esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Factors That May Influence Early Healing
#### 4.2.1 Anti-inflammatory Use  
Numerous drugs are used for treating postoperative pain, such as glucocorticoids or nonsteroidal anti-inflammatory drugs (NSAIDs). Some authors have noted that the use of these drugs can reduce healing/cointegration, such as [PERSON] et al. [15], who found that 44% of the failures involved patients with a history of NSAID use. Additionally, [PERSON] et al. [16] reported that the use of NSAIDs, more specifically COX2 inhibitors, could prevent osseointegration of the implant.  
However, [PERSON] et al. [17], who analysed the data from two groups of 30 patients to evaluate the effect of ibuprofen on alveolar bone, found that at three and six months after implant placement, there was no significant difference in the amount of marginal bone between the group that received ibuprofen and the group that did not.  
Other authors have focused on the effects of NSAIDs and their associations with the growth of the alveolar bone crest. One example is [PERSON] et al. [18], who evaluated 326 implants in 81 patients and reported a relationship between the use of NSAIDs and the growth of the alveolar bone crest. However, these patients also took other medications apart from NSAIDs, so this bone growth may have been due to the combination of several drugs, and thus the study results are not conclusive.  
Various animal studies have also been conducted on this topic. This includes the study by [PERSON] et al. [19], who analysed the effect of diclofenac on wounds caused by incisions in 20 rats and found a decrease in fibroblasts and reduced wound contraction, demonstrating the antiproliferative effect of diclofenac.  
In the study by [PERSON] et al. [20], the authors examined the effect of diclofenac and meloxicam on bone healing around the implant in 30 rats. The group that was administered diclofenac demonstrated delayed healing, whereas meloxicam had no effect on healing around the implant.  
All these studies may have been influenced by different biases and circumstances, however, and so more studies in humans are necessary to conclude that NSAIDs have an inhibitory effect on implant healing.  
In our study, all patients took paracetamol as an analgesic. We did not find a relationship between implant success at 2 months and the control of postoperative pain or discomfort. Only four patients reported pain equal to or greater than a 40 mm mark on the VAS line, but none of them experienced early implant failure.
## 2 Materials and Methods
### Statistical Analysis  
Graphs were produced and other statistical analyses and data fittings were performed by OriginPro 2017 (OriginLab, Northampton, MA). The statistical parameters applied for evaluating the goodness of fit were: mean absolute error (MAE), residuals (e), and the coefficient of determination (R\({}^{2}\)) according to Equations (3)-(5).  
\[MAE=\frac{1}{n}\sum_{j=1}^{n}\lvert y_{j}-\vartheta_{j}\rvert \tag{3}\]  
\[e=y-\vartheta \tag{4}\]  
\[R^{2}=1-\frac{\sum_{j=1}^{n}\left(y_{j}-\vartheta_{j}\right)^{2}}{\sum\left(y_ {j}-\overline{y}\right)^{2}} \tag{5}\]  
where \(y_{j}\) is the experimental value for sample j, \(\vartheta_{j}\) represents the predicted values for sample j, n is the number of samples and \(\overline{y}\) is the mean of experimental values.  
Model selection was made by the Akaike information criterion (AIC) (Equation (6)), which summarizes the fitting of models to the dataset from each substrate:  
\[\text{AIC}=n\cdot\ln\left(\frac{RSS}{n}\right)+2(N+1)+\frac{2(N+1)(N+2)}{(n-N-2)} \tag{6}\]  
where \(n\) is the number of data points, RSS is the residual sum of squares and N is the number of model parameters. AIC accounts for both the goodness of fit and the number of parameters in the models. Thus, the smaller the AIC value, the more a model is suitable.
# Off-Cloud Anchor Sharing Framework for Multi-User and Multi-Platform Mixed Reality Applications
## 5 Experiments  
After completing all the developments described in the previous sections, a series of experiments were performed to evaluate the proposed system. The objective was to assess the impact of spatial anchor size on performance during transmission and loading processes in a multi-user MR application.  
Specifically, the aim of the experiments was to measure the time required for creating, exporting, transmitting and loading spatial anchors of different sizes between the two smart glasses. For this, two experimental cases were designed to comprehensively evaluate the system under different network configurations and hardware roles. In the first case, two HoloLens 2 devices were connected to a local network, with one device acting as the host and the other as a client. The second case involved a remote setup where a PC acted as the host and the HoloLens device was connected as a client. This setup allowed us to add potentially less-stable or higher-latency network conditions, thereby assessing the robustness and scalability of the proposed solution.  
Thus, the proposed evaluation focused on how anchor complexity, which is strongly tied to the volume of HoloLens' spatial mapping data, affects synchronization performance throughout the application initializing process.  
Four scenarios were defined to simulate different anchor sizes and spatial complexity levels: small, medium, big and room-sized anchors. The different spatial mappings are compared in Figure 7 and are detailed below:  
1. Small anchor: Standing, slowly rotate head and Head-Mounted Displays (HMD) 180\({}^{\circ}\) on vertical and horizontal axis multiple times to capture a small, focused area.
2. Medium anchor: Similar to the small anchor setup, standing, the HMD performs full 360\({}^{\circ}\) head rotation, capturing a slightly larger spatial environment.
3. Big anchor: Standing on each corner of a 2 m by 2 m square; performing slow 360\({}^{\circ}\) rotations on vertical and horizontal axes multiple times to scan a broader area.
4. Room-sized anchor: walking around the room, scanning walls and surrounding objects by rotating the head and HMD, generating a large and detailed spatial map.  
Figure 6: Screenshot of the developed game: (**a**) View from a desktop computer. (**b**) View from a HoloLens device.
## 4 Results and Discussion
### Architecture Determination and Regression Performance of GBDT  
To compare the influences of different structural parameters on GBDT performance, the mean-square error (MSE), defined as \(\psi_{MSE}=10\log_{10}\left\{e^{2}(n)\right\}\), was used. The performance of the GBDT (Gradient Boosting Decision Tree) regressor was mainly affected by the loss function, maximum depth and number of estimators of the individual regression estimators.The regression loss functions commonly used by GBDT include Mean Squared Error (MSE), Mean Absolute Error (MAE) and Huber error. The mean de-noise effects for three different loss functions in the 10 types of noise datasets are shown in Figure 10.  
Figure 10: The de-noise effects of GBDT with MSE, MAE and Huber loss functions.  
Figure 9: Clustering results of different types of noise datasets with different methods, (**a**) K-means++ for Raw Noise Data, (**b**) K-means++ for raw noise data with PCA, (**c**) K-means++ for raw noise data with VMD, (**d**) K-means++ for raw noise data with VMD/PCA.  
It can be seen from Figure 10 that the GBDT regressor with Huber loss function yields the smallest MSE \(\psi_{MSE}\) for each of the 10 types of noise datasets. The MSE loss function curve is smooth and continuous and is, therefore, conducive to the iterative optimization through gradient descent method. However, it is sensitive to discrete points. In comparison, the MAE loss function is tolerant to discrete points, but it has high complexity in solving the gradient. As the fusion of MSE and MAE functions, Huber error can not only reduce the sensitivity to discrete points, but also allows differentiability in all situations. Therefore, the Huber error loss function is selected as the loss function of GBDT.  
Influences of maximum depth and number of estimators on the regression performance of GBDT were examined. In order to simplify the determination architecture of GBDT, it was assumed that different noise types have the same GBDT structure. Using a trial-and-error method, the regression performance indicator MSE \(\psi_{MSE}\) corresponding the maximum depth ranging from one to six and the number of estimators ranging from 100 to 700 were studied. Using factory noise as an example, the influence of maximum depth and number of estimators on the GBDT regression performance is shown in Figure 11. Therefore, the optimal maximum depth and number of estimators of GBDT used in this paper were four and 500, respectively.
# Transmission Angle Analysis of a Type of Parallel Manipulators
###### Abstract  
In most research works, the conditioning index is used to analyze the kinematic performance of a type of parallel manipulators whose traveling plate has only translation in the workspace, and each limb is made up of three joints and two moving links. However, it is difficult to determine the reasonable limit value of the conditioning index as the design criterion. Therefore, this paper defines two types of transmission angles to evaluate the kinematic performance of parallel manipulators, with which the direct and indirect singularities can be identified in a straightforward manner. Taking both the Diamond and Delta parallel manipulators as examples, the relationships are investigated between the angles and the condition number of the Jacobian, which is a vital performance index for parallel manipulators throughout the workspace. Then, simulation studies are conducted to prove the effectiveness of two kinds of transmission angles for the Diamond and Delta robots. It indicates that the defined angles are well characterized compared with the condition number of the Jacobian matrix in evaluating the kinematic performance of the parallel manipulators and determining the workspace.  
parallel manipulator; transmission angle; singular configuration +
Footnote †: journal: Applied sciences
# A Lightweight YOLOv8 Model for Apple Leaf Disease Detection
###### Abstract  
China holds the top position globally in apple production and consumption. Detecting diseases during the planting process is crucial for increasing yields and promoting the rapid development of the apple industry. This study proposes a lightweight algorithm for apple leaf disease detection in natural environments, which is conducive to application on mobile and embedded devices. Our approach modifies the YOLOv8n framework to improve accuracy and efficiency. Key improvements include replacing conventional Conv layers with GhostConv and parts of the C2\(t\) structure with C3 Ghost, reducing the model's parameter count, and enhancing performance. Additionally, we integrate a Global attention mechanism (GAM) to improve lesion detection by more accurately identifying affected areas. An improved Bi-Directional Feature Pyramid Network (BiFPN) is also incorporated for better feature fusion, enabling more effective detection of small lesions in complex environments. Experimental results show a 32.9% reduction in computational complexity and a 39.7% reduction in model size to 3.8 M, with performance metrics improving by 3.4% to a [EMAIL_ADDRESS] of 86.9%. Comparisons with popular models like YOLOv7-Tiny, YOLOv6, YOLOv5s, and YOLOv3-Tiny demonstrate that our YOLOv8n-GGi model offers superior detection accuracy, the smallest size, and the best overall performance for identifying critical apple diseases. It can serve as a guide for implementing real-time crop disease detection on mobile and embedded devices.  
identification of apple leaf diseases; object detection; lightweight network; ghost networks; GAM; improved BiFPN; YOLOv8n +
Footnote †: journal: Applied Sciences
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 5 Experiment and Analysis
### Impact of the Number of Users  
The experiment in this section mainly verifies the changes in WFO with respect to the number of user services. The primary consideration of the experiment was 100 ECs. The data volume is set at 300 MB. The bandwidth between the CC and ECs is generated according to the topology of the network, as recorded in the bandwidth matrix. The bandwidth was set from 3 MB/s to 10 MB/s. The link delay between nodes is set at 1-3 ms. The forwarding delay of the node is set at 1-5 ms. We assume that the data from the CC are not capped.  
Figure 6 shows the number of users of the WFO strategy with the delay tolerance. The tolerance delay varied from 0-30 ms. The tolerance time was set within 30 ms because some video data are worthless when the delay exceeds 30 ms, such as live streaming. The ordinate is the number of users, which indicates the service capacity of WFO at a certain tolerance time. We considered the serviceability of WFO in five different bandwidth situations, namely 100 MB/s, 150 MB/s, 200 MB/s, 300 MB/s, 400 MB/s, and 500 MB/s. As the delay tolerance increased, the number of WFO service users increased rapidly until the maximum number of users was reached. To further illustrate this, if the tolerance time of the node is set to 3 ms, the number of users that WFO can serve under 100 MB/s is 22. This represents the number of nodes that FCFS can serve. This is the same for the other conditions. The number of users under the tolerance delay is linear until the node meets all users. Thus, we can serve more user services with minimal delay, and it has almost no impact on the user experience.
## 5 AI-Based Microwave NDT Algorithms
The results of their approach advocate for their model's applicability in microwave imaging applications, with low values for structural similarity index (SSIM) and mean squared error (MSE) across different test conditions [143]. Machine learning techniques have also been used in active infrared thermography imaging algorithms for defect detection [146; 147]. Furthermore, [PERSON] and [PERSON] proposed a novel statistical method and compared its defect detection capabilities with perceptron and Kohonen neural networks[147]. Using this approach, it was shown that the perceptron neural network (PNN) yielded a correct characterization of 84% with regard to temperature, 77% with regard to phase, and 82% with regard to amplitude when evaluating the entire image produced by pulsed thermography, thus advocating for the integration of such neural network architectures within standard MNDT techniques. Advanced machine learning methodologies have been shown to enhance the detection and visualization of corrosion under insulation (CUI) severity [148]. These technologies not only diagnose the current state of corrosion but also forecast future CUI progression and accurately assess the remaining lifespan of the inspected materials, enabling proactive maintenance strategies [148]. In summary, implementing AI in MNDT has appreciable impacts on sensitivity, cost, and automation [141]. Furthermore, using AI to post-process data gathered from microwave sensors is a fundamental aspect of portable testing devices, enabling them to surpass traditional rack-mounted equipment fitted with large displays and requiring advanced plotting capabilities, thus making it a promising addition to MNDT that will attract further research in the coming years.
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 2 Methodology  
### Overview  
The proposed method aims at addressing the failure prediction problem of the rotating machinery accurately, and its functional block diagram, is shown in Figure 1.  
Considering that the original signals measured by the vibration sensors have much noise interference, CEMDAN and MIC are thus used to obtain the reconstructed signals in the fault feature extraction stage. Then, ApEn of the reconstructed signals is achieved as the health indicator of the rotating machinery. Details of the fault feature extraction are described in Section 2.2.  
The prediction starting time, on which the rotating machinery has a slight failure but can work for a longer period, is achieved with the Chebyshev inequality. By selecting a reasonable final failure threshold according to the actual situation, all time slots for performing RUL prediction can be identified. More details about initial and final failure prediction are described in Section 2.3.  
In the remaining life prediction stage, an efficient RUL prediction model by the combination of the AR model and UKF algorithm is developed to accurately obtain the RUL of the rotating machinery. More details about the RUL prediction are described in Section 2.4.
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 4 Performance Evaluation
### Simulation Scenario Construction for the Network Games  
Figure 7 shows a scenario concept for network games. The same concept applies to all game situations. As for the node arrangement, one client, one server, two switches and two routers have been deployed, and as for traffic, the author evaluated its performance by differentiating the transmission protocols (i.e., TCP, UDP, and RUDP), using the RUDP demand model. The simulation run time is one hour and the performance index has been determined with traffic data send/receive rates along with the end-to-end transmission delay time.
## 2 Materials and Methods
### Investigation Methods
#### 2.4.2 Investigation on Disease Incidence, Disease Index, and Control Efficacy  
Investigations were conducted in mid-to-late September during the maize waxripening stage.  
Disease investigation protocol in 2023: In each region where disease outbreaks occurred, one representative plot (5-8 \(\mathrm{hm^{2}}\)) was selected per county. Five sampling points were established per plot using a diagonal sampling pattern (four corners and center). To minimize edge effects, sampling avoided the outermost five corn rows along plot boundaries. At each sampling point, \(\geq\)20 plants were examined, ensuring a minimum total of 100 plants surveyed per plot.  
Disease investigation protocol in 2024: Each treatment contained three biological replicates, with \(>\)25 plants examined per replicate (minimum 75 plants per treatment). The disease evaluation protocols included the following: (1) _Fusarium_ stalk rot--quantification of infected plants among total sampled plants at each sampling point; (2) northern leaf blight--severity scoring based on the established classification system (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Treatment Number** & **Treatment Combinations** & **Formulation** & **Dosage per Hectare** \\ \hline
1 & 10\% Diferonoconazole & WP (Wettable powder) & 300 g \\
2 & 10\% Diferonoconazole + & WP (Wettable powder) + AS & 300 g + 1.5 L \\
2 & Biocontrol agent & (Aqueous solution) & \\
3 & 15\% Triradimefon & WP (Wettable powder) & 1200 g \\
4 & 0.3\% Tetramycin & AS (Aqueous solution) & 1050 g \\
5 & 250 g/L Azoxystrobin & SC (Suspension concentrate) & 300 mL \\
6 & 50\% Carbendazim & WP (Wettable powder) & 1500 g \\
7 & Biocontrol agent & AS (Aqueous solution) & 1.5 L \\
8 & CK & - & - \\ \hline \end{tabular}
\end{table}
Table 2: The dosage of the fungicide used in the research.  
The following formulas were used to calculate the disease incidence and inhibition rate [20]:  
\[\text{Disease incidence (\%)}=\text{(Number of infected plants/Total number of investigated plants)}\times 100;\] \[\text{Disease index}=\text{[}\sum\text{(Number of diseased leaves at each severity level }\times\text{Corresponding severity value)}/\text{(Total number of investigated leaves }\times\text{Maximum severity value)}\times 100;\] \[\text{The inhibition rate of NCLB = [(Disease index of control group \text{ }-\text{Disease index} of treatment group)/\text{Disease index of control group}]\times 100.\]  
The inhibition rate of _Fusarium_ stalk rot = [(Disease incidence of control group \text{ }-\text{Disease incidence of treatment group})/\text{Disease incidence of control group}]\times 100. When treatment groups demonstrated non-significant inhibitory effects, the inhibition rate was recorded as 0.
# A Compression-Based Toolkit for Modelling and Processing Natural Language Text
## 3 The Toolkit's Libraries
### A Selection of Methods Provided by the Toolkit's Libraries  
Table 2 provides a selection of the main methods provided by the libraries for each of the seven types of objects described in the previous section. Space precludes listing all the methods provided by the toolkit which can be found by downloading the software distribution. The table lists methods for each object type in alphabetical order. The C function name used in the libraries and its arguments are listed in column three of the table. Note that the prefixes TLM, TXT and TTM used in the function names are used to signify the library being used for language modelling operations, text-based operations and transformation operations respectively. Column one lists the main type of object being operated upon, and column two lists the type of object returned by the method. (Here _Void_ indicates the method returns nothing, _Unsign_. \(Int.\) indicates the method returns an unsigned integer, \(Transform\) a transform object, _Boolean_ a Boolean and \(Char\) a character with the other return types being one of the toolkit's object types). A brief description of these methods for each of the toolkit's object types now follows:  
* Methods for \(Symbol\) objects: TLM_decode_symbol (_Model_, \(Context\), \(Coder\)) returns the symbol decoded using the arithmetic \(Coder\) and also updates the \(Context\) for the \(Model\) so that the last symbol in the context becomes the decoded symbol. TLM_encode_symbol (_Model_, \(Context\), \(Symbol\), \(Coder\)) encodes the specified \(Symbol\) using the arithmetic \(Coder\) and also updates the \(Context\) for the \(Model\) so that the last symbol in the context becomes the encoded symbol. TXT_sentinel_symbol() returns the \"sentinel symbol\". This is used where there is a break required in the updating of the context, such as when the end of text has been reached or when more than one model is being used to encode different parts of a text. The effect of encoding the sentinel symbol is that the prior context is forced to the null string i.e., the subsequent context will contain just the sentinel symbol itself. This is useful during training if there are statistics that differ markedly at the start of some text than in the middle of it (for example, individual names, and titles within a long list). * \(File\) methods: TXT_close_file (\(File\)) closes the \(File\) and releases memory used by the file object. TXT_open_file (\(Filename\), \(Mode\),...) opens the file named \(Filename\) with the specified