esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Statistical Analysis  
Three models were tested in this study:  
1. **A multivariable linear model** with five variables (VI avg, VI max, VI min, \(\Delta\)VI and day of year) was used to predict weekly \(\Psi_{\text{stem}}\) (one model per week) in Mevo Beitar vineyard (hereafter, **MB-Mult** model).
2. **A single linear regression model** was used in Mevo Beitar to predict \(\Psi_{\text{stem}}\) from VIs for the entire season using the VI time series (hereafter, **MB-Reg** model).
3. **A single 'global' multivariable linear model** with the same variables as in **MB-Mult** was used to predict seasonal \(\Psi_{\text{stem}}\) from VI time series of the 81 commercial vineyards (hereafter, **Global-Mult**).  
In **Global-Mult**, variables from all four VIs were first used in a single multivariable model with a total of 20 variables, five variables per VI. Then, to avoid multicollinearity issues in the model only the statistically significant variables (\(p<0.05\)) with the highest size effect in the model were maintained, one VI per variable (total of five variables). The **Global-Mult** with the five variables was then used to predict \(\Psi_{\text{stem}}\) in the experimental vineyard of Mevo Beitar, comparing it with **MB-Mult** and **MB-Reg**. It should be noted that in **MB-Mult** and **Global-Mult**, the values of the variables change along the season, while the value of \(\Delta\)VI remains the same.  
Pearson's \(r\) of the correlation, coefficient of determination (R\({}^{2}\)), and root square mean error (RMSE) were used as measures of model performance and to compare between the models. The size effect of each variable in **Global-Mult**, which is the effectiveness of the variable in predicting \(\Psi_{\text{stem}}\), is presented as LogWorth, defined as \(-\)log10(\(p\)-value). The larger the LogWorth, the stronger is the effect of the variable in the model. All statistical analyses were carried out using JMP\({}^{\circledR}\) software version 14.0 (SAS Institute Inc., Cary, NC, USA).
## 3 Results and Discussion
### Analysis of Fire Severity Classification Accuracy for Different Spectral Bands  
Figure 10 shows the classification accuracy of fire severity based on spectral and spectral difference analysis for different fire severities. The classification accuracy of the post-fire image and the pre-fire and post-fire differential image varied significantly with wavelength. For the post-fire image, the spectral classification accuracy was optimal in the near-infrared to shortwave infrared range (722 nm-1341 nm), with the highest WF1 of 78.99% at 800 nm. However, classification accuracy in other spectral ranges was relatively low. In contrast, the classification accuracy of the spectral difference in the pre- and post-fire images was poor across the entire spectral range, with the highest WF1 of 37.89% occurring at 731 nm. These findings suggest that the spatial spectral differences induced by fire in the post-fire image are more pronounced than the temporal spectral differences in the pre- and post-fire images. The post-fire spectral differences can effectively differentiate burned from unburned areas, though distinguishing between different fire severities remains challenging. On the other hand, the spectral differences between the pre- and post-fire images were not useful for reliably identifying burned areas, and they failed to provide an effective classification of fire severity. In contrast, the post-fire spectral differences were more effective for distinguishing burned from unburned areas, whereas the spectral differences in the pre- and post-fire images are inadequate for determining fire severities.  
Figure 8: Comparison results of spectral separability among different fire severities. (**a**) Post-fire image; (**b**) Pre- and post-fire difference image.  
Figure 9: The separability comparison of different VIs in the post-fire image and different dVIs in the pre- and post-fire image combination. (**a**) DI category; (**b**) RI category; (**c**) NDI category; (**d**) dDI category; (**e**) dRI category; (**f**) dNDI category. Note: The spectral resolution is 10 nm below the 1032 nm mark and 20 nm above it. Similarly for subsequent cases.
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Critical Analysis of Contemporary Approaches Based on Evaluation Criteria  
After the comprehensive analysis of the above-mentioned state-of-the-art approaches, we have concluded that these classification approaches exploited different data sources so that some of these have used metadata while others have used the content of the documents. Based on the above discussed observations, we classified state-of-the-art approaches into three types:  
1. Content-based approaches which exploited the content data source and classified documents into only one class (single label) from the multiple classes.
2. Content-based approaches which exploit the content data source and classified documents into one or more than one class (multi-label).
3. Metadata-based approaches which exploited the metadata source of the documents and classified documents into either single label or multi-label from the multiple classes.
# Demand-Response Control in Smart Grids
## 3 Generic Formal Model for Demand Response  
In this section, we present a generic formal model for demand response where we consider that the smart grid is constituted by several consumer agents as well as a provider agent. Consumer Agents (CAi) are considered independent users who ensure their well-being. To enhance their benefits and satisfaction, each user needs a precise quantity of electricity. The purpose of a smart grid is to provide the requested power to all consumer agents and maintain a balance between the production and consumption of power (load).
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 3 Review of Edge Computing Technologies
### Caching  
Edge computing caching includes base station caching, distribution networks, and transparent caching, all of which significantly impact content distribution efficiency and service quality. Unlike traditional methods, edge computing caches can store content in edge nodes, enabling users to access data nearby instead of transmitting it from the cloud center. Additionally, edge computing caches reduce network delay and enhance service quality.  
[PERSON] et al. [70] proposed a cache-aware task scheduling strategy in edge computing to address issues related to improper placement and utilization of caching. They obtained an integrated utility function considering data transmission cost, caching value, and cache replacement penalty. Furthermore, they presented a cache locality-based task scheduling method, treating the task scheduling problem as a weighted graph affected by the positions of required data. Experimental results demonstrate that the proposed task scheduling algorithm performs better than other baseline algorithms in cache hit ratio, data transmission time, response time, and energy consumption.  
[PERSON] et al. [71] illustrated that edge caching can reduce the burden on the backhaul network, but existing works often treat storage and computing resources separately, ignoring the mobility characteristic of edge caching and portable end devices. They proposed a cooperative edge caching framework for 5G networks, utilizing edge computing resources to enhance edge caching capability. In the proposed framework, smart vehicles are employed as collaborative caching nodes for bringing contents to end equipment and sharingcontent cache tasks with base stations. The experiments show that the proposed framework alleviates content access latency and improves the utilization of cache resources.  
[PERSON] et al. [72] investigated the collaborative caching problem in the edge computing environment, aiming to reduce the data caching and migration cost. This collaborative edge data caching problem (CEDC) is formulated as a constrained optimization problem. An online algorithm called CEDC-Online (CEDC-O) based on Lyapunov optimization is proposed.
## 7 Silicon Building Blocks Containing H, OH and Alkoxy moieties as Leaving Groups
[40], a \({}^{18}\)F-silyl-bombesin derivative (**35**, Figure 11), exhibiting a most favorable _in vitro_ binding affinity to the GRP receptor (IC\({}_{50}\) = 22.9 nM), was evaluated _in vivo_ in a PC-3 tumor-bearing mouse model. In this study, the \({}^{18}\)F-labeled bombesin derivative unfavorably showed a low and unspecific _in vivo_ tumor uptake of only 0.6 %ID/g at 1 h p.i., despite its high affinity to the target receptor. This effect can most probably be attributed to the high lipophilicity of the tracer caused by the stable but bulky and unfortunately highly hydrophobic silicon-containing moiety, resulting in the observed high accumulation in liver, gallbladder and intestine. Thus, attempts were made to compensate the high lipophilicity of the organo-silicon moiety by introducing hydrophilic substituents into the peptide sequence in order to achieve a more favorable _in vivo_ biodistribution of SiFA-derivatized peptides [38]. In order to increase the hydrophilicity of peptidic compounds, PEG and carbohydrate building blocks have been used before [42; 43; 44]. Thus, Tyr\({}^{3}\)-octreatate derivatives were synthesized, comprising a SiFA moiety and a carbohydrate and/or a PEG moiety. The resulting peptide derivatives **36** and **37** (Figure 12) were radiolabeled, their  
Figure 11: Structure of the \({}^{18}\)F-silyl-bombesin derivative **35** used for _in vivo_ biodistribution studies in tumor-bearing mice. Figure 10: Course of labeling reactions described by [PERSON] _et al_. lipophilicity and _in vitro_ binding affinities towards the Somatostatin receptors were determined, and their _in vivo_ biodistribution characteristics determined and compared to the non-modified SiFA-Tyr\({}^{3}\)-ocretotate **29** (Figure 12). The derivatization of the Tyr\({}^{3}\)-ocretocate with hydrophilic building blocks showed only a minor influence on the binding affinities to the target receptors, resulting in potent target receptor binders while at the same time compensating, at least to some extent, the high lipophilicity of the organo-silicon moiety (log \(P_{\text{OW}}\textbf{36}\) = 1.23, log \(P_{\text{OW}}\textbf{37}\) = 0.96 _vs._ log \(P_{\text{OW}}\textbf{29}\) = 1.59). This positive effect was also reflected in the successful visualization of the tumor _in vivo_ in an AR42J tumor-bearing mouse model by **37**, whereas the non-modified \({}^{18}\)F-SiFA-peptide **29** showed only a very low and unspecific accumulation in the tumor tissue due to its high lipophilicity [38]. Thus, although many improvements have been made in the radiolabeling of peptides with \({}^{18}\)F using silicon building blocks carrying bulky substituents, some efforts still have to be undertaken to optimize the _in vivo_ biodistribution properties of the resulting \({}^{18}\)F-silicon-derivatized peptides.
## 3 Overview of OMF Architecture  
Before designing and implementing the OMF-enabled test environment, it is essential to understand the underlying structure and components of the OMF architecture. This section provides an in-depth look at the main components and how they interact to create a dynamic and scalable test environment. The step-by-step workflow for running an experiment using OMF is also examined, capturing the intricacies of each phase, from setup to data collection.
# Multi-Scale Features for Transformer Model to Improve the Performance of Sound Event Detection
## 4 Experimental Results  
### Experimental Condition  
Herein, we used the DCASE 2019 Task 4 dataset [1]. The training set consists of 3 parts: 2045 synthesized audio clips with strong labels, 1578 real-recording audio clips with weak labels, and 14,412 real-recording audio clips without labels. The real-recording audio clips were taken from AudioSet [29] and were converted to a 1-channel, 16-bit, 16 kHz sampling rate for consistency in the audio data format. Synthetic clips were generated using clean signals from the FSD dataset [30] and noises from the SINS dataset [31]. These training data are used to update the weights of the DNN during iterations. We also have validation data consisting of 1168 real recording clips. They are used in early stopping to avoid overfitting during the training. The length of each clip is 10 s, and there are 10 different sound classes, usually domestic or household. The details of the dataset are listed in Table 1. For the performance test of the proposed method, we presented the experimental results on the validation data as well as the evaluation data, which were from the official test dataset for the DCASE 2019 task 4 Challenge.  
We used the Adam [32] optimizer with a learning rate of 0.001 and batch size of 128. The StepLR scheduler from PyTorch was employed to reduce the learning rate during the training iterations. The ratio of data volume in each batch is 1:1:2 for the weakly labeled, strongly labeled, and unlabeled data. The training was performed for 3100 and 30,000  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Validation Set**} & **Evaluation Set** \\ \hline Label Type & Weak & Strong & Unlabeled & Strong & Strong \\ \hline No. of clips & 1578 & 2045 & 14,412 & 1168 & 692 \\ \hline Properties & Real recording & Synthetic & Real recording & Real recording & Real recording \\ \hline Clip length & & & 10 s & \\ \hline Classes (10) & Speech, Dog, Cat, Alarm bell ring, Dishes, Frying, Blender, Running water, Vacuum cleaner, Electric & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset used in the experiment.  
mini-batch iteration steps for the basic Transformer model and the mean-teacher model, respectively. More details regarding the training conditions are presented in Table 2.
## 2 Materials and Methods
### Description of the Selected DL Architectures  
CNNs and ViT-based systems have some differences that may make their application more appropriate in one scenario or another [38]. The most important ones are:  
* CNNs are designed to exploit local spatial correlations through the use of convolutional layers, which makes them effective at capturing local patterns and features in an image. In contrast, ViT-based systems employ an attention mechanism that allows them to capture global relationships between image patches, which makes these systems more suitable for handling long-range dependencies and capturing the global context of an image.
* CNNs require a larger number of parameters, which makes them computationally expensive. ViTs take advantage of attention mechanisms to capture the global context more efficiently with fewer parameters. This can be advantageous when computing resources or memory are limited.
* CNNs are intrinsically translation-invariant, thanks to the use of shared weights in the convolutional layers. This property is very useful for image classification. In contrast, ViTs do not have this inherent translation invariance due to their self-attenuation mechanism, although it can be incorporated by positional embedding.
* Both CNNs and ViT-based systems have been shown to have a high generalisability capacity when trained on large datasets. In this sense, ViT-based systems are more sensitive to the size of the training set, as they tend to require larger sets than CNNs.
* CNNs lack interpretability; it is difficult to understand how they make their decisions, and they are often used as \"black boxes\". In that sense, the attention mechanisms of ViT-based systems are more useful, as they allow for analysing which parts of an image are more relevant when making predictions.  
In the following subsections, we detail the most relevant features of all the DL models used in this study.
## 5 State-of-the-Art
### Secure Execution  
In [78], the authors introduced a portable and efficient implementation of a Crystals-Kyber post-quantum key encapsulation mechanism (KEM) algorithm based on Wasm. The overall software is written in JavaScript with core performance components in Wasm. This software was shown to significantly outperform other JavaScript-based Kyber implementations in terms of key generation, encapsulation, and decapsulation. In [79], an identity-based cryptography library called CryptID was designed for efficiency and portability across various platforms, including desktop, mobile, and IoT. Enabled by Wasm, CryptID can operate both client-side and server-side. One unique aspect of CryptID is the use of structured public keys that can contain arbitrary metadata, allowing for a wide range of domain-specific use cases. In [80], a comprehensive review of recent malware and security threats related to IoT systems is provided. This review proposes a solution that allows secure applications to run on IoT devices in a secure execution framework by using verified bytecode in an isolated environment. This ensures that security is enforced by software and does not rely on the security features provided by the hardware components. Another survey paper [81] studied various techniques and methods for Wasm binary security and proposed future research directions regarding the current lack of Wasm binary security research. Memory safety aspect is very important to Wasm environment. In [82], the authors introduced MS-Wasm, an extension to Wasm that allows developers to capture low-level C/C++ memory semantics at compile time. At deployment time, Wasm compilers can use these added semantics to enforce different models of memory safety depending on user preferences and the hardware available on the target platform. MS-Wasm offers a range of security-performance trade-offs, and allows users to upgrade to stronger models of memory safety as hardware evolves. In [83], the security implications of cross-compiling C programs to Wasm are investigated. This work found that the execution of certain binaries produced different results across platforms for various reasons, including the use of different standard library implementations, lack of security measures in Wasm, and differing semantics of the execution environments. These findings suggest that porting existing C programs to Wasm may require source code adaptations to maintain security. Swivel was proposed in [84]. It is a compiler framework designed to harden Wasm against Spectre attacks, which can bypass Wasm's isolation guarantees. Swivel ensures that potentially malicious code cannot exploit Spectre attacks to break out of the Wasm sandbox or force other Wasm clients to leak secret data. The work presents two designs, a software-only approach and a hardware-assisted approach, and demonstrates that Swivel's overhead is much lower than existing defenses. Finally, a multi-layer secure framework dedicated to hardware-constrained devices, which is increasingly important considering the proliferation of IoT devices, was developed in [85].
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 2 Related Studies
### Shadow Detection Methods in Drone Images
#### 2.2.1 Property-Based  
Property-based methods mainly focus on detecting shadows based on shadow properties in images. Considering that shadow properties in images can be explored in different ways, property-based methods are then further classified into thresholding, invariant color model, and object segmentation. Thresholding is a simple image processing method that creates binary images by setting a pixel value threshold. The global thresholding and local thresholding [12], bimodal histograms, and Otsu's threshold [13] are examples of approaches that are generally used due to the simplicity of the implementation. Unfortunately, there are a lot of weaknesses of these methods. Some thresholding methods apply various threshold values to different segments of the image [12]. Moreover, implementation of the thresholding methods highly depends on prior knowledge of shadow properties. One of the shadow properties used is the significant changes in the red band of RGB components in the Optimized Shadow Index of the shadow region [3].  
For the invariant color model methods, color information is exploited for shadow detection by using invariant properties of some color spaces, such as CIELAB [14] and C1C2C3 [15]. The drawback of these methods is that they depend mainly on the knowledge of the presence of shadow in different channels of color model. Proposing an object-oriented method, [16] performed a Gaussian Mixture Model to refine soft shadow and added the result to another result from image segmentation. While it managed to recover incomplete shapes and holes in the detected shadow, the accuracy obtained was not higher than 90%, while a lot of shadow analysis needed to be performed, such as clustering analysis. Another object-oriented segmentation approach was performed by [17], who used shadow features in the image segmentation step, followed by extraction of shadow regions using the statistical features of the images. They also used spatial information to avoid false shadow detection, but it still faced difficulty detecting small shadows.
## 3 Results
### The Ring-Shaped Structure. The Optical Vortex Beam  
A more complicated structure than the single soliton appears at higher values for the injection current, as can be seen in the picture corresponding to zone II in Figure 2. This emission pattern is normally made by a three-peak ring with a central hole in the middle and we can call it an optical vortex [23; 24]. Its dimensions are of about ten microns for the distance between peaks; a plot of this structure is shown in Figure 3.  
These were observed recently in coupled VCSELs--one operated as a gain device and one as a saturable absorber [25]. Vortices in self-focusing Ginzburg-Landau models were predicted [26]. References [27; 28] investigate a nonlinear cavity with a saturable or cubic, i.e., Ginzburg-Landau-like nonlinearity, coupled to an additional linear filter which provides a minimal model for a VCSEL with frequency-selective feedback, similar to our experiment.  
The homogeneous polarization, in the same way as in the previous section, describes a constant orientation for the whole area of the emission, obtaining one value or its orthogonal depending on the injection current value, with a very similar intensity pattern (\(S_{0}\)) in both cases. This structure is essentially identical to the vortex soliton observed in a prior work of our group [12], including a phase singularity in the dark center.  
Corresponding generalized vortex solitons were predicted [29] and indications observed [30] in single-pass conservative systems and termed azimuths. Theoretical predictions also exist for dissipative systems [31].  
Figure 6 shows the states of polarization for the vortex. The angle takes again a very similar value to that already shown for the single soliton. In addition, the linear and circular components have the same behavior already seen in the soliton case, i.e., a particular sense of rotation is associated to one of the linear orientations.