esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
A growing number of patients prefer tooth preservation, leading to increased demand for surgical endodontic treatments. Endodontic surgery is indicated for teeth with persistent periapical lesions of endodontic origin when nonsurgical treatments have failed or are not feasible. The primary goal is to eliminate infection and create an environment conducive to periapical tissue and bone healing [1,2]. During periradicular surgery, the root-end is resected, exposing an apical dentin surface coated in cement, which is sealed with a retrograde filling material to prevent microleakage while ensuring biocompatibility and material stability in the apical tissues [3].  
Success in endodontic surgery depends on case selection, instrumentation techniques, and importantly, the choice of obturation material [4]. The ideal root-end filling material should be biocompatible, promote tissue regeneration without inflammation, be stable dimensionally, be easy to handle, be minimally soluble in tissue fluids, bond to dental tissue, be radiopaque, be non-absorbable, and not stain surrounding tissues [5].  
Various materials have been employed, including amalgam, gutta-percha, zinc oxide-eugenol cements, glass-ionomer cements, composite resins, and silicates [6]. While these materials support tissue healing and bone regeneration, none consistently induce cement formation or full periodontal ligament repair.  
Mineral trioxide aggregate (MTA), a calcium silicate-based material derived from modified Portland cement, has become a preferred option due to its high biocompatibility and sealing ability [7]. Although success rates between MTA and traditional root-end filling materials like Super Ethoxybenzoic Acid (Super EBA) or Intermediate Restorative Material (IRM) show no consistent statistical differences, MTA offers distinct advantages in promoting complete regenerative healing [8]. Moreover, MTA's sealing properties remain effective even with moisture exposure during treatment.  
Nonetheless, MTA has limitations such as difficult handling and long setting time, leading clinicians to consider alternatives like IRM or Super EBA. To overcome these issues, newer calcium silicate-based materials have been developed, with Biodentine (Septodont, Saint Maur des Foses, France) emerging as a promising alternative. Biodentine features a shorter setting time and favorable physical and biological properties, making it suitable for dentin restoration and pulp capping procedures [9].  
A successful outcome in endodontic surgery relies heavily on the quality of apical root canal filling. However, the quest for the perfect retrograde filling material remains ongoing. Consequently, this study aims to present evidence-based findings and evaluate the advantages and drawbacks of commonly utilized materials. The objective is to empower clinicians with valuable insights during the preoperative planning phase of endodontic surgery.
# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 2 Materials and Methods
### 2 DCNN Architecture  
The 2D convolutional neural networks (2 DCNNs) are a type of deep learning architecture (see Figure 1) commonly used for image classification and detection tasks, including brain aneurysm detection. The network has multiple layers, including convolutional, pooling, and fully connected layers [15].  
In the context of brain aneurysm detection, the 2 DCNN takes in a 2D image of the brain, such as a computed tomography angiography (CTA) or magnetic resonance imaging (MRI) scan. The network then applies a series of convolutional filters to the image to extract features at different levels of abstraction. These features may include edges, corners, and other patterns that may indicate the presence of an aneurysm. After the convolutional layers, the outputs pass through pooling layers. They reduce the dimensionality of the feature maps while retaining the most salient features. This step helps improve the computational efficiency of the model while still preserving the essential information. The pooling layers' output passes through a series of fully connected layers, which use the extracted features to predict the presence or absence of an aneurysm. The final layer typically uses a Sigmoid or Softmax activation function to produce a probability value for each class [15].  
The 2 DCNN performance for aneurysm detection is optimized by various modifications to the architecture. For example, skipping connections between the convolutional layers can better capture the spatial information in medical images. Using residual blocks, which allow for better gradient flow through the network, can also improve the model's accuracy.
## 3 Results and Discussion
### Characterization of the Adsorbents
#### 3.1.2 Morphology Determination of MOP and AC-MOP
He obtained a surface area of 8.5 m\({}^{2}\)/g and a pore volume of 0.016 cm\({}^{3}\)/g. Mandarin orange peels were used by [PERSON] et al. (2022) [35] to adsorb acid Red 35 dye. The prepared adsorbent had a maximum adsorption capacity of 476.19 mg/g; the BET-specific surface area was 5.65 m\({}^{2}\)/g, the total pore volume value was 0.0175 cm\({}^{3}\)/g and the mean pore diameter was 11.74 nm. \begin{table}
\begin{tabular}{c c c} \hline
**Element** & **MOP** & **AC-MOP** \\ \hline C & 55.07 & 88.59 \\ \hline O & 44.78 & 9.99 \\ \hline K & 0.16 & 0.19 \\ \hline Mg & & 0.13 \\ \hline Cl & & 0.40 \\ \hline Ca & & 0.71 \\ \hline Total & 100 & 100 \\ \hline Gray color = N/A (not applicable for the specific material). & & \\ \end{tabular}
\end{table} 
Table 1: The surface element analysis of MOP and AC-MOP adsorbents. Figure 4: EDS spectra of (**a**) MOP and (**b**) AC-MOP.
# Energy-Efficient LoRa Routing for Smart Grids
## 1 Introduction
Forming a separate sensor network requires the random placement of a certain number of multi-functional sensor nodes throughout a remote region, which have finite battery power [14]. Low-power WSNs are the focus of most modern sensor-aided systems, which have also transformed industrial monitoring and diagnostics. These weakly powered nodes can communicate wirelessly and form a network quickly. Because WSNs are often put in unmanaged, difficult environments, retrieving batteries when they run out of power is challenging. Power quality and network lifetime are prioritised to improve energy-constrained WSNs. The best approach for sustaining the energy consumption in sensor nodes is thought to be clustering techniques [15]. Each cluster has a single cluster head (CH) and a few cluster member (CM) nodes. The CM sends the information to its corresponding CH in the data aggregation cluster and transfers it to the base station (BS). If a node runs out of energy during a round, then it is declared as dead, and the network is said to have come to an end. Delivering the combined data from all CHs to the BS for additional analysis is the goal of a WSN protocol. This kind of routing results in high energy costs and unexpected node deaths, both of which can potentially render the network inoperable; hence, it is essential to find ways to extend the network's lifetime effectively. Under adverse wireless communications situations, providing quality-aware data delivery such as scalability, network coverage, throughput, stability, lifetime, reliability, complexity, and latency comprises the critical design concerns in SGs [16]. Long range (LoRa) is one of the most-promising large-scale smart grid communication platforms. LoRa is a low-energy wide-area network specifically suited to transmitting sensor data [17]. LoRa technology allows power station sensors and controllers to communicate data to a monitoring centre through walls, buildings, and other obstacles. Thus, it requires fewer gateways than other wireless technologies. Because of the possibly complicated environment in which the sensor devices work, issues occur when deploying LoRa technology in dense and significantly varying wireless settings [18]. To solve these difficulties, hierarchical routing and sensor node clustering have been proven to extend the network lifetime [19]. Hence, this paper employed an energy-efficient LoRa smart grid system using optimal CH selection and path selection techniques. We can also apply the proposed method by considering the node as the energy router, which is primarily a routing device within a microgrid. At the same time, an energy hub is a more comprehensive energy management platform, which integrates multiple energy systems and microgrids [20].
## 2 Radio Access Technologies for Direct V2X
### IEEE 802.11p  
IEEE 802.11p (hereafter sometimes shortened as 11p for conciseness) was an amendment published in 2012 and is now part of the IEEE 802.11-2020 standard [17]. In the US, it is part of the wireless access in vehicular environment (WAVE) protocol stack that includes documents from IEEE and SAE, while in Europe it is at the basis of ITS-G5 [18].  
At the PHY layer, IEEE 802.11p adopts orthogonal frequency-division multiplexing (OFDM) with 64 subcarriers (including virtual subcarriers) and SCS of 156.25 kHz. Convolutional encoding is applied. The generic transmission uses all the subcarriers with the same modulation and coding scheme (MCS), which corresponds in most cases to quadrature phase shift keying (QPSK) with a coding rate of 1/2.  
At the MAC layer, carrier sense multiple access with collision avoidance (CSMA/CA) is used; when a vehicular user equipment (VUE) has a frame to transmit, it senses the channel to check if it is currently being used. If the sensed power is below a given threshold, the VUE assumes that the channel is not being used and transmits its frame. Otherwise, it waits until the channel is released, after which it also performs a random backoff procedure to avoid the risk of starting at the same time as some neighboring VUE.  
As an evolution of this standard, at the beginning of 2013, the new amendment IEEE 802.11 bd [19] was published (hereafter shortened as 11 bd for conciseness). Rather than a new solution, it is a fully backward-compatible enhancement to IEEE 802.11p with the scope to improve range and throughput under specific scenarios [20]. More specifically, it is based on the same access scheme (CSMA/CA) and shares the same IEEE 802.11p preamble, and adds a number of new optional features [20; 21]: new MCSs with modulations up to 256-QAM; additional pilot sequences, called midambles, to improve the channel estimation when the mobility is high and the packet length is particularly large [22]; up to three blind packet retransmissions (called blind because they are not related to acknowledgments) [23]; possible use of channel bonding [24]; and mmWave band [25].  
Dealing with co-channel coexistence, it is relevant to note that the CSMA/CA protocol in IEEE 802.11p (and IEEE 802.11 bd) has an implicit procedure to limit interference to other signal sources, no matter the technology used. It should be noted, however, that the power threshold to assume the channel as busy when the signal cannot be recognized is \(-65\) dBm, which is much higher than what is used when the signal is recognized instead. A frame being sent by another IEEE station can in fact be recognized by the preamble detection, which depends on the specific implementation of the receiver, but can be realistically assumed around \(-100\) dBm. The lower power level implies a reduction in the probability of collisions due to hidden terminals, as discussed in detail in [11].
## 5 Consensus Mechanism  
Gateways installed at different sites are vulnerable to other attacks, apart from DDoS, and the probability that the control of agents may be taken by malicious users is not negligible. In order to make the proposed protocol more robust and resilient to malicious nodes that would attempt to either hide an ongoing DDoS attack, or trigger a false alert, a validity checking mechanism has been employed.  
The mechanism to protect the integrity of the information exchanged through path messages is based on a blockchain infrastructure that is co-maintained by all nodes participating in the system. Each node is checking the validity of the most recent states the path message has gone through according to specific rules. If a violation is observed, then the agent/agents related to it is/are excluded from subsequent path-messages.  
Blockchain technology enables distributed nodes to collaborate without the requirement for a trusted third party. It provides the basis for collaboration of participating nodes upon a predefinedprotocol. Nodes are equal to each other and interact according to predefined rules. Accidental or deliberate actions that are invalid with respect to the rules are not allowed by the protocol. An abstract blockchain infrastructure has been employed in order to present how a smart contract could orchestrate the agents collaboration and provide the required means, in order to reach consensus between agents about hosts that may be victims of DDoS attacks. No specific blockchain platform has been chosen, as this is out of the scope of this paper. The proposed scheme can be implemented in every blockchain platform that offers smart contracts functionality.  
A smart contract is a set of rules that define how different nodes collaborate. Technically, it is a computer program and the main parts it consists of are a set of state variables and a set of functions which are called by nodes interacting with the smart contract. The calls to these functions are strictly ordered and the state variables of the contract have unquestionable values at any point in time, throughout the whole procedure. This makes smart contracts an appropriate scheme for supporting collaborating nodes that need to maintain a global state, without using a single central node to host this state.
## 6 Limitations and Strengths of the Study  
This review presents several limitations. Most notably, there is a scarcity of randomized controlled trials specifically evaluating acoustic interventions in dental settings. Much of the evidence is extrapolated from broader healthcare environments like ICUs, NICUs, and inpatient wards (e.g., [15; 24; 27]), which, while relevant, limits direct applicability to dentistry. Additionally, the geographic focus of the included studies skews toward high-income regions, underrepresenting dental environments in low- and middle-income countries [41; 58]. Another constraint is the emerging nature of innovations like AI-driven soundscapes and plant acoustics, which are still largely conceptual but suggested in the literature [85; 93; 104; 105; 106]. This makes it difficult to assess their real-world effectiveness in clinical dental settings. Finally, a notable limitation of this review is the lack of empirical data on the quantitative acoustic performance (e.g., RT60, dB(A), frequency spectra) of specific design interventions, as the included literature primarily focused on conceptual frameworks and subjective outcomes rather than technical measurements.  
Despite these limitations, the review's strength lies in its interdisciplinary synthesis. It is among the first to systematically link sustainable acoustics, lean practice, and patient-centered care in dentistry, drawing from 60 screened studies across environmental psychology, healthcare design, and clinical acoustics [78; 94; 95]. Practical tools, such as tables, checklists, and a multi-sensory patient journey map, enhance their translational value for clinicians, designers, and policymakers [20; 96; 97]. Importantly, this review reframes acoustics as a core component of dental care quality and sustainability. Integrating sound design with the principles of green dentistry and the circular economy promotes the transformation of dental clinics from purely utilitarian settings into spaces that actively support healing, emotional well-being, and sustainable operations [78]. Future research should prioritize participatory design methods, especially engaging vulnerable populations such as children and neurodivergent individuals, to ensure inclusive and adaptive dental environments. Empirical trials should evaluate the impact of real-time sound modulation, acoustic zoning, and biophilic design strategies tailored to clinical dentistry. Additionally, alignment with frameworks such as the WELL Building Standard, particularly its provisions on Comfort (Feature 80: Sound Reducing Surfaces), Mind (Feature 89: Adaptable Spaces), and Nourishment of Sensory Health, can guide the development of acoustically resilient, psychologically supportive, and environmentally responsive dental care spaces [82; 97; 98; 99; 106].
## 3 Results and Discussion
### Dma
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Resin**} & \multirow{2}{*}{**Specimen**} & \multicolumn{3}{c}{**1 st Run**} & \multicolumn{3}{c}{**2 nd Run**} \\ \cline{3-8}  & & \(T_{g}\), \(E^{\prime}\) & \(T_{g}\), tan \(\delta\) & **Failure** & \(T_{g}\), \(E^{\prime}\) & \(T_{g}\), \(\tan\delta\) & **Failure** \\ \hline \multirow{3}{*}{BVE} & BVE-1 & 54.9 & 94.9 & No & (-) & (-) & (-) \\  & BVE-2 & 62.1 & 98.3 & No & (-) & (-) & (-) \\  & BVEL-1 & 57.6 & 107.2 & No & 66.2 & 116.8 & Yes \\ \cline{1-1}  & BVEL-2 & 58.3 & 102.3 & No & 69.2 & 114.3 & Yes \\ \cline{1-1}  & BVEL-3 \({}^{\star}\) & 79.7 & 145.7 & Yes & (-) & (-) \\ \hline \hline \end{tabular}  
* Specim cured for 24 h at 110 \({}^{\circ}\)C (remaining specimens cured for 4 h at 100 \({}^{\circ}\)C).  
\end{table} 
Table 4: Summary of DMA results. Figure 4: DMA results: (**a**) storage modulus and (**b**) tangent delta curves of BVE and BVEL resins as a function of temperature (* specimen cured for 24 h at 110 \({}^{\circ}\)C; remaining specimens cured for 4 h at 100 \({}^{\circ}\)C). followed by failure) was observed. Additionally, as expected, these samples presented increased \(T_{\text{g}}\) in the second run (in the range of 66-69 \({}^{\circ}\)C from the onset of \(E^{\prime}\) decay and 114-116 \({}^{\circ}\)C from tan \(\delta\) peak) relative to the first run, suggesting increased crosslinking from the consecutive heating processes undergone during DMA. In fact, the results gathered from the BVEL-3 specimen (significantly increased \(T_{\text{g}}\) after longer post-curing at higher temperature), combined with the results from the second run of the BVEL-1 and 2 specimens, indicate that there was still significant potential for additional cross-linking in the BVEL resins even after post-curing at 100 \({}^{\circ}\)C for 4 h. It is reasonable to consider that the increases in \(E^{\prime}\) after glass-transition may have resulted from additional cross-linking being facilitated by the polymer's state of increased molecular mobility. Similar post-glass-transition increases of \(E^{\prime}\) have been reported in the literature for acrylate-based resins incorporating lignin, either as a filler or as part of a copolymerized network [33]. In that study, the authors also suggested that lignin may have contributed to further crosslinking at elevated temperatures, thus resulting in increased \(E^{\prime}\).
###### Abstract  
In any rock engineering project, the uniaxial compressive strength (UCS) is one of the most relevant parameters to be determined as it is used for a variety of purposes. Traditionally, the UCS of a rock sample is obtained by carrying out a uniaxial test on a rock core. The UCS can also be estimated indirectly by correlating it with different parameters such as the pulse velocity (\(V_{p}\)), Schmidt hammer rebound number (\(R_{n}\)), effective porosity (\(n_{e}\)), total porosity (\(n_{l}\)), dry density (\(\gamma_{d}\)), point load index (\(l_{s50}\)), shear wave velocity (\(V_{s}\)), Brazilian tensile strength (BTS), slake durability index (SDI), or by using an artificial neural network (ANN). This paper presents a comparative study for an additional approach to determine the UCS, namely by converting triaxial test results to the UCS. This research has been conducted to encourage further utilisation of laboratory test results obtained from triaxial tests. Triaxial tests are normally performed to determine the intact rock strength envelope. By further utilisation of triaxial test data to estimate the UCS, the database of the UCS can be enriched for rock engineering projects. The method can also be used in cases of limited samples or when samples are too challenged for carrying out a USC test. The research showed that the UCS derived by this method is more direct and more accurate than many empirical methods. Furthermore, the test procedure described herein (carrying out the UCS and triaxial tests on the same sample) can be used to evaluate the accuracy of the calculated UCS. It is important to bear in mind that the scope of this study is not to develop a new method or replace the traditional uniaxial compression test with the method presented in this paper. The purpose and intention is to document that the test results obtained from triaxial testing can be utilised to also provide values of UCS from the same samples.  
UCS; gramitic rock; empirical methods; estimate UCS; uniaxial test; triaxial test Article
## 4 Parameter Optimization of the Lightweight Drive Rod Based on Fully Symmetrical Structure
### Determining the Target Function  
According to the working conditions of the lightweight drive rod, after applying a corresponding force to it, it needs sufficient strength to ensure that the elastic deformation of the overall structure remains within a small range. In principle, the smaller the elastic deformation of the lightweight drive rod under working conditions, the better. Therefore, taking the elastic deformation of the driving rod in the force direction as the first optimization objective, a first objective function is established (Equation (7) [26]).  
\[minf_{1}(X)=min\left(\frac{F_{\Sigma}L}{EA}\right) \tag{7}\]  
where, \"\(F_{\Sigma}\)\" is the force on the drive rod; \"\(L\)\" is the length of the lightweight drive rod, namely the value of \"b\"; \"\(E\)\" is the elastic modulus of connecting rod material; and \"\(A\)\" is the cross-sectional area. According to the schematic diagram of the cross section of the lightweight drive rod shown in Figure 17, the calculation formula of \"\(A\)\" is:  
\[A=2\times\frac{7\pi c^{2}}{4}+d(g-2R)-2\times\left(\frac{\theta}{360}\pi R^{2 }-\frac{d(R-\mathrm{h})}{2}\right)\]  
\[R=r+c\]  
\[h=R-\mathrm{Rcos}\theta\]  
\[\theta=arccos\frac{2R^{2}-d^{2}}{2R^{2}}\]  
\[X=[x_{1},x_{2},x_{3},x_{4},x_{5}]^{T}=[a,b,c,d,e]^{T}\]  
Figure 16: Result of parameters sensitivity analysis.  
while ensuring the elastic deformation demand of the drive rod, the weight of the drive rod should be smaller. Therefore, taking the weight of the drive rod as the second optimization objective, a second objective function is established (Equation (8)):  
\[\begin{split} mint_{2}(X)=min(\rho V)\end{split} \tag{8}\]  
where, \"\(\rho\)\" is the density of the lightweight drive rod; \"\(V\)\" is the volume of the lightweight drive rod. \"\(V\)\" can be calculated as follows:  
\[\begin{split} V=3a\bigg{(}eg-\frac{7\pi R^{2}}{4}-\frac{R^{2}}{ 2}\bigg{)}+(\text{b}-3\text{a})\bigg{[}\frac{7\pi c^{2}}{2}+d(g-2R)-\bigg{(} \frac{\theta}{180}\pi R^{2}-d(R-\text{h})\bigg{)}\bigg{]}\end{split}\]