esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[22] predicted the compression index using ANN with a feed-forward back-propagation algorithm. [PERSON] et al. [32] used GA to optimize the hyperparameters of five models including a back-propagation neural network (BPNN). [PERSON] and [PERSON] [27] developed an XGB model to predict the compression index using five geotechnical parameters, while [PERSON] et al. [28] and [PERSON] et al. [29] employed XGB along with other models. Additionally, [PERSON] et al. [12] optimized the parameters of ANN and adaptive neuro-fuzzy inference system (ANFIS) models by using ten swarm intelligence algorithms. Of these, the ANFIS and PSO hybrid model demonstrated the best performance. [PERSON] and [PERSON] [33] developed a super-learner machine learning model for compression index prediction. The liquid limit, plasticity index, natural water content, and initial void ratio were used as input values, with the extra trees regressor and gradient boosting regressor algorithms serving as base learners and the random forest regressor algorithm serving as the meta learner. This resulted in a model with a strong predictive performance being developed, achieving a value of R\({}^{2}\) = 0.93. Although numerous studies have been conducted to predict geotechnically significant indices using artificial intelligence (AI) techniques, no data preprocessing was carried out prior to training the model. Indeed, even if preprocessing was performed, it was limited to simply removing statistical outliers or performing feature engineering based on computer science approaches. Furthermore, even if a prediction was made, the performance of the model was only analyzed numerically, with the geological elements of the study area not taken into consideration during the prediction and analysis process. Therefore, this study aimed to analyze the factors influencing variations in the predictive performance of machine learning models by dividing data based on topographical factors in the Saemangeum reclaimed land area, located on the west coast of South Korea. First, geotechnical data comprising five key geotechnical parameters affecting the consolidation of soft ground were extracted from the reports conducted on the study area. The area was then categorized into four zones based on sedimentary characteristics and the water systems. Four ML models, namely multiple linear regression (MLR), ridge regression (RR), lasso regression (LR), and elastic net regression (ENR), were then employed. After optimizing the hyperparameters of each model, prediction of the compression index then took place. The prediction performance of a total of 20 models was evaluated using the model performance metrics R\({}^{2}\) and root mean squared error (RMSE) for five cases using data from all four zones (1, 2, 3, and 4) as well as the entire Saemangeum data. Finally, the prediction accuracy of the ML models was analyzed in relation to the geotechnical and geological characteristics to verify the reliability of the selected models and methodologies.
## 3 Damping Effect of Saturated Sandy Soil
### Theoretical Analysis of Saturated Sandy Soil as a Damping Material  
Saturated sandy soil, when subjected to vibration, experiences a sharp increase in pore water pressure, which makes it highly prone to liquefaction. Earthquakes with a magnitude greater than 5 on the Richter scale can induce liquefaction within 20 m below the ground surface. For earthquakes of magnitude 8, the relative density of sand that remains unreached by liquefaction must reach 70%.  
Liquefied saturated sandy soil exhibits a high degree of similarity to fluids, with its stress-strain rate curve being a convex line that passes through the origin--a hallmark of typical 'non-Newtonian fluids'. The apparent viscosity, defined as the ratio of shear stress to shear strain rate, increases exponentially as shear strain grows and decreases with an increase in shear strain rate, demonstrating viscous properties. Consequently, liquefied saturated sandy soil can be regarded as a viscous fluid.  
The damping ratio is a crucial concept in structural dynamics, indicating the decay pattern of structural vibrations following excitation. The damping ratio of soil reflects the hysteresis of the soil's dynamic stress-strain relationship. In the viscous model, it is represented as the ratio of the viscous damping coefficient \(c\) to the viscous critical damping coefficient \(c_{r}\), as shown in Equation (3) [17]. The damping force \(F\) is expressed as a force proportional to the magnitude of the vibration velocity and opposite in direction, related to the viscous damping coefficient.  
\[D=\frac{c}{c_{r}} \tag{3}\]  
\[F=-c\overrightarrow{v} \tag{4}\]  
[PERSON], [PERSON], and colleagues [18] conducted dynamic shear tests on soil undergoing liquefied cyclic flow, establishing a definition for the damping ratio \(D\) during the soil's deformation phase within the liquefied cycle. They observed that the soil's damping ratio initially increases and then subsequently decreases with the augmentation of maximum shear strain. Notably, in soils of higher density, there remains a capacity to resist deformation even after complete liquefaction.  
\[D=\frac{1}{4\pi}\frac{A_{0}}{A_{r}} \tag{5}\]  
In Equation (5), \(A_{0}\) represents the area of the hysteresis loop in the shear stress-shear strain curve, while \(A_{r}\) denotes the area of the triangle formed by taking the maximum positive shear strain as the base and the maximum positive shear stress as the height in the shear stress-shear strain curve.  
Throughout the process of soil liquefaction and subsequent to complete liquefaction, the damping ratio remains below 1. Equations (3) and (4) indicate that, at a constant velocity, an increase in the damping ratio leads to a relative increase in the viscous damping coefficient \(c\) compared to the critical viscous damping coefficient \(c_{r}\), resulting in a proportional increase in the damping force. Once the soil has fully liquefied, the critical viscous damping coefficient \(c_{r}\) stabilizes, and both the damping ratio \(D\) and the damping force \(F\) will increase as \(c\) becomes larger.
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
## 2 Related Works
### IP Address Lookup Algorithms Utilizing On-Chip Memories  
In the parallel Bloom filter (PBF) architecture proposed by [PERSON] et al. [6], \(W\) Bloom filters associated with each prefix length are maintained, where \(W\) is the number of distinct prefix lengths. In programming, prefixes with the same length are programmed into the same Bloom filter. In search procedure, for an input IP address, all of the Bloom filters stored in on-chip memories are first queried in parallel. For the lengths where positive results are returned by the Bloom filters, an off-chip hash table is sequentially accessed starting from the longest length to find the output port of the best matching prefix.  
In the Bloom filter chaining (BF-chaining) architecture proposed by [PERSON] et. al. [20], a binary trie is primarily constructed. Every node in the binary trie is programmed into an on-chip Bloom filter, while prefixes are only programmed in an off-chip hash table. For an input IP address, the Bloom filter is sequentially queried from the root node until a negative result is produced. Since a node cannot exist without an ancestor node, the trie level (prefix length) of the last positive would have the longest matching node for the input (if the positive is true positive). The off-chip hash table is accessed for this length. If the positive is a false positive or if the node is an empty node without storing a prefix, a back-tracking should occur, which accesses for a shorter length. The back-tracking by the false positive cannot be avoided but the back-tracking by the empty node can be avoided by pre-computation, which makes each empty node store the output port of the direct ancestor prefix. An IP address lookup is achieved by a single off-chip memory access if the Bloom filter positive is true.  
However, the PBF and BF-chaining architectures require at least one off-chip memory access because the on-chip Bloom filter does not return the output port of the longest matching prefix.  
[PERSON] et al. [30] proposed a splitting approach for IP address lookup (SAIL). The SAIL architecture applies the splitting along with prefix lengthes and focuses to speed-up the IP address lookup with short lengths using on-chip bitmaps. The SAIL also requires at least one off-chip access because the on-chip bitmap does not return the matching output port.
## 3 Results and Discussion
### Accuracy of Support Vector Machine (SVM)-Based LULC Mapping for 1984 and 2018 in Harare  
The producer's accuracy for the classes was in the range of 68-93% for 1984 and 74-95% for 2018 (Table 4). The user's accuracy for the classes ranged from 82 to 97% for 1984 and from 83 to 92% for 2018. The overall accuracy values were above 86% for 1984 and 2018, which was good. The Kappa coefficient is one measure of inter-observer agreement, with values from 0.61-0.80 showing substantial agreement and values from 0.81 to 0.99 signifying almost perfect agreement [37]. The kappa coefficient values were above 0.82 for 1984 and 2018, just on the lower side of almost perfect agreement. This study obtained overall accuracies of 87.03% and 86.67% for 1984 and 2018, respectively. Due to improved Landsat 8 data, it was expected that accuracy would be higher in 2018 than in 1984, but results show otherwise. This could be explained by the fact that a city becomes more complex as it grows.
# Motion Estimation of Non-Cooperative Space Objects Based on Monocular Sequence Images
###### Abstract  
The motion estimation of non-cooperative space objects is an important prerequisite for successfully completing on-orbit servicing, debris removing and asteroid exploration missions. The motion estimation of non-cooperative space objects is a challenging task because of the lack of prior information about the object in unknown scenarios. In this paper, a motion estimation method of non-cooperative space objects for autonomous navigation based on a monocular sequence of images is proposed. The rotational attitude estimation of the non-cooperative space object is realized by matching the feature points of adjacent frame images, and the conditions required for translational estimation are discussed. This method has small calculation costs and runs stably, which meets the requirements of real-time computation on-board. As a result, this method implements the estimation of the complete unknown information of the object, including the attitude quaternion and angular velocity. Numerical simulations and results verify the effectiveness of the proposed method.  
autonomous navigation; non-cooperative space objects; monocular sequence image 1
## 3 New Generation Sensor Web Enablement
### KVP and SOAP Binding Introduced
#### 3.3.3 Tasking of Sensors
Further, the new _GetTask_ operation now allows clients to retrieve complete information about a given task or tasking request. Also, the _GetTask_ operation is designed to serve as an extension point for future extensions to SPS 2.0. The status concept has some consequences on the reporting behavior of SPS service instances. The new SPS 2.0 clearly defines syntax and semantics for the status reports of all possible statuses and their corresponding transitions. New Asynchronous Communication Concept  
The asynchronous communication concept has changed fundamentally from version 1.0 to version 2.0. SPS 1.0 used the Web Notification Service (WNS) (Section 3.3.4) to realize asynchronous communication between client and server. To use this feature, clients had to register with a WNS upfront and provided the notification endpoint in the various requests. SPS servers sent all results to this WNS, which acted as a forwarding mechanism using arbitrary communication protocols. SPS 2.0 is not tied to WNS anymore, but supports a _publish/subscribe_ model. This model defines a number of event types that can be published to interested consumers. Depending on the subscription model implementation, content based filtering and/or channel based filtering is supported. SOAP Binding Introduced  
The SPS 1.0 model only specified encodings appropriate for use of HTTP GET transfer of operation requests (using key-value pairs (KVP) passed to the service in the URL), and for use of HTTP POST transfer of operations requests (using plain XML or KVP encoding). SPS 2.0 introduces a SOAP binding based on what has been defined in the SWES specification (Section 3.3.1). Additional bindings can be added through extensions to the baseline specification. SPS EO Profile  
The fundamental changes to the SPS specifications have been reflected in a new version of the earth observation profile for SPS [82]. This profile is still _sui generis_, though it is expected that new profiles will be developed in the future focusing on resource-oriented implementations of the SPS model.
## 2 Experimental Section
### Enzyme Characterization  
The influence of temperature on \(\beta\)-mannanase activity was measured by performing a standard activity assay at temperatures ranging from 30 to 80 \({}^{\circ}\)C. The temperature stability of \(\beta\)-mannanase was determined by pre-incubating the enzyme samples (Man 58 and retentate) at 40, 50, 60, 70 and 80 \({}^{\circ}\)C and removing samples at intervals to measure the activity as described before. The influence of pH on \(\beta\)-mannanase activity was assessed by incubating 25 \(\mu\)L of enzyme sample, 50 \(\mu\)L of mannan (1% \(w\)/\(v\)) and 75 \(\mu\)L of each of the following buffers: 50 mM sodium acetate (pH 3.0-6.0), 50 mM sodium phosphate (pH 6.0-7.5) or 50 mM Tris-HCl (pH 7.5-9.0), at 50 \({}^{\circ}\)C. All buffers, regardless of pH, were adjusted to the same ionic strength with NaCl. The effects of several salts (MgSO\({}_{4}\), FeSO\({}_{4}\), CoCl\({}_{2}\), FeCl\({}_{3}\), CuSO\({}_{4}\), CaCl\({}_{2}\), ZnCl\({}_{2}\), ZnSO\({}_{4}\), CuCl\({}_{2}\), KCl and NaCl) and EDTA on \(\beta\)-mannanase activity were tested after 20 min of incubation at 28 \({}^{\circ}\)C in the presence of the individual reagents at final concentrations of 1.0 and 10 mM, followed by a standard \(\beta\)-mannanase assay under the following conditions: 25 \(\mu\)L of Man 58, 75 \(\mu\)L of the appropriate reagent and 50 \(\mu\)L of \(\beta\)-mannan. For the kinetic experiments, galactomannan was used as a substrate in a concentration range of 0.33-26.6 mg/mL. The substrates were saturating and the enzyme activities were proportional to the amount of enzyme added. \(K_{\rm m}\) and\(V_{\text{max}}\) values were estimated using the Michaelis-Menten equation with a non-linear regression data analysis program suitable for analysis of enzyme kinetics experiments [21]. The effect of phenolic compounds on Man 58 activity was measured by incubating the enzyme with ferulic acid, p-coumaric acid, vanillin, cinnamic acid, 4-hydroxybenzoic acid and tannic acid at concentrations of 1 mg/mL. This concentration of phenolic compounds showed no interference in the DNS assay. The reaction mixture was 4.5 \(\mu\)L of Man 58, 0.5 \(\mu\)L of phenolic compound and 10 \(\mu\)L of mannan (1% _w/v_). The inhibition effect was measured immediately after incubation of the enzymes with phenolic compounds. The assay conditions were as described above. Appropriate controls were used for each experiment and the acceptable standard deviation was less than 10% of the mean.
## 3 Method  
### Bridge Condition Index (BCI)  
One of the major concerns of organizations in charge of bridges is that repairs and maintenance of bridges should be implemented with respect to financial constraints. Bridge maintenance is a costly and long-term project, which has led to the development of various scientific tools and methods for optimal budget allocation [38]. Before allocating any budget, it is necessary to determine the current condition of the bridge and its present and possible future needs. The key to the successful assessment of a bridge condition is to recognize various damages. Bridge damage is a slow, progressive, and continuous process that is influenced by the imposing load, conditions of various bridge components, environmental factors, and the properties of materials [16].  
Figure 2: Weight importance of elements for a conventional concrete bridge.  
Many researchers have argued that the damage process is a blend of several mechanisms, such as corrosion, creep, shrinkage, cracking, fatigue, etc. [39]. The bridge damage is induced by a host of factors such as traffic, rainfall, freezing and melting cycles, climate change, and pollution, which can eventually lead to bridge failure [16]. The bridge failure can be either structural or functional. The methods for assessing various components of a bridge and their relative significance are key concepts in BMS [40].  
Different countries employ diverse methods to evaluate bridges so that they can develop a priority plan for bridge repair and maintenance with respect to budget constraints. Using a series of indicators is one of the most commonly used decision-making methods to prioritize maintenance. One perquisite of such indicators is determining the relative importance of different bridge components. Indicators can be categorized into two broad categories [16]:  
1. Bridge Health Index (BHI);
2. Maintenance Priority Index (MPI).  
The BHI is generally calculated as follows [16]:  
\[\text{BHI}=\sum\text{W}_{\text{i}}\times\text{C}_{\text{i}},\text{i}=1,\ldots, \text{n} \tag{11}\]  
where BHI is bridge health index, \(\text{W}_{\text{i}}\) is the weight of ith element, \(\text{C}_{\text{i}}\) is the condition of ith element, and n is the number of bridge elements.  
Moreover, MPI is usually calculated using the following equation [41]:  
\[\text{MPI}=\sum\text{K}_{\text{i}}\times\text{F}_{\text{i}}(\text{a},\text{ b},\text{c},\ldots), \tag{12}\]  
where MPI is maintenance priority index, \(\text{K}_{\text{i}}\) is the weight of ith damage, \(\text{F}_{\text{i}}\) is ith damage and a, b, c, etc., are damage characteristics.  
The above indicators, BHI and MPI, aim to determine the condition of the in-service bridges. In fact, both these indicators are somehow the same as the BCI. In this study, a method of BCI determination is proposed that is structurally similar to BHI.
# Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
## 3 Results
### CFD Simulations
In contrast, geometry 2 achieved higher flow velocities, though with reduced flow homogeneity. The choice of geometry should be made based on the specific application needs, whether prioritizing flow uniformity or achieving higher flow velocities. Geometry 1 was selected for the ERS compartments, where the high flow velocity is not crucial. On the other hand, geometry 2 was chosen for the saline solutions, as its higher flow velocities enhance ionic exchange across the membranes, promoting better mass transfer and more efficient ion transport.
## 4 Optimization for ISMLP
In contrast, the proposed ISMLP utilizes entropy regularization to mine the information of the unlabeled data, it makes no data distribution assumption; therefore, can be applied to broader scenes. Similar results can also be found in the other sub-figures. Therefore, the retrieval experiment on Corel 5K dataset can verify the superiority performance over the compared LSeMML approach. Figure 3: The typical retrieval result of the proposed ISMLP and LSeMML approach on the Corel 5K dataset with a training labeling rate of 30%. The leftmost figure denotes the query image and the first row of each sub-figure displays the result of the proposed method, whereas the second row shows the result of LSeMML. The green checkmark means the right retrieval result while the red cross means the wrong results. ### Classification Performance on CUB-200 and Cars-196 Datasets

We further conduct image recognition experiments on two fine-grained datasets to test the classification ability of the proposed ISMLP and its compared methods. The R@K index is utilized to quantify the performance of each method. Tables 5 and 6 show the classification results on the CUB-200 and Cars-196 datasets, respectively. We can draw the following conclusions from the figure: (1) All metric learning methods can benefit from the amount of labeled data; the more labeled data, the higher recognition performance. Since LMNN can only utilize the labeled data, when the labeling rate is low, LMNN can easily fall into the trap of over-fitting. Its performance is inferior to those semi-supervised metric learning methods under all tasks. This can prove the superiority of utilizing the unlabeled in metric learning. (2) Compared to those manifold-based semi-supervised approaches, the proposed ISMLP makes no assumptions about the smoothness or density of the data. Thus, ISMLP can be applied to broader scenes, and achieve better performance. (3) Both SERAPH and ISMLP utilize entropy regularization to mine the discrimination information of the unlabeled data; ISMLP adopts the proxy vectors to construct the probability model, which is more robust than SERAPH, and it obtains better performance across all the tasks on CUB-200 and Cars-196 datasets. (4) SSML-DR can obtain competing results due to its strong hierarchical feature extraction ability. (5) the proposed ISMLP can better mine the rich structural information of the unlabeled data; it achieves the best performance on 20 of all 24 tasks, which proves the efficiency of adopting the proxy vectors as surrogate points.
## 3 The Proposed Clustering-Based Bubble Meshing Method
### Procedures of the Proposed Bubble Method
#### 3.2.4 Procedure 4: Adaptive Population Control  
The number of bubbles is the key factor for obtaining a high quality of mesh for the proposed method presented in the previous section. The overlap ratio [33; 35] is introduced to control the number of bubbles adaptively for avoiding the overfitting. This metric checks a local bubble population, deletes excess bubbles that obviously overlap with neighboring bubbles, and adds bubbles around bubbles with significant gaps. The method is capable of removing from over-populated areas and adding bubbles in under-populated areas in each iteration. The overlap ratio is defined as:  
\[\alpha_{i}=\frac{1}{r_{i}}\sum_{j=0}^{n}(2r_{i}+r_{j}-l_{ij}) \tag{7}\]  
where \(r_{i}\) and \(r_{j}\) are the radii of bubbles \(i\) and \(j\), respectively. \(l_{ij}\) denotes the distance of bubbles \(i\) and \(j\), and \(n\) is the number of bubbles associated with bubble \(i\). [PERSON] [17] believed that the standard overlap ratio of bubbles on a surface or in the internal domain are 6 and 12, respectively, which form anoptimal mesh in the three-dimensional domains. Consequently, bubbles with large overlap ratio will be removed, while new bubbles will be added around those with small overlap ratio. In the present study, ten to fourteen bubbles are preserved in the domain. Our result reveals that bubble population may be controlled automatically when bubbles reach stabilized position. Finally, a set of nodes with the well-shape is obtained.