esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
## 4 The Proposed Scheduling
### CEM-RM Algorithm
We assume that the furthest leaf node is four hops away from the gateway in all topology structures. In industrial wireless networks, since there is the requirement of reliable and real-time performance, on the one hand, the big hop size can increase the rate of packet loss and, on the other hand, the large network cannot cater the demand of transmission deadlines. In our experience, four hops can overlap the general factory. Meanwhile, four hops can exactly overlap the experimental factory in this paper. The difference between each topology structure is that the number of nodes on each hop is generated according to different probabilities. For example, to generate one of the Tp1 topologies, we first traverse all the nodes, where the total number of nodes is determined previously. The traverse approach stipulates that a node has a 0.3 probability of being on the first hop, a 0.3 probability of being on the second, a 0.3 probability of being on the third, and a 0.1 probability of being on the fourth. Then, each node on the lower hop randomly selects two higher-level nodes as their parents to be the major path and redundant path. Thus, all the topologies of the Tp1 structure may be different from each other. Without loss of generality, we will stochastically generate enough topologies of every kind of structure. With the same method, the other kinds of topologies are generated according to the corresponding probabilities, which are listed in \[\sum_{i=0}^{k-1}|X_{i}|\cdot\left\lfloor\frac{p_{k}}{p_{i}}\right\rfloor+|X_{ k}|\leq\left\lfloor\frac{p_{k}}{\kappa}\right\rfloor\cdot|C|,\forall F_{k}\in F^{\prime} \tag{10}\]5
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD
### Navigation
#### 3.4.4 Command Executor  
In opposition to the traditional philosophy of an MPC, the result of the _Guidance_ module is not directly fed to the machine's actuators. It is first filtered and merged with previous results of the optimization process in order to always keep a consistent queue of commands that will sustain the operation of the vehicle for a short period of time. This filtering is carried out by the _Command Executor_ module. The goal of this module is to ensure that the signals sent to the actuators will be appropriate, both for avoiding long-term damage of the devices involved and also for keeping the operation running as expected.  
The _Command Executor's_ input is a \"trajectory\" of commands to be executed at specific times. Each command of the trajectory is inserted in a command queue. The queue insertion process entails finding the time at which the current command is to be inserted, erasing any command previously queued from that moment onwards. Then, the new command is appended at the end of the queue, effectively overriding outdated directives.  
Before the _Command Executor_ issues a new command to the machine actuators, the upcoming command is filtered. The velocity command \(u_{\
u}\) is limited to a maximum value \(u_{\
u,max}\) and a \"dead zone\" is applied to the steering command \(u_{\omega}\), namely:  
\[u_{\
u}=\begin{cases}u_{\
u}&if\ u_{\
u}<u_{\
u,max}\\ u_{\
u,max}&if\ u_{\
u,max}<u_{\
u}\end{cases} \tag{20}\]  
\[u_{\omega}=\begin{cases}u_{\omega}&if\ u_{\omega}<-u_{\omega,\ \mathit{min}}\ \ \textit{or}\ u_{\omega,\ \mathit{min}}\ <u_{\omega}\\ 0&if-u_{\omega,\ \mathit{min}}\ <u_{\omega}<u_{\omega,\ \mathit{min}}\end{cases} \tag{21}\]  
where \(u_{\omega,\ \mathit{min}}\) is a predefined constant value for the steering command \"dead zone\" and \(u_{\
u,max}\) is a value computed, so that if the machine were to be commanded to stop at the present time, it would effectively stop before the last queued command. That is, given a command queue with a total duration of \(Q_{dt}\) seconds and a machine deceleration of \(\overline{D}_{v}\) meters per second squared, then: \(u_{v,max}=\overline{D}_{v}\cdot Q_{dt}\), where \(\overline{D}_{v}\) is the mean deceleration of the machine when a full brake is applied, a parameter that can be determined experimentally.  
A diagram of the described process is shown in Figure 11 for a single command of the input command trajectory. As mentioned, the same steps are executed for all elements.
# Bacteriotherapy in Breast Cancer
## 4 Bacteria as a Carrier for Cancer Therapeutic Agents
### Lactobacillus plantarum
(ii) Lysosome fuses with phagosome to form a phagolysosome, where Lm can be killed and lysis, then, leading to loading of its antigens onto MHC class II for activation of CD4+ helper T cells. (iii) Lm can express the pore-forming toxin listeriolysin O (LLO) and two listerial phospholipases, PI-PLC and PC-PLC, to perforate phagosomes and gain entry into the cytosol. (iv) The _Listeria_-based vaccine has a dual mode of action, first, it is able to kill tumor cell via activation of NADP+ oxidase and, secondly, raise the intracellular level of Ca 2+, both resulting in the production of high ROS levels. A high level of ROS in tumor cell is able to induce the activation and maturation of dendritic cells (DC) which caused activation of CD8+ cytotoxic T lymphocytes. (v) Lm antigens into the cytosol where they can be degraded by proteasomes and loaded onto MHC class I for activation of TAA-specific (tumor-associated antigens) CD8+ cytotoxic T lymphocytes. (vi) Additionally, Lm is able to cell-to-cell spread via the formation of actin tails. This process results in the formation of double-membraned vacuoles from which the bacteria rapidly free themselves by LLO and cooperative action of the two listerial phospholipases PI-PLC and PC-PLC. Cancer stem cells (CSC) present in solid tumors and hematological malignancies have direct and indirect roles in the induction of metastasis [122]. CSCs can be used for molecular targeting with therapeutic potential for the effective cancer therapy [123]. Several transcription factors and signaling pathways participate in supporting the CSCs, such as hypoxia-inducible factor (HIF), which facilitates the transcriptional responses in both normal and cancer tissues under hypoxia conditions, and also regulates the tumorigenic potential of glioma stem cells in hypoxic conditions. Furthermore, they can induce particular signaling pathways and transcription factors, including Notch and Oct4, which are associated with the self-restoration of stem cells and multipotency [105; 124]. _Lactobacillus rhamns_ belongs to the genus _Lactobacillus_, which can downregulate the expression of HIF-1\(\alpha\) in breast cancer cell lines such as MDA-MB-231. HIF-1\(\alpha\) signaling in the stem cells of mouse lymphoma and human acute myeloid leukemia has a specific activity and effects on the responsible inhibitors for inhibiting the preferential eradication of CSCs in the mouse models. _L. rhamns_ with modulation of HIF-1\(\alpha\) signaling can be used as a therapeutic agent (Table 4) [105].
## 5 Conclusions  
Generally, the performance of semantic segmentation networks relies on two key factors: feature extraction ability and feature interpretation ability. In this paper, we reflect on traditional semantic segmentation models and propose a new network named MCDCNet that can achieve efficient semantic segmentation. MCDCNet employs a ViT-based module as the backbone to efficiently capture rich semantic information from input images, thereby enhancing the network's feature extraction ability. Moreover, MCDCNet improves the spatial feature learning process with the proposed SEM. To better leverage these features, a novel inference process is proposed to enable the IAM-based decoder to replace traditional methods in semantic segmentation, which further strengthens the network's feature interpretation ability. We demonstrate the excellent performance of MCDCNet with extensive experiments. During the experimental process, we found that blindly expanding the size of the network does not necessarily improve its performance. Therefore, in the future, we will test our network architecture in larger-scale networks and promote the application of the ideas presented in this paper for semantic segmentation. Particularly, we anticipate that this method can be further applied to cancer disease studies using biomedical images, providing strong support for early diagnosis and treatment of the disease [50]. Research in this field not only holds profound scientific significance but also directly relates to human health and well-being.  
Conceptualization, [PERSON], [PERSON] and B.S.; methodology, [PERSON], J.W. and B.S.; software, [PERSON], J.W. and B.S.; validation, [PERSON], B.S. and G.W.; formal analysis, [PERSON], B.S., G.W., X.Z., B.C. and T.L.; investigation, [PERSON], B.S., G.W., X.Z., B.C. and T.L.; resources, [PERSON], J.W. and B.S.; writing original draft preparation, [PERSON], G.W. and B.S.; writing - review and editing. [PERSON], G.W., B.S., X.Z., B.C. and T.L.; visualization, [PERSON] and G.W.; supervision, [PERSON]; project administration, G.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Experimental Research on In Situ Uniaxial Tensile Response of Silica-Based PLC Optical Splitters
## 4 Conclusions  
The mechanical and optical properties of the silica-based 1 \(\times\) 8 PLC optical splitter during the entire loading process until fracture were studied through in situ uniaxial tensile experiments. During the tensile test, the IL of the samples was measured in real-time to explore the in situ damage monitoring capability of PLC optical splitters. The following specific conclusions can be drawn:  
* Under uniaxial tensile loading, the tensile strength of the PLC optical splitter shows dependence on the loading rate. This phenomenon may be related to the viscoelastic effect of the adhesive layer material.
* The experimental results show that the fiber array-PLC chip adhesively-bonded joints are the weak areas of the PLC optical splitter. Compared with the adhesively-bonded 1-channel input fiber array-PLC chip joint, the adhesively-bonded 8-channel output fiber array-PLC chip joint is more susceptible to damage.
* The failure modes of the adhesively-bonded fiber array-PLC chip joints of all the samples showed mixed failure modes. Among them, cohesive failure usually occurs near the v-groove and the optical fiber, and adhesive/interfacial failure often occurs at the edge of the bonding interface.
* Based on the experimental results and phenomena, the corresponding relationship between the \(\Delta\)IL at 1.55 \(\upmu\)m wavelength and PLC optical splitters damage degree is established. That is, the \(\Delta\)IL corresponding to light damage, moderate damage, and severe damage are (\(-\)5 dB; 0 dB], (\(-\)15 dB; \(-\)5 dB], (\(-\)0, \(-\)15 dB], respectively.
* This study shows that the local damage behavior of the PLC optical splitter is reflected in the optical activity, and the measurable optical performance index IL provides valuable information about the damage process of its internal materials. A method is proposed to indirectly assess the degree of mechanical damage of PLC optical splitters through measurable optical properties.  
Conceptualization, Y.Z.; Data curation, Y.Z., L.J. and J.C.; Formal analysis, Y.Z. and L.J.; Funding acquisition, Y.Z. and J.D.; Investigation, J.C. and J.L.; Methodology, Y.Z. and L.J.; Project administration, Y.Z.; Resources, J.L. and J.D.; Supervision, J.D.; Writing--original draft, Y.Z. and L.J.; Writing--review & editing, Y.Z., L.J., J.C. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Grant no. 52175445), the State Key Laboratory of High Performance Complex Manufacturing, Central South University (Grant no. ZZY)KT2020-09), and the Natural Science Foundation of Hunan Province, China (Grant no. 2020 JJ4247).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Research Results and Discussion
### The Maximum of the Criterion Function--Distributions of the Random Variable of IFR and MTFR Classes
: _If the time function \(T_{1}\in\) IFR, \(\lambda_{1}\)(t) is differentiable, \(\lambda_{1}\)(\(\infty\)) \(\alpha\) ET\(T_{1}+\beta-\alpha<0\), \(p_{23}>(ET_{4}-ET_{2})\)/ET\({}_{3}\), then the analyzed criterion function g(x) reaches exactly one maximum value._  
**Proof of Conclusion 4. **  
In the case of the availability factor, the derivative of the criterion function is of the form:  
\[\text{g}^{\prime}(\text{x})=[\alpha\ [\text{f}_{1}(\text{x})\ \text{ET}_{1}(\text{x}) -\text{R}_{1}(\text{x})\ \text{F}_{1}(\text{x})]+\beta\ \text{R}_{1}(\text{x})]/M^{2}(\text{x})\]  
where  
M(x) is the denominator of the criterion function, g(x). \(\square\)  
The function H(x) increases if the damage intensity function, \(\lambda_{1}\)(t), increases. The derivative has the same sign as the base function:  
\[\text{h}(\text{x})=\alpha\ [\lambda_{1}(\text{x})\ \text{ET}_{1}(\text{x}) -\text{F}_{1}(\text{x})]+\beta\]  
It is known that H(0+) = 0, hence h(0+) = \(\beta>0\). From the fact that p\({}_{23}>(\text{ET}_{4}-\text{ET}_{2})/\text{ET}_{3}\), it results that \(\alpha<0\) and the function h(x) decreases from the value h(0+) = \(\beta>0\) to the value h(\(\infty\)). If h(\(\infty\)) = \(\lambda_{1}\)(\(\infty\)) \(\alpha\) ET\({}_{1}+\beta-\alpha<0\), it means that the derivative of g'(x) changes sign exactly once from \"+\" to \"\(-\)\". Thus, it is concluded that the availability factor g(x) reaches exactly one maximum. If \(\lambda_{1}\)(\(\infty\)) = \(\infty\), then the following conditions are sufficient for the existence of a maximum availability factor: T\({}_{1}\)\(\in\) IFR, p\({}_{23}>(\text{ET}_{4}-\text{ET}_{2})/\text{ET}_{3}\).
## 4 Evaluation and Experiments
### Experimental Setup  
The experimental setup encompassed both software and hardware configurations designed to ensure reproducibility and facilitate rigorous evaluation. The AI-driven label redaction system was implemented using Python 3.9. It leveraged the EasyOCR library (version 1.7.1) [9] and the Tesseract library (version 5.3.3 via pytesseract wrapper) [10] for text extraction, and the 'littellm' library (version 1.16.14) [24] for simplified, provider-agnostic interaction with various LLMs. Image processing tasks were performed using OpenCV (version 4.9.0) [25]. Experiments were conducted on a dedicated workstation equipped with an NVIDIA GeForce RTX 4090 GPU with 24 GB of VRAM, providing sufficient computational resources for efficient OCR processing and LLM inference.  
For redaction, we employed black masking, overlaying solid black rectangles on identified personal data bounding boxes to ensure unambiguous and visually clear concealment.  
The evaluation procedure involved iterating through each label image in the annotated dataset. This included performing text extraction using both EasyOCR and Tesseract, combining their outputs, feeding the extracted text segments to the Qwen (Qwen/Qwen2.5-72B-Instruct-AWQ) LLM [8] for PDPA-aware classification, applying black masking based on the LLM's output, and subsequently calculating the predefined OCR performance and redaction effectiveness metrics by comparing the system's output against the created ground truth annotations. We also employed Gemini 1.5 Flash to provide explanations for a subset of the labels, assessing its ability to interpret the label content and identify potential PDPA violations in a different context (explanation rather than redaction).
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 5 Experimental Results and Analysis
### Comparison of Computing Time  
Figure 8 shows comparison of computing times of different deblurring methods. Although our method, HTV-Lp, is slower than the others, the computing time is within 3.5 s, thus it is effective to employ convolution and the FFT theorem to avoid large computational complexity.  
Figure 8: _Cont._  
Figure 7: Comparison of drawing charts. (**a**–**h**) are the drawing charts of original “Store” image, degraded “Store” image with Gaussian blur (G, 5, 5, 7) and Gaussian noise standard deviation of 5, deblurring results of ATV, ITV, ATpV, FTV4 Lp, Hybrid TV, and HTV-Lp methods, respectively.
## 5 Results
### Weight Clustering  
To implement weight clustering, the cluster weight function was used from the TensorFlow optimization toolkit. All layers were clustered, except the first layer of GBRAS-Net. The maximum number of clusters was 32. We assessed the same optimization strategy with four different centroid initializations: density-based [53], K-means++ [54; 55], linear, and random. Tables 9-12 show all results for S-UNIWARD and WOW, for both 0.2 and 0.4 bpp.  
Tables 9 and 10 show the results for the S-UNIWARD algorithm at 0.4 bpp and 0.2 bpp, respectively. For S-UNIWARD at 0.4 bpp, when choosing the centroid initialization to be density-based, the model achieved a slight increase of 0.03% in terms of accuracy when compared to GBRAS-Net, while it could save approximately 73% of memory storage. When setting the centroid initialization as K-means++, the model experienced a slight accuracy decrease of 0.33%, with the same storage requirements as for the density-based centroid initialization. Table 10 shows that S-UNIWARD at 0.2 bpp experienced an accuracy decrease of more than 4% for all scenarios.  
Tables 11 and 12 show the results for the WOW algorithm at 0.4 bpp and 0.2 bpp, respectively. For the WOW algorithm at 0.4 bpp, when setting the centroid initialization to random, the model obtained accuracy of 89.20%, resulting in a decrease of 0.60% when compared with the original model. This configuration achieved memory storage of 0.13 MB, which was one fifth of that of GBRAS-Net. At 0.2 bpp, the best results in terms of accuracy were obtained when setting the centroid initialization as K-means++, with 78.05%, resulting in a decrease of 2.25% in accuracy and a memory requirement of 0.  
obtained by setting the pruning ratio as 50% or 70%, with 73.58% and 74.15% accuracy, respectively. In particular, by setting the pruning ratio to 70%, an increase of 0.55% in accuracy was obtained over the original model, with memory storage of 0.28 MB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategy** & **Accuracy** & **Memory Requirement** \\ \hline GBRAS-Net & 89.80\% & 0.65 MB \\ Pruning ratio: 20\% & 87.98\% & 0.54 MB \\ Pruning ratio: 30\% & 89.24\% & 0.50 MB \\ Pruning ratio: 40\% & 88.77\% & 0.45 MB \\ Pruning ratio: 50\% & 89.02\% & 0.39 MB \\ Pruning ratio: 60\% & 88.91\% & 0.34 MB \\ Pruning ratio: 70\% & 89.27\% & 0.28 MB \\ Pruning ratio: 80\% & 88.66\% & 0.22 MB \\ Pruning ratio: 90\% & 85.15\% & 0.16 MB \\ \hline \hline \end{tabular}
\end{table}
Table 15: Low-magnitude pruning applied to GBRAS-Net for WOW at 0.4 bpp.
Table 15 shows the results of the simulations with this strategy applied to WOW at 0.4 bpp. The best result was achieved by setting the pruning ratio at 70%, with accuracy of 89.27%, while obtaining a model size of 0.28 MB. The results for this strategy applied to WOW at 0.2 bpp are presented in Table 16. By defining the pruning ratio as 60%, it was possible to obtain slightly better accuracy compared to GBRAS-Net. In particular, we observed an increase of 0.16% in accuracy when compared with GBRAS-Net, while the model was compressed by approximately two times.
## 4 Conclusions  
This study experimentally evaluated the improvement of the initial hydration reactivity and carbonation resistance of a mortar mixture produced by partially substituting cement with BFS and adding an alkaline aqueous solution obtained through an electrolytic process in which potassium carbonate (K\({}_{2}\)CO\({}_{3}\)) was used as an electrolyte. The conclusions drawn from this study are as follows:  
Figure 17: Porosity results for BFS25 and ABFS25.  
Figure 18: Porosity results for BFS50 and ABFS50.  
1. The compressive strength at all ages decreased as the BFS replacement ratio increased. For mortar mixed with BFS, using the alkaline solution as mixing water led to a higher level of strength compared to that obtained when using pure water because the former created an alkaline environment inside the mortar; thus, the glassy film on the surface of the BFS particles was destroyed, improving the hydration reactivity.
2. From the SEM analysis results, it was confirmed that alkaline water destroyed the film on the surface of the BFS particles, thereby eluting the reactive materials in the BFS, resulting in more hydration products.
3. The accelerated carbonation test results confirmed that the mortar mixed using alkaline water had a higher level of carbonation resistance than the mortar mixed using conventional water because the carbonation proceeded slowly for the hardened body that used alkaline water. The internal pores became smaller and affected the permeation of the carbon gas, as shown in the MIP test results at 7 d of age.  
This study utilized an electrolysis alkaline aqueous solution as mixing water to improve the initial strength of mortar produced by substituting a specific cement content with BFS, a byproduct of the steel industry. Because this study has limitations in experimental factors and levels, more experiments are required in the future.  
Conceptualization, S.J. and J.K.; formal analysis, S.J.; experiment, S.J. and J.K. and H.K.; writing--original draft preparation, S.J.; writing--review and editing, S.J.; visualization, S.J. and J.K.; supervision, S.P.; project administration, S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF), grant funded by the South Korean government (MSIT) (No. NRF-2020R1A2C1011957).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Methodology
### Basic Watermarking Procedure
#### 2.1.2 Robustness Evaluation Index  
Evaluating a watermarking algorithm is commonly performed from several perspectives, e.g., peak signal-to-noise ratio (_PSNR_) is used to measure the degradation of image fidelity [13; 36], false negative probability (missed detection) or false positive probability (false alarms) are used to evaluate the authority and authentication of watermarking algorithms [15; 37]. Regarding robustness, it is common to use _NC_[10; 17; 23] or _BER_[17; 24] to compare the similarity between the extracted watermark sequence with the embedded one in a quantitative method. For further evaluation, taking _BER_ as an example, assume \(wm\) is the original watermark sequence and \(wm^{\prime\prime}\) is the extracted, _BER_ is calculated as:  
\[BER=\frac{1}{L}\sum_{i=1}^{L}{NOR(wm^{\prime\prime}[i],wm[i])}, \tag{2}\]  
where _NOR_ is the operation of exclusive _OR_. \(BER=0\) means there is no error in the extracted watermark. Commonly the threshold of _BER_ is set previously and empirically in robustness experiments.