esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Compression Behavior of Ground Rock with Bi-Directional Penetrating Crack
## 4 Discussion  
(1) The grout properties will inevitably affect the reinforcement. The grout properties that affect the mechanical properties of grouted rock mainly include grout strength and the cohesion between the grout and the original rock. Different proportions of raw materials will lead to different strength. In this paper, the mechanical strength of the slurry is greater than that of the original rock. When the reinforced body does not slide along the magmatic rock interface under pressure, the higher the strength of the grout, the better the reinforcement effect. However, the cohesion of the interface between grout and rock is far less than that of the original rock itself, which is also the deficiency of the grouting material commonly used in underground engineering. The cohesion between the grout-rock interface depends on the geometric form of the crack and the chemical reaction between the grout and the original rock, and the chemical reaction is the main factor. In actual engineering, the geometric form of the crack cannot be changed, but different grouting materials can be tried from the perspective of improving the chemical bond between the grout and the original rock to improve the cohesion between the grout-rock interface.  
(2) It can be seen from this article that when the angle between the principal stress direction and the crack is within a certain range, the grouting reinforcement effect is not obvious, such as the case where \(\beta\) was 30\({}^{\circ}\) in this article. During the excavation of underground space, the direction of principal stress will be deflected, and the direction of principal stress and cracks will be recombined. After grouting reinforcement, the weak position can be judged according to the conclusion of this paper. Due to the limitation of grouting's reinforcement effect, for the weak position after reinforcement, other reinforcement methods such as bolt or anchor cable can be combined.  
(3) In actual construction, there is no guarantee that the grout will completely fill the cracks. The spread of grout depends on the grouting construction parameters, including grout pressure, grouting hole layout, etc. The grouting effect is not as good as the results obtained in the laboratory, so in actual engineering, the reduction of the effect of grouting reinforcement should be considered.
# Real-Time Maintenance Optimization with Industrial Internet of Things
###### Abstract  
Efficient maintenance management is critical to ensuring the reliability and productivity of industrial systems. This article explores how the Industrial Internet of Things (IIoT) enables real-time maintenance optimization through data-driven decision-making. IIoT technologies, such as connected smart sensors and predictive analytics, provide continuous monitoring of equipment performance and state. Within the frame of this article, a novel mathematical model is proposed to support the real-time optimization of maintenance operations in production systems. The model makes this possible by using real-time state information to optimize maintenance operations, minimize maintenance costs, and maximize the efficiency of the production system. The results highlight the potential of IIoT to transform conventional maintenance strategies into dynamic, adaptive systems. This research contributes to advancing smart maintenance solutions for modern industrial applications.  
maintenance; forecasting; optimization; efficiency; Industrial Internet of Things solutions; recursive objective function; real-time monitoring Article
## 4 Applications of CDs in Cancer Theranosis
### Cancer Diagnosis via Fluorescence Imaging
#### 4.1.1 In Vitro Diagnosis
\begin{table}
\begin{tabular}{l c c l l l} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Nanocarrier**}} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Size** \\ **(nm)** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{**Advantage**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Limitation**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Ref.**}} \\ \hline \multirow{4}{*}{CD} & & - & Easy, cheap, and green synthesis & - & Controlled synthesis of uniform CDs \\  & & - & Easy surface modification & & remains challenging \\  & & - & Water soluble & - & Long-term biosafety is not \\  & & - & Stable photoluminescence & - & fully established & \\  & & - & Low toxicity & & \\ \hline \multirow{4}{*}{Micelle} & \multirow{4}{*}{5–100} & & - & Enhances solubility of & \multirow{4}{*}{-} & \multirow{4}{*}{Poor water solubility of} \\  & & - & & hydrophobic drugs & & small-sized micelles \\  & & - & Shields degradation and reduce systemic & & & \\  & & toxicity of drugs & & - & Poor physical stability in vivo \\  & & - & Biocompatible & & \\ \hline \multirow{4}{*}{Liposome} & \multirow{4}{*}{50–150} & & - & Carry both hydrophilic and & \multirow{4}{*}{-} & \multirow{4}{*}{Physical instability} \\  & & - & Biocompatible & & \\  & - & Protects drugs from degradation & - & Drug leakage before reaching the target & \\  & & - & Prolong circulation time to enhance & - & Limited drug loading capacity & \\  & & passive tumor targeting & & \\ \hline \multirow{2}{*}{Nanogel} & \multirow{2}{*}{\textless{200}} & - & High drug loading capacity & - & Polydispersity of polymers & \\  & & - & Biocompatible & - & Poor renal clearance & [131] \\ \hline \multirow{3}{*}{Polymer nanoparticle} & \multirow{3}{*}{10–1000} & - & Controlled drug release & - & Low scalability & \\  & & - & biocompatible & - & Low reproducibility & [132; 133] \\ \cline{1-1}  & & - & Possible surface functionalization & - & Variability in drug release profiles & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of CDs with other nanocarriers. FA-CDs to distinguish cancer cells from normal cells by culturing and analyzing a mixture of NIH-3T3 normal and HeLa cancer cells [152]. They observed that after 6 h incubation of the cell mixture with FA-CDs, only the HeLa cells exhibited bright fluorescence, while NIH-3T3 cells did not (Figure 8b(i-vi)), demonstrating the selective targeting of the FA-CDs to cancer cells.
_Int. J. Mol. Sci._**2013**, _14_, 14136-14170; doi:10.3390/ijms140714136  
International Journal of  
Molecular Sciences  
ISSN 1422-0067  
www.mdpi.com/journal/ijms  
_Review_  
**Modulation of Phytoalexin Biosynthesis in Engineered Plants**  
**for Disease Resistance**  
**[PERSON] \({}^{\star}\), [PERSON], [PERSON] and [PERSON]**  
Laboratory of Stress, Defenses and Plant Reproduction, Research Unit \"Vines and Wines of Champagne\", UPRES EA 4707, Faculty of Sciences, University of Reims, P.O. Box 1039, Reims 51687, France;  
E-Mails: christophe.clement(@univ-reims.fr (C.C.); [EMAIL_ADDRESS] (E.C.);  
[EMAIL_ADDRESS] (S.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +33-3-2691-3341; Fax: +33-3-2691-3340.  
_Received: 25 April 2013; in revised form: 19 June 2013 / Accepted: 25 June 2013 / Published: 8 July 2013_  
**Abstract:** Phytoalexins are antimicrobial substances of low molecular weight produced by plants in response to infection or stress, which form part of their active defense mechanisms. Starting in the 1950's, research on phytoalexins has begun with biochemistry and bio-organic chemistry, resulting in the determination of their structure, their biological activity as well as mechanisms of their synthesis and their catabolism by microorganisms. Elucidation of the biosynthesis of numerous phytoalexins has permitted the use of molecular biology tools for the exploration of the genes encoding enzymes of their synthesis pathways and their regulators. Genetic manipulation of phytoalexins has been investigated to increase the disease resistance of plants. The first example of a disease resistance resulting from foreign phytoalexin expression in a novel plant has concerned a phytoalexin from grapevine which was transferred to tobacco. Transformations were then operated to investigate the potential of other phytoalexin biosynthetic genes to confer resistance to pathogens. Unexpectedly, engineering phytoalexins for disease resistance in plants seem to have been limited to exploiting only a few phytoalexin biosynthetic genes, especially those encoding stilbenes and some isoflavuoids. Research has rather focused on indirect approaches which allow modulation of the accumulation of phytoalexin employing transcriptional regulators or components of upstream regulatory pathways. Genetic approaches using gain- or less-of functions in phytoalexin engineering together with modulation of phytoalexin accumulation through molecular engineering of plant hormones and defense-related marker and elicitor genes have been reviewed.  
**Keywords:** plant engineering; phytoaleoxins; transcriptional factors; hormones; elicitors; regulation
## 3 Theoretical Analysis
### The Power of Different Modes in the Cladding
In the designed sensor, the loss of the light source and the efficiency of converting the fundamental mode into a higher order mode of MSC are considered. Thus, in the sensing area of this sensor, the ratio of the power enhancement of the higher order mode to the fundamental mode is defined as  
\[\eta=\frac{\eta_{LP01-light}\times\gamma\times\eta_{LPnm-cl}-\eta_{LP01-light} \times\eta_{LP01-cl}}{\eta_{LP01-light}\times\eta_{LP01-cl}}=\frac{\gamma\times \eta_{LPnm-cl}-\eta_{LP01-cl}}{\eta_{LP01-cl}} \tag{4}\]  
where \(\eta\) represents the proportion of the power increase of the LP\({}_{\text{nm}}\) mode to the fundamental mode in the cladding, \(\eta_{LP01-light}\) indicates the efficiency of a light source producing the fundamental mode. \(\eta_{LPnm-cl}\) represents the proportion of the power of the LP\({}_{\text{nm}}\) mode in the cladding to the total power in the optical fiber. \(\gamma\) of the MSC is defined as the ratio between the generated higher-order mode power and the total power of the MSC. Figure 2: The proportion of power of the \(LP_{nm}\) mode in the cladding with the different normalized frequency. The inset clearly depicts this proportion when \(V>8\). As shown in Figure 3, in this sensing area, the proportion of the cladding power increases by approximately 100% and 150% when the LP\({}_{11}\) and LP\({}_{21}\) modes are adopted, where \(\gamma\) of the LP\({}_{01}\)-P\({}_{11}\) mode coupler is set as 0.8, and \(\gamma\) of the LP\({}_{01}\)-LP\({}_{21}\) mode coupler is set as 0.5, according to the calculation in Section 4.1. The abnormal peak of the blue curve is caused by the error of the numerical method, which coordinates with the inset of Figure 2. These results indicate that the advantage of applying higher-order modes for sensing is that higher order modes have more power in the cladding to excite a higher intensity of SPR, and the loss of these modes can be fully detected by intensity modulation, whose variation can reflect the change trend of RI. Therefore, the evanescent wave power enhanced by these modes contributes to the sensitivity enhancement of the RI, which is verified by simulations of the sensing area of this sensor.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 2 UVSQ-SAT Attitude Determination Considerations
### Sensors Description  
The UVSQ-SAT satellite described by [1] is equipped with different subsystems and sensors, among which some are used to determine its attitude. Those instruments are defined in the spacecraft body frame (B). The different faces of the spacecraft are named after this reference frame. Two opposite faces correspond to one axis. This is shown in Figure 1. The instrumental reference frame is fixed with respect to the satellite. It undergoes only one constant rotation with respect to the satellite reference frame. To simplify this, we apply this rotation and consider the measurements in the spacecraft body frame. We do not mention the instrument frame in the following explanations.  
The different inputs available to the algorithms are as follows:  
* Three-axis angular velocities. The gyrometer measures the three-axis angular velocities in the sensor frame to the inertial reference frame (I), defined by \(\omega_{g}=\{\omega_{X},\,\omega_{Y},\,\omega_{z}\}_{B/I}\), as the calibrated measurement.
* Three-axis magnetic field. The magnetometer measures the magnetic field along its three axes in the instrument's reference frame defined by \(B=\{B_{X},\,\,B_{Y},\,\,B_{Z}\}_{B}\), as the calibrated measurement.
* Six photodiodes in the visible domain. They measure solar and outgoing shortwave radiations in the 400-1100 nm wavelength range. They are defined as the calibrated fluxes \(\Phi p=\{\Phi p_{+X},\,\,\Phi p_{-X},\,\,\Phi p_{+Y},\,\,\Phi p_{-Y},\,\,\Phi p_{+ Z},\,\,\Phi p_{-Z}\}\). They are used as a Sun sensor.
* Six Earth radiative sensors (ERS) sensors with an Optical Solar Reflector (OSR). They aim to measure radiation between 0.2 and 3 \(\upmu\)m. They are defined as \(\Phi osr=\{\Phi osr_{+X},\,\,\Phi osr_{-X},\,\,\Phi osr_{+Y},\,\,\Phi osr_{-Y},\,\, \Phi osr_{+Z},\,\,\Phi osr_{-Z}\}\). Those sensors are used as an Earth sensor and aimed toward the nadir.
* Six ERS sensors with carbon nanotubes (CNT). Six sensors aim to measure total radiation between 0.2 and 100 \(\upmu\)m. They are defined as \(\Phi cnt=\{\Phi cnt_{+X},\,\,\Phi cnt_{-X},\,\,\Phi cnt_{+Y},\,\,\Phi cnt_{-Y},\,\,\Phi cnt_{+Z},\,\,\Phi cnt_{-Z}\}\). They are used as Earth sensors.  
An example of the time series of the UVSQ-SAT inputs is given in Figure 2. Oscillations are present in the magnetometer and gyrometer measurements as the satellite rotates. For the photodiode measurements, sunlight and eclipses periods appear clearly. ERS sensors measure all Earth and solar radiative fluxes. Eclipses periods appear also clearly.  
Figure 1: Spacecraft body frame of the UVSQ-SAT satellite.
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 3 Feature Extraction
### Voxel Sampling Strategy  
Essentially, the voxel-based scan matching process is to construct the associations between voxels. It is believed that voxels with high quantity of linearity or planarity tend to be more stable than others. So for each input point cloud, we classify its 3d grids according to the proposed quantities into three types: edge voxel, planar voxel and others. Furthermore, it should be noticed that the ground points usually contain more planar features and non-ground points usually contain both planar and edge features. With these born in mind, we first divide the whole point cloud into ground and non-ground segments using the method proposed in [31]. Figure 2a is an example of the ground segmentation result. Then, for the non-ground points, we extract planar and edge features, while only planar features are extracted for the ground points.  
In each scan, which is now represented as the voxel centroids, we sort the voxels based on their linearity values \(c\) and surface-ness values \(p\) and get two sorted lists. Two thresholds \(c_{th}\) and \(p_{th}\) are then employed to distinguish different types of features. We call the voxels with \(c\) larger than \(c_{th}\) edge features, and the voxels with \(p\) larger than \(p_{th}\) planar features. Then \(n_{F_{e}}\) edge features with the maximum \(c\) are selected from each scan. Non-ground and ground planar features with the maximum \(p\) are also selected, and the numbers of the two types of features are \(n_{F_{p}}\) and \(n_{F_{gp}}\), respectively. Edge and planar features extracted from all the scans are denoted as \(F_{e}\), \(F_{p}\) and \(F_{gp}\) thereafter. Visualization examples of these features are given in Figure 2b-d.  
Figure 1: Illustration of the surface feature and linear feature.
## 6 Future Work, Challenges, and Limitations
### Extensibility and Scalability  
There has been ongoing research addressing both NLoS detection and error correction to improve the performance of UWB IPS. However, it is worth noticing that the performance of the proposed approaches in the literature has not been evaluated in totally new environments yet. In such new environments, training datasets are collected using different techniques, and the collected data may vastly vary due to different factors, including device heterogeneity, such as different device topologies, the size of the room, the presence of objects, etc. Moreover, training separate models for each distinct environment requires considerable effort and time, and the UWB devices cannot remain in the same configuration [109]. Furthermore, the environments may change over time, necessitating frequent model updates. As a result, traditional ML algorithms are limited in their adaptability to entirely new environments. However, if collecting data and training different models for each unique environment, that would require considerable work and time (i.e., setting up the devices, performing large data collections, and executing the training model process). Even then, the environment may already change as time progresses, seriously requiring to update the models frequently [110]. Therefore, conventional techniques requiring new big datasets and completely new models are limited in their versatility to unseen and changing environments with different UWB configurations. To address this shortcoming, a transfer learning (TL) framework could be proposed, TL is an ML approach that can help with this task. Transferring the knowledge learned from one task to another similar task maynot reliably transfer knowledge from a known domain to a new target domain with a satisfactory level of accuracy.
## 2 Materials and Methods
### Autoencoder  
Autoencoder is an artificial neural network that compresses data and learns their important features. This type of network is often used in operations such as data size reduction and noise removal. An autoencoder consists of two main components: the encoder and the decoder. The encoder converts the input data into a smaller size, the latent space, and learns the critical features of the data by compressing them. The latent space contains a condensed representation of the data. The decoder takes this compressed representation and converts it back as close as possible to the original data. Autoencoders aim to minimize the information lost during data compression and reconstruct the data as close as possible to its original form. This process helps the model to learn patterns and structures in the data. Autoencoders are used in various fields, such as dimension reduction in high-dimensional data, producing clean data from noisy data, and learning important features in data [43].
## 3 Results and Discussion
### Data Compression Test  
The profile detection data of the buoy contains the following data: time, temperature, depth, and conductivity. The length of a single pack of data are a fixed 32 bytes; the maximum number of bytes received from the buoy main control board by the terminal at one time is 512; and the compression ratio of 50 groups of profile detection data are compared according to data compression algorithms. The compression ratio is expressed as the percentage of the byte length of the compressed data compared to the original data browser length. The test results are shown in Figure 9.  
Figure 8: Comparison chart of power tests in different states.  
The average compression ratios of the differential encoding, LZW, and Huffman algorithms are 59.1%, 87.4%, and 42.8%, respectively. When the differential encoding is combined with the LZW and Huffman algorithms for double data compression, the compression ratios are 53.3% and 25.9%, respectively. Therefore, differential encoding and Huffman algorithms are used to carry out double data compression on the profile detection data. The maximum number of bytes for a single Beidou civil card communication is 78 bytes; the maximum number of bytes for the telegram content section of a communication application is 60 bytes. After completing a profile run, the buoy transmits the profile exploration data, which has an average byte size of 4463 and contains 134 packets of data. This requires 75 transmission requests to be sent. After the detection data of a profile is employed by double data compression, both differential encoding and Huffman data, it only needs to send 20 communication applications, reducing the number of remote communications and lowering power consumption.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 3 Separability Improvement
### Numerical Comparison of the Methods for Improving Separability  
Let us compare the accuracy of the methods for improving separability using a simple example of a noisy sum of an exponential series and a sine wave:  
\[s_{n}=0.2\exp(0.05n)+4.12\cos(2\pi n/30),\quad n=1,\ldots,100.\]  
The noise is standard white Gaussian. The parameters are: the window length \(L=48\), the number of components \(t=3\), which is equal to the signal rank, the grouping for IOSSA \(\{1\}\cup\{2,3\}\). Table 2 shows the average and median MSEs calculated with 1000 repetitions; the smallest errors are marked in bold. It can be observed that the IOSSA and EOSSA methods perform similarly, with the EOSSA error even being significantly smaller. It is important to note that the EOSSA method is faster than a single iteration of IOSSA. On the other hand, Basic SSA yields incorrect results due to poor separability, while FOSSA improves separability but still produces less accurate results compared to both IOSSA and EOSSA. This is mainly due to the rather poor weak separability of the sinusoid with period 30 and the exponential series of length 100.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(N\) & SSA & FOSSA & EOSSA & IOSSA.true & IOSSA.true10 & IOSSA.wrong10 \\ \hline
1000 & 4.52 & 3.93 & 3.02 & 64.32 & 199.00 & 192.20 \\ \hline
10,000 & 36.12 & 36.12 & 18.06 & 166.83 & 596.83 & 690.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of computational time (ms).