esa-sceva/satcom-chunk-collection huggingface dataset:
# Robotic Manipulation of Cumulus-Oocyte Complexes for Cumulus Cell Removal
## 4 Experimental Results  
### Oocyte Maturity  
To train the EPSANet for oocyte maturity assessment, 1800 images were captured from 180 mouse COCs. For each COC, an image was captured every 12 seconds after enzymatic treatment with hyaluronidase for 2 min, mimicking different concentrations of cumulus cells surrounding the oocyte. The dataset was divided into 80% for training, 10% for validation, and 10% for testing. After image capture, the experienced embryologists manually removed cumulus cells and assessed the maturity of oocytes by observing the presence of the polar body. The assessment results (i.e., mature or immature) were used as the ground truth labels. The performance of the EPSANet50 network was compared with that of the ResNet50 [36], the DenseNet50 [37], and the SENet50 [38] networks, including accuracy, F1-score, and AUC. The accuracy and F1-score are defined as  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN},\ F1-score=\frac{2 TP}{2 TP+FP+FN} \tag{15}\]  
where _TP_ is the number of mature oocytes correctly classified as mature, _TN_ is the number of immature oocytes correctly classified as immature, _FP_ is the number of immature oocytes incorrectly classified as mature, and _FN_ is the number of mature oocytes incorrectly classified as immature. The area under the receiver operating characteristic (ROC) curve (AUC) is calculated to evaluate the model accuracy regardless of the classification threshold.  
As shown in Figure 8, the EPSANet50 network achieved an accuracy of 94.5%, an F1-score of 91.2%, and an AUC of 0.91, all of which outperformed the other three networks. The performance of the DenseNet50 network was similar to that of the SENet50 network in terms of accuracy (92.0% vs. 92.3%), F1-score (88.9% vs. 89.2%), and AUC (0.85 vs. 0.86), respectively. In comparison, the ResNet50 network had lower performance, i.e., 89.7% in accuracy, 85.6% in F1-score, and 0.78 in AUC. -6 pt  
Figure 8: Performance comparison among EPSANet50, ResNet50, DenseNet50, and SENet50. (**a**) Accuracy. (**b**) F1-score. (**c**) AUC.
## 5 Simulation and Experiment
### Simulation  
Firstly, we used the standard data set (Solomon benchmark datasets R201, C201, and RC201) [44], which includes the coordinates of nodes, to simulate the TGA algorithm, and verify the solution quality of TGA in path optimization.  
Secondly, to verify the advantages of combining algorithms (ILEACH-TGA and TGA-MOVING-DCMDR), in our simulations, 200 sensor nodes were distributed uniformly at random in an area of 10,000 m, with the UAV's take-off point (5000, 5000). The data gathering model corresponds to the context of the aforementioned real-world field tests.
## 3 Results and Discussion
### Effect of the Specific Surface Area  
The influence of the specific surface area (S.S.) on the outcome of hydrothermal treatments was studied in order to delve into the results obtained from the tests where samples of exactly the same size could not be used. This should allow us to determine the effect of this variable on a prospective scaling and provide us with a more realistic approach to the recycling of actual aeronautical components.  
For comparison purposes, two tests on composite samples of different sizes were used, which caused them to have different S.S.s, being 850 mm\({}^{2}\)/g for the sample used in the P6 test and 716 mm\({}^{2}\)/g for the one in the P10 test. The rest of the variables were set to the following values: 300 \({}^{\circ}\)C, 130 bar, 60 rpm, 80 min reaction time, 222 ml\({}_{\text{solv}}\)/g\({}_{\text{comp}}\) solvent/sample ratio, 5 wt.% IPA solution, as solvent and 0.1 M KOH as catalyst. The specific surface area (S.S.) was calculated by dividing the external surface area of the samples by their mass.  
Figure 3 shows the DR values obtained from the tests on different specific surface area samples. Based on these results, it can be observed that the test where the samples had a higher specific surface area (850 mm\({}^{2}\)/g) achieved a higher DR (90%) than the test where the sample had a lower S.S. (716 mm\({}^{2}\)/g), 84%. This allowed us to conclude that the higher the S.S., the higher the DR achieved, which is of special interest for the comparison of results between samples that are not strictly identical.  
Figure 3: DR values reached when processing pieces with different specific surface areas at 300 \({}^{\circ}\)C, 130 bar, 60 rpm, 80 min, 300 ml\({}_{\text{solv}}\)/g\({}_{\text{comp}}\), IPA 5 wt.%, KOH 0.1 M.
# An Empirical Study on Ensemble of Segmentation Approaches
## 2 Materials and Methods
### Loss Functions
#### 2.2.3 Weighted Intersection over Union  
Another well-known loss function is intersection over union (IoU) loss, which was introduced for the first time in [33]. The original formula was:  
\[\text{IoU}=\frac{|P\,\cap\,T|}{|P\,\cup\,T|} \tag{2}\]  
As mentioned earlier, \(T\) is the truth label image and \(P\) is the prediction for the output image.  
Unfortunately, the set symbols for Intersection and Union are not differentiable because \(P\) and \(T\) have to be either 0 s or 1 s. This is not true for \(P\), so the formula was then approximated with the following:  
\[\text{IoU}^{\prime}=\frac{|P\times T|}{|P+T-P\times T|} \tag{3}\]  
where \(P\times T\) is the element-wise multiplication of \(T\) and \(P\). For what concerns the denominator, we subtract the \"intersection\" between \(P\) and \(T\) because we do not want to consider the intersection twice.  
Once the set operators have been converted to arithmetic ones, the formula is differentiable, and it is possible to evaluate the gradient.  
However, IoUs an evaluation metric used for evaluating the goodness of the prediction. Hence, a value of \(1\) is equivalent to a perfect prediction. For this reason, the loss function will be:  
\[L_{\text{IoU}}=1-\text{IoU}^{\prime} \tag{4}\]  
Unfortunately, this function has to face the same problem of CE in inferring the label of the boundary of each object; therefore, as suggested in [34], we use the weighted intersection over union () wIoU, instead of the standard IoU.  
The formula of the weighted intersection over union loss is:  
\[L_{\text{wIoU}}=1-\frac{|w*P*T|}{|w*(P+T)-w*P*T|}=1-\frac{\sum_{i=1}^{N}w_{ik }*\sum_{k=1}^{K}T_{ik}*Y_{ik}+1}{\sum_{i=1}^{N}\sum_{k=1}^{K}w_{ik}(T_{ik}+Y_{ ik}-T_{ik}*Y_{ik})+1} \tag{5}\]  
where \(N\) is the number of pixels and \(K\) is the number of classes. The weights \(w_{ik}\) are calculated as aforementioned. \(T_{ik}\) and \(Y_{ik}\) are, respectively, the ground truth value and the prediction value for the pixel \(i\) belonging to the class \(k\). We added 1 to both the numerator and the denominator in order to prevent the undefined division \(\frac{1}{0}\).
# Enhanced and Combined Representations in Extended Reality through Creative Industries
## 4 Results and Discussion
### Discussion
#### 4.2.3 Ethics  
However, some ethical questions arise (RQ4) regarding the management and exploitation of digital solutions that directly or indirectly use representations based on CT scans.  
Ownership  
A key question that arises concerns the ownership of the CT scans and the generated representations. The answer is not simple, as the process involves several groups of people from different disciplines who justifiably have a stake in the property. The complexity of the situation is better understood by using the example of an archaeological find. Specifically, the team of archaeologists is excavating a complex artifact that needs further investigation into its internal structure for better documentation. The cultural organization that hosts or undertakes its temporary conservation needs the specialized services of a group of experts who will undertake the process of producing the tomography records. A team of experts will likely need to analyze the scans with special software and high-end computing systems, producing datasets that are more understandable and usable for culture professionals. Finally, a creative team of programmers will undertake the development of an interpretive tool for the multimodal representation of the artifact based on different types of data, including CT scans. Therefore, the precise ownership framework for each phase of the life of the data and its derivatives should be explored in depth, prioritizing the principles of open access to research.  
The second critical point that raises extensive discussion is the use of CT scans, indirectly or directly, especially in cases where the finding to be studied concerns human remains, whether it is an archaeological excavation or a patient in the case of medicine. The ethical question that arises is the way to use information and its representations. Thisdebate is ongoing, as it has already developed around the use of digitized 3D representations of bones and skeletons in the field of archaeanthropology. Therefore, the parameter that needs to be added here is the ethical way of handling and using the scans and their derivatives related to humans. Extending to the field of medicine and health, the discussion should center on ensuring the privacy and protection of sensitive personal data of patients, as well as the ethical use of data for research and educational purposes.
## 8 Analysis
### Technical Feasibility  
Our proof of concept has been deployed using Remix-ETH IDE. Remix IDE is an open-source web-based Integrated Development Environment (IDE) primarily used for developing and deploying smart contracts on the Ethereum blockchain. Remix supports solidity, the primary programming language for ETH smart contracts, and facilitates various stages of the development process, including writing, compiling, testing, and deploying smart contracts.  
However, deploying a DApp in a real blockchain (e.g., ETH mainnet, testnets, or sidechains like Polygon, among others) introduces significant technical feasibility challenges that we have not considered, but developers working in the off-chain part should do it:  
* Latency: Ethereum no longer uses a proof of work (PoW) mining mechanism; instead, it now uses proof of stake (PoS). Therefore, there is no mining time in the traditional sense. It means Ethereum no longer has a traditional mining process where miners compete to solve complex puzzles. In the case of our experiments with Remix, it executes near-instant blocks; however, Ethereum currently takes approximately 12.12 s [42]. Something to consider in the logic is to assume fast feedback or instant confirmations that will not hold in a production environment.
* Disconnections: Although public blockchain networks are highly reliable, the off-chain code must take into account availability troubles.
* Upgradability: our design does not contemplate any proxy pattern for contract upgrades. It means that if our smart contracts are deployed, they cannot be reset as easily as modifying them using Remix.
* Large storage: In our examples, such as _BirthCertificate_ and _ScholarCurriculum_, we only considered plain text stored directly in smart contracts. However, blockchain was not designed for storing large documents or binary data (e.g., scanned PDFs). To address this limitation, off-chain storage using the InterPlanetary File System (IPFS) could be employed in future token designs that require larger data capacity [43; 44]. In such cases, only the IPFS content hash would be stored on-chain, ensuring data integrity while maintaining cost efficiency.
## 4 PD Recognition System
### Measurement Environment  
Figure 5 shows a block diagram of the PD experiment in the laboratory. Construction of the PD experiment laboratory included a notebook (NB), high voltage control panel, an isolation transformer, a step-up transformer, test objects (power cable), a data acquisition (DAQ) card and a high-frequency inductive sensor (L sensor) to measure the electrical signal caused by the PD phenomenon in power cable. This experiment used the positive edge trigger function of the DAQ card to acquire the output voltage of the isolation transformer as the trigger signal. The electrical signals recorded by the L sensor were all synchronized in each experimental model.  
In the voltage step-up procedure of the PD experiment, a 25 kV cable should have 14.4 kV(U\({}_{0}\)) rated phase to ground voltage. However, according to IEC 60502-2 for a power cable from 6 kV to up to 30 kV, the test after installation voltage should be 1.7 U\({}_{0}\) for 5 min [20]. Because the voltage must exceed the partial discharge inception voltage to excite the PD phenomenon, the testing voltage should be 1.7 \(\times\) 14.4 kV = 24.5 kV. Thus, 25 kV was selected as the test voltage. The high voltage generator generated a rising voltage from 0 V to 25 kV. The inductive (L) sensor then measured the power cable joint and recorded the signals after 1 min. During the experimental process, the sampling rate was set at 20 M/s to acquire one power cycle (60 Hz) acoustic signal. All measured signals determined through the preamplifier and analog data were converted to digital data for storage in a computer. In the general practice of PD tests, a longer period of measurement is advantageous in PD identification. Because the experimental model of this study was established based on the common defect patterns of cable joints and the measurements were conducted in a shielded environment of a laboratory,  
Figure 4: Experimental defect models in XLPE power cable.  
clear PD signals could be measured for each single cycle. Therefore, only one cycle was taken for signal processing in this study. A longer sampling time should be taken for signal processing in practical PD measurement in the future.
# Performance Analysis of Pile Group Installation in Saturated Clay
## 3 Two-Pile Group Analyses  
In geotechnical engineering, jacked piles are generally installed in a continuous sequence, where the installation of a subsequent pile (the second pile) exerts lateral pressure and upward frictional force on the adjacent pile (the first pile), causing the adjacent pile to experience offset and uplift, as illustrated in Figure 6. Additionally, during the installation of the subsequent pile, the auxiliary tube can increase the resistance of the adjacent pile. Therefore, in subsequent analyses, the influence of the auxiliary tube on the adjacent pile should be excluded.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Calculation Parameters** & **Values** \\ \hline Effective unit weight [\(\gamma^{\prime}/(\text{kN}/\text{m}^{3})\)] & 8.75 \\ Effective internal friction angle [\(\varphi^{\prime}/(\text{c})\)] & 30 \\ Overconsolidation ratio [OCR] & 1.0 \\ Void ratio [\(\epsilon_{0}\)] & 0.98 \\ Elastic modulus [\(E/\)(MPa)] & 24 \\ Poisson’s ratio [\(v\)] & 0.45 \\ Cohesion [\(c/\)(kPa)] & 15 \\ Earth pressure coefficient at rest [\(K_{0}\)] & 0.55 \\ Coefficient of permeability [\(k/\)(cm/s)] & 2.65 \(\times\) 10\({}^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clay parameters in centrifuge tests.  
Figure 4: Dissipation of excess pore pressure in the soil consolidation stage [33].  
Figure 5: Load–displacement curve of the pile after 8.65 days of consolidation [33].  
In this study, the effects of subsequent pile installation on adjacent piles are explored. The separate auxiliary tube method is proposed for investigating the effect of adjacent piles on the installation of subsequent piles. In addition, the influence of \(L/D_{\text{e}}\) and pile length on the responses of adjacent piles are further discussed.  
The simulation of two-pile group installation and consolidation was typically carried out in three stages: (1) In the installation of the adjacent pile, both the pile and the auxiliary tube are set as rigid and moving downwards with a constant speed. (2) The auxiliary tube is separated from the pile, and the rigid body constraints on the pile are removed after the installation process of Pile 1, as illustrated in Figure 7. (3) The installation of the subsequent pile occurs. It should be noted that the installation of the two-pile group is continuous, so the intervening consolidation between Pile 1 and Pile 2 is excluded in this study.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### Impact of Training Dataset Size on Accuracy  
The measurement of power spectral densities required for training this model can be highly time-consuming, primarily due to the extensive number of OSA measurements needed, which directly depends on the number of active channels. As an example, the EDFA-17 dataset in the low-gain range was characterized using the following input parameters:  
1. **Total input power**: Ranging from \(-10\,\mathrm{dBm}\) to \(6\,\mathrm{dBm}\), with a step size of \(2\,\mathrm{dBm}\) (9 values);
2. **Gain**: Ranging from \(14\,\mathrm{dB}\) to \(20\,\mathrm{dB}\), with a step size of \(1\,\mathrm{dB}\) (7 values);
3. **Tilt**: Ranging from \(-5\,\mathrm{dB}\) to \(5\,\mathrm{dB}\), with a step size of \(1\,\mathrm{dB}\) (11 values).  
In this scenario, 9 input power spectra and 693 output power spectra are required. When the channel density is high and the spacing between channels is narrow, a lower resolution bandwidth is necessary to accurately capture the noise level between adjacent channels. However, since measurement time is inversely proportional to the square of the resolution bandwidth, this requirement can significantly increase the time required for data acquisition. This underscores the importance of carefully balancing model accuracy with dataset size and acquisition time. As previously mentioned, the model is designed to be adaptable, allowing for trade-offs between precision and the duration of data collection.  
To assess performance, two types of analyses are conducted. The first approach trains the model using the complete dataset of one or more devices while evaluating accuracy on the remaining datasets. This represents a worst-case scenario, where the correlation between devices is minimal. The second analysis aggregates all datasets and applies a random train/test split, analyzing accuracy as a function of the split ratio. For these evaluations, the Cisco L-band EDFA datasets will be utilized, as they provide six available datasets.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 3 Design and Implementation of CONE
### Neighbor Discovery and Degree Exchange  
As previously stated, nodes have to know their 1-hop neighbors and their degree to construct a DS. Algorithm 3 gives the pseudo-code of the neighbor discovery and degree exchange procedure. Nodes exchange their degree with their neighbors. For this purpose, a node periodically broadcast a degree_message which contains the sender's degree. During the degree exchange, nodes also discover their neighbors. If a node discovers a new neighbor, then it updates its degree. Also, each node stores the node which has--up to that time--sent the highest degree in order to elect this node for becoming a dominator later.  
```
1:receive degree_message
2:if sender's ID unknown then
3: mark sender's ID as known
4: increase degree by 1
5:endif
6:if received degree is greater than highest known degree then
7: consider sender as dominator
8: set highest known degree to sender's degree
9:else if received degree is equal to highest known degree then
10:if sender's ID is lower than the one of current dominator then
11: consider sender as new dominator
12:endif
13:endif
```  
**Algorithm 3** Neighbor discovery and degree exchange.
## 3 Proposed Method
### Counter-Propagation Networks  
The rapid progress and development of deep learning (DL) [31] technologies in recent years has contributed remarkably to the classification [32], recognition [33], understanding [34], and prediction [35] of diverse and numerous tasks. With the advent of very deep multilayered backbones such as Inception [36], Xception [37], ResNet [38], ResNeXt [39], and Res2 Net [40], the performance and accuracy of DL-based methods significantly outperform those of classical ML-based methods. However, to achieve their full performance, DL-based methods require the combination of large amounts of training and validation data combined with high-dimensional features. Therefore, DL-based methods mainly target large image datasets obtained from various cameras, including depth sensors [41]. For low-dimensional features and low-volume datasets, DL-based methods are unsuitable for training and validation because of the complexity of their networks and their numerous parameters. We infer that conventional DL-based methods have an important role and that they can make a meaningful contribution in this application range.  
For this study, we used CPNs [7], which are a conventional ML-based method, to classify and to recognize bed-leaving behavior patterns. Input features from the sensors to the input layer unit \(i\) (\(1\leq i\leq I\)) at time \(t\) are \(x_{i}(t)\). A weight from \(i\) to Kohonen layer unit \(j\) (\(1\leq j\leq I\)) at time \(t\) is \(w_{ij}(t)\). Also, a weight from Grossberg layer unit \(k\) (\(1\leq k\leq K\)) to Kohonen layer unit \(j\) at time \(t\) is \(w_{jk}(t)\). Herein, \(I\), \(J\), and \(K\) respectively denote the total numbers of input layer units, Kohonen layer units, and Grossberg layer units. Before learning, \(w_{ij}(t)\) are initialized randomly. Using the Euclidean distance between \(x_{i}(t)\) and \(w_{ij}(t)\), a winner unit \(c(t)\) is sought for the following:  
\[c(t)=\operatorname*{argmin}_{1\leq j\leq I}\sqrt{\sum_{i=1}^{I}(x_{i}(t)-w_{ij }(t))^{2}}. \tag{11}\]  
A neighborhood region \(N(t)\) is set from the center of \(c\) is  
\[N(t)=\lfloor N(0)\cdot J\cdot\left(1-\frac{t}{L}\right)+0.5\rfloor, \tag{12}\]  
where \(L\) stands for the maximum learning iteration. Teaching signals to the Grossberg layer units at time \(t\) are \(y_{k}(t)\). For supervised learning, \(w_{ij}\) and \(w_{jk}\) in \(N(t)\) are updated as  
\[w_{ij}(t+1)=w_{ij}(t)+\alpha(x_{i}(t)-w_{ij}(t)), \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node Number** & **Formula Index** & \(\alpha\) & \(\beta\) & \(\gamma\) \\ \hline N6 & (3) & 0.50 & – & – \\ N7 & (3) & 0.80 & – & – \\ N8 & (3) & 0.90 & – & – \\ N9 & (3) & 1.10 & – & – \\ N10 & (3) & 1.20 & – & – \\ N11 & (3) & 1.50 & – & – \\ N12  
\[w_{jk}(t+1)=w_{jk}(t)+\beta(y_{k}(t)-w_{jk}(t)), \tag{14}\]  
where \(\alpha\) and \(\beta\) are learning coefficients that decrease along with learning progress.