esa-sceva/satcom-chunk-collection huggingface dataset:
# Fertilization and Soil Microbial Community: A Review
## 1 Introduction
For this reason, they are used as indicators of soil quality and health [52; 59]. Soil quality is defined as the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintaining or enhancing water and air quality, and supporting human health and habitation [60]. Microbial populations vary depending on different abiotic factors such as soil type, presence/absence of plants and climate; their responses to similar fertilization treatments can thus be different depending on the above-mentioned abiotic factors. During the long-term process of evolution, soil, plants and microbes co-evolved to form relatively stable relationships within a given ecosystem. In fact, the soil microbial community has recently been termed the soil microbiome [53; 61; 62]. Changes in soil microbial communities induced by environmental changes could influence the relationships between microorganisms and plants and may negatively influence soil fertility and crop productivity. Consequently, studying the effects of chemical/organic fertilizers on the natural microbial community is of crucial importance. For example, understanding how NPK chemical fertilizers influence the microbial biomass, which is an indicator of soil fertility and quality, is a basic prerequisite for understanding microbiological processes [63], in order to preserve the ecosystem functions of soil. These research themes are an important part of soil ecology, as pointed out by several international authorities (e.g., EU commission, FAO [64; 65]). The use of inappropriate farming practices (e.g., excessive use of chemical fertilizers and pesticides) and frequent changes in land use may cause variability in soil microbial communities, which can have significant effects on soil fertility and productivity [66]. The present article reviews the importance of soil microbes for the soil ecosystem, with a particular emphasis on the influence of chemical and organic fertilizers on the soil microbial community. In particular, microbial biomass, activity and diversity were taken into account as parameters. For microbial activity, dehydrogenase activity, a general indicator of microbiological activity [67; 68] is considered.
## 3 Materials and Methods
### Extraction and Analysis Procedure  
As shown in Figure 10(left), the extraction tube was connected to the six-port valve instead of the sample loop. The six-port high pressure valve was switched between the load and inject states to perform the extraction and desorption processes. In the load state, the sample solution was transported through the extraction tube via an EasySep-1020 LC pump, and the target analytes were adsorbed by the selected wire. The sample was pumped and flushed from pore 1 to pore 6, then pressureed into the extraction tube and entered pore 3. After the preconcentrated target compounds in sample were continually entrapped by the extraction materials, the remaining solution was flushed to the waste bottle from pore 2. After the rotor was rotated about 60\({}^{\circ}\), the system changed to the injection state. In this case, the mobile phase flowed through the extraction tube from pore 4 to pore 3, the absorbed target compounds were desorbed by the mobile phase, and the mobile phase with the desorbed compounds was pumped to the separation column from pore 6 to pore 5 for UV detection, consequently. The extraction and the desorption could be completed by the switch between the load and inject modes in Figure 10(right).
# Graph Dilated Network with Rejection Mechanism
## 6 Experiments
### Evaluation between Rejection Mechanism and Gate Mechanism  
To further analyze the rejection mechanism, we conduct experiments to evaluate the performances between the rejection mechanism and the gate mechanism. In order to avoid the influence of multiple graph dilated convolution kernels, the variants of GraphDRej (including GraphDRej and GraphDRej-Gate) used in this section only adopt a single kernel, i.e., \(\gamma=1\). The nodes classification results with the different number of layers are reported in Table 4. It shows that the performance of GG-NN drops quickly when the number of layers increases. It indicates that although GG-NN adopts a gate mechanism, the over-smoothing problem still exists, whereas for GraphDRej-Gate, it successfully overcomes this problem and achieves comparable performance with GraphDRej. It shows the main factor to solve the over-smoothing problem is to reject the message from distant nodes with the penalty parameter, which can be implemented by a learnable parameter or a gate function.  
Figure 5: The results of GNNs combined with the rejection mechanism on Cora. “XX-Rej” refers to the model XX with the rejection mechanism, and GraphDRej-NRej refers to the version of GraphDRej without the rejection mechanism.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 3 WSN Architecture  
The proposed star/circle WSN topology seeks to create a sustainable, efficient monitoring system for long-term sensor node deployment across diverse forest types. A key aspect of the methodology is determining the optimal number and arrangement of sensors for balanced coverage. Strategic deployment tackles challenges posed by varying forest conditions, while efficient placement is essential for testing routing protocols, optimizing connectivity and data integrity over different distances and densities, and overcoming communication barriers in dense canopies.
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 4 Experimental Results and Discussion
### Comparative Experiment  
To validate the effectiveness of the attention mechanism introduced in this paper, we conducted a series of comparative experiments. We compared several common attention mechanisms and explored the effects of the proposed improvements. We improved the network structure and optimized parameters of the YOLOv8n model, using the dataset from this study for training and validation. We compared the effectiveness of various improvement methods through experimental results. The main attention mechanisms compared included the SE (squeeze-and-excitation) attention mechanism, D-attention (vision  
Figure 11: Performance graph of the improved YOLOv8.  
Figure 12: The effect of identifying fruits.  
transformer with deformable attention), and LocalWindowAttention. Additionally, we included the YOLOv5 algorithm for comparison. The results of the comparative experiments are shown in Figure 13. The outcomes of these comparisons regarding the pertinent metrics are presented in Table 5.  
As shown in Table 5, the precision-recall and mAP of the improved YOLOv8 were both higher than the other object detection methods, and its FPS value was also higher than the other three methods. Therefore, the improved YOLOv8 can quickly and accurately detect targets, and it meets industrial requirements. The results of object detection for the small target fruit dataset using the aforementioned four algorithms are shown in Figure 13.  
Our improved algorithm's detection results are shown in Figure 13. It can be observed that the false negative rate of our improved attention mechanism was significantly lower than that of the YOLOv5n, YOLOv8n-LocalWindowAttention, and D-attention improved algorithms. Additionally, the detection accuracy was also superior to the algorithms based on SE-attention improvements, and the YOLOv8n algorithm itself.  
At the same time, by comparing the impact of the commonly used loss functions IoU, DIoU (Distance-IoU), GIOU (Generalized-IoU), CIOU (Complete-IoU), and SIOU (SCYLLA-IoU) on model accuracy, we can see from the experimental results in Table 4 that the model using the SIOU loss function (YOLOv8n+SIOU) had higher accuracy. This indicates that the incorporation of this loss function effectively improved the mAP compared to the original model (YOLOv8n+CIOU). The results are shown in Table 6.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 3 Chitin
### Extraction Procedure of Chitin from Crustaceans  
One of the main advantages of chitin recovery from crustacean shells is that this unused resource can provide an additional source of chitin [35].  
The shells from which chitin is extracted have a significant influence on the final quality of the isolated chitin. For example, shrimp have a thinner shell wall, which makes the isolation of chitin easier. To obtain chitin from shellfish, the shells are first cleaned, dried, and crushed into small pieces at the end of the process. Then, the two main components of the shell are removed--proteins by deproteinization and inorganic calcium carbonate by demineralization. Furthermore, chemical or enzymatic treatments are provided to remove colorants and lipids. In addition to deproteinization by NaOH, a partial deacetylation of chitin will take place, as well as hydrolysis of the biopolymer, resulting in a reduction in molecular weight [35].  
Acid treatment in dilute hydrochloric acid is often used to demineralize calcium carbonate, which involves its breakdown into water-soluble calcium salts and the release of carbon dioxide. Salts may then be readily removed by filtering solid chitin followed by washing with deionized water [32]. Chitin has excellent film-forming characteristics as well as good stability, which is supported by the formation of a hydrogen bond network between the chains. Chitin imparts biocompatibility, biodegradability, non-toxicity, and antibacterial activity to the subsequently processed materials [33; 34].
###### Abstract  
Self-compacting concrete (SCC) shows to have some specificities when compared to normal vibrated concrete (NVC), namely higher cement paste dosage and smaller volume of coarse aggregates. In addition, the maximum size of coarse aggregates is also reduced in SCC to prevent blocking effect. Such specificities are likely to affect the results of non-destructive tests when compared to those obtained in NVC with similar compressive strength and materials. This study evaluates the applicability of some non-destructive tests to estimate the compressive strength of SCC. Selected tests included the ultrasonic pulse velocity test (PUNDIT), the surface hardness test (Schmidt rebound hammer type N), the pull-out test (Lok-test), and the concrete maturity test (COMA-meter). Seven sets of SCC specimens were produced in the laboratory from a single mixture and subjected to standard curing. The tests were applied at different ages, namely: 1, 2, 3, 7, 14, 28, and 94 days. The concrete compressive strength ranged from 45 MPa (at 24 h) to 97 MPa (at 94 days). Correlations were established between the non-destructive test results and the concrete compressive strength. A test variability analysis was performed and the 95% confidence limits for the obtained correlations were computed. The obtained results for SCC showed good correlations between the concrete compressive strength and the non-destructive tests results, although some differences exist when compared to the correlations obtained for NVC.  
self-compacting concrete; non-destructive test methods; compressive strength; ultrasonic pulse velocity test; surface hardness test; pull-out test; maturity test; within-test variability; normal vibrated concrete
# Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests
## 2 Problem Statement
### Test Uncertainty Analysis  
A traditional testability analysis is carried out based on reliable testing, but in practical engineering applications, the unreliable testing of a traction system is common and has a great impact on the judgment of equipment testing. This is because, in reality, various factors interfere with the traction system, such as environmental and operating factors, etc., due to its large size, many components, and complex electrical coupling relationship.  
\[ft=\begin{array}{ccccc}T_{1}&T_{2}&\cdots&T_{n}\\ F_{1}&f_{11}&ft_{12}&\cdots&ft_{1 nt}\\ F_{2}&ft_{21}&ft_{22}&\cdots&ft_{2 nt}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ F_{n}&ft_{m1}&ft_{m2}&\cdots&ft_{mn}\\ \end{array} \tag{1}\]  
In the fault-test dependency matrix \(ft\), the elements \(ft_{ij}\) in the formula are Boolean. \(ft_{ij}=1\) means that the fault \(f_{\rm i}\) is related to \(t_{j}\), and the test \(t_{j}\) can detect and isolate the fault \(f_{\rm i}\). \(ft_{ij}=0\) means that the fault \(f_{\rm i}\) is not related to \(t_{j}\), and the test \(t_{j}\) cannot detect and isolate the fault \(f_{\rm i}\). The \(i\)th row vector \(F_{i}=[ft_{1i}ft_{2}\ldots ft_{in}]\) represents the output of all available tests when the fault \(f_{\rm i}\) exists, describing the symptoms of the fault \(f_{\rm i}\). The jth row vector \(T_{j}=[ft_{1j}ft_{2j}\ldots ft_{mj}]^{T}\) represents the faults that the available test \(t_{j}\) can detect and isolate and describes the fault detection and isolation capability of the test \(t_{j}\). When the test results are unreliable, the test results reflect three forms: normal, missing inspection, and false inspection. Missing inspection and false inspection are essentially different manifestations of unreliable testing.  
The unreliability of the traction system test results shows the uncertainty of the traction system test, which means that the test process is affected to a certain extent; thus, it is necessary to analyze which factors affect the test.  
Therefore, this paper puts forward the concept of test reliability to uniformly describe the reliability of test results, so as to avoid classifying and analyzing the different forms of results obtained using unreliable traction system tests. We can think of test reliability is as describing the probability that the test can accurately detect the occurrence or non-occurrence of a system failure.
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 3 Grey-Total-Influence-Based Tolerance Rough Sets
### Grey-Total-Influence-Based Tolerance Relation  
The overall relationship index (\(g_{ij}\)) forms the foundation of the proposed grey-total-influence-based tolerance rough set (GTI-TRS). \(S_{A}^{GTI}(\mathbf{x}_{i},\ \mathbf{x}_{j})\), an overall GTI-based similarity measure, is defined as  
\[S_{A}^{GTI}(\mathbf{x}_{i},\mathbf{x}_{j})\ =g_{ij} \tag{28}\]  
Let \(\mathbf{x}_{i}\tau_{A}^{GTI}\mathbf{x}_{j}\) denote that \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) are similar regarding \(A\), where \(\tau_{A}^{GTI}\) is called a GTI-based tolerance relation with respect to all attributes \(A\). \(\tau_{A}^{GTI}\) can be related with \(S_{A}^{GTI}\) as  
\[\mathbf{x}_{i}\tau_{A}^{GTI}\mathbf{x}_{j}\ \Leftrightarrow\ S_{A}^{GTI}( \mathbf{x}_{i},\mathbf{x}_{j})\ \geq\ t_{A}^{GTI} \tag{29}\]  
where \(t_{A}^{GTI}\in[0,1]\) is a similarity threshold based on \(A\).  
GTI-TC(\(\mathbf{x}_{i}\)), a GTI-based tolerance class of \(\mathbf{x}_{i}\), can be generated by considering patterns that have a GTI-based tolerance relation with \(\mathbf{x}_{i}\):  
\[\text{GTI-TC}(\mathbf{x}_{i})\ =\ \{\mathbf{x}_{j}\in U\mid\mathbf{x}_{i}\tau_{A}^ {GTI}\mathbf{x}_{j}\} \tag{30}\]  
The higher the value of \(t_{ij}\), the more likely it is that \(\mathbf{x}_{j}\) can be included in GTI-TC(\(\mathbf{x}_{i}\)). The lower (\(\tau_{A}^{GTI}X\)) and upper approximations (\(\overline{\tau_{A}^{GTI}}X\)) of \(X\) can be determined by subset and concept approximations by replacing \(\underline{\tau_{A}}\), \(\overline{\tau_{A}}\), and \(TC(\mathbf{x})\) with \(\underline{\tau_{A}^{GTI}}\), \(\overline{\tau_{A}^{GTI}}\), and GTI-TC(\(\mathbf{x}\)), respectively. \(\langle\underline{\tau_{A}^{GTI}}X,\ \overline{\tau_{A}^{GTI}}X\rangle\) is called a GTI-TRS. As shown in Figure 1, by finding the grey total influence, the proposed GTI-TRSC can be set up by merging the proposed classifier with the computational steps of the TRSC.  
Figure 1: A flowchart for constructing the proposed classifier (GRA: Grey Relational Analysis, GTI-TRS: grey-total-influence-based tolerance rough set).
## 2 FTTR Architecture Description  
FTTR adopts the full network grouping method, as shown in Figure 1, which mainly contains the FTTR master gateway, FTTR slave gateway, and supporting optical components such as splitters. The functions of each component are described below.  
The FTIR master gateway is a new integrated device with a network location between the OLTs and slave gateways that the operator is responsible for maintaining. It connects upward to the external network via optical fiber to provide FTTH fiber to the home and downward to provide optical ports to connect the slave gateways in each room to form an FTIR network via P2 MP, as well as to provide user-side interfaces, such as Ethernet, Wi-Fi, etc., for the purpose of communicating with the terminal equipment. Compared with FTTH, the network deployment is more flexible as the master gateway is continuously expandable downward through the slave gateways, which are connected to the user equipment by wired or wireless means, and they are also connected to the FTIR master gateway upward by optical fiber. Slave gateways can set different service priorities according to specific different services. Slave gateways are created with exclusive functions, such as providing exclusive gaming slave gateways, HD video slave gateways, teleconferencing slave gateways, etc. Usually FTIR slave gateways are equipped with AP functions to provide Wi-Fi access points for wireless devices. Splitters are in between the FTIR master and slave gateways, and they are used to connect the master gateway and multiple slave gateway devices, thus dividing an optical fiber into multiple implementations to distribute the downlink data and centralize uplink data (similar to the role of switches).  
FTIR networks can connect up to 256 terminal devices, which is about eight times the maximum number of connections of a traditional network, and it can guarantee a variety of whole-house smart terminal networking. But an increase in the number of terminals will also bring some new problems.  
(1) Increased Traffic Volatility: The proliferation of terminals implies a potential increase in network traffic volatility as more devices simultaneously send and receive data.  
(2) Complexity in Traffic Patterns: Varied traffic demands and usage patterns among different terminals render traffic patterns more complex and harder to predict.  
(3) Decline in Prediction Accuracy: With the expansion of network scale, traditional traffic prediction models may struggle to accurately capture the traffic variations of all terminals, thus leading to decreased prediction accuracy and further exacerbating uneven bandwidth allocation within the network.  
Hence, in the context of FTIR, it is imperative to address the aforementioned issues by employing deep learning to construct traffic prediction models, thereby enhancing prediction accuracy and real-time performance. Simultaneously, the introduction of optimization algorithms for intelligent parameter tuning can reduce manual intervention, thus facilitating the intelligent management of FTIR networks.  
Figure 1: FTIR scene diagram.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 5 Results and Analysis
### Effective Permittivity, and Permeability  
From Figure 10, the 16 elements metasurface antenna permittivity and permeability before and after adding diodes were all negative. These results were obtained from the CST MWS software, which used a method by [67] to retrieve the effective constitutive parameters (permittivity and permeability) of a slab of metamaterial from the measurement of scattering parameters. Frequency reconfigurability can also be observed from this figure. Since \(\mu\) and \(\epsilon\) were negative at all resonating frequencies, as shown in Figure 10a,b, this indicates that both the designed 4 \(\times\) 4 metasurface antenna structure and its frequency reconfigurable structure were double negative metasurfaces, which exhibits the phenomenal ability of negative refraction. Furthermore, Equations (10) and (11) can be used to verify whether \(\mu\) and \(\epsilon\) are negative.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Capacitance (pF)** & **Angular Beamwidth (\%)** & **Gain (dBi)** & **Directivity (dBi)** & **Radiation Efficiency (\%)** \\ \hline Without diodes & 16.5 & 15 & 17.2 & 87.2 \\ \hline
0.03 & 15.3 & 27.23 & 43.37 & 62.79 \\ \hline
0.035 & 15.0 & 27.50 & 43.06 & 63.86 \\ \hline
0.04 & 14.5 & 27.93 & 44.28 & 63.08 \\ \hline
0.045 & 14.31 & 28.42 & 44.93 & 63.25 \\ \hline
0.05 & 13.9 & 28.8 & 45.6 & 63.31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the gain, directivity, and radiation efficiency.
# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 3 Comprehensive Survey of Deep Learning-Based Image Watermarking  
This section discusses the integration of deep neural networks in contemporary deep learning-based image watermarking, explaining the adaptation of traditional watermarking processes within this advanced framework. The modern deep learning-based image watermarking approaches are architectured akin to their traditional counterparts, encompassing transformation, watermark embedding, and extraction phases. For a structured analysis, we classify current deep learning-based techniques into three distinct categories based on the roles of deep learning in various papers: (1) embedder-extractor joint training, (2) deep networks for feature transformation, and (3) hybrid methods. In order to provide a clear and concise overview of each category, Table 2 acts as a navigational guide. Each category is outlined, with its key features, requirements, and potential drawbacks listed.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Methodology** & **Key Features/Requirements/Potential Drawbacks** \\ \hline Embedder–extractor Joint training & **Key features:** Automated watermarking schemes are learned from designated data through joint optimization of watermark embedding and extraction. \\  & **Requirements:** A robust dataset for training and an astute selection of noise levels in the noise layer to ensure robustness. \\  & **Potential drawbacks:** Efficacy may wane with inadequate training data or improper noise selection, emphasizing the necessity for a robust dataset and prudent noise level selection. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of methods in each proposed category.
# MAC-Based Compression Ratio Improvement for CAN Security
## 3 Proposed CAN Security Method
### Data Transmission and Reception  
Data transmission by the proposed algorithm can be defined by two cases based on the BRA-compressed data length.  
* Case I (\(0\leq\) BRA-compressed data length \(\leq 4\)): BRA-compressed data and 4-byte MAC are transmitted in the same data field using \(ID_{i}\).
* Case II (\(4<\) BRA-compressed data length \(\leq 7\)): BRA-compressed data and 4-byte MAC are transmitted using \(ID_{i+1}\) and \(ID_{i+2}\), respectively.  
Figure 4: 12-bit data representation using 4-byte MAC with the proposed method.  
The maximum length of the BRA-compressed data is 7. Therefore, unlike the Triple ID method, there is no need to consider an 8-byte uncompressed message. Figures 5 and 6 show the proposed data transmission and reception flow charts, respectively.
## 4 Results and Discussion
### Phase 1: Assess the Potential and Maturity for IoT Solution Implementation
#### 4.1.1 Does the Company Have an Organized Information System? (Network, Servers, Connectivity)?  
In Table 5, item 1 indicates that just over half of the companies in the agro-food sector that were part of the inquiry process have an organized information system, whether it consists of a data network, servers, or even connectivity. At this point, there is a need for the development of information and communication technology and implementation strategies in companies in this sector.