esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Patients with metastatic castration-resistant prostate cancer (mCRPC) have poor outcomes. Docetaxel (DTX)-based therapy is a current standard treatment for patients with mCRPC. Approaches combining conventional chemotherapeutic agents and nanoparticles (NPs), particularly iron oxide NPs, may overcome the serious side effects and drug resistance, resulting in the establishment of new therapeutic strategies. We previously reported the combined effects of Fe\({}_{3}\)O\({}_{4}\) nanoparticles (Fe\({}_{3}\)O\({}_{4}\) NPs) with DTX on prostate cancer cells in vitro. In this study, we investigated the combined effects of Fe\({}_{3}\)O\({}_{4}\) NPs and rapamycin or carboplatin on prostate cancer cells in vitro. Treatment of DU145 and PC-3 cells with Fe\({}_{3}\)O\({}_{4}\) NPs increased intracellular reactive oxygen species (ROS) levels in a concentration-dependent manner. Treatment of both cell lines with 100 \(\upmu\)g/mL Fe\({}_{3}\)O\({}_{4}\) NPs for 72 h resulted in significant inhibition of cell viability with a different inhibitory effect. Combination treatments with 100 \(\upmu\)g/mL Fe\({}_{3}\)O\({}_{4}\) NPs and 10 \(\upmu\)M carboplatin or 10 nM rapamycin in DU145 and PC-3 cells significantly decreased cell viability. Synergistic effects on apoptosis were observed in PC-3 cells treated with Fe\({}_{3}\)O\({}_{4}\) NPs and rapamycin and in DU145 cells with Fe\({}_{3}\)O\({}_{4}\) NPs and carboplatin. These results suggest the possibility of combination therapy with Fe\({}_{3}\)O\({}_{4}\) NPs and various chemotherapeutic agents as a novel therapeutic strategy for patients with mCRPC.  
**Keywords:** prostate cancer; Fe\({}_{3}\)O\({}_{4}\) nanoparticles; carboplatin; rapamycin; reactive oxygen species
## 2 The AMC and Antenna Designs  
### Design of AMC Structure  
Figure 1 depicts the configurations of the designed unit cell of the AMC. The AMC unit cell is composed of two FR-4 substrates (a material with a dielectric constant of 4.3, loss tangent of 0.025, and thickness of 1.52 mm), and a ferrite material called MP2106-0M0, made by Laird [23]. The ferrite material has high-permeability properties in the HF, VHF, and UHF bands. It is positioned between the two FR-4 substrates (Figure 1a) and the thickness is \(t_{f}\) = 10 mm. The upper side of the upper substrate has a patch and the lower side of the lower substrate has a ground area. The side length of the unit cell and square patch are \(W_{AMC}\) = 100 mm, and \(W_{P}\) = 60 mm, respectively.  
Figure 1b shows the simulation boundary condition of the unit cell. To set up the periodic boundary condition, the planes on the \(x\) and \(y\)-axis are designated as perfect electric conductor (PEC) and perfect magnetic conductor (PMC), respectively. To simulate the reflection phase of the unit cell, the waveport is located on the +2-axis.  
Figure 2 illustrates the simulated reflection phase of the designed unit cell. The unit cell's operating bandwidth is generally referred to as a frequency range with the reflection phase characteristics ranging from \(-90^{\circ}\) to \(+90^{\circ}\)[24]. The simulated operating frequency range of the unit cell is 19-485 MHz (25.5:1). The simulation results show that a compact broadband unit cell is designed using high-permeability ferrite material. In order to verify the performance of the designed AMC unit cell, the performance was compared with existing studies. The operating bandwidth and size of the designed AMC are specified in Table 1.  
Figure 1: Configuration of the proposed unit cell of the AMC: (a) perspective-view; (b) boundary condition.
## 3 Results
### Relationships between Anthropometric and Physical Fitness Data  
Correlations of practical value were found between anthropometric and physical fitness parameters (Table 5). The most prevalent and largest relevant relationships were detected for body fat and performance parameters. Sprint performance was highly correlated with body fat independent of age. The correlation coefficients ranged from r = \(-\)0.565 (U12, sprint 15 m) to r = \(-\)0.849 (U11, sprint 10 m). The largest correlation coefficient was calculated for U11 and the parameters of body fat and CMJ (r = \(-\)0.919). The medicine ball throw was the performance parameter with the fewest correlations concerning anthropometric variables. Only body fat (U11) showed a relevant relationship (r = \(-\)0.584) to the medicine ball throw. BMI had the least relevant correlations with physical fitness parameters. Only two relevant correlations were found (U10, CMJ: r = \(-\)0.531; U11, SJ: r = \(-\)0.505).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Aerobic Capacity** & **Throwing Performance** \\ \cline{2-3}  & **Yo-Yo IR 1 (m)** & **Medicine Ball Throw (m)** \\ \cline{2-3}  & **Median** & **Median** \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\  & **(95\% CI)** & **(95\% CI)** \\ \hline
**U10** (\(n\) = 12) & 400 & 4.50 \\  & 433 \(\pm\) 268 & 4.48 \(\pm\) 0.64 \\  & (239–628) & (4.03–4.93) \\  & 560 & 4.70 \\
**U11** (\(n\) = 11) & 596 \(\pm\) 387 & 4.76 \(\pm\) 0.55 \\  & (394–799) & (4.29–5.23) \\  & 560 & 5.45 \\
**U12** (\(n\) = 12) & 613 \(\pm\) 331 & 5.49 \(\pm\) 1.01 \\  & (419–808) & (5.04–5.94) \\ Kruskal–Wallis (\(p\)) & \(p\) = 0.228 & \(p\) = 0.022 \\ Significant Differences & & **U11/U12: \(p\) = 0.048** \\ between Adjacent Age Groups (\(p\)) & & **U11/U12: \(p\) = 0.048** \\ \hline \hline \end{tabular}  
* Yo-Yo IR1—Yo-Yo Intermittent Recovery Test Level 1.  
\end{table}
Table 4: Aerobic capacity measuring in the Yo-Yo IR1 and medicine ball throw in relation to age groups. Bold font denotes \(p\) < 0.05.
## 4 Results and Discussion
### Optimization Results for Two Degrees of Freedom
Despite that, the reproduction of the effect of one degree of freedom over the performance indicator and corresponding other optimal degrees of freedom is restricted to the global optimum point or limited region of the independent DOF investigated. This aspect should be taken into account in the association between the constructal design and the optimization methods based on computational intelligence.
## 4 VRFormer
### Data Fusion Techniques: Integrating VR and V2X Inputs  
The VRFormer model, operating within the VRDeepSafety platform, uses a novel probabilistic data fusion technique that merges VR-derived spatial data with real-time V2X communication inputs. These V2X inputs include positional data from neighboring vehicles, infrastructure signals, and environmental data, such as weather and lighting conditions. This approach provides a significant improvement over traditional methods that typically rely on deterministic fusion rules, by enabling the model to account for uncertainty and variability in input data.
## 4 Dynamic Reconfiguration of Distribution Network  
### Power Loss Model  
The reconfiguration of distribution network is a traditional operation problem, which is a process of shifting the state of sectionalizing switches and tie switches to minimize the power losses. Power flows in a distribution network are computed by the following equations and the related topology are shown in the blue dashed box of Figure 2.  
\[P_{i}(t)=\sum_{j\in C_{i}}\left[P_{j}(t)+r_{j}l_{j}(t)\right]+P_{li}(t) \tag{15}\]  
\[Q_{i}(t)=\sum_{j\in C_{i}}\left[Q_{j}(t)+x_{j}l_{j}(t)\right]+Q_{li}(t) \tag{16}\]  
\[\text{U}_{k}(\text{t})^{2}\text{=}\text{U}_{i}(\text{t})^{2}\text{+}2\left[r_{ i}P_{i}(t)+x_{i}Q_{i}(t)\right]+(r_{i}^{2}+x_{i}^{2})l_{i} \tag{17}\]  
where \(P_{i}(t)\) and \(Q_{i}(t)\) are the injection active and reactive power of upstream branch of node \(i\); \(C_{i}\) is the set of subnode of node \(i\); \(P_{li}(t)\) and \(Q_{li}(t)\) are the active power and reactive power of load of node \(i\), respectively; \(r_{j}\), \(x_{j}\) and \(l_{j}\) are the unit resistance, unit reactance and length of branch \(j\), respectively.  
Thus, the power loss at time \(t\) can be expressed as follows  
\[min\quad P_{loss}(t)=\sum_{i=1}^{N}r_{i}\frac{P_{i}(t)^{2}+Q_{i}(t)^{2}}{U_{ i}(t)^{2}} \tag{18}\]  
where \(N\) is the total number of branches.
## 2 Related Works
While sleep management methods for mobile devices have been standardized and widely implemented [27; 28], MECS operational state management problems present unique challenges. Unlike mobile devices, where operational states are primarily determined by individual user activity patterns, MECS state transitions must consider multiple complex factors such as the aggregate service demand from numerous users, the server's own load level, and the current load distribution across other MECSs in the MEC system. In [29], a threshold-based method is proposed, which gradually puts servers with loads below the lower threshold into sleep mode while maintaining the sum of load on active MECSs as close to the upper threshold as possible. In [30], the MECS sleep problem is formulated as a bin-packing problem, and a heuristic algorithm is proposed to address it by prioritizing the assignment of user tasks with the highest resource demands with respect to the remaining system resources to neighboring servers. In [31], the MECS sleep control problem is regarded as an operational mode differentiation problem among neighboring MECSs, and an MECS state management technique is proposed by mimicking the cell differentiation process in biological systems. The authors in [12] cast the problem of determining the operational states of MECSs as a combinatorial optimization problem and propose a method to determine the operational states of each MECS using a metaheuristic genetic algorithm after predicting the distribution of task loads imposed on the system. In [32], a technique for minimizing the operational cost of MEC networks is proposed. Instead of focusing on the energy consumption of MEC servers, this approach minimizes network costs by deactivating routers and links by taking into account response latency. The authors in [33] propose a method for determining whether to put MEC servers into sleep mode based on the energy consumption of the MEC network. They formulate the server shutdown problem as a CFLP (capacitated facility location problem) and propose a heuristic server sleep method based on a greedy algorithm.
## 3 Methodology
### Data Integration  
Data cleaning and normalization are applied to the acquired raw data to make them consistent and homogeneous.  
* _Data cleaning_ includes the detection of outliers, missing values, and noise reduction. This step is particularly important when dealing with raw sensor data, since they are typically characterized by uncertainty and the presence of noise. In this context, outlier detection has the goal of recognizing noisy sensor data and removing them. Indeed, these data may confuse the ML algorithm and damage the recognition performance. Outlier detection is performed using a statistical method [31]. Missing data are replaced by means of interpolation. Noise reduction is achieved by applying low-pass filters to inertial sensor data to eliminate high-frequency noise.
* Data provided by different smartphone sensors may use different reference scales. _Data normalization_ is applied to ensure that all the acquired data have the same scale. For this reason, data acquired from sound and light sensors are normalized in a \([0,1]\) range, while the reported distance is represented in a metric scale.
## 4 Evaluation on Food Domain
### Experimental Results
#### 4.2.2 Evaluation for MenuNER  
In our experiments, we tried to examine how the domain-adapted BERT language model improves performance in MenuNER task by considering the following factors:  
* What is the effect of domain adaptation (further pre-training of BERT-base model) on a MenuNER task in terms of its performance?
* What impact does a feature-based BERT model put on the NER task while combining with character-level and POS tag features? (see Table 2)
* Comparison of neural network architectures for the MenuNER task: \"BERT with fully-connected FC layer and a SoftMax layer\" vs. \"Bi-LSTM-CRF\" and a variant with multi-head self-attention.  
For the MenuNER task, various network model variants are compared to BERT-base and BERT-domain features. The results of Table 3 demonstrate that the 'Bi-LSTM-CRF'-based neural network model when used with BERT-domain embedding as a feature produces effectively better results. In the first NER network model, we fine-tune BERT with a SoftMax layer. It is intriguing to see that fine-tuning the BERT model using a simple neural network results in improved performance. However, it lacks in identifying informal user-generated text and multi-word menu entities as shown in Table 4. To tackle this issue, we employ feature-based BERT with character-level and POS tag features in the Bi-LSTM-CRF and its multi-head attention network variant. However, because the BERT low-level representation and the Bi-LSTM upper-level layers already incorporate contextual information such as multi-head attention, the effect of the multi-head attention network does not appear compelling in our experiment.  
Figure 6: Model training with different embedding combinations.  
Table 4 compares NER task model outputs when utilizing word features from BERT-base and BERT-domain model. From examples 1, 2, 3, and 4, it is shown that the pre-trained domain model learns better dependencies between menu token pairs. As in examples 3 and 5, in general, the context of the words \"_breasts_\" and \"_green_\" are related to body part and color, respectively; however, the domain-adapted model could recognize it as a menu name from the context. In example 6, \"_Firni_\" (sweet pudding), is a unique word which does not exist in the training or valid dataset. However, the pre-training domain model recognizes and learns from its context. From example 7, the misspelled out-of-vocabulary (OOV) word \"_tooOmg_\" is recognized as the menu entity by the base model, which is not valid.
# Metabolite Analysis of Hangzhou Gongmei White Tea of Different Varieties
## 3 Results and Discussion
### Differential Analysis of Volatile Metabolites
The proportion of alcohols, terpenes, and sulphur-containing compounds in FD was the highest, while the proportion of aldehydes, ketones, and furrans was the lowest. The total amount of aroma substances was also the highest, including clean aroma components such as (\(Z\))-hex-3-en-1-ol, 2-ethylhexanol, (\(Z\))-non-3-en-1-ol, (\(E\),\(Z\))-nona-3,6-dien-1-ol, and (\(E\),\(Z\))-nona-2,6-dienal [38]; floral aroma components such as linalool and its oxides, geraniol, and phenethyl alcohol [39]; and sweet aroma components such as dimethyl sulphide [40], \(\alpha\)-terpineol [41], and \(\beta\)-myrcene [42], with higher content than other varieties. The amalgamation of these substances resulted in a clean, sweet, and slightly floral aroma. The proportion of alkanes in JK was the lowest, while the proportion of ketones, esters, and furrans was relatively high. The presence of clean aroma components such as (\(Z\))-hex-3-en-1-ol, hex-2-enol, hex-2-enol, and nonanal was minimal, while the content of fruit aroma components such as 2-pentylfuran [43], neral [44], octanal [45], decanal [45,46], dodecanal [46], 6-methylhept-5-en-2-one [41], and (\(E\))-citral [47] was substantial. The manifestation of the aroma was fruity. The highest proportion of aldehydes, ketones, benzenes, and furrans was observed in LJ, while the content of alcohols and sulphur-containing compounds was the lowest. The content of floral components such as meridolol, jasmone, \(\beta\)-ionone, and geranic acid methyl ester was significantly higher than other varieties [48,49]. Furthermore, the highest concentration of hexanal, which was associated with a grassy aroma, had been detected in LJ [20,50]. Consequently, the aroma of LJ was characterised as floral and slightly green. YS was distinguished by its high ester content, accompanied by a comparatively low level of terpenes and other constituents. The proportion of most substances was moderate, and the aroma was clean and pure.
## 3 Results and Discussion
### Processing Outcomes and Product Differentiation (FFS vs. ESC)  
The comparative assessment with commercial reference products (defatted soybean meal, soybean cake, and raw soybeans; Table 6) demonstrates that ESC achieves a balanced profile with superior protein concentration, enhanced protein solubility, moderate anti-nutritional load, and elevated dietary fiber content. Such characteristics highlight ESC as a promising clean-label ingredient for both functional foods and high-protein feed formulations, especially where controlled oil content and digestibility optimization are desired. The elevated dietary fiber content of ESC (15.8-16.3%) has practical relevance for both food and feed applications. In human nutrition, this level of fiber supports gut microbiota modulation, glycemic control, and increased satiety, making ESC particularly suitable for functional foods aligned with clean-label and high-fiber dietary trends. In animal feed, it may improve digestive health and nutrient utilization, especially in mono-gastric species. Moreover, the protein concentration in ESC (37.4-39.0%) exceeds that of conventional soybean cake (typically 30-35%), offering enhanced nutritional value and formulation efficiency in high-protein diets. This composition positions ESC as a superior alternative in the development of plant-based, protein-enriched, or hybrid protein products, responding to both sustainability goals and evolving consumer demands.  
The barothermal-pressing process effectively reduced key anti-nutritional factors and improved protein digestibility in both full-fat soy (FFS) and expanded soybean cake (ESC). The trypsin inhibitor activity (TIA) was significantly lowered from 19.48 mg/g in raw soybeans to 2.86 mg/g in FFS and 3.33 mg/g in ESC (Table 6), fully complying with recommended safety thresholds for animal feed and human consumption [39; 40; 41].  
Protein functionality parameters showed favorable modifications upon processing. The protein solubility in potassium hydroxide (KOH-SP) improved markedly in ESC, reaching 90.98% of crude protein compared to 80.79% in FFS (\(p<0.05\)), indicating higher extractability of functional protein fractions after oil removal. Protein dispersibility index (PDI) values followed the opposite trend, with slightly lower values for ESC (26.46%) relative to FFS (35.01%), reflecting the thermal aggregation of certain protein fractions during pressure-expansion. Nonetheless, both values remain within acceptable ranges for most functional food and feed applications (Table 6).  
Thermal-pressure treatment reduced TIA to 2.86 mg/g FFS and 3.33 mg/g ESC compared to raw soy 19.48 mg/g (Table 6), within safe feed limits [40, 41]. Similarly, [PERSON] and [PERSON] [39] confirmed the benefit of heat treatment for deactivating trypsin inhibitors. Our products also showed reduced phytic acid (-0.25%) and RFO (-4%) relative to raw soy [42]. While RFOs support microbiota [43], their presence in feed may limit use for monogastric animals. Authors recommend further processing (fermentation, enzymatic treatment) to improve bioavailability [38, 44].
## 4 Results and Interpretations
### Facies Associations and Lobe Complexity
#### 4.3.3 Facies Association 3 (FA3): Lobe Fringe  
The lobe fringe is primarily characterized by muddy units, which comprise most of the percentage of sedimentary rocks. The main feature is rhythmic sandstone and mudstone units, which ranges in thickness from a few centimeters to tens of centimeters. The average thickness value was about 10.8 cm, where the thicknesses of the bed units ranged from 1 to 18 cm. The facies association included the S3, S4, Si1, Si2 and M facies. S1 facies are quite rare in this facies association. Furthermore, hybrid event beds (H) with a lower sand thickness are associated with lobe fringe deposits [49]. The rock units have sharp contact  
Figure 6: Lobe architecture and facies association of lobe settings. Thickening and thinning cycles are also marked on the bed scale for better understating of deep-marine lobe complex systems. L1 is the old event of lobe deposition, while L3 is the younger event partially overlapping the older lobe L1 and L2. Each lobe is further classified into axis (yellow color), off-axis (brown), fringe (brownish gray) and distal fringe (gray) from sand to shale or mud alterations [48].  
and are relatively continuous laterally. A complete Bouma sequence (from Ta to Te) could be present, but typically, the basal massive sequence (Ta) is usually absent in the lobe fringe settings [26; 94]. Thin-bedded sand and shale interbeds with a high fraction of mud and good lateral continuity are interpreted to be a part of lobe fringe deposits.
## 4 The kClusterHub Application
### Description
Our experiments showed that applying the elbow method for k-modes and k-prototypes takes significantly more time compared to k-means. Consequently, when running k-modes or k-prototypes on datasets with more than 1500 instances, kClusterHub employs a strategy of random sampling and applies the elbow method to the sampled data to enhance speed and efficiency. Also, kClusterHub provides a REST API that allows developers to submit their data, execute the elbow method and partition-based clustering and obtain the cluster assignments from their own applications, by incorporating simple API calls into them. The REST API is an advantageous feature of kClusterHub, expanding its usability beyond the web-based interface and allowing for more efficient and automated usage of partition-based clustering. kClusterHub is a user-centric application that mandates user registration and login as essential components of its functionality. This login system ensures a secure experience for users, enabling them to access the capabilities offered by the platform. The datasets uploaded by the registered users can be designated as either public or private, each carrying distinct accessibility characteristics. Private datasets are confined to the user who initiates the upload, ensuring a restricted scope of access. In contrast, public datasets are open to all registered users of kClusterHub, though exclusive upload privileges are extended solely to users holding advanced status. This facet holds particular significance for educators who aspire to impart datasets to their students for educational purposes. The application introduces a hierarchy of user roles (see Figure 5), comprising three distinct tiers: (i) simple users, (ii) public dataset creators, and (iii) administrators. Simple users encompass individuals with the ability to upload private datasets and leverage the suite of kClusterHub features. Additionally, they can use public datasets contributed by public dataset creators. The public dataset creators have the same attributes as those of simple users, while augmenting the capability to upload public datasets. This role caters  
Figure 4: Pipeline diagram of tasks in kClusterHub. to those with a proclivity for sharing data and insights. The higher tier of the hierarchy, occupied by administrators, wields the authority to elevate user accounts from the status of simple users to that of public dataset creators. This administrative control facilitates management in kClusterHub.
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 4 Results  
### Result of Shadow Models Using Drone Dataset and Non-Drone Dataset  
Quantitative analysis is performed using a test set consisting of drone images prepared during this project. The result of evaluation metrics in Table 4 shows that the model trained with the drone dataset performs much better than the non-drone ISTD model. The overall accuracies of shadow detection in aerial images using shadow models based on the ISTD and drone images are 0.7962 and 0.9551, respectively.