esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Clinical Parameters  
The present systematic review discusses the efficacy of topical HA at different concentrations in addition to SRP in non-surgical periodontal therapy for periodontal disease. Non-surgical periodontal therapy and adjunctive therapies have gained increasing attention, with several studies reporting a correlation between periodontitis and systemic disease [39, 40]. Non-surgical periodontal treatment seeks to eliminate biofilm through the use of manual, ultrasonic, or sonic devices [3]. Nonetheless, relying on mechanical removal alone may not be sufficient in some clinical scenarios, and the continued presence of periodontitis can result in persistent periodontal pockets [41].  
Previous research indicates that additional therapy with HA can reduce the need for surgical procedures and improve outcomes for probing pocket depth (PPD) [42]. Three recent systematic reviews [11, 43, 44] demonstrated the effectiveness of the addition of HA in improving PPD at the 12-week follow-up. A meta-analysis of the PPD parameter, measuring the difference (\(\Delta\)) between baseline and follow-up, found a statistically significant improvement with the application of HA gel. Nonetheless, due to the high risk of bias and heterogeneity, the need remains for further well-designed RCTs.  
In their review, [PERSON] et al. [45] examined highly heterogeneous studies on HA, its mode of application, and high risk of bias, thus precluding a meta-analysis. For this reason, the application of HA as an adjunct to non-surgical periodontal treatment could not be recommended. There was a statistically significant improvement in PD, CAL, and BOP at test sites in the HA treatment group at both < 0.3% HA and > 0.8% HA. An evaluation of the clinical parameters showed no statistically significant differences in PD between the two concentrations; however, a slightly higher efficacy was noted for treatment with HA > 0.8% than for HA < 0.3%. There was a statistically significant improvement in BOP at the test sites compared with the control sites. The reason for the difference may have been more for the effect of SRP than for the bacteriostatic and non-bactericidal activity of HA, however. A statistically significant difference in efficacy was found for CAL after treatment with HA > 0.8%.  
[PERSON] et al. [38] reported a statistically significant difference in PD and CAL and an acceleration of healing after treatment with HA. Their data suggested a more conspicuous difference between the test and the control group at 12 weeks than at the second follow-up at 24 weeks. Similarly, [PERSON] et al. [24] reported improvement in PD, CAL, and BOP, underscoring the beneficial properties of HA in reducing inflammation and accelerating healing. Finally, [PERSON] et al. [31] reported an improvement in CAL and PD, suggesting the anti-inflammatory action and periodontal regeneration of 1.6% HA.
# A Novel Hierarchical Multi-Stable Cylindrical Structure with Superior Energy Trapping
## 4 Experiments  
The HMCSs are fabricated by Ultimaker S7 series 3D printer (Ultimaker, Utrecht, The Netherlands) via Fused Deposition Modeling (FDM), and the consumable is chosen to be TPU, which exhibits excellent toughness and large recoverable elastic strain. Quasi-static compression tests were performed to investigate the fundamental mechanical properties, deformation modes, and energy trapping capability of the HMCS, using the INSTRON 5569 (INSTRON, Norwood, MA, USA), a universal vertical press for mechanical property testing. All compression tests were conducted with a constant displacement rate of 2 mm/min.  
The compressive deformation process of a layer of the HMCS was recorded by a camera as shown in Figure 6. Here we define the initial morphology as state 1 and the state at the end of the snap-through as state 2. The average of three specimens for each configuration were used as the final experimental results to ensure the reliability of the conclusions. As a comparison, the same experiment was performed for TMCSs.  
Figure 7 shows the force-displacement curves of the HMCS and TMCS. The experimental results are in good agreement with the simulation curves, exhibiting similar overall profiles as well as comparable peak and valley forces, which validates the accuracy of the finite element models. The peak force of the HMCS is roughly three times that of the TMCS and the trapped energy is nearly five times.  
Figure 6: (**a,b**) Snap process of TMCS during quasi-static compression experiments; (**c,d**) snap process of HMCS during quasi-static compression experiments.
# Topology-Aware Retinal Artery-Vein Classification via Deep Vascular Connectivity Prediction
## 2 Methods
### Vessel Topology Estimation
#### 2.3.2 Tree Tracing via Connectivity Prediction  
Algorithm 1 shows the pseudocode of our simple and intuitive algorithm for tree tracing. While the farthest part from the optic disc center is firstly labeled in [9], we start the tracing from the thinnest terminal segment (segment with a terminal pixel). Since vessels become thinner when being spread out, we believe that the criterion based on the thickness is more appropriate to choose the stable start point for tracing, which is located in a uncrowded region. The thickness of the \(j\)-th segment is defined by:  
\[t_{j}=t(v_{j})=\frac{1}{N_{j}}\sum_{m=1}^{N_{j}}\arg\max\ T(v_{jm}), \tag{5}\]  
where \(T(v_{jm})\) is the predicted class probability vector for the thickness on pixel \(v_{jm}\), which can be acquired as a side output from the connectivity classification network in Figure 3c.  
```
Input :Initial graph \(G(V,E)\),  Connectivity probabilities of all segment pairs \(P^{comn}\)  Thicknesses of all segments \(\left\{t_{j}|1\leq j\leq N\right\}\) Output :Tree label for each segment \(y_{j}\)  Initialize \(y_{cum}\) as 0 whiledo ifthere are untracked non-terminal segments with an assigned tree labelthen  Select the segment with the highest recorded connectivity probability  Stand at the recorded end pixel of the selected segment elseifthere are untracked terminal segmentsthen \(y_{cum}\)+=1  Select the thinnest segment among them based on \(t_{j}\)'s  Assign \(y_{cum}\) to the selected segment  Stand at the terminal pixel of the selected segment else  Break  end if Track along the current segment (arrive at the opposite end) ifthere are other segments judged as connected to the current segment using \(P^{comn}\)then  Assign \(y_{cum}\) to all the connected segments  Record the end we reach and the connectivity probability for each connected segment  end if
```  
**Algorithm 1**Tree tracing via connectivity prediction.  
In Algorithm 1, starting from the terminal pixel of a selected terminal segment, tracking along the segment and tree label propagation to connected segments are iteratively performed until all segments are tracked. In consequence, all vessel segments are assigned respective tree labels by which the initial graph is divided into multiple trees.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## Preliminaries
### Fundamental Concept of the Energy-Efficiency  
Consider a network composed by a set of communication units (nodes). Each unit usually has two operation modes: transmission mode \(TX\) and reception mode \(RX\). Each operation mode has a specific energy cost. In addition, the commutation between \(TX\) and \(RX\) also has a energetic cost associated. The costs are defined as: \(E_{TX}\) (denoting the transmission mode energy consumption), \(E_{RX}\) (denoting the reception mode energy consumption), and \(E_{SW}\) (denoting the energy spent during the switching between \(TX\) and \(RX\)). The transmission and reception energy costs depend on the packet size being transmitted, \(L_{PTX}\) and \(L_{PRX}\).  
The energy consumption associated with a transmission of a single packet is explicitly dependent on the hardware and the packet size (in bytes) [37], however, in a multi-hop network the total energy cost depends not only on the hardware but also on the reliability of the protocol implemented in the network. These protocols are responsible to set the packet retransmissions policy, and they are commonly used to ensure that the packets certainly arrive, drastically increasing the network reliability. To quantify the energy cost, the models used in this work are presented in [37], but models associated to other types of technologies could be used.
## 4 Review and Status of Cryogenic Laser Technology
### Other Cryogenic CW Lasers  
#### 4.2.1 Ho:YAG Cryogenic Lasers  
Other cryogenic CW lasers have been developed recently as well. In [140], the authors describe a cryogenically-cooled CW Ho:YAG laser pumped by a Tm:Glass fiber laser with output at 1932 nm, producing 8 W of CW power output at \(\sim\)2100 nm. In addition, still pumping at 1932 nm, the authors report 2.5 W of CW output power at 1976 nm, resulting in a very low quantum defect of 2.2%.
## 4 Performance Evaluation
### Security Analysis  
This section provides the security analysis of the RNM-SC approach for key generation, data privacy, integrity, and node-level authentication. Our RNM-SC approach coped with the secure distribution of key management among medical devices and achieved security objectives. It authenticates the communicating sensors while routing the healthcare data using private-public keys. Moreover, the session keys are generated for peer nodes and are valid up to a definite interval. Afterward, the session keys are revoked and associated nodes have to resend the request for the generation of new session keys. The unique nonce is added in the request/response packets of session keys; this avoids their re-generation for unknown nodes. Unlike most other solutions that impose a high computational cost in obtaining data secrecy, the RNM-SC approach uses the Blum-Goldwasser algorithm to distribute private-public asymmetric keys for achieving data privacy and integrity. Additionally, IoT data are divided into chunks, and each chunk is encrypted using a lightweight encryption mathematical function. The encryption process over the unreliable channel imposes less computing overhead on each level based on XoR operation. The proposed security scheme provides data privacy and integrity by utilizing the Blum Blum Shub pseudo numbers generator. Each time the healthcare data arrives towards the next hop using heuristics \(X(n)\), it must be carried through an XoR operation with \(h\) least significant bits of the key with data bits. Accordingly, the proposed approach increases the security limits iteratively from IoT sensors to cloud servers using hashes. Our proposed security scheme also reduces complexity because it is based on bidirectional routing methods and security assessments. Such a proposed method decreases the number of RREQ messages and makes communicating links less congested. Furthermore, it ensures data security from medical sensors to the cloud systems and provides privacy and shared authentication between cloud systems and end-users using a hybrid security algorithm. The generated session key is for a particular data send/receive, which is further encrypted and decrypted using private-public keys of the cloud system and end-users. The secret key is used for encrypting and decrypting the user request and cloud response. Table 2 illustrates the most common security attacks on network data for constraint nodes and how our proposed approach avoids them.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Initial energy & 2 j \\ IoT sensors & 250 \\ Deployment & Random \\ Jamming nodes & 3–15 \\ Traffic type & CBR \\ Transmission power & 10 mA \\ Reception power & 15 mA \\ ZigBee routers & 10 \\ Control bits & 20 bits \\ Simulation interval & 200 s \\ Network standard & IEEE 802.15.4 \\ Packet size & 64 bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments  
### Datasets  
We select several publicly available and highly recognized datasets in low-light image enhancement: LOL-Blur [25], LOL-V1 [9], LOL-V2 [26], LSRW [27], SICE [28], DICM [29], and MEF [30]. Among them, the datasets with paired images include LOL-Blur, LOL-V1, LOL-V2, LSRW, and SICE, the datasets without paired images include DICE and MEF. LOL-V1 was taken in a natural environment. The dataset contains 500 paired images. The image content contains multi-scene images. The training dataset contains 485 pairs of images. The test data contain 15 pairs of images, with image resolutions between 400 and 600. In LOL-V2, the image scenes are divided into indoor and outdoor. The first set includesreal low-light images, containing 689 pairs of images in the training set and 100 pairs in the test. The second set consists of synthetic images, containing 900 pairs of training images and 100 pairs of test images. The resolutions of the images are 400 \(\times\) 600 and 384 \(\times\) 384, respectively. LOL-Blur is a large-scale joint low-light enhancement and deblurring dataset. The dataset contains pairs of images with different darkness levels and motion blur in dark dynamic scenes. The training dataset contains 10,200 pairs of images. Testing data contain 1800 pairs of images with an image resolution of 1120 \(\times\) 640. LSRW is shot using Huawei equipment and Nikon cameras. The image content is primarily indoor objects or architectural images, including a few outdoor images. Huawei images contain 2450 pairs of training data and 30 pairs of test data. The Nikon part contains 3150 pairs of training images and 20 pairs of testing images. The image resolutions are 960 \(\times\) 720 and 960 \(\times\) 640, respectively. SICE contains 589 pairs of large-scale multiple-exposure images. The image scenes include natural scene images and indoor images, and the image size is 3872 \(\times\) 2592 to 6000 \(\times\) 4000. DICM comprises 69 dark images under natural lighting. The images are unpaired, and the image resolution ranges from 931 \(\times\) 480 to 720 \(\times\) 960. The MEF contains 17 unpaired images. The images include natural scenery, indoor and outdoor images, and artificial building images. The image resolution ranges from 512 \(\times\) 339 to 512 \(\times\) 384. We selected LOL-Blur as the training dataset and used test sets from other datasets to verify the effectiveness of our proposed method.
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 3 Experimentation and Results
### Example Analysis  
#### 3.3.1 Ablation Experiment  
In order to verify the effectiveness of the dilate block, DS path, and Im-CSAM proposed in this paper for segmentation, ablation experiments were conducted on the SWINySEG and TCDD datasets. In the experiment, the dilate block, DS path, and Im-CSAM were removed separately, and the ablation experiment results were analyzed using three evaluation metrics: accuracy, MIoU, and ER.  
Table 2 shows the ablation experiment results on two datasets. Taking the SWINySEG dataset as an example, the basic model in this paper (referred to as Model a) has an accuracy of 0.876, an MIoU of 0.763, and an ER of 0.124 during model training. Adding dilate blocks to Model a (now referred to as Model b) increases accuracy by 0.002 and the MIoU by 0.021, and decreases the ER by 0.002. Adding a DS path to Model a (now referred to as Model c) increases accuracy by 0.008 and the MIoU by 0.029, and decreases the ER by 0.008. Adding an Im-CSAM to Model a (now referred to as Model d) increases accuracy by 0.01 and the MIoU by 0.033, and decreases the ER by 0.01. Compared to Model a, the accuracy of the proposed model is increased by 0.019, the MIoU is increased by 0.038, and the ER is  
decreased by 0.019. Both the accuracy and MIoU are higher than in Model b, Model c, and Model d.  
Similarly, on the TCDD dataset, the performance metrics of Models b, c, and d all surpass those of the baseline Model a, while the proposed model in this paper exhibits the best performance. Compared to Model a, accuracy increases by 0.029, the MIoU increases by 0.049, and the ER decreases by 0.028. Compared to Models b, c, and d, all metric values show stable improvements, indicating that the proposed dilate blocks, DS path, and Im-CSAM all enhance the segmentation performance of the model.
## 4 Interdisciplinary Applications
### Computational Physics and Engineering  
**Turbulence Modeling (TEE-PINN Architecture).** The Turbulence-Focused Enhanced PINN (TFE-PINN) architecture exemplifies how physics-guided neural networks redefine industrial simulations. By coupling wavelet-based multiscale feature extraction with stochastic eddy-viscosity models, TFE-PINN corrects subgrid stress terms through dynamically predicted viscosity coefficients:  
\[\tau_{ij}=-2\
u_{t}\overline{S}_{ij} \tag{10}\]  
where the eddy viscosity \(\
u_{t}\) is forecasted by an LSTM network trained on high-fidelity LES data. In NASA's hypersonic thermal protection tests, TFE-PINN reduces wall heat flux prediction errors to 5% compared to 12.7% for traditional LES, while improving energy spectrum alignment in the inertial range by 40% [30]. TFE-PINN training data generation required 72 h of high-fidelity LES simulation. This hybrid approach demonstrates how PINNs inherit numerical rigor while leveraging data-driven adaptability.  
**Multiphysics Coupling (\(\mu\)PINN for Crystal Plasticity Analysis).** The \(\mu\)PINN framework addresses multiscale material modeling by jointly optimizing dislocation density fields and stress tensors. Validated against cyclic loading experiments on 304 stainless steel, \(\mu\)PINN achieves a mean square error (MSE) of 0.021 in predicting grain boundary slip--7.5\(\times\) lower than FEM--while accelerating microstructure evolution simulations by 1000\(\times\) compared to molecular dynamics [38].
# Continuous Fiber-Reinforced Material Extrusion with Hybrid Composites of Carbon and Aramid Fibers
## 3 Results and Discussion
### Mechanical Properties
The average deviation of all predicted values is about 5.6%, taking into account the comparatively small number of samples and the associated measurement uncertainty. The largest deviation, with about \(-\)10.5%, was determined for the flexural modulus with a mixing ratio of 70 CF/30 AF. No correlations can be observed between the hybrid effects in the tensile and flexural properties. In particular, +3.3%, +8.8% and +4.7% for the tensile modulus were entirely positive hybrid effects, while \(-\)10.5%, \(-\)9.5% and \(-\)5.6% for the flexural modulus were  
Figure 7: Results mechanical tests: (**a**) tensile and flexural strengths; (**b**) tensile and flexural moduli; (**c**) Charpy impact strength. entirely negative hybrid effects. The flexural strengths also show higher negative hybrid effects compared to the tensile strengths. This could be explained by a high influence of the layer arrangement in the flexural specimens. While in the uniaxial tensile test all layers are approximately equally stressed, no homogeneous stress distribution occurs in the flexural test. Therefore, the mechanical properties in the flexural tests are more dependent on the layer configuration. Since the stress in the neutral line is zero, the fiber layers become increasingly important with increasing distance from it. Alternating layers with widely different strain rates, which are additionally subjected to different stresses across the cross-section, could increase delamination effects that reduce the stiffness and strength of the composite. Figure 8: Comparison between experimental and predicted RoHM values: (**a**) tensile strength; (**b**) Young’s modulus; (**c**) flexural strength; (**d**) flexural modulus; (**e**) Charpy impact strength. For the Charpy impact strengths, especially with increasing aramid content, positive hybrid effects of +4.0% for 50 CF/50 AF and +9.0% for 30 CF/70 AF were shown while a negative hybrid effect of \(-5.2\%\) was observed at low aramid content of 30%. The negative hybrid effect for the 70 CF/30 AF specimens could be due to the two outer carbon fiber layers, since the outermost layers have the greatest influence on impact strength. This was also observed in a similar way by [PERSON] et al. [45], among others.
Sensors_**2013**, _13_, 9860-9877; doi:10.3390/s130809860  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Survey on Temperature-Aware Routing Protocols in Wireless Body Sensor Networks**  
**[PERSON] and [PERSON] * Department of Computer Engineering, Chosun University, 375 Seoseok-dong, Gwangju 501-759, Korea; E-Mail: [EMAIL_ADDRESS]**  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-62-230-6032; Fax: +82-62-233-6896.  
_Received: 28 May 2013; in revised form: 24 July 2013 / Accepted: 24 July 2013 / Published: 2 August 2013_  
**Abstract:** The rapid growth of the elderly population in the world and the rising cost of healthcare impose big issues for healthcare and medical monitoring. A Wireless Body Sensor Network (WBSN) is comprised of small sensor nodes attached inside, on or around a human body, the main purpose of which is to monitor the functions and surroundings of the human body. However, the heat generated by the node's circuitry and antenna could cause damage to the human tissue. Therefore, in designing a routing protocol for WBSNs, it is important to reduce the heat by incorporating temperature into the routing metric. The main contribution of this paper is to survey existing temperature-aware routing protocols that have been proposed for WBSNs. In this paper, we present a brief overview of WBSNs, review the existing routing protocols comparatively and discuss challenging open issues in the design of routing protocols.  
**Keywords:** wireless body sensor network; routing protocol; temperature awareness; bioeffect; wearable computer
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 5 Potential lncRNA-Based Biomarkers
### Hotair
HOTAIR serves as a mediator of hypoxia-induced metastasis, promoting the formation of vasculogenic mimicry and enhancing TNBC cell migration. Hypoxia increases HOTAIR expression, but its siRNA-mediated silencing prevents the development of hypoxia-induced vasculogenic mimicry and increases miR-204 levels, impacting cell migration. Additionally, the knockdown of HOTAIR alters cytoskeletal organization by reducing Focal Adhesion Kinase (FAK) expression. In BC stem cells derived from MCF-7 and MB-231 cell lines, HOTAIR indirectly suppresses miR-7, resulting in increased expression of SETDB1, STAT3, c-Myc, twist, and miR-9. This cascade of events leads to the downregulation of E-cadherin, promoting the EMT [35].