esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating BDS-3 Satellite Differential Code Biases with the Single-Frequency Uncombined PPP Model
## 2 Methods
### Conversion of Zero-Mean Conditions  
Currently, the majority of analysis centers utilize the zero-mean condition as the constraint for satellite DCBs [25; 26]. This condition assumes that the sum of all satellite DCBs is equal to zero. However, over longer observation periods, satellites may experience failures or replacements, leading to non-stationary availability of observable satellites for users. Consequently, satellite DCBs computed under the zero-mean constraint are considered as relative values. In situations where comparisons of satellite DCBs are required or when assessing the stability of satellite DCBs over extended time periods, it becomes necessary to convert satellite DCBs computed under different constraint conditions to a common constraint condition. This conversion ensures consistency and facilitates accurate comparisons and analyses of satellite DCBs.  
If a particular GNSS comprises a total of \(n\) satellites in normal operation, the zero-mean condition C1 for all satellites can be expressed as follows:  
\[\sum_{i=1}^{n}DCB_{\text{C1}}^{s}=0 \tag{9}\]  
where \(s\) represents the PRN of the satellite observed by the receiver.  
Assuming the receiver observes or utilizes \(m\) satellites from the satellite navigation system in actual observation, the new zero-mean condition C2 for all the observed satellites can be expressed as:  
\[\sum_{i=1}^{m}DCB_{\text{C2}}^{s}=0 \tag{10}\]  
Considering the two aforementioned zero-mean conditions, there exist \(m\) common satellites. The sum of the satellite DCBs for these \(m\) satellites under condition C1 can be expressed as follows:  
\[\sum_{i=1}^{m}DCB_{\text{C1}}^{s}=d\ (d\
eq 0) \tag{11}\]  
In order to transform the satellite DCBs from condition C1 to condition C2, the process involves subtracting a constant bias \(d/m\) from all satellite DCBs under condition C1. This transformation yields the satellite DCBs under condition C2, which can be expressed as follows:  
\[\left\{\begin{array}{l}\widehat{DCB}_{\text{C1}\to C2}^{s}=DCB_{\text{C1}}^{s} -\frac{d}{m}\\ \sum\limits_{i=1}^{m}\widehat{DCB}_{\text{C1}\to C2}^{s}=0\end{array}\right. \tag{12}\]
# Effects of Illuminance Level of Light Source on White Appearance of a Tablet Display
###### Abstract
explored white points (neutral and preferred) under various lighting conditions, finding that both shifted with ambient chromaticity, with neutral white ranging from 6600 K to 7300 K and preferred white from 5900 K to 6300 K. They also showed that chromatic adaptation is incomplete under lower color temperatures, with greater shifts and lower adaptation in those environments [7; 8]. [PERSON] and [PERSON] examined display white point preferences under five color temperatures, showing that the preferred white points were close to natural ones and shifted with the ambient CCT. Their research also indicated incomplete chromatic adaptation in low color temperature environments and proposed a function to optimize the CAT02 model based on these findings [9; 10]. [PERSON] et al. developed a model predicting the optimal display white point CCT based on ambient lighting CCT [11]. They found that transparent and opaque displays have similar preferred CCTs, which increase linearly with the ambient CCT. The preferred CCT is higher when the ambient CCT is below 5600 K and lower above 5600 K. Their research also highlighted the use of forced-choice experiments to track chromatic adaptation, showing that the white point CCT first increases and then stabilizes when transitioning from 3000 K to 6500 K [12]. Later studies found that the ambient CCT significantly affects display neutral points, with higher CCT environments leading to higher neutral points. The ratio of ambient light to display brightness strongly correlates with neutral point changes [13]. [PERSON] et al. modified a CIE-based model to improve white point predictions and found that the ambient CCT positively correlates with the white point CCT while adapting luminance has a negative correlation [14]. While previous studies have explored the impact of various illuminance levels, CCT, and \(D_{\text{uv}}\) on perceptual whiteness, the effects of a wide range of illuminance levels remain underexplored, especially for lower illuminance. To address this gap, this study aims to investigate a broader range of illuminance levels to better understand their impact on human visual perception of white appearance and contribute to developing a more comprehensive evaluation model. In addition, given the above findings from the related research, appropriate ambient lighting enhances visual perception when using displays. Since visual comfort is influenced by perceived whiteness, it is essential to investigate the effects of various illuminance levels and combinations of CCT on users' perceived whiteness. This research can optimize ambient lighting configurations and improve the overall user experience.
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 3 Results
### Ablation Study  
The ablation experiments are mainly aimed at evaluating the contributions of the multi-scale feature fusion network and the date-time encoding module to the overall performance of the MD-Autoformer model. The results of the ablation experiments are shown in Table 11.  
According to the results of the ablation experiments, in three datasets with different lengths and from different fields, MD-Autoformer, which simultaneously incorporates the multi-scale feature fusion network and the date-encoding module, outperforms other models in terms of the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **t-Statistic** & \(p\)**-Value** & **Mean Diff** \\ \hline Apparel Sales & 57.63 & 0.00000 *** & 0.298 \\ ILI & 47.91 & 0.00000 *** & 0.312 \\ Exchange & 20.70 & 0.00000 *** & 0.252 \\ Weather & 20.95 & 0.00000 *** & 0.047 \\ \hline \end{tabular} Values marked with *** indicate that the \(p\)-value is lower than the critical value \(\alpha\) = 0.01, suggesting that the difference between MD-Autoformer and Autoformer is statistically significant.  
\end{table}
Table 10: Statistical significance analysis of MD–Autoformer vs. Autoformer.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**MD–Autoformer**} & \multicolumn{2}{c}{**Autoformer + MSFFN**} & \multicolumn{2}{c}{**Autoformer + DE**} & \multicolumn{2}{c}{**Autoformer**} \\ \hline
**Metric** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Apparel \\ Sales \\ \end{tabular} } & 24 & 2.165 & 0.761 & 2.773 & 1.114 & 2.189 & 0.781 & 2.851 & 1.256 \\  & 36 & 2.257 & 0.847 & 2.870 & 1.168 & 2.277 & 0.842 & 2.879 & 1.158 \\  & 48 & 2.320 & 0.876 & 2.945 & 1.184 & 2.346 & 0.86 & 2.949 & 1.199 metrics, achieving the optimal performance level of the model. As the prediction step size continuously increases, the impact of the multi-scale feature fusion module on RMSE and MAE becomes more and more significant. This is particularly evident in the Exchange dataset. When the prediction step size is 720, compared with Autoformer, the MSE decreases by approximately 9.73% and the MAE decreases by approximately 11.26%. The effect is more obvious compared to short-term prediction tasks.  
On the one hand, the introduction of date-time encoding enhances the model's ability to capture time-related periodic information in the sequence. On the other hand, the multi-scale feature fusion module can mitigate the prediction errors caused by short-term fluctuations in the original sequence. It also reduces the interference of random components in the original data and decreases extreme errors, thus making the trend of the prediction results closer to the actual values.
## 3 Results
### Poultry Litter and Its Biochar on Jatrophola curcas L. Morphological Properties
#### 3.6.3 Number of Leaves  
From week 1 to week 6, the statistical analysis results revealed that the average number of leaves increased significantly with the application rate in both treatments (Figure 4). All treatments produced significantly higher number of leaves than the control plants. Plants grown in soil treated with PL, BC350, and PLBC600 showed a statistical difference (\(p\) < 0.05) from week 2 to week 6; however, no statistical difference (\(p\) > 0.05) was observed for any of the treatments during week 1. Plants grown in PLBC600 soil had the most leaves (18), followed by PL (14), BC350 (13), and PLBC350 (12) (Figure 4).  
Figure 4: Effects of poultry litter and its biochar on the Jatropha number of leaves at different application rates (g/kg) of 0, 15, 30, 60, and 120 g/kg from week 1 to week 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Application Rate (g/kg)** & **PL** & **BC350** & **BC600** & **PLBC350** & **PLBC600** \\ \hline \multicolumn{6}{c}{**8 Weeks**} \\
0 & 8.20 \(\pm\) 0.81 b & 9.17 \(\pm\) 0.64 a & 9.4 \(\pm\) 0.36 a & 8.87 \(\pm\) 0.50 ab & 9.13 \(\pm\) 0.74 a \\
15 & 11.30 \(\pm\) 0.62 ab & 9.77 \(\pm\) 0.37 a & 9.00 \(\pm\) 0.66 a & 10.63 \(\pm\) 0.92 a & 10.43 \(\pm\) 0.97 a \\
30 & 11.80 \(\pm\) 0.12 a & 9.90 \(\pm\) 0.45 a & 9.17 \(\pm\) 0.99 a & 10.60 \(\pm\) 0.76 a & 9.50 \(\pm\) 0.72 a \\
60 & 10.37 \(\pm\) 0.90 ab & 10.77 \(\pm\) 0.13 a & 7.93 \(\pm\) 0.43 a & 9.80 \(\pm\) 0.72 a & 11.40 \(\pm\) 0.65 a \\
120 & 6.47 \(\pm\) 0.61 b & 10.80 \(\pm\) 0.15 a & 8.40 \(\pm\) 0.59 a & 6.23 \(\pm\) 0.41 ab & 8.17 \(\pm\) 1.24 a \\ \(p\)-value & 0.01 & 0.07 & 0.51 & 0.01 & 0.18 \\ \hline \multicolumn{6}{c}{**48 Weeks**} \\
0 & 14.13 \(\pm\) 0.70 a & 15.17 \(\pm\) 0.42 b & 9.47 \(\pm\) 0.42 a & 13.00 \(\pm\) 0.90 b & 14.67 \(\pm\) 1.16 a \\
15 & 18.03 \(\pm\) 0.77 a & 15.80 \(\pm\) 0.51 b & 9.07 \(\pm\) 1.68 a & 16.33 \(\pm\) 0.38 b & 17.17 \(\pm\) 0.32 a \\
30 & 19.57 \(\pm\) 0.26 a & 17.67 \(\pm\) 0.38 a & 9.67 \(\pm\) 1.45 a & 16.90 \(\pm\) 0.55 a & 15.93 \(\pm\) 0.71 a \\
60 & 19.57 \(\pm\) 1.02 a & 18.60 \(\pm\) 0.42 a & 8.10 \(\pm\) 0.87 a & 16.97 \(\pm\) 0.97 a & 18.57 \(\pm\) 0.34 a \\
120 & 19.57 \(\pm\) 3.07 a & 19.13 \(\pm\) 0.22 a & 10.50 \(\pm\) 0.72 a & 10.97 \(\pm\) 2.34 a & 14.03 \(\pm\) 1.76 a \\ \(p\)-value & 0.04 & 0.01 & 0.67 & 0.02 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of poultry litter and its biochar on Jatropha stem diameter (mm) at different application rates.
## 5 Simulation Results and Discussion
### IEEE 39-Bus Case Study
This demonstrates the high degree of accuracy and the capability of the proposed method in effectively determining the optimal and secure operational point of the system when confronted with cyber-physical attacks. Table 2 provides a comparison of PM's performance with previous SOTA research in terms of total generation cost, total speed deviation and voltage deviation under various  
Figure 16: Voltage deviation of IEEE 39\(-\)Bus for scenario of line outage between bus 4 and 14. Figure 17: Output power and rotor angle generators in IEEE 39\(-\)Bus (for PM condition) for scenario of line outage between bus 4 and 14.  
scenarios involving cyber-physical attacks. It is evident from this table that PM outperforms previous methods significantly in identifying the secure and optimal generation point of the system following disturbances. This superiority is observed in terms of both accuracy and speed. Moreover, the table highlights that PM consistently achieves the lowest total generation cost across all scenarios. Additionally, when it comes to effectively dampening system oscillations with the highest speed and best accuracy, PM also stands out as the most capable approach among the considered methods.
## 3 Materials and Methods
### Model Architecture
#### (2) Adaptive Gating Fusion Layer (Global Integration)  
The second tier introduces an adaptive gating network to dynamically weight attention-refined features. Using the gating coefficient g (computed via Section 3.2.3), it balances text/image reliability (Equation (16)):  
\[V_{fused}=g\cdot Attn_{i}+(1-g)\cdot Attn_{t} \tag{16}\]  
Figure 7: CLIP-BCA-Gated framework structure.  
This global integration optimizes modality weights (e.g., prioritizing text for blurry images or imagery for ambiguous text).  
(3) Hierarchical Fusion: From Local to Global To further integrate pre-trained CLIP knowledge, global features \(\mathbf{f}_{\text{text}},\mathbf{f}_{\text{image}}\in\mathbb{R}^{512}\) (raw CLIP embeddings) are concatenated with \(V_{\text{fused}}\), forming a 1536-dimensional vector. A trainable projection layer refines this to 512 dimensions:  
\[V_{final}=\sigma\Big{(}W_{proj}\cdot\Big{[}V_{fused};v_{i};v_{i}\Big{]}+b_{ proj}\]  
Here, \(\sigma\) (ReLU) and learnable \(W_{proj}\), \(b_{proj}\) suppress noise and enhance discriminative interactions.  
(4) Classification Layer: Crisis Category Prediction The refined feature \(V_{\text{final}}\) is fed into a linear classifier with a softmax output to produce multiclass predictions (Equation (17)).  
\[y=softmax\Big{(}W_{c}\cdot V_{final}+b_{c}\Big{)} \tag{17}\]  
Here, \(W_{c}\in\mathbb{R}^{C\times 512}\) (C: number of crisis categories) and \(b_{c}\in\mathbb{R}^{C}\) are learnable parameters, adapting to crisis-specific boundaries (e.g., infrastructure damage vs. rescue efforts).  
By separating local interaction (bidirectional attention) and global weighting (adaptive gating), the framework balances fine-grained alignment (e.g., \"flood\" text \(\leftrightarrow\) water imagery) and modality reliability optimization (e.g., prioritizing clear text over blurry images). Integrating raw CLIP features further injects pre-trained semantic priors (e.g., \"earthquake\" universal semantics), enhancing generalization to noisy social media data.  
As shown in Figure 7, this layered architecture--grounded in local interaction and global integration principles--establishes a robust pipeline for crisis multimodal classification. It addresses both the semantic complexity (e.g., diverse crisis-related concepts) and noise challenges (e.g., distorted imagery, typo-ridden text) inherent in social media data.
## 2 Complex Network Analysis Applied to Subway Networks
### Robustness  
Robustness or resilience of a network can be defined as its ability to resist random failures or deliberate attacks and its capacity to solve them and return to a state of equilibrium [15; 28]. There are several metrics to evaluate the robustness of a network: robustness indicator, effective resistance, effective conductance, average efficiency, algebraic connectivity, etc. (see [29]). Of special interest in this type of studies are the modularity and the assortativity correlation that are described as follows.  
It is important to analyze the community structure of the networks in order to identify cascading impacts resulting from disruptions [4]. A _community_ is a subgraph of a network in such a way that within the nodes there is high probability of being connected to each other but between communities there are fewer links [30]. The computation of communities involves processes with a high computational load; several detection and partitioning schemes have been proposed: CNM scheme, BGLL scheme, etc. [31] A commonly used metric for evaluating the quality of the network partition into communities is given by a numerical index called _modularity_[30; 32; 33].  
Suppose the network is partitioned into \(g\) communities, and let \(\epsilon_{ij}\) be the fraction of edges in the original network that connect vertices between \(i\)-th community and \(j\)-th community. The _modularity index_ is defined as:  
\[Q=\sum_{1\leq i<j\leq g}\epsilon_{ij}-\sum_{1\leq i<j<k\leq g}\epsilon_{ij} \epsilon_{ki},\quad 0\leq Q\leq 1. \tag{14}\]  
The greater \(Q\) the stronger the connection within the communities is.  
The _assortativity correlation_ studies to what extent nodes link to nodes of similar or dissimilar nodal degree. Depending on the degree correlation, a network can be _assortative_, when nodes with height (resp. low-degree) connect with high (resp. low-degree) nodes, or _disassortive_, when connectionshappen between high and low degree nodes. The assortativity correlation can be measured by means of the Pearson correlation coefficient [34] as follows:  
\[r=\frac{M^{-1}\sum_{i_{j}\in E}k_{i}k_{j}-\left[M^{-1}\sum_{i_{j}\in E}\frac{k_{ i}+k_{j}}{2}\right]^{2}}{M^{-1}\sum_{i_{j}\in E}\frac{k_{i}^{2}+k_{j}^{2}}{2}- \left[M^{-1}\sum_{i_{j}\in E}\frac{k_{i}+k_{j}}{2}\right]^{2}}, \tag{15}\]  
where \(k_{i}\) and \(k_{j}\) are the degrees of the nodes connected by the edge \(e_{ij}\) and \(M\) is the total number of edges. This coefficient ranges from \(-1\) (disassortative network) to 1 (assortative network). It is important to note that in assortative networks failures of high-degree stations will have little impact since other high-degree stations are still connected to other high-degree stations [35].
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 3 Materials and Methods
### Preprocessed Signal Transformation  
After removing noise, the next step is to convert preprocessed 1-D multi-channel signals into 2-D spectrogram images using the short-time Fourier transform (STFT) method. The Short-time Fourier transform (STFT) is a time-frequency analysis used to analyze non-stationary signals. It derives from the discrete Fourier transform (DFT). The STFT method divides a long-time signal into segments with the same size as the window, and the Fourier transform is applied to each segment [57]. Figure 5 shows the spectrogram of random samples after applying STFT. The discrete STFT \(X\) of the signal \(x\) is given by Equation (5) [56]:  
\[X(m,k)=\sum_{n=0}^{N-1}x(n+mH)w(n)\text{exp}\left(\frac{-2\pi ikn}{N}\right) \tag{5}\]  
\[X(k)=\kappa^{\text{m}}\left(\frac{k}{N}\right)\sum_{n=0}^{N-1}x(n)\text{exp}(-2 \pi ikn) \tag{6}\]  
with \(m\in Z\) and \(k\in\) [0:K]. The frequency index is \(K=N/2\), corresponding to Nyquist frequencies (assuming \(N\) is even). \(X(m,k)\) is the complex number that denotes the kth Fourier coefficient for the \(m\)th time frame. For each fixed time frame m, one obtains a spectral vector of size \(K+1\) given by the coefficients \(X(m,k)\) for \(k\in\) [0:K]. The computation of each such spectral vector amounts to a DFT of size \(N\) as in Equation (6), which can be done efficiently using the FFT.  
Figure 5: Spectrogram of random signals after applying STFT.  
The ECG signal is sampled at a rate of around 200 to 1000 samples per second. If we consider a typical duration of 10 s and a sampling rate of 250 samples per second, the size of the ECG signal would be approximately 2500 data points (samples). In the described methodology, spectrograms were created from these data points through the utilization of the Short Time Fourier Transform (STFT). The resultant spectrograms are represented in RGB format and have dimensions of 512 \(\times\) 512 \(\times\) 3. The spectrogram's power values are encoded into an RGB color image, thereby accentuating the contrasts in one version. This approach aims to visually highlight the variations and power distribution of the signals in a graphical representation, facilitating the identification and understanding of patterns and features in the ECG data.
# Evolution of China's Building Energy Service Industry Based on Synergetic Theory
## 4 Order Parameter of BESI System
### Synergetics in BESI
#### 4.1.2 Synergy between Subsystems  
Synergies between subsystems are the co-evolution of mutual benefit symbiosis between subsystems by means of cooperation and interaction. This can be expressed as an interdependent and mutually beneficial relationship between subsystems. As mentioned above, the dominant subject can guide the whole system. The synergy between subsystems we discussed here mainly refers to the synergy between the core operating system and other subsystems.  
1. Offering of building energy service  
The core operating system offers the building energy service cooperation and interaction between ESCOs, owners, and other stakeholders. Other subsystems interact with the core operating system by means of cooperation. The production factor subsystem provides resources such as talent, technology, material, and equipment for the core operation subsystem. The core operating sub-system acts as the demander in the whole system, providing market-oriented resources, such as customers, for the production factor subsystem. The synergy between these two subsystems can enhance production efficiency and reduce costs, which is beneficial to both parties. This type of synergy can be regarded as a mutually beneficial synergy.  
2. Operation of BESI system  
A supportive relationship exists between the core operating and industry-standard subsystems. The industry standard subsystem guarantees the building energy service with the assistance of codes of practice, contract regulations, and other specifications and guidelines. These industry standards are crucial for the stable and healthy development of the BESI, as they protect the interests of the involved stakeholders. The practical experience of stakeholders can, in turn, provide a basis for the industry standard. This interrelationship can also be treated as a mutually beneficial synergy for a collaborative evolution.  
3. Driving forces of BESI system  
The core operation subsystem cannot develop without being driven by an external environment. On the other hand, the external environment gradually becomes complete because of the involvement of the core operating subsystem, thereby promoting other industrial development. For the BESI system, this mutually beneficial form of cooperation is an important pathway towards the development of subsystems.  
Cooperation between different sub-systems can include joint research and development with respect to dealing with technical problems, the sharing of advanced technology, channels of funding support, and the mutual exploitation of resources to compensate deficiencies. Such cooperation can promote the sharing of resources, thereby expanding the BESI. Based on the abovementioned cooperation between subsystems, the synergy formed can maximize the utilization efficiency of resources.