esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 3 Proposed Quasi-Uniform Quantizer for SCMA System
### The Proposed Quasi-Uniform Quantizer for SCMA  
In the aforementioned standard uniform quantizer, there are two approaches to enhance the performance of the SCMA detector. One is to increase the quantization precision without changing the quantization step \(\Delta\), i.e., increase the value of \(q\), which put forward higher requirements for the hardware implementation. Another method is to increase the quantization step \(\Delta\), which can expand the range of quantized probabilities, which scarifies the precision in application. To solve these problems, we aim to design a quasi-uniform quantizer that can handle the conditions below.  
* A large dynamic range of the probabilities is allowed.
* The quantization precision is less than the standard uniform quantizer; i.e., the quasi-uniform quantizer can achieve the same BER performance as the standard uniform quantizer with less quantized bits.
* The hardware implementation complexity can be controlled; in fact, the proposed quasi-uniform quantizer has the same hardware implementation complexity as the standard uniform one. The difference between the quasi-uniform and uniform quantizers lies in the quantization thresholds, i.e., the quantization thresholds of the uniform quantizer is uniformly distributed, while it becomes non-uniformly distributed for the quasi-uniform quantizer.  
To this end, we propose a quasi-uniform quantizer for SCMA detection, where the uniform quantization role is adopted at the low region of the probabilities and the nonuniform quantizer is applied at the high region of the messages. To describe this quantizer, we call it the \(q+1\)-bit quasi-uniform quantizer. Specifically, for the input values \(-\mu L\Delta<a<\mu L\Delta\), the uniform quantizer in (19) is used, while for the values in the intervals \((-\infty,\mu L\Delta]\) and \([\mu L\Delta,+\infty)\), the outputs of this quasi-uniform quantizer are in the form of \(\pm\mu^{m}L\Delta\), i.e., the corresponding intervals increase exponentially  
operates the same as the 4-bit uniform quantizer in the interval \([0,8)\), while it works in the exponentially increasing way when \(a\geq 8\).
## 2 Model Definition
### Model Implementation  
The proposed model is implemented as a computer simulation using the Python programming language (version 3.7). The environment \(E\) is represented by a 3D tensor, such that each node represents a space segment with a volume of \(\delta^{3}\). At the beginning of the simulation (i.e., \(t=0\)), the individuals that occupy the environment are set in the environment, such that each individual is allocated a unique inhaling and exhaling duration and volume values from a pre-defined distribution. In a similar manner, a number of pathogen particles needed to infecty an individual are randomly sampled from a given distribution and set to each individual. Afterward, at each step in time, five computational steps take place. First, based on the individuals' stage in the breathing cycle (inhaling,exhaling, neither), a force \((F_{int},F_{out},\) none) is allocated to the corresponding location of the individuals' face (see Section 2.2). Moreover, if an infected individual is exhaling, \(\phi_{g}\) pathogen particles are introduced to the room at \((l_{x},l_{y},l_{z})\). Second, an adaptive mesh for the CFD model is computed using the method proposed by [62], such that at least a single node of the mesh is located in each node in the \(M_{E}\) matrix in order to ensure a lower boundary of the size of the geometry's resolution. Third, the air movement in a single step in time is calculated by using the RANS method [55] for the CFD model (practically approximating Equation (1) for the mesh's nodes' location at a specific point in time). Fourth, using the \(\
abla u\) value computed on the geometry's mesh from the previous step, Equation (2) is computed, and the number of pathogen particles that enter individuals' bodies are updated. Finally, the epidemiological state of each individual in the population is updated based on the number of pathogen particles in its system and its current epidemiological state. This process repeats itself for \(T\) time steps. A schematic view of the simulator's process is presented in Figure 2.
## 5 Conclusions  
Aiming at the problem of low data rate of underwater acoustic spread-spectrum communication systems, this paper proposed an in-phase/quadrature index modulation-aided multiple sequences spread-spectrum (MSSS-I/Q-IM) system. By adding in-phase/quadrature index modulation, the MSSS system obtains a new dimension for conveying index bits, so the spectral efficiency and data rate of the system are improved. We also derived the theoretical BER of the MSSS-I/Q-IM system over an AWGN channel and verified it through simulations. Then, in order to verify the feasibility of the MSSS-I/Q-IM system for a UWA channel, the simulations over UWA channels and the pool experiments were carried out. The results have proved that the proposed system can achieve higher data rate and better BER performance in UWA channels by adjusting system key parameters, such as \(J\). In other words, the proposed system has shown a good trade-off between data rate and BER performance. In the future, we will further investigate the theoretical performance of the proposed scheme over multipath fading channels and UWA channels. In addition, to verify the robustness of the proposed scheme in complex environments, we will carry out some field experiments in sea or lake scenarios.  
Conceptualization, Y.W., L.Q. and W.X.; methodology, Y.W., L.Q., W.X. and D.W.; software, L.Q., W.X. and D.W.; validation, W.X.; formal analysis, Y.W., L.Q. and W.X.; writing--original draft preparation, Y.W., L.Q. and L.W.; writing--review and editing, Y.W., L.Q., W.X. and L.W.; visualization, Y.W.; supervision, W.X.; project administration, L.W.; funding acquisition, D.W. and W.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, Ministry of Natural Resources (MNR), No. KFJ20220201. This work was supported in part by the National Natural Science Foundation of China under Grant 62271427 and 61871337.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Conclusions  
This paper investigated the anti-jamming channel selection problem in UAV communication networks. Via constructing a cooperative anti-jamming mechanism, users can cooperate with each other and then take actions according to the interference level in the network. The channel switching cost and cooperation cost, which had a great impact on the users' utilities, were introduced. For the case where users were not influenced by co-channel interference, a Markov decision process was formulated for independent anti-jamming channel selection, and a single Q-learning method was designed to obtain the independent anti-jamming channel selection strategies. For the case where users were influenced by co-channel interference, a Markov game was formulated for the interactions between users and the malicious jammer, and a multi-agent Q-learning method was adopted to obtain the joint anti-jamming channel selection strategies. Simulation results depicted that the proposed ICADCSA algorithm can avoid malicious jamming and co-channel interference effectively.  
In the future, we will try to investigate the cooperative anti-jamming distributed channel selection with more jamming patterns. Moreover, as the strategy space of Q-learning is limited, we will investigate the cooperative anti-jamming channel selection problem via multi-agent deep Q-learning to extend the strategy space.  
[PERSON], [PERSON] and [PERSON] conceived of and designed the model. [PERSON], [PERSON] and [PERSON] performed the theoretical analysis and simulation. [PERSON] analyzed the simulation result. [PERSON] wrote the paper. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] also provided some valuable suggestions for this paper.  
This work was supported by the National Natural Science Foundation of China under Grant No. 61771488, in part by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034 and in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratory.  
The authors declare no conflict of interest.
## 2 Industrial Cloud-Edge Collaboration
### Cloud-Edge Collaboration  
Cloud computing [13; 14] is growing rapidly because it can provide flexible services and data-intensive processing power to end users over a wide area network (WAN). Users can use a large amount of computing resources through the cloud without having to build a new computing infrastructure. However, targeting low-latency and high-computational-performance IoT applications [15] such as ultra-high-definition video, augmented reality (AR), and virtual reality (VR), challenges the traditional cloud's scalable and flexible computing model. Edge servers have relatively low compute and storage capacity but have advantages such as low latency and flexible distribution. However, edge computing also poses complex resource management problems because end devices at the access side are characterized by fast business demands, high mobility, and large data volumes.  
For aspects related to the cloud, historically, many companies have grown and expanded from evolving technology and innovation. Cloud computing is seen as a unique solution for delivering applications to businesses [16; 17]. It uses different components to deliver services, especially for Internet businesses. However, most studies do not consider the importance of edge services, and the security of data transfer between edges is very understudied. For the automotive industry chain as shown in Figure 1, for the different components of the car, it usually requires multiple enterprises to work together, and these IoT device data can greatly reduce the network and computational pressure if they can directly circulate with each other among enterprises.
## 3 Results
### Beta Diversity  
Non-Metric Multidimensional Scaling (NMDS) analyses revealed a clear separation between the lava caves of Hawai'i and New Mexico (Figure 3). According to [PERSON] and [PERSON] (2018), NMDS should be the go-to beta diversity ordination for ecologists. When these data were analyzed by cave, Hawai'i and New Mexico caves had separate centroids. Similar to observations made in our alpha diversity analysis, Kula Kai stands out from the rest of the Hawaiian caves. Kula Kai overlaps slightly with New Mexico samples but does so, more extensively, with the Hawaiian samples. The four samples from Kula Kai do not group with Hawaiian or New Mexican samples. Instead, they are found to reside between the two locations (Figure 3). The sample types of those outliers are moonmilk, a mineral crust, a microbial mat, and a coralloid. The samples from Kula Kai that are well within the Hawai'i groupings are two spheroids and an ooze.  
Figure 2: Alpha diversity, by cave. Representations of library size, species richness, and inverse-Simpson diversity. Box plots represent medians, as well as all four quartiles.  
We confirmed that both locations (Hawai'i and New Mexico) were significantly different by establishing a Bray-Curtis dissimilarity matrix, thus allowing for statistical hypothesis testing. Applying a permutational analysis of variance (PERMANOVA) [37] showed that the two locations were significantly different from one another (\(p<0.05\)) (Figure 3). Additionally, if we apply the same PERMANOVA to analyze the difference between speleothems, we obtain a variety of results (Figure 4). However, the more curious results are that mineral crusts are not significantly different from any of the speleothems, with the exception of spheroids, which are significantly different from all other speleothems.  
When divided out by cave feature (Figure 4), the NMDS demonstrated a visual delineation between ooze, moonmilk, spheroids, and microbial mats, across both New Mexican and Hawaiian samples. Coralloids have a bit of overlap with spheroids, microbial mats, and moonmilk. Mineral crusts overlap with almost everything, excluding spheroids. Using a PERMANOVA hypothesis test with an alpha of 0.05, we can establish that spheroids  
Figure 4: NMDS, by speleothem. Graphed here are the normal confidence ellipses (where 95% of the data are expected to fall), generated by ggplot2.  
Figure 3: NMDS, by cave. Beta diversity ordination of our data, with normal confidence ellipses (where 95% of the data are expected to be found), generated by ggplot2. A’a, Pantheon, and Four Windows are New Mexican caves, and Kula Kai, Maelstrom, and Tapa are Hawaiian caves.  
are statistically different (rejecting the null hypothesis) from everything within the dataset (\(p<0.05\)). In the case of mineral crusts, we failed to reject the null hypothesis in all speleothem comparisons, with the exception of spheroids (\(p>0.05\)). In the case of coralloids, we reject the null hypothesis in favor of the alternate hypothesis in only the cases of ooze and spheroids (\(p<0.05\)). Moonmilk is statistically different from ooze, spheroids, and microbial mats.
## 4 Experiments
### Parameter Setting  
The performance of a deep learning model is largely dependent on the hyperparameters' selection. Selecting the appropriate hyperparameters is vital for developing a model that is both accurate and able to generalize effectively. For example, learning rate plays a crucial role during model training. While too low of a learning rate might result in slow convergence and needless training time, too high of a rate can cause early convergence and poor choice of solutions. Furthermore, whereas adding more epochs generally improves model performance, there is a point beyond which further increases may yield diminishing returns. In this study, using the IBM dataset, key parameters such as batch size, dropout rate, number of epochs, loss function, and learning rate have been evaluated. After extensive experimentation and careful consideration of the trade-offs among various parameters, the final configuration of parameters used in the experiments is presented in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **Value** \\ \hline All deep learning models & Epoch & 50 \\ All deep learning models & Optimizer & Adam (lr = 0.001) \\ All deep learning models & Loss function & Binary Crossentropy \\ All deep learning models & Bath Size & 128 \\ All deep learning models & Dropout Rate & 0.5 \\ CNN & Kernel & 3 \\ CNN & \# Filters & 100 \\ MLP, LSTM, Bi-LSTM, GRU, Bi-GRU & \# Units & 100 \\ Transformer & \# attention heads & 8 \\ Transformer & Head-dim & 64 \\ Transformer & Feedforward-dim & 2048 \\ Bi-TCN 1 (Proposed) & \# Filter & 32 \\ Bi-TCN 2 (Proposed) & \# Filter & 64 \\ Bi-TCN 1 (Proposed) & kernel & 3 \\ Bi-TCN 2 (Proposed) & kernel & 5 \\ Both Bi-TCN (Proposed) & dilations & [1, 2, 4] \\ Fully connected (Proposed) & \# Units & 128 \\ RF, AdaBoost, Gradient Boosting & \# Estimators & 100 \\ Gradient Boosting, CatBoost & Learning rate & 1.0 \\ CatBoost & Iterations & 10 \\ CatBoost & Depth & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameter values used for experiments.
### The Role of Objective Cognitive Tests and Subjective Assessments in Spaceflight  
Continuous high levels of astronaut cognitive performance are of high relevance as even small mistakes can have catastrophic consequences and cause mission failure. Astronauts are highly trained and selected, but they do not differ from the general population in their relative inability to self-assess performance, especially in chronic exposure situations [9]. This is why repeated objective cognitive performance testing is so important for mission success [6]. NASA's Cognition test battery was developed for high-performing astronauts [10]. It consists of 10 brief cognitive tests that cover a range of cognitive domains relevant for spaceflight, yet it typically takes less than 20 min to administer. The fact that Cognition has 15 unique forms is a strength of Cognition that allows for the repeated testing and monitoring of astronauts on long-duration space missions. Practice and stimulus difficulty effects have been established for Cognition [11].  
There is a long history of the administration of surveys in spaceflight and space-analog environments with the goal to assess various mental and emotional states. Commonly used instruments include the Profile of Moods States (POMS) [12] or the Beck Depression Inventory (BDI) [13; 14]. Most of these scales contain multiple items to capture a single domain (e.g., confusion-bewilderment on the POMS), with sub-scores for each domain and a total score across domains. The sheer number of questionnaires and the time required to fill them out has been a problem in spaceflight and especially in space-analog environments, leading to response fatigue and complaints by study participants.  
In many settings, Cognition administration is preceded by a brief 18-item Alertness and Mood Survey (AMS) that can be used to track astronaut self-assessed mental state before, during and after space missions. In order to reduce administration time and avoid response fatigue, Cognition AMS uses visual analog scales where each question covers a single domain of interest. So far, analyses have concentrated on individual AMS items as a combined AMS score does not exist. The latter would be a useful summary metric for astronaut behavioral health that could be tracked across space missions of different durations.
## 2 Materials and Methods
### Computational Modelling and Ligand Docking  
AlphaFold models for the maize and _Arabidopsis_ Z-ISO enzymes were downloaded from UniProt ([[https://www.uniprot.org/](https://www.uniprot.org/)]([https://www.uniprot.org/](https://www.uniprot.org/)); peptide sequence identities B4 FHU1 (AF-B4 FHU1-F1) and Q9 SAC0 (AF-Q9 SAC0-F1), respectively (accessed on 1 June 2022). The UniProt entry for Slr1599 is N-terminally truncated (see Section 3.1) so the model for _Synechocystis_ Z-ISO was generated using AlphaFold v2.1.2 [37] (git commit e93a9 eb) with full database searching and the monomer model parameter, which was run on a Dell PowerEdge C4130, housed within the University of Sheffield SHARC high-performance computing cluster, and equipped with an Intel Xenon E5-2630 v3 CPU, 64 GB DDR4 ECC-RAM, and eight NVIDIA Tesla K80 GPUs. Mutated Z-ISO structures were generated in ChimeraX [38]using the AlphaFold structures and scored using the Rosetta membrane protein-scoring suite [39]. Mutants were chosen based on previous biochemical studies [5; 6] and rational interrogation of the AlphaFold structures.  
The AlphaFold structures of the _Arabidopsis_ and maize Z-ISO enzymes were used to dock heme B, and these heme-bound structures were subsequently used for docking of 9,15,9'-tri-_cis-_c-carotene. The 3D ligand structure of heme B (HEM-model spatial data file (SDF)) was downloaded from RCSB PDB ([[https://www.rcsb.org/](https://www.rcsb.org/)]([https://www.rcsb.org/](https://www.rcsb.org/))). We could not find the 3D structure of 9,15,9'-tri-_cis-_c-carotene in any database and so generated one from the 2D SDF using ChimeraX, predicting bond lengths by bond order. AutoDock structure files containing structure, partial charges, and rotational freedoms (.pdbqt files) were generated with AutoDock Tools [40]. Rotational freedom was removed from any bonds involved in the 9,15,9'-tri-_cis-_c-carotene \(\pi\)-conjugation system. Heme B was docked into both structures by searching a 125,000 A\({}^{3}\) volume, investigating 10,000 different binding modes. Using the _Arabidopsis_ model, two binding sites were identified with estimated \(\sim\)9 kcal mol\({}^{-1}\) binding energy; however, only one was near prototypical bis-His ligands predicted by the AlphaFold structures. This informed a more targeted docking of heme B into the maize Z-ISO structure, where 1000 binding modes in a 2744 A\({}^{3}\) volume around the putative bis-His heme-binding site were investigated. The lowest-energy heme B-binding mode was used to generate a new maize Z-ISO-heme B PDB structure, which was used to dock 9,15,9'-tri-_cis-_c-carotene, searching 10,000 binding modes in a 125,000 A\({}^{3}\) volume.
## 5 Interference Analysis
### V-Ns Interference  
The interference between V-Ns at the recipient V-N may be represented as \(L_{I_{X}}(s)\), wherein \(X\) is \(V\) and is derived by presuming Rayleigh fading for \(g_{V}\). V-N interference is divided into two distinct categories: (i) interference from V-Ns that are not situated on a conventional road traversing the origin, and (ii) interference from V-Ns that belong on a road that traverses the origin [11]. The interference of non-typical road V-Ns may be obtained by supposing that there are two distinct types of V-Ns, i.e., (a) the V-Ns that are positioned at a distance of \(y\geq z_{V}\) (e.g., \(L_{I_{Y}}^{z}(s)\)), where \(y\) is the perpendicular distance to \(z_{V}\) such that \(z_{V}\) is the radius of the sphere centered at the origin, \(\mathcal{S}(o,z_{V})\), and (b) the V-Ns that are located at a distance of \(y<z_{V}\) (e.g., \(L_{I_{Y}}^{z}(s)\)). It is important to note that for the V-Ns with distance, \(y<z_{V}\), only the V-Ns located on a road (or a line segment) between \(\left[-\sqrt{z^{2}-y^{2}},-\sqrt{z_{V}^{2}-y^{2}}\right]\) and \(\left[\sqrt{z_{V}^{2}-y^{2}},\sqrt{z^{2}-y^{2}}\right]\) will create interference. Whereas, for the V-Ns with distance \(y\geq z_{V}\), the V-Ns located on a road (or a line segment) between \(\left[-\sqrt{z^{2}-y^{2}},\sqrt{z^{2}-y^{2}}\right]\) will create interference. The V-Ns' interference is placed at a straight line segment (road) \(\left[-\sqrt{z^{2}-y^{2}},\sqrt{z^{2}-y^{2}}\right]\). is obtained by ensuring that the V-Ns are placed on the road as a PPP, with the pdf provided by \(f(t_{1})=\dfrac{1}{2\sqrt{z^{2}-y^{2}}}\). Given that the total quantity of V-Ns on the road is \(j\), the interference is represented as  
\[L_{I_{V}}^{\sharp}(s)=\sum_{j\geq 0}\mathbf{P}\{\aleph_{V}=j\}\left(\int_{- \sqrt{z^{2}-y^{2}}}^{\sqrt{z^{2}-y^{2}}}\dfrac{f(t_{1})dt_{1}}{1+sP_{V}B \mathcal{G}_{t}\mathcal{G}_{r}\big{(}y^{2}+t_{1}^{2}\big{)}\dfrac{-\alpha_{r} }{2}}\right)^{j}. \tag{23}\]  
Plugging \(f(t_{1})\) into (23) and the probability of nodes, \(\mathbf{P}\{\aleph_{V}=j\}\), that follows PPP-distribution, the interference is expressed as  
\[L_{I_{V}}^{\sharp}(s)=\sum_{j\geq 0}\dfrac{e^{\left(-2\lambda_{V}\sqrt{z^{2}-y ^{2}}\right)}\left(2\lambda_{V}\sqrt{z^{2}-y^{2}}\right)^{j}}{j!\left(2\sqrt{ z^{2}-y^{2}}\right)^{j}}\left(\int_{-\sqrt{z^{2}-y^{2}}}^{\sqrt{z^{2}-y^{2}}} \dfrac{dt_{1}}{1+sP_{V}B\mathcal{G}_{t}\mathcal{G}_{r}\big{(}y^{2}+t_{1}^{2} \big{)}\dfrac{-\alpha_{r}}{2}}\right)^{j}. \tag{24}\]  
Using simple mathematics, the expression can be written as  
\[L_{I_{V}}^{\sharp}(s)=\sum_{j\geq 0}\dfrac{e^{\left(-2\lambda_{V}\sqrt{z^{2}-y ^{2}}\right)}}{j!}\left(\dfrac{\left(2\lambda_{V}\sqrt{z^{2}-y^{2}}\right)}{ \left(2\sqrt{z^{2}-y^{2}}\right)}\int_{-\sqrt{z^{2}-y^{2}}}^{\sqrt{z^{2}-y^{2 }}}\dfrac{dt_{1}}{1+sP_{V}B\mathcal{G}_{t}\mathcal{G}_{r}\big{(}y^{2}+t_{1}^{2} \big{)}\dfrac{-\alpha_{r}}{2}}\right)^{j}. \tag{25}\]  
Using \(\int_{-t_{2}}^{t_{2}}=2\int_{0}^{t_{2}}\) and \(\sum_{j=0}^{\infty}\dfrac{\mathcal{Y}^{j}}{j!