esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Hyperparameter Experiments
#### 5.4.6 Effect of Different Optimizers  
Figure 17 shows the impact of different optimizers, and the results suggest that adaptive optimizers (Adam, AdamW, and NAdam) generally outperform traditional gradient-based optimizers (SGD and Adagrad). The Adam optimizer combines momentum and adaptive learning rates, which help stabilize training and improve convergence. AdamW performed slightly worse than Adam but still achieved competitive results. Nadam uses the Nesterov momentum does not significantly enhance the performance in this task. SGD and Adagrad have a lower performance. Therefore, the best prediction result is achieved using the Adam optimizer.  
Figure 16: Impact of the number of spatial–temporal layers on metrics: (**a**) MAE; (**b**) RMSE.  
Figure 15: Impact of state expansion factor on metrics: (**a**) MAE; (**b**) RMSE.
## 5 System Overview
### SaveFile  
The data node, also responsible for the client application, runs/acts like a load balancer. This node sends the IP address of next data node that is responsible for file storage operation. Therefore, saving file operation is done in two steps. When the client application wants to store the file, it takes the IP value of the appropriate data node from corresponding data node. As mentioned before, corresponding data node means client applications has a proxy node which is responsible for routing operation.  
As shown in Figure 4, the client gets the IP address of the data node that will store the file from the proxy node before storing the file. Load balance functionality provides different data node IP for SaveFile operations. This operation is done with the GetAvailableDatanodeIP function. The system saves the client file to the data node which is nearly got from the proxy node by using SaveFile function. The SaveFile function saves the file in its virtual file system with a linked list structure and changes the corresponding bunch in bitmap (a hybrid of Unix bitmap and Windows FAT32 structure with revised version). Because of this operation, the client gets a unique file name that has the starting bunch number and the data node id of the file. In summary, when the client application wants to store the file, it gets the IP address of the data node that will store the file. In this way, the client switches to data communication with the target data node to store the file. In systems that use head node structure, the data is transferred to head node, and the head node writes this data to the data node. In this schema, the amount of data transferred is doubled. Because data first goes from client to head node and then goes to the data node. This type of work also reduces the number of concurrent connections to the head node. But in our system, the client communicates with the data node that will store the file, and the data is transferred once. In this way, the concurrent connection number is maximized.  
The client receiving these values deals with only this data node in subsequent operations (in the commands ReadFile and DeleteFile) and run the requests. Only when the ReadFile operation fails, the client app gets one of the other copies of the file with the GetFileFromReplica command.  
When a new client is added to the system, and change is made to the data nodes, the system administrator sends CheckServerList command to the all nodes.
# Assessment of Transformer Fault Severity from Online Dissolved Gas Analysis Using Positive CUSUM
## 1 Introduction  
Transformers are essential devices in power generation, transmission, and distribution systems, affecting the stability and reliability of the electrical system. The continuous operation of transformers is therefore crucial for electric utilities and electricity consumers. A fault within a transformer can disrupt the stability and reliability of the power system. The continuous operation of transformers is therefore crucial for utilities and their users. Problems within the transformer, however, can lead to the impairment of a vital and costly component of the power distribution system. Certain types of faults may escalate into catastrophic events, causing substantial destruction to nearby equipment and posing risks to individuals. Maintenance of electrical transformers at the onset of damage is necessary. Routine maintenance once a year and sampling of dielectric fluid for dissolved gas analysis may not be sufficient for assessing the health of heavily used transformers in plants.  
These plants opt for online dissolved gas analyzers in transformer oil. This provides continuous observation and assessment of the transformer's condition. This allows them to make prompt decisions if abnormalities arise because of internal faults. Since therelationship between transformer faults and combustible gases from oil decomposition was discovered, extensive research has been conducted to identify the types and quantities of these gases. Dissolved gas analysis has a long history, dating back to the 1960s [1], while online dissolved gas analyzers for transformer oil, manufactured by Morgan Schaffer [2], were first used in the 1970s. Dissolved gas analysis has been used to distinguish between transformers operating normally and those exhibiting abnormalities and to pinpoint the nature of these abnormalities. These analytical methods emerged during the 1970s to 1990s, such as the Doernenburg Ratios [3], Rogers Ratios [4], and Duval Triangles [5,6]. IEC 60599 and IEEE C57.104 [7,8], developed in 1977-1978, provide standards for interpreting dissolved gas analysis results. These standards were created by a group of experts using data collected from transformer oil. These two standards serve as the basis for interpretation and have been regularly updated for the present in the form of IEC 60599-2022 and IEEE C57.104-2019 [9,10]. The approach to assessing the severity of faults based on dissolved gas analysis (DGA) begins with the concept of prioritizing maintenance for transformers.
## 2 Methodology  
### General Design Considerations  
The first design basis considers the typical electrical energy charges for a one-family house as shown in Figure 1, where energy and hour daily operation are included for a day [17]. As can be observed from the figure, the peak hour is at 8 p.m., when the total electrical energy requirement increases up to 1015 Wh, being the period with the higher electrical energy consumption from 8 p.m. to 11 p.m. This is a daily typical profile of electrical energy charges for a Spanish house, which may widely vary depending on the country. Hence, the total amount of electrical demand per day for this case of study is 7.635 kWh/day.  
The second design consideration is the temporal horizon of the full month of January. Considering that this month is characterized by the lowest radiation level, and it is known to be the coldest one, if the electrical demand is supplied by the proposed, designed system, it could be generally considered that those requirements for any other month will be fulfilled, too. Hence, our specific case study considers the weather data of Ciudad Real, which is a city located in Spain. As a design basis, the electrical demand is the same for all days.  
The third design basis considers that the amount of energy to be stored in form of hydrogen should be enough for self-supplying the house for four consecutive days without any PV energy production, since it is based on the meteorological data from this city supplied by the PV-SYST software, and January is the most restrictive month with four consecutive days without any radiation at all. Thus, if there is a cloudy day or not enough solar radiation is available, the house can receive the stored energy in form of hydrogen via a fuel cell. The electrical demand of the house for four days is 30.54 kWh. Hence, considering the hydrogen consumption of the fuel cell for supplying this electrical demand, the amount of hydrogen to consider is 1.8 kg, which is the design basis for the hydrogen storage tank. Another base design is the efficiency of both the fuel cell and electrolyzer, i.e., 73.5% and 45%, respectively.
## 2 Model Setup and Validation
### Numerical Aspects and Boundary Conditions  
The commercial program of ANSYS 6.3.26 with the finite volume method is employed to solve the three-dimensional numerical simulations. To improve the accuracy of the numerical simulations, second-order discretization schemes are applied to the momentum, turbulent kinetic energy, and turbulent dissipation rate equations. Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm is used to evaluate pressure-velocity coupling in the continuity equations. Meanwhile, the standard wall-function method is applied to model the near wall regions with low Reynolds number [21].  
Figure 3: Simulation model of internal wall: (**a**) three-dimensional schematic of internal wall; (**b**) heat transfer boundary conditions on the wall surfaces.  
According to the \"Design standard for energy efficiency of residential buildings in hot summer and cold winter zone\" (JGJ 134-2010) [22], the cold air infiltration is 1 h\({}^{-1}\). The heat transfer coefficient for the window should be less than 4.7 W/(m\({}^{2.\circ}\)C), and the window: external wall area ratio should be less than 0.4 (north-facing) and 0.45 (south-facing), respectively. Thus, the area of window in the computational model is 3 m\({}^{2}\), and the heat transfer coefficient of window is 3.0 W/(m\({}^{2.\circ}\)C).  
At the inlet of the computational domain, uniform velocity and temperature are imposed, and the inlet values of turbulent kinetic energy (\(k_{0}\)) and dissipation rate (\(\varepsilon_{0}\)) profiles [23] are set as follows:  
\[T_{\rm u}=0.16(Re_{\rm L})^{-1/8} \tag{13}\]  
\[\varepsilon_{0}=C_{\mu}^{3/4}\frac{k^{3/2}}{l} \tag{14}\]  
where \(V\), \(T_{\rm u}\) and \(Re_{\rm L}\), respectively, denote the mean velocity, the turbulence intensity, and the Reynolds number at the inlet. \(l\) is a length scale defined as \(l=0.07\ L\), where \(L\) is the hydraulic diameter at the inlet. Furthermore, outflow boundary condition is applied at the outlet of the computational domain and non-slip conditions are imposed on the inner surfaces of solid walls.
## 2 Material and Methods
### Long Short-Term Memory (LSTM)  
RNNs are known to have difficulties in training because of the vanishing/exploding gradients. Therefore, long short-term memory (LSTM) has been introduced to overcome this difficulty [25]. LSTM is an advanced type of RNN. Its structure is composed mainly of three types of gates: input, output, and forget [26] (Figure 2). With reference to Figure 2, the transfer equations of an LSTM unit are described according to Equations (3)-(7). Note that RNN and GRU are particular structures of an LSTM. More details about DL algorithms as well as their structures and transfer equations can be found in [26] and [28; 29]. In LSTM, memory cells are introduced to detect hidden features and to continuously track long-termhistorical information. The main idea behind using memory cells is to model historical self-states of the network through self-connections [22].  
\[f_{t}=\sigma\Big{(}w_{f}.[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{3}\]  
\[i_{t}=\sigma(w_{t}.[h_{t-1},x_{t}]+b_{i}) \tag{4}\]  
\[o_{t}=\sigma(w_{o}.[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[C_{t}=f_{t}C_{t-1}+i_{t}.th(w_{c}.[h_{t-1},x_{t}]+b_{c}) \tag{6}\]  
\[h_{t}=O_{t}.th(C_{t}) \tag{7}\]  
All of the variables in Equations (3)-(7) are defined in Figure 2.
## 4 Distributed Power Control for Spectrum Sensor Nodes
### Noncooperative Optimal Solution
From Equation (28), the noncooperative optimal transmit power vector \(\mathbf{P}^{NC}\) for all IT and MT SSNs in the NoCoPC problem can be further combined as:  
\[\mathbf{P}^{NC}=\left\{\underbrace{p_{1}^{NC}(t),\cdots,p_{|\mathcal{N}_{IT}| }^{NC}(t)}_{\text{for }n_{IT}\text{IT}\text{SSNs}},\overbrace{p_{|\mathcal{N}_{IT}|+1}^{ \text{for }n_{MT}\text{MT}\text{SSNs}}}^{\text{for }n_{MT}\text{MT}\text{SSNs}}}_{\overbrace{p_{|\mathcal{N}_{IT}|+1}^{\text{ for }n_{MT}\text{MT}\text{SSNs}}}^{\text{for }n_{MT}\text{MT}\text{SSNs}}_{\text{ for }n_{MT}\text{MT}\text{SSNs}}} \right\} \tag{29}\]Given the noncooperative optimal solution \(p_{m}^{NC}(t)\), by substituting \(\mathcal{V}_{m}^{NC}(p_{m},E_{m})\) and \(p_{m}^{NC}(t)\) into Equation (26), the function \(\mathcal{V}_{m}^{NC}\big{(}p_{m}^{NC}(t),E_{m}^{NC}(t)\big{)}\) for the \(m\)-th SSN is further given by:  
\[\mathcal{V}_{m}^{NC}\big{(}p_{m}^{NC}(t),E_{m}^{NC}(t)\big{)}=\frac{\wp_{m}^{ C}}{r(r+1)}\Bigg{(}\tau_{rp}p_{m}^{\max}-\frac{p_{m}^{\max}H_{m}\big{(}\Upsilon_{d (m)}|\Upsilon_{f(m)}\big{)}\tau_{rp}^{2}\wp_{m}^{C}}{4(r+1)}-E_{A}(t)-rE_{m}^{ NC}(t)\Bigg{)} \tag{30}\]  
where \(E_{m}^{NC}(t)\) is the noncooperative optimal energy consumption value of the \(m\)-th SSN at time \(t\).
# Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
## 3 Automotive Ethernet Security Protocol
### Design of Data Encryption and Authentication Algorithms for In-Vehicle Multimedia Signals
#### 3.2.1 Improved AES-128 Encryption Algorithm Design (IAES-128)
\begin{array}{l}F(X,Y,Z)=(X\wedge Y)\vee(X\wedge Z)\\ G(X,Y,Z)=(X\wedge Z)\vee(Y\wedge|Z)\\ H(X,Y,Z)=(X\oplus Y\oplus Z)>>>1\\ I(X,Y,Z)=X\lor Z\oplus Y\end{array}  \tag{6}\]  
\[ \begin{array}{l}FF(a,b,c,d,M_{j},s,t_{i})=b+((a+F(b,c,d)+M_{j}+t_{i})<<S)\\ GG(a,b,c,d,M_{j},s,t_{i})=b+((a+G(b,c,d)+M_{j}+t_{i})<<S)\\ HH(a,b,c,d,M_{j},s,t_{i})=b+((a+H(b,c,d)+M_{j}+t_{i})<<S)\\ II(a,b,c,d,M_{j},s,t_{i})=b+((a+I(b,c,d)+M_{j}+t_{i})<<S)\end{array}  \tag{7}\]  
The variables, \(X\wedge Y\), \(X\oplus Y\), and \(X\lor Y\) represent the logical AND, logical XOR, and logical OR, respectively. \(\mid X\) represents the logical complement operation of \(X\). In the aforementioned equation, we optimize the nonlinear functions \(H\) and \(I\). Firstly, we cyclically shift function \(H\) to the right to promote its earlier propagation, thereby enhancing the speed of the avalanche effect. Additionally, by enhancing function \(I\) from \(I(X,Y,Z)=X\lor|Z\oplus Y\) to \(I(X,Y,Z)=X\lor Z\oplus Y\), we reduce the symmetry of the nonlinear function in the fourth round. This improvement enhances the operational efficiency of the algorithm and expedites the avalanche effect. Among them, \(M_{j}\) represents the \(j\)-th message block (ranging from 0 to 15), \(<<S\) denotes a circular left shift by \(S\) positions, and the \(+\) symbol indicates addition modulo 232. The IMD5 algorithm enhances the security by optimizing the iterative formula so that the generated 48 words are associated with the first 15 words. In addition, the improvednonlinear function diffuses in advance, which improves the avalanche effect and makes the algorithm more secure. The authentication speed is doubled with an output length of 64 bits, which improves the authentication efficiency of the algorithm. After research and analysis, this paper selects the MD5 algorithm with a 64-bit output value for in-vehicle multimedia information encryption and authentication, which ensures the design of a highly efficient security scheme.
## 4 Concluding Remarks
Finally, the dense distribution of experimental data can be fitted with NURBS and the parameters of interest can be evaluated at any arbitrarily chosen position (e.g., at the nodes of the corresponding numerical model), allowing for a seamless full integration with finite element simulations [34; 46]. It is important to note, however, that DIC experimental data do not necessarily represent the ground truth, as they are susceptible to errors analogous to numerical results obtained from FE analyses. As an example, there is an analogy between the two methods in terms of the representative size of the ROI discretization which is represented by the mesh element for the FEA and by the grid of pixel subsets for DIC [47]. Analogous to FEA, in the presence of large deformation gradients, the ROI must be sampled properly, i.e., for a given size of the pixel subset (usually not less than 10 \(\times\) 10 pixels), the image resolution (and hence the lens magnification factor) must increase to avoid strain underestimation [47; 48]. This is a significant limitation for in situ DIC measurement during welding, as the high temperature and other technological process constraints (e.g., shielding from bright light, sparks, and projected melted material) impose a large distance between the stereo camera and the welding area. This underlines the importance of setting the DIC system parameters on the basis of the expected experimental conditions [49] and to evaluate the measurement resolution and accuracy of the adopted setup before using DIC experimental data for validation purposes [35; 47]. In conclusion, this work and related results demonstrate the feasibility of integrating laboratory methods with industrial processes and, although the procedure has not yet been transferred to the shop floor level, it certainly encourages future development. Conceptualization, K.G. and R.M. ; Methodology, K.G. ; Validation, K.G. ; Investigation, K.G., N.N. and R.S. ; Resources, R.S. and R.M. ; Data curation, N.N. ; Writing--original draft, K.G. and R.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.
## 4 Discussion

Table 3: The association between PTFJ morphological parameters and knee alignment (the varus FTA group was chosen as the control group (\(n\) = 43)) for the valgus FTA group (\(n\) = 10), and the normal FTA group (\(n\) = 14). that anteroposterior region of tibial platform is smaller than mediolateral size [21]. This can decrease the valgus moment, subsequently decreasing the risk of developing a valgus knee. Thus, a higher relative articular height and lower declination angle were found to be associated with an increased likelihood of having a valgus knee. From our study, The fibula was used to be considered less critical for joint functionality than the other long bones of the lower limb. It has been freely used as a source for grafts and as a vascularized transplant to bridge large bony defects [22,23]. As proximal fibulectomy was applied to treat knee OA, the biomechanical influence of a fibula on the knee joint has been considered. According to [PERSON] et al. 's study [24], proximal fibulectomy weakened the lateral fibular support and led to the correction of varus deformity, which can subsequently shift the loading force from the medial compartment more laterally, leading to decreased pain and a satisfactory functional recovery. A cadaver study showed that, after fibular osteotomy, the peak force, contact area, and pressure in a medial tibial compartment decreased with significant differences at 15\({}^{\circ}\) of flexion (\(p\) < 0.05) [25]. [PERSON] et al. found that the range of motion (ROM) of the PTFJ increases and that the fibula head shifts posteriorly and downward due to rebalancing of the lateral soft tissues after fibulectomy [5]. These findings not only explain the mechanism of proximal fibulectomy to treat knee OA but also prove the biomechanical function of the fibula in the knee joint. It is important to further explore the association between the fibula and knee joint for a better understanding and treatment of knee OA in the future. This study had several limitations. Firstly, the sample size in this study was small. To improve reliability of the logistic regression model, a greater sample size should be enrolled to further explore the association between PTFJ morphology and knee alignment in future studies. Secondly, due to a lack of weight-bearing knee radiographs for healthy people, only participants with severe knee OA were included in this study. To further evaluate the relationship between PTFJ morphology and knee alignment, future studies should also incorporate healthy knees and knees with medial OA.
# Wideband E\({}_{00}\)-E\({}_{10}\) Silicon Mode Converter Based on 180 nm CMOS Technology
###### Abstract  
Mode division multiplexing (MDM) is a promising technology for the capacity enlargement of the optical transmission network. As a key element in the MDM system, the mode converter plays an important role in signal processing. In this work, a wideband E\({}_{00}\)-E\({}_{10}\) silicon mode converter constructed by Y-branch and cascaded multimode interference coupler is demonstrated. The theoretical mode crosstalk is less than -29.2 dB within the wavelength range from 1540 nm to 1600 nm. By 180 nm standard CMOS fabrication, the tested mode conversion efficiency of 91.5% and the crosstalk of \(-\)10.3 dB can be obtained at 1575.9 nm. The 3 dB bandwidth is over 60 nm. The proposed E\({}_{00}\)-E\({}_{10}\) silicon mode converter is applicable in mode multiplexing.  
integrated optical devices; silicon waveguide; mode multiplexing; mode converter +
Footnote †: journal: Applied Sciences
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 3 Dataset and Study Area  
For the experiments, we used three Kompsat-3A image sets taken over Incheon, Chilgok, and Seoul in South Korea. Table 1 shows the image acquisition dates for previous and recent images within the image sets. The time difference between previous and recent images was less than 10 days to minimize brightness changes due to seasonal differences.  
Figure 3: The process of feature-based change analysis: (**a**) a pixel-based change map; (**b**) the distribution of features within the marked area of the pixel-based change map; and (**c**) one ROI of 3 \(\times\) 3 pixels defined on a feature point.  
Figure 4 shows the images used for the experiments. The areas selected for study from each dataset had many changed and unchanged small objects, such as buses, trucks, ships, and containers. Study areas are marked with red boxes in Figure 4. From the Incheon images, one coastal area was selected. From the Chilgok images, one rural area was selected. From the Seoul images, one downtown area along the Han River was selected. The previous and recent images of each study area are shown in Figure 5.  
From each dataset, changed and unchanged small reference objects were extracted by manually digitizing the object boundaries. Since there were a lot of small objects within the test sites, we could not extract all of them. We extracted some representative objects with clear object boundaries widely distributed within the test areas. Extracted reference objects are shown in Figure 6. Objects marked in orange are the changed reference objects, and those marked in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Incheon** & **Chilgok** & **Seoul** \\ \hline Acquisition dates of previous images & 18.01.19 & 18.10.24 & 17.02.23 \\ \hline Acquisition dates of recent images & 18.01.27 & 18.11.01 & 17.02.24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Temporal specifications of the Kompsat-3A image sets.  
Figure 4: Study areas in Kompsat-3A images used. Red boxes indicate the areas for (**a**) Incheon, (**b**) Chilgok, and (**c**) Seoul.  
Figure 5: Previous images (top row) and recent images (bottom row) over the study areas from (**a**) Incheon, (**b**) Chilgok, and (**c**) Seoul.  
green are the unchanged reference objects. The extracted objects range from relatively large sizes, such as ships and containers, to small sizes, such as trucks and small vehicles. To increase the number of unchanged reference objects, the roofs of small buildings were included. Table 2 shows the quantity of reference objects and their average sizes in pixels and in square meters. The average changed object size in Incheon was larger than other sites due to the very large ships docked in the harbor. The average unchanged object size in Chilgok is larger than the others because agricultural fields were included as unchanged references.