esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Preprocessing  
The preprocessing steps included cropping, normalization, and data augmentation strategies applied to all volumes prior to training, as illustrated in Figure 1.  
For the cropping phase, indices were determined through statistical analysis of the binary masks obtained by applying [PERSON]'s thresholding filter on the T2-weighted scans and a sequence of additional morphological operations, i.e., erosion (radius of structuring element: \([1,1,1]\)), binary hole filling, and dilation (radius of structuring element: \([4,4,4]\)). Specifically, for each subject, the largest non-zero region in the xy-plane was computed, averaging the indices across all slices; the resulting cropping bounds were \([40,196]\) for \([x_{\text{min}},x_{\text{max}}]\) and \([30,222]\) for \([y_{\text{min}},y_{\text{max}}]\). Along the z-axis, ground truth masks were used to identify the last slice index containing a segmentation label. The volumes were then cropped while preserving the largest dimension between x and y across the volumes, resulting in a final size of \(192\times 192\times 150\).  
To ensure consistency, robustness, and generalizability during training, a series of transformations from the Medical Open Network for AI (MONAI) library [43] was applied on the imaging data, including orientation alignment, random flipping, intensity normalization, and intensity-based augmentations. Initially, all images and their corresponding segmentation masks were reoriented to the standard RAS coordinate system. Random flipping was then applied independently along each of the three spatial axes with a probability of 50%. Subsequently, intensity normalization was performed on non-zero voxels to standardize the signal intensity values across scans. To further improve model generalization to intensity variations commonly observed in MRI data, random intensity augmentations were applied, including random scaling and shifting of voxel intensities.
## 2 Theoretical Background
### Computational Intelligence Methods
#### 2.2.2 Particle Swarm Optimization  
The Particle swarm optimization (PSO) algorithm is another CI method, inspired by nature, used for the solution of problems of various types. Specifically, PSO proposes the existence of a group of particles, where each of them represents a potential solution to the problem. Actually, the integral part of collectivity is the cooperation among the members that comprise it. The particles that create a specific collectivity interact with each other in order to achieve their common aim by exchanging information. In this way, each problem that arises can be solved in a more efficient way compared to what a sole particle can do. In nature, various kinds of such collectivities have been observed, many of which are organized in swarms. The main idea for the creation of PSO was based on the study of the synchronized flying patterns of birds and their ability to regroup appropriately when being members of flocks [25].  
The notion of the swarm could be compared to the notion of population in genetic algorithms, whereas the notion of the particle could be linked to the one of the chromosome. The PSO particles work together in order to mimic the success of their neighboring particles, aiming at unveiling the best positions within the search space. In this algorithm the particles \"fly\" in the search space, thus, occupying certain positions which occur from the experience that has been gained from their own \"flying\" as well as from the one of their neighbors. Specifically, a certain number of particles is randomly placed in the search space and the result of the fitness function for the given position is assessed. Afterwards, for a predefined number of executions of the algorithm, each particle occupies a new position for which the fitness function gives a better result compared to the previous one. Each particle holds a memory of its best position so far; this is compared and contrasted with the best positions which have been occupied by the neighboring particles of the swarm and the best one is recorded. Therefore, as the number of executions of the algorithm is increased, the swarm achieves the fittest solution to the problem, having a predefined number of particles working for the accomplishment of their common goal. The search for the best solution through the PSO algorithm is an ongoing process which is executed continuously until the point a certain termination criterion is satisfied [26].
# A Reliability Engineering Approach for Managing Risks in CubeSats
###### Abstract  
Besides large-scale space missions, the spread of CubeSats for a variety of applications is increasingly requiring the development of systematic approaches for risk management. Being these applications are based on components with low TRL (Technology Readiness Level) or with limited performance data, it is required to define approaches which ensure a systematic perspective. This paper aims to present a reliability engineering approach based on FMECA (Failure Mode, Effects, and Criticality Analysis) to manage CubeSat reliability data and prioritize criticalities early in the design phase. The approach firstly proposes an alpha-numeric coding system to support the identification and labeling of failure modes for typical CubeSats' items. Subsequently, each FMECA coefficient (i.e., Severity, Occurrence, Detectability) has been linked to the CubeSat's structural properties, reducing subjectivity by means of techno-centric proxy indicators. The approach has been validated in the design phases of a 6-Units university CubeSat for the observation of M-Dwarf stars and binary systems. The performed analysis supported the design process and allowed to identify the major criticalities of the CubeSat design, as demonstrated in the extended case study included in the paper. The formalized method could be applied to design procedures for nano-satellites, as well as being expanded for research and development in a variety of space missions.  
FMECA; satellite reliability; reliability engineering; cubesat; space operations; space mission analysis; space reliability Article
# Study on Vibration Effects and Optimal Delay Time for Tunnel Cut-Blasting Beneath Existing Railways
## 3 The Mechanism and Effect of Vibration Reduction Through Waveform Interference
### Optimal Delay Time Determination for Various Classes of Surrounding Rock
When using 2 ms, 3 ms, or 5 ms delays within their respective ranges, vibration reduction through waveform interference outperforms separation waveform. However, vibration reduction through main vibration phase superimposition exhibits high randomness, with effects varying significantly by location. For Class V conditions, optimal vibration reduction is achieved with 1 ms delay blasting within the ranges \(-\)50 m\(\sim\)25 m. A 4 ms delay blasting yields vibration reduction within the ranges 25 m\(\sim\)50 m. Behind the tunnel face at 40 m, when 1.6 ms \(<\) T \(<\) 2.1 ms, 1 ms delay blasting achieves vibration reduction through main-period superposition with better efficacy than other delay times. However, at 25 m to 50 m in front of the tunnel face, 4 ms millisecond blasting provides the best vibration reduction effect. However, ahead of the tunnel face from 25 m to 50 m, 4 ms delay blasting provides the optimal vibration reduction effect. In this range, the vibration reduction effect of main-period superposition weakens, whereas that of main vibration phase superposition strengthens. At this time, vibration reduction through main vibration phase superimposition has a better vibration reduction effect. Table 10 summarizes the optimal delay time for different grades of surrounding rock in different ranges. In summary, for Class III conditions, when protected objects lie within 40 m before/after the tunnel face, using waveform separation provides optimal vibration reduction. Beyond 40 m, we should consider main-period superposition. For Class IV, waveform interference based on proximity for near-face protected objects should be selected. For distant objects, shorter delays should be considered to achieve main-period superposition. For Class V, due to the increase in the main period, vibration reduction through main period superposition can be realized according to the protected object. Figure 13: Optimal delay time for various classes of surrounding rock in different locations.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 2 Materials and Methods
### Data  
#### 2.2.1 Satellite Data and Preprocessing  
Data from Sentinel-2 and GF-7 were used in this study, and the specific information is shown in Table 1.  
* Sentinel-2 data Sentinel-2 consists of Sentinel-2A and Sentinel-2B, launched in 2015 and 2017, respectively. Both satellites carry a multispectral imaging instrument covering 13 bands from visible light to near-infrared and shortwave infrared, with a maximum spatial resolution of 10 m. After the two satellites are networked, the revisit period is shortened to 5 days. Given the rich band configuration, excellent spatial resolution, and shortened revisit period, Sentinel-2 data were selected as the data source for parcel extraction.  
Google Earth Engine (GEE) was used to download Sentinel images for our study areas. A cloud-free dataset was imperative for our analysis; hence, we ensured that the selected images for Youyi County, A1, and A2 each exhibited cloud cover of less than 1%. After stitching, the images were exported as PNG images in a false color combination.  
* GF-7 data GF-7, launched by China on 3 November 2019, is a high-resolution Earth observation satellite. The satellite is equipped with effective payloads such as a dual-line array stereo camera and a laser rangefinder. Its panchromatic and multispectral resolutions are 0.65 m and 2.65 m, respectively, enabling sub-meter-level stereo mapping. In this study, all GF-7 images had less than 1% cloudiness, and pre-processing such as geographic alignment and cropping was performed to ensure that they could be matched with the Sentinel-2 images.
## 4 Blockchain-Based Lightweight Point Cloud Data Authentication Framework
### Decentralized Data Authentication Procedures  
The Blockchain-based data access authorization and integrity verification procedures are presented as pseudo-code in Algorithm 1. Given a list of meta-data \(M\), data owner traverses each meta-data \(MD_{i}\) and uploads them to the DDS, then appends the returned Swarm hash \(D_{i}\) to \(ordered\_swarm\_hash\), as Lines 2-6 show. Following that, the data owner feeds \(ordered\_swarm\_hash\) to function \(BMT()\), which will construct a binary Merkle tree and output the root hash \(mk\_root\) (Line 7). Finally, the data owner calls the smart contract function \(set\_dataAC()\) to record \(mk\_root\) and \(ordered\_swarm\_hash\) into the distributed ledger as the public audit proof, which can be uniquely addressed by \(token\_id\) (Line 8).  
In the data verification procedure, the data user firstly uses \(token\_id\) as the input to call the smart contract function \(query\_dataAC()\), which will return the public audit proof information stored on the Blockchain (Line 10). Regarding token validation, the data user performs function \(BMT()\) on the received \(ordered\_swarm\_hash\) to recover the root hash \(mt\_root^{\prime}\), then checks if \(mt\_root^{\prime}\) is consistent with the audit proof \(mt\_root\). If the validation fails, it directly returns a false result. Otherwise, it goes ahead to the meta-data verification. Given the received \(ordered\_swarm\_hash\), the data user traverses each digest \(D_{i}\), which is used to download the meta-data \(MD_{i}\) from the DDS. Any wrong digest or corrupted meta-data will lead to a \(NULL\) result returned by the function \(download\_data()\). Finally, a valid list of the meta-data is returned only if all meta-data can be successfully retrieved, as Lines 16-23 show.  
```
1:procedure: authorize_data(token_id, M)
2:\(ordered\_swarm\_hash=[]\)
3:for\(MD_{i}\) in M do
4:\(D_{i}\leftarrow\) upload_data(\(MD_{i}\))
5:\(ordered\_swarm\_hash.append(D_{i}\))
6:endfor
7:\(mt\_root\leftarrow\)BMT(\(ordered\_swarm\_hash\))
8:\(receipt\leftarrow\)Contract_set_dataAC(\(token\_id,mt\_root,ordered\_swarm\_hash\))
9:procedure: verify_data(token_id)
10:\(mt\_root,ordered\_swarm\_hash\leftarrow\)Contract_query_dataAC(\(token\_id\))
11:\(mt\_root\leftarrow\)BMT(\(ordered\_swarm\_hash\))
12:if\(mt\_root\
eq mt\_root\)then
13:return\(False\)
14:endif
15:\(MD\) = []
16:for\(D_{i}\) in \(ordered\_swarm\_hash\)do
17:\(MD_{i}\leftarrow\)download_data(\(D_{i}\))
18:if\(MD_{i}==NULL\)then
19:return\(False\),NULL
20:endif
21:\(MD\).append(\(MD_{i}\))
22:endfor
23:return\(True\), MD
```  
**Algorithm 1** The data access authorization and integrity verification procedures
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 4 Results and Evaluation
### Ldr-to-Hdr Conversion: Subjective Evaluation
Conversely, our findings also suggest that participants tended to perceive both HDR and LDR video sequences as exhibiting similar quality when assessing scenes with lower light levels and reduced contrast, e.g., instances where dim lighting and a lack of distinct highlights prevailed. We also noticed that, in a few cases, participants selected the LDR version as the one with better quality. This observation underscores the idea that the advantages of HDR technology, such as a large luminance variation, were less apparent or less impactful in scenarios with limited visual dynamics. These results provide valuable insights into viewer preferences, showcasing a preference for HDR's visual enhancements in specific contexts while highlighting the nuances of quality perception in specific lighting and contrast conditions.
## 4 Multi-Objective Scheduling Optimization Model
### Multi-Objective Model Solution  
We proposed a multi-objective optimization model for the MECS operation. There are two methods for solving such a model. The first is to solve it using group intelligence algorithms, for example, genetic algorithms, particle swarm algorithms, and ant colony algorithms. As an intelligence algorithm can globally search for the optimal solution, the programming code is excessively complex, particularly in mathematical models with integers and continuous and discrete variables, thus yielding non-optimal results [10].  
The second method is to weight multi-objective functions into an integrated single-objective function. In general, the weighted coefficients can be obtained subjectively or via weight determination methods for different objective functions [28]. In a previous study, the weight coefficients were mainly calculated by using the entropy weight method [29]. The applicability of this method is limited by the uncertainty factors and imperfect information existing in the MECS operation. To overcome this problem, this paper uses the rough theory to handle the information in the proposed multi-objective model. The calculation process comprises three steps, namely, the construction of the payoff table, the calculation of the weight coefficients, and the conversion of the multi-objective functions.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 3 The Proposed Approach
### De-Noising Based on Wavelet Energy
#### 3.2.2 Calculating the Energy of Each Decomposition Layer  
Firstly, the detail coefficients of each layer are calculated according to the detcoef function of Matlab, and then the energy contained in the detail coefficients of each layer is calculated. The calculation formula is as follows:  
\[E_{j}=log_{e}\left(\sum_{i=0}^{N}{\left|{cof_{i}}\right|^{2}}\right) \tag{2}\]  
where \(E_{j}\) represents the detail coefficient energy of the \(j\)th layer, \(N\) represents the number of detail coefficient of the \(j\)th layer, and \(cof_{i}\) represents the \(i\)th detail coefficient of the \(j\)th layer.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 5 Comparative Assessment of Existing R-Peak Detection Methods
### Performance Relative to Match Window Tolerance  
This section systematically evaluates the influence of matching window tolerance on FECG peak detection accuracy across multiple methods. We examined three symmetric window widths centered on true R-peak annotations: \(\pm\)25 ms (50 ms total), \(\pm\)15 ms (30 ms), and \(\pm\)10 ms (20 ms), corresponding to progressively stricter localization criteria. Building on Section 5.1, we analyzed the top three methods by F1-score from the A&D FECG (AD_origin) and FECGSYN (SYN_origin) databases. For broader relevance, we included PTA_E2020, a widely adopted approach based on the ECGPUWAVE peak detector, despite its suboptimal ranking.  
Figure 8 reveals that PPV, SEN, and F1-score improve with wider windows. Figure 8a shows that Rodrigues2021 and PTA_E2020 exhibit significant performance degradation as windows narrow (e.g., 16.11-16.84% F1-score drop from 50 ms to 20 ms), reflecting inherent localization limitations. In contrast, our U-Net and Neurokit202 maintain stable metrics (\(\pm\)2.65% variation) across all window width. Notably, U-Net achieves superior performance (F1 = 83.79% at 20 ms and 0 dB), demonstrating superior detection accuracy and noise robustness. Figure 8b further highlights the exceptional accuracy of U-Net under tightened window width, reinforcing its efficacy in precise peak detection.  
The matching window tolerance in R-peak detection affects the determination of TP. While most methods perform well over a wider tolerance range, U-Net's consistent accuracy under tight windows makes it uniquely valuable in fetal HRV analysis and other applications that require precise peak localization.
## 3 Results and Discussion
### Selectivity Test  
It is well known that the chemical composition of water in natural environments varies in accordance with the predominance of weathering, dissolution of atmospheric gases, and leaching of salts from ore [38]. Indeed, natural (e.g., river, lake, spring) waters have a different composition [39]. In the study, we tried to cover the maximum diversity of ions by considering both widely presented environmental contaminants and agents influenced by such types of reaction [40] to make sure there is no influence of external ions.  
Figure 5: (**a**) The absorbance spectra of PAO-immobilized GNPs after different additions of Sb\({}^{3+}\) (normalized spectra, after subtracting the corresponding background signal); (**b**) the value of optical density (lower) and average hydrodynamic diameter (upper) of conjugated GNPs. All experiments were undertaken in three replicates.  
Figure 6: TEM image of PAO-modified GNPs after interaction with Sb\({}^{3+}\) (10 ng/mL).  
Figure 7 demonstrates the difference in intensities of optical density at 620 nm. As seen, insignificant nonspecific changes were observed. Only Sn\({}^{4+}\) and As\({}^{3+}\) influence the determination of Sb\({}^{3+}\). However, the influence from As\({}^{3+}\) is only observed when its concentration exceeds 1 mg/L, whereas an allowable concentration of As\({}^{3+}\) in drinking water is 0.01 mg/L [41]. As for the tin ion (Sn\({}^{4+}\)), finding it in this form is almost impossible. Moreover, the content of tin ions is not standardized in drinking water. Despite this, we note this effect in this study.  
Alkali metal ions (Na\({}^{+}\), K\({}^{+}\), Li\({}^{+}\), and Cs\({}^{+}\)) did not affect the assay results up to the concentrations of 22.5 mg/L, and the optical density value continued to correspond to the blank solution. Earth alkali metal (Mg\({}^{2+}\), Sr\({}^{2+}\), and Ba\({}^{2+}\)) did not influence the conjugate PAO-GNPs' stability, at least at 10 mg/L and Ca\({}^{2+}\) at 13.5 mg/L. Nickel ions (Ni\({}^{2+}\)) at 4 mg/L did not cause the aggregation of the used PAO-modified GNPs. Such ions as Mo\({}^{2+}\), Mn\({}^{2+}\), Zn\({}^{2+}\), Hg\({}^{2+}\), Co\({}^{2+}\), Ag\({}^{+}\), Cd\({}^{2+}\), Cu\({}^{2+}\), and Fe\({}^{3+}\) were taken at the concentration of 1 mg/L, and aggregation did not occur. Chromium ions (Cr\({}^{3+}\)) did not influence the stability of conjugates at 1 mg/L but influenced it at 1.8 mg/L. It should be noted that even the first concentration exceeded the maximum permissible level for drinking water. The chosen anions did not alter optical density even at 22.5 mg/L for chloride, 20.5 mg/L for sulfate, and 20.0 mg/L for nitrate ions. The low influence of other ions as interferences is explained by the selective interaction of the oligonucleotide sequence with Sb to induce aggregation.
## 4 CT Algorithm
### The Design of CT
#### 4.2.3 Phase 3: Trap Construction  
The node selected in phase 2 is the starting node of the algorithm of this phase. As shown in Algorithm 4, the steps in this phase are as follows:  
1. the starting node broadcasts \(TRAP\) message, and the message content is \(\langle TRAP,~{}i,~{}Next,~{}nSlot\rangle\), representing trap, sending node id, next target node, and minimum time slot respectively.
2. after receiving the \(TRAP\) information, the node selects a node that meets the following conditions as the next target node among all the nodes: (a) the target node is located in the annular region in Figure 3; (b) the target node is within the communication range; (c) \(\angle AOi\) has the largest angle, which is calculated from the positions of the current node A \((x_{a},y_{a})\), its one-hop neighbor I \((x_{i},y_{i})\) and sink O \((x_{o},y_{o})\) by cosine theorem; (d) the target node adjusts its own slot value to be the smallest of the neighbor nodes except the nodes on the selected path.
3. repeat step 2 until all nodes have been selected.  
Figure 3: Trap construction.  
```
1. BROADCAST\(<\)SEARCH, \(i\), Next, Distance\(>\);
2. IF\((i=\textit{Sink})\)THEN
3. Next\(\leftarrow\{n\mid n\in myN\wedge n.getY()=\text{Max}(myN.getY()\}\);
4. Distance\(\leftarrow\)Distance - 1;
5. END IF
6. IF\((n=i)\)THEN
7. IF\((d=0)\)THEN
8. startNode\(\leftarrow i\);
9. ELSE
10. Next\(\leftarrow\{n\mid n\in myN\wedge n.getY()=\text{Max}(myN.getY()\}\);
11. Distance\(\leftarrow\)Distance - 1;
12. END IF
13. END IF
```  
**Algorithm 3**Node location protocol for node \(i\)  
```
1. BROADCAST\(<\)TRAP, \(i\), Next, nSlot\(>\);
2. IF\((i=\textit{start})\)THEN
3. \(nSlot\leftarrow\min(\{n.slot\mid n\in myN\}\cup\{slot\})\);
4. Next\(\leftarrow\)choose(\(\{n\mid n\in\text{Circular Area}\wedge n\in\text{Sensing area of node }i\wedge\angle AOn=\max(\angle AOI\mid i\in nodes)\}\));
5. END IF
6. IF\((n=i)\)THEN
7. slot\(\leftarrow s-1\);
8. \(nSlot\leftarrow\min(\{n.slot\mid n\in myN\}\cup\{slot\})\);
9. Next\(\leftarrow\)choose(\(\{n\mid n\in\text{Circular Area}\wedge n\in\text{Sensing area of node }i\wedge\angle AOn=\max(\angle AOI\mid i\in nodes)\}\));
10. IF\((\textit{Next}\
otin\text{Trap Route})\)THEN
11. BROADCAST\(<\)TRAP, \(i\), Next, nSlot\(>\);
12. ELSE
13. BREAK;
14. END IF
15. END IF
```  
**Algorithm 4**Circular setting protocol of node \(i\)  
As shown in Figure 3, the order of \(\angle AOI\) for each node is as follows: \(\angle AO0<\angle AO1<\ldots\angle AOI\ldots<\angle AO13\). Therefore, the algorithm runs counterclockwise along the annular region in the figure and gradually adjusts the time slot.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 6 Evaluation Metrics for Localization Techniques
### Convergence Time  
Convergence time is the time taken for localizing every node in the network. Network size influences the convergence time, and hence, the convergence rate of the localization algorithm has to be analyzed with an increase in the network size. However, for specific applications involving a fixed number of nodes, convergence time is crucial as well. If the location precision of any algorithms is exceptionally high at the cost of longer localization time, it proves an algorithm's impracticality for that scenario. Additionally, for networks with moving nodes, the slower algorithm may fail to imitate the present structure of the network.