esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Application and Testing of Virtual Constellation in Apalachicola Bay  
#### 2.4.1 Selecting the Baseline Sensor  
The baseline sensor is designated as the target sensor while the peer sensors provide complementary observations to the target sensor in an effort to estimate missing data. In this study, we used NDVI derived from three satellite systems (L8, S2A, and ASTER) for data fusion. Overall, three primary factors were considered when selecting the baseline sensor: Longest available historical data record; maximum overlapping with peer sensors; and minimum percentage of monthly cloud obscured data.  
Cloud cover percentage (i.e., CC) was calculated as the number of cloud obscured pixels indicated in the imagery metadata divided by the total number of pixels in each image. The percentage of visible pixels (100-CC) for each sensor from 2013 to 2018 is shown in Figure 5.  
As shown in Figure 5, Landsat (including previous generations) has the longest record dating back to August 1972. ASTER has the next longest record beginning in July 2000 and S2A has the shortest record beginning in July 2015. Figure 5 also shows that the coincident time period for all three sensors begins in 2015, therefore the period of analysis presented herein is from 2015 to 2018 when all three sensors were simultaneously operational (42 months total). These three sensors have spectral and spatial similarity which minimizes pre-processing and aids in information persistence during the analysis, especially in the sensors chosen as peers rather than primary. Along with the longest  
Figure 5: Monthly percentage of visible pixels in the satellite imagery. The gray bars in the background are provided for scale and indicate 100% visibility.  
period of record, L8 has the highest percentage (\(\sim\)80%, S2A and ASTER have \(\sim\)71 and \(\sim\)52%, respectively) of visible data over the coincident time period from July 2015 to 2018, therefore it was selected as the primary sensor. Since L8 is the primary sensor, observations from the peer sensors can be integrated into the target L8 image when necessary. In other words, the output of virtual constellation is an L8 image where missing values due to clouds are estimated using S2A or ASTER.
# Fine-Grained Fault Sensitivity Analysis of Vision Transformers Under Soft Errors
## 3 Results
### Results and Analysis
#### 3.2.3 Type-Wise  
In this part of the study, we systematically injected faults into the projection matrices of the query (Q), key (K), and value (V) components at each layer within the attn.qkv sub-block to assess the impact of different weight matrix types on overall model accuracy. As shown in (3), a single linear transformation (fully connected layer) can be applied to compute Q, K, and V simultaneously, so the size of attn.qkv is \(3(N+1)\times D\), where \(N+1\) represents the length of input patches plus a _clk_token_, and \(D\) stands for the dimension of each 1-D patch. For each simulation, the weight matrix attn.qkv was divided into three parts (\(W_{Q}\), \(W_{K}\), \(W_{V}\)), and a weight parameter was selected from one of these three matrices (e.g., \(W_{Q}\)) at a designated layer (e.g., layer-x). The model was then executed 10 times to assess the effect of errors on \(W_{Q}\) in layer-x. Figure 8 presents the vulnerability factor of the Q, K, and V projection matrices across different layers for various model configurations. For the B16-384-augreg model with a 384-dimensional embedding and augmentation-based regularization, a notable peak in the vulnerability factor of the V matrix was observed at the first layers. Under the same conditions, the B32-384-augreg model showed a similar trend where the V matrix exhibited heightened vulnerability among the three projection matrices in the initial layers. This heightened sensitivity can be attributed to the role of the V matrix in directly shaping the attention outputs, as shown in (4). Unlike the Q and K matrices, which determine attention weights through similarity computation, the V matrix transforms the actual content that is propagated through the self-attention mechanism. Therefore, faults in the V matrix may have a more immediate and amplified effect on the resulting feature representations. The trend diminishes in later layers, and this phenomenon is similar to the trend in Section 3.2.2. For L16-384-augreg and L32-384-orig models, the vulnerability factors for Q, K, and V remain close to 1 across all layers, indicating a more uniform distribution of sensitivity. This phenomenon can be attributed to the relatively low vulnerability factor observed in ViT Large models. These results indicate that ViT Large models have much less reliance on the attn.qkv sub-block, particularly in the first layer, as shown in Figure 6b.  
Figure 7: Layer-wise vulnerability analysis of ViT-Huge-14 model. Layers 1, 3, and 13 exhibit the highest vulnerability factors, with early layers being the most sensitive overall (error bar up to 0.0030).
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 4 Results and Discussion
### Dimension Reduction  
The outcome of applying dimension reduction before the clustering process is examined in this experiment. Considering that the output embeddings created by the transformer-based models are highly dimensional, it is likely that numerous features are redundant [43]. Therefore, both t-SNE and UMAP, which are non-linear algorithms, were examined, comparing the results against the use of the original embedding. The algorithms' parameters were empirically tuned by randomly repeating the simulation multiple times. The best number of dimensions and neighbors was observed to be 2 and 70, respectively. Furthermore, for UMAP, the optimal minimum distance was 0, while for t-SNE, the optimal perplexity was 50.  
Figure 3 shows t-SNE and UMAP applied to [PERSON] and [PERSON]'s embeddings (the best-performing models using a single layer), respectively, and all layers of the models were examined. Using any of the dimension reduction algorithms led to a substantial performance improvement. Notably, it was observed that UMAP increased by almost  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Pairs (Layer Number! Layer Number)**} & \multicolumn{6}{c}{**Tuples (Layer Number! Layer Number)**} \\ \cline{2-13}  & 112 & 314 & 516 & 718 & 9110 & 11112 & 11213 & 41516 & 71819 & 101112 & 11213 & 41516 & 71819 & 1011112 \\ \hline BERT & **43.98** & 34.22 & 34.07 & 32.04 & 35.56 & 41.82 & 42.48 & 33.94 & 33.44 & 43.47 & 42.48 & 33.94 & 33.44 & 43.47 \\ RoBERTa & 28.89 & 32.19 & 32.79 & 34.94 & 33.99 & **37.96** & 29.91 & 32.54 & 33.76 & 36.88 & 29.91 & 32.54 & 33.76 & 36.88 \\ GPT-2 & **36.85** & 24.71 & 17.18 & 15.93 & 15.97 & 13.99 & 33.79 & 19.43 & 16.47 & 14.25 & 33.79 & 19.43 & 16.47 & 14.25 \\ XLNet & **20.31** & 16.19 & 10.06 & 9.71 & 10.18 & 7.17 & 19.04 & 11.58 & 9.79 & 8.86 & 19.04 & 11.58 & 9.79 & 8.86 \\ ALBERT & 18.22 & 17.58 & 19.01 & 16.83 & 17.01 & **26.51** & 18.67 & 18.00 & 17.21 & 23.77 & 18.67 & 18.00 & 17.21 & 23.77 \\ ELECTRA & **37.97** & 34.52 & 30.67 & 24.22 & 20.27 & 15.40 & 36.84 & 29.58 & 23.29 & 17.53 & 36.84 & 29.58 & 23.29 & 17.53 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean V-measure scores (in %) of the examined models with a concatenation of layers (the used layers are indicated by the numbers 1 to 12, where 1 is the top layer and 12 is the output layer) for sentence embedding.  
20% the performance of the model that uses BERT. It was also concluded that UMAP surpasses t-SNE in this setting. The vectors' size was reduced by a factor of 384 (from 768). Several values were investigated for the number of components to assess the potential loss of information due to compression. The results indicate slight variations in the vector dimensionality.
## 4 Discussion
[PERSON] evaluated if there were postural changes in patients monitored in different visual and dental conditions concluding that no important correlations were found between the two systems, although the existence of a compensation or masking effect on trigeminal receptors by inferences of the postural system could be assumed [27]. [PERSON] et al. have suggested that if there is a correlation between the stomatognathic system and posture, it is limited to the craniocervical tract of the column and its effect is reduced as descending along the spine [28]. In addition, [PERSON] et al. also reported in their overview that despite the probability of the stomatognathic system affecting the cervical region function, not enough scientific evidence is available to support this relation. It is clear that conflicting opinions can be found in literature about the correlation between posture structures, skeletal craniofacial growth, and dental occlusion [14]. Our study suggests that improvements in body posture are progressive during functional-orthopedic therapy. The absence of a control group clearly represents a major limitation. The comparison between an untreated Class II sample and a group treated with FR-2 could better investigate the influence of a functional-orthopedic treatment on the body posture in growing subjects. However, including an untreated group in a controlled clinical trial could preclude the correction of the malocclusion in this group, because the ideal correction timing--represented by the pubertal peak--would be overcome. Despite this limitation, this is to our knowledge the first study demonstrating postural improvements following orthodontic treatment of jaw advancement.
### Background on Wettability Measurements  
Quantifying reservoir wettability accurately has gained attention for some time now and has increased research to improve its understanding. The most important reason for determining the surface wettability of reservoir rocks is to increase and improve productivity by employing a suitable Enhanced Oil Recovery (EOR) technique. To decide about the type of EOR technique to employ to achieve maximum reservoir performance, detailed understanding, and prediction of surface wettability and the parameters that affect it is crucial. This statement means that parameters such as mineral composition, morphology, pore structures, surface roughness, salinity, and surface contaminations must be quantifiedat the lab and field scales. These parameters are of interest because a wrongly assumed parameter can have severe permanent damage to the reservoir and subsequently affect production, particularly water flooding and EOR techniques [16; 17; 18].  
For many decades, researchers have come up with several empirical models that best try to postulate a correlation between well log data and water saturation for wettability measurements, with most being a modification or extension of [PERSON]'s equation [19]:  
\[\mathrm{S_{W}}^{\mathrm{n}}=\frac{\mathrm{R_{W}}}{\varphi^{\mathrm{m}}\mathrm{ R_{t}}} \tag{1}\]  
where Sw is the water saturation, Rw is the water phase resistivity, \(\varphi\) represents porosity, R\({}_{t}\) is the sample's resistivity, and m, the cementation factor. Based on [PERSON]'s equation which the petroleum industry has widely accepted since 1940, wettability is affected by reservoir properties like fluid flow, pore space geometry, distribution, and connectivity. The fluid phase connectivity is also relied upon by resistivity; hence wettability directly has a partial effect on the resistivity parameter n. The importance of resistivity to wettability cannot be overlooked because of its wide depth of investigation range and direct influence on saturation measurements [20].  
Many researchers have come up with several methods over the last couple of decades to quantitatively and qualitatively measure surface wettability accurately in the laboratory and translate such results onto the field level. Wettability can be therefore be measured by several methods such as contact angles [4], Amott-Harvey [9], US Bureau of Mines (USBM) [10], electrical resistivity [21] and Nuclear Magnetic Resonance [11].
# Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions
## 2 Digital Forensics
### Host Forensics  
Host forensics is often called digital forensics because it encompasses forensics done on \"normal\" devices, such as desktops, servers, and other non-specialized sources of data. This method has been long established, but the tools used are constantly evolving as technology is progressing.  
Investigators can also utilize the method of weighting forensic evidence with blockchain technology. This can help with certifying the validity of digital evidence with it is presented in a court. This weighting system first collects evidence in a blockchain that records when the evidence was collected and who was in possession of it at the time. This data can then be categorized by relevance to the case and a timeline of events can be created [1]. This method allows investigators to confidently show that evidence was processed correctly and was not tampered with. It can also be helpful with IoT forensics because of the large amount of information gathered in those investigations [2]. An example to demonstrate how weighted forensic evidence can be used is if, after investigators collect evidence for a case, they need some way to prove to the jury that the evidence has not been tampered with. Because of the structure of blockchain and its unchangeable nature, investigators can document the chain of custody for the evidence, showing that it was never unaccounted for.  
Something investigators must take into account when performing forensics is the operating system of the device in question. Each operating system performs tasks differently and stores information in different places in the system, which affects all areas of digital forensics [3]. It is critical for investigators to be familiar with the many different types and versions of operating systems in order for them to be able to gather all relevant evidence.  
Another challenge that investigators face with host forensics is the randomization of kernel addresses. In order to face this problem, investigators can use four approaches to derandomize this information: brute force code, patched code, unpatched code, and read only kernel data. The brute force method simply scans the entire kernel code. For the patched code option, the kernel must know where to apply patches. The signature from this gives investigators insight into the organization of randomized address locations. The unpatched code signatures come from the code that has been identified as having not been patched. Finally, for read only kernel data, static pointers can help investigators shift data to find offsets, which will lead them to the proper address [4].
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 3 Optimization Algorithms
### Covariance Matrix Adaptation Evolution Strategy
\[C^{(t+1)}=\overbrace{\Big{(}1-c_{1}c_{c}(1-h_{\sigma})(2-c_{c}) -c_{1}-c_{\mu}\Big{)}C^{(t)}}^{\text{cumulative update}}\\ +\underbrace{c_{1}p_{C}p_{C}^{T}}_{\text{rank-one update}}+ \underbrace{c_{\mu}\sum_{i=1}^{n}w_{i}^{\prime}\delta^{(i)}\Big{(}\delta^{(i)} \Big{)}^{T}}_{\text{rank-$\mu$ update}} \tag{9}\]  
where \(c_{\mu}\leq 1\) is the learning rate for the _rank-\(\mu\) update_, \(c_{1}\leq 1-c_{\mu}\) is the learning rate for the _rank-one update_, \(c_{c}\in[0,1]\) is the learning rate for the _cumulative update_, \(h_{\sigma}\) is the evaluation of a unit step function used to properly update the evolution path, \(p_{C}\) is a cumulative vector used to update the covariance matrix, \(w_{i}^{\prime}\) is a modification of the coefficients \(w_{i}\), and \(\delta^{(i)}\) is the sampled deviations. In Equation (9), the first term (_cumulative update_) retains the information of the previous covariance matrix. The second term (_rank-one update_) allows for elongating the distribution along a favorable axis. The third term (_rank-\(\mu\) update_) increases the search in areas where good solutions are likely to exist. The combination of the three terms changes \(C\) to move the hyperellipse in a favorable direction.
# Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
## 3 Materials and Methods
### Classification  
Classification is ideal for identifying which set certain input data belongs to. In this investigation, the classification task was used to determine whether or not a potato leaf is infected with late blight. From the training of the SVM supervised learning classification models using linear kernel and CNN with the adaptation of the AlexNet architecture, the performance of each algorithm is compared when training it on the same data set.  
The trained SVM model is a supervised learning algorithm developed with a linear kernel; that is, a separation hyperplane is defined as a linear function that can separate the data set into two classes corresponding to the healthy and infected states. In Figure 8, the architecture used for the CNN models is shown; they are made up of three convolutional layers with Relu activation functions and three maximum pooling layers, a flattening of all layers, a dense layer with Relu activation, a dropout, and finally the classification layer corresponding to a dense layer with special sigmoidal activation for binary classification.
## 3 Results  
The average results (\(\overline{\text{X}}\)), standard deviation (SD), confidence interval, effect of the activation time (AT) in seconds, and the percentage of maximum voluntary contraction  
Figure 4: Electromyographic instrumentation of the athletes.  
(%MVC), as well as the comparisons according to different rackets, are shown in Table 2. The comparison between the studied muscles is also presented. Six significant values were collected (\(p\leq 0.05\)), each highlighted in Table 2 with an asterisk. Two of them refer to the intermuscular difference using the same racket when the activation time was evaluated.  
Figure 5 shows sEMG raw signal for one of the athletes, considering both rackets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Muscle**} & \multirow{2}{*}{\begin{tabular}{c} **Yonex Duora 33** \\ **(_u_ = 60)** \\ \(\overline{\text{X}}\)\(\pm\)**SD** \\ \end{tabular} } & \multirow{2}{*}{**CI 95\%**} & \multirow{2}{*}{\begin{tabular}{c} **Yonex Duora 88** \\ **(_u_ = 60)** \\ \(\overline{\text{X}}\)\(\pm\)**SD** \\ \end{tabular} } & \multirow{2}{*}{**CI 95\%**} & \multirow{2}{*}{**Effect Size**} & \multirow{2}{*}{\(p\)} \\ \cline{3-3} \cline{5-8}  & Bicp & 0.764 \(\pm\) 0.071 & 0.72; 0.80 & 0.750 \(\pm\) 0.121 & 0.71; 0.79 & \(-\)0.057 & 0.600 \\ \cline{2-8} AT (sec) & TLat & 0.666 \(\pm\) 0.102 & 0.63; 0.69 & 0.621 \(\pm\) 0.080 & 0.60; 0.65 & \(-\)0.118 & 0.173 \\ \cline{2-8}  & TLong & 0.540 \(\pm\) 0.058 & 0.52; 0.56 & 0.551 \(\pm\) 0.065 & 0.53; 0.57 & \(-\)0.331 & 0.116 \\ \hline \multirow{3}{*}{\%MVC} & \multirow{2}{*}{\begin{tabular}{c} Bicp \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 58.5 \(\pm\) 14.1 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 54.74; 61.94 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 65.2 \(\pm\) 16.3 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 61.12; 69.22 \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} \(-\)0.454 \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 0.028 \({}^{*}\) \\ \end{tabular} } \\ \cline{2-8} \cline{5-8}  & TLat & 91.8 \(\pm\) 17.0 & 88.38; 95.28 & 98.2 \(\pm\) 24.5 & 94.29; 102.78 & \(-\)0.070 & 0.046 \({}^{*}\) \\ \cline{2-8}  & TLong & 67.4 \(\pm\) 13.5 & 63.92; 70.17 & 58.2 \(\pm\) 12.3 & 56.04; 61.00 & \(-\)0.195 & 0.249 \\ \hline \multicolumn{2}{c}{\(p\)} & \multicolumn{2}{c}{0.016 \({}^{*}\)} & \multicolumn{2}{c}{0.009 \({}^{*}\)} & \multicolumn{2}{c}{} \\ \hline \hline \end{tabular}
\end{table}
Table 2: %MVC and AT for each of the rackets and respective comparisons.  
Figure 5: sEMG raw signal for one of the athletes: (**a**) [PERSON] 33; (**b**) [PERSON] 88.  
Figure 6 shows the average values of the normalized intermuscular amplitude (%MVC) and their standard deviation, comparing both rackets.  
Figure 7 shows the average values of the activation time and the respective standard deviation for both of the rackets.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Geographical Routing ICN Procotol
#### 3.2.1 Packet Structure  
As introduced in the related works, CAMs are sent on the G5 channel every 100 ms. These messages are encapsulated with security data. The CAM structure is extendable and so it is possible to insert some extra data. We choose to use this feature to include the ICN interest and the ICN data inside the CAM (Figure 3). Thus, we take advantage of the already existing security data. The overhead related to the ICN exchanges is, then, only dependent on the size and the name of the data. Analysis of the overhead is provided further in this study.  
Figure 4 shows the structure of the ICN payload in our approach. Compared to NDN packets [48], we removed the security data (this part is handled by the CAM) and we put new information about the sequence number and the hop limit (in order to bypass the single-hop nature of the CAM).
# Tire Bubble Defect Detection Using Incremental Learning
## 3 Experimental Results
### Results Obtained by the Tiny YOLO Architecture  
The Tiny-YOLO architecture is a YOLO-based object detector [7]. It is designed to contain a simpler network structure and utilize fewer computing resources. From Table 3, the sensitivity of the Tiny YOLO architecture is 79.77%. The incremental Tiny YOLO architecture achieves a better bubble detection rate by an additional 5%.