esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
First, the model personalization process requires administering an initial chemotherapy dose before estimating patient-specific parameters, which may introduce delays in optimizing the dosing protocol and pose risks to the patient. Additionally, once the neural network has been trained, the chemotherapy dosing period (cycle duration) remains fixed. Another challenge lies in the frequent sampling of tumor and immune cell populations, which may not always be feasible in clinical practice. Moreover, only parameters with a significant impact on tumor dynamics can be effectively personalized, potentially reducing the overall accuracy of the patient-specific model. Furthermore, a distinct neural network must be trained for each variation in the mathematical model, increasing the computational burden and limiting generalizability. The last drawback is that the computational complexity of generating the datasets and training the MLP NN is high. These limitations highlight the challenges that must be addressed for effective clinical implementation of the proposed approach. Future research should focus on addressing the limitations of the proposed approach and enhancing its clinical applicability. One key direction is the development of adaptive dosing strategies that allow real-time therapy adjustments (optimization) based on continuous patient monitoring providing new data. Improving the feasibility of patient-specific data collection is another critical area, which could involve integrating non-invasive biomarkers or imaging techniques to estimate tumor and immune cell populations more efficiently. Further work is also needed to test different and more complex structures of cancer models. Clinical validation through retrospective and prospective trials will be essential to assess the effectiveness and safety of the personalized dosing protocol in real-world settings.
## 2 ARD Framework
### Data Download and Access  
A list of the generated products is forwarded to the download stage on the completion of the scene discovery stage. The framework can ingest data from various data sources, thus enabling instantaneous access to products. The Python Sentinelsat API [46] can query and download data from the Copernicus Open Access Hub [47]. However, the retention policy mandates shifts of old data from the online archive to the Long-Term Archive (LTA); the retention period is 18 months for Copernicus Sentinel-2 L2A. Older products in the LTA can only be accessed upon a trigger generated by a request for each individual product  
Figure 2: Flow chart for the selection of Sentinel-1 and Sentinel-2 products.  
24 h in advance. The framework implements data retrieval from other platforms that mirror the Copernicus hub, such as the Alaska Satellite Facility (ASF) for Sentinel-1 [48], Google Cloud Storage for Sentinel-2 [49] and Amazon Web Services (AWS) for Sentinel-1 and Sentinel-2 [50; 51], limiting the delay for obtaining data. Authentication of the right to access is required from Copernicus, Google Cloud, AWS or ASF to initiate downloading. The downloading stage implements the caching of products to prevent re-downloads of existing products, a considerable time-consuming step.
## 3 Results
### Mechanical Properties  
In terms of mechanical properties, MOE, MOR and Brinell hardness have been evaluated. The mean value, standard deviation, and minimal and maximal values are listed in Table 4. Results of ANOVA are indicated by letters a-e and u-z. The first letter refers to the different raw material (for values with the same letter, the raw material has no significant influence), the second letter refers to the used adhesive (again, and for values with the same letter, the adhesive has no influence).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Properties** & **Board** & **Mean** & **Std. Dev.** & **Min** & **Max** \\ \hline MOE [GPa] & A & 1.13 a,u & 0.42 & 0.73 & 1.79 \\  & B & 1.15 b,v & 0.29 & 1.20 & 1.96 \\  & C & 2.57 c,w & 0.36 & 2.12 & 3.00 \\  & D & 1.32 d,u & 0.30 & 0.92 & 1.60 \\  & E & 1.30 d,x & 0.44 & 0.68 & 1.70 \\  & F & 0.86 e,y & 0.13 & 0.65 & 1.00 \\ \hline MOR [N/mm\({}^{2}\)] & A & 4.25 a,u & 1.21 & 2.55 & 5.47 \\  & B & 5.20 a,w & 1.02 & 3.79 & 6.53 \\  & C & 13.26 b,y & 2.04 & 11.09 & 16.26 \\  & D & 7.43 c,v & 1.34 & 5.36 & 8.65 \\  & E & 8.86 c,x & 2.75 & 5.35 & 12.18 \\  & F & 5.47 d,z & 0.75 & 4.33 & 6.32 \\ \hline Brinell hardness [N/mm\({}^{2}\)] & A & 62.50 a,u & 12.55 & 39.95 & 82.65 \\  & B & 43.70 b,v & 7.07 & 36.57 & 57.72 \\  & C & 28.00 c & 5.48 & 22.26 & 35.39 \\  & D & 53.90 d,u & 12.70 & 27.82 & 70.59 \\  & E & 55.50 d,w & 8.51 & 37.30 & 63.89 \\  & F & 18.00 e,y & 3.08 & 15.26 & 22.82 \\ \hline \end{tabular} a,b,c,d,e values with the same letter were not significantly different (raw material),u
## 3 Results and Discussion
### Wettability of the A-CF/EP/FEP Composites  
FEP is a low-surface-energy material, and its content affects the wettability of A-CF/EP/FEP composites. The WCAs of A-CF/EP/FEP composites with 10-50 wt% FEP is shown in Table 1. Obviously, all the A-CF/EP/FEP composites are hydrophobic, and the WCAs exceed 90\({}^{\circ}\). The WCA increases firstly and then decreases with the rise in FEP content, with a maximum of 109.9 \(\pm\) 2.6\({}^{\circ}\) at the FEP content of 20 wt%. Owing to the low surface energy of FEP, the hydrophobicity of the A-CF/EP/FEP composites shall be improved with the increasing FEP content, but this does not seem true, which brings us to the combination among EP, FEP, and the A-CF fillers.  
Figure 3: FTIR spectra of CF (**a**), E-CF (**b**), and A-CF (**c**).  
Figure 5a shows obvious holes in the A-CF/EP/FEP composites at higher FEP content (50 wt%), which is mainly due to the gap between the resin phase (EP, FEP) and the filler (Figure 5b). In other words, excessive FEP is unfavorable for the hydrophobicity of M-CF/EP composites. The content of the low-surface-energy FEP plays an important role in the construction of the hydrophobic surface, and 20 wt% FEP is confirmed as the best content.
# A Cyanobacteria-Based Biofilm System for Advanced Brewery Wastewater Treatment
## 3 Results & Discussion
### Practical Applications and Research Perspectives  
The photosynthetic biofilm approach described in this study could complement conventional brewery wastewater treatment systems such as activated sludge. Life cycle analyses indicate that photosynthetic systems provide a less energy-demanding option (\(\sim\)47% operating cost reduction) with a positive environmental impact [49]. The use of biofilms can further enhance the financial viability of photosynthesis-based wastewater treatment facilities as it entails efficient, low-cost, biomass harvesting [23]. The biomass composition of this study's microbial consortium adds a new dimension to the sustainability of the process. Due to its high carbohydrate content, the biofilm biomass can be used as feedstock for bioethanol, a product of significant commercial value [50]. To realize the system's full biotechnological potential more research should be carried out on analyzing the biofilm's EPSs profile, as certain cyanobacterial EPSs could be sustainable source materials for the production of hydrocolloids, bio-sourced materials, and biological agents [21; 38].
## 3 Numerical Model
### Finite Element Model
The contact  
Figure 11: Two-dimensional finite element model. type between the station structure and the surrounding soil is surface-to-surface contact, and the contact property is friction contact. The tangential behavior is considered as the friction force to prevent the relative sliding between surfaces, and the friction coefficient is set to be 0.4. A hard contact is established in the normal direction of the interface. In order to simplify the analysis model, the dividing plate element is not set in the middle column of the seismic mitigation structure. The friction contact is set between the two adjacent unit columns, and the friction coefficient is 0.8 [33]. The soil and structure are simulated by the four-node plane strain element (CPE4R) in ABAQUS. On the grid division, the grid size of the station structure is 0.5 m \(\times\) 0.5 m, and the soil structure is divided into 0.5 m \(\times\) 0.5 m grids within 2 times the width of the station. Outside the double width, the vertical grid height is 1 m, the width is 0.5 m, the transverse grid width is 1 m, and the height is 0.5 m.  
The Chi-chi wave, Duzce wave, and Manjil wave are selected as input ground motions for dynamic time history analysis. The ground motion input only considers the horizontal seismic action. In the calculation, the peak values of the three seismic waves should be adjusted to make the peak value of the seismic wave be adjusted to 0.1 g. The acceleration time history curve of the ground motion is shown in Figure 12.  
Figure 12: Input ground motion acceleration time history curve. (**a**) Chi-Chi earthquake. (**b**) Dzuce earthquake. (**c**) Manjil earthquake.
# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 3 Results
### Machine Learning Performance Assessment  
To obtain evaluation metrics, a confusion matrix was constructed for each error label (X):  
* \"True positives (TP) of \"X\" are all X instances that are classified as X;
* \"True negatives (TN) of \"X\" are all non-X instances that are not classified as X;
* \"False positives (FP) of \"X\" are all non-X instances that are classified as X;
* \"False negatives (FN) of \"X\" are all X instances that are not classified as X.  
True negative (TN) metric is not useful for object detection. Therefore, it was ignored. The following metrics were calculated as seen in Table 2 using the confusion matrix: recall (sensitivity) = TP/TP + FN, precision = TP/TP + FP, and F1 score = (2 \(\times\) Precision \(\times\) Recall)/(Precision + Recall), where TP, FP, FN represent true-positive, false-positive, and false-negative results, respectively.  
Figure 2: Representative samples of assessed contact area overlap; Cyan box: within enamel, White box: at DEJ, Off-white box: within dentin, Green box: within restoration (clear margins), Blue box: within restoration (nuclear margins).  
Results showed that the model excelled in detecting the following two classes: \"overlap within enamel\" (Precision: 0.893, Recall: 0.899, F1 score: 0.896) and \"overlap within restoration \"clear margins\" (Precision: 0.893, Recall: 0.619, F1 score: 0.764) (Table 2). However, for the other classes, the evaluation metrics were unreliable due to under-representation of those classes (Table 2). The overall system errors made by this built model was represented by the log loss value, which had a value of 0.15.
# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 4 Experiments and Results
### Datasets, Implementation Details, and Evaluation Protocols  
#### 4.1.1 Datasets  
The experiments were implemented on four public datasets, which include CUHK-01 [22], CUHK-03 [23], Market-1501 [24], and VIPeR [25]. Each dataset is described briefly as follows.  
CUHK-01 dataset: The CUHK-01 Campus dataset [22] contains 1816 persons and five pairs of camera views (P1-P5, ten camera views). They have 971, 306, 107, 193, and 239 persons, respectively. Each person has two images in each camera view. This dataset is used to evaluate the performance when camera views in the test are different than those in training. All images are normalized to 160 \(\times\) 60.  
CUHK-03 dataset: There are 13,164 images of 1360 identities contained in the CUHK-03 dataset [23]. All pedestrians are captured by six cameras, and each person is only taken form two camera views. The dataset provides both the manually cropped bounding box and the automatically cropped bounding box with a pedestrian detector [26]. We report results on both of these versions of the data. Following the protocol used in [23], we randomly partitioned the dataset into a training set of 1160 persons and a test set of 100 persons. This random partition was repeated for 20 times for computing the averaged performance.  
Market-1501 dataset: Market-1501 [24] contains 32,668 detected bounding boxes of 1501 persons, with each of them captured by six cameras at most and two cameras at  
Figure 7: Some typical samples from three public datasets. Each column shows two images of the same person from two different cameras under significant changes of the scale and illumination. (a) VIPeR dataset exhibits significant differences between different views. (b) CUHK-03 dataset with labeled version. (c) Market-1501 dataset has significant and consistent lighting changes. (d) CUHK-01 dataset is similar with CUHK-03.  
least. Similar to the CUHK-01 dataset, it also employs a DPM detector [26]. We used the provided fixed training and the test set, containing 750 and 751 identities, respectively, to conduct experiments.  
VIPeR dataset: The widely used VIPeR dataset was collected by [PERSON] and [PERSON] [25] and contains 1264 outdoor images obtained from two views of 632 persons. Each pair is made up of images of the same person from two different cameras, under different viewpoints, poses, and light conditions, respectively. All images were normalized to \(128\times 48\) pixels. View changes were the matched image pairs containing a viewpoint change of 90 degrees.
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 7 Experiments
### Evaluation Methodology  
#### 7.2.1 Balanced Initial Model  
The enhanced versions of HMMs published in other papers [16; 17; 18; 19; 20; 21; 22; 23] usually use some sort of prior knowledge to initialize the transition and emission matrices, which enables the adopted parameters' distribution comply with the real data distribution to be evaluated. As a result, we could not distinguish if the improvement in performance was due to the skewed initial model or the enhancement mechanisms created by us. Accordingly, to eliminate biasing factors, the AA-HMM was initialized as a Balance Model (BM)--the probabilities in the transition and emission matrices were evenly distributed, as shown in Tables 3 and 4. Since the initial model was not biased to either one of the two classes, any accuracy improvement would then be attributable to the actual mechanism of the algorithm.
## 4 Review and Comparative Evaluation of VQA Models
### Performance Comparison
Additionally, BH-VQA (LSVQ  
Figure 8: Achieved RMSE values between different VQA models (lower is better). version) showcases a strong correlation (PCC = 0.847, SROCC = 0.816), proving that the developed deep neural network-based quality-aware feature extractor effectively captures the temporal distortions brought by frame-rate variations and object and camera motion. GSTI (PCC = 0.791, SROCC = 0.7909) and some AVQBits variants (e.g., AVQBits | H0 | s and AVQBits | M3) achieve moderate correlations, indicating that their designs partially capture the perceptual aspects of HFR video quality. VMAF shows a respectable correlation, though it does not match the performance of fuzzy logic-based approaches. In contrast, classical metrics such as PSNR, SSIM, and MS-SSIM exhibit weaker alignment with subjective perception due to their limited ability to capture subtle motion artifacts and high-frame-rate dynamics. Advanced methods like ST-RRED, FSIM, FRQM, SpEED, and DeepVQA display varying performance but generally fall short compared to the leading models. For the sake of clarity, Table 4 compares the results depicted in Figures 8 and 9. These results align with earlier discussions, which anticipated that simpler, pixel-level metrics would yield higher prediction errors and weaker correlations with MOS. Notably, the new ANFIS-VQA model achieves comparable or superior accuracy with seven rules, in contrast to the 36-rule complexity of FLAME-VQA. This significant reduction in rule complexity not only minimizes computational overhead but also highlights the effectiveness of integrating adaptive neuro-fuzzy techniques to capture the perceptual quality of HFR video content. Notwithstanding, all comparative metrics reported for BH-VQA, DeepVQA, GSTI, VMAF, and related models were evaluated on the LIVE-YT-HFR database. However, the models vary in their use of input data: deep-learning approaches process raw video frames and learn hierarchical feature representations, whereas the new ANFIS-VQA consumes  
Figure 9: Achieved PCC (**a**) and SROCC (**b**) values (higher is better) on the LIVE-YT-HFR video database with different VQA models. only four precomputed video features. As a result, observed differences in predictive performance may partly arise from these distinct input modalities rather than purely from algorithmic effectiveness. This distinction should be borne in mind when contemplating the presented results.
# Mapping Based Quality Metrics for Mesh Deformation Algorithms Using Radial Basis Functions
###### Abstract  
The mesh deformation method based on radial basis functions is widely used in computational fluid dynamics (CFD) simulation with a moving boundary. The traditional method for generating CFD mesh quality metrics called mesh-based metrics use the information of deformed mesh with specific element node coordinates and a connectivity relationship. This paper develops a new mesh quality, metric evaluating method based on the mapping process between the initial and deformed mesh, which is named mapping-based metrics. Mapping-based metrics are evaluated based on the conception of the deformation principle in continuum mechanics. This method provides a new point for mesh quality evaluation without requirements of deformed mesh coordinates and element connectivity information. Three test cases show that, comparing with indirectly solving by a geometrical method, mapping-based metrics accurately reveal the changes of the angle and area over the whole deformed domain. Additionally, the mapping-based metrics give high applicability to the quality of deformed mesh compared to mesh-based metrics. The quality evaluation method for CFD mesh proposed in this paper is effective.  
mesh deformation; radial basis functions; mesh quality metrics; computational fluid dynamics +
Footnote †: journal: Applied Sciences
## 3 Methodology  
### Research Procedure  
Research was carried out on the influence of internationalization and competitive advantage in Taiwan's CNC machines industry on enterprise performance, using the industrial environment as the interference variable. This research adopts the questionnaire survey method as the research method. The domestic and foreign journals and the literature related to the industrial environment and enterprise performance were compiled to collect data related to the research topic, establish the research structure, compile questionnaires, and conduct questionnaire surveys. Firstly, the content validity of the research questionnaire was confirmed using the expert questionnaire and, after compiling expert opinions, the pre-test questionnaire was compiled, and then the questionnaire was pre-tested. After the pre-test questionnaire was collected, the discrimination, reliability, and internal consistency of the questionnaire items were analyzed using item analysis. And factor analysis was used to understand the construct validity of the questionnaire, as the basis for the revision of the questionnaire. After completing the project analysis and questionnaire revision, a formal questionnaire was formed, and then the formal questionnaire was administered. After the formal questionnaire was collected, statistical analysis was performed. After statistical analysis, the results of this study were compiled and, finally, the conclusions and recommendations from this study were written according to the research results [34; 42]. Based on the research purpose and the literature review results, this study proposes the following research hypotheses (Figure 1):  
**Hypotheses 1 (H1).**_The high degree of internationalization in Taiwan's CNC machines industry has a positive impact on enterprise performance._  
**Hypotheses 2 (H2).**_The high degree of competitive advantage in Taiwan's CNC machines industry has a positive impact on enterprise performance._  
**Hypotheses 3 (H3).**_The high degree of internationalization in Taiwan's CNC machines industry has a direct impact on the competitive advantage._  
**Hypotheses 4 (H4).**_The better the business operation ability, the stronger the positive relationship between internationalization and enterprise performance._
## 4 Implementation and Evaluation
### Implementation
#### 4.2.3 State Search and Insertion Time  
This experiment compares the amount of space required by MSSA and a table (cuckoo hash [46]) to store 10,000 8-byte flow states. MSSA has a 1024-bytes global state area; 64 flow tables, each with a 64 bytes table state area; and a dynamic packet state area that takes up very little space in the experiment. The experiment looked at two different sizes of flow-state areas: 8 and 64 bytes.  
The experimental results are depicted in Figure 15. The blue line's MSSA has an 8-byte flow state area, while the black line's MSSA-2 has a 64-byte flow state area.  
To sum up, we note the following:  
1. There is a fixed space overhead in MSSA. Even if no states are needed to be recorded, the global state area, the table state area, and the flow state area all take up space, and there is some space waste if only a few states are recorded.
2. MSSA consumes space linearly, with each additional flow increasing the flow state area space. This results in a lot of wasted space because not all flows need to keep states. It is possible to solve this issue by adding configuration to the southbound interface, specifying the size of each table's table state area and flow state area.  
Figure 14: Performance comparison using match fields or actions to compare states.  
Figure 13: Two state-comparison methods. (**a**) Compare state with match fields. (**b**) Compare state with actions.  
When the flow state area size is 8 bytes, MSSA storing more than 1000 flow states takes up much less space than the hash table. When the flow-state area size is 64 bytes, the MSSA takes up almost the same amount of space as the hash table. The 64 bytes of available space far exceeds the needs of most stateful applications for storing some count values or signs. Furthermore, because MSSA does not require extra space to avoid conflicts, it can use all of the available space to record the state, resulting in a higher space utilization rate than the hash table.