esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Spatial Layer Construction
#### 3.1.1 Dynamically Partitioned Geohash-Hilbert Encoding  
Geohash is a hierarchical spatial data structure. Its principle is to recursively divide the longitude and latitude codes into binary strings, and then merge the codes according to the rule of \"odd bits correspond to latitude codes, and even bits correspond to longitude codes\". Each code string corresponds to a rectangular area, and it is ensured that the longer the common prefix of two Geohash codes, the closer they are in space. The basic principle of Geohash encoding is shown in Figure 3.  
Figure 2: GLMI structure. Pn represents the partition obtained through Geohash, and f(x) denotes a linear function.  
The Geohash encoding method uses the Z-order curve to maintain local order, but the corner points of each Z-shape may cause abrupt changes [32; 33]. For example, the two partitions represented by the solid line in Figure 3 are continuous in encoding, but the partition distance is very far. This paper uses the Hilbert curve for encoding, which better preserves spatial proximity and is more conducive to spatial clustering and analysis. The Hilbert curve has four states, each of which maps to four Hilbert values and the subsequent state, as shown in Figure 4.  
The solid arrow represents the Hilbert structure in the current state. Along the direction of the arrow, the codes of the four partitions are 00, 01, 10, and 11, respectively. The dotted arrow represents the next state and the generated overall Hilbert structure. A mapping function can be constructed from the four states of the Hilbert curve. This function takes two parameters: the current state and the Z-order curve encoding bit pair. Based on the mapping rule in Figure 4, the function returns the output bit pair and the state value for the next bit pair. The Geohash-Hilbert coding algorithm mainly consists of two steps: (1) generate the Z-order curve binary code from the spatial point according to the Geohash principle; (2) iterate the bit pairs according to the mapping function ZtoH and add them to the output sequence.  
When GLMI constructs a partition, it uses a preset threshold as the criterion for partition splitting. When the number of data points in a partition exceeds the threshold, the partition is split. GLMI first initializes the first-level partitions with a lower precision, splits the partitions that meet the splitting conditions, and increases the coding length. Partitions that do not meet the splitting conditions have their codes added to the encoding set, continuing until all partitions no longer meet the splitting conditions. The number of data points in each spatial area after dynamic partitioning is less than the preset threshold, which not only optimizes the maintenance efficiency of the partitions but also significantly reduces the search for irrelevant data points during range queries. This effectively addresses the problem of data skew, thereby improving query performance.  
Figure 4: The four Hilbert curve states.  
Figure 3: Geohash encoding principle.
## 2 Materials and Methods
### Laboratory Food Analyses  
Regarding the number of samples, from 2021 to 2023, more than 2369 fruit and vegetable samples were analyzed by two laboratories accredited according to ISO 17025 [29], including 1415 fruit samples, 834 vegetable samples and 120 miscellaneous product samples (herbs, spices, sprouts, etc.). The method used was according to EN 15662:2018 [30]. The extraction and clean-up of the samples were carried out using the QuECHERS procedure (quick, easy, cheap, effective, rugged and safe). The tested samples were simultaneously analyzed using GC-MS/MS gas chromatography (7890A Agilent Technologies GC System) coupled with a triple-quadrupole tandem mass spectrometer (7000A Agilent Technologies GC/MS Triple Quad), and LC-MS/MS utilizing an Agilent 1200 system (Agilent Technologies, Santa Clara, CA, USA) with a triple quadrupole mass spectrometer Agilent 6410B QQQ (Agilent Technologies, Santa Clara, CA, USA) equipped with an electrospray ionization (ESI) source, and the ESI source operated in positive ionization mode. The samples were a combination of official surveillance of the goods or part of the external control of the company. Samples with values below the detection limit (LOD) were counted as LOD/2 [26].  
In total, 29 active substances were detected in 35 different fruits and vegetables, including 19 fruits--peaches, plums, blueberries, pears, blackberries, raspberries, strawberries, apples, cherries, lemons, grapes, bananas, oranges, tangerines, grapefruits, avocados, pomegranates, melons and apricots; and 16 vegetables--broccoli, carrots, green salad, bell pepper, Brussels sprouts, potato, ginger, tomato, celeriac, cherry tomato, cucumber, beetroot, parsley, chili pepper, onion and spinach.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 4 Chronological Review of Retrospective Approaches
### [2015-2018]
The saliency detection was used to separate the layers region and the background. The transformation model was estimated based on the higher weight on the saliency region, with different weights given to background and foreground. With the saliency map, the transformation model was estimated for each OCT slice based on the pixel intensity and gradient value. A center bias constraint was also included. Here, a Gaussian-based function was used to encourage that a drifted slice remained close to its initial position if the amount of drift in the fast axis was low. Lastly, transformation vectors of the objective function were obtained by considering two terms related to feature similarity in saliency and center bias constraint. In order to validate the proposed algorithm, seven OCT volumes were used to generate artefacts in 49 synthetic OCT volumes. Visual inspection of the blood vessel discontinuity was also considered. **[PERSON] et al. [84] (2017)** proposed an imaging method based on Lissajous scanning and a respective motion correction algorithm. Lissajous scan is a closed-loop scan obtained by operating along the horizontal and vertical axes using sinusoidal waveforms of different frequencies. The core idea of the algorithm proposed in this work was to correct motion artefacts by maximizing the similarity of overlapping areas between multiple scans. The first step of the algorithm (pre-processing) was to split the OCT measurement data into smaller sub-volumes. The correction process was divided into two parts, lateral motion correction and subsequent axial motion correction. For the lateral motion correction, the en face projection of each sub-volume was used. Here, a rough and a fine correction were applied based on the maximum of the cross-correlation function. For the axial motion, the 1D cross-correlation was calculated in the overlapping area between the reference and registered A-lines. The axial shifts of the registering A-lines were determined by the maximum peak positions of the cross-correlation function. For validation, they compared the motion corrected results with SLO and cross-sectional OCT along fast scan direction as reference standards. To validate the repeatability, two motion-corrected volumes, which were measured in the same location using the same subject but at different times were compared. In addition, visual inspection was used. This algorithm was later extended to OCT-A [98].
###### Abstract  
The wind-induced response of structures is typically studied in wind tunnels either on scaled models or using numerical approaches under similar transient load conditions. In early design phases--where the potential for impactful change is most significant--information is often limited. As a result, studies are frequently conducted on simplified or reduced-resolution structural models. Typical applications for dimensionally reduced engineering models include early design phases, deciding on the need for high-fidelity analyses, and verifying wind tunnel models, which are often constructed using beams with lumped masses. In this contribution, the validity of these approaches is tested. Various limitations intrinsically arising from such modeling assumptions, showcased on a generic high-rise under dynamic wind load conditions, are highlighted. The systematic parametric analysis focuses on the variations in transient structural responses, particularly displacement and accelerations at the top of a building. Various wind loading cases are studied, with the reduction of the resolution taking place either in the original or in modal space. Results indicate that a considerable reduction is possible, but characteristic design values tend to deteriorate in cases of a high reduction, particularly when higher mode contributions are truncated. It is observed that the top-floor acceleration and displacement can be captured with considerable accuracy with three lumped masses for tall buildings. It is critical to study the impact of simplifying models starting at the highest level of detail possible. Here, a three-DoF model was able to capture the displacement up to a deviation of 11% and accelerations up to 20%. These approximate models are useful for initial design stages, optimization, uncertainty quantification, etc., where fast, cheap, and moderately accurate model evaluations are necessary.  
wind loading; structural modeling; structural response; numerical methods; reduced-resolution models; computational wind engineering +
Footnote †: journal: Applied Sciences
# Interpretability Study of Gradient Information in Individual Travel Prediction
###### Abstract  
With the development of intelligent transportation systems (ITS), individual travel prediction has become a key technology for optimizing urban transportation. However, deep learning models are limited in decision-sensitive scenarios due to their lack of interpretability. To address the shortcomings of existing XAI methods in analyzing the dynamic features of historical travel sequences, this paper introduces an alternative interpretability method based on gradient information, overcoming the interpretability bottleneck of travel prediction models. This method calculates the gradient information of input features relative to the prediction result, breaking through the limitations of traditional interpreters that only analyze static features. It can trace the contribution weights of key time points in historical travel sequences while maintaining low computational cost. The experimental results show that features with higher gradients significantly affect predictions--masking the maximum-gradient feature reduces accuracy by approximately 30%. Descending-order masking strategies exhibit the strongest impact, highlighting non-linear interactions among features. Contribution maps visualize how gradients capture regular patterns and anomalies. The method proposed in this paper provides a valuable tool for understanding the underlying principles of travel prediction models, bridging the gap in existing methods for temporal sequence analysis.  
Keywords:interpretability; gradient information; individual travel prediction; deep learning model +
Footnote †: journal: Applied sciences
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 5 Empirical Testing
### Experimental Design  
Several randomized experiments were conducted under different conditions to empirically test our proposed orchestration algorithm (Algorithm 1) in comparison with a control orchestrator (Algorithm 2). The control orchestrator implemented a \"naive\" algorithm that added or removed containers such that, at any given time, the probability of each microservice operating reliably approximated \"nine-nine-nines reliability\" (99.99999999%).  
```
1:procedureOrchestrateCloud (Microservices, \(t\))
2: Delete failed microservice containers.
3: Ensure each microservice has at least one non-failed container
4:for each \(ms\in\)Microservicesdo
5:while\(P_{failure}(ms,\ t)\geq\)**1.0\(\times\)10\({}^{-9}\)do
6: Replicate \(ms\)
7:endwhile
8:while\(P_{failure}(ms,\ t)<\)**1.0\(\times\)10\({}^{-9}\)do
9: Satisfely shut down a container from \(ms\)
10:endwhile
11:endfor
12:endprocedure
```  
**Algorithm 2** The orchestration algorithm we used as a control for task \(T\)  
The same _SpotMarketProvider_ was used by both the control and experimental orchestrators so that resultant costs could be validly compared. Only the experimental orchestrator adjusted its behavior in response to market conditions.  
The cloud configuration for each of the experiments consisted of two microservices with exponential failure functions of the form \(F_{ms}(t)=1-e^{-\lambda t}\). All experiments were conducted over 500 samples and repeated \(n=1000\) times, with the spot market conditions varied according to the design of each experiment.  
In our experiments, we selected a value of 1 for the parameter \(\lambda\) as this gave a 99% probability of failure within 500 samples at the rate of 100 samples per second, thus approximating the exponential distribution within the execution time constraints of the simulator.  
In each experiment, the orchestrators were run at two frequencies: once at the sampling rate, modeling a system where failures are immediately detected and remedied, and again at a lower frequency of once per ten samples, modeling a system where diagnostics are run periodically to detect failures.  
We used a simplified model of a spot market in the experiments which was composed of two parameters. These parameters gave the marginal cost of running a microservice, \(\Phi_{MS}(t)\), according to (11), where \(\Phi(t)\) referred to a global \"spot price\" per second of  
Figure 5: High-level architecture of the open-source Cloud Reliability Simulator, showing our proposed spot market functionality.  
execution and \(\alpha\) referred to a static multiplier assigned to each microservice to represent its resource usage intensity per unit time.  
\[\Phi_{MS}(t)=\alpha\Phi(t) \tag{11}\]
## 2 System Model and Problem Formulation
### Problem Formulation  
In this part, we to aim to find the energy efficiency of network, the EE problem is formulated with maximum transmit power constraints and quality of service (QoS) for users. Hence, the EE optimization problem can be formulated as  
\[\max_{v,Y,p} EE_{t}= \frac{R}{P_{T}}\] (7a) subject to \[\sum_{i=1}^{\mathcal{N}}\sum_{l\in\mathcal{S}}v_{m,i,j}Y_{m,i,l} p_{m,i,l}\leq P_{m},\forall m\in\mathcal{M} \tag{7b}\] \[p_{m,i,l}\geq 0,\forall m\in M,i\in\mathcal{N},l\in\mathcal{S}\] (7c) \[\sum_{l\in\mathcal{S}}v_{m,i,l}Y_{m,i,l}R_{m,i,l}\geq R_{m,i,l}^ {th},\forall m\in\mathcal{M},i\in\mathcal{N}\] (7d) \[\sum_{l\in\mathcal{S}}v_{m,j,l}Y_{m,i,l}\leq 1,\forall m\in \mathcal{M},i\in\mathcal{N}. \tag{7e}\]  
The first constraint (7b) denotes the total transmit power constraint for all users served by BS \(m\). (7c) denotes the user's power constraint. (7d) is the minimum data rate requirements for user. (7e) means that the user can be associated with one BS and assigned to one subcarrier at the time.
## 3 Results and Discussion
### Application of WB-MAD Method for the Determination of Fluoride in Twelve Different Toothpastes  
The World Health Organization (WHO) established dental decay prevention by limiting concentration levels of fluoride ions to a maximum of 1500 mg/kg for adults and the maximum of 550 mg/kg for children [42]. In the interest of counterclockwise whether the declared fluoride ions in the toothpaste brands that are used daily comply with the recommended concentrations established by WHO or not, twelve different brands of toothpaste were purchased in a South African local supermarket (sample A-L) and subjected to the optimum conditions of the proposed WB-MAD method. Table 3 shows that all F\({}^{-}\) concentration levels detected using F-ISE after WB-MAD agree with the manufacturer's concentrations recorded on the product label and are within the acceptable ranges as prescribed by WHO [43; 44; 45]. Secondly, validation was carried out by comparing fluoride concentration levels obtained by using F-ISE and ion chromatography (IC) after the WB-MAD method. Table 3 shows that the F\({}^{-}\) concentration levels obtained from IC and F-ISE were insignificantly different, with excellent precision. Furthermore, Figure 8 presents chromatograms of three different digests from toothpaste samples A, B and I. The F\({}^{-}\) ions were eluted at 2.5 min, while Cl\({}^{-}\) and CO\({}_{3}\)\({}^{-2}\) ions were delayed and appeared at 4.3 and 8.4 min, respectively [41].  
Figure 6: Comparison of microwave-assisted acid digestion (MAAD) with newly developed water-based microwave-assisted digestion (WB-MAD).  
Figure 7: Reaction of fluoride in an acid environment to form HF.
## 2 Materials and Methods
### Classification of Tenon-Mortise Joints
#### 2.2.2. Tenon-Mortise Joints for Joining Horizontal and Vertical Structural Components  
Common horizontal tenon-mortise structures include three typical components: beams, tie beams and purlins, as shown in Figure 3. The beam is placed on the column along the depth, bearing the weight of the components and roof in the upper structure, as well as its own weight. Tie beams, which assist beams to enhance the whole stability of the structure, are placed perpendicular to the beams. Furthermore, the purlin is an important component to fix the rafter and support the roof, which is laid on the tie beam or the cushion board. The three of them are often connected to vertical structural components via the following types of tenon-mortise joints:  
1. Steamed-bread tenon: a tenon used to perpendicularly join the top end of a column to one end of a beam, its function lies in preventing the horizontal displacement of the beam, as shown in Figure 5a.
2. Dovetail tenon: a tenon commonly used for joining the other horizontal structural components, except the beams to the top end of a column, such as the eave tie beam connecting the eave columns (the outmost column), and the purlin tie beam connecting the hypostyle columns (within the eave column, all the other columns except the ones in the axis of the building), as shown in Figure 5b.
3. Beam end-locking tenon: a special tenon that joins two tie-beams and a column at an end or a corner, and is supposed to lock the column tightly. The use of such tenons brings a strong tying force to the corner column and protects the column at the same time, as shown in Figure 5c.
4. Through tenon: a tenon often made in the form of a \"big tongue to enter and small tongue to protrude\" at the end of a penetrating tie beam, which connects two columns in a row, as shown in Figure 5d.
5. Half tenon: as its applicable part is similar to a through tenon, it is only used when a through tenon is not suitable, such as when joining the center column and the beam, as shown in Figure 5e.  
Figure 4: Tenon–mortise joints for fixing vertical structural components. (**a**) Base tenon; (**b**) Top tenon; (**c**) Half tenon at the base of a short column.
## 4 Results
### Result on Depth Difference Between Two Objects and Its Precision--Experiment A
#### 4.2.1 Result on Effect of Light in Depth Difference Estimation  
To investigate the effect of light on depth difference estimation, we examined the success rate in discriminating between distance differences for three objects at different setups (Figure 4) for each method and each illumination condition. We considered distances greater than 34 cm, starting at \"d + 16\", since RealSense can only accurately provide a minimum amount of depth information. The Figure 13 shows the results of successfully estimating the depth difference for different illumination conditions. As expected, based on the results in Table 3, RealSense is quite consistent with a high success rate over the selected range of distances (34 cm to 98 cm) for different lighting conditions (99%). Using the MiDaS method for the available data, the smaller value is 72% for light number 6 and 91% for the overall success rate. The reason for the overall lower rate compared to RealSense is due to the two further distances \"d + 56\" and \"d + 64\", where the success rate in estimating the depth corresponding to the distance differences between objects seems to be lower at distances greater than 74 cm than at the closer distances. However, in our application, it is important to monitor hand interaction with objects whose total distance is less than 70 cm, so MiDaS still be a compatible option for our application.  
All in all, based on the above analysis and taking into account the disadvantages of 3D (depth) cameras mentioned at the beginning, as well as the advantages of 2D cameras (RGB cameras), we decided to use a 2D camera in our application and to use MiDaS for depth estimation in hand-object interaction detection.
# Influence of Cutter Head on Cavitation of Non-Jammed Submerged Grinder Pump
## 4 Numerical Results and Analysis
### Performance Comparison of Different Cutter Head Shapes  
Figure 18 shows the performance comparison of the GSP-22 pump with two different cutter head shapes, which were streamlined cutter head and non-streamlined cutter head-shaped, respectively. It can be seen from the figure that when the flow rate was less than 15 m\({}^{3}\)/h, the head of the streamlined cutter head grinding pump was higher than that of the non-streamlined cutter head; when the flow rate was greater than 15 m\({}^{3}\)/h, the head of the streamlined cutter head grinding pump was lower than that of the non-streamlined cutter head. The maximum efficiency of the streamlined cutter head grinding pump was 33.1%,  
Figure 16: Surface pressure distribution of the different numbers of cutter heads.  
while the maximum efficiency of the non-streamlined cutter head grinding pump was 27.5%. The maximum efficiency was increased by nearly 6% after changing to a streamlined cutter head. It can be seen that the overall power of the streamlined cutter head was lower than that of the non-streamlined cutter head. When the flow rate was 7.2 m\({}^{3}\)/h, the power of the streamlined cutter head grinding pump and the non-streamlined cutter head grinding pump were 1566.3 W and 1990.4 W, respectively. The power of the streamlined cutter head was 426.09 W--lower than that of the non-streamlined cutter head. When the flow rate was 36 m\({}^{3}\)/h, the power of the streamlined cutter head grinding pump and the non-streamlined cutter head grinding pump were 2344.1 W and 2702.1 W, respectively, with a difference of 357.98 W. The corresponding power of the non-streamlined cutter head was more than 13.2% higher than that of the streamline cutter head. This is because after the shape of the cutter head is streamlined, the corresponding displacement effect of the cutter head is reduced. The hydraulic loss and power consumed on the cutter head is therefore reduced, which results in the improvement in its efficiency.  
The analysis showed that the performance of the grinding pump had been greatly improved in general by changing the shape of the cutter head to streamlined. For the head of the streamlined cutter head, the change was not very large; however, the power of the grinding pump was reduced by more than 13.2%, and the maximum efficiency of the grinding pump was increased by 6%.