esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results Analysis
### Experimental Results
#### 4.4.2 POI Group Recommendation Results
The results show that the proposed method with intragroup divergence is better than CF algorithm with least misery strategy and average strategy for multi-person groups. Figure 8 is a histogram of the precision and recall of the four comparison algorithms on Tokyo dataset. Seen from Figure 8a, with the number of recommended locations increasing, the precision value decreased. Especially, the precision value is about 0.1 when the length of recommendation location list is 5. Seen From Figure 8b, with the number of recommended location increasing, the recall value also increases. The recall value of ours obtains 0.051, which is much higher than CFL, CFA and CMFC when \(N=5\), respectively. It obtains a good quality of precision and recall, which illustrates that the technique is reliable. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{_Precision@N_} & \multicolumn{3}{c}{_Recall@N_} \\ \cline{2-7}  & **5** & **10** & **20** & **5** & **10** & **20** \\ \hline CFL & 0.0313 & 0.0247 & 0.0179 & 0.1564 & 0.2471 & 0.3574 \\ CFA & 0.0338 & 0.0283 & 0.0206 & 0.1691 & 0.2828 & 0.4124 \\ CMFC & 0.0406 & 0.0287 & 0.0212 & 0.2029 & 0.2870 & 0.4237 \\ CFID & 0.0833 & 0.0522 & 0.0309 & 0.4166 & 0.5218 & 0.6186 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative results for two-person group on Tokyo dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{_Precision@N_} & \multicolumn{3}{c}{_Recall@N_} \\ \cline{2-7}  & **5** & **10** & **20** & **5** & **10** & **20** \\ \hline CFL & 0.0567 & 0.0385 & 0.0255 & 0.2836 & 0.3855 & 0.5091 \\ CFA & 0.0727 & 0.0545 & 0.0349 & 0.3636 & 0.5455 & 0.6982 \\ CMFC & 0.0843 & 0.0581 & 0.0375 & 0.4218 & 0.5818 & 0.7491 \\ CFID & 0.1112 & 0.0691 & 0.0422 & 0.5562 & 0.6909 & 0.8436 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative results for multi-person group on New York City dataset.
## 3 An Architecture for Self-Organization
### Self-Optimization  
The self-optimization module is responsible for selecting the parameters of the protocols AODV and OLSR that maximize network performance, while considering that WMN varies with the number of clients. The self-optimization module accounts for:  
* the OLSR and AODV parameters that mostly influence network performance;
* the type of the parameters (integer, real or boolean); and
* the size of the network at the current time, which is obtained from the NS-Agent.  
The first step is to model network performance as a function of the routing protocol parameters. The analysis of simulation data elicited that the hyperbolic cotangent [29] captures the average influence of the parameters on network performance. Thus, the function modeling the influence of a given parameter was obtained by solving a linear least squares problem to fit the function below to simulation data:  
\[FO(x)=\left(\frac{e^{2x}+1}{e^{2x}-1}\right) \tag{1}\]  
The solution was in this case, the use of non-linear optimization without restrictions, so all the features of the problem fit themselves in this optimization sub-area.  
In the self-optimization module of the parameters we note that it should be found a minimum local that could replace the standard values of the protocols' parameters OLSR and AODV in the size scales proposed as: small, medium or large.  
\[Minimize\sum_{i=1}^{n}\left[FO(i)-y(i)\right] \tag{2}\]  
In Equation (3) it was used to minimize the objective function (1). Where \(y\) is the result of the throughput analysis for each proposed interval of time.
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Fruits and Vegetables
#### 5.2.1 Heavy Metals
 & MB & Langmuir & N.R & 288.34 mg/g & 67–76 & [48] \\ \hline \multirow{3}{*}{Pomegranate peel} & \multirow{3}{*}{Cu(II)} & Langmuir, & & & \\  & [PERSON], [PERSON] & Pseudo-second order & 30.12 mg/g & 78.85 & [103] \\  & & [PERSON] & & & & \\  & and Temkin & & & & & \\ \hline \multirow{3}{*}{Pomelo peel} & MB & Langmuir & Pseudo-second order & 133 mg/g & 83 & [1; 83; 96] \\  & RB-114 & Langmuir & Pseudo-second order & 16.3 mg/g & 89 & \\ \hline \begin{tabular}{c} Red dragon fruit \\ peels (ABR) \\ \end{tabular}  & \multirow{3}{*}{Iodide (\(\bar{l}^{-}\))} & \multirow{3}{*}{Freundlich} & Pseudo-second order & 68.6 mg/g & 68.4 & [79] \\ \cline{2-2} \cline{4-6} \cline{5-6} \  
of lead after chemical modification was mango peel waste, retaining 76.2% of this metal rapidly (30-60 min) with a q\({}_{\rm m}\) of 99.05 mg/g based on the Langmuir isotherm model [10]. Many fruit residues have been reported to have a certain affinity for other heavy metals that may represent a hazard for human beings. The ability of watermelon rinds, mango peel waste, and banana, kiwi, and tangerine peel residues to retain Cd(II) from water sources was studied; the respective removal efficiencies obtained were 80%, 90.5%, 95%, 78%, and 73% [3, 10, 60, 61, 94]. The Langmuir isotherm and pseudo-first and second-order kinetic models were best suited for describing Cd uptake by these biomaterials. [PERSON], et al. [73] reported similar adsorption capacities for cadmium and nickel (0.096 and 0.097 mmol/g respectively) using raw corn cob as biosorbent, while coconut waste presented a maximum Ni(II) adsorption of 15.95 mg/g at pH 5.3 [59]. Raw corn cob was studied by [PERSON] et al. [104] for the almost complete reduction of Mn(II) (99.8%) in industrial wastewater after 60 min at pH 5.0 and 40 \({}^{\circ}\)C. The maximum adsorption capacity was 6.24 mg/g following the pseudo-second order kinetics. This removal efficiency was the highest when compared to that obtained with watermelon rinds (98.9%) or beet pulp (86.3%) at an adsorbent concentration of 1 g/L and pH 6 [3, 88]. Coconut waste represented an adequate option for the removal of Co(II) from water, showing a maximum adsorption capacity of 12.82 mg/g at pH 4.3 [59]. The kinetic and equilibrium parameters were well-explained by the Langmuir model, which demonstrated the presence of stable chemical bonds between the molecules across the surface area. Recently, [PERSON] et al. [3] used watermelon rinds as an adsorbent material in water sources and reported removal efficiencies for Co(II) (57%), Tl(I) (98-98.5%), As(III) (99%), As(V) (98%), Ni(II) (69-70%), and Fe(II) (98.3%).
## 2 Soil-Borne Plant Pathogens and Microbiota Determine Disease Suppression
### Soil Microbiome Influences Disease Suppression
#### 3.2.2 Contribution of Fungal Community  
Although fungi and micro-eukaryotes are closely associated with the suppressiveness, being crucial for crop protection, most of them are often neglected [112]. Microfavina and mesofauna can feed on pathogens, can help in nutrients recycling and their turnover, and can maintain specific biodiversity with dominant bacterial taxa [156]. Soils with higher disease suppressiveness rates are also associated with higher fungal diversity [157]. Since there exists a large diversity of uncultured fungi, culture-independent approaches such as amplicon sequencing must be developed to completely describe the whole fungal community and screen the mycobacteria with high suppressiveness along the soil health gradient [158]. Authors have assessed the fungal composition differences between suppressive, weakly suppressive, and conducive soils using the terminal restriction fragments length polymorphisms (T-RFLPs) method as a culture-independent technique [54]. In this regard, arbuscular mycorrhizal (AM) fungi can contribute to disease suppression in several ways [159]. Mycorrhizal plants can recruit more pathogen-antagonistic _Actinomycetes_ than the non-mycorrhizal_ones [160]. AM fungi does not compete with other bacteria as do, for instance, plant growth-promoting rhizobacteria (PGPR). Rather, PGPR interact for mutual establishment in order to increase plant disease resistance [161]. AM fungi are often known to increase the nutritional status of the host plant and to help indirectly the suppression of plant diseases, AM fungi increase availability of phosphorus (P) by increasing the tolerance of the plant to pathogen damage [159]. Other non-nutritional mechanisms of AM fungi such as change in exudation patterns, activation of plant defense systems, increased lignification of cell walls, and competition for colonization space and infection sites were documented [162]. AM fungi such as _Gomus fasciculatum_ releases a large variety of antibiotics and toxins acting against pathogens [163]. Besides AM fungi, the feeding preference of the main fungal BCAs like _Aspergillus_ spp., _Penicillium_ spp., _Giocladium_ spp. and _Trichoderma_ spp. is one of the major determinants in developing a specific rhizosphere microbiome [112]. The interactions between the different trophic levels can modify the nutrient cycling in the soil, influencing the soil nutrient status and plant vigor [164] and affecting suppression against, for instance, the common scab disease by accumulation of mineral elements in the tuber periderm of different potato cultivars [165].
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 1 Introduction
Our process effectively closes this gap by featuring dynamic learning rate adjustments along with pre-training on models trained on wide diversified augmented datasets, which shows enhanced performance in non-IID scenarios when compared with classic approaches. The key contributions of this article can be summarized as follows:  
* We identify the negative impact of non-IID data on federated learning performance, demonstrating that the effectiveness of federated learning is particularly inferior when applied to non-IID data compared to IID data. * We propose an optimization strategy using a cyclical learning rate to adjust the learning rate dynamically during the federated training process, with the goal of increasing accuracy and achieving faster model convergence. * We introduce a novel approach for data sharing and pre-training on augmented data further to improve the performance of FL under non-IID data conditions. * We validate our proposed methods through extensive experiments, showing significant improvements in accuracy and convergence speed compared to the baseline FedAvg approach with a fixed learning rate. * This research attempts to contribute to the field by providing new methods for applying federated learning to real-world scenarios, enhancing the efficiency of federated learning applications. The following Section 2, provides an overview of federated learning, including its architecture and a detailed explanation of the FedAvg algorithm. Section 3 describes the methodology used in this research, including the tools, datasets, data pre-processing, and models created. In Section 4, we present our proposed solutions, focusing on the Cyclical Learning Rate (CLR) and data sharing with pre-training on augmented data. Section 5 presents and compares the results of the experiments. Ultimately, in Section 6, we conclude the paper by summarizing the key contributions, outlining potential future work, and discussing the findings and their implications in real-world federated learning scenarios.
## 6 Discussion
### Comparison with Related Works  
In Table 4, we provide a comprehensive comparison of our proposed methodology with seven related works, highlighting the key differences in objectives, virtual sensing approaches, integration methods, predictive models, performance metrics, and scalability. The comparison underscores the unique contributions of our work, particularly in the integration of virtual and physical sensors for forest fire prediction. Unlike the related works, which focus on applications such as soil moisture estimation, fault tolerance, or secure resource allocation, our methodology demonstrates the ability to enhance scenario prediction accuracy by leveraging hybrid sensor data through advanced machine learning techniques.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 2 The Use of Neural Networks in the Global Positioning System
### Prediction of DGPS Differential Correction Using NN  
The autoregressive moving average (ARMA) neural network as shown in Figure 7 was proposed by [PERSON] [33] to predict data from DGPS pseudorange correction (PRC) message.  
Figure 6: Architecture of an RBFNN.  
The current work fits the profile of errors that are primarily controlled by ionospheric and tropospheric delays even without SA degradation. The ARMA NNs projected that PRC would give correction data with a large accuracy improvement when the PRC signal was temporarily lost. To construct the desired output of the PRC if the signals are lost, the stock market prediction methodology has been used in the NN input-output mapping architecture. The BPNN and GRNN types of ARMA NNs have both been used. The key advantage of employing GRNN during the training phase is the speed of computation. Results from the GRNN and BPNN are extremely similar in terms of accuracy. When the DGPS signals are temporarily absent, the accuracy of the system has greatly increased thanks to the inclusion of the ARMA NN mechanism.
## 3 Results
### Differential Gene Expression Analysis  
DEG in intestinal tissues were identified in the CON vs. DSS, DSS vs. HMLS, and DSS vs. TM groups. DEG were identified based on an FDR value cut-off of 0.1 using Cufflinks, and an absolute log FC of at least 2. In total, 786 (350 upregulated, 436 downregulated), 176 (40 upregulated, 136 downregulated), and 77 (58 upregulated, 19 downregulated) DEG were identified in the three above comparisons, respectively. DEG, along with the FC and FDR, are listed in Table S2, and details of the differences between samples are shown in Figure S1. Genes annotated in QuickGO ([[https://www.ebi.ac.uk/QuickGO/](https://www.ebi.ac.uk/QuickGO/)]([https://www.ebi.ac.uk/QuickGO/](https://www.ebi.ac.uk/QuickGO/))) were mapped; for the TJ genes annotated in QuickGO, there were no significant differences between the DSS and DSW groups (Figure S2). The scatter plots in Figure 6a-c show the FC values for all genes in relation to the adjusted \(p\)-value for differential expression in the DSS vs. HMLS and DSS vs. TM groups. DEG for inflammation-related genes in CON vs. DSS groups heatmaps are presented in Figure 6d. The expression pattern in the HMLS group was similar to that in the control group, and the expression patterns in the DSS and TM groups were alike. Inflammation-related DEG between the HMLS and DSS groups are listed in Table 2. In the HMLS group, the expression of nitric oxide synthase (NOS2), which produces free radicals, was the most significantly reduced when compared to its level the DSS group, whereas the expression of C-C motif  
Figure 4: Effects of DSW-derived mineral waters on colonic histopathological changes. Histopathological features of colons from BALB/c mice with 5% DSS-induced colitis. Hematoxylin and eosin staining. Magnification, 200\(\times\) (bar = 200 mm) (representative samples).  
Figure 5: Mineral waters do not improve DSS-induced intestinal permeability in mice. Mice were provided ad libitum with drinking water (CON), 5% DSS in water (DSS), 5% DSS in HMLS water (HMLS), or 5% DSS in TM water (TM) for seven days. A FD-4 permeability assay was performed on Day 7. Different letters above the bars indicate significant differences ([PERSON]’s multiple range test; \(p<0.05\)). The letters are the order of values from small to large.  
chemokine ligand 20 (CCL20), which migrates white blood cells to maintain homeostasis, was the most significantly induced (FDR < 0.1).  
Figure 6: Differential gene expression in colonic tissues in the treatment groups by RNA-seq analysis. (**a**–**c**) Correlation of mRNA expression levels (log) in CON vs. DSS, DSS vs. HMLS, and DSS vs. TM. (_n_; CON = 8, DSS = 5, HMLS = 4, and TM = 5). (**d**) Heatmap shows the z-scores based on the average of each expressed gene which is related inflammatory response. The squares display the respective frequencies according to the color scale shown on top.
# DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information
###### Abstract  
Word embedding is an important reference for natural language processing tasks, which can generate distribution presentations of words based on many text data. Recent evidence demonstrates that introducing sememe knowledge is a promising strategy to improve the performance of word embedding. However, previous works ignored the structure information of sememe knowledges. To fill the gap, this study implicitly synthesized the structural feature of sememes into word embedding models based on an attention mechanism. Specifically, we propose a novel double attention word-based embedding (DAWE) model that encodes the characteristics of sememes into words by a \"double attention\" strategy. DAWE is integrated with two specific word training models through context-aware semantic matching techniques. The experimental results show that, in word similarity task and word analogy reasoning task, the performance of word embedding can be effectively improved by synthesizing the structural information of sememe knowledge. The case study also verifies the power of DAWE model in word sense disambiguation task. Furthermore, the DAWE model is a general framework for encoding sememes into words, which can be integrated into other existing word embedding models to provide more options for various natural language processing downstream tasks.  
natural language processing; word representation learning; word2 vec; sememes; attention mechanism; structural information +
Footnote †: journal: Applied Sciences
## 1 Introduction
In particular, we consider the control of articulated robots with 3 degrees of freedom (3-DOF) in terms of picking up individual parts under the proposed framework, with limited computing resources. To this end, Raspberry Pi 4 is utilized as a controller of the 3-DOF articulated robots and of the demonstration equipment of the devised FL algorithm, which recognizes and analyzes the manufacturing environment and part picking conditions in real time. As mentioned above, articulated robot control technology is very important for the efficient operation of smart factories, and the proposed FL framework is important in that it shortens the learning time of robots under limited resources, ultimately improving manufacturing process efficiency. [PERSON] et al. [14] pointed out that small and medium-sized enterprises (SMEs) that have difficulty hiring highly qualified employees due to limited resources should prioritize the use of low-investment robots to introduce advanced technologies. Therefore, the proposed framework will contribute to the expansion of FL use for articulated robot control under limited resources, especially for SMEs. In the experiment, FL performance with four different articulated robots is evaluated in terms of training completion time, resource share ratio, network traffic, and the completion time of a picking task. The results show that when three articulated robots are used, the proposed FL-based framework can achieve object recognition with an accuracy of approximately 80 %, with a minimum number of learning rounds of 76 and with a network traffic intensity of 2303.5 MB. Therefore, this study will contribute to the expansion of FL use for articulated robot control in SME manufacturing environments with limited resources. The remaining sections are organized as follows: Section 2 reviews recent methodologies related to FL, the introduction of articulated robots in smart factories, and techniques for robot control such as forward kinematics, inverse kinematics, and Pulse Width Modulation (PWM). Then, the FL-based articulated robot control framework is proposed. Section 3 reports on the performance of the articulated robot control module and the FL module in the framework. Section 4 discusses the potential challenges that may be faced when applying the proposed framework to actual manufacturing sites and suggests solutions to address them. Finally, Section 5 presents the conclusion and suggests future work associated with this study.
## 4 Experiments and Discussion
### Fusion Results on TNO Dataset  
To test the performance of our DAGANFuse, we first employ the TNO Image Fusion Dataset. We select four pairs of typical images for intuitive qualitative comparison, as shown in Figure 6. The infrared targets and visible details are marked with red and blue frames, respectively. Judging from the fusion results, FusionGAN and DDCGAN inherit more infrared target information, but there are problems of blurred contours and lack of texture information, while MDLatRR and DenseFuse retain more texture information but lose some infrared target information. In contrast, Res2 Fusion, MFEIF, and SDDGAN are superior to the previous four methods in comprehensive performance. In general, from the comparison of the results of four typical image pairs, our method can retain more visible light texture information and infrared target information.  
To objectively evaluate the quality of fused images, we also conduct quantitative analysis. The index values are shown in Table 2. The best values are indicated in red and bold font, and the sub-optimal values are indicated by underlining. In Table 2, compared with other state-of-the-art fusion methods, our proposed method (DAGANFuse) achieves four best values (EN, AG, VIF, and SF) and two sub-optimal values (SD and MI). This means that DAGANFuse can retain more complementary information from the pixel level and feature level. In conclusion, our proposed DAGANFuse has a good fusion effect in both quantitative index evaluation and qualitative visual description.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **EN** & SD & **AG** & **MI** & **VIF** & **SF** \\ \hline Without\_CDAM & 6.9969 & 37.1377 & 1.7484 & 1.0673 & 0.1998 & 3.1063 \\ CBAM & 7.0451 & 40.5360 & 2.1700 & 1.6596 & 0.4171 & 4.1848 \\ Without\_DFPM & 7.1019 & 41.5828 & 3.9443 & 2.3943 & 0.5145 & 8.2949 \\ Without\_FFPM & 7.0958 & 41.6206 & 3.9841 & 2.8534 & 0.5373 & 8.4275 \\ ours & 7.1253 & 41.8938 & 4.0462 & 2.6519 & 0.5367 & 8.5184 \\ \hline \end{tabular}
\end{table}
Table 1: The objective results of ablation studies.  
Figure 5: The results of ablation study. (**a**) Infrared image: (**b**) Visible image: (**c**) Without\_CDAM; (**d**) CBAM; (**e**) Without\_DFPM; (**f**) Without\_FFPM; (**g**) Ours.  
Figure 6: Qualitative comparison of our method with seven other state-of-the-art methods on four typical infrared and visible image pairs. From top to bottom: infrared images, visible images, the results of FusionGAN, DDCGAN, MDLatLRR, DenseFuse, MFEIF, Res2 Fusion, SDDGAN, and our DAGANFuse.