esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiments
\begin{table}
\begin{tabular}{c c c} \hline
**Standard CNN** & **Flattened CNN** & **Proposed CNN** \\ \hline \multicolumn{3}{c}{Conv1: 64 filters, each filter constituted as:} \\ \hline \(3\times 3\times 3\) conv & \(3\times 1\times 1\) conv, \(1\times 3\times 1\) conv & \(\mathbf{w}_{1}\doteq\mathbf{p}_{1}(1\times 3\times 1)\otimes\mathbf{q}_{1}(1 \times 1\times 3)\otimes\mathbf{t}_{1}(3\times 1\times 1)\) \\ \(1\times 1\times 3\) conv & \(3\times 3\times 3\) conv \\ \hline \multicolumn{3}{c}{ReLU + Batch Normalization} \\ \hline \multicolumn{3}{c}{Conv2: 64 filters, each filter constituted as:} \\ \hline \(64\times 3\times 3\) conv & \(64\times 1\times 1\) conv, \(1\times 3\times 1\) conv & \(\mathbf{w}_{2}\doteq\mathbf{p}_{2}(1\times 3\times 1)\otimes\mathbf{q}_{2}(1 \times 1\times 3)\otimes\mathbf{t}_{2}(64\times 1\times 1)\) \\ \(1\times 1\times 3\) conv & \(64\times 3\times 3\) conv \\ \hline \multicolumn{3}{c}{ReLU + Max Pool \(\left(\frac{1}{2}\right)\) + Drop Out (P = 0.5)} \\ \hline \multicolumn{3}{c}{Conv3: 144 filters, each filter constituted as:} \\ \hline \(64\times 3\times 3\) conv & \(64\times 1\times 1\) conv, \(1\times 3\times 1\) conv & \(\mathbf{w}_{3}\doteq\mathbf{p}_{3}(1\times 3\times 1)\otimes\mathbf{q}_{3}(1 \times 3)\otimes\mathbf{t}_{3}(64\times 1\times 1)\) \\ \(1\times 1\times 3\) conv & \(64\times 3\times 3\) conv \\ \hline \multicolumn{3}{c}{ReLU + Batch Normalization} \\ \hline \multicolumn{3}{c}{Conv4: 144 filters, each filter constituted as:} \\ \hline \(144\times 3\times 3\) conv & \(144\times 1\) conv, \(1\times 3\times 1\) conv & \(\mathbf{w}_{4}\doteq\mathbf{p}_{4}(1\times 3\times 1)\otimes\mathbf{q}_{4}(1 \times 1\times 3)\otimes\mathbf{t}_{4}(144\times 1\times 1)\) \\ \(1\times 1\times 3\) conv & \(144\times 3\times 3\) conv \\ \hline \multicolumn{3}{c}{ReLU + Max Pool \(\left(\frac{1}{2}\right)\) +Drop Out (P = 0.5)} \\ \hline \multicolumn{3}{c}{Conv5: 256 filters, each filter constituted as:} \\ \hline \(144\times 3\times 3\) conv & \(144\times 1\times 1\) conv, \(1\times 3\times 1\) conv & \(\mathbf{w}_{5}\doteq\mathbf{p}_{5}(1\times 3\times 1)\otimes\mathbf{q}_{5}(1 \times 1\times 3)\otimes\mathbf{t}_{5}(144\times 1\times 1)\) \\ \(1\times 1\times 3\) conv & \(144\times 3\times 3\) conv \\ \hline \multicolumn{3}{c}{ReLU + Batch Normalization} \\ \hline \multicolumn{3}{c}{Conv6: 256 filters, each filter constituted as:} \\ \hline \(256\times 3\times 3\) conv & \(256\times 1\times 1\) conv, \(1\times 3\times 1\) conv & \(\mathbf{w}_{6}\doteq\mathbf{p}_{6}(1\times 3\times 1)\otimes\mathbf{q}_{6}(1 \times 1\times 3)\otimes\mathbf{t}_{6}(256\times 1\times 1)\) \\ \(1\times 1\times 3\) conv & \(256\times 3\times 3\) conv \\ \hline \multicolumn{3}{c}{ReLU + Max Pool \(\left(\frac{1}{2}\right)\) + Drop Out (P = 0.5)} \\ \hline \multicolumn{3}{c}{FC 1024 + Batch Normalization + ReLU + Drop Out (P = 0.5)} \\ \hline \multicolumn{3}{c}{FC 512 + Batch Normalization + ReLU + Drop Out (P = 0.5)} \\ \hline \multicolumn{3}{c}{FC 10} \\ \hline \multicolumn{3}{c}{Soft-Max} \\ \hline \end{tabular}
\end{table} 
Table 2: Structure of CNN2 for CIFAR-10 dataset.
# Effects of Organic Acid Coagulants on the Textural and Physical-Chemical Properties of Tofu
## 3 Results and Discussion
### Texture Profile Analysis (TPA)
This study shows that the tofu made with apple vinegar (T1C5) and rice vinegar (T2C3) exhibited higher hardness and fracturability values, requiring greater force to rupture. This is because the lower pH of these vinears promotes better protein flocculation and more robust gel networks [26]. The interactions of these proteins, facilitated by the acidic conditions from the vinegar, involve hydrogen bonds and disulfide bridges, which contribute to the rigidity and elasticity of the tofu [16]. Higher protein content, as seen in treatments T1C5 (3.93%) and T2C3 (3.25%), correlates with higher hardness and improved textural properties. Thus, the type and concentration of vinegar used in coagulation significantly influence the texture of the tofu by affecting the protein interactions and gel formation [26]. Several studies have determined that the gel properties and texture characteristics of tofu are positively related to the hardness, gummosity, and chewability [7; 28], for which it is mentioned that the textural properties of tofu are one of the most critical quality traits used to evaluate the acceptability of tofu. Firm tofu often has higher hardness and cohesion, so more power is needed to break its structure; it shows higher elasticity and chewability. Thus, the factors that determine the formation of the tofu structure are closely related to the coagulation process [29]. Regarding the properties of these proteins on the tofu texture, it is stated that glycinin (11S) and \(\beta\)-conglycin (7S), particularly in the hardness, elasticity, cohesion, and chewability of tofu, have a significantly positive relation with the 11S/7S ratio, for which they can form arranged structures, because, in the gelification process generated in the cooking stage, heat is induced at high temperatures (>78 \({}^{\circ}\)C) and the exposure of functional groups is achieved, so that, later, in incubation with a reduction in temperature, an interaction among the exposed functional groups occurs, where these groups provide more strength to the tofu structure through hydrogen bonds [30]. The exposure of these proteins to temperatures from 80 \({}^{\circ}\)C to 100 \({}^{\circ}\)C causes the dissociation of globulin 11S into polypeptides, thus obtaining more rigid gels (tofu); also, at 80 \({}^{\circ}\)C, SS bonds (disulfide) occur, which confer elasticity to the tofu. These findings at the level of texture parameters reported in the analyzed samples with different vinears are, in turn, related to the protein content results reported in Table 2, where the contents in treatments T1C5 (3.93%) and T2C3 (3.25%), the treatments which also reported the highest hardness values (T1C5, 31.7 N; T2C3, 64.16 N), stand out. Likewise, the pH values recorded in the used kinds of vinegar oscillate from 2 to 4.6, recording the lowest values in the apple vinegar. This subsequently promotes the texture properties of the hardness and fracturability mentioned above.
# Routing Design for Pipe System Considering Natural Frequency
## 1 Introduction  
Pipe design is among the most difficult, time-consuming tasks in the aerospace and ship industries [1; 2; 3]. Designers primarily focus on the geometric design of pipelines and often struggle to allocate sufficient time and expertise to considering the natural frequency. Applying the natural frequency calculation algorithm to the pipe's routing algorithm poses challenges. The main difficulty is that the routing algorithm does not know the final model during the process. Pipes requiring natural frequency evaluation are numerous with iterations. This problem complicates the integration of natural frequency considerations into established pipe routing methods.  
This paper proposes a novel path planning method that combines the genetic algorithm (GA) and the rapidly exploring random tree [4] (RRT) while considering the natural frequency for pipe routing. The approach begins by presenting an adaptive decomposition method to accurately define the routing space for aero engines. Then, this paper improves the initial population of the genetic algorithm based on the rapidly exploring random tree. Next, this paper establishes a numerical surrogate model for the natural frequency of the pipe. It introduces an evaluation function that incorporates the weighted values of the natural frequency and the length of the pipe. Additionally, this paper introduces several new operators to mitigate the issue of illegal initial paths during algorithm iterations. Finally, the correctness of the proposed algorithm is demonstrated by the experiments on two well-designed examples and an actual aero engine.  
The article is arranged as follows: Section 2 is related to works and the challenges when applying existing pipe routing algorithms to aero engines. Section 3 aims to model the routing space. Considering the numerous obstacles in actual engine models, this paper proposes an adaptive space decomposition method in Section 3. Section 4 provides a detailed introduction to the proposed method in this article. Several new genetic algorithmoperators and a fitness function containing natural frequency are proposed. In Section 4, this paper establishes a numerical surrogate model for the natural frequency of the pipe. In the fitness function, this article normalizes the length and natural frequency to obtain the optimal path for the selected pipe. Section 5 mainly introduces a co-evolutionary algorithm for the collaborative routing of multiple pipes. In this algorithm, a new method for selecting and calculating the fitness function is constructed. Sections 6 and 7 are numerical simulation examples and conclusions to verify the algorithm proposed in this paper.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 3 Experiments and Results
### Accuracy Analysis of Matching between ZY-1-02C Images and ETM+ Images
#### 3.3.1 Results of Initial Matching Value Prediction  
According to Sections 2.1 and 2.2, Table 4 shows the initial matching value parameters of Dataset 3. Fourteen tie points were used to connect 13 lines for calculating the rotation angle (\(A\)) and scale variance (\(k\)). Given the large image size, blocking technology was used in OPC; the overlapped regions were divided into 16 blocks of small images of the same size, to enhance the precision of detecting translation parameters and the operating efficiency. Table 4 shows the results of the image blocks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Quick View of Dataset 3** & \multicolumn{2}{c}{**Resampling Parameters of Dataset 3**} \\ \cline{2-3}  & \multicolumn{2}{c}{\(A\) = 11.715\({}^{\circ}\), \(k\) = 2.98} \\ \hline  & Block 1 & \(x_{0}\) = –187, \(y_{0}\) = 645 \\  & Block 2 & \(x_{0}\) = –183, \(y_{0}\) = 638 \\
## 4 Results and Discussion
### Rainfall Characteristics of the TP
Consequently, they contributed to the precipitation volumes, with the satellite-based estimates having more precipitation than the ground measurements at the light rain range. Figure 2: Spatial distributions of mean annual precipitation for (**a**) CGDPA, (**b**) IMERG-UC, (**c**) IMERG-C, (**d**) GSMaP-MVK, and (**e**) GSMaP-Gauge over the TP during the period of April 2014–March 2017.
## 5 Experiment and Results
### System Evaluation  
The proposed system was evaluated by users to obtain feedback on the system from their perspective. To this end, the following points were considered:  
* Selection of users: Fifty-two random volunteer users participated in the evaluation. These users were selected from different age groups, such as young (63.46%), middle-aged, (26.92%), and old (9.62%), to obtain feedback on the system from a broad range of user categories. About 15.38% of these users had visual problems. Furthermore, a test was carried out on the location of the users in different parts of the city of Seoul.
* The evaluation was performed based on two aspects, namely, system design, and acceptance of the output based on user ideas. To achieve this, the design of the system interface was evaluated in terms of effectiveness, efficiency, and user satisfaction. In this regard, the usability was determined based on user responses to the questionnaire outlined in Table 7[45; 46]; users selected the desired score for each item (i.e., question) on a 5-point Likert scale. In this scale, 1 represents the worst case--i.e., complete user dissatisfaction--and 5 indicates complete user satisfaction. Notably, when a usability component consists of several items, the average of these item-scores is considered the output score.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**User Context** & **Value** \\ \hline Age & 29 \\ Time of day (daytime) & Day \\ Vision problem & No \\ \hline \hline \end{tabular}
\end{table}
Table 6: User context for system test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Usability Component** & **Question** \\ \hline \hline Efficiency & Is the processing time adequately fast? (slow–fast) \\ How many steps does the system require to achieve the result? (many–few) \\ How quickly does the system provide the result? (slow–fast) \\ \hline \hline \end{tabular} \
\begin{tabular}{c} \hline \hline Is data entry for tourists complex or easy? (complex–easy) \\ Is the size of the screen reasonable, or is it difficult to interpret? (difficult-easy) \\ How much interpretability does the presented information offer? (poor–high) \\ \hline \hline \end{tabular}
\begin{tabular}{c} \hline \hline User satisfaction & What level of alignment with your expectations concerning the hotel recommendation and AR visualization services do the results of our application provide? (poor–high) \\ How likely are you to use this application as a tourist? (poor–high) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Questionnaire for usability testing.  
Table 8 and Figure 13 show the averages of the usability item-scores entered by all users for each usability component. This evaluation revealed that efficiency was the most accepted component, with a score of 4.159 reported in the evaluation report. Additionally, the average overall score among the users, which indicates the overall user satisfaction with the system, was 4.112. Notably, the overall score was estimated by averaging the efficiency, effectiveness, and user satisfaction components of the usability test.
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack
### Physical Layer  
On the physical layer, NB-IoT adopts the same numerologies as legacy LTE along with OFDM and SC-FDMA signal waveforms in downlink and uplink, respectively. However, the resource scheduling unit in NB-IoT is the subcarrier (or tone) instead of PRB, to foster the network scalability by serving multiple UEs in a 180 kHz bandwidth. The downlink and uplink frame structures are as depicted in Figures 3 and 4, respectively. In general, the base station uses DCI to specify the scheduling information for a downlink/uplink transmission in NB-IoT. Then NB-IoT UE learns the deployment mode (standalone, in-band, or guard-band) as well as the cell identity through its initial acquisition, and it figures out which resource elements are already used by LTE. This is the way by which the UE can map NPDCCH and NPDSCH symbols to available resource elements. For example, in the downlink, NPDCCH is transmitted by aggregating the narrowband control elements (element 0 and element 1) where element 0 is occupied in subcarrier 0 to 5 and element 1 occupies subcarrier 6 to 11 in a subframe. The elements are determined by the type of DCI which is carried by NPDCCH to deliver scheduling command. Either two DCIs can be multiplexed in one subframe, or one DCI can be mapped in one subframe, corresponding to the aggregation level used [48]. However, NPDCCH, NPDSCH, and NRS cannot be mapped to the already occupied resource elements for LTE signals such as cell-specific reference symbols (CRS) and LTE physical downlink control channel (PDCCH). When NB-IoT UE receives NPDCCH which carries DCI, it decodes it and uses the device's scheduling feature (k0) to know the delay over which it will start to receive NPDSCH. The scheduling information is used to identify the allocated resources over NPDSCH  
Figure 3: NB-IoT Downlink Frame Structure: subframe number 0 carries the Narrowband Physical Broadcast Channel (NPDCH), 1 to 4, and 6 to 8 carry the Narrowband Physical Downlink Control Channel (NPDCCH)/Narrowband Physical Downlink Shared Channel (NPDSCH), and 5 and 9 carry the Narrowband Primary Synchronization Signal (NPSS)/Narrowband Secondary Synchronization Signal (NSSS) (**A**) When the subframe is carrying control channels and (**B**) when the subframe is carrying data. and NPUSCH, respectively. In each NPDCCH, a maximum of two DCIs can be transported, and each UE can receive up to one DCI. The time interval between two successive NPDCCH opportunities is referred to as an NPDCCH period (PP) [48]. In the state of the art, different works have proposed solutions to the challenges that occur in PHY layer features, such as initial cell acquisition and synchronization, random access, channel estimation, error correction, and co-channel interference, as summarized in Table 2.
# Solar Extreme and Far Ultraviolet Radiation Modeling for Aeronomic Calculations
## 2 Source Data
### FUV Data  
At present, a large amount of data from satellite observations of solar radiation fluxes in the FUV spectrum region has been accumulated. The data obtained within the SORCE (Solar Radiation and Climate Experiment, 2003 to date) and TIMED, (2002 to the present time) are the most reliable, covering the entire solar cycle in time. The SORCE spectral range (115-308 nm) fully includes all wavelengths corresponding to the developed model, while the TIMED range (27-190 nm) contains only a part of the specified range. Based on this, the SORCE measurement data were taken to develop the model, and the TIMED data were used to validate the model. It should be noted that this use of the data is justified since the same calibration methods were used in both projects.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Technologies and AI Enhancements in VBDA  
The foundation of VBDA has been significantly influenced by advances in distributed computing, real-time stream processing, and intelligent data infrastructure. The shift from batch-oriented processing to real-time streaming and from centralized systems to edge-based deployments reflects increasing demands for low-latency, scalable, and privacy-aware video analytics solutions.
# PFC Inductor Design Considering Suppression of the Negative Effects of Fringing Flux
## 4 Practical Optimization of PFC Converter Using Analyzed Magnetic Components
### Performance Evaluation with Standard Alloy PFC Inductor  
Initial measurements were done in the way of efficiency evaluation in dependency on the value of output power, while the original inductor made on alloy core was used. The efficiency investigation was also done for higher switching frequencies to identify shortcomings associated with the non-optimized inductor solution.  
By increasing the frequency, we can assume that the losses increase not only on the transistors and diodes but also on the coils. As is stated within the theory of alloy cores, their practical applicability is up to 100 kHz due to a significant increase in losses in the core at higher frequencies [15; 16; 17]. Considering the inductance of the coil, as the frequency increases, the current ripple decreases, thus the increase in losses is partially compensated. As a consequence, reduction of the core saturation caused by the AC part will be reduced. Considering the results shown in Figure 20, the efficiency drop for 80 kHz of switching frequency is not critical, thus evaluation for 120 kHz was provided as well.  
At 120 kHz switching frequency, a decrease in efficiency of 0.3% was recorded, which represents a difference of 13 W compared to 41 kHz operation.
# A New Tooth Profile Modification Method of Cycloidal Gears in Precision Reducers for Robots
## 4 Meshing Contact Characteristics of the Modified Profile
### Minimum Backlash Angle of the Modified Profile  
Lost motion determines the positioning accuracy of a precision reducer. It is also one of the important indicators for evaluating the modification quality. One of the most important influencing factors of the lost motion is the minimum backlash of the modified tooth profile. In order to accurately evaluate the modification quality, the minimum backlash angle of the modified profile is discussed in this study. The schematic diagram of the minimum backlash required for the reverse meshing contact between pin gear and cycloidal gear is shown in Figure 9.  
Under the non-loading condition, only one pair of teeth engage in the transmission of the modified cycloidal gear and pin gear. In Figure 9, the No. 0 tooth is the current meshing tooth pair and the other tooth has no meshing contact. It can be seen that there are different backlashes between the gear teeth. The tooth pair with the smallest backlash can be contacted first when the pin gear is reversely rotated. Therefore, it is necessary to overcome the smallest backlash before reverse meshing transmission. The essence of the lost motion calculation is to obtain the angle corresponding to the first pair of tooth contacts by calculating the minimum backlash at the moment of the reverse rotation of the pin gear. The flowchart shown in Figure 10 clearly illustrates the process of determining the minimum backlash angle.  
Figure 8: Process of solving the transmission error.  
Figure 9: Backlash of cycloid-pin gear in reverse rotation.  
In the calculation process of the backlash angles, it is necessary to firstly determine the initial rotation angles \(\phi_{1}^{0}\) and \(\phi_{2}^{0}\) of the pin gear and the cycloidal gear in the current meshing state. When the cycloidal gear remains fixed and the pin gear rotates in the opposite direction, the backlash angle \(\phi_{1}\) of the pin gear in the meshing process is solved. In this way, a series of angles of the pin gear can be obtained and represented as \(\phi_{1}^{i}\) (\(i\) = 1\(\sim\)\(z_{p}\)). Thus, the backlash angle can be obtained with \(\Delta\phi_{1}^{i}=\left|\phi_{1}^{i}-\phi_{1}^{0}\right|\), which is the rotation angle of the \(i\)-th pin tooth required for eliminating the backlash and reverse meshing contact. The minimum backlash angle describes the lost motion and also determines the pin number of the first contact. With the pin tooth number as the abscissa and the rotation angle required for eliminating backlash as the ordinate, the minimum backlash angle curve can be plotted.