esa-sceva/satcom-chunk-collection huggingface dataset:
# Ant Colony Optimization with Warm-Up
## 3 Warm-Up
### The Procedure  
The warm-up emulates the update of the pheromone that, in classic ACO, is made during the first iterations of the algorithm, i.e., those generally aimed to explore the graph. The procedure is iterative and relatively easy. First of all, the pheromone matrix \(T\) is initialized, setting each element \(\tau_{i,j}=\tau_{0}\) ( \(\forall\tau_{i,j}\in\{1,\ldots,N\}|i\
eq j\)) and \(\tau_{i,j}=0\) ( \(\forall\tau_{i,j}\in\{1,\ldots,N\}|i=j\)). Similarly, to avoid divisions by zero, all the elements on the diagonal of the matrix of costsare made equal to \(1\) (\(C=C+I_{N}\)). At each iteration \(m\), the probability matrix \(P(m)\) is built by calculating each of its elements as in the following equation.  
\[p_{i,j}=\frac{(\tau_{i,j})^{\alpha}\cdot(\frac{1}{c_{i,j}})^{\beta}}{\sum_{j=0}^ {N}(\tau_{i,j})^{\alpha}\cdot(\frac{1}{c_{i,j}})^{\beta}}\quad\forall i,j\in 1, \ldots,N \tag{1}\]  
Note Equation (1) is the same used by many authors and mentioned by [4] to compute the probability to include the edge \((i,j)\) in the new generated solution at each iteration of the algorithm. Then, the matrix of updates \(U(m)\) is calculated as in Equation (2), according to [1].  
\[u_{i,j}=\frac{Q}{c_{i,j}}\quad\forall i,j\in 1,\ldots,N \tag{2}\]  
Then, the pheromone matrix \(T\) is updated according to Equation (3). In particular, the pheromone on each edge is updated according to its cost, and its corresponding value in the matrix of probabilities.  
\[T=T+[U(m)\circ P(m)] \tag{3}\]  
Finally, the pheromone evaporates as expressed in Equation (4).  
\[T=\rho_{uu}\cdot T \tag{4}\]  
The process is then repeated until the maximum number of iterations \(M\) is reached. In general, it is possible to see how the warm-up emulates exactly the same process that takes place during the iterations of the ACO. However, while during each iteration of the classic ACO only the pheromone on the edges owning to a new generated best solution is increased, in this case, at each iteration, all the edges of the graph see an increase of the pheromone, and this increase is proportional to their attractiveness. This is also a peculiarity of the proposed approach when compared to existing ones in literature (see for instance [25] or [26]), which generally initialize the pheromone simply depending on the cost associated to each edge of the graph. The author is aware that, over the years, several versions of the ACO were proposed by the scientific community, and most of them differ from the others for the formulas adopted to calculate the increase of pheromone [27], the evaporation [9], and the probability to choose an edge instead of the other [28]. On occasion of this study, reference is made to the first version by [1]. As several different versions of the ACO exist, many different versions of this warm-up procedure can be made by doing slight modifications to the formulas.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 3 Related Works
### Problem Formulation
#### 3.1.1 Cost-Based Calculation of Alternative Route  
The majority of existing FRR mechanisms, such as LFA [24; 31; 62], Remote LFA [14], Directed LFA [63], ECMP [23], MRC [11], and MRT [46; 47], calculate an alternative backup route according to link metrics. Alternative routes are usually calculated using a Dijkstra SPF algorithm, which calculates the route path as the minimal total cost of each individual link. The main problem with this type of calculation is that a valid alternative route can only be calculated if the internal algorithm of the FRR mechanism is able to find the correct alternative route according to specific metric conditions. In other words, there are topologies or situations where one FRR mechanism can find an alternative path but another FRR mechanism is unable to do so. If link costs exceed mathematical conditions, the FRR mechanism cannot find an alternative route, even if the alternative route physically exists. The positive effect of the cost-based FRR mechanisms is that they guarantee the calculation of the most advantageous alternative route in the event of a failure. On the other hand, it should be noted that they depend on the correct cost of links in topology. Therefore, there is a need for an FRR algorithm that is able to find an alternative route without cost-based calculation of an alternative route.
## 3 Results
### Anomalous X-ray Scattering (AXS)  
The crystal structure factors of the 101 and 112 planes of NaAlH\({}_{4}\) can be expressed as:  
\[F^{101}=2\sqrt{2}(f_{\rm Na}-f_{\rm Al})-2.4f_{\rm H} \tag{7}\]  
\[F^{112}=-4(f_{\rm Na}+f_{\rm Al})-0.62f_{\rm H} \tag{8}\]  
The crystal structure factors have different energy dependences in the case of Na and Al substituted by Ti. The intensity of 101 decreases by approximately 3%, and that of 112 is almost constant for the 2% Ti-substituted Al. On the other hand, the intensity of 101 increases for the Ti-substituted Na. Therefore, the relative intensity of 101/112 is available to identify the metal sites that are substituted by Ti.  
The diffraction peak intensities for 101 and 112 of NaAlH\({}_{4}\)-0.02 TiCl\({}_{3}\) were measured using incident X-ray energies of 4.6645 and 4.9395 keV, respectively. As the desorption reaction gradually progressed even at room temperature, all the peak intensities without the degradation were summed for alternately repeating measurements of the two diffraction peaks. Figure S3 shows the background with interpolation by linear function and integration range of \(d\) = 4.55 \(\pm\) 0.07 and 3.005 \(\pm\) 0.035 A for 101 and 112 of each Bragg peak.  
The relative intensities of the 101 and 112 peaks for different incident X-ray energies, \(I_{E/4.6645\ keV}^{\rm 101/112}\), were evaluated by simulation, as shown in Figure 3. \(I_{4.9395\ keV}^{\rm 101/112}\), \(I_{4.9395\ keV}^{\rm 101/112}\), \(I_{4.6645\ keV}^{\rm 101/112}\), and the reduction of 3.8% is significant because of the relatively low statistical error (0.82%). The simulation curves show the three cases of Ti-substituted  
Figure 2: Powder diffraction profiles of NaAlD\({}_{4}\) and NaAlD\({}_{4}\)–0.02 TiCl\({}_{3}\) before the desorption and after the re-absorption of deuterium by (**a**) neutron and (**b**) synchrotron X-ray. Rietveld refinement results: observed (circles), calculated (line), and residual (line below the vertical bars) diffraction profiles. The Bragg reflection positions are shown for NaAlO\({}_{4}\), Na\({}_{3}\)AlO\({}_{6}\), NaCl, and Al.  
Al, Ti-substituted Na, and nonsubstituted (interstitial Ti) in Figure 3. By comparing \(I_{\frac{101}{E}/4.6645~{}keV}^{101/112}\) with the simulation curves, we found that Ti clearly occupies the Al site in NaAlH\({}_{4}\). Additionally, the relationship between the relative intensity and the amount of Ti substitution at the Al site was estimated, as shown in Figure 4. This result shows that 1.3 \(\pm\) 0.3% of Ti is present at the Al site in NaAlH\({}_{4}\). The X-ray absorption edge shift should be considered to obtain an accurate substitution amount based on TiO\({}_{2}\), although it is difficult to determine the edge of Ti at the Al site in NaAlH\({}_{4}\). Typically, the oxidation state of Ti in compounds is higher than that in metallic Ti; hence \(E_{0}\) shifts to the high-energy side. In the case of the high-energy-shifted simulation, the substitution amount is increased by more than 1.3%, which is consistent with the Rietveld refinement result before the hydrogen desorption (Table 1).
# Boating Tourism and Fishing Interactions: A Social Network Analysis Using AIS Data
## 2 Literature Review
### Automatic Identification System (AIS)  
Vessel tracking performed by AIS has several advantages [19,20]. AIS records contain spatial-temporal information on passenger boats (regardless of their size), fishing boats (e.g., in the EU if they are at least 15 m long or 300 GT), and other types of boats [21]. These records are provided on a voluntary basis from a transponder, which is equipment mounted on the vessels themselves [22].  
AIS pings are a way to collect a large volume of data in a trusted and non-intrusive way [23]. In addition, AIS records allow for visualising the simultaneous occurrence of vessels in a given area, which in turn facilitates the recognition of the interactions that may exist between coastal area users [24].  
The spatial and temporal interception of activities such as maritime tourism and fishing have aroused some research interest due to their relevance in the busiest coastal zones [25]. An analysis of AIS data allows for verifying where there are the highest densities of spatial use by the stakeholders and where there are overlapping areas [26]. A proper understanding of these phenomena allows for improvement in management processes and can be effectively used for maritime spatial planning [27].
# Design and Optimization of Modular Solid Rocket Grain Matching Multi-Thrust Performance Curve
## 3 Results and Discussions
### Single-Thrust Case
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Items** & **Change of RMSE** & **Change of \(\mathbf{r^{2}}\)** \\ \hline \multirow{8}{*}{Star grains} & \(r_{\text{Ref, st},j}\) & 29.1\% & \(-\)0.986\% \\ \cline{2-4}  & \(L_{\text{st},j}\) & \(-\)39.2\% & 0.871\% \\ \cline{2-4}  & \(l_{j}\) & \(-\)67.0\% & 1.74\% \\ \cline{2-4}  & \(\varepsilon_{j}\) & \(-\)2.33\% & 0.0617\% \\ \cline{2-4}  & \(\theta_{j}\) & \(-\)1.86\% & 0.0674\% \\ \cline{2-4}  & \(r_{j}\) & \(-\)2.00\% & 0.0506\% \\ \cline{2-4}  & \(n_{\text{p, st},j}\) & \(-\)3.481\% & 0.0666\% \\ \hline \multirow{8}{*}{Slot grains} & \(r_{\text{Ref, st},j}\) & \(-\)0.268\% & 0.00967\% \\ \cline{2-4}  & \(L_{\text{sl},j}\) & \(-\)5.81\% & 0.0908\% \\ \cline{2-4}  & \(h_{i}\) & 1.16\% & \(-\)0.0131\% \\ \cline{2-4}  & \(n_{\text{p, st},j}\) & \(-\)0.0322\% & 0.00111\% \\ \hline Common & \(R\) & 332.5\% & \(-\)8.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parametric sensitivity study of single-thrust grain (each parameter increased by 1% independently). Similarly, using the PEF method, the burning surface regression and the pressure-time curve can be obtained in Figures 10 and 11. The analysis of the results reveals that the modification of the slot grain geometry from Figures 4-9 does not significantly affect the internal ballistic performance. The new grain still maintains a good match to the target pressure curve, with a maximum deviation of less than 6.1%. The relative mean squared error (RMSE) between two curves is 0.002545. The performance matching degree (\(r^{2}\)) calculated by Equation (34) is 0.9826, even higher than the results before slot modifications. A slight progressive burning is observed when the time \(t\) is less than 1.5 s. This progressive burning behavior can be attributed to the growth of the slot length at the beginning of the burning. In conclusion, the single-thrust case analysis demonstrates the potential of using a modular grain design. This design allows for the production of a stable pressure platform while maintaining a high propellant loading fraction, which can be mathematically guaranteed. Building upon this finding, the subsequent section will investigate the utilization of a two-segment modular grain to achieve a dual-thrust profile. Figure 11: Internal ballistic of the single-thrust grain (modified slot grain). Figure 9: Geometry of the modified slot grain.
# Energy Storage Overflow-Aware Data Delivery Scheme for Energy Harvesting Wireless Sensor Networks
## 1 Introduction  
In traditional battery-powered wireless sensor networks (WSNs), it is inevitable to replace batteries in the nodes when the battery energy no longer supports the nodes' normal operation. The inconvenience of replacing batteries can be overcome by energy harvesting WSN (EH-WSN) or battery-free WSN (BF-WSN). In the EH-WSN, the nodes do not use batteries, rather they harvest energy from ambient energy sources, such as sunlight, wind, radio, vibration, etc. [1].  
As the key task in the EH-WSN, the data delivery scheme needs at least one path from the source node to the destination node before the data can be delivered. The popular metrics applied in building the data delivery path(s) in the EH-WSN include energy harvest rate, energy consumption rate, and residual energy of the nodes, in addition to the common ones, such as packet delay, number of hops, data load, and more. Usually, energy storage is used in the EH-WSN nodes to store the harvested energy [2, 3]. Unfortunately, the existing data delivery schemes do not consider the energy missed by the nodes with full energy storage.  
Clearly, when an energy storage is full, the newly harvested energy cannot be stored anymore, which is referred to as Energy Storage Overflow (ESO). ESO is ignored in the existing data delivering schemes for EH-WSNs because these schemes usually focus on delivering data to the sink with minimum energy consumption, which may lead to the problem of \"energy hungry and surplus coexistence\" in the EH-WSN. That is, some nodes are hungry for energy, which may interrupt datadelivery when the energy-hungry nodes exhaust their energy, whereas other nodes continuously experience ESO. In fact, the data delivery process can last longer without interruption so that more data can be delivered if we consider ESO in the data delivery scheme. Motivated by this, we study the ESO-aware data delivery scheme. The main contributions of the paper are as follows:  
(1) We propose the ESO-aware multiple path (EAMP) data delivery scheme, in which multiple disjoint paths from the source node to the sink are constructed to deliver data. With the EAMP, the source node splits data into multiple pieces with each going through one of the paths, which mitigates ESO and avoids the problem of \"energy hungry and surplus coexistence\".  
(2) For the EAMP, we formulate an optimization problem that simultaneously minimizes the total ESO and maximizes the total residual energy. Then, we solve the optimal problem by using a genetic algorithm, which leads to the optimal Data Delivering Decision (DDD) applied in the EAMP.  
(3) Simulation results show that the proposed scheme can deliver more data than the existing schemes in addition to maintaining higher residual energy and lower ESO.  
The remainder of the paper is organized as follows. Related works are surveyed in Section 2. The EAMP, the optimization problem for the EAMP, and the genetic algorithm are proposed in Section 3. Simulation results are presented in Section 4. Section 5 concludes the paper.
## 3 Theoretical Analysis and Proof
### Parameter Descriptions  
In order to provide a clearer and more concrete representation of the strengths and weaknesses of each model in assisting CEM method (1), several metrics are introduced below: overall classification accuracy (OA), average classification accuracy (AA), product precision (PP), residual error (RE), and Kappa coefficient. Their calculation formulas are as follows:  
\[\left\{\begin{array}{l}\text{OA}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{ FP}+\text{TN}+\text{FN}},\\ \text{AA}=\frac{1}{2}\left(\frac{\text{TP}}{\text{TP}+\text{FN}}+\frac{\text{ TN}}{\text{TN}+\text{FP}}\right),\\ \text{PP}=\frac{\text{TP}}{\text{TP}+\text{FP}},\\ \text{Kappa}=\frac{p_{0}-p_{e}}{1-p_{e}},\end{array}\right.\]  
where TP means true positive; FP stands for false positive; TN denotes true negative; and FN represents false negative. Furthermore, for the Kappa, \(p_{0}\) and \(p_{e}\) are  
\[\left\{\begin{array}{l}p_{0}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN }+\text{FP}+\text{FN}},\\ p_{e}=\frac{(\text{TP}+\text{FN})(\text{TP}+\text{FP})+(\text{FP}+\text{TN})( \text{FN}+\text{TN})}{(\text{TP}+\text{TN}+\text{FP}+\text{FN})^{2}}.\end{array}\right.\]
## 4 Simulation and Results
### Scenario and Parameter Settings  
The simulation parameters are listed in Table 1. The scheduling interval is 50 ms, and the simulation time is 5 s. The simulation includes four typical types of phased array radar tasks: confirmation, precision tracking, general tracking, and airspace search. Confirmation tasks are triggered immediately upon target detection, and each target requires only a single confirmation instance. The expected execution times are uniformly and randomly distributed within the interval [0, 4 s]. The tracking task for each target is generated at a fixed data rate after the confirmation task is completed. The ratio of the number of targets tracked normally to those tracked precisely is 2:1. The threat degrees of precise tracked targets and general tracked targets are uniformly and randomly distributed within (0.5, 1) and (0, 0.5], respectively. The number of targets ranges from 12 to 108, with the experiment repeated 100 times for each increment of 12 targets, and the results are averaged. \(\alpha=0.5\), \(\beta=-1\); \(l=1\) ms, \(E_{th}=250\) J, and \(\tau=150\) ms.
# Integrated Replay Spoofing-Aware Text-Independent Speaker Verification
## 2 Related Work  
In this section, we introduce the two studies most relevant to this study [12; 16; 17]. First, [PERSON] et al. [12] propose a separate modeling of two Gaussian back-end systems with a unified threshold for both SV and PAD tasks. Their study explores various acoustic features to find which ones best simultaneously suited both tasks. As organizers of the ASVspoof challenges, official trials for the ISV task are released in this study. For our purposes, it is important to highlight that these trials include both ZE and replayed non-target, which we use throughout this paper. However, [PERSON] et al. [12] reported the average of two EERs--ZE-EER and PAD-EER--because they separately modeled two Gaussian mixture models for each task.  
[PERSON] et al. [16; 17] extended [PERSON] et al.'s work [12] by proposing an integrated ISV system; this study is the first that reports an ISV-EER. More specifically, they propose a three-phase training framework for extracting an embedding for the ISV task, followed by a probabilistic linear discriminant analysis (PLDA) back-end. In the first phase, a MTL [20] framework is employed to train a common embedding for both SV and PAD tasks. In the second and third phases, the embedding is adapted to fit the ISV task. However, because the DNN is adapted in the third phase to fit the enrollment speakers, it has limitations for real-world scenarios. In addition, because the performance is reported does not exploit organizer's official trials, it is difficult to compare the performance with the literature.  
In this paper, we first propose an E2E framework, illustrated in Figure 1a, that extends the work of [PERSON] et al. [16; 17] in two aspects: First, we adopt a single phase training approach by using three loss functions for SID, PAD, and ISV. Second, our framework directly outputs a spoofing-aware score without using a separate back-end system.
## 4 Methodology
### Temporal and Spatial Analysis  
Exchanging crucial information on social networks creates proper options for raising awareness of the disaster circumstance among individuals, and allows for more effective targeting of their attempts by officials and relief organizations. So, in this section, based on the results of multi-class classification, the temporal and spatial analysis will be introduced.  
* Temporal analysis  
Topics of the conversation differ on social networks throughout various crises. One variable that could lead the conversation topic to vary is the different support demands of the impacted individuals. To comprehend the temporal variability among various classes of information, the distribution of categorized tweets was examined over time. The classes incorporated in this research classification, which depict multiple requirements for situational awareness, were injured, dead, and missing people; infrastructure; donation; response effort; and other relevant information (this included information are about shelter and supplies, caution and advice, and so on) [10].  
* Spatial analysis (identifying region-particular topic)  
LQ historically has been applied to measure manufacturing or job requirements in the local and national economies. LQ could be used to identify the concentration of phenomena of interest in relation to other events in the special region. In this study, LQ was used to analyze the spatial concentration of discussion topics relating to earthquake crises on the social network. LQ is depicted as a percentage proportion of a specific topic at spatial county unit as compared to the proportion of that exact topic in the state of California in total. LQ is calculated according to Equation (1):  
\[LQ_{i}^{k}=\frac{X_{i}^{k}}{\sum_{i}^{n}X_{i}^{k}}/\frac{Y_{i}^{k}}{\sum_{i}^{n} Y_{i}^{k}} \tag{1}\]  
where \(X_{i}^{k}\) represents the number of messages with particular topic \(k\) in county \(i\), \(Y_{i}^{k}\) represents the total number messages in county \(i\), and \(n\) is the total number of counties in California state. In other words, LQ analyzes the comparative concentration of particular topics in a county in the state of California divided to other counties. A value of \(LQ_{i}^{k}\) higher than 1 represents the concentration of particular topic \(k\) in county \(i\) was lower than other counties in the state of California; and, a \(LQ_{i}^{k}\) lower than 1 shows a lesser concentration of particular topic \(k\) in county \(i\) than the state. Hence, the particular subject with the greatest LQ value greater than 1 is the most concentrated one for a specified county. In this regard, in this study, the topic with the greatest LQ value was chosen as the topic particular to the county.