esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion  
The results from Sections 5 and 6 indicate that the non-intrusive gPC is a valid method for quantifying uncertainties of time-averages and their sensitivities with respect to system parameters in chaotic systems. The gPC is an established method in propagating and evaluating uncertainties, yet the accuracy of the intrusive version deteriorates when applied to general unsteady (let alone chaotic) systems. This was demonstrated in the Lorenz attractor, where it was shown that the statistics of the trajectory cannot be captured correctly with an iPC approach. Approaches where the orthonormal basis is recalculated and the spectral order of the expansion is increased have appeared in the literature [30; 31]; however, these approaches have high computational cost and increased complexity.  
Non-intrusive approaches, on the other hand, do not suffer from the aforementioned problem. This is demonstrated on the Lorenz-96 system and the van der Pol oscillator, where the statistics of the time-averages are computed accurately for a low spectral chaos order \(c\), the results being in good agreement with the MC. The convergence of the gPC for low \(c\) is due to the fact that the time-averages display smooth variation to system parameters, as well as due to ergodicity are independent of the initial conditions used. The computational cost of this method scales with the number of stochastic parameters. In such high-dimensional cases, correlations may exist between the uncertain parameters. These issues are dealt with in the gPC literature with the use of sparse grids, data-driven techniques, or variational approaches, [1; 3; 32].  
Regarding the sensitivities, to properly evaluate their statistics, the gPC is coupled with the MSS algorithm, which can accurately compute the sensitivities of time-averages, even for a large number of uncertain parameters. The MSS algorithm is formulated in such a way to allow for the non-intrusive nature of the niPC to be preserved and for already developed software conducting the sensitivity analysis of the system to be employed. This coupling introduces a basic algorithmic framework for conducting UQ in chaotic systems and can exist as a basis for developing an approach to robust design in the presence of chaos.  
Figure 13: Shadowing and reference trajectory. Here, \(\beta_{ref}=0.3\), \(\beta_{shad}=0.301\), and \(T=20\).  
**Author Contributions**: Conceptualization, K.D.K. and G.P.; software, K.D.K; validation, K.D.K and G.P.; writing--original draft preparation, K.D.K.; writing--review and editing, G.P.; supervision, G.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the President's Scholarship Scheme at Imperial College London.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Methods
### Agent Training by Reinforcement Learning
#### 3.3.2 Observation Space  
The observation space is partitioned into two distinct components: time and location. The time variable indicates the exact moment that the agent performs the current action, with a precision level of one hour. The location specifies the present coordinates of the agent, with a precision level at the metre scale.  
\[Observation=\{time,\quad location,images_{1},images_{2},...images_{n}\} \tag{11}\]  
This equation defines an _observation_ as a set containing _time_, _location_, and a series of _images_ labelled as \(images_{1}\), \(images_{2}\), \(images_{n}\).  
During practical operation, we designate the agent's present location as the focal point and choose a certain area around it, known as the agent's viewport, with a buffer size. Next, we extract the remote sensing images that are included inside this specified region. This process guarantees that the agent may acquire environmental data in the vicinity of its present position, and by modifying the buffer size, we can effectively manage the dimensions of the viewport.  
When we crop images, we take into account the variety of information included in the image and may use different forms of remote sensing images that capture several bands of data. The data collected by these bands may provide valuable environmental characteristics, assisting the agent in obtaining a thorough sense and comprehension of the surrounding environment. In our study, we discretised the continuous variables, such as direction and distance, into bins and then applied one-hot encoding to these bins. For example, the direction was divided into eight bins (each representing a 45-degree range), and the distance was divided into several bins based on range intervals.
## 2 Mathematical Model  
### Assumptions  
As shown in Figure 1a, the shield tail void exists between the segment and soil during shield tunnel construction [30]. The synchronous grouting in the shield tail void is a process of a three-dimensional annular void filled by the grout. The grout filling in the shield tail void through the grouting hole is a complex spatial-temporal process. Initially, the grout fills the shield tail void circumferentially driven by injection pressure and gravity (Figure 1b). The completion of circumferential grout distribution along the segment generally takes tens to hundreds of seconds [31]. After the completion of circumferential grout filling, the grout fills the shield tail void longitudinally parallel to the movement direction of the shield machine. At the same time, the grout penetrates radially into the soil under the grout pressure, as shown in Figure 1c,d.  
The grout pressure formation is completed during the circumferential grout filling process, whereas the grout pressure dissipation contributes to longitudinal filling and radial penetration diffusion. Compared with the circumferential grout filling, the longitudinal filling and radial penetration diffusion take more time, usually several hours. Therefore, the grout pressure formation and dissipation are considered two consecutive processes. To establish the theoretical model of the grout pressure during synchronous grouting in shield tunnels, the following assumptions are made [32; 33].  
1. The grout is regarded as a homogeneous and incompressible fluid following [PERSON]'s law.
2. No grout dilution and blockage occur during synchronous grouting.
3. The disturbance of groundwater is neglected.
4. The pressure dissipation induced by grout consolidation is neglected.
5. The grout pressure formed during the circumferential grout filling phase is assumed to be the initial value of the longitudinal filling and radial penetration diffusion phase.
# Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation
## 2 Methods
### Learning from Positive Samples  
The underlying idea of the proposed approach is to use the proxy task for predicting the availability of a label for a data point and adapting it to the given classification task. A classifier is trained to infer the probability of whether a data point is labeled (\(s=1\)) or not (\(s=0\)). [PERSON] et al. showed that it is possible to convert this training by introducing class-costs during the training so that the final classifier predicts a given sample label \(y\) instead of labeling property \(s\)[26]. During the training of a cost-based classifiers, each class is assigned a specific cost for miss-classification, providing an effective method to control the class sensitivity of a classifier [27].  
The idea behind this approach is visualized in Figure 3 for an idealistic case with clearly separable classes. As only positive samples are annotated, the probability of annotation should be zero for negative samples and corresponds to the fraction of annotated positive samples for positive samples. If the importance of annotated samples increased so that the increased number of annotated samples matches the overall number of positive samples in the image, the probabilities match the class probability. This is of course a simplified example, assuming clear separability between the classes. However, the same underlying idea also works for non-trivial problems.  
For PU-learning, costs factors \(c_{P}\) and \(c_{U}\) of the positive and the unlabeled class depend on the class prior \(\pi=\mathrm{P}(y=1\mid\mathcal{U})\) as well as the positive sampling rate \(\eta=\frac{\eta}{n+\eta}\) with \(n\) and \(n^{\prime}\) being the numbers of labeled and unlabeled voxels, respectively. They are chosen in a way that the classification error for the classifier is minimized for the estimation of observation labels. We used, in our experiments, costs similar to the ones proposed by [PERSON] et al., while accounting for the greedy optimization strategy of random forests and the convex loss function [28].  
\[c_{P}=\frac{4\cdot\pi}{\eta}\quad;\quad c_{U}=\frac{1}{1-\eta} \tag{1}\]  
A common assumption in PU-learning is that the class and sampling ratios are constant for all observations, and as a consequence, costs are calculated for all samples of one class. We call this mode the 'global mode', reflecting the fact the weights are calculated globally for all samples. However, the visible fraction of the tissue type in a image can be variable, and as a consequence, we interpret each image as an individual group of observations or batch. This allows the calculation of individual weights per image, meaning that the class' cost depends on the image and not on the entire training dataset; we denote this approach as the 'batch mode'.
## 2 Antenna Design  
In this section, the design of the reconfigurable antenna is presented. First, a 3D view of the device with the antenna and all other parts will be shown. The antenna topology will be illustrated and explained in detail. The results of the parametric studies on the values of the lumped components and the equivalent model of the PIN diode are then given. Finally, a matching network is proposed to achieve a good impedance matching at interesting frequencies.
# Experimental Detection of Nonlinear Dynamics Using a Laser Profilometer
## 2 Experimental Test Measurements
### Application Case  
The case study for the detection of nonlinear dynamics using a laser profilometer is the Euler-Bernoulli cantilever beam, reported in Figure 1. It has been tested in three different configurations, as follows:  
1. The simple and typical clamped-free beam with a lumped mass at the free end. Although a nonlinearity can be present due to a large initial condition displacement, which does not allow the application of small deformation theory, it represents the reference linear case;
2. Two pairs of permanent magnets acting in attraction (#2a) or repulsion (#2r), as in Figure 2a. This introduces a nonlinearity owed to the asymmetric characteristic of the force carried out by these elements;
3. A non-holonomic constraint that acts similar to a pin located at a certain distance from the clamp during the time instants in which it is in contact with the beam, as in Figure 2b. This introduces a nonlinearity owed to the contact of the beam with the new introduced constraint.  
The clamp is composed by two steel parts connected by two screws, as reported in Figure 3a. The four threaded rods that properly lock the system give it great torsional stiffness. Furthermore, the Bosch profile in the upper part of the clamp is in contact only with the biggest part of it, ensuring an ideal clamp since the contact does not give more stiffness to the constraint. On the other side, the contact in the non-holonomic constraint is guaranteed along all the width of the beam, as depicted in Figure 3b,c.  
Figure 1: Euler–Bernoulli cantilever beam.  
Figure 2: Sketch of the nonlinear system in configurations #2a-#2r (**a**) and #3 (**b**) with relevant dimensions expressed in millimetres.
## 4 Discussion  
Although the accuracy of proposed ISP based on VLC is not particularly high, it has fewer applied limitations and special functions compared with other ISPs. In Reference [14], the paper proposed an IPS using a single transmitter and a rotatable single PD with less than 3 cm of error. However, the size of the proposed device must be compact because the positioning accuracy relies on the incidence angle gain profile. In Reference [15], the target device can be positioned within centimeters with multiple PDs, but the total number of LEDs and PDs is no smaller than four. We could obtain optimal performance on the condition of sufficient SNR, but it requires a higher computational complexity compared to currently proposed methods. The proposed reversed positioning system only needs two receivers, which is more convenient and cost-efficient.  
Besides, the proposed reversed VLC-based IPS is the first system with the function of personnel monitoring in some large electromagnetic-sensitive sites. Based on the proposed reversed VLC-based IPS, the security of workers will be guaranteed in nuclear power stations and aircraft carriers. Moreover, the proposed reversed positioning system does not need complex data-processing algorithms, which can improve the real-time positioning speed.  
Figure 6: The normalized error and error ratio at different distances.  
Since the effective positioning range of our system is 1.8 m, in order to locate the LED's position in a big room, we must install multiple receivers on the ceiling. In our future research, we will study the shape of the receiver array to achieve full-range positioning in a room. Furthermore, according to Reference [16], the minimum intensity is determined by the mitigation of systematic AOA errors. In the proposed reversed IPS, the minimum intensity is determined by the ADC value that the MCU can distinguish. Since the MCU can distinguish a 0.1 normalized ADC value, we can obtain the minimum intensity according to the minimum ADC value difference of two adjacent PINs.
# A New Integrated Multi-Criteria Decision-Making Model for Resilient Supplier Selection
## 2 Literature
#### Research Gaps
In addition, there may be insufficient information due to data confidentiality which causes difficulties for the evaluation process. In this case, the uncertain and incomplete data might limit the function of BWM in producing an accurate result. In previous supplier selection studies [9; 10; 11; 13], the fuzzy-based model was commonly applied to select the suppliers due to its strength to cope with uncertainties. However, in terms of handling incomplete data and information, the grey theory is an appropriate method. One of the major parts of the grey theory is Grey Relational Analysis (GRA) which is suitable for solving problems with complicated interrelationships between multiple factors and variables. In order to fulfill the third objective, GRA is integrated with BWM-TOPSIS to rank the resilient suppliers with the consideration of uncertainties and incomplete data. Furthermore, this study is an original effort in improving SC resilience by applying GRA-BWM-TOPSIS to execute resilient supplier selection in the food manufacturing industry. This research can help decision-makers to improve the supplier selection process by identifying the most resilient supplier.
## 2 Materials and Methods
### Data Analysis  
Data analysis was performed using MATLAB(r) R2015a software (MathWorks, USA). First of all, sensors responses in terms of resistance (\(\Omega\)) were normalized when compared to the first value of the acquisition (R\({}_{0}\)). For all the sensors, the difference between the first value and the minimum value during the analysis time was calculated. Hence, the dataset was composed of \(\Delta\)R/R\({}_{0}\) parameters.  
In the second step, the normal distribution of the variables was checked using the Jarque-Bera (JB) test, with a significance level equal to 0.05 chosen. This test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The null hypothesis is a joint hypothesis from both the skewness and the excess kurtosis being zero.  
Based on the test result, Partial Least Squares (PLS) method was used both to view how the groups of samples were represented thanks to sensors responses and to build the model that was used to classify the samples themselves.  
Finally, classification was performed, comparing two different classifiers: Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Networks (ANNs). PLS-DA was successfully applied in different fields where products had to be recognized according to their place of origin or the presence of contamination, such as in milk [41], honey [42], wine [43] and cheese [44]. ANNs are complex structures that try to mimic what the human brain does. They are formed by elemental units called neurons that work like real neurons: once information arrives, they elaborate it and give an output. Each neuron is characterized by an activation function and coefficients of connectivity called weights. The overall structure is mainly composed of an input layer, hidden layers and an output layer [45]. ANNs can be used to resolve regression and classification problems, and function approximation. They found a lot of space in food applications for the analysis of data collected with electronic noses [46; 47; 48; 49]. In this work, a feed-forward ANN trained with the Levenberg-Marquardt algorithm is used.  
For PLS, dataset was split in two parts--a training set and test set--using Venetian Blinds (VB) as the cross-validation procedure. This method divides the whole dataset in j cross-validation groups;  
Figure 1: (**A**) SEM image of SnO\({}_{2}\) nanowires. (**B**) SEM image of CuO nanowires. (**C**) Experimental setup formed by S3 and autosampler.  
in each one, one sample is put in the test set and the others in the training set on the first step. Subsequently, in every group the sample after the previous one is taken into the test set, and the others into the training set, and so on. In this work, the number of cross-validation groups chosen was equal to 10.  
For classification with PLS-DA, a toolbox made for MATLAB(r) and released by Milano Chemometrics was used [50]. Instead, ANNs were created using the function _nntool_ of the same software. This tool allows for the random splitting of the dataset in test and training sets by default.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 5 Practice
### Exercise 3  
A three-phase induction motor absorbs the power \(P_{abs}=10\) kW. The stator losses, \(P_{IS}+P_{JS}\), are 500 W. The slip of this motor is \(s=3\%\). Determine the rotor Joule effect losses.  
SolutionThe calculation of the rotor Joule effect losses follows the power balance shown in Figure 4.  
The transmitted power to the rotor is equal to \(P_{tr}=10-0.5=9.5\) kW. Then, since the slip is \(s=3\%\), the Joule effect losses in the rotor equal \(P_{JR}=s\cdot P_{tr}=0.03\cdot 9500=285\) W.
# An Efficient Steganographic Protocol for WebP Files
## 3 Results
### Format-Based Methods
#### 3.1.4 Chunk Injection  
The purpose of this experiment was to test the chunk injection method. These tests included the generation of a chunk with the selected name containing secret data, embedding it into a carrier, checking the file in applications, and extracting the message out of the stego medium. During this experiment, two approaches were tested: with updating the carrier main chunk size and without it. Chunk injection tests started by choosing a secret message and a chunk identifier. We have decided to embed 38-byte shellcode into a chunk named XYZW. Such a name is not part of the standard and the probability of using it in the future is low because of its meaninglessness. The final shape of the chunk is presented as follows; as can be seen, the four bytes after the identifier denote the chunk size.  
<58 59 5a 57 26 00 00 00 48 c7 c0 3b 00 00 48 |XYZW...H......H| 8d 3d 10 00 00 00 48 c7 c6 00 00 00 00 48 c7 c2 |............H..| 00 00 00 00 00 07 05 2f 62 69 6e 2f 73 68 00 |....../bin/sh.|  
Later, we attached it to a pure container as a last chunk. As expected, the file size increased by 46 bytes (header + payload). We also updated the RIFF chunk size to correspond to the current file length. After these operations, exiv2 correctly detects the embedded chunk.  
STRUCTURE OF WEBP FILE: stego-file1.webp Chunk | Length | Offset | Payload RIFF | 4642280 | 0 | WEBP VP8X | 10 | 12 |....o..... VP8 | 4624168 | 30 |....*p..>>.B.!$(Jx...gk.qd. EXIF | 15264 | 4624206 | II*..  
Also, the file has been tested in Firefox, GIMP, and ImageMagick. All these programs displayed the image without problems.  
The alternative approach is to inject the chunk without changing the RIFF chunk size. Then it is not detected with exif2 software.  
STRUCTURE OF WEBP FILE: stego-file2.webp  
Chunk | Length | Offset | Payload  
RIFF | 4642234 | 0 | WEBP  
VP8X | 10 | 12 |....o....  
VP8 | 4624168 | 30 |....*p...>)..B..1$(.Jx...gk.qd.  
EXIF | 15264 | 4624206 | II*.......................  
XMP | 2755 | 4639478 | <?xpacket begin=\"...\" id=\"WSMOMP  
At first glance, it seems to be a good idea, as hidden data are more difficult to spot. However, in such a case, some applications have problems with opening the file. Even though it is displayed in the browser (Firefox) and by ImageMagick, GIMP refuses to open the image, as the WebP plugin returns an error.  
To make this experiment more complete, we also recovered hidden shellcode from the container. It was realized with dd software, which is useful for copying parts of the file.  
dd if=stego-file1.webp count=$((38)) bs=1 skip=$((4642242+8)) > recovered  
Retrieving was possible as we knew payload length (38) and offset (4,642,242 + 8 bytes of header). We compared the recovered shellcode with the original and the files were identical. This means that the presented method is suitable for carrying even binary data which cannot be corrupted.
## 5 Conclusions  
We used long-term ground phenological observations along with leaf area index and carbon flux measurements made at Harvard Forest to evaluate six vegetation-index-based methods for retrieving phenology. Our analysis shows that, compared with the ground phenology reference chosen according to the definition in the Agro-IBIS dynamic ecosystem model, phenology derived using the evaluated methods generally had relatively large discrepancies, which could be attributed to the different definitions of phenology, the parameters used for a certain method, and the input data. However, two methods for leaf onset (_i.e._, LOGISTIC2 [22] and MOVING [23]) and one method for leaf offset (_i.e._, LOGISTIC2 [22]) showed a bias of less than a week, and could be further improved, suggesting that phenological metrics derived using these methods could potentially be used in dynamic ecosystem models similar to Agro-IBIS. Our analysis shows that, when remotely sensed phenological metrics are used to parameterize phenological models, the bias is generally maintained in the modeled phenology, and will propagate to cause errors in productivities simulated in dynamic ecosystem models. The different sensitivities of Agro-IBIS to leaf onset and offset suggest that the impact of spring and autumn phenology on carbon assimilation should be examined separately. Our evaluation was conducted at one site due to data availability, and subject to uncertainties associated with ground observations, model simulations, and data processing. However, the evaluation procedure proposed in this study, including quantitative selection of ground reference, comparison between multiple data sources and examination of bias propagation, can provide guidance for further evaluation at more locations.  
We thank three anonymous reviewers for their constructive comments and suggestions, which helped to improve our manuscript.