esa-sceva/satcom-chunk-collection huggingface dataset:
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 4 Structural Characteristics of TA Proteins from Pathogenic Bacteria
### MazEf  
The crystal structure of the MazF toxin from _S. aureus_ shows a well-defined dimer, each monomer consisting of a core five-stranded anti-parallel \(\beta\)-sheet surrounded by three \(\alpha\)-helices and a small two-stranded anti-parallel \(\beta\)-sheet (Figure 2A) [161], which is featured by the typical MazF/CcdB fold. Crystallographic and nuclear magnetic resonance (NMR) studies revealed structural and dynamics differences in three loop regions of the MazF toxin, two of which are relevant to the function [161]. The two loops, including residues Leu12-Gly22 and Ile61-Lys70, show a relatively low heteronuclear nuclear Overhauser effect (hetNOE) values and a decrease in the ratio of the transverse relaxation rate (R2) to the longitudinal relaxation rate (R1) indicative of increased mobility in the fast NMR timescale. NMR titration experiments showed that a MazE-derived peptide consisting of residues Met23-Glu56 causes significant chemical shift changes in loop 1 and strands S5 and S6 of MazF [161], suggesting that these regions are involved in MazE binding. The residues with a high mobility and the MazE binding site are colour-mapped onto the structure of MazF in Figure 2A. _S. aureus_ MazF is structurally most similar to _B. subtilis_ MazF: their structures show an RMSD value of 0.73 A for 110 C\(\alpha\) atoms. The structural difference is mainly found in the loop Gly48-Ile55 of _S. aureus_ MazF, which corresponds to the loop Ala48-Leu55 of _B. subtilis_ MazF. Another structural homologue of _S. aureus_ MazF is _E. coli_ MazF: their structures show an RMSD value of 1.69 A for 95 C\(\alpha\) atoms. _S. aureus_ MazF adopts different conformations in the loops Gly48-Ile55 and Leu9-Pro25 and the helix Ile29-Thr40, compared to _E. coli_ MazF. NMR dynamics studies showed that they share three loop regions with a relatively high flexibility: S1-S2, S3-S4 and S4-S5 [161]. In _B. subtilis_, there are the known structures of MazF bound to MazE and uncleavable UUdUACAUAA RNA substrate [179]. The TA proteins form a heterohexmeric complex with a stoichiometry of two MazF monomers per MazE (Figure 2B). The two subunits of MazF form an extensive dimeric interface with a concave surface. The overall structure
# Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition
## 2 Theoretical Background of POD  
The objective of POD is to identify a deterministic function, \(\phi\), that correlates the best with the entire ensemble of a physical field [32]. Given a random pressure, \(p(x,y,t)\), the maximum of the projection of pressure \(p(x,y,t)\) on the function \(\phi(x,y)\) can be sought by considering the following:  
\[\int\frac{p(x,y,t)dxdy}{\sqrt{\phi^{2}(x,y)dxdy}}=max \tag{1}\]  
where \(x\), \(y\), and \(t\) are the coordinates and time of the pressure.  
The maximization condition, given by Equation (1), is typically implemented in the mean square sense. This can be summarized as the following integral equation [43]:  
\[\int R_{p}\big{(}x,y,x^{\prime},y^{\prime}\big{)}\phi\big{(}x^{\prime},y^{ \prime}\big{)}dx^{\prime}dy^{\prime}=\lambda\phi(x,y) \tag{2}\]  
where \(R_{p}(x,y,x^{\prime},y^{\prime})=\langle p(x,y,t)p(x^{\prime},y^{\prime},t)\rangle\) is the space covariance of pressure and \(\langle\ \rangle\) denotes the expectation operator. The solution of Equation (2) is a set of eigenvalues \(\lambda_{i}\) and eigenvectors \(\phi_{i}(x,y)\), denoted hereafter as eigenmodes.  
The POD eigenmodes \(\phi(x,y)\) satisfy the following orthogonality condition:  
\[\int\phi_{i}(x,y)\phi_{j}(x,y)dxdy=\lambda\delta_{ij} \tag{3}\]  
where \(\delta_{ij}\) is the Kronecker delta with the property of \(\delta_{ij}=1\) or \(0\) if \(i=j\) or \(i\
eq j\).  
The eigenmodes can be used as the base functions in a series expansion to reconstruct the pressure:  
\[p(x,t)=\sum_{i}q_{i}(t)\phi_{i}(x,y) \tag{4}\]  
where the principal coordinates, \(q_{i}(t)\), are as follows:  
\[q_{i}(t)=\int p(x,y,t)\phi_{i}(x,y)dxdy. \tag{5}\]The principal coordinates, \(q_{i}(t)\), are statistically uncorrelated. They adhere to an orthogonality condition expressed as:  
\[\left\langle q_{i}(t)q_{j}(t)\right\rangle=\delta_{ij}\lambda_{i}. \tag{6}\]
## 4 Experiments
### Main Experiments
However, SCTNet-B has a much lower accuracy despite having a similar parameter count. Lightweight networks such as TopFormer [43] and SeaFormer [44] have fewer parameters and computation complexity, but their results are significantly worse compared to ours. At the same resolution, our method surpasses TopFormer by 4.0 mIoU and SeaFormer-B by 2.6 mIoU. **Results on ADE20K. ** Table 2 shows that our method achieves the best mIoU of 44.4 with 11.8 M parameters and 37.0 G of computation. Compared to conventional semantic segmentation methods such as PSPNet [3], DeeplabV3+ [16], and LR-ASPP [46] from the early years, our method demonstrates superior accuracy. Our method has a moderate Params and computational complexity, achieving a good balance. Among the newer methods, the one most similar to ours is SegNeXt-S [6]. Our method has 2 M fewer parameters, twice the computational cost, and 0.1 higher mIoU than SegNeXt-S.  
**Results on COCO-Stuff. ** Table 3 shows that our method achieves the best mIoU of 40.4 with 11.8 M parameters and 37.0 G of computation. Among the baselines that use dilated convolution, the DeepLabV3+ [16] achieves an accuracy of 31.4 mIoU with 17.1 M number of parameters. Our MCDCNet is 9.0 mIoU higher than DeepLabV3+. Among the ViT-based baselines, SegFormer [42] with both MIT-B0 and MIT-B1 backbones achieves lower performance than our MCDCNet. The SegFormer using MiT-B0 as the backbone reaches 35.6 mIoU on the COCO-Stuff. The SegFormer with MiT-B1 is very close to our MCDCNet, reaching 40.2 mIoU, but its parameter count is 2 M higher than ours.
## 4 Multi-Site Micro-Scale Diversity Scheme
### Numerical Results
Due to the fair comparison in terms of transmitted power, the capacity in clear-sky conditions remains the same, however, the use of multiple transmitting antennas under rainy conditions offers a substantial gain compared to the SISO case. Increasing the number of transmitting stations even further, a higher gain under rain is to be observed. As a final remark, the difference between the MISO and SIMO system is the gain at higher time percentages where SIMO clearly outperforms a corresponding SISO architecture. Figure 16: Uplink outage capacity statistics for the SISO and the MISO case with 2, 3 and 4 stations using the copula synthesizer. Figure 15: Downlink capacity vs. SNR for Ka-band at 19.701 GHz using SISO and SIMO configurations in clear-sky conditions achieved at 1% of the time.
## 1 Introduction  
Several types of underwater operations play an essential role for many players in the maritime sector. These operations are associated with high costs and, more importantly, high risks due to the fact that human resources (divers) are almost exclusively used. The importance of employing alternative solutions to divers in such operations has been the ultimate goal in this application domain for many decades. In certain cases, Autonomous Underwater Vehicles (AUVs) provide a plausible solution to this problem. By means of technological advancements, future AUVs will most likely be equivalent or superior to human divers regarding the complexity of the operations they can carry out. Although considerable amount of efforts must be made until this vision is realized, today AUVs provide a plausible solution to those missions associated with high costs as well as hazards, especially the ones related to endangering human lives.  
In addition to that, the possibility of deploying a set of collaborating AUVs rather than one or even a few scattered units opens up new possibilities. Improving quality and safety for missions, such as subsea mapping, as well as emergence of new missions which require context awareness and collaboration, can be tackled in a better way with several AUVs. As mentioned before, there are a number of challenges associated with development of advanced AUVs. Specifically, hardware interoperability is a major issue at the data level, as the autonomous vehicles supposed to interact with each other may not be able to do so if they send information that cannot be comprehended by a vehicle not manufactured by the same company.  
This manuscript puts forward a system aimed to solve these issues (information exchange in constrained environments, hardware interoperability among vehicles from different manufacturers, different data formats representations, etc.) through the usage of middleware for maritime vehicles. By using this approach, it will be possible to overcome all those difficulties and guarantee data transfer among the elements implied in the deployment.
# Batch and Column Adsorption of Phosphorus by Modified Montmorillonite
## 3 Results and Discussion
### Further Confirmation of Batch and Column Adsorptions of Phosphorus by XRD Analysis  
In the batch and column adsorption experiment, chemical analysis confirmed that Al\({}_{13}\)-O-MMt has a good ability to remove phosphorus in aqueous solution. To further determine the increase in the adsorption of phosphorus onto Al\({}_{13}\)-O-MMt, the structural characteristics of Al\({}_{13}\)-O-MMt before and after the adsorption of phosphorus were determined using XRD to verify whether alteration of the interlayer was related to such an increase in adsorption of phosphorus. According to Figure 7, the d\({}_{001}\) peak was shifted to lower angles after modification, indicating that the interlayer distance within the layers of MMt increased. The increase in interlayer distance in Al\({}_{13}\)-O-MMt was due to the intercalation of Keggin Al\({}_{13}\) complexes and HDTMA. The increase in interlayer distance could provide more sites for adsorbing phosphorus. After batch and column adsorptions, the d\({}_{001}\) peak of Al\({}_{13}\)-O-MMt was shifted to higher angles, suggesting a decrease in the interlayer distance of Al\({}_{13}\)-O-MMt. This decrease in the interlayer distance of Al\({}_{13}\)-O-MMt revealed that the phosphorus ions adsorbed by the Keggin Al\({}_{13}\) complex influenced the arrangement of modifiers in the interlayer [64]. Furthermore, the changed interlayer distance of modified MMt revealed that the increase in phosphorus adsorption was due to the increased interlayer, where the adsorption took place. Compared to the d\({}_{001}\) peak of Al\({}_{13}\)-O-MMt before and after batch adsorption, the d\({}_{001}\) peak after column adsorption became much broader. This could be explained as follows: the aqueous phosphorus ions retained by Al\({}_{13}\)-O-MMt in the column had a longer time to react with Keggin Al\({}_{13}\) complexes and HDTMA in the interlayer of Al\({}_{13}\)-O-MMt, resulting in the disordered arrangement of modifiers in the interlayer--this reflected by the broader d\({}_{001}\) peak as shown in Figure 7.
# Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images
## 3 Experimental Results  
### Datasets and Hardware  
Two widely utilized and publicly available datasets from the breast and colon cancer domain are the main sources of visual information that are exploited in this paper for the training and validation of the deep convolutional networks. The first dataset named Break Histological Image Classification (BreakHis) and consists of 7909 microscopic, breast tumor tissue images that are collected from 82 patients using different magnifying factors [50]. The images are:  
* Divided in 2480 benign and 5429 malignant samples;
* 3-channel RGB (8 bits in each channel);
* In PNG format;
* In four different magnifying factors (40\(\times\), 100\(\times\), 200\(\times\), 400\(\times\));
* Their dimensions are 700 \(\times\) 460 pixels. Separation of benign images in the following four distinct histological types is provided in the BreakHis dataset: adenosis (A), fibroadenoma (F), phyllodes tumor (PT), and tubular adenoma (TA). Four malignant tumor types are provided as well: ductal carcinoma (DC), lobular carcinoma (LC), mucinous carcinoma (MC), and papillary carcinoma (PC). Samples of the BreakHis dataset are shown in Figure 4 and the class distribution of the dataset is depicted in Table 2. The second dataset is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue. All images are 224 \(\times\) 224 pixels at 0.5 microns per pixel (MPP). Tissue classes are: adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), and colorectal adenocarcinoma epithelium (TUM) [51]. Samples of the second dataset are shown in Figure 5. The class distribution of the CRC dataset is depicted in Table 3. Training and validation of the developed implementations take place on a remote configuration of a double-GPU equipped server. The GPUs are the TITAN Xp (11 GB, corecount:30 and coreClock:1.582 GHz) and the GeForce GTX 970 (4 GB, corecount:13 and coreClock: 1.392 GHz). All of the basic algorithmic operations concerning the deep neural network approaches and the Grad-CAM technique are implemented by using the TensorFlow 2.3 framework for Python programming language.
## 4 Dynamic Assessment of Movement Performance
### Wearable Strain Gauges
#### 4.1.1 Sensing Gloves
A glove incorporating a single optical fiber with Bragg structures positioned at specific spots, such as finger joints, was evaluated for its functionality and performance. The sensor response exhibited linearity with hand movements for opening and closing. This sensor response allowed for the extraction of joint angle information, enabling the estimation of parameters like finger force. The developed glove provided real-time numerical data on hand posture angles. Its simplicity and performance make it suitable for various applications, including physical therapy, studying human kinematics in sports, virtual reality, and remote-control applications. Additionally, as the fiber Bragg gratings only require a single optical fiber end to access the sensor response, the overall system becomes straightforward concerning construction and maneuverability, which are crucial requirements for possible applications. Refs. [65; 70] independently proposed data gloves with IMUs containing an accelerometer, gyroscope, and magnetometer. Two existing data gloves could accurately measure finger motion but had limitations such as wired transmission and restrictions during hand function evaluation tasks. Other authors proposed in [71] a low-cost data glove with nine-axis sensors, but dynamic validation was lacking, limiting its applicability in the medical field. Ref. [67] developed a pair of data gloves with an attitude measurement algorithm, conducting static and dynamic validation. However, the non-modular hardware design rendered the glove unusable if a sensor was damaged. Considering the need for improvement in transmission methods and modular design, a hand-function assessment system was proposed [72]. This system utilized a data glove with six-axis IMUs, but it only had an accelerometer and gyroscope and could measure the Range of Motion (ROM) in specific settings. Furthermore, with only one IMU on the back of the hand, accurately capturing the ROM for the thumb and little finger's metacarpophalangeal joints was impossible.
# Adaptive Cooperative Control of Multiple Urban Rail Trains with Position Output Constraints
## 3 Control Law Design
### Single-Value Learning Adaptive Cooperative Control Design of Multiple Trains with POC  
In order to simplify the structure of the controller, this subsection proposes a novel single-parameter adaptive control method. By selecting a new type of parameter to be estimated, only one online parameter adjustment is required, which effectively improves the engineering practicability of the controller. Based on the data of neighboring trains, the adaptive cooperative control law for multiple urban rail trains is designed as follows:  
\[ \begin{array}{ll}u_{i}=&-\hat{\Theta}_{i}\psi^{2}(x_{i,2})\widetilde{x}_{i,2}+ \dot{x}_{i,2d}-\alpha_{i}\frac{\widetilde{x}_{i,1}}{i_{2}^{2}-\widetilde{x}_{ i,1}^{2}}\\ &-d_{i}\sum\limits_{j\in N_{i}}a_{ij}\
abla_{x_{i,1}}U_{i,j}+h_{i}\sum\limits_{j \in N_{i}}a_{ij}\big{(}x_{j,2}-x_{i,2}\big{)}\end{array}  \tag{20}\]  
where \(\psi(x_{i,2})=1+x_{i,2}+x_{i,2}^{2}\), \(\Theta_{i}=\max\{f_{i},a_{i,1},a_{i,2}\}\), \(\hat{\Theta}_{i}\) is the estimated value of \(\Theta_{i}\), the adaptive law of \(\hat{\Theta}_{i}\) is designed as follows:  
\[\dot{\hat{\Theta}}_{i}=\theta_{i}\psi^{2}(x_{i,2})\widetilde{x}_{i,2}^{2} \tag{21}\]  
where \(\theta_{i}>0\) is the control parameter. **Theorem 2**. : _For an ordered collection of \(n\) trains, the distributed controller in Equation (20) and the adaptive law in Equation (21) are designed so that each train can track the target speed-distance curve, the separation distance between two neighboring trains is always kept within the safe range of \((1_{1},1_{2})\), and the speed of each train can reach the desired speed. That is, the closed-loop dynamic system satisfies the control objective._  
Proof of Theorem 2.: Define the parameter estimation error \(\widetilde{\Theta}_{i}=\hat{\Theta}_{i}-\Theta_{i}\), based on the coordinates \(\widetilde{x}_{i,1}\), \(\widetilde{x}_{i,2}\), \(\widetilde{x}_{j,2}\), \(\widetilde{\Theta}_{i}\), and \(\widetilde{U}_{i,j}\), the dynamics of the multiple trains closed-loop system can be obtained as follows:  
\[\left\{ \begin{array}{l}\dot{\widetilde{x}}_{i,1}=\widetilde{x}_{i,2}\\ \dot{\widetilde{x}}_{i,2}=-\widetilde{\Theta}_{i}\psi^{2}(x_{i,2})\widetilde{ x}_{i,2}-\alpha_{i}\frac{\widetilde{x}_{i,1}}{i_{2}^{2}-\widetilde{x}_{i,1}^{2}}-d_{i} \sum\limits_{j\in N_{i}}a_{ij}\
abla_{\widetilde{x}_{i,1}}\widetilde{U}_{i,j} +h_{i}\sum\limits_{j\in N_{i}}a_{ij}\big{(}\widetilde{x}_{j,2}-\widetilde{x}_ {i,2}\big{)}\\ \\ \dot{\widetilde{\Theta}}_{i}=\theta_{i}\psi^{2}(x_{i,2})\widetilde{x}_{i,2}^{2} \end{array} \right. \tag{22}\]  
The following global positive definite Lyapunov function is selected so that  
\[Q_{2}=\frac{1}{2}d_{i}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}a_{ij}\widetilde {U}_{i,j}+\frac{1}{2}\sum\limits_{i=1}^{n}\widetilde{x}_{i,2}\widetilde{x}_{ i,2}+\frac{1}{2}\alpha_{i}\frac{\sum\limits_{i=1}^{n}\log\frac{k_{i}^{2}}{k_{i}^{2}- \widetilde{x}_{i,1}^{2}}}{2}+\frac{1}{2}\sum\limits_{i=1}^{n}\frac{\widetilde{ \Theta}_{i}^{2}}{\theta_{i}} \tag{23}\]  
The differential of \(Q_{2}\) is obtained as follows:  
\[
# High-Speed Running and Sprinting Thresholds in Elite Female Team Sports: A Systematic Review
###### Abstract  
Over the past decade, participation in female team sports has increased significantly, leading to greater interest in monitoring their training and competition load using wearable technology. Despite this, there is currently no systematic review or meta-analysis that has specifically focused on quantifying and comparing high-speed running (HSR) and sprinting thresholds in female team sports. This systematic review aimed (1) to summarize and describe the evidence on absolute speed thresholds used to classify HSR and sprinting in female team sports and (2) to compare HSR and sprinting thresholds between female team sports. A total of 82 studies were included, encompassing a range of female team sports such as Australian football, basketball, field hockey, soccer, Gaelic football, handball, lacrosse, and different codes of rugby (league, sevens, and union). This systematic review was conducted following the PRISMA 2020 guidelines. This review found that to date, there is no consensus on defining high-speed and sprint running thresholds in female team sports, showing considerable variability in the thresholds used to define HSR (ranging from 11.1 to 21.6 km\(\cdot\)h\({}^{-1}\)) and sprinting (from 15.0 to 30.0 km\(\cdot\)h\({}^{-1}\)). Our results showed that the mean velocity for the HSR threshold was 16 km\(\cdot\)h\({}^{-1}\), although the most frequently used velocity was 18.0 km\(\cdot\)h\({}^{-1}\). In relation to the sprint threshold, the mean and the mode were similar: 21 km\(\cdot\)h\({}^{-1}\) and 20.0 km\(\cdot\)h\({}^{-1}\), respectively. The lack of standardized thresholds highlights the need for personalized approaches when monitoring training loads in female athletes. Despite apparent variability, these findings provide valuable insights for practitioners in designing evidence-based training programs aimed at optimizing high-speed exposure in female team sports. Further research is needed to establish sport-specific and standardized velocity thresholds for women's team sports.  
women; external load; global navigation satellite system; global positioning system; local positioning system; tracking system +
Footnote †: journal: Applied Sciences
# Integrated Multifunctional Laryngoscope for Medical Diagnosis and Treatment
###### Abstract  
Laryngeal lesions can cause great inconvenience to patients. Early diagnosis and corresponding treatments are critical to the survival of patients. However, the diagnosis and precise removal of tumors remain a challenge under the use of a white light laryngoscope. In this work, an integrated, multifunctional laryngoscope was designed and tested for the imaging evaluation and precision laser surgery for laryngeal tissue. This integrated diagnostic and therapeutic endoscopic system included two imaging modes (i.e., optical coherence tomography and white light endoscopy) and a laser ablation treatment mode. The endoscope had a common-path design to ensure that the same position could be imaged and treated simultaneously. The ex vivo porcine larynx experimental results showed that the system imaging modes could simultaneously acquire both superficial and cross-sectional images of the sample tissue, and the ablation treatment could be performed under imaging guidance. This multifunctional laryngoscope has great potential for the early diagnosis of and accurate laser ablation surgery for laryngeal tumors.  
Keywords:integrated multifunctional laryngoscope; optical coherence tomography; white light endoscope; laser ablation +
Footnote †: journal: Medical Imaging
## 4 Discussion and Future Directions
### Answering Research Question Three  
\"Can the identified research gaps be filled? If so, what could be the recommendations that can pave ways for future research?\"  
Yes, the identified gaps can be filled through technological solutions. Table 10 presents a summary of the identified research gaps, their associated problems, and the suggested works to bridge the gaps as future work in this research domain.