esa-sceva/satcom-chunk-collection huggingface dataset:
# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 3 Methodology  
We propose a direction-aware lightweight framework for traditional Mongolian document layout analysis. As shown in Figure 1, our framework consists of four main components: (1) a modified MobileNetV3 backbone with AIR blocks for efficient feature extraction; (2) a dynamic feature enhancement module (DFEM) for adaptive multi-scale feature fusion; (3) a direction-aware detection head with three cooperative branches for classification, localization, and direction prediction; and (4) a multi-task loss function for joint optimization of these tasks.  
The modified MobileNetV3 backbone incorporates asymmetric convolutions and Adaptive Inverted Residual (AIR) blocks, specifically designed to capture the vertical characteristics of traditional Mongolian text while maintaining computational efficiency. The dynamic feature enhancement module adaptively integrates multi-scale features through channel attention, enabling the model to effectively process complex hierarchical structures in Mongolian documents. The direction-aware detection head employs a \((\sin\theta,\cos\theta)\) vector representation for accurate orientation modeling, with three cooperative branches handling classification, bounding box regression, and direction prediction. Finally, the multi-task loss function with adaptive weighting balances the contributions of different tasks during training, ensuring optimal performance across all evaluation metrics.  
Figure 1: Overview of our proposed framework for Traditional Mongolian document layout analysis. The framework consists of four main components: (1) a modified MobileNetV3 backbone with AIR blocks for feature extraction, (2) a dynamic feature enhancement module for multi-scale feature fusion, (3) a direction-aware detection head with three cooperative branches, and (4) a multi-task loss function for joint optimization. The input is a scanned image of a _Traditional Mongolian_ newspaper (_Inner Mongolia Daily_), and the output is the structured layout analysis result, including region segmentation and reading direction prediction.
## 4 Results and Analysis
### The Effect of the Limiter Height on the Dynamic Characteristics of the Section Valve  
The valve limiter had an important influence on dynamic characteristics of the reed. Figure 18a shows the lift of the suction reed valve at various limiter heights, indicating that the delayed closing time of the suction reed valve became longer as the limiter height increased from 2.0 mm to 3.6 mm. When the lift was higher, both the deformation of the reed and the restoring force became larger, but the closure of the valve was not entirely dependent on the elastic force. The pressure difference between the lower and upper surfaces of the reed was the critical factor for valve closure. The fluid flow velocity in the narrow region between the valve plate and the upper surface of the suction reed valve was higher when the valve lift was lower, the low-pressure region in the pressure distribution became more prominent, and the pressure difference was larger.  
When the limiter height decreased from 2.0 mm to 1.2 mm, an excessively high flow velocity in the narrow region was caused by the excessive low lift, and the low-pressure region in the pressure distribution made the reed valve close prematurely. This directly caused less flow rate suctions into the cylinder, and the pressure in the cylinder was lower than the suction pressure. As shown in Figure 18b, when the piston reached BDC, the reed valve opened again, resulting in a more serious backflow.  
Therefore, 2.0 mm was a more reasonable limiter height for the test compressor. The low-pressure region in the pressure distribution between the upper surface of the reed and the valve plate should be fully considered in valve design.  
Figure 17: (**a**) The movement of the suction valve at various rotation speeds. (**b**) The mass flow characteristics of the suction valve at various rotation speeds.
# A Review of Training and Guidance Systems in Medical Surgery
## 2 Theoretical Framework
### Surgical Simulators
#### 2.1.1 Immersive Simulators  
Virtual reality simulators (VR) are artificial environments provided by a computer and in which one's actions partially determine what happens in the environment. Virtual reality simulators use a graphic representation of the body parts and tools present in surgery. The user can operate in the virtual environment and see the results of the performed manipulations. Moreover, by using a headset, the user can face an immersive experience through sensory stimuli [13; 14].
# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 3 Applications in Medical Decision-Making  
### QC Applications  
QC can enhance drug discovery by enabling simulations of complex molecular interactions at the quantum level [33]. Traditional computational methods often struggle  
Figure 3: Illustration of machine learning algorithms applied to CT images for spleen injury detection. The algorithms include classification, object detection, and segmentation, focusing on determining the presence of injury, identifying the target lesion, and performing voxel-level classification. Reproduced from reference [29] under the Creative Commons Attribution 4.0 International License ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) (accessed on 24 January 2025)).  
to model the behavior of large molecules accurately, especially in the context of drug design. Quantum simulations, however, can calculate the precise interactions between drug molecules and their targets, helping researchers design drugs that are more effective and have fewer adverse effects. This could drastically reduce the time required for preclinical drug development and allow for the discovery of novel therapeutic agents that otherwise are missed [34].  
In radiotherapy, the challenge lies in accurately delivering radiation to the tumor while minimizing damage to surrounding healthy tissues. QC excels at solving optimization problems, which is crucial for radiotherapy treatment planning [35]. By leveraging quantum algorithms, healthcare providers can optimize the distribution of radiation doses across the tumor and surrounding tissue, improving treatment efficacy and minimizing side effects. This capability holds the potential to significantly improve outcomes in cancer treatment, making radiation therapy more precise and personalized for each patient [36].  
Genomic data analysis involves processing vast amounts of information, such as DNA sequences and gene expression data, to identify patterns and make predictions about disease risk and treatment responses [37]. Classical computers are often limited in their ability to process such large datasets efficiently. QC, with its parallel processing power, has the potential to modernize genomic data analysis by enabling faster processing and more accurate predictions. This could lead to breakthroughs in understanding complex genetic diseases and more personalized treatment options based on a patient's genetic makeup [38].
## Conventional GTR and GBR
### Bone Graft Materials
#### 2.4.2 Type of Bone Graft Material  
Autografts  
Autogenous bone grafts are bone tissue which is harvested from one site of the body and implanted into other sites in the same person. The grafts can be cortical or cancellous bone or cortico-cancellous grafts. Fresh autogenous bone contains the surviving cells and osteoinductive proteins including BMPs. They are the best materials due to lacking immunogenicity and fast vascularization [52]. Possible donor sites can be the distal radius, tibia, iliac crest, mandibular ramus, and maxillary tuberosity [53]. Autogenous bone grafts in periodontal regeneration are a very cheap and effective option, but there are some limitations such as fast resorption, donor site morbidity, and limited amount [52] (Table 1).  
Allografts are extracted from one person and implanted into another person from the same species. Due to several limitations of autografts, allografts are used clinically as a common alternative to autografts. They can be achieved from cadavers or living persons [54]. Cancellous allografts provide minimal to no structural strength, mild-to-moderate osteoconductivity, and mild osteoinductive properties. In contrast, cortical allografts show good strength but little osteoinductivity [49]. Freeze-dried and fresh-frozen bone allografts induce more prompt graft incorporation, vascularization, and bone regeneration than fresh bone allograft. Freeze-drying of bone allografts produces a safer bone graft regarding the decrease in the risk of immunologic responses and transmission of diseases [55; 56]. As the osteoblast and BMP are denatured through the processing course of allografts, freeze-dried bone allografts showed delayed bone incorporation and revascularization than autografts. The freeze-drying method also decreases the mechanical strength of the allografts, and the cost of processing the allografts is high. The demineralized allografts possess osteoconductive and osteoinductive properties, and they vascularize fast [55].  
3. Xenografts  
Xenografts are bone tissue harvested from different species, commonly derived from coral, bovine, and porcine sources, consisting of hydroxyapatite bone mineral. In the study comparing different species (canine bovine, porcine, and coral graft), bovine bone xenografts had organic substances extracted, and had a non-antigenic, natural porous matrix, identical to the mineral phase of bone tissue [57]; they were shown to have high osteoconductive properties and a meager absorption rate. Also, another study reported that an inorganic bovine bone has no osteoinductivity and its granular form makes it difficult to be fixed on surgical sites. Moreover, the bovine xenograft is non-absorbable in vivo [58].
# Optimum Geometries for Wireless Power Transfer Between an Oblong Coil and a Rectangular Coil
## 2 Wireless Power Transfer
### Mutual Inductance of a Magnetic Link in DIWPT Configurations
In DIWPT configurations using short coils, the design procedure usually does not convey much geometric intuition, often requiring numerical computation of non-elementary integrals. However, when the magnetic link is constituted of an oblong coil and a rectangular coil, simple geometric interpretations can be obtained for the relative dimensions and relative positions of some optimum configurations, if some simplifying hypotheses are adopted, as presented in Section 3.
## 3 Results and Discussion
### Effect of Different Sterilization Methods on Lipid Metabolites in Red Raspberry Juice
Table 2 listed 24 differentially metabolized lipids identified after HHP and HTST treatments, with 7 lipids altered in HHP-treated juice (1 upregulated, 6 downregulated) and 22 in HTST-treated juice (9 upregulated, 13 downregulated). This indicated a more pronounced effect of HTST treatment on the lipid profile, aligning with changes observed in the fatty acid composition. Notably, key metabolites, such as 13-peroxy octadecatrienoic acid, 9-peroxy octadecatrienoic acid, 9-hydroxy octadecatriene, and 9,10-epoxy octadecatrienoic acid, known to contribute to fruit and vegetable aroma synthesis [38], were notably impacted. These hydroperoxides of fatty acids, typical intermediates in both the LOX and \(\beta\)-oxidation pathways, can transform into C6/C9 aldehydes via the LOX pathway or into hydroxy acids and oxyacids through the \(\beta\)-oxidation pathway, eventually forming lactone aroma compounds [39]. Consequently, significantly downregulated 9-peroxy octadecatrienoic acid may be converted to C6/C9 aldehydes through the LOX pathway or converted to lactone aromas through the \(\beta\)-oxidation pathway, consistent with the increased downstream aroma contents of (\(Z\))-3-hexenol, hexanol, \(\delta\)-decanolactone, and dihydro-tinidiolide observed. Moreover, these four intermediates were notably reduced in the HTST-treated juice, indicating that oxidative degradation of unsaturated fatty acids during HTST processing may involve not just thermal oxidation but also enzymatic conversion by key enzymes in the fatty acid metabolic pathway. Figure 5: Partial least squares discrimination analysis (**a**) and cluster analysis (**b**) based on the lipid profile of red raspberry juice in raspberry juices after different sterilization treatments.
## 5 Case Studies  
### Case Study One: Khan Academy  
#### 5.1.1 Detailed Description of Khan Academy  
Khan Academy, a pioneer in online education, serves as an exemplary case study for implementing the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF) [49]. It offers various learning resources across various subjects, primarily targeting K-12 education. What sets Khan Academy apart is its adaptive learning technology that personalizes educational content based on individual learner performance and progression. The platform utilizes instructional videos, practice exercises, and a personalized learning dashboard that empowers learners to study independently in and outside the classroom [50].  
At the heart of Khan Academy's system lies a sophisticated data analytics engine that tracks each learner's interactions and progress, providing insights into their strengths and areas for improvement. This data-driven approach enables the platform to offer tailored learning experiences, making it an ideal environment for applying the DFDLOF model. By integrating DFDLOF, Khan Academy can enhance its adaptability, offering more nuanced personalization that responds to what students are learning and how they are learning, thereby optimizing the educational pathway for each student.
## 3 Results
### Nanoparticles for Gastric Cancer Treatment
Another study employed paclitaxel, encapsulated in cationic liposomes modified with PEG to enhance in vivo circulation of the liposomes, and loaded with a siRNA capable of inhibiting APAF1. APAF1 is a protein that blocks apoptosome assembly and caspase 9/3 activation. This therapy induced a decrease in cell colony formation and an increase in apoptosis induction (5% more compared to the free drug). In addition, in a xenograft in vivo experiment, it achieved a 55% tumoral volume reduction between the complete therapy and the treatment without gene silencing and chemotherapy [65]. [PERSON] et al. (2021), co-encapsulated arsenic oxide and a siRNA targeting Her-2 in a calcium phosphate nanoparticle, modified with PEG. The combined therapy increased the apoptosis rate by 12.1% and decreased the expression of different markers of tumor progression such as HER2, CXCR4, MMP2, and MMP9. Finally, the weight of the tumor mass in vivo was reduced by about 71.4% with the combined therapy with respect to the free drug and the number of metastases was reduced by 50% [66]. Therefore, in gastric cancer, the most widely used chemotherapeutics have been paclitaxel (conventional) together with other novel therapies such as arsenic oxide. Among the molecular targets silenced with siRNA, we find HER2 or APAF1, which are linked to chemoresistance in this type of cancer. Among the most relevant results, significant reductions in cell proliferation were shown in simultaneous treatment compared to individual treatments, in addition to a reduction in tumor growth in vivo (Table 4). \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Nanoformulation (Loading Method)** & **Chemotherapeutic Agent** & **siRNA Target** & **Targeting Ability** & **In Vitro Results** & **In Vivo Results** & **Ref.** \\ \hline Mucoadhesive vehicle composed of \(\beta\)-glucan and docoshexaenoic acid (modified solvent-metisolvent precipitation method) & & & & Increased cell cytotoxicity compared to the administration of the free drugs. & Reduced stomach tumor weight and 2.5-fold increase in apoptosis, with a 33% decrease in the expression of Ki67. & Reduced stomach tumor weight Reduced the tumour weight and 2.5-fold increase in apoptosis, with a 33% decrease in the expression of Ki67. & Reduced stomach tumor weight Reduced the tumour weight and 2.5-fold increase in apoptosis, with a 33% decrease in the expression of Ki67. & Reduced stomach tumor weight Reduced the tumour weight and 2.5-fold increase in apoptosis, with a 33% decrease in the expression of Ki67. & Tumor volume decreased by 50\% compared with the untreated controls. & [63] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Nanoparticle-siRNA-drug strategies developed for gastric cancer treatment.
## 4 Experiment Results
### Experimental Setting
However, we notice that the CPBD of the TFI outperformed other methods due to the TFI results with rich texture along with high noise and artifact. Our CSFR method which maintains sharpness and higher contrast, presented the second best performance in all the comparison methods. The 14.92% improvement of CPBD can be  
Figure 7: Different images of the reconstructions of the TFP, SNM, and our CFSR with the TFI. From left to right: (**a**) TFP (\(w\) = 160); (**b**) SNM; (**c**) CFSR. The larger the white areas on the image, the larger the difference. The results of our CFSR are with less noise and motion blur in the static and dynamic region, respectively. achieved by our CSFR model compared with SNM method on average. In summary, our CFSR methods can present appreciable performance. Furthermore, we also applied the reconstruction methods on a simulated dataset provided by [23], where the spike streams and ground truth images are generated from video-based virtual scenes with the camera's ego-motion. We compared the TFP, TFI, SNM, and CSR methods against the ground truth for quantitative evaluation. All parameters involved in our method and the competing methods were optimally assigned or selected as suggested in the reference papers. In our method, we set \(T=300\), \(\theta=10\), and \(w=30\), respectively. As shown in Figure 8, the proposed CSR method achieved the best results in terms of PSNR and comparable results with SNM in terms of SSIM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Method** & **Rotation1** & **Rotation2** & **Car** & **Mean** \\ \hline \multirow{5}{*}{STD} & TFI [18] & 25.1455 & 40.1673 & 53.6904 & 39.6677 \\ \cline{2-6}  & TFP [20] & 23.5241 & 35.9377 & 43.6953 & 34.3858 \\ \cline{2-6}  & SNM [24] & **39.9200** & **60.2485** & **58.1364** & **52.7683** \\ \cline{2-6}  & CSR & 24.3537 & 37.6037 & 55.8144 & 39.2573 \\ \cline{2-6}  & CFSR & **50.6763** & **75.0012** & **68.4223** & **64.6998** \\ \hline \multirow{5}{*}{CPBD [34]} & TFI & 0.9101 & **0.8189** & **0.9572** & **0.8954** \\ \cline{2-6}  & TFP & 0.8115 & 0.6153 & 0.5075 & 0.6448 \\ \cline{2-6}  & SNM & 0.8254 & 0.6376 & 0.6511 & 0.7041 \\ \cline{2-6}  & CSR & 0.8811 & 0.6781 & 0.7564 & 0.7719 \\ \cline{2-6}  & CFSR & **0.9262** & **0.6905** & **0.8109** & **0.8092** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The STD and CPBD metrics of the PKU-SPIKE-RECON dataset by different methods. Red and blue indicate the best and the second best performance, respectively. Figure 8: Comparison among different reconstruction methods on synthetic data. From left to right: ground truth, (**a**) TFP (\(w=160\)); (**b**) TFP (\(w=30\)); (**c**) TFI; (**d**) SNM; (**e**) CSR (\(w=30\), \(\theta=10\)). The PSNR and SSIM of the reconstructed images are also listed below the images, respectively.
## 3 Measurement Results
Normalized and linearized experimental spectra (blue curves) with the synthetic spectra (red curves), as well as their residuals are shown in Figure 9c,d for 2.656 \(\upmu\)m and 2.808 \(\upmu\)m respectively. In contrast to raw spectra in Figure 9a,b, normalized spectra are a result of averaging of seven AMU measurements per single measurement cycle. Parameters of spectral lines for the synthetic spectra were taken from HITRAN 2016 [13], Voight profile was exploited as line contour shape. As spectral ranges were chosen in such a way to include each spectral line of interest in separate, not an overlapped position, fitting of the experimental spectra is carried out by linear regression method. All spectral lines inside the observed spectral range were included in synthetic spectra. Self-broadening parameters were used for CO\({}_{2}\) lines, whereas air-broadening parameters were used for H\({}_{2}\)O due to lack of data on the pressure broadening of H\({}_{2}\)O in CO\({}_{2}\) in our spectral regions. Preliminary retrieved data of isotopologue abundance ratio from measured spectra in comparison with HITRAN isotopologue abundance are presented in Table 2. Isotopologue abundance ratio was calculated based on the following equation (on the example of \({}^{18}O\) contained in water vapor):  
\[{}^{18}R_{(H_{2}O)}=\frac{N(^{18}O)}{N(^{16}O)} \tag{4}\]  
where \(N(^{18}O)\)--number of water vapor molecules with \({}^{18}O\), \(N(^{16}O)\)--number of water vapor molecules with \({}^{16}O\). Comparison of the retrieved isotopologue abundance ratio with the ratio from _HITRAN_ was calculated by the equation (on the example of \({}^{18}O\) contained in water vapor):  
\[\delta^{18}O_{(H_{2}O)}=(\frac{{}^{18}R_{(H_{2}O)}}{{}^{18}R_{(H_{2}O)}}-1) \times 1000 \tag{5}\]where \({}^{18}R_{(H_{2}O), HITRAN}\)--oxygen isotopologue abundance ratio of the _HITRAN_. As for the retrieval precisions of the isotopologue abundance ratio, it was calculated based on a variation of parameters included to the model and are 0.87% and 3.35% for 2.656 \(\upmu\)m and 2.808 \(\upmu\)m respectively.
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 2 Average-Case Refinement
### Vulnerability, Channels, and Leakage
Since each observation \(y\) is produced with some probability \(a_{y}\), it is sometimes conceptually useful to consider that the _result of running a channel_\(C\), on the initial knowledge \(\pi\), is a \"hyper\" distribution \([\pi,C]\): a probability distribution on posteriors \(\delta^{y}\), each having probability \(a_{y}\). It is then natural to define the (average-case) _posterior vulnerability_ of the system by applying \(V\) to each posterior \(\delta^{y}\), then averaging by its probability \(a_{y}\) of being produced:  
\[V[\pi,C]\ \ :=\ \ \sum_{y}a_{y}V(\delta^{y}) \tag{4}\]  
when defining vulnerability in this way, it can be shown [9] that \(V\) has to be _convex on_\(\pi\); otherwise, fundamental properties (such as the data processing inequality) are violated. Any continuous and convex function \(V\) can be written as \(V_{g}\) for a properly chosen \(g\), so, when studying average-case leakage, we can safely restrict to using \(g\)-vulnerability. _Leakage_ can be finally defined by comparing the prior and posterior vulnerabilities, e.g., as \(\mathcal{L}_{g}^{+}(\pi,C)=V_{g}[\pi,C]-V_{g}(\pi)\). (Comparing vulnerabilities \"multiplicatively\" is also possible but is orthogonal to the goals of this paper.)