esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison Study with DNN  
To verify the superiority of the proposed framework, we adopt the plain DNN for comparison study. DNN is a typical deep learning network and has been employed in data anomaly detection with high accuracy [59]. During the training process, the model parameters of the DNN are firstly adjusted to ensure the best performance. Then, five subsets are adopted to train and test the model. In this part, we use F\({}_{1}\)-score on the testing set and Time/Epoch as indicators to compare the performance of the proposed model and the DNN, as shown in Figure 12.  
It can be observed that whether training with balanced or imbalanced sets, the proposed CNN-based method has higher detection accuracy and shorter training time compared with the DNN model. To quantitatively analyze the superiority of the proposed model, a performance improvement index (_PDI_) is established as follows.  
\[PDI=\big{|}I_{proposed}-I_{DNN}\big{|}/I_{DNN} \tag{10}\]  
where \(I_{proposed}\) and \(I_{DNN}\) represent the evaluation indicators of the proposed model and DNN model, respectively.  
According to Equation (10), the improvements in accuracy and cost of the proposed model are calculated in Table 6. Compared with the DNN model, the F\({}_{1}\)-score of the proposed model increases up to 3.4 times, and the training time is reduced by 0.6. It reflects the powerful spatial feature extraction capability of CNN. By contrast, the performance of the DNN model is poor. It can hardly detect slab arching, especially for the cases of using imbalanced sets. It is because the arching features included in track irregularities are very tiny and similar to normal waveforms. However, DNN changes the  
Figure 12: Detection performance comparison of the proposed model and the deep neural network (DNN) model. (**a**) F\({}_{1}\)-score on the testing set; (**b**) Time/Epoch.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Subset**} & \multicolumn{4}{c}{**Validation Set**} & \multicolumn{4}{c}{**Testing Set**} \\ \cline{2-9}  & **TP** & **TN** & **FP** & **FN** & **TP** & **TN** & **FP** & **FN** \\ \hline
1 & 473 & 977 & 15 & 23 & 5349 & 5863 & 85 & 599 \\
2 & 962 & 970 & 22 & 30 & 5823 & 5847 & 101 & 125 \\
3 & 477 & 1472 & 16 & 19 & 5693 & 5884 & 64 & 255 \\
4 & 262 & 1680 & 20 & 22 & 5467 & 5889 & 59 & 481 \\
5 & 956 & 2948 & 26 & 36 & 5741 & 5904 & 44 & 207 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection results of the validation set and testing set (one of five runs).  
original positional relations between pixels by transforming the two-dimensional matrix (image) into a one-dimensional vector, which may make it difficult to learn sufficient characteristics. Moreover, all results of the balanced set are better than that of the imbalanced sets, again proving the advantages of the balanced training set.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 4 Likelihood Derivation
### Doppler and Angle-of-Arrival Likelihood Derivation  
The Doppler likelihood of a single measurement \(k\) is derived from the probability density function of the Gaussian distribution with a standard deviation of \(\sigma_{\text{d}}\) as follows,  
\[g_{1}(f_{\text{d}}{}^{(k)}|\mathbf{x})=\mathcal{N}(f^{(k)}_{\text{d}};\mu^{(k )}_{\text{d}}(\mathbf{x}),\sigma^{2}_{\text{d}}), \tag{8}\]  
where \(\mu^{(k)}_{\text{d}}(.)\) is the true Doppler. Next, we obtain the AoA likelihood function from the probability distribution function (PDF) of the Gaussian estimated Kent distribution assuming that the errors in the azimuth and off-nadir are independent,  
\[g_{2}(\Phi^{(k)},\Theta^{(k)}|\mathbf{x}) =\mathcal{N}\bigg{(}\Phi^{(k)};\mu^{(k)}_{\Phi}(\mathbf{x}),\frac {1}{\kappa-2\beta}\bigg{)}\] \[\mathcal{N}\bigg{(}\Theta^{(k)};\mu^{(k)}_{\Theta}(\mathbf{x}), \frac{1}{\kappa+2\beta}\bigg{)}, \tag{9}\]  
where \(\mu^{(k)}_{\Phi}\) and \(\mu^{(k)}_{\Theta}\) are the true azimuth and the true off-nadir angle of arrival, respectively.
## 2 Materials and Methods
### Intra-Treatment Rectum Tumor Volume Change
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Patient**} & **GTV** & **CBCT** & **CBCT** & **CBCT** & **CBCT** & **CBCT** & **Total** & **Patient** & **No. of** & **Dose per** & **Patient** \\  & **(cm\({}^{3}\))** & **Week 2** & **Week 3** & **Week 4** & **Week 5** & **Dose** & **Age** & **Gender** & **Treatment** & **Fraction** & **Weight** \\  & **(cm\({}^{3}\))** & **(cm\({}^{3}\))** & **(cm\({}^{3}\))** & **(cm\({}^{3}\))** & **(cm\({}^{3}\))** & **(cm\({}^{3}\))** & **Gy** & **(Vears)** & **Gender** & **Fractions** & **(Gy)** & **(Gy)** \\ \hline Patient 1 & 89.9 & 87.07 & 74.97 & 49.35 & 55.3 & 64.79 & 50 & 71 & Female & 28 & 1.8 & 45 \\ Patient 2 & 47.6 & 48.62 & 39.39 & 40.37 & 40.65 & 40.46 & 50 & 60 & Male & 25 & 2 & 84 \\ Patient 3 & 57.21 & 51.498 & 43.099 & 40.199 & 40.698 & 45.188 & 50 & 58 & Female & 28 & 1.8 & 87 \\ Patient 4 & 41.42 & 34.157 & 37.807 & 35.105 & 35.437 & 31.321 & 50 & 45 & Female & 28 & 1.8 & 69 \\ Patient 5 & 90.66 & 89.871 & 93.785 & 92.89 & 90.643 & 87.908 & 50 & 53 & Female & 28 & 1.8 & 50 \\ Patient 6 & 227 & 223.25 & 232.55 & 247.72 & 228.54 & 227.98 & 50 & 50 & Male & 28 & 1.8 & 77 \\ Patient 7 & 40.86 & 36.986 & 38.564 & 37.654 & 39.876 & 34.874 & 50 & 67 & Female & 25 & 2 & 70 \\ Patient 8 & 68.03 & 61.985 & 63.986 & 64.963 & 67.734 & 67.097 & 50 & 50 & Male & 28 & 1.8 & - \\ Patient 9 & 105 & 102.95 & 101.79 & 104.88 & 106.78 & 102.65 & 50 & 74 & Male & 25 & 2 & - \\ Patient 10 & 51.86 & 35.482 & 36.725 & 38.125 & 40.228 & 31.245 & 50 & 75 & Male & 28 & 1.8 & 60 \\ Patient 11 & 32.06 & 46.151 & 33.645 & 37.559 & 44.052 & 38.312 & 50 & 56 & Male & 25 & 2 & 72 \\ Patient 12 & 47.45 & 38.38 & 42.522 & 45.725 & 41.711 & 48.162 & 50 & 56 & Male & 28 & 1.8 & - \\ Patient 13 & 35.9 & 34.486 & 32.686 & 33.512 & 28.759 & 29.051 & 59 & 59 & Male & 33 & 1.8 & 50 \\ Patient 14 & 67.48 & 36.735 & 82.028 & 103 & 96.808 & 96.957 & 45 & 39 & Female & 25 & 1.8 & - \\ Patient 15 & 89.92 & 78.076 & 74.978 & 68.008 & 65.305 & 64.289 & 45 & 71 & Female & 25 & 1.8 & 42 \\ Patient 16 & 68.87 & 67.142 & 57.852 & 88.589 & 57.852 & 57.895 & 50 & 37 & Male & 25 & 1.8 & 60 \\ Patient 17 & 64.85 & 63.481 & 53.649 & 61.882 & 54.191 & 51.232 & 50 & 59 & Male & 28 & 1.8 & 76 \\ Patient 18 & 74.34 & 69.04 & 42.188 & 43.357 & 29.12 & 32.043 & 50 & 68 & Male & 28 & 1.8 & 83 \\ Patient 19 & 56.46 & 61.975 & 59.67 & 58.91 & 58.569 & 60.864 & 45 & 38 & Female & 25 & 1.8 & 56 \\ Patient 20 & 74.98 & 80.324 & 78.983 & 77.973 & 73.848 & 74.868 & 50 & 71 & Male & 25 & 2 & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Patient data collected from King Abdulaziz University Hospital, Radiotherapy Department.
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 3 Results
### Summary of This Chapter  
In this study, CT scanning technology was used to obtain slice images of moldy corn grains. Structural models of corn grains at each stage were reconstructed to visually analyze the characteristic changes caused by mold infection. Additionally, a deep learning network model was integrated to facilitate the rapid detection of corn grain mildew. Compared with traditional methods such as dry slice culture, sensory detection, and microbial activity determination, the rapid detection method for identifying corn grain mildew defects, which integrated CT scanning technology and deep learning, could swiftly and accurately detect corn grain mildew in the early stages. Furthermore, it allowed for the non-destructive observation of internal structural changes in corn kernels following mold infection.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### Diffusion Coefficient Independent of the Absorption Coefficient in TD-DE  
The diffusion coefficient is given as \(D(\mathbf{r})=1/[3(\mu_{s}{}^{\prime}(\mathbf{r})+\mu_{a}(\mathbf{r}))]\) in the process of deriving the DE in the framework of the \(P_{1}\) approximation stated above. However, there was a long controversy about the expression of the diffusion coefficient and whether it depends on \(\mu_{a}\) or not. [PERSON] and [PERSON] [8] first discussed that in time-domain, \(D(\mathbf{r})\) is independent of \(\mu_{a}(\mathbf{r})\), i.e., \(D(\mathbf{r})=1/[3\mu_{s}{}^{\prime}(\mathbf{r})]\), while in the CW-domain \(D(\mathbf{r})\) may depend on \(\mu_{a}(\mathbf{r})\). For optically homogeneous media, the radiance in the RTE for an impulse source can be written as,  
\[I(\mathbf{r},\hat{s},t)=I_{0}(\mathbf{r},\hat{s},t)\exp(-\mu_{a}ct), \tag{8}\]  
where \(I_{0}(\mathbf{r},\hat{s},t)\) is the radiance for non-absorbing medium without absorption, Equation (9),  
\[\frac{\partial}{c\partial t}I_{0}(\mathbf{r},\hat{s},t)+\hat{s}\cdot\
abla I_ {0}(\mathbf{r},\hat{s},t)+\mu_{s}I_{0}(\mathbf{r},\hat{s},t)=\mu_{s}\int_{4 \pi}p(\hat{s},\hat{s}t)I_{0}(r,\hat{s},t)d\Omega. \tag{9}\]  
This is easily understood by substituting Equation (8) into Equation (1) neglecting the impulse source term, and Equation (8) is sometimes referred to as expressing the microscopic Beer-Lambert law [9]. The process of the \(P_{1}\) approximation described above can be applied to Equation (9), then the diffusion coefficient is clearly given as \(D=1/(3\mu_{s}{}^{\prime})\) independent of \(\mu_{a}\). For inhomogeneous media, the derivation of \(D(\mathbf{r})=1/[3\mu_{s}{}^{\prime}(\mathbf{r})]\) is not straightforward like for homogenous media, but [PERSON] and [PERSON] [8] proved it mathematically.  
Then, the controversy about the diffusion coefficient started regarding whether it should be included \(\mu_{a}\) or not [10; 11; 12]. But experimental and numerical studies supported \(D(\mathbf{r})=1/[3\mu_{s}{}^{\prime}(\mathbf{r})]\)[13; 14; 15], and mathematical studies using processes different from the \(P_{1}\) approximation derived \(D(\mathbf{r})\) independent of \(\mu_{a}\) for the TD-DE and \(D(\mathbf{r})\) dependent on \(\mu_{a}\) for the CW-DE [16; 17]. Finally, the following expressions are likely to be accepted in this field,  
\[D(\mathbf{r})=\frac{1}{3\mu_{s}{}^{\prime}(\mathbf{r})}\text{ for the TD }-\text{DE}, \tag{10}\]  
\[D(\mathbf{r})=\frac{1}{3[\mu_{s}{}^{\prime}(\mathbf{r})+\alpha\mu_{a}( \mathbf{r})]}\text{ }\alpha\text{ }=\text{ 0.2}\text{-0.8 for the CW}-\text{DE}. \tag{11}\]  
The absorption coefficient, \(\mu_{a}\), of biological tissue is much smaller than \(\mu_{s}{}^{\prime}\) in the NIR range (typically \(\mu_{a}\sim\)0.01 mm\({}^{-1}\) and \(\mu_{s}{}^{\prime}\sim\)1.0 mm\({}^{-1}\)), and the change in the magnitude of \(D\) upon including \(\mu_{a}\) is very small. Therefore, \(D(\mathbf{r})=1/[3\mu_{s}{}^{\prime}(\mathbf{r})]\) is a good choice for all cases.
# Video Temporal Grounding with Multi-Model Collaborative Learning
###### Abstract  
Given an untrimmed video and a natural language query, the video temporal grounding task aims to accurately locate the target segment within the video. Functioning as a critical conduit between computer vision and natural language processing, this task holds profound importance in advancing video comprehension. Current research predominantly centers on enhancing the performance of individual models, thereby overlooking the extensive possibilities afforded by multi-model synergy. While knowledge flow methods have been adopted for multi-model and cross-modal collaborative learning, several critical concerns persist, including the unidirectional transfer of knowledge, low-quality pseudo-label generation, and gradient conflicts inherent in cooperative training. To address these issues, this research proposes a Multi-Model Collaborative Learning (MMCL) framework. By incorporating a bidirectional knowledge transfer paradigm, the MMCL framework empowers models to engage in collaborative learning through the interchange of pseudo-labels. Concurrently, the mechanism for generating pseudo-labels is optimized using the CLIP model's prior knowledge, bolstering both the accuracy and coherence of these labels while efficiently discarding extraneous temporal fragments. The framework also integrates an iterative training algorithm for multi-model collaboration, mitigating gradient conflicts through alternate optimization and achieving a dynamic balance between collaborative and independent learning. Empirical evaluations across multiple benchmark datasets indicate that the MMCL framework markedly elevates the performance of video temporal grounding models, exceeding existing state-of-the-art approaches in terms of mIoU and Rank@1. Concurrently, the framework accommodates both homogeneous and heterogeneous model configurations, demonstrating its broad versatility and adaptability. This investigation furnishes an effective avenue for multi-model collaborative learning in video temporal grounding, bolstering efficient knowledge dissemination and charting novel pathways in the domain of video comprehension.  
video temporal grounding; collaborative learning; pseudo-label; iterative training 2025 15 3072
## 4 Simulation Results
### Case IV  
In the fourth case, we assume that PMUs are installed at all load buses. In addition, several loads change is considered. The loads at bus #3, #16, and #20 are gradually increased by 15% from the time instant 0.5 min to 1.5 min of the simulation.  
Figure 9 shows the traces of the selected pilot buses (bus #23 and #26 in this case) and \(VSM\) during the simulation of case IV. Since insufficient \(VSM\) (\(VSM=0.26<0.3\)) is detected at \(t=1.5\) min, the control strategy will be activated accordingly. Thus, the voltage levels and voltage stability margin are considerably enhanced after ASVC. This can be regarded clearly in Figure 9.  
Table 2 summarizes the test results for the above three cases. An inspection from the shown table indicates that both voltage profile and static voltage stability margin are greatly improved after applying the proposed control scheme.  
Figure 8: Simulation result of case III. (**a**) The trace of \(|V_{24}|\); (**b**) The trace of \(|V_{29}|\); (**c**) The trace of VSM.  
Figure 9: Simulation result of case IV. (**a**) The trace of \(|V_{23}|\); (**b**) The trace of \(|V_{26}|\); (**c**) The trace of VSM.
# Establishing a Geological Knowledge Base for Braided River Deltas Using Google Earth
## 2 Materials and Methods  
### Study Area  
The data in this study are derived from eight typical braided river delta systems worldwide: Wax Lake, Yukon-Kuskokwim delta, Lena River, Po River, Poyang Lake, Orinoco, Rufiji River, and Saloum delta.  
The formation of braided river deltas is a complex geological process governed by the interplay of fluvial processes, marine (or lacustrine) dynamics, wind forces, biological activities, and the sediment supply [8]. When rivers carrying abundant sediments reach the basin margin, sediment deposition initiates due to the combined effects of reduced flow energy, marine/lacustrine backwater, and wind-induced sediment redistribution [15]. In the initial development stage, frequent channel bifurcation and avulsion create intricate channel networks, during which bioturbation from benthic organisms may also modify sedimentary structures. As sediment accumulates over time, the delta progrades towards the basin center, sequentially developing the delta plain, delta front, and prodelta sub-facies.  
Several factors significantly determine the sedimentation rate of braided river deltas. The sediment supply remains a primary control, encompassing not only fluvial-derived clastics but also aeolian sediments in some regions. For example, the Yukon-Kuskokwim delta sustains continuous sedimentation not only from substantial terrigenous inputs but also from wind-transported sands [29]. Hydrodynamic conditions play a crucial role, with strong fluvial currents enhancing sediment transport and deposition through lateral erosion and vertical aggradation [1]. Marine processes, including waves, tides, and extreme events like storm surges, can rework deltaic sediments, altering deposition rates and patterns. Topography also impacts sedimentation; steeper slopes facilitate rapid sediment accumulation, while gentle slopes promote sediment dispersion. Additionally, local bathymetric variations can guide sediment distribution. Tectonic activities indirectly regulate sedimentation rates by controlling basin subsidence. Rapid subsidence in tectonically active zones provides more accommodation space, enabling higher sedimentation rates. Moreover, changes in the regional stress field can induce channel migration and avulsion, further influencing the depositional process.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Proposed Approach
#### 3.1.2 Global Information Enhancement Module  
The GIEM is designed to comprehensively enhance global feature representations by leveraging three attention mechanisms: Pixel Attention, Channel Attention, and Simple Pixel Attention. These mechanisms work collaboratively to strengthen global contextual information in the extracted features. Specifically, Pixel Attention [39] emphasizes spatially salient regions, Channel Attention [40] highlights important feature channels, and Simple Pixel Attention provides an efficient approach to refine pixel-level features with minimal complexity. The overall structure of the module is shown in Figure 3, and the detailed structures of the three attention mechanisms are illustrated in Figure 4.  
To further optimize the module, Pointwise Convolution (PWConv) [41] is employed. By performing \(1\times 1\) convolutions, PWConv effectively reduces the computational cost while maintaining the ability to process feature maps across all channels. This makes it a lightweight yet powerful operation, enabling the module to achieve global feature enhancement without imposing a heavy computational burden. Combined with the attention mechanisms, PWConv ensures that the module operates efficiently, making it well suited for remote sensing change detection tasks.  
Figure 2: The overall structure of the Siamese-SAM model.
# Analysis of Hydration-Mechanical-Durability Properties of Metakaolin Blended Concrete
## 3 Property Evaluations of MK Blended Concrete
### Results and Discussion  
#### 3.2.1 Reaction of Neat Cement Paste  
Based on the compound compositions of cement, the reaction parameters of the cement hydration model (Equation (1)) can be determined. Furthermore, the development of the cement hydration degree can be calculated as the curing time progresses. The CH content can be determined based on the cement hydration degree and the proportions of the concrete mix. As shown in Figure 2, as the water to cement ratio of the neat paste increased from 0.3 to 0.5, the CH content normalized by the cement mass also increased because when the water to cement ratio increases, the capillary water concentration \(C_{w-free}\) increases, as does the reaction degree of cement.
## 3 Methodology
### Statistical Methods
#### 3.3.3 The Arima Model  
ARIMA is one of the most popular and widely used statistical methods for time series forecasting. The acronym stands for AutoRegressive Integrated Moving Average. The model is defined using three distinct input parameters:  
* \(a\), which is the lag order or the number of lag observations included in the model;
* \(d\), which is the number of times that the raw observations are differentiated;
* \(q\), which is the size of the moving average window used to compute the mean.  
So for example if \(a=0\), \(d=2\) and \(q=2\) the forecast value \(\widehat{Y}_{t}\) that ARIMA will predict will be equal to :  
\[\widehat{Y}_{t}=2Y_{t-1}-Y_{t-2}-\theta_{1}e_{t-1}-\theta_{2}e_{t-2}. \tag{3}\]  
where \(\theta_{1}\) and \(\theta_{2}\) are the moving average coefficients while \(e_{t}\) is the forecasting error for the observation in time \(t\). The reason we chose to test this forecasting method for our data is that it takes into account the strong auto-correlation that exists in our data. Moreover, ARIMA is a popular method used successfully with different types of datasets giving interesting results.
## Data
### EO Data  
As the analysis of land use and land cover and associated changes over the Dilijan national park spans from 1991 to 2019, covering 28 years, it was decided to use two satellite constellations: Landsat and Sentinel. As part of the Landsat family, Landsat 4, 5 and 7 times-series at 30 m spatial resolution were used for the mapping of the park in 1991, 1995, 2000, 2002, 2005 and 2010. Landsat 8 times-series at 30 m spatial resolution were used for 2015 and Sentinel-2 images at 10 m spatial resolution were used for the most recent mapping of 2019 (see Figure 2).  
Two satellite VHSR data were used for calibration and validation purposes:  
* Ikonos at 4 m spatial resolution for the year 2007, used for epoch 2005
* Pleiades of 17 July 2014 and 24 July 2014 at 0.5 m resolution (Panchromatic) and 2 m (Multispectral) used for epoch 2015 (see Figure 3)
* Pleiades of 28 June 2018 at 0.5 m resolution (Panchromatic) and 2 m (Multispectral), used for epoch 2019.  
Data Pre-Processing  
For Landsat data, the Level-2 Surface Reflectances have been processed using the ESPA on demand service. A total of 909 Landsat 4/5, 685 Landsat 7 and 323 Landsat 8 have been ordered. Using QA data (cloud detection), mosaics have been processed and cropped to the Dilijan National Park border.  
For Sentinel data, the Level-2 flat Surface Reflectance using Maccs-Atcor Joint Algorithm (atmospheric correction and cloud screening software based on the MACCS processor, developed for CNES by CS-SI, from a method and a prototype developed at CESBIO) have been processed and downloaded from CNES Peps platform. After a resampling of the 20 m and 60 m bands to 10 m resolution, mosaics of Sentinel-2 imagery have been processed and cropped to the Dilijan National Park border.  
Figure 3: Péiades at 0.5 m spatial resolution (Pansharpened) acquired on 17 and 24 July 2014 @CNES (2014), Distribution AIRBUS DS.  
Figure 2: Sentinel-2 at 10 m spatial resolution acquired on 31 July 2019 @ESA.