esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Local Search Algorithms with Search Space Tightening
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & **P-I** & & & **P-II** & & & **P-IV** & & **P-IV** \\
**Test** & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{\(m\)} & \(E(D_{opt})_{SVM}\) & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{\(m\)} & \(E(D_{opt})_{SVM}\) & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{\(m\)} & \(E(D_{opt})_{SVM}\) & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{\(m\)} & \(E(D_{opt})_{SVM}\) \\
**No.** & & & **k(N)\({}^{2}\)** & & & **(k(N)\({}^{2}\)** & & & **(k(N))\({}^{2}\)** & & & **(k(N))\({}^{2}\)** \\ \hline
1 & 14 & 2578 & \(-707.86\) & 17 & 3202 & \(-707.86\) & 13 & 2523 & \(-767.65\) & 9 & 4528 & \(-754.88\) \\
2 & 4 & 107 & \(-707.85\) & 10 & 280 & \(-707.81\) & 14 & \(3992\) & \(-707.81\) & 12 & 3123 & \(-755.79\) \\
3 & 19 & 1762 & \(-707.86\) & 10 & 474 & \(-707.73\) & 4 & 21 & \(-707.77\) & 5 & 3723 & \(-759.56\) \\
4 & 47 & 2412 & \(-707.86\) & 15 & 2588 & \(-719.35\) & 5 & 723 & \(-707.78\) & 15 & 4847 & \(-740.06\) \\
5 & 59 & 2209 & \(-707.86\) & 12 & 4288 & \(-707.20\) & 12 & 2679 & \(-767.12\) & 11 & 4076 & \(-771.67\) \\
6 & 69 & 4838 & \(-707.86\) & 14 & 4635 & \(-772.15\) & 8 & 1626 & \(-707.78\) & 5 & 2165 & \(-741.28\) \\
7 & 13 & 1812 & \(-772.86\) & 19 & 515 & \(-707.70\) & 18 & 3224 & \(-707.77\) & 9 & 4601 & \(-756.77\) \\
8 & 47 & 2385 & \(-707.86\) & 12 & 424 & \(-707.71\) & 15 & 2626 & \(-766.46\) & 3 & 370 & \(-757.06\) \\
9 & 38 & 1311 & \(-772.87\) & 15 & 1122 & \(-771.10\) & 11 & 911 & \(-707.81\) & 8 & 470 & \(-752.52\) \\
10 & 48 & 2437 & \(-772.60\) & 10 & 945 & \(-707.83\) & 10 & 4490 & \(-707.85\) & 11 & 4935 & \(-753.29\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optimal objective function values with search algorithms run with different adjustments. case. It is worth noting that the random number generated with a standard deviation \(\sigma=0.5\) mm after 4000 iterations means a too large a search space near-global optimum, as the algorithm only managed to find a better function value only after one case of being run.
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Specificity of the Status-Byte-Assisted Transmission  
In the proposed status-byte-assisted transmission mechanism, an innovative memory management solution is proposed by implementing the concept of status byte, as illustrated in Figure 8. The status byte indicates the usage status of a memory block: 0 signifies the memory block is available, 1 indicates the memory block contains data, and 2 means the memory block is currently unavailable. Compared with the sliding window transmission mechanism, the status-byte-assisted transmission can handle memory blocks more flexibly.  
In burst scenarios, if the current receiving memory block is unavailable, the status byte of the corresponding memory block can be marked as 2. This mechanism allows the sending thread to skip the unavailable memory block and continue sending other memory  
Figure 8: Memory status in the status-byte-assisted transmission mechanism.  
blocks, thereby avoiding the blocking of threads. This flexibility makes the transmission mechanism more suitable for various edge-computing data transmission scenarios.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 6 Benefits of Using a Mobile Manipulator for Railway Track Maintenance
### Connectivity  
As 5G and cellular technologies become more robust, a fleet of connected mobile manipulators is becoming feasible. These connected mobile manipulators could securely communicate with the central command and control center. Upon receipt of a task command, a mobile manipulator could autonomously navigate to the designated intervention or possession area.
## 4 FHB Management
### Biocontrol
rosea_ has been reported as broadspectrum BCA with application against numerous plant pathogens, including _Fusarium_ pathogens and the mycotoxin ZEN [162, 163, 164]. It has shown ability to degrade DON, as well as minimise the effect of FHB in wheat [165, 164, 166]. Recently, the _C. rose_ ACM941 strain was isolated from peas in Manitoba and its formulation 'CLO-1' tested against FHB in field trials. The studies confirmed that CLO-1 reduces FHB severity in wheat, is most effective on moderately resistant cultivars, and to a large extent is effective as a conventional fungicide. Hence, it is evident that biological control is a promising option that can be a part of an integrated management system for the treatment of FHB and _Fusarium_ mycotoxins. However, there is a need to continue to investigate newly discovered BCAs that are Fusarium-host specific with potential to degrade different mycotoxins in the cereal crops. _Sphaerodes myoparasstica_ has been described in 2009 as specific myoparasite to phytopathogenic and mycotoxicine Fusarium hosts [150]. It represents a next-generation BCA-effective in controlling FHB [167, 168, 169]. According to [PERSON] and [PERSON] [170] it is among the best-studied myoparasites within the Sordariomycetes [168, 171]. It is an exemplary model system to study biotrophic mycoparasitism [172] and polyphagous lifestyle against multiple _Fusarium_ species [66] and associated mycotoxins involved in FHB [66]. This BCA is particularly suitable for prenatal plant care as protecting flowers and germinating seeds from _Fusarium_ infection and thus providing an early control of FHB [20]. It has been shown in numerous studies to downregulate expression of _Tri_ (trichothecene) and AUS (aurofusarin) genes in fusaria and degrade mycotoxins such as DON, 3 ADON, 15 ADON and ZEN [167, 168, 171, 173]. Evidence from ongoing studies has also shown that _S. mycoparasstica_ alone bolsters defense in wheat and barley against FHBand mycotoxins as well as in combination with low dose of synthetic chemical (Folicur(r)) pesticide in both greenhouse and field experiments [138]. These findings have received farmers' attention [174; 175] particularly when _S. mycoparsitica_ benefits resistant cereal varieties [173; 174; 175; 176; 177].
## 3 Results
### Virtual Inertia Optimal Allocation
Furthermore, the Rate of Change of Frequency (RoCoF) decreased significantly: in Region 1 from 0.42 Hz/s to 0.209 Hz/s (a 50.2% reduction), and in Region 2 from 0.46 Hz/s to 0.2205 Hz/s (a 52.1% reduction). Thus, the optimized system exhibits a slower frequency decline rate, reduced RoCoF, and shortened frequency recovery time. In the frequency recovery process of Region 1 and Region 2, 1.2 MW and 1.7 MW loads were added, respectively, to verify the accuracy of the model under different load sizes. The results are shown in Figure 9.  
This optimized allocation strategy enables the system to achieve frequency regulation across both regions without exceeding operational limits following disturbances. Compared to the pre-allocation scenario, the RoCoF is reduced, the frequency nadir is raised, and the recovery time is significantly shortened. Consequently, the optimal allocation of virtual inertia within the autonomous regions is achieved.
# Study of a Miniaturizable System for Optical Sensing Application to Human Cells
## 3 Results
### Method Characterization
#### 3.1.2 HDFs Confocal Microscopy  
To get more details on the intracellular distribution of Ru(bpy)3\({}^{2+}\), we analyzed the HDF treated cells by confocal laser scanning microscopy (CLSM), as shown in Figure 4. In order to detect the fluorophore signal, we used three lasers with 405 nm, 488 nm and 555 nm wavelengths for the analysis. Signal distribution across cell layers, detected with the 555 nm wavelength laser, proved the presence of the red fluorophore inside the cells after the uptake process. Moreover, CLSM allowed us to observe that Ru(bpy)3\({}^{2+}\) was, mostly, localized in the cytoplasmic compartment, as proven by the distribution of signal intensity across cell layers, and largely absent from the nucleus, as demonstrated by the lack, or at least by the very low presence, of red signals in the nuclear region (Figure 4).  
Figure 4: Fluorophore uptake in HDFs, treated with 0.7 uM Ru(bpy)3\({}^{2+}\) for 24 h, observed by CLSM with 555 nm wavelength laser. (**A**–L): Serial sections with 0.33 μm/section obtained as reported in [24]. (**M**): Signal intensity along the cell section indicated on the right. (**N**): 3-D reconstruction of the cell (sectioned along the broken line) shown in the panel (**M**). (**O**): Signal intensity detected in the A to L serial sections shown above, evaluated using the cell on the left. Pale blue and red bars indicate the fluorescence intensity of the entire cell and of its nucleus, respectively. S.E.: Standard error.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Ablation Study
#### 4.4.3 Ablation Study of Data Augmentation  
The results of the ablation study of the data augmentation are listed in Table 7. In the University-1652 dataset, there is a disproportion between the number of satellite and drone images, i.e., 1 satellite image corresponds to 54 drone images from different perspectives. If the UAV or satellite images are directly used for model training, the model convergence results will be poor due to an imbalance of samples. Data augmentation techniques are adopted during training to improve the diversity of satellite images to match the larger number of drone images. Previous studies [11; 29] for cross-view image data augmentation mainly contain rotation, flipping, and color distortion augmentations. These techniques are tested in this paper to be more suitable for UAV and satellite images. The data augmentation techniques tested in this paper include random edge filling and clipping in image size, random rotation and reversal in image orientation, and random adjustment in image brightness and contrast in image color. Regarding the ratio of data equalization, previous research [11] has shown that 1:1 can achieve the best results. Therefore, in this paper, the data ratio after data augmentation remains 1:1. The results show that the random edge filling and clipping in image size and the orientation augmentation can improve the testing performance by 5.06% at R@1 compared to the trained model without adding data augment. Compared to adding a random color adjustment, the proposed data augmentation method improved the testing performance by 10.17% at R@1. The results illustrate that randomly changing the size and direction of the image would help improve model performance, while randomly changing the color of the images (e.g., brightness and contrast) will seriously degrade the testing performance. The reason is that the original color distribution of drone images is quite different from satellite images. The random color adjustment will only make the difference more obvious, which is not conducive to the model learning the correct matching pattern.
## 3 Methodology  
### Overview  
The RGB-D mapping system used in this research contains two sensors: one RGB camera, and one IR sensor. The IR sensor is combined with an IR camera and an IR projector. This kind of sensor system can be highly mobile, and attached to an iPad, iPhone, or other mobile instrument. Unlike the traditional laser scanning system, the system captures 640 \(\times\) 480 registered RGB images and depth images at 30 frames per second, which is convenient for interactive RGB-D frame labeling. Figure 1 shows its hardware structure and the observed depth and RGB frames with timestamp.  
We next describe our methods to automate the identification and reconstruction of accurate and consistent indoor components from low-quality RGB-D sequences as shown in Figure 2. The main idea behind our approach is to extract indoor components by making full use of the information from RGB-D sequences, which contains the camera trajectory, labeling information, and low-quality point clouds. Because RGB-D frames are labeled by camera position and orientation, timestamp, and start-stop point associated with RGB-D sequences, the camera trajectory and the RGB-D sequences are divided into several subsections according to the range of each space and the positions of openings. Next, the point cloud of each subsection can be obtained by merging the corresponding RGB-D frames. Note that all point clouds to be processed are generated after applying the depth calibration model, and data preprocessing operations are applied to each point cloud.  
Two different processing flows are used for individual space reconstruction and opening reconstruction, respectively: (1) For space extraction, a region growing plane segmentation method is first used for plane detection. Based on the normal of the recognized planes, the surfaces are organized in vertical and horizontal directions. Each plane is described using a cluster of points and plane parameters. We distinguish the types of planes by following the classification rules, and wall candidates are derived from vertical surfaces observed in the scans. Subsequently, an interior element filtering algorithm is used to separate the wall planes from the vertical planes. Finally, space layout is generated based on a boundary generation algorithm, which considers the intersection of all wall candidate centerlines in the horizontal plane. (2) Rather than detecting the opening by finding the holes of the triangulated model of the wall planes, we propose a new noise robustness method based on the properties of convex hull, octrees structure, Euclidean clusters and the camera trajectory for opening generation, which is inapplicable to the data collected in the indoor environments due to the inevitable occlusion. A global optimization approach is designed to eliminate inconsistency between adjacent walls and maintain plausible connectivity between the walls and the relationships between the walls and openings. Finally, the final model is stored according to the CityGML3.0 standard.
## 3 Classification of Organic Matter in Shale and Its Morphological Characteristics
### Migrated Organic Matter  
Migrated organic matter refers to petroleum and bitumen formed during the thermal maturation of kerogen, which subsequently migrate into adjacent mineral pores or fractures, along with their byproducts, such as solid bitumen and pyrobitumen. The primary distinction between migrated organic matter and depositional organic matter lies in their temporal formation: migrated organic matter forms later in the maturation process and accumulates at the target site after migration, often accompanied by the development of authigenic minerals. A key characteristic of migrated organic matter is the early crystallization of surrounding minerals, with organic matter later filling the voids within the crystal frameworks of matrix minerals. The morphology of this organic matter is largely dictated by the spatial constraints of the host mineral structures. Based on its location and occurrence, migrated organic matter can be classified into three main types: intergranular pore, intragranular pore, and microfracture.  
Figure 6: SEM image of in situ remnants from post–hydrocarbon generation. **(a)** In situ remnants from post–hydrocarbon generation, Y206#, 3860.75 m; **(b)** In situ remnants from post–hydrocarbon generation, Y206#, 3859 m; **(c)** In situ remnants from post–hydrocarbon generation, Y206#, 3865.5 m; **(d)** In situ remnants from post–hydrocarbon generation, Y206#, 3873.7 m; **(e)** In situ remnants from post–hydrocarbon generation, Y206#, 3872 m; **(f)** In situ remnants from post–hydrocarbon generation, Y206#, 3872 m.
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 3 Weather Conditions and OCDMA Code  
In this section, a brief description about the two weather conditions and the construction of the OCDMA code used in this work (ZCC) will be given.
# SMS: A Secure Healthcare Model for Smart Cities
## 5 Role of MEC in the Proposed SMS  
In a typical edge computing paradigm, the data are processed in close proximity to the source from where the data is generated as close as possible. This is made possible by deploying dynamic intermediator servers optimally placed in a distributed manner. These servers are capable of performing basic data processing and analysis that can be used to service requests in real time or near-real time. Although there are several security vulnerabilities associated with the classical mobile edge computing paradigm that can affect the edge devices and gain access to the data and information, this paper proposes to implement the OAuth 2.0 protocol, twofish cryptographic approach, and blockchain technology to safeguard the system against such attacks. In the SMS, all the participating devices and entities need to register in the system beforehand. Each device in the SMS can be uniquely identified with a unique key formed by combining the device_hardware_id and the device_registration_id provided at the time of registration. This unique code along with the hardware id of the device together forms the uniquely identifying key for the device. Figure 4 shows the creation of a unique key for the IoT and edge devices.  
The users are authenticated using the \"OAuth 2.0\" protocol. The protocol provides \"role-based access\" to the legitimate user for a \"specific period of time.\" Once the time period expires, access to the system is revoked.  
\begin{table}
\begin{tabular}{l c l} \hline \hline \multicolumn{1}{c}{**Stakeholders**} & \multicolumn{2}{c}{**Impact of Data Analytics**} \\ \hline \multirow{4}{*}{Healthcare practitioners} & \(\bullet\) & Designing a personalizing treatment \\  & \(\bullet\) & Monitoring patient’s health \\  & \(\bullet\) & Remotely consult the patient \\  & \(\bullet\) & Decision-making using the predictive health analytics \\ \hline \multirow{4}{*}{Government} & \(\bullet\) & Manage, maintain, and monitor a unified data record of citizens \\  & \(\bullet\) & Easy identification of disease patterns \\  & \(\bullet\) & Analysis of regional, national, or disease-specific trends in a population \\  & \(\bullet\) & Design and develop health policies, preventive programs, and interventions \\  & \(\bullet\) & Based on the data from a particular demographic area and population \\  & \(\bullet\) & Efficient response in healthcare emergencies \\ \hline \multirow{2}{*}{Healthcare providers} & \(\bullet\) & Analysis of patients’ medical history \\  & \(\bullet\) & Better healthcare outcomes based on data \\ \hline \multirow{4}{*}{Pharmaceutical companies} & \(\bullet\) & Drug discoveries based on medical data \\  & \(\bullet\) & Market assessment using predictive data analytics \\ \cline{1-1}  & \(\bullet\) & Business intelligence using predictive data analytics \\ \cline{1-1}  & \(\bullet\) & Better customer outreach and engagement \\ \hline Patients & \(\bullet\) & Patients have equal participation and control during the overall care process \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data analytics assisting the stakeholders.
# A Robust and Effective Two-Factor Authentication (2 FA) Protocol Based on ECC for Mobile Computing
## 4 Security Analysis
### Heuristic Analysis
#### 4.2.6 Password-Guessing Attack  
In the 2 FA protocol, \(\mathcal{A}\) can eavesdrop on all information through the open channel and extract all parameters from the smart card of the user. Then, analyses of the password-guessing attack can be conducted from two angles: (i) On the one hand, with an unknown user's identity \(ID_{i}\), \(\mathcal{A}\) guesses \(\{ID_{i}^{\prime},PW_{i}^{\prime}\}\) within the dictionary space. Then, \(\mathcal{A}\) will be capable of calculating the relevant parameters \(RPW_{i}^{\prime}=H\big{(}ID_{i}^{\prime}\parallel PW_{i}^{\prime}\big{)}\) mod \(n_{0}\), and \(A_{i}^{\prime}=H\big{(}ID_{i}^{\prime}\parallel PW_{i}^{\prime}\parallel a_{i }\big{)}\) mod \(n_{0}\). Afterwards, \(\mathcal{A}\) verifies whether \(A_{i}^{\prime}\)? = \(A_{i}\). Finally, the above procedures are repeated until the password and identity are guessed by \(\mathcal{A}\) accurately. Obviously from the perspective of theory, the relevant \(ID_{i}^{\prime}\) and \(PW_{i}^{\prime}\) that meet \(A_{i}^{\prime}\)? = \(A_{i}\) can be estimated by \(\mathcal{A}\) in polynomial time. Factually, the guessing size for the password and identity space can be represented as \(\frac{|\mathcal{D}_{ID}|*|\mathcal{D}_{PW}|}{n_{0}}\), where \(2^{4}\leq n_{0}\leq 2^{8}\), and \(\mathcal{D}_{PW}\) and \(\mathcal{D}_{ID}\) represent the guessing spaces of the password and identity, respectively. Thus, the valid password and identity cannot be guessed by \(\mathcal{A}\) effectively because \(\frac{|\mathcal{D}_{ID}|*|\mathcal{D}_{PW}|}{n_{0}}\approx 2^{32}\)is larger than the finite \(Sum\) that denotes the time allowed by the smart card for an attacker until login failure, when \(|\mathcal{D}_{ID}|=|\mathcal{D}_{PW}|=10^{6}\) and \(n_{0}=2^{8}\)[41]. (ii) On the other hand, the identity \(ID_{i}\) of the user is likely to be leaked by \(\mathcal{A}\). Nevertheless, \(\mathcal{A}\) is unable to estimate the password \(PW_{i}\) correctly since there are still a large number of password candidates \(\frac{|\mathcal{D}_{PW}|}{n_{0}}\approx 2^{12}\) that meet the formula \(A_{i}=H(ID_{i}\parallel PW_{i}\parallel a_{i})\) mod \(n_{0}\). Therefore, no matter whether \(\frac{|\mathcal{D}_{ID}|*|\mathcal{D}_{PW}|}{n_{0}}\approx 2^{32}\) or \(\frac{|\mathcal{D}_{PW}|}{n_{0}}\approx 2^{12}\), the authentic prize cannot be attained by the adversary using a guessing attack.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for High-Throughput and Spatial Multi-Omics Integration
#### 2.3.5 Majorbio Cloud: A Cloud-Based Suite for Large-Scale Multi-Omics Research  
Majorbio Cloud is a comprehensive, cloud-based platform designed to support large-scale multi-omics studies by providing a centralized suite of bioinformatics tools and workflows for integrating genomics, transcriptomics, proteomics, metabolomics, and metagenomics data. With its focus on ease of access and broad analytical capabilities, Majorbio Cloud caters to high-throughput demands by offering pre-configured pipelines that facilitate data processing, analysis, and visualization across multiple omics types [28].  
The platform's cloud-based architecture allows researchers to manage and analyze substantial datasets without needing dedicated local infrastructure, thereby supporting institutions of varying scales. Majorbio Cloud incorporates over 300 distinct analysis tools, enabling users to perform complex analyses like differential expression, clustering, pathway enrichment, and gene interaction mapping within a single environment. These capabilities make it particularly suited for projects in areas such as cancer research, where examining interactions across various biological layers is crucial.  
A key feature of Majorbio Cloud is its user-friendly web interface, which allows for customizable data exploration through interactive visualizations, such as heatmaps, PCA plots, and condition-correlation matrices. This interactive environment aids researchers in identifying molecular relationships and pathway dynamics, contributing to the in-depth study of disease mechanisms and tissue-specific molecular activities. Furthermore, Majorbio Cloud emphasizes collaborative workflows, enabling researchers to share data and results across teams, thus fostering interdisciplinary research efforts in multi-omics.