esa-sceva/satcom-chunk-collection huggingface dataset:
# Upgrading _In Vitro_ Digestion Protocols with Absorption Models
## 2 Technological Approaches of Intestinal Epithelium Models
### Membrane-Based Transport Models  
Brush border or basolateral membrane vesicles do not contain intact cells but they are prepared from mammalian intestinal epithelial cells. They are purified fractions of the apical or the basolateral membranes of the intestine [22].  
The purified fraction of apical membranes is extracted from inverted intestine homogenate [22]. [PERSON] et al. [36] and [PERSON] et al. [37] describe methods for the purification of brush border membranes. According to [PERSON] et al. [37], several approaches exist to purify brush border membranes from intestine, for example:  
1. \"Tris disruption procedure, followed by density gradient centrifugation\";
2. the hypotonic EDTA procedure which can be complemented with density gradient centrifugations;
3. the Ca2+ precipitation protocol. In the presence of Ca2+, endoplasmic reticulum and mitochondria are converted to larger particles due to aggregation. These larger particles can be easily separated by centrifugation at 2000\(\times\)\(g\). Following this, the brush border fragments are separated by centrifugation of the supernatant at 20,000\(\times\)\(g\)[38];
4. \"isotonic homogenization and density gradient centrifugation\";
5. \"isolation of enterocytes, homogenization and density gradient centrifugation or free flow electrophoresis\".  
To obtain basolateral membrane vesicles [39], the mucosa of healthy, human jejunum tissue is scraped with a metal spatula. After homogenization of the mucosal scraping in an appropriate buffer, the homogenate was centrifuged at 2500\(\times\)\(g\) for 20 min. The supernatant was centrifuged at 22,000\(\times\)\(g\) for 25 min. After the removal of the supernatant, a whitish fluffy layer remained at the top of the solid yellow pellet. This layer was resuspended in a buffer and was homogenized. The homogenate was mixed with 15% (wt/wt) Percoll and centrifuged at 48,000\(\times\)\(g\) for 60 min. On the top of the Percoll pellet, basolateral membrane vesicles appeared as a distinct whitish layer. The vesicles were removed, resuspended in another buffer (called transport buffer) and centrifuged at 48,000\(\times\)\(g\) for 20 min. This washing step was repeated twice. The retrieved basolateral membrane vesicles can be used for transport studies after resuspension in the transport buffer. These models are not appropriate for the evaluation of paracellular transport. The information they provide regarding transcellular transport is also partial since they do not incorporate the apical and basolateral membranes at the same time [22].
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 3 Proposed Method
### Detection Based on Logits Dispersion by Other Three Features
#### 3.4.1 Detection Based on the Intra-Frame Mean Difference  
Figure 3 illustrates that the difference between the maximum value and the next largest value in logits differs for normal speech and adversarial examples. The dense logits also show the identical pattern as shown in Figure 5:  
These five adversarial examples are generated from the code [37] provided in the literature [15]. It indicates that adversarial examples have a smaller intra-frame difference  
Figure 4: Deployment of the detection system.  
Figure 5: Difference of each frame in Lingvo by five normal speech (**a**) and C&W adversarial examples (**b**).  
than normal speech. This paper quantifies this difference by calculating the sum between the maximum value and the next largest value for all decision frames. The phenomenon that long text speech with a significant difference sum is more likely to be judged as normal speech can be avoided by dividing by the number of decision frames:  
\[\Delta_{12}=\frac{\sum_{i\in D_{(n-t)}}\left(l_{i}^{max}-l_{i}^{sec}\right)}{(n -t)} \tag{6}\]  
where \(D_{(n-t)}\) denotes the set of serial numbers of character frames and \(l_{i}^{max}\) denotes the maximum value of the \(i-th\) frame. The obtained \(\Delta_{12}\) is compared with the threshold \(k\), and those less than \(k\) are judged as adversarial examples.
## 2 Materials and Methods
### Data Processing Layer  
#### 2.2.1 UAV Photogrammetric Data Processing  
Photogrammetric data processing is required to produce georeferenced 3D point cloud data from UAV-captured photographs. Exchangeable image format (Exif) metadata are generated from each captured airborne image to generate 3D point cloud data. Detailed reviews are provided by [PERSON] (2017) and [PERSON] (2018) for the generation of 3D point cloud data from captured photographs. Several algorithms are available to produce georeferenced 3D point cloud data using multiples mathematical and computational techniques ([PERSON], 2018; [PERSON], 2018). There are several commercial software programs available to convert georeferenced photographs into 3D point cloud data. Adequate overlap of adjacent photographs consisting of the same features and precise insertion of GCP is mandatory to improve the absolute accuracy of 3D point cloud data ([PERSON], 2018) (Figure 4). Image processing consist of four main steps: (1) alignment of photographs, (2) construction of geometry, (3) texture building, (4) exporting to colored point cloud/digital terrain model/orthophoto. Table 1 shows the details about the UAV photogrammetric data processing for the generation of georeferenced 3D point cloud data. RMSE value adjustment of GCPs is also provided in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & **Details** \\ \hline Average ground samplingDistance (GSD) & 2.36 cm \\ Area covered & 0.7305 km\({}^{2}\) \\ Images & Median of 52,717 key points per image \\ Dataset & 775 out of 775 images geolocated (100\%)529 out \\ \multicolumn{1}{c}{} & of 775 images calibrated (68\%) \\ Camera optimization & 0.41\% relative difference between initial and \\ Matching & Median of 7579.05 matches per calibrated image \\ Georeferencing & 22 GCPs (22 3D), mean RMS error = 0.074 m \\ Mean reprojection error (pixels) & 0.194 \\ Number of 3D densified points & 52,790,183 \\ Average density (per m\({}^{3}\)) & 113.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of UAV photogrammetric data processing for the generation of georeferenced 3D point cloud data.  
Figure 3: The typical range of frequencies for the application of GPR ([PERSON], 2018).
# Analytical Model of an Energy Detector for Ultra-Wideband Chaotic Communications
## 2 Energy Detector Model  
Here we consider an orthogonal system of signals, also called OOK (on-off keying) or ASK (amplitude shift keying), in which symbol '1' is encoded by a radio pulse on a prescribed time interval, and symbol '0' by no pulse on that time interval. In our case, the task of the energy detector is to detect the presence of a UWB radio pulse against an AWGN background.  
The detector is assumed quadratic (square-law), non-quadratic detectors are discussed in Section 4.
## 5 RFI Measurements
### Receiving Chain Compensation  
Given the architecture of MERITXELL, the spectrum retrieved by the back-end stage is a combination of the received signal spectrum, or the spectrum under analysis, and the frequency response of the receiving chain, including the antenna and front-end stage. The non-ideal frequency response of the combination of antenna, tuned filters, and amplifiers may mask the detection of weak RFI signals. However, the frequency response corresponding to the front-end and back-end stages can still be compensated or equalized by using the spectrum of the corresponding matched load. This compensation is performed dividing point by point the power spectrum taken at the antenna by the power spectrum measured at the matched load. The result of this operation gives a measurement of the relative power of the RFI signal after the antenna over the thermal noise present at the matched noise.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 4 Challenges and Future Research Directions
### Challenges
#### 4.1.1 Interoperability and Standardization  
Interoperability between IoT and O-RAN will be a significant challenge for integrating the two systems because they comprise various devices from different manufacturers, cross-layer networking topologies, and edge computing vendors, having their respective communication protocols and standards. These devices span various application domains (transportation, industry, healthcare, energy, etc.), where each application has its unique requirements and data formats.  
Second, O-RAN uses standard communication protocols like the 5G NR. In contrast, IoT devices use various IoT-specific protocols (e.g., MQTT, CoAP, AMQP, etc.). Most IoT devices have limited battery power. At the same time, the O-RAN concept is more complex and battery-demanding than the traditional RANs.  
Lastly, the development, validation, and testing tools are imperfect for extending the O-RAN concept into IoT systems. Most of the efforts reviewed in this study are either complex (computation-expensive), static (does not scale to environmental changes and demands), visionary (theory/hypothesis, yet to be implemented), vulnerable to threats, or even undeployable (needs thorough testing/validation) in some cases. There is a need for standards, tools, and specifications that guarantee the integration and interoperability operation of these systems.  
The O-RAN Alliance has dedicated several focus groups working to ensure that the O-RAN vision comes to fruition [87]. Similarly, there is a need for a dedicated group that engages O-RAN and IoT standardization and research communities to investigate the feasible ways O-RAN could be an idea match for IoT systems. At this juncture, there is not much published research reporting the possible ways of integrating O-RAN interfaces with IoT applications. How to implement IoT-specific protocols, such as RFID, NB-IoT, LoRAWAN, etc., in O-RAN still needs to be discovered and investigated. Most of the research efforts are visions and discussions for the future study. However, it is critical to dedicate a focus group within the O-RAN Alliance community that engages the IoT research community to develop standards and specifications for O-RAN to IoT integration. After that, the yielded specifications will be helpful to the research on a theoretical foundation and analysis, modeling and simulation testbeds, among others.
# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 3 Case Study
### Data Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **Min** & **Median** & **Max** & **Mean** & **Std** \\ \hline P & 2.607108 & 6.44266 & 55.94638 & 7.635982 & 5.634877 \\ T & 1536.277 & 3680.723 & 5613.279 & 3656.688 & 701.2206 \\ RPM & 2.239389 & 4.054824 & 5.258368 & 4.060949 & 0.7243764 \\ F & 14,476.57 & 23,237.92 & 31,188.26 & 23,230.56 & 4178.51 \\ V & 7.954941 & 26.01329 & 290.4988 & 30.78468 & 25.78844 \\ FPI & 5.44542 & 52.55251 & 130.6987 & 56.02042 & 23.09044 \\ TPI & 0.073619 & 0.81829 & 1.506045 & 0.82678 & 0.274542 \\ WR & 12.9168 & 158.0774 & 352.7145 & 153.7523 & 41.84008 \\ \hline \end{tabular}
\end{table} 
Table 5: Basic statistical information on TBM parameters. In order to investigate the causal relationship between multi-source features, the causal relationship between multi-source features was analysed using the Direct-LiNGAM method (see Figure 12 for the causal network). Each node in the figure corresponds to a distinct data feature, with arrows denoting the direction of causality. The arrows originate from the cause and terminate at the effect. The value assigned to each arrow signifies the strength of the causal relationship, with positive values indicating positive effects and negative values indicating negative effects. It is important to note that causality is based on a threshold of 0.1, and values less than 0.1 are generally considered to lack a significant causal relationship. As demonstrated in Figure 13, the strength of causal relationships between individual TBM features is illustrated, with the horizontal axis variable acting as the cause and the vertical axis variable denoting the effect. Null intersections in Figure 13 indicate a non-significant causal relationship between two variables, while solid circles indicate that the horizontal axis variable is the cause of the corresponding vertical axis variable. The findings from Figures 12 and 13 demonstrate that the features exhibiting the strongest causal relationship with surrounding rock grades among the three data sources are \(HSP\), \(F\), and \(Y\). This observation indicates that, in terms of causality, these three features are the most significant contributors to surrounding rock grades among the respective data sources. Concurrently, the causal relationship between these three features and other features is also relatively robust, and they correlate with each other as an intrinsic link between the data from multiple sources. Nevertheless, this does not imply that these three features have the greatest impact on the prediction process of surrounding rock grades, a topic that will be examined in Section 4.2. Figure 11: PLCC and MIC heat map matrix for multi\(-\)source data. Figure 12: Multi-source feature causal network diagram. Figure 13: Multi-Source Feature Causal Relationship Adjacency Matrix.
## 1 Regional Geological Background  
### Basin Location and Tectonic Unit Division  
The Mangeshlak Basin is one of the sedimentary basins rich in oil and gas resources in Central Asia. It has remaining recoverable petroleum reserves of more than 2.5 billion barrels of oil equivalent and has great exploration potential. At present, the oil and gas discovered in the basin mainly come from three reservoirs: Triassic, Jurassic, and Cretaceous. Most of the reserves come from the Jurassic reservoir [1]. Typical discoveries such as the Zhetybay Oilfield, Uzen Oilfield, and Tenge Gas Field are mainly in the Jurassic reservoir, which has received much attention and research. However, there is little discussion and research on the Triassic reservoir, with great potential to develop in the basin. The Triassic reservoir in the basin is not only a major source of rocks but also of high-quality carbonate reservoirs. The development and distribution characteristics of the reservoir are among the main controlling factors affecting oil and gas accumulation. Therefore, it is of great significance to understand the sedimentary environment and sedimentary facies model of the Triassic carbonate strata for the potential of Triassic oil and gas exploration. Starting with the analysis of the basin's geotectonic and sedimentary evolution, this work analyzed the Triassic carbonate sedimentary environment in the basin based on a variety of data such as drilling cores, rock thin sections, and logging and seismic data and explored the sedimentary facies model of this set of carbonate rocks to boost Triassic oil and gas exploration in the basin.  
The Mangeshlak Basin is located along the eastern coast of the Caspian Sea, covering an area of about 20 \(\times\) 10\({}^{4}\) km\({}^{2}\). While most of the basin lies within Kazakhstan, it extends eastward and southward into Uzbekistan and Turkmenistan. Geotectionically, the basin developed within the Tethys tectonic domain in southern Eurasia. It is a Mesozoic and Cenozoic basin located in the western part of the new Turan Platform [2]. The development of the Mesozoic sedimentary cover is like that of the North Ustyurt Basin, Terek-Caspian Basin, and Amu Darya Basin on the same platform. The basin includes three secondary tectonic units: the Mangeshlak-Central Ustyurt Uplift Zone, the South Mangeshlak, and the Turkmen Platform Anticline. The Mangeshlak-Central Ustyurt Uplift Zone is bounded by the Agraham-Atyrau fault in the west, disappears in the Kyzylkum massif in the east, strikes NWW-SEE, and includes a series of scattered goose-row bulges, such as the Gornyy Mangeshlak and the Kara Baur Uplift. The Southern Mangeshlak Sub-basin is connected to the Mangeshlak-Central Ustyurt Uplift Zone in the north and is bounded by the Turkmenia Anteclise Sub-basin in the south. A series of alternating depressions and local uplifts are developed in the interior. The southern Turkmenia Anteclise Sub-basin is mainly located in the Caspian Sea (Figure 1).
## 6 Discussion
According to the initial follow-up experiments, not reported in the paper due to space limitations, data augmentation based on the mix-up technique (generating new audio recordings by mixing the existing recording exhibiting the same spatial audio scenes) or involving a signal resampling technique (pitch shifting and time stretching) introduced only small improvements to the CNN performance, indicating that there is no \"information gain\" or any other network training benefit (e.g., reducing the overfitting effect), resulting from the artificial extension of the dataset by means of re-using the existing recordings in a modified form. Therefore, a more plausible explanation for the relatively poor performance of the CNN algorithm is that only eleven BRIR sets were used to generate the training dataset, hence limiting the \"amount\" of spatial acoustic information embedded into the training database and, consequently, preventing the algorithm from benefiting fully from its deep learning capabilities. Therefore, it could be hypothesized that extending the existing database by introducing new spatial audio information, e.g., by including audio recordings synthesized using new BRIR sets representing new electro-acoustic conditions (e.g., varied room acoustics and dummy heads) would improve the performance of the CNN algorithm. According to the results, the CNN algorithm substantially outperformed the traditional algorithms for one of the considered BRIR sets. However, there was another BRIR set for which the opposite outcome was observed (see Section 5.6). Consequently, it may be argued that there are some audio characteristics which are not captured by the spectrograms and/or the CNN algorithm, while they are \"picked up\" by the traditional feature extraction algorithms combined with the traditional classification algorithms (and vice versa). The identification of these unique characteristics is planned to be undertaken in the follow-up work. The calculation time required by the SVM, XGBoost, and Logit algorithms was approximately 4, 9, and 52 times shorter than computational time required by CNN algorithm, respectively, implying that the Logit algorithm is the least computationally demanding method (while still providing similar classification rates compared to the remaining methods), whereas the CNN algorithm imposes the highest computational load. However, the direct comparison of computation time might not reflect the actual computational load due to the fact that the CNN and XGBoost algorithms were GPU accelerated (NVIDIA RTX 2080 Ti), whereas the remaining methods utilized the central processor unit (Intel Core i7-7820X CPU 3.6 GHz).
# A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes
###### Abstract  
In the operation and maintenance of planetary gearboxes, the growth of monitoring data is often faster than its analysis and classification. Careful data analysis is generally considered to require more expertise. Rendering the machine learning algorithm able to provide more information, not just the diagnosis conclusion, is promising work. This paper proposes an analysis and diagnosis two-stage framework based on time-frequency information analysis. In the first stage, a U-net model is used for the semantic segmentation of vibration time-frequency spectrum to highlight faulty feature regions. Shape features are then calculated to extract useful information from the segmented image. In the second stage, the decision tree algorithm completes the health state classification of the planetary gearboxes using the input of shape features. The real data of wind turbine planetary gearboxes and augmented data are utilized to verify the proposed framework's effectiveness and superiority. The F1-score of segmentation and the classification accuracy reach 0.942 and 97.4%, respectively, while in the environmental robustness experiment, they reached 0.747 and 83.1%. Equipping the two-stage framework with different analytical methods and diagnostic algorithms can construct flexible diagnostic systems for similar problems in the community.  
planetary gearbox; time-frequency analysis; fault diagnosis; U-net; vibration Article
# Nonlinear Vibration Analysis of a Beam with a Breathing Crack
## 7 Results and Comparisons
### Experimental Platform  
Fatigue crack preparation is a key procedure for the following vibration testing. First, a beam with a notch is cut from a C45E steel plate, and its dimensions in mm are given in Figure 6a. The elasticity modulus of the material is \(E=206\) GPa and the mass density is \(\rho=7850\) kg/m\({}^{3}\). The actual fatigue crack is created by a servo hydraulic machine (SDS 200), as shown in Figure 6b. The bending fatigue testing for crack growth is conducted in accordance with the specification ASTM-E647. The constant amplitude loading test is performed with a loading frequency of 20 Hz and a stress ratio of 0.1. The crack length is 3.825 mm, measured by an optical microscope (AO-AF216C), as shown in Figure 6c. Then, to remove the notch starter, the height of the specimen is machined to 12 mm by the electrical discharge machine (EDM).  
Figure 7 shows the experimental setup. It consists of three parts: the exciting shaker system, the vibrating system, and the data acquisition system. The vibrating system consists of the cracked beam specimen, the solid base, the force sensor (VL25), the threaded stem, the shaker (HEA-50) and the aluminum block, as shown in Figure 7a. The cracked beam specimen is clamped on the solid base by bolts. A force sensor weighing 0.05 kg is fixed at the free end of the beam by bolts. An accelerometer weighing 0.01 kg is attached at the free end of the cantilever beam. The harmonic force generated by the shaker is transmitted to the beam through a threaded stem which connects the force sensor and the shaker by a screw. The shaker system consists of the signal generator, the power amplifier (HEAS-50) and the shaker. The data acquisition system consists of an accelerometer (JF-2050) and a PC equipped with a data acquisition board (DEWE-201), as shown in Figure 7b.  
Figure 6: Fatigue crack growth setup: (**a**) single edge notched beam specimen, (**b**) three-point bending test, (**c**) rack length measured by optical microscope.  
Figure 7: Vibration testing setup of the cantilever beam: (**a**) the structure of the vibrating system, (**b**) the system of the data acquisition.
## 6 Discussion
### Limitations  
In practical application scenarios, dynamic input clipping is a necessary part. The main idea of training clipping is to cut out a large number of redundant parameters through weights and input clipping operations, and then compensate for the loss caused by clipping through model training. The problem is that the weights are easy to obtain, but the input is dynamically changing in a range, and this dynamic range needs to be obtained by manual search, comparison, and experiment, which has a certain randomness. However, since the change of model accuracy is mainly caused by the weights, and the model training will compensate the loss of input clipping, in general, the impact on model accuracy is thus not significant.