esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion Related to the Evaluations of the Osseeointegration Process
### BII and BIC  
Concerning the BII distance, 10 \(\upmu\)m is used as a limit by [PERSON] et al. [21], [PERSON] et al. [24], and the main implant manufacturers of dental implants. In the present study, 1 pixel corresponds to 6.8 \(\upmu\)m, which means that the BII is 3.3 \(\upmu\)m, much less than 10 \(\upmu\)m, thus even more favorable.  
The BIC evaluations performed on the four sections displayed in Figure 12 are given in Table 2. An area located close to the external border of the side-closed implant, open at its bottom, was investigated for different angular positions at the cylinder periphery.  
Over the 603 pixels corresponding to the cumulative height of the 4 selected sections on the outer portion of the cylinder, 28 pixels representing bone are not in contact with pixels representing metal, i.e., 4.6%. The contact between the bone and the metal, related to the BIC, is therefore 95.4%. Such a high level of osseointegration promises the highest stability and duration of the implant in the bone. Indeed, classically BICs ranging between 50% and 80% are considered to be favorable scores [28].  
A similar count was also performed on the same sections, but on an area located close to the internal border. In this case, the analysis was conducted in the lattice region of the side-closed implant open at its bottom for different angular positions at the cylinder periphery. The results are given in Table 3. Out of the 653 pixels considered, 47 did not show bone/implant contact, a rate of 7.1%. The difference in the BIC between the internal and external borders of the implant mainly originates from one of the four considered sections, located at 90\({}^{\circ}\).  
The low thickness of the BII and the importance of the BIC may induce stress concentration at the bone-implant junction if there is a high stiffness difference between the bone and the implant. However, in the present study, the smaller difference between [PERSON]'s modulus, i.e., the metric representing the stiffness of the cortical bone (25-30 GPa) and the new used alloy (38 GPa), has reduced the effective stress compared to other commonly used materials.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Angular Position around the Cylinder** & **0\({}^{\circ}\)** & **45\({}^{\circ}\)** & **90\({}^{\circ}\)** & **135\({}^{\circ}\)** & **Total** \\ \hline Number of pixels in the selected image area & 148 & 151 & 150 & 154 & 603 \\ \hline Number of pixels for which bone and & & & & & \\ metal have no contact & 1 & 5 & 17 & 5 & 28 \\ \hline \% of pixels free of bone or metal & 0.6 & 3.3 & 11.3 & 3.2 & 4.6 \\ \hline BIC (\%) & 99.4 & 96.7 & 88.7 & 96.8 & 95.4 \\ \hline \end{tabular}
\end{table}
Table 2: BIC evaluations performed on an area located close to the external border of the side-closed implant, open at its bottom, labeled 1386, for different angular positions at the cylinder periphery.
## 1 Introduction and State of the Art
### Digital Twin  
In recent years, the concepts of \"cyber-physical systems\" (CPS) and the \"Internet of Things\" (IoT) have emerged, both of which have a similar but different impact on the design, development and implementation of mechatronic components and systems, as well as on more complex systems resulting from the combination and integration of these [25]. In the case of cyber-physical systems, individual components, often but not always mechatronic in nature, are combined and integrated through the use of advanced intelligent software. The Internet of Things represents a domain that is extended by access to and exchange of information to integrate both mechatronic and cyber-physical systems to create novel systems that are user-centric [26,27]. Starting with mechatronics, this section considers the design issues involved and discusses the product development process using model-based approaches. In this context, an important requirement is the interaction between systems at different hierarchical levels. This concerns modelling as well as simulation. The concept of a \"digital twin\" dates back to NASA's Apollo program, where at least two identical spacecraft were built, allowing engineers to mirror the conditions of the spacecraft during the mission, with the vehicle remaining on Earth referred to as the twin [26]. Nowadays, a digital twin refers to a computerized model of a product that is used for different purposes or product life phases. Currently, there are many definitions and explanations of the term \"digital twin\" in the literature. Depending on the information flow between the physical and digital object, a distinction is made between a \"digital master\", a \"digital shadow\" and a \"digital twin\" [28]. The problem is that a digital twin follows certain laws and properties in every application, but ultimately is different in every implementation. The research goal is to develop a generic meta-model [29; 30] that can then be instantiated by the development engineer according to the respective circumstances. This leads to the question of what the structure of the meta-model should look like as a generic template, how can a digital twin for a collaborative workplace be specified from it?
###### Abstract  
Given the increasing incidence of chronic metabolic diseases, fermented functional foods are receiving a growing demand due to their important functional activities. The aim of this pilot clinical study-nutritional intervention is to expand knowledge on how the habitual intake of a biofunctional miso-type sauce, enhanced with biocarotenoids, may affect biomarkers of lipidemia, glycemia, and oxidative stress in healthy volunteers. Using a randomized, cross-over, controlled, and single-blind design, ten healthy participants with a mean age of 23 years, who met the eligibility criteria, supplemented their daily diet with either 20 g of legume-based or the biofunctional miso-type sauce for 30 days, with a one-week washout. Blood samples were taken at baseline and after intervention. The measured parameters included serum total, HDL, and LDL cholesterol, triglycerides, uric acid, glucose, and plasma TAC. After 30 days, the miso-type sauce increased plasma TAC (\(p=0.04\)) and slightly decreased mean triglycerides (\(p=0.47\)) compared with the control sauce. Both sauces resulted in higher LDL cholesterol levels (\(p=0.001\)-\(0.02\)), indicating possible negative effects on lipidemic control. However, the miso group showed a lower grade of increment compared with the control. This long-term study partly supports the acute postprandial indications and motivates research expansion, demonstrating that biofunctional miso-type sauce, enhanced with biocarotenoids, may possess a preventive role in chronic dysmetabolism and oxidative stress.  
biofunctional miso-type sauce; biocarotenoids; habitual consumption; metabolic effect; bioactivity; oxidative stress; lipidemia; glycemia Article
## 3 Iterative Learning Construction and Analysis Based on RBF Neural Network
### Simulation Verification Analysis  
To standardize the simulation and verification of gait training trajectories for the lower limb exoskeleton rehabilitation robot, we employed an integrated Qualisys 3D motion capture system with CGA gait data to acquire human lower limb kinematics (Figure 4). Reflective markers were placed at the waist, hip, thigh, knee, calf, and ankle, as well as foot segments, connected using a tandem rigid body structure to establish a single-leg coordinate system for gait trajectory extraction. Subjects executed straight-line walking trials during 10-s intervals, with synchronized motion capture data and CGA parameters processedthrough MATLAB 2021. This analysis focused on sagittal plane trajectories including hip flexion/extension, knee flexion/extension, and ankle dorsiflexion/plantarflexion.  
Figure 5 shows the trajectory tracking curves of each joint of the lower limb exoskeleton rehabilitation robot. The blue line represents the desired lower limb rehabilitation training trajectory and the red line represents the actual lower limb rehabilitation training trajectory. When the number of iterations increases, the actual rehabilitation trajectory of the hip joint gradually converges to the desired trajectory, and when the iteration reaches the 10 th time, that is to say, after the joints track the desired trajectory and repeat the movement for 10 times, the actual rehabilitation trajectory almost completely coincides with the desired trajectory.  
The iterative learning trajectory tracking errors are shown in Figures 6 and 7. As the number of iterations increases, the tracking error value gradually decreases. Compared with the previous iteration cycle, the trajectory tracking performance in the next iteration cycle is stronger. In addition, the systematic error of the iterative learning controller based on the RBF neural network proposed in this paper is significantly smaller than that of the iterative learning controller. After three iterations, the error is controlled within a very  
Figure 4: Human Gait Measurement Experimental Environment.  
Figure 5: Trajectory tracking curves of each joint of a lower limb exoskeleton rehabilitation robot. (**a**) Hip Trajectory Curve; (**b**) knee Trajectory Curve; and (**c**) ankle Trajectory Curve.  
small range. Compared with the iterative learning control algorithm, the algorithm in this paper has higher accuracy and a faster convergence speed.
# A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
## 6 Benchmark Datasets
### Comparison on Flickr 30k and MS-COCO Datasets  
The Flickr 30k and MS-COCO datasets are widely employed for evaluating cross-modal representations. Our comparisons relied on the test sets from Flickr 30k and MS-COCO. We utilized metrics such as IR@K and TR@K (K = 1, 5, 10), where R_SUM represents the total recall. We present the comparison results on the Flickr 30k dataset in Table 5. Non-pre-trained models were trained from scratch. Generally, models incorporating pre-training exhibit superior performance compared to traditional methods without pre-training. For instance, SCAN [56] maps the features of each modality to a low-dimensional space, employing only shallow interactions for similarity calculation. CAAN [106] suggests collecting local information and investigating intra-modal properties. This approach overlooks significant cross-modal commonalities. While VSRN [107], MMCA [58], and DIME [108] utilize distinct methods to employ semantic reasoning or dynamic interaction, they need to gain early learning of common properties across modalities. Therefore, the effect of feature training is inferior to the result of the pre-training mode.  
Table 6 presents the comparison results on the MS-COCO test dataset. These findings suggest that the larger the pre-training dataset, the higher the recall in cross-modal retrieval is. ALIGN [29], which utilizes 1.8B pre-training data, outperforms algorithms with the 4M and 5.6M pre-training data in retrieval tasks. UNITER [39] and ViLT [35] employ single-stream encoders. However, since UNITER relies on a pre-trained object detection model, it introduces additional noise, leading to suboptimal retrieval performance. In contrast, ViLT incorporates an RPN-like candidate region generator, but the limited tags in supervised training pose challenges, resulting in performance constraints.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Performance Evaluation Metrics
#### 4.1.3 Packet Delivery Ratio  
The ratio of the packets received by other vehicles to those sent out by the transmitting vehicle. Higher PDR values indicate improved network performance.  
\[PDR=TotalP_{r}/TotalP_{s} \tag{7}\]
## 3 CFTL Log Parsing Method  
### Motivation  
The log parsing process learns the features of historical log data. Similar to the general machine learning process, the selection of features has a profound impact on learning outcomes. Too many selected features may lead to overfitting. Otherwise, underfitting may occur. Log data features can be divided into global and local features. Global features include log length, token spatial distribution, and token ratio of constants and variables. Local features refer to the specific token information, the specific token location, and the prefix and suffix information of the token. These features provide heuristic rule information for log parsing and provide an important basis for heuristic rule-based methods.  
The strategies used in heuristic rules heavily depend on the experience of the researcher. Researchers establish rules by observing these log message characteristics, and analytic methods rely on these rules to perform clustering and finally obtain log templates. AEL analyzes logs using the ratio of constants to variables. It considers only the global features of logs, excluding local features, and the extracted heuristic rules are insufficient; thus, it easily underfits in actual applications. IPLoM considers global features such as the log length and local features such as unique token locations and token pair mappings. Drain and Spell further complement the local feature of the token prefix. These methods extract an excessive number of heuristic rules, which results in high training accuracy but can easily lead to overfitting and poor generalizability.  
These findings demonstrate that both the global and local features of log messages should be considered; however, the extracted heuristic rules should be maintained at an appropriate granularity to balance training and practical application performance. The log length is widely recognized as the most representative global feature. To prevent overfitting of local features, the scale and complexity of selection for the number of tokens and related information should be controlled. This study focuses on the system logs of distributed servers. Different types of logs have different lengths, whereas logs of the same type have similar structures and token distributions. The most significant feature is the first token difference. Therefore, in the proposed CFTL method, the log length and first token are selected as global and local features, respectively, as a heuristic rule to train the analytic model to avoid underfitting. In addition, an excessive number of tokens or their auxiliary information is not selected as a rule to avoid overfitting. In summary, the combination of the log length and first token provides an appropriate heuristic rule granularity for the system logs of distributed servers, which prevents overfitting and underfitting.
# Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan
## 4 Discussion  
Based on the regionalization technique, six spatially contiguous zones were created in the province of Balochistan. A 24-month SPEI time series was used for regionalizing the study area. The zones  
Figure 16: (a) 3-month and (b) 12-month SPEI maximum drought duration.  
Figure 17: (a) 3-month and (b) 12-month SPEI minimum drought severity.  
Figure 18: (a) 3-month and (b) 12-month SPEI minimum drought intensity.  
produced not only reflect the climatology of the region, but the first four zones are also similar to the divisional boundaries of Balochistan.  
The results of this study are consistent with the existing literature. [PERSON] and [PERSON] (1998) conducted a study in 2015 in which they assessed the spatio-temporal characteristics of droughts in Balochistan using SPI as an indicator for the period of 1975-2010. They concluded that negative trends in precipitation exist in more than 70% of the climatic stations at both annual and seasonal scale. The only anomaly that was observed was at Ziarat station, which lies in Zone 5 in this study and showed a statistically significant positive trend in spring. Furthermore, [PERSON] (2014) concluded, based on climatic data from 1951 to 2015, that the annual evapotranspiration and maximum temperature in Balochistan showed a statistically significant positive increasing trend at a confidence interval of 95%. Coupled with other detrimental effects, the trend toward dryness may eventually lead to the desertification of the entire region ([PERSON] et al., 2015).  
Furthermore, the findings of this study also complement the results of global research focusing on the effects of climate change on droughts. [PERSON] et al. (2015) reported a dramatic increase in drought-affected areas in South Asia in the late 1990s and early 2000s. As identified in this study, the drought of 1998-2002 was among the worst that the entire region of Balochistan faced. It occurred due to an El Nino that started to develop in 1997 in the Pacific Ocean. They also concluded that the Drought Risk Index (DRI), defined by combining the effects of drought severity, frequency, crop yield, and extent of irrigation, in South Asia is expected to increase by up to 97.6% by the year 2050.
## 5 Experiments
### Experiment Settings
#### 5.1.2 Methods of Comparison  
To validate the effectiveness of the proposed method, we compare it with the following knowledge graph-based recommendation methods:  
* FM [35]: A classical latent factor model that captures pairwise feature interactions using second-order terms, widely applied in recommendation and ranking tasks.
* NFM [36]: An extension of FM that replaces manual interaction modeling with a neural network, enabling the capture of higher-order and nonlinear feature interactions.
* CKE [6]: A knowledge-aware recommendation model that integrates collaborative filtering with knowledge graph embedding (TransR), modeling both structural, and semantic item features.
* KGAT [10]: A graph neural network-based model that jointly learns embeddings from user-item interactions and KG triples via an attention-guided message-passing framework.
* KGIN [11]: A recent GNN-based model that separates collaborative and knowledge signals using relation-aware propagation and semantic-level attention mechanisms.
* VRKG4 Rec [34]: A virtual relational KG-based model that clusters original relations into high-level virtual categories to alleviate sparsity and enhance recommendation performance.
* LightGCN [37]: A simplified graph convolutional model that focuses on pure neighbor aggregation without feature transformation or nonlinear activation, achieving strong performance on collaborative filtering tasks.
* Wide & Deep [38]: A hybrid model combining linear (wide) and deep (nonlinear) components to jointly learn low- and high-order interactions, widely adopted in industrial recommender systems.
# Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
## 2 Materials and Methods
### Construction of Rock Physical Prior Information
#### 2.1.2 Wave Velocity and Formation Density
According to the core principle of FWI, the objective function Q(m) for the multi-parameter FWI of seismic waves can be defined in the following form:  
\[Q(\mathbf{m})=\frac{1}{2}\underset{\mathbf{x}_{s}}{\sum}\underset{\mathbf{x}_{r} }{\sum}\underset{t}{\sum}\left(\mathbf{R}\mathbf{w}(t,\mathbf{x},\mathbf{x}_{ s};\mathbf{m})-\mathbf{d}_{\mathrm{obs}}(t,\mathbf{x}_{r},\mathbf{x}_{s}) \right)^{2} \tag{3}\]  
where \(\mathbf{m}\) denotes the parameter vector of the model medium, \(\mathbf{x}_{s}\) represents the spatial coordinates of the excitation point, \(\mathbf{x}_{r}\) stands for the spatial coordinates of the receiver point, \(t\) is the observation time, \(\mathbf{R}\) signifies the constraint on the receiver point position, \(\mathbf{w}(t,\mathbf{x},\mathbf{x}_{s};\mathbf{m})\) is the simulated full wavefield information, and \(\mathbf{d}_{\mathrm{obs}}(t,\mathbf{x},\mathbf{x}_{s})\) is the observed wavefield. In this study, the variable-density acoustic wave equation is adopted as an example for multi-parameter FWI testing. In this equation, \(\mathbf{m}=[v(\mathbf{p}),\rho(\mathbf{r})]^{\mathrm{T}}\), where \(v\) is the velocity of the acoustic wave, \(\rho\) denotes the medium density, \(\mathbf{p}\) is the acoustic pressure, \(\mathbf{r}\) is the spatial coordinate vector of the receiver point, and \({}^{\mathrm{T}}\) is the transpose operator. To mitigate the ill-posed problem in the inversion problem, the traditional TV regularization strategy is used to formulate the regularization term based on the L1 norm. The research in [11] indicates that TV regularization can effectively mitigate the excessive smoothing of boundaries encountered in Tikhonov regularization, resulting in more pronounced distinctions between features and background regions, as well as sharper and more well-defined edges in the reconstructed target structures. As a regularization method based on penalty functions, TV regularization introduces an additional TV regularization constraint term to the conventional FWI objective function, thereby constructing a new objective function:  
\[Q_{TV}(\mathbf{m})=Q(\mathbf{m})+\lambda_{TV}\Phi_{m}(\mathbf{m}) \tag{4}\]  
where \(\lambda_{TV}\) is the TV regularization factor, \(\Phi m(\mathbf{m})\) is the model parameter objective function, and \(Q_{TV}(\mathbf{m})\) is the objective function under TV regularization. The objective function for the multi-parameter FWI of seismic waves, incorporating rock physical constraints as proposed in this study, is formulated as follows.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 2 Algorithm Overview
### Lemming Optimization Algorithm
#### 2.2.1 Position Initialization  
Before entering the iterative process, the LOA initializes the positions of all search agents. The initial candidate solutions are represented as a matrix \(\overset{\rightarrow}{Z}\) with dimensions \(N\times Dim\) (where \(N\) is the population size and \(Dim\) is the problem dimensionality), bounded by the lower and upper limits of the search space. This is formulated as in Equation (1):  
\[\overset{\rightarrow}{Z}=\left[\begin{array}{cccc}z_{1,1}&z_{1,2}&\dots&z_{ 1,Dim-1}&z_{1,Dim}\\ z_{2,1}&z_{2,1}&\dots&z_{2,Dim-1}&z_{2,Dim}\\ \dots&\dots&z_{ij}&\dots&\dots\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ z_{N-1,1}&z_{N-1,2}&\dots&z_{N-1,Dim-1}&z_{N-1,Dim}\\ z_{N,1}&z_{N,2}&\dots&z_{N,Dim-1}&z_{N,Dim}\\ \end{array}\right] \tag{1}\]  
Figure 2: Hybrid A\({}^{*}\) search method.  
In each iteration, the best position determined is considered to be the optimal solution or approximately optimal solution obtained so far. The decision variables \(Z_{i,\ j}\) for each dimension are calculated according to Equation (2):  
\[z_{ij}=LB_{j}+rand\times\left(UB_{j}-LB_{j}\right),i=1,2,\ldots,N,j=1,2,\ldots, Dim \tag{2}\]  
where this value is a random number between 0 and 1; \(LB_{j}\) is used to represent the lower limit of the \(j^{th}\) dimension, \(UB_{j}\) and \(j^{th}\) simultaneously represent the upper limit of the dimension.