esa-sceva/satcom-chunk-collection huggingface dataset:
# Emerging Roles of Toxin-Antitoxin Modules in Bacterial Pathogenesis
## 5 Persistence Emerges in Response to the Activation of Diverse TA Modules
### TA-Induced Persistence in Bacterial Species Other than _E. coli_  
The experimental data regarding persistence in bacterial species other than _E. coli_ are restricted to a few examples. Overproduction of the native HicA toxin results in growth arrest and increases the number of persisters insensitive to ciprofloxacin or ceftazidime in _Burkholderia pseudomallei_, the causative agent of melioidosis. The persistence frequency is modulated depending on the level of HicA production. Moreover, deleting the _hicAB_ locus from _B. pseudomallei_ significantly decreased persister cell formation after exposure to ciprofloxacin, but not to ceftazidime [124]. Similarly, inactivation of _relE_ toxin family genes influenced the frequency of persister cell formation in _Mycobacterium tuberculosis_ as outlined further below [125]. Moreover, overproduction of the VapC type II toxin led to the production of dormant cells in _Mycobacterium smegmatis_[126]. The _slupAB_ type II locus of _Salmonella_ mediates persistence in a (p)ppGpp independent manner [127]. TA systems also have been shown to induce persister cell formation of Salmonella in macrophages during infection as described further below [128].
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 3 Proposed Machine Learning Models
### Ensemble Methods
#### 3.5.2 AdaBoost  
Similar to bagging, the AdaBoost method [69] manipulates the training examples to generate multiple predictions to form the final prediction. The main difference with bagging is that AdaBoost applies a weight to each of the training examples. In each iteration, the weights are individually updated to minimize the weighted error on the training set. For example, the weights on those training examples incorrectly predicted in previous iteration increase, whereas the weights of the correctly predicted training examples decrease. Therefore, AdaBoost tends to construct progressively more difficult learning problems in subsequent iterations. Once the training process has finished, the predictions are combined through a weighted majority vote (or sum) to produce the final prediction. Therefore, the final classifier usually can achieve a high degree of accuracy in the test set.  
By combining four individual models as base estimators in AdaBoost, this study obtained four AdaBoost ensemble models. They were an LR AdaBoost ensemble model (abbreviated as Ada_LR), an SVR AdaBoost ensemble model (abbreviated as Ada_SPN), a BPNN AdaBoost ensemble model (abbreviated as Ada_BPNN), and a CART AdaBoost ensemble model (abbreviated as Ada_CART).
## 5 Experiments
### Comparison of FT Sensors  
As shown in Figure 10, there were two FT sensors on the cutting tool. We wanted to confirm if 2 FT sensors provide an advantage over a single FT sensor, as well as determine which FT sensor is better as a source of input for the control scheme.  
A piece of thick foam was used as the material to be cut. This foam reproduces the same type of shearing forces as meat cutting. Two experiments were performed. In the first one the robot was commanded to apply forces gradually from 0 to 50 N in the Y-direction of the world frame, which was parallel to the cutting direction of the knife. In the second experiment, a human cut the foam multiple times in the Y-direction, as shown in Figure 15.  
Based on the sensor readings from both the sensors, it was observed that for free motion in space (without any cutting), both the sensors record the same readings. However, for cutting motion, sensor A (refer to Figure 10) is blind to the cutting forces. Sensor B recorded forces when the robot cut the foam autonomously, but the recorded forces were feeble and almost negligible to the forces commanded to the robot. When a human cut the foam using impedance controller the sensor B registered adequate forces.  
Figure 14: Network diagram of the Exoscarne system. From left to right: control box of the KUKA LWR 4+ robot connected to one PC with FRI/KDL libraries; data acquisition boxes for FT sensors A and B connected to two PCs and an Arduino board for joystick acquisition connected to a laptop.  
Hence it was determined that it is reasonable to consider that the forces sensed by sensor B (below the joystick) capture the human applied forces on the knife and thus, the user's intention. During actual meat cutting experiments, both the sensors were alternated as sensor inputs for the controller in numerous trials and the user responded that he preferred the system behaviour when sensor B was the sensor input.
## 4 Discussion
### Limitations
#### 4.7.1 Issues with the Search and Screening Process  
The first three limitations have to do with our search and screening process. First, our search might have been over-constrained. Our inclusion criterion regarding only considering English-language papers might have led us to miss out on relevant papers in other languages. Searching only four databases might have missed papers exclusive to other databases; for example, the Google Scholar search afterwards found papers not in the four databases, but its lack of formality as opposed to the four-database search is questionable. Our search strings may have missed relevant papers in the databases that we searched due to not including synonyms for \"mnemonic\". As stated before, trying \"acronym\" and \"initialism\" separately provided no new good results, but what about other synonyms? Also, terms with similar meaning to \"serious game\", such as \"educational game\" or \"learning game\", were not used, possibly leading to missing more relevant papers. Second, some of the papers that we screened out may have been relevant after all, but were only ignored because of human error or subjective opinion. Third, there may have been new relevant papers introduced between the time of the original search and the time that this paper was completed.
## 3 Experimental Section
### Effect and Efficiency Analysis of De-Bubble Algorithm  
In order to verify the effectiveness of the proposed de-bubble algorithm, we tested the effect of bubble removal in four different bubble environments with a data rate of 7.2 Kbit/s, as shown in Figure 10. The results show that the proposed de-bubble algorithm based on sorting could effectively deal with bubbles in various situations, and the processing results were not affected by bubble densities, sizes, or distances. This is mainly because the multi-dimensional effects of bubbles on optical signals (such as reflection, refraction,  
Figure 8: The effect of different air bubble size: (**a**) Bubble size is larger than the duration of bit “1” and (**b**) bubble size is less than the duration of bit “1.”scattering, and backscattering) can be reflected into one-dimensional pixels in the RSE-based UOCC system.  
We also compared the proposed scheme with the traditional image inpainting algorithm based on the fast marching method (FMM) [27]. The basic idea of the traditional inpainting method is to get the mask of bubble region by maximum entropy segmentation [28], which represents the damaged area in the original image. Then the FMM algorithm is used to repair the image according to the mask so as to remove bubbles. The comparison between the FMM and our proposed de-bubble method is shown in Figure 11. Obviously, the maximum entropy segmentation cannot separate the correct mask for the fringe image with an especially complex background. In addition, the FMM algorithm needs to use the neighborhood pixels of masks, which will cause inter symbol interference (ISI) in the process of decoding.  
Figure 11: Comparison between fast marching method (FMM) and our proposed de-bubble method.  
Figure 10: Performance of the proposed method in different bubbly environments.  
Figure 9: Different imaging conditions in ISOs of (**a**) 50, (**b**) 250, (**c**) 640, and (**d**) 3200.  
Moreover, the processing latencies were evaluated. The computer used had an Intel i7 8565U processor @ 1.8 GHz with 8 GB random access memory (RAM). The processing times required for the FMM-based inpainting method and our proposed scheme were 11.63 s and 2.84 s. Apparently, the proposed de-bubble algorithm had lower computational complexity and higher efficiency and it was more suitable for underwater applications.
# Micro-Satellite Systems Design, Integration, and Flight
## 5 Conclusions
#### Discussion of the Results  
The PD controller method was proven valid by comparing the solution computationally to other industry-standard controllers. Tested control algorithms included the P+V controller, the P + V Double Integrator Controller, the Double Integrator Gain Tuning Controller, the 2 DOF Feed Forward Controller, the P + V Control Law Inversion Controller, the Open Loop Guidance Controller, and the Real-Time Optimal Controller. The PD controller had the lowest quadratic cost while maintaining a robust rise time (w.r.t. the computational timestep). Validation of the controller was performed through experimentation, including  
Figure 51: Magnetic flux due to changes in temperature with a three-degree polyfit.  
moment of inertia testing, amplification factor testing, and endurance tests of the hardware. These tests showed the functionality of the respective subsystems. Once testing was performed, an optimization code was written, and the optimized result showed the fastest convergence within a defined search region. The optimal Kane Damper moment of inertia and damping constant were 0.07 and 0.0025, respectively. Finally, tuning was performed for the IMU. A model was created to adjust for hard iron offsets, soft ion offsets, changing battery voltage, and temperature changes. Each regression was superimposed into one model. The resulting code was tested at arbitrary values. By keeping a systems perspective throughout the satellite lifecycle, the requirements for the developed systems were met.  
Conceptualization, P.N.; methodology, P.N.; software, P.N.; validation, P.N.; formal analysis, P.N.; investigation, P.N.; resources, T.S.; data curation, P.N.; writing--original draft preparation, P.N. and T.S.; writing--review and editing, P.N. and T.S.; visualization, P.N. and T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
This section acknowledges support given which is not covered by the author's contribution or funding sections. Materials used for experiments were provided by [PERSON] who also supervised the research and contributed to conceptualization, methodology, and project administration. [PERSON] collaborated with the first author performing the research.  
The authors declare no conflicts of interest.
# Coastal Zone Classification Based on U-Net and Remote Sensing
## 4 Discussion
### Benefit of Spectral and Spatial Features  
The combination of spectral and spatial features in the U-Net model achieved the best classification performance (OA over 93%) using GF-2 images of Shuangyue Bay, Guangdong Province, where the surface cover was complex. The analysis results indicated that NDVI was particularly sensitive to vegetation characteristics. This sensitivity arises because NDVI exploits the strong absorption of red light by green plants and their high reflectance  
Figure 7: Performance of U-Net model after fusion of multiple features.  
in the near-infrared, enhancing the spectral response difference between vegetation and other ground objects. In addition, texture features significantly improved the classification accuracy across all classes. As regional features, texture attributes utilized image information to describe the spatial distribution of each pixel, considering both macroscopic properties and fine structures more effectively than other features. The contrast feature was found to be particularly sensitive to water bodies and farm land. This sensitivity is due to the contrast feature's ability to combine the gray levels of low-frequency pixels and stretch the gray levels of high-frequency pixels, thereby highlighting image details and enhancing the distinction between water bodies, cultivated land, and other features. The fusion of texture, NDVI, and contrast features resulted in the best classification outcomes. This superior performance is mainly because each feature contributes differently to various categories. When combined, these features complement each other, allowing their advantages to be fully utilized, and thus achieving the highest classification accuracy.
## 7 Performance Analysis: Reliability and Scalability
### Experiment Results
#### 7.2.2 Performance Measurement in Terms of Container's Resources Usages at the Scale-Up  
The second experiment was conducted by increasing the number of nodes to track how the system behaved if it were scaled up. This experiment was conducted by setting the number of nodes to 3; the observations were measured using the same performance tools discussed (Prometheus and Grafana). In this experimental setup, the assumption was to scale up the containers to observe the system behaviors. The experiment was detailed with two entries: scaling the application, in terms of container services, and generating different requests to observe the system. In this experiment, the observations are sticking to CPU, Memory, and Traffic metrics behaviors.  
Figure 19 visualizes the memory and CPU usages in the scaled 3 nodes, using 1000 generated requests. The graph of memory usage showed that the increases in requests could increase the amount of consumed memory, but for the time being, there was no increase. The CPU usage graph showed that, while at the time, there was a small increase in usage, this increase may have depended on the internal cooperation of all container services involved in the data processing.  
Figure 18: Resource usage for memory and throughput, with 1,000,000 generated sensor requests.  
The performance metrics were concerned with the transmission cost of the network as shown in Figure 20. The main point of tracking those parameters was to observe how the internal containers were costly while exchanging message requests. In network transmission, the busy container was cAdvisor, as it was concerned with the huge number of requests being generated.  
Figure 21 shows the visualization of the system after increasing the number of requests up to 1,000,000. The graph shows that CPU and memory did not change significantly, but the trend increased compared to Figure 19.  
Figure 19: Performance of the platform after scaling up with the requests of 1000—memory and CPU usage.  
Figure 20: Performance of the platform after scaling up with requests to 1000 network data transmissions.  
The network transmission at the transfer side showed tremendous changes, but it was also observed that, at one hundred requests, the graph began to go down to ten thousand, but then increased.
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
## 3 Error Performance Analysis  
In order to derive the error probabilities, we first should know a distribution of effective channel gain for a signal at the cloud server. The effective channel gain, \(Z\), is equal to  
\[Z=\sum_{i=1}^{N}Z_{\gamma_{i}}^{K_{i}}=\sum_{i=1}^{N}d_{i}^{-\alpha}\|\mathbf{h }_{i}\|^{2}, \tag{4}\]  
where \(\gamma_{i}=d_{i}^{-\alpha}\) (\(i\in\{1,\ldots,N\}\)). Since the fact is that \(Z_{\gamma_{i}}^{K_{i}}\) follows the Erlang distribution with \(K_{i}\gamma_{i}\) mean and \(K_{i}\gamma_{i}^{2}\) variance represented as following probability density function (p.d.f.):  
\[f_{Z_{\gamma_{i}}^{K_{i}}}(z)=\frac{z^{K_{i}-1}}{(K_{i}-1)!\gamma_{i}^{K_{i}}} e^{-\frac{z}{\gamma_{i}}}. \tag{5}\]  
the distribution of the effective channel gain \(Z\) is the same as the distribution of the sum of \(N\)_non-identical_ and independent random variables, which follows Erlang distribution. It is known as hyper-Erlang distribution [15] which is the mixture of \(N\) mutually independent Erlang distribution and parallel \(N\)-phase Erlang distribution weighted with probabilities. Therefore, the p.d.f. of \(Z\) is given by  
\[f_{Z}(z)=\sum_{i=1}^{N}\sum_{j=1}^{K_{i}}A_{ij}f_{Z_{\gamma_{i}}^{j}}(z). \tag{6}\]  
The coefficient \(A_{ij}\) is iteratively obtained by the recursive method [15]. See Appendix A.
# The Emerging Role of Long Noncoding RNAs in Sorafenib Resistance Within Hepatocellular Carcinoma
### Secondary Resistance
#### 1.3.6 ATP-Binding Cassette (ABC) Transporters
oveexpression resulted in decreased intracellular sorafenibb concentrations and diminished drug efficacy [43]. This mechanism highlights the complex regulatory network involving lncRNAs and microRNAs that modulate ABC transporter activity and drug resistance. lncRNAs can also epigenetically regulate ABC transporters through histone modification and chromatin remodeling. LncRNA PVT1, for example, has been shown to interact with chromatin-modifying complexes to promote the transcription of ABCC1 (MRP1), a major transporter involved in drug efflux and MDR. In HCC, PVT1 binds to the promoter region of ABCC1 and recruits epigenetic modulators, such as histone methyltransferases, to activate its transcription. This upregulation of ABCC1 contributes to the efflux of sorafenibb and other chemotherapeutic agents, resulting in reduced drug sensitivity [42]. By targeting PVT1 or its associated epigenetic machinery, it may be possible to downregulate ABCC1 expression and reverse drug resistance in HCC. The mechanism of sorafenibb resistance and the involvement of lncRNAs are depicted in Figure 2. The figure illustrates the multifaceted mechanisms of sorafenibb resistance. OCT1 (SLC proteins) is shown to facilitate the uptake of sorafenibb into cells, while ABC transporters (BCRP, MRP2) mediate its efflux, thus regulating intracellular sorafenibb levels. The metabolism of sorafenibb through enzymes like CYP3A4 and UGT1A9 produces various metabolites, which can affect the efficacy of sorafenibb. Key targets of sorafenibb, such as EGFR, VEGFR, and PDGFR, are involved in critical cell signaling pathways that control survival and proliferation. The processes of autophagy and apoptosis are shown to influence resistance. The figure also highlights the role of several long noncoding RNAs (lncRNAs)--including NEAT1, ROR, SNHG1, H19, SESTIN2, TUC338, HOTAIR, CRNDE, and FOXD2-AS1--which can either promote or inhibit resistance to sorafenibb. Together, these components and pathways illustrate the multifactorial nature of sorafenibb resistance, emphasizing the need for a comprehensive understanding of these mechanisms to improve therapeutic strategies. Figure 2: Sorafenib induces the AMPK (AMP-activated protein kinase) pathway by increasing the ATP, initiating autophagy via AMPK, and through mTOR1 inhibition or ULK1 activation. Sorafenib can inhibit mTORC1 via the ERK pathway by inhibiting PTEN (Phosphatase and Tensin Homology), but it can also activate mTOR1 through Akt activation (key regulators of P13K/AKT/mTOR pathway); it also inhibits ERK, further reducing mTOR activity. In addition, miRNAs are involved in sorafenibb-mediated autophagy regulation. Sorafenib increases miR-21 and 25 expression, suppressing autophagy and decreasing FBXW7 protein expression, downregulated PTEN expression, and subsequently activating Akt.
## 3 Results and Discussion
### Kinematic Force  
The FEA residual displacements of the TE and ME waveforms are described in Figures 11 and 12, respectively. The displacement decreased with increasing distance from the top of the slope. The most significant displacement occurred at the top of the slope at 0.13 m and 0.25 m due to the TE and ME waveforms, respectively.  
Figures 13 and 14 depict examples of the kinematic force distribution calculated using the proposed method and the JRA method, respectively. The JRA method produces a more conservative smallest kinematic force (21 kN) than the proposed method (6.2 kN). The JRA method generates the same kinematic force at all distances from the top of the slope. In contrast, the proposed method demonstrates that the kinematic force is smaller at the toe of the slope than at the top of the slope because ground displacement is reduced with distance from the top of the slope. The kinematic force produced by the TE and ME waveforms differs. The ME waveform creates a larger kinematic force than the TE waveform due to its greater ground displacement (Figure 12). The JRA method does not account for waveform characteristics; it focuses only on soil unit weight and depth. Figure 14 depicts the triangular shape of the JRA kinematic force distribution and Figure 13 depicts the trapezoidal form of the proposed kinematic force distribution. The JRA method was computed based on the hypothesis that ground displacement occurs in the soft soil  
Figure 11: FEA of the TE waveform: Residual displacement.  
layer. Therefore, the kinematic force distribution is computed at the base of the Clay 2 soil layer. In comparison, the kinematic force distribution in the proposed method is based on the ground displacement determined by the FEA. Consequently, kinematic force is not estimated when there is no displacement in the soil layer.  
Figure 12: FEA of the ME waveform: Residual displacement.  
Figure 13: Examples of kinematic forces using the proposed method.