esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance under the Effect of Various Traffic Models  
In this experiment, the proposed approach was evaluated under the effects of different traffic models such as Pareto, Weibull, and fBM [10]; SNR = 8 dB; and NumSTA = 4. This experiment demonstrated how heterogeneous traffic patterns affected the optimal throughput performance with the minimum delay in the WLAN downlink channel. The FIFO FA (baseline approach), Equal Frame Size FA, Equal MPDUs Agg FA, Avg Num MPDUs FA, and Adaptive FA Conv. approach [11] were considered to evaluate the performance of the proposed approach in terms of achieving the maximum system throughput under the conditions of different traffic models.  
Figure 2: Performance of average system delay time under the effects of heterogenous traffic models when SNR = 8 dB and NumSTA = 4.  
As the results in Figure 3 show, due to the effects of heterogenous traffic patterns among streams, different system throughput performances were achieved by different traffic models. The proposed approach achieved the maximum performance of 583 Mbps using the Weibull traffic model and 504 Mbps using the fBM traffic model compared to the FIFO FA (baseline approach), which approached the maximum throughput performance of the Adaptive FA Conv. approach, which only considered the maximum throughput with the cost of maximum delay. On the contrary, the proposed approach achieved the minimum system throughput of 279 Mbps using the Pareto traffic model. This was because the highest bursty traffic behavior in the Pareto traffic allowed the waiting time of frames in a queue before transmission to maximize in terms of queue delay. Thus, the FIFO FA (baseline approach) contributed to the optimal system throughput with minimum system delay. This indicated that the performance of the proposed approach was better at coping with the less bursty traffic scenarios such as the Weibull and fBM traffic models. In general, these results indicated that traffic patterns in the network determined the system performance. The results also demonstrated that the proposed adaptive approach achieved a comparable result with the maximum system throughput achieved by the Adaptive FA Conv. approach, which was particularly focused on achieving the maximum system throughput at the expense of delay, particularly in the Weibull and fBM traffic models.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
## 3 Experiments and Results  
Before fine tuning the LLaMA3-8B model, we perform sentiment analysis's classification task using the base model in a subset of the Yelp dataset. The base (non-fine-tuned) model results are shown in Table 5.  
Figure 2: Neurons in FFN and attention layers.  
For fine tuning, we conduct our experiments mainly at two settings: (1) At the first setting, all the linear layers of the LoRA and QLoRA models are adapted; (2) at the second setting, only the attention layers of the LoRA and QLoRA models are adapted. We plan to explore other combinations and possibilities in future experiments.  
Next, we investigate and test whether a lower value of \(r\) results in poor adaptation (underfitting). A lower value of \(r\) implies fewer parameters, which can reduce the low-rank matrix's capacity to capture task-specific information. Similarly, we test whether a higher value of \(r\) (more trainable parameters) results in the model being overfitted in the downstream task.  
If all the linear layers are adapted in LoRA and QLoRA, the performance decreases after \(\alpha\) exceeds 32, whereas if only the attention layers are adapted in LoRA and QLoRA, the performance steadily increases as the value of \(\alpha\) exceeds 16. So, an increase in the value of \(\alpha\) can have a degrading effect when all the layers are adapted, but it performs better only when the attention layers are adapted.  
The experiment also demonstrates how QLoRA, for \((r=8,\alpha=32)\), delivers the same performance as LoRA when all the linear layers are adapted. Similarly, for \((r=8,\alpha=64)\), QLoRA delivers the same performance as that of LoRA when only the attention layers are adapted. LoRA's and QLoRA's performances peak at 68% (at \(r=8,\alpha=32\)) when all the linear layers are adapted. They perform equally well (at \(r=8,\alpha=64\)) when only the attention layers are adapted. According to this analysis, we recommend using QLoRA with a higher value of \(\alpha\) for attention layers and a lower value of \(\alpha\) (\(\leq\)32) if all the linear layers are to be adapted.  
These results in Figures 3 and 4 consistently show that the performance of the 4-bit QLoRA with the NF4 data type matches the 16-bit LoRA's fine-tuning performance in the classification task of the sentiment analysis. In other words, QLoRA can replicate the 16-bit LoRA's fine-tuning performance with a 4-bit base model.  
Figure 4: LLaMA3—attention layer adaptation, \((r=8)\).
# Common-Mode Voltage Analysis and Reduction for the Quasi-Z-Source Inverter with a Split Inductor
## 3 CMV Reduction Strategies for the VSI
For example, in Sector 1, the switching vectors are \(\mathbf{V}_{1}\), \(\mathbf{V}_{2}\), \(\mathbf{V}_{6}\) when the output angular \(\theta\) meets \(0\leq\theta<\pi/6\), while the switching vectors are \(\mathbf{V}_{1}\), \(\mathbf{V}_{2}\), \(\mathbf{V}_{3}\) when \(\pi/6\leq\theta\leq\pi/3\). NSPWM also uses only active vector states to reduce the CMV and maintain the same maximum DC-link voltage utilization ratio. However, when the ST vector state is inserted, the conventional CMV reduction strategies for the VSI are no longer available for the qZSI. Thus, further work should be done to tackle this issue, making the conventional CMV reduction methods appropriate for the qZSI system. Figure 4: Typical CMV reduction strategies with switching patterns and CMV when using: (**a**) discontinuous pulse width modulation (DPWM), (**b**) active zero state pulse width modulation (AZSPWM), (**c**) remote state pulse width modulation (RSPWM) and (**d**) near state pulse width modulation (NSPWM) strategies.
## 4 Discussion
The fatty acid content of the SBP3 strain, specifically referring to thermophilic bacilli [3; 47], is highly concordant with the high temperature (130 \({}^{\circ}\)C) of the vent environment from which it was isolated. After the wet heat treatment, changes in the Raman spectra in the bands that referred to proteins, nucleic acids and lipids indicated different degrees of damage in spores from each strain, which could be related to their viability. The PCA plot (Figure 6) shows the major distance between the untreated and wet-heat-treated spores of both mesophilic BHO and BSU strains, indicating the occurrence of denaturation processes in their structures that could justify their lower resistance to heat compared to SBP3. Evident variations were observed in the spectral regions that referred to spore-coat structures and, particularly, to lipids of the inner membranes of BHO spores (Figure 5). Moreover, the spectrum of the heated spores of BHO showed a \begin{table}
\begin{tabular}{c c c c} \hline \hline  & _Bacillus horneckiae_ SBP3 DSM & _Bacillus horneckiae_ DSM & _Bacillus subtilis_ \\  & **103063 [PERSON] et al. [3]** & **23495\({}^{\text{T}}\) [PERSON] et al. [10]** & **168 [PERSON] et al. [48]** \\ \hline Temperature range (\({}^{\circ}\)C) & 25–45 & 4–40 & 25–37 \\ Fatty acids & & & \\ Straight chain saturated & & & \\ C16:0 & 0.8 & 0.5 & 3.14 \\ Branched saturated & & & \\ iso-C14:0 & 3.4 & 4 & 1.13 \\ iso-C15:0 & 63.1 & 54.3 & 29.27 \\ iso-C16:0 & 3.5 & 2.5 & \\ iso-C17:0 & 2 & 1.3 & 9.59 \\ anteiso-C15:0 & 8.8 & 19.9 & 40.19 \\ anteiso-C17:0 & 1.8 & 2.3 & 9.38 \\ Monounsaturated & & & \\ C16:1 o\(7\) c-alcohol & 7.8 & 6.8 & 0.23 \\ iso-C17:1 o\(10\) c & 3.7 & 2.3 & 1.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentages of the relative abundance of fatty acid methyl esters in _B. horneckiae_ SBP3, _B. horneckiae_ DSM 23495\({}^{\text{T}}\) and _B. subtilis_ 168.  
peak shift at 824 cm\({}^{-1}\), indicating that DPA in the spore's core was released, possibly as a consequence of the extensive protein damage, as described by [PERSON] et al. [49] in the case of _B. subtilis_ spores. In the spectra of the treated BSU, evident peak shifts were observed in the regions associated with nucleic acids and protein structures, including SASPs [32]. These results suggest that the wet heat damage mainly involves the denaturation processes of the spore protein components rather than DNA damage, according to previously reported studies [4; 41; 42]. Therefore, the wet heat could damage spores, resulting from the release of DPA and SASPs [50]. Differently, the Raman spectra from the most resistant SBP3 spores showed small changes in the band that referred to proteins after heat treatment (Figure 5). What is even more interesting is the slight separation observed in the PCA between untreated and wet-heat-treated SBP3 spores, indicating the major spore stability of the thermophilic strain (Figure 5).
## 2 Results and Discussion
### Fermentation Characteristics of Goji Juice
The concentrations of soluble proteins were \(39.23\pm 0.67\) mg/100 mL in the unfermented goji juice, and increased by 90.54%, 31.18%, and 110.91% in the goji juice samples fermented with SLV, SZP, and SZVP, respectively. [PERSON] et al. (2018) also obtained a similar result from fermentation of wheat bran with \(L\). _plantarum_ and yeast, where they found that fermentation increased the protein content of the fermented wheat bran by 20%. Some bacterial strains produced or were activated by a variety of enzymes, such as endogenous proteases during fermentation ([PERSON] et al., 2018). Proteases can break down macromolecular proteins and convert them into small protein molecules. It is possible that the soluble protein contents increased in the goji juice samples due to the action of proteases. The soluble protein contents were higher in the juices fermented with SZVP and SLV, which may be related to the presence of _B. velezensis_ and \(L\). _reuteri_. It was reported that _B. velezensis_ can produce bacteriostatic substances comprising cyclic lipoprotein peptides during fermentation ([PERSON] et al., 2018). The number of live bacteria was slightly lower in the juice fermented with SLV compared with that in the juices fermented with SZP and SZVP, where the cell numbers in both exceeded \(10^{9}\) CFU/mL. [PERSON] et al. (2018) showed that some strains can produce endoglucanases during fermentation, which can degrade cellulose into cellooligosaccharides, and the growth of \(L\). _plantarum_ can be promoted by this process. _Lactobacillus plantarum_ and _B. licheniformis_ were present in the SZP and SZVP mixtures, but not in the SLV fermentation mixture. Therefore, it is proposed that _B. licheniformis_ may produce a cellulase similar to endoglucanase to promote the growth of \(L\). _plantarum_. The number of live bacteria was higher in the fermentation mixture containing \(L\). _plantarum_ and _B. licheniformis_. The sensory quality scores were clearly improved after fermentation compared with the raw juice. Fermentation did not cause color change and unpleasant odor production in the goji juice, but the taste was improved significantly. There was no significant difference in the sensory quality of the goji juices fermented with SLV and SZVP, but their sensory quality scores were better than that for the juice fermented with SZP. Overall, the juices fermented with SZVP and SLV groups had a greater capacity for acid production and higher soluble protein contents. The strains present in these mixtures could grow normally in the goji juice, and the sensory quality was better after fermentation. The SZVP and SLV mixtures had a greater capacity for fermentation than SZP.
# Late Line-of-Sight Check and Prioritized Trees for Path Planning
## 3 Forward Estimation and Online Optimizing  
A* is the basis of many any-angle path planning algorithms, including the Theta* family. The basic A* defines the cost of node \(n\) with a synthetic value \(f(n)=g(n)+h(n)\). The value \(g(n)\) is the real cost from the start node to node \(n\). The value \(h(n)\) represents the estimation cost from node \(n\) to the goal node, which is often called the heuristic function. Weighted A* [53] is a variant of A* introducing the technique of multiplying the heuristic function by a weight \(w\), which is a real value greater than or equal to one. The final cost of node \(n\) is defined as \(f(n)=g(n)+w*h(n)\). We change the \(f\) value of Weighted A* (WA*) into the following form:  
\[f(n)=(1-\lambda)*g(n)+\lambda*h(n),\qquad\lambda\in[0,1] \tag{2}\]  
According to Formula (2), when \(\lambda\) = 0, WA* will become Dijkstra [6], and when \(\lambda\) = 1, WA* will become the greedy best-first search [54]. When \(\lambda\) = 0.5, it will become A*. We control the greedy degree of WA* by changing the equilibrium factor \(\lambda\). In this paper, we use the Euclidean distance as the heuristic, which satisfies both admissibility and consistency [7], and the two properties can guarantee optimality. A heuristic is admissible if \(h(n)<Cost(n,goal)\), and it is consistent if \(h(n)<Cost(n,n^{\prime})+h(n^{\prime})\), where node \(n^{\prime}\) represents every successor of node \(n\).  
The A* algorithm contains two states sets, i.e., \(open\) and \(closed\). The states in the \(open\) set represent that they are visited, but have not been validated to be optimal, and the states in the \(closed\) set represent that they have been verified to be optimal. The \(open\) set is a prioritized queue, and the state that has the least \(f\) value will be chosen to expand new states in each iteration. After the state expansion, this chosen state will be moved from the \(open\) set to the \(closed\) set. The A* algorithm is both resolution complete and resolution optimal because it satisfies the monotone condition that the solution cost estimate (i.e., the \(f\) value) from a parent to its child state must be monotonically increasing [34].  
The contribution of A* is advising the heuristic function \(h(n)\) to speed up the search, which is a kind of forward estimation about the future cost. Meanwhile, the contribution of Theta* is introducing the online LoS-Check to optimize the value \(g(n)\), which reduces the real cost.
## 2 Methods
### Seamless Deployment  
JAVIS was designed to facilitate effortless transitions across multiple levels of deployment, from a **personal/small** team setup to full **hospital/institution**-wide integration. This scalability has been achieved through a carefully orchestrated, containerized workflow using Docker Compose for JAVIS and Docker for LLM/VLM containers, ensuring that libraries, dependencies, databases, and core components are consistently configured at every stage.  
**Transition of scale levels:** Transitioning JAVIS from a development environment to a production setting is as simple as running a single Docker build command. Furthermore, JAVIS integrates smoothly with the previously described versatile mode (for small user bases such as personal computers or small teams) and with the scaling mode (for hospital or institution-level user loads). Replacing the modality of containers is as simple as using a different command. For the versatile mode, wicorn run_llm:app -host 8000 -port 0.0.0.0 is used, and for running the scaling mode, python run_ray_llms_v1 ms.py command is employed.  
**Addition of LLMs:** Integrating additional large language models (LLMs) is straightforward, as it involves updating the container configuration and launching new containers. JAVIS automatically routes queries to the newly added models through predefined APIs, facilitating easy expansion and experimentation.  
**Upgrading system capabilities:** By decoupling the JAVIS core stack from the container, you can effortlessly upgrade the system's capabilities to handle large user volumes without affecting the primary JAVIS services. Moreover, these decoupling capabilities are especially valuable when the hospital requires a direct API for LLM/VLM deployment for any other use case, such as a common data warehouse (CDW) connection or powering any other hospital service. By leveraging a modular, container-based solution, we can initially support a small group of users for experimentation. Then, with just a simple two-command scaling process, the container can be expanded to serve a broad user base at the institutional level, all within the hospital's internal network.  
Rapid Container Modality Switching for Scaling  
JAVIS supports two primary container templates to accommodate varying operational scales:  
* **A versatile mode,** which is ideal for single-machine or small lab setups.
* **A scaling mode,** which is optimized for large, institution-wide deployments. Furthermore, JAVIS is split into two versions:
* **Development JAVIS,** which is ideal for tailoring the system to a given hospital or institution.
* **Production JAVIS,** which was built for large, institution-wide deployments.  
In summary, each of the four components is designed with a modular approach and combining them as needed offers a solution to satisfy varying needs, from system tailoring and serving a small user base to scaling to an institutional level.
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 4 Discussion  
The Assumption of Equivalence between Fiber Bending Impact and Gaussian Noise Added to the Bucket Detector Signal  
The approximation of Gaussian noise for disturbances caused by fiber bending is reasonable based on the following assumption and discussion. In our study, fiber bending involved complex scenarios where a 50 cm fiber fixed at both ends and attached to a two-dimensional translation stage experienced configuration changes that resulted in pixel-level intensity variations rather than simple pattern drifts caused by simple bending and temperature changes. These changes were independent and random. In the context of SPI, each speckle pattern corresponds to one measurement, and the errors from 2500 measurements were independent. The disturbance measurement process is represented as \(\mathbf{y}_{\text{disturbed}}=(\mathbf{A}+\triangle\mathbf{A})\mathbf{x}\), where \(\triangle\mathbf{A}\mathbf{x}\) is the noise induced via bending. Given the independent and random nature of these changes, approximating this noise with additive Gaussian noise for \(\mathbf{y}\) simplifies the computational process and provides a practical framework for image recovery in MMF-SPI. Therefore, \(\mathbf{y}+\mathbf{e}\) is a reasonable assumption with respect to noise in the model, where \(\mathbf{e}\) includes bending noise and the initial electric BD noise.
# Indole-3-Acetic Acid Action in Outdoor and Indoor Cultures of _Spirulina_ in Open Raceway Reactors
## 5 Conclusions  
Different from other studies involving phytohormones and microalgae, this is the first study involving microalgae within an outdoor setting, utilizing raceway-type reactors and a nonsterile medium and supplemented with indole-3-acetic acid phytohormone, which has yielded promising outcomes. When contrasted with data acquired from an indoor environment lacking phytohormone supplementation, the outdoor experiment with indole-3-acetic acid supplemented in _Spirulina_ sp. LEB 18 cultivations demonstrated a notable increase of 122.5% and 130.9% in biomass concentration and productivity, respectively, resulting in a 55% reduced-cost product. Moreover, the carbohydrate concentration underwent a remarkable surge of 230%, a remarkable 697% boost in biomolecule productivity, and an 86% decrease in production costs. However, further studies are necessary to apply this technology on an industrial scale and usher in a new era of microalgae supplementation. Nonetheless, this study establishes that phytohormone supplementation represents a viable industrial strategy for reducing microalgae production costs in an environmentally friendly manner and enhancing large-scale microalgae production. Hence, a significant gap in the research regarding phytohormone supplementation in microalgae cultivation has been addressed.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14093715/s1](https://www.mdpi.com/article/10.3390/app14093715/s1)]([https://www.mdpi.com/article/10.3390/app14093715/s1](https://www.mdpi.com/article/10.3390/app14093715/s1)), Table S1: Price of reagents used to compose Zarrouk medium obtained on March 2023, from the manufacturer's website (Sigma-Aldrich Brazil).  
Conceptualization, J.T.d.S., A.P.C.d.R., M.G.d.M. and J.A.V.C.; methodology, J.T.d.S. and A.P.C.d.R.; formal analysis and investigation, J.T.d.S.; writing--original draft preparation, J.T.d.S.; writing--review and editing, J.T.d.S., A.P.C.d.R., M.G.d.M. and J.A.V.C.; funding acquisition, J.A.V.C.; resources, J.A.V.C. and M.G.d.M.; supervision, A.P.C.d.R. and J.A.V.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001 and also had the support of the MCTI (Ministry of Science, Technology, and Innovation).  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 5 KAFIN for Option Pricing
### KAFIN for Option Pricing
#### 5.2.3 Boundary Loss for Call Option Pricing  
To mathematically model the boundary loss for call option pricing using a neural network, we ensure that the network's output satisfies the boundary conditions of the option pricing model. For a European call option, the boundary conditions are determined by the behavior of the option price as the underlying asset price approaches zero and infinity, and at the expiration time.  
For a European call option, the boundary conditions include zero boundary and infinity boundary. For zero boundary, as the underlying asset price \(S\) approaches zero, the option price should also approach zero:  
\[C(0,t)=0\quad\forall t \tag{22}\]  
for infinity boundary, as the underlying asset price \(S\) approaches infinity, the option price should asymptotically approach \(S-K\):  
\[\lim_{S\to\infty}C(S,t)=S-K\quad\forall t \tag{23}\]  
The boundary loss \(L_{\text{boundary}}\) ensures that the neural network output satisfies these boundary conditions. Mathematically, this loss can be defined as the sum of the mean squared errors at the boundary points.  
The zero boundary loss can be formulated as  
\[\mathcal{L}_{\text{boundary},0}=\frac{1}{N_{0}}\sum_{i=1}^{N_{0}}(u_{\theta }(0,t_{i}))^{2}, \tag{24}\]  
where \(N_{0}\) is the number of sampled points \(t_{i}\). The infinity boundary can be formulated as  
\[\mathcal{L}_{\text{boundary},\infty}=\frac{1}{N_{\infty}}\sum_{i=1}^{N_{0}}( u_{\theta}(S_{i},t)-(S_{i}-K))^{2} \tag{25}\]  
where \(N_{\infty}\) is the number of sampled points \(S_{i}\).  
The total boundary loss \(\mathcal{L}_{\text{boundary}}\) is the sum of these individual losses:  
\[\mathcal{L}_{\text{boundary}}=\mathcal{L}_{\text{boundary};0}+\mathcal{L}_{ \text{boundary},\infty}. \tag{26}\]