esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 3 Service Models of Cloud Computing
### Infrastructure as a Service (IaaS)  
This service model provides access to fundamental computing resources such as virtual machines, servers, storage, and networking infrastructure. While IaaS offers several advantages, it is not without its challenges. One notable issue is the potential misalignment of security between the cloud infrastructure and the virtual machines hosted within it. This misalignment can lead to security vulnerabilities. For example, both the cloud customer and the cloud provider may have different policies regarding data retention and deletion, which can pose security risks. Additionally, when clients use legacy code to build applications on IaaS platforms, they may inadvertently introduce security vulnerabilities from outdated code.  
Security Challenges:  
* Securing virtual machine instances and hypervisors.
* Protecting against unauthorized access to IaaS resources.
* Ensuring data security and isolation in a multitenant environment.
## 2 Materials and Methods  
We selected digital libraries for the automated search approach using well-known databases such as Scopus, PubMed, Scholar Google, and Science Direct for the following reasons: (i) the breadth of research it provides, in a variety of scientific domains, and (ii) the availability of powerful tools for conducting thorough searches. The keywordsused were \"grape\" AND \"grape pomace\" AND \"UV\" AND \"skin protection\" AND \"photoptection\" AND \"cosmetics\" AND \"sunscreen\" AND \"sun oils\" AND \"applications\" AND \"effect\" AND \"bioactives\" AND \"antioxidant\" AND \"anti-inflammatory\" AND \"by-products\" AND \"sunburn\" AND \"erythema\" AND \"pigmentation\" AND \"skin cancer\" AND \"melanoma\" AND \"anti-wrinkle\" AND anti-thrombotic AND \"antitumor\" AND \"tomato\" AND \"lycopene\" AND \"a topcherol\" AND \"L-ascorbic acid\" AND \"SPF\" AND \"carotenoids\" AND \"in vivo experiments\" AND \"in vitro experiments\" AND \"flavanols\" AND \"catechins\" AND \"epigallocatechin gallate\" AND \"quercetin\" AND \"rutin\" AND \"resveratrol\". The same terms were also used in several searches, in which instead of \"AND\" the term \"OR\" was used.  
Initially, the search was focused on the dates 2020-2024; however, the results were not sufficient, and thus the search time frame was expanded to 2014-2024. In some cases, some important studies published earlier than 2014 were also included, which were not previously thoroughly reviewed. Articles in other languages than English, duplicates, and the majority of review articles were excluded, apart from some important review articles on general information about these bioactives, photoprotection, and sun oils.
## 4 Results and Discussion
### Comparisons with Other Reported Systems  
For more contribution and to prove the current proposed systems' capability, reliability, applicability, and practical use the current systems' results are compared with the related results in other published works. The maximum TGOR and maximum freshwater productivity of co-generation and tri-generation systems are chosen as performance and productivity indicators for comparison to the currently proposed systems. However, they differ in operating conditions from our chosen conditions, as shown in Table 5. As shown in Table 5, the current system (MGS-II) has maximum TGOR and freshwater productivity better than the comparable systems.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment  
### Dataset  
To comprehensively assess the performance of our network, we trained and evaluated it on several benchmark datasets known for low-light image enhancement tasks. Initially, we utilized the LOLv1 dataset [58], which includes 1000 synthetically generated low- and normal-light image pairs alongside 500 real-world pairs. From the real-world subset, 15 pairs were designated for evaluation, while the remainder contributed to the training process. Expanding our evaluation, we incorporated two additional real-world paired datasets: LOLv2-real [59]. The LOLv2-real dataset contains 789 image pairs, with 689 pairs utilized for training and 100 pairs allocated for evaluation.  
Furthermore, to assess the generalization capability of our method in unseen scenarios, we tested it on five widely recognized real-world unpaired benchmarks: DICM [60], LIME [10], NPE [61], MEF [62], and VV [63]. These datasets present a variety of challenging conditions, enabling us to evaluate the robustness and adaptability of our approach in practical applications.
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 3 Results and Discussion
### Biology
#### 3.2.2 Evaluation of the Antibacterial Activity by the Determination of IZDs
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Compounds**} & \multicolumn{4}{c}{**Diameter of the Zone of Inhibition (mm)**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Gram-Positive Bacteria**} & \multicolumn{2}{c}{**Gram-Negative Bacteria**} \\ \cline{3-6}  & & _S. aureus_ & _B. subtilis_ & _E. coli_ & _S. enteritidis_ \\ \hline \multirow{2}{*}{**4**} & 3 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 5 \(\upmu\)g & NI & NI & NI & NI \\ \hline \multirow{2}{*}{**7**} & 3 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 5 \(\upmu\)g & NI & NI & NI & NI \\ \hline \multirow{2}{*}{**12**} & 3 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 5 \(\upmu\)g & NI & NI & NI & NI \\ \hline \multirow{2}{*}{**22**} & 5 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 5 \(\upmu\)g & NI & NI & NI & NI \\ \hline \multirow{2}{*}{**23**} & 3 \(\upmu\)g & 6.73 \(\pm\) 0.01 & 10.38 \(\pm\) 0.04 & NI & NI \\ \cline{2-6}  & 5 \(\upmu\)g & 14.03 \(\pm\) 0.01 & 16.07 \(\pm\) 014 & NI & NI \\ \hline \multicolumn{6}{c}{Ciprofloxacinacin, 5 \(\upmu\)g} & 26.00 \(\pm\) 1.41 & 26.50 \(\pm\) 0.71 & 31.50 \(\pm\) 0.71 & 29.50 \(\pm\) 2.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Zone of inhibition of the synthesized compounds **4**, **7**, **12**, **22**, and **23** against Gram-positive and Gram-negative bacteria by the disk diffusion method. after using volumes containing 3 \(\upmu\)g and 5 \(\upmu\)g of compounds, respectively. The size of the IZDs correlated with the amount of the compound and were 10.38 \(\pm\) 0.04 mm and 16.07 \(\pm\) 0.14 mm for _B. subtilis_ as well.
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 2 Related Works
### Multiple Object Tracking  
Most studies on MOT focus on DBT, which aims to connect the same objects frame to frame according to the detection results of the detector. There are online and offline tracking methods for DBT systems [5]. An online tracking method locates and tracks objects in the previous frame according to the current frame. By contrast, an offline tracking method uses all frames as the input to provide the tracking results. It considers past, as well as future frames for tracking. Therefore, online tracking is used in real-time tracking.  
For the association stage, the correlated information of different frames should be used to determine the similarity between objects. One of the basic tracking methods employs the intersection over union (IoU) metric. The detection result is linked by selecting the track with the largest IoU in the previous tracks, as in Figure 2.  
The well-known SORT predicts the position of a bounding box CNN detection and estimates the trajectory by using a Kalman filter [21], then matches the position by using the Hungarian algorithm [22]. Using IoU-based methods can be effective for tracking a few objects. However, the performance degrades when a large number of objects exist in the images, and also, it incorrectly connects the objects in overlapped or separated situations. Recently, DeepSORT [9] overcame this problem adopting a CNN model for the association stage. The network can consider image information, as well as location information in the bounding boxes. However, the performance degrades when the tracked object has a similar appearance.  
To overcome this problem, we considered a triplet loss that can discriminate the details of the visual appearance. Moreover, we improved the association network by employing a convolutional attention module.
## 2 Data and Methods  
The seismic monitoring network operating at Mt. Vesuvius comprises a permanent network, whose signals are transmitted in real time to the monitoring center for surveillance purposes [18], and an offline mobile network [19], whose data are characterized by a better continuity of signals and are mainly used for monitoring and scientific research. The dataset used in this work consists of continuous signals recorded in the years 2019, 2020 and 2021 by stand-alone seismic stations. The latter consisted of three-channel MarsLite Lennartz digitizers equipped with Gurlap CMG40T broadband seismometers (60s). For our analysis, we selected those with the lowest seismic background noise (Figure 1), i.e., those installed at high altitude (BKSG, BKWG) and in less-populated areas (SVAG). Once a possible seismic event has been identified by our automatic procedure, we also take into account signals recorded by the other seismic stations available in the area (BKE, VBKN, etc.; Figure 1) and catalogs of local and regional earthquakes. In the present work, two analysis methodologies operating in the frequency domain have been used jointly: (a) the spectral analysis obtained with the statistical moments and (b) the coherence analysis. A brief description of the two adopted techniques is given in the following two sections.
## 3 Results
### Growing Season
Although MIROC-ESM does not show the highest spatial correlation due to a few area of disagreement mainly located in Alaska, it shows one of the lower RMSE (27 days); the other models have errors ranging between 26 days (BCC-CSM1) and almost 3 months (IPSL-CM5A-MR). Compared to other CMIP5 models, GFDL-ESM2G has a smaller RMSE than the average. However it should be noted that it has been computed considering only a few grid points, due to the incorrect representation of the seasonal amplitude. Looking at the satellite data, the growing season length (Figure 6) is found to increase dramatically with decreasing latitude [93]. It is the shortest in central and eastern Siberia along the Arctic coast, with a duration of only 4 months. In contrast, most of Europe, Eastern China and Southern North  
Figure 6: Mean growing season length (days) for 11 Earth System Models (ESMs) and satellite observations over the Northern Hemisphere (30\({}^{\circ}\)-90\({}^{\circ}\)N). For each model we masked out all the grid points where the seasonal amplitude is less than 0.5 (see Figure 3). In the box the value of root mean square error (in days) and spatial correlation, as computed from mean annual data, are presented for each model against the observations. America have long growing seasons. The growing season length shows a good agreement between satellite data and models, although individual models like IPSL-CM5A-MR, and HadGEM2-CC still exhibit large errors in reproducing the observed spatial pattern. Specifically, the best results are found in BNU-ESM, INMCM4, being the correlation systematically greater than 0.6. Besides, in the few grid points covered by deciduous forests GFDL-ESM2G shows a good agreement with satellite GSL and this explains the relative high correlation and low RMSE compared to other ESMs. Consistent with previous results, HadGEM2-CC shows a negative correlation, indicating the inability of this model to reproduce the observed spatial variability. In addition HadGEM2-CC and IPSL-CM5A-MR also show the highest RMSE for the GSL, the error being more than 2 times larger than the lowest RMSE found in CanESM2.
# Link Prediction with Hypergraphs via Network Embedding
## 2 Materials and Methods
### Hypergraph Construction  
A hypergraph is defined as \(H\) = (\(V\), \(E\)) where \(V\) = {\(v_{1}\), \(v_{2}\),..., \(v_{n}\)} and \(E\) = {\(E_{1}\), \(E_{2}\),..., \(E_{m}\)} [41]. \(V\) is a set of \(n\) hypernodes and \(E\) is a set of \(m\) hyperlinks. The hyperlink \(E_{i}\) = {\(v_{i1}\), \(v_{i2}\),..., \(v_{i\bar{j}}\)}, (\(i\) = 1, 2,..., \(m\); \(j\) = 1, 2,..., \(n\)) contains \(j\) nodes, that is, the size of \(E_{i}\) is \(j\). The \(\mid V\mid\times\mid E\mid\) incidence matrix can be represented by \(H\).  
\[H(v,e)=\begin{cases}1,&if\ v\in e\\ 0,&if\ v\
otin e\end{cases} \tag{1}\]  
Figure 1: The framework of link prediction for hypergraphs via network embedding (HNE). (**a**) The heterogeneous network contains two types of nodes, _Nodes I_ and _II_, with their interactions; it can be constructed by a hypergraph model. The incidence matrix represents the node–hyperlink interactions and the adjacency matrix describes node–node associations. (**b**) The Deepwalk model is applied to learn the node embedding vectors. (**c**) The random forest classifier is trained to predict link labels.  
Based on \(H\), the node degree \(d(v)\) of each node \(v\) meaning the number of neighbor nodes of node \(v\) is represented as  
\[d(v)=\sum_{e\in E}H(v,e). \tag{2}\]  
The hyperdegree \(d_{H}(v)\) of node \(v\) denotes the number of hyperlinks which the node \(v\) participates in. The degree \(\delta(e)\) of hyperlink \(e\) is the total number of neighbor hyperlinks of hyperlink \(e\) as follows,  
\[\delta(e)=\sum_{v\in V}H(v,e). \tag{3}\]  
The hyperdegree \(\delta_{H}(e)\) of hyperlink \(e\) denotes the number of nodes of hyperlink \(e\)[42].
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 3 Approach
### Blockchain Network Procedure
In our scheme, we put the process of validating the routing process on the blockchain network and all the routing nodes get the relevant routing information from the blockchain network instead of their neighbor routing nodes. After the packets are transmitted, \(R_{i}\) trigger the \"transfer\" function on the token contract to indicate the state change including the information of the amount \(n\) of tokens sent to \(R_{\pi}\) to the blockchain network. Then the token amount \(n\) is released on the token contract. While \(R_{\pi}\) trigger the \"confirm\" function on the token contract to confirm the amount \(n^{\prime}\) of the received packets to the blockchain network. The number \(n^{\prime}\) is based on the amount of the packets \(R_{\pi}\) actually received in the routing network. 3. Then the token contract checks whether \(n\) equals \(n^{\prime}\), and if \(n=n^{\prime}\), the token balances for \(R_{i}\) and \(R_{\pi}\) end with \(B_{R_{i}}=p-n\) and \(B_{R_{\pi}}=q+n\). If \(n\
eq n^{\prime}\), the token balances for \(R_{i}\) and \(R_{\pi}\) remain \(B_{R_{i}}=p\) and \(B_{R_{\pi}}=q\). The whole token transaction is confirmed by the PoA consensus of the server nodes, i.e., only the validation of more than half of the authenticated server nodes can allow a server node to upload the transaction to the blockchain network. We stipulated that the whole transaction process should be completed within one time slot. The unconfirmed transactions are cancelled and a failed transaction is not recorded in the blockchain without affecting the routing information. Meanwhile, every token transaction is recorded on the blockchain by an authenticated server node, that all the routing nodes and server nodes have a back-up of the transaction recorder available for traceability. Each token transaction records the token name, the timestamp \(t_{i}\), the amount \(n_{i}\) of tokens transmitted and the route addresses \(A_{t}\) of each hop arrived. Since all the transaction information is supervised jointly by all network nodes, our blockchain-based platform provides a distributed routing environment, enabling the acquisition of global routing information. With this blockchain-based routing scheduling model, it is possible for routing nodes to apply and obtain the routing information dynamically from the blockchain network. It can also provide a trusted routing environment for traditional routing algorithms (e.g., the backpressure routing algorithm). However, its routing information has not been effectively utilized, and the performance needs to be improved. We introduce the reinforcement learning algorithm to dynamically learn this information, and all of  
Figure 5: Implementation of the token transaction. the information will be captured by the reinforcement learning model of the routing node to output subsequent routing policies and help the routing node choose the better routing links.
## 3 Results
### Secondary Particle Characterisation
On the other hand, charged particles such as electrons, scattered protons and positrons may interact with the sensor with a high probability of Coulomb interaction with the sensor material, depositing their energy in several steps and creating a track-like cluster. For a more in-depth look at the cluster sizes of the most common particles detected in the system, Figure 6 makes use of Geant4 simulation data to create a comparison graph between gammas, electrons and neutrons in terms of their mean cluster size for each beam energy. Particles such as positrons and scattered protons are not included in this graph since they are not present for all energies. The figure reinforces the theory that, by thresholding the pixels based solely on cluster size, it is simpler to preserve events that are of interest while discarding the sources of noise coming from particles of a different nature. Ultimately, to validate the hypothesis of achieving the identification of gamma particles only according to the event cluster size, the clusters with a size of up to two pixels were selected for direct comparison with the simulated data coming from particles that are already known to be gammas. This comparison is shown in Figure 7 where the energy deposition spectra are juxtaposed. Although both curves exhibit similar shape and count ranges, the experimental data are slightly skewed towards higher energies and show an increase in counts for energy losses between 100 and 200 keV, which indicates that the discrimination by cluster size, although useful, still shows the effects of the experimental background noise when comparing the experiment to the model. Figure 6: Cluster size comparison by particle for each beam energy under study. The figure discriminates between electrons, neutrons and gamma particles.
###### Abstract
Moreover, [PERSON]. [18] evaluated the fundamental rheological characterization of unmodified and polymer-modified binders with SBR and SBR-RGP using a dynamic shear rheometer (DSR). The results of the DSR test demonstrated that adding RGP has an adverse effect on the rutting resistance of the SBR-modified binder. Further studies were conducted by the same research group, Ref. [18], to investigate the rheological and mechanical properties of asphalt binders and mixtures modified with crumb rubber (CR) and RGP. The outcome of the study suggested that the best combination that improved the rheological and mechanical properties of the asphalt mixture was 5% CR and 5% RGP. Moreover, it was concluded that mixtures modified with different percentages of CR and RGP showed better resistance to the initiation and propagation of cracks, since CR and RGP exhibited a high elasticity behaviour. Finally, most of the studies that were conducted to evaluate the performance of asphalt mixtures modified with PCM and GP did not fully address the rheological and mechanical properties of the mixes. From the authors' point of view, only limited previously published data have been found to provide comprehensive insight on incorporating these additives in asphalt binders. Therefore, this study aims to investigate the rheological, spectroscopic, and chemical characterization of asphalt binders that are modified with GP and PCM using a viscometer, an environmental scanning electron microscope (ESEM), Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimetry (DSC), and a dynamic shear rheometer (DSR). Moreover, this study significantly contributes to advancing a better understanding of using PCM and GP in asphalt applications.
# Significance of Camera Pixel Error in the Calibration Process of a Robotic Vision System
## 2 Experimental Setup and Measurement Uncertainties
### Flange to Camera Calibration  
The estimation of unknown matrix \(X\) from equation \(AX=XB\) is also known as hand-eye calibration. The system equation can be formulated in two components, namely, orientational and positional components, which are defined below:  
\[\begin{bmatrix}R_{A}&t_{A}\\ 0&1\end{bmatrix}\begin{bmatrix}R_{X}&t_{X}\\ 0&1\end{bmatrix} =\begin{bmatrix}R_{X}&t_{X}\\ 0&1\end{bmatrix}\begin{bmatrix}R_{B}&t_{B}\\ 0&1\end{bmatrix} \tag{18}\] \[\begin{bmatrix}R_{A}R_{X}&R_{A}t_{X}+t_{A}\\ 0&1\end{bmatrix} =\begin{bmatrix}R_{X}R_{B}&R_{X}t_{B}+t_{X}\\ 0&1\end{bmatrix} \tag{19}\]  
Using the above expression the respective orientational and positional components can be defined as:  
\[R_{A}R_{X} =R_{X}R_{B} \tag{20}\] \[R_{A}t_{X}+t_{A} =R_{X}t_{B}+t_{X}. \tag{21}\]  
The hand-eye calibration technique is broadly classified in two different categories, that is, either the rotation and translation parts of the matrix \(X\) are evaluated either individually or in combined form. In the former category, [PERSON] [10] and [PERSON] [11] have estimated the rotation matrix and translation vector individually using a least squares fitting algorithm. The least square fitting algorithms are fast and simple, but might result in an inaccurate solution [19]. For estimating the rotation matrix of \(X\), the concept of quaternions has been used by [PERSON] et al. [20], while [PERSON] [21] has simplified the expression by linearising the equation (\(XB=0\)) before estimating the solution using singular value decomposition. [PERSON] [12] has formulated the rotation matrix with quaternions and conducted a non-linear optimisation on rotation matrix and translation vector.  
In the latter category, [PERSON] et al. [13] have proposed a formulation in which the number of unknown parameters remains constant and constrained this condition over complete data set. [PERSON] et al. [14] estimated the solution of system equation by using the Kronecker product on reformulated linear system. [PERSON] [22] have applied non-linear optimisation for both rotation matrix and translation vector and introduced the possibility to disregard the orientation of camera for parameter estimation. A similar approach has been presented by [PERSON] [23]. [PERSON] [24] also conducted a similar nonlinear optimisation with the help of the lie group theory. To estimate the relative position and orientation between the reference frames and robot link, [PERSON] [25] has combined the kinematic equations of rotation and translation into two simple and well structured linear systems.  
The major possible sources of uncertainty in hand-eye calibration is erroneous estimation of matrices \(A\) and \(B\). It is well known that the robotic vision systems are not completely free from errors, which make hand-eye calibration process more challenging. The hand-eye calibration algorithms discussed above perform ideally under error-free theoretical setups, but might fail to deliver the same performance for practical setups.