esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Characterization of SRLNC Transmission Model  
The effectiveness of the SRLNC algorithm for efficient data transmission over a multipath channel was assessed in terms of throughput while varying payload (samples per generation), packet loss rate, throughput with link loss variation and need for redundant packets, etc. The proposed SRLNC algorithm applies a Galois field of size 8 that enabled it to time as well as the computation efficient. Typically, higher redundant packets enable more accurate decoding at the receiver, however, at the cost of added computational overheads. Considering it, SRLNC was tested with one and two redundant packets per generation and relative performance was obtained (Figure 4). Initially the number of redundant packets was considered as 1, and the generation size was fixed at 10. Considering practical UWSN, where there can be continuous variations in the link quality to assess  
Figure 3: Channel gain.  
Figure 2: Time advancement of the magnitude baseband impulse response.  
To enable an optimal computationally efficient routing scheme over UWSNs, enriching FEC is vital. Maintaining a minimum number of redundant packets to decode the data packets can be advantageous. It can reduce the computational overheads as well as unwanted bandwidth utilization. To estimate the minimum number of redundant packets per generation to have maximum data decoding at the receiver node, we tested SRLNC with one and two redundant packets per generation, where the proposed approach exhibited higher throughput with two redundant packets per generation. It signifies that with two redundant packets per generation, SRLNC can provide higher throughput and, hence, optimal performance towards efficient communication (Figure 7).  
Observing the results obtained, it can be found that the proposed S-RLNC scheme achieves throughput up to 99-100% with ideal (or near ideal) network condition.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 3 Results
### Field Emission Scanning Electron Microscopy  
Field Emission Scanning Electron Microscopy (SEM) clearly observes material microstructure and organization, crucial for revealing mechanisms of soil improvement through a stabilizer and recycled aggregate combination. This section discusses and analyzes SEM microstructure images of the specimens (R20-C10, R20-C15, R20-C20, R40-C20, R60-C20).  
Figure 11a,b show the R20-C10 specimen microstructure at 500\(\times\) and 2000\(\times\) magnification. Figure 11a shows recycled aggregates fully encapsulated by red clay, but with notable void structures and cracks; Figure 11b shows limited cementitious materials from soil stabilizer hydration between red clay and recycled aggregates, partially filling inter-particle voids. However, large cracks and wide voids remained, forming aggregates that constitute the main skeletal structure supporting the specimens and bearing unconfined compressive strength.  
Figure 12a,b show the R20-C20 specimen microstructure at 500\(\times\) and 2000\(\times\) magnification, revealing recycled aggregates fully encapsulated by red clay and voids between soil particles and recycled aggregates filled with hydration products, forming denser aggregates. This explains the R20-C20 specimens' highest unconfined compressive strength.  
Figure 11: R20C10-SEM micrographs (**a,b**).  
Figure 12: R20C20-SEM micrographs (**a,b**).  
Figure 13a,b show the R60-C20 specimen microstructure at 500\(\times\) and 2000\(\times\) magnification, where increased recycled aggregate particles created large voids that red clay particles could not completely fill and encapsulate, leading to unstable skeletal structure due to movement and sliding between recycled aggregate particles, resulting in the R60-C20 specimens' lowest unconfined compressive strength.
# Vitamin D and Rehabilitation after Stroke: Status of Art
## 3 Genetic and Epigenetic of Vitamin D Pathway
### Gene Polymorphisms
#### 3.1.3 Cyp24a1  
Several polymorphisms have been identified in _CYP24A1_, many of which are associated with the reduction in CYP24A1 enzyme activity [76; 77].  
Two vitamin D response elements (VDRE) are present in the _CYP24A1_ promoter region that regulates the transcription activity of this gene [78].  
The polymorphism T > C, identified in position -279 within the distal vitamin D response element (VDRE2), results in decreased protein binding and _CYP24A1_ expression levels in cultured primary of human lymphocytes [76].  
A study on the Amish population showed that _CYP24A1_ rs2762939 SNP is associated with coronary artery calcification (CAC), suggesting that vitamin D metabolism is involved in coronary atherosclerosis [47].  
Moreover, [PERSON] et al. analyzed the correlation between _CYP24A1_ rs927650 genotype and the risk of ischemic stroke together with hypertension, diabetes, smoking status, and obesity in the Turkish population [33]. The authors found that CT + TT alleles significantly increase the risk of stroke considering hypertensive versus normotensive, diabetic versus non-diabetic, smoker versus nonsmokerand obese versus non-obese subgroups [33]. Probably, the enzyme carrying this SNP increases the active vitamin D degradation, thus reducing its levels, with a higher risk for ischemic stroke.
## 3 Conventional SFO Estimation Scheme  
Post-FFT synchronization strategies can be practically implemented by performing correlation between dedicated pilots of successive OFDM symbols in the frequency domain [16; 17; 18; 19; 20; 21; 22]. In contrast, the DD estimation scheme is proposed to find the phase rotation of the OFDM signal caused by the frequency offset without relying on a dedicated pilot. To do this, a priori knowledge of the transmitted data is of paramount importance [23; 24]. The DD estimation scheme spends a large number of complex multiplications in comparison to the pilot-based estimation scheme. Although the latter is too computationally demanding, it can be considered as a promising candidate for the estimation strategy rather than the pilot-based estimation scheme because the Homeplug GP system contains no dedicated pilot symbols for synchronization. Hereafter, we limit our study to the DD estimation strategy.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Print Quality Problems
#### 3.3.8 Visible Lines on the Side of the Print  
Visible lines appear on the side or across the top layer of the print, often diagonally from one side to another. This may be due to incorrect print settings, combing, scratching scars caused by dragging of the nozzle, and/or over-extrusion. Lower quality filaments also tend to ooze from the nozzle due to the residual heat of the extruder, leaving a trail as the nozzle shifts. Solutions include the following:  
1. Checking the extrusion flow rate. Reduce the print flow by 5% and print a calibration cube to check if the issue is solved. Ensure that the filaments are extruding correctly to avoid under-extrusion or over-extrusion (refer to 3.2.2: Over-extrusion).
2. Combing allows the nozzle to run over the printed area to substitute the need for retractions, but this may cause scarring of the part. Turn off combing will curb the problem but may lead to longer print time. Increase the amount of retraction if this issue continues after combing is turned off.
3. Scaring occurs when the nozzle is set not high enough (refer to 3.1.2: (1) Incorrect built platform levelling).
4. Adjust the extrusion temperature for lower quality or older filaments which may have been exposed to sunlight or moisture over a period of time as their tolerance to the print temperature may be reduced. Decrease the hot end temperature by 5 \({}^{\circ}\)C and repeat until a good result is obtained.
## 3 RUL Prediction Methodology
### First Detection Time, End-of-Life Time and RUL Prediction Time
#### 3.3.3 RUL Prediction Time (PT)  
As the RUL is a dynamic variable dependent on the current health condition, the prediction of RUL at the healthy stage may be untenable. On the other hand, the rate of fault deterioration is not known in advance and is often increasingly fast due to the accumulation of historical defects. To allow the optimal predictive maintenance planning,the inspection window between the FDT (\(t_{FDT}\)) and the PT of RUL (\(t_{PT}\)) is expected to be as short as possible to enable timely prediction of RUL at the early degradation stage.  
On the above basis, a typical example of a bearing degradation process with respect to the machine operation time based on the health indicator is shown in Figure 3. The HI is assumed to start from a relatively stable plateau at the healthy stage and step up to enter the degradation stage once the first fault event is detected. Then, it further increases with the accumulation of faults and potential failures. As defined in Equation (6), the bearing is thought to suffer from functional failures when the HI first passes the average EOL threshold \(\overline{\gamma}\) at time \(t_{EOL}\). In the case of ESI, the threshold \(\overline{\gamma}\) is determined based on the mean maximal magnitude of all available historical ESI trajectories, each of which extracts the spectral energy of the bearing FCF (defect) components in the averaged envelope spectrum. It is considered that the selected \(\overline{\gamma}\) signifies the utmost extent of bearing deterioration, reaching a critical point where immediate replacement is required. Thereby, the threshold also facilitates the determination of the end-of-life time for the RUL prediction in practical applications.
# Mobility Prediction of Mobile Wireless Nodes
## 2 Mobility Models
### Random Waypoint  
The random waypoint (RWP) model is used to mimic users' movement in a network. User nodes in an RWP model pick a random and independent location and destination in a specific scope with an assigned speed. Then, the node moves as directed by the previous information until it reaches the target destination, where it pauses for a moment before beginning the new selection [8; 9].
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
## 2 Related Work
### Neural Architecture Search  
Automated machine learning (AutoML) has been gaining increased attention in recent years, with a strong emphasis on the automation of parameter selection and the fine-tuning of the model architecture [27]. A notable idea in this domain is model architecture adaptation, which reduces the need to design neural networks from scratch. Neural architecture search (NAS) [28; 29; 30], in particular, aims to find the optimal architecture for a specific problem by using methods such as reinforcement learning and evolutionary algorithms [25; 31; 32]. Both AutoML and NAS have been employed to adapt neural architectures with notable results [33; 34].  
Furthermore, model adaptation has also been integrated with other optimization avenues, such as quantization. Previous research has focused on joint optimization for the neural architecture and quantization space, which has been shown to be effective in achieving significant performance gains and has inspired broad interest. Specifically, [PERSON] et al. [35] explored the end-to-end co-optimization of NAS and mixed-precision quantization. Furthermore, [PERSON] et al. [36] used a predictor-transfer technique to obtain a quantization-aware predictor, which was then fed into the evolutionary search to jointly optimize the neural network architecture, pruning policy, and quantization policy.
## 3 Results
### Monitoring of Photobleaching  
Perspectives of the developed approach in monitoring of a PDT procedure were studied in the course of the BPD photobleaching. The phantom was irradiated at the wavelength of 695 nm, with the intensity at the surface amounting 150 mW/cm\({}^{2}\), which is typical for an anti-tumor PDT procedure. The irradiation lasted until the delivered dose amounted to 270 J/cm\({}^{2}\), typical for antitumor treatment. Fluorescence images acquired at different stages of irradiation are shown in Figure 10b, together with the corresponding delivered doses. Figure 10a shows the decrease of the fluorescence intensity with the increase of the accumulated dose which indicates photobleaching of the PS. One can see that this dependence is accurately (R\({}^{2}\) = 0.989) approximated by an exponential decay law. A photobleaching level of 50% was achieved at the dose of 120 J/cm\({}^{2}\), and the level of 80% was reached after the full dose of 270 J/cm \({}^{2}\). Figure 10c,d show the OA B-scans of the phantom with agar mixture obtained at 690 nm before (Figure 10c) and after (Figure 10d) the PDT-mimicking procedure. One can observe a decrease of OA signals from BPD, indicating decrease of absorption as a result of photobleaching. The OA image contrast of BPD at 690 nm, calculated using Equation (1), decreased from 1.1 to 0.5 after the procedure and corresponds to the relative signal decrease of 28% due to photobleaching, which was significantly lower than the corresponding FL signal decrease. This fact indicates that fluorescence imaging is more sensitive to detecting photobleaching compared to OA, which is in good agreement with the results of other researchers [28].
## 2 Machine Learning Applications in Big Data
### Manufacturing and Industry 4.0  
In manufacturing, ML is a key enabler of Industry 4.0, driving advancements in automation, predictive maintenance, and quality control. By analyzing data from sensors, machines, and production lines, ML models can predict equipment failures, optimize production schedules, and improve product quality. These applications lead to increased efficiency, reduced downtime, and lower production costs [114; 115; 116].  
Predictive maintenance is one of the most widely adopted applications of ML in manufacturing. By analyzing data from sensors installed in machinery, ML models can predict when equipment is likely to fail and schedule maintenance before a breakdown occurs. This approach reduces unplanned downtime, extends equipment life, and lowers maintenance costs [117; 118; 119].  
In quality control, ML models analyze data from production lines, including images, measurements, and sensor readings, to detect defects and ensure that products meet quality standards. These models can identify patterns and anomalies that human inspectors may miss, leading to higher-quality products and reduced waste [120; 121; 122].  
Furthermore, ML is used in supply chain optimization, where models analyze data from suppliers, logistics providers, and customers to optimize inventory levels, reduce lead times, and improve supply chain resilience. This leads to more efficient operations, lower costs, and better customer service [123; 124; 125].
## Appendix B  
The coefficient of determination \(R^{2}\) is a statistical measure that indicates the proportion of the variance in the dependent variable that is predictable from the independent variable(s). Its value ranges from 0 to 1. The formula is as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\ \left(y_{i}-\widehat{y}_{i}\right)^{2}}{\sum_{i=1}^ {n}\ \left(y_{i}-\bar{y}\right)^{2}} \tag{1}\]  
where \(y_{i}\) is the actual value, \(\widehat{y}_{i}\) is the predicted value, and \(\bar{y}\) is the mean of the actual values. A higher \(R^{2}\) indicates a stronger explanatory power of the model.  
The root mean squared error (RMSE) is the square root of the average of the squares of the differences between the predicted and actual values. A smaller RMSE indicates a lower prediction error. The formula is as follows:  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}\ \left(y_{i}-\widehat{y}_{i}\right)^{2}}} \tag{2}\]  
The mean absolute error (MAE) is the average of the absolute differences between the predicted and actual values. A smaller MAE indicates a lower prediction error. The formula is as follows:  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}\ \left|y_{i}-\widehat{y}_{i}\right|} \tag{3}\]The standardized root mean square error (SI) is the ratio of the RMSE to the mean of the observed values. A smaller SI indicates a better model performance. The formula is as follows:  
\[SI=\frac{RMSE}{\tilde{y}} \tag{10}\]  
The Akaike information criterion (AIC) is used to evaluate the goodness-of-fit and complexity of a model. The formula is as follows:  
\[AIC=2k-2 ln(L) \tag{11}\]  
where \(k\) is the number of parameters in the model, and \(L\) is the likelihood function. A smaller AIC value indicates a better model.  
The Bayesian information criterion (BIC) is similar to AIC but penalizes model complexity more strictly. The formula is as follows:  
\[BIC=k\ln(n)-2\ln(L) \tag{12}\]  
where \(n\) is the sample size. A smaller BIC value indicates a better model.  
The Kling-Gupta efficiency (KGE) considers the correlation, bias, and variability. The formula is as follows:  
\[KGE=1-\sqrt{(r-1)^{2}+(a-1)^{2}+(b-1)^{2}} \tag{13}\]  
where \(r\) is the correlation coefficient, \(b\) is the mean ratio, and \(a\) is the variability ratio. A KGE value closer to 1 indicates a better model performance.  
The mean bias error (MBE) measures the systematic bias between the predicted and actual values. A positive MBE indicates a systematic overestimation by the model, while a negative MBE indicates a systematic underestimation. The formula is as follows:  
\[MBE=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\tilde{y}_{i}\right) \tag{14}\]
_applied_  
_sciences_  
_Article_  
**Secondary Mandible Reconstruction with**  
**Computer-Assisted-Surgical Simulation and Patient-Specific**  
**Pre-Bent Plates: The Algorithm of Virtual Planning and**  
**Limitations Revisited**  
**[PERSON] 1, [PERSON] 2,3 and [PERSON] 2,3,4,5,*(c)**  
* Department of Oral and Maxillofacial Surgery, Bir Hospital, National Academy of Medical Sciences,  
Kathmandu 44600, Nepal, [EMAIL_ADDRESS]
* Division of Trauma Plastic Surgery, Department of Plastic and Reconstructive Surgery,  
Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan; [EMAIL_ADDRESS]
* College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
* Craniofacial Research Center, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
* Department of Plastic Surgery, Xiamen Chang Gung Hospital, Xiamen 361000, China
* Correspondence: [EMAIL_ADDRESS]; Tel.: +886-3-3281200 (ext. 2946)  
**Abstract:** Despite the known advantages of virtual surgical planning and three-dimensional (3D) printing, translation of virtual planning to actual operation is a challenge, especially in secondary mandibular reconstruction. Patients who underwent secondary microvascular mandibular reconstruction were retrospectively reviewed and categorized into three categories as follows: (i) defect not crossing the midline (category I); (ii) defects crossing the midline with availability of previous imaging data (category II) and; (iii) defects crossing the midline with unavailability of previous imaging data (category III). The resulting 3D printed models were used as an effective guide for plate bending during secondary reconstruction surgery. Accuracy of the reconstruction was evaluated by superimposing post-operative images over virtual plan. Out of eleven patients, five were category I, three were category II, and three were category III. The mean linear discrepancy between the planned and post-operative position was measured. A Mann-Whitney U test was conducted to compare mean discrepancy among the groups showed no significant difference between group I and group II (\(p>0.05\)) whereas comparison of groups I and II with group III showed a significant difference (\(p<0.01\)). The proposed algorithm for the generation of defect template for manual plate bending during secondary reconstruction of mandibular defects is valid with acceptable accuracy in various defect configurations.  
**Keywords:** mandibular defect; plate bending; secondary reconstruction; virtual surgical planning (VSP); vascularized bone flap; 3D printing
## 3 Results  
In this section, we conducted numerical experiments to evaluate the performance of our proposed SC-MADDPG. The communication mechanism between UAVs in SC-MADDPG is improved based on the MADDPG algorithm, integrating four parts to achieve UAV communication functions. The observation encoding part consists of two layers of multilayer perceptron (MLP) and a self-attention model; the estimation inference part is composed of a gated recurrent unit (GRU) and two layers of MLP, with the number of hidden layer units in the GRU being 32; the message sending part is implemented using a graph neural network (GNN); the Critic network in the decision part is set to three layers, with the number of neurons being the sum of all UAV state space sizes, 192, and 1, respectively. The Planner network is also set to three layers, with the number of neurons being the state space size of UAV \(i\), 192, and \(m\), respectively. The Executor network has input as UAV \(i\)'s observation situation \(o_{i}\) and \(g_{i}\), and output is the UAV's action. At the same time, a safety layer is added to the MADDPG algorithm, so SC-MADDPG is composed of Actor network, Constraint network, and Critic network. The Constraint network consists of two fully connected layers, with the activation function being the ReLu function, and the initial learning rate is \(5\times 10^{-4}\). The Actor network consists of three fully connected layers and two hidden layers, with the number of neurons in the hidden layers being 100. The initial learning rate is \(1\times 10^{-4}\). The Critic network also includes three fully connected layers and two hidden layers, with the number of neurons in the hidden layers being 500, and the initial learning rate is \(1\times 10^{-3}\). Detailed parameters can be found in Table 1 below.  
The system model considers a system environment composed of 80 user devices, 4 UAVs, and 2 edge servers. The flight range of UAVs is a rectangular area of \(300\) m \(\times\)\(300\) m. The parameter settings are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of hidden layer units & 32 \\ \hline Number of critic network layers & 3 \\ \hline Number of neurons & The sum of the state space sizes of all \\  & drones, 192 and 1 \\ \hline Number of Planner network & 3 \\ \hline Number of neurons & State space size, 192, and m of UAV \(i\) \\ \hline Input to the Executor network & Observation of UAV \(i\) \\ \hline Output to the Executor network & Drone actions a\({}_{i}\) \\ \hline Activation function & ReLu \\ \hline Initial learning rate of constraint network & \(5\times 10^{-4}\) \\ \hline The number of neurons in the hidden layer of the & \\ actor network & 100 \\ \hline The initial learning rate of the actor network & \(1\times 10^{-4}\) \\ \hline The number of neurons in the hidden layer of the & 500 \\ \hline The initial learning rate of the critic network & \(1\times 10^{-3}\) \\ \hline Episode & 5000 \\ \hline Step & 50 \\ \hline Discount factor & 0.99 \\ \hline Batch size & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment parameters.