esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump
## 4 Design of Water Hammer Fault Monitoring and Diagnosis System  
### Composition of Monitoring System  
The water hammer detection system consists of two parts: a data collector on the edge side and a server on the cloud side. The topology structure is shown in Figure 4. The monitoring system works in the way of edge-cloud collaboration [22; 23]. Vibration data acquisition, water hammer characteristic calculation and water hammer characteristic value alarm judgment are completed in the collector on the edge side. And symptoms related to centrifugal pump failures are also calculated in the collector. These failure symptoms mainly include imbalance, misalignment, bearing wear, impeller wear, cavitation, loosening, etc. [24]. Common centrifugal pump faults have different fault characteristics in different frequency domains. Therefore, vibration signals can be divided into different frequency bands, and alarm thresholds can be set for each frequency band to achieve the use of predefined thresholds to distinguish common centrifugal pump faults. The data sent by each edge collector are stored in the cloud server. And the trend analysis and modeling of the data are performed by software running on the cloud server. The server software corrects the alarm thresholds of each fault symptom according to the data trend and sends it to the edge collector.
## 3 Experiments and Results
### Image Fusion  
In the image fusion stage, two identical encoders extract two different source images to get two feature maps. The two feature maps are merged into one feature map through the fusion strategy. The decoder is used to restore the feature map to an image and output \(320\ast 320\) fused images.
# Load Frequency Control for Multi-Area Power Plants with Integrated Wind Resources
## 1 Introduction
\begin{array}{l}\dot{x}_{i}(t)=A_{i}x_{i}(t)+B_{i}u_{i}(t)+\sum\limits_{ \begin{array}{c}j=1\\ j\
eq i\end{array}}^{N}H_{ij}x_{j}(t)+F_{i}\Delta P_{di}(t)\\ y_{i}(t)=C_{i}x_{i}(t)\end{array}  \tag{6}\]  
The system matrices \(A_{i},B_{i},F_{i},H_{ij}\) in the state space model are given below:  
\[A_{i}=\left[ \begin{array}{cccc}\frac{-1}{T_{Pi}}&\frac{K_{Bi}}{T_{Pi}}&0&0&- \frac{K_{Bi}}{2\pi T_{Pi}}\sum\limits_{i=1,j\
eq i}^{N}K_{ij}\\ 0&-\frac{1}{T_{Ti}}&\frac{1}{T_{Ti}}&0&0\\ -\frac{1}{R_{i}T_{Gi}}&0&-\frac{1}{T_{Gi}}&0&0\\ K_{Bi}K_{Ei}&0&0&0&\frac{K_{Bi}}{2\pi}\sum\limits_{i=1,j\
eq i}^{N}K_{ij}\\ 2\pi&0&0&0&0\end{array} \right],H_{ij}=\left[ \begin{array}{cccc}0&0&0&0&- \frac{K_{Bi}}{2\pi T_{Pi}}\sum\limits_{i=1,j\
eq i}^{N}K_{ij}\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&\frac{K_{Ti}}{2\pi}\sum\limits_{i=1,j\
eq i}^{N}K_{ij}\\ 0&0&0&0\end{array} \right]\]  
\[,B_{i}=\left[ \begin{array}{c}0\\ 0\\ \frac{1}{T_{Gi}}\\ 0\\ 0\end{array} \right]\text{and }F_{i}=\left[ \begin{array}{c}-\frac{1}{2T_{Ti}}&\frac{1}{2T_{Ti}}\\ 0&0\\ 0&0\\ 0&0\\ 0&0\end{array} \right]\]  
\[x_{i}(t)=\left[ \begin{array}{cc}\Delta f_{i}(t)&\Delta P_{mi}(t)&\Delta P_{gi }(t)&E_{i}(t)&\Delta\dot{\delta_{i}}(t)\end{array} \right]^{T}\]  
represent the state variables in the state space model. \(u_{i}(t)\in R^{m}\) is the control input. \(\Delta P_{di}(t)\) is the load uncertainty. The model of the wind farm in the \(i\)th area is further discussed in the next section.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
###### Abstract  
To study the mechanical properties of stainless-steel-reinforced concrete (SSRC) columns under eccentric compression, one ordinary reinforced concrete column and eight SSRC columns were designed for eccentric compression load tests. The eccentricity and reinforcement ratio were considered as the variation parameters. The cracking and failure modes of the SSRC columns were studied. The effects of the variation parameters on the longitudinal strain, concrete strain, lateral displacement, and ultimate load were analyzed. The test results demonstrated that the failure modes of SSRC columns under eccentric compression are similar to those of ordinary reinforced columns. Eccentricity significantly affected the failure modes of the SSRC columns. Under the same eccentricity, the reinforcement ratio had negligible effect on the lateral displacement corresponding to the ultimate load. The mechanical properties of SSRC columns under small eccentric compression were better than expected; however, the ultimate loads under large eccentric compression were proposed. The ultimate load-bending moment curves obtained were consistent with the results of the test and finite element analysis. Based on the experimental results, the force characteristic coefficient was set as 2.7 for calculating the long-term crack width.  
SSRC; eccentric compression; mechanical performance; ultimate load; crack width Article
# A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems
## 3 Proposed Model
### Complexity and Latency  
In this section, we consider the complexity of our proposed model and that of previous studies in Table 2. The operators are counted in the equalizer to detect a bit. In Table 2, we can see the trade-off between the BER performance and the complexity of the models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Mul/Div** & **Add/Sub** \\ \hline Structure 1 & 1998 & 1955 \\ Structure 2 & 4578 & 4435 \\ Structure 3 & 2623 & 2555 \\ Structure 4 & 4581 & 4438 \\ Structure 5 & 6489 & 6271 \\ Structure 6 & 7114 & 6871 \\ Structure 7 & 7739 & 7471 \\ Structure 8 & 7123 & 6817 \\ Conventional equalizer in [18] & 33 & 48 \\ DFE with reliability factor [2] & \textgreater{}36 & \textgreater{}34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity of our proposed model and previous studies.  
Figure 9: BER performance of the CNN equalizer with 1.85 blur and 10% misalignment.  
In Table 3, we consider the processing time for each CNN structure to detect one bit. These results present the latency/delay of the proposed model.
## Data  
### Demeter  
DEMETER was a low-altitude satellite (710 km) launched in June 2004 into a polar and circular orbit. The altitude of the satellite was decreased to 660 km in December 2005. The scientific mission of DEMETER ended in December 2010. The orbit of DEMETER was nearly sun-synchronous and the up-going half-orbits corresponded to nighttime (10:30 p.m. LT), whereas the down-going half-orbits corresponded to daytime (10:30 a.m. LT). The main scientific objectives of DEMETER were to study the disturbances of the ionosphere due to seismo-electromagnetic effects and anthropogenic activities (power line harmonic radiation, VLF transmitters, and HF broadcasting stations). For this purpose, DEMETER measured the electromagnetic waves and plasma parameters all around the globe, except in the auroral zones. Regarding the wave experiment, the VLF range for the electric field was from DC up to 20 kHz. There were two scientific modes: a survey mode where spectra of one electric component were onboard computed up to 20 kHz (time resolution of 2 s and frequency resolution of 20 Hz), and a burst mode where, in addition to the onboard computed spectra, waveforms of the same electric field component were recorded up to 20 kHz. Details of the electric field experiment can be found in [37]. The electron density was measured by the experiment ISL (Instrument Sonde de Langmuir; [38]), whereas the ion density was given by the experiment IAP (Instrument d'Analyse du Plasma; [39]).
## 3 Ecotoxicity
### Aquatic Biomodels
#### 3.1.2 Crustacean  
_Daphnia mgna_ (_D. magna_) is a planktonic crustacean highly sensitive to environmental changes. It is used in toxicity studies due to its short life cycle and standardized protocols, with the mobility of the _Daphnia_ being a quantifiable toxicity parameter [96,97]. Bibliographic information on the toxicity of DESs towards _D. magna_ assays is presented in Table 4.  
As far as we know, there has only been one previous study, carried out by [PERSON] et al., in which this biomodel was used to test the ecotoxicity of DESs [84]; They studied some DESs and their mixtures with water:glycerine (Gly) (1:2 choline chloride:glycerol), reline (Rel) (1:2 choline chloride:urea), ethaline (Etha) (1:2 choline chloride:ethylenglicl), glycerine and water (GlyW) (1:2:1 choline chloride:urea:water), and ethaline and water (EthaW) (1:2:1 choline chloride:water). Results obtained with _D. magna_ were compared to those obtained for other biomodels, such as _A. fischeri and Rapidiocelis subcapiata_ (_R. subcapiata_). The ranges of concentrations tested for _D. magna_ studies were 150-4500 mg/L for Rel, 250-8200 mg/L for RelW, 300-1000 mg/L for Gly, 350-12,000 mg/L for GlyW, 190-6000 mg/L for Etha, and 250-8000 mg/L for EthaW. The results of this study showed the dependence of the biomodel used. In this case, _D. magna_ turned out to be the most sensitive biomodel. None of the mixtures were considered toxic, since the EC\({}_{50}\) values found were higher than 1000 mg/L. The addition of water to the compound modified its toxicological properties; however, the reason for this phenomenon could not be ascertained.  
On the other hand, there are studies on phosphonium-type DESs [73] and ChCl-DESs [88] tested in _Artemia salina_ (_A. salina_). Tests performed with this bioindicator are widely used for the detection of many toxic substances (heavy metals, pesticides, detection of mycotoxins, etc.), and are very useful for a preliminary study of toxicity [98].  
It seems that _A. salina_ was very susceptible to phosphonium-based DESs and ChCl-based compounds. In both assays, to determine the toxicity, the mortality was quantified, counting the number of dead brine shrimp over time. The time it took for 2, 4, 6, and 10 specimens to die was recorded, in minutes. The phosphonium-based DESs were formed from methyltriphenylphosphonium bromide (MTPB) combined with glycerine (Gly), ethylene glycol (EG), and triethylene glycol (TEG) as HBDs. It was observed that the highest survival time for 10 specimens was only 3.58 min for MTPB:TEG, followed by MTPB:EG (3.27 min) and MTPB:Gly (2.32 min) [73]. For the ChCl-based compounds, the tested DESs were based on ChCl as an HBA and glycerine (GI), ethylene glycol (EG), triethylene glycol (TEG), and urea (U) as HBDs. In this case, the survival times were higher, but still not enough: 5.32 min for ChCl:GI, 10.13 min for ChCl:EG, 2.58 min for ChCl:TEG, and 4.05 min in ChCl:U DESs. In both assays, the toxicity of the primary materials was lower than the DESs' toxicity [88].
## 4 Experimental Section
### Experimental Results
#### 4.3.4 Tracking Accuracy  
Figure 12 provides a comparison of the three techniques on the basis of tracking accuracy. Given that our measure of tracking accuracy is based on the Euclidean distance between estimated and actual location, the lower the measure, the better the tracking accuracy. As can be seen in this figure, tracking accuracy improves with the relative adaptability of the clustering technique. Comparing the Static technique with its fixed clusters to the dynamic technique with its ability to share Anchor Nodes between clusters, we see an improvement in tracking accuracy. Similarly, the _ad hoc_ technique's ability to freely form clusters results in the best tracking accuracy.  
These results are a consequence of the quality of service issues that occur as Mobile Nodes reach the boundaries of sensor node clusters. For example, fixed clustering techniques like the static technique can result in deteriorating conditions (lost estimations, weak signals, _etc._) as Mobile Nodes reach the boundary of the tracking cluster since they only deploy Anchor Nodes within their cluster; in  
Figure 11: Interval plot for resource consumption. \(n=160\), ANOVA, \(1\)_df_, \(p<0.05\).  
contrast, a very adaptable technique like the _ad hoc_ technique does not have any restrictions concerning Anchor Node commitments to clusters when choosing among available Anchor Nodes.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 1 Introduction
Compared with the commonly used multiphase flow models, the CFD-DEM coupling strategy has several advantages. First, the motion of solid particles is solved in a Lagrange coordinate system; therefore, the position of each particle can be tracked. Second, the interaction between particle and liquid is considered, which can then be used to plumb the flow characteristics. Third, this model can be applied at high SVFs [14]. The CFD-DEM coupling strategy has been used in studies of the fluidized bed, cyclone, hopper flow, and pneumatic conveyor [15, 16, 17, 18]. The maximum diameter of solid particles is 15.0 mm. The present study aims to reveal two-phase flow characteristics in a vertical pipe as manganese nodules rise with seawater. Particle diameters ranging from 5.0 mm to 30.0 mm were selected. The CFD-DEM coupling method was used to simulate the two-phase flows and to evaluate the operational performance. The initial solid volume fraction (SVF) was varied from 1% to 10%. Initial mixture velocities of 3.0 m/s, 4.0 m/s, and 4.5 m/s were selected. A comprehensive comparison was implemented between the results obtained under different operating conditions. It is expected to shed light on the hydraulic lifting of minerals from deep sea and to provide a sound support for the design of the deep-sea mining system.
## Appendix A Signal Expression  
The spread spectrum signal waveform can be expressed as follows (data symbol \(d_{0}\) corresponds to spread spectrum code chip \(c_{0}\sim c_{N-1}\)):  
\[s(t)=\sum\limits_{p=-\infty}^{\infty}d_{p}\sum\limits_{m=-\infty}^{\infty}c_{ m}w(mT_{c}-pT)h_{T}(t-mT_{c}) \tag{1}\]  
where \(d_{p}\) is the data symbol sent, the chip period is \(T_{c}\), the spread spectrum multiple is \(N\) and the symbol width is \(T=NT_{c}\). \(h_{T}(t)\) is the signal forming pulse, assumed to be the root rising cosine pulse in this paper, where \(\alpha\) is the roll-off factor and has \(0<\alpha\leq 1\)[25]. \(w(t)\) is the window function,  
\[w(t)=\left\{\begin{array}{ll}1&0\leq t<NT_{c}\\ 0&else\end{array}\right. \tag{2}\]  
The receiver applies a matched filter with an impulse response of \(T_{c}^{-1}h_{T}(t)\) to the signal in Formula (1). Then the channel response has the raised cosine characteristic. Considering the complex baseband model in an ideal Gaussian channel, the matched filtered signal can be approximated as  
\[r(t)=As(t-\tau T_{c})e^{i(2\pi ft+\varphi)}+n(t) \tag{3}\]  
where \(s(t-\tau T_{c})\) is the baseband signal waveform and \(n(t)\) is the noise after matched filtering. In addition, \(A\) is the signal amplitude, \(\tau\) is the normalized signal transmission delay, \(f\) is the carrier frequency and \(\varphi\) is the carrier phase after matched filtering.  
Sample \(s(t-\tau T_{c})\) is set according to the chip rate and the sampled data \(s(k)\). Assuming that the local spread spectrum code is not strictly aligned with the received signal, the normalized spread spectrum code synchronization error is \(\tau T_{c}\). When the signal of Formula (3) is sampled at \(t=kT_{c}\), the baseband signal after sampling can be expressed as  
\[\begin{array}{ll}&r(k)=As(k)e^{i(2\pi fkT_{c}+\varphi)}+n(k)\\ &=A\sum\limits_{p=-\infty}^{\infty}d_{p}\sum\limits_{m=-\infty}^{\infty}c_{ m}w(mT_{c}-pT)h(kT_{c}-mT_{c}-\tau T_{c})e^{i(2\pi fkT_{c}+\varphi)}+n(k)\end{array} \tag{4}\]
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Standardization and Technology of NB-IoT
#### 2.3.1 Standardization of NB-IoT  
NB-IoT has passed through a series of standardization by 3 GPP. Figure 8 describes a brief history of NB-IoT standardization from inception to its current freezing state. The diagram is a unique representation of the standardization history as it captures the various stages at a single glance. The standardization started in 2005 when 3 GPP Release 8 (R8) was specified to cater for massive device connections, billing of usage, addressing, machine type communication mode, and security issues. All the standardization versions have a start date and end date with overlapping of some specific technological fields of concern. Examples are the versions R12 and R13 with various standardization numbers and specific areas of concern. In 2015, Version R13 was released with features that further reduced the energy consumption rate of NB-IoT through the implementation of eDRX and PSM mechanisms [15].  
Figure 9 shows the major technology companies involved in the standardization process [79]. These companies include Vodafone and Huawei; they paired together to work on NB-IoT M2M communication features and made their submission in May 2014; Qualcomm presented NB-IoT Orthogonal Frequency-Division Multiplexing (OFDM) in August 2014. The works of the above formed the NB Cloud IoT (NB CIoT); a year later, Ericsson presented the LTE features of NB. These works were fused by rapporteurs in September 2015 to form the NB-IoT 3 GPP Version R13 work item with a standardization number of 45.820. In this version, the following technological features were addressed: reduced latency, improved indoor coverage, ability to be compatible with existing infrastructure (LTE), support for a low data and low cost terminal.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Traffic Types** & **Areas of Application** & **Reliability** & **Latency** & **Criticality** \\ \hline Emergency traffic & Safety traffic & Ultra-high & Sub-milliseconds & Most \\ Critical control traffic & Robotic motion control & Ultra-high & Milliseconds & \\ Remote control traffic & Unmanned vehicle control & High & Milliseconds & \\ Critical Alerting & Periodic control checks & Medium & Seconds & \\ Non-critical Alerting & Temperature measurement & Low & Minutes & \\ Monitoring & Static feedback & Low & Minutes & Least \\ \hline \end{tabular}
\end{table}
Table 4: Latency and reliability summary for industrial communication traffic.
## 2 Connection Options and Topology Optimization after Site Densification
### System Gain Calculation
A microwave or millimeter-wave link is operational, as long as the system gain is higher than the sum of link losses minus the sum of link gains in a hop. \[Link\ losses^{\left[\text{dB}\right]}-Link\ gains^{\left[\text{dB} \right]}<SG^{\left[\text{dB}\right]}. \tag{3}\]  
When designing \(\upmu\)/mmW links, the network planners should use the \(SG\) values carefully, i.e., they must remember which definition is used by the equipment manufacturers when the parameters are specified. The calculation of a complete link is presented in the next section. Figure 6: System gain definitions—with and without antennas [1].
## 4 Discussion
### Strengths and Limitations  
The main strength of the proposed rad-path model is that it provides a way for radiologists and pathologists to create coherent, correlated, and integrated diagnostic summaries. The rich set of radiology/pathology characteristics, as well as the machine learning signatures produced from them, will improve our understanding of gliomas, and can help with precision diagnoses.  
There are several important limitations to this study. The most important is that the pathology images were outlined in a semiautomatic fashion, which is a user-dependent and laborious task. Delineation of the region of interest in a full-automated fashion would facilitate automation of the analysis, reduce user bias, and empower larger-scale analysis. Another important limitation is that a retrospective dataset was used in this study, whereas the comparison of our methods to clinically measure OS by using a prospective dataset would further validate our models.
## 5 Experimental Results  
This section presents a structured validation of the proposed model through a progressive experimental framework, with each stage targeting a specific component of the detection pipeline. The evaluation begins with feature space visualization to illustrate the distributional separability between benign and malicious traffic, thereby providing a conceptual basis for downstream analysis. Subsequently, clustering parameter tuning is conducted to assess the model's adaptability and stability across varying traffic scenarios. An ablation study is performed in a hierarchical manner by progressively removing individual component, allowing for a quantitative assessment of the contribution of each component to the classifier performance. Finally, comparative benchmarking against baseline models is conducted using standard metrics such as accuracy, FPR, and ROC curves.  
In the overall work, the embedding dimension is 384; the hidden layer dimension is 512; eight attention heads are used; the feed-forward network dimension is 384, and the model consists of eight encoder blocks. The number of training rounds is 25. The learning rate is set to 0.000006, and a weight decay rate of 0.0004 is used to prevent overfitting. The AdamW optimizer is used during training, and the OneCycleLR learning rate scheduler is used to dynamically adjust the learning rate. In addition, the model also applies a multi-head attention mechanism, layer normalization, Dropout regularization, and other technologies to enhance the feature extraction ability of the model by processing four different stream aggregation channels in parallel.
## 3 The Proposed Satellite Images DSM Generation Method
### Stereo Reconstruction  
Stereo reconstruction includes image rectification and stereo matching. The image rectification process transforms the satellite images in order to reduce the computational burden of stereo matching. Stereo matching generally uses dense matching methods combined with triangulation to generate 3D point clouds.  
The current satellite images are all acquired by pushbroom cameras. In fact, the epipolar line that satisfies the epipolar constraint on the stereo pair of the pushbroom images is not a straight line but a curve [6]. However, for a small Area of Interest (AOI), the epipolar curve of the pushbroom images can be approximated as a group of parallel straight lines [8]. Therefore, the stereo rectification on the pushbroom images can be generated by the rigid 2D transformation of the images. If the processed satellite image tile is small (1000 \(\times\) 1000 pixels), it can be rectified by affine transformation [7]. The proposed method designs two steps for stereo rectification of satellite images, as shown in Figure 3. Firstly, the affine transformation is calculated according to the point correspondences generated in Section 3.1, and then the affine transformation is performed on the image.  
After rectification, the stereo pairs also need to be stereo matched. Semi-Global Matching (SGM) is commonly used for binocular stereovision systems. In the SGM algorithm, the parallax map of the stereo pair is obtained by minimizing the cost function of the dynamic programming. Then the stereo matching process combines the parallax map with the refined RPC model to triangulate. Finally, the 3D point cloud and the DSM can be generated. In order to improve the efficiency of the algorithm, the proposed method uses an improved SGM algorithm to perform dense matching [7; 10]. We adopt Census cost [38] for SGM, and replace the one direction of the dynamic programming with the mean of the parallax information in the two vertical directions.