esa-sceva/satcom-chunk-collection huggingface dataset:
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 3 Essentiality of LPS and the Minimal LPS Structure
Moreover, \(\Delta\)(_lpxL lpxP lpxM_) strains exhibit gross alterations in terms of accumulation of primarily glycoform IV/V with three Kdo residues and truncation of the terminal disaccharide [7]. Thus, complete synthesis of LPS is a requirement for bacterial fitness and survival. During the detailed genetic construction of strains to define the minimal LPS structure in the absence of any suppressors, it was shown that strains with LPS composed of either Kdo\({}_{2}\)-lipid IV\({}_{\text{A}}\)\(\Delta\)(_waaC lpxL lpxM lpxP_) or only lipid IV\({}_{\text{A}}\) (absence of WaaA Kdo transferase) can be constructed under slow growth conditions on minimal medium at low temperatures (21-23 \({}^{\circ}\)C) [7]. Since lipid IV\({}_{\text{A}}\) is a poor substrate for the MsbA IM LPS flippase, suppressor mutations that improved growth characteristics of \(\Delta\)(_waaC lpxL lpxM lpxP_) or \(\Delta\)_waaA_ were found to map to the _msbA_ gene [7]. It is likely that such MsbA variants exhibit altered binding properties of lipid A and might be more relaxed in substrate selectivity or changes in ATP binding/hydrolysis. Interestingly, \(\Delta\)_waaA_ suppressor-free strains synthesizing lipid IV\({}_{\text{A}}\) were found to accumulate excess of phospholipids consistent with a balanced synthesis of LPS and phospholipids [7]. Moreover, such \(\Delta\)_waaA_ strains under slow growth conditions were also found to accumulate pentaacylated and hexaacylated species of lipid IV\({}_{\text{A}}\) without any requirement for the Kdo presence. Thus, lipid IV\({}_{\text{A}}\) derivatives with myristoyl, lauroyl, palmitolyl or palmitoleate chains could be identified from \(\Delta\)_waaA_ strains, indicating that under such conditions late acyltransferases can use lipid IV\({}_{\text{A}}\) as a precursor without requirement for Kdo [7]. Consistent with these results, overexpression of the _lpxL_ gene suppress the lethality of _waaA_ deletions on nutrient broth up to 37 \({}^{\circ}\)C without the need for MsbA overproduction [25].
## 6 Effectiveness of the Proposed Solutions
### Estimating Performance of the Control System  
When assessing the obtained results, it should be emphasized that some solutions become unstable at some point of the time response; see Figure 5a,b (open-loop control at low frequency of parametric and loading variation), Figures 6, 9a,b and 11a. It is worth emphasizing that the classical PID controller operating in a closed control system proved to be the best when controlling the rotational speed of the driven disk subjected to a variable additional load of low frequency \(T_{la}\) (ITAE = 0.0368; see Table 2). On the other hand, the same controller, operating under the same conditions, failed during the variation of the damping parameter \(b_{la}\) (ITAE = 1.5168), where a divergent response was obtained as the simulation time passed. The ITAE index has been computed numerically with the use of the 1/3 Simpson's rule.  
It turns out that the proposed adaptive PID controller with PI compensation of the second stage error dynamics works very well in the presence of both considered types of disturbances, acting on the control object in the low-frequency regime.  
Among the FOPID results, the best one is for a closed-loop control system in which the control object is subjected to a low-frequency disturbance of the torque loading the drive (ITAE = 0.0962). In general, this type of controller does not perform well in the given operating conditions, and the convergence of the rotational speed to the given trajectory of the reference model is satisfactory.  
It is important to note that after introducing the high-frequency disturbance characteristics of \(b_{la}\) and \(T_{la}\), the classical PID controller turned out to be the best.  
Paying attention only to the local overshoots of the dynamic time responses, being in relation to the given reference time histories \(\dot{\theta}_{rl}\), those shown in Figures 6b, 7b, 8a, and 12a prove high efficiency of the tested controllers, of which the proposed adaptive PID with a second-stage PI compensation of error dynamics is found to be qualitatively the best.  
Figure 12: The closed-loop reference model control system response at the presence of \(b_{la}(t)\) (**a**) and \(T_{la}(t)\) (**b**) and an adaptive PID + PI (high frequency)—see case B in the block diagram in Figure 4.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 2 Preliminaries
### AntHocMMP Algorithm  
Due to the dynamic topology of the MANET network, the link is easy to disconnect. Therefore, it is necessary to design reliable and adaptable routing protocols. In Reference [30], an improved ant colony optimization algorithm named AntHocMMP is proposed, which combines three methods to obtain higher packet delivery rate, prolong the survival time of the network, and reduce the number of re-transmissions, thus reducing the end-to-end delay. The first method considers the residual energy and energy consumption of the node, and calculates the quality of the path by using the maximum minimum path algorithm; the second method updates the pheromone by the forward ant and considers the energy consumption of the path. Periodic pheromone updates can improve the adaptability of the algorithm to dynamic topologies. The third method is to predict the link failure time and use the maximum minimum path algorithm to find the alternate path. In order to detect the link failure in time, each node broadcasts a hello packet to the neighbor node. If a link failure is detected, the alternative path is selected using the transition probability criterion, and the transfer rule [30] is as shown in formula (3):  
\[p_{ij}(t)=\left\{\begin{array}{cl}\frac{\tau_{ij}^{\alpha}(t)}{\sum\limits_ {d\in N_{i}}\tau_{il}^{\alpha}(t)},&\text{if}\quad j\in N_{i},\\ 0,&\text{else}.\end{array}\right. \tag{3}\]  
After the ants arrive at the destination node, reverse ants are immediately generated and return along the original path. The pheromone table in each node is updated at the same time.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 4 Experiment Results
### Recognition Accuracy of Two Classifiers
#### 4.1.1 Comparison between Conventional Features and Unconventional Features  
According to [19; 21] and the down-sampling method used here, the dimensions of the baseline features adopted are sizes of 16, 36 and 81, which are represented by DM\({}^{1}\), DM\({}^{2}\) and DM\({}^{3}\), respectively. In order to compare them to the conventional features, the unconventional feature extraction methods reduce the dimensions of the cropped image to the same size as the former. Considering the big differences within the baseline features, the SVM does not adopt the feature normalization method in this experiment.  
Figure 4 shows the recognition accuracy of the two classifiers under different feature spaces. Figure 4a plots the recognition accuracy under different feature spaces of SRC, and Figure 4b plots that of SVM. Compared with the unconventional features, the recognition rates of the two classifiers under baseline features are nearly highest. So in the low-dimensional feature spaces, baseline features are better for describing the targets. Simultaneously, the highest recognition rates of SRC and SVM are almost identical, but SRC implies that the precise choice of feature space is no longer critical [14]. That is to say that when the dimensions of the feature space surpasses a certain threshold, unconventional features perform just as well as conventional features towards SRC. From Figure 4a, the dimension threshold in this experiment is near 80.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 5 Experiment and Analysis
### Ablation Experiments  
In this paper, the SE module is first improved and then introduced into the encoder as the channel attention module, so that the modulation signal features can be better extracted and the computational complexity of the model can be reduced. In order to validate the efficiency of the improved SE module as the channel attention mechanism, we carry out the first ablation experiment based on the control variable method. We compare the improved SE module (impr-SE) using the convolutional layer with the original SE module (orig-SE) using FC and the denoising autoencoder without a channel attention mechanism (withoutCA). In this case, the SE module is removed in the coding block in Figure 3b. Figure 6a displays the training loss (tra-loss) and validation loss (val-loss) of the three methods. As is shown, the module with the improved SE module gives better performance. When the epoch equals 40, it lower the validation loss by 35.7% and 17.9% compared to the module with the original SE and the model without a channel attention mechanism. In addition, it should be noted that the denoising model without a channel attention mechanism is even superior to the model with the original SE. This can be attributed to the loss of feature information caused by data dimension transformation. Therefore, a mismatched channel attention module may cause network performance degradation, and the improved SE is quite effective.  
Figure 5: Classification accuracy at different depths.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Parameter** \\ \hline DRdA-CA2 & 181,408 \\ DRdA-CA3 & 263,416 \\ DRdA-CA4 & 345,424 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Parameters of Different Depths.  
In the proposed DRdA-CA, \"dual\" represents the residual connections added not only between different encoding (decoding) blocks but also inside the encoding (decoding) blocks of different depths, so it is called \"dual\". In contrast, \"single\" refers to the residual connection only existing inside the encoding (decoding) block, and each encoding (decoding) block is the improved SE-ResNet module, which contains one residual connection. In order to demonstrate the advantages of \"dual residual connections\", one of the main innovations, we have also conducted an ablation experiment based on the control variable method, comparing it with the \"single residual connection\" method, where the residual connection only exists inside the encoding (decoding) block. The loss curves are given in Figure 6b. As shown in the figure, the dual residual model achieves a lower training loss and validation loss than the single residual model. When the epoch increases to 40, the validation loss of the dual residual connections is only 50% of that of the single residual connection. Comparing Figure 6a,b, apparently, both the improved SE module and dual residual connections play an important role in improving denoising performance, and dual residual connections are more effective.
## 5 Results and Discussion
### Gaps and Challenges  
The harmonized use of L8/OLI and S2/MSI has helped to correct historical gaps and uncertainties in LULC analysis [28]; for example, the long interval between two valid observations of the same point and the LULC mixing in the pixels lying on change boundaries [124]. However, three crucial gaps still limit LULC classification in heterogeneous and large landscapes: (i) the scarcity of field samples to train classifiers, (ii) the big data processing, and (iii) the difficulty to standardize both L8/OLI and S2/MSI. In addition, the accuracy of LULC classification and LULCC detection still depends on the performances of the classification methods [50]. Different results demonstrate that, by addressing or mitigating the above-mentioned crucial gaps, the classifiers can perform classification tasks, which makes this problem more manageable.
## 4 Discussion
### Correlation between Therapeutic Parameters and Mechanical Variables  
According to the literature, the variation in the lordosis angle could be considered a therapeutic indicator. Nevertheless, the pressure generated by the belt at the intervertebral discs should be a direct effect for reducing back pain.  
A cross-correlation analysis shows that there is a strong correlation between the variation of the lordosis angle resulting from the application of the belt and the mechanical quantities obtained at the intervertebral discs (see Table 4). For example, the lower the absolute pressure in the vertebral discs, the greater the variation in the lordosis angle.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation  
Energy conservation methods try to reduce energy consumption of the sensor nodes to extend the lifetime of the nodes and, therefore, the lifetime of the network. Since radio communication is the main consumer of energy in a node, the design of communicationsprotocols (MAC and routing software) is a key element. In the past, without rechargeable batteries, prolonging the life of the network was the major focus in the communication unit design, leaving network latency and throughput optimization at a second place. Since energy-harvesting capabilities has been added to nodes, Quality of Service (QoS) has come back to be its main focus, though energy constraints remain. Energy-Neutral Operation (ENO) arises as a new paradigm, and achieving the best performance of the application/network (in terms of throughput and time response) balancing the energy expenditure caused by sensor activity with the energy flow being able to harvest has attached attention in recent times.  
There is abundant literature relating to energy-saving in WSNs in recent years that have been collected and classified in different works. The survey in [9] uses a simple classification based on the network structure (flat or hierarchical) for data aggregation and routing schemes. This year two surveys have been published. In [4] energy-saving schemes such as duty-cycling, efficient routing, efficient MAC, data aggregation, cross-layer design and error control code are addressed. This survey includes works until 2019 and it is noticeable that the most active year was 2017.  
Another recent review by [PERSON] et al. [5] uses an uncommon classification, considering battery management schemes (nodal management and energy balancing), transmission power-management schemes (MAC protocols, routing protocols and transmission policy), system power-management schemes (processor power and device management) and other miscellaneous schemes like load balancing, duty-cycling or cross-layer optimization. In this survey we decide to use the four main categories proposed in [10] (see Figure 1). In every category we explain the fundamentals of each type of solution, we consider some subcategories and when we find a particularly active area of research in the last two years, we review the most relevant works found in the literature.
## 4 Results
### Feature Importance
Time-series of satellite imagery that covers the advanced phenological phases of both summer and winter crops is characterized by high discriminatory qualities. Furthermore, the image sensed during May proves of great significance in all nomenclature levels, explained by the dataset's abundance in soft wheat and barley and thus in cereals and winter crops. In volume-wise skewed datasets, the dependence of variable importance in classes' prior probability would affect the weights correspondingly. With respect to the spectral characteristics, it is the VIs and the bands of 20 m spatial resolution that contribute the most, despite the small volume of the  
Figure 7: Proportion of feature contribution bar graphs for the Sentinel-2 scenario, calculated based on the feature importance values. The two figures are plotted for two thematic categories (**a**) month of image acquisition and (**b**) spectral information. first and the lower spatial resolution of the latter. More specifically, what the red-edge, narrow-NIR, and SWIR data lack in spatial resolution, they gain in spectral resolution (narrower spectral windows) and reflectance profile distinctiveness among the different crop types. Inspecting the higher levels of nomenclature in Figure 7b, crop season classification exposes considerable differences, with May and August being the main contributors in the discrimination process. Summer crops in May are germinating, while winter crops are already flowering or even ripening. On the other hand, summer crops in August are fully grown, whereas winter crops have already been harvested. Hence, the two months describe periods of maximal vegetation density difference between the two classes. This in turn explains the significant contribution of vegetation indices in crop season classification.
## 3 Results and Discussion  
### Generated Calcium Carbonate  
As can be seen from Figures 4 and 5, after curing by microorganism, the content of calcium carbonate in silt is between 9% and 20%, and the generated calcium carbonate will attach to the surface of silt particles or fill in the pores between particles, cementing the loose silt particles into columns with a certain strength. This was confirmed by [PERSON] et al. [22], it is believed that precipitated calcium carbonate influences soil behavior by bonding particles together and densifying the soil by pore space precipitation, which decreases its void ratio. When the injection amount of cementing fluid was increased to 300 mL each round, the production amount of CaCO\({}_{3}\) was still significantly increased by controlling other factors unchanged. This indicated that the production amount of CaCO\({}_{3}\) could be increased by appropriately increasing the injection amount of cementing fluid. However, the content of calcium carbonate does not increase linearly with the amount of cement injection. On the one hand, the urease activity of the bacterial fluid is limited, which cannot promote the hydrolysis of more urea to produce CO\({}_{3}\)\({}^{2-}\) and Ca\({}^{2+}\) binding. On the other hand, with the injection of bacterial liquid and cementing liquid, CaCO\({}_{3}\) is constantly generated in the pore between particles, which makes the silt structure more compact, and it is difficult to inject subsequent bacterial liquid and cementing liquid.  
Figure 4: Effect of different pore ratio on calcium carbonate production.  
As can be seen from Figure 4, when the content of fine particles is the same, more CaCO\({}_{3}\) is generated in silt with a pore ratio of 0.75. This is because the silt sample with a pore ratio of 0.9 has a loose structure and a large proportion of large pores. In the process of grouting, bacterial liquid and cementing liquid are more likely to flow out under the action of peristaltic pump, which is not conducive to the generation of CaCO\({}_{3}\). As can be seen from Figure 5, when the porosity ratio is the same, CaCO\({}_{3}\) production increases with the increase in fine particles content, fine particles will increase the adhesion rate of bacterial fluid, and CaCO\({}_{3}\) is more easily generated on the surface of particles. It can be seen that after curing by microorganism, the maximum CaCO\({}_{3}\) production amount is produced in silt A with pore ratio of 0.75 and fine particle content of 75%. [PERSON] et al. [23] believed that this increase in calcium carbonate precipitation with the increase in clay content indicates that the activity of soil bacteria present in the clay portion of the soil was essential for precipitating calcite.
## 3 Proposed Method
### ECTSA Method  
Wireless links pose network difficulties, which are overcome using a cross-layer design. For example, consider a traditional transmitter that misinterprets a wireless fault as a network congestion notification. Using additional cross-layer design techniques, one of the most important properties of the wireless medium is utilized. In this work, an energy-efficient cross-layer routing is accomplished by developing the ECTSA for the MANET. This ECTSA selects the optimal path by considering four distinct objective functions: residual energy, communication cost, DSR, and mobility. Among these, the energy, DSR, and mobility are calculated from the network layer. Additionally, the network contention is calculated from the MAC layer and the CW is adjusted according to the residual energy and contention to minimize the energy consumption of the nodes. The flow chart for the ECTSA-based cross-layer routing is shown in Figure 3.  
The steps for the flowchart are given as follows,  
Step 1: Start the process of cross-layer routing.  
Step 2: Initialize the parameters present in the network.  
Step 3: Deploy the nodes in a random manner.  
Step 4: Calculate the fitness function parameters.  
Step 5: Update the fitness evaluation with the help of ECTSA-based cross-layer routing.  
Step 6: After the update, the adaptive congestion window gets adjusted.  
Step 7: Once the adaptive congestion window gets adjusted, data are transmitted.  
Step 8: Check the simulation criteria by evaluating the simulation time.  
Step 9: Display the performance measures.  
Step 10: Stop the process.  
Figure 3: Flow chart for cross-layer routing using ECTSA.  
A clear explanation of cross-layer routing using ECTSA is described at Section 3.4.
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
## 1 Introduction
For tropospheric correction, the following two new datasets are worth noting: the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 [25] and the Generic Atmospheric Correction Online Service for InSAR (GACOS) [26; 27; 28]. The accuracy and resolution of ERA5 have reached new heights compared with previous atmospheric reanalysis data, and the GACOS attempts to simplify tropospheric correction using the zenith delay mapping (ZDM) method. In terms of elevation data, the high-quality WorldDEM achieves much higher vertical accuracy than the widely used Shuttle Radar Topography Mission (SRTM). However, the two tropospheric datasets have not yet been introduced to SAR geolocation, and it is unknown how much improvement will be achieved by replacing the SRTM with the WorldDEM. To fully utilize the aforementioned advanced auxiliary data and simplify the procedure while ensuring geolocation accuracy, we geolocated measurement points on TerraSAR-X and Tianhui-2 images. For the tropospheric correction, we did not use ERA5 data directly but instead input it into the Weather Research and Forecasting Model (WRF) to obtain tropospheric parameters with higher resolution. We calculated the delay using both the WRF results and the GACOS data. We studied the influence of the ground elevation error by comparing the geolocation result based on GNSS-measured height and the height data from the WorldDEM and the SRTM. By conducting experiments on Tianhui-2 images, we found that the traditional range-Doppler model is not applicable, so we proposed a bistatic model for Tianhui-2. After removing the influence of the orbit error, the Tianhui-2 geolocation results showed patterns consistent with those of TerraSAR-X, proving the effectiveness of our model and the simplified geolocation procedure. Our research demonstrates the advantages of the GACOS and the WorldDEM and can serve as a reference for other high-resolution SAR geolocation studies. This article is organized as follows: Section 2 analyzes the principles of the geolocation errors and introduces the geolocation procedure. Section 3 presents the variation ranges of the errors and compares the effectiveness of different correction methods. Section 4 discusses deficiencies in our experiment and other factors affecting geolocation accuracy. Section 5 contains the conclusions of this research.