esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Studies
### Quality Evaluation of OHC Information Services  
Currently, the construction of OHC information service quality evaluation systems primarily focuses on several key directions: multi-dimensional evaluation, user experience, reliability and authority, cultural and design factors, and consequence and antecedent indicators [10]. First, in terms of multi-dimensional evaluation, [PERSON] and [PERSON] (2020) proposed a four-dimensional framework including information accuracy, timeliness, comprehensibility, and usability [11], while [PERSON] et al. (2020) combined technical levels, user perception, information quality, and service models to construct a more comprehensive evaluation system using fuzzy comprehensive evaluation method [12]. [PERSON] et al. (2019) conducted a systematic literature review, summarizing 25 evaluation criteria and 165 specific indicators covering credibility, professionalism, and objectivity, providing comprehensive evidence for OHC information quality assessment [13].  
In terms of user experience, [PERSON] and [PERSON] (2021) constructed a user experience evaluation index system based on five elements (strategic layer, scope layer, structural layer, framework layer, and presentation layer) using the fuzzy hierarchical analysis method, and proposed suggestions to enhance user satisfaction and continuous usage intention [14]. In measuring reliability and authority, [PERSON] (2007) proposed eight core indicators through the HON certification system, including content authority and privacy protection [15], and [PERSON] (2021) constructed 15 reliability evaluation indicators (technical reliability, access flexibility, time management flexibility, and communication capability across time and distance, etc.) to ensure information credibility and authority [16]. Some studies focused on cultural and design factors. For example, [PERSON] et al. (2021) used the LIDA tool to evaluate website layout, color scheme, and search tools, emphasizing the impact of website design on user experience and information acquisition efficiency [17]. Finally, [PERSON] and [PERSON] (2022) constructed a consequence and antecedent indicator system through meta-analysis, deeply exploring the influence of individual differences and information sources on information quality, as well as cognitive evaluation and behavioral intention indicators, which systematically elucidating the influence mechanism of OHC information service quality [18].  
A critical comparison of these evaluation approaches reveals several limitations. Multi-dimensional frameworks often lack contextual sensitivity to different medical specialties, while user experience models frequently overlook professional dimensions of healthcare quality. Reliability measures tend to focus on technical aspects rather than knowledge exchange quality, and cultural factors are often underrepresented in standardized evaluation systems. Our research addresses these gaps by integrating network structural analysis with recommendation metrics, providing a more comprehensive understanding of how professional networks influence service quality perception.
# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 7 Energy-Wave Problem and High Power Communication
### Hierarchical Connectivity Bottleneck  
The Internet of Things (IoT) revolution promises to make \"things\" including electronic, industry, health devices and home appliances part of the Internet world. Most of the devices in Internet of Things paradigm have to be constrained in energy supply powered by batteries with wireless connectivity. Therefore, energy efficiency is a driving factor regarding design of IoT applications' architecture. Communication over wireless channels is responsible for the large portions of energy consumption of the IoT devices. Therefore, IEEE 802.15.4 wireless communication standard was designed to extend the lifetime of IoT power sources by using low power transmissions. It provides low-rate, short range connectivity between communicating devices. Hop-by-hop communication-based mesh architecture is suggested in long range needing IoT applications [33]. In multi-hop topology, end-to-end communication between a data sender and destination is achieved by using intermediate nodes as forwarders in routing path. This architecture provides the connectivity between ends through multi-hop paths by using low power low range transmissions in hope of energy saving but communications' quality of service is deteriorated [34].  
We tested the assumption of communications suffering in hop-by-hop communication for long range data transmissions. We suggest direct one-hop high power transmissions for achieving long range connectivity for better quality of service. We tested our case on real IoT research motes on a basic set of experiments of increasing data rates and found supporting results to our position. For high power direct connectivity, we obtained up to 15 times less average delays and more than four times higher response receiving rate at high data sending rates. Even energy consumption is better conserved in high power case due to in-time high rate data delivery, causing less duty cycling for IoT devices. We obtained the best result of energy saving at one request per second rate and it is six times lower than multi-hop data delivery. Single hop communication causes much less duty cycling for the sender in high power communication case, which is also one of the main reasons for low energy consumption.
# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
###### Abstract  
Differentiation between benign and malignant breast cancer cases in X-ray images can be difficult due to their similar features. In recent studies, the transfer learning technique has been used to classify benign and malignant breast cancer by fine-tuning various pre-trained networks such as AlexNet, visual geometry group (VGG), GoogLeNet, and residual network (ResNet) on breast cancer datasets. However, these pre-trained networks have been trained on large benchmark datasets such as ImageNet, which do not contain labeled images related to breast cancers which lead to poor performance. In this research, we introduce a novel technique based on the concept of transfer learning, called double-shot transfer learning (DSTL). DSTL is used to improve the overall accuracy and performance of the pre-trained networks for breast cancer classification. DSTL updates the learnable parameters (weights and biases) of any pre-trained network by fine-tuning them on a large dataset that is similar to the target dataset. Then, the updated networks are fine-tuned with the target dataset. Moreover, the number of X-ray images is enlarged by a combination of augmentation methods including different variations of rotation, brightness, flipping, and contrast to reduce overfitting and produce robust results. The proposed approach has demonstrated a significant improvement in classification accuracy and performance of the pre-trained networks, making them more suitable for medical imaging.  
breast cancer; classification; image augmentation; medical images; transfer learning +
Footnote †: journal: Medical Image Computing
## 4 Methodology and Setup  
In this section, we present the research methodology that we have used to evaluate the interference robustness of the SUN-OFDM modes and compare it with the OQPSK-DSSS mode, both defined in the IEEE 802.15.4-2015 PHYs.
## 3 Results
### Entrapment Efficiency (EE) of the SBO-NLCs  
The predicted EE value of the SBO-NLCs for center point STDs was 82.76%, whereas the actual EE of SBO-NLCs was varied (90.88, 76.75 and 81.42%). The highest EE (90.88%) was observed in one of the center points runs (STD 15; 5% Wax, 5% Surfactant, 20% PEG400 in the surfactant). The actual lowest EE (30.42%) was found in STD 6 (Table 2). The reduced cubic model was adapted for the prediction of EE of the SBO-NLCs. The model was significant (\(p\) = 0.0019) and the lack-of-fit value was non-significant (\(p\) = 0.9703). Acceptable ranges of adjusted R\({}^{2}\) (0.9609) and predicted R\({}^{2}\) (0.9587) values were detected in the reduced cubic model (Table 3). The results recommended that the reduced cubic model was appropriate for predicting the EE of SBO-NLCs.  
The CCD-generated reduced cubic model equation for the EE of the SBO-NLCs was as follows (Formula (6)):  
\[\%EE=10^{0.9123+0.2949A-0.0990B+0.0644C+0.0304 AB-0.0163 AC-0.0050 BC-0.0213A^{ 2}+0.0227B^{2}-0.0003C^{2}+0.0015 ABC+0.0006A^{2}C-0.0065 AB^{2}} \tag{6}\]  
where A = Percentage of wax in formulation (2.5 to 7.5%), B = Percentage of surfactant in formulation (2.5 to 7.5%), C = Percentage of PEG400 in the surfactant (10 to 30%).  
All the studied variable factors substantially affected the EE of the SBO-NLCs. The higher content of wax, surfactant and PEG400 percentage in the surfactant increased the EE of the SBO-NLCs (Figure 4).  
Figure 3: The response surface plot presents the effects of wax, surfactant, and PEG percentage in the surfactant on the zeta potential of the SBO-NLCs. (**a**) Interaction of wax and surfactant. (**b**) Interaction of wax and PEG400 percentage in the surfactant.  
The wax (%) (\(p\) = 0.003) and surfactant concentration (%) (\(p\) = 0.013) and %wax2 (\(p\) = 0.0225) significantly influenced the particle size of the SBO-NLCs.  
Surfactant concentration (%; \(p\) = 0.0083), wax \(\times\) surfactant concentration (%; \(p\) = 0.240), and %surfactant \(\times\) %PEG400 in surfactant (\(p\) = 0.0017) significantly manipulated the PDI of the SBO-NLCs.  
Wax concentration (%; \(p\) = 0.0070), %PEG400 in surfactant (\(p\) = 0.0008), and %wax \(\times\) %surfactant2 (\(p\) = 0.0343) substantially affected the ZP of the SBO-NLCs.  
The EE of the SBO-NLCs was influenced by the percentage of PEG400 in the surfactant (\(p\) = 0.0212), %wax \(\times\) %surfactant (\(p\) = 0.0210), %wax \(\times\) %PEG400 in surfactant (\(p\) = 0.0010), %surfactant \(\times\) %PEG400 in surfactant (\(p\) = 0.0040), %wax2 (\(p\) = 0.0016), %surfactant2 (\(p\) = 0.0015), %PEG400 in surfactant2 (\(p\) = 0.016), %wax \(\times\) %surfactant \(\times\) %PEG400 in surfactant (\(p\) = 0.0007), and %wax2 \(\times\) %PEG400 in surfactant (\(p\) = 0.0023) (Table 4).
## 3 Results
### Sea-ice Concentration Evaluation
#### 3.2.4 Merging with Good-Quality MODIS Sea-Ice Concentration

Table 2: Comparison of the different SIC datasets in Figure 9. We show the mean \(\mu\), the mean difference \(\Delta\) between the thin- and thick-ice Sentinel-2 SIC and the respective dataset and the RMSD with regard to the thin- and thick-ice Sentinel-2 SIC and the open water extent (area of pixels with less than 85% SIC). A positive difference means that the Sentinel-2 SIC is higher. All quantities except the open water extent are given as fraction of 1. Only pixels where all datasets are available are considered for the comparison. For this scene, this amounts to 8191 of 11,881 pixels, equivalent to an area of 8191 km\({}^{2}\). Details about location and acquisition time of the scene are given in the caption of Figure 9.  
Figure 11: Sentinel-2 scene in the Beaufort Sea on 22 May 2019 and the corresponding SIC maps. (**a**) Sentinel-2 RGB image. Sentinel-2 SIC with thin ice considered as ice (**b**) and water (**c**). (**d–f**) The merged SIC and their difference to the thin- and thick ice Sentinel-2 SIC. Panels (**g–l**) show the same for the MODIS and AMSR2 SIC, respectively. The acquisition times for the single scenes in UTC are given in brackets in the titles of the maps in the left row. Thus, in this case we conclude that the merged SIC shows clear advantages over the AMSR2 SIC because of its higher resolution and ability to resolve almost all leads identified in the Sentinel-2 reference SIC. Moreover, compared to the MODIS SIC, the merged SIC shows clear advantages because the bias and RMSD compared to the reference SIC are lower and unnatural fluctuations in the MODIS SIC are removed.
# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 3 The Method
### Spatial Feature Fusion
Specifically, this stage begins with the initial fusion of the outputs \(F_{L}\) and \(F_{H}\) from the previous stage: the initial fused features \(F_{\text{sum}}\) are generated through element-wise summation, as shown below:  
Figure 2: Overview of the proposed spatial feature fusion. \[F_{\text{sum}}=F_{L}+F_{H} \tag{5}\]  
This addition operation preserves the core information of both features while providing a foundation for subsequent nonlinear transformations. Subsequently, to enhance the feature representation capabilities further, we design a nonlinear mapping module. This module can capture the complex dependencies between \(F_{L}\) and \(F_{H}\). Through this approach, we not only allow interactions between the information of the features but also adaptively adjust the weights of the contributions of the low-level detail features and the high-level overall illumination features, thereby generating more discriminative fused features. Next, the resulting features \(F_{\text{sum}}\) are passed into a bottleneck module. Thus, we obtain the fusion weights \(\alpha\) and \(\beta\) as follows:  
\[\alpha=\text{Sigmoid}(\text{Conv}_{1\times 1}(\text{ReLU}(\text{Conv}_{1 \times 1}(F_{\text{sum}})))),\beta=\ 1\ -\ \alpha \tag{6}\]  
It is worth noting that the weight values range between 0 and 1, which enables the network to calibrate the shallow features \(F_{L}\) and deep features \(F_{H}\) better. Finally, the final output \(F_{\text{out}}\) is obtained through linear combination of the weights, which can be described as follows:  
\[F_{\text{out}}=\text{Conv}_{1\times 1}(\text{Conv}_{1\times 1}(\alpha\otimes F _{L})+\text{Conv}_{1\times 1}(\beta\otimes F_{H})) \tag{7}\]  
where \(\otimes\) denotes element-wise multiplication. Through the aforementioned adaptive fusion process, the final feature map is rich in both detailed and semantic information.
## 3 Results and Discussion
### Carotenoid Content  
The major carotenoid in _D. viridis_TAV01 was lutein, making over 85% of the total carotenoids. However, neoxanthin, violaxanthin, and \(\beta\)-carotene could also be detected in minor quantities (Table 6). This carotenoid profile agrees with other studies on _D. viridis_. The lutein content in _D. viridis_TAV01 was 2.15 \(\pm\) 0.12 mg g\({}^{-1}\) DW, after 15 days of growth. However, improved growth conditions and shorter cultivation times could lead to higher contents, as shown in a different study on another _D. viridis_ strain, in which lutein contents as high as 8.87 mg g\({}^{-1}\) DW were obtained after 5 days [81]. Furthermore, sulphate deprivation could be a good inducer for lutein accumulation; in _D. salina_, sulphate deprivation in combination with bicarbonate supplementation, led to a significant increase  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Macroelements (mg g\({}^{-1}\) DW)** & & & & & & & & & & \\ \hline Ca & Fe & K & Mg & Na & & P & \\ \hline
3.18 \(\pm\) 0.04 & 3.67 \(\pm\) 0.03 & 5.10 \(\pm\) 0.04 & 5.78 \(\pm\) 0.07 & 41.5 \(\pm\) 0.1 & & 9.79 \(\pm\) 0.04 & \\ \hline
**Microelements (mg 100 g\({}^{-1}\) DW)** & & & & & & & & \\ \hline Si & Sr & Zn & Cd & V & Ba & Cu & As & Pb & Mn & Cr & Al \\ \hline
13.11 \(\pm\) 1.56 & 8.40 \(\pm\) 0.39 & 8.66 \(\pm\) 0.02 & 0.07 \(\pm\) 0.01 & 4.95 \(\pm\) 0.10 & 0.21 \(\pm\) 0.01 & 2.16 \(\pm\) 0.01 & 4.30 \(\pm\) 1.56 & 7.83 \(\pm\) 0.01 & 4.53 \(\pm\) 0.03 & 0.04 \(\pm\) 0.01 & 1.01 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mineral profile of _Dunaliella_ sp. TAV01. Values are means \(\pm\) SEM.  
in lutein, reaching contents of 0.26 mg g\({}^{-1}\) DW [82, 83]. As the literature suggests, _D. viridis_ TAV01 does not accumulate \(\beta\)-carotene under high salinity nor under nitrogen depletion as seen for _D. salina_, the major \(\beta\)-carotene microalgal producer [84]. \(\beta\)-carotene supplements from _D. salina_ are already approved for human consumption by the EU; this might facilitate the introduction of _D. viridis_ as a source of lutein. This might be particularly relevant as the global lutein market value is expect to reach USD 170 M in 2029 [85].
## 1 Introduction
### Statistically Validated Performance Ranking  
By executing each model over 30 independent runs and applying the Friedman test with Benjamini-Hochberg correction, we objectively establish which algorithms differ significantly in predictive accuracy (median test-set MSE) and which perform comparably. This statistical layer provides practitioners with confidence bounds rather than anecdotal rankings.
## 5 Discussion  
In the above sections, we have introduced an iterative algorithm ISATR to tackle the joint optimization problem of UAV-assisted communication, involving trajectory planning, power control, and bandwidth allocation. This proposed algorithm is dedicated to providing the pre-planning scheme of a UAV as a temporary base station in an emergency scenario. It can serve as a necessary baseline in case of emergencies, benefiting from its high accuracy.  
Therefore, it is necessary to combine ISATR with other real-time algorithms, such as deep reinforcement learning (DRL), for practical implications. The main difference between ISATR and DRL is that their application scenarios are complementary. The former is applied to pre-planning which requires high accuracy, while the latter is applied to dynamic planning that requires real-time response. DRL-based UAV planning algorithm will be studied in future work to provide a dynamic response.  
Physical limitations of UAVs are also essential in UAV-assisted systems. One notable drawback lies in the current weight and complexity of UAV systems, which can pose challenges in rapid deployment, particularly in emergency scenarios where swift action is imperative. To address these limitations, future research could focus on advancing lightweight UAV designs and streamlined deployment mechanisms. Integration of advanced materials and miniaturized components could significantly reduce the weight and size of UAVs, facilitating quick and agile deployment even in constrained environments.
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 4 Engineering Analysis and Performance Comparison
### Dynamic Analysis  
In order to better verify the dynamic mechanical performance of the camera, random analysis of the camera was carried out. From Section 3.2 in Table 3, the fundamental frequency meets the design requirements, indicating that the dynamic stiffness of the camera is high enough to avoid resonance with a rocket carrier under the action of low-frequency sinusoidal excitation. The test conditions of random vibration are given by the technicians of rocket design, as shown in Table 5. The dynamic analysis showed that the node with the largest acceleration excitation response is located at the edge of the primary mirror, which is set as the sampling point. The acceleration responses of the sampling point in the X, Y, and Z directions are summarized in Table 6. The results show that the direction corresponding to the maximum response of the sampling point is the X direction (vertical to the optical axis), and the amplification factor is 4.71, which meets the mechanical performance requirements. It demonstrates that the dynamic performances of the system meet the engineering application.
# Bi-Stability in Flexure Beams: Introducing a Setup for Experimental Characterization
## 3 Results and Discussion  
### Bi-Stability Validation and Total Linear Displacement  
In this section, the results of the two experiments are presented and discussed. The tests demonstrated that all the samples under compression can trigger the bi-stability, regardless of their thickness. Figure 5a-e shows the bi-stable behavior in the load-displacement curves for the five thicknesses. The graphs represent the first part of the bi-stable curve (from state 1 to the snap-through point), as the second part (snap-back) cannot be measured by the texture analyzer. Each graph shows the curves for each of the three samples (a, b and c), and a thicker black curve represents the mean values of the three curves. All curves rise to their _CF_ and then drop to zero (snap-through point), demonstrating bi-stability. The shaded gray areas correspond to the standard deviation caused by the variation among the three samples. Videos of the experiments can be found in the Supplementary Material section.  
Although the critical force and the snap-through points vary within the same thickness, all samples exhibit linear displacement of the same total distance of 45 mm, which is the entire length of the slider (this observation can be seen in the videos of the experiments found in the Supplementary Material section). This indicates that the same linear displacement can be reached regardless of the thickness of the flexure beams and within a wide range of critical forces.
## 2 Materials and Methods
### Isolation of RNA and Construction of cDNA Library  
Total RNA samples were isolated from leaves harvested 14 days after planting. The standard protocol and chemicals supplied with the QIAprep Spin Miniprep kit (QIAGEN, Cat. 27106, Hilden, Germany) were used. The cDNA was synthesized using a qPCRio cDNA Synthesis kit (PCR Biosystems, Cat. PB30.11.10, Wayne, PA, USA) based on the manufacturer's instructions. Specific primers (forward: 5'-ATGCGGCGGCGATGATTCTCTC CTGCA-3'; reverse: 5'-TCAGGCATTGCAAGTTCGAATCCTA ACAAG-3') for the ORF with _Bam_H1 and _XhO_1 restriction sites were designed for the PCR. PCR was performed with Pfu high-fidelity polymerase enzyme (Bioneer, Cat. K-2301, Daedeok, Daejeon, Korea) in a total volume of 50 \(\mu\)L. The following PCR conditions were used: initial denaturation at 94 \({}^{\circ}\)C for 5 min, followed by 40 cycles of denaturation at 94 \({}^{\circ}\)C for 30 s, annealing at 58 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 1 min; and a final extension at 72 \({}^{\circ}\)C for 5 min. The amplicons were then purified from 1% agarose gel.