esa-sceva/satcom-chunk-collection huggingface dataset:
In summary, these spatiotemporal analysis approaches commonly fail to effectively model spatiotemporally integrated processes. Thus, the relevant methods cannot be efficiently used to represent the spatiotemporal patterns of GDP evolution. To investigate the characteristics of internal variations in GDP growth in the Wuhan urban agglomeration, GDP dasymetric maps of the study area from 1992 to 2012 were created using GDP statistical data and saturation-corrected nighttime light images obtained by the Defense Meteorological Satellite Program (DMSP)-Operational Linescan System (OLS). Additionally, a map spectrum-based, spatiotemporally integrated clustering model is proposed to classify the patterns of variation. The proposed model simultaneously considers both spatial and temporal characteristics in clustering and it differs from existing methods, in which spatial and temporal characteristics are considered separately in clustering. The results of this study show that the proposed model can reflect regional discrepancies in the variations of GDP growth, especially in the urban, suburban, and rural areas of the Wuhan urban agglomeration.
# A Robust Security Architecture for SDN-Based 5G Networks
## 4 Security Analysis
### Replay Attack  
Finally, we will illustrate how the security architecture prevents the replay attack. In a replay attack, the attack will repeat or relay a message from a 5G device by eavesdropping. Since our security authentication will change the secret during each communication, the messages from replay attack will always have an outdated secret, namely, can always be found by our architecture.
## 5 Experimentation
### Results and Discussions
#### 5.2.2 Voltage Limits  
Figure 14 shows the waveforms of bus-voltage and machine speed during the bus-voltage fault protection process.  
It can be seen from Figure 14a that the fault point was set at 0.04 s, and the bus-voltage started to rise, resulting in the increase in speed. The protection action was triggered and switch \(T7\) was conducted as soon as bus-voltage was up to 320 V, which led to decreases in voltage. Consequently, speed was back to the normal value. The power was cut off and the machine was shut down rapidly after an adjustment process when bus-voltage dropped to 200 V, as shown in Figure 14b.  
What needs to be pointed out is that the boost converter started to work to make up the difference when bus-voltage fell (\(K_{3}=1\)). Figure 14b shows that the boost converter pulled up the bus-voltage back to the rated level of 270 V quickly when it had not yet dropped to the protection threshold value of 200 V. As a result, the machine speed returned to the rated value and the system maintained normal performance despite the lower power supply. This process commonly helps to deal with under-voltage power faults without turning power off completely. Note that the dynamic period should be less than \(t_{s}\), or the under-voltage protection action will be triggered as well.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 2 Related Work
While matching and graph theory have specifically been used for frequency assignment problems, other optimization techniques along with machine learning has been used to solve a wide variety of resource sharing problems.
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 4 Discussion  
Tumors of different sizes located at different locations inside the phantom were detected. The proposed system shows great accuracy at distinguishing cancerous masses inside human breast. Tumors were detected near the skin layer, as shown in Figures 20 and 21. Although the skin layer has a relatively high dielectric constant of 36, the image reconstruction algorithm was capable of clearly identifying cancerous masses. In Figures 22 and 23, tumors were detected further away from the skin layer inside the tissue layer. The system was also capable of detecting small tumors of size 5 mm and higher. The worst-case scenario is when the tumor is located at the furthest point inside the phantom, the center. In this setup, the initial radar measurement set and the rotated measurement set are similar, which complicates the detection. Figure 24 shows a 5 mm tumor located at (0,0), where it is clearly identified from healthy cells. However, in all cases, an acceptable amount of clutter is present in the reconstructed images due to the limited bandwidth of the antenna.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
### Existing Reviews and Our Contributions  
Previous surveys have examined the role of AI and IoT in solar energy monitoring and control, but most studies focus on these technologies in isolation rather than exploring their combined potential. While some reviews address AI-driven energy forecasting and optimization techniques, others concentrate on IoT-enabled monitoring and automation systems. However, significant gaps remain in the literature, including the absence of a unified framework for AIoT in solar energy systems, limited analysis of how AI and IoT collaboratively optimize performance, and a lack of comprehensive taxonomies categorizing their applications. To fill these gaps, this survey offers a structured taxonomy and a detailed analysis of AIoT applications in solar energy. We classify AIoT implementations into key areas such as fault detection, predictive maintenance, energy forecasting, solar monitoring systems, and optimization techniques like MPPT, solar tracking, and automated cleaning. Furthermore, we compare existing studies to highlight differences in methodologies, findings, and technological approaches, providing a broader perspective on AIoT's role in solar energy. By consolidating advancements, identifying challenges, and analyzing key differences across studies, this paper aims to offer valuable insights that can inform future research and development in AIoT-driven solar energy solutions.  
Table 1 summarizes the strengths and weaknesses of existing reviews and highlights the research gaps that our survey addresses.
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 4 Model Category Classification  
There is a large number of models with complex structures and textures in the landmark building model. View-based methods require projections from various angles to acquire different views. When utilizing common descriptors such as color histograms and contour descriptors to extract view features, the resultant features often lack stability. Even a minor alteration in the viewing angle can lead to substantial changes in the corresponding view features, compromising the reliability of retrieval outcomes. In contrast, topological features exhibit greater stability, consistently yielding similar characteristics for adjacent views. (Using the contour as an example (see Figure 1), when the model is rotated, the contour will be deformed, a rectangle may become a square, a circle may become an ellipse, and features related to the contour such as curvature features, aspect ratio features, and features extracted by the Fourier descriptor will become very different. Color- and texture-related features are similar to contours. On the contrary, the topological structure of the image does not change easily and it still retains the \"hole\" features). Therefore, extracting topological features is a reliable strategy to effectively minimize the interference of complex structures and textures during the retrieval process.  
Therefore, instead of focusing on features such as texture, color, and local curvature changes of the view, this paper focuses on the topological differences of the graphs. With the help of topological perception theory, we are able to classify models from a novel perspective. Topological perception theory posits three critical topological features: holes, connectivity, and internal/external relations, which take precedence over other geometric aspects. This paper initially categorizes images into four primary types: hole images, separated images, concave unclosed images, and non-concave images, as show in Figure 2.  
Subsequently, considering that the views with significant topological features are primarily located on the top or side views of the model, this paper firstly divided the landmark building model into 16 shapes by combining the 4 top  
Figure 1: Examples of the stability of topological features: (**a**,**b**) views of the same model from different viewpoints.  
Figure 2: Example of topology structure: (**a**) hole, (**b**) separated, (**c**) concave unclosed, and (**d**) non-concave.  
top view and the 4 topological structures of the side view in pairwise combinations. Since some of the combinations cannot exist in reality or the number collected did not meet the requirements, we ultimately selected six categories of 3D topological structure models as the subsequent experimental dataset. The various categories of models are shown in Figure 3.
## 2 Models and Methods
### Substructure Coupling
\begin{array}{l}\mathbf{ML}\ddot{\boldsymbol{q}}(t)+\mathbf{CL} \dot{\boldsymbol{q}}(t)+\mathbf{KL}\boldsymbol{q}(t)=\boldsymbol{f}(t)+ \boldsymbol{g}(t)\\ \mathbf{L}^{T}\boldsymbol{g}(t)=\boldsymbol{0}\end{array} \right. \tag{10}\]  
By pre-multiplying the dynamic equilibrium equation by \(\mathbf{L}^{T}\) and considering that the interface equilibrium is automatically satisfied (\(\mathbf{L}^{T}\boldsymbol{g}(\boldsymbol{t})=\boldsymbol{0}\)), the assembled system becomes:  
\[\mathbf{L}^{T}\mathbf{ML}\ddot{\boldsymbol{q}}(t)+\mathbf{L}^{T}\mathbf{CL} \dot{\boldsymbol{q}}(t)+\mathbf{L}^{T}\mathbf{KL}\boldsymbol{q}(t)=\mathbf{L} ^{T}\boldsymbol{f}(t) \tag{11}\]  
i.e.,  
\[\mathbf{M}\ddot{\boldsymbol{q}}(t)+\mathbf{\tilde{C}}\dot{\boldsymbol{q}}(t)+ \mathbf{\tilde{K}}\boldsymbol{q}(t)=\boldsymbol{\tilde{f}}(t). \tag{12}\]  
To obtain the receptance matrix \(\mathbf{H}(j\omega)\) of the assembled system, Equation (12) must be expressed in the frequency domain considering \(\boldsymbol{\tilde{f}}(t)=\mathbf{\tilde{F}}(\omega)\,e^{j\omega t}\) and \(\boldsymbol{q}(t)=\boldsymbol{Q}(\omega)\,e^{j\omega t}\):  
\[\left[-\omega^{2}\mathbf{\dot{M}}+j\omega\mathbf{\tilde{C}}+\mathbf{\tilde{K} }\right]\boldsymbol{Q}(\omega)=\mathbf{\tilde{F}}(\omega) \tag{13}\]  
Therefore,  
\[\mathbf{H}(j\omega)=\left[-\omega^{2}\mathbf{\dot{M}}+j\omega\mathbf{\tilde{C} }+\mathbf{\tilde{K}}\right]^{-1}. \tag{14}\]
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 7 Simulations and Results
### Number of Vehicles vs. System Throughput under Variable SINR  
The system throughput (Mbps) is calculated by the summation of the total throughput of V2P users and total throughput of CUE given by Equation (16):  
\[\left(\sum_{\
u=1}^{\
u}\bigl{[}\log_{2}(1+\gamma_{\
u})\bigr{]}+\sum_{c=1}^{C} \bigl{[}\log_{2}(1+\gamma_{c})\bigr{]}\right) \tag{16}\]  
Cellular users with greater SINR threshold contribute better to the overall system throughput as evident from the formula and shown in Figure 5.
# Reliability of Critical Fitness Tests for Police Officers
## 3 Results  
Considering all the participants (n = 122), the following was observed: (i) in the HG test, the average sum of the best performance in each hand in the first moment (T1) was 91.70 \(\pm\) 18.04 kg, and, in the second moment (T2), it was 93.38 \(\pm\) 18.87 kg (average difference of 1.68 \(\pm\) 6.54 kg), and the ICC was 0.934; (ii) in the HJ test, participants achieved 2.27 \(\pm\) 0.23 m in T1 and 2.26 \(\pm\) 0.22 m in T2 (average difference of 0.01 \(\pm\) 0.10 m), and the ICC was 0.904; (iii) in the 60 s sit-ups test, participants achieved 53.68 \(\pm\) 8.04 repetitions in T1 and 55.61 \(\pm\) 7.56 repetitions in T2 (average difference of 1.93 \(\pm\) 4.31 repetitions), and the ICC was 0.824; and (iv) in the 20 m shuttle run test, participants achieved 71.24 \(\pm\) 20.08 shuttles in T1 and 74.16 \(\pm\) 20.30 shuttles in T2 (average difference of 2.92 \(\pm\) 9.01 shuttles), and the ICC was 0.892. The highest ICC was observed at the HG test (0.934) and the lowest at the 60 s sit-ups test (0.824). Considering the two evaluation moments, the variables of the sum of HG, sit-ups, and 20 m shuttle run presented higher performance values in the retest than in the test. In HJ, a higher value was observed in the test, showing the slightest difference between the test and retest. Regarding the SEM, it was lower than 1 (one) in all variables, with the 20 m shuttle run (0.82) and HG sum (0.59) being the tests with the highest values (both above 0.50) and HJ the lowest (0.01). Results are presented in Table 2.  
In the graphical representations of applying the Bland-Altman test (Figure 3), the average difference was almost zero (close to zero) in the HJ. In the remaining fitness tests (HG sum, 60 s of sit-ups, and 20 m shuttle run), it was higher in the retest (T2). In female participants (Table 3), the following was observed: (i) in HG sum, the difference between the means of the two moments was 0.16 \(\pm\) 4.52 kg, and the ICC was 0.868; (ii) in the HJ, they reached 1.97 m in both assessments, presenting an ICC of 0.795 (the lowest ICC, the only one below 0.80); (iii) in 60 s of sit-ups, the difference between the means of the two moments was 1.90 \(\pm\) 3.18 repetitions and the ICC was 0.896; and (iv) in the 20 m shuttle run, the difference in the two moments was 2.63 \(\pm\) 8.43 shuttles and the ICC was 0.856. Regarding male participants (Table 3), the following was observed: (i) in HG sum, the difference in the two evaluations was 2.18 \(\pm\) 7.02 kg and the highest ICC (0.875); (ii) in the HJ, they obtained the same value in both assessments (2.36 m), presenting an ICC of 0.789; (iii) in the 60 s sit-ups test, the difference was 1.93 \(\pm\) 4.63 repetitions, and the ICC was 0.789; and (iv) in the aerobic capacity test, the difference between the means of the two moments was 3.01 \(\pm\) 9.24 shuttles, and the ICC was higher than 0.85 (0.862).
## 3 Proposed Algorithm for Energy-Efficient Node Mobility
### A Time Interval Calculated According to the Moving Speed and Distance of a Mobile Node  
#### 3.2.1 Velocity Estimation of the Mobile Node  
As a mobile node moves from one location to another, a frequency difference occurs in the received signal owing to the Doppler effect. This frequency difference is referred to as Doppler frequency. The maximum Doppler frequency in the received signal is proportional to the speed of motion of the mobile node. The relative speed of the mobile node is related to the signal sent from the neighboring router node. The signal can be used to find the speed and direction of the mobile node [13; 16]  
If a neighboring router node is in motion relative to a mobile node, the frequency of the signal received by the router node is different from that of source signal. If a mobile node moves closer to a router node, the received signal has a higher frequency than the transmitted signal. When a mobile node moves farther away from a router node, the received frequency decreases.  
If the speed of motion of a mobile node is \(V_{m}\), the angle between the mobile node and the parent node is \(\theta\), the speed of the electromagnetic (EM) wave is \(c\), \(f_{d}\) is the Doppler frequency, the received frequency is \(f_{r}\) and the frequency transmitted from router nodes is \(f_{t}\), then the Doppler frequency is given by Equation (1):  
\[f_{d}=f_{r}-f_{t}=V_{m}\cdot\cos\theta\cdot f_{t}/c \tag{1}\]  
To find the dynamic time interval proposed in this paper, we first need to know the speed of the mobile node. Using Equation (1), the speed of a mobile node can be estimated as shown in Equation (2):  
\[V_{m}=f_{d}\cdot c/\cos\theta\cdot f_{t} \tag{2}\]
## 5 Discussion  
Through the observation of mining pressure in the practice of mining working faces with high heights and ultralong lengths, as well as research on existing theories of mining pressure and rock layer control, it is shown that changes in the length of the working face will lead to changes in the roof pressure during the mining process, which indicates that the height of the roof rock layer movement and collapse in the mining area changes, and this change conforms to the pressure balance arch in the Proctor theory. The Proctor theory suggests that the roof strata above a certain span space (working face length) will experience arch collapse, and the height of the arch is related not only to the collapse but also to the strength of the rock. The specific relationship is shown in Formula (1). The height of the roof rock caving is  
\[H_{M,L_{g}}=\frac{L_{g}}{2\text{f}} \tag{1}\]  
where \(H_{M,L_{g}}\) is the falling height, m; \(L_{g}\) is the working face length, m; and f is Proctor's coefficient.
## 1 Introduction
In conventional SSK, every transmitter has an equal pulse duration and amplitude, but in order to further enhance this modulation technique, the pulse for each symbol has a unique time duration, creating the enhanced space shift keying (eSSK) modulation technique [21; 22]. In order to investigate how the adjustment of an optical filter at the receiver enhances its performance, the bit error rate (BER) metric will be estimated for an indoor environment in the presence of sunlight radiation. To the best of our knowledge, it is the first time that such a low-cost, thin-film, VO\({}_{2}\) optical filter has been proposed and experimentally evaluated as a component of a typical VLC system in order to reduce the background noise and improve the performance characteristics of the link. More specifically, as it will be shown in Section 6, taking into account the experimentally determined properties of the optical filter, the BER performance of a typical VLC link reaches a six orders of magnitude improvement. The remainder of the work is organized as follows: In Section 2, the VO\({}_{2}\) optical filter design and implementation will be analyzed; in Section 3, the characteristics of the system model will be presented; in Section 4, the signal-to-noise ratio (SNR) of the system will be estimated; in Section 5, the BER performance will be estimated; and in Section 6, the corresponding numerical results will be presented.
# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
###### Abstract  
Sound event detection (SED) is the task of finding the identities of sound events, as well as their onset and offset timings from audio recordings. When complete timing information is not available in the training data, but only the event identities are known, SED should be solved by weakly supervised learning. The conventional U-Net with global weighted rank pooling (GWRP) has shown a decent performance, but extensive computation is demanded. We propose a novel U-Net with limited upsampling (LUU-Net) and global threshold average pooling (GTAP) to reduce the model size, as well as the computational overhead. The expansion along the frequency axis in the U-Net decoder was minimized, so that the output map sizes were reduced by 40% at the convolutional layers and 12.5% at the fully connected layers without SED performance degradation. The experimental results on a mixed dataset of DCASE 2018 Tasks 1 and 2 showed that our limited upsampling U-Net (LUU-Net) with GTAP was about 23% faster in training and achieved 0.644 in audio tagging and 0.531 in weakly supervised SED tasks in terms of F1 scores, while U-Net with GWRP showed 0.629 and 0.492, respectively. The major contribution of the proposed LUU-Net is the reduction in the computation time with the SED performance being maintained or improved. The other proposed method, GTAP, further improved the training time reduction and provides versatility for various audio mixing conditions by adjusting a single hyperparameter.  
sound event detection; U-Net; weakly supervised learning; pooling +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 4 Light Attenuation
### Reflectivity Attenuation  
The experimental reflectivity of a good mirror in the UV-visible-near-infrared range is usually between 0.85 and 0.95. In these simulations, a conservative approach has been adopted, using the lowest value (to account for dust, moisture, corrosion, wind, etc.):  
\[\eta_{ref}\approx 0.85 \tag{9}\]  
Figure 7: The successive cross product vectors always point (**a**) to the same side (inside point) or (**b**) to opposite sides (outside point), permitting us to distinguish between the two situations.  
The overall light attenuation can then be estimated for each simulated ray, multiplying the above attenuation factors:  
\[\eta=\eta_{attm}\;\eta_{cos}\;\;\eta_{ref} \tag{10}\]  
Other effects, such as interception efficiency (see [7]), are currently not considered in our software.
## 3 Materials and Methods
### Packed Tower  
Figure 2 shows the packed tower system constructed to determine the effect of the liquid-gas contact ratio on the stripping factors. The tower was designed with a diameter of 0.2 m and a height of 1.5 m to enable observation of the physical characteristics of NH\({}_{3}\) air stripping at the bench scale. The precise specifications are shown in Table 2. The internal packing material consisted of 18 mm PP pall rings, and the height of the packing was 0.48 m. The maximum capacity of the tank was 147 L; the effective capacity for the stripping experiment was set to 100 L. The stripping temperature was set to 70 \({}^{\circ}\)C, and a heater in the tank and a heat gun were used to control the stripping temperature. Air was supplied using the heat gun into the bottom of the tower and was emitted from the top of the tower. The flow rate of air was controlled using a valve and measured through a flow meter. The liquid was supplied into the top of the tower from the tank using a pump; after spraying through a nozzle, the liquid passes through the packing layer, then cycled back into the tank. The cycling rate of the liquid and the flow rate of the gas are specified along with the other operating conditions in Table 3. A temperature-adjusted pH sensor was installed inside the tank to monitor the pH and temperature, and the NaOH solution was supplied into the tank using a pump to control the pH. Ammonia stripping using the packed tower was performed at 70 \({}^{\circ}\)C and pH 8.5 (NH\({}_{3}\) fraction 76.1%). To ascertain the optimal operating conditions for the tower, the liquid flow rate was fixed at 117.0 L/h, and the stripping efficiency was assessed according to the liquid-gas ratio by varying the gas flow rate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature** & **pH** & **Initial NH\({}_{3}\) Concentration** \\ (\({}^{\circ}\)**C**) & **(-)** & **(mg/L)** \\ \hline  & 8.9 & 2900 \\  & 9.4 & 1500–2900 \\
20 & 10.2 & 2900 \\  & 10.8 & 2900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions of NH\({}_{3}\) stripping using a stripping column.  
Figure 2: Schematic view of the ammonia stripping packed tower system.