esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 2 Experimental Investigation
### Experimental Tests of SCC
#### 2.6.2 Impact Strength of SCC  
For surface crack assessments and impact strength tests, cylindrical SCC samples with a diameter of 150 mm and a height of 63.50 mm were utilized in this study. The cylindrical shape was chosen because it ensures uniform heating during different heating cycles, and any surface cracks that develop on the exposed face are expected to be evenly distributed across the cross-section. The drop weight test method and device depicted in Figure 5 were employed in accordance with the guidelines provided by ACI 544 [31]. To apply the impact load to the specimen, a 45-N hammer was dropped repeatedly from a height of 457 mm onto a hardened steel ball with a diameter of 64 mm. This process was repeated until the specimen failed. The number of blows required for the initial failure and final failure of each specimen was recorded. The initial failure was determined when the first hairline crack appeared on the specimen's surface, while the ultimate failure was observed when the specimen was completely split into multiple parts.  
Figure 4: Compressive test results of SCC mixes.
# Hydration of Calcium Sulfoaluminate-Based Binder Incorporating Red Mud and Silica Fume
## 3 Results and Discussion
### Drying Shrinkage  
The results of drying shrinkage or length change of the samples are presented in Figure 3. With respect to the amount of SF addition, Figure 3a shows that, at a given SF content (0% SF addition), drying shrinkage is reduced as the CSA content in the sample is increased. When 5% SF is added, there is no significant difference in the drying shrinkage behavior of samples as the CSA content in samples is increased or decreased, except for the C50R45S5 sample. All samples with 5% SF have an almost constant drying shrinkage after 28 days. The addition of 10% SF indicates that there is no consistency in terms of the CSA content for drying shrinkage.  
Previous studies [21; 29] found that the addition of SF influences the drying shrinkage of samples and that drying shrinkage increases with increase in SF content. This was also confirmed for the samples with 40% CSA content in this study. The drying shrinkage of the sample increased with increasing the amount of SF. The opposite result was observed in the samples with 10-30% CSA content. That is, the drying shrinkage of the samples tends to decrease with increasing amounts of SF. [PERSON] [21] and [PERSON] et al. [29] reported that the increase in shrinkage of samples is influenced by the pore size refinement and pozzolanic reaction due to the SF content. [PERSON] et al. [29] also reported that the refinement of pore size leads to increase in capillary tension, causing the increase of shrinkage.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **C10R9050** & **C10R85S5** & **C10R80S10** \\ \hline
1-day & 3.9 (1.12) & 4.0 (0.86) & 4.2 (1.15) \\
28-day & 6.2 (1.57) & 6.7 (1.22) & 6.9 (1.34) \\ \hline  & **C20R8050** & **C20R75S5** & **C20R70S10** \\ \hline
1-day & 7.7 (0.86) & 11.3 (1.05) & 10.8 (1.02) \\
28-day & 11.7 (1.20) & 20.3 (1.04) & 19.8 (1.25) \\ \hline  & **C30R7050** & **C30R65S5** & **C30R60S10** \\ \hline
1-day & 18.3 (0.80) & 18.6 (0.74) & 16.4 (1.13) \\
28-day & 24.6 (0.87) & 36.6 (0.88) & 34.8 (1.05) \\ \hline  & **C40R6050** & **C40R555** & **C40R50S10** \\ \hline
1-day & 18.4 (1.05) & 18.3 (0.88) & 15.2 (0.86) \\
28-day & 32.1 (1.32) & 41.4 (1.38) & 36.9 (1.11) \\ \hline  & **C50R5050** & **C50R455** & **C50R40S10** \\ \hline
1-day & 10.9 (0.85) & 10.9 (1.03) & 8.4 (1.16) \\
28-day & 28.6 (1.14) & 34.7 (1.39) & 29.6 (0.75) \\ \hline \end{tabular}
\end{table}
Table 3: Strength (standard deviation) of samples at 1 and 28 days (MPa).  
Figure 3: Drrying shrinkage for the paste samples with various mixes: (**a**) Group I (10–50% CSA + 90–50% RM + 0% SF), (**b**) Group II (10–50% CSA + 85–45% RM + 5% SF), and (**c**) Group III (10–50% CSA + 80–40% RM + 10% SF).  
In the case of the 50% CSA replacement level, the sample with 0% SF addition showed expansion during the testing period. When 5% SF was added, the sample showed reduced expansion; however, its length change was still positive. The sample with 10% SF addition showed shrinkage after 24 days.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 4 PIDL for UQ
### PIDL-UQ for TSE
#### 4.1.3 Physics-Informed Flow-Based GAN (PhysFlowGAN)  
**PhysFlowGAN** combines the merits of GAN, normalizing flow, and PIDL. It uses normalizing flow as the generator for explicit likelihood estimation, while exploiting adversarial training with the discriminator to ensure sample quality. The structure of PhysFlowGAN is shown in Figure 11c, which consists of a normalizing flow, a discriminator, and a PICG. The data loss \(L_{o}\) is composed of of two parts, i.e., \(L_{o}^{GAN}\), that is calculated from the discriminator and \(L_{o}^{flow}\), that is calculated from the normalizing flow. The physics loss is calculated in the same way as PI-GAN. One PhysFlowGAN model, TrafficFlowGAN [98], has been applied to the UQ-TSE problem.  
Table 4 summarizes the hybrid architecture used for the UQ-TSE.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Descriptions** & **Pros** & **Ref.** \\ \hline \multirow{4}{*}{PI-GAN} & \(L_{c}=\frac{1}{N_{c}}\sum_{i=1}^{N_{c}}|r_{c}|^{2}\) is added to the & The most & [65; 90; 95; 96] \\  & & generators loss function using the & widely used & [65; 90; 95; 96] \\ \cline{3-4}  & & Residual is fed into the & & \\  & & discriminator, & & \\  & & & the gradient & \\  & & & then averaged over collocation & \\  & & points to calculate \(L_{c}\). & & \\ \hline \multirow{4}{*}{PI-GAN} & \multirow{4}{*}{Mean-GAN} & Residual is averaged over the & Can encode & \\  & & physics parameter \(\lambda\): & stochastic & \\  & & \(L_{c}=\frac{1}{N_{c}}\sum_{i=1}^{N_{c}}|\frac{1}{N_{c}}\sum_{i=1}^{N_{b}}r_{c}| ^{2}\). & physics model & \\ \cline{1-1} \cline{2-4}  & PI-GAN- & The \(\rho-u\) relation is approximated & Requires & \\ \cline{1-1} \cline{2-4}  & FDL & by ML surrogates; physics loss \(L_{c}\) is & minimal & \\ \cline{1-1} \cline{2-4}  & & the same as PI-GAN. & physical & \\ \cline{1-1} \cline{2-4}  & & information & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Architecture used for the UQ-TSE.
## 2 Materials and Methods
### Materials
#### 2.1.2 Satellite Imagery  
As the first raster component, we use polarimetric SAR intensity data (Ground Range Detected, GRD) from the Copernicus Sentinel-1 mission. Due to highly dynamic soil moisture and even plausible short-lived snow cover conditions during the time window, it is advantageous to use a mean-valued mosaic image composed of several images over the time period. The Finnish Meteorological Institute (FMI) publishes a preprocessed 11-day Sentinel-1 mean gamma-nought mosaic product [38]. See Supplement S1 for additional information and availability of the mosaic. We use an instance of a VV and VH polarity dataset from April 2018 (11 th to 21 st). Although the underlying spatial resolution of a Sentinel-1 Interferometric Wide Swath image is 5 \(\times\) 20 m for an image of a 250 km swath [39], FMI mosaic preprocessing [38] resamples the data to 20 m spatial resolution. As a restriction, measurements prior to 2019 were quantized to 1 dB intensity intervals by the FMI data pipeline, posing a hard limit to discretization, that is, the binning of the measurements for the density estimators. Individual Sentinel-1 images that coincide with the time period and location of the mosaic and our ROI are listed in Supplement S1.  
As a second raster component, we spatially mosaic three Sentinel-2 multispectral images selected from as close to the time period and ROI of the Sentinel-1 mosaic as possible (see Supplement S2 for the image identifiers), resampled to 10 m spatial resolution. An additional criterion for this image selection was a relatively low overall (\(<\)10%) percentage of pixels containing cloud or snow in the quality indicator (QI) metadata of the images. We also filter out individual pixels with a cloud or snow confidence value of \(\geq\)10%. This reduces the amount of pixel observations per parcel and makes the pixel sets discontinuous, but these properties do not cause problems for the proposed approach.  
From the Sentinel-2 spectral channels, we calculate relevant basic spectral indices--the Normalized Differential Vegetation Index (\(NDVI\)) [40] and Normalized Differential Village Index (\(NDTI\)) [41; 42]--as features for our density estimates. Consequently, we have \(D=2\) for MSI images. The formulas for the indices in the context of Sentinel-2 bands are:  
\[NDVI=(B8-B4)/(B8+B4) \tag{1}\]  
and  
\[NDTI=(B11-B12)/(B11+B12), \tag{2}\]  
where the Sentinel-2 band center wavelengths are: \(B4\) (Red): 670 nm, \(B8\) (NIR): 830 nm, \(B11\) (SWIR): 1610 nm, and \(B12\) (SWIR): 2200 nm.  
The SAR VH/VV bands and the two optical spectral indices per pixel represent two disparate data sources at different resolutions and image extents (as seen in Figure 2). In Section 2.2 below we combine these to a common representation per parcel by extracting the pixels that coincide with the parcel delineation polygons.
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 3 Results
### Model Comparison for CLQ Evaluation
The obtained models based on the 294 training samples are compared in Figure 4.  
Based on the scattered graphs from the training samples in Figure 4, the points of estimated vs. observed CLQ are overall randomly distributed at both sides of the 1:1 lines. However, the accuracies of the predicted CLQ values vary greatly depending on the models. Overall, the estimates of CLQ from the PLSR models for 2011 to 2015 have smaller R\({}^{2}\) values and greater RMSE and NRMSE values, then the SVR models and the GA-BPNN models. This indicates that the GA-BPNN models performed best, implying that CLQ was nonlinearly correlated with GPPs. \begin{table} \begin{tabular}{c c c c c} \hline \hline YearsGrowth Stages & Tillering & Jointing & Heading & Maturity \\ \hline
2011 & 1.971 & 1.981 & 4.611 & 2.874 \\
2012 & 1.407 & 2.687 & 4.130 & 3.451 \\
2013 & 1.274 & 4.092 & 7.679 & 4.468 \\
2014 & 1.421 & 1.667 & 3.257 & 3.448 \\
2015 & 2.073 & 1.699 & 2.655 & 1.026 \\ \hline \hline \end{tabular}  \In addition, the predicted CLQ values from the models were validated for their accuracy using 126 validation samples in Figure 5. Given a year and a model, the points of predicted vs. measured values of CLQ were randomly placed at both sides of the 1:1 line. But, the PLSR models led to obvious overestimations and overestimations for the smaller and greater CLQ values, respectively. The overestimations were mitigated by SVR models and more mitigation was achieved by the GA-BPNN models. Among the three kinds of models, the GA-BPNN models have the smallest average RMSE of  
Figure 4: Scatterplots of measured versus estimated CLQ using the training dataset from 2011 to 2015: (**a-e**) partial least squares regression (PLSR) model; (**f-j**) support vector regression (SVR) model; (**k–o**) GA-BPNN model. 67.37, than the SVR models with average RMSE of 73.04 and the PLSR models with average RMSE of 92.45 for years from 2011 to 2015, indicating that the GA-BPNN models have the strongest ability of predicting CLQ.
# Translation-Based Embeddings with Octonion for Knowledge Graph Completion
## 4 Experiments and Discussion
### Experimental Setup  
We use the WN18 [9] benchmark dataset to test the link prediction effect of TransO compared with the traditional translation-based KGC models, and use the WN18 RR [11] and FB15k-237 [42] benchmark datasets to test both TransO and PTransO compared with the existing advanced KGC models.  
The WN18 dataset is derived from the cognitive-based English vocabulary knowledge network WordNet [43], and the WN18 RR dataset is a subset of WN18 by removing the inverse relations in WN18. The FB15k-237 dataset is a subset of the FB15k [9] dataset, which is derived from the large knowledge base Freebase [44]. Similar to WN18 RR, FB15k-237 removes the inverse relations in the original FB15k dataset. The main relational patterns in WN18 are \"symmetric\", \"asymmetric\", and \"inversion\", whereas those in the WN18 RR and FB15k-237 datasets are \"symmetric\", \"asymmetric\", and \"combinatorial\". Significantly, WN18 RR is hierarchical, but FB15k-237 is not.  
Because WN18 RR and FB15k-237 delete the inverse relations that may lead to data leakage from the original datasets WN18 and FB15k, respectively, these two datasets can more accurately reflect the effects of the models, and are generally less able to obtain satisfactory link prediction results than the original datasets. However, since the translation-based KGC models appeared earlier, they were mostly experimented on the WN18 dataset. In order to effectively compare TransO with the same type of translation models, we still retain WN18 as one of the experimental datasets to verify the effectiveness of the TransO model. The dataset statistics are shown in Table 1.  
For the WN18 dataset, we conduct the link prediction experiment only in high-dimensional (\(d\in\{200,400,496\}\)) embedded space in order to compare the best performance with traditional translation-based models, while we use the WN18 RR and FB15k-237 datasets in both high-dimensional (\(d\in\{200,400,496\}\)) and low-dimensional (\(d=32\)) embedded spaces in order to analyze the influence of embedded dimensions on the experimental results.  
The learning rate of our models is set to 7.5 \(\times\) 10\({}^{-5}\); the weight decay in the high-dimensional embedded space is set to 1.5 \(\times\) 10\({}^{-2}\), and that in the low-dimensional embedded space is set to 7.5 \(\times\) 10\({}^{-3}\); the batch size for training is set to 128; the number of negative samples is set to 50.
## 2 Materials and Methods
### Model Validation  
The model validation was performed based on the cross-validation method [62]. The model that better replicated the temperature behaviour in the precalciner was selected by quantifying the degree of accuracy with respect to the validation data. This was carried out by mean of a performance index (\(FIT\)), which was attained by the following expression [62].  
\[FIT=\left[1-\frac{\left\|y-\hat{y}\right\|}{\left\|y-\overline{y}\right\|} \right]\;*\;100\% \tag{1}\]  
where \(y(t)\) is the measured output, \(\hat{y}(t)\) is the estimated output, and \(\overline{y}(t)\) is the mean of \(y(t)\). The obtained validation results are shown in Table 1.  
Table 1 exhibits that the better attained model is a fourth-order model with ARMAX structure and time delay, which is characterized by the expression [62]:  
\[y(t)=\frac{B(q)}{A(q)}q^{-nk}u(t)+\frac{C(q)}{A(q)}e(t) \tag{2}\]  
where:  
\[A(q)=1-1.783q^{-1}+0.0356q^{-2}+1.729q^{-3}-0.5489q^{-4} \tag{3}\]  
\[B(q)=19.91q^{-1}-46.14q^{-2}+33.1q^{-3}-6.842q^{-4} \tag{4}\]  
\[C(q)=1-1.164q^{-1}-0.109q^{-2}+0.5668q^{-3}-0.2937q^{-4} \tag{5}\]  
and \(e(t)\) is the uncorrelated random white noise with zero mean.  
The validation results of the mathematical model attained are shown in Figure 3 and depict a high adjustment index (FIT).  
The obtained discrete-time mathematical model (1) of the precalciner temperature can, thus, be characterized by the next transfer function with dominant time delay:  
\[G(z)=\frac{19.91z^{3}-46.14z^{2}+33.1z-6.842}{z^{4}-1.783z^{3}+0.0356z^{2}+1.2 9z-0.5489}z^{-10} \tag{6}\]
## 4 Experiments
### Ablation Studies
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{4}{c}{**Model Configuration**} & \multicolumn{4}{c}{**Nordland**} \\ \hline
**Adapter** & **CIE** & **lr** & **Scheduler** & **R@1** & **R@5** & **R@10** \\ \hline DSConv-DiNOv2 & ✓ & 6 \(\times\) 10\({}^{-5}\) & Linear & **89.3** & **95.7** & **97.2** \\ DSConv-DiNOv2 & ✓ & 0.0001 & Linear & 80.9 & 90.4 & 93.4 \\ DSConv-DiNOv2 & ✓ & 6 \(\times\) 10\({}^{-5}\) & Step & 85.0 & 93.1 & 95.3 \\ MultiConv-DiNOv2 & ✓ & 6 \(\times\) 10\({}^{-5}\) & Linear & 84.8 & 93.1 & 95.3 \\ MLP-DiNOv2 & ✓ & 6 \(\times\) 10\({}^{-5}\) & Linear & 86.4 & 94.1 & 95.2 \\ DSConv-DiNOv2 & \(\times\) & 6 \(\times\) 10\({}^{-5}\) & Step & 69.4 & 80.9 & 85.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation studies on the model’s design choices. CIE stands for the cross-image encoder, lr stands for the learning rate, DSConv stands for the depthwise separable convolutional adapter, and MultiConv stands for the multi-resolution convolutional adapter. Although the MLP adapter incurred lower computational cost, its lack of convolutional operations limited its capacity to learn expressive features. As a result, its performance remained suboptimal, as evidenced by the results in Table 2. Our adapter replaced the traditional convolution operations with depthwise separable convolutions. As shown in Table 3, our depthwise separable convolutional adapter outperformed CricaVPR's multi-resolution convolutional adapter in the Multiply-Accumulate Operations (MACs), Floating Point Operations (FLOPs), and parameter count, demonstrating that our adapter achieved higher performance and significantly enhanced computational efficiency and model compactness. Cross-Image Encoder: To assess the contribution of our cross-image encoder, we conducted an ablation experiment by removing it from the architecture. The results, presented in the last row of Table 2, showed a significant performance drop. Without the cross-image encoder to facilitate information sharing between neighboring images, the average Top-1 recall dropped from 89.3% to just 69.4%. This underscores the critical role of the cross-image encoder in enhancing feature contextualization and improving retrieval accuracy by leveraging multi-image relationships.
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 3 Background Knowledge  
### Lstm  
The problems of gradient explosion and gradient vanishing happen in RNN when it processes long series, which makes RNN perform worse in long series forecasting. LSTM is a unique type of RNN with improved memory cells which obviously overcome the problems of gradient explosion and vanishing gradient during the training process.  
In LSTM, a special hidden state and three controlling gates are adopted to control long and short memory. In each time step, LSTM receives current input value, previous output value, and hidden state. Memory state is used to store all the historical information about the earlier sequences related to the current state [17]. As shown in Figure 1, the most essential mechanisms of LSTM are gate mechanism, input gate (\(i_{g}\)), forget gate (\(f_  
the new memory state will forget the previous memory state and take the current input candidate as new memory state.  
\[h_{t}=o_{g}*tanh(c_{t}) \tag{6}\]  
Similarly, the output value of a current memory cell \(h_{t}\) is decided by output gate \(o_{g}\).  
In this way, LSTM utilizes the flexible gate mechanism to control the value of memory state and obtain meaningful information of the whole series. Hence, LSTM is widely used in electricity load forecasting.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 1 Introduction
Hence, a lot of research has been reported by many authors using economical adsorbents such as lignin, bark, chitosan, clay, zeolite, activated carbons, synthetic polymeric adsorbents, and so on [18]. The present review focuses on the use of various types of adsorbent for the removal of heavy metal ions. There are not many research papers that use multiple types of adsorbents in a single frame to eliminate them. The classification of various types of adsorbent like natural, synthetic and modified adsorbents have been covered in this review which otherwise was not included in any of the reviews published before [19; 20; 21; 22; 23]. Adsorption is a commonly proposed technique since it is highly effective in the extraction process and is easy to apply; adsorbents are available in different ranges and are cheaper in cost [24]. Several chelating resins were synthesised by the polymerisation of traditional chelating monomers i.e., methacrylic acid, acrylamide, vinylpyridine and vinylimidazole [25; 26]. Researchers synthesised a polymer by reacting this with ligands having a low molecular weight which resulted in a new polymer with some functionality or new group in the modified polymer [27; 28]. Additionally, the functionalisation of the polymer matrix results in the formation of new chelating resin [29]. Because of the existence of reactive hydroxyl and amino moieties, chitosan is a natural biopolymer that is abundant in nature and has the ability to adsorb a considerable amount of metals. Furthermore, modification of chitosan through physical and chemical techniques were able to increase its sorption abilities for metal ions like As\({}^{3+}\) or As\({}^{5+}\)[30; 31; 32], Cr\({}^{6+}\)[33; 34; 35], Pb\({}^{2+}\)[36; 37], Cu\({}^{2+}\)[38; 39; 40], Hg\({}^{2+}\)[41; 42; 43], Cd\({}^{2+}\)[44; 45; 46], Ni\({}^{2+}\)[47; 48; 49], Zn\({}^{2+}\)[50; 51], Ag\({}^{+}\)[52; 53], Co\({}^{2+}\)[54], Mn\({}^{2+}\)[55], U\({}^{6+}\)[56; 57], V (3+\(A\)+ and 5+) [58], and Pt\({}^{4+}\)[59]. Similarly, [PERSON] et al., synthesised polystyrene-supported chitosan beads cross-linked with glutaraldehyde for rapid extraction of copper ions [38]. [PERSON] et al. (2010) studied whether the coagulation procedure was useful in the removal of arsenic (As), and found that Al and Fe salts were effective coagulants for extraction. Furthermore, since 2000, the amount of data available on adsorption in water treatment has steadily risen (Figure 1), suggesting a rise in research interest in this area [60].
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 4 Proposed Framework: FL-LoRaMAC
### Optimizing Communication Bandwidth  
The artificial intelligence models are quite large in terms of the number of parameters in the weight matrix. However, LoRa is a low-bandwidth and low-data-rate communication technology. Hence, the transmission of these model parameters to the server will require a large amount of time and may consume all the network bandwidth. Therefore, the total transmission time and communication bandwidth used need to be optimized without significantly affecting the model's performance. To achieve this, we propose the framework depicted in Figure 8 that uses principal component analysis (PCA) and model pruning in conjunction with a spreading factor allocation scheme that is explained in subsequent sections. PCA is a popular dimensionality reduction method that is widely used in ML to reduce large datasets [26].