esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Prediction Block
#### 2.2.2 Temporal Feature Extraction  
\(O_{SF}\) will be continuously sent to the attention LSTM and a fully connected network to obtain the temporal features. Attention LSTM is effective in predicting traffic flow [22; 23; 24]. Traditional conventional attention LSTM is used to capture the weight scores of different time intervals, usually by assigning heavier weight scores to adjacent time intervals and lower ones to those further apart [21]. However, passenger flow prediction models are affected by many factors, such as weather conditions [25], emergencies, passenger flow, network topology, and so on. Thus, applying the traditional conventional attention LSTM to assign weights for outputs in LSTM is insufficient. Therefore, based on previous work by [PERSON] et al. [26], we use a fully connected network to obtain weights that can be scored according to the output of LSTM based on Equations (28) and (29).  
\[\alpha=f(W\circ Out+b) \tag{28}\]  
\[AttenLSTM=\alpha\circ Out \tag{29}\]  
where \(Out\in\mathbb{R}^{S_{i}\times Neu}\), \(S_{i}\) refers to the number of stations in the \(i\)th category, and \(Neu\) represents the number of neurons used in LSTM. \(W\) is the trainable weights, \(b\) is the trainable bias, and \(f\) represents the activation function in the fully connected layer. The term \(\alpha\) is a trainable weight matrix whose shape is identical to \(Out\). The term \"\(\circ\)\" denotes the Hadamard product. \(AttenLSTM\) is the output of attention LSTM.  
\(AttenLSTM\) will be flattened and sent to a feed-forward network as \(O_{FFN\_AttenLSTM}\) for full connection. \(O_{FFN\_AttenLSTM}\) is the output of temporal feature abstraction, which is also the prediction result for the same type of station. Every type of station has the same processing to construct its own prediction model and obtain the corresponding prediction result. By integrating the predicted passenger flow for every type of station, the final result for all the stations in the whole network will be obtained.
# QRLIT: Quantum Reinforcement Learning for Database Index Tuning
## 3 Related Work
### Quantum Algorithms for Index Tuning  
There exists little research in the area of quantum index tuning. The article [2] proposes the conversion of the classical algorithm DINA (deep reinforcement divergent index advisor) [28] to a quantum version. However, the paper is at an early stage; it has not provided quantum implementation details and experimental results.  
Besides the capabilities that reinforcement learning provides to automate index tuning problems, other techniques are used. The paper [29] leverages the capabilities of quantum annealers by proposing novel techniques to map the database indexes into the qubits of the quantum annealer. One technique exploits the qubits more efficiently by reducing the asymptotic qubit growth from quadratic to linear by incorporating additional auxiliary variables. The second technique is embedded within the transformation function, where efficiency is achieved through a process of extensive pre-processing before the run time. This technique generates a library of embedding templates, which cover a subset of index selection problem instances.  
The paper in [30] proposes SQIA, a quantum-classical (hybrid) index advisor that delivers optimal solutions with high probability by using a novel Grover search-based approach. This approach implements an efficient quantum Oracle used in the Grover search algorithm, which loads the problem data into the qubit phases. In other words, this technique loads and encodes the storage cost, benefits, and constraints.  
The present literature review reveals that, in addition to the vision paper proposing a quantum counterpart of DINA [21], there is currently no quantum counterpart implementation with experimental results of index advisory using reinforcement learning.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 5 Simulation and Results Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Arrival Rate** & **10** & **15** & **20** & **25** & **30** & **35** & **40** \\ \hline \hline \multicolumn{1}{c}{PPOSFC} & 557.5 & 715.0 & 814.6 & 860.0 & 897.5 & 899.2 & 904.5 \\ \multicolumn{1}{c}{PPOSFC *} & 538.3 & 684.3 & 776.1 & 814.6 & 845.3 & 843.4 & 846.8 \\ \hline \hline \end{tabular}  
* Without network topology information.  
\end{table} 
Table 3: Algorithm performance on cumulative profit in terms of whether to incorporate future network topology information. Finally, we examined the impact of hyperparameters on training effectiveness in the PPOSFC algorithm. In DRL algorithms, training effectiveness is primarily evaluated through two aspects: training convergence efficiency and convergence performance. For DRL algorithms, these factors are assessed by observing changes in the agent's episodic cumulative profit over training steps. We investigated scenarios with a batch size of 256 and learning rates of 0.0001, 0.0005, and 0.001. The simulation results, as shown in Figure 6, demonstrate varying cumulative rewards under different learning rates. With a learning rate of 0.0001, the algorithm achieved the fastest convergence, while a learning rate of 0.001 resulted in the slowest convergence. This occurs because higher learning rates increase the magnitude of updates, and despite PPO's clipping mechanism, may lead to excessive policy updates that deviate from the optimal policy path, causing instability in the learning process. Conversely, lower learning rates tend to maintain smoother policy updates, enabling more stable algorithm convergence and a gradual approach toward optimal solutions, thus achieving superior convergence results. We also examined scenarios with a fixed learning rate of 0.0001 and varying batch sizes of 64, 256, and 512. The algorithm demonstrated different convergence efficiencies across these batch sizes. As shown in Figure 6, with a batch size of 64, the algorithm initially converged more rapidly but achieved the lowest final reward. At a batch size of 512, the algorithm reached final rewards comparable to those with a batch size of 256 but exhibited slower convergence. This behavior occurs because smaller batch sizes typically result in higher gradient update variance. While this enables faster convergence due to more frequent parameter updates, it also increases the likelihood of the model becoming trapped in local optima, thereby affecting the final cumulative reward. Conversely, larger batch sizes provide more accurate gradient updates. The increased number of data samples enables more precise gradient direction estimation, reducing update randomness and volatility. This facilitates steady optimization in the parameter space, leading to higher final rewards, albeit with slower update progression.
## 3 Aim, Materials, and Methods
### Integrated Intelligent Perception Mode of \"Sky Air and Ground\"
#### 4.1.3 \"Ground\" Command System  
The \"Ground\" command system not only receives real-time data from the airborne UAV system but also transmits these crucial data to the rear command platform, front mobile command system, and on-site rescue personnel. This enables decision-makers to quickly obtain an accurate understanding of the situation and develop more scientific and efficient rescue strategies. During actual rescue operations, the UAV provides aerial surveillance of the entire disaster area and relays high-definition images in real time. Equipped with various sensors, it collects environmental data such as temperature, humidity, and gas concentration. The \"Ground\" command system integrates and analyzes this information to facilitate prompt decision-making. Specifically, in earthquake or mudslide scenarios and  
Figure 2: Integrated intelligent perception mode of “sky air and ground”. Source: Own processing according to the literature.  
other disasters, the \"Ground\" command system assists in assessing damage severity and establishing rescue priorities by prioritizing the areas most severely affected for immediate assistance. Simultaneously, the rear command platform coordinates resources including materials, equipment, and manpower to provide robust support for on-site rescue efforts. The front mobile command system closely collaborates with on-site rescue teams while adjusting plans in real time to ensure an orderly and efficient execution process.
## 4 Experimentation and Results
### Experimental Setup
#### 4.1.2 Metrics  
We evaluate the compressors based on the following critical metrics:  
1. CR: The compression ratio (CR) is the ratio between the sizes of the original data and their compressed latent representation. The compressed data of our model are outputs of the quantizer in an integer format. We define CR as: \[CR=\frac{original\_size}{ compressed\_size}\] (9)
2. MAE: Reconstruction quality is measured using traditional error metrics such as the mean squared error (MSE) and the mean absolute error (MAE). \[MAE(x,\hat{x})=\max_{1\leq i\leq N}|x_{i}-\hat{x}_{i}|\] (10)
3. PSNR: This metric measures the performance of compression schemes. PSNR is defined via the mean squared error (MSE). MSE is given in Equation (11). \[MSE(x,\hat{x})=\frac{1}{n}\|\ x-\hat{x}\ \|_{2}^{2}\] (11) where \(x\) and \(\hat{x}\) are the original and reconstructed data, respectively.  
PSNR is then defined as  
\[PSNR=10\times\log_{10}\left(\frac{\left(MAX-MIN\right)^{2}}{MSE}\right) \tag{12}\]  
where \(MAX\) is the max value in the dataset and \(MIN\) is the min value in the dataset.  
PSNR is inversely proportional to MSE. When the error between input and output data is small, MSE is a small number, which leads to a large PSNR. Therefore, it is desired to maximize PSNR for any compression model.
## 2 Molecular Mechanisms of Antimicrobial Agents Recalcitrance in Clinically Important Biofilms
### Nutritional Limitation and Stress Responses
#### 2.2.1 Hypoxia and Reduced Growth Rate  
Studies [35; 36; 37] showed that antibiotics targeting metabolic activities were ineffective in hypoxic biofilm areas, although they could effectively penetrate those zones, while steel kept their antimicrobial activity in other biofilm regions normally supplied with oxygen. The reverse effect was reported in _P. aeruginosa_ biofilms exposed to colistin and aminoglycoside antibiotics [38]. It was suggested that the tolerance of _P. aeruginosa_ biofilms to tobramycin is due to the reduction of the cell membrane potential in hypoxic zones. The intracellular uptake of aminoglycososide antibiotics requires the generation of an electrochemical gradient across the bacterial cell membrane, the so-named proton motive force (PMF), which is composed of an electrical potential gradient (\(\Delta\Psi\)) and a chemical gradient in proton concentration (\(\Delta\)pH), which is acidic outside the plasma membrane and interior alkaline. Since at physiological pH aminoglycosides are polycationic molecules, their internalisation is driven by \(\Delta\Psi\), which is interior negative. However, a critical transmembrane electrical potential threshold level of at least \(-155\) mV [39; 40; 41; 42] is needed for the intracellular uptake of aminoglycosides and further rRNA binding and protein synthesis inhibition. According to the chemiosmotic theory, the generation of \(\Delta\Psi\) is dependent on some key respiratory complexes of the electron transfer chain (ECT). Under hypoxic conditions, expression of the latter is downregulated [43], resulting in a reduction in \(\Delta\Psi\) and a consequently decreased intracellular uptake of aminoglycos antibiotics.  
A recent study [44] conducted by [PERSON] et al. revealed another mechanism responsible for antibiotic resistance under hypoxic conditions. The genes encoding the MexCD-OprJ efflux pump were shown to be transcriptionally upregulated in _P. aeruginosa_ biofilms grown under low-oxygen partial pressure. Overexpression of this pump renders the biofilm resistant to several classes of antibiotics, including fluoroquinolones, \(\beta\)-lactams, tetracycline, chloramphenicol, macrolides, trimethoprim, and novobiocin.
## 3 Experimental Section
### RNA Extractions  
Total RNA was isolated from _S. aureus_ MW2 cells grown to exponential growth phase (OD\({}_{600}\) approximately 0.5) either treated with dimethyl sulfoxide (DMSO) or 8\(\times\) the MIC of SK-03-92 dissolved in DMSO using TRIzol extraction (Life Technologies, Carlsbad, CA, USA) according to manufacturer's instructions with an additional lysostaphin treatment step to help lyse the _S. aureus_ cell walls. The RNA samples were digested with DNase I (New England Biolabs, Ipswich, MA, USA) followed by phenol and chloroform extractions to remove the protein. RNAs were run on 0.8% agarose gels to confirm concentration and integrins of the RNAs. To assess DNA contamination of the samples, PCRs were performed on the RNA samples using SaFtsZ1 and SaFtsZ2 primers (see Table 4). The PCR conditions for amplification with the SaFtsZ1/SaFtsZ2 primers was as follows: 94 \({}^{\circ}\)C, 1 min; 55 \({}^{\circ}\)C, 1 min; and 72 \({}^{\circ}\)C, 1 min for 35 cycles.  
\begin{table}
\begin{tabular}{l c l} \hline \hline
**Primer** & **Gene** & **Sequence** \\ \hline SaFtsZ1 & _ftsZ_ & 5′-GGTGTAGGTGGGCGTAA-3′ \\ SaFtsZ2 & & 5′-TCATTGGCGTAGATTGTGT-3′ \\ GuaBF1 & _guaB_ & 5′-GCTCGCATCAGGTGTTTAGGTG-3′ \\ GuaBR1 & & 5′-TAAGACATGCACACCTGCG-3′ \\ StrA1 & _srtA_ & 5′-TGCGTGTGTGTGTATTAC-3′ \\ StrA2 & & 5′-CAGGTGTGTGTCCTGA-3′ \\ MW2284A & _MW2284_ & 5′-CAATGCAAATGAGACCGAAATT-3′ \\ MW2284B & & 5′-GAGAGAATAGTGTATGGTGTCAT-3′ \\ MW2285A & _MW2285_ & 5′-GTATGITATTATTGCAGAGCGCAA-3′ \\ MW2285B & _s_-AAAGGCAAAAATCCGCAGATACG-3′ \\ SA2043A & _tdk_ & 5′-CTGTGTCTCACTGCACGCATCA-3′ \\ SA2043B & & 5′-ACGCACGACTTAACTAATG-3′ \\ SaPurD1 & _purD_ & 5′-CAGCCGCAATTGATGATT-3′ \\ SaPurD2 & & 5′-AGCACTTCTGCTGCTTCAAT-3′ \\ SaPurH1 & _purH_ & 5′-CCAGAAAATAATGATGGCGCGCGT-3′ \\ SaPurH2 & & 5′-TGCCGATACAATTGTGTGT-3′ \\ SaPurL1 & _purL_ & 5′-GTATATGTGAGATGAAACATTGC-3′ \\ SaPurL2 & & 5′-ACCCCCAATAGAGACAATGTC-3′′ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Oligonucleotide primers used in this study.
# Realisation of a Novel Functionally Redundant Actuation System for a Railway Track-Switch
## 4 Controller Design, Implementation and Model Validation
### Validation of the Co-Simulation Model  
In this section, the results of step-response in the co-simulation for the full switch model are presented, with a single actuator bearer in operation. The results from the dynamic model with controllers are plotted in Figure 11, they show that the differences between the two cam angles (cams A and B) are seen to be negligible (shown in Figure 11b), which ensures no tilting of the rail carrier. Figure 11c shows that the differences in the motor velocities in both the motors are negligible in the co-simulation which ensures synchronised motion of the two motors and the cams. The plots of the motor velocities of the two motors within an actuator show agreement as would be expected. The nature of the current plots for both the assemblies also matches. The maximum current also satisfies the current limitation of the system. Overall this suggests that the controllers designed will appropriately govern the position of the switch rails.  
The experimental results from the implemented system are presented along-side the simulation results and used to validate the co-simulation model. The validation results with one active bearer (the third bearer (B3) in Figure 2) are presented in Figure 12. The  
Figure 10: Location cabinet with hardware installed, for testing at Great Central Railway Quorn and Woodhouse Station, UK.  
results with a step-change in command from 0\({}^{\circ}\) to 180\({}^{\circ}\) shows that the cam rotates as desired. The 0\({}^{\circ}\) corresponding to the cam position that places the switch rails in the Normal position, whereas the 180\({}^{\circ}\) is reached by the cam when the switch rails are placed in the Reverse position. In the cam angle plot in Figure 12, the result from the co-simulation is shown with cam sensor data from the experiment and it shows a good agreement apart from the small difference in the start-up phase of the plot. The cam angle sensor data from the experiment shows that the cam settles at an angle of 180\({}^{\circ}\), which is perfect because the hoppers carrying the switch rails rest on the locking block before the full 180\({}^{\circ}\) rotation. The other two data which are available from the experiment are the motor angular velocity and motor input current which are also shown to agree with those from the co-simulation result in Figure 12. It is seen from Figure 12 that the control requirements are satisfied such as settling time (4.8 s), maximum motor current (<10.4 A) and maximum overshoot (0\({}^{\circ}\)).  
Figure 11: Comparison of performances for both the cams within the actuator: (**a**) angular position of the cams; (**b**) error in the angular position between the cams; (**c**) motor angular velocities; (**d**) motor input currents.  
Figure 12: Validation of the co-simulation model with the experimental results of the track switch system, working by single actuator bearer (third bearer B3, motor side-B).
## 2 Model and Formulation
### Formulation of the Deformed Plate
#### 2.2.1 Centrifugal Moment of the Deformed Disc  
Before calculating the centrifugal force and moment, the deformation-induced angle is defined as:  
\[\theta_{p}=\arctan\frac{w}{r\sin\theta} \tag{11}\]  
Here, \(\theta_{p}\) is defined as the angle between the point mass and the tilting axis generated from the deformation. This angle differs from the deformed disc to the flat discs. As explained in Figure 6, the deformation of the disc generates an angle between the selected points and the tilting axis. For instance, when the tilting angle \(\theta_{Z}\) of the deformed disc is 0, the deformed points still hold an angle to the axis so that the actual is nonzero. Moreover, as the disc tilts, this angle has different impacts on the absolute value of tilting angles: it decreases the tilting angle of upper parts while increasing the value of lower parts. This angle thus causes asymmetry of the centrifugal force of the upper and lower parts. The deformation-induced angle is expressed as a function of local variables and the shape function:  
The corresponding centrifugal force of one point of the deformed disc can be expressed as:  
\[\tilde{F_{i}}=\underbrace{ph}_{m}\underbrace{\sqrt{(r\sin\theta)^{2}+w^{2}} \cos{(\theta_{Z}+\theta_{P})}}_{\tilde{r_{i}}}\omega^{2} \tag{12}\]  
This formula has the same mass and angular velocity terms as the flat discs. The new distance between the mass point and the rotating axis is defined as \(\tilde{r_{i}}\), which accounts for the effect of the deformation. Then, the centrifugal moment of the disc can be written as:  
\[\begin{split}\tilde{M_{t}}&=\int_{a}^{(b-a)\cos{ \alpha}+a}\int_{0}^{2\pi}\underbrace{ph}_{m}\underbrace{\sqrt{(r\sin\theta)^{2 }+w^{2}}}_{\tilde{r_{i}}}\cos{(\theta_{Z}+\theta_{P})}\omega^{2}\underbrace {\sqrt{(r\sin\theta)^{2}+w^{2}}}_{\tilde{l_{i}}}\sin{(\theta_{Z}+\theta_{P})}r \,dr\,d\theta\\ &=\frac{1}{2}ph\omega^{2}\sin{(2\theta_{Z})}\int_{a}^{(b-a)\cos{ \alpha}+a}\int_{0}^{2\pi}[(r\sin\theta)^{2}+w^{2}]\cos{(2\theta_{P})}r\,dr\,d \theta\\ &+\frac{1}{2}ph\omega^{2}\cos{(2\theta_{Z})}\int_{a}^{(b-a)\cos{ \alpha}+a}\int_{0}^{2\pi}[(r\sin\theta)^{2}+w^{2}]\sin{(2\theta_{P})}r\,dr\,d \theta\\ \end{split} \tag{13}\]of the integration varies due to deformation. The same change can apply to the moment of inertia.
# IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance
## 3 Results
### Beam Profiles and Off-Axis Ratios  
The profiles of all CyberKnife beams measured by myQA(r) SRS are shown in Figure 4, panel a. The visual inspection shows the good performance of the detector in terms of resolution, also when compared (examples are reported in panels b and d for 5 mm and 60 mm fields, respectively) with commissioning data acquired with the PTW 60017 diode (PTW, Freiburg, Germany). The field size numerical results in terms of FWHM confirm the agreement with the commissioning values at a depth of 15 mm in water. It should be noted that all field dimensions are within 0.4 mm tolerance, which is the detector's resolution limit. The other beam parameters (flatness, symmetry, and penumbra) calculated according to the same protocol as the baseline commissioning data, show values within the tolerance level used in clinical practice. This confirms the suitability of using myQA(r) SRS detector for small beams in all those circumstances requiring constancy checks by reason of its sampling resolution.  
Conventional linacs mount a flattening filter through the photon beam path with the aim of making uniform the energy intensity across the treatment field (obtaining the so-called flat beams). Conversely, the CyberKnife system delivers flattening-filter-free (FFF) radiation, characterized by \"_a different beam profile and higher dose rate, but also a different photon energy spectrum and different head-scatter properties_\" ([PERSON], 2018). Figure 4 shows the typical FFF beam profiles, characterized by a sharper penumbra, a smaller out-of-field dose, and a more intense central region with respect to flat photon beams.  
Figure 4: Beam profiles measured by myQA® SRS device compared with PTW 60017 commissioning data: (**a**) all CyberKnife® fixed collimators measured by myQA SRS device; (**b**) 5 mm fixed cone comparison between myQA SRS (continuous line) and PTW 60017 (dotted line); (**c**) 60 mm fixed cone comparison between myQA SRS (continuous line) and PTW 60017 (dotted line).