esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper proposes a series of parallel optimizations on a high-resolution ocean model, the LASG/IAP Climate System Ocean Model (LICOM), which was independently developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences. The version of LICOM that we used was LICOM 2.1. In order to improve the parallel performance of LICOM, a series of parallel optimization methods were applied. We optimized the parallelization scheme to tackle the problem of load imbalance. Some communication optimizations were implemented, including data packing, the application of the least communication algorithm, and the replacement of communications with calculations. Furthermore, for the calculation procedures, we implemented some mature optimizations and expanded functions in a loop. Additionally, a hybrid of MPI and OpenMP, as well as an asynchronous parallel IO, was used. In this work, the optimized version of LICOM 2.1 was able to achieve a speedup of more than two times compared with the original code. The parallelization scheme optimization and the communication optimization produced considerable improvement in performance in the large-scale parallelization. Meanwhile, the newly optimized LICOM could scale up to 245,760 processor cores. However, for the original version, there was no speedup when scaled up to over 10,000 processor cores. Additionally, the problem of jumpy wall time during the time integration process was also tackled with this optimization. Finally, we conducted a practical simulation from 1993 to 2007 by using the optimized version of LICOM 2.1. The results showed that the mesoscale vortex was well simulated by the model.  
LICOM; meteorological model; parallel optimization
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 3 Structure of the ARIBC
### ARIBC Setup Phase
#### 3.3.2 Selection of the Master (Server) Secret Key  
We randomly pick the _Master (Server) Secret key_\((KSAK)\in\mathbb{Z}_{p}^{*}\) of the ARIBC instance, where \(p\) is the order of the bilinear map cyclic groups \(G_{\text{T}}\). The \(KSAK\) is even more security-sensitive than the Master Private Key of a Certification Authority in an X.509 PKI, because an adversary that knows the Master Private Key of a Certification Authority of a PKI is only able to create spoofed certificates of public keys; this is in contrast to an adversary who knows the \(KSAK\), who is able to create the private key for any user. Accordingly, the operator of the ARIBC instance is responsible for taking all necessary measures to ensure the security and availability of the \(KSAK\), and the appropriate procedures for recovering it, should the need arise.  
One way to solve the problem that the key generation center gets to know the entire private keys of all users is the use of certificate-less public key cryptography [46]. In this approach the key generation center only computes a partial private key of user \(A\), based on the identity \(ID_{A}\); the user combines this partial private key with some secret information (only known to the user). Then the user combines the secret information with the public parameters of the key generation center to obtain the user's public key. The advantage of this approach is that this public key no longer needs to be certified, since it contains the identity of the user, and if the key generation center is trusted (and the public parameters of the key generation center are authentic) one can rasonably assume that the user associated to \(ID_{A}\) really corresponds to \(A\) and holds the corresponding private key.
## 1 Introduction
Consequently, the National Centre for Complementary and Integrative Health (NCCIH), a branch of the U.S. National Institute of Health (NIH), launched their strategic plan which emphasises the need to comprehensively research the biological mechanisms of action as well as the safety profiles of putative NP food supplement interventions [17]. Perclinical animal studies have been an essential component in the pipeline to bring new drugs to market and especially in addressing the issues with NP immunomodulators highlighted by the NCCIH Strategic Plan. These studies are considered expensive, require a high level of maintenance, and most importantly are considered unethical [18; 19]. Using the framework established by the 3 Rs (replacement, reduction, and refinement) [20] there is a move within the research community to find alternative invertebrate model replacements than can reliably confirm the safety and efficacy of novel NP immunomodulators. Non-vertebrate alternatives, such as the nematode _Caenorhabditis elegans_ (_C. elegans_), the fruit fly _Drosophila melanogaster_, and the zebrafish (_Danio rerio_), have been extensively used but the _Galeria mellonella_ (_G. mellonella_) larvae possess several key advantages as a replacement preclinical in vivo model. _G. mellonella_ belongs in the _Pyralidae_ family of the order _Lepidoptera_ and can be found in the egg, larval, pupal, or moth form [21]. In the larval stage, _G. mellonella_ is found in beehives feeding on the honeycomb of the hive colony before pupation and finally metamorphosing into adult moths. The complete life cycle lasts for 8-12 weeks, of which 5-6 weeks are in the larval stage in which they typically grow up to 3 cm in length and between 300 and 500 g in weight [22]. This larvae stage makes an ideal in vivo model for assessing drug safety and efficacy as well as host-pathogen interactions [23]. _G. mellonella_ are superior to other invertebrate models due to their larger size facilitating experimental manipulation (e.g., drug administration) and the ability to grow at a wide range of temperatures (20-37 \({}^{\circ}\)C). In addition, they confer several key advantages over small animal models due to their short data generation time (24-48 h versus weeks), being relatively inexpensive, being not constrained by stringent ethical requirements, and in many cases, producing comparable data to animal studies [24; 25; 26]. Recently, its genome was fully sequenced, opening opportunities for genetic and epigenetic studies [27]. Larvae of the greater wax moth _G. mellonella_ are a commonly used in vivo invertebrate infection model to study bacterial, fungal and nematodal infections and for assessing the efficacy of antimicrobial drugs or insecticides, which has been extensively reviewed elsewhere [28; 29; 30]. A summary of the advantages and disadvantages can be seen in Table 1.  
This systematic review describes studies that have used the invertebrate _G. mellonella_ in vivo model to assess immunomodulating activity of synthetic and natural product-derived immunomodulators and suggest strategies to enhance the use of this model in future research.
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 3 Taguchi Method  
The Taguchi method, pioneered by [PERSON], is a statistical approach used to enhance quality and optimize processes in engineering and manufacturing. When applied to antenna arrays, this method involves systematically adjusting various design parameters to improve the array's performance [48,49].  
In the realm of antenna arrays, employing the Taguchi method entails structured experimentation to refine factors influencing performance. These factors encompass elements such as spacing, geometry, materials, feeding techniques, and other design attributes. Engineers conduct experiments based on this approach with the aim of identifying the ideal combination of parameters that enhance metrics such as gain, directivity, radiation pattern, and sidelobe suppression [50,51] (please refer to Appendix B for further details).  
Utilizing predefined experimental designs known as orthogonal arrays, the Taguchi method efficiently explores numerous parameter combinations through a limited set of experiments. Statistical analysis of these outcomes aids in pinpointing the most influential parameters and determining the optimal settings for superior antenna performance.  
By leveraging the Taguchi method in antenna array design, the goal is to elevate overall performance, reliability, and efficiency by systematically optimizing parameters using a statistically-driven approach [52]. In their quest to improve a phased-array satellite communication antenna, engineers utilized the Taguchi method to optimize its design parameters. They systematically experimented with element spacing, feeding techniques, and substrate materials while varying them across different levels. Analyzing the resulting data on gain, beamwidth, and sidelobe levels using statistical methods, they pinpointed the most impactful factors and identified the ideal combination of element spacing, feeding technique, and substrate material. This optimized configuration maximized gain, narrowed beamwidth, and minimized sidelobes, significantly enhancing the antenna's performance in satellite communication [53,54].
# Effects of Ethanol Concentration on Oral Aroma Release After Wine Consumption
###### Abstract  
This paper evaluates, for the first time, the effects of ethanol concentration on the dynamics of oral (immediate and prolonged) aroma release after wine consumption. To do this, the intraoral aroma release of 10 panelists was monitored at two sampling points (0 and 4 min) after they rinsed their mouths with three rose wines with different ethanol content (0.5% \(v\)/\(v\), 5% \(v\)/\(v\) and 10% \(v\)/\(v\)) that were aromatized with six fruity esters (ethyl butanoate, isoamyl acetate, ethyl pentanoate, ethyl hexanoate, ethyl octanoate and ethyl decanoate). Overall, the results indicated that the extent of the effects of ethanol content on the oral aroma release were influenced by the subject, the ethanolconcentration and the type of aroma compound. This effect was also different in the immediate than in the prolonged aroma release. In the first in-mouth aroma monitoring, an increase in the ethanol content provoked a higher release of the more polar and volatile esters (ethyl butanoate, ethyl pentanoate), but a lower release for the more apolar and less volatile esters (ethyl octanoate, ethyl decanoate). Regarding the prolonged oral aroma release, an increase of ethanol content in wine increased the oral aroma release of the six esters, which might also increase the fruity aroma persistence in the wines. Future works with a higher number of individuals will be needed to understand the mechanisms behind this phenomenon.  
Keywords:wine; ethanol; intra-oral SPME; oral aroma release; aroma persistence +
Footnote †: journal:
## 3 Results and Discussion
### Optimization of Perovskite Absorber Layer Thickness  
The absorption layer critically affects solar cell efficiency because it absorbs solar light and produces electron-hole pairs. Therefore, the optimization of PAL thickness is most important.  
Figure 6 shows the current density-voltage (\(J\)-\(V\)) plot for various PAL thicknesses, and Figure 7 shows \(V_{oc}\), \(J_{sc}\), \(FF\), and \(PCE\) values as functions of PAL thickness (100-1300 nm). As shown in Figure 7d, with an increasing PAL thickness, the \(PCE\) gradually increases until the thickness of the PAL reaches 1300 nm with the highest conversion efficiency of 19.30%. Afterwards, the \(PCE\) tends to decrease with an increasing PAL thickness. This decrease in \(PCE\) was attributed to the increasing electron-hole recombination in the absorption layer, rather than the generation of electron-hole pairs [64; 65; 66]. The \(J_{sc}\) value increases as the thickness of PAL increases, which is due to improved light absorption [67]. It increases sharply from 15.18 mA/cm\({}^{2}\) to 23.70 mA/cm\({}^{2}\) for the range of 100-700 nm. Beyond this thickness, it increases slowly until it saturates at 24.91 mA/cm\({}^{2}\). Additionally, as \(V_{oc}\) increases from 100 nm to 1200 nm, \(PCE\) also increases. Afterward, from 1300 nm to 2000 nm (not shown in Figure 6), \(V_{oc}\) slightly decreases. The initial increase in \(V_{oc}\) indicates a reduction in charge carrier recombination within the thin absorber layer, but as the thickness increases beyond 1300 nm, the decrease in \(V_{oc}\) could be due to a reduction in the effective bandgap and increased recombination [68].  
Figure 5: Acceptor density–\(PCE\) plot for HTL thickness of 100 nm.  
Figure 6: Current density–voltage (_J–V_) curves for various PAL thicknesses.  
Figure 7: Effects of PAL thickness on (**a**) \(V_{\infty}\), (**b**) \(I_{\infty}\), (**c**) \(FF\), and (**d**) \(PCE\).
## 4 Our Proposed EC-PSO Algorithm
### First Period CH Selection  
During the initial phase after the sensors are deployed, the energy of the network is homogeneous and no energy centers exist. Therefore, we use geometric method to distribute the CHs evenly and the number of the CHs is given using the Formula (8).  
\[CH\_N=\left\lfloor\sqrt{N\cdot Per\_CH}\right\rfloor \tag{8}\]  
where \(Per\_CH\) denotes the percentage of CHs in sensors and \(N\) represents the number of sensors. The distribution of CHs after first period CHs selection is shown as Figure 4.  
After CHs are elected, each CH broads a Cluster_Join message and nodes join a closest CH in accordance with the receiving signal strength. The optimal communication distance is used by each node to forward data to the CH and the topology is described in Figures 4 and 5. Then the greedy  
Figure 4: First period CHs distribution.  
Figure 3: Optimal communication distance.  
algorithm is used to generate a chain or tree for the CHs to transmit data to sink as is discussed in Section 4.7. The data fusion is conducted in each CH in order to preserve energy.  
When the network spends its first period, it enters into a steady phase. Then the PSO algorithm is executed for clustering and the topology of the network will maintain for 20 rounds. We visualize the residual energy of the network using a 3D picture as is shown in Figure 6. It illustrates that there are energy holes near the CHs because nodes close to CHs take the role of forwarder and they consume more energy. Our main purpose is to select the CHs near the energy centers to avoid energy holes and balance the energy consumption of nodes.
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 4 Dichotomy Graph Sketch Mechanism
### Time and Space Complexity Analysis  
In this section, we analyze the space cost and time complexity of DGS. The memory cost of DGS is \(O(\alpha^{2})\), where \(\alpha\) is the width of the light sketch. Specifically, the memory cost of DGS includes the light sketch, the heavy sketch, the heavy node candidate list and the edge classifier. Since the sample generator is temporal and its memory can be freed when all edges have been processed, we do not calculate its memory cost here. Supposing that \(\alpha_{h}\) denotes the width of heavy sketch, the memory cost of heavy sketch is \(O(\alpha_{h}^{2})\). Since \(\alpha_{h}<\alpha\) (see Section 4.1 for detail), \(O(\alpha_{h}^{2})\) is below \(O(\alpha^{2})\). The memory cost of heavy node candidate list is a constant \(c_{1}\) and in practice \(c_{1}\) is small (\(c_{1}<200\)). Thus, it is below \(O(\alpha^{2})\). The  
Figure 5: The details of the proposed edge classifier.  
memory cost of edge classifier is a constant \(c_{2}\) since the size of DNN is fixed. In addition, we set \(c_{2}<\alpha^{2}\) in practice, and thus \(c_{2}\) is also below \(O(\alpha^{2})\).  
The edge update of DGS includes three steps: inferring with the edge classifier, locating the right position in the light sketch/heavy sketch, and updating the value in that position. Supposing that the number of neurons in the \(i\)th fully connected layer is \(\beta_{i}\), and the input feature vector contains \(\beta_{input}\) elements, the time cost of DNN inference is \(O(\beta_{input}\beta_{1}+\beta_{1}\beta_{2}+\beta_{2}\beta_{3})\). Locating the right position in the sketch is \(O(1)\), since we only need to calculate two hash values. Obviously, updating the value is also \(O(1)\). Thus, the total time cost of edge update is \(O(\beta_{input}\beta_{1}+\beta_{1}\beta_{2}+\beta_{2}\beta_{3})\). In practice, since \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\) is fixed in the DNN inference process, the time cost can be regarded as \(O(\beta_{input})\). The process of edge query is similar to that of edge update, and thus the time cost of edge query is \(O(\beta_{input})\) as well. For top-k heavy nodes query, since we can directly obtain top-k nodes from the heavy node candidate list, the time cost is \(O(k)\).
# Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging +
## 6 Simulation Results
The detecting probability is given in (**a**) and the false alarm rate is given in (**b**). Note that Algorithms 1 and 2, as developed in [21; 32], respectively, are simulated as benchmark methods. Additionally note that Algorithm 2[21] cannot properly sense a target with the echo delay over the CP duration, and hence its detecting probability is much lower than our algorithms. Please note that the symbol “-” before all numbers, as automatically generated by MATLAB during plotting the figure, denotes the negative sign. Table 2 compares the four algorithms simulated above by highlighting their key features/results. We see that the proposed Algorithms 3 and 4 are able to sense longer distances than the benchmark Algorithm 2[21]. We also see that the proposed Algorithms 3 and 4 can remove false targets with lower false alarm rates achieved, as compared with the benchmark Algorithm 1[32]. Moreover, we see that the proposed Algorithm 4 not only reduces false alarm rate but also maintains a high detecting probability, while Algorithm 3 reduces the false alarm rate at the cost of decreasing its detecting probability. In addition, comparing the first and last rows in the table, we see that Algorithm 4 is able to reduce the false alarm rate by over 50% of that achieved by Algorithm 1.
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 2 Non-Binary LDPC Codes Based on High Girth Hamiltonian Graphs
### Cycle and Cycle Cancellation  
The cycle in the Tanner graph is related to the sub-optimality of the iterative decoding. The length of the shortest cycle where a node is located represents the shortest path or minimum number of iterations a node's message needs to travel back to the node itself. When the number of iterations increases, the message propagates to other parts of the graph. Therefore, enlarging the girth of an LDPC code helps to improve the iterative decoding performance. Figure 2a shows a Hamiltonian graph that is composed of 26 vertices and 26 chord edges, and it can be used to derive a parity-check matrix with a code rate of 1/2. In this figure, the shortest cycle length is six, i.e., the girth of the derived parity-check matrix is 12.  
Although there are no short cycles in the parity-check matrix constructed while using the high-girth Hamiltonian graph, in the case of an improper configuration of non-zero entries, the existence of cycles can generate low-weight codewords, thereby reducing the minimum distance and introducing a higher error floor. Fortunately, it is possible to prevent the cycle from generating low-weight codewords by ensuring that the determinant value of the matrix corresponding to the cycle is not equal to zero, which means that the cycle is cancelled [15].  
Let \(\alpha_{1},\alpha_{2},\cdots,\alpha_{l}\) be the non-zero elements of Galois field GF(\(q\)) related to a cycle of length \(l\), and the matrix form of the cycle is equivalent to the \(\frac{l}{2}\times\frac{l}{2}\) square matrix that is expressed in Equation (2).  
\[\mathbf{C}_{d}=\left[\begin{array}{cccccc}\alpha_{2}&\alpha_{3}&0&\cdots& \cdots&0\\ 0&\alpha_{4}&\alpha_{5}&&&\vdots\\ \vdots&&\ddots&\ddots&&\vdots\\ \vdots&&&\ddots&\ddots&0\\ 0&&&\alpha_{l-2}&\alpha_{l-1}\\ \alpha_{1}&0&\cdots&\cdots&0&\alpha_{l}\end{array}\right]. \tag{2}\]  
The determinant of \(\mathbf{C}_{d}\) is given by  
\[\det(\mathbf{C}_{d})=\prod_{i=1}^{l/2}\alpha_{2i}-\prod_{i=1}^{l/2}\alpha_{2i-1} \tag{3}\]  
The multiplication and addition operations are all defined on Galois field GF(\(q\)). Subsequently, the FRC of the cycle can be expressed as  
\[\prod_{i=1}^{l/2}\alpha_{2i}-\prod_{i=1}^{l/2}\alpha_{2i-1}\
eq 0. \tag{4}\]  
Furthermore, by applying the addition property of the finite field, the FRC is equivalent to  
\[\prod_{i=1}^{l/2}\alpha_{2i}\
eq\prod_{i=1}^{l/2}\alpha_{2i-1}. \tag{5}\]  
Figure 2b shows the FRC of a cycle of length 12. Therefore, a cycle can avoid the generation of low-weight codewords by appropriately configuring its corresponding non-zero elements to satisfy the FRC.
## 5 Discussion
### Use of UAVs in Impact Assessment
#### 5.3.2 Limitations of the Use of UAVs  
The use of UAVs is governed by complex regulatory frameworks that vary significantly across regions, creating challenges for their seamless deployment in post-flood assessments. Regulations often address aspects such as airspace management, licensing, and privacy concerns. Taking these into account, the operators of the UAV in our research team were certified and licensed pilots, adhered to the permitted altitude limits, and followed the necessary flight safety protocols. Non-compliance with these rules can result in legal consequences, including fines or other penalties [49; 50; 51; 52; 53]. Such regulatory constraints could delay UAV operations, impacting the effectiveness of our response efforts during the flood event in our case study. Additionally, the functionality of UAVs in post-flood assessments is particularly sensitive to the weather conditions. Adverse weather, such as strong winds, heavy rain, or fog, can hinder flight operations and degrade the quality of the collected data. In the case of the Lilas River flood, we encountered unpredictable weather conditions, including localized intermittent rainfall and cloud cover, which exacerbated these challenges. As a result, the UAV flights were sometimes delayed, and the risk of equipment damage increased if appropriate flight plans were not implemented. The adverse weather conditions we faced also accelerated the battery consumption, necessitating additional flights to complete the data collection [25; 49; 54; 55; 56; 57]. In addition, the operators relied on accurate weather forecasts and exercised caution when planning the UAV operations to minimize disruptions and ensure mission safety. Another significant limitation was the flight duration. The operational constraints of most commercial UAVs, including our own, involve limited flight endurance (typically 20-40 min). This forced us to limit the scope and duration of our data collection missions [46; 58; 59]. While UAVs excel in capturing high-resolution imagery and other datasets, the processing and interpretation of these data can be resource-intensive. The large datasets generated during the UAV operations required advanced computational tools and methods for their analysis. Additionally, interpreting the UAV-derived data, such as aerial imagery and videos, demanded specialized knowledge in fields like geospatial analysis. Errors in the data interpretation could lead to inaccuracies or mistakes, potentially undermining the flood damage assessments and hindering effective recovery efforts [60; 61; 62].