esa-sceva/satcom-chunk-collection huggingface dataset:
# The Cell Membrane of _Sulfolobus_ spp.--Homeoviscous Adaption and Biotechnological Applications
## 3 Homeoviscous Adaption  
Homeoviscous adaption has been studied in several Sulfolobales species, especially in the most commonly used _S. solfataricus_, _Sulfolobus islandicus_ and _S. acidocaldarius_. The focus of the research is on (1) the DEL:TEL ratio; (2) the amount of cyclopentane rings; and (3) the polar head groups. The amount of cyclopentane rings (= cyclization number) is commonly described via an averaged parameter called ring index (RI) [77]. RI is the weighted average number of cyclopentane rings in the entire TEL pool of the membrane lipids (Equation (1)). The ring number of individual lipid species is indicated with a number ranging from 0-8 added after the lipid class (e.g., GDGT-4). Polar head groups are connected at the glycerol's _sn_-1 position and are found to be hexoses (Hex), inositolphosphate (IP) and sulfonated trihexose (sulfono-Hex3) [66].  
Equation (1) shows the ring index (RI) formula. n represents the number of cyclopentane rings and GDGT-n.  
\[RI\ =\ \frac{\sum_{n\ =\ 0}^{8}n\times[\%GDGT(n)]}{100} \tag{1}\]  
Table 2 gives an overview of the correlations between the alterations in the membrane lipid composition and the applied growth conditions according to present knowledge. The connections between the modifications and the changes in temperature, pH and growth rate are discussed further in the next sections.
## 4 Experimental Results and Discussions
### Data Preprocessing Results  
Following the acquisition of point cloud data, preprocessing was conducted as detailed in Section 3.1. Figure 5 displays the denoising results for a segment of Dataset 1. As illustrated in Figure 5, the preprocessing effectively removed points associated with rails, auxiliary facilities, ground, and bolt holes. These results demonstrate that the capability of the method to minimize point cloud noise and mitigate interference in leakage detection, thereby establishing a robust foundation for the subsequent detection process.  
The preprocessed data now exclusively comprises tunnel linings and leakage points. The subsequent steps involve sample selection for model training and testing. Due to the relatively small proportion of leakage points compared to tunnel lining points, sections with a higher incidence of leakage were chosen for training. Table 2 summarizes the key characteristics of the selected samples, including the leakage type, point density, tunnel length, number of points, and leakage-lin  
Figure 4: TLSD system for tunnel point cloud data collection.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 3 Selected Beamforming Techniques
### Power-Based Capon Beamformer  
In the presence of replicas that are correlated with the LOSS of interest, matrix \(\mathbf{R}_{\mathbf{X}}\) is composed of the LOSS auto-correlation plus some cross-correlation terms between the LOSS and the replicas. As a consequence, the CAP beamformer \(\mathbf{w}_{\text{CAP}}\) can mix both contributions when minimizing the power at its output, thus eventually leading to an undesired cancellation of the LOSS. In that sense, the _power-based Capon_ beamformer (PBC) circumvents this limitation by determining the cross-correlation terms in \(\mathbf{R}_{\mathbf{X}}\) and eliminating their effect from the latter prior to applying beamforming onto \(\mathbf{x}(n)\).  
Briefly, we define \(\kappa(\tau_{0},\varphi_{0})\doteq\alpha_{0}\mathbf{r}_{\mathbf{Xs}}(\tau_{0},\varphi_{0})-\alpha_{0}^{2}\mathbf{a}_{0}\). The cross-correlation terms in \(\mathbf{R}_{\mathbf{X}}\), denoted as \(\mathbf{\Gamma}_{\mathbf{X}}(\tau_{0},\varphi_{0})\in\mathbb{C}^{L\times L}\), are as follows [8]:  
\[\mathbf{\Gamma}_{\mathbf{X}}(\tau_{0},\varphi_{0})=\mathbf{a}_{0}\kappa(\tau_ {0},\varphi_{0})^{H}+\kappa(\tau_{0},\varphi_{0})\mathbf{a}_{0}^{H}. \tag{10}\]Then, by defining a new auto-correlation matrix \(\mathbf{\tilde{R}_{X}}(\tau_{0},\varphi_{0})\doteq\mathbf{R_{X}}-\mathbf{\Gamma_{ X}}(\tau_{0},\varphi_{0})\), the PBC beamformer is given by  
\[\mathbf{w}_{\text{PBC}}(\tau_{0},\varphi_{0})=\frac{\mathbf{\tilde{R}_{X}}^{-1} (\tau_{0},\varphi_{0})\mathbf{a}_{0}}{\mathbf{a}_{0}^{H}\mathbf{\tilde{R}_{X}}^ {-1}(\tau_{0},\varphi_{0})\mathbf{a}_{0}} \tag{11}\]  
which is the CAP in (6) after replacing \(\mathbf{R_{X}}\) with \(\mathbf{\tilde{R}_{X}}(\tau_{0},\varphi_{0})\).
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel without Assistance  
#### 3.2.1 Coherent Integration Only with Parallel Implementation  
As mentioned previously, in the case where there is no non-coherent accumulation, there is only one stage of accumulator-based memory, and therefore there is no interest in having a temporary memory between the Inverse FFT (IFFT) output and the coherent accumulator(s).  
The parallel implementation is already given in Reference [23], and shown in Figure 4 (where it is assumed that the coherent integration time is a multiple of the secondary code period), and the corresponding timing diagram is shown in Figure 5. The processing time for one frequency bin is thus  
\[\begin{split}& T_{\text{FB}}=KN_{\text{FFT}}N_{\text{S}}+2(N_{ \text{FFT}}+L)-(N_{\text{P}}+N_{\text{Z}})\\ &=KN_{\text{S}}(2N_{\text{P}}+N_{\text{Z}})+3N_{\text{P}}+N_{ \text{Z}}+2L\\ &=N_{\text{P}}(2 KN_{\text{S}}+3)+N_{\text{Z}}(KN_{\text{S}}+1)+2 L,\end{split} \tag{9}\]  
where \(K\) is the number of secondary code periods accumulated coherently. Thus, \(K_{\text{C}}=KN_{\text{S}}\) correlation results are coherently accumulated (or written in another way, the coherent integration time is \(T_{\text{C}}=K_{\text{C}}T_{\text{P}}=KN_{\text{S}}T_{\text{P}}\)).  
Figure 4: Implementation of a pilot channel acquisition, with coherent integration only, in a parallel way (timing diagram available in Figure 5).  
Figure 3: Timing diagram of a data channel acquisition shown in Figure 2, with \(N_{\text{D}}=4\) for the illustration (in reality \(N_{\text{D}}=20\) for the GPS L1 C/A signal). Colors indicate different portions corresponding to the length of one data bit. Grey color indicates intermediate results (i.e., the value is not yet the final one).  
Regarding the amount of memory necessary, the memory-based accumulators require  
\[\begin{array}{l}M=N_{\text{S}}2N_{\text{P}}B_{\text{C}}\text{ bits}\\ =N_{\text{S}}2N_{\text{P}}(B_{\text{R}}+\lceil\log_{2}(KN_{\text{S}})\rceil) \text{ bits},\end{array} \tag{10}\]  
where \(B_{\text{C}}=B_{\text{R}}+\lceil\log_{2}(KN_{\text{S}})\rceil\) is the number of bits used to quantize the output of the coherent accumulators (\(\mathbf{y}_{i}\)).
# Semi-Active Vibration Control Based on a Smart Exciter with an Optimized Electrical Shunt Circuit
## 5 Concluding Remarks and Outlook on Future Work  
It has been found that vibration control with a SE-RL-shunt system can be realized very effectively. The SE alone can reduce vibration and sound pressure. However, its vibration reduction effectivity is significantly reduced in higher frequency ranges. After coupling the SE with an RL-shunt, the SE can reduce the acceleration and sound pressure more effectively. Simultaneously, it can reduce the vibrations much faster than it can without the RL-shunt. Whether the RL-shunt is connected in series or parallel makes little difference in its effectiveness.  
The main findings according to the feasibility of the proposed semi-active damping approach can be summarized as follows. The results of the numerical investigations prove that the time span of the step response of a panel with the SE and RL-shunt is reduced by a factor of 10 compared to a panel without the semi-active damping device. Furthermore, it has been found that it is possible to reduce the steady-state response of the panel (excited in its first natural mode) down to 32.4 dB, if the proposed semi-active control approach is taken into account during time-harmonic simulations. The experimental results prove that the proposed approach is capable to reduce the overall vibration level in the frequency band 0-400 Hz down to nearly 8.5 dB, if the SE is placed in the center of the panel. Under free-field conditions, a reduction of the sound pressure level close to 2 dB has been detected at the position of a microphone that has been placed at a 1 m distance to the panel.  
In the present paper, only RL-shunts have been investigated. For future studies, shunt combinations and the coupling positions of the SE should be investigated in more detail, in order to achieve more effective solutions for vibration reduction and sound pressure absorption. At the same time, the increases in weight should be carefully considered. In addition, non-isothermal behavior due to the heat generation in the resistor should also be investigated to consider regulations in aircraft design.  
Conceptualization, T.K. and Y.W.; methodology, T.K.; software, Y.W.; validation, T.K. and Y.W.; formal analysis, Y.W.; investigation, T.K. and Y.W.; resources, T.K.; data curation, T.K.; writing--original draft preparation, T.K. and Y.W.; writing--review and editing, T.K.; visualization, T.K. and Y.W.; supervision, T.K. and Y.W.; project administration, T.K.; funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the city of Hamburg in cooperation with Hamburg Innovation.  
Not applicable.  
No additional data available.  
We thank the Zentrum fur Angewandte Luftfahrtforschung GmbH (ZAL) for support and the manufacturing of the RL-shunt box. In the same manner, we acknowledge the Behorde fur Wissenschaft, Forschung, Gleichstellung und Bezirke (BWFG) of the city of Hamburg.  
The authors declare no conflict of interest.
# Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
## 2 Materials and Methods
### Machine Learning  
#### 2.3.1 Random Forest  
Random forest is a multifunctional machine-learning algorithm that was first proposed by [PERSON] and [PERSON]. It is a data dimensionality reduction tool used to handle missing values, outliers, and other important steps in data exploration with good results [32]. Many decision trees (weak classifiers) are generated in the random forest, and each tree gives its own classification choice. By combining multiple weak classifiers in a voting or averaging way, the overall model has a high accuracy and generalization performance. In this experiment, the number of decision trees was set to 100, and used Gini impurity to measure the quality of a split. Gini impurity is a common metric to measure classification performance. The smaller the Gini impurity, the higher the purity, the higher the degree of order of the set, and the better the classification.
## 3 State-of-the-Art
### Agricultural Residual Biomass
#### 3.2.4 Palm Kernel Shells
In this context, analytical techniques combining pyrolysis with gas chromatography (GC) and mass spectroscopy (MS), forming the Py-CG-MS system, can be used to identify the degree of thermochemical conversion of biomass and biochar, allowing to (i) upgrade the standards of quality evaluation of these materials, (ii) track the termed \"combustion continuum\" fundamental in the dynamics of environmental black carbon and carbon sequestration, and (iii) deepen the insight about the true technological potential of these byproducts.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Weed Detection
[121] can handle occluded targets well through Swin Transformer. It uses data augmentation to effectively solve the problem of data imbalance. It can identify multiple weed species and is more suitable for actual farmland. However, it has low real-time per \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Target** & **Approach** & **Performance** & **Hardware Specifications** & **Number of Datasets** \\ \hline
[122] & Fields Weed Detection & YOLOv4, CSPDarknet53, CBAM & mAP50: 86.89\% & NVIDIA Tesla V100 & \multirow{4}{*}{3000} \\  & & YOLOv8, LSK, DySample & & & \\  & & YOLOv8, LSK, DySample & & & \\  & Semantic Segmentation of Crops and Weeds & & & & \\  & & YOLOv5s, CSPDarkNet53, SKAttention & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._formance and high computational complexity. It is suitable for weed detection in complex environments with low real-time requirements. In [127], a BP neural network model is constructed using multiple features such as color, texture, and shape as inputs for weed detection inference. Color features are extracted using the Color Moments algorithm in combination with the RGB and HSV color spaces. Texture features are extracted using the Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP). Shape features are extracted using Hu invariant moments and geometric parameters. After the multi-feature complementary fusion, the input is fed into a backpropagation network for weed detection in asparagus fields. For the feature fusion method, ref. [124] employs a multi-polar feature weighted fusion strategy. Features are weighted using Softmax to highlight crop and weed features while suppressing background interference. Cross-layer feature fusion is then performed, combining texture and shape information from different layers. This dynamic strategy significantly improves the accuracy of crop and weed segmentation. However, in the rice seedling dataset, the model misclassifies crop shadows as weeds. This indicates that the model is not robust enough against light changes and shadows. This may be because there are few shadow samples in the training data, or the annotations are insufficient. When tested in actual field environments, its performance may decline due to complex lighting and other conditions.
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 4 Planetary Defense: State-of-the-Art
### Potentially Hazardous Objects and Detection  
According to [137], NEOs include asteroids and comets with perihelion distance \(q\) less than 1.3 AU, while near-Earth comets (NECs) are specifically restricted to only short-period comets with an orbital period of fewer than 200 years. Near-Earth asteroids (NEAs) consist of the majority of NEOs, which are categorized into four types, namely, Atira, Aten, Apollo, and Amor, according to their perihelion distance (q), aphelion distance (Q), and their semi-major axes (a). The definitions and relations of these four types are given and illustrated in Figure 13.  
Furthermore, potentially hazardous objects (PHOs) are currently defined in terms of parameters that evaluate the asteroid's potential to make threatening close approaches to Earth [137]. For example, all objects with an Earth minimum orbit intersection distance (MOID) of 0.05 AU (7,480,000 km or 19.5 lunar distances) or less and an absolute magnitude (H) of 22.0 or less (equivalent to a diameter of at least 140 m) are considered PHOs. This dimension (140 m in diameter) is large enough to cause unprecedented regional devastation to human settlements in case of a land impact or a very dangerous tsunami in case of an ocean impact.  
As of 1 May 2022, there are a total of 28,874 NEOs discovered and surveyed [138]. Only 117 (0.4%) of them are NECs, and the remaining are all NEAs. Furthermore, as illustrated in Figure 14, 10,070 of all NEAs are NEA-140 m (with diameters 140 m and larger), while 877 are NEA-km (with diameters 1 km and larger). Moreover, 2262 and 156 of all NEOs are categorized into PHA and PHA-km, respectively [138]. Eventually, the orbits of all known PHOs can be plotted for real-time tracking, such as in Figure 15.  
Figure 14: Near-Earth asteroids discovered as of 1 May 2022 [139].  
Figure 13: Four types of near-Earth asteroids known as Atira, Aten, Apollo, and Amor [137].  
In terms of theoretical estimations, impact events caused by PHOs in the 140-m and larger dimension may occur on average around once at least every 10,000 years, while a meteoroid the size of a football field hits Earth every 2000 years or so [140]. However, due to many uncertainties and factors, it is hard to predict when and where the impact will happen or if humanity will be ready to handle it. In addition, an impact event that is caused by PHOs smaller than 140 m and occurs in a shorter time period cannot be ignored since the aforementioned Tunguska event, and Chelyabinsk event were both caused by meteorites much smaller than 140 m. In fact, authors in [141] implied that 'Tunguska' impact rate on Earth is one event every 300 years and probably rather more frequently, which is much shorter than the current preferred value of 2000-3000 year.
# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 3 System Model and System Architecture
### Hardware Architecture
Since both cores are identical and share the same memory, tasks can be run interchangeably between them. The ESP32-S2 is an excellent choice for our data storage system, thanks to its long-range transmission capability of up to 200 m, as well as its low power consumption and excellent radio frequency performance. This repeater allows us to effectively transmit data to the platform over medium and long distances, making it an ideal choice for our dataset generator.
# A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
## 3 Result Analysis and Discussion
### The Influence of Uneven Geostress on Casing
#### 3.2.2 The Influence of Uneven Geostress on Casing Eccentricity  
According to Table 8, when keeping the stress heterogeneity constant, we can observe that the larger the eccentricity, the overall trend of stress increase is increasing. Especially, the changes in radial stress and shear stress are the most dramatic. However, the changes in hoop stress and equivalent stress at different eccentricities are relatively small. From this, we can conclude that radial stress and shear stress are more sensitive to eccentricity, and their numerical changes are larger compared to hoop stress and equivalent stress. Therefore, when considering the influence of eccentricity on casing stress, special attention needs to be paid to the changes in radial stress and shear stress.
## 3 Results
### DEMs from Sentinel-1 and Landslide Volume Estimation  
Figure 7 shows the results of the post-event DEMs generated using the Sentinel-1 image pairs from different orbit tracks, and the pre-event ArcticDEM for comparison.  
For the volume estimation of the landslide, each of the three generated DEMs was subtracted from the ArcticDEM. Then a spatial subset for the landslide was created using the OBIA delineation of the landslide deposition area for 2018 (Figure 8). The DEM from track 16 shows maximum elevation differences to the ArcticDEM of more than 80 m, whereas the other two DEMs (track number 118 and 155) show lower, more homogenous and realistic elevation differences in the landslide deposition area.  
Figure 6: Object-based geomorphological feature mapping using Sentinel-1 and Sentinel-2 datasets. (**a**) shows the riverbed with water before the Hitardalur landslide using the pre-event Sentinel-2 image only; (**b**) shows the landslide, the landslide deposition area, the landslide-dammed lake, and the (now partly interrupted) riverbeds with water after the landslide event in 2018; (**c**) shows the landslide, the landslide-dammed lake, and the riverbeds with water in 2019; (**d**) shows the reference map from National Land Survey of Iceland IS 50V 24 September 2019 Vatnafar Flakar (© NISI) [77]. The hillshade derived from the ArcticDEM was used as a background layer.  
Figure 7: Illustration of the three post-event DEMs derived from the Sentinel-1 image pairs. (**a**) for orbit track number 16 (11 July 2018 and 17 July 2018), ascending; (**b**) orbit track number 118 (5 August 2018 and 11 August 2018), ascending; (**c**) orbit track number 155 (4 July 2019 and 10 July 219), descending; (**d**) ArcticDEM. For visualization purposes, each DEM is overlaid with the hillshade derived from it.  
However, both DEMs from tracks 118 and 155 partly show negative values upslope, which indicate the removal of material and potential issues in the delineation of the deposition area.  
Table 3 shows the landslide deposition volumes. The DEM from track 16 gives a much higher volume than the other two DEMs. The landslide volume estimations using the DEMs from track 118 and 155 are in a similar range.
# Design and Testing of Torveastro: An Outer Space Service Robot
###### Abstract  
Space robots are one of the most promising solutions for on-orbit servicing (OOS) duties like docking, berthing, refueling, re-pairing, upgrading, transporting, rescuing, and orbital trash disposal. Numerous enabling techniques and technological demonstration missions have been developed and completed over the past two decades. There have been several successful manned on-orbit service missions, but unmanned service missions have not yet been conducted. Robotic maintenance continues to be an important area of investigation with numerous technical challenges. This report outlines the design and initial testing of Torveastro, an astronaut service robot. The specifications are provided concurrently with the design and simulation. In comparison with the simulation results, preliminary tests demonstrated promising behavior for future development.  
Keywords:space robotics; service robotics; experimental robotics; robot design; robot testing +
Footnote †: journal: Applied
# The Novel Role of Solvents in Non-Surgical Endodontic Retreatment
## 9 Future Directions  
The causative agents of post-treatment disease have been exhaustively investigated, confirming a less diverse set of microbiota and lower cell counts in well-treated teeth. _Streptococcus_ species and the usually reported _E. faecalis_ are among the most common bacteria that are isolated in secondary infections [69]. _E. faecalis_ has been especially implicated, probably due to its ability to survive in mono-infections under adverse conditions [69]. Nevertheless, nearly 55% of the microbial community belongs to uncultivated or uncharacterized phylotypes, which may be dominant in some cases, and the common association of _E. faecalis_ with secondary infections is not definitively supported [70]. Moreover, _Fusobacterium_ spp. and _Pseudomonas_ spp. with _Streptococcus_ and _Actinobacteria_ spp. have recently been reported as the most dominant taxa. Regarding fungi, _C. albicans_ is recognized as the most frequently identified [61].  
In addition to the wider microbial knowledge regarding endodontic microbial diversity, new concepts are developing, such as the awareness of proteins, which are often associated with virulence and with resistance to antibiotics, and the dependency on the host's individual profile [61]. Rapid and accurate chair-side tests for quick microbial detection, together with the knowledge of antibiotic resistance genes, could eventually address a rapid microbiological diagnosis, enabling the best therapy. Meanwhile, advances in antibioticfilm-effective adjunctive protocols might be important for reducing the bacterial load, improving the success rate of endodontic treatments.