key to authenticate the drone by any ground station while the drone is flying. To demonstrate the feasibility of the proposed scheme, we implemented a prototype of SENTINEL with ECDSA, PBKDF2 and HMAC-SHA256. The experiment results demonstrated that the average execution time of the authentication protocol in SENTINEL was about 3.1 times faster than the \"TLS for IoT\" protocol. We also formally proved the security of SENTINEL using ProVerif that is an automatic cryptographic protocol verifier.  
unmanned aerial vehicle; authentication; key management; digital certificate; internet of drones +
Footnote †: journal: Applied Soft
# Clustering of Road Traffic Accidents as a Gestalt Problem
## 3 Methods
### Basic Notions  
A road traffic accident \(n_{i}\) is represented as follows:  
\[n_{i}=(id_{i},\varphi_{i},\lambda_{i})\, \tag{3}\]  
where  
* \(id_{i}\) is a unique identification number of \(n_{i}\);
* \(\varphi_{i}\) and \(\lambda_{i}\) are positional coordinates of \(n_{i}\), i.e., latitude and longitude expressed in radians, respectively.  
Spatial distance between traffic accidents \(n_{i}\) and \(n_{j}\) is calculated based on the haversine formula [17]:  
\[d(n_{i},n_{j})=2\cdot R\cdot\text{atan2}(\sqrt{a(n_{i},n_{j})}\cdot\sqrt{1-a(n _{i},n_{j})})\, \tag{4}\]  
where  
\[a(n_{i},n_{j})=\sin^{2}\frac{\varphi_{2}-\varphi_{1}}{2}+\cos\varphi_{1}\cos \varphi_{2}\sin^{2}\frac{\lambda_{2}-\lambda_{1}}{2}\, \tag{5}\]  
function atan2 is an adoption of the arctangent function designed to calculate an unambiguous angle value, and \(R=6371\cdot 10^{3}\)m (i.e., mean Earth radius). In addition, let \(\tau\) be a spatial threshold value representing an input parameter to the clustering algorithm, and let \(N=\{n_{1},n_{2},\ldots,n_{k}\}\) be a set of traffic accidents that occurred in a given period.
## 4 Discussion
### Uncertainties of this Study  
Soil moisture variability is the main driver of crop yield variability and accurate estimates of soil moisture are important for understanding crop status and estimating crop yields [26; 49; 50]. Our soil moisture retrieval results showed that the accuracy of three SM images was acceptable and the maximum R\({}^{2}\) was 0.53 and minimum RMSE was 7.43%. However, large magnitude soil moisture error can bring a large uncertainty to crop models. The soil moisture retrieval error comes from both field measurements and the characteristics of the satellite data. It has been found that VV polarized radiation, which was used in this study, is effective for monitoring early vegetation, whereas it will cause more errors during the later stages of crop development [51].  
In our analysis, the accuracy of the winter wheat yield estimation was improved by the EnKF assimilation method. The R\({}^{2}\) increased from 0.21 to 0.35 and RMSE decreased from 1330 kg/ha to 934 kg/ha and more realistic spatial differences throughout the study area were shown. This indicates the potential of crop yield estimation by combining the WOFOST model and the water cloud model, which jointly used the Sentinel-1 C-band SAR data and Sentinel-2 MSI optical data. However, compared with our previous study about assimilating time series LAI into the WOFOST model using the EnKF method (R\({}^{2}\) = 0.43, RMSE = 439 kg/ha) [1], this study still has a relatively high error (RMSE = 934 kg/ha). The main reasons for the errors are likely from the following points: First, the uncertainty of soil moisture retrieved from the water cloud model is a main source of error. About 7% error for soil moisture is slightly high, because the field capacity of the soil and the wilting point of the crop are around 0.30 m\({}^{3}\)/m\({}^{3}\) and 0.07 m\({}^{3}\)/m\({}^{3}\) in this study area, respectively. Second, the yield breakdown rate, which will cause overestimation of the field measured winter wheat yield, was not considered when calculating the field measured yield. Third, the amount of observation data (there are only three dates of SM images on April 1 st, May 7 th, and June 1 st) was very limited for the crop model to conduct data assimilation. Sufficient observations during the crop growth period are of great importance for data assimilation to improve crop yield estimation. It is proven that the data assimilation algorithm is sensitive to sampling size and update frequency [44] and the EnKF method is limited by the finite ensemble size, which will bring sampling error into the background covariance [52; 53]. Fourth, the soil moisture in WOFOST model is an average quantity, which is obtained by dividing the total water content of the root zone by the root length. In this study, we only used the 0-10 cm soil moisture data in the water cloud model. Thus, it is better to use soil moisture data at different depths.
## 3 Results and Discussion
Therefore, the maximum and standard deviations of the RH were also important parameters and are listed in Table 2. Although the mean RH values of cases D-1, D-2, D-3, and D-4 were less than 3%, the maximum RH values were as high as 22.4-30.4%. In addition, the smallest standard deviation of the RH (i.e., 0.4%) occurred in case D-9, which indicated that the RH uniformity for this case was the best. Furthermore, Table 2 indicates that case D-6 had the best moisture prevention performance, because the smallest values for both the mean and maximum RH values (i.e., 1.3% and 9.5%, respectively) were obtained. Figure 9: RH evolutions as function of CDA flow rate to air curtain at given CDA flow rate of 80 L/min to panel-purge system.
## 3 Feasibility Study on GNM Nmf2  
After processing the electronic density profile of COSMIC based on the kriging interpolation algorithm, the GNM models and the fitting error (root mean square error) were obtained. Figure 2 shows the Nmf2 spatial variation for a period of 0-2 h. In accordance with the spatial distribution of TEC, there was a high electron density on both sides of the geomagnetic equator in the range of 60\({}^{\circ}\)W-120\({}^{\circ}\)E, 30\({}^{\circ}\)N-30\({}^{\circ}\)S. This \"double hump\" structure was influenced by the effect of the magnetic equatorial fountain [35].  
To prove the reliability of Nmf2 construction, the applicability Nmf2 model in different latitudes was verified using the GIM data and the Nmf2 observations. To quantitatively assess the Abel retrieval and kriging interpolation, we set the cell of the GNM with the same size as the GIM (Global Ionosphere Map). All the cells were compared with the corresponding GIM TEC, as shown in Figure 3. The main panel represents the case when the Nmf2 and TEC scatter on the global level (87.5\({}^{\circ}\)N-87.5\({}^{\circ}\)S, 180\({}^{\circ}\)W-180\({}^{\circ}\)E) is statistical. The attached top/bottom panel shows the case when both cell values in the mid-low latitude (60\({}^{\circ}\)N-60\({}^{\circ}\)S) and the high latitude (90\({}^{\circ}\)-60\({}^{\circ}\)N or 90\({}^{\circ}\)-60\({}^{\circ}\)S) are accounted for, respectively. The corresponding correlation coefficients (R) are presented in respective panels. The GNMs (attached top panel) in the mid-low latitude showed good modeling accuracy, with the correlation coefficient increased from 0.9165 (global in the main panel) to 0.9654. In the bottom panel, their correlation became weaker with R = 0.6779. This is because the zones with weak correlations were all adjacent to high-latitude polar regions. Note that, along the descent latitude, the spatial sparsity and error amplification of Nmf2 observations led to Nmf2 models in high-latitude cells (especially in the polar zone) equal to zero or even less than zero, and this is nonexistent in reality. The results presented that the GNM Nmf2 in the mid-low latitude with a superior correlation for GIM TEC had a better modeling accuracy than the other zones. Naturally, this is suitable for pre-earthquake ionosphere analysis.  
The initial GNM model was output under the inertial sun-fixed coordinate system (local time and geomagnetic latitude), and the above operations for analyzing external conditions were completed in this system. In the following pre-earthquake ionosphere analysis, the background variations are analyzed based on an earth-fixed assumption to capture the daily abnormal signals of daily periods.  
Figure 2: Nmf2 spatial variation based on the kriging method (UT 12–14 h).
## 3 Strategy and Performance Analysis of Spread Spectrum Code Acquisition
### Acquisition Success Probability and Incoherent Accumulation Length Analysis  
The system false alarm probability is the same as the unit false alarm probability, that is,  
\[P_{F}=P_{f} \tag{9}\]  
As can be seen from Figure 7, if a false alarm occurs in the frame burst period before the successfully captured decision position, the receiver will directly transfer to the tracking state and cancel the acquisition. This will miss the successful acquisition decision position of the burst signal, resulting in a missed signal. If the number of decisions in a period is \(M=2 NL\), there will be \(M-1\) false alarms of the decision position in the previous frame period of the signal, which may lead to missing alarms. The probability of no missing alarms due to false alarms in the previous frame period is \(\left(1-P_{f}\right)^{M-1}\). Then, the system missing probability, that is, the acquisition failure probability, is the missing probability of the correct decision position plus the failure probability caused by false alarms,  
\[P_{M}=\left(1-P_{f}\right)^{M-1}P_{m}+1-\left(1-P_{f}\right)^{M-1} \tag{10}\]Therefore, the acquisition success probability is the system detection probability, which is \(M-1\) (no false alarm) and \(1\) (no missing alarm), and its calculation formula is as follows:  
\[\begin{split}& P_{acq}=P_{D}=1-P_{M}=(1-P_{f})^{M-1}P_{d}\\ &=(1-e^{-\lambda}\sum\limits_{k=0}^{L-1}\frac{\lambda^{k}}{k!})^{ 2 NL-1}Q_{L}(\sqrt{2L\mu},\sqrt{2\lambda})\end{split} \tag{11}\]  
In addition, it can be seen from Equation (11) that the acquisition success probability is jointly determined by the incoherent accumulation length \(L\) and the normalized decision threshold \(\lambda\). The larger the number of incoherent accumulations, the higher the maximum value of \(P_{acq}\), but an increase in \(L\) will increase the hardware implementation complexity. Based on the goal of improving the success probability of the acquisition of burst spread spectrum signal, the length \(L\) determination method is presented in Section 4.1; a matched filter acquisition implementation method based on high-power clock multiplexing is proposed in Section 4.2 to effectively reduce hardware complexity and resource consumption; and an adaptive decision threshold design method based on the auxiliary channel is proposed in Section 5. Based on the above designs, the success probability of burst spread spectrum acquisition can be optimized.