esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
###### Abstract  
Post-earthquake fires (PEFs) pose a significant secondary hazard in earthquake-prone regions, compounding the destruction caused by seismic events and threatening structural safety. This review explores the interplay between seismic damage and fire resistance, focusing on ignition sources such as damaged utility systems and overturned appliances, and their cascading effects on structural integrity. Advanced performance-based design approaches are evaluated, emphasizing the integration of probabilistic risk assessments, sequential analysis, and hybrid fire simulations to address multi-hazard scenarios. Key findings of current studies reveal that seismic damage, including spalling, cracking, and loss of fireproofing, substantially reduces the fire resistance of materials like steel and reinforced concrete, exacerbating structural vulnerabilities. Despite advancements, critical gaps persist in experimental data, probabilistic modeling, and comprehensive performance-based design guidelines for PEF scenarios. Addressing these deficiencies requires enhanced data collection, improved modeling techniques, and the integration of PEF considerations into building codes. This study provides a comprehensive review of PEF damage assessment and underscores the need for a holistic, multi-hazard design paradigm to enhance structural resilience and ensure safety in regions subject to seismic and fire risks. These insights provide a foundation for future research and practical applications aimed at mitigating the compounded effects of earthquakes and fires.  
Post-earthquake fire (PEF); multi-hazard structural design; fire resistance; structural resilience; seismic damage; performance-based design +
Footnote †: journal: Applied Sciences
## 4 Numerical Evaluations
### Evaluation of Transmission Delay  
Figure 5 shows the simulation results of end-to-end transmission delays per packet transmission generated with different numbers of nodes under RWK at the speed of 100 m/s and with different ranges for packet transmission. Here, end-to-end delay corresponds to the average time period for completing a packet transmission from the source node to the destination node, which is equivalent to the number of hops under the condition that each hop-by-hop transmission time is equal. In smaller wireless ranges, transmission delays are observed as almost zero in all conditions, due to the lack of reachability, and in addition, in larger wireless ranges, delays converge to around 15-20 hops in all conditions, due to almost full reachability. In middle wireless ranges, we can recognize some additional transmission delays, due to the firework procedure. However, even in the worst conditions, delays are in total less than 40 hops in Figure 5. In addition, it turned out that delay differences between results with and without firework were limited to at most eight hops in all cases. This fact indicates that our MTFR is feasible enough for real communication scenarios. As a result, MTFR produces better reachability at the expense of a small additional transmission delay from the overall viewpoint.  
In addition, Figure 6 shows the simulation results of the end-to-end delay increase per packet transmission generated with different numbers of nodes under RWK at the speed of 100 m/s and with different ranges for packet transmission. Here, the difference of end-to-end delay is the discrepancy in delay between with firework and without firework for the absolute values shown in Figure 5. In smaller and middle wireless transmission ranges, the tendency of maximum delay increase shows similar results as those of reachability. However, in larger wireless transmission ranges, degradation is saturated at the level of about 4 hops. This is because the temperature gradient sustains the basic principal communication route on one side, but it does not allow shortcut routes on the other side. The sufficient number of nodes with a sufficient wireless transmission range redundantly increases the average number of links that each node has. This produces a higher probability of equal cost links than in the situations with a smaller density of wireless coverage, and hence, a random selection feature is taken into account for path selection.  
Figure 4: Reachability improvement analysis as the difference between results with and without firework shows the largest improvements for intermediate transmission ranges.  
In addition, our firework mechanism emphasizes this randomness more. Hence, it produces a certain fixed delay.
## 2 Materials and Methods  
### Project Scheduling Problems--The Overview  
Current research on project scheduling problems is focused on searching for optimal schedules considering existing constraints [10; 11]. Construction cases of such problems are usually considered to be NP-hard optimization problems, as explained in [12; 13]. Numerous categories--models of project scheduling problems (PSP) are being studied depending on various constraints and schedules' objective functions. The most common approach for solving PSP optimization is a deterministic approach (due to its complexity and practical aspect). There are many well-known reviews of PSP deterministic models, e.g., [14; 15; 16; 17]. One of the most frequently described and researched models is the Resource-Constrained Project Scheduling Problem (RCPSP) [18]. This is due to the wide applicability of this problem in the worldwide industrial production and the construction sector [10].  
Classic RCPSP focuses on the makespan minimization. However, different objective functions, constraints, conditions (and the whole model) can be used in order to best meet the needs of the decision maker. The most frequently described objective functions in construction include optimization of total cost [19], quality [20], NPV [21], total project delay [22], monthly cash demand [23], cost minimization in regard to the base-plan [24], deviations from average employment level [25], schedule robustness [26], etc.  
The procedures used to solve PSPs can be classified as: exact, heuristic, and metaheuristic [10; 13]. Practical scheduling problems in construction are often classified as NP-hard (non-deterministic, polynomial-time hard) problems. Computing time needed for solving such problems grows exponentially with the increase of the problem's size [12; 13; 27]. Therefore, according to many researchers, metaheuristic algorithms seem to be the most appropriate measures for task sequencing and scheduling (despite the fact that they do not guarantee finding the optimal solution) [19; 21; 27; 28; 29; 30]. Different metaheuristic algorithms are being used for solving PSPs, e.g., Simulated Annealing (SA) [21], Genetic Algorithms (GA) [19; 29], Tabu Search (TS) [21; 31], Ant Colony Optimization (ACO) [32], Particle Swarm Optimization (PSO) [33], or hybrid algorithms [23; 34; 35].
# Chloroplasts at the Crossroad of Photosynthesis, Pathogen Infection and Plant Defense
## 8 Chloroplasts are Targeted by Pathogen Effectors  
Plant pathogens also elicit effector proteins to suppress host defense and promote pathogenicity [298, 299]. For example, individual _P. syringae_ strains use the T3 SS to deliver approximately 15-30 T3 Es into plants [300, 301, 302, 303]. After entry into plant cells, some effectors move into discrete subcellular compartments, such as the plasma membrane, the endoplasmic reticulum, the nucleus, the tonoplast, vesicles, mitochondria, and chloroplasts [298, 299, 304, 305]. A number of T3 Es were found or proposed to localize to the chloroplast (Table 1) [35, 305, 306, 307, 308]. These T3 Es act as virulence factors and manipulate chloroplast structure and functions. The N-terminal region of AvrRps4 (avirulence protein resistance to _P. syringae_ 4) and HopK1 (Hrp outer protein K1) has been shown to be cleaved _in planta_ and the processed AvrRps4 and HopK1 are localized in the chloroplast [308, 309]. Although relevant experimental evidence is still lacking, HopO1-1, HopO1-2, and HopR1 (Hrp outer protein O1-1, O1-2, and R1) were predicted to have a cleavable transit peptide as well, according to LOCALIZER, a subcellular localization prediction program for plant proteins and pathogen effectors in plant cells [310]. HopI1 and HopN1 (Hrp outer protein II and N1) use a non-cleavable transit peptide to localize themselves to the chloroplast [306, 307]. HopB1 and HopM1(Hrp outer protein BB1 and M1) are another two possibly chloroplast-targeted T3 Es, although it is not yet clear whether they use a cleavable transit peptide (Table 1). To assist pathogen proliferation and virulence, these effectors manipulate chloroplast structure and functions, for example, remodel thylakoid membranes [306], reprogram the expression of nuclear-encoded chloroplast-targeted protein genes and chloroplast-encoded genes [35; 36], disrupt photosynthetic water splitting, electron transport, and CO\({}_{2}\) assimilation [35; 307], minimize chloroplastic ROS production [37; 307; 308], alter enzyme redox status, and suppress SA accumulation [306].
## 3 Results and Discussion
### Correlations between Physicochemical Indices and the Microbial Community  
The correlations between microbial community and the differences in physicochemical indices in the storage process of rice grains were determined (Figure 5). A total of two interaction networks were constructed; one was the correlation between bacterial community and rice grains' quality, and the other was the correlation between fungal community and rice grains' quality. Most bacteria genera including _Bacillus, Microbacterium,_ etc., showed negative correlations with the moisture content, while some ASVs belonging to the genera of _Staphylococcus, Sphingomonas,_ Alloprerotella,_ etc., showed positive correlations with moisture content. _Staphylococcus aureus_ is a pathogenic bacterium and should be given more attention in the process of rice storage. Therefore, moisture content should obtain good control during rice storage. _Sphingomonas_ was positively correlated with fatty acid value, which was considered to be an important index for evaluating the suitability of rice grains (Figure 5A). Some bacterial genera including _Bacillus, Staphylococcus,_ etc., showed significant correlations with albumin, globulin, gliadin, and glutenin (Spearman correlation, \(p<0.05\)). According to previous studies, some microorganisms utilize the protein of rice grains to provide nutrition, which destroys the structure of  
Figure 4: Shared and unique microbial community in rice grains stored under three storage environments. Venn diagrams show the number of shared and unique bacterial (**A**) and fungal ASVs (**B**). RA: room temperature, horizontal ventilation, nitrogen gas atmosphere; RB: room temperature, vertical ventilation, nitrogen gas atmosphere; RC: quasi-low temperature, horizontal ventilation, nitrogen gas atmosphere.  
rice grains at the same time [57; 58]. Bacteria might have an impact on the quality of rice and are worthy of attention. Some fungi can grow and produce metabolites on rice, causing toxin contamination. From the correlation, the volatile compounds including butyl 1,2-benzenedicarboxylate, 5-methyloctadecane, heptadecane, 4-methyladecane, and hexadecane could be suggested to inhibit _Moesziomyces_. 10-methylesicosane, 2,3,5,8-tetramethyldecane, and _Fusarium_ showed negative correlations. _Fusarium_ is closely related to _Fusarium_ toxins. Butyl 1,2-benzenedicarboxylate and 4-methylundecane might have a bad effect on _Alternaria_. Similarly, butyl 1,2-benzenedicarboxylate and 2-hexyldecanol showed negative correlations with _Microdochium_ (Figure 5B). Abnormal activities of microbial communities can lead to an accelerated deterioration in rice quality. Microbial community activities can lead to weight loss, volume reduction, dry matter loss, germination rate reduction, mildew, and even toxin pollution of rice. Therefore, the microbial community is closely related to rice quality. In the future, these volatile components can be developed as green antibacterial ingredients.
## 4 Results and Discussion
Future study could include integrating sensors into autonomous and swarm-based robot systems. Swarm robots or unmanned vehicles can monitor the environment, search for the hazardous environment [60; 61], or grade the risk value [62]. In this case, new artificial intelligence-based research activities such as efficient communication protocols, efficient use of sensor energy, optimal environment coverage in mobile sensor nodes, autonomous movement, CBRN risk assessment, intelligent decision making and self-organized response strategies without human commanders in swarm robots can be carried out. Last, but not least, the system's design can be used in further research and development activities regarding the detection, identification, and monitoring of biological or radioactive contamination. In the case of biosensors, the available techniques for the complete detection of pollutants are insufficient [63] and the diagnosis of biological contamination is still a considerable challenge. Radiological standard detectors are often burdened with weaknesses, such as a low signal level at normal radiation protection dose rates or susceptibility to temperature and humidity corrections [64]. The use of machine learning algorithms may at least improve detection capabilities.
## 2 System Model
### Relay Selection Methods
#### 2.4.2 Best Opportunistic Relay Selection (B-ORS)  
In the B-ORS protocol, the best relay is chosen to maximize the e2e SNR, i.e.,  
\[\mathrm{R}_{b}:\min\left(\Psi_{1b},\Psi_{2b}\right)=\max_{m=1,2,...,M}\left( \min\left(\Psi_{1m},\Psi_{2m}\right)\right), \tag{26}\]where \(b\in\left\{1,2,...,M\right\}.\)  
Then, the e2e performances of this scheme are given, respectively, by  
\[\begin{array}{ll}\text{OP}_{\text{B-ORS}}&=\Pr\left(\frac{(1-\alpha)\text{T}}{ 2}\text{log}_{2}\left(1+\min\left(\Psi_{1b},\Psi_{2b}\right)\right)<C_{\text{th }}\right),\\ \text{TP}_{\text{B-ORS}}&=\left(1-\alpha\right)\text{TC}_{\text{th}}\left(1- \text{OP}_{\text{B-ORS}}\right).\end{array} \tag{27}\]
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 3 Idealized Model of Mimblewimble-Based Blockchain
### Executions  
There can be attempts to execute a transaction on a state that does not verify the precondition of that transaction. In the presence of such situation the system answers with a corresponding error code (of type _ErrorCode_). Executing a transaction \(t\) over a valid state \(s\) (_valid_state\((s)\)_) (when dealing with global states, _valid_state_ is _validChain_) produces a new state \(s^{\prime}\) and a corresponding answer \(r\) (denoted \(s\cdot\frac{t/r}{r}\), \(s^{\prime}\)), where the relation between the former state and the new one is given by the postcondition relation _Post_.  
\[\begin{array}{c}\underline{valid\_state(s)}\ \ \textit{Pre}(s,t)\ \ \textit{Post}(s,t,s^{\prime})\\ s\stackrel{{ t/ok}}{{\longleftrightarrow}}s^{\prime}\\ \\ \underline{valid\_state(s)}\ \ \textit{ErrorMsg}(s,t,ec)\\ s\stackrel{{ t/error(\alpha)}}{{\longleftrightarrow}}s\end{array}\]  
Whenever a transaction occurs for which the precondition holds, the valid state may change in such a way that the transaction postcondition is established. The notation \(s\stackrel{{ t/ok}}{{\longleftrightarrow}}s^{\prime}\) may be read as _the execution of the transaction \(t\) in a valid state \(s\) results in a new state \(s^{\prime}\)_. However, if the precondition is not satisfied, then the valid state \(s\) remains unchanged and the system answer is the error message determined by a relation _ErrorMsg_. (Given a state \(s\), a transaction \(t\) and an error code \(ec\), \(ErrorMsg(s,t,ec)\) holds iff \(error\,ec\) is an acceptable response when the execution of \(t\) is requested on state \(s\)). Formally, the possible answers of the system are defined by the type:  
\[\textit{Response}\stackrel{{\text{def}}}{{=}}ok\ \ |\ \textit{ error}\ (ec:ErrorCode),\]  
where \(ok\) is the answer resulting from a successful execution of a transaction.  
One-step execution with error management preserves valid states.  
**Lemma 3** (Validity is invariant).:  
\(\forall\ (s\ s^{\prime}:\textit{State})(t:\textit{Transaction})(r:\textit{ Response}),\\ \underline{valid\_transaction}(t)\to s\stackrel{{ t/r}}{{ \longrightarrow}}s^{\prime}\rightarrow\underline{valid\_state}(s^{\prime})\)  
The proof follows by case analysis on \(s\stackrel{{ t/r}}{{\longrightarrow}}s^{\prime}\). When \(\textit{Pre}(s,t)\) does not hold, \(s=s^{\prime}\). From this equality and \(\underline{valid\_state}(s)\) then \(\underline{valid\_state}(s^{\prime})\). Otherwise, \(\textit{Pos}(s,t,s^{\prime})\) must holdand we proceed by case analysis on \(t\), considering that \(t\) is a valid transaction and \(s\) is a valid state.  
System state invariants, such as state validity, are useful for analyzing other relevant properties of the model. In particular, the properties in this work are obtained from valid states of the system.
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Bias-free MRI Scan:
#### 3.1.9 Data Augmentation
The model uses the style transfer technique, selects distributed features across the multiple latent spaces, and captures the local patterns to enhance the image resemblance. In a stroke disease, the brain cells start dying due to insufficient blood to the brain (cerebral ischemia) or moments of internal bleeding (intracranial hemorrhage). CGAN generator is trained on specially altered lesion masks to create synthetic brain images to enlarge the training dataset. CNN segmentation network includes depth-wise-convolution-based X-blocks and feature similarity module (FSM) [152]. IsoData (Iterative Self-Organizing Data Analysis Technique) is an unsupervised segregation method measuring the means of the classes uniformly dispersed within data space and clusters the rest of them iteratively based on the minimum distance methods. Every iteration computes the new mean and classifies pixels. The WGAN-based process depends upon the image histogram by generalizing more than two classes and splitting, merging, and deleting the class depending on the input threshold parameters [153]. Functional connectivity GAN (FC-GAN) generates the functional brain connectivity (FC) patterns obtained from fMRI data amplifying the efficiency of the neural network classifier. VAE and WGAN-based network contains three parts, the encoder, the generator, and the discriminator [154]. The connectome-based sample generation is another approach for data augmentation. A generative adversarial neural network auto-encoder (AAE) framework produces synthetic structural brain connectivity instances of MS patients even for an unbalanced dataset [155]. The number of samples in the regular fMRI datasets is insufficient for training. Multiple GAN architecture generates new multi-subject fMRI points. The multiple GAN architectures used in this method are cycleGAN, starGAN, and RadialGAN and do not need label details to determine the relation matrix. The cycle-GAN is not expandable to multiple domains because of the N (N-1) mappings to be learned for N domains. StarGAN is expandable to multiple domains using a single generator for multidomain translation tasks. RadialGAN can successfully extend the target dataset by employing multiple source datasets [156]. Dual-encoder BiGAN architecture duplicates abnormal samples within a normal distribution. Anomaly detection in BiGAN reduces bad cycle consistency loss due to insufficient sample data information [157]. The approach in [158] generates annotated diffusion-weighted images (DWIs) of brains showing an ischemic stroke (IS). Realistic DWIs are generated from axial slices of these 3D segmentation maps with the help of three generative models: Pix2 Pix, SPADE, and cycleGAN.
# Allocating Small Transporters to Large Jobs
## 5 The Saturated Transporter Allocation Problem
### Greedy Algorithms
#### 5.3.2 Greedy Algorithms by Transporter  
Algorithms \((\mathrm{SGR}c)\) and \((\mathrm{SGR}i)\) assign transporters one by one in order of non-increasing filling times.  
Given currently assigned transporters, Algorithm \((\mathrm{SGR}c)\) assigns the next transporter to the non-saturated job with maximum completion time, whereas Algorithm \((\mathrm{SGR}i)\) assigns it to the non-saturated job with maximum idle time per period.  
If every transporter has been assigned but the solution is still not saturated, these algorithms do not find any saturating solution. However, as opposed to algorithm \((\mathrm{SGJ})\), the found solution is still feasible for the unsaturated problem as long as the number of transporters is at least as large as the number of jobs, i.e., if the instance itself is feasible. Indeed, both algorithms \((\mathrm{SGR}c)\) and \((\mathrm{SGR}i)\) assign the first \(N_{J}\) transporters to different jobs.  
Recall that by Equation (1):  
\[C(j,\overline{X}):=C^{\mathrm{sat}}(j)\cdot\frac{L(j,\overline{X})}{L(j, \overline{X})-1(j,\overline{X})},\]  
and by Equation (2):  
\[L(j,\overline{X}):=\max\Big{\{}\sum_{r\in\mathcal{R}}\overline{X}_{r,j}\cdot \kappa(r),\ \delta(j)+\max_{r\in\mathcal{R}}\big{[}\overline{X}_{r,j}\cdot\kappa(r)\big{]} \Big{\}}.\]  
Therefore, at every step of Algorithms \((\mathrm{SGR}c)\) and \((\mathrm{SGR}i)\), we only need to save the current values of the total and of the maximum assigned transporter capacities in order to decide where to assign the next transporter. We deduce from Equation (2) whether a job is saturated or not.  
Theorem 4: _Algorithms \((\mathrm{SGR}c)\) and \((\mathrm{SGR}i)\) run in \(O(N_{R}\cdot\log[N_{R}])\)._  
Proof.: In both algorithms, we first sort the transporters by decreasing filling time in \(O(N_{R}\cdot\log[N_{R}])\). Then, the two algorithms use a different strategy to select the best job to which the next transporter is assigned. For Algorithm \((\mathrm{SGR}i)\), the best job maximizes the idle time per period. For Algorithm \((\mathrm{SGR}c)\), the best job maximizes the completion time.  
In either case, we sort the jobs in \(O(N_{J}\cdot\log[N_{J}])\) using e.g., a heap map. Then, the algorithm extracts the best job in \(O(\log[N_{J}])\) time, update its value along with its total and its maximum transporter filling times in \(O(1)\) time, and re-insert it into the heap in \(O(\log[N_{J}])\).  
Since our problem requires \(N_{J}\leq N_{R}\), the time complexity of the algorithm is \(O(N_{R}\cdot\log[N_{R}])\).