esa-sceva/satcom-chunk-collection huggingface dataset:
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 5 Agent-Based VM Migration
### VM Migration Heuristics  
The VM migration heuristic also serves to reduce the load of overloaded SAs and transfer VMs whose security levels do not match. For the former, it assists overloaded SAs in selecting VMs to migrate to underloaded SAs by ranking VMs according to their resource usage. When VM migration thresholds, as well as the activation threshold, are exceeded, an SA employs migration heuristics to select VMs for migration. For the latter, the security matching level serves as a \"one-vote veto.\" This means that if the security level of a VM is not equivalent to that of the host, it must be mitigated to the host.  
SAs have access to resource-based migration algorithms that prioritize VMs based on CPU and memory utilization data. The migration heuristic considers the ranking of CPU and memory usage, and the security level.  
An SA employing a resource-based migration heuristic selects the VM to be migrated using the following process (Algorithm 1).  
```
Input: \(N\) (Vector of VMs), VM CPU usage, VM memory usage Output: VM for migration (\(N^{k}\))
1. Sorting vector \(N\) based on ascending VM CPU usage
2. \(N^{k}_{CPU\_Random}=k\), i.e., VM \(k\) has a CPU weight of \(k\), VMs with higher CPU usage have higher CPU weight
3. Sorting vector M based on ascending VM memory usage
4. \(N^{k}_{MEM\_RandomOrder}=k\), i.e., VM \(k\) has a memory weight of \(k\), VMs with higher memory usage have higher memory weight
5. Let \(N^{k}_{Overall\_RankOrder}=N^{k}_{CPU\_Based\_Order}+N^{k}_{MEM\_RandomOrder}\)
6. Selecting the VM \(N^{k}\) with the highest value of \(N^{k}_{Overall\_RankOrder}\) (ties are broken randomly)
```  
**Algorithm 1** Resource-based migration heuristic  
To select a VM to be migrated, an SA ranks VMs by CPU consumption in ascending order (Algorithm 1, step 1) and assigns a weight to each VM based on its CPU rank order (Algorithm 1, step 2). The SA then ranks the VMs by memory consumption in ascending order (Algorithm 1, step 3) and assigns a weight to each VM based on its memory rank order (Algorithm 1, step 4). The SA calculates the total rank order by adding the CPU and memory rank orders (Algorithm 1, step 5) and selects the VM with the highest total rank order (Algorithm 1, step 6).  
An SA that employs a security-based migration heuristic selects the VM to be migrated using a much simpler process. Once the SA detects a mismatch of VM security levels, it starts the migration heuristic, and selects all the VMs with the mismatch security levels.
# Unveiling Biological Activities of Marine Fungi: The Effect of Sea Salt
## 2 Materials and Methods
### Detection of Antibacterial Activity Based on Dual Culture Test-Variant  
Fungi were inoculated on one side of 90 mm plates with PDA (with and without 3% sea salt) in triplicate and incubated at 25 \({}^{\circ}\)C until the mycelium reached the middle of the plate. Three Gram-positive bacteria, _Staphylococcus aureus_ (ATCC 6538), _Kocuria rhizophila_ (ATCC 9341) and _Enterococcus faecalis_ (ATCC 29212), and three Gram-negative bacteria, _Escherichia coli_ (ATCC 25922), multiresistant _Klebisella pneumoniae_ (P9.5) and _Pseudomonas aeruginosa_ (4P6.2), obtained from MicroLab from Department of Biology, University of Aveiro, Aveiro, Portugal, were used. These two multiresistant strains were isolated from the Lis River (Central Portugal) and were resistant to \(\beta\)-lactams and other classes of antibiotics (Stein
## 4 Congestion-Avoidance and Vehicle-Detection Modules of ACO-PT for Vehicles
### Automatic-Vehicle-Detection Module
#### 4.2.3 Matching Contours  
The goal of matching contours is to determine whether a contour in the image corresponds to a predefined contour template of vehicles. First, matching contours entails geometric matching that compares the shape and size of contours. Then, this method performs structural matching to compare the relations between contours, thus matching based on appearance to compare the texture, color, or other features of the contours. The result of the matching process is a similarity score, which indicates how similar the contour in the image is to the predefined template. The score is usually represented as a value between 0 and 1, where a value close to 1 indicates high similarity and a value close to 0 indicates low similarity. To do so, each contour found is checked for similarity to the image of the vehicle. For this purpose, a database of vehicle contours is prepared and predefined in advance. The easiest way to compare two contours is to calculate the contour moments. Roughly speaking, the moment is a rough characteristic of the contour, calculated by integrating (or summing) all the pixels of the contour. In general, the moment of contour \((p,q)\) is defined as follows:  
\[m_{p+q}=\sum_{i=1}^{n}i(x,y)x^{p}y^{q} \tag{26}\]  
where \(p\) is the order of \(x\) and \(q\) is the order of \(y\).  
The term order is employed, which indicates the degree to which the component of the sum is raised. Summation is carried out over all pixels of the contour boundary.  
The integrity of the contours is critical for obtaining an accurate automated-detection method.  
The selection of contours indicates that each contour is limited to being an integral unit in matching. Let each contour integrity be \(C_{V}^{l}\) for the vehicle that requires edge pixels \(P_{i}^{(v)}\). Hence, there are two options: Either all of the edge pixels participate in the matching or none of them does. Contours that partially match are not permitted. The same constraint is employed to model contours \(C_{v}^{M}\). The contour-selection indicators are introduced, which should be written as \(A_{I}^{sel}\in\{0,1\}^{|1|\times 1}\) in the whole vehicle image and \(B_{I}^{sel}\in\{0,1\}^{|M|\times 1}\) in the vehicle modeling. Thus, an image for vehicle-contour-integrity selection and a model for vehicle-contour-integrity selection can be determined as follows:  
\[A_{I}^{sel}=\begin{cases}1\text{ contour }C_{V}^{l}\text{ {is selected}}\\ 0\text{ {otherwise}}\end{cases} \tag{27}\]  
\[B_{I}^{sel}=\begin{cases}1\text{ contour }C_{V}^{l}\text{ {is selected}}\\ 0\text{ {otherwise}}\end{cases} \tag{28}\]
## 6 Space-Segment Optimization Aspects
### Constellation Maintenance
#### 6.6.5 End-of-Life (EOL) De-Orbiting  
In addition to the maintenance during the mission lifetime, a Sat in a constellation requires an EOL scheme to remove satellites from their orbit upon failure or termination. At the end of life, a Sat is required to fulfill the disposal requirement to re-enter the atmosphere within a 25-year limit. This limitation is implemented in national legislation in order to reduce the accumulation of orbital debris and lower collision risks. Moreover, a dysfunctional satellite in a mega-constellation might pose a threat to the constellation itself via collision risk. The orbital decay depends on the ballistic coefficients of Sat and the solar activity at the EOL time. The ballistic coefficient \(B_{C}\) is given as:  
\[B_{C}=C_{d}\frac{\mathcal{A}}{m} \tag{9}\]  
where \(C_{d}\) is the coefficient of drag, \(m\) is the satellite mass, and \(\mathcal{A}\) is the cross-sectional area. The solar activity is indicated by the solar radio flux at a wavelength of 10.7 cm, also referred to as the solar index. F10.7 changes with the 11-year solar cycle changing the minima and maxima. This parameter is important to estimate the Sat orbital decay as drag correlates with the solar cycle.  
If the 25-year limitation cannot be satisfied, the mission designers need to employ alternative disposal strategies. These include:  
* **Uncontrolled Reentry,** where the decay time of Sat is decreased by changing the Sat area-to-mass ratio physically. This is the most cost-effective disposal scheme. Sometimes, this scheme may initially require one or more maneuvers to facilitate the decay;
* **Controlled Re-entry,** where orbital maneuver are carried out to induce controlled orbital decay and burn-up in the atmosphere. This would require the Sat to maintain attitude and have a propulsion system for de-orbiting. This would also require the maneuvers to be incorporated into design with EOL fuel budget, resulting in increased platform mass and overall costs;
* **Graveyard Orbit Placement,** where the Sat(s) maneuver to graveyard orbit, defined due to lack of its value for space missions. This is common for GEO and MEO satellites [87; 137]. The mission design for a LEO constellation would require incorporating an EOL strategy for Sat.
## 4 SHINE Additional Building Blocks
### Hardware-in-the-Loop  
A very advanced feature of SHINe is the possibility to connect it in a Hardware-in-the-Loop (HIL) configuration with IngeniArs SpaceART [15] (see Figure 7). SpaceART [16] is a cutting-edge test equipment for the analysis of SpaceFibre and SpaceWire devices, equipped with two SpaceFibre ports, four SpaceWire ports and an Ethernet port for the connection with the Host Personal Computer (PC).  
SpaceART can be used to: (i) monitor the traffic on the links, providing snapshots of the data flowing from and to the external devices; (ii) inject errors on the links; and (iii) internally produce and consume data at specific rates to simulate different load conditions. In addition to these, it allows streaming user-defined packets from the Host PC to one or more of the SpaceFibre and SpaceWire ports and vice-versa, greatly increasing the versatility of the test equipment.  
Together with SpaceART, the SpaceWire PXI Analyser [17] from IngeniArs has been used in the Hardware-in-the-Loop configuration. It has similar functionality to SpaceART but it is based on the National Instruments PXI platform. As shown in Figure 8, a _HILSPfiNode_ node (for SpaceFibre) and a _HILSPwNode_ node (for SpaceWire) have been developed to communicate with SpaceART. They can be configured to connect one of the six hardware output ports of SpaceART with the Ethernet port. From the user perspective, the HIL nodes provide an interface equal to the correspondent Endpoint node, completely hiding the complexity of the communication with SpaceART. As a result, it is possible to plug an external SpaceWire or SpaceFibre Unit Under Test (UUT) into a fully simulated network without altering the packet stream. Some example use cases are:  
* Test of an RMAP Initiator or Target UUT in a networking scenario, without the need of deploying several hardware components;
* Test of a new user-defined protocol running on top of SpaceFibre or SpaceWire. Using SHINe, it is extremely easy to analyse packets or inject errors to deeply test the UUT under different conditions.  
Again, the Hardware-in-the-Loop is totally transparent for both the UUT connected to SpaceART and for the rest of the simulated nodes in SHINe. Because obviously the simulation speed is much slower than the data-rate of a real device, the communication will not happen in real-time. This means that the UUT will see a far end device that is \"slow\" in consuming and producing data. However, thanks to the flow control mechanisms implemented in both SpaceFibre and SpaceWire, no data will  
Figure 6: RMAP additional layer between the user application and the Endpoint.  
be lost. Note that this is true as long as the UUT does not rely on any time-dependent mechanism such as timeouts, in which case the different time speed might, and probably will, affect the correct behaviour. If the UUT is not time-dependent, as it happens for raw SpaceFibre and SpaceWire data transmission, the integration in SHINe is transparent.
# Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations
## 3 Results
### Comparison between Models  
The difference in performance between the transfer learning model and the custom model was substantial. The transfer learning model exhibited a swifter convergence and achieved higher accuracy on the validation set primarily due to the utilization of pre-trained weights, which encompassed knowledge from a vast dataset (ImageNet) and was fine-tuned on the specific task of fruit classification. In contrast, the custom model began with random weights, and thus had to learn features from scratch, which is often less efficient.  
Figure 5: Feature maps of all the convolutional layers of the custom CNN model.  
Figure 6: An enhanced view of a feature map, activations can be seen at damaged sections of the fruit.  
The training and validation loss and accuracy for both custom and transfer learning models are illustrated in Figure 7.  
As can be seen, due to the similarity of images, the small dataset used, and the sheer size and capability of these networks, the accuracy and loss rapidly reached a steady state, even for the less powerful custom network. Figure 8 demonstrates the same diagrams for five epochs.  
As mentioned previously, in some instances, the validation accuracy was higher than the training accuracy for the transfer learning model. This phenomenon is generally counterintuitive as models tend to perform better on the training dataset. However, in this case, data augmentation was applied to the training set; thus, the model was exposed to a more diverse set of images during training than during validation. This rigorous training process made the model more robust, and therefore it sometimes found the non-augmented validation set easier to handle, resulting in higher validation accuracy.
## 7 Estimating the Confusion Matrices of GEVA and GEFA
### Geva Confusion Matrices  
From the audio data obtained during the experiments, with natural and faked emotions, two confusion matrices have been obtained: one for each classifier used (J48 and JRIP). The rows show the real emotions expressed by users (natural and faked), and the columns show the detected user emotion by the classifier. The sum of the values of the entire row must be equal to 100.  
In Table 1, it can be observed that the classifier, J48, is not very accurate at distinguishing between the tone of voice for happiness and neutral when happiness is the real expressed emotion. Nevertheless, this  
\begin{table}
\begin{tabular}{l c c c c} \multicolumn{4}{c}{**J48 Classifier**} \\ \hline  & **Happy** & **Neutral** & **Sad** & **Surprise** \\ \hline
**Happy** & 50 & 50 & 0 & 0 \\
**Neutral** & 0 & 80.28 & 19.71 & 0 \\
**Sad** & 0 & 33.33 & 66.66 & 0 \\
**Surprise** & 28.57 & 42.85 & 0 & 28.57 \\ \hline \end{tabular}
\end{table}
Table 1: Confusion matrices for GEVA (rows: real emotions; columns: detected emotions).  
same classifier is quite precise (80.28%), recognizing the neutral emotion of the user. On the other hand, when the user is sad, the classifier is able to detect this emotion 66.66% of the time, and the rest of the time, it incorrectly detects that the expressed emotion is neutral. Finally, J48 has problems detecting the surprise emotion successfully. In this case, the success rate is just 28.57%, although it can be observed that it never gives a false positive when detecting this emotion. The J48 just gives the surprise output when the user expresses this same emotion.  
In order to calculate the average success of the classifiers, it is necessary to sum the elements of the diagonal and divide this result by the number of emotional states (4). _Therefore, for the classifier, J48, the average success rate is 56.37%._  
In relation to the other classifier, the JRIP, it can be observed that it is quite accurate at detecting the neutral and sad emotions. On the contrary, it has problems recognizing happiness and surprise. _Finally, the average success rate in JRIP is 57.10%._  
Although these matrices have been calculated using the natural and the faked interaction, it has been observed that the failure rate significantly increases when the user tries to simulate the emotion asked by the supervisor and the user does not do it in a very expressive way. In these cases, even the supervisor had problems recognizing the user emotion, and the classifiers give more incorrect answers. For example, the happiness or sadness emotions expressed in a timid way may be confused with the neutral one. In the same way, if the user expresses the neutral emotion in a very relaxed way, it can be confused with sadness.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 4 Simulation and Evaluation
### Results of Power Allocation for SM-MPC
#### 4.3.3 Comparison of Hydrogen Consumption  
For the NEDC test, the hydrogen consumption at 1180 s varies among the three methods. The SM-MPC method has the lowest hydrogen consumption at 268.63 g, which is 7.11 g less than the MPC method and 18.22 g less than the SM method. Similarly, for the HWFET test with 1500 s, the SM-MPC method uses the least hydrogen, reducing hydrogen consumption by 1.89 g compared to the MPC method and by 10.18 g compared to the SM method, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Hydrogen Consumption (g)**} \\ \cline{2-3}  & **NEDC** & **HWFET** \\ \hline SM & 286.85 & 705.23 \\ MPC & 275.74 & 696.31 \\ SM-MPC & 268.63 & 694.42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of hydrogen consumption for three methods.  
Figure 7: Power allocation results from three methods in the NEDC: (**a**) SM; (**b**) MPC; (**c**) SM-MPC.  
Figure 8: Power allocation results of three methods in the HWFET: **(a)** SM; **(b)** MPC; **(c)** SM-MPC.  
Figure 10: SOC variation curve of three methods in the HWFET.  
Figure 9: SOC variation curve of three methods in the NEDC.
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 3 Masked Face Datasets
### Real Mask Datasets
[26] introduced the Face-Mask Label Dataset (FMLD), a challenging dataset comprising 41,934 images categorized into three classes: correct mask (29,532 images), incorrect mask (1528 images), and without mask (32,012 images). Similarly, [PERSON], [PERSON], [PERSON], [PERSON], et al. [27] amalgamated data from MAFA and Wider Face, and additional manually curated images sourced from various online platforms to create a dataset encompassing 7500 images, referred to here as [PERSON]'s custom dataset. Additionally, [PERSON], [PERSON], et al. [28] developed a practical dataset known here as [PERSON]'s practical dataset, comprising a total of 4672 images. This dataset includes 4188 images sourced from the public MAFA dataset and 484 images sourced from the internet. The images in [PERSON]'s practical dataset are categorized into five types: clean face, hand-masked face, non-hand-masked face, masked incorrect face, and masked correct face, with the first three types grouped into the without-mask class and the remaining two classes designated as mask_correct and mask_incorrect. Likewise, the Interactive Systems Labs Unconstrained Face Mask Dataset (ISL-UFMD) [29] is compiled from a variety of sources, including publicly available face datasets like Wider Face [36], FFHQ [37], CelebA [5], and LFW [6], in addition to YouTube videos and online sources. This comprehensive dataset comprises 21,316 face images designed for face mask recognition scenarios, encompassing 10,618 images of individuals wearing masks and 10,698 images of individuals without masks. Moreover, several other datasets cater to face mask recognition tasks. For instance, the Masked dataset (MD-Kaggle) [20] hosted on Kaggle comprises 853 images annotated into three categories. Similarly, the Properly Wearing Masked Face Detection Dataset (PWMFD) [30] contains 9205 images categorized into three groups. Additionally, datasets like Moxa3K [25] and TFM [34] focus on binary classifications, distinguishing between images with and without masks. Moxa3K features 3000 images, while TFM boasts a larger private collection of 107,598 images. Furthermore, the Wearing Mask Detection (WMD) [31] dataset provides 7804 images for training detection models. Meanwhile, the Wearing Mask Classification (WMC) [31] dataset consists of two classes, with 19,590 images containing masked faces and 18,555 background samples, resulting in a total of 38,145 images. The Bias-Aware Face Mask Detection (BAFMD) dataset [35] comprises 6264 images, featuring 13,492 faces with masks and 3118 faces without masks. Noteworthy is that each image contains multiple faces.
###### Abstract  
This study aimed to evaluate the impact of different closures used in second fermentation on the aromatic fraction of sparkling wine. Six types of closures (cork stoppers and screw caps) and 94 months of aging in a bottle were investigated. Headspace solid-phase microextraction (HS-SPME) and thermal desorption (TD) procedures coupled to gas chromatography-mass spectrometry (GCMSMS) analysis were applied. The vectors containing the relative abundance of the volatile compounds are compositional vectors. The statistical analysis of compositional data requires specific techniques that differ from standard techniques. Overall, 101 volatile compounds were identified. HS-SPME extracted the highest percentage of esters, ketones and other compounds, while TD was a useful tool for the obtention of alcohol, acid, ether and alkane compounds. Esters were the most abundant family of compounds. Compositional data analysis, which was applied to study the impact of different closures used in bottle aging after second fermentation on the volatile composition of sparkling wine, concluded that there are differences in the relative abundance of certain volatile compounds between cork stoppers and screw-cap closures. Overall, the most abundant part in screw-cap closures was ethyl hexanoate, and it was ethyl octanoate in cork stoppers. Also, the proportional amount of dimethylamine was higher in screw-cap closures than cork stoppers relative to the entire sample.  
sparkling wine; second fermentation; aging; aroma compounds; compositional data; log-ratio; MANOVA Article
# Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density
## 2 Preliminaries
### Fuzzy Logic and Fuzzy Morphological Operators  
This paper uses the fuzzy mathematical morphology, which is based on fuzzy conjunctions and fuzzy implication functions. It was introduced in [17], and it was used successfully in many applications (see [14; 18]). We recall the definitions of fuzzy logic operators that are used in this framework (see [19; 20; 21]).  
**Definition 1**.: _A \(t\)-norm is a commutative, associative, increasing function \(T:[0,1]^{2}\to[0,1]\) which has neutral element \(1\) (that is, \(T(1,x)=x\) for all \(x\in[0,1]\))._  
**Definition 2**.: _A binary operator \(I:[0,1]^{2}\to[0,1]\) is a fuzzy implication function if it is decreasing in the first variable, increasing in the second one and it satisfies \(I(0,0)=I(1,1)=1\) and \(I(1,0)=0\)._  
Using the previous definitions, the basic fuzzy morphological operators can be defined by using a t-norm \(T\) as a conjunction, a fuzzy implication function \(I\), and taking a gray-level image \(A\) and a gray-level structuring element \(B\).  
**Definition 3** ([11]).: _The fuzzy dilation \(D_{T}(A,B)\) and the fuzzy erosion \(E_{I}(A,B)\) of \(A\) by \(B\) are the gray level images defined by_  
\[D_{T}(A,B)(y) =\sup_{x\in d_{A}\cap T_{y}(d_{B})}T(B(x-y),A(x)),\] \[E_{I}(A,B)(y) =\inf_{x\in d_{A}\cap T_{y}(d_{B})}I(B(x-y),A(x)),\]  
_where \(d_{A}=\{(i,j)\mid A(i,j)\text{ is defined}\}\) and \(T_{\mathbb{P}}(A)\) with \(v\in\mathbb{R}^{n}\) is given by \(T_{\mathbb{P}}(A)(x)=A(x-v)\)._  
Two basic fuzzy morphological operators such as the fuzzy closing and the fuzzy opening are defined as follows.  
**Definition 4** ([17]).: _The fuzzy closing \(C_{T,I}(A,B)\) and the fuzzy opening \(O_{T,I}(A,B)\) of \(A\) by \(B\) are the gray level images defined by_  
\[C_{T,I}(A,B)(y) =E_{I}(D_{T}(A,B),\overline{B})(y),\] \[O_{T,I}(A,B)(y) =D_{T}(E_{I}(A,B),\overline{B})(y),\]  
_where \(\overline{B}(x)=B(-x)\)._  
A more detailed account on these operators, their properties and applications can be found in [11; 17; 22; 23].