esa-sceva/satcom-chunk-collection huggingface dataset:
# Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
###### Abstract  
Model calibration refines design-stage inputs to align with real-world building performance. Accurate parameter selection, especially for highly sensitive variables like air leakage, is crucial. This study compared two building energy model calibration methods. The \"classic\" method adjusted indoor air capacitance, internal mass, and air infiltration, while a novel method focused on capacitance and internal mass, using empirical data for infiltration. The infiltration values were calculated using the decay equation and the EnergyPlus equations with site-specific coefficients. A triple validation assessed model performance in terms of temperature (CIBSE TM63), energy consumption (minimization), and indoor air quality (represented by CO\({}_{2}\) levels in accordance with the ASTM D5157 Standard). Results demonstrated the novel method's superiority across all three performance metrics. All calibrated models met the CIBSE TM63 criteria even during the validation period, which was five times longer than the training period. Compared to the classic method, models incorporating dynamic empirical infiltration showed a 29% and 26% improvement in MAE and RMSE, respectively, in temperature prediction. In energy consumption results, the novel method models presented a 31% reduction, and for CO\({}_{2}\) level agreement, these models achieved a 130% higher R\({}^{2}\) value than the classic model. In addition, the classic method's infiltration values failed to meet ASTM D5157 requirements, suggesting reliance on unrealistic parameter values for accurate temperature representation. The incorporation of calculated air leakage data into the BEM allowed a more realistic estimation of capacitance and internal mass values, emphasizing the importance of accurate air infiltration modeling for overall model reliability.  
model calibration; building energy model (BEM); infiltration modeling; decay method; model validation; energy efficiency
# Technological Development and Application of Photo and Video Theodolites
## 3 Measurement and Processing Approaches  
Depending on the developed hardware and software, integrated sensors, and different functions through the implementation of various algorithms in TS, we can distinguish types of TS (Table 3).  
State-of-the-art IATS and IASTS represent a new kind of RTS, unifying the geodetic precision of TS with the areal coverage of images, as well as laser scans. IATS offer the user an image-capturing system with integrated camera(s) using image sensor(s) in addition to a polar 3D point measurement system. IASTS are equipped with a scanning function with lower scan rates comparable to classical terrestrial laser scanners. The greatest advantage of using such a (multi-sensor) instrument is the common coordinate system of all built-in sensors, as long as appropriate a priori calibration is provided [55].  
These multi-sensor systems can capture images for documentation and for photogrammetric measurements. They can even merge more of these captured images into a panoramic image mosaic using camera rotation, i.e., telescope rotation. With appropriate calibration, these images are accurately georeferenced and oriented, such that they can be immediately used for direction measurements with no need for object control points or further photogrammetric orientation processes [15].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TS** & **RTS** & **IATS** & **IASTS** \\ \hline EDM & ✓ & ✓ & ✓ & ✓ \\ Reflectorless & \(\bullet\) & \(\bullet\) & ✓ & ✓ \\ EDM & \(\bullet\) & \(\bullet\) & ✓ & ✓ \\ Data registration & ✓ & ✓ & ✓ & ✓ \\ Motorization & ✗ & ✓ & ✓ & ✓ \\ Image sensor & ✗ & ✓/\(\bullet\) & ✓ & ✓ \\ Automated & ✗ & ✓/\(\bullet\) & ✓ & ✓ \\ aiming & ✗ & ✓/\(\bullet\) & ✓ & ✓ \\ Tracking & ✗ & ✓/\(\bullet\) & ✓ & ✓ \\ Imaging & ✗ & ✗ & ✓ & ✓ \\ Scanning & ✗ & ✗ & ✗/\(\bullet\) & ✓ \\ \hline \hline \end{tabular} \(\mathcal{J}\)—integrated; ✗–not integrated; \(\bullet\)—optional.  
\end{table}
Table 3: Different types of TS according to integrated sensors and functions.
## 4 Experimental Results and Analysis
### Comparative Experiments and Analysis  
#### 4.5.1 General Assessment  
(A) Binary segmentation  
Two commonly performed binary segmentation methods (the Otsu and Tsallis-BE methods) were selected to be compared with the proposed Cauchy-TB to demonstrate its advantages. We selected 90 slice images numbered 30-119 from the BrainWeb dataset and grouped them into nine groups, with each group containing 10 images in sequential order. Additionally, we selected 15 images from the MRBrainS13 dataset and divided them evenly into three groups. Tables 2 and 3 show the comparative experimental results for 13 groups of slices. We present the average results for each group of images. Obviously, Cauchy-TB outperformed all other methods in terms of the metrics JC, DSC, SSIM. On the BrainWeb dataset, our average accuracy, JC, and DSC reached 98.62%, 98.36%, and 98.17%, respectively. Compared to the Tsallis-BE method, our method performed better by 0.3%, 0.4%, and 0.2%, respectively. On the MRBrainS13 dataset, our average accuracy, JC, and DSC reached 91.71%, 88.30%, and 93.69%, respectively. Compared to the Tsallis-BE method, our method performed better by 0.6%, 0.96%, and 0.55%, respectively. In the MRBrainS13-3 group alone, there was a very small difference in accuracy compared to the Tsallis-BE method, with a reduction of only 0.03%. Similar superiority was also reflected in the PSNR, FSIM, and SSIM metrics, where our method showed an average performance improvement across both datasets. Only in the Group BrainWeb-3 and MRBrainS13-2 groups did our PSNR and FSIM scores slightly lag behind those of Tsallis-BE. Overall, across the six proposed metrics, our method demonstrated outstanding overall performance. Table 4 presents the average runtime of the three methods across the two datasets. The Otsu method was implemented using the existing cv2 library, and due to issues with code development and optimization, its average runtime is relatively short. Both Tsallis-BE and Cauchy-TB are self-implemented methods, and it can be observed that their average runtimes were similar, both within the order of seconds. This runtime is acceptable for the segmentation of a relatively small number of brain MRI images. As shown in Figure 8, Cauchy-TB also demonstrated superior performance on the metrics of sensitivity and precision, with average values reaching over 97%, slightly better than the Tsallis-BE method. However, our method lagged slightly behind in specificity, although the results were still satisfactory. The Tsallis-BE method performed well in all
## 1 Introduction
In addition, the chlorine decay rate within drinking water sources depends on several variables, particularly temperature. Hence, we require a real-time quantity analysis of drinking water. The main intention of the proposed framework is to sustain a continuous supply of drinkable water. Its quality concerning chlorine content must be checked in real-time [13]. Additionally, the existing system makes most laboratory-based testing methodologies time-consuming and expensive. Even though advancements have been made in water monitoring systems [14], they still rely on wireless sensor networks or wireless network technology, which has several drawbacks, including inadequate information security, transmission coverage, and power consumption control [15]. In this characteristic, the Internet of Things (IoT) has benefited since it enables the construction of real-time systems that are much more effective, reliable, and cost-efficient. The proposed model continuously updates the chlorine level values to the sensor hub. Further, that information is transferred through a communications channel for data analysis to evaluate chlorine levels in drinking water and residual chlorine concentration over time in the drinking water.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 1 Introduction
Meanwhile, its application objects have also been gradually extended from the early still digital images to other fields, such as videos, audios, and documents. With the increasing demand of digital video applications in people's daily life, videos gradually occupy an important position in multimedia digital works. At the same time, video content has gradually become the mainstream of information displayed on the Internet, so the copyright protection of video products has become more and more important. Consequently, video watermarking technology has become a research hotspot [11]. The application of video watermark in copyright protection is shown in Figure 2.  
According to the characteristics of watermarks, they can be divided into three categories: robust watermark, fragile watermark, and semi-fragile watermark [12]. The robust watermark can resist various attacks, so it is often used for copyright protection. The fragile watermark is very sensitive to tampering attacks. Any attacks, including common signal processing attacks, may destroy the watermark information. As a result, it is mainly used for content authentication and tampering  
Figure 1: Main branches of information hiding. detection. The semi-fragile watermark combines the advantages of the robust watermark and fragile watermark. It can distinguish the common signal processing operations from malicious attacks. In this paper, we will focus on robust video watermarking algorithms for copyright protection. The rest of this paper is organized as follows. Section 2 expounds the basic models of robust video watermarking algorithms, which include the watermark generation, watermark embedding, and watermark extraction. Properties of video watermarking algorithms are described in Section 3, and evaluation indexes of every property are also introduced. The literature study on robust watermarking algorithms based on original videos is presented in Section 4. Section 5 overviews robust watermarking algorithms based on compressed videos. Conclusions and possible further development directions are given finally in Section 6.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 3 Air Resistance Approximation and Motion Primitive Generation
### Linearization Approximation of Air Resistance Model  
This article considers the external disturbances experienced by quadcopters from two perspectives (4), including airframe resistance \(\hat{f}_{rb}\) and rotor resistance \(f_{rp}\):  
\[\hat{f}_{rb}=-\hat{k}_{rb}\mathbf{v}^{2} \tag{11}\]  
\[f_{rp}=-k_{rp}a_{t}\propto\omega_{p}^{2} \tag{12}\]  
Among them, \(\hat{k}_{rb}\) and \(k_{rp}\) are the drag coefficients, \(\mathbf{v}\) is the quadcopter velocity, and \(\omega_{p}^{2}\) is the propeller speed.  
Using a linearized model to approximate resistance (11):  
\[\mathbf{f}_{rb}=-k_{rb}\mathbf{v} \tag{13}\]  
where \(\hat{k}_{rb}\) is the approximate resistance coefficient.  
Take a single axis for analysis and express the similarity between the standard model (11) and the approximate model (13) in the maximum operating speed range \(v_{\max}\) of the quadcopter in the form of definite integrals:  
\[L(k_{rb})=\int_{0}^{v_{\max}}\left(\hat{k}_{rb}\mathbf{v}^{2}-k_{rb}\mathbf{v}\right)^ {2}\!dv \tag{14}\]  
Figure 2: Diagram of the interception process of invading drones, where blue represents the interceptor, red represents the intruder, and arrows represent the vector of velocity.  
Find the extreme point of function (14) relative to the approximate resistance coefficient \(\hat{k}_{rb}\), and the approximate result is shown in Figure 3.  
\[\frac{dL}{dk_{rb}}=\frac{v_{\max}^{3}(-3\hat{k}_{rb}v_{\max}+4k_{rb})}{6}=0 \tag{15}\]  
\[k_{rb}=\frac{3\hat{k}_{rb}v_{\max}}{4} \tag{16}\]  
Therefore, Equation (4) is rewritten as:  
\[\mathbf{a}_{e}=f_{rb}+\mathbf{R}\mathbf{e}_{3}f_{rp} \tag{17}\]
# Silver Nanowires: Synthesis, Antibacterialativity and Biomedical Applications
## 4 Toxicity of Silver Nanowires
They could not find a specific effect linked to length only, but they observed that both short (2 um) and long (20 um) AgNPs induced a dose-dependent foreign-body response. Differences in the inflammatory response were linked not only to the difference in length, but also to dissimilar doses (as a dose by mass of the two species corresponded to a different number of individual wires administered) and a different rate of Ag\({}^{+}\) release due to the difference in surface area. Further _in vitro_ studies looked at the mechanisms of defense present in the lungs that are able to respond to AgNPs exposure [74]. It was found that proteins, present in the pulmonary surfactant secretion, bind to AgNPs reducing their ability to be uptaken by alveolar cells. Furthermore, these proteins were also responsible for a reduced rate of Ag\({}^{+}\) release that resulted in longer-term effects. Interestingly, those AgNPs that were uptaken, entered the cells either by membrane piercing or were observed inside endosome/lysosome vesicles and showed evidence of sulfidation, as previously described by [PERSON] et al. [71]. These results highlight once more the complexity of the interaction between AgNPs and the biological environment reiterating the need for further studies to be conducted. Some studies have evaluated the toxicity of AgNPs in aquatic species finding that again toxicity is linked to the rate of release of Ag\({}^{+}\) form the nanomaterial with lower toxicity induced by AgNPsNPs compared to AgNPs [75; 76]. Ecotoxicity studies reported similar findings; in earthworms, longer AgNWs had a lower toxicity compared to shorter ones. The toxic activity of AgNPs was linked to a decrease in intracellular esterase activity combined with earlier finding about ROS production and the role of sulfur rich proteins [77]. [PERSON] et al. also demonstrated that the _in vitro_ toxicity was due to cell adsorption, rather than internalization, and that _in vivo_ toxicity in earthworms was elicited by oral assumption rather than topical contact. It would be interesting to compare these results in other animal models. Studies so far tend to show that AgNPsNH might present lower toxicity compared to free Ag\({}^{+}\) and AgNPs. However, our current knowledge on the toxicity of AgNPs is extremely limited and both _in vitro_ and _in vivo_ studies must be performed to better understand the fate of AgNPs in contact with the biological environment.
## 4 Research on Critical Technologies  
From analyzing the comprehensive research status at home and abroad, we found that, in terms of communication payload airborne applications, the following limitations and improvements mainly exist at this stage in the study of lightweight and small-sized uncrewed aircraft optical communication: (1). With the continuous development of unmanned aircraft technology, people need to realize high-speed and stable data transmission to meet the needs of real-time monitoring, data collection, and other applications. Therefore, in practical applications, the high-speed data transmission capability and stability of optical communication need to be further improved. (2). The high speed, uncertain trajectory, and random errors generated by changes in the platform's altitude exhibited by the airborne platform in flight pose a significant challenge to the rapid establishment of laser links. These factors increase the difficulty of establishing stable communication links in dynamic environments. (3). UAVs have a limited payload, so reducing the size and weight of the payload while maintaining the performance of the laser communication  
Figure 8: Schematic diagram of the communication link between the ground station and drone through atmospheric turbulence communication.  
payload is critical to the successful establishment of links. (4). The environmental factors faced by UAV optical communication payloads include the unfavorable factors of the airborne platforms themselves and the negative impacts of the complexity and variability in the external space environment. These factors include, but are not limited to, vibration, temperature changes, atmospheric disturbances, etc., all of which may impact the performance of a communication system. Therefore, the design of these systems must consider these factors to ensure they can operate stably in various environments.  
Modulation and demodulation techniques, space environment adaptability techniques, capture tracking alignment techniques, and lightweight and miniaturized design techniques are the keys to solving the issues.
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
###### Abstract  
Inter-system bias (ISB) will affect accuracy and processing time in integrated precise point positioning (PPP), and ISB stochastic models will largely determine the quality of ISB estimation. Thus, the impacts of four different stochastic models of ISB processing will be assessed and studied in detail to further reveal the influence of ISB in positioning. They are ISB-PW considering ISB as a piece-wise constant, ISB-RW considering ISB as random walk, ISB-AD considering ISB as an arc-dependent constant, and ISB-WN considering ISB as white noise. Together with the model without introducing ISB called ISB-OFF, i.e., five different schemes, ISB-OFF, ISB-PW, ISB-RW, ISB-AD, and ISB-WN, will be designed and tested in this study. From the results of pseudorange residuals, it can be noticed that when considering ISB, the Root-Mean-Square (RMS) of ionosphere-free combined pseudorange residuals are much smaller than without ISB (ISB-OFF). The results of convergence time and positioning accuracy analysis show that PPP performance with ISB-AD is even worse than ISB-OFF, when using the precise products from the German Research Centre for Geosciences (GFZ) named as GBM products here; while the strategies of ISB-RW, and ISB-WN achieve the best results. For the products from Wuhan University called WUM products, a completely different result is achieved. PPP with the stochastic models of ISB-PW and ISB-AD perform best. The most likely reason is the ISB stochastic models applied by the analysis centers are consistent with those used in the PPP on the user side. So, ISB-RW, or ISB-WN is recommended when GBM products are used, and for the WUM products, ISB-PW, or ISB-AD is chosen. From the statistics of PPP precision during the convergence period, it can be concluded that considering ISB also has a great improvement on combined PPP accuracy during the initialization phase.  
inter-system bias; stochastic model; piece-wise; random walk; white noise +
Footnote †: journal:
# An Improved Method for Ship Target Detection Based on YOLOv4
## 4 Experiments and Results
### Ablation Experiments  
We conducted a series of ablation experiments on our dataset. As shown in Table 5, we considered the influence of different combinations of four factors, backbone parameter, Kmeans++, CBAM and RFB_s, on the experimental results. We used the mAP and AP of each ship class as evaluation criteria to verify the effectiveness of our model.  
It can be seen that, with YOLOv4 as the benchmark experiment, the YOLOv4-Kmeans++ model improves the mAP of the model by 7.81% through using pre-trained backbone parameters instead of random initialization and the Kmeans++ algorithm to select better initial anchors. Based on this, the YOLOv4-CBAM model adds the CBAM module to adaptively adjust different feature layer weights, the mAP of the model improves by 9.81% and the AP of merchant ships improves by 11.11%. As a result of a large number of small ships in the labeled merchant ships, CBAM can be validated as effective at improving the detection of small targets. The mAP of the YOLOv4-RFB_s model increased by 10.93%, and the AP of the merchant ship increased by 10.68%, indicating that the RFB_s module can also improve the small target detection effect. The mAP of the improved model with the introduction of CBAM and RFB increased by 13.74%, and the AP of the merchant ship increased by 12.43%, proving that the improved model improved the small target detection effect.  
As can be seen from Figure 11, the original model does not detect the small ships and the ship at the edge of the image (Figure 11a). The model with the CBAM module detects the ship at the edge, and some of the small ships are missed by the original model, but it still does not detect small ships completely (Figure 11b). The model with the RFB_s module detects the ship at the edge, and small ships are missed by the original model but miss small ships disturbed by the background (Figure 11c). The improved model correctly detects all ships (Figure 11d).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**YOLOv4**} & \multicolumn{2}{c}{**YOLOv4-Kmeans++**} & \multirow{2}{*}{**YOLOv4-CBAM**} & \multirow{2}{*}{**YOLOv4-RFB\_s**} & \multirow{2}{*}{**Ours**} \\  & & **(Baseline)** & & & \\ \hline Backbone-Parameter & - & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Kmeans++ & - & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ CBAM & - & - & \(\surd\) & - & \(\surd\) \\ RFB\_s & - & - & - & \(\surd\) & \(\surd\) \\ \hline mAP (\%) & 77.66 & 85.47 & 87.47 & 88.59 & 91.40 \\ AP\_Arcerst carrier (\%) & 93.59 & 87.68 & 84.37 & 86.30 & 91.33 \\ AP\_Submarine (\%) & 86.02 & 89.03 & 91.15 & 92.19 & 96.44 \\ AP\_Mechant (\%) & 73.50 & 80.65 & 84.61 & 84.18 & 85.93 \\ AP\_Warship (\%) & 57.55 & 84.51 & 89.74 & 91.70 & 91.88 \\ FPS & 22.33 & 21.97 & 20.92 & 21.17 & 20.34 \\ \hline \hline \end{tabular} \(\surd\) means the module or trick in the first column is used in the corresponding model.  
\end{table}
Table 5: Comparison of ablation experiment.
## 4 Results and Discussion
### Permeability Test Results  
Figure 13 shows the permeability coefficient of three mixtures at temperature of 20 \({}^{\circ}\)C. The \(K_{20^{\circ}\text{C}}\) value of OGFC, SBS-OGFC and SM-OGFC were found to 0.19, 0.28 and 0.24. According to the technical requirements of the open grade asphalt mixture in the Specifications for design of Highway Asphalt Pavement in China (JTG D50-2006), It is recommended that the permeability coefficient (\(K_{20^{\circ}\text{C}}\)) should be greater than 0.01 cm/s, and the \(K_{20^{\circ}\text{C}}\) value of three mixtures meets the specifications. It is well known that asphalt-aggregate ratio plays a key role in permeability performance of OGFC. The lower the asphalt-aggregate ratio, the better the permeability performance of OGFC. Thus, SBS-OGFC shows better permeability performance than OGFC. What is interesting is that SM-OGFC has a higher asphalt-aggregate ratio than OGFC, but has better permeability performance. The main reason for this phenomenon is that SM-OGFC has relatively less free asphalt, which leads to a lower probability of free asphalt blocking the interconnected pores, thus showing better permeability performance.  
Figure 11: Spring-thawing stability for all OGFC mixtures.  
Figure 12: Immersion Marshall stability and Immersion residual stability for all OGFC stability.  
Figure 13: Permeability coefficient for all OGFC mixtures.