esa-sceva/satcom-chunk-collection huggingface dataset:
# RF Energy Harvesting Wireless Communications:
## 6 Secure Communications
### Challenges in Secure Communication  
In the physical layer, communication security is commonly enhanced by exploiting noises, interferences, or channel fading [83]. For instance, when the hybrid base station (HBS) serves as both data source and energy source, it can utilize an energy beam to create a strong co-channel interference to jam the communication channel of eavesdroppers [84]. In a multi-user network, the transmitting vector of HBSs can be optimized to maximize the secrecy rate, which is defined as the transmission rate that guarantees the perfect secrecy [85].  
The power sensitivity of data communication and energy harvest modules have significant differences. In general, a data packet can be successfully decoded when the strength of the receiving signal is as low as \(-70\,\mathrm{dBm}\) (\(0.1\,\mathrm{nW}\)). By contrast, many energy harvesters need the minimum intensity of the incident power higher than \(-20\,\mathrm{dBm}\) (\(0.01\,\mathrm{mW}\)). It implies that HBS needs to consider the completely different power requirement of data and energy packets. It will be especially challenged when the HBS needs to deliver the energy and data information in a single packet to improve time efficiency. A high transmission power will increase the risk of information disclosure due to the broadcast nature of the wireless channel. Therefore, how to communicate securely while providing sufficient energy to RF-EHWC nodes is a critical problem.
# Lacase in Biorefinery of Lignocellulosic Biomass
## 6 Laccase-Based Biological Pretreatment Lignocellulosic Biomass  
Laccase biological pretreatment plays a critical role in biomass pretreatment by efficiently degrading the lignin component of lignocellulosic biomasses. It involves the use of laccase enzymes to oxidize phenolic compounds in lignocellulosic biomasses. The phenolic compounds act as natural cross-linking agents that bind lignin to the cellulose and hemicellulose fibers in the biomass, making it difficult to break down. The lignin component acts as a barrier that prevents the efficient hydrolysis of cellulose and hemicellulose components by the enzymatic hydrolysis. Oxidizing these compounds, laccase breaks the cross-links and makes the lignocellulosic more accessible to the enzymatic hydrolysis step. By degrading the lignin, laccase enhances the accessibility of the cellulose and hemicellulose components, resulting in higher sugar yields during the subsequent hydrolysis step. The optimal conditions for laccase biological pretreatment include a temperature range of 30-60 \({}^{\circ}\)C and a pH of 4.5-5.5. The advantages of laccase biological pretreatment include the selective removal of lignin and the enhancement of enzymatic hydrolysis. The disadvantages include the high cost of the enzyme and the need for additional steps to remove the residual enzyme [5].
## 2 Results
### Identifying Differentially Expressed Genes (DEGs) in Plants with Transient Expression of O/CYP142  
Transcriptome profiling of the transgenic flowers and flowers transformed with the empty vector was performed to facilitate a deeper understanding of the possible molecular mechanisms of _OfCYP142_ in stimulating linalool synthesis (Figure S5). Use of criteria of expression change >2-fold and \(p\) < 0.05 showed that 1079 genes were DEGs in the transgenic flowers compared with flowers transformed with the empty vector (Figure 8A). The DEGs in the transgenic flowers were clustered into 19 categories and enriched in 94 pathways in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Figure S6 and Table S9). Some overrepresented categories included genes involved in metabolism of terpenoids and polyketides, signal transduction, membrane transport, biosynthesis of other secondary metabolites, and environmental adaptation, implying that _OfCYP142_ overexpression may affect synthesis of monoterpene compounds and stress signaling and response (Figure 8C). Among the DEGs, expression of _O/SDR1_ was markedly upregulated.  
Figure 7: Functional characterization of _OfCYP142_ by transient expression. **(A)** qRT-PCR analysis of the _OfCYP142_ relative expression levels in control and transgenic _O. fragrams_ flowers. **(B)** The GFP fluorescence of p355:OfCYP142:GFP and empty vector fusion proteins. **(C)** Score scatter plot of the flower GC–MS profiles of two types of petals with transient expression of _OfCYP142_ and empty vector in _O. fragrams_, determined with PCA-X analysis in SIMCA14.1. Each type of petals consisted of three replicates. **(D)** Score scatter plot of the flower GC–MS profiles of two types of petals with transient expression of _OfCYP142_ and empty vector in _O. fragrams_, determined with OPLS-DA analysis in SIMCA14.1. **(E)** Loading scatter plot analysis of floral volatile organic compounds in petals after transient transformation with 355:O/_OfCYP142_:GFP and empty vectors. The red arrow marks the linalool. **(F)** Information of key aromatic components selected based on VIP from OPLS-DA (VIP > 1). **(G)** Relative content analysis of linalool after transient transformation with 355:O/_OfCYP142_:GFP and empty vectors. “\({}^{**}\)” represents significant difference at \(p\) < 0.05.  
Additionally, expression of some other genes in the 2-C-methyl-D-erythnitol 4-phosphate (MEP) pathway including _OfDXS2_, _OfDXR_, and _OfTPS2_ was upregulated while _OfGPS1_ was downregulated in transgenic flowers. Their expression patterns were further validated by qRT-PCR (Figure 8B).
## Materials and Methods
### The Method to Determine Optimal Offshore Tsunami Observation Instrument Regions  
When the observation network is far from the potential tsunami source, the tsunami observation instruments should be deployed along the real tsunami propagation path to accurately predict the damage to the coastal areas [19]. Historically, tsunamis propagated from Japan's west coast only  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Domain**} & \multicolumn{2}{c}{**Grid Size**} & \multicolumn{2}{c}{**Mesh**} & \multicolumn{1}{c}{**Time Step**} & \multicolumn{1}{c}{**Numerical**} \\ \hline
**Longitude** & **Latitude** & \(\Delta x\) & \(\Delta y\) & **Number** & **Size (\(\Delta t\))** & **Model** \\ \hline
116.9–142.9\({}^{\circ}\) E & 29.9–49.9\({}^{\circ}\) N & 0.5 arc minute & 0.5 arc minute & 3119 \(\times\) 2400 & 1 s & Linear \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical modeling information.  
Figure 3: Representative results of numerical modeling with potential tsunami scenarios in Table 1 (Appendix A): (**a**) Case 04: left—initial sea surface displacement (m), right—maximum wave height distribution (m); (**b**) Case 10: left—initial sea surface displacement (m), right—maximum wave height distribution (m); (**c**) Case 30: left—initial sea surface displacement (m), right—maximum wave height distribution (m) [18].  
damaged specific eastern Korean coastal cities [20]. This means that tsunami propagation is affected by refraction (due to bottom topography, e.g., Yamato Rise in Figure 2), which creates a tsunami energy convergence region. Thus, using various fault parameters, we analyzed the main tsunami propagation area in the eastern sea of Korea. It is also essential to maximize the evacuation time (warning time) through offshore tsunami observation and consideration of the tsunami energy convergence region. Therefore, to reduce the loss of lives from a tsunami, warning time must also be considered [21; 22].  
To achieve this goal of the study, we set the three steps of research based on previous studies; water depth, travel time, and installation point of view [3; 19; 21; 22]. Figure 4 shows the steps and considerations used to determine an optical deployment region for offshore tsunami observation instruments. We started by investigating maximum tsunami wave distribution resulting from our potential tsunami scenarios by numerical modeling. The fault parameters include various conditions (e.g., source location, strike angle, dip angle, and slip distribution), as shown in Table 1, which shows the diverse aspects of tsunami propagation. After narrowing down the probability area, we applied two specific criteria from previous studies, maximization warning time and installation conditions [21; 22]. There are other physical, technical, financial, and legal limitations, such as public fishing rights, to consider for installation locations as well [23; 24]. Still, this paper only deals with the ocean bottom slope angle as an installation condition.
# Path Algorithms for Contact Sequence Temporal Graphs +
## 2 Materials and Methods
### Algorithms for General TRG_Ours
#### 2.2.1 Shortest Paths Algorithm for TRG_Ours  
Our shortest paths algorithm is a modification of the well-known [PERSON]'s algorithm. The main difference is that we need to account for chain neighbors. Whenever the distance to a TRG node \((u,t)\) is fixed, the distance to all non-distance fixed nodes \((u,t^{\prime})\in V_{out}(u),t^{\prime}\geq t\) should be fixed to the same value. The neighbors of those chain neighbors should be updated in turn.  
Algorithm 1 shows our shortest path algorithm. The input is the TRG_Ours representation of the temporal graph, the source node (in the original graph), and the start and end times. The output is a vector of the shortest distances from the source node to the nodes in the original graph (the \(\textit{shortest}[]\) vector). A local vector (\(\textit{distance}[]\)) keeps track of the TRG graph's node distances. We initialize the shortest distance for the source node to 0 and for all the other nodes to infinity. Lines 6 to 28 implement the modified [PERSON]'s function using a min priority queue (\(pq\)). The main modification over [PERSON]'s algorithm happens in the for loop in lines 16-27. This loop traverses the chain neighbors of \((u,t)\) (including itself) whose distances are not fixed yet and for each chain neighbor \((u,t^{\prime})\), updates the shortest distance to its neighbors \((u^{\prime\prime},t^{\prime\prime})\).  
The time complexity is \(O(n+elogn)\), where \(n\) and \(e\) are the number of nodes and vertices in the transformed graph, respectively.  
```
1:functionShortestPaths(\(G\), \(s\), \(t_{start}\), \(t_{end}\)) \(\triangleright\)\(G=(V,E)\) is TRG_Ours graph and s is the source node
2:returnArray\(shortest[]\) of shortest path costs
3: Initialize \(shortest[s]=0\), all other distances to infinity
4: Let \(pq\) be a priority queue \(\triangleright\) Nodes with lower distance have higher priority
5: Sort nodes in \(V_{out}(s)\) in increasing order of time
6: Place the first node in \(V_{out}(s)\) with \(t\geq t_{start}\) in \(pq\)
7:while\(pq\) is not empty do
8: Let \((u,t)\) be head of \(pq\)
9: Remove \((u,t)\) from \(pq\)
10:if\((u,t)\) is distance_fixed then
11: continue
12:endif
13:if\(shortest[u]\) equals infinity then
14:\(shortest[u]\gets distance[(u,t)]\)
15:endif
16:for all non distance_fixed\((u,t^{\prime})\in V_{out}(u)\) with \(t\leq t^{\prime}\leq t_{end}\)do
17:if\(t^{\prime}\
eq t\)then
18:\(distance[(u,t^{\prime})]\gets distance[(u,t)]\)
19:endif
20: Mark \((u,t^{\prime})\) as distance_fixed
21:for\((u^{\prime\prime},t^{\prime\prime})\in neighbors((u,t^{\prime}))\)do
22: Let \(w_{link}\) be the weight of the link from \((u,t^{\prime})\) to \((u^{\prime\prime},t^{\prime\prime})\)
23:if\(t^{\prime\prime}\leq t_{end}\) and \(distance[(u,t^{\prime})]+w_{link}<distance[(u^{\prime\prime},t^{\prime \prime})]\)then
24:\(distance[(u^{\prime\prime},t^{\prime\prime})]\gets distance[(u,t^{ \prime})]+w_{link}\)
25:endif
26:endfor
27:endfor
28:endwhile
29:endfunction
```  
**Algorithm 1** Shortest paths algorithm for TRG_Ours
## 4 Understanding Centralised Identity Systems
### OAuth2.0: The Open Authorization Standard
#### 4.8.3 Limitations and Mitigations  
OAuth has a few security flaws which makes it prone to attacks like replay attacks, CSRF attacks, implementation faults, misconfigurations, and authorization code interception. To some extent, they can be avoided by utilising strategies like stronger encryption techniques to prevent token misuse, Unified Client App Type to decrease complexity and use of digital signatures, and 2 FA for added protection [44].  
A few common vulnerabilities and its mitigation [45, 46] are as follows:  
* **Excessive Scope Requests**: Many applications request more information and access than necessary, such as location data, which raises privacy concerns. This issue can be mitigated by encouraging developers to request limited scopes for their apps functionality and by implementing strict policies and standards that promote low-scope usage.
* **Lack of User Control**: Manipulative UI designs often lead users to grant all requested permissions without proper consent, allowing more access than necessary. To mitigate this, fine-grained permission control should be implemented, enabling users to select specific scopes of access. Additionally, user-friendly authorization screens should clearly display the requested scopes in a straightforward manner.
* **Token Replay Attacks**: Here, the attacker obtains unauthorised access to resources by intercepting and reusing the OAuth token. To mitigate this, the tokens should have a smaller time span and should be immediately revoked if misuse is discovered.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs
### Self-Healing Architecture  
Self-healing architecture is another important survivability technique for the optical sensor network. As presented in Figure 2, self-healing architecture consists of the fiber Bragg grating (FBG) network and a central office. This self-healing architecture offers a survival function during link failure by reconfiguring the fiber network with remote nodes (RNs) that are responsible for performing self-healing functions if link failure occurs in the lower level network. Self-healing architecture has also been widely researched. In [46], the authors presented a fiber-laser-based sensor network with a self-healing function. The survivability of fiber-laser-based sensor networks was enhanced by adding switches in the self-healing ring architecture. In [47], the authors proposed self-healing architecture for FBG sensor networks. They designed remote nodes using simple optical switches to check the breakpoint and reconfigure the FBG network if any links failed. In [48], the authors proposed two large-scale self-healing architectures for FBG sensor networks. They designed branch nodes using simple optical switches and couplers to reconfigure the FBG sensing subnets if any links failed. In [49], the authors proposed and demonstrated a novel 3-D ring-mesh sensing system with a comprehensive self-healing function. The proposed ring-mesh topology is constructed by \(2\times 2\) optical switches to link the ring-based subnets in a meshed architecture. In [50], the authors proposed and demonstrated novel FBG-based passive sensor architecture that could be used to protect the fiber cut and monitor multiple sensors simultaneously. In [51], the authors proposed an FBG sensor system with two-level  
Figure 1: The fiber bus protection scheme [40]; BP: branching point, SU: sensor units, \(\mathrm{W_{in}}\): working cable input, \(\mathrm{W_{out}}\): working cable output, \(\mathrm{P_{in}}\): protection cable input, and \(\mathrm{P_{out}}\) protection cable output.  
ring architecture. The survivability of an FBG for a multipoint sensor system is enhanced by adding remote nodes and optical switches to the two-level ring architecture. In [52], the authors presented a novel star-ring-bus sensor system. The main trunk of the proposed sensor system is a star topology and the sensing branches comprise a series of bus subnets. Any weakness in the reliability of the sensor system is overcome by adding remote nodes and switches to the ring and bus subnets. In [53], the authors presented hybrid star-ring architecture for a highly reliable FBG sensor system. The main trunk of the proposed sensor network is a star topology, and the sensing branches comprise a series of concatenated ring subnets.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 3 System Model  
This section describes the network architecture under consideration, including the implementation details of the user measurement. The typical handover procedure that is assumed in this paper is described as well.
## 2 Theoretical Derivation of PIC Model  
Fretting wear can profoundly impact the service life of cable-stayed structures. To quantitatively assess the extent of fretting wear, it is imperative to calculate the contact pressure at each contact point within the cable-stayed system. The general idea of this paper is illustrated in Figure 1. To comprehensively consider the diverse factors influencing the contact pressure between wires, this paper classifies contact pressure into three types based on their sources. Corresponding methods for calculating contact pressure under each circumstance were derived. Subsequently, finite element simulations were conducted in later sections, wherein the distribution patterns of contact pressure across the entire cross-section of the cable were discussed.  
Specifically, during the manufacturing process, parallel wires in the cable-stayed system undergo concentric leftward twisting after bundling. Subsequently, a high-density polyethylene sheath is thermally extruded onto the outer surface of the wire bundle. In this process, internal parallel wires experience inward extrusion pressure, giving rise to contact forces. During the normal operation stage of the cable-stayed structure, the cables undergo bending due to vibration, leading to contact extrusion of internal parallel wires, and consequently generating contact pressure. From these three aspects, three sources of contact pressure between the wires can be obtained:  
Figure 1: (**a**) Cable-stayed bridge; (**b**) stay cable; (**c**) cable vibrates and bends; (**d**) contact caused by wire twist; (**e**) contact caused by the sheath compression; (**f**) contact caused by cable bending; (**g**) twisting model; (**h**) radial compression model; (**i**) wire bending contact model; (**j**) contact pressure distribution from wire twist; (**k**) contact pressure distribution from sheathing compression; (**l**) contact pressure distribution from wire bending.  
Contact pressure caused by wire twisting (CWT): contact pressure generated by the twisting of parallel wires during the manufacturing process;
2. Contact pressure caused by sheath compression (CSC): constriction due to external radial forces, such as sheath and clamp;
3. Contact pressure caused by cable bending (CCB): contact of wires in different layers caused by the bending during the operation stage.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### ULDK Service Integration  
The ULDK service provides spatial localization of cadastral parcels. It returns detailed information like parcel numbers, administrative divisions, and geometries in the Well-Known Binary (WKB) format. This publicly available service is accessible without any limitations or authentication, allowing for seamless integration and data retrieval.
## 3 Results and Discussion
### Seasonal Variation
Table 2 presents the performance results of the simple linear regression model for each meteorological parameter. The table includes the employed model and the selected criteria for performance evaluation. Upon reviewing the results, it is evident that the model's performance improved for all parameters as the ellipsoid height increased. When the R-squared values approached one and the MAE and RMSE values approached zero, the model performed successfully in all parameters. The estimation of the parameters was carried out with the least squares method. The hypothesis tests determined the regression model's suitability and the regression coefficients' significance.
## 4 Fabrication and Experimental Verification  
### Displacement Measurement Experiment  
The optimal model of the proposed horn was fabricated, and a displacement measurement experiment was conducted to verify the optimal design. Figure 9a shows the experimental set-up, which consists of a block-type ultrasonic cutting machine (developed block-type horn and oscillator), laser Doppler vibrometer sensor (LDV, Polytec), signal analyzer (B&K PULSE), and laptop. Figure 9b shows the prototype horn and the displacement measurement points on the lower face of the horn. The value of the data measured over 1 s was used for the output displacement at the lower face of the horn.  
The ultrasonic horn was excited by a resonator at 28.5 kHz, as we intended from the design stage, and the experiments were performed repeatedly at nine points at the lower face. Figure 10 shows the simulated and measured displacement at the lower face of the horn. Table 5 shows the maximum and minimum displacements of the results and displacement difference at the lower face of the horn. The measured maximum displacement of 17.56 \(\upmu\)m occurred at point 5. The reason for this is that point 5 is located at the center of the horn, and the shaft, which is the maximum vibration source, is also located in the same position. The minimum displacement of 15.30 \(\upmu\)m occurred near point 8. This is because the width at the lower part of the horn was designed to be reduced to amplify the vibration at each edge of the horn. The difference between the measured and simulated displacements was less than 4%, which shows that the fabricated horn well matched with simulation horn.  
Figure 8: Simulated output displacement of the optimal and initial models at the horn lower face.  
Figure 9: (**a**) Experimental set-up for a displacement measurement experiment and (**b**) fabricated optimal model and measuring points.