esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal-Organic Frameworks as Promising Textile Flame Retardants: Importance and Application Methods
## 4 Metal-Organic Framework
### Metal-Organic Frameworks for Flame Retardants and Their Mechanism
#### 4.2.1 Uio-66  
The metal-organic framework UiO-66 was first developed at the University of Oslo, which is reflected in its name (UiO). This porous metal-organic network, based on zirconium (Zr), has the formula Zr6O4(OH)4(BDC)6. The inorganic component consists of a core unit Zr6O4(OH)4 linked by twelve carboxylates (-CO2) from dicarboxylic acids, forming a face-centered cubic network [111; 112; 113; 114]. The Zr6O4(OH)4 nodes feature six zirconium ions (Zr4+) arranged with octahedral geometry, with four oxygen or hydroxyl atoms at the centers of each of the octahedral faces [115]. This structure provides UiO-66 with high chemical, thermal, and mechanical stability while supporting structural defects around the metal core r and presenting a high surface area [111].  
Variation in the binder can result in different metallic structure, as demonstrated by MOF-808. Also based on Zr6O4(OH)4(CO2)6(HCOO)6 as SBU, MOF-808 has six BTC (trimesic acid) units acting as an organic binder to form a three-dimensional porous struc  
Figure 3: (a) ZIF-8: SEM, structuring and SBU; (b) Uio-66: SEM, structuring and SBU.  
ture [116; 117]. Although MOF-808 has zirconium clusters similar to those of UiO-66, this structure is connected by the BTC binder and has two different hole cages of approximately 18 and 14 [118].  
UiO-66 has a 7.5 A tetrahedral cage and a 12 A octahedral cage, with a pore opening of 6 A and a theoretical pore volume of 0.77 cm\({}^{3}\) g\({}^{-1}\), and its surface area depends on the preparation method and the presence of defects, which can vary from 800 to 1200 m\({}^{2}\) g\({}^{-1}\)[112; 115; 119]. MOFs synthesized from transition metal elements represented by zirconium can catalyze the char formation reaction at high temperatures [117]. Upon decomposition, Zr-MOFs generally produce zirconia, which acts as a physical barrier on the substrate, as well as non-flammable gases that dilute the concentration of flammable gases, thus facilitating smoke suppression [105; 120; 121].  
This MOF exhibits high hydrothermal and chemical stability as well as mechanical stability, and in addition to being stable at 500 \({}^{\circ}\)C and able to maintain its stable structure in water, DMF, benzene, or acetone, it has strong acid stability and some alkaline stability [114]. Therefore, as a robust material accommodates various types of synthesis, whether modulated or not, and different synthetic post-modifications, it is an attractive material for numerous applications, including as a flame retardant.
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 4 Simulation Verification
### Segmented Mirror Active Optical Control
Thus improving the control efficiency and enhancing the segmented mirror co-phase maintenance cost performance. In summary, the co-phase maintenance model proposed in Section 2.2.2 reduces the control system complexity and support system design and processing requirements and improves the co-phase maintenance efficiency and cost-effectiveness compared to the traditional model without the involvement of edge sensors.
# Prediction of Ship-Unloading Time Using Neural Networks
## 2 Literature Review
among conductivity, temperature, and component concentrations in Bayer alumina production, with experimental results indicating high prediction accuracy. [PERSON] et al. [42] presented an improved SCN model for predicting ammonia nitrogen concentrations in water quality monitoring. They introduced a new inequality during the network construction process and proposed a node-selection method. The experimental results confirmed the effectiveness of the enhanced SCN model. In summary, while there is a substantial amount of research on predicting ship-unloading times, most studies focus on container ships rather than bulk carriers. Furthermore, existing research on bulk carriers primarily addresses unloading scheduling rather than unloading-time prediction, which does not adequately reflect the significance of this issue. The study aims to address this gap.
# Detecting Suspicious Texts Using Machine Learning Techniques
## 4 Proposed System
### Training
#### 4.3.2 Logistic Regression  
Logistic regression [43] is well suited for the binary classification problem. Equations (5)-(6) define the logistic function that determines the output of logistic regression:  
\[h_{\theta}(x)=\frac{1}{1+\exp(-\theta^{T}x)} \tag{5}\]  
Cost function is,  
\[C(\theta)=\frac{1}{m}\sum_{i-1}^{m}c(h_{\theta}(x^{i}),y^{i}) \tag{6}\]  
\[c(h_{\theta}(x),y)=\begin{cases}-\log(1-h_{\theta}(x))&\texttt{if}\ y=0\\ -\log(h_{\theta}(x))&\texttt{if}\ y=1\end{cases}\]  
Here, \(m\) indicates the number of training examples, \(h_{\theta}(x^{i})\) presents the hypothesis function of the \(ith\) training example, and \(y^{i}\) is the input label of \(ith\) training example. We used the 'l2' norm to penalize the classifier, and the 'lbfgs' optimizer is used for a maximum of 100 iterations. The default value of the inverse of regularization strength is used with a random state 0.
# Influence of Particulate Matter and Carbon Dioxide on Students' Emotions in a Smart Classroom
## 1 Introduction
### Importance of Monitoring and Regulation  
Given the significant health risks associated with poor air quality, various health organizations have established strict guidelines to ensure safe indoor environments. The World Health Organization [7] recommends a PM\({}_{2.5}\) limit of 10 ug/m\({}^{3}\) as an annual mean, while a CO\({}_{2}\) concentration of 1000 ppm is considered the threshold for good IAQ. These standards are particularly important in educational settings, where students spend extended periods indoors and are more vulnerable to pollutants.  
Ref. [8] provides further criteria for maintaining healthy and comfortable indoor environments in non-residential buildings. Table 1 summarizes the acceptable levels for CO\({}_{2}\) and temperature according to this standard:  
This regulation classifies IAQ into three categories based on CO\({}_{2}\) concentration and temperature, ensuring that buildings meet acceptable health and comfort standards. Exceeding these levels can have significant health implications, particularly for students and staff, making it essential for schools to regularly monitor air quality and take appropriate actions to reduce exposure [9; 10].  
Technological advancements in environmental monitoring, particularly IoT-based systems and wearable sensors, have significantly improved the ability to track IAQ in real time. These technologies allow for schools not only to ensure compliance with health standards but also to create dynamic environments that can quickly respond to changes in air quality. For example, a study conducted at a K-12 school in Melbourne demonstrated how these systems can monitor CO\({}_{2}\), PM, temperature, and humidity levels, providing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{
\begin{tabular}{c} **CO\({}_{2}\) Concentration (Above** \\ **Outdoor, ppm**) \\ \end{tabular} } & \multicolumn{2}{c}{**CO\({}_{2}\) Concentration**} & \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} \\  & & & \\ \hline
1 st (High Quality) & 550 & 1030 & 21–23 \\
2 nd (Moderate Quality) & 800 & 1280 & 20–24 \\
3 rd (Low Quality) & 1350 & 1830 & 19–25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: European standards for CO\({}_{2}\) and temperature (EN 16798-1:2019) [8].  
insights into how indoor climate affects students' emotions, engagement, and overall well-being [11].  
In line with these developments, the study [12] recommends the widespread adoption of IAQ sensors in schools. These sensors, compliant with the UL 2905 standard [13], monitor key parameters such as PM\({}_{1}\), PM\({}_{2.5}\), CO\({}_{2}\), temperature, humidity, and total volatile organic compounds (TVOCs). However, despite these advancements, [14] found that CO\({}_{2}\) levels in classrooms often exceed recommended limits, sometimes reaching as high as 4000 ppm due to insufficient ventilation. This highlights the ongoing need for better ventilation management to prevent discomfort and health risks associated with poor air quality.
# An Anomaly Detection Ensemble for Protection Systems in Distribution Networks
## 4 Case Study
In this study, we referred to the PCA anomaly detection model used in references [48; 49], reviewed the LOF anomaly detection model used in references [50; 51], and employed the IF anomaly detection model used in references [52; 53] as a reference for comparison. After a detailed comparison, it can be concluded that the KPCI-IF method still has a significant advantage in terms of timeliness. This advantage is reflected in several aspects. First, compared with the PCA model, the KPCI-IF method has a higher computational efficiency that reduces the running time of the program, enabling users to obtain results within a short period. Secondly, compared to the LOF model, the KPCI-IF method can avoid issues caused by the presence of duplicate points where the average reachable distance is zero and the local reachable density becomes infinite, causing computational difficulties, and it even has a lower computational cost, making it more suitable for high-dimensional and large-scale data. Finally, compared to the IF model, the KPCI-IF method has a faster processing speed and can more effectively process and identify anomalies in the data due to its excellent timeliness. When dealing with large amounts of real-time data input, the KPCI-IF method can quickly find and identify anomalies in these data. Therefore, our research results show that compared to other anomaly detection models, the KPCI-IF method has a certain advantage in terms of its timeliness performance and can better meet users' requirements for the rapid identification and processing of data anomalies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Anomaly Detection Method** & **Calculation Time (s)** & **Degree of Improvement (\%)** \\ \hline KPCI-IF & 0.7728 & — \\ PCI-IF & 1.0232 & 24.5 \\ IF & 1.8724 & 58.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of efficiency between methods.
## 3 Congestion Prediction Model
### Data Transmission Time
#### 3.5.1 Optimal Parameters Estimation: the Interval Reduction Method  
Finding the optimal parameter values minimizes the difference between the estimated traffic from the model and the actual network traffic. We simulated several conditions for the WSNs as a training set. We consider the training set as a \"miniaturized actual WSNs\". Nodes deployed random form; other conditions are default in NS-2. Conditions are shown in Table 3.  
It can be expressed as a minimization problem as follows:  
\[minZ(\alpha,\beta)=\frac{1}{2}\sum_{b}(T_{b}-\overline{T_{b}})^{2},\
u_{b}=\
u _{b}(t_{a}(\alpha,\beta)) \tag{13}\]  
The following Equation (14) can be obtained by substituting the above Equation (13) into the BPR Equation:  
\[minZ(\alpha,\beta)=\frac{1}{2}\sum_{b}\left[\overline{V_{a}}-C_{a}(\frac{ \overline{T_{a}}-1}{a})^{-\frac{1}{\beta}}\right]^{2} \tag{14}\]  
The minimization problem can be solved through various previously-presented optimization techniques. In this paper, the interval expected to have an optimal solution is precisely analyzed by reduction of the iterative calculation, known as the golden section search. As shown in Figure 7, the initial parameter \(\alpha^{0}\) was first fixed on the \(\alpha\)-axis. The optimal \(\beta^{0}\) value was calculated by a golden section search on the \(\beta\)-axis. Then, the optimal \(\alpha^{1}\) value was calculated again with \(\beta^{0}\) fixed. If this process is repeated several times, the optimal parameter \(\alpha^{*}\) and \(\beta^{*}\) can be obtained. The process to obtain the overall optimal solution is detailed as follows:  
[Step 1] Initialization: The actual amount of network traffic (\(\overline{V_{a}}\)), initial (\(\alpha^{0}\), \(\beta^{0}\)) setting, \(n=0\)  
[Step 2] By fixing \(\alpha^{n}\), \(\beta^{n}\) can be calculated by using a golden section search.  
The objective function used here is Equation 14 and \(\
u_{b}\) is the predicted network traffic.  
[Step 3] By fixing \(\beta^{n}\), \(\alpha^{n+1}\) can be calculated using a golden section search.  
[Step 4] Convergence Review: if \(|\alpha^{n+1}-\alpha^{n}|<\varepsilon\ ||\ \ \left|\beta^{n+1}-\beta^{n}\right|<\ \varepsilon\ \text{ then stop}\).  
If not, proceed to [Step 1].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Option** \\ \hline Number of nodes in 1 zone & 100, 500, 1,000 \\
1 Zone extent & 10 m\({}^{2}\), 100 m\({}^{2}\), 500 m\({}^{2}\) \\ Periodic Data(number of times/s) & 1/1, 1/5, 1/10, 1/60, 1/300 \\ Event Data Probability (1 times per 1 min) & 20\%, 40\%, 60\%, 80\%, 100\% \\ Number of Sink in 1 zone & 1, 5, 10 \\ Bit Error Rate & 20\%, 40\%, 60\%, 80\%, 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training set condition.
## 6 Sensitivity Analysis
### Sensitivity Analysis under Decentralised Decision-Making
#### 6.2.2 Impact of Logistics Cost-Effectiveness Factors  
We analysed the impact of changes in the logistics cost-effectiveness coefficient \(k\) on the optimal profit, optimal demand, and optimal decision-making in the supply chain, and to ensure that the range of values of \(k\) met the basic Assumption 5, taking the value of \(k\) between 20 and 60. Only the value of \(k\) was changed, and the values of other parameters were consistent with the base setting. The graphs describing the relationship between the optimal profit, optimal demand, optimal decision variables, and the logistics cost-effectiveness coefficient in supply chains with different channel types under decentralised decision-making were plotted.  
From Figures 9 and 10, it can be seen that in the dual-channel supply chain model under decentralised decision-making the profit \(\pi_{C}\) of the supply chain, the profit \(\pi_{S}\) of the producer, the profit \(\pi_{r}\) of the retailer, the market demand \(Q_{T}\) of the offline channel, and the demand \(Q_{O}\) of the online channel all decrease with the increase in the logistics cost-effectiveness factor \(k\). In the dual-channel supply chain model, the overall supply chain profit \(\pi_{C}\) increases significantly, the producer profit \(\pi_{S}\) increases more, the retailer profit \(\pi_{r}\) decreases slightly, and the market demand \(Q_{T}\) increases in the offline channel. Under decentralised decision-making, the producer's profit is higher than the retailer's  
Figure 8: Effect of \(k_{T}\) on optimal decision variables (FD).  
Figure 7: Effect of \(k_{T}\) on optimal profits and demand (FD).  
in all cases. As the logistics cost-effectiveness coefficient k increases, the overall supply chain profit differential between the dual-channel supply chain models decreases, and the producer's profit differential, the retailer's profit differential, and the offline market demand differential all decrease.  
Observing the slope of the curve, it can be found that as the coefficient of logistics cost-effectiveness k increases, the decreasing speed of optimal profit, optimal demand, and optimal decision variables is slower and slower, which indicates that as the unit logistics cost corresponding to the level of logistics service increases, the marginal benefits of decreasing profit, demand, and optimal decision variables brought by the increase in unit logistics cost are also decreasing, and the decision-making under higher unit logistics cost shows that the decline in the overall profit of the supply chain under decentralisation slows down.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 4 Experiment Evaluation  
### Experimental Environment and Parameter Settings  
To verify the effectiveness of the proposed cloud storage \"EC-Kad\" solution, we used three HP ProLiant DL380 Gen10 servers (manufacturer: Hewlett Packard Enterprise; Country: USA; primary production site: Houston, TX, USA), with the specifications including a Xeon 2.1 GHz Intel 16-core CPU, 64 GB DDR4 RAM, 2 TB HDD, and eight 1 Gbps Ethernet NICs, to build a cloud storage platform based on Hadoop. The three servers in the platform serve as three cloud data centers, and we virtualized the physical servers in each data center. One server had 6 virtual machines, while the other two servers had 7 virtual machines each. The cloud storage platform had a total of 20 virtual machines, and our experiment ran on the 64-bit Ubuntu operating system 20.04.2.0 LTS. In addition, the Hadoop version we used in the experiment was 3.3.2. Virtual machines from different data centers were interconnected through a VPN, and virtual machines from the same data center were directly connected to achieve collaborative computing among the virtual machines in the Hadoop clusters.  
On the private cloud storage platform built with Hadoop, we implemented the storage solution \"EC-Kad\" proposed in this paper. The implementation was primarily carried out in the form of cloud tasks submitted to the cloud data center to conduct experiments on the data storage process of cloud storage services. During data storage, our erasure coding module was mainly responsible for encoding and splitting the submitted storage files. Assuming that the size of an original file is \(M\), and the number of erasure-coded chunks for this file is \(k\), then the size of each data chunk is \(M\)/\(k\). If the last chunk is not a full block, then it is padded, and m represents the number of redundant data chunks.  
The Kademlia data retrieval module first generates a corresponding key for the encoded data blocks using SHA-1. The data are then stored on the node whose ID has the closest XOR distance to the key. The module performs iterative searches to quickly retrieve and merge the data chunks from the virtual nodes and then decodes them. The specific operations are implemented by the virtual machine work nodes assigned by the data center hosts, with all operations scheduled by the master node.  
We selected storage files of different sizes for the experiment. The file sizes were 100 MB, 200 MB, 300 MB, 400 MB, 500 MB, and 600 MB.
# Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles
## 4 Resource Allocation via Matching  
As can be observed in (3.2), the formulated problem is a nonconvex MINLP problem, which is difficult to solve in practical settings with large sets of vehicles and RBs. Since the interference only exists within each reuse pair consisting of a CUE and a VUE, problem (3.2) can be decomposed into two subproblems, which are power control and spectrum allocation. The power control subproblem has been addressed in [4; 8]. Hence, in this paper, we only consider the spectrum resource allocation for problem (3.2). We define \(\mathbb{E}\{R_{m,k}^{C}\}\) as the optimal ergodic capacity solutions of the power control subproblem. \(R_{m,k}^{C}\) is the transmission capacity when CUE \(m\) reuses RB \(n\) with VUE \(k\) under the optimal power control \(\{P_{m,n}^{C}\},P_{k}^{V*}\}\), which is  
\[R_{m,k}^{C}=W\text{log}\left(1+\frac{P_{m,n}^{C}\ast^{\alpha}R_{m,n}^{C}S_{m,n }^{C}}{\sigma^{2}+P_{k}^{V\ast}\ast^{\alpha}C_{k,m,n}^{C}S_{k,m,n}^{C}}\right) \tag{12}\]  
Then, problem (3.2) can be transformed into:\[\max\sum_{m\in\mathcal{M}}\sum_{k\in\mathcal{K}}x_{m,k}U_{a}\left( \mathbb{E}\Big{\{}{R_{m,k}^{C}}^{*}\Big{\}}\right)\] \[\text{s.t.}\] \[\text{C1:}\quad 0\leq\sum_{m\in\mathcal{M}}x_{m,k}\leq 1,\ \forall k \in\mathcal{K} \tag{13}\] \[\text{C2:}\quad 0\leq\sum_{k\in\mathcal{K}}x_{m,k}\leq n_{q},\ \forall m \in\mathcal{M}\] \[\text{C3:}\quad x_{m,k}\in\{0,1\},\ \forall m\in\mathcal{M}, \forall k\in\mathcal{K}\]  
where constraint C1 represents that one VUE is allowed to access the RBs of a single CUE, and constraint C2 represents that the spectrum of one CUE can be shared with at most \(n_{q}\) VUEs.
# Construction of Nonblocking Wavelength/Space Switches with AWGs and WSSes
## 2 A New Decomposition Technique for Constructing AWG/WC-Based Nonblocking Switches
### Wavelength Dilated Network
Conceptually, it is much simpler than that described in [11]. Instead of decomposing the AWGs and WC modules in the original topology as done in [11], we show that it is possible to carry out the decomposition in the expanded space domain directly. This approach is simpler and more intuitive. Note that in the wavelength dilated topology (as such Figure 1b), the node stages are numbered from 1 and the links from 0. Each output of node stage 1 can be written as a two-tuple (\(x_{1}\), \(x_{2}\)), where \(0\leq x_{1}\leq r-1\), \(0\leq x_{2}\leq n-1\), where \(x_{1}\), called the _switch_ field, represents the switch on which the link is located and \(x_{2}\), called the _member_ field, represents the link's number inside \(x_{1}\). Similarly, an input of node stage 2 can also be written as a two-tuple (\(y_{1}\), \(y_{2}\))  where \(0\leq y_{1}\leq n-1\), \(0\leq y_{2}\leq r-1\). Similarly, \(y_{1}\) is the switch field and \(y_{2}\) the member field. In the three-stage space equivalent topology, the interconnection pattern between the first and the second node stage can be written as a _mapping_ from the outputs of node stage 1 to the inputs of node stage 2. Given (\(x_{1}\), \(x_{2}\)), the mapping can be described as:  
\[(x_{1},x_{2})\rightarrow(y_{1}=x_{2},y_{2}=x_{1}) \tag{2}\]
## 5 Implementation and Deployment
### Message Broker  
V2X is a typical distributed system with high-speed dynamic topology changes and many participants. The TS must achieve high-concurrency data transmission, affecting its reliability and availability.  
Modern messaging systems are built on top of Message-Oriented Middleware (MOM) architectures, leveraging two paradigms, the publish-subscribe mechanism and message queue, to help build scalable and loosely coupled systems [35]. Familiar message brokers include Kafka, ActiveMQ, and RabbitMQ. Among the three, Kafka has the best throughput and high-availability performance, ActiveMQ is more frequently used in enterprises, and RabbitMQ has a good concurrency capability owing to the Erlang development language [35; 36; 37]. Furthermore, RabbitMQ [38] supports multiple open-source standard protocols.  
We selected RabbitMQ as the message broker for high-concurrent information transmission between modules.
# GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks
## 3 Multi-Path Routing Algorithm Based on the Improved Breadth First Search  
Breadth-first search algorithm (BFS) is a search algorithm that visits neighbor nodes sequentially from the source, then visits unsearched neighbor nodes sequentially layer by layer, and finally stops traversing once the destination is searched [17]. But only one shortest path is obtained, and other shortest paths cannot be output in BFS, which may result in one of the neighbor nodes of the source being used for data forwarding all the time, causing network congestion. Therefore, this paper designs a new multi-path algorithm, which is an improvement on the breadth-first search algorithm. The algorithm can continuously iterate  
Figure 2: Architecture of the proposed algorithm.  
over the link information to output all shortest paths. It has two key steps, breadth-first search, and multipath output.