esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dental Biomaterials for VPT
### Recent Advancements in the Vital Pulp Therapy (Table 2)
#### 4.2.3 Laser Therapy in Vital Pulp Therapy  
Laser therapy has gained attention as a potential adjunct to traditional VPT techniques. Lasers, such as CO\({}_{2}\), erbium (e.g., Er: YAG, Er, and Cr: YSGG), and diode lasers, each with different wavelengths and clinical applications, can exert diverse biological effects on pulp tissue. These effects include bacterial reduction, coagulation and hemostasis, enhanced cellular proliferation, and stimulation of dentin bridge formation [51; 66]. However, reported clinical outcomes have been variable due to several factors, including differences in laser type and power settings, exposure duration, tissue penetration depth, operator technique, and case selection. Randomized controlled trials (RCTs) have demonstrated that laser-assisted VPT can result in reduced postoperative pain, accelerated healing, andimproved clinical success rates in some cases. Yet, other studies have reported no significant difference or inconsistent histological outcomes compared to conventional treatments.  
This variability underscores the need for standardized protocols and practitioner training. Furthermore, high-quality long-term studies are necessary to determine whether laser application consistently enhances clinical outcomes or if benefits are technique- and context-dependent [66]. Clinicians should weigh the potential benefits of laser disinfection and biological stimulation against practical considerations such as equipment cost, training requirements, and treatment complexity. Until more conclusive evidence is available, lasers may be best considered as an adjunct to, rather than a replacement for established VPT modalities.
# The Duality of Similarity and Metric Spaces
## Appendix A
### Proof of Theorem 7  
We must show that a distance equals the symmetric difference of two sets \(d(x,y)=\mu(x\triangle y)\)[21; 22; 23]. If \(\mu\) is a \(\sigma\)-finite measure on a \(\sigma\)-ring \(\mathbf{S}\), this function is pseudometric on \(\mathbf{S}\) (D1 and D2 must be satisfied), assuming \(x,y,z\in\mathbf{S}\)  
\[\begin{split} d(x,z)&=\mu(x\triangle z)=\mu(z \triangle x)=\mu((x\triangle y))\triangle(y\triangle z))\\ &\leq\mu((x\triangle y)\cup(y\triangle z))\\ &\leq\mu(x\triangle y)+\mu(y\triangle z)\\ &=d(x,y)+d(y,z)\end{split} \tag{10}\]  
The relation (D3) \(x\sim y\Longleftrightarrow d(x,y)=0\) is an equivalence relation on \(\mathbf{S}\), so \(d\) becomes a metric on the set \(\mathbf{S}\). We need also to prove sequential continuity with Cauchy sequence, i.e., \(\{x_{n}\}_{n\in\mathbb{N}_{0}}\),  
\[\lim_{n\to\infty}d(x_{n},x)=0\implies\lim_{n\to\infty}|\mu(x_{n})-\mu(x)|=0 \tag{11}\]  
This implies that  
\[\begin{split} d(x,y)&=|\mu(x)-\mu(y)|=|(\mu(x \setminus y)+\mu(x\cap y))-(\mu(x\cap y)+\mu(y\setminus x))|\\ &=|\mu(x\setminus y)-\mu(y\setminus x)|\\ &\leq|\mu(x\setminus y)|+|\mu(y\setminus x)|=\mu(x\setminus y )+\mu(y\setminus x)=\mu(x\setminus y)\cup\mu(y\setminus x)\\ &=\mu(x\triangle y)=\int|\chi_{x}-\chi_{y}|d\mu\end{split} \tag{12}\]  
We call \(d\) the symmetric difference metric. The symmetric difference between two sets can be considered a measure of how 'far apart' they are.
# Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
## 1 Introduction
As low-order mode shapes are relatively easy to obtain, combining them with CNNs could greatly increase the effectiveness of structural damage detection. At present, there are few studies combining CNNs with low order mode shapes. On the other hand, although CNNs have been applied to structural damage detection by many studies owing to its good performance, it was only used as a powerful \"black box\", its feature extraction mechanisms are to be explored, which will undoubtedly promote the further development of CNN methods. In this paper, the first order mode shape is taken as the input of a CNN to study the CNN accuracy in locating the damaged rods of a 3D steel truss. At the same time, because the curvature difference of the first order mode shape has distinguishable peak features at the damaged segment, this paper also takes it as the CNN input to study its locating accuracy. As the CNN has been widely used in SHM as a powerful \"black box\", it is necessary to have more in-depth understanding of its working mechanism, which is important for the further application of the CNN, as people can analyze whether the predicted results are accurate and identify the possible causes of errors, and even can obtain more damage information from measurement data through the CNN methods. To this end, this paper explores the basis of damage classification of the CNN by comparing the difference between the inputs and outputs of each convolutional layer. To use this powerful tool to find more structural damage indexes and the extraction methods to obtain these indexes, some internal working mechanisms of the CNN are studied.
# The Evolution of Volatile Memory Forensics
## 3 Memory Acquisition
### Acquisition Techniques
#### 3.2.2 Kernel Level  
At the kernel level, there are many techniques for acquisition, implementing tools as kernel drivers, generating crash dumps or hibernation files, and debuggers running on top of executables. Tools implemented as kernel drivers can typically be deployed post-incident, at the expense of some atomicity and integrity. _WinPmem_ (an open source, Windows-based memory dump tool included in the _Rekall_ suite) [14], _LiME_ (an open source Linux kernel module) [15], and _ProCump_ and _WinKD_ (Windows-based tools from Sysinternals which  
Figure 1: Taxonomy for memory acquisition methods as defined by [PERSON] et al. [4].  
contain several functionalities, including memory dump capabilities) [16, 17] are specific examples of tools implemented as kernel drivers. These tools can create memory dumps from the command line while a target program is running or be configured to watch for certain events, such as a process crash or window hang. Another technique, the utilization of crash dumps, relies on the operating systems' integrated memory acquisition abilities. When the system encounters a critical state, an image of the virtual memory is taken by the operating system for investigative reasons. Because crash dumps are built into the operating system (OS), they should be considered pre-incident, but the programs and the OS need to be properly configured in order to produce dumps. However, crash dumps are, by nature, terminating. Similarly, file hibernation is a built-in capability (and thus a pre-incident tool) that can be used for memory acquisition. When a computer hibernates, most of its physical memory is written to the disk in a hibernation file for storage while the power is not available. This file contains the volatile memory, and can thus be used for analysis as long as there is not full disk encryption on the system and the hibernation mode has been enabled. In Windows, for example, the hibernating file is located at \(C\):\(\backslash\)_hiberfil.sys_, needs no setup to produce, and can be read by _Volatility_[18].  
Lastly, software debuggers can be employed for memory acquisition because the debugger has full access to the debuggee's memory. Most modern operating systems come with debugging system calls that can accomplish this. A debugger can be deployed pre-incident by launching the debuggee from the debugger, or post-incident by attaching the debugger to a running process. The _GNU Project Debugger (GDB)_[19] is commonly used for UNIX systems and _WinDbg_[20] or _Visual Studio_[21] for Windows systems. _GDB_ can attach to a running process via the command line, or WinDbg can attach from its graphical user interface (GUI) menu.
## 3 Selection of Input Variables for Influencing Factors of Mechanical Properties of UHPC
### Box 1
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Water/Binder Ratio (\%)**} & \multicolumn{3}{c}{**Experimental Results (MPa)**} \\ \cline{2-4}  & **7d** & **14d** & **28d** \\ \hline
0.14 & 288.97 & 324.15 & 335.85 \\
0.15 & 294.82 & 328.15 & 338.51 \\
0.16 & 299.17 & 333.88 & 342.61 \\
0.17 & 307.42 & 337.15 & 351.31 \\
0.18 & 311.61 & 341.01 & 355.15 \\
0.19 & 317.46 & 347.16 & 359.14 \\
0.2 & 321.93 & 352.33 & 363.12 \\
0.21 & 315.61 & 341.05 & 355.15 \\
0.22 & 310.17 & 335.15 & 348.25 \\
0.23 & 302.15 & 331.95 & 340.91 \\
0.24 & 299.23 & 328.08 & 333.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effect of water/binder ratio on the compressive strength of UHPC. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Steel Fiber (\%)**} & \multicolumn{3}{c}{**Experimental Results (MPa)**} \\ \cline{2-4}  & **7d** & **14d** & **28d** \\ \hline
0 & 280.14 & 302.01 & 327.51 \\
0.2 & 283.82 & 308.21 & 333.51 \\
0.4 & 287.09 & 312.87 & 338.64 \\
0.6 & 292.77 & 316.31 & 343.85 \\
0.8 & 294.64 & 320.98 & 347.02 \\
1 & 297.31 & 323.05 & 352.78 \\
1.2 & 304.12 & 327.04 & 358.15 \\
1.4 & 307.22 & 331.61 & 362.71 \\
1.6 & 309.42 & 337.02 & 365.56 \\
1.8 & 311.61 & 340.45 & 367.61 \\
2 & 312.42 & 342.21 & 368.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of steel fiber content on the compressive strength of UHPC. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Water-Reducing Agent (\%)**} & \multicolumn{3}{c}{**Experimental Results (MPa)**} \\ \cline{2-4}  & **7d** & **14d** & **28d** \\ \hline
1.4 & 301.01 & 326.46 & 362.13 \\
1.5 & 307.23 & 330.58 & 368.14 \\
1.6 & 312.01 & 335.21 & 373.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of water-reducing agent content on the compressive strength of UHPC.
# Factorial Numbers and Their Practical Applications
## Application of Factorial Numbers in Permutation Problems
### Errors in Permutations  
A feature of permutations is that the appearance of identical elements in a permutation, for example, two ones or more, is a sign of an error [7]. An indicator of an error can be the original sum of the values of all its digits, which is the same for permutations of equal length. For Table 2, it is equal to six. Violation of this sum in the permutations of Table 2 indicates the presence of an error in them.  
Which of the identical elements of the permutation is erroneous is identified in another way, for example, by using XOR checksum for them [31]. There are other more sophisticated methods for detecting and correcting errors based on permutations [7,8].  
Detection of errors in permutations is possible because in all possible code combinations generated by \(n\) symbols, permutations that form allowed code combinations can be found. The remaining code combinations are forbidden combinations, the appearance of which is a sign of an error.  
The number of allowed combinations is determined by the range of permutations \(P=n!\) of length \(n\), and the number of forbidden permutations is determined by the difference \(n^{n}-n!\). Using factorial numbers to generate permutations makes it possible to obtain them of almost any length \(n\), which means achieving a high level of noise immunity, which increases with increasing \(n\), as follows from Table 4.  
Table 4 makes it possible to determine the probability of detecting errors in permutations of different lengths, containing from one to eight elements. This thereby makes it possible to select an error-resistant code based on permutations. The probability of detected errors in permutations is defined as follows:  
\[p=1-\frac{n!}{n^{n}}. \tag{2}\]  
Table 5 shows the values of probability \(p\) for values of factorial numbers with bit depth \(n\) varying from one to eight.  
Table 5 and Figure 2 clearly show that as the length of permutations \(n\) increases, the probability of detecting errors \(p\) tends to 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(n\) & \(n!\) & \(n^{n}\) & \(n^{n}-n!\) & \(n\) & \(n!\) & \(n^{n}\) & \(n^{n}-n!\) \\ \hline
1 & 1 & 1 & 0 & 5 & 120 & 3125 & 3005 \\
2 & 2 & 4 & 2 & 6 & 720 & 46,656 & 45,936 \\
3 & 6 & 27 & 21 & 7 & 5040 & 823,543 & 818,503 \\
4 & 24 & 256 & 232 & 8 & 40,320 & 16,777,216 & 16,736,896 \\ \hline \end{tabular}
\end{table}
Table 4: Allowed and forbidden states for permutations.
## 3 Transfer and Harvesting Energy Metamaterials
### Monitoring Systems with Wireless Energy Harvesting Sensors  
This work focuses its attention on possible venues for the use of metamaterials as environmentally sustainable materials. We wish to highlight, in this detailed section with energy harvesting, two outstanding uses for this application. The use of sustainable technologies is a response to a need in the technology market and to the 17 sustainable goals of the UN. The proposed solutions focus their attention on several of them: clean water and sanitation; affordable and clean energy; industry, innovation, and infrastructure; sustainable cities and communities and climate action. Help in those areas can be delivered using a monitoring system with wireless energy efficient sensors.  
Desired areas for application similar system can be on offshore wind farms. A whole wind farm is a complicated system with desired stages of use of multiple different sensors [69; 70]. The sensors used on offshore wind farms are typically powered by a combination of on-board batteries and external power sources, but, even now, there are ideas of piezoelectric materials in the sensors [71; 72]. Sensors, such as anemometers and meteorological sensors, are powered by batteries. The batteries are recharged using small solar panels or other renewable energy sources. Others, such as LIDAR, require more consistent and reliable power sources. Those sensors are powered by electrical cables that are connected to the wind farm's substation or by a standalone generator [73]. Anemometers and meteorological sensors typically require very low power inputs, typically in the range of a few milli amperes. Specific vibration sensors can be powered in the range of few milliamperes to a few amperes, too. One potential advantage of proposing energy harvesting technology into this monitoring system is that it eliminates the need for traditional power sources, such as batteries or external electrical cables. This can reduce the overall cost and complexity of the offshore wind farm, as well as reduce the risk of power failures or other issues. Wind farms are still undergoing a tremendous amount of application of new technologies, and the question of how to reduce energy costs and effectively monitor the system using sustainable solutions is still being asked.  
The next proposed area is in sewer monitoring systems. Especially, in Poland, the number of hydrologic events and floods in 40 years increased four times, and, due to climate changes, it can be worse. The monitoring of sewer systems, underground drainage, or sewage is an active area of work by scientists. It can prevent urban floods and result in good water quality. [PERSON] et al. [74], for example, proposed a similar web-based real-time monitoring service with the use of a wireless sensor.  
These are only two of possible areas wherein to use monitoring systems containing several wireless sensors to highlight the importance of smart cities and IoT.
# Future Wireless Networking Experiments Escaping Simulations
## 6 Result Analysis  
### Powder  
The POWDER lab setup was made using four nodes. The results show increased bandwidth for ring topology compared to linear topology because of the use of the 5 GHz lower channel which increases the speed of data transmission, though with low coverage as the second path for ring topology uses the combination of a 5 GHz lower channel and 2.4 GHz channels. Due to fewer available nodes, only two topologies are possible in POWDER, i.e., linear and ring topology. Results for POWDER lab are as follows:  
1. **UDP Bandwidth:** The UDP bandwidth for the POWDER lab setup shows that the maximum bandwidth is around 8 Mbps for hop 0 and it is decreased to around 7 Mbps for hop 1 and, for hop 2, to around 6 Mbps for linear topology (Figure 14). For ring topology, only two hops are present on both paths. The results show that for ring topology for hop 0 the bandwidth is around 9 Mbps and, for hop 1, the bandwidth is around 7.5 Mbps (Figure 14).  
Figure 14: POWDER lab UDP bandwidth.  
Figure 13: CityLab ring topology node setup.  
2. **UDP Loss:** The UDP loss for POWDER lab shows that the percentage loss is much higher in linear topology than in ring topology (Figure 15). In linear topology and ring topology, for hop 0 there is no loss whereas for hop 1 in linear topology the loss is increased to around 22 percent and for hop 2 it is around 25 percent. In ring topology, for hop 1 loss is increased to around 8 percent. There is no hop 2 in ring topology as it has only two hops in both paths.
3. **UDP Jitter:** The UDP jitter is much higher in ring topology than in linear topology (Figure 16). In linear topology and ring topology, for hop 0 the jitter is around 1 ms and around 5 ms, respectively, whereas for hop 1 in linear and ring topology the jitter is increased to around 4.9 ms and 5.5 ms, respectively. For hop 2 it is around 25%. In ring topology, for hop 1 loss is increased to around 8 percent.
4. **TCP and SCTP Bandwidth:** TCP and SCTP bandwidth are greater for hop 0 in both linear and ring topology, at around 10.7 Mb/s for TCP and 9 Mb/s in SCTP for ring  
Figure 16: POWDER lab UDP jitter.  
Figure 15: POWDER lab UDP loss.  
topology, whereas it is 8.2 Mb/s for TCP and 6.1 Mb/s for SCTP in linear topology. For hop 1, TCP and SCTP in linear topology are around 5.5 Mb/s whereas in ring topology TCP is 5.5 Mb/s and SCTP is around 4.5 Mb/s. Hop 2 exists in linear topology only and it is 3 Mb/s for TCP and around 2.9 Mb/s for SCTP (Figure 17).
# The Efficiency of Binaural Beats on Anxiety and Depression--A Systematic Review
## 3 Results
### Depression Assessment  
Among the studies under review, [PERSON] et al. [29] examined the impact of binaural beats on individuals diagnosed with depression. The researchers employed the PHQ-9 [37] scale to compare outcomes between groups and across multiple assessments. Their analysis of the PHQ-9 scores revealed a decrease in depression symptoms during the second and third evaluations, conducted at 4 weeks and 8 weeks, respectively, for the group exposed to binaural beats. Additionally, to support their conclusion regarding the efficacy of binaural beats in alleviating depression symptoms, they did not observe significant differences in the PHQ-9 scores among the other two groups at any point during the study [29].
# Avian Influenza Virus Strain Specificity in the Volatile Metabolome
## 4 Discussion
While our previous study analyzed intact feces collected from cage floors, the current work relied on cloacal swabs, which do not consistently include visible fecal material. Consequently, the sample matrix available for volatile analysis differed substantially. Cloacal swabs in the current study were treated with 6N guanidine hydrochloride to inactivate the shed virus. Guanidine HCl is a potent chaotrope known to disrupt protein-ligand interactions and may interfere with volatile recovery. Although we did not perform direct controls for its effect in this study, the existing literature suggests that chaotropic agents can significantly alter volatile extraction efficiency [18; 19]. During method development, acetoin fortification of swab matrices treated with guanidine HCl demonstrated an unexpectedly high detection threshold (~20 ppm), suggesting that matrix-related suppression or interference may have prevented its detection in this experiment. Alternatively, the presence of acetoin in the previous study may have been a direct result of gamma irradiation used to inactivate shed virus in fecal samples [20]. Although ionizing radiation is known to generate volatiles, it does not produce acetoin from pyruvate; instead, irradiation typically results in the formation of 2,3-dimethyltartaric acid or acetic acid. The gamma irradiation of lipids has similarly been shown to reduce, rather than increase, ketone levels, including acetoin [21]. More importantly, in a subsequent study with animal biosensors (ferrets) trained to detect AIV infection, the ferrets initially trained to recognize increased concentrations of acetoin in test solutions generalized their response to non-irradiated fecal samples from infected ducks [8]. In other words, irradiation was not necessary to produce the increased acetoin concentrations detected by the ferrets. These findings collectively suggest that gamma irradiation was not responsible for the appearance of acetoin in fecal headspace profiles in the original study. Finally, it is also possible, while less likely, that microbial acetoin production occurred post-excretion, perhaps facilitated by microbes introduced from the duck's skin or feathers or the environment. Indeed, several acetoin-producing species, including those commonly found on human skin, are known to persist on dry surfaces and rapidly colonize biological material [22]. While any of these schemes may explain why acetoin was not observedin the headspace of cloacal swabs, the most probable reason was likely a combination of the limited amount of feces collected by swabbing and poor detectability in headspace resulting from the use of guanidine HCl.
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
###### Abstract  
Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. However, traditional FL architectures face challenges such as centralization and lack of effective defense mechanisms against malicious nodes, particularly in large-scale edge computing scenarios, which can lead to system instability. To address these challenges, this paper proposes a cross-zone secure federated learning method with blockchain and credibility mechanism, named CS-FL. By constructing a dual-layer blockchain network and introducing a blockchain ledger between zone servers, CS-FL establishes a decentralized trust mechanism for index detection and model aggregation. In node selection, CS-FL considers multiple dimensions, including node quality, communication resources, and historical credibility, and employs a three-stage mechanism that introduces lightweight probe tasks to assess node status before formal FL training, ensuring high-quality nodes participate. Additionally, the credibility incentive mechanism penalizes nodes that bypass probe mechanism and engage in malicious behaviors, effectively mitigating the impact of deceptive attacks. Experimental results show that CS-FL significantly improves the defense performance of FL, reducing attack success rates from 75-85% to below 5-20% when facing different types of threats, and effectively maintaining the training accuracy of the FL model. This demonstrates the potential of CS-FL to enhance the security and stability of FL systems in complex edge computing scenarios.  
federated learning; edge computing; blockchain; credibility mechanism; node selection strategy; hybrid encryption; Industrial Internet +
Footnote †: journal: Applied  
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