esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
## 6 Conclusions and Future Work  
In this paper, a novel and efficient model-based object pose estimation algorithm is proposed to achieve an accurate and stable pose tracking function for an OOI having a multi-planar structure. This property can help to implement many computer vision applications. Thanks to the GPU acceleration and the template tracking technologies, the proposed algorithm can efficiently and robustly detect and track the target in real time. Based on the template tracking result, the 3D pose of the OOI can be obtained accurately from the proposed model-based pose estimator, which combines an HD-based initial pose solver and a model-based PnP solver. Experimental results show that the maximum estimation error of the proposed algorithm is in the case of estimating the rotation in the \(y\)-axis and is about 1.64\({}^{\circ}\) on average. Otherwise, the proposed algorithm can provide accurate pose estimation results. The rotation estimation errors in both the \(x\)- and \(z\)-axis are about 0.81\({}^{\circ}\) and 0.22\({}^{\circ}\) on average, respectively. The translation estimation errors in the three axes are all smaller than 0.33 cm on average. Moreover, the entire system achieves an extremely high processing speed on a desktop computer equipped with a GPU accelerator. These advantages significantly increase the applicability of the proposed algorithm in practical applications.  
In the future, a robust visual tracker can be integrated with the template tracker to improve the tracking robustness of the system to overcome external uncertainties during the pose tracking process. Furthermore, it is also crucial to combine a robust object detection method, such as a deep learning-based object detector [34], with the proposed algorithm to detect the target in complex scenes, which helps to improve the robustness and computational efficiency of the proposed algorithm in practical applications.  
Conceptualization, [PERSON], [PERSON] and [PERSON] Methodology, [PERSON], C.Y. Software, [PERSON], [PERSON] and [PERSON], [PERSON] Validation, [PERSON], [PERSON] and [PERSON], [PERSON] Formal analysis, [PERSON], [PERSON] Investigation, [PERSON], [PERSON] Resources, [PERSON], [PERSON] and [PERSON] Data curation, [PERSON], [PERSON] Writing, original draft preparation, [PERSON], [PERSON] Writing, review and editing, [PERSON], [PERSON] and [PERSON] Visualization, [PERSON] Supervision, [PERSON], [PERSON] Project administration, [PERSON], [PERSON] Funding acquisition, [PERSON], [PERSON].  
Figure 6: Experimental results of multi-object pose tracking of the proposed algorithm [32].  
**Acknowledgments:** This work was supported by the Ministry of Science and Technology of Taiwan, ROC, under Grant MOST 107-2221-E-032-049 and was supported in part by NUWA Robotics Co., Ltd, Nanjing East Road, Zhongshan District, Taipei City 104, Taiwan.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Analysis
### Performance Analysis  
Based on the results, it should be noted that the COSGO algorithm performed significantly in finding the threshold value in images. In this problem, the image levels were studied on levels 2, 3, 4, and 5, respectively. The findings obtained for the algorithm in concern were near to one, which shows good improvement of the algorithm and a considerable performance of the algorithm, according to Tables 6 and 7, which have been analyzed  
Figure 4: Comparison of SSIM and FSIM values obtained from 10 test images using different algorithms at threshold level 4.  
Figure 5: Comparison of SSIM and FSIM values obtained from 10 test images using different algorithms at threshold level 5.  
on the PSNR, SSIM, and FSIM criteria. An increase in the number of threshold levels generally results in more vivid and detailed segmented images. While images segmented using the COSGO algorithm are often visually distinguishable, the method exhibits certain weaknesses in specific regions. In addition, Figure 6 shows the segmented images with different metaheuristic algorithms.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 2 Materials and Methods
### Regional Regulations
#### 2.2.1 Europe  
The European Telecommunication Standards Institute (ETSI) and the Electronic Communications Committee (part of the European Conference of Postal and Telecommunications Administrations, CEPT) define the set of recommendations and standards [19; 23] for Short Range Devices operating in the 25-1000 MHz frequency band. Considering that the CEPT includes 48 European countries (national level) and ETSI has more than 800 members (regulatory bodies, governments, companies, universities, research bodies) from 66 countries and five continents, their normative and regulations are complex, as consensus within the parties have to be made. In addition, some countries may differ on the designated bands or impose certain conditions on uses of specific bands. The normative for EU wide harmonized National Radio Interfaces cuts the 863-870 MHz band into sub-bands, as detailed in Table 4. Table 5 shows conformance to any National Radio Interface, in the case of using the entire 7 MHz band. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rate Mode** & \
\begin{tabular}{c} **Chip Rate** \\ **kchip/s** \\ \end{tabular} &
\begin{tabular}{c} **Data Rate** \\ **kbps** \\ \end{tabular} & **Alias** \\ \hline
0 & 100 & 6.25 kbps & QQPSK-6.25 \\
1 & 100 & 12.5 kbps & QQPSK-12.5 \\
2 & 100 & 25 kbps & QQPSK-25 \\
3 & 100 & 50 kbps & QQPSK-50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Radio settings using O-QPSK modulation. The values of these parameters are defined in the standard [22]. The rate mode defines the resulting data rate. The radio signal is composed by chips; in the 868 MHz frequency band, the standard uses a fixed 100 kchip/s. The spreading mode defines the amount of chips needed to encode a bit of information. The duty cycle restriction of 0.1% in some of the sub-bands could be eased and taken up to 2.8% if polite spectrum access is applied to the transmitters. Polite spectrum access refers to Listen Before Talk (LBT) and Adaptability Frequency Agility (AFA). Prior to transmission, a \"polite\" device performs Clear Channel Assessment (CCA) to check its availability. If the channel is free, the device continues its transmission. If it is not, the device should not retry its transmission until a random time has passed. Optionally, the device can change the intended transmitting frequency and listens again before starts the transmission. The timing parameters of polite spectrum access are:  
* Minimum CCA time: 160 us
* Maximum single TX duration: 1 s
* Maximum cumulative TX time in one hour: 100 s (duty cycle of 2.8%) per 200 kHz spectrum
## 3 Results  
### Laboratory Measurements  
At first, the microphone measurement was processed by means of the convolution between the recorded ESS and inverse ESS, thus obtaining the time domain Impulse  
Figure 8: (**a**) View from above of the model of the car’s hood adopted for the numerical vibrational analysis. The red dot indicates the force application point. (**b**) View from below. The blue dots indicate the constraint application points.  
Figure 9: (**a**) Velocity magnitude over the bonnet at 1 kHz. (**b**) Grid of \(N=368\) points where the velocity data are evaluated on the upper surface of the bonnet.  
Response (IR) of the system. The linear part of the IR was cut in the time domain before the first reflection of the environment and converted to the frequency domain by applying Fast Fourier Transform (FFT). Doing so, the spectrum of the sound pressure was obtained for the acoustics reference measurement performed with the B&K microphone on-axis at a distance of 1 m. By applying an equivalent processing, the velocity spectrum in each of the \(N\) = 77 points measured by the LDV was obtained too. Then, both the numerical solution and experimental measurement were processed to reconstruct the sound pressure at the microphone position by applying [PERSON]'s integral with the formulation for the velocity data:  
\[p(r_{2},f)=\frac{j\omega\rho_{0}}{2\pi}\sum_{n=1}^{N}v(r_{n},\ k)\ \frac{e^{-i\cdot k \cdot d}}{d}\Delta S_{n} \tag{2}\]  
where \(d\) is the distance between the observing point (\(r_{2}\)) and vibrating point (\(r_{n}\)), \(\rho=1.2\) kg/m\({}^{3}\) is the air density, \(\omega\) is the angular frequency, \(v\) is the mobility datum, \(f\) is the frequency, \(n\) is the node index, \(\Delta S_{n}\) is the area of each element and \(k\) is the frequency index. In the case of a numerical solution, one can note that \(v\) is directly obtained in the frequency domain, therefore making the FFT unnecessary.  
In all the three cases, the SPL curve is obtained with the standard conversion formula:  
\[SPL(f)=20\cdot\log_{10}\left(\frac{p(f)}{p_{0}}\right) \tag{3}\]  
where \(f\) is the frequency, \(p\) is the sound pressure, and \(p_{0}=20\) uPa is the standard reference value for the sound pressure level. The three results (acoustic measurement with B&K microphone, [PERSON]'s integral applied over measured and numerical data) are compared in Figure 10. One can note a good superimposition of the curves, particularly above 300 Hz. At very low frequencies, below 200 Hz, one can note a turnaround of the microphone peaks with the laser evaluated ones. This is caused by the effect of the IR cut in the time domain before the first reflection, which makes the IR too short to have sufficient information at the lower frequencies. The spectrum obtained by processing numerically calculated data does not present the peak at 550 Hz, which is caused by the nonideality of the experimental setup.  
Figure 10: SPL recorded by microphone (solid line) superimposed with SPL calculated by [PERSON]’s integral (dash line).
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research  
Interference monitoring technology is an important part of anti-interference technology in communication systems. The interference monitoring technology in this study needs to judge whether the interference signal exists, and give the information such as the type of interference, the bandwidth range of the interference, the interference intensity and the interference frequency. Interference monitoring provides a priori information for communication anti-interference technology. It is not only the premise and foundation of cognitive and intelligent anti-interference systems, but also one of the key technologies.  
In order to study the interference in satellite communication systems, this study first analyzes and models the satellite communication channel, and then analyzes the style and mathematical model of existing man-made interference signals [19]. Aiming at the problem of interference monitoring, this paper studies the advantages and disadvantages of the existing artificial feature extraction methods, and finally puts forward the interference monitoring technology based on deep learning.
# Thermal Impact of 5G Antenna Systems in Sandwich Walls
## 2 Materials and Methods
### Analytical Approaches  
The U-value (W/m\({}^{2}\)K) of a building component is calculated using the following formula [22]:  
\[U=\frac{1}{R_{T}} \tag{1}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Material** & **Density (kg/m\({}^{3}\))** & **Thermal Conductivity (W/mK)** & **Specific Heat (J/kgK)** \\ \hline Concrete & 2400 & 2 & 1000 \\ Rockwood & 140 & 0.035 & 840 \\ Styrofoam & 15 & 0.04 & 1500 \\ PCB & 2200 & 0.2 & 960 \\ Rubber & 850 & 0.2 & 1900 \\ Stainless steel & 8000 & 15.2 & 500 \\ PTFE (Teflon) & 2000 & 0.24 & 1500 \\ Copper & 8960 & 400 & 385 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wall and antenna material properties.  
where \(R_{T}\) is the total thermal resistance of the building component (m\({}^{2}\)K/W). If the building component is constituted of homogeneous and parallel layers, \(R_{T}\) is computed as [22]:  
\[R_{T}=R_{si}+\sum_{j}^{N}R_{j}+R_{se}=\frac{(T_{i}-T_{o})}{q} \tag{2}\]  
where \(R_{si}\) and \(R_{se}\) are the internal and external surface resistance (m\({}^{2}\)K/W), \(R_{j}\) is the thermal resistance of layer \(j\) (m\({}^{2}\)K/W), \(T_{i}\) and \(T_{o}\) are the inside and outside temperatures (\({}^{\circ}\)C), and \(q\) is the heat flux through the building component (W/m\({}^{2}\)). \(R_{si}\), \(R_{se}\), and \(R_{j}\) are given as:  
\[R_{si}=\frac{1}{h_{si}},R_{se}=\frac{1}{h_{se}},R_{j}=\frac{l_{j}}{k_{j}} \tag{3}\]  
where \(h_{si}\) and \(h_{se}\) are the indoor and outdoor heat transfer coefficients (W/m\({}^{2}\)K), \(k_{j}\) is the thermal conductivity of layer \(j\) (W/mK), and \(l_{j}\) is the thickness of layer \(j\) (m). \(h_{si}\) and \(h_{se}\) are the normally set as 4 (W/m\({}^{2}\)K) and 25 (W/m\({}^{2}\)K) in Finland. There are several analytical methods available for calculating the U-values of building components with inhomogeneous layers [24]. In this study, we present three commonly used methods: the parallel path, the isothermal planes, and ISO 6946 combined methods. To illustrate the implementation of these methods, we use an example wall (as depicted in Figure 5) in the subsequent subsections.
## 4 Discussion
### Rice Plant Growth, Nutrient Uptake, and Nutrient Efficiency  
The rice plant height, filler number, panicle number, and greenness in the treatments amended with RSRH biochar were significantly higher than in the rest of the treatments. This finding suggests a positive effect of biochar addition on the growth of rice plants. The plant growth increased with the application of RSRH biochar. This was due to the improvement of soil chemical properties such as increased availability of plant nutrients (N, P, and K) and a decrease of soil acidity. The availability of N, P, and K was readily available in soil for efficient plant uptake. Improved rice plant physical growth directly increased the dry weight of the plants under RSRH biochar treatments.  
In terms of rice plant N uptake, T3 (10 h\({}^{-1}\) RSRH biochar) significantly improved the N concentration and uptake, and this indicated the reason for less N loss from the soil-plant system. Similarly, the N use efficiency was the highest in T3 compared to other treatments (Table 12). This was because the soil-biochar mixture might have encouraged the formation of NH\({}_{4}\)\({}^{+}\) and NO\({}_{3}\)\({}^{-}\) over NH\({}_{3}\). [PERSON] et al. [29] reported similar observations. [PERSON] et al. [75] also stated that the plant N uptake and use efficiency could be related with the ability of organic amendments to reduce NH\({}_{3}\) volatilization. Further, the adsorbed N, NH\({}_{4}\)\({}^{+}\), and NO\({}_{3}\)\({}^{-}\) in biochar exchange sites were being released slowly into the soil such that it met the demand of rice plant growth stages. It was proven that biochar can be an excellent alternative source for a slow release agent of nutrients [76]. However, there was no significant difference in N uptake between T1, T2, T4, and T5. This might be due to the lesser biochar application rate in T2, where the capability to adsorb nutrients was lesser and almost equal to non-biochar-amended treatments (T1). Further, lesser adsorption capability may have led to minor urea volatilization and denitrification. This may have been the cause of lesser N uptake and use efficiency in T1, T2, T4, and T5.  
The P, K, and Ca concentrations, uptake, and use efficiency of rice plant in T2 and T3 were significantly higher compared to other treatments. Addition of RSRH biochar increased the soil pH and adsorption of PO\({}_{4}\)\({}^{3-}\) onto its exchange sites by reducing fixation by Al and Fe. The PO\({}_{4}\)\({}^{3-}\) ions are not released immediately and this favored absorption by the plant root system. This could have improved better root growth in the rice plants which received biochar amendments (T2 and T3), thus increasing P, K, Ca, and Mg uptake in rice plants. Similarly, the plant nutrient use efficiency increased in line with plant nutrient uptake.
## 3 Experiments and Results
### Experimental Results  
#### 3.4.1 Experiment on the Effectiveness of Algorithm Improvements  
MPSR has two improvements. Firstly, because the texture of the infrared image is not obvious, the image details are enhanced before the super-resolution reconstruction, and then the image is reconstructed with the improved biregular objective function. Secondly, the regularization objective function is combined with sub-pixel convolution. Instead of taking the enlarged high-resolution image as the input, the low-resolution image is taken as the input. Finally, the sub-pixel convolution is used to enlarge the image to the required multiple, which not only reduces the complexity, but also enhances the reconstruction effect. The effectiveness of these two parts is verified by experiments, as shown in Figure 6.  
Figure 6a is a comparison of the objective functions between single regular item and double regular items. It can be seen from the figure that the effect of image reconstruction with the objective function of double regular items is better than that with single regular item, and the PSNR value is increased by about 1.5 dB  
Figure 6b is the comparison of PSNR values before and after algorithm improvement. The former algorithm refers to the traditional regularization algorithm, the improved algorithm is MPSR. It can be seen from the figure that the improved PSNR value has increased by 2.5 dB. As shown above, both improvements in this experiment are effective.
## 3 Materials
### Data Management, Storage, and Visualization Tools  
Efficient data management is essential in any IoT implementation, particularly in rural settings, where cloud connectivity may be limited. To ensure autonomy and resilience, the Smart Rural IoT Lab integrates open-source tools covering the entire data lifecycle.  
The core of the system is Node-RED v4.0.9, a flow-based programming tool that simplifies the integration of IoT devices and services through a visual interface [42], which allows users to define the system logic by connecting functional nodes. In the laboratory infrastructure, Node-RED acts as the control layer, managing the data flow between sensors and storage systems.  
Sensor data are stored in two databases. InfluxDB v2.7.6 is used for time-series data, due to its high performance in storing timestamped records, such as environmental measurements and motion detections [43; 44]. The laboratory also uses MongoDB v7.0.9, a document-oriented NoSQL database [45]. In rural IoT applications, where data structures may evolve over time, MongoDB provides schema flexibility, allowing IoT systems to  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Protocol** & **Communication Model** & **Energy Consumption** & **Latency** & **Native Security** & **Reliability** \\ \hline MQTT & Publish-Subscribe & Low & Low & No (requires TLS) & Medium \\ HTTP & Request-Response & High & High & Yes (HTIPS/TLS) & High \\ UDP & Connectionless & Very low & Very low & No & Low \\ CoAP & Request-Response & Very low & Very low & Yes (DTLS) & Medium \\ LoRaWAN & LPWAN (low power) & Optimized for batteries & High & Yes (AES-128) & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: IoT protocol feature comparison review.  
store sensor metadata, configuration settings, and historical records in a way that is easily adaptable to changing requirements.  
To make sensor data accessible and interpretable, the laboratory includes Grafana v11.0.0 [46], an open-source visualization platform widely used in IoT projects, allowing the creation of interactive dashboards. Through these dashboards, users can explore sensor data such as temperature trends and motion sensor activity.  
All services are deployed in Docker [47] containers on a Debian-based virtual machine, for modularity and reproducibility. This setup ensures that the laboratory can be replicated in other environments, supporting collaborative research.  
By combining these tools, the Smart Rural IoT Laboratory demonstrates that it is possible to implement robust data infrastructure in low-resource environments.
# Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
## 3 Transaction Support and Integrity
### Proposed Extension--Data Reflection  
The already defined types as the extensions of the conventional approach are not suitable for the temporal manner. During the research in the temporal sphere, we came to a significant problem, which is not covered by those categories complexly. Therefore, in this paper, we introduce the Data reflection category. Data in the temporal system are stored for a long time. The above principles, however, do not reflect the possible changes in the structure, as well as the evolution in the constraints definition. Multiple sources and data origins are consecutively added to the temporality, therefore the demand for the structural changes is present and required almost always. For the temporal evolution, just the changes in the data model can be placed, so the state must reflect the particular data model defined in the validity time frame. Note, however, that the data model can be changed during the valid data state itself. Similarly, the state can be defined with the future planned validity, however, data model reflection must be done directly during the transaction processing sooner in a timeline manner. As the result, the transaction only reflects states referencing the \"AS-IS\" data model, however, the validity is shifted to the \"AS-WILL BE\" model. Thus, the particular state can be approved with the temporal reflection. Physically, before getting the validity state mark, a particular model is checked, whether all constraints are there passed or not. For suitability and completeness, any change, even done automatically during the transaction shifting, is monitored and stored in the temporal layer by the flags. The state itself can hold the following status:  
* Valid--the state is currently valid or has been planned in the past by moving the state into the valid representation currently.
* Corrected--the state was planned and later state values were corrected (mostly by using bi or multi-temporal architectural model).
* Transaction refused--the planned state was removed as the consequence of breaking any of the concerning integrity rules.
* User rollbacked--the whole transaction is user rollbacked.
* Refused--the state does not cover the already planned model in the future.
* Planned--the state is planned to be valid in the future, the particular model is not defined yet, or all conditions reflecting it are passed.
* Modified--this option reflects the transaction rules, by which the original state validity is changed, either shortened or shifted in a timely manner.
## 3 System Model  
In this paper, we primarily focus on vehicular network scenarios and select the most representative straight road scenario for modeling [28; 29; 30; 31]. In this scenario, vehicles travel in opposite directions along a straight road, utilizing OFDM-IM technology to achieve V2V communication. We define the communication model and OFDM-IM transmission framework for this scenario. By considering the impact of scatterers on both sides of the road on the signal propagation path and intensity, we analyze in detail the contributions of line-of-sight (LOS) and non-line-of-sight (NLOS) path components to channel fading. Additionally, we provide the transmission framework and transceiver module model for this system, laying the foundation for subsequent simulations.