esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
_Aim_: the aim of this retrospective clinical study was to evaluate clinically and radiographically full-arch implant prosthetic rehabilitations supported by six axial implants or four implants placed according to the All-on-Four protocol. _Materials and methods_: Patients in need of immediately loaded full-arch implant prosthetic rehabilitations of one or both arches were considered. Follow-up visits were performed one week after surgery, at 3 and 6 months and then once a year for the next 6 years to record implant survival, peri-implant parameters, marginal bone loss, bone cortical thickness and possible muscular, articular and prosthetic complications. A satisfaction evaluation questionnaire was also made available to all patients at the end of the follow-up period. _Results_: A total of 30 patients with total dentulism of one or both arches or need for avulsion of residual impaired teeth were enrolled for this study and underwent implant prosthetic rehabilitation on four or six implants. The overall implant survival rate at the six-year follow-up was 98.09%. Statistical analysis revealed a significant association between implant site and higher plaque and bleeding index in the posterior site. The extent of keratinized tissue was statistically superior in anteriorly located implants. Statistical analysis showed a significant difference between the values of bone-marginal levels as a function of time. The vestibular and palatal/lingual bone cortices were preserved, with an increasing trend in the apical direction. Regarding muscular and articular complications, the results of this study showed statistically significant differences between the mean values recorded in male and female subjects, while the most frequently encountered problems during the clinical evaluation were the presence of joint clicks and pain in the masseter muscles. Two of the patients examined showed problems with mandibular latero-deviation and prosthetic fracture. _Conclusion_: within the limitations of the present study, immediate implant-retained prosthetic rehabilitations supported by six axial implants or four fixtures placed according to the All-on-Four method could represent a valid therapeutic alternative to provide edentulous patients with restoration of masticatory function and aesthetics and promote their acceptance.  
Keywords:implant prosthetic rehabilitations; implant follow-up; All-on-Four protocol +
Footnote †: journal:  
_Article_  
Clinical and Radiographic Follow-Up of Full-Arch Implant Prosthetic Rehabilitations: Retrospective Clinical Study at 6-Year Follow-Up
# Intermitt FOCV Using an I-V Curve Tracer for Minimizing Energy Loss
## 2 Conventional MPPT Algorithms
### Successive Approximate Register (SAR)  
The SAR MPPT includes an active mode and a power down mode that alternate periodically [6]. The SAR MPPT tracks the MPP in the active mode, and maintains the final voltage of the previous active mode during the power down mode. Unlike the P & O algorithm that tracks the MPP with a single step, the SAR MPPT uses the BWS to make the tracking speed faster than the P & O. Therefore, like the P & O, it is not appropriate for low power and low energy environments. Figure 2 shows the MPP tracking procedure of the conventional P & O and the SAR over time.  
Figure 1: PV cell: (**a**) Electrical model and (**b**) I-V and P-V characteristics of a PV cell.  
Figure 2: The MPP tracking procedure of the conventional P & O and the SAR over time.  
The combined algorithm based on the adaptively binary-weighted search (ABWS) and the monotonically decreased step (MDS) aims at tracking the MPP as quickly as possible in order to reduce the power loss under rapidly changing environmental conditions [8]. Similar to the BWS, the ABWS tracks the MPP by increasing the voltage step, but starts from the LSB until the operating voltage step passes the MPP. Then, the ABWS stops and the MDS starts to operate, tracking the exact MPP. The MDS monotonically reduces the voltage step until the MPP is found. If the variation of the MPP drops below 1 LSB resolution, the MPP remains unchanged and the MDS operation is finished. Compared to the P & O and the SAR, this algorithm tracks the MPP faster than the P & O and consumes less power under stable environments.
## 4 Proposed Video Latency Measurement Technique
### Camera Client  
This application node, which is also running on the real-time target, initiates connection and receives video data from the camera server application. The functionality related to parsing the RTSP packets is the same as in the server application. The client application listens on a pre-defined TCP port for incoming connections, which will be propagated to the IP camera over the camera server application. Similarly, the video stream data, which is decrypted in the client application, is sent back to the latency measurement application. Like the camera server application, the camera client application is also organized in two threads, as shown in Figure 7. The first thread handles the RTSP protocol, while the second one is responsible for parsing the video data.  
The first step after receiving a fixed size data packet from the server is to the extract timestamp information from the packet (Step 1). After that, the packet is forwarded to the FPGA application node, which performs data decryption (Step 2). When the FPGA finishes decryption and decrypted data is read by the client application (Step 3), the network latency is calculated by subtracting the timestamp, captured from the real-time operating system time after decryption is completed (Step 4)  
Figure 6: Field programmable gate array (FPGA) node for encryption/decryption of the data.  
Figure 7: Camera Client application.  
from the timestamp, which arrived together with the data packet (extracted in Step 1). The calculated network latency is logged to file on the real-time target (Step 4). In Step 5, the decrypted video data is forwarded to the main application. It is worth noting that the data after decryption is again encoded with H264 or MJPEG algorithms (depending on the video source encoding type).
## 3 Results
### Results of the Proposed Method  
In combination with the passenger distribution and corridor and stairway layout, the initial characteristics of personnel movement are shown in Table 11, in which \(C\) is the _ID_ of corridor, \(D\) represents the _ID_ of door, and \(S\) denotes the _ID_ of stairway in the ship. The calculated escaping time of MVZ 1, MVZ 4, and MVZ 5 are listed in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Deck**} & \multicolumn{3}{c}{**MVE 1**} & \multicolumn{3}{c}{**MVZ 4**} & \multicolumn{3}{c}{**MVZ 5**} \\ \cline{2-13}  & \(t_{\text{stair}}\) & \(t_{\text{deck}}\) & \(t_{\text{f}}\) & \(t_{\text{f}}\) & \(t_{\text{f}}\) & \(t_{\text{stair}}\) & \(t_{\text{deck}}\) & \(t_{\text{f}}\) & \(t_{\text{f}}\) & \(t_{\text{f}}\) & \(t_{\text{f}}\) & \(t_{\text{f}}\) & \(T\) \\  & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** & **(s)** \\ \hline
1 & 23.2 & 44.4 & 301.7 & 369.3 & 849.4 & 21.4 & 59.0 & 100.8 & 181.3 & 416.9 & 23.1 & 40.4 & 45.5 & 109.1 & 250.9 \\
2 & 12.1 & 103.9 & 301.7 & 417.7 & 960.7 & 12.5 & 67.5 & 100.8 & 180.9 & 416.1 & 14.0 & 39.8 & 45.5 & 99.3 & 228.4 \\
4 & 18.6 & 0.0 & 301.7 & 320.3 & 736.6 & 12.8 & 0.0 & 100.8 & 113.7 & 261.5 & 12.8 & 0.0 & 49.0 & 61.8 & 142.2 \\
5 & 28.6 & 43.7 & 301.7 & 374.0 & 860.3 & 25.7 & 0.0 & 100.8 & 126.5 & 291.0 & 26.1 & 0.0 & 49.0 & 75.1 & 172.8 \\
6 & 40.4 & 42.4 & 301.7 & 384.6 & 884.5 & 39.2 & 65.9 & 128.6 & 233.7 & 537.6 & 39.7 & 38.1 & 49.0 & 126.7 & 291.3 \\
7 & 49.6 & 40.1 & 301.7 & 391.4 & 900.2 & 51.0 & 67.2 & 128.6 & 246.7 & 567.5 & 51.4 & 40.6 & 49.0 & 141.0 & 324.3 \\
8 & 58.8 & 71.3 & 301.7 & 431.8 & 993.2 & 61.8 & 66.9 & 128.6 & 257.3 & 591.9 & 62.3 & 39.9 & 49.0 & 151.1 & 347.6 \\
9 & 68.1 & 38.9 & 301.7 & 408.7 & 940.0 & 70.2 & 84.2 & 128.6 & 283.0 & 650.9 & 69.0 & 40.4 & 49.0 & 158.3 & 364.2 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The allocation of personnel in the night scene in MVZs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deck** & **Facility ID** & **Initial Persons** & \(D\) **(p/m\({}^{2}\))** & \(F_{\text{s}}\) **[p/(m \(\times\) s)]** & \(S\) **(m/s)** & \(F_{\text{C}}\) **(p/s)** \\ \hline  & C111 & & & & & & & 1.3 \\  & D111 & 66 & 0.9 & 0.8 & 1.0 & 1.2 \\  & S111 & & & & & &
# Effective Attention-Based Mechanism for Masked Face Recognition
## 6 Conclusions  
This paper presents a new method to solve the masked face recognition problems using deep learning technology. Traditional FR methods based on deep learning can address normal face recognition problems and achieve high performance. However, such methods show dramatically reduced performance when a face is covered with a mask. Through the analysis of the masked face images, we found that some of the key facial features are covered by a facial mask which makes the FR methods cannot recognize the face properly. To tackle the problem, this study introduced a new network architecture based on an attention mechanism that can focus on the most informative part around the eyes of the masked face images and obtain more discriminative feature information. Moreover, one of the most widely used ArcFace loss functions is implemented into the proposed network to optimize the feature embedding and to increase the similarity of the intra-class samples and diversity of the inter-class sample. To handle the problem of insufficient masked face datasets, new simulated masked face images are generated by using data augmentation for model training and evaluation. Through the various experiments, the following points summarize the findings in this paper:  
* The attention module can focus on the non-occluded part of the masked face and significantly improve the recognition performance.
* The newly generated masked face dataset can effectively help the model training and evaluation.
* The results show that the proposed method provides outstanding performance and a better recognition rate on both generated masked face and real masked image datasets compared to the state-of-the-art methods.  
We hope this research study becomes a useful solution to solve the masked face recognition problem. In future work, the improvement of the method to solve masked face recognition with different postures, expressions, illumination, and the presence of a hat are considered.  
[PERSON] designed and developed the proposed method, conducted the experiments and wrote the manuscript. [PERSON] designed the new concept, provided the conceptual idea and insightful suggestions to refine it further, and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (GR2019R1D1A3A03103736).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Figure 7: Training curves of the proposed method. (**a**) Training loss curve. (**b**) Training accuracy curve.  
Data Availability Statement: The datasets are available at the following link: [[https://github.com/MaskedFaceDataSet/SimulatedMaskedFaceDataset](https://github.com/MaskedFaceDataSet/SimulatedMaskedFaceDataset)]([https://github.com/MaskedFaceDataSet/SimulatedMaskedFaceDataset](https://github.com/MaskedFaceDataSet/SimulatedMaskedFaceDataset)) (accessed on 6 May 2022).
## 3 Simulation Framework
### Modules  
The current simulation framework includes the following software modules: Hybrid User Terminal (HUT), Transmitter/Receiver (T/R) and Channel. These modules are instances of simulator components, the HUT Software V1.0 Component and Device Software V1.0 Component, which are defined in [45].  
The HUT module is an instance of the HUT Software Component and is deployed in a PC. It is basically a GUI application responsible for generating user data and selecting the active communication channel. Additionally, it configures other simulation framework components such as the T/R and Channel modules.  
The T/R and Channel modules are instances of the Device Software Component and are deployed in a dedicated embedded platform that can enable processing acceleration. The T/R module adds a specific frame header to Data Messages, which contain user data, and forms Frame Messages that contain coded data. This module is also responsible for the decoding process, e.g., extracting Data Messages from received Frame Messages.  
The Channel module is responsible for modeling the behavior of terrestrial and satellite channels.  
Figure 2: Data message format.
# Analytical Model of Gear Thermal Stiffness Based on the Potential Energy Method
## 4 Case Study and Discussion
### Influence of Temperature and Temperature Distribution on Gear Thermal Stiffness  
Figure 8 presents the gear thermal stiffness at different temperatures. From Figure 8, the higher the gear temperature rise is, the greater the decrease in the gear stiffness and the smaller the thermal stiffness. In the process of gear transmission, the greater the load is, the higher the temperature rise [22]. Therefore, the greater the load on the gear pair is, the smaller the thermal mesh stiffness. The variation trend of the time-varying mesh stiffness with temperature is consistent with Figure 7a in Reference [24]. This further verifies the correctness of the proposed model in this paper.  
In the process of gear meshing, the temperature distribution in the tooth profile plane is very complex. Reference [22] obtained the bulk temperature distribution of the gear by the finite element method. It showed that the temperature distribution in the tooth profile plane had an obvious gradient. However, it did not give the specific temperature distribution function. A steady temperature distribution function was used to study the thermal deformation of gears in Reference [19] (Liu 2020). To study the influence of different temperature distributions on the gear thermal stiffness, the gear thermal stiffness under three different temperature distributions--uniform distribution, linear distribution and the temperature distribution of Liu 2020--are calculated in this paper, as shown in Figure 9.  
The distribution of the gear thermal mesh stiffness along the line of action is basically consistent with the changing trend of the time-varying thermal mesh stiffness under different temperature distributions, as shown in Figure 9. Especially under the condition of linear distribution and temperature distribution adopted in the literature [19], the thermal stiffness of gears remains highly consistent in both trend and absolute value. The gear thermal stiffness under uniform distribution has a larger gap than that under the previous two conditions. The study in [22] showed that there is a significant temperature gradient  
Figure 8: Gear thermal stiffness at different temperatures. (**a**) Thermal mesh stiffness of a single tooth pair; (**b**) Time-varying gear thermal mesh stiffness.  
in the body temperature of the gear. The temperature in the tooth profile plane is not uniformly distributed in the actual gear meshing process. Therefore, the influence of the temperature distribution in the tooth profile plane on the gear thermal stiffness is small and can be ignored when approximating the gear thermal stiffness.
## 4 Results and Discussion
### Performance Prediction and Analysis
The maximum predicted and tested values at 15 N.m are approximately 1.5 mV, whereas the maximum values are approximately 2 mV when the current load is 12 N.m.  
We conclude that the cell's consistency deteriorates as the current load increases based on the three figures above. In D5 (6-8-5-100) and D4 (6-8-5-50), the increase in consistency is exponential, while the consistency changes are essentially linear for D3 (6-8-5-20),D2 (6-8-5-10), and D1 (6-8-5-5), with the slope decreasing as the number of groups decreases. In the case of a high-load current and an assembly torque at 12 N.m, D4 (6-8-5-50) and D5 (6-8-5-100) even show a logarithmic growth trend. This is because under high-current loads, the rapid flow rate of reaction gas in the reactor has distributed flow field that can result in a turbulent and unstable gas supply. The reaction flow rate and consumption are moderate in the middle range of the load current, and the fluid distribution of each cell is stable, leading the uniform distribution of cell voltages. Figure 8 (a2,b2,c2) depicts the predicted and tested _ranges_ with different load currents, assembly torques, and grouping strategies. The _range_ has a similar growing trend to the  
Figure 7: Comparison between the model and experiment results of cell voltage distributions with the assembly torque of 14 N.m under different load current densities: (**a1**,**a2**,**a3**,**a4**,**a5**) 0.6 A/cm\({}^{2}\); (**b1**,**b2**,**b3**,**b4**,**b5**) 0.9 A/cm\({}^{2}\); (**c1**,**c2**,**c3**,**c4**,**c5**) 1.2 A/cm\({}^{2}\). \(U_{cell}\), but the fluctuations in _range_ are smaller. The predicted and tested _ranges_ in each group under different load currents are the smallest when the assembly torque is 14 N.m. When the assembly torque is 12 N.m or 15 N.m, the maximum predicted and tested values are around 28 mV, while they are 25 mV at 14 N.m. In D4 (6-8-5-50) and D5 (6-8-5-100), both the predicted and tested _range_ curves show an exponential growth trend with increasing current load; in D3 (6-8-5-20), D2 (6-8-5-10), and D1 (6-8-5-5), the linear growth trend is shown, but the slope is lower, and the change is gentler compared with the curve of the \(U_{cell}\). High _ranges_ may trigger cell voltage drops, resulting in irreversible fuel cell degradation. As a result, a high _range_ should be avoided even if the \(U_{cell}\) values are low. Figure 8: Comparison between the predicted voltages under different grouping strategies and the experimental voltages of 100 cells under varied load currents and assembly torques: (**a1**,**b1**,**c1**) for \(U_{cell}\); (**a2**,**b2**,**c2**) for _range_.
## 2 Methodology
### Error Derivation  
Either the spatial-resolution induced error or the temporal sampling scheme induced error (\(E\)) is the average of all the pixel results, which could be derived by Equation (5):  
\[E=\frac{\sum\limits_{j=1}^{m}E_{j}^{\mathrm{p}}}{m} \tag{5}\]  
where \(m\) is the number of pixels; \(E_{j}^{\mathrm{p}}\) is the estimated error of pixel \(j\). The pixel-scale error (\(E^{\mathrm{p}}\)) could be derived by the root mean square of the error of all of the sub grid points within the pixel whereby the Equation (6):  
\[E^{\mathrm{p}}=\sqrt{\sum\limits_{i=1}^{N}\frac{\left(E_{i}\mathrm{int}\right) ^{2}}{N}} \tag{6}\]  
where \(N\) is the total number of sub grid points within the pixel, which could be derived through Equation (4); \(E_{i}^{\mathrm{int}}\) is the measurement error of the discretized point \(i\), which can be derived from the Equation (7):  
\[E_{i}^{\mathrm{int}}=\frac{\sum\limits_{i=1}^{tn}\left|S^{\mathrm{p}}(t)-V_{i} (t)\right|}{tn} \tag{7}\]  
where \(tn\) is the number of time points within the estimated period (March 2019); \(V_{i}\) is the true OSR/OLR time series (1 min temporal resolution) of the sub grid point \(i\) within the pixel; \(S^{\mathrm{p}}\) is the simulated MERO-measured OSR/OLR time series of the pixel, which could be derived through Equations (1)-(4).
## 3 Materials and Methods
### Experimental Methods
In the decoding stage, the model innovatively introduces a dynamic confidence threshold mechanism: for each mask position, the probability difference between the original character and the predicted character is calculated at the same time, and the replacement is performed only when the difference exceeds a preset threshold (CONFIDENCE_THRESHOLD = 0.05), the confidence threshold (0.05) was empirically determined via grid search on the validation set, maximizing F1 while minimizing overcorrection. This probabilistic difference-based decision-making mechanism reduces 28% (for the validation set) of excessive error correction cases compared to the traditional Top-1 decoding strategy [19], which is especially suitable for input scenarios with systematic biases such as speech recognition results. At the same time, the system integrates a manual checking rule base to force the replacement of high-frequency error-prone combinations such as \"\(\mathcal{H}\)\(\subseteq\)\(\rightarrow\)\(\mathcal{H}\)\(\subseteq\)\", and improves the overall robustness through the collaborative work of the rule engine and the neural network. The deep fusion of acoustic sensors and semantic disambiguation operates through a closed-loop pipeline comprising three core phases: (1) real-time environmental feature extraction. Acoustic sensors sample ambient noise (0-100 dB), temperature (\(-\)10 \({}^{\circ}\)C to 50 \({}^{\circ}\)C), and humidity (0-100% RH) at 1 Hz. A ring buffer stores 60 s temporal windows, generating spatiotemporal feature vectors et \(\in\) R180 (3 channels \(\times\) 60 time steps). (2) Cross-modal attention alignment. During disambiguation, et is projected to a latent space \(\phi(\text{et})\in\) R768 via a learnable adapter. This environmental embedding modulates the confidence threshold dynamically: \(\tau\)t = \(\tau\)0 + \(\alpha\cdot\)sigmoid(\(\beta\cdot\)/\(\psi\)(et)\(\|\)2\(\tau\)t) = \(\tau\)0 + \(\alpha\cdot\)sigmoid(\(\beta\cdot\)/\(\psi\)(et)\(\|\)2), where \(\tau\)0 = 0.05 is the baseline threshold, \(\alpha\) = 0.02 and \(\beta\) = 0.5 are scaling factors. For noise > 65 dB (empirically calibrated), \(\tau\)t relaxes to 0.07 to suppress overcorrection. (3) Contextualized error correction. The modulated threshold \(\tau\)t gates the replacement decision in the BLAF decoder. Concurrently, an Environment-Aware Dynamic Lexicon (EDL) rescales candidate probabilities for high-risk homophone pairs (e.g., \"\(\frac{\alpha\cdot\text{ERL}}{\text{FRL}}\)\" vs. \"\(\frac{\alpha\cdot\text{ERL}}{\text{FRL}}\)\") based on sensor-derived thermal stress indices. This dual-path integration reduces acoustic-conditioned WER by 12.7%.Figure 2 shows our mixed model structure diagram.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
###### Abstract  
Aiming at the unmanned aerial vehicle (UAV)-assisted federated learning wireless-network scenario, and considering the influence of the UAV altitude on the coverage area, we propose a joint optimization algorithm of UAV placement, computation and communication resources. Considering the energy efficiency and federated learning performance, we defined the cost function of the system. Under the constraint of the total delay of federated learning completion, we formulated an optimization problem of minimizing the cost function to achieve the balance between the total energy consumption of users and the federated learning performance. Since the formulated optimization problem is a non-convex problem, in order to solve this problem, we decomposed it into three optimization subproblems: UAV horizontal placement, local accuracy and computation and communication resources. We used successive convex approximation (SCA), the Dinkelbach Method, the Bisection method and KKT condition, respectively, to solve the three subproblems, and finally obtain the optimal solutions through iteration of the three subproblems. Simulation results show that compared with the federated learning scenario under fixed UAV altitude, our proposed algorithm not only guarantees the learning performance, but also reduces more users' total energy consumption.  
machine learning; energy efficiency; learning performance; Dinkelbach method +
Footnote †: journal: Applied  
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