esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Phase Encoding  
Similar ideas to the ones presented in Section 2 can also be used in other implementations of the decoy-state BB84 protocol with a different signal encoding. For instance, in those QKD experiments based on phase encoding, which is more suitable to use with optical fibers than polarization encoding, which is particularly relevant in the context of free-space QKD [1,2].  
The basic setup is illustrated in Figure 4.  
Again, for simplicity, let us consider first the case where the input signals \(\rho_{i}\), with \(i\in\{1,\ldots,4\}\), are pure coherent states with arbitrary phase relationship: \(\left|\sqrt{2\mu}e^{i\theta_{1}}\right\rangle_{a_{0},+45^{\circ}}\) and \(\left|\sqrt{2\mu}e^{i\theta_{2}}\right\rangle_{b_{0},+45^{\circ}}\) (of frequency \(w_{1}\)), and \(\left|\sqrt{\mu}e^{i\theta_{3}}\right\rangle_{a_{2},+45^{\circ}}\) and \(\left|\sqrt{\mu}e^{i\theta_{4}}\right\rangle_{b_{2},+45^{\circ}}\) (of frequency \(w_{2}\)). Let \(\Delta t\) denote the time  
Figure 4: Basic setup of a passive decoy-state BB84 QKD source with phase encoding. The delay introduced by one arm of the interferometer is equal to half the time difference \(\Delta t\) between two consecutive pulses.  
difference between two consecutive pulses generated by the sources. Then, from Section 2 we have that the signals in modes \(c_{2}\) and \(d_{2}\) at time instances \(t\) and \(t+\Delta t/2\) can be written as  
\[|\sqrt{\tfrac{\zeta(\theta)}{2}}e^{i\phi}\rangle_{c_{2},+45^{\circ}}^{t}\otimes| \sqrt{\tfrac{\zeta(\theta)}{2}}e^{i\phi^{\prime}}\rangle_{d_{2},+45^{\circ}}^{t +\Delta t/2}, \tag{15}\]  
where \(\phi^{\prime}=\phi+\theta_{4}-\theta_{3}\). Similarly, we find that the quantum states in modes \(c_{3}\) and \(d_{3}\) are given by, respectively,  
\[|\tfrac{\sqrt{\gamma(\theta)}}{2}e^{i\phi}\rangle_{c_{3},+45^{ \circ}}^{t} \otimes |\tfrac{\sqrt{\gamma(\theta)}}{2}e^{i\phi^{\prime}}\rangle_{c_{3},+45^{\circ}}^{t+\Delta t/2},\] \[|\tfrac{\sqrt{\zeta(\theta)}}{2}e^{i\phi}\rangle_{d_{3},+45^{ \circ}}^{t} \otimes |\tfrac{\sqrt{\zeta(\theta)}}{2}e^{i\phi^{\prime}}\rangle_{d_{3},+45^{\circ}}^{t+\Delta t/2}. \tag{16}\]  
The case of phase-randomized strong coherent pulses is completely analogous to that of Section 2 and we omit it here for simplicity; it results in an uniform distribution for the angles \(\theta\), \(\phi\), and \(\phi^{\prime}\) for both pairs of pulses given by Equation (16). The strong signals in mode \(d_{3}\) are used to measure both their phases, relative to some local reference phase, and their intensities by means of an intensity and phase measurement, while [PERSON] sends the weak signals in mode \(c_{3}\) to [PERSON]. Again, just like in the passive source with polarization encoding shown in Figure 1, [PERSON] can now select some valid regions for the measured phases and also distinguish between different intensity settings. Then, we have that the analysis and results presented in Section 3 also apply straightforwardly to this scenario.
## 2 Materials and Methods
### Acoustic Data Treatment  
An operator with strong knowledge of French Guiana fish species and their ecology analyzed the acoustic data.  
**Identification**: Identification of demersal species was attempted via morphological criteria of the acoustic signal and acoustic shadow (a fish within an acoustic beam produces a shadow representing its silhouette, just as a fish does in a light beam) as suggest by [20] and [3], respectively.  
**Validation of the identification**: Because of the extreme turbidity in French Guiana waters, it was not possible to use video images to validate the identification of fish on acoustic images. Acoustic imagery was validated during fishing surveys, where an acoustic camera survey was conducted at the same time as the capture of individuals with fishing rods. When brought aboard the vessel, individuals were subsequently identified, measured then released alive. Acoustic camera signals of the captured fish were therefore associated with a specific fish species.  
**Acoustic videos analyses**: ProViewer 3.6, @BlueView Technologies, Inc software, Slangerup, Denmark, 2011 allowed visualization of the acoustic files with \"son\" format. Several tools were available to analyze the acoustic videos (scroll rate setting, measure of items, zoom, and acoustic wave representation mode) that allowed watching videos as many times as necessary to identify and measure fish.  
**Counting**: To avoid counting the same fish twice during stationary acoustic surveys, individuals that left the field-of-view were only counted as new fish upon return if they were a different size than the fish that left or if they were the same size but appeared more than 2 min later. In the same way, counting errors between mobile and stationary surveys were reduced by removing the same size individuals detected at the exact same location.  
To verify the consistency of counts during mobile surveys, they were run with the same vertical view angle twice on two islands (Mamelles and Grand Connetable). Mamelles islands surveys were run one day apart, and Grand Connetable Island surveys were run a few minutes apart.  
**Measure**: Size measurements of the same fish were made when fish signals were straight (tail lined up with the head and not curved during swimming undulations) at least two times with ProViewer software. The measurement tool of the ProViewer software provided a precision of the measure of \(\pm\) 10 cm. Because the orientation of the fish may reduce the estimate of actual size (and because this error of measurement cannot be estimated, Figure 3), the largest size measured of a single fish was considered the best measure.
## 2 Materials and Methods
### 3D Microstructural Analyses via X-ray Computed Tomography  
The cuboidal samples extracted from the ingots (see Figure 2) were scanned in a lab-scale Zeiss Versa 620 (Zeiss Microscopy, Dublin, CA, USA) X-ray Microscope (XRM). The XRM was used to capture 1600 radiographic projections of the samples as it was rotated about a fixed axis of rotation (see Figure 3a). The 1600 projections were then used to obtain a 3D reconstruction of the microstructure using a filtered back projection algorithm [38]. The four samples outlined in Table 1 were scanned using an average incident photon energy of 30 keV (accelerating voltage 60 kV, and power of 10 W) to ensure maximal difference in X-ray absorptivity of the three main elements in the microstructure (see Figure 3b). Furthermore, the scans were carried out at a resolution of 0.6 \(\upmu\)m/voxel. This ensured a high contrast between the MgZn intermetallic phase and Mg matrix. The XCT analysis of metals is a very well-established technique used to capture the 3D morphology of microstructural features non-destructively [32; 35; 43]. To further ensure repeatability of the particle analyses, only those particles that were larger than 200 \(\upmu\)m\({}^{3}\) were used in the analyses. This ensured that we had a minimum of at least 5 voxels (3D pixel) across each particle to accurately capture the morphology [43; 44]. Further, note that due to the relatively small difference in the X-ray absorptivity between Mg and Si elements at 30 keV, the Mg\({}_{2}\)Si phase could not be distinguished from the matrix.  
Figure 2: After casting, coupons of the cast alloys are extracted and prepared for X-ray Computed Tomography: extracted samples from the center of the cylindrical ingot were cuboidal in shape with a square cross-section and had an approximately 1.3 mm edge length. The samples were derived from multiple locations corresponding to different cooling rates within the ingots.
## 3 Model Development
### Model Efficiency  
The surface model inherits the same \"array-block\" data structures as all other IFS physics parameterisation packages and is therefore capable of benefiting from modern processors and GPUs. These data structures allow for the exploitation of shared memory parallelism via OpenMP as multiple threads can work independently on distinct array-blocks, therefore leading to a reduction in memory consumption and improved load balancing, with the scaling of these blocks tailored depending on the target architecture. This makes ECLand a very well adapted and efficiently integrated component of the IFS. The offline ECLand model version equally supports distributed memory parallelism via the Message Passing Interface (MPI). The new implementation allows to significantly accelerate the computational performance of ECLand in offline mode at all resolutions and enables efficient simulation at very high resolutions such as 1 km (with a throughput of one year of global simulation per day). This throughput is important for a range of new developments including accounting for urban areas (see later Figure 26 in Section 3.6) and their carbon emissions within the future Copernicus CO2 service as well as the foreseen developments under the Destination Earth initiative (DestinE), which aims at creating a high-precision digital twins of the Earth [74].  
One of the largest scalability bottlenecks when running ECLand in offline mode at very high resolutions (e.g., 1 km) is input/output (I/O). Ongoing development would attempt to mitigate this by exploiting the capabilities of modern parallel file systems and higher level libraries such as NetCDF4. Parallel efforts focus to consistently adapt ECLand to ECMWF's multi-IO development including optional output in different formats such as NETCDF or GRIB2.  
Additional development towards increased model efficiency is the ability to run on accelerated hardware such as Graphics Processing Units (GPUs). An effort towards porting the surface model to run efficiently on GPUs has already started at ECMWF under the umbrella of the Scalability programme [75].
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs  
Routing approaches based on game theory vary depending on the types of game used. In this section, the existing game theory-based routing protocols for WSNs are reviewed and discussed in detail. Possible future improvements along with their applications are discussed too. They can be categorized as in Figure 1, according to the taxonomy discussed earlier. All the routing protocols that will be discussed in this section are listed in this figure.  
Figure 1: Categories of game theory-based routing protocols for wireless WSNs: CH-C-TEEM: Cluster Head Cooperative Trustworthy Energy-Efficient multiinput multioutput; RCFR: Reliable Coalition Formation Routing; PRGT: Probabilistic Routing Based on Game Theory; COMO: Cooperation Optimal Protocol for Multirate Opportunistic Routing; GERA: Game-Theory-Based Energy-Efficient Routing Algorithm; GTEB: Game Theoretic Energy Balanced Routing; EEREG: Energy-Efficient Routing Protocol based on Evolutionary Game; GEEC: Game-Theory-Based Energy-Efficient Clustering; ERG: Evolutionary Routing Game; GBRA: Game-Theory-Based Routing Algorithm.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 3 Results
### Illustrative Example  
As shown in Figure 14, each row of y, y\({}_{1}\), and y\({}_{2}\) consists of six images, each of which is the head field data of the study area at the 10 th, 20 th, 30 th, 40 th, 50 th, and 60 th time steps. Row y is a randomly selected set of head field data obtained from the physical model execution of Case 3. Row y\({}_{1}\) and row y\({}_{2}\) are the predicted head field data given by the CNN and DSCNN-GRU models, respectively, corresponding to the data in row y. The lines \(\left|\mathrm{y-y_{1}}\right|\) and \(\left|\mathrm{y-y_{2}}\right|\) are cloud maps, showing the absolute differences between the head fields in the corresponding time steps in the y, y\({}_{1}\), and y\({}_{2}\) rows.  
Figure 14: The groundwater head field at t = [10, 20, 30, 40, 50, 60] months for the random test samples of the original model Case 3 (y), the general CNN surrogate model (y\({}_{1}\)), and DSCNN-GRU surrogate model (y\({}_{2}\)) with five convolutional layers and Adam and Tanh as the optimizer and loss function, respectively. The absolute error between the original model and the alternative model is represented as \(\left|\mathrm{y-y_{1}}\right|\) and \(\left|\mathrm{y-y_{2}}\right|\).  
Comparing the water level difference data at the same time step for rows \(\mid\)y - y\({}_{1}\mid\) and \(\mid\)y - y\({}_{2}\mid\), it is clear that the prediction accuracy of CNN is lower than that of DSCNN-GRU. However, the head field data predicted by these two types of models generally give a consistent head distribution across the entire study area with the physical model. In addition, both models exhibited obvious prediction errors in the southeast and northeast regions of the study area, respectively. This difference may be due to the fact that the water level changes in the eastern region of the area during the sampling period of this study were larger than those in the western region, resulting in a higher complexity of the time-varying water head field data in the eastern region, which led to a decrease in model learning accuracy.  
When comparing the prediction results of each time step of the DSCNN-GRU model, it was found that the model has high accuracy in predicting continuous time series. However, it should be pointed out that the error in predicting the head field at each time step is not completely consistent. This inconsistency may stem from the fact that the data contained in the input sample varies unevenly over time.
## 2 Materials and Methods
### Mechanical Properties
Using iron powders reduces the shrinkage of the struts, thereby improving the bonding quality between the struts and leading to superior compressive strength [30]. No significant improvement was observed in samples with 15% iron when compared to those with 10% iron. However, both these samples (15% and 10%) exhibited superior energy absorption capacity and compressive strength compared to the samples with 5% iron. The reason behind this behavior can be attributed to the difference between the thermal expansion coefficients of PLA and iron, which are 60 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\) and 11.8 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\), respectively. During the filament-making melting process, the higher thermal expansion coefficient of PLA compared to that of iron powders results in the powders being bonded due to the compression caused by PLA expansion. This causes compressive residual stress in the materials and some hemispherical cracks [38]. During the compression test, all cell walls act like a beam and undergo both tensile and compressive stress. This residual compressive stress improved the tensile strength of the walls (because the imposed force in the structure should first overcome on it), so walls can withstand higher stress; however, by further increasing the amount of iron powders, the number of hemispherical cracks will increase and this diminishes the efficacy of the residual compressive stress. A summary of these results is represented in Table 6.  
The SEA for pure PLA is measured at 0.16 kJ/kg, while Fe10, which contains 10 wt% iron (Fe) by weight, exhibits a significantly higher SEA of 2.051 kJ/kg. Additionally, the SEA values for composites with 5% and 15% iron content, referred to as Fe5 and Fe15, are 1.645 kJ/kg and 1.545 kJ/kg, respectively. This data (Figure 11) provides valuable information about the energy absorption capabilities of these materials, highlighting that the Fe10 composite material surpasses both pure PLA and the other composite variations and is the most efficient at absorbing energy when subjected to compressive forces. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Material** & **Peak Stress (MPa)** & **Densification Strain** & **Max Stress in Plateau (MPa)** \\ \hline Neat PLA & 2.7 & 0.4 & 2 \\ \hline PLA/5\% Fe & 2.8 & 0.37 & 2.3 \\ \hline PLA/10\% Fe & 3.1 & 0.36 & 3.57 \\ \hline PLA/15\% Fe & 2.9 & 0.36 & 3.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of compressive properties. \begin{table}
\begin{tabular}{l|l|l|l|l} \hline \hline
**Strain** & **Neat PLA** & **PLA/Fe5** & **PLA/Fe10** & **PLA/Fe15** \\ \hline
0 & & & \\ \hline
0.1 & & & \\ \hline
0.2 & & & \\ \hline
0.3 & & & \\ \hline
0.4 & & & \\ \hline
0.5 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Deformation of samples during compressive test.
## 4 Description of the Machine Learning Methods  
### k-Nearest Neighbor  
Instance-based learning reduces the learning effort by simply storing the examples presented to the learning agent and classifying the new instances based on the closeness to their \"neighbors\", i.e., previously encountered instances (i.e., from the training set) with similar attribute values. The nearest neighbor (NN) algorithm classifies a new instance in the same class as the closest stored instance in the attributed space. A straightforward extension is the \(k\)-NN, where \(k\) neighbors (instead of 1) are taken into account when determining the membership of an instance to a class. This approach is especially useful when data are affected by noise and the nearest neighbor of an instance may indicate an erroneous class [29].  
The k-nearest neighbor (kNN) algorithm is based on the choice of k-nearest neighbors using a distance function as a criterion. The output is computed by aggregating the outputs of those k training instances. As a distance function, one can use the Euclidean or Manhattan distance, and usually particularizations of the Minkowski distance. Choosing the value of k is important. If k is too small, then the classification can be affected by the noise in the training data, and if the value of k is too large, then distant neighbors can affect the correctness of the results. To avoid the difficulty of finding an optimum value for k, one can weight the neighbor influence. The neighbors have a greater weight as they are closer to the instance, while those farther apart weigh less [30].  
The advantage of nearest-neighbor algorithms is the very quick learning (simple storing of instances) and high prediction capability. The disadvantage is the slow process of searching the nearest instance by processing the whole training set.
## 3 Emergence of Resilience-Oriented Post-Shock Emergency Responses  
### The Concept of Post-Shock Emergency Responses  
Despite considerable advancements in resilience assessment research, it is increasingly acknowledged that state-of-the-art frameworks fall short of capturing the dynamics of contemporary urban communities, as highlighted by recent real-world hazard events [28]. In particular, existing frameworks exhibit a critical limitation: they fail to adequately characterize the criticality of different time windows during post-shock recovery [68]. In the context of real-world catastrophes such as damaging earthquakes, the immediate physical, economic, and societal losses resulting from the mainshock can be substantially exacerbated in the subsequent short time frame. These cascading effects have far-reaching implications for the society as a whole. For instance, the functionality losses or deterioration of local hospital and road networks significantly impact earthquake-injured inhabitants. Those who cannot be promptly evacuated face a heightened risk of mortality within days or even hours after the seismic event due to the limited access to life-saving treatments [69; 70]. Meanwhile, in the absence of well-planned disaster management, the societal consequence of earthquake-initiated releases of radioactive contamination or other toxic chemicals become both financially burdensome and irreparable over the subsequent yearsand decades [71; 72; 73]. Additionally, the potential aftershock sequences following the mainshock could even further amplify these consequences [74; 75]. Moreover, the ever-growing interdependencies among CISs would accelerate the propagation of earthquake-initiated disruptions. Consequently, planning and executing effective post-shock restorations face increasingly complex challenges.  
Against this backdrop, emergency response--incorporating an array of actions that the operators and stakeholders responsible for post-earthquake restorations can take immediately after the shock--has emerged as a new pathway toward future earthquake-resilient urban communities [76]. Conceptually, the emergency response (ER) empowers each operator of a single CIS or networked CISs to mitigate the functionality deficit in the immediate aftermath of earthquakes (IAoEs), as mathematically in Equation (2):  
\[ER=\underset{a}{arg\;min}(\int_{t_{0}}^{t_{\sigma}}\left(D(t)-DF(t,a)\right)dt) \tag{2}\]  
In this context, \(DF(t,a)\) stands for the deliverable functionality of those CISs, conditioned on the action \(a\) taken by their operators; additionally, \(t_{\sigma}\) denotes the time point when the deliverable functionality has been restored to an acceptable level, ensuring that the emergency functionality demand from the community can be met (which essentially signifies the end of IAoEs), as illustrated in Figure 2.
## 4 Simulations Results and Discussion with Performance Evaluation
### Simulation Results
#### 4.3.5 Rper  
The EC per packet in the presence of different packet errors is discussed in this section. If RPER is high, the reliability of data and EC of packets transmission increases. When the packets are received at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**EC**} & \multicolumn{3}{c}{**PS = 400 bytes**} \\ \cline{2-4}  & **DR = 10 Kbps** & **DR = 20 Kbps** & **DR = 30 Kbps** \\ \hline FLMPC-One & 623.4360 & 477.0100 & 420.3573 \\ FLMPC-Two & 556.2920 & 328.1460 & 252.0973 \\ SPF-Three & 192.4860 & 146.2430 & 130.8287 \\ BFS-SPF-Three & 161.3635 & 107.6785 & 89.7835 \\ \hline \hline \end{tabular}
\end{table}
Table 8: EC Values with PS of 400 bytes.  
Figure 12: PRR of All Protocols.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**EC**} & \multicolumn{3}{c}{**PS = 500 bytes**} \\ \cline{2-4}  & **DR = 10 Kbps** & **DR = 20 Kbps** & **DR = 30 Kbps** \\ \hline FLMPC-One & 696.3850 & 521.2662 & 454.5842 \\ FLMPC-Two & 649.9825 & 389.2062 & 290.1217 \\ SPF-Three & 215.6075 & 157.8037 & 138.5358 \\ BFS-SPF-Three & 188.2060 & 121.0997 & 98.7310 \\ \hline \hline \end{tabular}
\end{table}
Table 9: EC Values with PS of 500 bytes.  
sink the error rate is calculated. If the error exceeds its error limit then this packet is dropped by the protocols. Increase in packet error means that the reliability of data packet decreases, accordingly.  
In Figure 13, the RPER of all protocols is demonstrated. It is clear from the figure that the EC of packets with different PERs of existing protocols is high than the proposed protocols. Although the difference is minor, because of multiple copies generation in all protocols using binary trees formation. Both proposed protocols select the best route for communication and control the effect of data loss. The proposed protocols are differentiated from the existing protocols because they deal with erroneous bits in a much better way than the existing protocols. Both SPF-Three and BFS-SPF-Three combines the data packets at the sink to demolish the effect of erroneous bits completely.  
High messages exchange and void hole formation in existing protocols cause high energy dissipation. Meanwhile in SPF-Three single shortest path reduces the active nodes with a minimum number of messages passing. This selected path decreases the EC by completely removing void hole regions from the underwater network. The proposed protocols deal with void hole regions in an efficient way to achieve the objectives. It is clearly seen from Figure 13, that EC is greater than 0.35 J at 6 th second. Hence, the proposed protocols efficiently improved the EC improvements.