esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 3 System Design
### Bandwidth Allocation Scheme
 \tag{13}\]  
At present, the bandwidth allocation for the new flow has been established, and after a designated period (max(RTT\({}_{\text{i}}\)), i \(\in\) N\({}_{\text{BRF}}\)), the actual flow rate is measured. If \(\text{BW}_{\text{i-real}}<\text{BW}_{\text{i}}*\alpha\), the flow is considered app_limited, while if \(\text{BW}_{\text{i-real}}>\text{BW}_{\text{i}}*\alpha\), it is deemed app_no_limited; typically, \(\alpha\) falls between 0.95 and 1.0. In this scenario, the bandwidth allocation scheme for flow i is as follows:  
\[\text{BW}_{\text{i}}= \begin{cases}\text{BW}_{\text{i-real}}*\beta,\beta\in[1. 05-1.10],\text{i}\in\text{N}_{\text{BRF-app-limited}}\\ \frac{\text{C}_{\text{BRF}}-\sum_{i=0}^{\text{L}_{\text{i}}}\text{SNR}_{\text{ BPF-app-limited}}\text{BW}_{\text{i}}}{\text{N}_{\text{BRF-N}_{\text{BRF-app-limited}}}}, \text{i}\in\text{N}_{\text{BRF-app-no-limited}}\end{cases}  \tag{14}\]  
At this juncture, the allocation of bandwidth has been finalized, and the phase of measuring bandwidth commences. Once the measurement is completed, it will proceed to the bandwidth allocation phase if no new flows are added or transition to the bandwidth initialization phase if new flows are added, and so on. In the event of link congestion, the system enters a phase of congestion elimination whereby bandwidth allocation is determined based on the type of congestion present. The variable \(\theta\) represents the specific type of congestion, while \(\theta_{1}\) and \(\theta_{2}\) represent queuing and instantaneous burst traffic-induced congestion, respectively. The process for reducing such congestion is as follows:Let t be the time required to eliminate congestion and \(\Delta\)t be the time interval between the next bandwidth allocation and the previous allocation, which can be expressed as follows. \[\Delta\mathrm{t}=\max(\mathrm{RTT_{i}}),\mathrm{i}\in\mathrm{N_{BRF}} \tag{15}\]  
where q(t) is the queue length and MSS is the maximum packet segment length; then  
\[\left(\sum_{\mathrm{i}\in\mathrm{N_{BRF}}}\mathrm{BW_{i}}-\sum_{\mathrm{i}\in \mathrm{N_{BRF}}}\mathrm{BW_{i+\Delta t}}\right)*\mathrm{t}=\mathrm{q(t)}* \mathrm{MSS},\ \theta\in\theta_{1} \tag{16}\]  
From Equation (16), it can be concluded that  
\[\mathrm{BW_{i+\Delta t}}=\mathrm{BW_{i}}+\frac{\gamma*(\mathrm{q(t)*MSS})-\sum _{\mathrm{i}\in\mathrm{N_{BRF}}}\mathrm{BW_{i}}}{\sum_{\mathrm{i}\in\mathrm{N_ {BRF}}}\mathrm{BW_{i}}},\ \theta\in\theta_{1} \tag{17}\]  
where y is the emptying coefficient, and the lower the value of this coefficient, the faster the congestion is relieved, usually one or two max (\(\mathrm{RTT_{i}}\)) values. If congestion is caused by excessive transient burst traffic, the allocated bandwidth is scaled down:  
\[\mathrm{BW_{i+\Delta t}}=\frac{\sum_{\mathrm{i}\in\mathrm{N_{BRF}}}\mathrm{BW_ {i-real}}}{\alpha*\mathrm{C_{BRF}}}*\mathrm{BW_{i}},\theta\in\theta_{2} \tag{18}\]  
where \(\alpha\) is the maximum allowed link utilization, typically between [0.95, 1.0].
## 4 Experimental Validation
### Datasets  
#### 4.1.1 Pollen Grain Microscopy Images  
The proposed method is evaluated on a pollen dataset. The pollen files are scanned by biologists in pure pollen files and uploaded to Cytomine [46]. The resolution of each file is up to 30,000 by 30,000, which limits the training of the model, so we crop 800 by 800 images in those large images. Fifty of these 800 \(\times\) 800 images are extracted to be manually labeled. An additional set of 300 images is extracted from other high-resolution original images. Of these, 250 remain unlabeled, while the other 50 are manually labeled to define a test set.
## 3 Experimental Setup  
Our experimental system consisted of a computer, an arbitrary waveform generator, a power amplifier, an oscilloscope and ultrasound transducers. Its schematic diagram is shown in Figure 4a. Piezoelectric transducers (PZTs) were employed as the ultrasound transducers in our SHM system, where the diameter of each PZT was 5 mm. The central frequency of the transducer was 250 KHz. There were 20 transducers in the transmitting and receiving arrays respectively, which were attached to a monitored aluminum plate with the dimension of 500 mm \(\times\) 500 mm \(\times\) 1.5 mm. The transducer spacing was 10 mm for both the transmitting and receiving arrays. The distance between the two arrays was 200 mm. Therefore, the monitored area was 200 \(\times\) 200 mm\({}^{2}\). The monitored area was divided into different numbers of grids.  
A five-cycle Hanning-windowed sinusoidal signal with a central frequency of 250 KHz, which was generated using an arbitrary waveform generator (33220 A, manufactured by Agilent Technologies, Santa Clara, CA, USA) and then amplified using a power amplifier (AG1021, manufactured by T and C Power Conversion, Inc., Rochester, NY, USA), was sent to the transmitters. An oscilloscope (54622A, manufactured by Agilent Technologies, Santa Clara, CA, USA) was employed to sample the ultrasonic signals received by the receivers. A computer was used to read the sample data from the oscilloscope. Figure 4b shows a typical original received signal, where both the transmitter and receiver transducer were 10 rd ones. The received signal contained A0 and S0 guided modes. From Figure 4b, it can be seen that the amplitude of the A0 guided mode was greater than that of S0. To ensure the imaging quality, a time gating function similar to Reference [40] was applied to remove unwanted components. The gating function was set to one between the start time and the end time. Outside the region, the gating function reduced from one to zero. The received signal was enveloped via a Hilbert transform. The arrival time of the S0 guided mode was taken as the maxima of the S0 guided mode envelope. The start time was taken as the arrival time of the S0 guided mode plus 200 \(\upmu\)s. The end time was taken as the start time plus 500 \(\upmu\)s. After that, the A0 guided mode was reserved to extract the TOF. The A0 guided mode was enveloped via a Hilbert transform. The TOF was taken as the point at which the enveloped signal intersected a threshold at 50% of the signal's maximum. The TOF was then employed for the inversion.
## 3 Stratospheric Broadband Aeronautical Network  
Civil aircraft can be connected efficiently to broadband Internet services during flight by stratospheric basestations compared to LEO satellite systems. The stratospheric basestations fly at altitudes ranging from 20 to 50 km high in the stratosphere in the form of helium-filled airships carrying communications payloads. Other platform configurations can be utilized such as unpiloted aircrafts or unmanned aerial vehicles which require flying in circular paths to maintain its corresponding coverage area. However, stationary airship SBs will be considered in this paper due to its reliable and stable performance with longer service duration. The optimal altitude for SBs is approximately 20 km where the wind speed is minimum at this height that help in reducing the positional drifts. At this altitude, a SB can cover a circular area of approximately 1000 km diameter on the ground [10; 24] which can be further extended for flying aircraft. Figure 1 displays the system architecture for stratospheric broadband aeronautical network where aircrafts can be served by the line-of-sight SBs during flight period. Broadband communications services provision can be provided continuously even during landing or staying at airports. A network of SBs can provide broadband services during the whole flight time if several SBs are linked through microwave or optical links. A local central gateway station for broadband services provision is linked to the stratospheric network through backhaul links to one of the nearby main SB, while other neighbouring SBs can be linked with this main SB to extend the network coverage for remote regions such as oceans or wide deserts. The network can be expanded to cover several neighbouring countries forming a worldwide stratospheric aeronautical communication network. Over oceanic regions, the high altitude of flying aircrafts provides longer communication range, so a smaller number of SBs is required over these regions.
## 5 Conclusions  
This paper presents a novel darkness-free infrared and visible image fusion framework based on visual characteristics adjustment. The proposed adaptive low-light adjustment model effectively eliminates degraded illumination while preserving weak details. By employing multi-scale structure decomposition and tailored fusion strategies for the base, detail, and saliency layers, our method enhances both thermal and visual information. Comprehensive evaluations demonstrate that our approach surpasses eight existing methods in both objective metrics and visual quality, delivering fused images with superior detail preservation and contrast. The proposed technique holds significant potential for applications such as low-light image enhancement, surveillance, and object detection in challenging environments.  
Despite these advantages, several limitations warrant further investigation. First, the current framework assumes well-registered input images; the performance may degrade under significant sensor misalignment, necessitating robust registration preprocessing. Second, while the model adapts to varying illumination, parameter-tuning may still be required for extreme conditions, such as dense fog, occlusion, or thermal saturation. Additionally, real-time deployment under computational constraints remains an open challenge. Future work will explore task-driven fusion strategies and optimization for real-time processing to broaden the method's applicability.  
Conceptualization, J.L.; data curation, J.L.; methodology, J.L., J.P. and R.Z.; software, J.L.; validation, J.L., J.P. and Z.H.; formal analysis, J.L.; resources, J.P.; investigation, J.P.; writing--original draft preparation, J.L.; writing--review and editing, Z.H. and R.Z.; supervision, X.Z. and R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Presidential Foundation of CAEP] grant number [YZJ]ZQ202001. This research was funded by [Presidential Foundation of China Academy of Engineering Physics], grant number YZJ]ZQ2022001.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request after the completion of ongoing related studies.  
Figure 18: The fused images with and without saliency-preserving (SAP). The red box marks the target area used for comparison.  
**Acknowledgments:** We sincerely thank [PERSON], [PERSON], and [PERSON] for their valuable suggestions on graphical visualization that significantly improved the manuscript's presentation.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Mod2 VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2
## 6 Related Classical and Quantum Methods for Solving Linear Systems
### Classical Approach to Mod2 Linear Solving  
Lacking prior literature on quantum binary linear solvers, we instead compare Mod2 VQLS with the computational complexity of the block Wiedemann method, which is the fastest known classical algorithm for dealing with sparse unstructured systems over finite fields [29]. This method requires a linear number of matrix-vector multiplications plus a quadratic number of arithmetic operations in \(\mathbb{F}_{2}\). In fact, large-scale implementations, like the one leveraged in the record-breaking RSA-240 calculation described in [5], have used \(\geq 3n\) matrix-vector multiplications and it is not known if less than \(2n\) can be used [30].  
In our quantum setting, we consider the cost of executing our matrix-vector product circuit \(\mathcal{A}\) from Section 3. The results from our numerical experiments using the rotations ansatz described in Section 4.1, as recorded in Table 1, indicate that the gate complexity of our simple quantum algorithm is on par with the state-of-the-art block Wiedemann approach: the number of matrix-vector multiplications needed to achieve a solution is linear in the system dimension and we do not require the additional quadratic number of field operations or linear storage. (However, our method requires at least one qubit per variable, so large-scale systems encountered in practice will require larger quantum computers).
## 3 Results and Discussion
### Antibacterial Activity
Furthermore, the Gram-negative outer membrane contains a group of protein-channels called porins, which are essential for the passage of small hydrophilic antibiotics and metabolites into the cell. The antibacterial activity of silver NPs has been also related to the presence of these outer membrane porin proteins. On the contrary, when indolicidin peptide was tested on its own, the MIC revealed a stronger activity of indolicidin against \(S\). _aureus_ (MIC at 20 \(\mathrm{\SIUnitSymbolMicro mL}\)) and a lower inhibitory effect against both \(E\). _coli_ and \(P\). _aeruginosa_ (MIC at 30 \(\mathrm{\SIUnitSymbolMicro g/mL}\) for both). Surprisingly, both IndAgNP1 and IndAgNP2 levelled these differences and provided a stronger antibacterial effect with similar MIC value for both Gram-positive and Gram-negative bacteria (5-12.5 \(\mathrm{\SIUnitSymbolMicro g/mL}\)). The difference between AgNPs and Ind AgNPs is not of considerable degree, but it is important to notice that the amount of peptide inserted into the nanoparticle is negligible compared to the amount of indolicidin able to exert an antibacterial effect when used on its own as antibacterial. Therefore, the addition of indolicidin, more than adding a clear and definitive boost to the known antibacterial property of silver NPs, is indeed of primary importance for the physical characteristics of the produced NPs, both in terms of size and cytotoxicity reduction.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.1 Adaptability Analysis  
We selected 200 acoustic emission signals as sample data and set the characteristic parameters as rise time d1, ring count d2, energy d3, duration d4, amplitude d5, average frequency d6, RMS voltage d7, average signal level d8, peak frequency d9, and initial frequency d10, denoted as D = (d1, d2, d3, d4, d5, d6, d7, d8, d9, d10). To verify whether these data are suitable for factor analysis, we conducted a Kaiser-Meyer-Olkin (KMO) test and [PERSON]'s test of sphericity and assessed the Sig value and KMO value. The results are shown in Table 1.  
In Table 1, the KMO value is 0.842, which is greater than the critical value of 0.6, indicating that the data are suitable for factor analysis. Additionally, the results of [PERSON]'s test of sphericity show a significance level of 0.000, which is less than the standard of 0.05. Therefore, the data are appropriate for factor analysis.  
The following is a description of the scree plot, as shown in Figure 4:  
The scree plot displays the components (variables) on the horizontal axis and the corresponding eigenvalues of the indicator variables on the vertical axis. The eigenvalues are arranged in descending order. As shown in the plot, there is a steep slope from the first to the second indicator, with a noticeable inflection point at the third factor. The subsequent indicator variables gradually form a relatively flat curve. This suggests that selecting three factors is appropriate.  
\begin{table}
\begin{tabular}{c c} \hline \hline KMO and Bartlett’s Test & \\ \hline Kaiser–Meyer–Olkin Measure of Sampling Adequacy & 0.842 \\ Bartlett’s test of sphericity & Approximate Chi-Square & 151,302.068 \\ Degree of freedom & 45 \\ Significance & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of KMO and Bartlett’s Test of sphericity.  
Figure 4: The screen plot.
## 3 Results
### Performance of Upscaled Daily SIF to Track GPP at the Seasonal Scale
At the half-hour interval, the predicted SIF using the PAR-based method has a stronger correlation with the GPP than that using the cos-based method, with an increasing R\({}^{2}\) from 0.658 to 0.785 at the DM site and from 0.807 to 0.875 at the XTS site. More importantly, at daily intervals, the upscaled SIF\({}_{\text{daily}}\) using the PAR-based method also had an obviously stronger correlation with GPP\({}_{\text{daily}}\) than with SIF\({}_{\text{Inst}}\) or the cos-based SIF\({}_{\text{daily}}\); the R\({}^{2}\) values increased from approximately 0.68 to 0.76 at the DM site and from approximately 0.78 to 0.87 at the XTS site. However, the cos-based method did not reduce the differences between SIF\({}_{\text{inst}}\) and SIF\({}_{\text{daily}}\) at the seasonal scale, with nearly the same R\({}^{2}\) values as those for SIF\({}_{\text{inst}}\). Note that the higher R\({}^{2}\) values of the SIF-GPP correlation at the XTS site than those at the DM site were caused by the simulated GPP at the XTS site as opposed to the measured GPP at the DM site, which was independent of the different vegetation functional types for the C3 and C4 crops. In conclusion, at the seasonal scale, the SIF-GPP correlations can be obviously improved when comparing the PAR-based SIF\({}_{\text{daily}}\) with the GPP\({}_{\text{daily}}\) instead of SIF\({}_{\text{inst}}\) and the cos-based SIF\({}_{\text{daily}}\) at both half-hour and daily intervals. This improvement was important for more reliable GPP estimation at the seasonal scale.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 6 Example of Application of the Energy-Efficiency Protocol
### Results  
For comparison purposes, it is first designed a dynamic output-feedback controller using the classic control approach (no packet loss, \(L=\infty\), that is, \(P_{S}=1\) in Equation (15) for all routes). Applying this assumption in Lemmas 2 and 3 for the design of the controller, an \(\mathcal{H}_{\infty}\) guaranteed cost of 63.2076 (computed through Lemma 1) is obtained for the closed-loop system (13). Additionally, for a full-reliable communication, the mathematical expectation of the global number of transmission \(\mathcal{E}(M)\) for each route \(R_{1}\), \(R_{2}\), \(R_{3}\) and \(R_{4}\) is, respectively, 36, 24, 35.8025 and 43.75. The value of \(\mathcal{E}(M)\) is computed for each route with \(L=\infty\), \(p\) and \(N\) given in Table 4. Consequently, the energy consumed by route (according to Equation (17)) is, respectively, 4.3027 J/h, 2.8685 J/h, 4.1451 J/h and 4.9014 J/h, and the global energy consumption is 16.2177 J/h.
## 2 Materials
### In Situ Data  
The main characteristics (i.e., dates, range and mean values) of topsoil moisture (TSM), soil texture and surface roughness measurements collected in bare soil conditions are summarized in the Table 1. The protocols and the mean features of the collected soil descriptors are described hereinafter.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Soil** & **Variable** & **Measurement** & **Values** \\ \hline
**Descriptor** & **Name (Unit)** & **Date** & **[Min–Max]** & **Mean** \\ \hline Topsoil Moisture & (\% m\({}^{3}\).m\({}^{-3}\)) & 20 February 2010 & [24.0–32.9] & 28.9 \\  & & 27 February 2010 & [20.1–25.2] & 23.3 \\  & & 5 March 2010 & [21.2–31.0] & 27.0 \\  & & 16 March 2010 & [11.8–23.7] & 17.9 \\  & & 26 March 2010 & [11.5–25.6] & 20.8 \\  & & 8 April 2010 & [6.7–24.6] & 15.9 \\  & & 14 April 2010 & [3.5–18.9] & 11.2 \\  & & 1 May 2010 & [25.8–31.2] & 28.7 \\  & & 10 May 2010 & [20.0–24.1] & 21.8 \\  & & 20 May 2010 & [12.5–15.5] & 14.0 \\  & & 29 July 2010 & [7.0–17.0] & 11.1 \\  & & 17 August 2010 & [2.7–10.8] & 5.9 \\  & & 30 August 2010 & [2.5–6.0] & 3.8 \\  & & 15 September 2010 & [2.4–6.4] & 4.2 \\  & & 4 October 2010 & [7.0–16.3] & 11.2 \\  & & 12 October 2010 & [21.3–31.3] & 25.5 \\  & & 18 October 2010 & [9.8–21.9] & 14.5 \\  & & 22 October 2010 & [7.7–17.8] & 12.1 \\  & & 2 November 2010 & [18.1–29.2] & 23.3 \\  & & 12 November 2010 & [22.6–35.3] & 26.6 \\  & & 24 November 2010 & [22.0–29.6] & 26.3 \\ Soil Texture & Clay (\%) & Once during & [9–58] & 24 \\  & Silt (\%) & the experimental & [22–77] & 52 \\  & Sand (\%) & period & [4–53] & 24 \\ \hline \end{tabular}
\end{table}
Table 1: Overview of the measurements collected on the network of plots. Surface roughness is divided into perpendicular () and parallel () measurements compared to the row direction.  
Figure 2: Relative number of bare soil plots (\(\%\)) during the field campaign.
## 2 Molecular Mechanisms of Antimicrobial Agents Recalcitrance in Clinically Important Biofilms  
Planktonic bacteria are more sensitive to antimicrobials than biofilm bacteria are. Killing biofilm-associated pathogens may require up to a 1000-fold increase of the antibiotic dose that usually kills free-floating bacteria [12; 13], and most antibiotic-resistant infections are provoked by pathogenic bacteria in the biofilm mode of growth.  
Biofilm resistance to antimicrobial agents may be defined as the capacity of microorganisms to continue to grow in the presence of bactericidal and bacteriostatic agents in a concentration higher than the minimum inhibitory concentration (MIC). On the other hand, tolerance to antimicrobials means that the microorganisms neither die nor grow but are able to survive in the presence of a bactericidal antimicrobial agent. In the biofilm mode of life, microorganisms develop both resistance and tolerance to antimicrobials. Therefore, a new term named recalcitrance was introduced to describe the decreased sensitivity of biofilms to antibiotics as a consequence of a complex mixture of resistance and tolerance mechanisms [7; 14]. It is this acting in concert and the interplay of many self-defense mechanisms, some of them not fully understand nor investigated, which is still the subject of intense scientific debates, that makes the eradication of infectious biofilms such a difficult task to achieve. Consequently, a better understanding of various mechanisms contributing to biofilms antibiotics recalcitrance is crucial to identify and validate new drug targets and develop new significantly improved treatments that are able to overcome and/or bypass these mechanisms. The spectrum of molecular resistance and tolerance mechanisms elicited by biofilm bacteria is quite wide depending mainly on the particular bacterial species and strain, on the type of the antimicrobial agent, on the stage of biofilm maturation, and the particular growth conditions. However, five main types of determinants have been identified by researchers so far:  
1. EPS matrix--especially the interaction of antimicrobials with its components;
2. nutritional limitations and stress responses;
3. persister cells;
4. cell-to-cell communication in biofilms resulting in the enhanced expression of various specific genetic determinants of antibiotic resistance and tolerance. Transcriptomic and proteomic studies revealed sharp differences in the gene expression of genetically identical bacteria when they grow as aggregates in the biofilm mode of life and when they are isolated in the planktonic free-floating form. Some estimates indicate that as much as 40% of the genes of a bacterium may undergo up or downregulation and no less than half of the proteome is being differentially expressed in the transition from the planktonic to the biofilm state;
5. mutational (conventional) antibiotic resistance.  
In the next paragraphs, some major insights into the molecular mechanisms of biofilms recalcitrance to antibiotics will be outlined.