esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Resistive Sensor Design Utilizing the Geometric Freedom of Additive Manufacturing
###### Abstract  
Direct additive manufacturing (AM) of sensors has in recent years become possible, but still remains a largely unexplored area. This work proposes a novel resistive sensor design that utilizes the geometric freedom offered by AM, especially by material extrusion, to enable a customizable and amplified response to force and deformation. This is achieved by using a multi-material design made of an elastomer and an electrically conductive polymer that enables a physical shortening of the conductive path under compressive load through a specific definition of shape. A number of different variants of this novel sensor design are tested, measuring their mechanical and electrical behavior under compression. The results of these tests confirm a strong resistive response to mechanical loading. Furthermore, the results provide insight into the influencing factors of the design, i.e., the gap size between the conductive pathing and the stiffness of the sense element support structure are found to be primary influencing factors governing sensor behavior.  
3D printing; additive manufacturing; design for additive manufacturing; resistive sensors; electrical conductive filament; material extrusion; fused deposition modeling +
Footnote †: journal: Applied Sciences
# Performance Evaluation of Carbon Black Nano-Particle Reinforced Asphalt Mixture
## 3 Results and Discussion
### Study of CBNPs Effect on Rut Depth  
A wheel tracking test was performed to study the effect of CBNPs on the rut depth. Graphs between number of wheel passes and rut depth at 40 \({}^{\circ}\)C and 55 \({}^{\circ}\)C are shown in Figure 5a,b. Base asphalt showed the highest rut depth values against the standard 10,000 wheel passes. Addition of 5% CBNPs reduced the rut depth to 3 mm and 5.6 mm at 40 \({}^{\circ}\)C and 55 \({}^{\circ}\)C respectively (after 10,000 wheels passes). Furthermore, 10% CBNPs-modified asphalt has shown the maximum reduction in rut depth at both temperatures and after 10,000 passes i.e., 20% reduction at 40 \({}^{\circ}\)C and 37.8% reduction at 55 \({}^{\circ}\)C. Nano-particles increase the surface area of the asphalt binder and may enhance the interaction within the asphalt which results in more resistance against rutting [28]. [PERSON] et al. experienced an increase in rutting resistance when nano-carbon black modified asphalt was subjected to 8000 loading cycles during an asphalt pavement analyzer rutting test. This illustrates that CBNPs successfully enhances the high-temperature performance of asphalt by increasing its resistance against rutting.  
Figure 5: (a) Graph of rut depth and number of passes at 40 \({}^{\circ}\)C; (b) graph of rut depth and number of passes at 55 \({}^{\circ}\)C.
# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
## 2 Signal Sampling and Reconstruction Using Compressed Sensing
### Compressed Sampling of Broadband Oscillation Signal  
When the signal is compressible or sparse in the transform domain, an observation matrix that is independent of the sparse transform basis can be used to project the original high-dimensional signal into a low-dimensional space, allowing the original signal to be represented by a small amount of observation data [20]. Therefore, the compression and sampling of broadband oscillation signals mainly involve sparse transform basis selection and the sensing matrix calculation.  
1. Sparse transformation basis selection. Assuming that the broadband signal of length \(N\) is compressible, then, under the sparse matrix \(\Psi\), the coefficient vector \(\theta\) represents the broadband signal \(x\): \[x=\sum_{i=1}^{N}\theta_{i}\Psi_{i}=\theta\cdot\Psi\] (1) where \(\theta\) represents the coefficient vector and \(\Psi\) represents the sparse matrix.
2. Compressing the signal. Assuming that the sparsity of the coefficient vector \(\theta\) is \(K\) (\(K<<N\)), the measurement matrix \(\Phi\) can be used to compress the broadband signal; the compressed signal is expressed as:\[y=\Phi\cdot x \tag{2}\]  
where y indicates the compressed signal, and the length is \(M\) (\(M<<N\)); the measurement matrix is unrelated to the sparse matrix \(\Psi\).
3. Calculating the sensing matrix. The sensing matrix \(\Theta\) is obtained by inserting Equation (1) into Equation (2). \[y=\Phi\cdot\Psi\cdot\theta=\Theta\cdot\theta\] (3)  
According to Equation (3), the sensing matrix \(\Theta\) is determined by \(\Phi\) and \(\Psi\). The frequency domain coefficient of the broadband oscillation signal is not 0, only at the oscillation point, so it has a certain sparsity in the frequency domain. The discrete data points are transformed into the frequency domain on a discrete cosine transform (DCT) basis. DCT is expressed as follows:  
\[\left\{\begin{array}{l} X(0)=\dfrac{1}{\sqrt{N}}\\ X(k)=\sqrt{\dfrac{2}{N}}\sum\limits_{n=0}^{N-1}\left(x(n)\cos\dfrac{(n+0.5)_{ \pi}}{N}\right)\end{array}\right.\quad(k=1,2,\cdots,N-1) \tag{4}\]  
In the reconstruction process of the compressed signal \(y\), a Gaussian random matrix unrelated to the sparse matrix \(\Psi\) is selected as the measurement matrix \(\Phi\), which follows a normal Gaussian distribution.
# A Comparative Analysis of High Availability for Linux Container Infrastructures
## 3 Materials and Methods
### LXC and Proxmox as a Container Orchestrator  
Tools used in container virtualization include LXC. This is an operating system-level virtualization that allows multiple isolated containers to run and virtualize simultaneously. LXC uses the features of the Linux system to limit and prioritize system resources without the need to start a virtual machine. Namespaces are used to isolate the access of a given container within the operating system, allowing the ownership of the container files to be separated from the ownership of the operating system files themselves.  
LXC is able to create a virtual environment that isolates applications from other running applications on the host system and ensures that the applications do not share resources. In this way, applications can run in the container independently of other applications and system resources. LXC offers fast creation and the launch of virtual environments, simplifies application development and testing, and reduces server management costs. In addition, it provides the ability to create layers for containers, allowing multiple containers to be created that share the same base but differ in specific applications or dependencies. This leads to savings in disk space and allows easier and faster updates [23; 24; 25].  
Proxmox or Proxmox Virtual Environment (Proxmox VE) is a converged open source environment used in container virtualization. Its web-based environment enables the management and orchestration of operating systems and virtualized environments. Promoxsubsupports the virtualization of LXC containers and their use in high availability, Figure 1. To manage and orchestrate containers in Proxmox, a cluster manager is used to enable interconnection and communication between Proxmox instances. The manager provides redundancy and error detection through a method called fencing. Proxmox has several fencing options, such as external power switches, network-level node isolation, and the use of timers to recover failed nodes. If a node cannot be automatically recovered, a manual intervention by an administrator is required [26; 27; 28].
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 6 Conclusions  
This study presents a novel wireless fire monitoring system tailored for industrial environments, leveraging MQTT, ESP-NOW, and a radio environment map (REM) to address the unique challenges of such settings. The system's architecture offers a flexible, scalable, and energy-efficient solution for real-time fire detection, capable of maintaining consistent performance, even in expansive and interference-prone industrial layouts. By employing REM-guided node placement, the system achieved zero packet loss, enhancing the reliability of data transmission over extended distances and through multiple relay nodes.  
The integration of ESP-NOW and MQTT further enhanced the system's efficiency, achieving low-latency communication and minimal power consumption. This combination makes the system particularly suitable for battery-operated sensors deployed in remote or hard-to-access areas. The robust framework supports real-time detection and transmission of fire alerts, providing a scalable, resilient, and cost-effective solution that meets the rigorous demands of industrial safety applications.  
Future research will explore expanding the system's range and scalability by integrating additional protocols, such as LoRaWAN. This integration will enable coverage in even larger or more challenging terrains, making the system suitable for wide-area monitoring in diverse industrial settings. Further refinement of anomaly detection models through advanced machine learning techniques will improve the accuracy of fire detection, reducing false positives and enhancing operational efficiency. Additionally, incorporating adaptive algorithms and real-time network optimization tools will allow dynamic reconfiguration of node placements, addressing environmental changes, such as machinery layout shifts or the emergence of new obstacles. These advancements aim to enhance the system's versatility, reliability, and applicability across an even broader range of industrial scenarios.  
Conceptualization, T.H. and I.K.; methodology, T.H., M.U., R.U. and T.A.; validation, T.H., M.U., R.U. and T.A.; writing--original draft preparation, M.U.; formal analysis, supervision, funding acquisition, and final review, I.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) under Grant 2021 RIS-003.  
Not applicable.  
The dataset used is available from the references cited.  
We thank [PERSON] for his support.  
The authors declare they have no conflicts of interest related to the publication of this paper.
## 3 Results
### Sociodemographic Data of AMPH-add Participants
#### 3.3.3.  
AST to ALT Ratio  
Interestingly, the AST/ALT ratio was significantly (\(p\) < 0.001) increased in AMPH-adds compared to the corresponding normal healthy control group. The serum AST/ALT ratio increased from 0.84 \(\pm\) 0.084 to 7.48 \(\pm\) 0.59 (Table 3 and Figure 4c).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **ALT** & **AST** &
\begin{tabular}{c} **AST/ALT** \\ **Ratio** \\ \end{tabular} & **ACT** & **PEDF** \\ \hline AST (r) & \(-\)0.617 *** & - & - & - & - \\ \hline Sig. & 0.000 & & & & \\ \hline AST/ALT ratio (r) & \(-\)0.800 *** & 0.915 *** & - & - & - \\ \hline Sig. & 0.000 & 0.000 & & & \\ \hline ACT (r) & \(-\)0.296 & 0.183 & 0.250 & - & - \\ \hline Sig. & 0.080 & 0.285 & 0.142 & & \\ \hline PEDF (r) & \(-\)0.618 *** & 0.551 *** & 0.651 *** & 0.237 & - \\ \hline Sig. & 0.000 & 0.000 & 0.000 & 0.164 & \\ \hline MIP4 (r) & \(-\)0.052 & 0.069 & 0.077 & 0.078 & 0.181 \\ \hline Sig. & 0.772 & 0.699 & 0.663 & 0.661 & 0.306 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlations between the biomarkers and proinflammatory cytokines and liver transaminases (ALT and AST) among all study groups.  
Figure 4: Comparison of ALT (**a**), AST (**b**) and AST/ALT ratio (**c**) serum level AMPH-adds and control. Data are expressed as mean \(\pm\) standard error. Statistical analysis was carried out using unpaired Student’s \(t\)-test. ***: statistically significant at \(p\) < 0.001.  
Pearson's Correlation between the Biomarkers and Proinflammatory Cytokines and Liver Transaminases (ALT and AST) in Both AMPH-adds' Serum and Control Subjects  
Because amphetamine-induced liver injury can explain the change in neurodegenerative biomarkers, the correlation between liver function markers and neurodegenerative biomarkers has been investigated.  
As shown in Table 3, there was significant inverse correlation between PEDF and ALT (\(r=-0.618\), ***\(p=0.001\)), with positive significant correlations with AST (\(r=0.551\), ***\(p=0.0001\)) and the AST/ALT ratio (\(r=0.651\), ***\(p=0.0001\)); moreover, there were non-significant correlations between liver function tests versus ACT and MIP-4.
# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 3 Experiments and Discussion
### Comparisions with State-of-the-Art Methods  
We compare our proposed algorithm with 10 state-of-the-art RGB-D saliency detection models, including ACSD [26], DESM [12], LHM [13], GP [27], DCMC [37], LBE [28], SE [16], CDCP [18], CDB [24], and DTM [38]. For fair comparison, we employ saliency maps provided by the [51]. Table 1 and Figure 10 show the quantitative results of different RGBD  
Figure 9: Valuation of different components. (**a**) PR curves. (**b**) S-measure, E-measure, and F-measure at adaptive threshold, mean absolute error (MAE). G1: two-layer graph; G2: two-layer spare graph; BAMC (SL): bilateral absorbing Markov chain based on single-modal multi-graph learning (color mode), CL: cross-modal multi-graph learning model (color and depth modes); BSSM: background seed screening mechanism; DOM: depth-guided optimization model; NLC: non-local connection. saliency detection models. We also report saliency maps with various scenes as shown in Figure 11.  
We report PR curves of three datasets in Figure 10 and list \(S_{\alpha}\), \(E_{m}\), \(F_{\beta}\) and MAE in Table 1. As shown in Figure 10, our method achieves better PR curves on the three datasets, especially on NLPR and STERE datasets. This indicates that our method can obtain higher precision and recall compared with other methods. On the NJU2K dataset, although the end of our PR curve drops faster than some methods, we always maintain a robust curve on each dataset and keep a good balance between precision and recall overall. As listed in Table 1, we can intuitively observe the superiority of our method among all the methods, which can be proved with the best results over all the three datasets. This demonstrates that our algorithm can generate more accurate salient regions and is more adaptable to various scenes than others. In addition to the quantitative comparisons, to prove the effectiveness of our model visually, we also display some saliency maps in Figure 11. As we can see, the most saliency detection methods can effectively handle the cases with relatively simple backgrounds and homogenous objects. However, these methods fail to handle the complicated cases. In contrast, our method can deal with these intricate scenarios more effectively. To make it more convincing, we compare these methods in the following four aspects: (1) the effectiveness of dealing with boundary touch issues; (2) the effectiveness of the background suppression; (3) the effectiveness of solving similar appearances; and (4) the effectiveness of detection with a poor depth map.
## 6 Simulations and Result Analysis
### Simulation Parameters  
In our simulation experiments, the NSFNET shown in Figure 10 with 14 nodes and 21 links is adopted as the physical network topology, in which each node is capable of core switching. We assume that each fiber link is bidirectional and configured with MCF. Three connection request groups with different (300, 400, 500) requests are generated as representatives of light, medium, and heavy traffic loads, respectively. The number of FSs required for each connection request in each group is generated in [1; 10] uniformly and randomly, and all requests are evenly distributed among source-destination nodes pairs. To avoid the linear and/or nonlinear impairment of MCF, the guardband requires one FS, so that the total number of FSs required for each request is actually in [2; 11]. We set the number of shortest paths \(K=5\). The number of cores of each fiber \(\left\lvert\mspace{-1.0 mu }C\mspace{-1.0 mu }\right\rvert\) equals 3, 5, and 7, and the total number of FSs on each core \(\left\lvert\mspace{-1.0 mu }F\mspace{-1.0 mu }\right\rvert\) is set to 30. We assume that the bandwidth of each FS is 12.5 GHz, and the modulation format for all requests is BPSK with a transmission distance of 9600 km. The crosstalk threshold \(\Theta_{m}=1.8\). Table 2 presents the list of physical parameters and their values used in the proposed algorithm.  
The baseline algorithm is the bi-objective genetic algorithm based on Tchebycheff decomposition (BOGA/TD) proposed in reference [12]. Specifically, it encodes the routing and core into two populations to evolve, uses uniform design and random method to generate the initial routing and core populations, respectively, and sets evenly distributed weight vectors to decompose the bi-objective optimization problem into a series of single-objective optimization sub-problems on the basis of Tchebycheff decomposition. The values of \(N\), \(\delta\) and \(G_{max}\) are consistent with the proposed algorithm in this paper. The differential evolution probability, the mutation probability, and the differential coefficient are 0.5, 0.7, and 0.5.
## 1 Introduction
### State of the Art. Data Handling and Synergic Applications
A way to cluster XRF spectra is the use of SAM (Spectra Angle Mapper): in this method, a spectrum is considered as a point in a space of \(N\) dimensions, where \(N\) is the number of the channels. The difference between two spectra can be evaluated as the angle that divides the vectors heading to the two \(N\)-dimensional points:  
\[\alpha_{(i,j)_{k}}=\arccos\left(\frac{\sum_{l=1}^{N}\rho_{kl}\rho_{(i,j)_{l}} ^{\prime}}{L_{pk}L_{p}^{\prime}}\right) \tag{2}\]  
where \(\rho_{l}\) and \(\rho_{l}^{\prime}\) are the values of the channels of the two spectra to confront. This technique has been widely used for the analysis of multispectral data, but also for XRF data [42; 43]. For each group, an endmember can be found, which exemplifies the chemical composition of all the sample points of the same group. Another way to obtain the same results is the use of Artificial Neural Networks (ANNs) [44; 45]. Usually, ANNs require training to calculate the weights associated with each neuron, and then the weights can be employed to analyze the dataset. In the work of [PERSON] et al. [26], the authors use a Self-Organizing Map (SOM) method that skips the training process using the same dataset as a training set, allowing a fully automated clustering. As an example of the flexibility and of the enormous potential of XRF mapping, we are introducing and discussing a pioneering analysis protocol called STEAM, Statistically Tailored Elemental Angle Mapper. Based on the SAM algorithm and on the Global Spectrum (GS) analysis introduced by [PERSON] and co-workers [46], STEAM first accomplishes the tasks of the MA-XRF typical approach (i.e., it maps the elemental distribution [10; 14]) and then, in synergy with punctual XRF, it exploits the information gathered from the mapped area(s) as a database to extend the comprehension to data outside the scanned region(s). The protocol works independently from the acquisition set-up and, once defined the main features of the database, it sheds light on uncharacterized data, disregarding their origin and their relationships with the scanned region(s).
###### Abstract  
Red mud (RM) is a strongly alkaline waste residue produced during alumina production, and its high alkali and fine particle characteristics are prone to cause soil, water, and air pollution. Phosphogypsum (PG), as a by-product of the wet process phosphoric acid industry, poses a significant risk of fluorine leaching and threatens the ecological environment and human health due to its high fluorine content and strong acidic properties. In this study, RM-based cemented paste backfill (RCPB) based on the synergistic curing of PG and ordinary Portland cement (OPC) was proposed, aiming to achieve a synergistic enhancement of the material's mechanical properties and fluorine fixation efficacy by optimizing the slurry concentration (63-69%). Experimental results demonstrated that increasing slurry concentration significantly improved unconfined compressive strength (UCS). The 67% concentration group achieved a UCS of 3.60 MPa after 28 days, while the 63%, 65%, and 69% groups reached 2.50 MPa, 3.20 MPa, and 3.40 MPa, respectively. Fluoride leaching concentrations for all groups were below the Class I groundwater standard (\(\leq\)1.0 mg/L), with the 67% concentration exhibiting the lowest leaching value (0.6076 mg/L). The dual immobilization mechanism of fluoride ions was revealed by XRD, TGA, and SEM-EDS characterization: (1) Ca\({}^{2+}\) and F\({}^{-}\) to generate CaF\({}_{2}\) precipitation; (2) hydration products (C-S-H gel and calixarenes) immobilized F\({}^{-}\) by physical adsorption and chemical bonding, where the alkaline component of the RM (Na\({}_{2}\)O) further promotes the formation of sodium hexafluorocaluminate (Na\({}_{3}\)AlF\({}_{6}\)) precipitation. The system pH stabilized at 9.0 \(\pm\) 0.3 after 28 days, mitigating alkalinity risks. High slurry concentrations (67-69%) reduced material porosity by 40-60%, enhancing mechanical performance. It was confirmed that the synergistic effect of RM and PG in the RCPB system could effectively neutralize the alkaline environment and optimize the hydration environment, and, at the same time, form CaF\({}_{2}\) as well as complexes encapsulating and adsorbing fluoride ions, thus significantly reducing the risk of fluorine migration. The aim is to improve the mechanical properties of materials and the fluorine-fixing efficiency by optimizing the slurry concentration (63-69%). The results provide a theoretical basis for the efficient resource utilization of PG and RM and open up a new way for the development of environmentally friendly building materials.  
red mud; phosphogypsum; cemented paste backfill; fluorine fixation; hydration products; environmental safety +
Footnote †: journal: Applied Sciences  
Article