esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Digital Investigations
Forensics applications are often tailored to specific types of devices, hardware or software. These can include mobile devices, cloud infrastructure, memory or hard disks, and server logs or databases. Moreover, the forensics applications are designed for a specific OS, e.g., Windows, Linux, Android systems, or can be cross-platform, capable of running on multiple OS. Finally, regarding licensing, forensic software can be either commercial or open-source. Table 1 includes a list of widely used digital forensics applications and summarizes their main features and capabilities. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Application** & **Function** & **Device type** & **OS** & **Licensing** \\ \hline Magnet Axiom Cyber [28] & Remote collections, Processing, Analysis & Computers, Cloud & Cross-platform & Commercial \\ \hline Magnet Axiom Outrider [29] & Triage mobiles, Live system artifacts and memory, Processing, Analysis & Computers, Mobile Devices & Cross-platform & Commercial \\ \hline X-Ways Forensics [30] & Live data acquisition, Processing, Analysis & Windows-based Computers & Windows & Commercial \\ \hline X-Ways Investigator [31] & Processing, Analysis & - & Windows & Commercial \\ \hline Tovek Intelligence Platform [32] & Web searching, Processing, Analysis & - & Cross-platform & Commercial \\ \hline OpenText Forensic [33] & Data acquisition, Processing, Analysis & Computers, Cloud, Mobile Devices & Windows & Commercial \\ \hline Autopsy [34] & Web artifacts, Processing, Analysis & Computers & Cross-platform & Open-source \\ \hline The Sleuth Kit (TSK) [35] & Collection of command line tools, Disk image investigation & Computers & Cross-platform & Open-source \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of digital forensics applications by function, device type, hosting OS, and licensing.
# Uncovering Hidden Community Structure in Multi-Layer Networks
## 5 Experimental Setup
### Evaluation Metric  
Evaluation (also called benchmarking) is the assessment of how well an algorithm detects the desired set of communities in a network. We used the modularity metric as a performance indicator. Informally, the modularity Q of each possible partition can be calculated as:  
\[Q=(\text{fraction of edges among communities})-(\text{expected fraction of edges}).\]  
Nowadays, the indicators for evaluating the performance of community detection algorithms are numerous. In this paper, we used one of the classical performance indicators, i.e., modularity \(Q\). The more specific definition of how we calculate \(Q\) in this work is given in Section 3.1. We compare our proposed method with the same algorithm that we used as a baseline method in our work, i.e., Louvain, which is the fast method, popular for greedy modularity optimization and also used as the baseline method in the proposed method. Other metrics like Normalized Mutual Information (NMI) can be used if the focus is on finding the similarity of partitions [24] or the maximum matching ratio (MMR), which is designed specifically for biological networks [25].  
Figure 4: Results of modularity comparison of HCD algorithm with Infomap and Louvain algorithms.
## 1 Introduction
[27] focused on the comparisons of the thermal effect and chemical effect of CO\({}_{2}\) on the LBVs of O\({}_{2}\)/methane under various O\({}_{2}\) mole fraction and equivalence ratios by numerical simulation under ordinary pressure; the results showed that the chemical effect was smaller than the thermal effect, but much bigger than the radiative effect in the calculation domain. Meanwhile, the third body effect was also analyzed to identify important reactions. [PERSON] et al. conducted numerical simulation to illustrate that the dilution effect [28] of CO\({}_{2}\) can decrease the maximum temperature of the one-dimensional diffusion flame and, when the CO\({}_{2}\) added to the fuel side [29], the chemical effect, thermal effect, and radiative effect on the temperature were compared in detail. They conclude that both the chemical and the thermal effect of CO\({}_{2}\) can significantly change the flame temperature. However, it is worthy to note that the above studies of [PERSON] et al. were conducted under atmospheric-pressure. What is more, the NOx formation and emission of the gas flame with CO\({}_{2}\) dilution by distinguishing the chemical effect and thermal effect were analyzed as well [12; 13; 30; 31]. To summarize the above review, what could be extracted from the literature is that there is no report on the isolate effects of CO\({}_{2}\) on the counterflow diffusion flame along with improving pressure. The chemical effect and thermal effect of CO\({}_{2}\) on the flame (premixed flame or diffusion flame) have been investigated sufficiently. However, the reports investigating the dominant role of the chemical effect and thermal effects of gas dilution (taking CO\({}_{2}\) as an example) based on different flame characteristics are rare. More importantly, whether the chemical effect and the thermal effect of the CO\({}_{2}\) on the specific parameters (LBV, temperature, NOx emission, main species) will behave differently with the increase of pressure, and whether the dominant role of the two effects along with the increase pressure will exchange, remained to be studied in deeply. The objective of the present study is to quantitatively explore the effects of CO\({}_{2}\), which is added into the oxidant side of the CH\({}_{4}\)/O\({}_{2}\) counterflow diffusion flame on the flame temperature with high pressure and a large CO\({}_{2}\) dilution ratio, and reveal the trends of the chemical effect and thermal effect of CO\({}_{2}\) caused by pressure. The emphasis is on the comparison of the chemical effect and thermal effect under different pressure and confirming the dominant role between the two effects on temperature. Meanwhile, the effect of strain rate on the temperature was also carried out to quantitatively clarify the CO\({}_{2}\) effect on the flame temperature under a low strain rate and high strain rate.
## 2 PB-DA Design and Fabrication
### Dimension and Reinforcement Design of PB-DA  
This section focuses on designing a PB-DA based on a 30 m long bridge prototype. The PB-DA in this study was designed using fully prestressed concrete, ensuring that no tensile stress is generated under the maximum design load, thus preventing cracking. The maximum static design load for this model bridge is 43 kN, and within this load range, the concrete should not crack. According to existing research [27; 28; 29], a 1:5 geometric scale can accurately simulate the fatigue behavior of bridges. This paper also adopts a 1:5 geometric scale, with the scaled model's materials being the same as the prototype's. Based on the derivations in Section 2.1, when \(k=5\), the concentrated force is reduced to 1/25 of the prototype, resulting in the scaled model strain being the same as that of the prototype.  
The detailed dimensions of the scaled model are shown in Figure 1, with the scaled model being 0.4 m high and 6 m long, with an effective span of 5.7 m. The scaled model's cross-section is T-shaped, with a total flange width of 0.43 m, the outer edge of the flange at 36 mm, and the thickness at the juncture of the flange and web at 54 mm. The reinforcement layout and the reinforcement detail list are shown in Figure 2 and Table 3, respectively.  
In this experimental model, the ordinary reinforcement in the beams consists of HPB235 grade steel bars, and the prestressed reinforcement consists of steel strands. These strands were produced by Tianjin Metallurgical Group Zhongxing Shenda Steel Co., Ltd., China, with a nominal diameter of 9.53 mm and a nominal area of 54.8 mm\({}^{2}\). To ensure the lateral balance of the beam, the following tensioning sequence was adopted: 50% N2 \(\rightarrow\) 100% N3 \(\rightarrow\) 100% N2 \(\rightarrow\) 100% N1. Here, the tension control force for N1 was 62.05 kN, and for N2 and N3, it was 69.8 kN. After tensioning the prestressed tendons, the beam exhibited an upward deflection between 10 mm and 15 mm.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 3 State of the Art Methods for Laser Line Segmentation
### Grey Level Co-Occurrence Matrix Based Method  
Grey level co-occurrence matrix (GLCM) [26] computes the frequencies of different combinations of pixel values or grey levels occurring in an image and then forms a matrix to represent these frequencies. Therefore, it is usually used to segment textured images or objects with an unevenly distributed background. The second order GLCM is usually used for segmentation and it is formulated as:  
\[P(i,j|d,\theta)=\frac{\#\{k,l\in D|f(k)=i,f(l)=j,\;\parallel k-l\parallel=d, \;\angle(k-l)=\theta\}}{\#\{m,n\in D|\parallel m-n\parallel=d,\angle(m-n)= \theta\}}, \tag{3}\]  
where \(d\) is the distance of pixel \(k\) and pixel \(l\). \(\theta\) is the angle of vector \((k-l)\) with the horizontal line or vertical line. The combination of \(d\) and \(\theta\) represents the relative position of pixel \(k\) and pixel \(l\), which have gray-scale value \(i\) and \(j\) respectively.  
During our implementation, we update all the intensity values in the moving window by subtracting its minimal intensity value and then adding one. The quantization level of the GLCM matrix becomes one and the maximal intensity value of the updated moving window. Following the computation of the GLCM, the contrast measure is used to form the GLCM image. Then, the GLCM image is segmented by a global threshold.
## 2 Setup and Modeling
### Modeling
#### 2.2.2 RSOA Response  
The power of the encoded signal that exits the RSOA is expressed as  
\[P_{RSOA}(t)=|E_{RSOA}(t)|^{2} \tag{2}\]  
where the corresponding electric field, \(E_{RSOA}(t)\), which is normalized so that it represents power, is given by [23]  
\[E_{RSOA}(t)=\sqrt{P_{CW}}\exp\Big{[}\big{(}1-j\alpha_{LEF}\big{)}h\big{(}t-2L_ {RSOA}n_{g}/c\big{)}\Big{]} \tag{3}\]  
where \(\alpha_{LEF}\) is the RSOA linewidth enhancement factor, \(L_{RSOA}\) is the RSOA active region length, \(n_{g}\) is the group refractive index of the semiconductor material and \(h(t)\) is the RSOA  
Figure 1: Illustration of RSOA directly modulated by NRZ data followed by serially connected BFL. (**a**) Block diagram. (**b**) P-I curve. (**c**) Stand-alone RSOA temporal output profile. (**d**) RSOA electrical modulation response. (**e**) BFL spectral response. (**f**) BFL-assisted RSOA temporal output profile.  
integrated gain response, which obeys the following one-dimensional ordinary differential equation [23]  
\[\frac{dh(t)}{dt}=-\frac{h(t)-\Gamma aN_{0}L_{RSOA}\bigg{[}\frac{I(t)}{I_{0}}-1 \bigg{]}}{T_{car}}-\frac{\exp\big{[}2h(t)\big{]}-1}{P_{sat}T_{car}}P_{CW} \tag{4}\]  
where \(\Gamma\) is RSOA confinement factor, \(a\) is RSOA differential gain, \(N_{0}\) is RSOA carrier density at transparency, \(T_{car}\) is RSOA carrier lifetime, \(I_{0}\) is RSOA current required for transparency, and \(P_{sat}\) is RSOA material saturation power.
## 4 Proposed Framework
### Mathematical Formulation  
Within the framework of our studies on authentication based on Self-Sovereign Identity (SSI), we have further explored the mathematical characterization of the underlying mechanisms. We have clarified the roles and relationships through in-depth analysis, offering a thorough grasp of how every part interacts with the system as a whole. Digital identity creation, credential issuance and signing, credential verification, and access decision-making are some of these functions. We have also described the relationships that exist between credentials and the corresponding signatures, as well as between entities and credentials. This mathematical framework clarifies the nuances of important operations and how they interact with one another within the system, providing insightful information about the mechanics of SSI-based authentication.  
The Algorithm 1 represents the process of SSI-based authentication. It starts with the creation of digital identities for each entity in the system. Then, for each entity and each credential type, credentials are issued and signed. Finally, service providers verify the authenticity of the received credentials. This algorithm provides a clear and concise representation of the SSI-based authentication process. Here, we present a more detailed step-by-step mathematical representation of the proposed framework.  
```
1:procedureSSI Authentication
2:for each entity \(e\) in \(\mathcal{E}\)do
3:\((k_{\text{priv}},k_{\text{pub}})\leftarrow\text{Createl Identity}(e)\)
4:\(\text{Register}(e,k_{\text{pub}})\)
5:endfor
6:for each entity \(e\) in \(\mathcal{E}\)do
7:for each credential type \(t\) in \(\mathcal{T}\)do
8:\(c\leftarrow\text{IssueCred}(e,t)\)
9:\(s\leftarrow\text{Sign}(c,k_{\text{priv}})\)
10: Add \((e,c)\) to \(\mathcal{R}_{\text{ec}}\)
11: Add \((c,s)\) to \(\mathcal{R}_{\text{cs}}\)
12:endfor
13:endfor
14:for each service provider do
15:\(\text{Verify}(c,s)\)
16: GrantAccess(verification result)
17:endfor
18:endprocedure
```  
**Algorithm 1** Detailed Self-Sovereign Identity (SSI)-based authentication
## 1 Introduction
### Diagnostic Imaging Modalities in Space  
In addition to routine physical examination, various medical imaging modalities may be required to monitor and diagnose medical conditions during long-duration space travel. To date, ultrasound imaging acquired on space stations has proven to be helpful in diagnosing a wide array of medical conditions, including venous thrombosis, renal and biliary stones, and decompression sickness [29; 237; 238; 239; 240; 241; 242]. Moreover, the Focused Assessment with Sonography for Trauma (FAST), utilized by physicians to rapidly evaluate trauma patients, may be employed during space missions to rule out life-threatening intra-abdominal, intra-thoracic, or intra-ocular pathology [243]. Remote telemetored ultrasound (aka tele-ultrasound) has been previously investigated during the NASA Extreme Environment Missions Operations (NEEMO) expeditions [244]. Today, the Butterfly iQ portable ultrasound probe can be linked directly to a smartphone through cloud computing, allowing physicians/specialists to promptly analyze remote ultrasound images [245].  
Currently, alternative imaging modalities such as X-ray, CT, PET and MRI scan are unable to be used in space due to substantial limitations (e.g., limited space for large imaging structures, difficulties in interpretation due to microgravity). However, it is possible that the future development of a photocathode-based X-ray source may one day make this a possibility [101; 246]. If X-ray imaging was possible, certain caveats would need to be taken into account for accurate interpretation. For example, pleural effusions, air-fluid levels, and pulmonary cephalization commonly seen on terrestrial imaging, would need to be interpreted in an entirely different way due to the effect of microgravity [247]. While this adjustment might be challenging, the altered principles of weightless physiology may provide some advantages as well. For example, one study found that intra-abdominal fluid was better able to be detected in space than in the terrestrial environment due to gravitational alterations in fluid dynamics [248]. Further research is required to identify and optimize inflight imaging modalities for the detection and treatment of various medical conditions.
# Landsat Surface Product Validation Instrumentation:
###### Abstract  
Users of remotely sensed Earth optical imagery are increasingly demanding a surface reflectance or surface temperature product instead of the top-of-atmosphere products that have been produced historically. Validating the accuracy of surface products remains a difficult task since it involves assessment across a range of atmospheric profiles, as well as many different land surface types. Thus, the standard approaches from the satellite calibration community do not apply, and new technologies need to be developed. The Big Multi-Agency Campaign (BigMAC) was developed to assess current technologies that might be used for the validation of surface products derived from satellite imagery, with emphasis on Landsat. Conducted in August 2021, in Brookings, SD, USA, a variety of measurement technologies were fielded and assessed for accuracy, precision, and deployability. Each technology exhibited its strengths and weaknesses. Handheld spectroradiometers are capable of surface reflectance measurements with accuracies within the 0.01-0.02 absolute reflectance units, but these are expensive to deploy. Unmanned Aircraft System (UAS)-based radiometers have the potential of making measurements with similar accuracy, but these are also difficult to deploy. Mirror-based empirical line methods showed improved accuracy potential, but their deployment also remains an issue. However, there are inexpensive radiometers designed for long-term autonomous use that exhibited good accuracy and precision, in addition to being easy to deploy. Thermal measurement technologies showed an accuracy potential in the 1-2 K range, and some easily deployable instruments are available. The results from the BigMAC indicate that there are technologies available today for conducting operational surface reflectance/temperature measurements, with strong potential for improvements in the future.  
L +
Footnote †: journal: Sensors  
0000-0002-2320-3320]M. M.
## 2 Configuration of the System  
Figure 1 shows 2D and 3D views of the entire structure, and Table 1 summarizes major design parameters of the tunnel and mooring lines. The tunnel, which has a diameter of 23 m and a length of 700 m, is made of high-density concrete. Since the structure in this study is a section of the 30-km-long SFT, the fixed-fixed boundary condition at both ends are applied assuming that strong fixtures (or  
towers) will be built at 700-m intervals, as shown in the Figure 1. Considering that the water depth of the planned site is 100 m, the submergence depth, a vertical distance between free surface and the tunnel centerline, is set to be 61.5 m. The BWR is fixed at 1.3, and the tunnel thickness is 2.3 m. The tunnel thickness is actually greater than the real value to have the equivalent tunnel bending stiffness including inner compartment structures. The axial and bending stiffnesses are calculated based on the given data of Table 1.  
Chain mooring lines with a nominal diameter of 180 mm are used. High static and dynamic mooring tensions are expected based on the given BWR and wave condition [28]. In addition, the maximum mooring tension should be smaller than the MBL (minimum breaking load) divided by safety factor (SF). Thus, chain might be the best choice considering high MBL of 30,689 kN for Grade R5. As shown in Figure 1, four 60-degree-inclined mooring lines are installed for every 25 m interval toward the center locations. The lengths of mooring lines are 51.1 m for line #1 and #2 and 37.8 m for line #3 and #4. The wet natural frequencies of the tunnel hydro-elastic responses coupled with mooring lines are calculated and presented in Table 2.
# Ru(II)-Dppz Derivatives and Their Interactions with DNA: Thirty Years and Counting
## 3 DNA Binding
### Few Examples of Applications: Cellular Imaging, Photodynamic Therapy (PDT), and Sensing
The use of the dinuclear [Ru(bpy)\({}_{2}\)tpphzRu(bpy)\({}_{2}\)]\({}^{4+}\) and [Ru(phen)\({}_{2}\)tpphzRu(phen)\({}_{2}\)]\({}^{4+}\) molecular complexes as multifunctional biological imaging agent for DNA was reported. In particular, the efficiency of these species to \"switch -on\" nuclear DNA of living cells without requiring prior membrane permeabilization was demonstrated for both luminescence and transition electron microscopy [172]. Recently, a new derivative of [Ru(bpy)\({}_{2}\)(dppz)], demonstrating a dramatic improvement in photobiological activity by introducing a pyenyl substituent, was proposed [173]. The studies demonstrated that the population of an LC triplet state was much more performant in terms of singlet oxygen yield and potentially usable in clinical PDT applications. In addition to DNA, Ru-dppz-type complexes are able to interact with proteins. One example is the heteroleptic ruthenium species based on the ligands 7,8-dimethyl dppz, 1,10-phenanthroline-4-carboxamide, and 5,6-dimethyl-1,10-phenanthroline [174], which is able to bind to acetylcholinesteraseerase (AChE), exhibiting the typical light-switch behavior when binding to the hydrophobic domains of the protein. A further example is the more classic [Ru(bpy)\({}_{2}\)dppz]\({}^{2+}\), which is not photoluminescent in aqueous solution, even in the presence of monomeric amyloid beta (A\(\beta\)) (a peptide fragment containing 36-43 amino acids.) Monomeric A\(\beta\) self-assembles to form aggregates (amyloid fibrils) that are crucially involved in [PERSON]'s disease. In the presence of aggregates of A\(\beta\) fibrils, the photoluminescence of [Ru(bpy)\({}_{2}\)dppz]\({}^{2+}\) is recovered [175; 176]. A further interesting application arises from the modification of Ru-dppz complexes by introducing an aptamer fragment into the system. Although the aptamers are single-stranded DNA oligonucleotides, they are able to fold into three-dimensional structures through base pairing. This process leads to protection of the dppz system from the solvent, which results in luminescence. More specifically, the system is applicable by exploiting the combination of the light switch effect with the selective ability of aptamers towards specific analytes. Recognition by the aptamers of a specific analyte (e.g., immunoglobulin E (IgM)) [177], abrin toxin [178], and ATP [179; 180]) leads to the unfolding of its structure and the exposure of the dppz complex to the aqueous solvent, with consequent extinction of the luminescence.
# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 6 Accuracy and Suitability Analysis
Not only automated occupancy labeling itself is resource intensive for such datasets, but also achieving a class balance between \"occupied\" and \"unoccupied\" label classes is an equally challenging task [37]. It has also been shown that typical ML algorithms only accept training data with fixed sizes; thus, a networked node occupancy detection problem which can have a variable number of sensor nodes (due to occasional communication or hardware failure), would need to reformat the data into a fixed format, which often requires data fusion to happen prior to ML training phase [37; 45]. Furthermore, deep learning ML models require large datasets to train. Thus, it is infeasible to re-collect a large amount of data to retrain to handle any novelty in the occupancy patterns [46]. In the Bayesian inference methods sub-category, node-level occupancy estimation is usually performed via ML algorithms or knowledge-based methods. In contrast, the network-level occupancy estimation is performed by fusing the node-level assessment through a Bayesian inference-based framework [45]. Although this approach has produced accuracies up to 93% [21] in uncontrolled experiments, generating ML inference for each node, on-device, is resource intensive. Based on the above discussion on categorizing occupancy sensing methods for networked nodes, a taxonomy chart is presented in Figure 3.  
In the past, many review articles attempted to vaguely attribute the categories of occupancy sensing methods to specific application areas. As such, no one-to-one or one-to-many correspondence exists between the method categories shown in Figure 3 and the application areas mentioned in Table 2. The problem of suitability of applications to methods is much more nuanced and requires specific criteria to be met before suitability can be established. To achieve this end, Table 5 details the breakdown of how the ADPs for each application area map to each of the reviewed solutions in Table 4.