esa-sceva/satcom-chunk-collection huggingface dataset:
# A Strawberry Ripeness Detection Method Based on Improved YOLOv8
## 3 Analysis of Experimental Results
### Comparative Experiment
Similarly, Figure 11b reveals that the precision curve of the YOLOv8-CDW model demonstrates significant improvement with a smoother progression and fewer fluctuations compared to the original model. This suggests a reduction in false positive detections and enhanced accuracy in distinguishing objects. Figure 11c presents the [EMAIL_ADDRESS] metric, where the YOLOv8-CDW model attains rapid gains early in training and maintains a consistent advantage over all epochs. This reflects the model's improved performance in both object localization and classification. Finally, Figure 11d shows the most stringent metric, [EMAIL_ADDRESS]:0.95, which further highlights the enhanced performance of the YOLOv8-CDW model, particularly at higher Intersection over Union (IoU) thresholds. This improvement suggests a superior ability for fine-grained object detection and more precise boundary delineation. Collectively, these results demonstrate that integrating the CDW mechanism enhances the YOLOv8 model's detection accuracy, stability, and generalization ability, supporting its robust deployment in complex real-world detection scenarios. Figure 12: Comparison of various performance metrics between YOLOv8 and the improved YOLOv8: (a) original YOLOv8 model; (b) YOLOv8-CDW. In Figure 13, a comparative analysis of the detection performance is presented among three configurations: the original image (Origin), the baseline YOLOv8 model, and the proposed YOLOv8-CDW model. It is evident that each column corresponds to a distinct test scenario in real-world strawberry cultivation environments. These environments are characterized by varying lighting conditions, occlusion, and fruit maturity levels. The top row (Origin) displays the raw images without any detection overlay, serving as reference visuals. The middle row of the figure displays the results obtained from the standard YOLOv8 model. Despite the fact that YOLOv8 is capable of detecting multiple strawberries, it exhibits notable limitations. These include excessive overlapping bounding boxes, false positives, and ambiguous classification, particularly in scenes where there is a high density of objects, occlusion, and immature fruits (e.g., third column). In contrast, the bottom row illustrates the detection results produced by the enhanced YOLOv8-CDW model. This version incorporates enhanced mechanisms for contextual discrimination and weighting, thereby facilitating more precise object localization and clearer class differentiation. YOLOv8-CDW has been demonstrated to reduce redundant detections and to successfully categorize fruits into distinct maturity stages, including \"raw\", \"turning\", and \"ripe\". Such performance enhancements are especially pronounced in complex environments, where fruit appearance and occlusion vary significantly. The visual outcomes demonstrate that YOLOv8-CDW enhances detection precision and semantic segmentation, thus demonstrating its superior applicability in intelligent agricultural tasks such as automated fruit grading and yield estimation.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Quantitative (Holistic) Approach
#### 4.2.3 QRMP  
The Quantitative Resilience Management Tool [44] has been introduced by the RESILENCE project [45], which encompasses a set of indices and characteristics (ICs) that are employed to describe and evaluate the infrastructure at both physical and operational levels. All measures related to the resilience project are categorized based on three event phases (before, during, and after). A set of weights captures the relative importance of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **State** & **Resilience Metric** & **Symbol** \\ \hline I & Disturbance progress & How fast R drops? & \(\phi\) \\  & & How low R drops? & \(\Lambda\) \\ \hline II & Post-disturbance & How extensive is the & \(E\) \\  & & post-disturbance degraded state? & \\ \hline III & Restorative & How promptly does the & \(\Pi\) \\  & & Network recover? & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The (\(\phi\Lambda\)E\(\Pi\)) Resilience Metrics [43].  
Figure 7: The multi-phase resilience [43].  
each IC. Upon collecting and aggregating the associated weight for each IC, the overall resilience scores can be identified, which include resilience at different timelines such as preparation, prevention and protection, response, and recovery. Moreover, this tool considers the resilience associated with each level (physical and operational). The Quantitative Resilience Management Tool can be divided into the following four steps to assess the overall resilience, as shown in Figure 8, which displays the input entries for each phase (previous, during, and after disaster). The columns illustrate the different system levels:  
Definition of the structure of indices and characteristics (ICs) of the infrastructure. The most significant IC that can optimally represent the infrastructure must be identified, and based on that, each stage of the level receives a corresponding rank. This ranking and classification occur during this phase. For example, let \(L=1,2,3\) and \(S=1,2,3\) denote the level and the stage sequentially. Next, there will be \(N_{L}S_{l}C_{s}\) associated with every \(L,S\) pair, denoted by \(IC=(IC_{L}^{i},S,i\in N_{L,S})\).  
1. Description of the table of weights. The weights associated with each IC are expressed as \(W=W_{L}^{i}S,i\in N_{L,S}\). It is stated that the weights have to be selected, such that: \[\sum_{i=1...N_{L,S}}{W^{i}}_{L,S}=1\] (4)
2. Statistical representation of the ICs.
3. Obtaining the resilience sums. The expression provides the resilience scores corresponding to each \(L,S\) pair: \[R_{L}S=\frac{1}{N_{L,S}}\sum i=1...N_{L,S}W_{L,S}^{i}\;IC_{L,S}^{i}\] (5) The resilience scores related to each level are calculated using: \[R_{L}=\frac{1}{3}\sum_{L}R_{L,S}\] (6)  
Moreover, the resilience scores related to each stage are calculated using the following expression:  
\[R_{S}=\frac{1}{3}\sum_{S}R_{L,S} \tag{7}\]
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm
#### 3.4.2 Center of Gravity (CoG) Estimation  
To assess potential falls, the system computes an approximate Center of Gravity (CoG) for each person, based on a subset of keypoints associated with major body mass regions:  
\[CoG=\frac{1}{N}\sum_{i\in K}(x_{i},y_{i}), \tag{1}\]  
where K = {0,5,6,11,12,13,14} includes keypoints for the head, shoulders, hips, and knees, and N is the number of valid keypoints with c\({}_{i}\) > 0.5.  
A rapid downward shift in the vertical coordinate of the CoG across consecutive frames is considered a strong indicator of a fall.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Challenges Related to Kinematic Devices  
There are also kinematic devices that are responsible for the activity happening at the operator and teleoperator end. They catch or track movements of the operator and reproduce the same at the teleoperator end. Based on these devices, there are also kinematic sensors and kinematic actuators. Kinematic sensors capture the movements at the operator end, while kinematic actuators reproduce the same at the teleoperator end.
# Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data
## 5 Missing Multiple Vectors
### Both the Posterior and the Likelihood Estimate Have Missing Values  
In the cases when there is a great number of missing values in both \(B^{2}_{ij}\) and \(B^{3}_{ij}\) (for example, the case when only the possibility of the first drug used for each symptom is known, and other likelihood estimates and posterior probabilities are unknown.), To fill in the missing values, we need to make use of the eigenvalue and eigenvector of the Markov matrix.  
First, the eigenvector of the Markov matrix is known, i.e., \(e=\begin{pmatrix}p\big{(}T\big{)}\\ p(S)\end{pmatrix}\). The eigenvalue \(\lambda\) corresponding to e is equal to 1, and it is also the maximum eigenvalue of matrix B (other eigenvalues are less than 1). Therefore, we can calculate \(\frac{e^{T}Be}{e^{T}e}\) according to Formula (6):  
\[\frac{e^{T}Be}{e^{T}e}=\frac{e^{T}(Be)}{e^{T}e}=\frac{e^{T}\lambda e}{e^{T}e}=\lambda \tag{8}\]  
\(e\) in Formula (6) is the eigenvector of the final stable state matrix, so its corresponding eigenvalue is the maximum eigenvector.  
\[Maximize\bigg{\|}\frac{e^{T}Be}{e^{T}e}\bigg{\|}_{2}\]  
_subject to_ :  
\[0\leq b_{ij}\leq 1\] \[\sum_{i=1}^{m+n}b_{ij}=1;\] \[b^{3}_{ij}e_{j}=b^{2}_{ji}e_{i}\]  
According to this optimization model, a matrix can be optimized by eigenvectors, where the elements are the curative effects of different medication regimens on complications. The upper part of the eigenvector is the probability of patients using different medications (regimens) in the sample data, and the lower part is the probability of patients having different indications in the sample data.  
However, when there is little data, we can only fit an approximate matrix. Specifically, since matrix \(B\) is a Markov matrix, the limit state of B after it is multiplied by \(n\) times is:  
\[\lim_{n\rightarrow\infty}B^{n}=E=(e,e,\ldots e),\ e=\begin{pmatrix}e_{1}\\ \vdots\\ e_{m+n}\end{pmatrix} \tag{9}\]  
And:  
\[BE=EB=E \tag{10}\]  
This leads to:  
\[\frac{e^{T}BEBe}{e^{T}e}=\frac{e^{T}Ee}{e^{T}e}=\lambda \tag{11}\]  
So, we can build a maximization optimization model of an energy matrix:  
\[Maximize\frac{e^{T}BEBe}{e^{T}e}\]\[\begin{split} subject\ to:\\ & 0\leq b_{ij}\leq 1;\\ &\sum\limits_{i=1}^{m+n}b_{ij}=1;\\ & b_{ij}^{3}e_{j}=b_{ji}^{2}e_{i}\end{split}\]  
In conclusion, we can find a matrix B based on the known eigenvector \(e\) and a few matrix elements to satisfy all the requirements. According to the obtained posterior probability, doctors can provide more accurate medication plans.
## Abbreviations  
* 5G The Fifth Generation Mobile Networks AN Artificial Noise AWGN Additive White Gaussian Noise CSI Channel state information DC Difference of convex function DL Deep learning DNN Deep neural network DQL Deep Q-learning DQN Deep Q-learning DRL Deep reinforcement learning FDM Frequency division multiplexing FP Fractional programming GIG Gaussian interference game GNEP Generalized Nash equilibrium problem KPI Key performance indicator ML Machine learning MSE Mean squared error NP-hard Non-polynomial hard OFDMA Orthogonal frequency-division multiple access PSD Power spectral density QoS Quality of service RB Resource block ReLU Rectified linear activation unit RL Reinforcement learning SIPNR Signal-to-interference-plus-noise-ratio WMMSE Weighted-minimum mean squared error
## 3 Model and Assumptions
### Network Model  
We study the problem of detecting duplicates in a two-dimensional sensor network that is stationary, where the communication between nodes is bidirectional. In our framework, the sensor nodes are not mobile and, hence, do not change locations after being deployed in a two-dimensional sensor field. As is typical of most sensor networks, communication links between nodes are treated as bidirectional. The important assumption in our model, as adopted from [PERSON] _et al._[18], is that the nodes are deployed in groups. We describe this group deployment structure in the section below.
# The Impact of an Electric Machine Body on EM Wave Propagation in RTMS
## 5 Conclusions  
Our research aimed to evaluate how the physical structure of electric machines affects signal propagation. We proposed systems involving a stationary antenna mounted on a shaft which communicates wirelessly with an external receiver. These systems are designed to operate optimally under open-space transmission conditions, highlighting the critical need for reliable sensor communication within electrical machines.  
Ensuring reliable sensor transmission and reception is a major challenge for rotor temperature monitoring system (RTMS) engineers. Typically, RTMS use a transmitting antenna embedded within a shaft which wirelessly connects to a receiver unit outside the machine. These systems are often designed for optimal performance in free-space conditions. This study introduces a metric called the propagation factor to assess the effectiveness of electromagnetic (EM) propagation, taking into account antenna efficiency and reflection losses. Simulations show that strategically placed and aligned RN-171 antenna can nearly achieve the theoretical peak in free-space propagation above a ground plane.  
The electrical machine body significantly degrades the propagation factor, making it over 100 times worse than the ideal free-space benchmark. This highlights the need for antenna designs that do not prioritize free-space communication. Instead, a better strategy is to design antennas that use surface waves traveling along the electrical machine body.  
To validate this potential, a simple experiment was conducted. Although this design may not be the best for any specific RTMS application, it clearly shows the importance of leveraging the electrical machine body to improve the efficiency of wireless RTMS communications.  
A more effective approach would be to develop antennas that leverage wave transmission through machine components. Future work will involve conducting preliminary tests to confirm that such antennas can achieve a higher propagation factor, even in open-space conditions without obstructions. While this design may not be universally applicable, it underscores the potential benefits of integrating electrical machine components to enhance the efficiency of rotating temperature monitoring systems (RTMS).  
Methodology, S.B.B. and S.D.; Validation, S.B.B. and R.B.; Formal analysis, J.D.; Resources, J.D.; Writing--original draft, S.B.B.; Writing--review & editing, A.H.; Supervision, R.B.; Project administration, R.B., T.-H.V. and J.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
We would like to thank the Deanship of Scientific Research at Shaqra University for supporting this work.  
The authors declare no conflict of interest.
## 3 Uncertainty Evaluation
One is the coefficient of determination \(R^{2}\), while another is the reduced chi-square \(\chi^{2}\), which for the polynomial fitted to \(e_{Z}(x,y)\) are defined, as follows:  
\[R^{2}=1-\frac{\sum\limits_{l=1}^{MN}\left[\eta_{l}-\sum\
olimits_{k=0}^{K-1} \hat{a}_{k}\Xi_{k}(\xi_{l})\right]}{\sum\limits_{l=1}^{MN}\left[\eta_{l}-\sum \
olimits_{l=1}^{MN}\eta_{l}/MN\right]}, \tag{34a}\] \[\chi^{2}_{v}=\frac{\chi^{2}_{\min}}{v}=\frac{1}{MN-K}\sum\limits_{l=1}^{MN} \left[\frac{\eta_{l}-\sum\
olimits_{k=0}^{K-1}\hat{a}_{k}\Xi_{k}(\xi_{l})}{ \sigma(\eta_{l})}\right]^{2}, \tag{34b}\]where \(v=MN\times K\) signifies the degree-of-freedom in the least-square regression. Typically, \(0<R^{2}<1\), and the larger the \(R^{2}\) is, the better the polynomial model fits the data. For the reduced chi-square, \(\chi_{v}{}^{2}=1\) indicates that the model properly fits the data, while \(\chi_{v}^{2}>1\) indicates that the fit does not fully capture the data (or that the variance \(\sigma^{2}(\eta_{l})\) is underestimated) and \(\chi_{v}^{2}<1\) indicates that the model overly fits the data (either the model improperly fits the noise or the variance \(\sigma^{2}(\eta_{l})\) is overestimated). According to the value of \(\chi_{v}^{2}\), the variance of \(a_{k}\) that is represented in Equation (27) is usually rescaled as follows in order to pass the \(\chi^{2}\)-test:  
\[\sigma_{r}^{2}(\hat{a}_{k})=\chi_{v}^{2}C_{kk}, \tag{35}\]  
In addition, as illustrated in Equation (26), the least-squares estimation of vector a can be obtained by calculating the Moore-Penrose pseudo-inverse of the design matrix \(\mathbf{D}\). It should be pointed out that this solving approach is susceptible to round-off error in practice and it will lead to errors in both the solution \(\mathbf{a}\) and its uncertainty, especially when the variance \(\sigma^{2}(\eta_{l})\) (corresponds to the variance \(\sigma^{2}(e_{Z}(x_{i}\),\(y_{j}))\)) is not uniform at different pixel positions. To relieve the round-off problem, the solution by use of the singular value decomposition is recommended [22].
## 2 Numerical Analysis
### Analysis of Mesh Independence  
The quality of the computational mesh, or the resolution of the mesh, strongly affects the final CFD result accuracy. On the other hand, mesh resolution also affects computation time. Normally, finer meshes produce better mesh qualities leading to higher accuracies for final CFD results, but prolonging calculation time. As a result, to ensure the final CFD result accuracy and calculation time reasonableness, a mesh independence (sensitive) analysis was carried out. Three different simulations using three various computational meshes, including coarse-, medium-, and fine-resolution meshes, were conducted. A minimum mesh size value of 0.075 mm was set for the fuel injection region of the combustion chamber to ensure that it accurately reflects the fuel injection characteristics of the engine. The final CFD results had then been compared. Table 4 lists the mesh properties and corresponding calculation times of these three various mesh resolutions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Boundary Conditions** & **Boundary Type/Specific Condition** \\ \hline Cylinder head surface & Impermeable wall/temp./297 \({}^{\circ}\)C \\ Cylinder liner surface & Impermeable wall /temp./197 \({}^{\circ}\)C \\ Piston surface & Movable wall/temp./297 \({}^{\circ}\)C \\ Cutting surfaces & Periodic/inlet/outlet \\ \hline
**Initial Conditions** & **Value** \\ \hline In-cylinder pressure at IVC & 2.5 Bar \\ In-cylinder temperature at IVC & 37 \({}^{\circ}\)C \\ Swirl/tumble & 2880 1/min \\ IVC & 30 CADs ABDC \\ EVO & 50 CADs BBDC \\ SOI & 12 CADs BTDC \\ Fuel injection duration & 32 CADs \\ \hline \hline \end{tabular}
\end{table}
Table 3: BCs and initial conditions for CFD simulations.  
Figure 3: Single injection configuration of the engine.  
Figure 4 presents the comparison of the results of the CFD simulations that use those three various mesh resolutions. It is clear that the final CFD results were independent of the computational mesh resolution. Technically, all those three meshes can thus be employed for simulations in this study to achieve not only accurate, but also mesh independent final CFD results. However, the medium-resolution mesh was chosen for the simulations in this study since it took a reasonable calculation time while providing mesh independent final CFD results. It also offers a good resolution for contour analyses.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Resolutions** & **Coarse** & **Medium** & **Fine** \\ \hline Number of faces on 2D mesh at TDC & 7613 & 8595 & 9636 \\ Number of cells of 3D mesh at TDC & 83,743 & 94,545 & 105,996 \\ Number of cells of 3D mesh at BDC & 368,684 & 397,232 & 445,472 \\ Calculation time & 19 h & 25 h & 35 h \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mesh metrics and corresponding calculation time.  
Figure 4: Results of the CFD simulations using the three various mesh resolutions: (**a**) in-Cylinder, mean pressure, (**b**) in-Cylinder mean temperature, (**c**) NO mass fraction, (**d**) CO\({}_{2}\) mass fraction; (**e**) TKE, (**f**) RoHR.
## 5 Photogrammetry: Knowledge Base  
Photogrammetry is the art, science and technology of obtaining reliable information about physical objects and the environment through processes of recording, measuring and interpreting photographic images and patterns of recorded radiant electromagnetic energy and other phenomena [28]. Photogrammetry is divided according to:  
1. Positions;
2. Method of evaluation;
3. Number of pictures taken;
4. Evaluations.  
Figure 6 shows the scheme of photogrammetry organization.  
Modern photogrammetry technologies, which are used to create a variety of environments and tools, have undergone tremendous development in recent decades. During this period, they have passed several milestones from the visual side through the quality of textures to large-scale 3D models. The basic principle of photogrammetry [29], which also results from the above scheme, is convergent imaging. When a photogrammetric image is taken, a central projection is created. The relationship between the subject and its captured image at the time of exposure is given by a photogrammetric beam passing through the center of the projection.  
Figure 4: Correspondence view of Kunerad Mansion basement in FARO SCENE software.  
Figure 5: Comparison of raw (**a**) and denoised/filtered (**b**) point clouds.  
The shape of these rays is determined mainly by the elements of the internal orientation [30] and their position in space is determined by six rays from the external orientation. With respect to the elements of internal and external orientation and the sufficient offset, it is possible to create a point cloud representing the digitized object. Further processing of these points results in the creation of a triangulated irregular network (TIN) model, which forms the basis for further postproduction to achieve required details. Finally, the obtained texture based on the quality of the image is applied. It is important to note that the quality of the data will significantly affect the result. The main advantages of photogrammetry are high accuracy and massive collection of special data.  
Like laser scanning, photogrammetry can be used in reverse engineering. Sufficient photographs in ground and aerial photogrammetry form a good spatial model, but also more detailed information about the individual details of the object [31]. The technology can also be used in hard-to-reach and inaccessible places. Created photorealistic 3D models (PR3 DM) [32] find further application in the field of construction, architecture, medicine and also in the gaming industry. Within the already mentioned architectural [33] and cultural heritage digitization [34], sufficient interoperability is ensured, which allows integral use of photogrammetry in the digitization process as one of the basic tools.