esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### CNN Configuration  
For image classification, a CNN based on the ResNet50 architecture was used, employing a transfer learning framework. The model was configured with include_top = False to remove the default fully connected classification head and replace it with a customized classifier suitable for this study's objectives.  
No pretrained weights were used; instead, all layers were initialized randomly and trained from scratch. The input image size was scaled from 32 \(\times\) 32 to 224 \(\times\) 224 \(\times\) 3 to match the model's expected dimensions. The architecture was experimentally tuned for stable training, as detailed in Table 1, and the training parameters were set as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** &
\begin{tabular}{c} **Layer** \\ **Configuration** \\ \end{tabular} & **Activation Function** & **Output Dimension** & **Remark** \\ \hline Feature & ResNET50 (top layer & & \(7\times 7\times 2048\) & Input 224 \(\times\) 224 \(\times\) 3 \\ extractor & removed; weights = none) & & \(100\),352 & \(7\times 7\times 2048\) - \(>\) 1D \\ Flatten layer & Flatten & & \(512\) & Fully connected layer \\  & Dense (512) & ReLU & \(512\) & Feature normalization \\ FC layer 1 & Batch normalization & & \(512\) & Overfitting prevention \\  & Dropout (0.4) & & \(512\) & Fully connected layer \\  & Dense (256) & & \(256\) & Feature normalization \\ FC layer 2 & Batch normalization & & \(256\) & Overfitting prevention \\  & Dropout (0.3) & & \(32\) & No. of classes: 3 \\ Output layer & Dense (3) & Softmax & \(3\) & No. of classes: 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CNN classifier architecture.  
Figure 8: Input images after applying SpecAugment: (**a**) 3 Phase-RGB, (**b**) 3 Phase-DWT, (**c**) 3 Phase-STFT, (**d**) Id-Linear, (**e**) Id-DWT, and (**f**) Id-STFT.  
In this study, three strategies were implemented to prevent overfitting:  
1. Dropout layers (with rates of 0.4 and 0.3) were applied to the fully connected layers to randomly deactivate neurons during training;
2. Batch normalization was used to stabilize learning and accelerate convergence;
3. ReduceLROnPlateau was employed to automatically decrease the learning rate when the validation loss plateaued.  
The effectiveness of these strategies can be observed in Figure 9.  
Across all encoding methods, validation accuracy increased in line with training accuracy, and both training and validation loss decreased steadily--indicating that overfitting did not occur during training.  
However, in the 3 Phase-RGB method, oscillations were observed during the early and middle training stages, and the Id-DWT method exhibited a noticeable gap between training and validation accuracy, suggesting a potential risk of overfitting.  
Nevertheless, since all six configurations were trained under identical conditions, and four out of the six methods showed stable and consistent learning curves, the fluctuations in the two cases are likely attributed to sensitivity to the specific input encoding rather than fundamental model instability.
## 3 Analysis and Reliability Verification of an Engineering Case  
### Operating Conditions  
This study considered rectification of the lateral deviation of a tunnel in Tianjin, China, as an engineering case study [2,11]. As shown in Figure 5, the tunnel is located next to the ground wall of a foundation pit project. Due to the unloading effect of the foundation pit excavation, the tunnel has undergone some horizontal deformation. In order to correct the horizontal displacement of the tunnel, two sleeve valve pipes were set at a distance of 10.3 m from the side wall of the tunnel. The diameter of the sleeve valve pipes was 30 mm, and the distance between the two sleeve valve pipes was 4 m. In addition, the two sleeve valve pipes worked simultaneously during grouting. The grouting volume of a single sleeve valve pipe was 4 m\({}^{3}\), and the grouting depth ranged from 15 to 20 m. The selected grouting reinforcement area was a cylinder with a height of 5 m and a radius of 0.8 m. The outside diameter of the tunnel that should be deflected was 6.2 m. The tunnel was lined with tubular C50 concrete precast sheets. The depth of the soil layer of the tunnel and grouting section was in the range of 13.8-20 m, and the soil layer was all silty clay. The other relevant parameters of the tunnel were as follows: \(j=0.3\), \(k_{\mathrm{s}}=7.45\times 10^{5}\) kN/m, \(k_{\mathrm{t}}=1.94\times 10^{6}\) kN/m, \(N=175\), and \(D_{\mathrm{t}}=1.5\) m. The compressive modulus (\(E_{\mathrm{s}}\)), the elastic modulus (\(E\)), the tunnel equivalent bending stiffness (\(EI\)), and the Poisson's ratio (\(\mu\)) of the soil were 9.32 MPa, 25 MPa, \(1.1\times 10^{8}\) kN/m\({}^{2}\), and 0.3 respectively.
## 3 Results  
### Predicted Targets of Salforaphane  
To identify new protein targets of sulforaphane, three bioinformatics platforms (SwissTargetPrediction, SuperPred and TargetNet) were used, each using different algorithms to predict the most likely targets of this compound. The SwissTargetPrediction search identified 102 targets, SuperPred generated 115 targets and TargetNet revealed 623 targets. Through a comprehensive analysis of the candidate proteins, 11 proteins were consistently present in all three lists, establishing them as the most likely targets of sulforaphane (Table 1).  
Among the 11 proteins identified, previous evidence of interaction with sulforaphane exists for MIF (Macrophage migration inhibitory factor) [50], CDK2 (Cyclin-dependent kinase 2) [51], GSK3\(\beta\) (Glycogen synthase kinase-3\(\beta\)) [52; 53; 54; 55] and PARP1 (Poly [ADP-ribose] polymerase 1) [56; 57]. However, no previous evidence of interaction with sulforaphane was found for the remaining seven proteins (CYP19A1 [ Cytochrome P450 19A1], NAMPT [Nicotinamide phosphoribosyltransferaseense], P2 RX7 [P2X purinoceptor 7], CTSL [Procathep  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & **Gene** & **Uniprot ID** & **ChEMBL ID** & **Target Class** \\ \hline Macrophage migration & MIF & P14174 & CHEMBL2085 & Tautomerase \\ inhibitory factor & CYP19A1 & P11511 & CHEMBL1978 & Aromatase \\ Vicotinamide & NAMPT & P43490 & CHEMBL1744525 & Transferase \\ phosphoribosyltransferase & & & & \\ Poly [ADP-ribose] & PARP1 & P09874 & CHEMBL3105 & Transferase \\ polymerase-1 & & & & \\ P2X purineceptor 7 & P2 RX7 & Q99572 & CHEMBL4805 & Ligand-gated ion channel \\ Glyccogen synthase kinase-3 \(\beta\) & GSK3B & P49841 & CHEMBL262 & Kinase \\ Cyclin-dependent kinase 2 & CDK2 & P24941 & CHEMBL301 & Kinase \\ Cathepsin L & CTSL & P07711 & CHEMBL3837 & Protease \\ Adenosine A1 receptor & ADORA1 & P30542 & CHEMBL226 & Family A G \\ Adenosine A2a receptor & ADORA2A & P29274 & CHEMBL251 &
\begin{tabular}{c} Family A G \\ protein-coupled receptor \\ Oxidoreductase \\ \end{tabular} \\ Monoamine oxidase A & MAOA & P21397 & CHEMBL1951 & Oxidoreductase \\ \hline \hline \end{tabular}
\end{table}
Table 1: Consensus sulforaphane targets predicted using SwissTargetPrediction, SuperPred and TargetNet.  
sin L], ADORA1 [Adenosine receptor A1], ADORA2A [Adenosine receptor A2A], and MAOA [Monoamine oxidase A]].
## 3 Control Methods
### Fuzzy Inference System
#### 3.2.2 Yellow Light  
The same three linguistic variables employed for the \(X_{r}\) set in the green light are used for the yellow light: _entering_, _approaching_, and _departing_ (Figure 4(a)). No changes are made on the type of MF for each variable. However, their limits are changed. The _entering_ zone is reduced to a half of its original size. On the other hand, the _approaching_ zone is extended three times. Similarly, all the variables of \(\dot{x}_{norm}\) in the green light set are reused (Figure 4(b)). The limits and properties of the _slow_, _normal_, and _fast_ MF are updated. _Slow_ and _normal_ MFs are narrowed and displaced to the left without touching a zero value. _Fast_ is just shifted to the left. _Reverse_ remains unchanged. The three original MFs of \(\tau_{norm}\) are preserved. _Neutral_ and _forward_ continue as they were originally defined. _Reverse_ is shifted to the left. A new triangular MF with symmetric boundaries is introduced to add a _soft reverse_ variable. This new MF is designed to perform braking and regenerative actions for the CEV.  
Nine rules describe the designed navigation navigation behavior for \(L_{s}=\) yellow (Algorithm 3). The main goal is to greatly reduce the speed of the CEV according to its pose and velocity without reaching a full stop, as a part of the RB/EAD strategy. During this maneuver, power can be regenerated because of the performance the electric motor. Crossing the intersection during the yellow light should be avoided. However,  
Figure 4: Green light fuzzy sets.  
traversing the crossroad is encouraged if the speed is high and safety constraints are compromised. Reverse and over the speed limit navigation situations are avoided through specific rules.  
```
0:\(X_{r}\), \(\hat{x}_{norm}\)
0:\(\tau_{norm}\) if\(X_{r}\)\(\rightarrow\) entering &&\(\dot{x}_{norm}\)\(\rightarrow\) reverse then \(\tau_{norm}\)\(\leftarrow\) forward elseif\(X_{r}\)\(\rightarrow\) entering &&\(\dot{x}_{norm}\)\(\rightarrow\) slow then \(\tau_{norm}\)\(\leftarrow\) neutral elseif\(X_{r}\)\(\rightarrow\) entering &&\(\dot{x}_{norm}\)\(\rightarrow\) normal then \(\tau_{norm}\)\(\leftarrow\) soft reverse elseif\(X_{r}\)\(\rightarrow\) entering &&\(\dot{x}_{norm}\)\(\rightarrow\) fast then \(\tau_{norm}\)\(\leftarrow\) reverse elseif\(X_{r}\)\(\rightarrow\) approaching &&\(\dot{x}_{norm}\)\(\rightarrow\) reverse then \(\tau_{norm}\)\(\leftarrow\) forward elseif\(X_{r}\)\(\rightarrow\) approaching &&\(\dot{x}_{norm}\)\(\rightarrow\) slow then \(\tau_{norm}\)\(\leftarrow\) neutral elseif\(X_{r}\)\(\rightarrow\) approaching &&\(\dot{x}_{norm}\)\(\rightarrow\) normal then \(\tau_{norm}\)\(\leftarrow\) soft reverse elseif\(X_{r}\)\(\rightarrow\) approaching &&\(\dot{x}_{norm}\)\(\rightarrow\) fast then \(\tau_{norm}\)\(\leftarrow\) reverse elseif\(X_{r}\)\(\rightarrow\) departing then \(\tau_{norm}\)\(\leftarrow\) forward endif
```  
**Algorithm 3** Yellow light FIS knowledge base
## 2 System Introduction
### Data Collection  
In the cover test, the examiner covers one of the subject's eyes and uncovers it, repeatedly, to see whether the non-occluded eye moves or not. If the movement of the uncovered eye can be observed, the subject is thought to have strabismus. The cover test can be divided into three subtypes--the unilateral cover test, the alternating cover test, and the prism alternate cover test. The unilateral cover test is used principally to reveal the presence of a strabismic deviation. If the occlusion time is  
Figure 1: The proposed intelligent evaluation system of strabismus.  
extended, it is also called the cover-uncover test [29]. The alternating cover test is used to quantify the deviation [30]. The prism alternate cover test is known as the gold standard test to obtain the angle of ocular misalignment [31]. In our proposed system, we sequentially performed the unilateral cover test, the alternate cover test, and the cover-uncover test, for each subject, to check the reliability of the assessment.  
The protocol of the cover tests is as follows. Once the operator sends out the code \"0h07\", the automatic stimulus module waits for 6 s to let the application \"AMCap\" react, and then the occlusion operation begins. The occluder is initially held in front of the left eye. The first is the unilateral cover test for the left eye--the occluder moves away from the left eye, waiting for 1 s, then moves back to cover the left eye for 1 s. This process is repeated for three times. The unilateral cover test for the right eye is the same as that of the left eye. When this procedure ends, the occluder is at the position of occluding the right eye. Then the alternate cover test begins. The occluder moves to the left to cover the left eye for 1 s and then moves to the right to cover the right eye. This is considered as one round, and it needs to be repeated for three rounds. Finally, the cover-uncover test is performed for both eyes. The only difference from the above unilateral cover test is that the time of the occluder's occluding eyes is increased to 2 s. Eventually, the occluder returns to the initial position.  
We cooperated with the Hong Kong Association of Squint and Double Vision Sufferers to collect strabismic data. In total 15 members of the association consented to participate in our experiments. In addition to the 15 adult subjects, 4 children were invited to join our study. The adults and children, including both male and female, were within the age ranges of 25 to 63 years and 7 to 10 years, respectively. The camera was configured to capture a resolution of \(1280\times 720\) pixels at a frame rate of 60 fps. The distance between the target and eyes was 33 cm. If wearing corrective lenses, the subject was requested to perform the tests twice, once wearing it and once without it. After ethics approval and informed consent, the 19 subjects followed the data acquisition procedure introduced above, to participate in the experiments. Finally, 24 samples were collected, five of which were with glasses.
## 2 Related Works
[30] conducted experiments based on DeepLab v3+ with different backbone networks, including ResNet, DenseNet, and EfficientNet. Randomly weighted enhancement was applied to the network to balance the different types of defects in the training set. The experimental results show that ResNet-101 or EfficientNet as the backbone network could achieve the best IoU on the test set, that is, approximately 0.57, whereas the IoU was 0.325 when DenseNet was used as the backbone network. Similarly, DeepLab v3+ achieved the least training time when ResNet-101 was used as the backbone network. In summary, surface defect detection networks based on semantic segmentation are still developing. At present, surface defect detection networks based on FCN, SegNet, Unet, or DeepLab face some problems, such as loss of high-resolution features and low accuracy detection of defect boundary. [PERSON] et al. [21] proposed a new high-resolution feature extraction network named High-Resolution Net (HRNet) for human pose estimation. First, HRNet uses a high-resolution subnetwork to extract features and then inputs the feature into high-to-low resolution subnetworks. Meanwhile, different subnetworks with different resolutions are connected. In feature extraction, HRNet extracts high-resolution features by repeatedly fusing features between subnetworks. The existing semantic segmentation networks solve the defect boundary detection problem only by increasing the feature resolution and do not consider the relationship between the body and edge parts of objects in images. [PERSON] et al. [18] modeled the body and edge of objects in images and mapped the body and edge to the low- and high-frequency parts of images. After learning the optical flow field, the image feature is warped to make the body consistent, thereby obtaining the body feature. The edge feature is obtained by subtracting the body feature from the image feature. Through the supervised learning of body and edge features, the accuracy of detecting the defect boundary is enhanced. To design the surface defect detection network for automobile wheel hub based on DeepLab v3+, the following problems must be improved. The spatial resolution of features must be maintained during feature extraction to accurately predict the location and boundary of the defect area. The multi-scale features extracted by ASPP must be processed due to the gridding effect of the atrous convolution in the ASPP of DeepLab v3+, which causes local information loss and other problems. The boundary of the defect is similar to the normal area, resulting in low accuracy. During feature extraction and resolution restoration, high-resolution and multi-scale features must be fused.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 2 Background and Related Work  
### Situation Awareness  
From a _systems_ perspective, _situation awareness_ can be defined as enabling systems to sense, adapt, and react, based on the environment. During the last decades, within various application domains, a considerable number of research publications has been presented on situation awareness such as military [11; 12; 13], disaster management of different kinds [2; 14], smart manufacturing [15], tourism [16], connected cars [17], and advanced aerial mobility applications [18]. In [1], situation awareness is studied conceptually from the perspective of human behavior.  
The publications on situational awareness can be classified from several perspectives. Although there are similarities, the application domain is the one of the determining factors in the way a system with situation awareness is designed. Most publications on situation awareness are, therefore, 'problem specific' [11; 12; 13; 14; 15; 16; 17; 18]. For example, in [13], conceptual models are presented to evaluate the quality of symbolic information, and to semantically integrate heterogeneous and dynamic environments with cyberspace exploration. The initiatives presented are interesting but are specific to defense and security domains. A considerable effort may be needed to adapt the proposed techniques to reduce disaster situation awareness problems.  
Furthermore, the majority of publications propose a particular system and discuss how the system accomplishes its design objectives. These publications are'system specific'.  
Models for representing environments are considered important in the way data are gathered and interpreted. In this context, for example, data fusion techniques are evaluated [15]. Various analysis methods are proposed, for example, based on statistical analysis [19], Bayesian networks [20], and ontology-based techniques [21]. Multiplicity of data sources, such as crowd-sourcing, sensors, UAVs, and fusion of these are also investigated [22].
## 3 The Role of Mining Tailings in Advancing Sustainable Construction Practices
### Mining Tailings as Aggregates in Concrete  
Mining tailings are commonly used as aggregates in concrete production but their application varies significantly depending on the type of mine from which these tailings are sourced. This distinction is crucial as the chemical composition, particle size, and overall suitability for concrete production can differ dramatically, influencing both the environmental impact and the mechanical properties of the resulting concrete. For instance, tailings from iron ore mines have been successfully used in concrete as they typically possess high density and few deleterious materials, which can enhance the strength and weight of the final product [153]. This makes iron ore tailings particularly suitable for heavy-duty applications, such as foundations and load-bearing structures [154]. A case study from an iron ore processing facility in China demonstrated that replacing up to 30% of traditional aggregates with iron tailings could significantly increase the compressive strength and durability of dam concrete for hydraulic engineering applications. Furthermore, the concrete prepared with iron ore tailings exhibited superior durability compared to that prepared with normal aggregate, with the abrasion resistance being higher by 1.32-2.90 h m\({}^{2}\)/kg. Due also to the higher compressive strength, lower saturated-surface-dry water absorption, and angular shape of the iron ore tailing aggregates of raw rock, the modified concrete showed overall durability performance. Significant economic and environmental benefits were also noted, including a cost savings of approximately 8.98 million USD in two practical hydraulic engineering applications. Additionally, the use of the modified aggregate resulted in a reduction of occupied land space, CO\({}_{2}\) emissions, and water consumption by 151,900 m\({}^{2}\), 8157 tons, and 942,000 m\({}^{3}\), respectively, with relative reductions in CO\({}_{2}\) emissions and water consumption of 15.7% and 27.4% compared to the normal aggregate [154]. Conversely, tailings from copper and zinc mines often contain higher levels of sulfides, which can lead to durability issues such as acid generation and expansion when exposed to moisture and air [155]. This makes them less suitable for use in environments exposed to water or in marine construction. However, these tailings have been effectively utilized in controlled low-strength materials for backfill and non-structural applications, where such limitations are less critical [150]. Research from a copper mine in Chile explored the use of these tailings for paving paths and lightweight concrete blocks, where they performed well within the acceptable limits for non-structural use [156]. In the study conducted by [PERSON] et al. [157], the use of process tailings in the concrete industry was closely examined with several key outcomes identified. It was reported that
## 4 Results and Discussion
### Effect of Downstream Boundary Conditions on Lateral Migration  
The base level is considered to be the key downstream boundary condition of an alluvial river, and the change in base level can lead to significant morphological adjustments [20]. Previous studies  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **LM-MQ Reach** & **MQ-JMK Reach** & **JMK-TG Reach** & **Xiaobeiganliu Reach** \\ \hline
1987 & 470 & 147 & 189 & 230 \\
1988 & 536 & 225 & 281 & 319 \\
1989 & 1082 & 930 & 292 & 586 \\
1990 & 1603 & 350 & 347 & 543 \\
1991 & 1393 & 625 & 485 & 707 \\
1992 & 974 & 671 & 312 & 535 \\
1993 & 270 & 508 & 324 & 347 \\
1994 & 245 & 620 & 165 & 265 \\
1995 & 592 & 127 & 424 & 337 \\
1996 & 469 & 421 & 510 & 472 \\
1997 & 441 & 360 & 172 & 276 \\
1998 & 281 & 274 & 421 & 333 \\
1999 & 375 & 287 & 275 & 304 \\
2000 & 164 & 314 & 194 & 210 \\
2001 & 276 & 235 & 389 & 307 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reach-scale thalweg migration distance in the Xiaobeiganliu reach and three sub-reaches (m).  
Figure 7: Relationships between the lateral migration index and the upstream boundary conditions in the Xiaobeiganliu reach: (**a**) previous 3–year average water discharge; (**b**) previous 4-year sediment concentration; (**c**) comprehensive parameter.  
have shown that changes in TG elevation can greatly affect the aggradation and degradation processes in the Xiaobeiganliu reach [43; 44]. To investigate the effect of downstream boundary conditions on lateral migration in the Xiaobeiganliu reach, the relationship was established between the channel lateral migration index and the variation in the annual TG elevation (Figure 8). It was found that the lateral migration index increased with an increase in \(\Delta\)Z, and the relationship could be written as \(M=0.3607\times\left(\Delta\mathcal{Z}\right)^{0.2536}\), with a correlation coefficient of 0.62. This result implied that the downstream boundary conditions played an important role in the channel lateral migration, and the drastic change in the annual TG elevation could greatly increase the channel lateral migration in the Xiaobeiganliu reach.