esa-sceva/satcom-chunk-collection huggingface dataset:
# A Brain-Inspired Goal-Oriented Robot Navigation System
## 2 Mathematical Model  
### System Structure  
Inspired by the SPAUN neural structure and the mechanism of neural navigation in animals' brains, a brain-like biologically plausible cognitive model was proposed and applied to robotic spatial navigation tasks. The model's architecture is shown in Figure 1.  
The model includes three layers: cerebral cortex, hippocampal region and basal ganglia. The basal ganglia determine and perform the most highly recommended behavior, in this case, leading the robot to the target. The cerebral cortex mainly handles the working memory and reward function. When new knowledge is learned, the memory capacity of ART can increase adaptively without destroying the previous knowledge of the network. The ART method can build environmental information dynamically and stably. In this paper we used the ART network to dynamically process information, helping to achieve rapid and stable navigation.  
Sitting between the cerebral cortex and basal ganglia is the hippocampal region, which includes the hippocampus and the EC. This plays a crucial role in spatial learning and navigation [32], where external information (HD and speed information) are converted to expressions of the internal environment. The hippocampal region is an area where grid cells, HD cells, and speed cells converge to transfer place information to the place cells.  
Initially, speed cells and HD cells are inputs to layer III of the EC, which handles the ring attractor pre-processing. Then, they go through layer II of the EC, where they obtain the grid cells' activities. The grid cells in EC II pass through the perforant pathway into the dentate gyrus (DG). Then, the granular cells in the DG are associated with the CA3 of the hippocampus by moss fibers. CA3 projects to the CA1 by Schaffer collaterals. The deep V/VI layer performs top-down feedback from the hippocampus to the EC to help stabilize memories.  
Figure 1: System diagram of navigation model based on Semantic Pointer Architecture Unified Network (SPAUN) and brain neural system. This framework deals with data from input signals to final action generation to achieve autonomous navigation. The model consists of three layers: hippocampal region, dealing with input signals (head direction and instantaneous speed signals) by path integration to expression position; cerebral cortex, creating and modifying the topological map of environmental information and passing the reward information to the decision-making layer; and basal ganglia, determining the final action by winner-take-all rule. Meaning of abbreviations: DG, dentate gyrus; CA3 and CA1, internal area of hippocampus; EC, entorhinal cortex; PPC, posterior parietal cortex; DLPFC, dorsolateral prefrontal cortex.
## 1 Introduction
It demonstrated that neglecting spatial variability will cause an underestimation of the tunnel convergence, especially when it is severely disturbed by nearby environments, such as the large surface surcharge. [PERSON] et al. [24] established a 3D analytical model of a shield tunnel to examine longitudinal deformation under surcharge, comparing these findings with experimental results. The results showed that the greater the ground surcharge, the greater the deformation of the tunnel. The further away from the ground surcharge, the smaller the deformation of the tunnel. [PERSON] et al. [25] combined hyperstatic reaction methods and numerical simulations to study horseshoe-shaped tunnels in saturated soft rocks, finding consistency between the two approaches. The results showed that ground surcharge has a significant impact on the lining normal forces when the tunnel buried depth is approximately four times its height. [PERSON] et al. [26] adopted adaptive finite element limit analysis to investigate single and double circular tunnels at varying depths within cohesive-frictional soils or rock masses. They developed a novel approach that utilizes tunnel stability numbers to assess the stability of these tunnels, providing both upper and lower bounds for these critical values. Previous research has primarily utilized the stratum structure method to analyze the deformation of shield tunnel linings under ground surcharge, effectively simulating soil-lining interactions. However, lining structures are often simplified by using stiffness reduction methods to represent the lining structure as a homogeneous annulus. Alternatively, linear springs or joint units are employed to simulate the behavior of segment joints,or the interaction between bolts and segments is simplified by embedding bolts within the concrete of the segments. These approaches fail to accurately reflect the true mechanical behavior of joints, and most numerical simulations overlook the role of reinforcement. Moreover, most research does not take into account the interaction between lining rings, which makes it challenging to accurately represent the mechanical behavior of shield tunnel linings assembled with staggered joints. Alternatively, the loading structure method offers a more streamlined approach by representing soil complexities with equivalent loading, enhancing computational efficiency. This study proposes leveraging this method to develop a sophisticated finite element model of shield tunnel linings. Our aim is to investigate in greater depth the deformation characteristics under ground surcharge, addressing the limitations of current methodologies and providing a more comprehensive understanding of structural responses in these critical urban infrastructures.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 3 Experimental Setup and HEM Algorithm
### Hardware
#### 3.1.2 Refrigerator Setup  
The working cycle of refrigerator or air conditioner refers to their operations based on which the refrigerant (in this case gas) heat exchange cycles change status, or cools down the medium. It starts at the compressor phase at which it pulls the warm refrigerant and increases its pressure and temperature. The refrigerant then travels to the condenser, where it goes through several fins with the use of a running fan that helps to release its heat to the outer environment, thus reducing its temperature. During this process, the refrigerant changes its state from the gaseous to a high-temperature liquid phase. The liquid is then passed to a valve that converts it into mist. This sudden drop of pressure results in a rapid cooling of the refrigerant, which is then passed to the evaporator coil located in front of a fan that circulates the chamber air, thus resulting in its cooling. Some heater are tuned on and the compressor is turned off during the deforest cycle to avoid ice formation and blocking the cool air flow in the chamber. This deforest cycles could last for 30 to 45 min. [26]. This clearly shows that the refrigerator could be used as a schedulable load if temperature is kept within the acceptable limits. In this study, the refrigerator was connected to another smart socket and a waterproof temperature sensor was placed inside it (Figure 9).  
The internal temperature information of the refrigerator and its power consumption were wirelessly transmitted to the main controller by Zigbee communication modules that were built into the condition monitoring circuitry and the smart plug. The refrigerator could also be turned on/off by remote commands that actuated the relay inside the smart socket. Note that the internal temperature is continuously monitored by monitoring circuit described in Section 2.2.4.
## 2 Methodology
### Experimental Design and Statistical Analysis  
Each participant performed seven tasks, including a paper list picking task and six AR device picking tasks (2 devices \(\times\) 3 order information displays). The sequence of the trials was randomly arranged for each participant so that the effects of learning and fatigue could be evenly distributed into each experimental condition. The experiment then shifted to a completely randomized block design experiment. Each participant was regarded as a block. Means and standard deviations were calculated for all the dependent variables. The time to accomplish the task and the number of incorrect boxes picked were used as indicators of task performance. An analysis of variance (ANOVA) was performed to analyze the effects of gender, the device adopted, and the order information display on the time to accomplish the task. The dependent variable may be described using the model in Equation (1) if the effects of the participant were ignored:  
\[y_{ijk}=\mu+\alpha_{i}+\beta_{j}+\gamma_{k}+(\alpha\beta)_{ij}+(\alpha\gamma)_ {ik}+(\beta\gamma)_{jk}+(\alpha\beta\gamma)_{ijk}+\epsilon_{ijk} \tag{1}\]  
where \(y_{ijk}\) and \(\epsilon_{ijk}\) are the dependent variable and random error in \(i\)th gender, \(j\)th AR device, and \(k\)th order information display conditions, respectively, and \(\epsilon_{ijk}\) is normally distributed with a mean value of 0 and a standard deviation of \(\epsilon\); \(\alpha_{i}\), \(\beta_{j}\), and \(\gamma_{k}\) are the effects of \(i\)th gender, \(j\)th device, and \(k\)th order information display, respectively; \((\alpha\beta)_{ij}\), \((\alpha\gamma)_{ik}\), and \((\beta\gamma)_{jk}\) are the two-way interaction effects of gender \(\times\) device, gender \(\times\) order information display, and device \(\times\) order information display; and \((\alpha\beta\gamma)_{ijk}\) is the three-way interaction effect of gender \(\times\) device \(\times\) order information display.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & **Order Information Display** & **Mean** & **STD** & **Range** \\ \hline Paper list & Paper list & 0.27 & 0.64 & 2 \\ \hline \multirow{3}{*}{M400} & QR code & 0 & 0 & 0 \\  & Order map & 0.90 & 1.67 & 4 \\ \cline{1-1}  & 3D graph & 0.17 & 0.53 & 2 \\ \hline \hline \end{tabular} Note: There was no incorrect box picking when the participants were wearing the HoloLens 2. All the medians for the order information displays were 0.  
\end{table}
Table 3: Number of incorrect boxes picked up.  
Figure 7: QR code information displayed in the M400 device.  
Student-Newman-Keuls (_SNK_) tests were performed for posterior comparisons if the main effect of a factor was significant. Non-parametric statistical methods, including the Wilcoxon rank-sum test and Kruskal-Wallis test, were adopted to compare the number of incorrect boxes picked and the ratings of the SSQ and CRS. Paper and paper list were included as one of the levels when analyzing the factors of device adopted and order information display, respectively. The statistical analyses were performed using the SPSS 20.0 software (IBM(r), Armonk, NY, USA).
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 5 Conclusions  
Associated multisource data for transmission line fault-cause diagnosis are divided and extracted as waveform and contextual features in this paper. MVL is introduced to appropriately combine these features for performance improvement. A novel hierarchical multiview feature selection method based on an \(\varepsilon\)-dragging technique and sparsity regularization is proposed to perform hierarchical feature selection with multiview data. The \(\varepsilon\)-dragging is applied in the loss function to enlarge sample distance between classes. \(l_{2,1}\)-norm and \(F\)-norm conduct row-wise and view-level selection, respectively, which can be viewed as the low-level and high-level feature selection. We also develop the optimization algorithm and prove its convergence theoretically. The proposed HMVFS is evaluated by comparisons on yield data. The results reveal that HMVFS outperforms conventional feature selection methods in single-view and early fusion strategies. The further experiments concerning ML classifiers also demonstrate the superiority and effectiveness of the proposed method with high generalization learners. This study has shown the combined use of waveform and contextual features with HMVFS can cause significant improvement for fault-cause identification. In future work, more multiview data and further fault signature study are needed to refine the feature pools, and the performance of HMVFS is expected to be further improved.  
Conceptualization, S.J. and X.P.; methodology, H.Y. and C.S.L.; software, S.J.; experiment, validation and analysis, S.J. and H.Y.; investigation, S.J. and X.P.; resources, X.P.; data curation, S.J.; writing--original draft preparation, S.J. and X.P.; writing--review and editing, S.J., H.Y., C.S.L. and L.L.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (61903091) and the Science and Technology Project of China Southern Power Grid Company Limited (031800 KK52180074).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Composition and Structure of CHT Films
#### 3.1.1 FTIR Spectroscopy
The second effect of PEGDE was to increase the absorption at 1370 cm\({}^{-1}\) suggesting an increase in CH\({}_{2}\) groups from PEGDE. [PERSON] et al. [31] analyzed the FTIR spectrum of a matrix of HA crosslinked with PEGDE. They indicated that the epoxy group of PEGDE can react with the groups -COOH (1035 cm\({}^{-1}\)) and -OH (3200-3600 cm\({}^{-1}\)) in HA to form ester and ether linkages after reacting with PEGDE. However, neither their study nor ours, representative bands of ester bond formation (1730 cm\({}^{-1}\)) were observed indicating that the ratio of ester bond formation is quite low. However, it was reported that the crosslinking of HNa and PEGDE is mainly based on the formation of ether bonds between the hydroxyl groups of AH and the epoxy groups of PEGDE suggesting the formation of an interpenetrated network [31]. Figure 1a,b also shows the infrared spectra of the CHT/HNa/GA crosslinked films. When this crosslinking agent was present it was expected that NH and OH groups to  
Figure 1: FTIR spectra of PEGDE crosslinked CHT films. HNa 15% (**a**) and HNa 30% (**b**). be reduced or broaden, limiting the formation of a complex. The hydroxyl groups of hyaluronic acid can be crosslinked with the aldehyde groups of glutaraldehyde through an acetalization reaction while imide formation is expected for the case of GA reacting with primary amines from CHT. In this regard, [PERSON] et al. (2016) found broad bands at 3262 cm\({}^{-1}\) and 3247 cm\({}^{-1}\), attributed to hydroxyl groups being this broadening related to their chemical reaction, which was proved also by the 1639 cm\({}^{-1}\) imide formation. In addition, a shift from the band at 1417 to 1405 cm\({}^{-1}\) and changes at 1375 cm\({}^{-1}\) (CH\({}_{2}\)) during GA crosslinking were also observed. In our study, samples crosslinked with GA, the band located at 1630 cm\({}^{-1}\) was observed with less intensity in comparison to the band at 1550 cm\({}^{-1}\).
## 3 Extending the Telco USN-Platform towards the Semantic Sensor Web
### Semantic Annotation of Smart Device Observations  
To extend the USN-Platform some way towards the Semantic Sensor Web paradigm we first chose an annotation scheme for the observations generated by smart devices. Developing OWL customized ontologies for OGC Observations can be very complex [26]. In the work presented here, however, we have limited the task to implementing a simple semantic representation of the OGC O&M language. The representation in question consists in the encoding of O&M in OWL, henceforth referred to as O&M-OWL. (The interested reader can find more details on the mapping from O&M to O&M-OWL in [14].) Figure 4 shows the main O&M-OWL concepts and relations, namely, _featureOfInterest_, _observedProperty_, _procedure_, _observationLocation_, _result_ and _samplingTime_. Figure 5 shows an example of a semantically annotated observation expressed as a set of RDF triples, corresponding to the electric-car scenario that will be described in Section 4.  
Figure 4: Adding semantic features to the Smart Device and Telco USN-Platform.  
After choosing an annotation scheme for smart device observations, two functional areas were extended in the USN-Platform (see Figure 4):  
1. USN-Gateway functions, to manage semantically annotated observations in two ways: either by supporting already annotated measurements or by automatically annotating the observations generated by smart devices.
2. The _InsertObservation_ operation provided by the USN-Enabler, to receive these semantically annotated observations, which are then stored in a Semantic Web repository, as we describe below.
## 2 Data and Methods
### Study Methods
#### 2.2.2 Howland Forest, Maine--Hyperion NDVI Comparison to MODIS NDVI  
Three Alfux tower sites (US-Ho1: 45.20\({}^{\circ}\)N, \(-\)68.74\({}^{\circ}\)W; US-Ho2: 45.21\({}^{\circ}\)N, \(-\)68.74\({}^{\circ}\)W; and US-Ho3: 45.21\({}^{\circ}\)N, \(-\)68.73\({}^{\circ}\)W) in the Howland Experimental Forest, Maine, were chosen for the analysis of Hyperion NDVI vs. MODIS NDVI, utilizing imagery collected between 2003 and 2014, with representation from all seasons. All three sites were mixed evergreen forest with a warm summer climate and significant precipitation in all seasons. The mean average temperatures are slightly over 5 \({}^{\circ}\)C and the elevation ranged from 60 to 90 m. The spatial resolution of nadir MODIS data is either 500 m or 1 km, while the spatial resolution of Hyperion data is 30 m. Thus, the reflectance values of Hyperion data have been aggregated spatially to match MODIS pixels at either 500 m or 1 km resolution. To see phenology variability using the EO-1/Hyperion reflectance data product, the Hyperion NDVI data were compared with NDVI derived from MODIS by differencing the spectral pixel values.  
The Hyperion Level 1 GST (L1 GST) product having radiometric and geometric corrections as well as a systematic terrain correction were downloaded and used for this analysis. The Hyperion LIGST images were atmospherically corrected using the Atmosphere Removal Algorithm (ATREM) [29; 30]. Hyperion NDVI data (using Equation (1)) were calculated from the surface reflectance using bands 27-32 and 49-54 of the L1 GST product. The NDVI data from the MODIS satellite instrument were calculated from surface reflectance in MODIS band 1 (620-670 nm) and band 2 (841-876 nm). MODIS Level 1B calibrated radiance data (MOD21 KM and MOD02 HKM) and geolocation data (MOD03) were downloaded from the NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) [31] website. A modified gridding approach was used in this study, where the flux tower was located in the center of related 500 m or 1 km grids [32]. MODIS L1B radiance data from each swath were then gridded at 500 m or 1 km resolution for MODIS bands 1-2 with appropriate area weights of each MODIS observation. MODIS data were processed by the modified gridding method and the gridded observations were atmospherically corrected by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm [33].
# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
###### Abstract  
With the development of deep learning, convolutional neural networks (CNNs) and Transformer-based methods have become key techniques for medical image classification tasks. However, many current neural network models have problems such as high complexity, a large number of parameters, and large model sizes; such models obtain higher classification accuracy at the expense of lightweight networks. Moreover, such larger-scale models pose a great challenge for practical clinical applications. Meanwhile, Transformer and multi-layer perceptron (MLP) methods have some shortcomings in terms of local modeling capability and high model complexity, and need to be used on larger datasets to show good performance. This makes it difficult to utilize these networks in clinical medicine. Based on this, we propose a lightweight and efficient pure CNN network for medical image classification (Eff-PCNet). On the one hand, we propose a multi-branch multi-scale CNN (M2C) module, which divides the feature map into four parallel branches along the channel dimensions by a certain scale factor and carries out a deep convolution operation using different scale convolution kernels, and this multi-branch multi-scale operation effectively replaces the large kernel convolution. This multi-branch multi-scale operation effectively replaces the large kernel convolution. It reduces the computational cost of the module while fusing the feature information between different channels and thus obtains richer feature information. Finally, the four feature maps are then spliced along the channel dimensions to fuse the multi-scale and multi-dimensional feature information. On the other hand, we introduce the structural reparameterization technique and propose the structural reparameterized CNN (Rep-C) module. Specifically, it utilizes multiple linear operators to generate different feature maps during the training process and fuses all the participants into one through parameter fusion to achieve fast inference while providing a more effective solution for feature reuse. A number of experimental results show that our Eff-PCNet performs better than current methods based on CNN, Transformer, and MLP in the classification of three publicly available medical image datasets. Among them, we achieve 87.4% Acc on the HAM10000 dataset, 91.06% Acc on the SkinCancer dataset, and 97.03% Acc on the Chest-Xray dataset. Meanwhile, our approach achieves a better trade-off between the number of parameters; computation; and other performance metrics as well.  
CNN; multi-branch; multi-scale; medical image classification +
Footnote †: journal: Medical Image Classification
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 3 Results
### Measurement of the ARC
#### 3.3.4 Linearity of the ARC Receiver  
Linearity is one the key features of the ARC receiver [24]. After manufacturing the ARC, the linearity was measured using the signal generator. The signal was input into the ARC, and the ARC receiver collected the signal. The linearity errors for the full dynamic range were acquired from the collected data, and they are plotted in Figure 14. It can be seen from Figure 14 that the linearity errors are mostly below 0.2 dB, which suggests very precise antenna pattern recognition, as the pattern is mathematically derived from the received power.  
Figure 13: The gain flatness: (a) L-band; (b) C-band; (c) X-band; (d) Ka-band.
# An Efficient Routing Protocol for Quantum Key Distribution Networks
## 2 The Proposed QOLSR Protocol
### Analysis of the Limitations of AODV, DSDV, and OLSR in QKD Networks
Therefore, OLSR can be optimized by adding the information of the remaining quantum secure keys in the HELLO packet to pre-sense the link state. In addition, parameters such as quantum secure key generation capabilities should be added to the routing metrics. Unlike AODV, the routing table of OLSR is constructed locally using topology awareness and does not require the routing discovery procedure. Based on the above analysis, OLSR is the most suitable of the three protocols for QKD networks. This is also confirmed by the simulation results in Section 3.1. Therefore, a more efficient routing protocol based on OLSR for QKD networks is investigated in this research.