esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Reinforcement Learning-Based Scheduling  
In the context of Industry 4.0, there are several applications of reinforcement learning in process control and scheduling, ranging from guiding robots [53] to devising policies [54]. These applications are mostly aimed at tackling either job-shop [55], or flow-shop scheduling [56]. For instance, [PERSON] et al. [57] presented a digital twin-integrated manufacturing environment, wherein a reinforcement learning framework optimizes flow-shop scheduling on the basis of machine availability. Furthermore, publications combine reinforcement learning with other methodologies, e.g., evolutionary algorithms, in an effort to tackle dynamic and complex scheduling problems. For example, [PERSON] et al. [58] focused on improving the job make-span and workload in uncertain manufacturing environments, coupling reinforcement learning decision-making with evolutionary algorithms. Addressing dynamic scheduling problem, [PERSON] et al. [59] presented a CPS that generates and compiles job scheduling rules by means of genetic programming and reinforcement learning, respectively. Finally, in terms of sustainable production, reinforcement learning has been applied to circular manufacturing environments, focusing on improving resilience and productivity. An example of such an application is presented in [60]. In this study, the authors coupled reinforcement learning with manufacturing control policies endeavoring to enhance the responsiveness of circular manufacturing system under fluctuating customer demand and recurring faults.  
Table 1 summarizes the contribution of the cited papers in the pertinent literature. According to this table, a large portion of studies focused on the derivation of policies that merely authorize single type of activities on the basis of specific sustainability aspect, such as energy consumption. To this end, they applied complex mathematical models, e.g., mixed integer model, that capture a snapshot of the investigated problem. Furthermore, the literature review has revealed that reinforcement learning is scarcely applied within the context of sustainable production. To this end, the present paper presents a machine learning-based decision-making mechanism integrated in a multi-stage manufacturing system. The proposed decision-making methodology integrates a RL method and lean green practices. Implementing these techniques, the proposed mechanism strives to evaluate the synergy between Industry 4.0 and lean green manufacturing concepts and within smart manufacturing system. The aim of such synergy is to reduce the impact of the considered systems upon the environment and improve sustainability with circular economy and lean thinking practices. To do so, the mechanism endeavors to jointly optimize processes related to manufacturing, remanufacturing, maintenance, raw-material procurement, and refurbishing.
## 3 Materials and Methods
### Materials
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **S1** \(\mid\) **Soil 1** & **S2** \(\mid\) **Clayey Soil** &
\begin{tabular}{c} **Resultant Soil Mix** \\ **S1** + **S2** = **S12** \\ \end{tabular} \\ \hline \multicolumn{4}{c}{Particle size distribution (\%)} \\ \hline Gravel (2–75 mm) & 48.6 & - & 28.2 \\ Coarse sand (0.425–2 mm) & 22.5 & - & 13.1 \\ Fine sand (0.075–0.425 mm) & 14.5 & - & 8.4 \\ Silt and clay (\textless{}0.075 mm) & 14.4 & 100 & 50.3 \\ \hline \multicolumn{4}{c}{Afterberg limits (\%)} \\ \hline Liquid limit (LL) & - & 52 & 38 \\ Plasticity limit (PL) & - & 22 & 20 \\ Plasticity index (PI) & NP * & 30 & 18 \\ \hline \multicolumn{4}{c}{Proctor compaction test} \\ \hline Optimum water content (w\({}_{\text{opt}}\)) (\%) & 6.02 & - & 13.4 \\ Maximum dry unit weight (\(\gamma_{\text{d,max}}\)) & 2290 & - & 1910 \\ (kg/m\({}^{3}\)) & & & \\ \hline AASHTO soil classification & A-3 & A-7-6 & A-6 \\ \hline \multicolumn{4}{c}{NP * = non-plastic.} \\ \end{tabular}
\end{table} 
Table 2: Properties of soils used for the investigation. In particular, the groups of signals at _m/z_ 343, 387 and 431 represent dimeric products, probably of the phenylcoumaran type, the resinol type, and the dimeric species of sinapyl alcohol (S), respectively. All the other signals can be ascribed to the superior homologue species (trimer, tetramer, pentamer, and hexamer) derived from oligomerization of monolignol coniferyl alcohol (G).
## 3 Results
### VHIopt Accuracy Evaluation
#### 3.2.2 Evaluation Based on SPEI03  
SPEI03 is well-suited for studying vegetation drought. To further evaluate the performance of VHIopt, it was compared with SPEI03 using R, RB, and RMSE. Figure A3 presents a scatter plot of SPEI03 versus VHIori and VHIopt. The figure shows that compared to VHIori, VHIopt demonstrates a significant increase in the correlation coefficient with SPEI03 across most vegetation types, with the exception of farmland. Specifically, the overall correlation coefficient in the YRB increased by 0.03, with improvements of 0.03 and 0.04 in forest and grassland areas, respectively. The R for farmland did not improve due to the limited extent of farmland vegetation in the YRB and its susceptibility to human influence. The RB between VHIopt and SMA decreased by 0.28, 0.21,0.22, 0.21, and 0.20 in forest, grassland, farmland, changing vegetation types, and total vegetation, respectively. Similarly, the RMSE between VHIopt and SMA decreased by 0.29, 0.24, 0.24, 0.24, and 0.23 for the same categories. These results indicate that VHIopt is more effective than VHIori in capturing variations in precipitation and temperature.  
Using the detrended VHIopt, SPEI03, and SMA, we constructed annual time series charts for the YRB and its upstream, midstream, and downstream from 2001 to 2021 to analyze the volatility of these indices more accurately. Figure 4 illustrates that VHIopt, SPEI03, and SMA exhibit similar temporal patterns across the YRB, its upstream, and midstream over the past 21 years. This consistency in annual fluctuations among the three indices further confirms that VHIopt effectively reflects the trends in agricultural drought within the YRB.
# Automatic False Alarm Detection Based on XAI and Reliability Analysis
## 2 Related Work  
### AI Cyber Threat Detection  
With the advancement of technology, Internet usage is increasing, and cyberattacks are also increasing. Most cyberattacks can be detected based on signatures, but unknown attacks are difficult to detect. To solve this problem, various AI technologies are being applied, and through this, cyberattacks such as malware and intrusion can be detected [7; 8]. [PERSON] et al. proposed a new, integrated hybrid deep learning and visualization approach for effective malware detection and experimentally calculated high classification accuracy to verify the performance of the proposed framework [9]. [PERSON] et al. proposed training an IDS model based on convolution neural networks (CNN), a typical deep learning method, using an IDS dataset [10]. A security expert should form an appropriate response depending on the analysis results of the events that have occurred, the logs, and the detection system. A wrong judgment can cause serious damage, so it is necessary to accurately distinguish between what is normal and what is an attack. However, there are many cases when the AI detection system is actually normal but predicts an attack (FN), or when there is actually an attack that is predicted to be normal (FP). There are two ways to respond to false alarms. The first is a method that reduces false alarms by increasing the accuracy of the model itself, while the second fine-tunes the post-processing of false alarms. Improving the performance of the model is difficult; furthermore, even if the performance improvement is successful and the accuracy is high, the performance may be inadequate to detect cyberattacks that feature future technological advances and evolving attack strategies. Therefore, it is better to improve the quality of false alarms through post-processing. There are studies that have succeeded in processing an alarm set based on the distribution of false alarms [11, 12]. Although the rate of false alarms in the overall alarm set has decreased, it can be seen that the number of true alarms has also decreased [13].
# Assessing the Impact of Varied Dark Chocolate Concentrations on Enamel and Dentine Microhardness
## 3 Results
### Comparision of Enamel and Dentin Microhardness between Pairs of Groups
The results indicated that there was no noteworthy distinction in surface hardness or mineral loss/gain between the group using bitter chocolate and the group using fluoride toothpaste [36]. Another in vitro investigation compared the antibacterial activity of three distinct toothpastes. First up is theobromine toothpaste, a fluoride-free chocolate-based toothpaste; the other two are two types of toothpaste with fluoride. Results revealed that toothpaste containing theobromine not only displayed a larger zone of microbial inhibitions when compared to other fluoride toothpastes, but also demonstrated enamel remineralization because nontoxic fluoride was absent. As a result, it blended easily with phosphate and calcium to speed up the process of remineralizing the enamel [37]. In addition, individuals with dental fluorosis, tooth discolorations, and hyperplasia of the enamel responded better to theobromine toothpaste [38]. Another study comparing the antibacterial properties of chlorexidine mouthwash with cacao bean husk extract mouthwash found equivalent substantial decreases in salivary Streptococcus mutans counts in both groups during every follow-up period, demonstrating that this mouthwash is an acceptable substitute for chlorexidine [39]. Historically, fluoride has been the initial choice in dental practice for preventive purposes [40]. More recently, casein phosphopeptide amorphous calcium phosphate [41] and biomimetic hydroxyapatite [42] have been introduced and have demonstrated encouraging outcomes. In the future, it would be intriguing to assess the synergistic effects of these fluoride-free products when used in conjunction with chocolate-based toothpastes and mouthwashes. The study has limitations, as it was carried out in a controlled laboratory setting, making it difficult to completely replicate the authentic characteristics of the human mouth. Consequently, the findings may not fully capture the complexities of real-world oral conditions. To enhance the relevance and applicability of the research, it would be advantageous to replicate similar studies in a natural oral environment.
# Design of Outer Membrane Vesicles as Cancer Vaccines: A New Toolkit for Cancer Therapy
## 3 New OMV Applications: Tumor Vaccines
### Advantages of OMVs as Cancer Vaccines
Taken together, the design of the location of tumor antigen is a complex process, which needs more research to elucidate and verify; especially, various antigen delivery strategies should pave the way for the genetic design of OMVs as cancer vaccines. A second factor contributing to OMV potency is their inherent adjuvant properties. Owing to the presence of PAMPs in OMVs, TLR-mediated recognition can occur, thereby driving the inflammatory response in conjunction with complement system activation [101]. PAMPs can also elicit potent Th1-skewed immune responses without the need to add additional adjuvants or delivery systems [83; 84]. A third aspect is the integrated delivery system. It is well known that tumor cells are ubiquitous, making the drug diffusion process difficult to control, which may lead to multiple-drug resistance [102]. However, two characteristics of OMVs can solve this problem. OMV size is on a nanometer scale (\(<\)200 nm), which means that they can extravasate into the tumor tissues via the leaky vessels using the enhanced permeability and retention effect; drugs can then be released into the vicinity of the tumor cells [103; 104]. OMVs are stable and rigid and are therefore not susceptible to drug leakage or degradation in the circulation that occurs commonly with polymeric or liposome-based carriers. OMVs also accumulate selectively in tumor tissue when administered systemically. After arriving at the destination, OMVs can kill tumor cells through direct and indirect effects. To deliver siRNA and other proteins or peptides that are toxic to tumor cells, OMVs can recognize and bind to target cells through ligand-receptor interactions. This occurs via components uniquely expressed on the cell surface [102], such as anti-HER\({}_{2}\) affibody and HER\({}_{2}\) (overexpressed in the tumor cells) [105]. In this context, bound carriers are internalized before the drug is released inside the cell. While OMVs can enter APCs via receptor-mediated entrance, transport of materials into cells through membrane fusion has also been reported [101]. More importantly, antigen and immune stimulators that enhance the intensity of immune responses and modulate the direction of the response could be delivered to the same APCs simultaneously, facilitating the production of a robust and effective antigen-specific immune response [101]. In conclusion, OMVs have their distinct immunological and structural features, including their nanometer-scale vesicle structure, self-adjuvant properties, ability to be genetically modified, capacity to present large exogenous proteins, and ability to carry immune stimulators. All these features make the OMV an ideal antigen carrier. Currently, many studies are underway to test the applicability of OMVs as vectors in cancer vaccines.
# FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion
## 3 Background
### DoH-encrypted DNS Covert Channel  
In this paper, we focus on DoH-encrypted DNS covert channels and domain name resolution using DoH in two cases: one is to use browsers such as Google, Firefox, etc., that support the DoH protocol, where all DNS traffic is directly encapsulated into a TLS encrypted HTTP message and sent to DoH servers, which are then forwarded to domain name servers on the Internet for resolution; the second is to use hosts that do not support the DoH protocol by building a local DoH proxy for forwarding (available proxy tools include QuantumlX, Surge, Loon, etc.). The host forwards all network requests to the local DoH proxy, the proxy will forward the DoH traffic to the Internet DoH server, and, finally, the DoH server will perform domain name resolution.  
As shown in Figure 1, in the C&C stage of an APT attack, the data carrier in the DoH-encrypted DNS covert channel does not use the DNS covert channel directly, but rather, the attack is implemented by encapsulating it into a TLS-encrypted HTTP message. Firstly, the victim host sends a DoH request containing the DNS covert channel domain name, _updata.tunnel.com_, through a local DoH proxy or directly to the DoH server. _Update_ refer to sensitive information leaked from the victim host or command requests sent to the attacker. Secondly, the DoH server parses the DNS request and performs an iterative query, which is eventually forwarded to a disguised authoritative domain name server controlled by the attacker, i.e., C&C Server. Finally, the attacker obtains the _updata_ sent by the victim host through the C&C server. The attacker also issues commands, i.e., _downloada_, through a disguised authoritative domain name server and delivers _downloada_ to the victim host by DNS response and DoH response.  
In general, the DoH-encrypted DNS covert communication between the attacker and the victim host is similar to the scenario of non-encrypted DNS covert communication: the principles of building DNS covert communication are the same. As shown in Figure 1, both _updata_ and _downdata_ are iteratively queried, and the disguised authoritative domain name server is used as the C&C server to relay between the attacker and the victim host.  
The difference between a non-encrypted DNS covert channel and a DoH-encrypted DNS covert channel is reflected in two aspects. First, as a data carrier for leakage and command and control, DNS covert channels in DoH traffic are encrypted, making it impossible to apply existing deep packet inspection techniques. Second, DNS covert channels are hidden in HTTPS traffic, and the DoH server acts as a local DNS server to forward DNS traffic, which also makes it impossible for the local network administrator to find the malicious activities of the victim through DNS. At the same time, the victim host reduces the frequency of DNS requests, reducing the suspicion of malicious activities. The above two characteristics bring a greater challenge for DoH-encrypted DNS covert channel detection.
# A Vulnerability Index for Multimodal Transportation Networks: The Case of Korea
## 5 Discussion  
We utilized the vulnerability function to quantitatively analyze Korea's transportation network, objectively assessing the structural characteristics of the network and regional disparities in vulnerability. The results show that central regions, such as Seoul, maintain network stability and serve as critical hubs, whereas most regional areas exhibit relatively lower vulnerability. This indicates stronger connectivity in these areas, emphasizing the need for policy efforts to address regional imbalances.  
In particular, we confirmed that the vulnerability function is an effective and objective tool for quantitatively evaluating the likelihood of disconnection for specific nodes or regions, unlike traditional centrality measures. Gyeonggi exhibited the highest level of structural vulnerability, indicating a strong reliance on concentrated connections within the transportation network. Although non-capital regions display lower structural vulnerability, their comparatively limited accessibility to transport services remains a critical concern for policy development.  
Through a review of the literature, we observed that previous approaches have often focused on centrality analysis and simulations. In contrast, we modeled vulnerability using graph theory and mathematical functions, allowing us to intuitively identify weaknesses in the network through the derived results. This analysis revealed the regional imbalances within the transportation network and demonstrated its potential to contribute significantly to improving the efficiency of regional transportation systems, both academically and in policymaking.  
The proposed methodology offers generalizability as a methodological advantage.  
Its structural foundation, independent of flow-specific assumptions, allows for application across varying spatial and modal transportation systems, providing a basis for comparative analysis. Future research should expand upon the proposed vulnerability function and analytical method by applying them to other countries or large-scale urban networks to compare vulnerabilities across different transportation environments. Additionally, policy simulations based on the proposed vulnerability index would provide  
Figure 5: Comparison of centrality and vulnerability by node.  
valuable insights for verifying the effectiveness of specific transportation improvement measures and policy interventions.  
In conclusion, we propose a robust tool for objectively and quantitatively evaluating the structural characteristics of transportation networks using the vulnerability function. This tool extends beyond academic analysis and provides a crucial foundation for practical transportation policy and regional development aimed at achieving balanced growth.
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 4 Experimental Results and Analysis
### Watermark Robustness
AWGN and JPEG compression could be ensured. Finally, the embedding of watermark information mainly depended on the statistical property of the histogram and was independent of the size of the image; thus, this proposed watermark method could be applied to images of any size.
## 2 The Applied Methodology
### Analysis: Maximum Power Conditions  
Recall again that the delay time, \(t_{1}\), in (25) is an independent variable set by the controller's command, whereas \(t_{2}\), given by (20), (21) is a function of \(t_{1}\) as well as the OLMEH's parameters and operation conditions. Thus, the question arises regarding the proper choice of the delay time to optimize/maximize the OLMEH's power output. Symmetry considerations, see Appendix A for details, suggest that the maximum output power can be attained when the power transfer window is aligned symmetrically to the line current peak:  
\[t_{2_{mpp}}=\frac{T}{2}-t_{1_{mpp}}, \tag{26}\]  
Substituting (26) into (25), the maximum power that can be obtained from the OLMEH-AR under the MPP condition is  
\[P_{ompp}=\frac{4\sqrt{2}}{\pi N}V_{b}I_{1_{rms}}\text{cos}\left(\omega t_{1_{ mpp}}\right), \tag{27}\]  
Whereas the optimum delay time, \(t_{1_{mpp}}\), required to implement the MPP condition is found by normalizing the condition (26) and substituting it into (15). Further manipulation of the expression leads to the following quadratic equation:  
\[\alpha_{mpp}t_{1 Nmpp}^{2}+\beta_{mpp}t_{1 Nmpp}+\gamma_{mpp}=0, \tag{28}\]  
where  
\[\alpha_{mpp}=-2\pi V_{N}(1+K_{1}I_{N}), \tag{29}\]  
\[\beta_{mpp}=-[(2K_{2}-3\pi)V_{N}+\pi K_{1}(2-V_{N})I_{N}], \tag{30}\]  
\[\gamma_{mpp}=(K_{2}-\pi)V_{N}, \tag{31}\]  
Only the positive solution for \(t_{1 Nmpp}\) has a physical meaning:  
\[t_{1 Nmpp}=\frac{-\beta_{mpp}-\sqrt{\beta_{mpp}^{2}-4\alpha_{mpp}\gamma_{mpp} }}{2\alpha_{mpp}}, \tag{32}\]  
Translating the normalized time (32) to the real-time yields:  
\[t_{1_{mpp}}=\frac{T}{2}t_{1_{Nmpp}}, \tag{33}\]  
The results of (32), and (33) allow for optimally locating the power transfer window for optimal energy harvesting; see (27).
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 4 Experiments and Analysis
### Ablation Experiment of Two Metrics
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **Loss** & **mAP** & **Training Time (min)** & **FLOPs (G)** \\ \hline \multirow{3}{*}{ResNet18} & CE & 69.48 \(\pm\) 1.54 & 1.05 & 2.375 \\  & NCE + RCE & 87.21 \(\pm\) 1.15 & 1.07 \(\times\) 12 & 2.375 \\  & A-NCE + RCE & 87.78 \(\pm\) 0.78 \({}^{\star}\) & 1.40 & 2.375 \\ \hline \multirow{3}{*}{ResNet50} & CE & 78.89 \(\pm\) 0.87 & 1.83 & 5.368 \\  & NCE + RCE & 92.56 \(\pm\) 0.31 & 1.88 \(\times\) 12 & 5.368 \\  & A-NCE + RCE & 93.00 \(\pm\) 0.32 \({}^{\star}\) & 2.98 & 5.368 \\ \hline \multirow{3}{*}{ResNet101} & CE & 79.55 \(\pm\) 0.79 & 2.73 & 10.230 \\  & NCE + RCE & 89.72 \(\pm\) 1.34 & 3.33 \(\times\) 12 & 10.230 \\  & A-NCE + RCE & 91.94 \(\pm\) 0.67 \({}^{\star}\) & 4.75 & 10.230 \\ \hline \multirow{3}{*}{DenseNet169} & CE & 87.46 \(\pm\) 0.17 & 2.40 & 4.436 \\  & NCE + RCE & 94.93 \(\pm\) 0.55 & 2.40 \(\times\) 12 & 4.436 \\  & A-NCE + RCE & 95.45 \(\pm\) 0.50 \({}^{\star}\) & 4.1 & 4.436 \\ \hline \multirow{3}{*}{MobileNetV3\_large} & CE & 81.18 \(\pm\) 0.86 & 0.95 & 0.292 \\  & NCE + RCE & 89.63 \(\pm\) 0.86 & 0.97 \(\times\) 12 & 0.292 \\  & A-NCE + RCE & 90.99 \(\pm\) 0.33 \({}^{\star}\) & 1.37 & 0.292 \\ \hline \multirow{3}{*}{MobileNetV3\_small} & CE & 75.43 \(\pm\) 1.13 & 0.68 & 0.076 \\  & NCE + RCE & 84.59 \(\pm\) 0.27 & 0.68 \(\times\) 12 & 0.076 \\ \cline{1-1}  & A-NCE + RCE & 87.80 \(\pm\) 0.15 \({}^{\star}\) & 0.88 & 0.076 \\ \hline \hline \end{tabular}  \({}^{\star}\)  
* *  
retrieval accuracy compared to using two or three hidden layers while also requiring fewer parameters and thus reducing calculation costs. In addition, a hidden layer with 100 neurons is better suited for fitting the relationship between training samples and weighting hyperparameters, resulting in a higher retrieval accuracy.