esa-sceva/satcom-chunk-collection huggingface dataset:
# Socioeconomic Impact on Urban Resilience against Flood Damage
## 3 Results
### Socieconomic Cost
#### 3.2.2 Traffic Congestion Cost  
TCC is calculated based on the social damage caused by traffic congestion resulting from the detouring of flooded roads (Figure 6). The city with the highest occurrence of TCC was L city, and there was approximately a 23% difference compared to the second highest city, F. For the cities with the second (F city) and third highest (D city) TCC, F city, with only a 0.5% flood area ratio, had a higher TCC than D city, which had a 4.8% flood area ratio. This indicates that even with a smaller flooded area in a city, the cost of traffic congestion is affected by how many different routes the flooded roads are part of. This  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{3}{c}{**SI**} & \multirow{2}{*}{**RA**} & \multirow{2}{*}{**SR**} & \multirow{2}{*}{**DP**} & \multirow{2}{*}{**R**} & \multirow{2}{*}{**Change in City Resilience (\%)**} \\ \cline{2-2} \cline{5-8}  & \(\mathbf{SI_{b}}\) & & & & & & & \\ \hline B & 1.000 & 1.000 & 1.000 & 1.000 & 0.084 & 0.771 & 0.000 \\ I & 0.995 & 1.000 & 1.000 & 1.000 & 0.048 & 0.761 & 0.075 \\ S & 1.000 & 1.000 & 1.000 & 1.000 & 0.032 & 0.758 & 0.000 \\ T & 1.000 & 1.000 & 1.000 & 1.000 & 0.030 & 0.758 & 0.000 \\ U & 1.000 & 1.000 & 1.000 & 1.000 & 0.020 & 0.755 & 0.000 \\ H & 0.986 & 0.994 & 0.192 & 0.961 & 0.051 & 0.548 & \(-\)28.122 \\ K & 0.995 & 0.997 & 0.120 & 0.972 & 0.046 & 0.533 & \(-\)29.954 \\ R & 0.992 & 0.992 & 0.118 & 0.915 & 0.034 & 0.515 & \(-\)32.122 \\ N & 0.990 & 0.986 & 0.038 & 0.938 & 0.044 & 0.502 & \(-\)34.051 \\ F & 0.985 & 0.990 & 0.041 & 0.918 & 0.057 & 0.501 & \(-\)34.466 \\ P & 0.995 & 0.990 & 0.039 & 0.924 & 0.039 & 0.499 & \(-\)34.374 \\ Q & 0.985 & 0.975 & 0.121 & 0.855 & 0.038 & 0.498 & \(-\)34.387 \\ M & 0.987 & 0.975 & 0.046 & 0.834 & 0.045 & 0.476 & \(-\)37.408 \\ O & 0.857 & 0.979 & 0.085 & 0.834 & 0.039 & 0.469 & \(-\)38.287 \\ D & 0.942 & 0.972 & 0.064 & 0.789 & 0.058 & 0.467 & \(-\)38.921 \\ L & 0.958 & 0.952 & 0.010 & 0.627 & 0.045 & 0.409 & \(-\)46.267 \\ C & 0.973 & 0.942 & 0.004 & 0.442 & 0.071 & 0.369 & \(-\)51.990 \\ A & 0.766 & 0.924 & 0.019 & 0.423 & 0.119 & 0.352 & \(-\)54.925 \\ E & 0.941 & 0.878 & 0.029 & 0.402 & 0.058 & 0.349 & \(-\)54.312 \\ G & 0.925 & 0.912 & 0.006 & 0.303 & 0.056 & 0.321 & \(-\)57.985 \\ J & 0.974 & 0.824 & 0.046 & 0.260 & 0.047 & 0.313 & \(-\)58.925 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Change in resilience of each city after 2022 flood event.  
explanation also applies to cities where road damage occurred, but no TCC was assessed. For example, city H, with an \(\text{Si}_{\text{r}}\) of 0.994, had less structural damage compared to city F, which had an \(\text{Si}_{\text{r}}\) of 0.990. However, due to the flooding of detour routes with minimal before-and-after differences and other roads that have a minor impact on route changes, TCC did not occur.
# Scale-Free Random SAT Instances
## 5 Phase Transition in Scale-Free Random 2-SAT Formulas
### A Criterion for Phase Transition in 2-Sat
Using Bayes, we have  
\[\mathrm{E}[K_{x}\mid\pm x\lor y] = \sum_{k=0}^{\infty}k\,P(K_{x}=k\mid\pm x\lor y)\] \[= \sum_{k=0}^{\infty}k\,\frac{P(K_{x}=k\wedge\pm x\lor y)}{P(\pm x \lor y)}=\sum_{k=0}^{\infty}k\,\frac{P(\pm x\lor y\mid K_{x}=k)P(K_{x}=k)}{P(\pm x \lor y)}\]  
Given a pair of literals \(x\) and \(y\), the probability that either \(x\lor y\) or \(\
eg x\lor y\) are one of the clauses of the formula, conditioned by the fact that the number of occurrences of variable \(x\) is \(k\) (and assuming that clauses are not repeated) is: \(P(\pm x\lor y\mid K_{x}=k)=\frac{k}{2(n-1)}\) and, the probability of the same fact without condition: \(P(\pm x\lor y)=\frac{\mathrm{E}[K_{x}]}{2(n-1)}\). Therefore,  
\[\mathrm{E}[K_{x}\mid\pm x\lor y] = \sum_{k=0}^{\infty}k\,\frac{\frac{k}{2(n-1)}P(K_{x}=k)}{\frac{ \mathrm{E}[K_{x}]}{2(n-1)}}=\frac{\sum_{k=0}^{\infty}k^{2}\,P(K_{x}=k)}{ \mathrm{E}[K_{x}]}\] \[Q(\lambda)=\sum_{i>0}i(i-2)\lambda_{i}=0 \tag{9}\]0  
defines the unsatisfiability threshold. The previous theorems ensure that, when the criterion is satisfied, there is a giant bicycle containing a fraction of the literals, and the formula is unsatisfiable. However, if the formula is unsatisfiable, it can be due to a _small_ bicycle. Therefore, the reverse implication is not necessarily true. In other words, Theorems 2 and 3 establish a sufficient (but not necessary) condition for unsatisfiability of random 2-SAT formulas, which results in an upper bound for the phase transition point. However, we conjecture that, either giant bicycles are more probable than small bicycles and the percolation threshold (obtained with the criterion) is equal to the phase transition point, or, if small bicycles are more probable, the phase transition point is at \(c=0\).
# Lateral Capacity of URM Walls: A Parametric Study Using Macro and Micro Limit Analysis Predictions
## 1 Introduction
[47] investigated the effects of different geometrical (panel ratio, block ratio, and bond type) and mechanical (friction coefficient) parameters on the in-plane structural response of dry-stack masonry panels. The analytical simulation was performed using a kinematic upper bound micro LA model with an associative flow rule. The work demonstrates a negligible effect of the bond types. [PERSON] et al. [16] conducted parametric DEM analyses on masonry walls with different bond patterns (Flemish, English, Dutch cross-bond, Header, and Running bonds). The authors remarked how the walls' initial stiffness and lateral capacity increased with the level of pre-compression and the decrease of wall aspect ratio. They also observed that the bond types significantly affected the results at high block aspect ratios. However, few researchers investigated the influence of such geometrical and mechanical parameters by using more than one computational approach. In particular, studies comparing micro and macro strategies and assessing their pros and cons remain scarce. For example, [PERSON] et al. [48; 49] parametrically compared micro and macro LA formulations for IP and OOP failure mechanisms made with regular patterns by varying geometrical (panel aspect ratio, block aspect ratio), mechanical (friction coefficient) and overload levels. This study performs a parametric analysis involving 81 combinations by varying geometrical (panel aspect ratio, block aspect ratio, bond type) and mechanical (friction coefficient) parameters. The ANOVA approach is used to investigate one-way and two-way factor interactions and assess how each parameter affects the horizontal load multiplier. Macro and micro limit analyses are compared, and the differences in terms of lateral load-carrying capacity and failure mechanism are critically discussed. The manuscript's structure is designed as follows: Section 2 describes macro and micro LA formulations. The design of the parametric analysis is reported in Section 3. Section 4 discusses the simulations' outcomes. Finally, relevant conclusions are drawn in Section 5.
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
## 3 Methodology
### Network Model Setup, Simulation, and Network Scenarios for Data Collection
#### 3.2.2 Protocol-Specific (RA) Attack Model Simulation  
Two network models are designed for the protocol-specific attack simulation based on: (1) the number of nodes and (2) the state of nodes, each of them further divided into two use cases. The first use case of the first scenario comprises twenty nodes in the network, where one node is malicious and nineteen nodes are normal. In the second case of the first scenario, the network comprises fifty nodes, where two nodes are malicious and forty-eight are normal with one sink node in each case. The second scenario depends on the state of the node, which is either static or mobile. The first instance of the second scenario contains all nodes in a static disposition, while the second use case consists of a network with partially mobile nodes.  
The rank attack is simulated from the protocol-specific attack category. The rank attack network model is cumulatively illustrated in Figure 2, where three nodes, 10, 19, and 20, broadcast the decreased rank and favorable characteristics through DIO messages to attract the network traffic. In the attack model, malicious nodes are placed near the root node strategically for the victim nodes to select them as parents. Algorithm 1 depicts the simulation of rank attack in the RPL-based IoT network in the Cooja simulator.  
```
1. Input: rank attack building block, DIO control message
2. Output: DIO message with the decreased rank
3. Begin
4. True: DIO with an illegitimately decreased rank
5. If
6. RPL_Conf_Min_HopRankInc = 0,
7. RPL_Max_RankInc = 0,
8. Infinite_Rank limited to 256, and
9. Rpl_recalculate_ranks = null, then
10. Child nodes select the parent,
11. Attack instigated
12. Else
13. False: node = benign
14. Until decreased rank attack is launched
15. Network = attacked
16. End
```  
**Algorithm 1**Protocol-specific RA Scenario
# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 4 Experiments
### Experiment Settings  
#### 4.1.1 Datasets  
Our evaluation is based on five public datasets (FB15K237 [34], YAGO3-10 [35], WN18 RR [36], NELL-995 [37], DBpedia-242 [19]) and an industrial KG datasets collecting from the workshop of actual factory. Table 3 shows the statistics of public and industry datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **\#Rel** & **\#Ent** & **\#Train** & **\#Valid** & **\#Test** \\ \hline FB15K237 & 237 & 14,541 & 272,115 & 17,535 & 20,466 \\ YAGO3-10 & 37 & 123,182 & 1,079,040 & 5000 & 5000 \\ WN18 RR & 11 & 40,943 & 86,835 & 3034 & 3134 \\ NELL-995 & 200 & 75,492 & 123,370 & 15,000 & 15,843 \\ Dbpedia-242 & 298 & 99,744 & 529,654 & 35,850 & 30,000 \\ Industrial KG & 15 & 57,373 & 78,787 & 3000 & 3000 \\ \hline \end{tabular}
\end{table}
Table 3: Statistics of datasets.  
FB15K237 is a link prediction dataset created from FB15k. FB15k-237 was created by [PERSON] and [PERSON] to ensure that the test and evaluation datasets do not have an inverse relation with test leakage. YAGO3-10 is a benchmark dataset for knowledge base completion. It is a subset of YAGO3 (YAGO3 is an extension of YAGO) and contains entities associated with at least ten different relations. WN18 RR is a link prediction dataset created by WN18, a subset of WordNet. However, many text triples are obtained by inversely finding triples from the training set. The WN18 RR dataset was therefore created to ensure that the evaluation dataset does not have an inverse relation with test leaks. NELL-995 is a subset of NELL suitable for multi-hop inference proposed from the 995 th iteration of the NELL system. Useless triples are first removed using relations that occur more than 2 M times in the NELL dataset. After this step, the triples with Top-200 relations are selected, and the dataset is obtained after adding the inverse triples. DBpedia-242 is extracted from DBpedia [6], which contains 242 concepts. It is worth mentioning that the entities in FB15K237, YAGO3-10 and NELL-995 have a corresponding ontology, while in WN18 RR, The entity does not have a corresponding ontology.
## 2 Methodology
### Loss Model Selection  
Loss models can be categorized into internal loss and external loss. The internal loss is defined as how much the actual process is far from the isentropic process. It affects the efficiency as well as the pressure ratio of the compressor. The external loss is defined as the energy loss outside the main flow path. It is associated with minor flows leaking from the main flow through the compressor. It only affects the efficiency of the compressor. Thus, the internal loss models selection is carried out with the pressure ratio performance data and the external loss models selection is based on the efficiency data.  
There are three representative loss model sets, which are Aungier loss model set [25], Oh loss model set [26] and Galvas loss model set [27]. However, Aungier is based on pressure loss mechanism while Oh and Galvas are based on enthalpy loss mechanism. Since KAIST-TMD adopted the enthalpy based calculation procedure, Oh set and Galvas set were tested for the loss models. As shown in Figures 8 and 9, the prediction results from KAIST-TMD are compared to the test data of Sandia National Laboratory (SNL) to validate the adopted loss models. According to Figure 8, the Oh set provides good agreement with experimental data on the pressure ratio while the Galvas set is less satisfying. The difference in predictions is due to the skin friction loss calculated from the Galvas model which seems to over-predict the values. Thus, KAIST-TMD adopted the internal model set proposed by Oh.  
In Figure 9, the Oh set predicts slightly higher efficiency than the test data while the Galvas set provides poor agreement with the efficiency data. Even though the Oh set is reasonable, a modification was necessary on the disk friction loss model because it assumes a constant disk gap to radius ratio. The original disk friction model in the Oh set is developed by [PERSON] and [PERSON] [28] and the ratio of disk gap to the radius in the disk friction coefficient is not taken as a variable. Thus, the disk friction loss is insensitive to the ratio of disk gap to radius despite the ratio plays a crucial role in the loss mechanism. From the literature survey, an empirical disk friction loss model suggested by [PERSON] [29] shows better agreement than that of [PERSON] and [PERSON] as it can be observed from the Figure 9 Oh set with  
Figure 8: Predicted pressure ratio results with SNL test data [24].  
Figure 7: Velocity triangles and flow slip mechanism at impeller exit.  
modification. Table 1 summarizes the modified loss model set from [PERSON]'s suggestion and this set is applied to KAIST-TMD code.
## 3 Results
### ML Prediction and Verification
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Siaolin Village**} & \multicolumn{2}{c}{**Putunpunas River**} \\ \cline{2-5}
**ML** & \begin{tabular}{c} **With InSAR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Without InSAR** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **With InSAR** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Without InSAR** \\ **(\%)** \\ \end{tabular} \\ \hline Naive Bayes & 70.93 & 70.85 & 68.19 & 68.19 \\ DT & 68.02 & 62.02 & 75.45 & 75.07 \\ Random Forest & 82.95 & 79.84 & 80.52 & 78.79 \\ AdaBoost & 78.49 & 77.52 & 75.64 & 75.64 \\ XGBoost & 79.31 & 75.97 & 78.80 & 75.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average prediction accuracies before and after including InSAR data in different ML methods.
# On the Trade-Off between the Main Parameters of Planar Antenna Arrays
## 3 Sparse Array Synthesis
### The Directivity of the Scanned Beams  
The previous result seems to be excellent: the same directivity of a fully populated array using less than 50% of its radiators. Unfortunately, all that glitters is not gold.  
If we look at the normalized array factor in the \(uv\) plane, we can see a reduction of the side lobes in the region \(w\leq 1\), which is the region that is taken into account for the calculation of the broadside directivity of Equation (10). We can see that the side-lobe level increases in the \(w>1\) region: the side lobes are lower than the specification threshold, but we should expect a reduction in directivity when scanning the beam.  
In Figure 7, we have represented the variation of the directivity for the four arrays that we have considered up to this point. As expected, the performances of the 310 elements sparse array are very good but rapidly decrease as soon as the beam is focused in a direction different than the broadside one.  
This effect represents one of the main issues of sparse arrays: since the position of the elements is optimized for a particular beam, focusing in a direction different from the optimized one results in a reduction of the performances.  
It is worth underlining that this decrease does not occur, or is very limited, in equispaced arrays: because of the periodicity introduced by the lattice, the side-lobes are automatically lowered in the \(w>1\) region.  
In sparse arrays we have, instead, to perform the synthesis taking into account the desired reconfiguration of the beam, like in [42]; the scanning of the beam for large angular ranges must be explicitly managed.  
Figure 6: The 310 elements sparse layout. Left subplot: position and normalized excitation amplitude (dB) of the synthesized layout; center subplot: normalized Array Factor in the \(uv\) plane; right subplot: some relevant cuts. Negative values of \(w\) are related to the cuts \(\phi^{\prime}=\phi+\pi\).  
Figure 7: Directivity as a function of the focusing angle for some of the synthesized array layouts.  
A first choice, could be to manually increase the side-lobe level requirements for the \(w>1\) region, but this choice should be avoided since imposing such a constraint would require the use of a larger starting array. Furthermore, lowering the threshold to a not-needed level could make us avoid some good solutions.  
A second possibility could be to explicitly optimize for a discrete set of scanning angles, but this choice would increase the computational effort of the synthesis algorithm since we could not use simple formulas like Equation (10).  
Fortunately, it is possible to use a smart approach to this problem, which will be described in the next section.
## 5 Conclusions  
To understand the possibility of extracting weak TIR signals from loaded rock in a strong interference background under outdoor conditions, the AIRT signals from the cyclic loaded rock surface in both the monotonic and fluctuant conditions were obtained and analyzed. The study shows the following conclusions:  
1. The AIRT is strongly influenced by the ambient temperature and environmental radiation. The AIRT related to stress is relatively weak and submerged by the ambient temperature, which cannot be identified directly.
2. The stress-induced infrared radiation signal from the cyclic loaded rock can be extracted via wavelet analysis method. The correlation coefficient between the decomposed \(\Delta\)AIRT and stress is larger than 0.8, and the amplitude of the extracted stress-induced infrared radiation is close to the theoretical result. Therefore, it is feasible to extract the weak TIR signals induced by stress in a strong interference background.  
The experimental results provide an experimental foundation for crustal stress field monitoring in the strong background and has impact for science. Although the crustal stress monitoring via RS remains difficult and challenging, this work is still worthy of exploration.  
All of the authors contributed extensively to the present paper. [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] helped perform the experiments; [PERSON], [PERSON], and [PERSON] processed and analyzed the data; [PERSON] wrote the first draft; [PERSON] and [PERSON] revised the manuscript extensively.  
This research was funded by the [National Natural Science Foundation of China] grant number [41,440,032] and [41,771,404].  
The authors thank [PERSON], Northeast Institute of Geography and Agro-ecology, Chinese Academy of Sciences, for his help on the experimental design and field.  
The authors declare no conflict of interest.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 4 Right-of-Way Trade Strategy
### Privacy-Preserving Static Game-Based Optimal Bids Problem  
In this part, we discuss the optimal bids problem of two neighbor-order convoys for the right-of-way trade based on a two-party static Bayesian Game. The set of participants in the game is \(\Gamma=\{A,B\}\).  
Suppose that \(\Delta t_{AB}\), \(\Delta t_{BA}\), and \(\alpha_{B}\) are public information that is known by both A and B. However, \(\alpha_{A}\) is private information whose exact value is known only to A itself. Assume that the value space of the time cost factor is \(\Theta_{A}=\Theta_{B}=[0,\ 1]\). The time cost factor of A is denoted by \(\alpha_{A}\), \(\alpha_{A}\in\Theta_{A}\). Similarly, the time cost factor of B is denoted by \(\alpha_{B}\), \(\alpha_{B}\in\Theta_{B}\). Let \(A_{A}\) and \(A_{B}\) denote the set of optional bidding actions for convoys A and B, respectively, where \(A_{A}=[\Delta C_{A},\Delta C_{B}]\) and \(A_{B}=[0,\Delta C_{B}]\).  
The cost to A of giving the right-of-way is  
\[\Delta C_{A}=\alpha_{A}\Delta t_{AB}. \tag{13}\]  
B's profit from passing the crossroad early is  
\[\Delta C_{B}=\alpha_{B}\Delta t_{BA}. \tag{14}\]  
To reach an agreement in a short time, A and B bid simultaneously according to the static game model. If buyer B's bid \(S_{B}\) is higher than seller A's bid \(S_{A}\), both sides reach an agreement and B pays the average of the two bids to A. Otherwise, no agreement can be reached. Thus, the function of reaching a deal is  
\[f(S_{A},S_{B})=\left\{\begin{array}{ll}1,&\quad\text{if }S_{A}\leq S_{B}\\ 0,&\quad\text{if }S_{A}>S_{B}\end{array}.\right. \tag{15}\]  
The profit functions of A and B are  
\[G_{A}(S_{A},S_{B})=f(S_{A},S_{B})\bigg{(}\frac{S_{A}+S_{B}}{2}-\Delta C_{A} \bigg{)} \tag{16}\]  
\[G_{B}(S_{A},S_{B})=f(S_{A},S_{B})\bigg{(}\Delta C_{B}-\frac{S_{A}+S_{B}}{2} \bigg{)}. \tag{17}\]The two convoys each maximize their profit and create an optimization problem to solve for the optimal bids \(\left(S_{A}^{*},S_{B}^{*}\right)\) of both convoys.  
Convoy A knows the true value of \(\alpha_{B}\). Thus, the optimal bid \(S_{A}^{*}\) is  
\[S_{A}^{*}(\alpha_{A})\in\max_{S_{A}^{*}(\alpha_{A})\in A_{A}(\alpha_{A})}G_{A}( S_{A}(\alpha_{A}),S  
The problem is solved as  
\[S_{A}^{*}=S_{B}^{*}=\frac{\Delta C_{A}+\Delta C_{B}}{2}. \tag{26}\]