esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Section and Results Analysis
### Experiments and Result Analysis
#### 4.3.3 Dynamic Window Size and Computation  
Figure 6 shows the size of the dynamic window and the corresponding time required to train each streaming data during the OASVR training process on different datasets. The training time for each streaming data is approximately 5 s, and the addition of the dynamic window effectively controls the training time as the size of dataset increases compared to the original model. Furthermore, it allows for the adjustment of appropriate window sizes in various industrial scenarios to meet diverse time constraints. According to Figure 6c, it can be observed that the window size reaches only up to 394 when the train number reaches 2000 due to the presence of a dynamic window mechanism. This significantly reduces computational load while still maintaining favorable model performance.
###### Abstract  
The use of lipid nanoparticles as drug delivery systems has been growing over recent decades. Their biodegradable and biocompatible profile, capacity to prevent chemical degradation of loaded drugs/actives and controlled release for several administration routes are some of their advantages. Lipid nanoparticles are of particular interest for the loading of lipophilic compounds, as happens with essential oils. Several interesting properties, e.g., anti-microbial, antitumoral and antioxidant activities, are attributed to carvacrol, a monoterenoid phenol present in the composition of essential oils of several species, including _Origanum vulgare_, _Thymus vulgaris_, _Nigella sativa_ and _Origanum majorana_. As these essential oils have been proposed as the liquid lipid in the composition of nanostructured lipid carriers (NLCs), we aimed at evaluating the influence of carvacrol on the crystallinity profile of solid lipids commonly in use in the production of NLCs. Different ratios of solid lipid (stearic acid, beeswax or carnauba wax) and carvacrol were prepared, which were then subjected to thermal treatment to mimic the production of NLCs. The obtained binary mixtures were then characterized by thermogravimetry (TG), differential scanning calorimetry (DSC), small angle X-ray scattering (SAXS) and polarized light microscopy (PLM). The increased concentration of monoterenoid in the mixtures resulted in an increase in the mass loss recorded by TG, together with a shift of the melting point recorded by DSC to lower temperatures, and the decrease in the enthalpy in comparison to the bulk solid lipids. The miscibility of carvacrol with the melted solid lipids was also confirmed by DSC in the tested concentration range. The increase in carvacrol content in the mixtures resulted in a decrease in the crystallinity of the solid bulks, as shown by SAXS and PLM. The decrease in the crystallinity of lipid matrices is postulated as an advantage to increase the loading capacity of these carriers. Carvacrol may thus be further exploited as liquid lipid in the composition of green NLCs for a range of pharmaceutical applications.**  
carvacrol; stearic acid; beeswax; carnauba wax; nanostructured lipid carriers; crystallinity
# A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
## 4 Filter Pruning Based on Feature Maps Clustering
### K-Means Method
#### 4.1.1 Feature Map Dimension Reduction  
The method for a feature map's dimension reduction mainly includes SVD (singular value decomposition) and PCA (principal component analysis) [13]. Similar to the SVD method, the basic idea of the PCA method is to project the original data onto a new coordinate system. If the variance of all data on a coordinate axis is the largest, this axis is recorded as A1. That is, the projection of data is most scattered in the direction of the A1 axis, which means that more information is retained. Then, A1 is the first principal component. Then, look at A2. If the covariance between A2 and A1 is 0, that is, the information of A2 and A1 does not overlap and the variance of data in this direction is as large as possible, then A2 is the second principal component. By analogy, it can find the third, fourth, and Nth principal components.  
Usually, only the first two principal components of the data need to be preserved so that the high-dimensional image can be transformed into a two-dimensional vector.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 3 Existing Problems  
For the purpose of the final inversion application, this section will analyze and discuss the existing problems in the GNSS-R vegetation study.
# Coseismic Stress Change and Viscoelastic Relaxation after the 2008 Great Sichuan Earthquake
## 4 Discussion
### Effect of Lower Crust or Mantle Rheological Properties on the Stress Evolution  
In this section, the main factors that may lead to uncertainty in the numerical results are discussed. The uncertainty in stress change may be produced by all the uncertain parameters used in the stress calculation. The main influencing factors include the inhomogeneity of the crust, the use of a large source model, the viscosity, the locations of seismic events, and the regional stress state (e.g., [80; 81; 82]). In particular, our results may be affected by changes in viscosity. According to the study of deformation and rheological properties  
Figure 10: Magnitude and cumulative numbers of seismic events in the study area from 2000 to 2020 using various cutoff magnitudes.  
Figure 9: Scatter plot of magnitude relative to the sequential number of events in this study area.  
after earthquakes [23; 62; 63], a low viscosity in the lower crust is required by crustal flow beneath the Tibetan Plateau. Various methods are used to estimate the viscosity of the area, yielding values from \(10^{16}\) to \(10^{21}\) Pa-s. We assume that the mantle or lower crust has this viscosity range and test the range of stress variations to check the stability of the results. To test the effect of viscosity uncertainty, we change the viscosities of layers 3-6 of the model (Table 1) from \(10^{16}\) to \(10^{21}\) but maintain the original value in other layers. Considering that there various models use different combinations of viscosities for both the upper mantle and lower crust, only two models with maximum values and minimum values of viscosity are used to analyze the effect of stress changes (Table 5). Using these models, the stress changes on the Jiuzhaigou event induced by five previous earthquakes are calculated. Other parameters remain unchanged. The results are listed in Table 6. Furthermore, we also use the friction coefficients \(\mu^{\prime}\) = 0.2, \(\mu^{\prime}\) = 0.4 and \(\mu^{\prime}\) = 0.6 to validate the stability of the results; these results are also listed in Table 6. The deviation in the stress change caused by different combinations of viscosities and friction coefficients is small and does not have a significant effect.
# Efficient and Secure Federated Learning for Financial Applications
## 2 Related Works
### Secure Aggregation
#### 2.2.1 Sparse the Encrypted Gradient  
After local training, each participant calculates the gradient information to be sent and uses the gradient sparsification method for Top-k selection to record the location of the gradient to be sent. Subsequently, the encrypted mask is added to the original gradient, and then only the information at the position corresponding to the record on the masked gradient is sent according to the previous record. In this way, the gradient is protected and the server cannot collect the real gradient information. However, there is a problem with this method. Even if each participant has a relatively similar trend toward the descending direction of the gradient, it cannot guarantee that all Top-k positions will completely overlap in each round of training. As a result, when a participant encrypts a specific part of the gradient vector, another participant holding the symmetric mask may not have the corresponding Top-k gradient at that location. Therefore, the encryption mask at that location will not be sent and the mask cannot be eliminated in the aggregation process. The mask that cannot be eliminated is equivalent to adding noise to a component position of the gradient vector. If the influence of the mask is too large, it will have a great impact on the overall convergence direction, as well as the convergence speed and accuracy, of the global model. In the case of multi-party participation, this situation may be more serious and it is not feasible to directly sparsify the encrypted gradient vector.
# The Effect of the Improvement Technology on the Quality of Midu Pork Roll
## 3 Results and Discussion
### The Influence of Process Optimization on the Texture of MPR  
The texture properties of MPR in different groups are shown in Table 3. Significant differences were found in texture between C, T1 and T2. Hardness, cohesion, chewiness and shearing force in C were all significantly higher than in T1 and T2 (\(p<0.05\)). Studies have found that the addition of exogenous enzymes during the processing of meat products can accelerate the degradation of flavor precursors in a short time and cause the structure to break [34]. When protein is degraded, it can lead to physical, chemical and structural changes in the myofibril. Muscle fibers and tendons were broken down and the cellular structure integrity was degraded. Subsequently, this improved the tenderness of the meat [35]. Usually, a reduction in hardness is considered positive in fermented meat [36]. Meanwhile, the addition of fat could significantly improve the rheological properties and cohesion of MPR so that MPR from the new process had a smooth taste [37].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Physical and Chemical** & **C** & **T1** & **T2** & **Sig.** \\
**Indicators** & & & & \\ \hline pH & 4.26 \(\pm\) 0.01 \({}^{\text{c}}\) & 5.54 \(\pm\) 0.03 \({}^{\text{b}}\) & 5.79 \(\pm\) 0.02 \({}^{\text{a}}\) & **\(\ast\ast\)** \\ Aw & 0.96 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.95 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.96 \(\pm\) 0.01 \({}^{\text{a}}\) & ns \\ Moisture content (\%) & 56.05 \(\pm\) 0.49 \({}^{\text{c}}\) & 60.32 \(\pm\) 1.13 \({}^{\text{a}}\) & 59.18 \(\pm\) 0.83 \({}^{\text{b}}\) & **\(\ast\ast\)** \\ \(L^{*}\) & 58.70 \(\pm\) 1.68 \({}^{\text{a}}\) & 47.42 \(\pm\) 0.47 \({}^{\text{c}}\) & 53.28 \(\pm\) 0.41 \({}^{\text{b}}\) & **\(\ast\ast\)** \\ \(a^{*}\) & 14.49 \(\pm\) 0.25 \({}^{\text{c}}\) & 24.11 \(\pm\) 0.86 \({}^{\text{a}}\) & 21.57 \(\pm\) 0.45 \({}^{\text{b}}\) & **\(\ast\ast\)** \\ \(b^{*}\) & 13.99 \(\pm\) 0.13 \({}^{\text{c}}\) & 20.85 \(\pm\) 1.28 \({}^{\text{a}}\) & 15.80 \(\pm\) 0.30 \({}^{\text{b}}\) & **\(\ast\ast\)** \\ Cooking loss rate (100\%) & 18.21\(\pm\) 1.76 \({}^{\text{a}}\) & 8.79\% \(\pm\) 0.40 \({}^{\text{a}}\) & 9.80\% \(\pm\) 0.73 \({}^{\text{b}}\) & **\(\ast\ast\)** \\ Yield (100\%) & 75.23\(\pm\) 1.37 \({}^{\text{b}}\) & 85.91\% \(\pm\) 0.25 \({}^{\text{a}}\) & 86.03\% \(\pm\) 0.47 \({}^{\text{a}}\) & **\(\ast\ast\)** \\ \hline \hline \end{tabular}  
* C: Control group; T1: test group 1 (using the improved technology of Sichuan spicy flavor formula); T2: test group 2 (using the improved technology of halogen flavor formula). The results are expressed as mean \(\pm\) standard deviation (\(n=3\)). \({}^{\text{a}\text{-}\text{c}}\) within the same row, different lowercase letters indicate significant differences (\(p<0.05\)). Sig. is the significance regarding the MPR treatment. ns: not significant (\(p>0.05\)); \({}^{\text{m}\text{-}\text{v}}\) very important (\(p<0.01\)).  
\end{table}
Table 2: The effect of the improvement in pH, water activity, water content and color of MPR.
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Ablation Study
#### 3.3.3 Location of SSE Module  
In order to discuss the effect of the location of SSE module on the detection accuracy, we ensure that there are no ASPP modules in each model, and the reduction ratio \(r\) and other hyperparameters are the same. Since there are 6 convolutional layers in the model, we select 5 groups of adjacent convolutional layers as the input of SSE module in turn. As shown in Table 4, the detection accuracy of SSE module with Con_2 and Con_3 as input is the highest, reaching 96.87%. The number of feature map channels obtained in the shallow layer is small, and the global information obtained by squeeze operator is limited, which blocks the capacity and ability of the model. Meanwhile, the number of channels of the feature map obtained in the deeper layer is larger, which increases the risk of over fitting when the datasets are small. Therefore, different locations of SSE module should be chosen for different datasets to achieve the best detection accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SE Module** & **SSE Module** & **Epochs** & **Accuracy** \\ \hline - & - & 100 & 95.53\% \\ \(\surd\) & - & 100 & 96.23\% \\ - & \(\surd\) & 100 & 97.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with or without the SSE module.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reduction Ratio** & **Epochs** & **Input** & **Accuracy** \\ \hline
0.5 & 100 & Con_2, Con_3 & 97.77\% \\
1 & 100 & Con_2, Con_3 & 96.29\% \\
2 & 100 & Con_2, Con_3 & 96.29\% \\
4 & 100 & Con_2, Con_3 & 95.30\% \\
8 & 100 & Con_2, Con_3 & 94.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between SSE module at different reduction ratios.
# Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management
## 4 Blockchain Technology  
A Blockchain is defined as a \"digital, decentralized and distributed ledger in which transactions are logged and added in chronological order with the goal of creating permanent and tamperproof records\" [34] (p. 547). Essentially, it is a novel mechanism for storing, securing and sharing data between multiple nodes in a network [68]. A Blockchain breaks away from the traditional centralized approach by managing chain data across a distributed and interlinked network of nodes. The main characteristics of Blockchains are shared recordkeeping, immutability, decentralization, distributed trust, multiple-party consensus, independent validation, tamper evidence, and tamper resistance [69; 70]. The term 'Blockchain' gained its popularity as the output of a combination of configured technologies, tools and methods underpinning the cryptocurrency Bitcoin. In itself, Bitcoin is a decentralized digital currency based on an open system of computer networks and online communication protocols [33] and was the first successful application built on an online Blockchain.  
Blockchains can be configured to encrypt and store on-chain or off-chain data and record timestamped transactions. Furthermore, they can automate agreements through the utilization of smart contracts to run procedures based upon a set of conditions, terms, and rules that participants in the system have agreed upon [71]. A Blockchain platform can support multi-party exchange relationships in global supply chains by authenticating participant identities, authorizing their access and enhancing recordkeeping of transactions. This capability is possible by cryptographic mechanisms and recursive hashing of blocks. Each block contains a header and a body, the former of which contains the hash of the previous block, thus connecting the individual blocks. Any attempt to tamper with a block necessitates that the headers of previous and consecutive blocks be changed accordingly to avoid detection, and it gets progressively more difficult to tamper with as the chain gets longer. Since their pervasiveness and distributed nature characterize IoT networks, a centralized approach to collecting, storing, and analyzing all relevant supply chain data may cause delays and lead to a situation often referred to a single point of failure. A Blockchain, therefore, has the potential to address the challenges mentioned above and provide supply chain exchange partners with trust based on decentralization [31]. The lack of centralized controls in Blockchains ensures a high-level of scalability and robustness by using resources of all involved nodes and eliminating many-to-one traffic flows [30].
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 2 Related Work
### Change Detection in Remote Sensing  
Change detection in remote sensing has evolved significantly since its inception [15], starting with pixel-based methods that utilize algebraic, statistical, and transformation-based techniques. Algebraic methods, such as image differencing and change vector analysis (CVA) [16], construct difference images and apply thresholds to generate change maps, but their reliance on fixed thresholds often limits robustness. Statistical methods, like those used for hyperspectral or SAR images [17; 18], detect changes based on pixel distribution attributes, though they often struggle with complex scenes. Transformation-based approaches, including principal component analysis (PCA) and multivariate alteration detection (MAD) [19], enhance changes by transforming images into a feature space, but they still face challenges with interpretability.  
Unsupervised methods, which extend traditional techniques, aim to detect changes without labeled data. For example, logical verification CVA (LV-CVA) [20] introduces logical reasoning to reduce false detections, while Markov random field (MRF)-based methods [21; 22] incorporate spatial and contextual information to improve detection accuracy. Although these methods enhance robustness, they remain constrained by their dependence on predefined rules and assumptions, limiting their applicability in highly complex scenarios.  
Overall, while traditional methods have contributed to the development of CD, their limitations in accuracy and adaptability highlight the need for modern, data-driven approaches that can better handle the complexities of high-resolution remote sensing data.
# Grasping Claws of Bionic Climbing Robot for Rough Wall Surface: Modeling and Analysis
## 3 Design and Analysis of the Miniature Claws
### Calculation of Hook Top Size  
The maximum top size of the micro-hook is associated with the linear profile roughness of wall surface \(R_{u}\), the frictional coefficient between hook and wall \(\mu\), and the average width of wall profile unit \(S_{m}\). The maximum top size of the micro-hook is derived from Reference [21]. The top size of the micro-hook should be lower than the maximum size. [PERSON] et al. [9] established the models of hook and surface profile, and experimentally measured the relationships of the number of unit centimeter micro-bulges with the hook top size, hook load angle, and surface roughness. Spinybottl, the wall-climbing robot, as proposed by [PERSON], had a hook top diameter of 25 \(\upmu\)m and a hook neck diameter of 200 \(\upmu\)m. [PERSON] et al. [22] established the relationship between the hook top sizes with the surface roughness of objects and pointed out that, the stable stressing point could be achieved as long as the hook top radius was lower than the average radius of the micro-bulges on the wall surface. The smaller that the hook top size is, the stronger is the adhering ability on the rough wall surface.  
Figure 1: The grasping module of cross-arranged claws. 1—Secondary claws, 2—connecting rod, 3—cylinder, 4—main body, 5—fixture screw thread, 6—single tiny hook, 7—crew nut, 8—spring washer, 9—elastic steel sheet, 10—connecting plate, 11—support frame, 12—hose insertion of the cylinder.  
In present study, the concrete surface was taken as an example. Related parameters were measured as follows: surface profile linear roughness \(R_{a}=93.0\)\(\upmu\)m, frictional factor between hook and concrete surface \(\mu=0.5\), average width of wall profile unit \(S_{m}=100\)\(\upmu\)m, distance from the centroid of the robot to the wall \(L=30\) mm, and distance of the upper and lower claws of the robot \(D=120\) mm. We derived \(\tan\alpha=2L/D\) and the contact angle \(\alpha=\arctan(\frac{2\times 30}{120})=26.5651^{\circ}<45^{\circ}\). Using MATLAB simulation, we calculated that the maximum radius of the micro-hook top (Figure 2) was \(r_{\text{max}}=43.1397\)\(\upmu\)m. We set the radius of the micro-hook top as \(r_{s}=20\)\(\upmu\)m, which was approximately equivalent to the size calculated by [PERSON]. The accuracy of the interaction model of the micro-hook and the micro-bulges on the wall was also validated.
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 2 Related Work
### Outliers  
There is evidence from multiple authors that contextualized representations of transformer models (in particular, the BERT family) contain undesirable outliers (interestingly, abnormal dimensions were also previously observed for GloVe vectors in [128]). These are certain positions in word vectors with unusually high values. The authors of [129] called it outlier dimensions; in [130], it is referred to as outlier neurons, while the authors of [102] call it rogue dimensions. In all of these works, it is agreed that it is a significant contributor to the anisotropy of the representation space.  
There is debate about what causes outliers. The authors of [129] found that outliers are essential for good downstream performance. They regard it as a distinct model property that emerges during training and even encourage one to consider it during weight initialization. In contrast, in [102], it was found that the behavior of the model is not driven by outliers; rather, only a small subset of linguistic abilities is handled there (such as positional information; its relationship with outliers was revealed in [130]). Even more strangely, the authors of [125] found that the embedding space of the multilingual BERT model does not have outliers as the English BERT does, but is still anisotropic.  
There are several options to account for outliers. The most obvious solution is to remove them. The authors of [130] showed that this brings improvements for tasks that directly use the geometry of the embeddings, such as semantic textual similarity; however, ref. [129] showed that disabled outliers significantly degrade both Masked Language Modeling (MLM) loss and downstream task performance. The authors of [102] suggest that rogue dimensions be accounted for through the standardization transformation. Both options aim to reduce the dominating effect of the highest-value dimensionsduring the similarity measure, such as cosine similarity or Pearson correlation. On the other hand, one can argue that the similarity measure we use, not the representation space, needs improvements.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 3 Definition of the User Requirements
### Precision Requirements
#### 3.3.3 Image Quality and Geo-Location Requirements  
A threshold Modulation Transfer Function requirement for the IR optics is defined in Table 6. In order to collect accurate FRP measurements, the MWIR and LWIR bands must collect spatially explicit radiometric measurements. The intent of this requirement is to ensure that the majority of the observed energy originates from within the pixel foot print area.  
Wildfire management staff relying on the products from a satellite in an operational setting require that the data is accurately geo-referenced to ensure the location of target wildfires is known with a high degree of certainty (Table 6). Furthermore, FMF-4 and -5 require the data to be compared to observations made from other sources; this cross-platform data fusion necessitates precise geo-location to enable accurate comparisons [88].