esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation and Evaluation
### Recommendation Testing  
In the recommendation score testing, we used the same dataset that we have used for quality assessment, examining recommendation scores under various noise levels while keeping similarity scores and publishing intervals fixed. Suppose a subscriber requests data from sensor_4, which was not in the smart broker at the time. Since sensor_4 correlates closely with sensor_1 data (correlation coefficient of 0.64), it will appear on the recommendation list in the smart broker for sensor_4. Figure 11 shows the recommendation score of sensor_1 varying under different noise levels, with Sensor_1 having the highest recommendation score in \(\Gamma^{\prime\prime\prime}\).  
Figure 10: Reputation score of the topic varying with levels of noise and delay in the data.  
Figure 9: Time taken by the smart broker to detect drop in \(q_{\tau}\) below 0.7, 0.6, and 0.5, as the level of noise increases over time (\(\delta=15\) s, and there is no drift).  
The rest of the data streams will follow the same pattern but at lower levels than sensor_1. We consider the three possibilities of a similarity score of 0.9, 0.95, and 0.99, and most of the tags associated are the same. We can see that with increasing noise from the potential substitute itself (in this case, sensor_1), the recommendation score of the recommended substitute decreases.
# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 4 Results and Discussion
### Performance Analysis on Benchmark Functions
#### 4.1.1 Benchmark Functions  
The algorithm's accuracy was tested using unimodal functions (F1 to F7). As for multimodal functions, they contain multiple local minimum points, which serve as a measure of the algorithm's exploration capacity. This capacity pertains to its ability to move seamlessly from local to global minima without getting stuck in a single position. Table 4 shows the F8 to F13 multimodal benchmark functions that have many local minima. The F14 to F21 functions also have multiple local minima with fixed dimensions. These functions help to evaluate the stability of optimization algorithms. It is necessary to mentionthat the first 13 benchmark functions have higher dimension values of 30, 100, 500, and 1000 to evaluate the proposed HHAOA.  
The remaining functions, from F22 to F30, are hybrid functions as detailed in Table 5. While working with unimodal functions, finding the optimal solutions was comparatively effortless as they were conveniently located and easily reachable from the current location. The likelihood of being trapped in a particular search area is minimal. Finding the optimal position and movement between hybrid functions F19 and F33 is very difficult because there are many local minimum values to consider in the search space. Figure 6 displays the surface plots for all functions, providing a clear visualization of the search space needed to identify the global minimum for both single and multi-minima functions. Functions can be classified into different categories based on their surface area and shape. These categories include bowl-shaped, plate-shaped, and valley-shaped functions. There are also functions with single or multiple local minima. The latter type of function has a broad search space, wide range, numerous layers, and multi-dimensional features due to its multiple local minima values.
## 3 Results and Discussion  
### Effect of Soil pH on Soil Fertility  
Plants need a suitable environment for normal growth, and the nutrient status of the soil is one of the most important factors. Soil nutrient levels directly affect the yield or quality of plants [27]. In this study, the chemical indexes of the rhizosphere soil of tea trees planted in soils with different pH levels were analyzed. The results showed (Table 1) that the contents of the soil CEC and OM, AN, AP, and AK increased significantly with an increasing soil pH (3.29-5.32), as shown by increases from 7.26 to 22.48 cmoL/kg, 8.32 to 17.96 g/kg, from 35.23 to 91.38 mg/kg, from 3.13 to 15.12 mg/kg and from 49.25 to 124.95 mg/kg, respectively, while the soil contents TN, TP and TK did not change significantly. The soil chemical indexes are closely related to the soil fertility grade and can be used to analyze the fertility status of the soil [28]. According to the grade evaluation standard of the environmental fertility of tea planting soil (Table S2), the results of this study found that when the soil pH was 5.32 and reached grade I, all the chemical indexes measured in the soil reached grade I except for the TP and TN contents, which were grade II; when the soil pH was 4.74 and reached grade II, the contents of TN, TP and AP in the soil reached grade I, and the other indexes were grade II; when the soil pH was 4.13 or 3.29 and was grade III, the contents of TN and TP in soil still reached grade I, the TK content was grade II and the other indexes were grade III.  
The soil nutrient content and availability are reflections of the soil's fertilizer supply capacity, especially the available nutrients, and affect the nutrient uptake capacity of plants [29]. The pH of soil has little effect on the change in the total nutrient content in the soil, but it has a great effect on the content of available nutrients, and a high content of available nutrients is conducive to the growth of plants and improves their yield [19]. In addition, it has been reported that an increase in soil pH, most importantly, enhances the multiplication of nutrient-cycling-related microorganisms and enzyme activities in the soil, thereby promoting nutrient cycling in the soil and increasing the available nutrient content in the soil which, in turn, promotes the growth of tea trees [30; 31]. It can be seen that with an increase in the soil pH, the contents of TN, TP and TK in the tea rhizosphere soil did not change significantly, but the soil CEC and the contents of OM, AN, AP and AK increased significantly with higher levels of soil fertility.
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 4 Results and Discussion
### Discussion  
Several experiments were conducted in this study to evaluate the performance of the proposed SCSAEN method. In the experimental setup, we employed four public datasets, which are widely recognized in the field of object counting. These datasets encompass a variety of scenes and object types, providing a comprehensive testing environment for SCSAEN.  
In the comparative experiments, SCSAEN was evaluated against several state-of-the-art models, including FamNet, FCN, VLCounter, CSRNet, and SAFECount. The results indicate that SCSAEN exhibits a superior counting performance relative to the other models. Moreover, ablation studies were carried out on three datasets to validate that both the SSA module and the CAE module play significant roles in enhancing the overall counting performance of the network. Finally, the results comparing SCSAEN with the SAFECount algorithm were visualized, further demonstrating the effectiveness of SCSAEN, especially in scenarios with diverse object angles.  
These experiments clearly illustrate the superiority of SCSAEN in terms of counting accuracy, suggesting that the proposed method can more accurately estimate the number of objects in images under conditions of significant variation in object angles--an ability that is crucial for applications such as traffic monitoring, crowd counting, and resource management. Nonetheless, the proposed method has certain limitations, primarily because the experimental outcomes are dependent on the datasets used. Although four public datasets were employed in the experiments, the performance of the proposed method may still be influenced by the inherent characteristics of these datasets. For instance, if the datasets do not encompass all possible object angles, our method may not generalize well to other unseen datasets. To enhance the generalization capability of our approach, future research could focus on utilizing larger and more diverse datasets, which would enable the method to better adapt to different scenes and object types.
## 1 Introduction  
Thermal poling, a technique to induce second-order nonlinear effects and linear electro-optic effects in the center-symmetric fused silica fiber [1], allows the realization of all-fiber electro-optic modulators, all-fiber optical-to-frequency converters [2], switching [3] and polarization-entangled photon pair generation [4]. Poling temperature and poling voltage are important influence factors of second-order nonlinearity induced in the fiber core [5]. Besides, the poling duration also affects the value of the \(\chi_{\text{eff}}^{(2)}\)[6].  
Double anodes were addressed by [PERSON] et al. [7], who demonstrated that it is possible to avoid electrical breakdown and improve the stability and strength of \(\chi_{\text{eff}}^{(2)}\) induced by thermal poling. To further understand the physical process based on the double anodes model and optimize its poling parameters, [PERSON] studied the microscopic mechanism of ion movement in thermally poled double-hole fibers using COMSOL Multiphysics finite element analysis [8]. The impact of poling parameters (voltage, temperature, initial ionic concentrations, etc.) on the induced \(\chi_{\text{eff}}^{(2)}\) is also analyzed. Recently, a new type of thermal poling technology, electrostatic induction, developed by [PERSON] et al., lifts a number of restrictions on the use of complex microstructured optical fibers (MOF) for poling, as it is no longer necessary to individually contact internal electrodes [9].  
In this paper, a new type of double-hole fiber is designed, and the thermal poling process of this new double-hole fiber and circular double-hole fiber is analyzed by using COMSOL Multiphysics platform. This model is based on a previously proposed two-dimensional charge dynamics model [10]. In this model, Na+ ions which are initially uniformly distributed in the fiber and hydrogen-related species (H\({}_{3}\)O+) which are injected from electrodes are mainly considered. Different from the circular double-hole electrode structure, the new double-hole electrode structure proposed in this paper is a rectangle with a single rounded edge, and its single rounded edge causes a larger concentration of ions migrating toward the core. Based on this, a simulation model is established to calculate and analyze the migration process of ions inside the fiber and time evolution of the internal electric field on the core. The results show that the \(\chi^{(2)}_{\text{eff}}\) of the new double-hole poled fiber is higher than that of the circular double-hole fiber at a temperature of 285 \({}^{\circ}\)C and a voltage of 5 kV.
# Target Image Mask Correction Based on Skeleton Divergence
## 3 Results and Discussion  
In order to verify the efficacy of the proposed algorithm, RGB-depth (RGB-D) data (Technical University of Munich, Germany) were used to manually mark data set sequences, the RGB-depth (RGB-D) data (Technical University of Munich, Germany) is a video sequence containing depth information and RGB information for background motion recorded in an office environment using a handheld kinect. We manually labeled the moving target and background pixels of each frame in the image sequence. The computational equipment used in this study were as follows. CPU: Xeon (Xeon) E5 2.40 GHz six core, GPU: GTX1060 6G, memory: 1866 MHz 32 GB. The algorithms were programmed in C++ and then called on the MATLAB 2017b platform under the Windows 10 operating system.  
In the traditional motion compensation algorithm, due to the influence of texture loss and computation error accumulation in the target pixel region, the target region is excessively discrete and distorted. In order to achieve the correction of the target pixel region after the traditional motion compensation, the source images in this paper are discrete pixel regions generated after the same motion compensation [24] for moving targets, refer to [24], the square length of the selected sub-block is 7. Suppose that the internal pixel distribution of the moving target \(O_{1}\) at time t is \(S_{1}(t)\), and the moving target \(O_{1}\) is restored by motion compensation for the internal pixel distribution \(S_{1}(t+1)\) of the moving target \(O_{1}\) at time t + 1. The pixel distribution \(S_{1}^{\prime}(t)\), \(S_{1}^{\prime}(t)\), at the moment is the source image, and \(S_{1}^{\prime}(t)\) is mask corrected to obtain \(S_{1}^{\prime\prime}(t)\), and \(S_{1}(t)\) and \(S_{1}^{\prime\prime}(t)\) are compared to evaluate the effect of mask correction, in Section 3.1, Section 3.2, and Section 3.4, in order to give the reader an intuitive demonstration of the effect of mask correction on shape contours and demonstrate the positive effects of the structure of each part of the paper, this paper used a self-contrast approach. In Sections 3.3 and 3.5, comparing \(S_{1}(t)\) and \(S_{1}^{\prime\prime}(t)\), the mask correction effect is demonstrated by a large number of quantitative experiments. The number of image pairs used in this study is 1357, which is approximately 1.57 G, and the image resolution is \(640\times 480\), PNG format. The size of the images shown in the experimental results in this article is 380 dpi \(\times 382\) dpi.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 3 Deep Learning Techniques
### Enabling ML in the Mobile Edge
#### 3.3.2 Distributed ML Models  
DNN is a widely adopted ML technique, but the full burden of training a DNN model is too intensive for a single resource-constrained device at the mobile edge. Distributed ML models are well-adapted to mobile edge computing because the work is distributed across many computing centers in the network (cloud, base stations, edge nodes, end devices) [43] to collectively train the DL model by giving them each a small portion of the work to perform and then combining the results [44; 45]. Sub-tasks for the training can be allocated based on the edge device's resource constraints and distributed work stealing models that prioritize load balancing in inference tasks [46].  
Within mobile edge computing, distributed learning aims to use multiple smaller edge devices rather than one large data center. Distributed DNNs are composed of both local and global parameter adjustments during the learning processes combined with global aggregation steps in the cloud to achieve a single well-trained model [43; 47]. Optimization of the aggregation step can include methods, such as federated drop out, prioritized local updates, fast convergence, and compression [43; 44] while local learning can be optimized using efficient ML models as described in the previous section.  
However, distributed learning can introduce new challenges. In [48], the authors found that the latency and cost of sharing the learned gradients between devices constituted a bottleneck during the training processes. To overcome the communication bottleneck, gradients used during the back-propagation process of model training were compressed to reduce bandwidth requirements and redundancy. Additional efforts to selectively share only the important gradients in the training process have reduced communication costs with minimal impact on accuracy [49] and help reduce core network traffic and memory footprint on resource-constrained devices.
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation
### Statement of Main Interfaces  
M-Emu is still under development. The main interfaces that we currently open include:  
* DR and cRP deployment function. The location of the DR determines the distribution of data sources and receivers in the network. The number and position of cRPs determine the position of multicast root nodes [1; 2; 3].
* RP-selection algorithm. This algorithm determines how the source-side DR selects the RP from the cRPs set [16; 17; 18; 19; 20; 33].
* GP-selection algorithm. The algorithm determines how the receiver-side DR selects a node from the multicast tree to join [21; 22; 23].
* State reduction algorithm. The algorithm determines which nodes on the multicast path can create MFT entries [24; 34; 35; 36].
* Statistical tools. These are used to calculate the network status and evaluate the advantages and disadvantages of each algorithm.  
We elaborate on how these interfaces are used in Section 4, using the deployment of PIM-SM as an example.  
Figure 3: The packet information format.
## 4 Discussion  
The density was different for hollowed and non-hollowed briquettes. Smaller diameter hollow briquettes (T1) compressed better than larger diameter ones (T2), as the same pressure of press was exerted on a smaller surface area [54]. The standard deviation  
Figure 8: The influence of the ash content on the calorific value.  
Figure 9: Content of ash, volatile content, and fixed carbon for wheat straw.  
was lower for briquettes with a smaller diameter T1 (with a value of 18.24 kg/m\({}^{3}\)), even though the effective density value of 1237 kg/m\({}^{3}\) was the highest. Briquettes with T3 hollows had a slightly higher density than those without T2 voids, for the same reason as the T1-T2 comparison, namely due to the reduced pressure application surface on the ground straws [55; 56; 57; 58].  
With a view to a comparative analysis, the data on the straw characteristics are given in Table 3, next to similar characteristics of European beech biomass (_Fagus syluttica_ L.).  
In terms of the general values of the properties of wheat straw briquettes, the experimental briquettes obtained from wheat straw were made with powerful installations, thus obtaining a high density. Due to the high densities (1.1-1.2 g/cm\({}^{3}\)), the other mechanical properties (compression and splitting) also had high values (over 1.6 N/mm\({}^{2}\)).  
The three types of wheat straw briquettes had similar characteristics, such as a higher ash content (8.8-9.3%) and good calorific value (17.4-17.8 MJ/kg). The high ash content was due to the large amounts of secondary compounds, such as oxalates and carbonates, at 6-8% compared to 1-3% for solid wood [43; 59]. This is why a comparison was needed with briquettes made from hardwood residues (beech biomass). This also emerged from the fact that the limiting values of the properties according to the ONORM standard were expressed for the entire range of briquettes, especially for those based on wood, which was the most common on the world market. For this, research was carried out for a comparison with briquettes made from beech chips (_Fagus syluttica_ L.).  
An increase of 1% of ash led to a decrease of 0.2 MJ/kg of the calorific value since the ash does not contribute substantially to the heat developed by combustion, although some mineral elements in the ash can act as catalysts for the thermal decomposition of the fuel. Additionally, an increase in the ash content led to a proportional decrease in the carbon content, the latter being the main chemical compound that determines the increase in the calorific value of wood.  
Compared to other woody biomass such as pine [10], the volatile content of straw was much lower, by about 17%, and the fixed carbon content of wheat straw was 30.4% lower. Compared to beech wood, which has a volatile content of 66.3% and fixed carbon of 32.8%, wheat straw had higher values of volatiles by 3.2% and lower values of fixed carbon by 28.5%.
# Person Re-Identification Based on Attention Mechanism and Context Information Fusion
## 2 Methods
### Multi-Scale Information Fusion Module  
In the process of person ReID, we not only need to extract the high-level features of the pedestrian, but also need to extract the small-scale context information of the pedestrian, such as hats, sunglasses, etc. The deep neural network (DNN) is used to extract the representation of pedestrian features. With the increase of network depth, although high-level features of pedestrians can be obtained, these small features will be lost as the network continuously uses pooling operation to conduct down-sampling of features. Considering this problem, this paper proposes an effective solution that uses convolutions with different dilation ratios to sample shallow feature maps and concatenates them to high-level feature maps, so that we can obtain feature representations containing multi-scale context information of the image. The problem of small-scale information loss due to pooling operations is solved.  
The dilated convolutions [27] operation can expand the size of the ordinary convolution kernel according to the dilation ratio. The relationship between the size of the dilated convolution kernel and the size of the original convolution kernel is shown in Formula (4):  
\[Filter_{2}=d\times(Filter_{1}-1)+1, \tag{4}\]  
Figure 3: The details of the improved ResNet50 using ECA module.  
where \(Filter_{2}\) represents the size of the convolution kernel after dilation, \(d\) represents the dilation ratio, and \(Filter_{1}\) represents the size of the original convolution kernel. It can be seen from Formula (4) that we can use the original convolution of the same size and use different dilation ratios to obtain dilated convolutions of different sizes.  
Figure 4 is a schematic diagram of dilated convolution with a core size of 3 \(\times\) 3 and dilation ratios of 1, 2, and 3 respectively. Convolution with dilation ratio of 1, 2, and 3 and kernel size of 3 \(\times\) 3 is adopted instead of convolution with kernel size of 3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7, respectively, because the latter will generate more redundant information and increase computation.  
Inspired by the literature [14], we design a multi-scale information fusion module to extract the context information of pedestrians. As shown in Figure 5, the backbone network extracts shallow features and outputs a feature map with a size of 256 \(\times\) 64 \(\times\) 32. We use 3 \(\times\) 3 convolutions with the dilation ratios of 1, 2, 3, 4 respectively to sample the feature maps, and then concatenate them together. In order to concatenate the feature map after processing to the high-level feature map output by the backbone network, this paper uses 1 \(\times\) 1 convolution to adjust the size of the feature map to 1024 \(\times\) 16 \(\times\) 8.  
Figure 4: Diagram of dilated convolution. The size of the convolution kernel is 3 \(\times\) 3 and the dilation ratio is 1, 2, and 3, respectively. (**a**) Convolution with dilation ratio of 1; (**b**) Convolution with dilation ratio of 2; (**c**) Convolution with dilation ratio of 3.