esa-sceva/satcom-chunk-collection huggingface dataset:
# Twitter-Based Safety Confirmation System for Disaster Situations
## 2 Related Studies and Services
### Rescue Request Tweets Posted during Large-scale Disasters in Japan  
Certain studies have reported the use of social media posts as emergency-dispatch platforms outside Japan [33; 34]. However, as of yet, no official rules or procedures have been established to respond to rescue request posts in Japan.  
A large number of tweets, including safety information and rescue requests, were posted on Twitter during the Great East Japan Earthquake in 2011. Regarding the earthquake, [PERSON] et al. developed an extraction system using natural language processing (NLP) for disaster-response situations to extract tweets containing safety information [35]. [PERSON] et al. developed a website which extracted the rescue request information [36]. The website automatically listed similar statements by text filtering to extract the rescue requests from Twitter. This article reported that the website could contribute to execute emergency calls.  
As mentioned in Section 1, tweets with rescue request hashtags such as ###[] (#Rescue) and #### (meaning #Rescue_request) were posted in the recent major flood events. The use of such hashtags in tweets posted during the 2017 Northern Kyushu Flood has been analyzed in multiple studies [20; 21; 22]. [PERSON] indicated that not many tweets with such hashtags included detailed information on the location of the victims, the current state of damage and injuries, or the number of injured victims, which are necessary to achieve rescue. In fact, only a few rescues were accomplished based on the rescue request tweets [21]. On the other hand, [PERSON] et al. collected and analyzed the tweets with rescue request hashtags posted during the 2018 Japan floods [22]. The result indicated that the tweets requesting rescues included area names, landmark names, or street numbers, which could prove to be useful when specifying the place where the rescue was needed. However, in both two flood events, many tweets did not include the information directly related to rescue requests. Therefore, we have difficulty in extracting tweets truly requesting rescues. [PERSON] and [PERSON] studied a method to efficiently extract the tweets requesting rescues from the dataset of tweets with hashtag #rescue posted the 2018 Japan Floods [37]. It performed with a relatively high accuracy rate of 64.7%. To increase the accuracy of extracting the tweets requesting rescues, the victims are required to provide accurately detailed information. However, even people who are familiar with smartphones or similar devices and social media cannot easily provide detailed information. Therefore, a system is required to facilitate sharing the rescue request tweets.
## 3 Results and Discussion
### Proposed Identification of Chemicals in Avocado (P. americana Mill.) Semil 34 Seed  
The chemical characterization of Semil 34 was conducted using negative and positive electrospray ionization (ESI) modes via UPLC-MS (Figure S5). A total of 53 putative compounds were identified, based on their predicted chemical formulas and high mass accuracy (Table 2). The chemical classification tools Classyfire and NPClassifier were also utilized to group the previously identified compounds [37; 38; 39], allowing the classification of the metabolites into carboxylic acids and derivatives, cinnamic acid and derivatives, fatty acids, flavonoids, turnaoid ligans, glycerolipids, glycerophospholipids, organic sulfuric acids and derivatives/arylsulfates/phenylsulfates, organooxygen compounds, and phenol lipids. Three molecules were tentatively identified among the carboxylic acids and derivates. For example, compound 2 showed a precursor ion at \(m/z\) [M-H]\({}^{-}\) 191.0188, assigned to citric acid. Previous studies have identified citric acid in avocado seeds with precursor ions at \(m/z\) 191.021 and 191.0539, which aligns with our findings [19; 40]. Within cinnamic acid and derivatives, a coumaoylquinic acid isomer with \(m/z\) [M-H]\({}^{-}\) 337.0922 was found. This compound was previously reported as 5-O-p-coumaroylquinic acid and cis-3-p-coumarylquinic acid at \(m/z\) 337.04 and 337, respectively [41; 42]. Caffe acid [M+H-H2O]\({}^{+}\) with \(m/z\) 163.0387 was also identified, a compound that is widely studied in avocado [20; 41; 43; 44]. An example within the fatty acyl compounds is 2-linoleoylglycerol [M+H-H2O]\({}^{+}\) with \(m/z\) 337.2732, which has also been previously reported with a fragment ion at 337 [45]. Examples of organooxygen compounds include perseitol, observed at \(m/z\) 211.0815 [M-H]\({}^{-}\), previously identified by [PERSON] et al. at \(m/z\) 211.0805, as well as other authors [19; 45; 46]. Quinic acid with \(m/z\) 191.0553 [M-H]\({}^{-}\) and neochlorogenic acid at \(m/z\) 353.0878 [M-H]\({}^{-}\) were also identified and corroborated by the literature [19; 45; 47]. Regarding flavonoids, compounds 29 and 30 were found, with proposed compounds Procyanidin B1 and B2, with \(m/z\) 579.1490 and 579.1493 [M+H]\({}^{+}\), respectively, agreeing with previous studies focused on the [PERSON] and Fuerte varieties [48]. Molecules specifically found in avocado, such as compounds 15, 16, 17, and 20, were presented as avocadend A with \(m/z\) 231.2107 [M+H]\({}^{+}\), 4-AcO-avocadyne with \(m/z\) 309.2424 [M+H-H2O]\({}^{+}\), avocadene atte with \(m/z\) 293.2472 [M+H-2H2O]\({}^{+}\), and avocadyne with \(m/z\) 249.2210 [M+H-2H2O]\({}^{+}\), which have also been previously reported [45; 49; 50].
# Effect of Non-Homogeneous Soil Characteristics on Substation Grounding-Grid Performances: A Review
## 3 Effect of Horizontal Three-Layer Soil Characteristics on Grounding System Performances
### Vertical Grounding Rods  
Similar to horizontal two-layer soil, the length of vertical rods connected to a grounding grid plays a vital part in defining grounding behavior and protection. The effect of vertical grounding rod length in a horizontal three-layer soil configuration is studied in Ref [16]. The effect of vertical grounding rods on the decrease rate of grounding resistance is seen in Figure 12. High resistivity layer (H) of 1000 \(\Omega\)m, medium resistivity layer (M) of 500 \(\Omega\)m and low resistivity layer (L) of 100 \(\Omega\)m comprise a three-layer soil structure. The first soil layer is 10-m deep, while the second soil layer is 50-m deep. The graph in Figure 12 shows the grounding resistance reduction rate, \(\zeta\) at the y-axis, and the ratio of the corresponding radius and the vertical rod-length (L/Req) at the x-axis, where Req is the equivalent radius of the horizontal grounding grid calculated using Equation (4) from Section 2.5.  
Figure 11: Current density and potential distribution graphs for the X and Y directions of the ring-shaped grounding electrode. (**a**) Potential distribution (**b**) current density distribution [60].  
Figure 12: The effect of additional vertical rods on the decreasing rate of grounding resistance [16].  
From the graph, it can be seen that a similar behavioural pattern is present in horizontal two-layer soil structures. The findings show that long rods will not be able to reduce the grounding resistance when the first layer soil resistivity is low, as can be seen at 'L, H, M' (5) and 'L, M, H' (6). It is also found that the grounding resistance reduction rate of the 'M, L, H' (4) is lower than that of 'M, H, L' (3) before the vertical rods reach the bottom layer. This is attributed to the effect of the second soil layer of low resistivity. The low resistivity second layer causes more current to disperse thus the reduction rate of grounding resistance is lower compared to the 'M, H, L' soil structure. Therefore, based on the results, it is recommended that it is best for vertical rods to remain in the second soil layer in 'M, L, H' soil structure, compared to the 'M, H, L', soil structure where the vertical rods need to long enough to enter the bottom layer.
## 4 Practical Application  
In this section, a practical case study to verify all of the concepts discussed in the previous sections is developed. Consider the scheme in Figure 8 that shows an unbalanced four-wire three-phase linear system connected to an infinite short-circuit power network with unbalanced voltages. The load is modeled at a constant impedance and its values are indicated in the same figure. The voltages are unbalanced and sinusoidal in PCC (Point of Common Coupling), in which  
\[\underline{V_{an}}=231.00\;e^{i0}\quad\quad\underline{V_{bn}}=240.00\;e^{-j110 }\quad\underline{V_{cn}}=195.00\;e^{j90}\]  
Using the 'PSPICE V.9.2' analysis software, the currents circulating in the load are obtained, in which  
\[\underline{I_{a}}=0.00\;e^{-j0.00}\quad\quad\underline{I_{b}}=84.853\;e^{-j155.00}\quad\underline{I_{c}}=38.243\;e^{j76.69}\]  
Figure 9 displays the waveform of line currents in each phase obtained from the simulation of the system of Figure 8.  
Considering the values of line-to-neutral voltages and line currents, Tables 1 and 2 show these values in symmetric components. These values have been determined from the Fortescue transformation matrix.  
Figure 8: Proposed system for practical application.  
Figure 9: Line currents before compensation in PCC.  
The total apparent power of Buchholz that supplied the network in the bus was determined by Equation (2) and its value is as follows:  
\[S_{T}=3\sqrt{(212.891^{2}+42.094^{2}+50.67^{2})\ (40.979^{2}+25.938^{2}+23.141^{2})} =35924.80\ \text{VA}\]  
Then, we proceeded to calculate the reactance values in each phase of the ZSCC, the SVC and NSCC compensators as per the scheme of Figure 2. As described in the previous sections, it is very important to calculate the compensators in the following order: first the ZSCC compensator, then the SVC compensator, and finally the NSCC compensator.
## 3 Related Work
This technique dynamically manages hash expansion on persistent memory (PM) and guarantees a constant lookup time for the hash table. By setting the size of each bucket to match a cache line, the number of cache-line accesses is minimized, resulting in reduced overhead when accessing data spanning multiple cache lines. An alternative approach, as described in [18], utilizes a PM tree structure to avoid unnecessary reads during record probing. This approach incorporates fingerprinting techniques from PM trees and introduces a lightweight one-byte hash to detect keys efficiently, thereby conserving PM read/write bandwidth. The strategy employed by this approach involves postponing segment splits, which leads to improved space utilization. The implementation of this approach for PM utilizes the PMDK libraries [51]. A comprehensive study of main-memory hash joins in storage class memory (SCM) and evaluation of two state-of-the-art join algorithms is studied in [52]. It explores the design space, identifies the most crucial factors to implement an SCM-friendly join, and compares the performance of partitioned hash join (PHJ) and non-partitioned hash join (NPH) in real SCM environments. It provides valuable insights into the design and evaluation of main-memory hash joins in SCM, and the experimental results can help guide future research in this area. As the size of the dataset grows, the process of rehashing all the records becomes a challenge and a resource-intensive operation. To address this issue, extendible hashing is specifically designed for applications that prioritize time sensitivity. This hashing technique allows for incremental rehashing operations as the data size increases or when bucket overflow occurs. During the process of rehashing, the goal is to allocate all records to their respective buckets based on their hash keys. In this process, the directory entry pointers in the extendible hashing scheme are doubled as the depth increases, as illustrated in Figure 2. The directory, represented by the bucket address pointer, is responsible for pointing to the hash buckets [14]. As the size of the hash table increases, the directory is updated accordingly. Developing a dynamic and scalable hash table that is compatible with the new architecture of persistent memory hardware, and capable of operating efficiently under high load factors and providing instant recovery, is of the utmost importance [15; 18; 47; 53]. Furthermore, the design of hashing schemes that minimize the overhead associated with dynamic memory management, while improving hash table lookup time and other related operations, is equally crucial. This work aims to contribute to these ongoing efforts.
## 3 Results and Discussion  
### Solid Fuel Analysis  
Table 2 presents the fuel analysis results, including proximate and ultimate analysis, the lower heating value of the biomass fuels, and the chemical composition of the fuel ash. According to Table 2, all the untreated biomass fuels are characterized by an ash content (AC) of 1.85-5.04 wt.% db. The moisture content (MC) of all tested samples is in the range of approximately 2.71-10.7 wt.% wb. The fuel ash analysis results show predominant ash forming elements being compounds of K, Ca, Al, Mg, Na, P, S, and Si. The K concentration was high in the untreated biomass fuels but reduced significantly along with other impurities after the chemical pre-treatment. Observed silica content in the untreated and chemically pre-treated biomass fuel ash increased from 11.53-44.13 to 38.62-70.74 wt.% db, respectively. Similar silica enrichment after acidic pre-treatment of biomass fuels has been reported elsewhere [35; 36]. The high carbon and oxygen contents of 44.4-50.10 wt.% db suggest the presence of oxygen-containing functional groups on thebiomass fuels' surface and the lignocellulosic nature of samples [57]. The lower heating values (LHV) define the amount of heat released during the combustion ranged between 16.36-18.8 MJ/kg db as indicated in Table 2.
## 3 DC Fault Characteristics and Effects  
Generally, two typical electrical faults (line-to-line and line-to-ground) can occur in a DC electrical system. In most cases, a short circuit is happening in the line-to-ground fault because of the easy connection between the line to the ground by any natural event or unintentional action. Comparatively, the line-to-line fault impedance is lower than the line-to-ground short-circuit condition, which causes a huge fault current during line-to-line faults. The impedance of line-to-ground faults can be either as a low or high-impedance depending on the type of the grounding system. Also, the types of grounding, characteristics of the distribution line, and system components have a direct effect on the voltage sag and the value of the fault current [14]. During any DC short-circuit accident, the resistance of the fault path can converge to its minimum value, and the fault current can increase to a peak value in a short time.  
In this study, three typical DC short circuits were applied at three different points, as depicted in Figure 1. When faults occur, the current which flows through the related branch diverges from the normal value. Usually, five types of protection devices, such as mechanical breakers (Molded-Case Circuit Breakers (MCCB) and Miniature Circuit-Breakers (MCB)), fuses, SSCB, and the hybrid SSCB have been used for DC fault interruptions. However, their operation speed is different, which can range from a few (\(\upmu\)s) for SSCB and about tens of milliseconds (ms) for mechanical breakers [21]. On the other hand, the maximum allowable time for the fault interruption can be decided based on how much the fault can withstand the capability of the system components. Fast short-circuit detection and segregation are necessary for system recovery and the reduction of electrical stresses on converters and the system components. For the proper selection of the protection device, the functionality, operation speed, time coordination, and controllability considerations are essential to deal with fast fault interruption and arc extinguishing. The design of a protection system needs to abide by the following characteristics [22]:  
* Operating speed;
* Selectivity;
* Reliability;
* Reactivity;
* Strength.  
Figure 4: Feedback PI control diagram of the battery’s bidirectional converter.  
Figure 3: Circuit diagram of bidirectional converter for the battery and PI controller.
## 3 Results and Discussions  
Herein, the dependencies of the permittivity components of a hypercrystal and nanocomposite upon frequency are modelled aiming to detect the frequency ranges of Dyakonov surface waves (DSWs) and the existence of SPP waves (Figure 2).  
In the range the frequency \(\omega_{\,\parallel\,0}\) the semiconductor-dielectric metamaterial possesses hyperbolic properties. It can be seen from Figure 2, that the propagation of DSW is possible in the case of \(\epsilon_{n}=2.25\), \(\epsilon_{d}=11.8\). It is worthwhile to note that the regime of DSWpropagation is possible in the case of \(\epsilon_{||}\), \(\epsilon_{nc}>0\). Moreover, it is possible to increase the frequency range of DSW existence by changing the nature of inclusions, i.e., by replacing AZO with ITO.  
It is of particular interest to analyze two cases, i.e., when \(\epsilon_{n}>\epsilon_{d}\) and \(\epsilon_{d}>\epsilon_{n}\). The case when \(\epsilon_{n}=2.25\), \(\epsilon_{d}=11.8\) is depicted in Figure 3a. It can be observed that dispersion curves in these cases exhibit an exotic behavior in contrary to the conventional surface plasmons propagating at the metal/dielectric interface [23]. The case when \(\epsilon_{n}=11.8\), \(\epsilon_{d}=2.25\) is depicted in Figure 3b. By having a glance into the properties of DSW obtained by changing the filling factor of the nanocrystal as displayed in Figure 3a, one may observe that in contrast to the case depicted in Figure 3a, DSW does not propagate if \(\epsilon_{n}=11.8\), \(\epsilon_{d}=2.25\) (Figure 3c).  
Figure 4 demonstrates the transmission characteristics on frequency for various filling ratios. It can be seen in Figure 4a,c that with the increase of frequency, the imaginary part of \(\beta\) decreases at certain intervals, and the transmission distance increases. The former reveals the reduction of transmission loss with increasing frequency. Figure 4 shows that the filling ratio has a dramatic impact on the transmission characteristics of  
Figure 3: Solution of the wave equation for different filling ratio \(f\): (**a,b**)—\(\epsilon_{n}=2.25\), \(\epsilon_{d}=11.8\); (**c,d**)—\(\epsilon_{n}=11.8\), \(\epsilon_{d}=2.25\). Herein AZO (**a,c**) and ITO (**b,d**) inclusions are employed in nanocomposite and hypercrystal.  
the SPPs. In this relationship, the realization of dimension-tunable SPPs is enabled. It is worth noting that the results presented in Figure 4a,c are very important for practical application. The increase of the absorption enhancement (imaginary part of the propagation constant) makes a dramatic impact while creating photoconductive antennas with this enhanced performance [24].
## 4 Lagrangian Relaxation-Based Solution Procedures
### Procedures of Steps 2 and 3: Decomposition and Solving Sub-Problems
#### 4.2.6 Sub-Problem 6 (Related to \(\eta_{\pi i}\))  
By considering the variables related to \(\eta_{\pi i}\), the optimization problem is shown as  
\[\text{Objective function}:\] \[\text{min}\ \sum_{\pi\in\Pi}\sum_{i\in I}(F_{i}-\sum_{s\in S}\mu^{8}_{ \pi i})\eta_{\pi i} \tag{8}\] \[\text{subject to}: \eta_{\pi i}=0\text{ or }1.\]  
The objective function of (8) is also a cost minimization problem that can be divided into \(|\Pi||I|\) sub-problems. If the coefficient of each sub-problem is negative, then the decision variable \(\eta_{\pi i}\) is set to one at index migration interval \(\pi\) and task \(i\). The minimum objective value is determined by the summation of all the objective values of \(|\Pi||I|\) subproblems with the assigned parameter \(\eta_{\pi i}\) set to one. The running time is \(O(|\Pi||I|)\), and the pseudo-code is illustrated as shown in Algorithm 6.  
```
Input : Given parameters \(F\) and Lagrangian multipliers \(\mu^{8}\). Output : Decision variable \(\eta\).
1 Initialize : \(\eta_{xi}\gets 0\), \(\forall\tau\in\Pi\), \(i\in I\)
2 for\(\pi=0\) to \((|\Pi|-1)\)do
3 for\(i=0\) to \((|I|-1)\)do
4\(c\gets F_{i}\)
5 for\(s=0\) to \((|S|-1)\)do
6\(c\gets c-\mu^{8}_{\pi,i,s}\)
7 end for
8 if\(c<0\)then
9\(\eta_{\pi,i}\gets 1\)
10 end if
11 end for
12
13 end for
```  
**Algorithm 6** Sub-problem 6
## 5 Communication Overhead Analysis
### Proposed SCA Method  
The message structure of the proposed authentication SCA method is shown in Figure 10b, we analyzed the SCA message structure using Equation (22):  
\[\text{m}=\{m_{i},L_{ZID},\;pid_{i},T_{s},pk_{C},\sigma_{C}\} \tag{22}\]  
where \(pk_{c}=N|pk_{i}|\), and \(\sigma_{C}=N|\sigma_{i}|\). Therefore, the total communication overhead was \(|L_{ZID}|+|pid_{i}|+|T_{s}|+|pk_{c}|+|\sigma_{c}|4+1+4+64N+32N=9+96N\) bytes. The communication overhead for the SCA method depend on the number of communicated zones \(N\).  
Figure 10: The proposed protocol message structure. (**a**) TCA method, (**b**) SCA method, and (**c**) RCA method. Where \(m_{i}\) represents the message payload, \(pid_{i}\) represents the pseudo-identity of \(v_{i}\), and \(L_{ZID}\) represents a list of zone IDs. \(T_{s}\) represents the time stamp, \(pk_{i}\) represents the public key of \(v_{i}\), and \(\sigma_{i}\) represents the signature over the message. In our implementation, a signature \(\sigma_{i}\in G_{1}\) and the public key \(pk_{i}\) is \(\in G_{2}\). The total communication overhead of one message is \(|L_{ZID}|+|pid_{i}|+|T_{s}|+|pk_{i}|+|\sigma_{i}|=4+4+1+64+32=105\) bytes.