esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results  
### Dataset Description  
To evaluate the effectiveness of the proposed method, we used the bearing dataset, generated by the University of Cincinnati Center for Intelligent Maintenance Systems (IMS). The IMS bearing test rig is illustrated in Figure 5. It consists of an AC motor coupled to the shaft via a rub belt and four double row bearings installed on the shaft. The sampling rate of the record data was 20 kHz under the radial load of 6000 lbs for each of the four bearings at the constant rotation speed of 2000 rpm. Data acquisition was made every 10 minutes and for each assessment interval; a 1-s vibrational signal snapshot, which included 20,480 points, was recorded.  
The IMS bearing datasets contain three run-to-failure tests; both normal stage and failure stage data exist in each test. For each test, data collection continued until any failure in inner race, outer race, or roller elements occurred at least for one bearing. At the end of the test, bearings 3 and 4 from the first dataset, bearing 2 from the second dataset, and bearing 3 from the third dataset showed signs of failure. In the current work, the datasets for all three cases were considered. The time-domain vibrational signals for the four bearings are shown in Figure 6. For each bearing datum, the failure threshold was identified by the adaptive failure threshold method, and healthy samples and faulty samples were  
Figure 4: The overall convolutional autoencoder architecture used.  
established. Details of IMS bearing datasets are described in Table 1. Since different bearing defect frequencies are proportional to the revolutions per minute, all defects occurring in the bearing are revealed in every revolution. Therefore, given the acquisition frequency of 20 kHz and rotation speed of 2000 rpm, the vibrational signals were split into equal chunks of length \(L_{m}=600\) points. For each chunk, the CWT was performed, and the power spectrum image was obtained.  
Figure 5: Intelligent maintenance systems (IMS) test rig [45].  
Figure 6: Original vibration signals of the IMS bearing dataset [45]: (**a**) subset 1 bearing 3, (**b**) subset 1 bearing 4, (**c**) subset 2 bearing 1, and (**d**) subset 3 bearing 3.
# PpyNT/NR/NBR Composites with Excellent Microwave Absorbing Performance in X-Band
## 3 Results and Discussion
### MA Performance of PPyNT/NR/NBR Composites
#### 3.2.2 Optimization of the NR/NBR Blend Ratio
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \(\mathbf{RL_{min}}\) & **EAB** & **Thickness** & **Filler Loading** & **Ref.** \\  & **dB** & **GHz** & **mm** & **(wt.\%)** & **Ref.** \\ \hline Fe-Ni/C/NBR & \(-8\)—\(-10\) & 0 & 1.5 & 70 & [11] \\ CBSFr/NBR & \(-16\) & 1.5 & 5 & 20.73 & [5] \\ CB/NR & \(>\)\(-5\) & 0 & 5 & 14.2 & [8] \\ CCB/NR/ENR (70/30) & \(>\)\(-10\) & 0 & 5.5 & 30 & [14] \\ MWCNT/HNBR & \(-21.3\) & Not mentioned & 2 & 8.26 & [6] \\ rGO@Fe\({}_{3}\)O\({}_{4}\)/SR & \(-22.5\) & 2.1 & 1.5 & 50 & [3] \\ NiZn/TPNR & \(-38.3\) & Not mentioned & 7 & 12 & [35] \\ RGO/NR & \(-57\) & 3.8 & 3 & 10 & [4] \\ PPyNT/NR/NBR (90/10) & \(-56.67\) & 3.7 & 2.9 & 5.24 & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the MA performance of the PPyNT filled NR/NBR (90/10) composite with other rubber-based absorbers in the frequency range of 8.2–12.4 GHz. Figure 5: Three-dimensional RL plots for 6 phr PPyNT filled NR/NBR composites with different ratios of NR/NBR: (**a**) 10/90; (**b**) 30/70; (**c**) 50/50; (**d**) 70/30; (**e**) 90/10.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 3 System Model and Problem Description  
### System Model  
Mining operations encompass the critical production stages of \"drilling, blasting, extraction, loading, transportation, and dumping\". Drilling and blasting predominantly occur at the surface, requiring substantial computational resources for training sophisticated mining algorithms. Conversely, extraction, loading, and transportation activities are typically conducted in underground mining areas. The spatial limitations and power supply constraints inherent in such underground environments necessitate the use of smaller, low-power devices. Consequently, deploying complex computational models on these resource-constrained devices is often impractical, posing significant challenges to efficient task execution in these scenarios. Moreover, large volumes of data are typically transmitted to surface-level facilities for processing before being returned to the underground operations, resulting in significant latency and reduced overall system reliability. Thus, the strategic deployment of edge nodes directly at the mining site emerges as a crucial approach, enabling task offloading to local edge processing. This approach not only mitigates latency but also enhances the system's robustness. This section focuses on addressing the complex problem of identifying optimal deployment locations for edge nodes, specifically tailored to both fixed and mobile devices. For clarity, Table 1 defines the symbols and notations used throughout this section.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 3 System Model and Problem Statement
### Cognitive Metrics
#### 3.2.2 Weighted Average of Received Signal Strength  
The link quality between the child node and the parent node is highly dependent on distance and obstacles. If a node chooses the node with the shortest hop to the sink node as a parent node but the link quality is poor, packets may be lost and additional energy consumption may increase owing to retransmission. To build a stable path, we use the received signal strength between the child node and the candidate parent node. Each node can easily obtain the received signal strength when it receives a hello message from neighboring nodes.  
Contrary to the cognitive metrics proposed in our previous study, we use weighted moving average (WMA) to prevent sudden changes in measured values. The WMA gives more weights on recent data and less on past data. That is, we multiply each measured value by a weight. Here, the time \(t\) is based on the time of a hello message. We define a \(n\)-period WMA of the received signal strength (\(P\)) for neighboring node \(i\) as follows:  
\[P_{i}=\frac{(w_{1}*P_{i}^{t-1}+w_{2}*P_{i}^{t-2},\ldots,+w_{n}*P_{i}^{t-n})}{( w_{1}+w_{2},\ldots,+w_{n})}, \tag{1}\]
## 2 Materials and Methods
### Preparation and Characterization of CoPc@s-BC  
To prepare CoPc@s-BC, the following steps were carried out: (1) 20 mg of pure BC was incubated with a 30 mmol/L NaIO\({}_{4}\) solution and reacted for 8 h. The resulting oxidized BC was thoroughly washed with ultrapure water and dried at 50 \({}^{\circ}\)C for 12 h. The content of aldehyde groups on oxidized BC was determined according to the literatures with minor modifications [42; 43]. (2) The dried, oxidized BC was submerged into a spacer solution (DT or TT or TP or PH) and shaken on a temperature-controlled shaker. The content of the attached spacer was indirectly determined by measuring the number of amino groups on the BC substrate using a ninhydrin-based monitoring system, as described in the literature [30]. Ninhydrin reagent can react with primary amines to form a colored complex which is soluble in C\({}_{2}\)H\({}_{5}\)OH/H\({}_{2}\)O mixed solvent and highly conjugated, with a strong absorption at ca. 570 nm. Two hundred milliliters of ninhydrin reagent and 100 \(\mu\)L of H\({}_{2}\)O were added to 1 mg of spacer-attached BC and heated in a 100 \({}^{\circ}\)C water bath for 1 h. Five milliliters of C\({}_{2}\)H\({}_{5}\)OH/H\({}_{2}\)O (50:50, \(v/v\)) mixed solvent was added, and the mixture was analyzed on a UV-vis absorption spectrometer (UV-2450) at the wavelength of maximum absorbance: 570 nm. (3) The spacer-attached BC was submerged into 12 mL of glutaraldehyde solution and shaken at 25 \({}^{\circ}\)C for 2 h. The glutaraldehyde-activated BC was washed 3 times with ultrapure water and then dried at 50 \({}^{\circ}\)C for 12 h. The residual attached spacer content after glutaraldehyde activation was determined with the above-mentioned ninhydrin method. (4) CoPc@s-BC was harvested by submerging the glutaraldehyde-activated BC in a 2 \(\times\) 10\({}^{-2}\) mol/L CoPc solution and reacted for 2 h at 25 \({}^{\circ}\)C. The product was washed 3 times with dimethylformamide, 3 times with ultrapure water and then dried at 50 \({}^{\circ}\)C for 12 h. The content of immobilized CoPc was calculated according to the cobalt content on CoPc@s-BC, measured by atomic absorption spectrometry (Thermo solar M6, Thermo  
Figure 1: Chemical structures of (**a**) diethylenetriamine (DT); (**b**) triethylenetetramine (TT); (**c**) tetraethylenepentamine (TP); and (**d**) pentaethyleneexamine (PH).  
Fisher, Waltham, MA, USA). All of the modification and functionalization processes are schematically shown in Figure 2. The elemental compositions and chemical bonding of BC, oxidized BC, TP-attached BC, glutaraldehyde BC and CoPc@TP-BC were analyzed by X-ray photoelectron spectroscopy (XPS). XPS spectra were recorded on a Kratos Axis Ultra XPS system with Al (mono) K\(\alpha\) irradiation (hv = 1486.6 eV). The binding energy peaks of the XPS spectra were calibrated by placing the principal C 1s binding energy peak at 284.6 eV.
# Active Learning Strategy for Surrogate-Based Quantile Estimation of Field Function
## 3 Active Learning for Surrogate-Based Stochastic Process Quantile Estimation
### Infill Criterion
Figure 2: (a) output stochastic process realizations and 99% quantile prediction, (b) the GPs involved in the surrogate model of the output stochastic process present uncertainty in the prediction and different GP realizations may be sampled (red curves), (c) for each GP realization, a quantile of the output stochastic process may be estimated (black dot lines), (d) based on the set of quantile estimations an confidence interval on the exact quantile may be estimated. ```
Inputs : \(\mathcal{U}_{M},\mathcal{Y}_{M}\), \(\bar{\mathbf{u}}\), \(\bar{\mathbf{X}}(t,\mathbf{U})\) Initialization: \(\eta\), \(\beta\), \(N_{Q}\), \(N_{GP}\) (1) Define new input and output \"virtual\" extended datasets \(\mathcal{\tilde{U}}_{M+1}=\{\mathbf{u}_{1},\ldots,\mathbf{u}_{M},\bar{\mathbf{ u}}\}\) and \(\mathcal{\tilde{Y}}_{K_{M+1}}=\left\{y_{1}=\xi_{k}(\mathbf{u}_{1}),\ldots,y_{M }=\xi_{k}(\mathbf{u}_{M}),y_{M+1}=\underline{\xi}_{k}(\bar{\mathbf{u}})\right\}\) (2) Train the new \"virtual\" GPs \(\xi_{k}^{s}(\cdot)\) based on \(\mathcal{\tilde{U}}_{M+1}\) and \(\mathcal{\tilde{Y}}_{K_{M+1}}\) (3) Generate \(N_{GP}\) Gaussian process trajectories for the \(N_{KL}\) GPs in KL \(\left\{\xi_{k}^{s(1)}(\cdot),\ldots,\xi_{k}^{s(s)}(\cdot),\ldots\xi_{k}^{s(N_ {GP})}(\cdot)\right\}\) (4) for s = 1 : \(N_{GP}\)do (a) Define a new \"virtual\" surrogate model: \(\hat{\mathbf{X}}^{(s)}(t,\mathbf{U})=\hat{\mu}(t)+\sum_{k=1}^{N_{KL}}\sqrt{ \lambda_{k}}\xi_{k}^{s(s)}(\mathbf{u})\hat{\mathcal{L}}_{k}(t)\) (b) Carry out quantile estimation with Monte Carlo sampling: \(\hat{q}_{\eta}^{(s)}(t)=\hat{\Phi}_{\bar{\mathbf{X}}^{(s)}(t,\mathbf{U})}^{-1}(\eta)\) end for (5) Define the set \(\mathbb{Q}_{N_{GP}}=\left\{\hat{q}_{\eta}^{(1)}(t),\ldots,\hat{q}_{\eta}^{(s)} (t),\ \ \ldots,\hat{q}_{\eta}^{(N_{GP})}(t)\right\}\) of \(N_{GP}\) quantiles (6) Determine \(\bar{\hat{q}}_{\eta}(t)\) and \(\underline{\hat{q}}_{\eta}(t)\) as quantiles with \(\beta\) and \(1-\beta\) levels over the set \(\mathbb{Q}_{N_{GP}}\). (7) Compute \(A(\bar{\mathbf{u}})=\int\left(\bar{\hat{q}}_{\eta}(t)-\underline{\hat{q}}_{ \eta}(t)\right)\mathrm{d}t\) over the time mesh \(\mathcal{T}_{n_{v}}\) return\(A(\bar{\mathbf{u}})\)
```  
**Algorithm 1**Confidence interval area calculation for quantile estimation based on GP trajectories for a candidate sample \(\bar{\mathbf{u}}\)
# Concrete with Organic Waste Materials as Aggregate Replacement
## 2 Materials and Methods
### Concrete Mix Design  
Ordinary Portland Cement CEM I, 42.5R traditional-grade, supplied by the company Dragon Alfa located in Sharpness, UK, was used as the binder cement. Sands and crushed granules were used as fine and coarse aggregates for each mixture. The sand used was graded as having granules below 4 mm in size. The coarse aggregate grade was between 4 mm and 10 mm in diameter for the granules. The waste materials replacing sand (see Section 2.1) are readily available without the need for further processing, with a particle size of less than 4 mm.  
A constant water-to-cement ratio of 0.45 was used in all concrete mixes. A ratio of 1:2:4 was considered for cement, fine aggregate, and coarse aggregate for all of the batches. In Table 1, the composition of the different types of concrete is presented. Traditional concrete, with only sand as the fine aggregate, was used for comparative purposes. The alternative concrete, referred to as concrete with organic waste from now on, had the same composition as traditional concrete, but 10% of the fine aggregate was replaced with organic waste. The preparation of the materials, batching, mixing, and sampling were carried out in accordance with BS 1881 part 125: Methods for mixing and sampling fresh concrete in the laboratory [26]. Concrete cubes (100 \(\times\) 100 \(\times\) 100 mm) and prisms (100 \(\times\) 100 \(\times\) 500 mm) were prepared and cured in water until testing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & \begin{tabular}{c} **Portland Cement** \\ **(kg)** \\ \end{tabular} & **Water (kg)** & \begin{tabular}{c} **Coarse Aggregate** \\ **(kg)** \\ \end{tabular} & \
\begin{tabular}{c} **Fine Aggregate** \\ **(kg)** \\ \end{tabular} &
\begin{tabular}{c} **Organic Waste** \\ **(kg)** \\ \end{tabular} \\ \hline Traditional concrete & 316 & 142 & 1276 & 704 & - \\ Concrete with organic waste & 316 & 142 & 1276 & 634 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete mix composition in 1 m3.
## 1 Introduction
In particular, the LCI is a compilation of all mass flows and emissions associated with the activities within the waste management system as well as upstream and downstream activities linked to the management of the waste. It relies on recent, representative, and accurate data such as waste types and their individual material fractions, detailed physico-chemical composition, mass balances for all relevant material fractions, energy balances for all processes and technologies, records of the emissions, and inventories of all relevant upstream and downstream processes [19]. The LCI covers all consumptions and emissions of environmental importance [18]. However, it is difficult to find case-specific data or LCI that include waste composition, energy and resource inputs, and material substitution in an LCA implementation study [20]. Establishing a relevant and high-accurate LCI is often demanding but crucial since it is the technical basis for assessing the waste management system. Existing models and software offer some assistance and databases in setting up the LCI, but it is always important to ensure relevance and consistency in the technical data of the specific study. LCA methodologies and advanced software such as EASETECH (developed by DTU) are based on fragmentation analysis to follow elemental balances throughout the processes. Inventories on existing facilities managing the OFMSW from unsorted mixed waste are scarce [21]. In most cases, the treatment of such materials is held by private facilities, and the available data concerning full-size treatment are not published. Although several composing technologies have been studied in European countries, a few have developed LCI for composting systems treating segregated biowaste [22]. The available information about the materials of OFMSW and nutrient flows are inconsistent, making it difficult to develop alternative scenarios during urban planning [23]. Therefore, there is an increasing need for predictive models to support environmental policy and decision-making. Fewstudies have investigated compositing of mechanically sorted OFMSW obtained at MBT plants. Thus, this research is urgently needed [24]  
This study aims to create a life cycle inventory (LCI) based on the mechanical recycling and compositing facility in Chania (Crete, Greece). The objectives are to model the compositing unit by mapping the fragmental mass balance between its sub-processes, to monitor the release of C and N as emissions to the environment, and to record the water, electricity, and fuel consumption for the treatment of one Mg of OFMSW introduced in the facility. Two-year waste sampling and data collection are comprehensively analyzed. The outcomes from this study can be used as a tool for the waste management practitioners to foresee the outputs and cost of treating OFMSW and source segregated biowaste.
## 4 Experiments and Results
The correlation index between the SNR and error (point-line distance) was then computed for both methods, confirming a positive correlation between the amount of noise and the resulting error. It was observed that SIFT was more sensitive to noise levels than YOLO. The literature highlights that SIFT is a feature-based method (keypoints) and is more sensitive to noise because the calculation of local gradients can be directly affected. YOLO, on the other hand, is a deep neural network-based model trained to detect objects in images, making it more \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Group** \\ \end{tabular} } & \multicolumn{2}{c}{**(\%) Point-Line**} & \multicolumn{2}{c}{**(\%) Line Angle**} \\  & \multicolumn{2}{c}{**Distance \textless{} 3 mm**} & \multicolumn{2}{c}{**Deviation \textless{} 3\({}^{\circ}\)**} \\ \cline{2-5}  & **YOLO** & **SIFT** & **YOLO** & **SIFT** \\ \hline
1 & 100.00 & 86.67 & 100.00 & 80.00 \\ \hline
2 & 93.33 & 66.67 & 93.33 & 73.33 \\ \hline
3 & 100.00 & 85.71 & 92.86 & 85.71 \\ \hline
4 & 93.33 & 80.00 & 100.00 & 73.33 \\ \hline
5 & 100.00 & 86.67 & 100.00 & 86.67 \\ \hline
6 & 93.33 & 80.00 & 93.33 & 86.67 \\ \hline
7 & 93.33 & 73.33 & 93.33 & 80.00 \\ \hline
8 & 93.33 & 66.67 & 100.00 & 73.33 \\ \hline
9 & 100.00 & 86.67 & 100.00 & 80.00 \\ \hline
10 & 93.33 & 93.33 & 93.33 & 86.67 \\ \hline Mean & 96.00 & 80.57 & 96.62 & 80.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy comparison based on the metrics of point–line distance and line angle deviation. In both metrics, the YOLO-based method had a higher accuracy than the SIFT-based method. robust to moderate noise compared to feature-based methods, as it uses deep convolutions that capture global contextual information. In the statistical significance tests to validate the performance difference between the SIFT-based and YOLO-based approaches, regarding the point line distance metric, an employed \(t\)-test resulted in a \(p\)-value of 0.000087. Compared to the significance level of \(\alpha=0.05\), we can conclude that there is statistically significant evidence to reject the null hypothesis and affirm that there is a statistical difference between the groups, confirming the conclusion that the YOLO-based method demonstrates better performance. Similarly, for the line angle deviation metric, the \(t\)-test resulted in a \(p\)-value of 0.00000595, which also rejects the null hypothesis and confirms a statistically significant difference for \(\alpha=0.05\).
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
## 4 Shear Modulus of Sand with Different Saturations  
The shear modulus \(G\) is an important parameter in the characterization of the dynamic properties of soil. Normally, when the shear strain \(\gamma\) exceeds a certain threshold (\(\gamma>10^{-5}\)), the shear modulus of soil decays with the increase in shear strain. There are many factors that affect the decay characteristics in the dynamic shear modulus of soil and this study is mainly focused on the influences of saturation and net stress on the dynamic shear modulus of sand.
## 5 Conclusions and Future Work  
In this work, we present VSSE, a verifiable DSSE scheme based on blockchain for 6G-based intelligent application systems. This scheme ensures forward privacy of data in the 6G network through a state chain structure. VSSE leverages the immutable nature of blockchain to perform verification operations on the blockchain. Meanwhile, it combines bitmap index and MAC technology to achieve dynamic verification of search results. Our scheme also possesses effective access control functionality. Finally, the evaluation of the scheme's functionality and performance demonstrates that VSSE aligns with the requirements of 6G-based intelligent application systems.  
Regarding future work, in this paper, we conducted a security analysis focusing solely on threats related to update and search operations. However, potential threats still exist in the verification process between the cloud server and the blockchain, as well as in the access control mechanism for users with different privileges. Therefore, a more comprehensive security analysis of VSSE is needed, including the consideration of various attack scenarios, such as collusion attacks, and the proposal of corresponding security measures. Furthermore, although the scheme presented in this paper exhibits high search efficiency for single keyword, the search cost increases linearly with the number of keywords when users perform conjunctive searches involving multiple keywords. This is not favorable for a truly practical DSSE scheme. Therefore, our future research direction will focus on extending our scheme from single-keyword searches to multi-keyword searches while maximizing search efficiency. When our scheme achieves both rich expressive capability and efficient search performance, along with dynamic verification functionality, it will represent a more ideal DSSE solution in the context of 6G networks.  
Writing--original draft, T.P.; Writing--review & editing, J.Z.; Funding acquisition, B.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Project of China under Grant 2019 YFB2102303, and in part by the National Natural Science Foundation of China under Grant 61971014.  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 8: Time of verification. (**a**) The influence of varying numbers of matching files on the verification time for each scheme. (**b**) The impact of the bitmap index length on the verification time of VSSE. [PERSON] et al. (2020) refers to the scheme in ref. [32].