esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 6 Experimental Results and Analyses  
### Energy Consumption  
Omnet++ simulator [38] is used to evaluate the effectiveness of RICC. Figure 13 presents the simulation results of the energy consumptions of different nodes. With more nodes in a cluster, much energy is consumed because it must handle more amount of data packets, which can be seen from Equation (22). Since the power for transmitting a packet is increased with RICC, nodes always have a larger energy consumption compared with NCCC, except for those in the area very close to the sink. It shows that RICC scheme can improve the energy utilization rate.  
Figure 12: Comparison of weighted end-to-end MFR.  
Figure 13: Comparison of energy consumptions.  
Figure 14 shows that in NCCCC, the energy consumption of nodes in hotspots is unbalanced in the network. In Figure 15, RICC causes the far-sink nodes to consume more energy; that is, the nodes in the network tend to have the same energy consumption. Hence, the energy usage of the network is more balanced, which improves the energy utilization rate and network reliability.  
The corresponding energy used for delivering a data packet per hop is shown in Figure 16. The node that is closest to the sink uses the same energy power for transmitting a packet, but unlike in NCCC, in RICC, the power increases with the distance from the sink node, resulting in lower one-hop MFR of packets. The energy consumption for each hop in the area far from the sink in the RICC scheme is higher than that of each hop in the NCCC scheme due to the larger N in this area in the RICC scheme.  
Figure 16: Energy used for delivering a packet per hop.  
Figure 14: Energy consumption of NCCC (3D).  
Figure 15: Energy consumption of Reliability Improved Cooperative Communications (RICC) (3D).
## 4 Discussion
### Feature Analysis  
As we see from Equations (8) and (9), the 28-D feature vector consists of three parts: 20 amino acid compositions; 7 correlation factors; and 1 ALE-index. One may be interested in knowing whether or not the last two parts are significant. First and foremost, let us see what would happen if only the first part was used? Without loss of generality, suppose \(S\) is a protein sequence and the counts of 20 native amino acids are \(n_{1},n_{2},\cdots,n_{20}\), respectively. Then we have a multi-set \(M\left(S\right)=\left\{n_{1}\cdot A,n_{2}\cdot C,\cdots,n_{20}\cdot Y\right\}\). Based on the knowledge of combinatorics, it is not difficult to see that there are a total of \(\frac{\left|S\right|!}{n_{1}!\cdot n_{2}!\cdot\cdots n_{20}!}=\frac{(n_{1}+n_{2 }+\cdots-n_{20})!}{n_{1}!\cdot n_{2}!\cdot\cdots n_{20}!}\) different sequence/strings possessing the same amino acid composition. This suggests that the amino acid composition alone is not sufficient to represent and compare protein sequences. What would happen if only the first two parts were used (i.e., without using the ALE-index)? By using the vector with the first 27 components, experiments I and II are performed. For the first dataset, there is no significant difference between the tree constructed with the 27-D vector and that with the 28-D vector. For the second dataset, the corresponding relationship tree of coronavirus spike proteins is shown in Figure 4. From Figure 4, it is easy to see that MERS-CoVs belonging to _Betacoronavirus_ appear to cluster together with the three _Gammacoronaviruses_, instead of the other _Betacoronaviruses_. This phenomenon is disappointing. For the third dataset, we repeat the three cross-validation tests with the 27-D vector and list the corresponding results in Table 7. By comparing Table 7 with Table 6, we can find that the prediction quality diminished slightly. These results indicate that the ALE-index can make a very positive contribution to the performance of experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **1** & **2** & **3** & **Average** \\ \hline \(S_{n}\) (\%) & 95.28 & 94.72 & 95.00 & 95.00 \\ \(S_{p}\) (\%) & 94.57 & 95.06 & 95.06 & 94.90 \\ \(Acc\) (\%) & 94.90 & 94.90 & 95.03 & 94.94 \\ MCC & 0.8978 & 0.8977 & 0.9004 & 0.8986 \\ \(F1_{-}M\) (\%) & 94.62 & 94.59 & 94.73 & 94.65 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of three different cross-validation tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **1** & **2** & **3** & **Average** \\ \hline \(S_{n}\) (\%) & 95.00 & 93.61 & 94.44 & 94.35 \\ \(S_{p}\) (\%) & 94.32 & 94.32 & 95.06 & 94.57 \\ \(Acc\) (\%) & 94.64 & 93.99 & 94.78 & 94.47 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the three cross-validation tests with the 27-D vector.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 5 Prototype Development and Validation
### Prototype Validation and Usability
#### 5.4.2. Usability of the Captive Portal with Security Keys in Mobile Devices  
As part of the validation of the solution, we designed and conducted a usability experiment with 15 subjects. For this experiment, subjects used their own smartphone devices, which they are used to, as clients of the Wi-Fi network running our captive portal. This follows the mock environment described in Section 5.3.  
Users were asked to connect to the Wi-Fi network and authenticate with the security key and a username, after a brief explanation. The security key and username were already registered in the system by an administrator. After the first try, the subjects were asked about their satisfaction and completeness of the process. Then, they were asked to perform the task two more times and finally complete a small questionnaire.  
For measuring the usability of the solution, we considered three different measures based on ISO 9241 [28]: effectiveness, completeness and efficiency:  
* Effectiveness \"is the accuracy and completeness with which users achieve specified goals\", that is, that complete the connection to the Wi-Fi network with the security keys. Also, we measure the error rate while performing the task by observing the difficulties or technical issues that the user encounters. The error rate measures the average number of errors per performed task, calculated as the division of total error occurrences by the number of tasks.
* Efficiency \"is the resources used in relation to the results achieved\" that we measure as Time-Based Efficiency and Overall Relative Efficiency.
* Satisfaction \"is the extent to which the user's physical, cognitive and emotional responses that result from use of a system, product or service meet user's needs and expectations\". We measured satisfaction with a custom questionnaire after performing the task.
###### Abstract  
In order to realize the accurate and reliable prediction of the change trend of dissolved oxygen (DO) content in California perch aquaculture water, this paper proposes a second-order hybrid optimization support vector machine (SVR) model based on Differential Evolution (DE) and Gray Wolf Optimizer (GWO), shortened to DE-GWO-SVR, to predict the DO content with the characteristics of nonlinear and non-smooth water quality data. Experimentally, data for the water quality, including pH, water temperature, conductivity, salinity, total dissolved solids, and DO, were collected. Pearson's correlation coefficient (PPMCC) was applied to explore the correlation between each water quality parameter and DO content. The optimal DE-GWO-SVR model was established and compared with models based on SVR, back-propagation neural network (BPNN), and their optimization models. The results show that the DE-GWO-SVR model proposed in this paper can effectively realize the nonlinear prediction and global optimization performance. Its R\({}^{2}\), MSE, MAE and RMSE can be up to 0.94, 0.108, 0.2629, and 0.3293, respectively, which is better than those of other models. This research provides guidance for the efficient prediction of DO in perch aquaculture water bodies for increasing the aquaculture effectiveness and reducing the aquaculture risk, providing a new exploratory path for water quality monitoring.  
dissolved oxygen; water quality prediction; differential evolution; gray wolf optimizer +
Footnote †: journal: Applied sciences
## 3 Numerical Methods  
### Meshing  
In this study, an unstructured grid was used to divide the area under calculation. Considering the large gradient variation in various parameters in the breathing zone of passengers, air inlets, and air outlets, the grid adopted a method of dividing the carriage as a whole and local mesh encryption. After the trial calculations, when the number of grids exceeded 2,474,927, the change was less than 2% in the PP of CO2 in the passenger breathing zone and 1% in the average temperature, as shown in Figure 5. Therefore, this set of grids could meet the requirements of grid independence.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Stations & Guangzhou & Chenzhou & Changsha & Wuchang & Zhengzhou & Xi’an & Lanzhou & Xining & Delingha \\ Altitude (m) & 41.7 & 184.9 & 44.7 & 23.1 & 110.4 & 397.5 & 1517.3 & 2261.2 & 2881.5 \\ Arrival time & 11:52 & 15:34 & 19:08 & 22:34 & 3.33 & 9:36 & 16:26 & 19:11 & 23:33 \\ Longitude (\({}^{\circ}\)) & 113.26 & 113.04 & 113.02 & 114.32 & 113.67 & 108.97 & 103.86 & 101.82 & 97.39 \\ Latitude (\({}^{\circ}\)) & 23.15 & 25.81 & 28.20 & 30.53 & 34.75 & 34.29 & 36.04 & 36.63 & 37.32 \\ \hline Number & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 \\ Stations & Golmud & Nachitai & Wudaoliang & Tuotuhoe & Tanggula & Amdo & Nagou & Damxung & Lhasa \\ Altitude (m) & 2807.7 & 3575.2 & 4645.1 & 4533.1 & 5207.0 & 4801.4 & 4507.8 & 4200.7 & 3650.2 \\ Arrival time & 2.20 & 4.09 & 620 & 8:15 & 9:59 & 10:54 & 12:17 & 15.02 & 1645 \\ Longitude (\({}^{\circ}\)) & 94.91 & 94.57 & 93.78 & 93.76 & 91.79 & 91.67 & 92.00 & 91.11 & 91.08 \\ Latitude (\({}^{\circ}\)) & 36.39 & 35.88 & 35.50 & 35.67 & 33.09 & 32.26 & 31.45 & 30.48 & 29.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Train passing stations and time (GMT + 8).  
Figure 4: Inner wall temperature of the running train.
# InMemQK: A Product Quantization Based MatMul Module for Compute-in-Memory Attention Macro
## 1 Introduction  
Large Language Models (LLMs), exemplified by ChatGPT [1], have found extensive applications across various domains, including AI-generated content (AIGC), medical diagnostics [2, 3], and autonomous vehicles [4]. During inference, these models, based on the transformer architecture and its derivatives [5, 6], generally require extensive computational density and frequent memory access. With the deceleration of Moore's law, conventional processor-centric computing architectures are encountering limitations in both performance and energy efficiency. Against this backdrop, compute-in-memory (CIM) technology presents itself as a promising solution that fundamentally alleviates data movement bottlenecks.  
Non-volatile memory (NVM) [7, 8], characterized by high density, rapid access, and low power leakage, fulfills crucial requirements of LLMs for computational density and memory bandwidth. Researchers have successfully implemented CIM techniques based on NVM technologies like ReRAM to accelerate Convolutional Neural Networks (CNNs) [9] and Recurrent Neural Networks (RNNs) [10]. Among these studies, time-domain CIM has garnered significant attention due to its capacity to minimize extensive AD/DA conversions prevalent in conventional CIM accelerators. Nevertheless, the distinctive scaled dot-product attention computation process in transformers poses challenges for directly applying thesetechniques. Additionally, effective management of intermediate results in matrix-matrix multiplication (MatMul) remains a significant challenge for CIM accelerators [11; 12; 13].  
To address these challenges, we introduce InMemQK, an NVM-based software-hardware co-design module aimed at accelerating MatMul computations. From a software perspective, InMemQK utilizes product quantization (PQ) techniques to avoid writing intermediate results. We clustered intermediate computational results into codebooks to eliminate data dependencies and propose an optimized PQ quantization method based on reconstructed feature map loss to suppress accuracy degradation. In terms of hardware, we designed an attention score computation module using time-domain MAC macros. Compared with the voltage-domain interface, the peripheral circuit of the proposed method is very compact, which is crucial to achieving high energy efficiency and speed at the system level. Experiments show that the PQ-based InMemQK reduces power consumption by avoiding delays associated with writing intermediate results to the NVM array, thereby enhancing overall efficiency.
# Multi-Tag Fusion Localization Method Based on Geometric Constraints
###### Abstract  
In environments where Global Navigation Satellite System (GNSS) signals are unavailable, our proposed multi-tag fusion localization method offers a robust solution for the precise positioning of vehicles or robots. During our research, we observed variations in the positioning information estimated from tags located at different positions within the same frame. Our goal was to extract reliable positioning information from this noisy data. By constructing geometric constraints, our method introduces an outlier factor to quantify the differences between tags. After effectively eliminating outliers, we enhanced the Kalman filter framework to accommodate the fusion of data from two or more tags, with the outlier factor dynamically adjusting the observation noise during the fusion process. The experimental results demonstrate that, even under the influence of motion and obstacles, our method maintains position errors within a 3 cm range and orientation errors within 3\({}^{\circ}\). This indicates that our method possesses high positioning accuracy and stability.  
localization; visual tag; data fusion; geometric constraints; AprilTag 2024202414540. [[https://doi.org/10.3390/app14135480](https://doi.org/10.3390/app14135480)]([https://doi.org/10.3390/app14135480](https://doi.org/10.3390/app14135480))
###### Abstract  
Vehicular communication is a promising technology that has been announced as a main use-case of the fifth-generation cellular system (5G). Vehicle-to-everything (V2X) is the vehicular communication paradigm that enables the communications and interactions between vehicles and other network entities, e.g., road-side units (RSUs). This promising technology faces many challenges related to reliability, availability and security of the exchanged data. To this end, this work aims to solve the scientific problem of building a vehicular network architecture for reliable delivery of correct and uncompromised data within the V2X concept to improve the safety of road users, using blockchain technology and mobile edge computing (MEC). The proposed work provides a formalized mathematical model of the system, taking into account the interconnection of objects and V2X information channels and an energy-efficient offloading algorithm to manage traffic offloading to the MEC server. The main applications of the blockchain and MEC technology in the developed system are discussed. Furthermore, the developed system, with the introduced sub-systems and algorithms, was evaluated over a reliable environment, for different simulation scenarios, and the obtained results are discussed.  
e 20221 11 / 173. [[https://doi.org/10.3390/electronics11020173](https://doi.org/10.3390/electronics11020173)]([https://doi.org/10.3390/electronics11020173](https://doi.org/10.3390/electronics11020173))  
1  
Vehicular communications; vehicle-to-everything; edge computing; blockchain
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 4 Image Encryption and Decryption  
Based on the above results on the chaotic responses and non-linearity exhibited by the discrete fractional map, we employ the DFSCM (8) to generate a chaotic sequence to perform image encryption. This section presents the application of the proposed encryption of images with a discrete time fractional sine map. To ensure the high level of security of the information transmission through images, the concept behind blockchain technology is adapted to ensure the decryption of the image by the authentic receiver. A schematic representation of the image encryption algorithm is presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(b\)**} & \multirow{2}{*}{**Tolerance (r)**} & \multicolumn{2}{c}{\(\varpi=0.5\)} & \multicolumn{2}{c}{\(\varpi=0.65\)} & \multicolumn{2}{c}{\(\varpi=0.8\)} \\ \cline{3-8}  & & \(M=1500\) & \(M=4500\) & \(M=1500\) & \(M=4500\) & \(M=1500\) & \(M=4500\) \\ \hline \multirow{8}{*}{12} & \(r=0.2\) & 0.3720 & 0.3303 & 0.7683 & 0.6081 & 1.2507 & 0.9844 \\  & \(r=0.3\) & 0.7107 & 0.6485 & 1.1946 & 1.0224 & 1.4293 & 1.3437 \\  & \(r=0.5\) & 1.2702 & 1.2302 & 1.5761 & 1.5362 & 1.2964 & 1.4599 \\  & \(r=0.7\) & 1.6311 & 1.6054 & 1.5980 & 1.6885 & 1.0919 & 1.3150 \\  & \(r=0.9\) & 1.8136 & 1.8045 & 1.5009 & 1.6473 & 0.9106 & 1.1575 \\ \hline \multirow{8}{*}{16} & \(r=0.2\) & 0.3492 & 0.3529 & 0.6176 & 0.5384 & 0.9864 & 0.8880 \\  & \(r=0.3\) & 0.6694 & 0.6804 & 1.0379 & 0.9209 & 1.3353 & 1.2855 \\ \cline{1-1}  & \(r=0.5\) & 1.2414 & 1.2545 & 1.5230 & 1.4719 & 1.4534 & 1.5390 \\ \cline{1-1}  & \(r=0.7\) & 1.6152 & 1.6117 & 1.6474 & 1.6830 & 1.3228 & 1.4595 \\ \cline{1-1}  & \(r=0.9\) & 1.8104 & 1.8104 & 1.6017 & 1.7159 & 1.1599 & 1.3328 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Approximate entropy of (8).  
Figure 4: Sensitivity of (8) with \(\varpi=0.5\), \(b=12\), \(c=50\) with initial states \((0.01,0.01)\) (red) and \((0.01001,0.01001)\) (blue).  
Figure 5: 2D plot of approximate entropy calculated for varying tolerance and fractional order with fixed \(c=50\); (**a**) \(b=12\), \(M=1500\); (**b**) \(b=12\), \(M=4500\); (**c**) \(b=16\), \(M=1500\); (**d**) \(b=16\), \(M=4500\).
## 2 Materials and Methods
### Loading Scheme and Sensor Arrangement  
The model box has dimensions of 1.35 m (length) \(\times\) 1.2 m (width) \(\times\) 0.9 m (height). Given that the distance between the model pile and the border of the model box is more than 10 d (where d is the pile diameter), the boundary criteria are satisfied and the boundary effect may be disregarded.  
To obtain the main parameters reflecting the accumulated horizontal cyclic deformation of the skirt-pile, the physical quantities to be measured in this test are the pile bending moment, the horizontal displacement, the turning angle of the pile, and the output load of the loading apparatus. In this regard, different monitoring sensors were used, including 7 pairs of strain gauges, 2 displacement gauges, and 1 tensile pressure sensor. Figure 4 shows a schematic diagram of the sensors used in the test.  
Figure 3: Schematic diagram of the model pile: (a) skirt–pile I; (b) skirt–pile II; (c) skirt–pile III (unit: mm).  
Figure 2: Schematic diagram after completion of model soil filling.  
In the test, the sensors were arranged according to the following design: For measuring the internal force and pile deformation, 7 pairs of strain gauges were set symmetrically along the pile in the loading direction. To monitor the model force-deformation response in real time, one displacement meter (J1) was placed at the loading point's height, and another displacement meter (J2) was placed at the top of the pile to measure horizontal displacement and determine the turning angle by comparing the displacements at the two locations. At the loading site, a single tensile pressure sensor (B1) was attached to output the load applied by the loading system in real time. The detailed loading scheme is shown in Table 2.
# Prediction of a Two-Transistor Vertical QNOT Gate
## 1 Introduction  
The current electronics era was made possible by digital systems that maximize their simplicity in converting, processing, and memorizing a wide range of data into only two available electrical signal levels (\"0\" and \"1\"). However, such a standard binary logic architecture seems to hardly meet the increasing requirements for larger information capacities, which laid out the foundation of multi-valued logic (MVL) systems with \(n\geq 3\), where \(n\) is the number of distinguishable data levels [1, 2, 3]. To date, the realization of MVL has no fully established protocols; thus, various strategies are being pursued at the materials, device, and system levels [4, 5, 6]. For instance, [PERSON] et al., in 2018, proposed a ternary (\(n=3\)) inverter based on a lateral heterojunction of organic semiconductors [7, 8]. In 2019, [PERSON] et al. proposed a full-swing ternary circuit using a negative transconductance heterojunction transistor with a modified structure [9]. Such reports suggested that, up to \(n=3\), a two-transistor topology can be effectively maintained (one unipolar and one anti-ambipolar). However, for a higher \(n\), previous research mostly adopted circuit-based implementations with the number of transistors roughly corresponding to the number of data levels [10, 11], implying the huge technological interest in advanced designs that reduce the transistor count.  
In this study, we demonstrate an aggressive projection of quaternary (\(n=4\)) digital inverter (or QNOT gate) made of only two field-effect transistors (FETs), by carrying out physically based finite-element simulation. In addition to saving the number of transistors, our proposed logic gate circuit is built by vertical (or three dimensional, 3-D) stacking of its component transistors, also showing the high potential for area-efficient on-chip deployment. In short, this particular geometry combines the two important trending design concepts in current electronics research, namely MVL and 3-D integration, in enabling a greatly simplified QNOT gate with a more efficient use of raw materials, manufacturing resources, and physical support areas. Importantly, the systematical variation of simulation parameters provides an in-depth understanding of how such an operation is obtained and also offers practical engineering guidelines toward targeted performances, which are intended to motivate wide-ranging experimental follow-ups.