esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Centrality
#### 4.1.1 Betweenness Centrality  
**Definition 4** (**Betweenness Centrality [133]**).: _Given a graph \(G\), the betweenness centrality of a vertex \(v\in V(G)\) is:_  
\[bc(v)=\sum_{s\in V(G)}\frac{\delta(s,t|v)}{\delta(s,t)},\]  
_where \(\delta(s,t|v)\) is the number of shortest paths between \(s\) and \(t\) that include \(v\), and \(\delta(s,t)\) is the number of shortest paths between \(s\) and \(t\)._  
For betweenness centrality, the fastest static algorithm to compute exact betweenness centralities for all the vertices is proposed in [133] and it requires \(O(nm)\) and \(O(nm+n^{2}\log n)\) time on weighted and unweighted graphs, respectively. Regarding the dynamic setting, Ref. [17] is the first work that proposes an efficient algorithm, QUBE, to handle the update of the betweenness centralities of vertices in a graph. QUBE decomposes the input graph into independent components using minimum union cycles, such that the change in one component does not affect the betweenness centrality of nodes in other components. After a graph update, QUBE recomputes the betweenness centrality for the vertices in the affected component. Compared with recomputing from scratch, QUBE significantly reduces the number of explored vertices. Therefore, QUBE achieves great speedup practically.  
After [17], [PERSON]. [18] devises an incremental algorithm for betweenness centrality by maintaining the BFS trees for each vertex in the streaming graph. Ref. [40] maintains the betweenness centralities by updating the all-pairs shortest paths. It keeps a directed acyclic subgraph for each vertex containing all the edges that belong to at least one shortest path from the vertex and updates the shortest paths by a Dijkstra-like algorithm. Based on QUBE, [PERSON]. [43] proposes a dynamic algorithm supporting vertex insertion/deletion. [PERSON]. [25] further extends QUBE to support the insertion and deletion of bridge edges. [PERSON]. [26] parallelizes [18] by introducing a novel multi-level data structure and providing an efficient workload estimation strategy. [PERSON]. [39] proposes a parallel incremental algorithm, named iCentral, for the betweenness centrality computation. Different from QUBE, iCentral decomposes the input graph into biconnected components such that the betweenness centrality values of vertices not belonging to the affected biconnected components do not change after the update. Following iCentral, Ref. [37] further explores the opportunities to reduce unnecessary computation for the batch update case. In addition, Refs. [38; 41] discuss the approximating betweenness centrality computation in dynamic graphs. Recently, Ref. [42] further improves the performance of the betweenness centrality maintenance on streaming graphs, where edges arrive over time.
# Line-of-Sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification
###### Abstract  
In dynamic space networks, achieving high precision and fast initial pointing of the optical line of sight (LOS) is the key goal in developing this technology. It is the premise and basis of realizing optical LOS capture. Based on the composition and working principle of space optical networking systems, and the effect of real-time position and attitude changes on LOS initial pointing between networks, the matrix transformation and transfer principle is used to establish a multi-link LOS initial pointing model and analyze the factors affecting the size of the field of uncertainty (FOU). In a dynamic space optical networking experiment, the \"one-to-two\" simultaneous LOS pointing test is carried out, which shows that the model can realize the function of multi-link LOS initial pointing. The sizes of the FOU of the test terminal are 8.67 mrad and 8.34 mrad, respectively, with an average capture time of 18.3 s.  
space optical networking multi-link line-of-sight pointing attitude; matrix +
Footnote †: journal: _Photonics_
## 2 Materials and Methods
### Sensing Electrode Array  
Characterization of the physicochemical properties of bulk materials has spearheaded the application of EIS in many areas to study the effect of electrode shape, size and material on electrode-solution interaction. Since [PERSON] studied the influence of electrode geometry on current distribution continuity to understand frequency dispersion processes [12; 13; 14; 15], significant advances are continually reported in developing sensing electrodes to understand the correlation of different compounds in bulk solutions with frequency response to identify and quantify the presence of particular substances. [PERSON] et al. demonstrated that a boron-doped diamond electrode can detect ambroxol in concentrations of 0.05-0.7 \(\upmu\)m [16]. [PERSON] and [PERSON] presented a silver-filled multi-walled carbon nanotube for determination of sulfamethoxazole, in the range 0.05-70 \(\upmu\)m, in pharmaceutical formulations and urine [17]. [PERSON] et al. developed a screen-printed electrochemical sensor for the detection of sulfamethoxazole and trimethoprim without pre-treating the sample [18]. Recently, [PERSON] et al. presented a graphene-based electrode for the determination of sulfamethoxazole in aqueous environments [19].  
Despite the fact that improved measurement technologies, including electrode sensing technologies, are continuously introduced, and the experimental setup may be relatively simple (i.e., bulk solution in the presence of a set of electrodes), the process of modelling electrical properties of bulk solutions is by no means complex because there are many physicochemical processes involved.  
Figure 1: Frequency measurement range of six common impedance measurement methods [11]. Authors’ graphical summary extracted from product information. Reproduced with permission, courtesy of ©Keysight Technologies, Inc., 2009–2016.
## 4 Conclusions  
In this study, a modified MCHX was proposed to improve heat exchanger frosting performance by extending the fins on the windward side. The heat transfer rate, frost accumulation rate, and pressure drop characteristics of the MCHX-A were measured and compared with the MCHX-B under different frosting conditions. Based on this experimental analysis, the main conclusions are as follows:  
1. Frost is mainly deposited on the windward side of the heat exchanger. Extending the length of windward fins can effectively capture a large amount of frost and make the frost distribution more uniform, thus inhibiting rapid frost blocking on the front side.
2. In the early stage of frosting, frost mainly grows in the area where the fin and the flat tube are in contact because of the lower surface temperature. As the frost progresses, the extended fins on the windward side begin to capture the frost. Therefore, the heat transfer rate of the MCHX-A is nearly the same as that of the MCHX-B at the early frosting stage, and the former shows better heat transfer performance than the latter as the frost progresses.
3. During the 60 min frosting cycle, the total heat transfer capacity and the mass of frost of the MCHX-A are 9.6-49.7% and 10.3-46.9% higher than the MCHX-B, respectively. In addition, the pressure drop of MCHX-B is larger than that of MCHX-A under all frosting conditions. This is mainly because the extended fins on the windward side capture a large amount of frost, which reduces the airflow attenuation rate and increases the heat transfer rate and frost accumulation rate of the heat exchanger.
4. The MCHX-A has greater potential to improve the thermal-hydraulic performance of MCHX at lower air velocity (0.6 m/s), where the frost layer distribution is more uneven. With the increase of air velocity, the frost layer uniformity is improved, and the proportion of MCHX-A improving heat transfer performance decreases gradually. In addition, as the inlet coolant temperature and RH change to promote frost layer growth, the potential of MCHX-A to improve heat transfer capacity increases. In conclusion, the more uneven the frost layer distribution, the greater the potential of MCHX-A to improve the heat transfer performance.  
Methodology, T.X.; Formal analysis, G.Y.; Data curation, T.X.; Writing--original draft, T.X.; Writing--review & editing, G.L.; Supervision, G.Y.; Funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data is contained within the article. The authors declare no conflict of interest.
## 3 Results
### Case 1--Manual On/Off Switch-On  
Once the number and absorbed power of the appliances to be installed in the interior spaces were determined, the manual on/off system was analysed. The devices installed are those proposed in Table 6 (post-intervention, LED).  
Table 8 shows the level of daylighting perceived within the spaces. As an example, the analysis was carried out for the four selected spaces. In this case, the lamps were turned off, and the objective was to evaluate in the three time periods--at 8:00 a.m., 12:00 p.m., and 4:00 p.m.--on 21 December the level of daylighting. Since this scenario included a manual luminaire on/off switch in the system, the lamps were only used when the daylighting did not meet the minimum lighting requirements. As the rooms analysed had different orientations, it was possible to see how the daylighting varied according to their layouts. The verification of illuminance was carried out (negative checks in red) according to the intended use of each room. This table suggests manually turning on the light when there is not enough daylight to positively verify illuminance values.  
Table 9 shows the level of illuminance for the four rooms selected in the time periods, considering the contributions of both the lamps (lamps on) and the daylight. All the illuminance values were verified.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average Illuminance (lx)** & \\ \hline
**Spaces** & **Pre-Intervention, Fluorescent Luminaires** & **Post-Intervention, LED** \\ \hline Room 5 & 726 & 789 \\ \hline Library 1 & 988 & 804 \\ \hline Gym & 635 & 567 \\ \hline Men’s bathroom & 205 & 249 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Illuminance values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**LED LAMPS OFF (Daylighting Contribution)**} \\ \hline \multicolumn{4}{c}{**Average Illuminance (lx)**} \\ \hline Spaces & 8 a.m. & 12 p.m. & 4 p.m. \\ \hline Room & 743 & 8850 & 28 \\ \hline Library & 189 & 1538 & 14 \\ \hline Gym & 530 & 2991 & 88 \\ \hline Men’s bathroom & 884 & 233 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Case 1—Evaluation of illuminance with LED lamps off.  
At 8:00 a.m., the lamps needed to be turned on in the library and bathroom to meet regulatory limits. When turning on the lights at 8:00 a.m., it can be seen that 888 lx was achieved in the library due to the artificial lighting system, and 296 lx was achieved in the bathroom.  
At 12:00 p.m., the daylight was sufficient to achieve useful illuminance on the work surfaces in all rooms. When the artificial light was on, the minimum values of illuminance were exceeded for the simultaneous presence of natural and artificial light.  
At 4:00 p.m., the daylight was very weak; therefore, it would be necessary to turn on the lamps in all the rooms. The minimum value of illuminance was not verified in any room; therefore, the presence of the lighting system was necessary. This happened because we analysed the worst day of the year, 21 December, when the sun set very early.
## 7 Numerical Results of NSDI  
Three structures are used as numerical examples in this work to illustrate the robustness and accuracy of the proposed NSDI approach. These structures are:  
1. 31-bar planer truss,
2. 120-bar dome truss, and
3. four-story steel structure model.  
Damage to the structure is modeled as a relative reduction in the elasticity modulus of each individual element, with the mass matrix assumed to remain constant. For the optimization process, the number of slime molds for ESMA was fixed to 20 for each run along a maximum of 100 iterations. The fit-test design in the current iteration is compared to that of 20 prior iterations as a measure of convergence for engineering optimization problems. As a result, if the difference between these two results is smaller than a certain tolerance value, it is recorded as converged. The desired value for this example is set as \(10^{-3}\). If the converge does not occur, the algorithm will be terminated by implementing a fixed maximum number of iterations.
## 2 Materials and Methods
### Validation
#### 2.6.1 Field Data  
FPC was calculated from field measurements made at 76 sites across NSW, collected between 2009 and 2015 (Figure 3). Each field site was located away from disturbances (>100 m from roads or buildings) and steep terrain, in a patch of homogeneous vegetation. They were measured during other projects, and although they were not randomly distributed across all vegetation communities, they cover a wide range of FPC values. At each site, the star transect method [62] was used to record 300 vertical sighting tube observations of the overstorey (woody vegetation >2 m height), midstorey (woody vegetation \(\leqslant\)2 m height) and understorey (herbaceous plants <2 m height), from a circular area with a radius of 50 m. The location of each centre point was recorded using differential GPS to less than \(\pm\)5 m. Overstorey FPC (\(FPC_{o}\)) was calculated according to the following equation from Armston, Denham, Danaher, Scarth and Moffiet (2016):  
\[FPC_{o}=\frac{P_{o,g}}{\left(1-P_{o,b}\right)} \tag{3}\]  
where \(P_{o,g}\) was the proportion of overstorey green foliage observations and \(P_{o,b}\) was the proportion of overstorey branch observations (which are likely to occlude foliage from the observer). As the satellite derived FPC model would likely be sensitive to both overstorey and midstorey foliage, we also calculated the combined FPC of the overstorey and midstorey (\(FPC_{o+m}\)) as:  
\[FPC_{o+m}=\frac{P_{o,g}}{\left(1-P_{o,b}\right)}+P_{m,g}\left(1-P_{o,g}-P_{o, b}\right) \tag{4}\]  
where \(P_{m,g}\) was the proportion of midstorey green foliage observations.
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 7 Following Stages of Ultrasound Research
### Toxicity  
Even though ultrasound offers several advantages in the pasteurization area, indeed, ultrasound still has some current challenges. Because of the cavitation effect, some macromolecules break down, not only water, releasing free radicals. These radicals can combine with other compounds in the product and generate new ones. Hence, these new compounds must be studied and quantified to ensure the product does not contain toxic substances.  
Recently, ultrasound has been tested for pesticide degradation in some food products. The comprehensive study of the intermediate and after-sonication chemical composition has been targeted in several experiments [108,109]. In specific research to degrade the pesticide parathion methyl in milk, scientists discovered a reduction of up to 97% of the pesticide concentration in milk, affected by the intensity of the treatment and the initial concentration of the chemical. However, they also found three transformation products, P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\), likely from oxidation during sonication; these products presented higher toxicity than the original pesticide [109]. Opposite to these results, [PERSON] et al. [110] studied the degradation of an organophosphate insecticide chlorpyrifos in milk using sonication and ultraviolet. The researchers found that both technologies could degrade the chemical up to 97% because of the electrophilic attack on the P=5 bond, converting it into a P=O bond, besides other chemical reactions, reducing the toxicity of the chlorpyrifos.  
Although the experiments mentioned above were conducted to evaluate pesticide degradation, processing conditions are similar to the research aimed at pasteurization. Also, the presence of pesticides is familiar in many food commodities. Then, the generation of these compounds is likely probable to find in pasteurized products by ultrasound. More research is needed in the area of toxicology, not only to find the concentration of these toxic compounds in different products but also to detect the processing conditions that decrease the formation of these chemicals while keeping the microbial safety and quality of the product.
# Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
## 4 Detection Using Deep Learning
### Feature Set  
IDSs use behavioral categorization and response to classify malicious and benign communication. No single message, or feature on a communication cycle can determine the behavior or the nature of the communication. Both the qualitative and quantitative features of a communication cycle are required to be observed over a period of time to yield its behavioral characteristics. We propose the features presented in Table 1 to characterize these qualitative and quantitative aspects of wireless communication messages intercepted by the detection module in IoT systems.  
In a typical communication cycle between a distinct pair of sender and receiver nodes, the transmission and reception rates are expected to be similar. These values are however different when the system is under an attack such as denial-of-service or sinkhole attack. Similarly, transmission-to-reception ratio is a reasonable indicator of spoofing and masquerading attacks when used in conjunction with activity duration. Transmission mode determines the state and protocol of the communication. Based on the pre-trained behavior, IDS can distinguish if the message headers in a communication cycle have a known vulnerable transmission mode, an unintended sender or receiver (through IP addresses), a malicious payload, or all of these. To classify malicious and benign traffic, the proposed IDS thus gathers above features such as transmission-rate, reception-rate, transmission-to-reception ratio, duration, transmission mode, source-IP, destination-IP, and the data-value information from the network traffic. These features are selected in consideration of theand the processing to performance ratio of portable low-powered, resource-constrained IoT devices. Our system caches these features and generates meta-features by preprocessing the data. During data preprocessing, IID calculates the probability distribution of the extracted meta-features as shown in Equation (1).  
\[f_{p}=\{P(B_{0}),P(B_{1}),....,P(B_{7})\} \tag{1}\]  
where \(P(B_{i})\) is the probability of each Byte \"1\" observed in the \(i^{th}\) Byte position, and  
\[f=L(f_{0}), \tag{2}\]  
where the function \(L:R^{8}\mapsto R^{8}\) in Equation (2) is the logical mapping, i.e., if \(P(B_{i})\) is greater than a half, the probability is mapped to 1, or else, 0.  
The set of features represents a tuple of input data for the machine-learning algorithm. Each tuple is represented as a data-vector \(d_{v}\) reduced from \(f\), before feeding data into the neural network. Consequently, each meta-feature set can be represented as a feature vector \(f_{v}\) at a time instance \(n\), generated as,  
\[f_{v}(n)=d_{v}(n)\oplus d_{v}(n-1), \tag{3}\]  
where \(\oplus\) is an exclusive-or operator applied to each position of bits in the vector.
## 2 Inductance Model Description
For multi-turn inductors, there is mutual coupling between each turn, and then the magnetic field strength through the inductor and the linked flux significantly increases with the number of turns, as shown in Figure 3. Therefore, the overall inductance of the inductor is the sum of the self-inductance and increased mutual inductance between each turn [22]. For the proposed Archimedean spiral coil with a rectangular cross section area and \(n\) concentric turns, the total inductance \(L_{\text{T}}\) can be written as the sum over the self-inductance \(L_{\text{S}}\) and the mutual inductance \(L_{\text{M}}\) of all turns,  
\[L_{\text{T}}=L_{\text{S}}+L_{\text{M}}=L_{\text{S}}+\sum_{i=1}^{n}\sum_{j=1}^ {n}M_{ij} \tag{1}\]  
where \(M_{\text{ij}}\) represent the mutual inductance between the \(i\)th turn coil and \(j\)th turn coil. The overall self-inductance \(L_{\text{S}}\) can be further approximated as a function of coil geometry [23],  
\[L_{\text{S}}=\frac{\mu ln_{eng}}{2\pi}\times\left(\log\left(\frac{2l_{eng}}{w+ t}\right)+0.5+\frac{w+t}{3l_{eng}}\right) \tag{2}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Inductor Technology** & **L (nH)** & **Q@f** &
\begin{tabular}{c} **RDC** \\ **(m\(\Omega\))** \\ \end{tabular} & **H (\%)** \\ \hline This work & Backside integrated air core & 208 & 15\#32 MHz & 1200 & 87.3 \\ \hline
[19] 2011 &
\begin{tabular}{c} Silicon embedded \\ silicon core \\ \end{tabular} & 15.8 & 5.5\#16 MHz & 291 & 53.2 \\ \hline
[21] 2018 &
\begin{tabular}{c} In-Si 3D toroid \\ silicon core \\ \end{tabular} & 43.67 & 9\#20 MHz & 1250 & 77.6 \\ \hline
[8] 2019 &
\begin{tabular}{c} In-Si 3D toroid \\ magnetic core \\ \end{tabular} & 229.5 & 1.74\#5 MHz & 2000 & 55.2 \\ \hline
[7] 2019 &
\begin{tabular}{c} In-Si 3D toroid \\ magnetic core \\ \end{tabular} & 112 & 11.5\#12.5 MHz & 265 & 90.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed backside integrated inductor with the prior integrated power inductors. Figure 2: Schematic of the major fabrication steps:(**a**) via formation, (**b**) via filling, (**c**) RDLs and vias formation, (**d**) Temporary bonding and backside grinding, (**e**) inductor formation, (**f**) TSV module assemble. where \(l_{\rm eng}\) is the total length of the \(n\)-turn coil, and \(\mu\) = \(4\pi\times 10^{-7}\) H/m is the permittivity of the surrounding medium. For an Archimedean spiral coil with an inner radius \(R_{0}\) and screw pitch \(p\) (\(p\) = \(w+s\)), \(l_{\rm eng}\) can be calculated by  
\[l_{\rm eng}=\int\limits_{0}^{2\pi\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{  
+++ ==WARNING: Truncated because of repetitions==
\rm{\rm{ \rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{ \rm{ \rm{ \rm{           }}}}}}}}}}}}} \sqrt\]  
++  
Substituting Equations (3) and (8) into Equation (1), the total inductance of the proposed Archimedean coil can be calculated.
# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
## 2 System Model  
This section introduces the system model employed in the distributed coordinated scheduling of space-ground multiresources for remote sensing missions. Firstly, the space-ground coordination network scenario and the problem of multiresources coordinated scheduling are introduced. Subsequently, we modified the time-expanded graph model to capture the dynamics of the network. Finally, the multiresources resource coordinated scheduling problem is formulated into an MILP problem based on the modified time-expanded graph.