esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Biofilm Inhibition and Eradication Ability
In this regard, it is necessary to underline that biofilm-related antimicrobial resistance is partly due to the presence of some dormant _S. aureus_ cells, also known as persister cells. These cells maintain their dormancy condition during antimicrobial treatment and become active again as soon as the treatment is withdrawn, thus causing a chronic recurrent infection [91]. As already demonstrated for _Pseudomonas aeruginosa_, it could be supposed that the overall density achieved by nanoemulsions can be quite high within the biofilm. Furthermore, the continued release of the antibiotic from biofilm-localized nanostructures could provide a sustained level of the antibiotic near persister cells. It is conceivable that this subpopulation may be better addressed by antibiotic-loaded nanocarriers compared to free drugs [92]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Biofilm Inhibition**} & \multicolumn{4}{c}{**Biofilm Eradication**} \\ \hline
**Strains** & **NEs** & **NEsL** & **LVX** & **LO** & **NEs** & **NEsL** & **LVX** & **LO** \\ \hline _S. aureus_ ATCC 6538P & 0.3 \(\pm\) 1.3 * & 14.0 \(\pm\) 12.8 * & 0.6 \(\pm\) 1.0 & 14.7 \(\pm\) 2.9 * & 24.4 \(\pm\) 2.9 * & 3.2 \(\pm\) 3.0 * & 16.0 \(\pm\) 3.5 * & 4.5 \(\pm\) 2.1 \\ \hline _S. aureus_ DA & 25.0 \(\pm\) 2.5 & 36.25\(\pm\) 12.3 & 24.7 \(\pm\) 22.0 & 4.75 \(\pm\) 9.5 & 21.0 \(\pm\) 15.6 & 71.0 \(\pm\) 10.0 & 30.8 \(\pm\) 3.2 & 31.0 \(\pm\) 4.3 \\ \hline _S. aureus_ HC & 24.1 \(\pm\) 5.6 & 8.0 \(\pm\) 6.0 * & 23.0 \(\pm\) 9.5 & 19.5 \(\pm\) 6.3 & 40.0 \(\pm\) 17.0 & 41.0 \(\pm\) 0.3 & 33.7 \(\pm\) 2.0 * & 33.0 \(\pm\) 4.8 \\ \hline _S. epidermidis_ ATCC 35984 & 0.2 \(\pm\) 1.5 & 30.0 \(\pm\) 5.0 & 56.0 \(\pm\) 7.0 & 29.3 \(\pm\) 9.0 & 35.0 \(\pm\) 2.3 & 21.0 \(\pm\) 7.5 & 15.0 \(\pm\) 4.2 & 15.5 \(\pm\) 4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Biofilm inhibition and eradication ability of NEs, NEsL, LVX and LO. All substances were used at 0.1 \(\upmu\)g/mL, with the exception of the conditions with * that were tested at 0.01 \(\upmu\)g/mL because the MIC value was lower. Values > than 40% were considered significant in inhibition and eradication of biofilm.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 5 Performance Evaluation
### Comparison of Network Lifetime  
We next explore the lifetime of the network after executing different algorithms. Generally, the network lifetime is defined as the time when nodes in the network begin to die. The simulation result is illustrated in Figure 11. In LEACH, the first node dies at about 2300 s and the other three algorithms are much superior to LEACH in aspects of network lifetime. Uneven energy dissipation of nodes, especially the cluster heads and super-long-distance communication, result in the premature death of sensors in LEACH. The adoption of mobile collector greatly decreases the communication distance between the source node and the gatherer. Therefore, the network lifetime of those schemas with a mobile data collector is prolonged in various degree compared to LEACH. Multi-hop communication in MWR increases the burden of compatible pairs which function as the forwarders and that is the primary reason for the short lifetime of MWR. Although TRP-MC and SHDGP both adopt single-hop communication, TRP-MC minimizes the overlapped nodes of SPs and further improves the gather efficiency of the mobile collector.  
Figure 10: Comparison of energy consumption between different algorithms.
## 2 Related Work  
Currently, there are two prevailing techniques for fine-grained image recognition. The first approach is known as localization classification subnetworks, and the other one is end-to-end feature encoding. The two-stage method (localization classification subnetworks), in particular, depends on the object and part localization annotations, region proposal networks [21], or attention mechanisms to acquire discriminatory areas, which are subsequently fed to the classifier. Study [22] built object and part detectors using bounding box datasets to find the most effective local semantic parts, and afterward applied a classifier to retrieve final classifications. Ref. [23] generated portions using segmentation and a posture graph, followed by moving them to a classification model. The authors of [24] layered a series of branches comprising a part cascade, an object cascade, and part landmark localization to merge feature maps carrying information from each component along with the bounding box. In [25], researchers combined classification and semantic part recognition. Study [26] designed a multi-granularity algorithm for learning with two stages: a targeted search to discover ROI (regions of interest) followed by classification. Ref. [27] utilized a weakly supervised approach to locate various relevant regions coming from proposals and subsequently apply them to generate a broader representation for classification. An attention mechanism was employed by [28] to train a coarse-grained model to identify relevant regions, which were then forwarded through a fine-grained network to enhance categorization. In terms of conclusion, each of those techniques attempts to use object-level or local-level details to eliminate unnecessary information, then feed the relevant information to the classifier for the classification task. Considering the complex two-stage pipeline and tedious and resource-intensive datasets, the present work emphasizes end-to-end feature encoding through deep learning neural networks to identify minute differences within subcategories. This strategy relies on maximizing classification outcomes through improved feature representations. A couple of research studies [29; 30] proposed paired interaction learning approaches to gather semantic differences. The authors of [31] employed the self-attention mechanism to retrieve discriminative features. Study [11] suggested a hierarchical architecture that performs cross-layer bilinear pooling. A small number of studies have focused on fine-grained image recognition with one-stage object detectors, which similarly adopt the feature encoding approach with the object localization system. Consequently, in this study, we intended to explore the performance and ability of the single-stage detector [32] to fill the void in the fine-grained image recognition problem.
# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 3 Results
### Classification Modeling
#### 3.2.2 Validation Results of the Model  
The confusion matrix for each model is shown in Table 6.  
In addition, the AUC scores were calculated, and the ROC curves were obtained (Figure 8). The AUC scores obtained of models except the polynomial (2d) model were over 0.9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Relative Bearing (\(\psi_{r}\))** & **Aspect** & **Speed Rate (K)** \\ \hline Relative bearing (\(\psi_{r}\)) & 1 & & \\ Aspect & \(-0.63\) & 1 & \\ Speed rate (\(K\)) & 0.04 & \(-0.01\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation matrix between the predictors.  
Figure 7: Distribution of the predictors. Classes (Y) 0, 1, and 2 denote Head-on, Give-away, and Stand-on, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Linear**} & \multicolumn{3}{c}{**Polynomial (2d)**} & \multicolumn{3}{c}{**Polynomial (3d)**} & \multicolumn{3}{c}{**Gaussian (RBF)**} \\ \hline \multirow{2}{*}{**True**} & \multicolumn{3}{c}{**Predicted**} & \multirow{2}{*}{**True**} & \multicolumn{3}{c}{**Predicted**} & \multirow{2}{*}{**True**} & \multicolumn{3}{c}{**Predicted**} & \multirow{2}{*}{**True**} & \multicolumn{3}{c}{**Predicted**} \\ \cline{2-2} \cline{8-11}  & **0** & **1** & **2** & & **0** & **1** & **2** & & **0** & **1** & **2** & **0** & **1** & **2** \\ \hline
0 & 320 & 35 & 21 & 0 & 343 & 20 & 13 & 0 & 335 & 20 & 21 & 0 & 302 & 41 & 33 \\
1 & 43 & 366 & 0 & 1 & 49 & 243 & 117 & 1 & 47 & 362 & 0 & 1 & 33 & 376 & 0 \\
2 & 70 & 0 & 337 & 2 & 81 & 174 & 152 & 2 & 74 & 0 & 333 & 2 & 60 & 0 & 347 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrices for high-performance SVM kernels.  
Figure 8: ROC curve and AUC score of the kernels in case: (**a**) Linear, (**b**) Polynomial (2d), (**c**) Polynomial (3d), and (**d**) Gaussian (RBF).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Kernel**} & \multirow{2}{*}{**C**} & \multirow{2}{*}{**Gamma**} & \multirow{2}{*}{**Acc.**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1 Score**} \\ \cline{5-10}  & & & & & & **Macro** & **Macro** & **Macro** & **Micro** \\ \hline \multirow{4}{*}{SVM} & Linear & 0.001 & - & 0.86 & 0.86 & 0.86 & 0.86 & 0.86 & 0.86 \\  & Polynomial (2d) & 10 & - & 0.62 & 0.61 & 0.63 & 0.61 & 0.62 \\  & Polynomial (3d) & 10 & - & 0.86 & 0.87 & 0.87 & 0.87 & 0.86 \\  & Gaussian (RBF) & 100 & 0.01 & 0.86 & 0.86 & 0.86 & 0.86 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Estimation of the optimal hyperparameters of the model.  
The polynomial (3d) model obtained the highest accuracy and performance (F1 score) among the constructed models. In addition, it is not overfitted because it has a training score of 0.88. In addition, the ROC and AUC metric also secured a high performance of 0.9 or higher. Therefore, the polynomial (3d) model performed best among the compared models.
# SEM Studies Assessing the Efficacy of Laser Treatment for Primary Teetis: A Systematic Review
## 3 Results
### Main Study Outcomes
#### 3.3.2. Enamel Surface Characterization  
Treating the enamel surface with Nd:YAG [32; 33; 34; 35; 36], Er,Cr:YSGG [15; 28; 29; 31], or Er:YAG [18; 19; 22; 23; 25] lasers results in a rough, irregular surface, cracks, craters, and ablation areas in the tissue. This is supported by the findings of [PERSON] et al. [15], [PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [33], [PERSON] et al. [18], [PERSON] et al. [19], [PERSON] et al. [22], [PERSON] et al. [23], [PERSON] et al. [34], [PERSON] et al. [25], and [PERSON] et al. [31]. According to [PERSON] et al. [25], the irregularity of the enamel surface increases with higher frequency and energy settings in used lasers. Several articles that assessed the enamel surface under SEM focused on the usage of fluoride before or after laser treatment to improve the evenness of the surface. [PERSON] et al. [32], [PERSON] et al. [33], and [PERSON] et al. [34] agreed that the smoothness was greater with the use of fluoride. [PERSON] et al. [15] reported an image of a honeycomb with a cloudy surface in the fluoride-treated group. [PERSON] et al. [34] compared sequences of such treatment and reported better results when treating enamel with a laser first and then applying fluoride than the other way around. [PERSON] et al. [15] stated that the craters that appeared after laser treatment were deeper in primary teeth than in permanent dentition.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 4 Problem Formulation and Solution
### Problem Analysis and Solution
#### 4.2.2 Time Resource Allocation Problem  
Problem \(P2\) can be proven to be a convex optimization problem. The fourth term in the objective function \(F(x)=\frac{1}{{x_{ij}}}\psi(x)\) of problem \(P2\) is continuous in the range of \(x\in[0,1]\). It has first and second derivatives in \(x\in(0,1)\), and \(F(x)^{\prime\prime}>0\) can be obtained by derivation. Therefore, the fourth term in the objective function is a concave function, and after adding the minus sign before the parentheses, the fourth term becomes a convex function. The second term is a linear function of variables \(b_{j}\), and the remaining first and third terms do not contain variables, so the objective function of \(P2\) is a convex function. At the same time, because the corresponding constraints are convex, problem \(P2\) is a convex optimization problem. We can use the Lagrangian method and the KKT condition in the convex optimization method [39] to solve it. Moreover, the Lambert function is used [40]. After solving, it can be obtained as follows:  
\[b_{j}^{*}=\frac{\theta\ R_{j}\ln 2}{G\ T(W\!\left(-\frac{1}{e}+\frac{\mu^{P} \ C_{\alpha}\ c_{j}}{e\ C}\right)+1)} \tag{12}\]  
\[a^{*}=1-\sum_{j\in U_{1}}\frac{\theta\ R_{j}\ln 2}{GT(W\!\left(-\frac{1}{e}+ \frac{\mu^{P}\ C_{\alpha}c_{j}}{e\ C}\right)+1)} \tag{13}\]  
Thus far, we have solved the optimal time allocation under the given task offloading decision \(U\).
Evaluation of the Potential of Natural Mining By-Products as Constituents of Stabilized Rammed Earth Building Materials  
[PERSON]  
1 Institute of Smart Cities (ISC), Department of Engineering, Public University of Navarre (UPNA), 31006 Pamplona, Spain; [PERSON]  
referenced in the RE literature; however, these zones should not be considered strict rules, as practical experience has shown that soils outside these recommended zones have still performed well in practice, while some that met the criteria have not [10; 11; 12]. In some cases, the reason for not achieving the desired properties is related to the clay content or type. In this situation it is common practice to stabilize the mix by adding a material that can act as a supplementary binder to enhance its performance, resulting in what is known as Stabilized Rammed Earth (SRE). Various researchers have also observed that modifying the soil's PSD by adding other granulometric materials can enhance the properties of the soil in SRE building [13; 14; 15]. This is particularly relevant as it allows for the use of mining by-products to adjust the soil's PSD, as has been achieved in other construction methods [16; 17; 18]. In this way, large quantities of mining by-products or waste that currently end up in landfills could be valorized. In fact, the mining industry is one of the largest contributors to global waste production [19], generating approximately 100 billion tons per year worldwide [20]. In the European Union, waste from this industry accounts for around 22.7% of the total waste generated [21]. Therefore, finding a practical application for these by-products is of significant interest, in order to develop sustainable solutions that preserve a healthy environment [22].  
Thus, in this research, different mining by-products were used to modify the PSD of a soil to evaluate their potential in SRE building. A laboratory experimental campaign was conducted, in which three different mixes were manufactured (a mix composed entirely of clayey soil, a mix consisting of mining by-products and clayey soil, and a mix entirely based on mining by-products), along with specimens of both unstabilized and stabilized mixes using two cement dosages (2.5% and 5%). The testing methodology was carried out following the new methodology to characterize SRE materials explained in [23]. The Standard Proctor (SP) test was employed to determine the Optimal Moisture Content (OMC) of each combination. Unconfined Compressive Strength (UCS) tests were conducted at 7, 28, and 90 days to study the evolution of the mechanical properties of the mixes. To assess the durability of the combinations, two different durability tests were performed: a UCS test at 28 days under soaked conditions to analyze the sensitivity of the mixes to liquid water, and a wetting and drying test at 28 days to determine the erosion withstanding capability of the combinations.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 5 Conclusions and Outlook
### Current Challenges
Meta-learning frameworks like PGML [34] have shown promise in few-shot adaptation and have considered the model's recognition performance when facing new samples. Future research needs to continue the diversified development in this area. The lack of a unified evaluation metric system complicates cross-study comparisons, as accuracy claims vary with the SNR range. 5. Real-time processing and energy efficiency trade-offs Real-time MFR in dynamic channels, such as vehicular VLC, requires sub-millisecond latency, which conflicts with the computational overhead of complex feature fusion and attention mechanisms. In terms of energy efficiency optimization, existing research has not yet broken through the strong correlation between algorithm complexity and hardware power consumption. Emerging architectures such as neuromorphic computing, although theoretically possessing event-driven and ultra-low power consumption characteristics, are still rarely applied in the VLC-MFR field. 6. Standardization and cross-layer design gap The fragmented development of VLC-MFR technology has to some extent hindered its engineering process. Compared with the established standardized datasets in the RFcommunication field, such as RadioML and HisarMod, VLC-MFR relies on fragmented, non-reproducible, and non-public local scenario datasets. This data heterogeneity makes performance comparisons between different studies more complex. The absence of cross-layer co-design mechanisms further constrains system-level performance optimization. There is a deep coupling relationship between physical-layer modulation recognition and upper-layer adaptive modulation and coding (AMC), hybrid automatic repeat request (HARQ), but existing research mostly adopts isolated optimization strategies. Establishing a joint optimization framework across the physical and link layers has become a key technical bottleneck for achieving 6G global coverage. These challenges highlight the necessity for a paradigm shift in VLC-MFR research, elevating robustness, efficiency, and cross-domain collaboration to the same level of importance as incremental accuracy improvements. Future work should primarily focus on bridging the gap between controlled experiments and real-world deployments to accelerate technological maturity.
## 5 Simulation and Results Analysis  
### Simulation Model  
In order to analyze the performance of the IA-SPM, the detection probability of the cognitive radio network is calculated.  
Let \(N\) be the number of samples in silence and \(\varepsilon\) be the selected detection threshold. The detection probability of the system can be expressed as [20]  
\[P_{D}=\frac{1}{2}\times\frac{erfc(\varepsilon-N-N\times\gamma)}{\sqrt{(2\times (2\times N+4\times N\times\gamma))}} \tag{13}\]  
where \(\gamma\) represents the power ratio between the PU signal and the SU signal. In this paper, it is named the SNR. It is well known that in the time-varying channel, the SNR is time-varying. In order to ensure that the study is not affected by other factors, we assume that the SNR is time varying in different periods and that the SNR is kept constant in the same period. The detection points have different energy-aware samples in different periods.  
The state equilibrium equation of the Markov chain represented by the Equation (8) can be used to solve the detection probability  
\[P_{(D,i)}=\frac{1}{2}\times\frac{erfc(\varepsilon_{i}-N_{i}-N_{i}\times\gamma _{i})}{\sqrt{(2\times(2\times N_{i}+4\times N_{i}\times\gamma_{i}))}} \tag{14}\]  
\[P_{D}=\sum_{i=1}^{k}\frac{\lambda_{(1,i)}P_{(D,i)}}{\sum_{j=1}^{k}\lambda_{(1,j)}} \tag{15}\]  
Assuming that each silence period is \(\tau_{i}\) and the sensing period is \(T\), the signal will be sampled at the Nyquist sampling frequency \(f_{s}\). Then we can get the following relationship between the silence period and the number of samples per period  
\[N_{i}=\tau_{i}\times f_{s} \tag{16}\]  
Since the impulse response of the fading channel is time-varying, the SNR of the channel varies randomly within a finite interval. Assuming that the SU uses the energy detection method for spectrum sensing and that the SUs do not know the current SNR, the threshold for energy detection should be represented by the equation  
\[\varepsilon=\left(N+\sqrt{(2\times N)}\times Q^{-1}\left(P_{f}\right)\right) \tag{17}\]  
where \(P_{f}\) is the false dismissal probability.
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 2 Modeling and Assumptions
### Definition of Two Categories of Robustness  
In the presence of uncertainty, robust schedules are expected to maintain their original state or performance under an uncertain environment. [PERSON] and [PERSON] [29] distinguished scheduling robustness into quality robustness (QR) and solution robustness (SR). Quality robustness is defined as the difference between the makespan of the actual schedule and the initial schedule. Solution robustness refers to the similarity between the actual schedule and the initial schedule.  
Quality robustness is calculated as follows:  
\[QR\big{(}\delta_{p}\big{)}=E\big{[}\big{|}C_{max}(\delta_{r})-C_{max}\big{(} \delta_{p}\big{)}\big{|}\big{]} \tag{1}\]  
where \(C_{max}(\delta_{p})\) is the makespan of the initial schedule, \(\delta_{r}\) is the actual schedule when \(\delta_{p}\) after disruption, and \(C_{max}(\delta_{r})\) is the makespan of the actual schedule \(\delta_{r}\).  
Since \(\delta_{p}\) is affected by RMBs, \(C_{max}(\delta_{r})\) is only more likely to be longer; that is, \(C_{max}(\delta_{r})\geq C_{max}(\delta_{p})\). Meanwhile, once the schedule is determined, \(C_{max}(\delta_{p})\) is constant. Quality robustness \(QR\big{(}\delta_{p}\big{)}\) can be rewritten as follows:  
\[QR\big{(}\delta_{p}\big{)}=E[C_{max}(\delta_{r})]-C_{max}\big{(}\delta_{p}\big{)} \tag{2}\]  
In this research, we use the sum of the operational completion time difference between initial and actual scheduling to indicate the solution's robustness. The solution robustness can be written as follows:  
\[SR\big{(}\delta_{p}\big{)}=E\big{[}\sum_{i=1}^{n}\sum_{j=1}^{m}\big{(}\big{|} C_{i,j}(\delta_{r})-C_{i,j}\big{(}\delta_{p}\big{)}\big{|}\big{)}\big{]} \tag{3}\]  
where \(C_{i,j}\big{(}\delta_{p}\big{)}\) is the completion time of \(O_{i,j}\) in the initial schedule \(\delta_{p}\) and \(C_{i,j}(\delta_{r})\) is the completion time of \(O_{i,j}\) in the actual schedule \(\delta_{r}\).  
When RMB takes place on an operation, the completion time of the operation will be directly pushed back, as \(C_{i,j}(\delta_{r})\geq C_{i,j}(\delta_{p})\). \(C_{i,j}(\delta_{p})\) is constant once the schedule is determined. Solution robustness, \(SR\big{(}\delta_{p}\big{)}\), can be rewritten as follows:  
\[SR\big{(}\delta_{p}\big{)}=\sum_{i=1}^{n}\sum_{j=1}^{m}\big{(}E\big{[}C_{i,j} (\delta_{r})\big{]}-C_{i,j}\big{(}\delta_{p}\big{)}\big{)} \tag{4}\]  
It is clear that \(C_{max}(\delta_{r})\) is equal to the maximum completion time of the last scheduled operation in the actual schedule \(\delta_{r}\). Thus, the two types of scheduling robustness evaluation can transform into solving the expected completion time of the operation. However, before the completion of production, the actual completion time of each operation cannot be obtained, which makes it difficult to evaluate the scheduling robustness.
## 4 Experimental Results
### Possible Applications
Thus, the QR code may be read (for example, with a smartphone app), the signature and the compressed data extracted from the Base45 encoding, and finally the signature can be verified: if the signature is correct the compressed data are decompressed and the original record is shown to the user to give her the information on the tagged object. Another application of the proposed method is the embedding of extra data into Base85 encoded files: metadata, signatures, integrity protection data are examples of information that can use the extra space obtained with the proposed framework.