esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Based Smart Monitoring Framework for Livestock Farms
## 6 Conclusions  
There are many challenges in the dissemination of intelligent solutions that support smart livestock farming. In this paper, we proposed an intelligent monitoring system framework by applying artificial intelligence technology and implemented the RT-DETR model for smart livestock farms. To achieve real-time detection more efficiently, we adopted the RT-DETR method rather than the commonly used CNN-based YOLO models. RT-DETR reduces computational costs and optimizes query initialization, making it more suitable for real-time object detection. We defined four classes to detect cattle lesions and abnormal behaviors, collected images from the CCTV video images of real ranches, and performed preprocessing and labeling tasks to build a high-quality training dataset for deep learning. An AI model was constructed by designing an RT-DETR network architecture, and its performance was evaluated using a test dataset and validation dataset.  
The experimental results confirmed that the RT-DETR model ensures high recognition accuracy. Furthermore, to verify the effectiveness of the proposed AI model, a comparative analysis was conducted with YOLO models. Through these experiments, the class loss values of RT-DETR, YOLOv5, and YOLOv8 were further analyzed, and it was confirmed that  
Figure 19: Classification loss curves.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Train/Class\_Loss** & **Val/Class\_Loss** \\ \hline YOLOv5 & 0.1327 & 0.2875 \\ \hline YOLOv8 & 0.1478 & 0.2365 \\ \hline RT-DETR & 0.0781 & 0.2049 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different models on class loss.  
the RT-DETR model outperformed the YOLO models, demonstrating superior classification performance.  
The proposed RT-DETR model-based smart livestock monitoring framework is expected to be highly practical as it does not require the installation of additional devices because it exploits the CCTV systems already installed on livestock farms. In future work, we are going to further enhance the model and develop an integrated solution that includes a user-friendly interface.  
Conceptualization, M.S. and B.K.; methodology, S.H. and M.S.; investigation, S.H., M.S. and B.K.; validation, M.S. and S.H.; writing--original draft, S.H. and M.S.; writing--review and editing, B.K. and M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of this manuscript.  
This paper was funded by Konkuk University in 2023.  
Not applicable.  
Dataset available on request from the authors.  
[PERSON] and [PERSON] are the corresponding authors.  
The authors declare no conflicts of interest.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 5 3D Estimation of Observation Scene
### Effect of Errors in 3D Position Estimation of Spherical Mirror on 3D Estimation  
Section 4.2 discussed the estimation of the 3D position of the spherical mirror. However, it is crucial to consider the potential errors in this position estimation and their impact on the overall 3D estimation of the object. Figure 8 illustrates this relationship.  
In Figure 8, the estimated 3D information is represented by the distance \(|\mathbf{P}|\) between the camera center and the 3D point. This distance is calculated using Equation (13), which considers the triangle formed by the camera center, the reflection points on the spherical mirror, and the 3D point.  
\[\frac{R}{\sin\phi} = \frac{d_{s}}{\sin\theta_{s}}=\frac{\|\mathbf{C_{s}}\|}{\sin\left( \pi-\theta_{i}\right)}\] \[\theta_{i} = \arcsin\|\mathbf{C_{s}}\|\frac{\sin\phi}{R}(=\arcsin S)\] \[\theta_{s} = \arcsin S-\phi\] \[d_{s} = \|\mathbf{C_{s}}\|\cos\phi-R\sqrt{1-S^{2}}\] \[\|P\| = \frac{\|\mathbf{C_{s}}\|\cos\phi-R\sqrt{1-S^{2}}}{\frac{1-2S^{2}}{2S \sqrt{1-S^{2}}}\sin\theta_{n}+\cos\theta_{n}} \tag{13}\]  
Equation (13) allows us to observe the changes in the estimated 3D information resulting from variations in the 3D position of the spherical mirror. In other words, it is  
Figure 7: Search for Matching Point by Color Histogram: Calculate the color histograms of the mirrored image at the reflection point and the corresponding omnidirectional image. Then, search for corresponding points based on their similarity.  
possible to analyze how errors in the estimation of the spherical mirror's position impact the accuracy of the 3D estimation.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Scanner**} & **Number and Position of** & **Group** & **RMS \(\pm\) 95\% CI (\(\upmu\)m)** & **Min; Max** & \(n\) & \(p\) \\ \cline{2-7}  & **Implants** & & & & & \\ \hline \multirow{4}{*}{IO 3 Shape Trios 3} & 6 (CCI A) & Tr6\_A & 12.66 [11.12; 14.20] & 5.80; 30.90 & 45 & \(*\) & \\ \cline{2-7}  & 4 (CCI B) & Tr4\_B & 16.96 [13.59; 20.33] & 6.10; 45.60 & 45 & \(*\) & \(<\)0.001 \\ \cline{2-7}  & 4 (CCI C) & Tr4\_C & 5.96 [5.52; 6.41] & 3.80; 10.10 & 45 & & \\ \hline \multirow{4}{*}{IO Medit i500} & 6 (CCI A) & Md6\_A & 11.36 [10.61; 12.10] & 7.00; 17.00 & 45 & & \\ \cline{2-7}  & 4 (CCI B) & Md4\_B & 16.69 [15.04; 18.33] & 7.90; 37.10 & 45 & & \(<\)0.001 \\ \cline{2-7}  & 4 (CCI C) & Md4\_C & 7.36 [6.73; 7.98] & 2.80; 12.80 & 45 & & \\ \hline \multirow{4}{*}{Photogrammetry PIC Dental} & 6 (CCI A) & Pi6\_A & 11.28 [9.80; 12.76] & 3.30; 22.60 & 45 & & \\ \cline{2-7}  & 4 (CCI B) & Pi4\_B & 14.44 [12.24; 16.63] & 1.90; 27.20 & 45 & - & 0.075 \\ \cline{1-1} \cline{2-7}  & 4 (CCI C) & Pi4\_C & 11.54 [9.65; 13.43] & 2.20; 26.60 & 45 & - & \\ \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics and comparison of digital parameters in precision tests by implant distribution, per device (\(\upmu\)m). \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Number and Position of** **Implants**} & **RMS \(\pm\) 95\% CI (\(\upmu\)m)** & **Min; Max** & \(n\) & \(p\) \\ \hline
6 (CCI A) & 11.77 [11.02; 12.51] & 3.30; 30.90 & 135 & \\ \hline
4 (CCI B) & 16.03 [14.61; 17.45] & 1.90; 45.60 & 135 & \(<\)0.001 \\ \hline
4 (CCI C) & 8.29 [7.51; 9.06] & 2.20; 26.60 & 135 & \\ \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics and comparison of precision test parameters among different implant distributions (\(\upmu\)m).
## 2 New Membrane Operations Aligned with PIS
### Energy Production in Desalination Sector  
New developments and progresses in membrane engineering are creating exciting opportunities to generate renewable and sustainable energy in the desalination sector. This can be achieved by mixing two streams with different salinity gradients. The power generated is termed as blue energy or salinity gradient power. The total global potential of blue energy has been estimated to be about 1.4-2.6 TW out of which \(\sim\)980 GW is extractable depending on the technology applied [25]. It has been demonstrated that salinity gradient energy is able to fulfil 20% of the current global energy demand. This can impart a significant contribution in lowering the dependence on carbon based energy production. PRO and RED are two of the most interesting membrane operations to harness this energy. Integration of these operations with conventional and emerging desalination operations produces synergetic effects. Besides reducing the net energy consumption of desalination processes, these operations also make desalination more clean and green by producing electricity with zero carbon emission and by diluting the concentrated brine which otherwise is a nuisance. By combining PRO or RED with membrane-based desalination systems like RO and MD, a synergetic advantage of both systems can be obtained in the logic of process intensification.
# Enhanced Linearity in Intracranial Pressure Monitoring System Through Sample Isolation Bridge ROIC
## 1 Introduction
First, the sample isolation technology decouples the IA output from the ADC's sampling network, eliminating switching noise and harmonic distortion without relying on additional ripple reduction circuits. Second, the cross-connection IA enhances power efficiency and gains accuracy while minimizing input-referred noise, a crucial feature for low-power implantable applications. Third, the isolation structure leverages intrinsic device matching to mitigate coefficient variations, thereby removing the need for trimming circuits and reducing chip area. Together, these innovations enable a compact, energy-efficient solution that maintains signal integrity and precision, addressing the limitations of conventional Wheatstone bridge-based ROICs in ICP monitoring. This paper is organized as follows. Section 2 introduces the proposed sampling isolation system scheme. Section 3 describes in detail the designed cross-connection IA and sampled-data isolation-based SAR ADC. Section 4 provides measurement results obtained from two different ROIC chips to validate the performance of the proposed structure. The conclusions are presented in Section 5.
###### Abstract  
Aerosol scattering is an important source of error in CO\({}_{2}\) retrievals from satellite. This paper presents an analysis of aerosol information content from the Cloud and Aerosol Polarimetric Imager (CAPI) onboard the Chinese Carbon Dioxide Observation Satellite (TanSat) to be launched in 2016. Based on optimal estimation theory, aerosol information content is quantified from radiance and polarization observed by CAPI in terms of the degrees of freedom for the signal (DFS). A linearized vector radiative transfer model is used with a linearized Mie code to simulate observation and sensitivity (or Jacobians) with respect to aerosol parameters. In satellite nadir mode, the DFS for aerosol optical depth is the largest, but for mode radius, it is only 0.55. Observation geometry is found to affect aerosol DFS based on the aerosol scattering phase function from the comparison between different viewing zenith angles or solar zenith angles. When TanSat is operated in target mode, we note that multi-angle retrieval represented by three along-track measurements provides additional 0.31 DFS on average, mainly from mode radius. When adding another two measurements, the a posteriori error decreases by another 2%-6%. The correlation coefficients between retrieved parameters show that aerosol is strongly correlated with surface reflectance, but multi-angle retrieval can weaken this correlation.  
aerosol; CAPI; DFS; retrieval error +
Footnote †: journal:  
0000-0001-4500-558X][PERSON]
# Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation
## 2 Uncertainty Contributions in the PMU Measurement Chain  
A PMU-based measurement system is comprised of several components, each characterized by specific properties and functions. In this sense, we can identify three main components. The Instrument Transformer (IT) stage converts the voltage and current signals of interest into a range compatible with the PMU analog front-end. The PMU acquires the IT secondary signals and processes them to produce time-stamped estimates of phasor, frequency, and ROCOF. A synchronization source guarantees the time-stamp alignment to the Universal Coordinated Time (UTC).  
Each component contributes to the overall uncertainty of the measurement chain and is regulated by specific standards and performance requirements. From a metrological point of view, it is important to classify the different uncertainty contributions and to define the most suitable tests and methods to rigorously assess them.  
In this context, Figure 1 proposes a possible classification of the uncertainties inherent in a PMU-based measurement system. It is worth noticing that such a classification is neither univocal nor standardized. Still, it takes inspiration from the similar classification employed for Power Quality (PQ) meters in the IEC Std 62586-2 [29]. The so-called intrinsic uncertainty accounts for the PMU performance in nominal conditions, which can usually be deduced from the manufacturer's specifications. The measurement uncertainty considers the contribution of power system variations and is assessed in the IEC Std tests. Finally, the overall uncertainty also considers the contribution of all non-PMU components, such as the synchronization source or the IT class.  
This last component is typically way larger than the others and dominates the overall uncertainty budget. For this reason, in the absence of more specific information, the IT class is traditionally considered a reliability metric of the PMU-based measurement result and used as a weight in WLS or DKF state estimators. Such an approach yields pros and cons. On the one hand, it accounts for the most significant source of uncertainty. On the other hand, though, it relies on _a priori_ and sporadically updated information like the IT class. Consequently, the weight associated with a single measurement result is constant and totally independent of the current operating conditions.
## 2 Simulation Model and Methods
The size of each magnet is 9 mm \(\times\) 13 mm \(\times\) 50 mm, the maximum magnetic field intensity is 1 T, and the magnetization direction is along the direction of \"13 mm\"[22]. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Simulation Parameters** & \\ \hline Hydrogen gas pressure & \(P\) & 0.6 Pa \\ Hydrogen gas temperature & \(T_{g}\) & 1200 K \\ Initial electron density on the emitter surface & \(N_{e}\) & \(5\times 10^{18}\) m\({}^{-3}\) \\ Hydrogen gas density & \(N_{H2}\) & \(4\times 10^{19}\) m\({}^{-3}\) \\ Atom to molecule density ratio & \(N_{H}\)/\(N_{H2}\) & 0.2 \\ Density ratio of vibrationally excited to ground state hydrogen molecules & \(N_{H2[p>3]}\) / \(N_{H2[p=0]}\) & 0.01 \\ Charged particle injection ratios & \(e-:H+:H_{2}+:H_{3}+\) & 1:0:2:0.6:0.2 \\ Electron temperature & \(T_{\rm e}\) & 10 eV \\ Chamber wall potential & \(\Phi\) & 0 V \\ Grid size & \(\Delta x=\Delta y=\Delta z\) & 4 mm \\ Grid number & \(N_{x}\times N_{y}\times N_{z}\) & 50.250 \\ Macro particle number of electron & \(N\) & 2.2\(-\)2.7 \(\times 10^{6}\) \\ CoSm magnet size & \(x^{\prime},y^{\prime},z^{\prime}\) & 9 mm \(\times\) 50 mm \(\times\) 13 mm \\ Extraction chamber size & \(X,Y,Z\) & 18 cm \(\times\) 60 cm \(\times\) 30 cm \\ Timestep & \(\Delta t\) & 0.1 ns \\ Simulation time & \(T\) & 15 \(\mu\)s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters used in code [32; 33; 34]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Collision Type** & **Collision Species** & **Threshold** \\ \hline
1 & Elastic & \(\text{e}+H\rightarrow\text{e}+H\) & 0.01 \\
2 & Ionization & \(\text{e}+H^{*}\rightarrow\text{2e}+H^{+}\) & 13.6 \\
3 & Ionization & \(\text{e}+H^{*}_{2}\rightarrow\text{2e}+H^{+}_{2}\) & 15.42 \\
4 & Dissociative Ionization & \(\text{e}+H^{*}_{2}\rightarrow\text{2e}+H+H^{+}\) & 21.1 \\
5 & Dissociative Recombination & \(\text{e}+H^{*}_{3}\to 3H\) & 0.1 \\
6 & Dissociative Recombination & \(e+H^{+}_{2}\) (\(0\leq v\leq 9\)) \(\to H(1\text{s})+H^{*}(n\geq 2)\) & 0.01 \\
7 & Dissociative Attachment & \(\text{e}+H^{*}_{2}\) (\(v>3\)) \(\to H^{-}+H\) & 0.1 \\
8 & Dissociation & \(\text{e}+H_{2}\) (\(X^{\dagger}T^{+}_{y^{\prime}},\
u^{\prime\prime}=0\)) \(\rightarrow\text{e}+H(1\text{s})+H^{*}(2\text{s})\) & 14.9 \\
9 & Vibrational Excitation & \(e+H_{2}\)(\(\
u=0\))\(\rightarrow\)H\({}_{2}\)(\(\
u=1\)
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 6 Results: Statistical Geographic Performance Validation and Analysis of Resource Requirements
### On-Board Surface Reflection Prioritization  
In theory, all of the signals tracked by the GNSS navigation receiver can be tracked and targeted for surface observations. However, only a smaller subset of reflections are usable due to the GNSS-R instrument's antenna gain surface coverage. Only a subset of the total signals tracked will result in reflections that fall within viable regions of the ground projected antenna footprint(s). This requires an on-board reflection prioritization algorithm to determine the optimal surface reflections to open loop track. A basic prioritization algorithm consists of the following steps.  
1. Determine the surface specular points for a set of GNSS transmitters as described in Section 3. Generate a set of vectors from the GNSS-R satellite to the estimated specular reflection points.
2. Map the vectors from the satellite receiver to the surface specular point into the satellite body frame, using a transformation derived using the satellite attitude matrix.
3. Map the specular point direction in the body frame to an on board map of the GNSS-R instrument science antenna patterns figure of merit. The figure of merit can be simply the antenna gain, or a range corrected antenna gain [28].
4. Rank the measurement points for all the GPS transmitters from highest to lowest figure of merit. Thus, sorting the surface reflections with high antenna gain to the highest ranking.  
Figure 6: Open loop tracking errors in delay and Doppler for PRNs 26 and 21 tracks. (**a**) Differences between predicted and detected reflected code phases. (**b**) Differences between predicted and detected Doppler frequencies.  
Prioritize the reflections based on the highest figure of merit estimates and the available processing resources available within the GNSS-R receiver digital signal processing channels.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 4 Conclusions
Therefore, in all, NNDiffuse and PCA have some extent of advantage in high-resolution image fusion, being better in both spatial information enhancement and spectral preservation. The edges of the cultured floating raft and enclosure seine are clear on the fusion image of these two methods, and both the spatial texture and spectral information preservation are better. For WV-2, PCA is better. This study shows that no image fusion method is suitable for all remote sensing data. The so-called \"best\" fusion method is relative. Different fusion methods should be considered for each data type and image acquisition area. Preferred fusion techniques can perform better in spectral inheritance, spatial resolution, and information diversity. As such, at the same time in practical applications, the time efficiency of the algorithm and the accuracy of the evaluation results are comprehensively considered to select the best evaluation index. Meanwhile, for high-resolution remote sensing image fusion, there is currently no standard for evaluating fusion quality. Conceptualization, W.Z. ; methodology, F.W. ; software, X.W. and F.W. ; validation, F.W., F.T. and J.L. ; formal analysis, X.W. ; investigation, F.T. and J.L. ; resources, W.Z. ; data curation, W.Z. ; writing--original draft preparation, X.W. ; writing--review and editing, W.Z. ; visualization, X.W. ; supervision, W.Z. ; project administration, W.Z. ; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript. This research was financially supported by Shanghai Sailing Program (NO. 19 YF1460000) and National Key Research and Development Project of China (2019 YFD0901405). Not applicable. The authors declare no conflict of interest.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 5 Anomaly-Based Model
### Retransmissions  
The MAC layer starts a timer with a value \(t_{w}\) seconds when node \(i\) sends a data packet to node \(j\), if an acknowledge (ACK) packet from node \(j\) arrives before the timer expiration then a successful delivering of the data has been carried out and no further action of the node \(i\) is needed, if the ACK packet does not arrive during the waiting time then data packet is retransmitted, and the MAC layer starts again a timer with a value \(t_{w}\). According to the IEEE 802.15.4 standard, we set three as the maximum number of retransmissions. The average number of retransmissions for AODV, DSR, and MPH is depicted in Figures 10-12, from that it can be concluded that the use of the average of the number of retransmissions may be another suitable metric for jamming detection. We can observe that for AODV and DSR, most of the nodes of the network increases the number of retransmissions under the presence of a jammer; in a similar way to the other metrics mentioned above, retransmission are taken into account for the anomaly model.  
Figure 10: Average number of packet re-transmissions of the nodes from Figure 3 for AODV (Ad hoc On Demand Distance Vector) under normal (blue) and jamming (red) conditions.  
Figure 9: Average number of retries (MAC algorithm) for the nodes from Figure 3 for MPH (Multi-Parent Hierarchical) under normal (blue) and jamming (red) conditions.
## 3 Methodology  
### Problem Formulation  
Generally, passenger information primarily comprises three main variables: passenger flow, density, and speed. In this paper, the goal of multistep pedestrian flow forecasting is to predict multistep passenger flow in a certain future period based on historical passenger flow.  
**Definition 1** (Pedestrian Flow Network).: _We define a pedestrian flow network as an undirected graph \(G=(V,E)\) and treat sensors as graph nodes, where \(V\) is a finite set of \(\mid V\mid=N\) nodes and \(E\) is a set of \(\mid E\mid=M\) edges. \(N\) and \(M\) represent the number of nodes and edges in the graph topology structure.\(A\in\mathbb{R}^{N\times N}\)is the adjacency matrix of the link set accommodating the structural information of graph \(G\). The variable \(A(1_{i},\;l_{j})\)denotes whether link \(i\) and link \(j\) are connected, i.e., \(A(1_{i},\;l_{j})=1\)indicates \(l_{i}\)and \(l_{j}\)are connected or \(A(1_{i},\;l_{j})=0\), otherwise._Definition 2(Signal Matrix): The observation on the graph \(G\) at the time step \(t\) is regarded as matrix \(X_{t}\)= \(\big{(}x_{t,1},\ x_{t,2},\ \cdots,x_{t,N}\big{)}^{T}\in\mathbb{R}^{N\times C}\), where \(x_{t,v}\in\mathbb{R}^{C}\)represents the feature vector of node \(v\) at time \(t\) and \(C\) is the number of features (a collection of variables, e.g., passenger flow, density, speed).  
Given a spatiotemporal series of historical pedestrian flow signal matrices \(X\in\mathbb{R}^{T_{h}\times N\times C}\) over the past \(T_{h}\) time steps, the goal is to learn a mapping function \(f\) to predict the spatiotemporal series of future signal matrices \(Y=\big{(}X_{t+1},X_{t+2},\cdots,X_{t+T_{p}}\big{)}\in\mathbb{R}^{T_{p}\times N \times C}\) over the next \(T_{p}\) time steps. To accurately align with the long-term historical data and capture repeated patterns in passenger flow, such as peak hours, we aggregate recent-term and long-term historical passenger flow signal matrices. We divide the historical sequence \(T_{h}\) into two time series segments of lengths \(T_{r}\) and \(T_{l}\) along the time axis as the input of the recent-term and long-term, respectively. As illustrated in Figure 1, the segment \(X_{r}=(X_{t-T_{r}+1},X_{t-T_{r}+2},\cdots,X_{t})\in\mathbb{R}^{T_{r}\times N \times C}\) represents the recent-term segment. Additionally, we select data from the past d days in extensive historical records that are closest to the recent-term segment and correspond to the same time slice as the long-term segment \(X_{I}=\big{(}X_{1},X_{2},\cdots,X_{T_{l}}\big{)}\in\mathbb{R}^{T_{l}\times N \times C}\), where \(T_{l}\) is d times \(T_{p}\). As a result, the passenger flow prediction problem can be formulated as:  
\[\big{[}X_{l},X_{r};A\big{]}\overset{f_{0}(\cdot)}{\to}\gamma \tag{1}\]