esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Pathogen Removal  
The overall reduction in _E. coli_ in the VFF and HFF system was \(2.95\) log\({}_{10}\) units, compared with \(3.75\)-\(4.49\) log\({}_{10}\) CFU mL\({}^{-1}\) in the incoming wastewater. This means that substantial numbers of _E. coli_ were present in the effluent from the system. Removal of _E. coli_ in the VFF and HFF was only achieved by filtration and straining in the pores of the biochar, and conditions for inactivation of bacteria (e.g., high or low pH, high temperature) were not present. Similarly, the VFF and HFF did not achieve effective removal of _Salmonella_ spp., but rather attenuated its concentration and released it gradually.  
It should be pointed out that the levels of _Salmonella_ spp. spiked in the wastewater were very high (7.29-9.40 log\({}_{10}\) CFU mL\({}^{-1}\)) compared with those expected to be found in wastewater. Previous studies report that wastewater can contain 0.001-100 CFU mL\({}^{-1}\)[31], which is much lower than the level in the present study. However, if wastewater contains up to 3.0 log\({}_{10}\) CFU mL\({}^{-1}\), a combined VFF-HFF biochar system might be able to remove _Salmonella_ spp. and the effluent would be considered safe for disposal.  
Other studies have reported similar removal of bacteria using biochar filters. For example, [PERSON], [PERSON] [20] reported 1.6-4.5 log\({}_{10}\) unit removal of _E. coli_ and enterococci bacteria from vertical flow biochar filters treating irrigation water. The efficiency of removal of microbes from wastewater by passage through filters depends on the adsorption capacity of the filter material, the characteristics of the biofilm formed on filter surfaces and physical entrapment (straining) in small pore spaces [32; 33; 34]. Adsorption is the dominant mechanism when the microbes are smaller than the pore size. It depends on several characteristics of the filter material, some of which (e.g., surface area, total porosity, size and distribution of pores) show more suitable values in biochar than in sand [35; 36].
## 3 Results and Discussion  
### Physiological Parameters of Sygurt  
The physiochemical parameters of the yogurt samples before cold storage are shown in Table 1. All samples were moved to cold storage when the pH dropped to 4.5 (commonly practiced pH level in yogurt production), and there was no significant difference (\(p>0.05\)) among all the samples in terms of the incubation time needed to reach this pH level. Titractable acidity of the yogurt samples before cold storage were from 0.87% to 0.90%. Fat content of the control sample S1 (3.11%) was almost the same as that (3.10%) of the probiotic yogurt S2, but the fat content of the symbiotic yogurt S5 (3.05%) was slightly lower due to addition of inulin. Addition of inulin also slightly increased total solid contents in the symbiotic yogurt samples (S3, S4, and S5), while the protein contents of all the samples before storage had no significant differences (\(p>0.05\)). Changes of physiochemical parameters of yogurt including pH, acidity, fat, and protein content during four weeks of storage at 4 \({}^{\circ}\)C are shown in Figure 1. The pH of all the yogurt samples decreased slightly in the storage period, while the titratable acidity increased, ranging from 0.92 to 1.03, during the storage (Figure 1A). Addition of inulin at different concentrations and _S. boulardii_ had no significant (\(p>0.05\)) effect on the change of pH or acidity of yogurt, suggesting that both the prebiotic and yeast did not alter the capacity of acid production by the starter LAB in yogurt. Previously, no significant effect of the addition of inulin on the pH of yogurt was also observed, though there was slightly more decrease in pH of yogurt during the third and fourth weeks of storage [29]. The increased acidity of yogurt during cold storage could be due to microbial activity and enzymes produced during fermentation that promoted conversion of residual carbohydrates (mainly lactose) to lactic acid, CO\({}_{2}\), and formic acid [30; 31; 32; 33; 34; 11]. Syneresis was considered a leading problem during the prolonged storage of yogurt, and separation of the released whey from the shrank gel part of yogurt deteriorated the texture and mouthfeel of the product [11; 28]. Addition of inulin obviously decreased syneresis of the yogurt with less syneresis at higher concentration of inulin because of the water-holding capacity of inulin (Figure 1B). Regarding the compositional changes of the yogurt during cold storage, though addition of inulin and _S. boulardii_ did not affect significantly the protein and total solid contents of yogurt, the fat content decreased significantly (\(p<0.05\)) during the later stage of cold storage (Figure 1C). A similar decrease in fat content of yogurt was also reported earlier by [PERSON] et al. [29] when inulin was used for making yogurt. However, whether inulin played a role in the metabolism of fat by _S. boulardii_ leading to decreased fat content in yogurt needs to be further studied.
## 1 Introduction  
Several periodontal surgical methods have been proposed to treat periodontitis, but further evaluation of these treatments remains crucial. Postoperative evaluations typically rely on the symptoms of the patient, radiographic imaging, and clinical examinations. However, superficial assessments and indirect radiographic evaluations make it challenging to accurately assess changes in soft tissue healing. Histological evaluation is currently the most accurate method for assessing soft tissue healing patterns [1, 2]. However, performing histological evaluation on all patients is impractical, highlighting the need for a more convenient method.  
The use of laser systems in periodontal treatment is becoming more common. Various positive results indicate that lasers can facilitate easier and faster removal of granulation tissue compared to traditional methods using a blade [3; 4]. Lasers are also effective in treating peri-implantitis [5; 6], making this an emerging field of research. Evaluating the healing outcomes of periodontal surgery with laser systems is crucial for their effectiveness.  
Optical coherence tomography (OCT) is a valuable imaging device that provides optical cross-sectional images of biological tissues. Initially used in ophthalmology to examine microstructures, including the retina [7], OCT has also been applied to study skin regeneration and re-epithelialization [8; 9]. Ongoing efforts were observed to extend its use to various other fields [10; 11]. In dentistry, OCT is now used to measure hard tissues such as dental caries, cracks, and calculus [12; 13; 14; 15]. Efforts are being made to apply OCT in periodontal areas, including gingival sulcus, tissue thickness, and biological width [16; 17; 18; 19; 20]. Based on these efforts, OCT is also being introduced as a diagnostic tool in dentistry, emerging as a highly useful evaluation method. This imaging system uses near-infrared laser light, eliminating the risk of radiation exposure. Furthermore, this modality can create high-resolution, real-time cross-sectional images chair-side. Therefore, OCT is proposed as a tool that can perform postoperative evaluations more simply and accurately than histological and radiographic methods. To properly evaluate periodontal treatments, supplementary examination methods are needed alongside radiographic and clinical evaluations.  
Therefore, with the hypothesis that using OCT to assess periodontal tissue provides a more direct indication of healing in real time, this study aims to investigate the effectiveness of OCT by comparing the healing patterns of laser treatment and general surgical tools in the periodontal tissue of animal models.
## 4 Support Scheme Optimization
### Simulation Results Analysis
#### 4.3.1 Deformation Analysis  
Figure 15 shows the contour maps of horizontal displacement, vertical displacement, and total displacement of the surrounding rock after the construction of the two support schemes. It can be observed that the horizontal deformation on the left and right sides is symmetrically distributed. The vertical displacement of the roof is slightly greater than the uplift displacement of the floor. From the total displacement contour map, it is evident that the maximum displacement deformation occurs at the tunnel roof position.  
Figure 15: Displacement contour map: (**a**) horizontal displacement contour map of the original support scheme; (**b**) horizontal displacement contour map of the new support scheme; (**c**) vertical displacement contour map of the original support scheme; (**d**) vertical displacement contour map of the new support scheme; (**e**) total displacement contour map of the original support scheme; (**f**) total displacement contour map of the new support scheme.  
In the roof, monitoring points were set every 1 m in the radial direction to record the displacement changes in the surrounding rock at different depths throughout the excavation process. Figure 16 shows the deep surrounding rock displacement curves for six monitoring points on the roof for the two support schemes.  
For the original support scheme, the deformation curves at different positions exhibit varying patterns. The deformation of the surrounding rock significantly decreases at depths greater than 3 m, with a risk of block fall near the tunnel surface.  
In contrast, the new support scheme shows consistent deformation patterns at different positions. The deformation gradually increases with stress release in the surrounding rock. The difference in deformation between the tunnel surface and a depth of 5 m is 32 mm. The maximum displacement values for the original support scheme and the new support scheme are 94.70 mm and 70 mm, respectively. This indicates that changing the support scheme has significantly improved the control of the surrounding rock.
# Modulation of Inflammatory Cytokine Production in Human Monocytes by cGMP and IRAK3
###### Abstract  
Interleukin-1 receptor-associated kinase-3 (IRAK3) is a critical checkpoint molecule of inflammatory responses in the innate immune system. The pseudokinase domain of IRAK3 contains a guanylate cyclase (GC) centre that generates small amounts of cyclic guanosine monophosphate (cGMP) associated with IRAK3 functions in inflammation. However, the mechanisms of IRAK3 actions are poorly understood. The effects of low cGMP levels on inflammation are unknown, therefore a dose-response effect of cGMP on inflammatory markers was assessed in THP-1 monocytes challenged with lipopolysaccharide-LPS. Sub-nanomolar concentrations of membrane permeable 8-Br-cGMP reduced LPS-induced NF&B activity, IL-6 and TNF-\(\alpha\) cytokine levels. Pharmacologically upregulating cellular cGMP levels using a nitric oxide donor reduced cytokine secretion. Downregulating cellular cGMP using a soluble GC inhibitor increased cytokine levels. Knocking down IRAK3 in THP-1 cells revealed that unlike the wild type cells, 8-Br-cGMP did not suppress inflammatory responses. Complementation of IRAK3 knockdown cells with wild type IRAK3 suppressed cytokine production while complementation with an IRAK3 mutant at GC centre only partially restored this function. Together these findings indicate low levels of cGMP form a critical component in suppressing cytokine production and in mediating IRAK3 action, and this may be via a cGMP enriched nanodomain formed by IRAK3 itself.  
interleukin-1 receptor-associated kinase 3 (IRAK3 or IRAK-M); cyclic guanosine monophosphate (cGMP); guanylate cyclase; nitric oxide; nuclear factor kappa-light-chain enhancer of activated B cells (NF&B); inflammatory cytokines
## 1 Introduction  
Optical image splitters are often used to build autostereoscopic displays. A high number of image splitter elements is necessary to shorten the internal projection way and to create tiny projections on the level of pixels and subpixels. That causes a flat 3D display design. Some switchable techniques allow an opening and closing of alternative barrier apertures on the image splitter. These components are known as spatial light modulators [1].  
The image splitter is placed either directly in front or behind the pixel plane of the display panel. Common forms of image splitters are parallax barrier and lenticular lens arrays [2]. Special kinds of barriers are wavelength-selective [3; 4; 5]. Stripe, step and hole masks complete the possible geometric shapes of the barrier and can be designed with various ratios and slant angles.  
Autostereoscopic multiview displays are distinguished in several techniques characterized by the geometrical relation of the display and image splitter [1; 2; 6]. Therefore, classifications differ in two-view, multiview and light field displays.  
Response times of tunable color filters and optical color shutters in the millisecond regime enable novel applications already in the area of image recording, image displaying and processing of video sources [7; 8; 9]. The strong birefringence of liquid crystals, the possibility to align them by confining surfaces and to trigger their alignment by an external electric field makes them well suitable for tunable interference filters [10; 11; 12]. The reorientation of nematic liquid crystals (NLCs) has been proven in various interference filter designs to generate fast switching times and wide shifting ranges. These include Lyot filter stacks [13; 14] and the Fabry-Perot filter [11; 15; 16]. A Lyot filter stack consists of several stages of a birefringent layer and a subsequent polarizer, whereby the birefringent layer of each stage has half the thickness of the preceding one. Electrically-tunable NLC Lyot filter stacks operating in the visible light range were already commercialized [12].  
In this paper, we report a novel barrier technique for autostereoscopic displays using fast switchable wavelength-selective filters as time-sequential image splitter elements. In the following sections, we describe the methodology of designing wavelength-selective filter barriers for autostereoscopic displays. We outline two example solutions we have found with the applied search strategy. We show an experimental setup for the assessment of the design, and we report about an experimental evaluation of a fast switching cell design with Fabry-Perot characteristics.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 8 Discussion on Evaluation
### Benefits and Limitations of SoSLM in General  
One of the advantages of the model is that, although it is a new approach, its core is provided by the general SLM and PLM models, and process engineering frameworks, so it is easy to migrate from those models in many areas. Of course, such migrations or model replacements have to take context-specific issues into account. Still, with minor additions, SoSLM can serve as a complete alternative to these two models. Due to its universal nature, in addition to systems, several other product types can also be inserted into the SoSLM model.  
Another advantage is that, following the model, every participant is aware of when, how, and what system, application, or different products should be used, leading to more efficient supply chain processes.  
Although the model provides a continuous flow of information, it is essential to note that information management is not as detailed as in ILM models. SoSLM approaches the issue of information management from the perspective of technological infrastructure. In its currently proposed version, it does not deal specifically with, e.g., the handling of personal or sensitive personal data. However, this requirement is not so typical in this part of the SoS architecture and must usually appear in the particular system's functional specification. The focus of information management is more on the processes affecting the technology infrastructure and the operating systems, creating the opportunity for continuous optimization and proactive maintenance and addressing threats and vulnerabilities affecting the SoS.  
It can also be stated that SoSLM itself is also a kind of SDLC, as it carries information about the main processes and subprocesses involved in the development of a system, although it is not detailed as an SDLC so far. However, the SDLC can be considered from the SoS perspective rather than a developer perspective.  
Furthermore, SoSLM makes the tools used for development and operation available in a transparent way, which is a great advantage for describing the specific SoS toolchain.
# Analysis of 3D-Printed Zirconia Implant Overdenture Bars
## 5 Conclusions  
Additive manufacturing has experienced rapid growth in recent years, with its applications in implant dentistry expanding substantially. The present study suggests that additive-manufactured zirconia is a viable option for implant-supported superstructures. The lattice design used less raw material, reducing costs and improving efficiency.  
To the best of our knowledge, this study presents the first use of a 3D-printed zirconia solid and lattice-structured implant overdenture bar. Such complex geometries are not manufacturable with subtractive methods, reinforcing the advantages of additive manufacturing for alternative dental components. This novel design is a metal-free option in implant dentistry, offering high accessibility and sustainability without compromising mechanical performance.
## 2 Time-Series Deep Neural Networks
### Recurrent Neural Network (RNN)  
Recurrent neural network (RNN) is one of the neural network models. Compared with the fundamental DNN, the calculated output of each hidden layer in DNN can only be passed to the output of the next layer in one direction, and the interrelationships of context cannot be analyzed. RNN transfers the weight of the hidden layer to the next time sequence and can perceive the influence of the data before and after on the current data. Compared with DNN and Convolutional Neural Networks (CNN), RNN is suitable for sequence-related data, such as sentence generation, speech recognition, language translation, stock forecasting, etc. [39; 40]. The RNN structure is shown in Figure 1. In this architecture, U, V and W are shared weights. There will be a non-linear transformation from U to the hidden layer. The nonlinear transfer function is usually tanh, and the error function uses CrossEntropy. In addition, the multi-category output is converted through the Softmax Function before V to output.  
In the RNN architecture, the length of input and output is not fixed and synchronized. Depending on the application category, there can be the following extensions:  
1. One to one: fixed-length input/output. For example, input the stock price of the day, and output the stock price of the next day.
2. One to many: single input, multiple output. For example, take the stock price of the day as the input, and output the estimated stock price in the next few days.
3. Many to one: Multiple inputs, single output. For example, input the stock price in a week, and output next week's stock price.
4. Synchronization many to many: Both input and output are multiple. For example, input one week's stock price and synchronously output the next week's stock price in real time.
5. Asynchronous many to many: Input one week's stock price, after the input is over, output the next week's stock price asynchronously.
6. Asynchronous sequence-to-sequence (Seq2 Seq): This type of input and output are sequence data, and before output is generated, the input message and the code are encoded into a set of context vectors, and the output is to decode the feature vector. The Seq2 Seq model can obtain the context dependence of the input more accurately.
# A Biologically Inspired Self-Organizing Underwater Sensor Network
## 6 Experiment
### Testing the Forwarding Guide  
We then test the performance of the forwarding guide and compare its performance with that of the classic Aloha protocol. The distribution of slots is as in Section 6.1. Assume Node 24 detects a target at a random time and needs to report this message to node 0, i.e., the base station. Without losing generality, during transmission of the message, other nodes will not keep silent because they need to exchange information, such as sending a message packet to check the connectivity of the network or message packets generated by other nodes.  
We compare the performance of our forwarding guide with that of the classic Aloha protocol. For our forwarding guide, the communication table is the one generated in Section 6.1. According to the analysis of the slot distributor, the communication table finally generated features each node occupying at least one slot. Thus, the exchanging of information by other nodes will not bother the transmission of the message packet from Node 24. However, for the classic Aloha protocol, if other nodes do not need to exchange information, they can keep silent. As we assume nodes always need to exchange information, we set each node with the same probability of broadcasting a message. As the time for message propagation is related to the probability of a node sending a message packet at a slot, we test the situation with sending probability \(p=0.1,0.2,0.3,0.4\). For each setting, we repeat the experiment 20 times. The result is shown in Figure 14.  
Figure 13: Communication result in each frame for network consisting of 100 nodes.  
In Figure 14, we measure the time needed to send the message packet from Node 24 to Node 0 with the number of slots. We highlight the performance of our forwarding guide in red, while that of the classic Aloha protocol is in blue. It is shown that it takes 30 slots on average to relay a message packet from the node farthest from the base station to its destination. However, for the Aloha protocol, it takes at least 40 slots for the message packet to reach its destination, around 30% longer than that of our forwarding guide. Further, the variation of our forwarding guide is smaller than that of the Aloha protocol with different sending probabilities, indicating that our forwarding guide performs not only better but also more steadily. Additionally, with the increase of \(p\), the time spent by the Aloha protocol shows a tendency to first decrease and then increase. This is because when the \(p\) is small, the probability of a node speaking is too small, such that a node needs to wait a long time before sending a message. When the \(p\) is big, the chance of communication collision increases, extending the time for the message to reach its destination successfully in return. This implies that with the Aloha protocol, the sending probability needs to be set carefully to get the best performance. Meanwhile, our forwarding guide has no parameters that need to be adjusted, making it easier to be adopted in an application.