esa-sceva/satcom-chunk-collection huggingface dataset:
# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 5 Results  
### MVTec Dataset Findings  
In our experimentation, we employed a specific subset of the MVTec dataset as our foundational testing ground, seeking to probe the effectiveness of masked convolutions embedded within skip connections. The structured yet challenging nature of this image dataset, especially juxtaposed against the intricacies of video anomaly detection, offered a controlled environment to rigorously ascertain the algorithm's capability in anomaly recognition. With the MVTec dataset encompassing both defect-free and defective samples, it provided an ideal base to validate the efficiency and scalability of our method.  
Figure 6 shows the Receiver Operating Characteristic (ROC) curves for the AutoEncoder, UNet, and MaskedSkipUNet, accompanying their specific AUC scores. With the UNet registering an 86.6% AUC, the MaskedSkipUNet, boasting a 93.8% AUC, only marginally surpasses the AutoEncoder's score of 92.9%.  
A more granular, qualitative examination further elucidates our conviction in advancing this methodology for the more complex domain of video anomaly detection. Figure 7 displays results from the hazelnut category of the MVTec dataset. Ground truth images are arrayed in the first column, displaying both regular and anomalous specimens. Successive columns illuminate the output produced by the AutoEncoder, UNet, and MaskedSkipUNet. The inherent differences in reconstructions, spanning from the blurring by the AutoEncoder to the precise detailing by the UNet are notable. The MaskedSkipUNet's extrapolation of normal features is particularly compelling, as it seemingly leverages the contextual cues to supplant anomalous regions.  
Figure 6: Receiver Operating Characteristic (ROC) curves comparing the performance of AutoEncoder, UNet, and MaskedSkipUNet on the hazelnut category of the MVTec dataset. The AUC scores for each method are also provided, underlining the supremacy of MaskedSkipUNet in anomaly detection.
## 4 Simulation and Discussion
### Performance Analysis for a Single Link Pair  
This subsection analyzes the performance of our proposed scheme for a single link pair. Continuing with Figure 4, we will observe whether the proposed scheme can guarantee the minimum required QoS of either link. Then, we will verify the advantages of our scheme based on [PERSON]'s method in terms of computational time.  
We fix the distance between PT-PR and ST-SR pairs as 500 m each. The required QoS for the PL is fixed as 10 bps/Hz, and we set the QoS requirement for the SL to vary from 5 to 15 bps/Hz.  
Figure 6 provides a description of the change in the link SE when the minimum QoS requirement of the SL increases. We still focus on the transmission within the first frame. All considered schemes (except the random scheme) ensure the minimum QoS requirements, with their link spectral efficiency (SE) precisely matching the required QoS. This demonstrates our solution's capability to guarantee QoS for heterogeneous users.  
In Figure 7, we show the computational time of all the schemes versus the QoS requirement of the SL. It can be noted that the optimal scheme and the optimal WSEE maximization scheme perform the same, and their average computational time is over 15 s. As expected, the random scheme gains the shortest computational time, i.e., \(3.27\times 10^{-5}\) s. The computational time of the proposed one is consistent with that of the random scheme in order of magnitude, i.e., \(5.28\times 10^{-5}\) s. The average computational time of the DRL-based scheme is approximately \(3.17\times 10^{-3}\) s. The above results indicate that our proposed scheme is not only optimal but also converges very quickly. Moreover, the proposed scheme does not need complex hardware conditions required by artificial intelligence methods, making it suitable for practical real-time deployment.  
Figure 6: Link SE vs. the QoS requirement of the SL.  
Figure 7: Computational time vs. the QoS requirement of the SL.
# An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
## 2 Related Work
### Deep Learning-Based Methods
They used fine-tuning and transfer learning to propose an ensemble model based on VGG19 and NasNetLarge architecture. [PERSON] et al. [37] proposed a hybrid model based on VGG16 and MobileNet for the classification of ALL cell images. Global average pooling (GAP) was used between VGG16 and MobileNet to extract high-level features from ALL cell images. LeukoNet was proposed by [PERSON] et al. [38] for the classification of ALL cell images. The proposed model combines the discrete cosine transform (DCT) domain features extracted using CNN with the optical density (OD) space features to build an effective classifier. Although these researchers used the C-NMC 2019 dataset composed of more than 10/000 images, but they did not segregate the training data based on subject-level variability. Instead, the classifier was trained by combining all of the subject's data into healthy and cancer classes. Because subject-specific features help in class discrimination, this pooling can restrict the classifier's performance. As a result, a practical classifier would require training on data from certain subjects and testing using data from entirely unknown subjects. In this paper we used the C-NMC 2019 dataset for the diagnosis of ALL. The cell images in the C-NMC dataset are selected carefully based on subject-level variability. In order to get more robust results, we split the dataset into seven-fold cross-validation so that no two splits overlap in terms of subject data. This strategy also ensures that none of the cell images of a subject used in training were used to test the classifier. In comparison to other ALL datasets such as the ispat general hospital (IGH) [17], ALL image database for image processing (ALL-IDB) [39], medical image and signal processing research center (MISP) [22], and American society of hematology (ASH) [32], the C-NMC 2019 dataset has a substantially more significant number of images, which can aid in the development of a robust and scalable ALL diagnostic classifier. Our proposed simple model uses an attention module to refine features more and overcome the problem of morphological similarity bringing more reliability to the results and thus classifying the images as healthy or not. To the best of our knowledge, we are the first to use the attention module with a CNN model for ALL classification.
## 2 Materials and Methods
### Structure of QSA
#### 2.1.14. Acceptance Criterion (Greedy or Otherwise)  
A common QSA approach is a **greedy** acceptance rule:  
\[\text{if }F\big{(}x_{\text{improved}}^{(k)}\big{)}<F\big{(}x^{(k)}\big{)}\implies x ^{(k)}\gets x_{\text{improved}}^{(k)}. \tag{13}\]  
otherwise, the old \(x^{(k)}\) is retained. This ensures that the overall best cost in the population does not degrade. Alternative acceptance rules (e.g., with a small probability of accepting a worse solution) are possible. This is simpler than classical simulated annealing, which might accept worse solutions. Since the quantum aspects (tunneling, collision) already maintain exploration, a purely greedy acceptance is often sufficient.
## 4 Evaluation of Test Results
### Current Situation Assessment  
After the completion of the Cho'ponota L1 Bridge construction phase, a deflection problem occurred in span S2, and it was requested to verify the compliance of the project and construction through vehicle load tests and non-destructive OMA measurements. For this purpose, the provided finite-element model of the bridge was updated based on the OMA results. The assessment of the current condition has been conducted using this updated model and is presented below.  
After the static vehicle load tests, numerical analyses showed that the operational displacement values obtained from the 30 t and 50 t vehicle load tests were consistent with the displacement values obtained from the finite-element analysis for the same loading conditions. This indicates that the finite-element model of the bridge can be reliably considered. In the project calculation report, a displacement value of 41.8 mm was obtained for a 100 t loading condition, and a similar value was derived using the updated finite-element model. Since this value is lower than the limit value of 56.25 mm (45,000/800), it can be stated that the bridge remains safely within usable limits.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 2 Literature Review  
### Temperature Thresholds in Cold Chain Transportation  
Temperature abuse in the cold chain can compromise food safety and quality. Therefore, to ensure high-quality food and prevent spoilage, transportation systems need to keep temperatures within close proximity to their safe, recommended refrigeration value [50]. In general terms, refrigerated food can be divided into four categories as to storage temperature: frozen, at \(-18\)\({}^{\circ}\)C or below; cold-chilled, between \(0\)\({}^{\circ}\)C and \(1\)\({}^{\circ}\)C; medium-chilled, at \(5\)\({}^{\circ}\)C; and exotic-chilled, between \(10\)\({}^{\circ}\)C and \(15\)\({}^{\circ}\)C [51].  
Chilled food, understood as any food product stored and kept at a temperature below that of ambient temperature, but above one where its water content will not start to freeze, is often preferred as a preservation method as it does not alter significantly quality characteristics such as texture, appearance and taste. Medium-chilled foods (\(5\)\({}^{\circ}\)C), the type used in the present work, may include minced meat, processed fish, prepacked meat, ready-to-eat foods [52], cooked products, dairy products [53], and bakery products, among others [51].  
Likewise, for frozen food, the integrity of the product, its packaging, and its temperature along the cold chain are all crucial quality factors. No other procedure can retain the quality attributes and properties of the original fresh food for such a long time as can contemporary freezing techniques and an uninterrupted chain of low-temperature storage and distribution. Due to the wide array of different foodstuffs each with their own physical and chemical composition, optimal temperatures for each type of frozen food product can vary and may be stored at temperatures more suited to their individual requirements (e.g., ice cream, dairy products, raw ingredients). While a maximum temperature of \(-12\)\({}^{\circ}\)C is usually accepted as being acceptable practice for other frozen items, \(-18\)\({}^{\circ}\)C is the standard (subject to adequate operational tolerances) for quick-frozen meals and legal requirement in the European Union (EC) and Great Britain [54]. It is worth noticing, however, that no frozen food poses a risk of microbiological development until the temperature is permitted to rise beyond \(-12\)\({}^{\circ}\)C for an extended length of time [54]. In any case, transportation systems in the food supply chains must maintain the temperature of the food within close limits. The reasons for this are not only to ensure the safety of the food, as temperatures higher than \(-12\)\({}^{\circ}\)C can pose a threat in terms of microbial growth, but also to guarantee that food retains its quality attributes, and maximum shelf-life.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 2 Laser Crosslink Mission
### Orbit Scenarios
#### 2.2.3 Phase III--Normal Operation Phase (NOP)  
In the NOP, fine relative navigation based on the differential GPS (DGPS) algorithm was implemented for satellites to seek one another [13]. Moreover, to evaluate the optical link performance, orbit maneuvers were conducted for 50, 100, 200, 500, and 1000 km baselines: for several laser crosslink tests, the distance between satellites was kept within 10% of each baseline over 10 days. When the LOS was precisely aligned, the sensors on the LCT detected beams from each other. Finally, the PAT system operated to maintain the spot position by utilizing the FSM within the active area of the detectors. While they carry out the laser crosslink, the solar panels orienting the sun can mitigate a solar direct noise incident on the LCT detectors. In addition, the GNSS antenna and star tracker's aperture share the zenith to enhance visibility, improving the navigation performance. The mission operations were repeated by varying the baseline throughout the mission lifetime.  
Figure 3: Profiles of inter-satellite distance with orbit maneuver scenarios using multiple burns. Blue circles denote a repeat count of the reconfiguration and station keeping maneuvers: (**a**) entire phases from launch and separation; (**b**) normal operation phase with reconfiguration and station-keeping maneuvers for adjusting inter-satellite distances.
## 4 Discussion
### How to Choose Smoothing Parameters  
The efficiency of each smoothing method depended on the choice of parameters. For example, applying upper-envelope iterations generally improved the results. This emphasizes the need to further compensate time-series of MODIS 8-day composite NDVI data for remaining negative bias due to cloud and aerosols in the atmosphere. At the sites Barah and Demokeya, where upper-envelope iterations did not improve results, vegetation density was very low, which means that the data were probably more affected by soil background characteristics, which would lead to decreased noise bias. When comparing against GPP, upper-envelope iterations also generally improved results, except when using DL and SPCV at the Norunda forest site. In the third experiment, upper-envelope iteration of SPCV, AG and DL did not improve \(\rho\) in correlating end of season timing and elevation, which could help explain why upper-envelope iterations also did not improve DL and SPCV fits at the Norunda forest site.  
The smoothing parameters, \(q\) in SG and LO, and \(p\) in SP, were much more influential than the envelope parameters \(u\) and \(a\) in the first experiment. As a consequence, more attention needs to be paid to adjusting \(q\) and \(p\). In addition, the ability to vary the smoothing parameters \(q\) and \(p\) means that SG, LO, and SP have larger flexibility than AG, and DL, but this flexibility means that the methods are less robust to user selection of parameter values. A higher flexibility means that a method is more capable of matching satellite data with ground reference data, but accurate matching also requires ground observations for calibration. In most situations, this calibration is impossible due to a lack of ground observations, and, as seen from the large ranges observed in the second and third experiments, a robust method with fewer parameters may be preferred.  
Automatically selecting smoothing parameters by CV can be useful for SG, LO and SP if no ground reference data are available for calibration, as demonstrated by the above-average results achieved with this technique. However, applying CV can sometimes lead to very poor results, as seen in a few cases (e.g., Abisko and Hyytiala in Figure S2). CV may also result in different parameter settings being used for different pixels of the same land cover class, and it can be discussed if this is reasonable. It can also be noted that CV had different effects on SP as compared to SG and LO. In SP, the smoothing parameter is continuous, whereas it is discrete in SG and LO. The discrete parameters in SG and LO may lead to unsatisfactory results if the data quality is poor and the smoothing parameter significantly influences the fitting, which could be the reason that CV is less reliable for SG and LO than for SP.  
Although most smoothing methods improved the ability of the satellite time-series to represent the ground reference data, it was not possible to find a single parameter setting that was always able to generate suitable results for all environments, which supports the finding by [PERSON] and [PERSON] (2016) and [PERSON], et al. (2016).
# Development of a Datalogger for Submarine Glider: Integration of Fault-Tolerant Software Layers
## 2 Datalogger Design
### Study and Choice of the ECC
To do so, there exist a number of binary BCH codes with a correction capability of \(t\leq 2^{m-1}\), and \((n-k)\geq mt\) parity bits [54]. With these parameters, the analysis was performed for some BCH codes with \(m=8\) and \(m=9\), that is, of lengths \(n=255\) bits and \(n=511\) bits. For example, for the arrangement BCH (255, 223, 4) that corrects 4 errors per 255 bits at each code, and using a NAND flash memory chip with a storage capability of 4,194,304 codes of 255 bits each one, with an RBER of \(p=236\times 10^{-4}\), so the probability for \(i\) errors in a code of 255 bits is computed by Equation (4), where \(i\) is the interval \([0-4]\), in which each number indicates the correction capability. \[P_{i}=\binom{255}{i}p^{i}(1-p)^{255-i}\quad i=0,1,2,3,4 \tag{4}\]  
where \(i\) can take the values from 0 to 4 which is the correction capability. The expression that indicates the fraction of devices that may have an error is  
\[CEP_{out}=1-\left\{1-\left(\binom{255}{0}p^{0}(1-p)^{255}+\cdots+\binom{255}{ 4}p^{4}(1-p)^{251}\right)\right\}^{4194304} \tag{5}\]  
In the case that the type of error was another type, for example, bursts, then the type of model is not binomial and the previous expression would not represent a good approximation. In our case, it is used, as NAND flash memories present random errors, so it can be modeled in the form of Bernoulli process [44,53]. Figure 4 shows a comparative graph of different BCH codes of code length \(n=255\) and \(n=511\) and with different correction capabilities. The left vertical axis represents the probability of error in the NAND flash device after performing error correction. The right vertical axis represents the added parity bits. When more parity bits are added, the correction capability increases, however, the data rate \(k/n\) also decreases, that is, less representative data is transmitted in a sent code. The data rate is also an indicator of how much area of NAND flash memory chip is used for parity bits. The number of parity bits is an indicator of how long it may take for the microcontroller to perform the BCH encoding. In Figure 4, it can be seen that as the correction capability of the BCH increases, the probability of error in the device also decreases and the added parity bits increase. The code chosen was a BCH (255, 223, 4). In Figure 6, it can be seen that to the right of the BCH code (255, 223, 4) there are other codes that may have a higher correction capability; however, the data rate is also lower. With the chosen BCH code, the data rate \(k/n\) is 0.87. BCH encoding is performed using the LFSR of Figure 6, in which it can be seen that there are AND and XOR operations, shift registers, and a multiplexer [49]. The real form of the LFSR and the operations to be carried out depend on the parameters \(n\), \(k\), and \(t\) of the BCH code and on a generator polynomial that is mentioned in the next section.
# A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation
## 2 Numerical Model  
This paper supposes that a flow is moving at a uniform speed \(U\) along the positive x-axis direction. Considering the inviscid incompressible fluid of infinite depth without rotational flow, ignoring the influence of surface tension, the potential flow theory is applied. Therefore, a source singularity of strength \(m\) is introduced at a distance \(L\) below the surface, as illustrated in Figure 1. The transient waves can be generated with the disturbance of source. The free surface wave height and flow field velocity potential can be expressed as \(z=\zeta(x,y)\) and \(\Phi(x,y,z)\).  
Dimensionless analysis is performed with fluid velocity \(U\) and distance \(L\). The velocity potential \(\Phi(x,y,z)\) satisfies [PERSON]'s equation, the free surface kinematic and dynamic boundary condition, the radiation condition and the limiting behavior of source singularity. With \(\phi(x,y)=\Phi(x,y,\zeta(x,y))\), the boundary integral equation is written:  
\[2\pi(\phi(q)-x)=\int_{0}^{\infty}\int_{-\infty}^{\infty}[\phi(p) -\phi(q)+x-\rho]K_{1}d\sigma d\rho\] \[+\int_{0}^{\infty}\int_{-\infty}^{\infty}\zeta_{\rho}(P)K_{2}d \sigma d\rho-\frac{\epsilon}{[y^{2}+x^{2}+(\zeta(q)+1)^{2}]^{\frac{1}{2}}} \tag{1}\]  
where the \(K_{1}\) and \(K_{2}\) are kernel functions [12].  
Moreover, the free surface conditions can be simplified by the symbol \(\phi(x,y)\). Then, the kinematic and dynamic boundary conditions of the free surface are combined to be  
\[\frac{(1+\zeta_{x}^{2})\phi_{y}^{2}+\left(1+\zeta_{y}^{2}\right)\phi_{x}^{2}-2 \zeta_{x}\zeta_{y}\phi_{x}\phi_{y}}{2(1+\zeta_{x}^{2}+\zeta_{y}^{2})}+\frac{ \zeta}{F^{2}}=\frac{1}{2} \tag{2}\]  
To solve the above nonlinear problem numerically, the \(N\times M\) mesh is established on the free surface (\(N\) and \(M\) represent the number of longitude and latitude lines of the mesh, respectively). The \(x\)-coordinates and \(y\)-coordinates of nodes are \(x_{1},x_{2},\ldots,x_{N}\) and \(y_{1},y_{2},\ldots,y_{N}\) with regular intervals in the coordinate system; thus, the vector \(\mathbf{u}\) of \(2(N+1)M\) unknowns is  
\[\mathbf{u}= [\phi_{1,1},\left(\phi_{x}\right)_{1,1},\ldots,\left(\phi_{x} \right)_{N,1},\ldots,\phi_{1,M},\left(\phi_{x}\right)_{1,M},\ldots,\left(\phi _{x}\right)_{N,M}\] \[\zeta_{1,1},\left(\zeta_{x}\right)_{1,1},\ldots,\left(\zeta_{x} \right)_{N,1},\ldots,\zeta_{1,M},\left(\zeta_{x}\right)_{1,M},\ldots,\left( \zeta_{x}\right)_{N,M}]^{T}. \tag{3}\]  
More \(4M\) equations are provided by applying the radiation condition as follows:  
\[x_{1}((\phi_{x})_{1,l}-1)+\gamma(\phi_{1,l}-x_{1})=0 \tag{4}\] \[x_{1}((\phi_{xx})_{1,l}-1)+\gamma((\phi_{x})_{1,l}-1)=0\] (5) \[x_{1}(\zeta_{x})_{1,l}+\gamma\zeta_{1,l}=0\] (6) \[x_{1}(\zeta_{xx})_{1,l}+\gamma(\zeta_{x})_{1,l}=0 \tag{7}\]  
where \(\gamma\) is the decay coefficient.  
Furthermore, more details about the governing equations, the boundary integral method and numerical discretization are provided by [PERSON] et al. [12].  
Figure 1: Flow field diagram.
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 4 Overview of the Proposed Solution  
### Bloom Filter Initialization  
This section details the initial setup phase of the proposed mitigation method, focusing on the process of creating and distributing a Bloom filter for secure device enrollment within the network. This Bloom filter plays a crucial role in efficiently authenticating devices and preventing unauthorized access.
* Hash Chain Creation: Each node generates a hash chain by repeatedly applying a cryptographic hash function to a chosen random number (\(x\) times). This process creates a chain of hash values, typically denoted as \(h^{0}\) (root) to \(h(k)\) (highest level). These hashed values are then stored locally on the device.
* Root Hash Transmission: Subsequently, each device transmits the root hash value (\(h^{0}\)) of its locally generated hash chain to the designated LBR. This root hash acts as a unique identifier for the device within the Bloom filter.
* Bloom Filter Construction: The LBR aggregates the root hash values from all connected devices and integrates them into the Bloom filter data structure. The method of integration varies depending on the specific Bloom filter implementation.
* Bloom Filter Dissemination: After the Bloom filter is constructed, the LBR broadcasts it to all devices within the network. Each device then stores the Bloom filter locally to enable future membership checks.