esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Eco-Friendly Zero-Cement Particle Board under Harsh Environment
## 3 Results
### Flexural Strength  
#### 3.6.1 Flexural Strength in Repeated Wetting and Drying Cycles  
Wetting-drying curing conditions were used to analyze the aging characteristics of the alkali-activated particle board. Flexural strength tests were carried out on an alkali-activated particle board under 25 cycles of repeated wetting-drying curing conditions. The impact of cyclic wetting-drying on the flexural behavior and results of the flexural tests, encompassing parameters such as strength and flexural load deflection, is graphically shown in Figure 10 and Figure 11, respectively.  
Generally, the repeated wetting-drying cycles resulted in decreased flexural strength. The flexural performance of the alkali-activated particle board under wetting-drying curing conditions typically declined compared to the specimens that underwent standard curing conditions. For instance, the 25 WD200 particle board specimen experienced more pronounced degradation, resulting in a complete loss of flexural strength. In contrast, the NC 200 variant exhibited a flexural strength of 42.24 Psi (0.29 MPa). The results were better with an agro-waste/slag ratio of 0.25 compared to 0.5. For instance, the flexural strengths of 25 WD300 and 25 WD400 are about 56 Psi (0.38 MPa) and 63 Psi (0.43 MPa), respectively. Moreover, for the same deflection value, mixtures exposed to freezing and thawing cycles sustained lower loads. This finding reflects the internal degradation and assassinated damage induced by wetting and drying cycles.  
Figure 9: Effect of freezing–thawing and wetting–drying cycles on flexural strength of agro-waste alkali-activated particle board (1 Psi = 0.0069 MPa).
# Blockchain-Based Internet of Medical Things
## 3 Analysis
### Privacy and Security  
Some of the privacy and security benefits that may be realized when a blockchain is integrated with IoMT systems are provided in the following:  
* To ensure that only those who need access to a patient's medical records do so by the rules put out by the lawful administrator, smart contracts may give access to control property.
* Each participant in an IoMT system values their privacy and does not want it invaded in any way by the exchange of information. The digital identity of the transactions is used by blockchain to make transactional data unreadable to other parties.
* CIA (confidentiality, integrity, availability): because the blocks containing data are signed, blockchain provides high levels of integrity protection. In addition, the linking through hashes and the unanimity requirement make it very difficult, if not impossible, to alter the contents inside a block. In addition, a complete version of the data is copied and stored in all nodes, which means a high degree of availability is supplied by construction. However, privacy is compromised due to blockchain's inbuilt transparency and verifiability checks for every data transaction. Since blockchain implementation focuses more on ensuring the data's integrity and availability than its secrecy, the latter is less strictly enforced. Since application-level encryption and other techniques where (sensitive) data is not immediately accessible by unauthorized nodes are beyond the purview of this study, the system must offer extra security if a high degree of secrecy is needed [60].  
While integrating blockchain and IoMT systems offers security and privacy advantages, serious privacy concerns may occur.
# Aortic Hemodynamics with Accelerated Dual-Venc 4D Flow MRI in Type B Aortic Dissection
## 2 Materials and Methods
### Image Acquisition  
All 4D flow magnetic resonance imaging (MRI) scans were performed on a 1.5T MRI system (MAGNETOM Sola, Siemens Healthcare, Erlangen, Germany). The volumetric coverage of the whole heart and entire aorta during the free-breathing non-contrast scan was provided and retrospective ECG gating was used. Respiratory navigator gating was used only for sagittally acquired dual-venc (DV) acquisitions as single-venc (SV) scans were acquired in coronal orientation with phase encoding left to right, which is less sensitive to respiratory motion artifacts. The DV scans were sagittally performed to reduce the scan time, as coronal 4D flow MRI scans take longer time. The scan protocol included one conventional GRAPPA-accelerated (R = 2) acquisition SV and one compressed sensing (CS) (R = 7.7) accelerated DV 4D flow MRI scans. The same scans were repeated five to ten days after the initial scan in all subjects for interscan reliability assessment. The flip angle was the same (\(\mathcal{T}^{\circ}\)) for all scan types. The velocity encoding (venc) value was 160 cm/s for GRAPPA-accelerated SV acquisitions, whereas both low (80 cm/s) and HV (160 cm/s) were used for the DV acquisitions. The venc strategy was symmetric for the SV scans whereas an asymmetric strategy was used for the DV scans. High-venc (HV) (160 cm/s) data from the DV acquisitions were separately analyzed and the results were compared with the other scan types as well. Table 1 summarizes the rest of the scan parameters for each acquisition type. The field of view and in-plane spatial resolution were not constant between the SV and DV acquisitions as low-venc (LV) scan (part of DV) needs stronger gradients for the velocity encoding. Automatic reconstructions on the scanner were performed before the analysis and phase images from the LV and HV data were jointly processed using the Bayesian method to derive velocity for the DV 4D flow MRI acquisitions [24; 25; 26]. The HV was calculated as the phase difference between velocity compensated reference set and high velocity encoded set.
## 4 Experiments and Analysis of Results
### Security  
#### 4.4.1 Computational Complexity  
Computational complexity denotes the computational workload needed to execute an algorithm and serves as a pivotal metric for assessing performance. Presently, stream ciphers and public key cryptography algorithms are prevalent in the RDH-EI algorithm domain. Table 7 displays the computational complexity of executing the algorithm across various encryption methodologies.  
AES encryption and decryption of a pixel exhibit a computational complexity of \(O(1)\). When considering a private key length of \(r\), the computational complexities for encrypting and decrypting a pixel using LWE and Paillier encryption methods are \(O(r^{2})\) and \(O(r^{3})\), respectively. Employing \((k,n)\)-threshold secret sharing encryption for a pixel results in a computational complexity of \(O(n)\) for encryption and \(O(n\text{log}^{2}n)\) for decryption, as rigorously proven by [PERSON] et al. [31].  
In our algorithm, the computational complexity of AMED prediction and re-encoding operations is \(O(1)\). During the secret splitting process, data embedding occurs by establishing a mapping relationship between secret data and vector parameters, maintaining a computational complexity of \(O(n)\). Information extraction involves recovering non-leading parameters of the shared vector; [PERSON] et al. [23] proposed a recursive algorithm with a computational complexity of \(O(k^{2}\text{log}^{2}k)\). For image recovery, only the leading parameter of the shared vector is necessary, mirroring the process in MSS secret sharing, showcasing a computational complexity of \(O(k\text{log}^{2}k)\).
## 4 WirelessHART Scheduling Schemes
### Comparison of the Scheduling Protocols
The solution presented in the Han algorithm [52] has a particular feature: this proposal uses shared slots for possible retries; thus, the devices that need a retry compete for the possibility of using the transmission medium. Implementation: Most of the proposals implement their ideas using simulation, which is reasonable for reasons of cost and reliability issues, as can be seen in [100]. The C-LLF and Zhang algorithms use testbeds, but the latter does not provide more details on it. The most deviant form of validation is the analytical comparison presented for the Dang algorithm, which uses calculations based on the number of hops and the reliability of the links. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Charactersteristic/ Proposal** & **Dang Alg.**[78] & **Zhang Alg.**[79] & **C-LLF**[81] & **Han Alg.**[52] & **Zhang Policy**[82] \\ \hline Objective & \multirow{3}{*}{Accommodate all Txs} & Latency and High reliability & Find schedule if there is & \multirow{3}{*}{Reliability} & Time and \\ \cline{3-4} \cline{6-6} Metric & & & Time window (Laxity) & & Deadline and \\ \cline{3-4} \cline{6-6} Multiple superframe & & Yes & No & No & Yes \\ \hline Redundancy & Yes & Yes & No & & Yes, uses shared slots for possible retries & No \\ \hline Implementation & No & algorithm; uses validation by & Yes, testbed, but no further details on implementation & Yes, optimal and heuristic algorithms and validation with testbeds and simulation in [55] & Yes, with simulations in [52] and a full simulated in [55] & No explicit algorithm is presented, but uses MATLAB® and COOA simulations for validation \\ \hline Flow differentiation & No & No & No & Yes & No \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of WirelessHART scheduling proposals. C-LLF, conflict-aware least laxity first; COOJA, Contiki OS Java. Flow differentiation: Only the Han algorithm [52] implements the flow differentiation feature in order to define each region of the superframe that would be occupied by each flow, as presented in Table 5. All other proposals deal with the flows indiscriminately.
## 4 Survey Results
### Dominant Factors Predicting Student Learning Outcomes  
Our survey revealed that 23 (37.09%) studies explored the impact of one to three factors on the attainment of student outcomes. However, 32 (51.61%) studies used more than three features to forecast student performance. The range of features varied between 4 (e.g., [53]) to 263 (e.g., [55]). Seven studies did not indicate the number of factors used to forecast student success. However, the dominant factors that were demonstrated to influence the attainment of student outcomes were substantially fewer. The strength of the evidence was grouped into three classes, namely strong, medium, and weak. Thirty-one (50%) studies reported finding strong evidence (i.e., statistical evidence or high prediction accuracy) about the predictive power of their factors. Six models showed medium evidence of the effects of the factors, while seven models reported a weak significance of the predictive factors they inspected. However, 18 studies were inconclusive about the strength of their findings.  
We coded the factors (100 occurrences with 14 studies not reporting the influential factors) that were found to impact the performance of students into themes. Overall, six primary themes emerged from our qualitative analysis. Figure 13 shows that online learning activities and patterns (19 times) were the key predictors of student learning outcomes. This was mainly relevant to virtual or blended learning studies, where all or part of the student learning occurs online. Examples of online learning behavior included resource access time [84], site engagement [62], and time and number of online sessions [76]. The next prominent predictor of student performance was the assessment data during the semester (17 times), such as assignment [102] and quiz scores [44; 82], and exam grades [69]. A surprising dominant factor of student achievements that prevailed was student academic emotions, which refer to student interests and enthusiasm [83], intrinsic motivations [92], and professor-student rapport [71]. The next influential features were grouped under previous academic achievements [45; 46; 48; 66] and the teaching environment and style [81; 98; 99].
# Platelet-Rich Fibrin: A Viable Therapy for Endodontic-Periodantal Lesions? A Preliminary Assessment
## 3 Results
### Clinical Assessments for the Diagnosis of Endodontic-Periodontal Lesions  
For the diagnosis of the endodontic-periodontal lesions, a detailed medical history was taken, and a clinical examination was performed. One patient reported that the affected tooth had previously suffered trauma [21]. Inspection for cavities, incorrect restorations, discoloration, cracks, fractures, erosion, and abrasion was assessed. In one case, the affected tooth presented a discoloration and an abfraction on the buccal side [22]. Also, the mucosa and gingiva were examined for ulcerations, inflammation or for sinus tracts, which are often associated with necrosis [1]. Gingival inflammation was observed in two cases [19; 21] and a sinus tract opening on the labial aspect of the tooth was reported in one case [21]. In order to detect the presence of a periradicular inflammatory process, palpation and percussion were assessed, however this could not predict if the lesion was of endodontic or periodontal origin [1]. Three patients [19; 21] presented pain on percussion and one of them [21] had the tooth tender on palpation also.  
For a certain diagnosis, a pulp vitality test was performed, and the probing depths were measured [30]. A negative response to the vitality tests was obtained, meaning the teeth were non-vital [19; 20; 21; 22; 23]. Significant values of the probing depth were reported after periodontal examination, from 8 mm to 11 mm in the case reports [19; 21; 22]. The inclusion criteria for the clinical trial conducted by [PERSON] and coworkers [23] were probing depths equal to or more than 5 mm and infrabony defects with 2 or 3 walls with a depth over 3 mm, after endodontic and non-surgical periodontal treatment. In the clinical trial conducted by [PERSON] et al. [20], patients with probing depth greater than 6 mm were included. Also, tooth mobility was tested. A mandatory assessment for the diagnosis of the endodontic-periodontal cases is represented by radiographic examination, which was  
Figure 2: Flow chart of methodology used to identify relevant literature according to PRISMA.  
performed in all the cases included [19; 20; 21; 22; 23; 24]. It showed the bone loss around the affected tooth and it could be useful in detecting the cause of the lesions, as for example a deep cavity or a failed root canal treatment. One study did not report the procedure of diagnosis of the endodontic-periodontal lesion [24].
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 7 Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **DPTracer** & **History Tree**[12] & **CAT**[23] & **AoT**[24] \\ \hline \multirow{3}{*}{**Tamper-Evidence**} & Tamper-evidence with & Tamper-evidence with & Tamper-evidence & Focuses on concurrency & Time-sparse tree with \\  & mutual auditing across & through the & via Concurrent & parity integrity for \\  & the entire data flow. & Merkle-tree-based & Authenticated Tree & constant storage \\ \hline \multirow{3}{*}{**Target Applications**} & Data provisioning & \multirow{3}{*}{Logging systems with } & \multirow{3}{*}{Large-scale distributed } & \multirow{3}{*}{\
\begin{tabular}{c} Large-scale IoT \\ environments with \\ minimal storage and \\ periodic \\ tamper-evidence. \\ \end{tabular} } \\  & networks requiring & & efficient static log & systems with high & minimal storage and \\  & and strong & verification. & concurrency needs. & periodic \\  & tamper-evidence. & & tamper-evidence. \\ \hline \multirow{3}{*}{**Data Flow Tracking**} & Provides full & \multirow{3}{*}{No data flow tracking.} & Focuses on static log & \multirow{3}{*}{
\begin{tabular}{c} Focuses on \\ accountability between \\ devices without flow \\ tracking. \\ \end{tabular} } \\  & & ability to reconstruct & integrity, not data flow & No data flow tracking. & \\  & data flow from input to & tracking. & \\  & output. & & \\ \hline \multirow{3}{*}{**Mutual Auditing**} & Integrates real-time & \multirow{3}{*}{Supports immediate } & \multirow{3}{*}{Supports one-way log } & Integrates periodic \\  & mutual audits between & & one-way log audit. & audit. & audits in data \\  & nodes and the logger to & the protocol. & & reporting. \\ \hline \multirow{3}{*}{**Scalability**} & Scalable for DPNs but & \multirow{3}{*}{Highly scalable for } & Optimized for \\  & incurs higher overhead & & large-scale, \\  & due to data-flow & static datasets. & high-concurrency & \\  & tracking. & & environments. & overhead. \\ \hline \multirow{3}{*}{**Storage Requirements**} & Higher storage due to & Moderate storage due & Moderate storage, \\  & Process Tree & to Merkle-tree-based & efficient handling of \\  & serialization of data & structure. & concurrent logs. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of DPTracer with existing tamper-evident logging systems. DPTraceris a tamper-evident logging system tailored for DPNs that provides data-flow tracking and reconstruction and mutual auditing. While it imposes some scalability and storage overhead, these are reasonable trade-offs for the enhanced verifiability in DPNs.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 5 PA-Based Communication and Localization
### PA-Enabled Underwater Localization
To cross the air-water barrier, a proposed technique relies on the use of Visual Light Communication (VLC) to transmit information from an off-water node to the underwater nodes [99; 100]. Moreover, preliminary investigation has been made on how to use the VLC to localize underwater nodes using RSSI [101]. In the proposed scheme, the airborne node transmits its GPS location using the VLC communication towards the water surface where the underwater nodes reside. The underwater node then detects the VLC and determines its location based on the intensity of the received light signal. By factoring the loss encountered by the visual light through its path, the authors propose a localization scheme where the underwater node can know its relative location from the transmitter. Magnetic Induction (MI) is also used to overcome the challenges faced in the air-water interface. Several studies have been made on the use of MI to communicate in underwater environments [102; 103]. Neither VLC nor MI can reach a longer distance in the underwater environment. Utilizing the PA effect to establish long distance cross-medium communication is deemed to be a more efficient method for underwater node localization. Particularly, an airborne unit generates virtual surface anchors to localize underwater networks. Leveraging a laser source, the airborne unit chooses a point on the water surface where a high energy pulsed laser is directed. Upon striking the water surface, the energy in the laser is absorbed by the water and acoustic signals are generated in underwater environments through the PA effect. By employing a suitable PA modulation methodology to transmit the GPS position of the airborne unit, [PERSON] et al. [104] have devised a localization scheme that handles various configurations. Specifically, the airborne unit generates multiple virtual anchorsthat are non-collinearly positioned to localize underwater nodes. The authors also explore the ability to localize a mobile under node using a single airborne unit where multiple readings can be obtained by moving the underwater node from the range of one virtual anchor to another. Generally, research on PA-enabled underwater localization is in early stages and more progress is expected in the next few years.
## 3 Data Processing Methods
### GB-SAR Deformation Detection with SAR Images  
#### 3.4.1 The GB-SAR Deformation Detection Principle  
A GB-SAR system observes the target point both before and after deformation, thereby facilitating the acquisition of two distinct SAR images. Subsequently, the phase difference between these two SAR images can be expressed as follows [66]:  
\[\Delta\varphi=-\frac{4\pi}{\lambda}\Delta r+\varphi_{atm}+\varphi_{noise} \tag{13}\]  
where \(\Delta r\) is the deformation value in the Line of Sight (LOS), \(\varphi_{atm}\) is the atmospheric phase, and \(\varphi_{noise}\) is the noise phase. Generally, if the time between two observations is short, atmospheric effects can be neglected. Meanwhile, by filtering to reduce the noise impact, the LOS deformation value can be expressed as follows:  
\[\Delta r=-\frac{\lambda}{4\pi}\varphi \tag{14}\]  
Due to the specific working geometry of GB-SAR observations, major steps such as image registration, baseline estimation, and removal of flat-earth effects are not required in data processing [66]. Therefore, the main steps of GB-SAR are as follows:  
1. Interferometric Phase Generation: An interferometric phase map, or interferogram, is generated by conjugate multiplying two GB-SAR images, which encapsulates the deformation information of the target area.
2. Phase filtering: Phase noise can adversely affect the accuracy of phase measurements; thus, this issue is typically addressed using techniques such as mean filtering. When processing a large number of interferograms, temporal window filtering is employed. Conversely, for a smaller number of interferograms, spatial window filtering is preferred.
3. Phase unwrapping: Since the interferometric phase value is wrapped within a certain range, it must be adjusted by adding integer multiples of \(2\pi\). Phase unwrapping is a critical step in obtaining the true phase value of the target. Commonly used methods for this process include the branch-cut method and the region-growing algorithm.
4. Deformation value calculation: Upon completion of the phase unwrapping process, the LOS deformation value of the target area can be computed from the true interferometric phase value using Equation (14).
## 3 Results and Discussion
### Virtual Water Trade in Gansu Province from 2007 to 2012
#### 3.2.2 Industrial Structure of Virtual Water Inter-Provincial Flow Direction from Gansu Province  
The inter-provincial trade of virtual water is carried out using industrial and agricultural products as carriers [74]. From 2007 to 2012, the largest virtual water exporter was primary industry, exporting 1.462 billion m\({}^{3}\), 2.862 billion m\({}^{3}\) and 7.215 billion m\({}^{3}\) of virtual water in 2007, 2010, and 2012, respectively, accounting for 96.81%, 96.78%, and 97.13% of the total output (Table 4). As such, the virtual water flow of the primary industry determines the trend of the virtual water current in Gansu province as a whole [75]. Because the agricultural sector dominates in Gansu province, it is the main contributor to virtual water exports. From 2007 to 2012, virtual water from primary industries mainly flowed to Beijing, Shanghai, Guangdong, Tianjin, Jiangsu, Zhejiang and Hebei, and the total output volume was over 80 million m\({}^{3}\), consistently increasing over time. These large export destinations are also the principal virtual water sinks, further supporting the concept that the basic pattern of virtual water output in Gansu province is affected by the trade flow of primary industry products [76]. However, most virtual water produced in Gansu province was consumed there, as agriculture, the primary industry of the province, suffers from low utilization efficiency in terms of irrigation water and rainfall [52; 54].  
In Gansu province, the secondary industry imported the highest virtual water volume from 2007 to 2012 (Table 4), at 1.294, 1.968 and 5.740 billion m\({}^{3}\), accounting for 80.17%, 80.26% and 66.24% of the total virtual water import, respectively. This virtual water stream flowed from Jiangsu, Zhejiang, and Henan, each of which retained more than 80 million m\({}^{3}\) during the study time period. This trend increased consistently, as these regions, in the midst of industrialization, consumed large amounts of water for industrial processes [77; 78].