esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Image Data
#### 3.2.1 Sentinel-2 MSI Image Time-series  
The Sentinel-2 MSI image time-series of the study area came from both Sentinel-2A and Sentinel-2B satellites and covered the vegetation growing season from 26 April 2018 to 10 October 2018. The temporal intervals of these images were 2 to 3 days. For the entire period, there were 67 observed Sentinel-2 images. Among them, there were only 14 (20.90%) clear-sky images (cloud coverage \(<\)5%) and 21 (31.34%) partially cloud-covered images (cloud coverage \(<\) 50%), as well as 32 (47.76%) fully cloud-covered images (cloud coverage \(\geq\) 50%). Figure 5 shows the cloud-free pixel distribution in this time series.  
As presented in Figure 5, majority of the pixels (about 60% of the total pixels) have 33 to 35 cloud-free days (about 18% pixels of 33 days, 22% of 34 days, and 19% of 35 days) that corresponded to about half of the total 67 image acquisition dates. Besides these, the number of pixels drop quickly with an increasing or decreasing number of cloud-free days. For example, while there were about 12.5% pixels with 36 cloud-free days, the percentage was down to 5% with 37 clear-sky days, and only about 1% with 38 days. These numbers indicated that, within the time period, fewer than half of the total image pixels of the time-period were useable. In the reconstruction process of the time-series, all cloud contaminated pixels were replaced by the corresponding pixels of the synthetic images while keeping and maximizing the value of all observed cloud-free pixels.
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 4 Experimental Results  
In this section, we compare the proposed SCKG with other traditional models under different channels, including the AWGN channel and the Rayleigh fading channel to verify the effectiveness of SCKG. In Table 2, we introduce the models used in the experiments, including their general features and technical methods. It is worth noting that the traditional communication models are not the only ones mentioned in Table 2. The source codingcan also choose arithmetic coding, L-Z coding, and other coding methods. Identically, the channel coding can also choose turbo code, polar code, and other coding methods.
## 3 Hanger-Specific Testing and Analysis
### Hanger Disease Causes Analysis
#### 3.4.3 Investigation of Hanger Water Buildup  
The results of the open box inspection of the hanger anchor head showed that the main reasons for the severe water accumulation inside the hanger were as follows:  
1. Rainwater penetrated through cracks in the sealing anchor concrete, entered through the gap in the upper anchor box cover, and then flowed into the lower-anchor box  
Figure 8: Window-opening detection of upper anchor box cover plate.  
Figure 7: A disease of the upper anchor head: (**a**) sealed anchor concrete, (**b**) diagram of sealing anchor concrete disease, (**c**) upper anchor box cover plate welding defects, (**d**) corrosion of upper anchor head and water accumulation, and (**e**) front view of the bridge.  
along the inner wall of the outer steel pipe. The distribution of the primary diseases on the anchor head of the bridge and the water flow lines are shown in Figure 9b.
2. The structure of the bridging hanger is important. The finished cable has a seamless steel pipe on the outside, and the wet air enters the inside of the steel pipe when the temperature of the inner wall of the steel pipe is lower than the dew point temperature of the wet air. Furthermore, condensation will form in the pipe, and because the steel pipe is closed, the water does not easily evaporate.
3. In order to facilitate a description of the disease degree of the anchor head, according to the 'Technical Condition Assessment Standard for Highway Bridges' (JTG/TH 21-2011) [20], the anchor head corrosion can be divided into two grades according to the corrosion area, namely, slight corrosion (cumulative corrosion area \(\leq\) 3% of the component area) and severe corrosion (cumulative corrosion area \(>\) 10% of the component area). The water accumulation in the anchor head is divided into two grades according to the amount of water, namely slight water accumulation (there is a small amount of water or water vapor in the anchor head, and the air humidity is large) and serious water accumulation (the water accumulation in the anchor head is very serious or the air is very humid, and the anchor head is seriously corroded). The distribution of anchor head disease is shown in Figure 9a. As the lower anchor box is filled with anti-corrosion grease, the water flow cannot be discharged, resulting in rainwater and condensation gathering inside the lower anchor box and the outer steel pipe and causing the anti-corrosion grease to age and fail, which, in turn, causes different degrees of rusting of the anchor head components. The water accumulation in the outer steel pipe is shown in Figure 10.  
Figure 10: Detection of internal water accumulation in steel pipe: (a) pipe cutting, (b) steel pipe window opening, and (c) water inside the steel pipe.  
Figure 9: Waterlogged disease inside the hanger: (a) disease distribution of anchor head, and (b) water flow inside the hanger.
# Evaluating the Impact of Drone Signaling in Crosswalk Scenario
## 3 Materials and Methods
### Metrics of Interest
#### 3.2.3 Perception-Reaction Distance  
The perception-reaction distance depends on the perception-reaction time. Typical values of \(t_{p-r}\) in different situations and for different drivers profiles can be found in Reference [47]. It reaches from less than 1 s to more than 2.5 s to specifically accommodate very elderly, debilitated, intoxated, or distracted drivers.
## 3 Results and Discussion  
### Oil Content  
Optimisation of extraction was performed using different solvents (hexane and hexane:isopropanol = 3:2), time of extraction and consecutive extractions (data not shown). Most of the lipids were extracted using the final method described in Section 2.2.  
The content of oil obtained from our samples varies between 9.1% and 21.7% (Table 1). Differences are likely to be random or dependent on environmental factors (e.g., geographical location, soil and weather conditions). The results are comparable with those found in the study by [PERSON] et al. [9], where a yield of 12.9% was obtained for _T. caucascica_. In contrast, the content for _Tilia cordata_ referenced in [15] was higher (27.9%), while [PERSON] et al. reported a lower content of 5.6%, 8.12% and 6.16% in _T. argentea_, _T. platyphyllos_ and _T. rubra_, respectively, growing in Turkey [16]. In our study, the average content in _T. platyphyllos_ seeds was found to be higher (19.2%) than in _T. cordata_ seeds (14.1%).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Oil** & \multirow{2}{*}{**Oil Colour**} & \multirow{2}{*}{**Antioxidant Activity**} & \multicolumn{4}{c}{**Unsaponifiable Substance (mg/100 g Total Unsaponifiable Matter)**} \\ \cline{4-9}  & **Content** & & & **Activity** & & **Phylol** & **Square** & **\(\Delta\)-** & **\(\gamma\)-** & **Stigmaster** & **\(\beta\)-** \\  & **(\%)** & & & **(\%)** & & **Tocopherol** & **Tocopherol** & **Stigmaster** & **Stigosterol** \\ \hline
1 \(\bullet\) & 9.3 & brown & 18.0 & / & / & / & / & / & / \\
2 \(\bullet\) & 15.1 & yellow & 54.8 & / & / & / & / & / & / \\
3 \(\bullet\) & 12.5 & yellow & 15.6 & / & / & / & / & / & / \\
4 \(\bullet\) & 21.7 & yellow & 62.7 & / & / & / & / & / & / \\
5 \(\bullet\) & 14.9 & yellow & 52.5 & 81.5 & 17.6 & 4.0 & 13.3 & 0.7 & 54.4 \\
6 \(\bullet\) & 9.1 & brown & 8.9 & / & / & / & / & / & / \\
7 \(\bullet\) & 14.7 & yellow & 42.0 & 78.1 & 17.1 & 4.9 & 13.3 & 11.8 & 99.8 \\
8 \(\bullet\) & 15.6 & yellow & 51.9 & / & / & / & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oil content, oil colour, antioxidant activity, and content and composition of unsaponifiable matter. Unsaponifiable matter denoted with “/” was not determined due to limited amounts of oil samples. Unsaponifiable matter denoted with “\(<\)LOQ” was below limit of quantification. _Tilia cordata_ samples are marked with circles and _Tilia platyphyllos_ samples are marked with triangles.  
In terms of the oil content in relation to traditional, commercially important seeds used for the industrial production of linoleic acid-based vegetable oils, _Tilia_ seeds are not a very rich source. For example, sunflower seeds contain 50% oil and pumpkin seeds contain between 40% and 50% oil. _Tilia_ seeds are more similar to soy (20%) and corn seeds (15%) [17; 18].
# Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring
## 3 Results and Discussion
### Discussion  
The sensor test results show the following characteristics: real-time acquisition bandwidth of 5.12 MHz; sensitivity of \(-100\) dBm for the handheld version and \(-120\) dBm for the fixed version; dynamic range of the input signal: \(-100\) to \(-30\) dBm; frequency  
Figure 16: Evaluating the distance between two consecutive measurement points (Google Earth) for space resolution assessment in the case of the moving sensor.  
Figure 17: TETRA channel measured power with the mobile sensor: the speed is not influencing the correctness of the readings—exemplified by the speeds of 60 and 120 km/h.  
resolution better than 2.5 kHz; sensor made up of COTS hardware components; small form-factor: 13.5 (width) \(\times\) 7 (height) \(\times\) 7 cm (depth); weight (cables included): 529 g; weight (no cables): 506 g; weight (no battery): 280 g. The approximate cost of the sensor is EUR 320.  
Compared to other SDR sensors reported recently [6; 7; 8], our sensor brings the following contributions:  
1. _Improved real-time acquisition bandwidth_ Our sensor has been tested under conditions of an instantaneous acquisition bandwidth of 5.12 MHz. Depending on the characteristics of the signals to be monitored, we can extend this instantaneous bandwidth up to 20 MHz.
2. _Good amplitude frequency response and improved sensor sensitivity_ The selection of SDR platforms with good resolution for analogue-to-digital conversion (12 bits for the portable version of the sensor and 14 bits for the fixed version) improves sensitivity as well as dynamic range. Compared to 8-bit SDR platforms such as those reported in [6; 8], we estimate an improvement of at least 10 dB in sensitivity terms.
3. _Improved data storage and data transfer rate with the addition of supporting applications_ Direct storage of IQ data can lead to a large amount of data. This can limit both data transfer and real-time data processing. From this point of view, it is useful to implement support applications that are directly operated by the sensor. In [8], it is shown that the acquisition of IQ samples with a bandwidth of 2.4 MHz generates a data transfer rate of about 50 Mb/s. Adding a support application to the sensor for power spectral density (PSD) computation, after data averaging and data compression, reduces the throughput to 50-100 kb/s. In the case of our sensor, the support application for the TETRA channel power computation directly by the sensor further reduces the data transfer rate. A rate of 15 kb/s is obtained even if the acquisition bandwidth is 5.12 MHz and the SDR platform resolution is 12 bits.  
To our knowledge, this is the first SDR sensor for real-time monitoring of TETRA signals. The sensor can be easily accommodated in any vehicle without imposing additional requirements (space, power supply, etc.). Sensor portability, autonomy in operation and low price offer the possibility to increase the number of sensors. A complete characterization of the TETRA spectrum becomes possible in four dimensions: amplitude, frequency, space and time.
# The Effect of Particle Type and Size on CoCr Surface Properties by Fine-Particle Shot Peaning
## 2 Materials and Methods
### Surface Observation  
Surface morphology plays a vital role in a material's properties and performance and can affect various applications [17]. The physical structure and appearance of a material's surface, including its texture, roughness, and topography, can be quantified using various techniques such as scanning electron microscopy (SEM), atomic force microscopy (AFM), and profilometry [18]. Profilometry measures surface roughness and other surface features, while SEM and AFM provide high-resolution images of the surface topography. Surface morphology can be modified using various techniques and processes such as polishing, etching, and surface treatments to optimize its properties and performance [19]. In biomedical implants, for example, surface roughness plays a crucial role in the osseointegration process, and its optimization can enhance the implant's biocompatibility [20].  
For this study, surface morphology was investigated by optical microscope (DP21, Olympus Corporation, Tokyo, Japan) with a magnification of 10 times on the top surface. BF/DF stands for brightfield/darkfield, which are two contrasting illumination techniques used in optical microscopy to visualize different types of samples. For studying the effect of particle type and size on CoCr alloy surface properties using the fine-particle shot peening technique, it is recommended to use the darkfield (DF) mode of the optical microscope. The fine-particle shot peening technique involves bombarding the surface of the CoCr alloy with small, high-velocity ceramic particles to induce compressive stresses and improve the surface properties. During the process, the surface of the CoCr alloy can become rough, and the fine ceramic particles can become embedded in the surface. Darkfield illumination is useful for visualizing the embedded ceramic particles in the CoCr alloy surface because it enhances the contrast between the particles and the surrounding matrix. The dark background in darkfield mode helps to highlight the particles and minimize background interference, which can be useful for characterizing the size, distribution, and orientation of the particles.
## 3 Derivation of the Theoretical Formula of the Surrounding Rock Loose Zone
### Theoretical Derivation of the Stress Method  
In the process of chamber excavation, the crushing effect of blasting on rock is regarded as the coupling effect of the shock wave and the explosive gas, as shown in Figure 3. The crushing action on rock can be mainly divided into the crushing action on the rock mass of the tunnel face and the crushing action on the surrounding rock [35].  
Figure 2: Formation of a loose zone in chamber excavation: (**a**) Initial stress state of the unexcavated chamber; (**b**) initial loose zone under in situ stress; (**c**) final loose zone after blasting expansion. \(P_{0}\) and \(\lambda P_{0}\) are original rock stress, \(P\) is in situ stress, \(\sigma\) is blasting stress, \(S_{1}\) is the in situ stress loose zone, and \(S_{2}\) is the blasting loose zone.  
A shock wave is generated at the moment of the explosion, and the explosion expands along the tiny cracks in the tunnel face and breaks the rock mass. The high temperature generated by the explosion generates an explosive gas, which throws the broken rock mass on the tunnel face and peels back the original rock mass to form a chamber.  
At this time, the stress state around the chamber changes, and the in situ stress in the surrounding rock is redistributed. In this process, part of the surrounding rock will be broken to form a stress-loosening circle. When the blasting shock wave is transmitted to the edge of the surrounding rock, it attenuates into a stress wave, expands the cracks in the surrounding rock, and forms a new blasting-expansion loose zone. The scope of the loosening force corresponding to the blasting circle and the scope of the loosening force corresponding to the blasting circle are deduced below.
# An Overview of Smart Composites for the Aerospace Sector
## 4 Discussion
### Trends in Top Publications
Moreover, a universal method has been proposed to program soft, magnetoactive materials, enabling the creation of bioinspired robots like a jellyfish and an artificial cilium that replicate biological beating patterns, with applications in biomedical engineering and microfluidics [29]. The third trend is the development of piezoelectric materials, which have been widely used in sensors and actuators. These materials have evolved into flexible polymer composites reinforced with ceramic nanoparticles to enhance performance. Flexible piezoelectric composites have seen significant advancements. Materials based on PVDF-PZT have been developed to combine the flexibility of polymers with the high piezoelectric response of ceramics, enabling greater sensitivity in advanced sensors. These improvements are particularly useful in structural health [34]. The fourth and latest trend detected is the use of aerogels and ultralight materials. Carbon aerogels and other porous structures have shown great potential in aerospace applications due to their low density and multifunctional properties. One of the most notable developments is the creation of highly elastic carbon aerogels. A newly designed aerogel exhibits reversible elongation of 200% and fatigue resistance exceeding 10\({}^{6}\) cycles. These characteristics make it ideal for use in advanced strain sensors capable of identifying complex shape changes in aerospace applications [35].
## 4 Results and Discussion
### Open Notch Crack  
In addition to the internal defects, a turbine blade also tends to suffer from surface cracks. Therefore, investigations were also conducted to evaluate the surface cracks based on the proposed method. Open notches with different orientations were introduced to mimic the surface crack. The size of the notch was set as a constant at 5 mm long and 2 mm wide. Only the influence of the orientation was studied and three directions (\(\theta=0^{\circ},45^{\circ},90^{\circ}\)) were investigated here. To investigate the capability of FWI to detect open notch defects with different angles, three numerical experiments were conducted. The transducer configuration and inversion scheme remained the same as those in the previous cases. The FWI tomographs of the three notches are shown in Figure 12a,c,e. Generally, the successive-frequency FWI was able to reconstruct the surface crack with a high resolution. Similarly, to quantitatively evaluate the accuracy, relative errors defined as the profiles along the width and length were represented as \(E_{S}\) and \(E_{L}\). The trends of the FWHM errors with a successive-frequency are presented in Figure 12b,d,f. \(E_{L}\) decreased significantly when the frequency was smaller than 1 MHz and converged to around 10%. However, different from the result of the circular defect in Section 4.1, \(E_{S}\) was generally much smaller than \(E_{L}\) and it increased with the successive-frequency. The reason might lie in the fact that ultrasound is much more capable of reconstructing the plane defects. Moreover, when the orientation of the crack was 45\({}^{\circ}\), the final error was slightly higher than the other two cases. It implied that the alignment of the transducer network probably introduced some inaccuracy into the FWI results.  
Figure 12: _Cont._
# Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
## 3 Proposed Solution
### Overall Scheme
After the function \(T\) is defined for \(g\) on all intensities on a gray scale, histogram equalization is performed by finding and mapping a value corresponding to **L** in the input image **I**, using \(T\). In Step 3 of the proposed algorithm, an estimate of **Z**, or equivalently **E** and **A**, is needed. From the given super-resolution problem, the following constrained least-squares problem is formulated as:  
\[\underset{\text{arg min}}{\text{min}}\left\|\textbf{I}\ -\ \textbf{REA} \right\|_{\text{F}}^{2} \tag{7a}\] \[\underset{\text{subject to }}{a_{ij}}\geq 0\ \forall\ i,\ j \tag{7b}\]  
with \(\left\|\ \cdot\ \right\|_{\text{F}}\) denoting the Frobenius norm, and \(a_{ij}\) is elements of **A** and constrained to non-negative values.
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 2 Materials and Methods
### UWB Positioning
#### 2.2.2 Performance Comparison Experiment between [PERSON] Algorithm and Taylor Algorithm  
In the white Gaussian noise environment, the MATLAB2018b simulation is used to verify the impact of different base station numbers and different TDOA errors on the positioning performance of the [PERSON] algorithm and [PERSON] algorithm. The standard deviations of the TDOA error are 0.1 us, 0.2 us, 0.3 us, 0.4 us, and 0.5 us, and the simulation radius is 200 m.  
Figure 3 shows that in the Gaussian noise environment, the positioning accuracy of the [PERSON] positioning algorithm increases with an increase in the number of base stations involved in positioning, which is due to the increase in the number of TDOA measurements that can be utilized. However, it can also be seen that when there are more than 5 base stations, the positioning accuracy does not change much, and the positioning performance reaches an optimal level. When the number of base stations is more than 4, the positioning performance of the [PERSON] algorithm reaches a better level, but it is inferior to that of the Chan algorithm. Therefore, the number of positioning base stations of the rail-mounted robot material distribution system in this paper is selected as 4.  
Figure 3: Error curves of the [PERSON] algorithm and [PERSON] algorithm with different numbers of base stations and different TDOA errors. **(a)** Error curves of the Chan algorithm; **(b)** error curves of the [PERSON] algorithm.  
Next, the positioning performance of the Chan algorithm and [PERSON] algorithm is compared based on 4 base stations. Figure 4 shows the performance comparison of the algorithm with the standard deviation of TDOA error as 0.1 \(\mu\)s, 0.2 \(\mu\)s, 0.3 \(\mu\)s, 0.4 \(\mu\)s, and 0.5 \(\mu\)s; the simulation radius of 200 m; and the mean of 100 positioning errors.  
Figure 4 shows that the performance of the [PERSON] algorithm is superior to that of the [PERSON] algorithm when four base stations are used in the positioning system in the Gaussian noise environment. The Chan algorithm can use all TDOA information to obtain a more accurate solution, so it can adapt to different measurement environments.