esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Tuning Model Parameters  
After evaluating the model, we engage in fine-tuning the learning process to enhance its performance. This step involves adjusting specific parameters within the model, such as the number of neurons, learning functions, and synaptic weights, among others. By iteratively refining these parameters, we can optimize the model's learning process and achieve improved overall performance.
## 3 Proposed Method
### Image Fusion
#### 3.1.1 KAResNet  
To enhance the accuracy of feature extraction under complex working conditions, this paper proposes the KAResNet model as the backbone network. The architecture of KAResNet is illustrated in Figure 4, with specific hyperparameters provided in Table 1. The KAResNet model comprises a Conv1 layer; H1, H2, and H3 layers; a global average pooling layer; and two KABlock layers. Each of the H1, H2, and H3 layers is composed of multiple stacked convolutions. To address the issues of gradient vanishing and exploding during training, residual connections are utilized to form residual blocks.  
Unlike the conventional ResNet structure, this paper introduces the Kolmogorov-Arnold network architecture [24], which utilizes the KABlock in place of the original fully connected layer using a fixed activation function. This modification enhances the model's efficiency in approximating complex functions. Notably, the KABlock represents an optimized and improved implementation of the Kolmogorov-Arnold representation theorem. The KABlock consists of a combination of basis and spline functions. The basis function employs a fixed SiLU activation function, while the spl  
Figure 4: KAResNet framework.  
B-Spline functions. According to the Kolmogorov-Arnold representation theorem, it is necessary to find a suitable univariate function to approximate complex functions. The KABlock selects B-Spline as the fundamental univariate function. The KABlock maintains flexibility and adjustability while providing high smoothness and numerical stability, enabling more efficient approximations of complex functions. This design enhances the overall performance of the model, achieving more complex function simulations using simpler functions.
## 3 Results and Discussion
### Splitting Tensile Strength
The maximum tensile strength achieved by any QFS-GPC specimen was attained by GPC-D (2.12 MPa), which is slightly less (6.6%) than the control OPC specimen at  
Figure 5: The splitting tensile strength of control OPC and QFS-GPC samples at 7, 28, and 56 days. 56 days. The improvement in the strength of the QFS-GPC specimens at all ages was due to an increase of calcium-bearing mixtures in the dissolution binders, which yielded a reaction product from both SG and FA [49]. The QRD has maximum CaO content (47%) among all the source materials. The increase in strength with increasing the replacement levels of QRD is due to the increased CaO contents which reduce the pore sizes in the geopolymer matrix consequently improving mechanical properties. However, the replacement level of QRD beyond 15% decreased the tensile strength due to the incomplete hydration of CaO contents of QRD present in excess quantity (47%) in the alkaline medium to form Ca(OH)\({}_{2}\)[45]. The tensile strength also reduces due to the deficient geopolymerization process [47].
## 4 Experiments
### Metrics  
**Metrics for Segmentation** Two widely used metrics, mean intersection over union (mIOU) and mean pixel accuracy (mPA), were utilized to evaluate the segmentation network proposed in this study. mIOU is the average of IOUs of all classes, in which the IOU of each class is calculated by dividing the intersection of the predicted area and actual ground truth area by the union of the predicted and ground truth areas. The mPA is the average pixel accuracy of all classes, in which the pixel accuracy of each class is defined as the proportion of accurate pixels to the total pixels.  
**Metrics for Pose Estimation** ADD-S (average closest distance) and AUC (area under the ADD-S curve) were adopted to evaluate the performance of the 6D pose estimation network proposed in the study on the SIAT dataset and YCB-Video dataset. The ADD-S is defined as the average distance between each point of the 3D object model transformed by the ground truth pose P\({}_{gt}\) and the nearest point of the 3D object model transformed by P\({}_{pred}\). Accuracy was defined as the percentage of testing samples with an ADD-S below a certain threshold. In this study, we report the accuracy of an ADD-S of less than 2 cm. In addition, an accuracy threshold curve of the ADD-S can be obtained by adjusting the threshold. The AUC is defined as the area under the curve from 0 to a certain threshold. For a fair comparison, we followed previous work to set this threshold to 0.1 m.
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 4 The Experiments of Liver Tumor Segmentation
### Evaluation Metrics
#### 4.2.1 Dice Similarity Coefficient Metric  
When training for semantic segmentation, assessing the model's performance using a metric that contrasts the ground truth with the predicted mask is essential. The key terms included \"true positive (_TP_)\", \"false positive (_FP_)\", and \"false negative (_FN_)\".  
\[DSC=\frac{|2 TP|}{|2 TP|+|FP|+|FN|^{\prime}} \tag{3}\]
# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 3 Experiments
### Experimental Analysis
This causal insight enables a more precise prioritization of shipping strategies in risk mitigation, particularly for high-risk shipments that could benefit from optimized shipping mode selection. Moreover, our analysis revealed a direct causal link between **delivery status** and **late delivery risk**, with a significant causal strength (10.66). This finding diverges from studies such as [PERSON] and [PERSON] [15], which focus primarily on reactive inventory adjustments to minimize delays. While inventory management is certainly relevant, our results suggest that monitoring delivery status provides a more immediate indicator of risk, allowing for preemptive interventions. This causal relationship, which our method detects with reinforced confidence, emphasizes the importance of real-time monitoring over purely inventory-based adjustments, thus proposing a shift in risk management priorities. Interestingly, our method also found moderate causal effects of customer-related variables, such as **order quantity** and **sales per customer**, on late delivery risk (causal strengths of 1.10 and 1.12, respectively). This nuance contributes to the discussion by supporting findings from [PERSON] and [PERSON] [15], who noted that large orders often disrupt logistics planning, leading to delays. However, unlike traditional studies that treat these customer variables as indirect or secondary risk factors, our model establishes them as contributing causal drivers, albeit with lower causal strength. This insight underscores the interconnectedness of logistical and customer factors in influencing delivery outcomes. In summary, our causal discovery approach not only corroborates established relationships identified in prior studies but also provides additional granularity by quantifying the causal strengths. This level of detail advances current knowledge by facilitating targeted risk mitigation strategies that address high-impact factors with greater precision. Although these insights are not directly related to the root causes of late deliveries, they point to key areas for further research and optimization in supply chain management. Focusing on these factors could lead to substantial improvements in profitability, highlighting the broader applicability of our causal discovery framework in uncovering critical drivers of business performance beyond delivery risks.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 2 Spatial Deformation and Soil Pressure Distribution of Retaining Structure
### Spatial Deformation of Retaining Structure
#### 2.1.2 Pile Deformation  
According to research, the deformation of piles in pile-anchorretaining structures consists of both horizontal and vertical deformations. However, horizontal deformation is generally considered to be the main deformation mode of piles [16; 17; 18]. Previous studies have shown that the maximum horizontal deformation of piles usually occurs near the pit bottom [19; 20; 21]. Therefore, this study assumes that the horizontal deformation curve of piles can be approximated by a quadratic curve fitting. The simplified horizontal deformation mode of the pile is shown in Figure 3.  
Let us make the excavation depth of the foundation pit \(h\) = \(\lambda H\). \(H\) is the length of the pile. \(\lambda\) is the ratio of the depth of the foundation pit to the length of pile, abbreviated as the depth coefficient of the foundation pit. The displacement of the pile top is the horizontal deformation of the crown beam \(\delta\). Assume that the embedded end of the support pile does not displace and the maximum displacement
## 1 Introduction  
Whey protein is a by-product of cheese and casein manufacture, and it serves as an ingredient widely used in the food and drink industry due to its valuable nutritional and bioactive characteristic, and multiple functional properties such as solubility, viscosity, and emulsifying and foaming properties [1; 2]. Currently, there is a growing demand to improve functional properties of natural food ingredients to have a variety of functions for food and drink application. Whey protein has been studied to enhance or alter its functional properties applications by various methods such as physical, enzymatic, and chemical techniques [3]. The modification of protein by conjugate and polymerization with phytochemical compounds has received attention recently because it can improve the functional properties of protein and improve human health [4].  
Plant phenolics are phytochemical compounds with one or more aromatic rings containing a hydroxyl substituent, which can be found in plant origins such as fruits and vegetables [5]. Phenolic compounds can interact with proteins by covalent and non-covalent interactions. However, covalent interaction appears to play a crucial role in protein-phenolic interaction, which is used to enhance the functional properties of proteins [4]. Such an interaction affects the functional properties of proteins, such as emulsion formation [6], gelling properties [7] and antioxidant activity [5]. Due to cross-linking and complex formation, the conformation of proteins is changed and the exposure of some additional hydrophobic regions previously buried takes place [4; 5; 6; 8]. Moreover, the increase in phenolic groups in modified protein can increase hydrophobicity and antioxidant activity [9].  
Therefore, the interaction of whey protein with plant phenolic compounds is an interesting method that could possibly lead to an increase in the functional properties of the resulting whey protein. However, there is limited information on the modification of whey protein with phenolic compounds. The objective of this study was to investigate the effects of whey protein modified with phenolic compounds including gallic acid, ferulic acid, and tannic acid with different sizes and numbers of hydroxyl groups (Figure 1) on the characteristics and functional properties of the modified whey protein.
## 2 Establishment of a 3D Rock-Breaking Model for Disc Cutters  
### Structure of Disc Cutters  
Disc cutters are a critical component of TBMs used for rock breaking. Their structure typically comprises four main parts: the cutter ring, cutter hub, cutter shaft, and disc-cutter seal. This modular design facilitates the easy disassembly and replacement of individual components. The cutter ring, an annular body with cutting edges, serves as the primary cutting element and is responsible for rock fragmentation, as shown in Figure 2. It is securely attached to the cutter body, which constitutes the hub section of the disc cutter.  
Based on the number of cutter rings on the cutter hub, disc cutters can be classified into single-disc, double-disc, and multi-disc cutters. Among these, single-disc cutters are the most commonly employed configuration in full-section TBMs. The primary distinction between single- and double-disc cutters lies in the number and arrangement of blades on the cutter ring, which significantly influence their rock-breaking mechanisms and application scenarios. Single-disc cutters produce larger rock fragments and more extensive rock fissures, whereas double-disc cutters apply higher extrusion pressure to the rock mass, resulting in finer gravel and rock debris. Single-disc cutters fracture rock through repeated compressive forces that generate tensile cracks, while double-disc cutters rely on synergistic interactions between the hobs for effective rock fragmentation. Each type has distinct advantages, and the choice between single- and double-disc cutters should be determined based on specific geological conditions and engineering requirements.  
Figure 1: TBM cutting head model used in China.  
A TBM cutter head can accommodate dozens to hundreds of disc cutters. Ensuring optimal rock-breaking efficiency requires determining the ideal cutter spacing. If the spacing, \(S\), is too large, the cracks generated by adjacent disc cutters may not intersect, leaving unbroken rock between them. Conversely, excessively small cutter spacing leads to the over-penetration of cracks, resulting in unnecessary energy loss. Optimal cutter spacing is achieved when the cracks generated by adjacent disc cutters fully penetrate the rock. This optimal spacing enhances the rock-breaking efficiency, minimizes cutter wear, accelerates the driving speed, improves the construction schedule, and reduces the risk of abnormal TBM failures.
## 4 Applying Noise to Encrypted Images
### The \(\chi^{2}\) Noise  
For the \(\chi^{2}\) noise, Equation (36) is used and previously described in Section 2. This random variable approximates a normal distribution with \(\mu=255\) and standard deviation \(\sigma=22.58\). The operation starts choosing \(n\) pixels randomly of the encrypted image in the spatial domain. Each of them is associated with a frequency \(f_{c}(x,y)\), where \(c\) indicates the color, and \((x,y)\) is the pixel position. The range of this variable is \(0\leq f_{c}(x,y)\leq 255\).  
Later, a value \(z_{c}(x,y)\sim\text{N}(0,1)\) is chosen randomly for each pixel and color. Then, the variable \(f_{c}^{\prime}(x,y)\) is calculated according to Equation (40). In the case of \(z_{c}<-3\), the value of \(-3\) is assigned to the variable. If \(z_{c}>3\), it is given the value of 3. In such a way, \(-3\leq z_{c}(x,y)\leq 3\).  
\[f_{c}^{\prime}(x,y)=255+z_{c}(x,y)22.58 \tag{40}\]  
Figure 2: Images used for testing AICLDH: (**a**) Peppers; (**b**) Donkey; (**c**) Lena; (**d**) Barbara.  
Afterward, the variable \(f^{\prime}_{c}(x,y)\) is discretized using floor and ceiling functions. The symbol \(\lfloor\ \rfloor\) is applied if the decimal part of \(f^{\prime}_{c}(x,y)\) is less than or equal to 0.5. Then, the integer part of \(f^{\prime}_{c}(x,y)\) is taken, and the discrete value of the frequency, \(f^{\prime}_{dc}(x,y)\), is obtained according to Equation (41). When the decimal part is greater than 0.5, the second symbol \(\lceil\ \rceil\) is applied. In this case, one unit is added to the integer part, and the discrete value of the frequency is computed according to Equation (42).  
\[f^{\prime}_{dc}(x,y)=\lfloor 255+z_{c}(x,y)22.58\ \ \ \ \mathrm{mod}\ 256 \tag{41}\]  
If a decimal fraction is greater than 0.5, then \(f^{\prime}_{dc}(x,y)\) is calculated according to Equation (42).  
\[f^{\prime}_{dc}(x,y)=\lceil 255+z_{c}(x,y)22.58\ \ \ \ \mathrm{mod}\ 256 \tag{42}\]  
In this paper, it is proposed to substitute the value of \(f_{c}(x,y)\) for \(f^{\prime}_{dc}(x,y)\) as the case may be. Additionally, this type of noise mostly replaces values located at the level extremes. In other words, values from the range 0-64 or 191-255.
## 4 Experiment and Analysis
### Performance Comparison with State-of-the-Art Methods  
Performance comparison of multiple state-of-the-art approaches including PROVID [11], MICRO [16], Light vgg m+SCAN [30], VRSDNet [37], RAM [29], SCCN-Ft+CLBL-8-Ft [8], and GS-TRE loss W/mean VGGM [12] with our proposed approach on the VeRi dataset is shown in Table 1. The VeRi dataset was chosen for comparison because it consists of supplementary viewpoints, different illumination, and resolutions as compared to other datasets. The VeRi dataset fulfills all the aspects of real-world camera surveillance data for vehicle-related applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & _mAP_ & **HIT-1\%** & **HIT-5\%** \\ \hline PROVID [11] & 53.42 & 81.56 & 95.11 \\ FACT [26] & 18.75 & 52.21 & 72.88 \\ GoogLeNet [23] & 17.89 & 52.32 & 72.17 \\ VGG [38] & 12.76 & 44.10 & 62.63 \\ FACT + Plate-SNR + STR [39] & 27.77 & 61.44 & 78.78 \\ GS-TRE loss W/mean VGGM [12] & 99.47 & 96.24 & 98.97 \\ JFSDL [40] & 53.53 & 82.90 & 91.60 \\ ABLN-Ft-16 [41] & 24.92 & 60.49 & 77.33 \\ SCN-Ft+CLBL-8-Ft [8] & 25.12 & 60.83 & 78.55 \\ Appearance + Color + Model + Re-Ranking [14] & 61.11 & 89.27 & 94.76 \\ Siamese-CNN-Path-LSTM [42] & 58.27 & 83.49 & 90.04 \\ RAM [29] & 61.5 & 88.6 & 94.0 \\ OT+ST [28] & 51.4 & 92.35 & \\ MTCRO [16] & 62.61 & 87.96 & 94.63 \\ ResNet-50 + GoogleNet + Feature fusion via correlative sparse ranking [43] & 58.21 & 90.52 & 93.38 \\ Light\_vgg\_m+SCAN [30] & 49.87 & 82.24 & 90.76 \\ VAM [13] & 50.13 & 77.03 & 90.82 \\ XVGAN [44] & 24.65 & 60.20 & 77.03 \\ VGG+C+T [19] & 58.78 & 86.41 & 92.91 \\ VRSDNet [37] & 53.45 & 83.49 & 92.55 \\ QD-PLF [85] & 61.83 & 88.50 & 94.46 \\ Combining Network [46] & 33.78 & 60.19 & 77.40 \\ Appearance (Ours) & 59.43 & 82.01 & 94.40 \\ Data Augmentation + Appearance (Ours) & 60.21 & 82.91 & 94.50 \\ Data Augmentation + Appearance + License plate verification (Ours) & 62.80 & 85.07 & 95.23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with some state-of-the-art approaches. _mAP_: mean average precision.  
Figure 9: Cumulative matching characteristics (CMC) on the VeRi-776 dataset.  
Firstly, from Table 1, it is observed that our method outperforms on the VeRi dataset as compared to all recent methods. It achieves 62.80% in mAP, 85.07 HIT@1 and 95.23 HIT@5. The VeRi dataset contains spatio-temporal information and most of the methods [11; 13; 16; 28; 39] exploit spatio-temporal information in the vehicle Re-Id system. It is worth mentioning here that our approach outperforms without using any spatio-temporal information, whereas Orientation Invariant Feature Embedding (OIFE) [28] needs additional annotations of key points during training for their orientation invariant embedded learning. It is noticeable in our approach that the training procedure was more convenient than many other methods presented in Table 1.
## 3 Azimuth Spectrum Reconstruction  
In view of the azimuth non-uniform sampling for hypersonic platform SAR, this section first analyzes the relative Doppler parameters, and then provides a high-efficient algorithm to reconstruct the azimuth echo signal, which can pre-eliminate the effects of the spectrum ambiguity on SAR imaging.
# The Case for a More Holistic Approach to Dry Eye Disease: Is It Time to Move beyond Antibiotics?
## 2 Dry Eye Disease as an Inflammatory Disorder  
Increasing evidence reveals a model of dry eye as a localized mucosal autoimmune disease originating from an imbalance in the protective immunoregulatory and proinflammatory pathways of the ocular surface [7]. In a mouse DED model, desiccating stress is sufficient to induce acute response cytokines that further enhance pro-inflammatory cytokine/chemokine production, followed by activation of pathogenic Th1 and Th17 cell responses [26; 27]. Autoreactive T cells traffic to the ocular surface tissues potentiating chronic autoimmune response and pathology [7]. Anti-inflammatory work to inhibit the hallmark cognate deleterious cascade of DED.  
Eye drop preservative toxicity affects ocular mucosal tolerance causing dry eye [28]. In a mouse model, dry eye can be eliminated through restoration of immune function, suggesting again that DED arises from immune dysregulation [26].  
As such, more promising DED agents are those directed towards reducing inflammation and restoring normal tear film though currently there is no specific immunotherapy for DED [29]. A 2017 review of cyclospor A (CsA) for the treatment of chronic dry eye [30] indicated strong evidence of efficacy in treating signs but not symptoms. However, this conclusion is at odds with a 2019 review that suggested significant improvement only in corneal staining [18] and side-effects including significant ocular irritation. CsA highlights a number of ocular therapeutics issues such as retention time [30], burning and stinging and installation site irritation [31].  
Interleukin -1 (IL-1) mediates the inflammatory cascade but products aimed at antagonizing IL-1 have had mixed results in clinical trials as reported by [PERSON] and colleagues [18]. One promising inhibitor of the inflammatory cascade is Lifitegrast, the first dry eye product to target LFA-1 (a T-cell receptor that amplifies inflammatory pathways through cytokine release) [22]. Lifitegrast trials offer tantalizing evidence for achieving attenuation of both signs and symptoms. However, these milestones were not achieved in any single study but shown independently in separate studies [18]. This proved the test case for the FDA to forgo requiring the need for improvement against placebo in both signs and symptoms within the same study [32].
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method
### Artificial Intelligence Method
#### 5.3.4 Hidden Markov Model (HMM)-Based Method  
The HMM is composed of Markov chains and state transition matrixes and contains four key components: hidden state, state transition probability, observable state and initial state. The model treats each mode as a hidden state by default, but the duration of the state is required to obey a geometric distribution. This problem can be overcome by the hidden semi-Markov model (HSMM), constructed by introducing the probability of state duration based on HMM. If the HMM transition probability prior distribution is defined using the multi-layer Dirichlet process hybrid model to cluster the data, the HDP-HSMM method constructed with Mahalanobis distance as the local monitoring statistic can automatically identify and classify the data mode after using the HSMM to monitor the mode conversion in the transition mode [93]. After being trained by the Baum-Welch method, HMM can be combined with kernel principal component analysis (KPCA), or variable selection methods and moving windows, to significantly improve the monitoring performance of transition modes and reduce rates of false alarm [94; 95; 96].