esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Corrosion Testing  
Potentiodynamic polarisation testing was performed by a Gamry Reference 600 (Gamry Instruments, Warminster, PA, USA) potentiostat/galvanostat with the employment of a standard three-electrode cell (saturated calomel as the reference electrode (SCE) and a graphite gauge used as the counter electrode). Corrosion behaviour was studied in aerated 3.5% NaCl solution (pH 7). Open-circuit potential (Eocp) was determined after 1 h of immersion in the solution. The scan rate for the polarisation testing was 10 mV/ min. Four measurements were performed for each composition.
# Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
## 5 Intelligent Servo Tuning Results
### Simulation Results  
We established the model with the optimized servo parameters with values shown in Table 5, and a spiral trajectory, shown in Figure 12, with high feed rate generated to evaluate the efficacy of intelligent servo tuning. From Table 6, we observed that both tracking error and contouring error were decreased.  
There is another simulation for rotation axes, which is the harmonic movement of the \(A\)-axis and \(C\)-axis. We generated a sine wave with a particular frequency, which was treated as the desired reference. As shown in Table 7, the tracking error was lower than both the \(A\)-axis and \(C\)-axis. It was explicated that the servo parameters obtained from intelligent servo tuning took effect on the rotation axes.  
The next simulation was the circular test, with a reference command consisting of 8 Hz angular frequency and a 1 mm radius circular path. Any two of the three axes would follow the command and the simulation results are shown in Figure 13, where Figure 13a,c,e are results of before optimization and Figure 13b,d,f are results of after optimization. The circular motion test illustrated that the performance of machine tool would be improved by an intelligent tuning servo approach. The reason why we selected 8 Hz as the reference command was that this frequency was near the bandwidth, and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tracking Error** & \(X\)**-Axis** & \(Y\)**-Axis** & \(Z\)**-Axis** & **Contouring Error** \\ \hline Before optimization & 0.0017 & 0.0299 & 0.0027 & 0.0173 \\ After optimization & 0.0013 & 0.0197 & 0.0023 & 0.0164 \\ improvement & 23.53\% & 34.11\% & 14.81\% & 5.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation result of spiral trajectory.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tracking Error** & \(A\)**-Axis** & \(C\)**-Axis** \\ \hline Before optimization & \(1.65\times 10^{-4}\) & \(2.53\times 10^{-4}\) \\ After optimization & \(0.99\times 10^{-5}\) & \(1.23\times 10^{-4}\) \\ improvement & 40.28\% & 51.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation result of rotation axes.  
Figure 12: Spiral trajectory.  
if the bandwidth increased, the system performance would be obviously improved. Compared to the frequency within the bandwidth, the optimized FRF showed that 8 Hz had an obvious rise. As shown in Table 8, there was a significant improvement in both contouring errors and tracking errors.  
Figure 13: Simulation result of circular motion tests. (**a**) Before optimization (\(X\)-\(Y\)), (**b**) After optimization (\(X\)-\(Y\)), (**c**) Before optimization (\(X\)-\(Z\)), (**d**) After optimization (\(X\)-\(Z\)), (**e**) Before optimization (\(Y\)-\(Z\)), (**f**) After optimization (\(Y\)-\(Z\)).
## 2 Comparison of SIFT Descriptors and YOLO Neural Network  
### Scale-Invariant Feature Transform (SIFT)  
SIFT is a feature detection and description algorithm widely used in computer vision. It is renowned for its ability to identify and match features in images, even under varying conditions such as scale, rotation, and lighting changes [10]. Initially introduced as a descriptor with robust properties against scale and rotation changes, SIFT has demonstrated superior performance compared to other local descriptors, establishing itself as a reference in the field [11]. Modern computer vision libraries, such as VLFeat, include implementations of SIFT [12]. The applications of SIFT extend to various domains, including healthcare, agriculture, industry, and security [13; 14; 15; 16].  
SIFT offers several strengths. First, it is highly robust to changes in image scale, orientation, and lighting, making it suitable for identifying consistent features in MRI images [10]. Second, it provides accurate feature matching, which is essential for precise image registration and alignment in MRI [17]. Third, SIFT has been extensively studied and used in various applications, including medical imaging, ensuring well-understood and reliable performance [17].  
However, SIFT also has some weaknesses. It can be computationally expensive, especially for real-time applications, due to its extensive feature extraction and matching processes [17]. Additionally, SIFT is primarily a feature matching algorithm and may not be as effective for complex tasks such as object detection and classification [10].  
In the context of MRI, SIFT is used for aligning MRI images taken at different times or from different angles, ensuring consistent and accurate image comparison [17]. It is also employed for real-time motion tracking of organs, such as the liver, in MRI-guided radiotherapy to ensure precise treatment delivery [17].
## 3 PDT in Veterinary Oncology
### Sarcomas
PDT using ZnPcS4 was performed on a dog with malignant fibrous histiocytoma in the elbow, a dog with spindle cell sarcoma in the carpus and a dog with intranasal undifferentiated sarcoma. The two animals with lesions in the elbow and carpus partially responded to therapy. However, the patient with intranasal sarcoma showed a complete response [84]. Using an aluminum-chloride-phthalocyanine nanoemulsion (AlCIPc-nano) administered intra and peritumally, PDT was performed in eight dogs with cutaneous hemangiigascomas. Two to four PDT sessions were repeated every seven days. Seven of the eight cases showed complete remission of neoplasia. Microscopic analysis of the excisional biopsies showed necrosis and hemorrhage, with no cancer cells, except in one case. Inflammation and necrosis were macroscopically observed in the treated areas [37]. Ten dogs and six cats with malignant soft tissue sarcoma were submitted to a combined therapeutic approach. The treatment consisted of surgical resection followed by hyperthermia and PDT based on indocyanine green (ICG), irradiation with broadband light, and chemotherapy. No severe side effects were reported. Seven dogs and three cats did not show recurrence [122]. A combination of photodynamic surgery (PDS) and PDT using AO was explored as a new approach to avoid feline injection-site sarcoma (FISS) recurrence. In this study, thirty-seven cats with FISS were included. PDT was performed on seven cats, with AO administration to the surgical field, then irradiated with visible light for 10 min. Findings revealed that, at the last follow-up, five of seven PDS-PDT-treated cats did not present relapse and/or metastatic disease. This combined therapy was associated with a higher disease-free survival rate than PDS-treated cats. Two PDS-PDT-treated cats were euthanized during the follow-up due to other treatment-non-related health problems. Nevertheless, none of these animals showed recurrence at the time of death. These results suggested that PDT can be an effective adjuvant therapeutic modality to prevent FISS recurrence [123]. To determine the photodynamic threshold, soft tissue optical properties were estimated. The threshold dose for three photosensizers (porfimer sodium, AlCIPc, and SnET2) was evaluated in dogs with spontaneous tumors. AlCIPc presented the highest threshold, thus needing more photon absorption than porfimer sodium and SnET2 [124]. PDT protocols used to treat sarcomas are presented in Table 3.
## 5 Conclusions and Future Work  
In this paper, a Road-based Multi-metric Forwarder selection Evaluation in Multipath Video Streaming (RMF-MVS) scheme with DSW is proposed and simulated in order to assess its performance against the existing schemes including MSLND and 3 MRP with and without DSW. The aim of the proposed scheme is to achieve optimal selection of the next forwarding vehicle for quality video streaming transmission in a multipath setting. The scheme employed most important road-based parameters including vehicle density on the road, vehicle trajectory, hello message received rate, accessible bandwidth availability and signal to interference plus noise ratio for evaluating next forwarding vehicle for video streaming. The simulation results demonstrate that the proposed scheme outperforms the state-of-the-art schemes. However, to further enhance the suggested scheme, future research study would be centered on multi-metric optimization based on quality parameters in order to attain quality video streaming in urban vehicular communication. Energy aware video offloading into edge of road things [36], vehicular trust based video transmission [37] and access control in video transmission under dense traffic environment [38] will be some of the quests in our future research direction in next hop based vehicular video transmission [39].  
Conceptualization, A.A.; Formal analysis, T.S.J.D.; Investigation, I.F.I.; Methodology, A.A., R.Z.R.M.R.; Resources, I.F.I.; Supervision, A.H.A., R.Z.R.M.R.; Validation, U.M.J., A.K.; Writing, A.A.; Review & Editing, T.S.J.D. All authors have read and agreed to the published version of the manuscript.  
The research is supported by Ministry of Higher Education Malaysia (MOHE) and conducted in collaboration with Research Management Center (RMC) at University Teknologi Malaysia (UTM) under Fundamental Research Grant Scheme with grant number: RJ130000.7851.5F029. The authors appreciate greatly for the support.  
The authors declare no conflict of interest.
## 3 Case Study B
### Maximum Angle maxAGL and Quantity N for Training Sets Generation  
Firstly, when generating the training set, the maximum value of the _maxAGL_ should be less than half of the minimum angle between selected point normal vectors. This ensures that each training set is independent and non-overlapping with others. In case B, the minimum angle between selected point normal vectors is 88.5\({}^{\circ}\). Therefore, the training sets should be generated within the range of angles less than 44.25\({}^{\circ}\).  
The generated training sets must exhibit uniform distribution within the chosen range to ensure ample stability and representativeness. To determine the optimal values for the _maxAGL_ and \(N\), this study iteratively generated 100 distinct training sets for each different combination of _maxAGL_ and \(N\) around selected point normal vectors. The variance in angles between the central vector of the training set and the selected point normal vector served as a criterion for evaluating the stability of the generated training sets. Figure 15 illustrates the variation in the stability of generated training sets with changes in the _maxAGL_ and \(N\) over 100 repetitions.  
In Figure 15a, the stability of the generated training sets increases with the rise of quantity \(N\), and the variation becomes relatively stable when \(N\) exceeds 200. However, there are still differences in stability among different _maxAGL_. In Figure 15b, under varying \(N\), an increase in _maxAGL_ corresponds to a decrease in stability. Moreover, higher values of quantity \(N\) result in less sensitivity of stability to _maxAGL_. Nevertheless, the impact of quantity \(N\) on stability diminishes beyond 150, consistent with the trend shown in Figure 15a.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Discontinuity** & **Number of Points** & **Number of Discontinuities** & **Lato (\({}^{\circ}\))** & **Proposed Method (\({}^{\circ}\))** & **Deviation (\({}^{\circ}\))** \\ \hline
1 & 229,036 & 173 & 194/34 & 194/31 & 0/3 \\
2 & 1,080,620 & 188 & 29/76 & 35/80 & 6/4 \\
3 & 450,066 & 149 & 309/90 & 136/86 & 7/4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of orientation results for case B.  
Figure 15: Variation curves of stability for generated point sets: (**a**) different _maxAGL_; (**b**) different \(N\).  
In summary, the study configures the parameters for generating the training sets using the proposed method with a _maxAGL_ set at \(5^{\circ}\) and an \(N\) set at 200, ensuring optimal performance of the algorithm.
# An Approach of Path Optimization Algorithm for 3D Concrete Printing Based on Graph Theory
## 5 Conclusions  
A path planning algorithm is proposed in this paper to optimize the problems of excessive printing time and poor forming quality in 3D concrete printing. In the algorithm, the commonly used concrete components in 3D concrete printing are taken as the research object. Shortening the printing time and improving the forming quality are the research goals; combined with the partitioning algorithm based on graph theory and the ant colony algorithm, the 3D concrete printing path is optimized, and the following conclusions are drawn:  
1. Compared with the conventional path planning algorithm, the algorithm in this paper has more advantages. The idle strokes and the head-up times of the nozzle running path are significantly reduced when using the algorithm in this paper to plan the printing path. This effectively saves the printing time and significantly improves the forming quality of concrete components.
2. The algorithm in this paper is feasible and has specific practical significance. The final output results will be an array of coordinates when planning the nozzle running path according to the algorithm in this paper. Therefore, a corresponding printing program can be quickly written to perform actual printing.  
However, the algorithm in this paper still has many limitations. Compared with the reinforcement learning algorithm, the algorithm in this paper has no significant advantages in planning the nozzle running path for the irregular concrete components. Therefore, how to use the principle of graph theory to split irregular concrete components into regular components will be our next research direction.
## 3 Basic Strategy
### Strategy of Angle-Based Selection  
The same scalarization method used is TCH, and it can be applied to both convex and non-convex regions. In MOEA/D, weights are assigned to subproblems to naturally balance diversity and convergence. The precondition is for each subproblem to produce decentralized localized Pareto-optimal solutions. However, some cases may not conform to this assumption, and Figure 4 presents a case where A is defined by \(w^{1}\). A plays a crucial role in diversity; however, it is likely to be displaced from B and C by other individuals. One problem with the current replacement strategy is that it is based on the choice of scalar values. This strategy suggests that if the aggregation function value of the offspring solution is equal to or lower than that of the parent solution, the offspring may replace the parent. To address this issue, a new selection strategy, called the strategy of angle-based selection, is introduced in this paper to retain diverse solutions [28; 29; 30].  
Figure 3: Low-carbon dispatch Gantt chart for ship segmental painting.  
Equation (28) initially establishes the acute angle formed by \(w^{i}\) and \(x^{k}\), where \(w^{i_{1}},\ldots w^{i_{T}}\) and \(x^{i_{1}},\ldots x^{i_{T}}\) are the weight vectors and solutions of the \(T\) neighborhoods of \(w^{i}\), respectively, and sets \(B(i)=\{i_{1},\ldots,i_{T}\}\).  
\[angle(i,k)=\arccos(\frac{{(F(x^{k})-z^{*})}^{T}w^{i}}{{\left|\left|F(x^{k})-z^{* }\right|\right|}{\left|\left|\left|w^{i}\right|\right|\right|}}) \tag{28}\]  
Next, define the maximum angle \(\varphi_{i}\) among the weight vectors \(w^{i}\) and \(x^{k}(k=1,\ldots,N)\). If in the angle \(angle(i,k)\) of candidate solutions \(x^{i}\) and \(w^{j}\), each subproblem is less than or equal to \(\varphi_{i}\), then \(x^{i}\) is a candidate solution for the parent individual and may be replaced. As shown in Figure 5 below, the vector \(w^{i}\) has three adjacent weight vectors, \((w^{i_{1}},w^{i_{2}},w^{i_{3}})\), where \(i_{1}=i\). Points A, B, and C represent the three individuals of these three weight vectors, respectively. The maximum angle between the point and the weight vectors is \(\varphi_{i}\). The area delineated by the two red dashed lines signifies the improved region. The core idea of the strategy of angle-based selection is to replace the old individuals using a selection strategy based on angles and scalarized values.  
Figure 4: Schematic diagram of the dual-objective spatial solution.  
Figure 5: Improved regions based on angle selection strategies.
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
## 5 Performance Analysis
### Network Delay  
The total time required for the information to be generated by the source device and to be received by the destination device is defined here as the network delay. Let, \(D_{j}\) be the delay that the packet is transmitted successfully at time \(j+1\) and the packet has failed for \(j\) times. Then  
\[D_{j}=T_{s}+jT_{c}+\sum_{i=0}^{j}D_{b} \tag{29}\]  
where, \(D_{b}\) is the delay that the device successfully found the channel idle during \(maxMacBackoff\) number of backoff slots. \(T_{s}\) is the mean time of successful transmission and \(T_{c}\) is the mean time that the channel is occupied because of the collision. Let \(B_{k}\) be the event that the channel access is successful for \((k+1)\)th times and the channel access is failed for \(k\)th times. Let \(B\) be the event that the channel access is successful within \(maxMacBackoff\) number of backoff. Hence, the expected backoff delay in one transmission attempt is  
\[D_{b}=\sum_{i=0}^{m}\frac{P_{backoff}^{i}(1-P_{backoff})}{1-P_{backoff}^{m+1}} \sum_{k=0}^{i}\frac{W_{i}-1}{2} \tag{30}\]  
If the packet is generated outside its allocated CAP, then it has to wait until the next allocated CAP comes. Therefore, if the packet arrival follows the Poisson process with packet arrival rate \(\lambda\), then  
\[waiting\text{ }time=\int_{0}^{BI}(BI-x)\lambda e^{-\lambda x}dx. \tag{31}\]Propagation delay is the amount of time it takes for the head of the signal to travel from the sender to the receiver. It can be computed as the ratio between the link length and the propagation speed over the specific medium. Let \(E_{j}\) be the event that the packet is transmitted successfully \(j+1\) times and the packet has failed for \(j\) times. Let \(E\) be the event that the packet is transmitted successfully within \(maxFrameRetries\). Considering the \(maxFrameRetries=r\),  
\[Pr(E_{j}/E)=\frac{(1-P_{S})^{j}P_{S}}{1-(1-P_{S})^{r+1}}. \tag{32}\]  
Therefore the network delay is  
\[D=\sum_{j=0}^{r}Pr(E_{j}/E)D_{j}+waiting\;time+propagation\;delay. \tag{33}\]  
The packet may not be transmitted in the current CAP period if \(D>CAP_{period}\) and has to wait for the next superframe. Therefore, the total average delay is  
\[Delay=\lfloor\frac{D}{CAP_{period}}\rfloor BI+D\;mod\;CAP_{period}. \tag{34}\]
# Advances in IoMT for Healthcare Systems
### Wearable Healthcare Devices and Edge Computing  
The use of wearable devices has become an important part of IoMT services for the remote measurement and continuous tracking of patients' vital signs. These devices can easily be used as accessories, integrated into clothing, applied to the skin as tattoos, or even embedded into the bodies of older adults or those with chronic illnesses when worn. Despite the many advantages of wearable devices, the conventional cloud computing systems are under increasing strain due to the influx of their data generation on a large scale. On the other hand, edge computing stands out as a candidate solution to the issues of the increased data volumes. In this regard, the benefits of fast data processing can be achieved along with the improved data security, since in edge computing, data analysis occurs closer to the source rather than sending it to the cloud at a remote location. However, wearable devices in edge infrastructure have certain limitations due to their resource constraints, such as high cost, short battery life, limited functionality and processing power, lower interoperability, and higher data security and privacy risks [7]. Additionally, managing distributed devices increases orchestration complexity in edge infrastructure. As a result, more sophisticated solutions are needed to address many of these challenges and incorporate edge computing as an integral part of IoMT infrastructure.
## 1 Introduction  
Recently, industries in major countries have shifted from a labor-intensive production structure to a knowledge-intensive structure. As a result, competition for product performance and quality is intensifying. Korean manufacturing industries, such as semiconductors, automobiles, and mobiles, use standardized communication technologies to respond immediately to customer changes and prepare measures for mass production of various types [1]. In this environment, Industry 4.0 is a paradigm that has evolved into a smart factory or an intelligent factory in the traditional manufacturing industry [2].  
The complexity of a smart factory has increased significantly due to changes in the market according to the various needs of consumers, development of cyber-physical systems (CPS), Internet of things (IoT), wireless communication technologies, and so on [3].  
Recently, services such as communication, banking, and shopping provided by companies have also been fused with information and communication technology. They can be performed without interactions between individuals. In addition, as heterogeneous computing devices, software, and communication methods are very closely coupled, computing technologies are evolving into complex, diverse, and large-scale systems that interwork with each other in a distributed environment and achieve a common purpose or goal.  
When various manufacturing facilities, such as smart factories that communicate with each other and form a network to perform various processes, are operated, there is a very high possibility of more error situations than before. The complexity of the system is very high due to the connection between manufacturing facilities that perform various manufacturing processes and the communication network. Accordingly, the possibility of unexpected errors or situations also increases [4].  
In particular, the implementation of smart factories is being promoted by the development of cyber-physical systems and IoT technologies, which are complex aggregates of information and communication technologies. For more reliable implementation, communication middleware/protocol, autonomous computing technology, and manufacturingsimulation or facility, research in various fields, such as a data platform that can secure interoperability, is essential. In other words, a methodology that guarantees a smart factory environment known to require high reliability is absolutely necessary. Reliability in the smart factory field cannot be guaranteed only by combining embedded systems developed by traditional methods. It is essential to develop technology for solving problems that occur during operation [5; 6].