esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Overall Loss Function  
As shown in Figure 1, in this paper, the feature maps \(G_{i}^{j(4)}\) with the same scale are finally concatenated along the channel dimension to obtain three fused feature maps \(G^{i}(i\in\{1,2,3\})\). Then, these three fused feature maps are upsampled to the original image size and input into their respective CNN-based classifiers, which have the same structure. Finally, three predicted maps \(P_{i}\) are output by the CNN-based classifiers. Let \(Y\) be the ground-truth value. Then the overall loss function of MDNet based on cross-entropy (CE) loss is:  
\[L_{total}=L_{ce}(P_{1},Y)+L_{ce}(P_{2},Y)+L_{ce}(P_{3},Y) \tag{9}\]  
where \(L_{ce}(P_{1},Y)\) is the cross-entropy loss between the predicted change map \(P_{1}\) and the ground-truth value \(Y\), and the same applies to \(L_{ce}(P_{2},Y)\) and \(L_{ce}(P_{3},Y)\).
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
###### Abstract  
Application of safety-related information interaction among vehicles has always been a research frontier in Vehicular Ad-hoc NETworks (VANETs). These messages require high real-time performance. There is a lot of research dependant on creating optimization model for communication task scheduling or routing protocols to reduce communication delay. In this paper, we analyze characteristics of safety-related information and introduce Interference Alignment (IA) technology in VANETs. To further improve routing efficiency, a data-driven assisted transmission routing and broadcast model framework for Vehicle to Vehicle(V2V) and Vehicle to Infrastructure (V2I) communication are constructed which are the basis for IA. Depending on the proposed model, we propose an optimization problem of minimizing total number of time slots required for safety information sharing in VANETs. Then a clustering algorithm is designed to narrow feasible solution space. Simulation results show that the approach can effectively reduce the number of time slots required and improve link use by 20% percent compared with no IA applied.  
interference alignment; modeling and optimization; VANETs 12 October 2019; 19, 4988; doi:10.3390/s19224988
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 1 Introduction
The modified MMSE criteria is designed to minimize the Frobenius norm for non-diagonal components of equivalent channel that causes IUI. The partial nulling scheme obtains diversity gain by utilizing degrees of freedom. The proposed scheme is operated like a GBD precoder which retains some IUIs to increase diversity gain and uses postcoding to eliminate the remaining IUI. Also, the proposed scheme is optimized through the modified MMSE criterion like RBD precoder. Therefore, the MGBD precoder provides improved BER performance compared to the GBD precoder and guarantees higher throughput performance than the RBD precoder in an unfavorable channel state by adjusting the number of streams. Additionally, in order to maximize the throughput performance according to channel state, this paper proposes an adaptive precoder in order to maximize throughput at all SNRs. The adaptive algorithm performs precoding by adjusting the partial nulling subset according to SNR. In other words, this precoder increases the partial nulling space at high SNRs for high throughput and decreases the partial nulling space at low SNRs for improved BER performance. Thus, UEs in MU MIMO downlink system obtain maximum throughput and BER performance. This paper is organized as follows. Section 2 presents a MU MIMO downlink system model. Section 3 explains conventional precoding schemes, such as RBD, GBD, etc. In Section 4, the MGBD precoder and the adaptive MGBD algorithm are proposed. The simulation results for BER performance and throughput are represented in Section 5. Section 5 also provides computational complexity for MGBD and RBD. Finally, Section 6 gives brief conclusions.
# Predictive Scheduling with Markov Chains and ARIMA Models
## 3 Production Scheduling under Technological Machine Failure Constraint  
### Objectives  
This paper formulates a predictive production scheduling process model in the job-shop environment under technological machine failure established with the help of Markov chains and ARIMA models. Our solutions predict the time of machine failure, as well as the time of repair, and constitute an alternative method to the models proposed in the literature. The objective function of our predictive production scheduling is to minimize the makespan, i.e., to produce a schedule with a minimum completion time of all jobs.
## 3 System Model Description and Problem Statement
### DQN Model for RAN-Level Slice Admission Control
#### 3.4.2 Action Space  
At each decision step, the agent selects an action from the following set:  
\[A_{t}=\{a_{1},a_{2},\ldots,a_{7}\},\]  
where \(a_{1},a_{2},\ldots,a_{6}\) correspond to accepting the slice request and assigning it to a specific slice type, while \(a_{7}\) represents rejecting the request due to insufficient resources. The action space is further categorized as follows:  
* **Slice Admission (\(a_{1}-a_{6}\)):** The agent assigns a slice request to its corresponding slice type based on available resources.
* **Slice Rejection (\(a_{7}\)):** If resources are insufficient, the request is rejected, and a blocking report is sent to the non-RT RIC.
## 3 Results and Discussion
### Comparison with Other Recently Proposed Algorithms
#### 3.4.2 Experimental Results on 50D and 100D Test Problems  
High-dimensional optimization problems are widely available in reality, and thus, this paper further validates the scalability of the MSAMT through 50D and 100D test problems. In addition to the four competing algorithms previously described, two additional algorithms are added in this section for comparison with the MSAMT, namely GL-SADE and LSADE. The GL-SADE alternately uses RBF and Kriging to assist DE to perform both global and local searches and then performs local searches again utilizing the rewarding mechanism if the local search yields a better result. The LSADE suggests a new surrogate model that is based on the Lipschitz and adopts DE as an optimization algorithm. Both the GL-SADE and LSADE were proposed for expensive optimization problems. Note that on 50D and 100D test problems, the initial population size \(N\) for GL-SADE was set to 200, while for the LSADE it was set to 100 and 200.  
Table 17 shows that the MSAMT achieved the optimal mean results on 60.7% of the test functions, thereby exhibiting a superior performance compared to the four competing SAEAs. For 18 of the 28 test functions, the MSAMT performed better than the HSAOA. Compared with the DSP-SAEA and the FSAPSO, the MSAMT achieved better results on 19 test functions. The MSAMT had a better performance than the FSAPSO on 71.4% of the functions. Table 18 presents the comparison results of the MSAMT with the GL-SADE and the LSADE on functions with 50 and 100 dimensions. The MSAMT obtained better results on 19 out of 28 functions. Compared with the GL-SADE, the MSAMT was more competitive on 20 test functions. In contrast to the LSADE, the MSAMT had better mean values on 19 test functions.  
Convergence curves of the MSAMT, HSAOA, DSP-SAEA, BiS-SAHA, and FSAPSO for 50D and 100D test functions are given in Figures 17 and 18, respectively. The figures demonstrate that the MSAMT had excellent convergence and could produce outcomes that are either better or comparable on most of the test functions. The HSAOA obtainedthe optimal results only on the F1-50 and F3-50 functions. The DSP-SAEA outperformed the other algorithms on the F9-100 and F11-100 functions, while the BiS-SAEA was most effective only for the F8-100 function. The FSAPSO obtained the best average value on five test problems, i.e., F6-50, F8-50, F9-50, F10-50, and F11-50. Figures 6 and 7 show the convergence curves of MSAMT, GL-SADE, and LSADE on the 50- and 100-dimensional test problems, respectively. The MSAMT achieved the best mean values for all functions except F6-50, F8, F9, F10-50, and F11-50. The GL-SADE beat the other algorithms only on the F9-100 function. The LSADE won on 7 out of 28 functions. In summary, the MSAMT achieved the mean values on most of the test problems and was competitive in solving high-dimensional problems.
## 2 Frequency Shift of Intensity Modulators
In addition, Figure 2 shows the modulation voltages corresponding to these signal pulses. Obviously, the states modulated by the IM with different intensities do not overlap completely in the frequency domain. The distinction will be more evident when increasing the repetition rate and narrowing the pulse width to increase the key distribution rate. Therefore, we tested it using the same method and obtained the normalized intensity distribution of 33-MHz optical pulses with 300 and 100 ps pulse widths, which is shown in Figure 3a,b, respectively. From Figures 1b and 3a,b, we can see that the distinction of the signal states (also the strong-decoy states) and the weak-decoy states is evident. This is because the amplitude of the modulation voltages of the signal and strong-decoy states are higher, which will induce a greater frequency shift. Thus, the peaks of the signal and strong-decoy states are lower than that of the weak-decoy states. There are also slight differences between the signal and strong-decoy states. On this foundation, Eve could take advantage of this side channel to distinguish different states, which will threaten the decoy-state method. Figure 1: **(a)** The wavelength spectrum of the 1 ns signal pulses with intensity ratio as 0.1: 0.384: 0.447 (\(\mu_{a}=0.1\), \(\mu_{b}=0.384\), \(\mu_{z}=0.447\)) and the original continuous light between 1549.94 nm and 1550.04 nm. **(b)** The normalized intensity distribution of these 1 ns signal pulses in the frequency domain. Three internals \(T_{a}\), \(T_{b}\), \(T_{z}\) with central wavelength 1549.976 nm, 1550.018 nm, 1549.982 nm and radius 0.001 nm are set to distinguish these signal pulses. Figure 2: The 33 MHz modulation voltages with 3.34% duty ratio \(V_{z}\), \(V_{b}\) and \(V_{a}\), which correspond to the coherent states with intensities \(\mu_{z}\), \(\mu_{b}\) and \(\mu_{a}\).
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 4 Experimental Results
### Analysis of Time Consumption in Model Training  
The time consumption of model training, which encompasses the total time expended to finalize the entire training process, stands as a pivotal metric for gauging the efficiency of machine learning and deep learning models. We executed an assessment of classifier performance across seven distinct scenarios. Specifically, we documented the temporal consumption of the baseline classifier, the classifier equipped solely with multilayer filters, the classifier with an exclusive noise injection mechanism, and our proposed classifier. During the experimental configuration, both CNN and DNN models underwent 20 iterations on the MNIST dataset, 100 iterations on the CIFAR10 dataset using both CNN and VGG16, 30 iterations on ResNet50, 200 iterations on VGG16 for the CIFAR100 dataset, and 18 iterations on ResNet50. We employed three datasets, segregating them into training and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **Architecture** & **Generated** & **Params** & **Clean** & **AA** \\ \hline \multirow{3}{*}{CIFAR10} & DiffPure (2022) [63] & WRN-28-10 & 1M & - & 89.37 & 75.68 \\  & [PERSON] et al. (2023) [33] & WRN-28-10 & 1M & - & 90.24 & 76.34 \\  & MLFD (Ours) & WRN-28-10 & - & - & 90.61 & 78.93 \\ \hline \multirow{3}{*}{_mini_ImageNet} & LPIF (2023) [40] & EfficientNet-B0 & - & 5.3M & 72.64 & 57.38 \\  & MLFD (Ours) & EfficientNet-B0 & - & 5.3M & 74.18 & 60.63 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The testing accuracy (%) under clean images and AutoAttack (AA) on the CIFAR-10 and ImageNet datasets.  
validation subsets at an 8:2 ratio. To mitigate experimental inconsistencies, we conducted three iterations of model training for each classification methodology.  
Table 8 provides an overview of the time expenses required to train the model across seven distinct scenarios. A comparative analysis reveals that the additional time overhead resulting from the incorporation of multilayer filters is relatively minor, with a growth rate of less than 10%. However, the majority of the time is spent during the noise injection phase, where the mechanism doubles the size of the training dataset. Although the noise injection mechanism is essential for enhancing the model's robustness, it does lead to considerably longer training durations. Future research could investigate more efficient noise injection strategies or refined training methodologies to reduce this time overhead, aiming to achieve a more favorable balance between model robustness and training efficiency.
## 3 Results and Discussion
### Characterization of Main Spectral Regions
#### 3.1.3 Spectral Region 2500-3500 cm\({}^{-1}\)  
A broad band around 2900 cm\({}^{-1}\) was identified in all samples--both in stems as well as the standard cellulose sample (Figure 4). The band from this range was attributed to the C-H stretching from the aromatic methyl group of cellulose and hemicellulose also identified following FT-IR analysis of _Abutilion indicum_ fibers [10]. _Mauritia flexuosa_ fibers were shown to present two bands around 2918 and 2849 cm\({}^{-1}\) that were associated with increased levels of organic extractives such as phenolic acid methyl esters and fatty acid methyl esters. This, in addition to increased water content, might indicate an easier degradation onset of these fibers [40], which is not a desired characteristic. At 2922 cm\({}^{-1}\), _Thesesia populnea_ fibers also displayed a band that was attributed to stretching vibration of the C-H molecular bond from functional groups CH\({}_{2}\) and CH\({}_{3}\) (Table 3). This band was shown to shrink once with stem maturity [39].  
A broad band at 3000-3700 cm\({}^{-1}\) was present in all samples (Figure 4). A similar broad band was identified in cotton samples and was attributed to OH stretching vibration that is due to the hydrogen bonds of cellulose. Particularly, the peaks at 3286 cm\({}^{-1}\) and 3336 cm\({}^{-1}\) were assigned to intra-molecular bonds from cellulose fibers, with the first one becoming evident earlier and second one becoming evident later during cotton fiber development [53]. Peaks at 3432 cm\({}^{-1}\) and 3342 cm\({}^{-1}\) were attributed to intramolecular hydrogen bonds from cellulose, while the band at 3568-3577 cm\({}^{-1}\) was assigned to the signal of intramolecular hydrogen bonds from a phenolic functional group of lignin [40].
## 3 Effects of Antenna Types on Static Positioning  
Figure 4 shows the horizontal and vertical plots representing the daily solution as an independent site (gray), the daily solution that all sites were processed simultaneously (blue), and the solution for each set as well as the overall estimates (green). While the horizontal components were estimated at a precision (repeatability) of a few millimeters, the vertical one shows a significant variation between sets (up to 1 cm). It should be mentioned here that the estimated final coordinates of each site are based on the most recent reference frame (IGb08) at the mean epoch of data collection (2013.85), and therefore it is necessary to transform the resulting coordinates into NAD83(2011) epoch 2010.00 because all RT solutions in this study were provided in that frame. The transformation between reference frames was accomplished using HTDP (Horizontal Time-Dependent Positioning) by NGS (version 3.2.3).  
Figure 5 shows the antenna types used in Phase 1. The UP component from the same antenna type behaves similarly at two sites, whereas the other two present positive and negative differences, indicating that a similar behavior of the antenna type at each site (positive or negative) cannot be always expected. That is, the TRM57970.00 NONE antenna is positively biased for all sites, and the TRMR8_GNSS3 antenna is offset downward (Figure 6). The difference between the antenna types in the estimated ellipsoidal heights with respect to the mean OPUS-Projects solution reaches up to two centimeters, even though the phase center variation of each antenna type was modeled in the processing software using IGS absolute antenna models.  
Figure 4: OPUS-Projects processing results for station EAST; horizontal (left); and vertical (right).  
Figure 5: Antennas used in Phase 1 [18].
## 3 Case Study and Parametric Analysis
### Factors Influencing the Sealing Capacity of the EPDM Gasket
#### 3.3.2 The Joint Offset of Lining Segments  
The joint gap is often accompanied by joint offset. The effect of offset on leakage has multiple aspects: On the one hand, the offset will reduce the leakage path, increase the hydraulic gradient, and further increase the leakage. On the other hand, the offset reduces the contact area, and the contact surface stress increases, further reducing the leakage.  
As shown in Figure 11, the calculation curve of the joint offset is in good agreement with the qualitative analysis. When the joint gap is 11.24 mm, the leakage increases from 0.22 to 0.46 L/m\({}^{2}\)/day  
Figure 10: Effect of a joint gap and groundwater pressure on the leakage; the dashed line represents that the daily average leakage is 0.1 L/m\({}^{2}\), and the abscissa of the intersection points between it and curves with different symbols are the maximum allowable joint gaps under groundwater pressures of 0.4, 0.8, 1.2, 1.6 and 2 MPa.  
as the joint offset increases from 0 to 15 mm, and their relationship seems to be linear, which means the leakage may be exacerbated by the growing joint offset. The reason is that the increase of joint offset shortens the seepage path and increases the hydraulic gradient. However, when the joint gap is less (9.73, 8.21, and 4.42), the curve concaves down. When the joint gap is 9.73 mm, the leak rate ascended to a peak value of 0.160 L/m\({}^{2}\)/day at an offset of 11 mm and then descendes. As the joint gap decreases, the peak value of the leakage appears earlier. When the joint gap is 8.21 and 4.42 mm, respectively, the peak value is at an offset of 0. The two peak values of the leak rate are 0.095 and 0.048 L/m\({}^{2}\)/day, respectively. This is because when the joint gap is at a certain value, although the increase of the joint offset will shorten the seepage path, it also causes the reduction of the contact area and, thus, increases the contact stress and reduces the leakage. Furthermore, the limit of the joint offset is related to the joint gap; the joint offset will not exceed the product of the joint gap and the cotangent of the groove inclination (shown in Figure 2). In our case, when the joint gap is 11.24, 9.73, 8.21, and 4.42 mm, the corresponding maximum offset is 6.49, 5.62, 4.74, and 2.55 mm, respectively. The reason why the leakage increases correspondingly mainly depends on the increasing gap, and the effect of the offset change on the leakage is slight.
## 4 Discussion
The first row shows the images generated by the base processes and the second row shows the images by SelectStitch. Each column indicates also the number of reader and each reader gives the score of 2 for both base and the proposed image. Figure 9: Exemplary composite images for the cases where the results deteriorated after SelectStitch. The first row shows the images generated by the base processes and the second row shows the images by SelectStitch. In all cases, the readers gave a score of 3 for base process composite image and 2 for the SelectStitch composite image. the composite images resulting from the proposed framework that received a lesser score than the base composite images suffered from over-exposure as the de-hazing algorithm failed to preserve the color information. To improve the diagnostic capability of the composite image, it should be enhanced by preserving its original color as much as possible. We also noted that in no evaluation, SelectStitch image received the score of 1 while the base process image score was 3. There was a significant difference in scoring between the first and second rounds at \(p\) value of 0.001, and the second round has 1.44 times higher risk of scoring less than the first round. It is possible that ENTs with Round-1 experience might have better judgement at second round. Although this work provides a proof-of-concept approach to obtain one representative composite image of the TM, the intra-class variability is a substantial challenge. Thus, to obtain a robust segmentation, more training samples from scarred eardrums, e.g., tympanosclerosis are required. With a larger database, a multi-modal image segmentation could also be developed. There are some limitations to this study. First, the relevant frames were determined according to the amount of eardrum they capture. If the amount of eardrum in a frame is above a certain threshold, then it was considered a relevant frame. While our study shows promising results, we expect that additional criteria could be developed to select the frames (or even parts of the frames) to be stitched. This would increase the probability that all the relevant frames (and parts of the frames) could be used to properly represent pathologies present in eardrums. In our study, we did not evaluate the diagnostic accuracy of composite images over either single images selected from the video or the entire of otoscopic video. Although, we expect that the proposed composite images will be superior (or at least equal) to the manually selected single images in terms of diagnostic capability, a comparison study is needed but is beyond the scope of the current study and will be the subject of our future studies.