esa-sceva/satcom-chunk-collection huggingface dataset:
# Road-Side Unit Anomaly Detection
## 3 RSU Failure Detection
We can see that RSUs 2, 9, 10, 13, 14, 17, 18, and 23 have the Pearson's correlation coefficient condition as false, meaning that there is no sufficient inverse linear relationship between range and signal intensity in their schemes. For RSU 2, it is a known failure for it is in the interurban context; as for the rest of the RSUs, their context is urban, and the low correlation may be due to the variation in signal intensity due to the environment or due to a failure. We can also observe that for RSUs 2, 6, 13, 14, 19, and 22, the OD inferior to the ID condition is false; for RSU 2, it is due to a failure, whereas for RSU 6 it is likely due to environmental reasons even though its context is interurban. Another explanation could be as the one discussed in [14], where the ID is smaller than the OD due to the time taken to validate the signal coming from the RSU. This is also applicable to RSU 19; both RSUs have significant correlation between signal and range. Meanwhile, RSUs 13, 14, 19, and 22 are urban RSUs, and thus the condition is false due to environmental reasons. Moreover, we used the K-Medoids algorithm in order to classify the behavior of RSUs using the following variables: range, signal strength, vehicle speed, and Pearson's coefficient of the signal-to-range correlation. The latter attribute is repeated for all packets from the same RSU. The classification results are shown in Figure 9, wherein the orange cluster regroups the packets that are transmitted from a failing RSU. The Medoids of each cluster are represented by the cyan larger dots. This classification will be enhanced in order to give a more precise classification, which will be used in the future on all vehicles in order to be able to detect non-proper RSUs. Then, an alarm will be sent through a specific message defined in the next section. Figure 4.0 The Correlation between Signal and Distance for a Failing RSU in Reims. Values are normalized between 0 and 1.
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **R@5** & **R@10** & **R@20** & **MRR@5** & **MRR@10** & **MRR@20** \\ \hline Most Popular & 0.1269 & 0.1830 & 0.2529 & 0.0835 & 0.0908 & 0.0957 \\ II-RNN-AP & 0.3024 & 0.3827 & 0.4653 & 0.1971 & 0.2079 & 0.2136 \\ IP-RNN-LHS & 0.3191 & 0.4013 & 0.4837 & 0.2073 & 0.2182 & 0.2239 \\ \multirow{2}{*}{ASLM-AP} & 0.3255 & 0.3991 & 0.4768 & 0.2336 & 0.2434 & 0.2488 \\  & (+2.02\%) & (\(-\)0.54\%) & (\(-\)1.43\%) & (+12.72\%) & (+11.55\%) & (+11.10\%) \\ \multirow{2}{*}{ASLM-LHS} & **0.3489** & **0.4218** & **0.4982** & **0.2527** & **0.2624** & **0.2677** \\  & **(+9.34\%)** & **(+5.12\%)** & **(+3.00\%)** & **(+21.90\%)** & **(+20.26\%)** & **(+19.56\%)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Group A: Recall and MRR scores for the ASLM models and the baselines for the validation sets in Reddit and Last.fm datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **R@5** & **R@10** & **R@20** & **MRR@5** & **MRR@10** & **MRR@20** \\ \hline Most Popular & 0.1516 & 0.2206 & 0.2913 & 0.1004 & 0.1095 & 0.1145 \\ IP-RNN-AP & 0.1551 & 0.2215 & 0.2966 & 0.1026 & 0.1113 & 0.1165 \\ \multirow{2}{*}{ILS} & 0.1702 & 0.2366 & 0.3116 & 0.1108 & 0.1196 & 0.1248 \\  & **0.1982** & **0.2563** & 0.3282 & **0.1315** & **0.1392** & **0.1442** \\  & **(+16.47\%)** & **(+8.33\%)** & (+5.32\%) & **(+18.68\%)** & **(+16.42\%)** & **(+15.58\%)** \\ \multirow{2}{*}{ASLM-LHS} & 0.1935 & 0.2549 & **0.3302** & 0.1275 & 0.1356 & 0.1408 \\  & (+13.71\%) & (+7.76\%) & **(+5.96\%)** & (+15.04\%) & (+13.38\%) & (+12.82\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Group B: Recall and MRR scores for the ASLM models and the baselines for the validation sets in \(1/10\) Reddit and \(1/80\) Last.fm datasets.
## 4 All-Silicon Photodetector Structures
### MBA-Based Devices
Although MBA has been well-known for quite some time, very few papers concerning silicon-based bulk devices have been reported in the literature, and yet several integrated devices are fabricated and characterized. In 2001, [PERSON] _et al_. [59] observed a remarkable increase of IR absorbance in surface microstructured silicon wafers produced by laser irradiation in the presence of SF\({}_{6}\). They found that in the microstructuring process, a high concentration of impurities and structural defects were incorporated into the silicon lattice, most likely producing bands of impurity states in the band gap that could absorb infrared radiation. The ion channelling spectra obtained from microstructured wafer show a background level higher than that of crystalline silicon but lower than that of amorphous silicon. These measurements indicate that the material is crystalline and is likely to have a high density of defects. The background remains high in ion channelling spectra from samples annealed for 3 hours at 1,200 K, indicating that a significant amount of disorder in the microstructured material is unaffected by annealing under these conditions. Moreover, the resulting subgap absorption is enhanced by the surface texture. These remarkable properties were then exploited for the fabrication of a  
Figure 10: Photospense of a 15-μm-radius-ring detector at \(-\)2 V [54]. Reprinted with permission from [PERSON], _et al_. Copyright © 2010 IEEE. silicon-based detector for infrared radiation [60]. The diode characteristics and responsivity depend strongly on processing conditions including laser fluence, substrate doping, and thermal annealing temperature. Figure 11 shows a schematic diagram of the device, whose active area is approximately 5 mm\({}^{2}\). This optimized sample exhibits responsivity of 50 mA/W at 1,330 nm and 35 mA/W at 1,550 nm at 0.5 V of applied reverse bias; its capacitance is 64 nF/cm\({}^{2}\), while the signal rise time is 10 ns and the fall time is 30 ns. Device leakage current is 120 \(\upmu\)A/cm\({}^{2}\) at 0.5 V of applied reverse bias.
## Materials and Methods
### Synergistic Antimicrobial Effect of Enterocin Gr17 and EOs  
The minimum inhibitory concentrations (MIC) of enterocin Gr17 (ENT) and EOs against the indicator strains were determined using the broth microdilution method in 96-well microplates, according to [PERSON] et al. [19]. Each of the wells was filled with 100 \(\mu\)L of microbial suspensions, with an inoculum concentration of 10\({}^{6}\) CFU/mL. Then, serial dilutions of ENT and EOs in TSB/PDB medium were added to the wells, with the final concentrations of 20, 10, 5, 2.5, 1.25, 0.625, 0.3125, 0.1563, 0.0781, 0.0390, and 0.0195 mg/mL. The wells that only had microbial suspensions (10\({}^{6}\) CFU/mL) were used as a negative control. The microplates of pathogens (_S. aureus_, _B. cereus_, _L. monocytogenes_, _S. enterica_, and _E. coli_) were incubated at 37 \({}^{\circ}\)C for 24 h; the spoilage microorganism (_P. aeruginosa_, _P. fluorescens_, and _S. putefaciens_) were cultivated at 30 \({}^{\circ}\)C; and the plates of fungi (_C. albicans_, _A. flavus_, _P. expansum_, and _A. alternata_) were incubated at 25 \({}^{\circ}\)C for 48 h. After cultivation, the mixtures in the 96-well microplates were measured at 600 nm. The MIC was the concentration of antimicrobial agent causing a 50% growth inhibition for the indicator strains, and it is expressed by mg/mL.  
The synergistic antimicrobial effect of ENT and the EOs was studied using the broth microdilution checkerboard method [19]. Serial dilutions of both antimicrobials were mixed to obtain a fixed amount of the first agent and increasing amounts of the second agent in each row (and column). Eight two-fold serial dilutions (from 2 MIC to MIC/64) of ENT and the EOs were prepared. The 50 \(\upmu\)L dilutions with different concentrations of ENT and EOs were added to the vertical and horizontal orientations of the 96-well microplates, respectively. A total of 100 \(\upmu\)L of fresh microbial suspension (10\({}^{6}\) CFU/mL) was added to each well and cultured, as described above. The combined effects were evaluated in terms of the fractional inhibitory concentration index (FICI). The FICI was calculated as follows:  
\[\text{FICI}=\left(\text{A}\right)/\left(\text{MIC}\right)_{\text{a}}+\left( \text{B}\right)/\left(\text{MIC}\right)_{\text{b}} \tag{1}\]  
(A): MIC of ENT in combination with EO; (B): MIC of EO in combination with ENT; (MIC)\({}_{\text{a}}\) and (MIC)\({}_{\text{b}}\): MIC of the ENT and EO alone, respectively. The FICI was interpreted as follows: \(\leq\)0.5: synergistic activity, 0.5-1: additive activity, 1-2: indifference, \(>\)2: antagonism.
# Input Selection Methods for Soft Sensor Design: A Survey
## 5 Feature Selection
### Hybrid Methods  
Merging different methods often brings better results and less computational demand. Different combinations of input selection methods and classes can be performed to further reduce the number of inputs. Filter methods can be used, such as the pre-filtering method in [1], where correlation coefficients and scatter plots are used as a preselection and then partial correlation and Mallows' \(C_{p}\) statistics are used for input selection. In [193], a combination of wrapper and embedded methods are proposed and called SBS-MLP. It presents low computational cost and tends to equally-perform or outperform other state-of-the-art methods it was compared with.  
In [194], a selection method is proposed in which Nearest Correlation Spectral Clustering Variable Selection (NCSCVS), a method that clusters inputs into groups based on the correlation between variables by nearest correlation spectral clustering, is used as a filter step and then integrated with group LASSO. This method is called Nearest Correlation Spectral Clustering Group LASSO (NCSC-GL). In [195], the NC-based method is used to search for inputs correlated with the output, and then the correlation similarity between the inputs and the output is used to weight the respective input in the model. The method is called Nearest Correlation-Based Input Variable Weighting (NCVW).  
In [29], a self-organizing map (SOM) is used to reduce the dimensionality of the input space and obtain independent inputs. Then, to determine which inputs have a significant relationship with the output, a hybrid approach exploiting GA with a General Regression Neural Network (GRNN) is proposed and called GAGRNN.  
In [83], variable selection is performed by first ranking the candidate inputs exploiting correlation coefficient analysis, then the optimal subset is chosen with a wrapper approach by evaluating the prediction performance of different models.  
The gradient-based leave-one-out gene selection (GLGS) algorithm [196] combines a variant of the Leave-One-Out Cross-Validation (LOOCV) with the Gradient Descent Optimization algorithm to PCA, to perform input dimensionality reduction.  
In [197], an ensemble input set that maintains informative inputs from the original set is formed as a combination of the output feature set of a population of LASSO models. The regularizing factors of these selectors are estimated via cross-validation procedures.  
In [49], filter methods such as CC analysis, ITSS and Lipschitz quotients analysis are combined with either LASSO and plant experts' knowledge, halving the original input set of an SS of a refinery process and outperforming the model trained with all the candidate inputs.  
Other cases of hybrid approaches are reported in [5].
# A New Approach to Image-Based Estimation of Food Volume
###### Abstract  
A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people's health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user's smartphone. From such a video, six frames are selected based on the pictures' viewpoints as determined by the smartphone's orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92% on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s.  
automatic diet monitoring; image analysis; interactive segmentation; image-based modeling; volume estimation +
Footnote †: journal:  
Article
## 5 Experimental Setup Development
### Critical Field and Breakdown Field Analysis of Fabricated FGIL Models  
Concerning the GIL, constraints regarding dielectric design require that the (_E/P_)\({}_{Breakdown}\) should be relatively lesser than the (_E/P_)\({}_{Critical}\) of the respective dielectric gas. The critical reduced field strength (_E/P_)\({}_{Critical}\) at (\(\alpha-\eta\)) = 0 regarding air and SF\({}_{6}\) per the computations using the BOLSIG+ tool were estimated as 30 kV/cm/bar and 89 kV/cm/bar [41]. Here, \(\eta\) represents the electron attachment rate and \(\alpha\) is the coefficient of ionization. Regarding the developed experimental setup, the pressure normalized maximum field strength at \(U_{50}\), (_Emax/P_)\({}_{Breakdown}\), could be evaluated by rewriting Equation (7) as Equation (9), where \(r\) represents the conductor's radius in mm, \(R\) represents the enclosure's radius in mm, and \(P\) is the gas pressure in kPa. Figure 23 shows the computations by Equation (9) regarding the experimental findings of lightning impulse discharge characteristics for air- and SF\({}_{6}\)-insulated FGIL models with simple and SCF-coated stranded conductors. Critical analysis of Figure 23 reveals that the (_E/P_)\({}_{Breakdown}\) was lesser than the (_E/P_)\({}_{Critical}\) for all scenarios, and furthermore, the SCF coating over the  
Figure 21: Lightning impulse discharge investigations regarding air and SF\({}_{6}\) for different FGIL models.  
Figure 22: Measured lightning impulse discharge voltage regarding air and SF\({}_{6}\) in fabricated FGIL model at 2.38 bar.  
stranded conductor enhanced the breakdown field level for both air- and SF\({}_{6}\)-insulated FGIL models. Thus, the developed FGIL models fulfill the above-stated dielectric design requirements for GILs.  
\[\left(E_{max}/P\right)_{Breakdown}\ =\ \frac{Us_{0}}{r\cdot\ln\left(\frac{\pi}{P} \right)\cdot p} \tag{9}\]
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 3 Performance Evaluation of Infrared and Visible Image Fusion
### Qualitative Evaluation (Subjective Evaluation Method)  
The human visual system serves as the foundation for the subjective evaluation method, which assesses the combined image's quality. Professionals in the field will often receive the fused image for subjective review and provide a professional assessment of the quality of the fused image. The standard technique divides image quality into five levels, with each level's ratings being 5, 4, 3, 2, and 1, respectively, denoting \"very good\", \"good\", \"average\", \"poor\", and \"particularly poor\" [69]. A score evaluation of the quality of the fused image will be produced by the expert after scoring the image from their own professional standpoint. The issue with this method is that the results are highly subjective and biased due to the erroneous description. The subjective evaluation approach has the benefits of being logical, uncomplicated, and consistent with the visual features of human eyes, among other things. It must thoroughly examine a number of factors, including spatial details, object size, color brightness, and others. The final evaluation findings will be impacted since many parameters are varied and individual visual variances are significant. A large amount of assessment data from various subjective evaluation individuals is required to improve the accuracy in order to eliminate these uncertainties. This approach, however, is ineffective, time-consuming, and expensive. Therefore, judging the quality of a fusion image solely on the basis of subjective evaluation is unreliable, biased, and ineffective.
## 2 Related Work
### Image-Tracking-Based Indoor Navigation  
[PERSON] et al. [24] pointed out that, unlike sensor-based or network-based methods, vision-based methods provide low-cost and easy-to-use solutions for indoor navigation. The technologies that originated in the field of robotics, such as Visual Inertial Odometry (VIO) [13; 14] and camera-based Simultaneous Localization and Mapping (SLAM) [25], have become commonly available in smartphones [15] and other consumer electronics. VIO combines data from visual sources (like cameras) and inertial measurement units (IMUs, commonly embedded in smartphones) to estimate the position and orientation of a device relative to its environment. The integration of such technologies in smartphones has boosted the precision of AR applications without relying on external references like GPS or beacons. [PERSON] [26] also indicated that AR is easy to employ in museums compared to Virtual Reality (VR) and Mixed Reality (MR) because it is mostly based on the Bring Your Own Device (BYOD) concept as it reduces costs and is easier for users to handle their own device. The survey by [PERSON] et al. [27] revealed that, for the purpose of indoor tracking, communication technology-based tracking can be faulty for applications that need accurate tracking and registration, while image-based tracking can be more accurate and reliable. However, they also pointed out the scalability problems of image-tracking-based solutions.  
[PERSON] et al. [28] proposed a methodology for smartphone-based indoor navigation that integrates AR and the Semantic Web. Their implementation utilizes the Unity3D engine's AR Foundation framework to integrate smartphones' AR frameworks to realize positioning. [PERSON] et al. [29] developed an indoor navigation system with Visual SLAM based on the smartphone camera and gyroscope. The implementations in both researches still need additional QR codes for location initialization, which we consider unnecessary because the reference markers can be any pre-defined image as long as a smartphone camera can track it.  
AR applications commonly place contents close to the initial location, as accumulative positioning errors may disrupt the user experience when the distance to the reference marker becomes longer. We consider this problem a main obstacle to practical image-tracking-based indoor navigation applications, as the users are supposed to move away from the initial marker to the destinations. [PERSON] et al. [29] discussed navigation accuracy from the viewpoint of the relationship between the navigation task success rate and smartphone angles. However, the numerical positioning error and its relation to the moving distance are not mentioned. [PERSON] et al. [28] also analyzed the accumulative positioning error as distance offset in different buildings and tasks. The error has a positive relation with the navigation distance but lacks consistency. The authors considered the illumination conditions to be the cause, but they did not come up with solutions from the application side. We suggest that periodical calibration by tracking reference markers can be a feasible solution.
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 2 LTE  
### The LTE Network  
The LTE network consists of LTE entities that are wireless access network technology and Evolved Packet Core (EPC) entities that are core network technology [13]. UE refers to user devices such as smart-phones among LTE entities. eNB is the base station [14; 15]. eNB provides the user with wireless interface and wireless Remote Resource Management (RRM) features such as radio bearer control, wireless admission control, dynamic wireless resource allocation, load balancing, and Inter Cell Interference Control (ICIC). MME communicates with Home Subscriber Server (HSS) for downloading user authentication and user profiles and executes initial attach and authentication. In other words, authentication of user personal profile information that is saved in HSS is executed using MME; at this time, eNB is used as a connection instrument [16; 17]. However, there are various threats that can harm the entire LTE network by tracing attacks, terminal tracing attacks, and privacy infringement attacks during a LTE initial attach process [18; 19; 20].
## 2 Artificial Neural Network Flow Law
### Artificial Neural Network Equations  
#### 2.1.1 Network Architecture  
As illustrated in Figure 2, the ANN used for computing \(\sigma\) from \(\varepsilon\), \(\dot{\varepsilon}\) and \(T\) is a two hidden layers network.  
Figure 1: Stress/strain curves of P20 alloy extracted from the Gleeble device for the five temperatures (\(T\)) and six strain rates (\(\dot{\varepsilon}\)).  
The input of the neural network is a three component vector noted \(\overrightarrow{y}\). Layer \([k]\), composed of \(n\) neurons, computes the weighted sum of the outputs \(\overrightarrow{\overrightarrow{y}}_{[k-1]}\) of previous layer \([k-1]\), composed of \(m\) neurons, according the equation:  
\[\overrightarrow{y}_{[k]}=\mathsf{w}_{[k]}\cdot\overrightarrow{y}_{[k-1]}+ \overrightarrow{b}_{[k]}, \tag{2}\]  
where \(\overrightarrow{y}_{[k]}\) are the internal values of the neurons resulting the summation at the layer level \([k]\), \(\mathsf{w}_{[k]}\) is the weights matrix \([n\times m]\) linking layer \([k]\) and layer \([k-1]\), \(\overrightarrow{b}_{[k]}\) is the bias vector of layer \([k]\) and \(\overrightarrow{y}_{[k-1]}\) is the output vector of layer \([k-1]\) result of the activation function defined hereafter.  
The number of learning parameters \(N\) for any layer \([k]\) is the sum of the weights and biases in that layer, expressed as \(N=n(m+1)\). Following the summation operation outlined in Equation (2), each hidden layer \([k]\) produces an output vector \(\overrightarrow{\overrightarrow{y}}_{[k]}\) computed through an activation function \(f_{[k]}\), as defined by the subsequent equation:  
\[\overrightarrow{y}_{[k]}=f_{[k]}(\overrightarrow{y}_{[k]}). \tag{3}\]  
This process is repeated for each hidden layer of the ANN until we reach the output layer where the formulation differ, so that the output \(s\) of the neural network is given by:  
\[s=\overrightarrow{w}^{T}\cdot\overrightarrow{\overrightarrow{y}_{[2]}}+b, \tag{4}\]  
where \(\overrightarrow{w}\) is the vector of the output weights of the ANN and \(b\) is the bias associated with the output neuron. As usually done in a regression approach, there is no activation function associated with the output neuron of the network (or some authors consider here a linear activation function).