esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Prominent Pathway Networks and Upstream Regulators in Cones after T3 Treatment  
The pathway network analysis yielded two networks. The first network was related to developmental disorders, hereditary disorders, and ophthalmic disease. Genes _Nr2e3_, _Nrl_, and _Crx_, involved in retinal development, were down-regulated after T3 treatment, implying developmental defect/disorder and hereditary disease (Supplementary Figure S1A). Also, genes involved in cone phototransduction, including _Pde6c_, _Pde6h_, and _Cngb3_, were down-regulated after T3 treatment, implying that there was a defect/disorder in cone phototransduction (Supplementary Figure S1A). The second network was involved in cell death and survival, cellular compromise, and neurological disease. The down-regulation of _Opn1 sw_, _Opn1 mw_, and _Arr3_ was linked to decreased photoreceptor function and cell death (Supplementary Figure S1B). Genes involved in cellular compromise/stress, including _Dhajc5_, _Add1_, _Add3_, Rabgef1_, _Grm8_, _Nkfp1_, _Laptm4b_, _Clathrin_, _Ccdc136_, _Ccd88a_, and _Glmn_, were also down-regulated (Supplementary Figure S1B). In contrast, the _Stat3_ and _Otx2_ genes, which are involved in development, death signaling, and diseases, were upregulated (Supplementary Figure S1B). The gene network summary and pathway overlap were analyzed, showing high levels of interaction among the pathways (Supplementary Figure S2).  
The upstream regulator analysis identified six regulators that were inhibited and one regulator that was activated in the cones after the T3 treatment. The inhibited upstream regulators included _Rabgef1_ (log2 FC = \(-\)2.28, \(p\) = 6.05 \(\times\) 10\({}^{-16}\)), _Crx_ (log2 FC = \(-\)0.81, \(p\) = 5.07 \(\times\) 10\({}^{-3}\)), _Icmt_, _Rax_, _Esrrb_, and _Gucy2f_ (Supplementary Figure S3). These upstream modulators down-regulate the target genes, e.g., _Nrl_, _Rho_, _Cngb3_, _Prb2_, _Pde6b_, _Gnat1_, _Gucy2d_, _Pde6g_, _Aipl1_, and _Cdhr1_, which, in turn, decrease the function of retinal cells/photoreceptors, reduce the quantity of photoreceptors, and together with _Cx3c1I_, activate the death pathways (Supplementary Figure S3).
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 1 Introduction
Our algorithm realizes the automatic formulation of MILPs for given combinatorial optimization problems. This study extends our MILP version to quantum annealing models, where quadratic functions of binary variables need to be formulated. As far as we know, this is the first study to apply Petri net modeling to the quantum annealing. In this paper, we introduce a new notation called binary quadratic nets, which denote Ising or QUBO models, respectively. Our method incrementally targets binary quadratic nets from problem-domain Petri net models that represent target optimization problems. This paper is organized as follows: Section 2 summarizes the basic knowledge on combinatorial optimization problems, quantum annealing, Ising models, and Petri nets. Section 3 introduces a new class of Petri nets, binary quadratic nets, for representing Ising or QUBO models. Section 4 proposes a method for formulating Ising or QUBO models from problem-domain Petri nets and presents some examples. Finally, in Section 5, we present some concluding remarks and areas of future tasks.
## 4 Experiments
### Multi-Hop QA  
#### 4.3.1 Settings for Multi-Hop QA  
Datasets  
HotpotQA [43] is a dataset of challenging multi-hop QA tasks. It extracts relevant facts and performs multi-hop reasoning based on article passages from Wikipedia. The answers to the questions in HotpotQA cover a wide range of domains, reflecting its diversity and complexity for QA tasks. We randomly selected 500 questions to construct the test set for evaluation.  
Baselines  
AMOR [48] decomposes the problem solving process into a series of modular steps, each corresponding to a state in a Finite State Machine.  
UALA [68] quantifies the degree of certainty of a language model in answering a question through various existing measures of uncertainty.  
Self-ask [69] allows the model to explicitly ask and answer a series of subquestions before answering the final question. In this way, the model is able to decompose complex questions more systematically and construct the final answer step by step.  
HGOT [49] leverages the planning capabilities of LLMs. It uses a divide strategy to decompose complex queries into subqueries and build dependency graphs at a deeper level.  
CoT, ReAct, Self-Consistency are the same as the baselines in Sections 4.1.1 and 4.2.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy**} \\
**Methods** & **FuncQA (Multi-Hop)** & **SVAMP** & **Tokens** \\ \hline CoT & 17.64 & 79.8 & 433 \\ ReAct & 41.17 & 80.3 & 2260 \\ Reflexion & 42.65 & 80.5 & 2312 \\ Self-Consistency & 45.59 & 84.6 & 5750 \\ EASYTOOL & 48.53 & 84.9 & 1411 \\ Self-Contrast & - & 89.0 & - \\ CoReaAgents & **57.35** & **90.3** & 3900 \\ \hline w/o TA & 50.00 & 87.1 & \\ w/o RA & 47.05 & 85.5 & \\ w/o TA\&RA & 44.12 & 82.4 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of different baselines and the ablation study on math reasoning. The table highlights the highest scores in bold, with underscores indicating the highest scores achieved by prior methods. - indicates that the method is not publicly available. The rightmost column in the table shows the number of tokens completing this task for each baseline.  
Evaluation metrics  
For HotpotQA, we evaluated it with Exact Match (EM) and F1 scores. EM identifies the proportion of predictions that are in precise agreement with the correct answer, while the F1 score evaluates the average token overlap between the prediction and the correct answer.
# Embedded Deep Learning for Ship Detection and Recognition
## 2 Related Work
### Deep Learning  
The concept of deep learning originates from the study of artificial neural networks, proposed by [PERSON] et al. [6]. Deep learning have made remarkable achievements in the field of image processing, especially for object detection. SSD is a typical one stage detector proposed in [7], which processes images in a single network, and and has good efficiency and accuracy. Faster R-CNN [8] is a two-stage detector, which uses RPN (Region Proposal Network) to produce high-quality region proposals and then detect them with Fast R-CNN [9].  
[PERSON] presents a single neural network named YOLO, which abandons anchor boxes, and predicts bounding boxes and class probabilities directly from a full image in one evaluation [10]. YOLO considers object detection as a regression problem to predict bounding boxes and class probabilities. It can be optimized as end-to-end directly with good detection performance. Fast YOLO can process 155 frames per second. Compared with other state-of-the-art detection algorithms, YOLO makes more localization errors.  
YOLOV2 [11] is based on YOLO [10]. YOLOV2 removes the fully connected layers from YOLO and uses anchor boxes to predict bounding boxes. The YOLOV2 model can run with various image sizes, and it is easy to make a trade-off between speed and accuracy. YOLOV2 is faster than YOLO, which can process 200 frames per second with the Tiny model. Table 1 shows the performance of these algorithms.
# Real-Time Prediction of Operating Parameter of TBM during Tunneling
## 4 Prediction Results
### Prediction Analysis  
Figures 4 and 5 show the prediction results in projects A-1 and B-2, respectively. As shown in Figure 4, the ARIMAX predictor outperformed the non-time series-based ordinary least squares (OLS) predictor in project A-1. This phenomenon can also be seen in project B-2 (Figure 5). These results imply that the previous input values used in ARIMAX provide meaningful information for predicting the values of current thrust, and that the thrust has time-varying features.  
Figures 4 and 5 are the prediction results under the condition that the real-time prediction distance (out-of-sample size) is one segmental lining. For practical applications, an additional analysis was performed according to the real-time prediction distance (Figure 6). As the real-time prediction distance increases, the MAPE (%) also increased logarithmically in both ARIMAX and OLS. As for the comparison between the two predictors, the ARIMAX outperforms OLS regardless of the real-time prediction distance in project A-1. The result in project B-2 also showed a similar trend, but when the real-time prediction distance is more than 8 rings, the performance for the ARIMAX began to be lower than the performance of OLS. The output of the ARIMAX predictor is dependent on time series, whereas OLS is not. If the real-time prediction distance increases, that of the future period to be predicted will also increase, which means ARIMAX does not guarantee a higher prediction accuracy in a longer future period. Based on the experimental tests with the two projects, it can be interpreted that time series-based ARIMAX provides meaningful results where the real-time prediction distance is under 8 rings for practical application.  
To compare the relative performance between WF based on ARIMAX and OLS, we calculated the difference in error in the unit of times (Figure 7). When the real-time prediction distance is 8 rings, the ARIMAX outperformed OLS by approximately 1.3 times in project A-1, and 1.1 times in project B-2. As the real-time prediction distance gradually decreased from 8 rings, the difference in error showed increasing patterns in both projects. The distance of 8 rings corresponds to about 10 m (1.2 m/ring), and such a time interval gives enough time for TBM operators to make a decision. However, a universal value for allowable real-time prediction distance cannot been specified because it varies with the project's conditions. Accordingly, this value (11 m) can be used only as a reference, and application to more projects would be needed to approximate the universal value.  
Figure 4: Prediction results of current thrust in project A-1.
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 2 Background
### AI Use Cases in Healthcare  
AI can automate operations and analyze large patient data sets to provide better healthcare sooner and at lower costs. Insider Intelligence estimates that 30% of healthcare budgets are spent on administration. Time and resources spent by healthcare workers on administrative tasks such as insurance pre-authorization and billing follow-up may be saved if AI were to automate these tasks [27; 33; 36; 37; 38]. According to research compiled by Insider Intelligence, 30% of healthcare budgets are spent on administrative costs [39]. AI has the potential to save healthcare providers' time and money by automating tasks such as insurance pre-authorization, invoice follow-up, and record keeping. AI is being integrated into wearable medical equipment to serve patients better. FitBits and smartwatches driven by AI might analyze data to warn users and physicians about potential health risks. One may lessen the load on doctors and avoid unnecessary hospitalizations and readmissions by using health monitoring technologies [40; 41; 42].
## 3 Results and Discussion
### Assessment of Building Base Shear and Displacement (Drift)
#### 3.1.2 Dowerin--Medium Risk Seismic Area  
Dowerin, categorized as a moderate seismic risk location in Australia with a hazard design factor of 0.20, demonstrates an increase in base shear force compared to traditional masonry panels, as illustrated in Figure 20.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Load Case**} & \multirow{2}{*}{**Location**} & \multicolumn{3}{c}{**Frictional Capacity of the SIM Panels (Type 1 and Type 2)**} \\ \cline{3-5}  & & **Level 3 (L3) Panel** & **Level 2 (L2) Panel** & **Level 1 (L1) Panel** \\ \hline
12 & Dowerin & 52\% & FULL & FULL \\
13 & Macquarie Island & 58\% & FULL & FULL \\
14 & Macquarie Island & 92\% & FULL & FULL \\
15 & Macquarie Island & 77\% & FULL & FULL \\
16 & Macquarie Island & FULL & FULL & FULL \\
17 & Macquarie Island & FULL & FULL & FULL \\
18 & Macquarie Island & FULL & FULL & FULL \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 19: Variation of shear force and building drift for low-risk seismic regions: (**a**) Base shear; (**b**) Building drift.  
The base shear ranges from approximately 120 kN to 400 kN in Dowerin. This significant base shear force raises concerns due to the limited movement allowed in traditional masonry before unit cracking occurs. Consequently, the utilization of SIM panels emerges as a feasible solution for this region, offering greater flexibility and reducing the risk of structural damage.  
The implementation of SIM panels, both Type 1 with a gap and Type 2 without a gap, resulted in a notable reduction in base shear, ranging from 61% to 77% and 73% to 86%, respectively. The effectiveness of Type 1 SIM panels with a gap in mitigating the risk of structural failure due to seismic events in multi-story buildings is particularly evident in this region. However, for Case 12, characterized by high importance levels and soft soil conditions, the use of Type 2 SIM panels without a gap is recommended, as they exhibit lower drift compared to Type 1 SIM panels.
## 5 Uncertainty
### Uncertainty in Localization  
For reporting the locating uncertainty, the concept F.2.4.5 of the GUM [30] is applied. In this case, a known correction factor \(\mathrm{\bar{b}}\) for a systematic effect is not applied but reported in the uncertainty of the measurement. Measurements are reported according to Equation (14) as three dimensional coordinates \(\mathrm{(x,y,z)}\) including a systematic effect with unknown direction and a combined standard uncertainty also with unknown direction. P is the (unknown) value of the measurand. One should note that Equation (14) cannot be evaluated but is just a formal method for reporting results and should be interpreted as described herein.  
\[\left\|\mathrm{P-(x,y,z)^{T}}\right\|=\mathrm{\bar{b}}\mp\mathrm{u_{c}} \tag{14}\]  
The reported combined standard uncertainty \(\mathrm{u_{c}}\) is defined as the positive square root of Equation (15). This complicated expression is explained in Figure 8. The index k identifies the position on the belt  
Figure 8: Graphical example for the calculation of uncertainty showing unknown value at the bottom, a systematic difference b(k) at each position, the uncertainty in determining b(k) as interval \(\mathrm{u^{2}[b(k)]}\) on the line tracking the value and the uncertainty of the individual measurements \(\mathrm{u^{2}[P(k)]}\).  
(23 positions in our case) whereas P(k) defines a sufficient set of measurements at this position (20 in our case).  
\[\mathrm{u}_{\mathrm{c}}^{2}(\mathrm{P})=\overline{\mathrm{u}^{2}[\mathrm{P}( \mathrm{k})]}+\overline{\mathrm{u}^{2}[\mathrm{b}(\mathrm{k})]}+\mathrm{u}^{2} \overline{[\mathrm{b}(\mathrm{k})]} \tag{15}\]  
The term \(\mathrm{u}^{2}[\mathrm{P}(\mathrm{k})]\) is simply the variance for the measurements mainly resulting from the uncertainty of the TDoA measurements and the DOP. At each position, a systematic correction factor \(\mathrm{b}(\mathrm{k})\) can be determined being the mean distance of the measurements to the interpolated reference position. As the value is not known the uncertainty for determining this factor is expressed by \(\mathrm{u}^{2}[\mathrm{b}(\mathrm{k})]\) resulting from the inaccurate reference positions and the variance of the mean value of the measurements. As only a single systematic value \(\overline{\mathrm{b}}\) is determined \(\mathrm{u}^{2}\overline{[\mathrm{b}(\mathrm{k})]}\) describes the variance of the factor itself. An important aspect of applying this formalism is that results can be transferred to different room positions as well. For example \(\mathrm{u}^{2}(\mathrm{P}(\mathrm{k}))\) which is closely related to the DOP can be estimated by calculating the DOP and reevaluating Equation (15) again.
## 7 Limitations  
While Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES) offers significant advancements in extractive summarization, it is important to acknowledge certain limitations inherent in our approach:  
* **Computational complexity**: The construction and processing of hypergraphs, especially with multiple types of hyperedges (semantic, narrative, and discourse), can be computationally intensive. This complexity might limit the scalability of MCHES for very large datasets or real-time applications.
* **Training data requirements**: Like many advanced NLP models, MCHES requires a substantial number of training data to perform effectively. The requirement for large annotated datasets can be a barrier, particularly in specialized domains where such data may not be readily available.
* **Domain specificity**: While MCHES has shown superior performance across various datasets, the model's effectiveness might vary when applied to domain-specific texts not represented in the training data. Adaptation to new domains may require additional fine tuning.
* **Interpretability**: The complexity of the hypergraph-based model and the dual-level attention mechanism may reduce the interpretability of the summarization process. Users might find it challenging to understand how specific sentences are selected for inclusion in the summary.
* **Resource-intensive nature**: The model's requirements for computational resources, including memory and processing power, are higher compared with simpler models. This could be a limiting factor for deployment in resource-constrained environments.
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays  
[PERSON]  
1 ElectrIT, 8200 Veszprem, Hungary; [EMAIL_ADDRESS] (G.Z.  
the sensor orientation must be either measured or known (typical use case is when the sensor is heading upwards) [4; 5]. The main advantage of the 2D approach is that the computational complexity can be decreased significantly [6]. The measurement method proposed in this paper provides 2D bearing measurements.  
For the measurement of bearings of light sources image sensors (e.g., cameras) can be utilized. The tilting of the sensor can be either measured by auxiliary sensors [4; 5] or can be estimated from the image itself [7; 8].  
Light sensors (e.g., photodiodes) offer a more cost-effective solution; such devices utilize the directional sensitivity of the light sensors. The quadrant photodiode angular diversity aperture (QADA), which utilizes four specially arranged photodiodes on a plane, with an auxiliary aperture was proposed [9]. Traditional photodiodes (PDs), arranged in various geometrical formations were also proposed to measure bearings: in [10] and [11] the PDs were attached on the edges of a cube, while the planar circular photodiode array (PCPA) utilizes PDs placed on the surface of a cylinder [12; 13].  
In this paper the performance properties of the PCPA are analyzed. The PCPA was proposed with a corresponding least squares (LS) bearing estimation in [12]. The hardware architecture was improved in [13]. In this paper a novel computationally efficient bearing estimation, using frequency domain, is proposed. The performance of the measurement methods in the presence of various physical error sources is analyzed. The results demonstrate that the effect of reflections can be significant, while the tilting of the sensor has only minor effect on the bearing estimation. The applicability of the measurement device in indoor localization applications is illustrated by real localization examples.
## 3 Results and Discussion  
### Proximate Composition  
The proximate composition of SPI flour, control noodles and SPI noodles is shown in Table 1. The SPI powder was rich in protein (78.54%), while the carbohydrate, lipid and ash contents were only 3.24%, 0.27% and 4.59%, respectively. Control noodles and noodles supplemented with 5%, 10%, 15% and 20% SPI were analyzed for their proximate compositions. The protein content of control noodles was 16.17%, while carbohydrates were the main components and accounted for 47.05% in the noodles. In contrast, 20% SPI noodles were mainly composed of protein with a 30.69% protein content, which was higher than the carbohydrate content of 23.06%. Therefore, the protein content of 20% in SPI noodles had a significant increase of about 1.89-fold compared to control noodles. Additionally, the ash content exhibited an upward trend, while there was no significant difference observed in the lipid content. The result indicated that the addition of SPI to the high-gluten flour contributed to the increase in the crude protein content of the noodles. The high digestibility of soy protein also makes it a promising protein source, especially for the vegetarian population, compared to other proteins. Protein digestibility refers to the percentage of ingested amino acids that may be used by the body after digestion and absorption. About 95-98% digestibility has been reported for soy protein with a Protein Digestibility Corrected Amino Acid Score of 0.92-1.00, which is comparable with animal protein (milk, egg, beef and whey) [32; 33]. Similar findings were also observed in pasta made with sunflower meal protein isolate or with high-protein raw materials [34; 35]. The protein content of the pasta supplemented with a 9% sunflower meal protein isolate was enhanced to 19.33% compared to the control pasta (11.00%) while the carbohydrate content decreased from 80.73% to 71.43% [34]. In addition, noodles with sorghum flour and soybean flour [12] or with lentil, pea, and faba bean hulls [36] were also shown to have a higher ash content. High ash content indicates that SPI noodles contain higher levels of minerals and vitamins.
# Extraction of Catastrophe Boundary and Evolution of Expressway Traffic Flow State
## 3 Catastrophe Boundary Extraction with Spectral Clustering Analysis Algorithm
### Example Verification  
In order to further verify the evaluation effect of the catastrophe boundary calculation model of traffic state, this paper selected the 24 h speed data measured in the west-to-east direction in the middle of the weaving section of Anshan No. 2 Road, Hang'an Expressway, Qingdao City on 24 November 2021 (Wednesday). Then, time-series graphs of speed parameters were plotted with an interval of 5 min. Finally, according to the speed catastrophe boundary obtained from the simulation data, this paper determined the traffic flow status in each period of the actual expressway weaving section and analyzed the results.  
As shown in Figure 6, the evolution of traffic flow state over time was in line with the traffic congestion during the peak period of morning and evening commuting on weekdays. There was a transient synchronized flow state between free flow and congested flow. It can be seen that the extraction method of catastrophe boundary of the traffic flow state based on cusp catastrophe theory is scientific and reasonable for the discrimination results of traffic state in different periods. There were three abnormal values outside of the adjacent data clusters in the state discrimination results, and the accuracy rate was 98.96%. Compared with the neural network algorithm [28] and decision tree algorithm [29] commonly used in traffic state discrimination, improvements of 7.91% and 9.34% were achieved, respectively, indicating good performance.  
Figure 5: Sensitivity of speed catastrophe boundary based on all parameters.