esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Earth's Radiation Budget (ERB) is a critical component for understanding the planet's climate system, as it governs the balance between incoming solar energy and outgoing thermal radiation. Accurate monitoring of the ERB, combined with Ocean Heat Content (OHC) measurements, is essential to assess Earth's Energy Imbalance (EEI) and its implications for global warming. This paper presents new results on the ERB based on data from the Uvsq-Sat and Inspire-Sat nanosatellite missions, which operated from 2021 to 2024. These satellites constitute the first European constellation demonstrator designed for broadband, Wide Field-Of-View (WFOV) measurements of the ERB. While WFOV instruments provide enhanced temporal and spatial coverage, they do not replace the need for Narrow Field-Of-View (NFOV) measurements, such as those provided by the established Clouds and the Earth's Radiant Energy System (CERES) instruments. Instead, they are designed to complement them. By using data from both the WFOV constellation and CERES instruments to measure Reflected Solar Radiation (RSR) and Outgoing Longwave Radiation (OLR), we estimate the EEI and monitor its evolution. Our analysis reveals a generally good agreement between Uvsq-Sat and CERES data for EEI from 2021 through the end of 2024. Over this period, EEI derived from Uvsq-Sat averaged \(+0.87\pm 0.23\) Wm\({}^{-2}\), closely matching the recent CERES trend. Both datasets indicate a peak in EEI in mid-2023, followed by a decline throughout 2024, likely reflecting stabilizing feedbacks triggered by the 2023 El Nino event. Importantly, this short-term decline occurred within a sustained upward trend in EEI since 2013, as shown by CERES observations, with solar activity having a negligible impact. Comparisons with OHC measurements confirm ongoing ocean heat accumulation, consistent with the rising decadal trend in EEI. These insights underscore the importance of continuous, high-frequency observations to capture the complex and rapidly evolving processes influencing Earth's energy balance. Demonstrations using nanosatellites at different local times illustrate the advantages of small satellite constellations for improved monitoring frequency and coverage, particularly for variables that change over short time scales, such as RSR, also known as Outgoing Shortwave Radiation (OSR).  
Earth's Radiation Budget; Earth's Energy Imbalance; climate change; satellites 2025172751
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 3 Sample Results
### TC-008: Broadband Inteference  
The test aims to verify the robustness of the receiver-to-broadband interference. As shown in the lower left box of Figure 6, there is a small decrease in C/N\({}_{0}\) under interference, but the receiver is still able to re-acquire and deliver nominal position accuracy. The 95% horizontal and vertical errors are 3.16 m and 3.4 m, respectively. The receiver is able to re-acquire the satellites in the presence of interference, as shown in the lower left box.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 3 The Proposed VSS-QR
### VSS-QR Decoder
#### 3.2.1 Color Stacking  
To reconstruct the secret QR code, the decoder stacks the three shares pixel by pixel:  
\[\textbf{T}[i,\ j]=\cap_{\ell=1}^{3}\textbf{S}_{\ell}[i,\ j]. \tag{14}\]  
for \(1\leq i,j\leq LD\). One illustrating result is shown in Figure 3.
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
## 2 Problem Statements  
### Spatial Error in Sound Field Reproduction  
Figure 1 illustrates a typical sound field reproduction system that consists of multiple loudspeakers in an array. The number of the loudspeakers is denoted as \(L\), and the position of the \(l\)th loudspeaker is denoted as \(\overrightarrow{r}_{s}^{(l)}\). The reproduced sound pressure at an arbitrary point in a room \(\overrightarrow{r}\) can be described as:  
\[P\Big{(}\overrightarrow{r};\omega\Big{)}=\sum_{l=1}^{L}H^{(l)}\bigg{(} \overrightarrow{r}\bigg{|}\overrightarrow{r}_{s}^{(l)};\omega\bigg{)}Q^{(l)} (\omega)\ S(\omega), \tag{1}\]  
where \(H^{(l)}\bigg{(}\overrightarrow{r}\bigg{|}\overrightarrow{r}_{s}^{(l)};\omega \bigg{)}\) is the transfer function characterizing sound propagation from the \(l\)th loudspeaker and sound pressure at the point in the room \(\overrightarrow{r},Q^{(l)}(\omega)\) is the filter for the \(l\)th loudspeaker, and \(S(\omega)\) is the input signal. For simplicity, \(S(\omega)\) is assumed to be 1, and the frequency \(\omega\) is omitted in what follows.  
A plane wave or a sound wave generated by a monopole in free field condition is defined as the desired field in many studies because any sound fields can be expressed as the superposition of plane waves or monopoles [5; 6; 7; 9; 10; 11]. In this study, the desired field is defined by a monopole:\[\widetilde{P}_{f}\left(\overset{\rightarrow}{r}\right)=A\frac{e^{ikR_{p}}}{R_{v}}, \tag{2}\]  
where \(A\) is the monopole amplitude, \(R_{v}\) is the distance between the position of the monopole \(\overset{\rightarrow}{r}_{v}\) and a point in the control region \(\overset{\rightarrow}{r}\left(R_{v}=\left|\overset{\rightarrow}{r}- \overset{\rightarrow}{r}_{v}\right|\right)\).  
The filter \(Q^{(l)}\) is obtained by the reproduction methods, such as WFS and HOA. These methods normally model the loudspeakers as monopoles or plane waves in free-field condition. Thus, the transfer function in a free field can be denoted as \(H_{f}^{(l)}\left(\overset{\rightarrow}{r}\right|\overset{\rightarrow}{r} _{s}^{(l)}\right)\). The reproduction methods attempt to make the reproduced field identical to the desired field in the control region \(V_{c}\):  
\[\widetilde{P}_{f}\left(\overset{\rightarrow}{r}\right)\approx P_{f}\left( \overset{\rightarrow}{r}\right)=\sum_{l=1}^{L}H_{f}^{(l)}\left(\overset{ \rightarrow}{r}\right|\overset{\rightarrow}{r}_{s}^{(l)}\right)Q^{(l)}, \quad\overset{\rightarrow}{r}\in V_{c} \tag{3}\]  
The spatially-averaged error \(\widetilde{e}_{f}\) is defined as the error between the desired and the reproduced fields:  
\[\widetilde{e}_{f}{}^{2}=\frac{\int_{V_{\mathrm{C}}}\left|\widetilde{P}_{f} \left(\overset{\rightarrow}{r}\right)-P_{f}\left(\overset{\rightarrow}{r} \right)\right|^{2}dV}{\int_{V_{\mathrm{C}}}\left|\widetilde{P}_{f}\left( \overset{\rightarrow}{r}\right)\right|^{2}dV} \tag{4}\]  
This measure quantifies how similar the reproduced field is to the desired in the space domain in terms of the pressure distribution.
## 3 Results
### The Response of LCE Changes to Different Driving Factors  
#### 3.4.1 Analysis of the Driving Factors Affecting LCE Changes at the Global Dimension  
As shown in Figure 9, the average correlation coefficients between LCE and driving factors (NTL, PD, and GPP) in the study area from 2000 to 2019 were 0.13, 0.21, and \(-\)0.05, respectively. Specifically, the area of positive correlation between LCE and NTL in the GZA was about three times that of negative correlation, accounting for 77.39% and 22.61% of the total area, respectively. The area with a significant positive correlation (0-0.5) accounted for 38.84% of the total area. The negative correlation area (\(-\)0.5\(--\)1) only accounted for 5.31%, which was mainly observed to be distributed in Xi'an and Xianyang. The proportions of positive and negative correlations between LCE and PD were relatively close. The positive correlation area (0-0.5) accounted for 30.68% of the total area, which was mainly found in the urban area of Xi'an, while other areas were scattered in a dotted pattern. The negative correlation area (0\(--\)0.5) occupied 29.19% of the total area, which was mainly distributed in the eastern part of Baoji, the western part of Weinan, and the southern part of Xianyang. The area of positive correlation between LCE and GPP in the study area was slightly smaller than that of negative correlation, accounting for 66.44% and 33.56% of the total area, respectively. In general, the area of positive and negative correlation was more concentrated. The area with a positive correlation (0-0.5) accounted for 51.59% of the total area, mainly distributed in the northwest of Baoji, the middle of Weinan, and the central and western regions. The area with a negative correlation (0\(\sim\)\(-\)0.5) occupied 30.29%, mainly distributed at the junction of southern Xianyang and northern Xi'an, the central and eastern part of Baoji, and the eastern part of Weinan. The analysis showed that the correlation between LCE and the above three factors in the study area has obvious spatial heterogeneity, which may be related to the different responses of LCE to the change of factors.
# Research on the Tooth Surface Integrity of Non-Circular Gear WEDM Based on HPSO Optimization SVR
## 3 Experimentation and Methods
### Measurement and Malculation of Evaluation Indicators  
There are many evaluation indicators for surface integrity. In this research, three indicators that have the most significant impact on surface integrity: surface roughness (SR), surface residual stress (RS), and surface microhardness (MH) were selected as process indicators for the measurements. Considering the symmetry of non-circular gears, this research chose three teeth in the upper half of the gear, measured the selected teeth, in the order of surface roughness, surface residual stress, and surface microhardness, and took the average of the measured results. The instruments used in the experiment are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level Factor**} & **Peak Current** & **Pulse-On Time** & **Pulse-Off Time** & **Tracking** \\  & **(I/A)** & **(I/s)** & **(I/s)** & **(I/s)** & **(HZ/s)** \\  & **A** & **B** & **C** & **D** \\ \hline
1 & 1 & 8 & 5 & 300 \\
2 & 2 & 16 & 6 & 350 \\
3 & 3 & 24 & 7 & 400 \\
4 & 4 & 32 & 8 & 450 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Factors and levels of the orthogonal test.  
Figure 5: Experimental instruments (**(a)** InfiniteFocus G4; **(b)** D8 Advance; **(c)** W1102D37).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trial** & **A** & **B** & **C** & **D** \\ \hline
2 & 1 & 2 & 2 & 2 \\
3 & 1 & 3 & 3 & 3 \\
4 & 1 & 4 & 4 & 4 \\
5 & 2 & 1 & 2 & 3 \\
6 & 2 & 2 & 1 & 4 \\
7 & 2 & 3 & 4 & 1 \\
8 & 2 & 4 & 3 & 2 \\
9 & 3 & 1 & 3 & 4 \\
10 & 3 & 2 & 4 & 3 \\
11 & 3 & 3 & 1 & 2 \\
12 & 3 & 4 & 2 & 1 \\
13 & 4 & 1 & 4 & 2 \\
14 & 4 & 2 & 3 & 1 \\
15 & 4 & 3 & 2 & 4 \\
16 & 4 & 4 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Orthogonal array for the L16 Taguchi design.  
The surface roughness was measured using an automatic zoom three-dimensional surface measuring instrument (InfiniteFocus G4, Alicona, Austria).  
The surface residual stress was measured using X-ray diffraction (XRD, D8 Advance, Bruker, Germany). In order to minimize the measurement error as much as possible, the surface of the sample was cleaned before the test, to remove surface stains.  
The surface microhardness was measured using an automatic microhardness measuring instrument (W1102D37, Buehler, American). The loading load was 9.807 N, and the load holding time was 15 s. The formula of microhardness is shown in Equation (10):  
\[HV=\frac{1854.4P}{d^{2}} \tag{10}\]  
where \(d\) denotes the length of the indentation diagonal (\(\upmu\)m); \(P\) denotes the load applied in the experiment (mN).
# Future of IoT Networks: A Survey
## Future IoT Network
### Network Security
#### 5.5.1 Data Security  
Data security is one of the important issues in the security area. The confidentiality of the IoT data may not be regarded significantly because many IoT data can contain simple data such as temperature, humidity, and others. However, some of the data such as credit data or request data to control IoT devices may indirectly contain the user's private information and affect the user's daily patterns. For instance, when these data are forged or falsified, a malicious attacker can easily control the user's IoT devices. In this situation, a malicious user can easily connect the user's IoT device and obtain information without any restrictions. Furthermore, the attacker can easily change the user's device to use it as an attack by utilizing the user's cheap and disposable IoT device to conduct a DDoS attack [91]. With IoT devices, DDoS attack can harmfully affect the IoT network and services. Consequently, a malicious attempt to control the things of the user can affect the user's life and even the industry.  
One of the simplest solutions to effectively secure data can be to utilize the existing techniques such as Public Key Infrastructure (PKI) or Elliptic Curve Cryptography (ECC) [92; 93]. However, these techniques require enormous amounts of computational power and battery resources. Although current mobile devices may support the security schemes, many IoT devices have limited power and computational resources. Therefore, a light weight encryption algorithm [94] and key management protocol are needed for IoT devices. Additionally, there are some DDoS-preventing algorithms [95], which use null routing or unauthorized packet filtering. However, these solutions cannot defend the reduced throughput because the router must receive the DDoS packet and filter it. Furthermore, data requests from various light weight sensors may lead to similar behavior to a DDoS  
Figure 4: The concept of physical grouping and logical grouping.  
attack in the IoT environment. Specifically, if the user requests tremendous amounts of data from various light weight sensors, even if the data may be temperature or other light density data, receiving these data can cause behavior similar to a DDoS attack for the user. Thus, even if the user has not intended to receive a DDoS attack, similar behavior to a DDoS attack may occur if the data security and control algorithm is not researched.
## 2 Principle
### Principle of BiGRU  
Bidirectional Gated Recurrent Unit (BiGRU) networks are an extension of Gated Recurrent Unit (GRU) networks. GRU simplifies the structure of Long Short-Term Memory (LSTM) networks by merging the input gate and forget gate into a single update gate and  
Figure 2: Principles of three different channel modeling methods. **(a)** The principle of LSTM. **(b)** The principle of GRU. **(c)** The principle of Transformer.  
removing the separate cell state, using only two gates (reset gate and update gate) to control the flow of information.  
BiGRU consists of two GRU networks: one processes the sequence from the beginning to the end (forward direction), while the other processes the sequence from the end to the beginning (backward direction). The outputs of both GRUs are then concatenated, providing a complete context for each time step in the sequence.  
The reset gate determines how much of the previous hidden state \(h_{t-1}\) is used to compute the candidate hidden state \(\tilde{h}_{t}\). It controls the contribution of the past information to the new candidate state:  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]+b_{r}) \tag{6}\]  
The update gate determines how much of the previous hidden state \(h_{t-1}\) is retained and passed to the current hidden state \(h_{t}\). It controls the balance between the old state and the new candidate state:  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]+b_{z}) \tag{7}\]  
The candidate hidden state combines the new input \(x_{t}\) and the previous hidden state \(h_{t-1}\), modified by the reset gate. The reset gate \(r_{t}\) controls the extent to which the previous hidden state is used:  
\[\tilde{h}_{t}=tanh(W\cdot[r_{t}\odot h_{t-1},x_{t}]+b) \tag{8}\]  
The current hidden state ht is a linear interpolation between the previous hidden state \(h_{t-1}\) and the candidate hidden state \(h_{t}\), controlled by the update gate \(z_{t}\):  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\tilde{h}_{t} \tag{9}\]  
By processing sequences in both forward and backward directions, BiGRU networks can capture information from the entire sequence context, leading to a more comprehensive understanding of the data. This capability makes BiGRU particularly effective for tasks requiring an understanding of temporal dependencies, such as VLC channel modeling for DCO-OFDM systems.
## 4 Discussion  
This study highlights the importance of mandibular movement type when measuring condylar guidance angles. Protrusive movements provided more stable values, supporting their use in articulator programming. These findings align with the literature suggesting protrusion as the most reliable reference for sagittal condylar inclination. The use of digital axiography provides enhanced diagnostic capabilities in prosthodontics, reducing variability and supporting individualized treatment planning.  
This study investigated the measurement of condylar guidance angles during protrusion and mouth opening using digital axiography. The findings offer valuable insights into the variations and potential reliability of these measurements, providing a basis for refining clinical approaches to temporomandibular joint (TMI) analysis.  
A significant correlation was observed between the condylar guidance angles measured during protrusion and mouth opening. Notably, the guidance angles obtained during mouth opening exhibited greater deviations from normative values compared to those measured during protrusion. This observation underscores that the type of mandibular movement assessed plays an important role in the precision and reproducibility of condylar guidance angle measurements. Specifically, protrusive movements yielded more stable and less variable results, supporting their potential as a preferred standard for these evaluations.
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 4 Discussion
Although our model demonstrated marked improvements compared to Internals and ResNet34 [74], there remains room for enhancement in garbage detection, Mean Absolute Error and depth accuracy. Third, in the absence of ground truth data, the standards for data annotation need to be established and refined more thoroughly. Finally, the evaluation framework for assessing model accuracy is an area that requires further exploration by experts in the relevant fields. Addressing these limitations will be crucial for advancing road anomaly monitoring technologies and ensuring their effectiveness in practical applications. As urban road anomaly challenges intensify due to climate change and urbanization, future research in flood monitoring and management should focus on several critical areas: (1) Future studies should consider the combination of diverse data sources, including satellite imagery, IoT sensor data, social media reports, and historical records. This multi-modal approach would contribute to enhancing models' performance and provide deeper insights into road anomalies and flood dynamics [15,60]. (2) Research should concentrate on developing explainable AI models, or the combination of models of generative AI and mathematics models that clarify the decision-making processes behind generation [75]. A transparent and logical model inference process will enable stakeholders to better understand and rely on these systems during emergencies. (3) Adaptive learning techniques should be established to allow models to continuously update and improve as new data become available [76]. This capability is essential in a rapidly changing climate, as it enhances the accuracy and robustness of predictions over time [77]. (4) Future systems should incorporate IoT devices and edge computing technologies to strengthen real-time capabilities [19,22].
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 3 Description of the Uvsq-Sat NG Space-Based Mission
### Calibration and Validation--Cal/Val
#### 3.2.3 Uvsq-Sat NG NanoCam for Observing the Earth in the Visible Range  
The camera is used to capture images of the Earth at Nadir and limb. It allows for high-precision geolocation in orbit, necessary for the post-processing of Uvsq-Sat NG data observations. Several Pre-Launch Calibrations of the NanoCam will be necessary for ensuring high-quality images, such as dark current calibration (100 e-/pixel/s at 55 \({}^{\circ}\)C), flat-field calibration, SNR (43 dB maximum), dynamic range (61 dB), geometric calibration, radiometric calibration, white balance calibration, focusing calibration, exposure calibration, alignment calibration, vignetting correction, temporal calibration, resolution calibration, sensor uniformity calibration, or even point spread function (psf) characterization.
## 3 Results
### Relationship between SIF and GPP  
Statistical analysis was conducted to reveal the relationship between SIF\({}_{760}\) and GPP. As shown in Figure 5, we found a significant correlation between SIF\({}_{760}\) and GPP, and their relationship could be characterized by a linear function, regardless of using individual SIF\({}_{760}\) of the four group measurements (GPP = 0.570 \(\times\) SIF\({}_{760}\) + 0.666) or the averaged measurements (GPP = 0.621 \(\times\) SIF\({}_{760}\) + 0.614), which suggests that SIF\({}_{760}\) can be used as a direct proxy for GPP.
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 2 New Orthogonal Transforms
### Full Non-Exponential Matrix  
The matrix \(A(n)\) has a square shape and size \(N\times N\) (\(n=\log_{2}N,\;n=1,2,3,\ldots\)). This form of matrix has a recurrent structure and can be generated by using the below formula:  
\[A(n)=\begin{bmatrix}A_{1}(n-1)&A_{2}(n-1)\\ A_{2}(n-1)&-A_{1}(n-1)\end{bmatrix} \tag{1}\]  
and  
\[A(1)=\begin{bmatrix}a_{1}&a_{2}\\ a_{2}&-a_{1}\end{bmatrix} \tag{2}\]  
A sample matrix for \(n=2\) is presented below:  
\[A(2)=\begin{bmatrix}A_{1}(1)&A_{2}(1)\\ A_{2}(1)&-A_{1}(1)\end{bmatrix}=\left[\begin{array}{cccc}a_{1}&a_{2}&\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
In this way, the basis sequence consisting of \(N\) elements (\(N\geq 4\)) is obtained. Then, the following relations hold true:  
\[A(n)=A^{T}(n)\,\ A(n)\cdot A^{T}(n)=C\cdot I\,\ \frac{1}{C}\cdot A^{T}(n)=A^{-1}(n) \tag{5}\]  
where:  
\(A^{T}(n)\)--transpose matrix \(I\)--unit matrix  
\(A^{-1}\)--inverse matrix  
\(C\)--coefficients \(C=\sum\limits_{i=1}^{N}a_{i}^{2}\)  
Satisfying Condition (4) and following Formula (1) guarantee that the matrix \(A(n)\) is symmetric, i.e., the forward matrix is equal to its transpose: \(A(n)=A^{T}(n)\) and, upon taking into account the coefficient \(C\), orthogonal--its rows (and columns) are orthonormal vectors:  
\[\langle v_{i},v_{j}\rangle=\begin{cases}0,&i\
eq j\\ 1,&i=j\end{cases} \tag{6}\]