esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### X-ray Tomography Acquisitions  
X-ray tomography acquisitions were conducted at the PSICHE beamline of the Synchrotron SOLEIL [36]. Monochromatic beam (25 keV) was used in order to optimize contrast, and thus reduce the required X-ray dose, limiting beam damage to the grains (especially drying and collapse of living cells if they die). [PERSON]'s phase retrieval algorithm was applied to the projections to enhance contrast [37]. The sets of 1000 projection images were transformed to volumetric images using filtered back projections [38].  
Two different settings were used: the low-resolution cut settings allowed the coverage of the entire grains while the high-resolution settings allowed the observation of specific regions of the grain with an increased level of detail.  
At \"low resolution\" settings, up to five grains of the same developmental stage were placed within the same tube. Using these settings, the time of acquisition was only about 1 min per grain. For each stage, at least 4 grains were imaged. The propagation distance between sample and detector for phase contrast was 70 mm. The size of the reconstructed images ranged from around 1000 \(\times\) 900 \(\times\) 1200 voxels to around 1700 \(\times\) 1700 \(\times\) 2800 voxels, depending on the size of the grain. The voxel size of reconstructed images was equal to 2.86 um.  
With the \"high resolution\" settings, three z-positions (middle, basal and upper) in three targeted regions of interest (crease or ventral groove, dorsal part, and lobes) were acquired for each grain. The propagation distance was set to 22 mm. Using these settings, grains were imaged one by one and carefully positioned in the middle of the tube. The resulting images had a size of 2048 \(\times\) 2048 \(\times\) 2048, with a voxel size equal to 0.64 um. In this case, a complete tomogram required 10 min.
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 2 Materials and Methods
### Antifungal Effectiveness of the Biogenic IONPs against the Tested Fungal Strain  
The antifungal efficacy of the biogenic IONPs was assessed using the food poisoning method against the tested fungal pathogens under investigation. Different concentrations of the biogenic IONPs (50, 100, 200, 400, and 800 ppm) were introduced into the PDA medium after sterilization. Following this, the plates were inoculated with a 6 mm disc containing fungal growth and placed in an incubator set at a temperature of 25 \(\pm\) 2 \({}^{\circ}\)C for a period of 7 days. On the other hand, control PDA plates were employed, wherein only a 6 mm disc of fungal growth was inoculated. The diameter of the pathogenic fungal strain was measured using a Vernier caliper. The calculation of the growth inhibition ratio (%) was performed utilizing the following formula: % inhibition = (A \(-\) B)/A \(\times\) 100. In this equation, A represents the growth diameter observed in the control plates, while B represents the growth diameter observed in the treated plates.
## 2 Materials and Methods
### Data
#### 2.2.2 The Environmental Data  
In this study, we used environmental data from the National Earth System Science Data Center (NESSDC). The data included the monthly maximum temperature (Tmax), precipitation (Pre), potential evapotranspiration (PET), relative humidity (RH), and soil moisture (SM). For Tmax, we used the dataset constructed by [PERSON] et al. [41] using machine learning methods. This dataset, based on meteorological station observations and considering terrain and geographical factors, covered the period from 1954 to 2020 with a spatial resolution of 1 km. Compared to commonly used temperature datasets such as Terra Climate, ERA5, and FLDAS (Famine Early Warning Systems Network Land Data Assimilation System), this dataset showed significant advantages in terms of accuracy and spatial detail. For Pre, we used the dataset developed by [PERSON] et al. [42]. This study generated monthly Pre data at 1 km resolution for China from 1901 to 2022, using the Delta spatial downscaling scheme based on global 0.5\({}^{\circ}\) climate data from CRU (Climatic Research  
Figure 1: Study area: (**a**) geographical location of Hainan Island; (**b**) elevation; (**c**) distribution of rubber plantations.  
Unit) and high-resolution climate data from World Clim. For PET, we employed the dataset from [PERSON] et al. [43], which provided monthly PET data at 1 km resolution for China from 1901 to 2022, calculated using the Hargreaves formula [44], which estimates PET based on monthly mean, maximum, and minimum temperatures. For RH, we utilized the dataset created by [PERSON] et al. [45; 46]. They produced monthly RH data at 1 km resolution for China from 2000 to 2020 through spatial downscaling of reanalysis climate data. For SM, we adopted the dataset from [PERSON] et al. [47]. They generated a daily SM dataset at 1 km resolution for 10 soil layers (10-100 cm depth, at 10 cm intervals) from 2000 to 2020. This dataset was produced using machine learning methods, with observations from 1648 stations provided by the China Meteorological Administration (CMA) as a benchmark. ERA5_Land meteorological forcing data, leaf area index, land cover type, terrain, and soil characteristics were used as covariates. In this study, we mainly focused on the surface layer (0-10 cm) of SM. We aggregated the daily data into monthly values to obtain monthly SM data.
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 5 Numerical Results
### Effects of Various Parameters on Ergodic Capacity
With the increase of \(\eta\), it is observed that the ergodic capacity of both protocol increase. This is because \(P_{R}\) increases with the increase of \(\eta\), which will enhance the transmit capabilities of the BC phase. And since \(P_{R}\) is provided by energy harvesting, the value of \(P_{R}\) is usually not very large. Thus the BC transmission ability dominates the value of ergodic capacity. Thus, with the increase of \(\eta\), the ergodic capacities increase. The value of \(\eta\) also effects the saturation rate of the DF protocol and the gap between the curves of AF protocol. This phenomenon is due to the value of \(\eta\) effects the value of distortion noises. The effects of the power splitting ratio \(\rho\) on ergodic capacities are evaluated for DF (left) and AF (right) protocols in Figure 5 under the parameters setting: \(\eta=0.8\), \((d_{1},d_{2})=(5,5)\) m, \(\kappa_{\rm{ave}}=0.1\), \(\rho=[0.1,0.5,0.9]\). The ergodic capacities of both protocols obtained by the closed-form expressions are in accordance with that obtained via Monte Carlo simulations (Simu). It can be noticed that the maximum ergodic capacities of both DF and AF protocols with \(\rho=0.9\) has best performance in low \(P_{t}\) region; while with the increase of \(P_{t}\), the maximum ergodic capacity change to the value with parameter setting \(\rho=0.5\) and further change to that with \(\rho=0.1\). This configuration verifies that the ergodic capacities can be enhanced through the design of \(\rho\), which validates the meaning of OPS design analyzed in Section 4. Figure 4: The effect of \(\eta\) on Ergodic Capacity for DF (left) and AF (right) Protocols. Figure 5: The effect of \(\rho\) on Ergodic Capacity for DF (left) and AF (right) Protocols.
# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
## 2 Mathematical Preliminaries  
### Non-Local Means  
NLMs [21] are a novel denoising technique that was recently proposed. The method makes full use of the redundant information in an image, and is able to maintain the amount of detail in an image. The NLM uses mean-square error (MSE) to calculate the similarity of two neighborhood blocks. The rows and columns of the neighborhood blocks are the same. Assuming neighborhood blocks all consist of m rows and n columns, the MSE can then be calculated as follows (Equation (1)), where \(A(i,j)\) is the pixel value of point \((i,j)\) in the neighborhood block centered on point \(A\) and \(B(i,j)\) is the pixel value of the point at the same position in the neighborhood block centered on point \(B\).  
\[MSE(A,B)=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}(A(i,j)-B(i,j))^{2}, \tag{1}\]  
Let the image with noise be \(v\) and the denoised image be \(\widetilde{u}\), the filter value of point \(A\)\(\widetilde{u}(A)\), namely the denoised image of \(A\), can be obtained by the weighted average of the pixel values of all points in the search window, as is shown in Equation (2). \(w(A,B)\) is the Gaussian weight of point \(A\) and any point \(B\) in the search window, which can be calculated by Equation (3), where the sum is calculated by Equation (4), and \(h\) is a smoothing parameter which is proportional to the standard deviation of the noise.  
\[\widetilde{u}(A)=\sum w(A,B)v(B), \tag{2}\]  
\[w(A,B)=\frac{1}{sum}e^{-\frac{MSE(A,B)}{h^{2}}} \tag{3}\]  
\[sum=\sum e^{-\frac{MSE(A,B)}{h^{2}}} \tag{4}\]
## 4 Assessment of Performance  
Simulations validate and support the effectiveness of the proposed algorithm. The simulation setup, measurements of performance, and simulation results are all covered in this section. The OMNeT++ platform is used to verify the results. Table 3 summarises all of the simulation variables. For the simulation, source and destination are chosen by the user. The initial energy allocation for each node is the same. We identify each node's neighbours based on the battery life and transmission range. We determine the shortest path by using the neighbour and distance. The mathematical model is used to compute the transmission energy. As a result, we determine the network's remaining energy after routing.  
The simulation's output shows that the initial deployment of node topology is taken into account by deploying nodes on a uniform path, and that each node's energy consumption and the network's overall energy consumption are taken into account and compared with a random placement pattern.
# Robotic System for Post Office Package Handling
## 2 Related Work
The trained model was able to generalize to basic (prismatic or cylindrical), typical (more complex geometry), and adversarial (with few available suction-grasp points) objects with success rates of 98%, 82%, and 58%, respectively. A more recent implementation of Dex-Net [23] (Dex-Net 4.0) containing five million synthetic depth images was used to train policies for a parallel-jaw and vacuum gripper, resulting in a 95% accuracy in gripping novel objects. In [24], a generative grasping convolutional neural network (GG-CNN) was proposed, where the quality and pose of grasps are predicted at every pixel, achieving an 83% grasp success rate on unseen objects with adversarial geometry. These methods are well suited for closed-loop control up to 50 Hz, enabling successful manipulation in dynamic environments where objects are moving. Furthermore, multiple grasp candidates are provided simultaneously, and the highest quality candidate ensures a relatively high accuracy in previously unknown environments. A significant challenge is grasping objects in a cluttered real-world scene. In such a setting, not only should the object to be grasped be analyzed, but also the surroundings for the robot should not collide with it. GraspNet [25] addresses this challenge and learns to generate diverse grasps covering the whole graspable surface. Gripper collisions are avoided by considering them during training and by predicting grasps directly without relying on accurate segmentation. Several object manipulation systems, applicable to post office automation, have been developed in the past. A system for package handling automation employing a robot arm with a vacuum gripper is described in [26]. Mailpiece singulation, i.e., removing items one by one from a moving conveyor, was described in [27]. They considered a bin-picking task, where a mix of objects with different shapes and weights should be grasped and removed, and designed a system comprising a range imaging camera, sparse data range imaging algorithm, grasp target selection algorithm, and a vacuum gripper. An implementation of a system for object manipulation was presented in [28] and showcased for two use cases: fish picking and postal parcel sorting. It employs model-free object localization based on Mask R-CNN for object detection and finding a grasp point for each segmented instance. A context-aware model for parcel sorting and delivery was proposed in [29]. The system includes the detection and localization of the parcel, recognizing the barcode and reading the delivery address, and performing robotic manipulation to transfer the parcel to the designated delivery box. The literature review reveals several promising strategies for addressing this challenge. However, it is deficient in essential implementation specifics and comprehensive architectural recommendations for real-world systems. This article presents a proposed robotic system designed for post office package handling. It provides a detailed account of the key system components and offers insights into implementation aspects in physical systems.
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 2 Materials and Methods
### Intra-Layer Knowledge Transfer  
As discussed in the previous section, the learned features \(\textbf{t}_{i}\) implicitly inherently encompass the knowledge acquired by SCSNet. To enhance the efficiency of the student network 1/n-SCSNet, a simple yet powerful Intra-KT module is designed, which enables the sequential transfer of hierarchical knowledge from SCSNet to 1/n-SCSNet. Specifically, \(\textbf{s}_{i}\) is responsible for learning the semantic patterns of \(\textbf{t}_{i}\) and optimizing the parameters of 1/n-SCSNet by enhancing their distributional similarity.  
Concretely, the Intra-KT module consists of two steps: channel reduction and knowledge transfer. Since the number of channels of features \(\textbf{s}_{i}\) and \(\textbf{t}_{i}\) is different, it is not suitable to directly calculate their similarity. To address this issue, a set of embedded features \(\textbf{E}=\{\textbf{e}_{1},\,\textbf{e}_{2},\cdots,\textbf{e}_{8}\}\) is generated by inputting each feature \(\textbf{s}_{i}\) into a \(1\times 1\) convolutional layer for channel reduction, which can be expressed as follows:  
\[\textbf{e}_{i}=\textbf{s}_{i}*\textbf{k}_{1\times 1}, \tag{3}\]  
where \(\textbf{k}_{1\times 1}\) denotes the to-be-learned parameters in the \(1\times 1\) convolutional layer. The output \(\textbf{e}_{i}\) is the embedded feature of \(\textbf{s}_{i}\), and it has the same channel number with \(\textbf{t}_{i}\).  
The second step entails a similarity calculation and knowledge transfer. Given that the Euclidean distance is excessively strict and may result in mere rote memorization, a relatively relaxed metric, cosine similarity, is employed to quantify the similarity between two features. In particular, the similarity between \(\textbf{t}_{i}\) and \(\textbf{e}_{i}\) at position \((x,y)\) is calculated as follows:  
\[\textbf{sim}_{i}(x,y)=\cos(\textbf{t}_{i}(x,y),\textbf{e}_{i}(x,y))=\sum_{c=1 }^{C}\frac{\textbf{t}_{i}(x,y,c)\cdot\textbf{e}(x,y,c)}{|\textbf{t}_{i}(x,y)| \cdot|\textbf{e}_{i}(x,y)|}, \tag{4}\]  
where \(C\) denotes the channel number \(\textbf{t}_{i}\), and \(\textbf{t}_{i}(x,y,c)\) indicates the response value at position \((x,y)\) of the \(c\)-th channel in \(\textbf{t}_{i}\). \(|\cdot|\) denotes the length of a vector. Therefore,  
\[\textbf{L}_{\text{intra}}=\sum_{x=1}^{H_{i}}\sum_{y=1}^{W_{i}}\textbf{1}- \textbf{sim}_{i}(x,y), \tag{5}\]  
where \(H_{i}\) and \(W_{i}\) denote the spatial size of \(\textbf{t}_{i}\). By minimizing this loss, the parameters in 1/n-SCSNet can be effectively optimized. In comparison to computationally expensive pair-wise distillation that can only be performed on low-resolution high-level features, our Intra-KT is more efficient to transfer implicit knowledge embedded in each layer.
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 4 Surface Area in Supercapacitors and Lithium-Ion Batteries  
Within the ever-evolving realm of energy storage technologies, graphene, a two-dimensional carbon allotrope, has emerged as a transformative material, harnessing its exceptional properties to revolutionize the performance of SCs and lithium-ion batteries. Because of its extraordinary surface area, graphene is becoming a major focus of research and development efforts as the need for more efficient and sustainable solutions for storing energy increases globally. This extraordinary material holds the promise of enabling energy storage systems with augmented capacities, swifter charge and discharge rates, extended service life, and superior overall efficiency. This scientific investigation embarks on a comprehensive exploration of graphene's surface area, delving into the fundamental principles, experimental findings, and practical applications that underpin its pivotal role in the relentless pursuit of enhanced energy storage capabilities within the framework of scientific inquiry and innovation. Some of the applications of its surface area in the field of supercapacitors and lithium-ion batteries are discussed here.
###### Abstract  
Industrial wireless applications often share the communication channel with other wireless technologies and communication protocols. This coexistence produces interferences and transmission errors which require appropriate mechanisms to manage retransmissions. Nevertheless, these mechanisms increase the network latency and overhead due to the retransmissions. Thus, the loss of data packets and the measures to handle them produce an undesirable drop in the QoS and hinder the overall robustness and energy efficiency of the network. Interference avoidance mechanisms, such as frequency hopping techniques, reduce the need for retransmissions due to interferences but they are often tailored to specific scenarios and are not easily adapted to other use cases. On the other hand, the total absence of interference avoidance mechanisms introduces a security risk because the communication channel may be intentionally attacked and interfered with to hinder or totally block it. In this paper we propose a method for supporting the design of communication solutions under dynamic channel interference conditions and we implement dynamic management policies for frequency hopping technique and channel selection at runtime. The method considers several standard frequency hopping techniques and quality metrics, and the quality and status of the available frequency channels to propose the best combined solution to minimize the side effects of interferences. A simulation tool has been developed and used in this work to validate the method.  
wireless sensor networks; robustness; coexistence mechanisms; interference avoidance; security; frequency hopping; channel characterization +
Footnote †: journal: Sensors
## 7 Conclusions  
In this paper, we introduce a novel adaptive low-cost INS/GNSS tightly-coupled integration architecture that can provide reliable navigation solutions within disturbed GNSS communication environments. The proposed architecture features an adaptive redundant measurement noise covariance estimation (RMNCE) approach, which is characterized by only employing the measurement system information. Different from traditional algorithms, this method avoids the effect of Kalman filter state vector estimation error. The RMNCE approach is applied to design a fast satellite selection algorithm and an adaptive UKF in our proposed system, which has increasingly improved system performance and accuracy. Both semi-physical simulation and field experiments have been carried out to demonstrate its overall better performance compared to the standard tightly-coupled schemes, RAE-based adaptive scheme, and CNR and satellite elevation-based adaptive tightly-coupled integration scheme. The experimental results lead us to conclude that: (1) the RMNCE approach can achieve a comprehensive better and robust measurement noise estimation results than the traditional noise estimation algorithms; (2) the RMNCE-based satellite selection algorithm takes  
Figure 19: Single point positioning errors of the different satellite selection algorithms during segments S1, S2, S3, and S4.  
Figure 20: 3D positioning errors of the different test schemes during segments S1, S2, S3, and S4.  
both measurement noise and GDOP into consideration to derive an optimal satellites combination, and hence can avoid the risk of unexpectedly introducing large measurement errors into the system; (3) the RMNCE-based adaptive UKF is advantageous in reducing the positioning errors when the GNSS measurements are contaminated; (4) the proposed architecture effectively limits positioning errors when the GNSS measurement quality is poor, and can provide 91.2% positioning reliability (2 m positioning error), which performs the best among all of the test schemes.  
This work is partially supported by the National Key Research and Development Program of China with a grant number as 2016 YFB0502004 and partially supported by the National Natural Science Foundation of China with a grant number as 61320106010.  
[PERSON] implemented the proposed architecture, conducted the experiments and wrote this paper. [PERSON] conceived the RMNCE based methods and designed the hardware platform. [PERSON] helped to implement the proposed system and provide much valuable revising advice on writing this paper. [PERSON] helped to perform the experiment and analyze the data.  
The authors declare no conflict of interest.