esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The primary wick in a loop heat pipe device is a key component that is central to the operation of the device. Both high permeability and capillary pumping capacity, two properties highly dependent on wick structure, are strongly desirable for a satisfactory thermal performance. In this paper, selective laser melting (SLM), a three-dimensional (3D) printing technology, is used to create a primary wick for an 80 W heat transfer application. The permeability and capillarity values of this wick, experimentally measured, are compared with those built with the most widely used technologies nowadays, such as powder sintering and meshes. In this study, the SLM scaffold is shown to satisfy the minimum values required by the application in terms of capillarity and permeability: 0.031 mm/s and \(4\times 10^{-12}\) m\({}^{2}\), respectively. Our comparative study revealed that the wick produced with the SLM technology presented higher values of permeability, by two orders of magnitude, and slightly higher capillary figures than those corresponding to powder sintering for such application. However, it had capillary values well below those of a stainless-steel mesh. The hydraulic behavior of the SLM wick was better than that of the sintered copper powder, because it not only met the above-mentioned specifications, but it also improved its performance.  
capillarity; loop heat pipe; permeability; three-dimensional (3D) printing; porosity; selective laser melting (SLM); wick; thermal performance
## 3 Results  
### Characterization of Physical Properties  
#### 3.1.1 X-Ray Diffraction for Structural Analysis of the Films  
Figure 2 shows the XRD patterns recorded for coated transparent polymer foils. As observed, in the case of Ag-based SiO\({}_{2}\) or TiO\({}_{2}\) matrix, respectively, there is an evidence of crystalline Ag phase formation (JCDPS No. 1-071-4613). The cubic structure exhibited a (111) preferred orientation and can be identified at the 20 position of 40.57\({}^{\circ}\) in Ag+SiO\({}_{2}\) thin film, whereas in Ag+TiO\({}_{2}\), the silver reflection is located at 39.76\({}^{\circ}\). As calculated, the grains exhibited nanometric sizes in the range of \(\sim\)12-17 nm. Similar data were found [47], where they investigated magnetron-sputtered Ag:TiO\({}_{2}\) layers. Based on TEM cross-section micrographs of as-deposited samples [47], Ag aggregates were found to be randomly distributed in the amorphous matrix and have various shapes with nanometer sizes of lateral diameters (nano-clusters). According to the mentioned standard, the position of the Ag maximum peak oriented along the (111) plane is 38.1\({}^{\circ}\). Therefore, a shift towards higher angles is evident in both measured patterns, suggesting a partial oxidation due to the oxidic character of SiO\({}_{2}\) and TiO\({}_{2}\). The XRD analysis showed no evidence of crystallinity for any of these compounds. As previously reported, an amorphous structure can accelerate the diffusion process and facilitate the formation of larger Ag particles, the deposited layer having a heterogeneous structure [48].  
Regarding the Ag+TiO\({}_{2}\)+O\({}_{2}\)-coated foils, the addition of 2.5% of oxygen in the total gas flow had a direct influence on the structural evolution, Ag being finely dispersed in the dielectric matrix. The hypothesis can be proven by the absence of the silver characteristic peak in the XRD-associated patterns, the dispersion process of the metal particles representing a consequence of the energetic impact of the oxygen ions during sputtering [49].  
Figure 1: Flow chart of working steps for testing the antibacterial activity of SiO\({}_{2}\)+Ag, TiO\({}_{2}\)+Ag and TiO\({}_{2}\)+Ag+O\({}_{2}\)-deposited films using MC Media Pad E. coli and Coliform media as culture medium.  
In this case, the only signal recorded was observed at 18.53\({}^{\circ}\), being characteristic of the foil substrate.
# A Modeling Method of Agents and SOA in Advanced Avionics System Based on AADL
## 2 Background
### Research and Modeling Methods for Agents  
[PERSON] defined an agent as a software entity that can carry out a group of operations on behalf of users or plans with certain independence or autonomy, and feel and automatically perform tasks in the computing system environment of complex dynamic environments [16]. [PERSON] pointed out that an agent is a computer (hardware or software) system that meets specific design needs, and it is located in a specific environment with a high degree of flexibility and autonomy [17; 18]. For example, the agents in avionics systems can autonomously identify targets and plan routes. At present, agents have been used in avionics systems, and there are many studies on modeling methods for agents. Agents are divided into typical agents according to functions and typical states based on the discrete event system (DEVS) [19; 20]. An agent can also be defined as a mapping, which maps the local environment perceived by an agent into general behavior attempts rather than specific actions [21]. The adaptive avionics system software based on agents has been applied in avionics fault diagnosis and air traffic systems [22; 23]. Adaptive software in avionics systems can be modeled by using multi-agent systems based on the belief, desire, and intention (BDI) model or hybrid automata [24; 25; 26]. In addition, the formal verification of agents based on the BDI model also has some achievements [27]. The abstract state machine (ASM) is used to model the agents in avionics systems, and based on this method, an intelligent landing gear system was modeled [28]. Agents have been applied to model and simulate pilot behavior, which means that agents have great potential in the aviation field [29].
## 4 Results
### Evaluation Metrics Comparison Among Each Scaled Multi-Spectrogram Settings
The rows represent the ground truth labels, while the columns indicate the predicted classifications. The matrices display true positives, true negatives, false positives, and false negatives for every instrument classification. Darker shades represent higher values, illustrating areas of strong classification accuracy and confusion points between certain instruments. Recall comparison (Figure 4) reveals consistently high detection rates for accordion, banjo, bass, piano, synthesizer, trumpet, ukulele, violin, and voice, showcasing the model's effectiveness. Variability in recall for cello, clarinet, cymbals, flute, guitar, marimba, mandolin, and saxophone, with the \"Log-Mel 128 CST Magnified Full Size\" often achieving higher recall, suggests larger feature sizes capture more relevant characteristics. Lower and more variable recall rates for drums, organs, and trombones indicate that these instruments are less distinct or more challenging to detect accurately. High and consistent F1 scores for instruments like accordion, banjo, bass, drums, guitar, piano, synthesizer, and trumpet reflect a good balance between precision and recall. In contrast, cello, clarinet, cymbals, flute, mandolin, organ, saxophone, and trombone show fluctuating F1 scores, with the \"Log-Mel 128 CST Magnified 1/4 Size\" and \"Log-Mel 128 CST Magnified Full Size\" configurations often performing better. This indicates these configurations provide a better trade-off between detecting instruments and minimizing false predictions, while voice, violin, marimba, and ukulele show variability in F1 scores, suggesting room for optimization in feature size and attention mechanisms.
# Unveiling the Landscape of Operating System Vulnerabilities
## 4 Research Methodology
### Data Collection and Processing
#### 4.1.1 Data Sources: CVE Mitre and NVD  
We collected the CVE IDs for the data extraction from Common Vulnerabilities and Exposure (CVE) Mitre. In CVE Mitre, each vulnerability report is thoroughly studied by the security team and provided with a CVE ID, i.e., every vulnerability report has a unique CVE ID. All these CVE IDs along with their description are updated daily by the CVE Mitre Corporation, and the overall data are maintained on their official website [[https://www.cve.org/Downloads](https://www.cve.org/Downloads)]([https://www.cve.org/Downloads](https://www.cve.org/Downloads)) accessed on 28 June 2023. Since the data are updated regularly, it is quite important to note the exact timestamp of the collected data. We downloaded a total of 274073 unique CVE entries as a single CSV file from CVE Mitre on 28 June 2023 22:32:46:334345 timestamp. This research was conducted in early 2023, so we did not have enough data for 2023; hence, we retained only those data within the range of 1999 to 2022. At the time of the download, there was a total of 278,146 CVE entries, and after excluding 20673 CVE entries for the year 2023, we were left with a total of 257,473 data entries consisting of CVE IDs and their corresponding description until 2022. We downloaded the CVE Mitre data only to collect the CVE IDs, since these do not provide the required information. Therefore, the next challenging task was to leverage the collected CVE IDs to scrape further valuable information from the National Vulnerability Database (NVD).  
The National Vulnerability Database (NVD) [[https://nvd.nist.gov](https://nvd.nist.gov)]([https://nvd.nist.gov](https://nvd.nist.gov)) accessed on 29 June 2023, is a platform recognized by the US government and therefore is authentic and reliable. The NVD provides complete reported details about the CVE ID. We used the CVE entries from CVE Mitre and prepared a Python script using the BeautifulSoup library to scrape the corresponding details of the entire CVE entries according to Algorithm 1. We collected product type, product name, vendor name, vulnerable versions, CWE IDs, and CVSS scores. Each CVE entry may change over time, and therefore we also recorded the timestamp at the time of individual data scraping from the NVD. The entire web scraping was conducted in 2 days starting from 29 June 2023 to 30 June 2023.
## 3 Model Development and Analysis
### Performance Analysis of CPC  
Let us consider a CPC having aperture width W, length L, and acceptance angle (\(2\mathrm{\theta_{a}}\)). Assume that the CPC collects both diffuse and beam radiation having a large acceptance angle. Then the beam radiation falling on the aperture plane is given by \(\mathrm{I_{b}}\mathrm{r_{b}}\)  
The diffuse radiation within the acceptance angle is given by \(\frac{\mathrm{1d}}{\mathrm{c}}\)  
\[\mathrm{The\ area of\ the\ aperture\ \mathrm{A_{P}}=L\times W}\] \[\mathrm{The\ area of\ the\ absorber\ \mathrm{A_{a}}=L\times b}\] \[\mathrm{The\ concentration\ ratio\ \mathrm{C}=\frac{\mathrm{A_{P}}}{ \mathrm{A_{a}}}}\]  
Then solar flux entering the aperture is given by [50]  
\[\mathrm{S}=\left(\mathrm{I_{b}}\mathrm{r_{b}}+\frac{\mathrm{I_{d}}}{\mathrm{ C}}\right)\mathrm{cos}(\tau\mathrm{\rho_{e}}\alpha) \tag{14}\]  
The useful heat gain is given by [41]  
\[\mathrm{q_{u}}=\mathrm{F_{R}}\mathrm{WL}(\mathrm{S}-\frac{\mathrm{U_{l}}}{ \mathrm{C}}(\mathrm{T_{fi}}-\mathrm{T_{a}})) \tag{15}\]  
Similarly, the heat gain from the collector by the fluid with a certain mass flow rate is given by [50]  
\[\mathrm{q_{u}}=\mathrm{m}\times\mathrm{c_{P}}(\mathrm{T_{fluid\ out}}-\mathrm{ T_{fluid\ in}}) \tag{16}\]  
From Equations (15) and (16), we get  
\[\mathrm{\eta}=\frac{\mathrm{q_{u}}}{\mathrm{A_{c}}\mathrm{I_{T}}}=\mathrm{F_{ R}}\mathrm{\tau\alpha}-\frac{\left(\mathrm{F_{R}}\frac{\mathrm{U_{l}}}{\mathrm{C}}( \mathrm{T_{fi}}-\mathrm{T_{a}})\right)}{\mathrm{A_{c}}\mathrm{I_{T}}} \tag{17}\]  
The instantaneous efficiency is given by [50]  
\[\mathrm{\eta}=\frac{\mathrm{q_{u}}}{(\mathrm{I_{b}}\mathrm{r_{b}}+\mathrm{I_{d }}\mathrm{r_{d}})\mathrm{WL}} \tag{18}\]
## 5 Combining Regional Graphs to Generate Concept Graphs
### Combination of Region Graphs  
The combination of region graphs allows the reduction of the number of nodes and edges to provide a much more tightly bound image concept graph with dense connections. The proposed method of graph combination derives inspiration from the approximate maximal common subgraph computation proposed in [3]. The process is composed of several phases:  
**Phase I: Merging entities. **: The first phase aims to find out which entity nodes from the different region graphs can be merged. This is performed on the basis of the following rules:  
* Same lemma. If two nodes have the same lemma and have an intersection-over-min (IOM) ratio above 0.5, they can be merged. The IOM ratio for two bounding boxes A and B is given by \[IOM=\frac{area(A\cap B)}{\min(area(A),area(B))}\] (2)
* Different lemma. Entities with different lemmas may also be merged if they belong to the same synset in the WordNet hypernym chain. This can be detected when the Wu-Palmer similarity [60] is 1.0. For example, pairs such as \(<dog,bound>\) or \(<baby,infant>\) belong to the same synset and, hence, can be merged. **Phase II: Merging actions and attributes. **: Once we have a pair of entities, one from each of two different region graphs that can be merged, the matching set of actions and entities connected to that entity in both regions graphs can also be merged. **Phase III: Drawing special edges. **: Finally, some entities demonstrate more detail in a specific region graph. In cases where two regions have an IOM ratio over 0.5 and the lemma of the two corresponding entities belongs to different generations in the same WordNet hypernym chain, they can be connected using a special edge. For example, if we have two entities in two regions such as \"furniture\" and \"table\", then an edge may be added that states \"furniture is table\".
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology
### Datasets
#### 3.2.2 BGL  
The BlueGene/L (BGL) dataset represents a significant resource in the field of large-scale computing system analysis and anomaly detection research. This open-access dataset comprises logs collected from the BlueGene/L supercomputer system, a high-performance computing (HPC) infrastructure operated by Lawrence Livermore National Labs (LLNL) in Livermore, CA, USA. The system's architecture is characterized by its impressive scale, incorporating 131,072 processors and 32,768 GB of memory and thus providing a rich source of operational data from a complex, distributed computing environment. BGL logs are particularly valuable due to their comprehensive nature, encompassing both alert and non-alert messages, which are distinctly identified through alert category tags. This binary classification is facilitated via a simple labeling scheme: non-alert messages are denoted with a \"-\" in the first column of the log, while alert messages are indicated with alternative markers. Such a clear delineation of alert statuses within the dataset renders it exceptionally suitable for research in anomaly detection. The BGL dataset has consequently gained prominence in the academic community, serving as a benchmark in numerous studies focusing on log-parsing techniques, anomaly detection algorithms, and failure prediction models in large-scale computing environments.  
The statistics for both datasets are presented in Table 2, with a focus on anomaly sessions. InfoLogger contains only 387 confirmed anomalous sessions (0.48%), reflecting  
the limited availability of expert-verified anomalies, which poses challenges for evaluation. In contrast, BGL provides a more balanced distribution with 31,374 anomalous sessions (45.31%), making it well suited for comparative analysis. Both datasets share a key characteristic: the inclusion of a property that indicates the severity level of log content. Specifically, this property is referred to as \"severity\" in InfoLogger and \"level\" in BGL. We utilize this attribute to identify anomalies in log messages, serving as the ground truth labels for evaluation. This shared property forms a robust foundation for benchmarking our methodology. By leveraging the similarities between InfoLogger and BGL, we ensure a fair basis for experimental comparison.
## 2 Methods
### Data Processing
#### 2.4.1 Reflectance Data Process  
At the leaf and canopy scales, the reflectance was averaged over the wavelength interval of the SGLI sensor (Table 1) to calculate VIs. At the canopy scale, HSSR data between 10:20 a.m. and 10:40 a.m. (Terra overpass time) were averaged for 16-day periods. At the satellite scale, values of band reflectance and quality assurance (QA) descriptions of the surface reflectance data were extracted from the MOD09A1.005 data [64]. Only data with 'no cloud shadow' and 'clear' cloud-state flags were used; data flagged as 'cloudy,''mixed,' and 'not set, assumed clear' were excluded. We then averaged8-day data to 16-day periods under the following conditions. If the cloud-state flag was 'clear' in both 8-day periods, we used their average value. If 'not clear' appeared in only one dataset, we selected the 8-day period with the clear-state flag. If 'not clear' appear in both datasets, we discarded both datasets.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 1 Introduction
### Steelmaking Processes  
The basic oxygen furnace (BOF), also known as the Linz-Donawitz (LD) process, produces crude steel from the pig iron or molten iron received from the blast furnace. The liquid metal and fluxing agents (e.g., limestone or dolomite) are charged in a furnace, and a lance injects oxygen into the mix, removing impurities from the charge by way of by-product gases, such as carbon monoxide, and by molten slag formation. The molten crude steel is tapped into a ladle, while the molten slag is tapped and removed. In the United States, 30% of the crude steel is produced by the BOF process [18], while around 71% of the worldwide steel production uses the BOF process [6].  
The electric arc furnace (EAF) process starts with a cold charge of scrap and recycled steel and fluxing agents. An electric current is passed through graphite electrodes to produce an arc that melts the charge. The injection of oxygen removes impurities, and additional metals or alloyants are added to refine the steel chemistry. The molten crude steel is tapped into a ladle, while the molten slag is tapped and removed. An estimated 29% of steelmaking worldwide uses the EAF process [6], but 70% of the crude steel is produced by the EAF process in the United States [18].  
The ladle metallurgy furnace (LMF) is a secondary steelmaking process and is an additional refining step after the BOF or EAF process. The LMF process is similar to the EAF process, except that additional refinements are used. Additional fluxing agents and alloyants are added to the ladle with the molten crude steel to further remove any impurities and to adjust the steel chemistry.  
The open-hearth furnace (OHF) process is obsolete and has been largely replaced by the modern BOF and EAF processes. An estimated 0.4% of all steel worldwide is still produced by the OHF process [6].  
The argon oxygen decarburization (AOD) process is used for producing stainless and specialty steels. For recycling stainless steels, the process usually starts with an EAF process, which is followed by the AOD process. The AOD process refines the molten metal through decarburization, reduction, and desulfurization. This process results in a slag that is typically very high in calcium and silicon and may contain more fluorine and chromium than EAF and BOF slags [19].
# Doped Epoxy Resins as an Alternative to Luminescent Optical Sensors
## 3 Results and Discussion
### Thermal and Spectroscopic Analysis  
Two types of polymers are generally used in optical fiber technology. These are linear polymers, from which optical fibers are produced in the thermal processing process, and cross-linked polymers used as protective coatings for glass or polymer optical fibers and as a material for special fibers. In order to produce an optical fiber, in the first stage a preform is produced. Obtaining it from a linear polymer requires precise control of the thermopolymerization process. If the process is not carried out properly, it leads to heterogeneity of the polymer structure and additionally causes cross-linking of the system. As mentioned, cross-linked polymers are used as protective coatings and, as the name suggests, provide protection for brittle glass, and in the case of polymer optical fibers, are also materials that trap traveling light in the fiber core. Therefore, such material should have a lower refractive index than the material used to produce the core, in order to meet the principle of total internal reflection. To be able to properly protect the newly formed fiber, such coatings are applied immediately, using the photopolymerization process, after the fiber is drawn out. Thermal resistance and mechanical strength are some of the most important parameters checked for materials potentially useful as coatings. The presented research focused on thermal properties. Table 4 contains the all-important parameters related to thermal resistance, which were read based on data obtained from TG/DTG/DSC analyses [42; 43; 44; 45; 14]. The table shows the temperatures at 1, 3 and 5 wt.% mass loss. On this basis, it is possible to compare the polymerization conditions at which the temperature parameters are the most favorable. The maximum values presented for the DTG and DSC curves additionally allow for determining the thermal resistance of the obtained materials. Table 4 also includes information on the amount of remaining unreacted mass. Graphs from these analyzes are provided in the Supplementary Materials (Figure S1).