esa-sceva/satcom-chunk-collection huggingface dataset:
# The Accuracy Assessment of Lithospheric Density Models
## 2 Numerical Model
### Sub-Lithospheric Mantle Geoid
#### 2.2.3 Mantle Disturbing Potential  
To reveal a gravitational signature attributed to a mantle density structure, the Moho geometry signature has to be subtracted from the crust-stripped disturbing potential. This procedure yields the mantle disturbing potential. According to [PERSON] et al. [50], the mantle disturbing potential \(T^{\rm M}\) is obtained from the crust-stripped disturbing potential \(T^{\rm CS}\) after subtracting the gravitational potential of Moho geometry \(V^{M,\Delta\rho^{c/m}}\). We then write the following:  
\[T^{\rm M}=T^{\rm CS}-V^{M,\Delta\rho^{c/m}}. \tag{31}\]  
The potential \(V^{M,\Delta\rho^{c/m}}\) (for the average Moho density contrast \(\Delta\rho^{c/m}\)) is defined in the following form:  
\[V^{M,\Delta\rho^{c/m}}\big{(}r_{g},\Omega\big{)}=3\frac{\rm GM}{\rm R}\sum_{n= 0}^{\eta}\sum_{m=-n}^{n}\rm F^{\Delta\rho M}_{n,m}\,Y_{n,m}(\Omega)\, \tag{32}\]  
where the numerical coefficients \(\rm F^{\Delta\rho M}_{n,m}\) are given by the following:  
\[\rm F^{\Delta\rho M}_{n,m}=-\frac{\Delta\rho^{c/m}}{(2n+1)(n+3)}\sum_{k=1}^{ n+3}\binom{n+3}{k}\frac{(-1)^{k}}{\rm R^{k}}\hat{M}^{(k)}_{n,m}. \tag{33}\]  
The Moho depth spherical functions \(\rm\hat{M}_{n}\) and their higher-order terms \(\{\rm\hat{M}^{(k)}_{n}\ :\ k=2,\ 3,\ \dots\ \}\) read as follows:  
\[\rm\hat{M}^{(k)}_{n}=\frac{2n+1}{4\pi}\iint\limits_{\Phi}M^{k}\big{(}\Omega^{ \prime}\big{)}\ P_{n}(t)\ d\Omega^{\prime}\]  
\[=\sum_{m=-n}^{n}\rm\hat{M}^{(k)}_{n,m}\,Y_{n,m}(\Omega). \tag{34}\]  
As seen in Equation (34), the coefficients \(\{\rm\hat{M}^{(k)}_{n,m}\ :\ k=1,\ 2,\ \dots\}\) are generated from values of the Moho depth \(M\). The Moho density contrast in Equation (33) is defined as the average lithospheric mantle density \(\rho^{\rm LM}\) from which the (constant) reference crustal density \(\rho^{\rm c}\) is subtracted. Hence,  
\[\Delta\rho^{c/m}=\rho^{\rm LM}-\rho^{\rm c}. \tag{35}\]
###### Abstract  
The Copernicus program, an initiative by the European Union, offers open-access Earth observation data and high-level products through its services. However, these services are less well known in Latin America, underscoring the need to strengthen capacity-building efforts. In this context, this research examines the design and implementation of training workshops in Ecuador and panel discussions in Bolivia, focusing on the role of Copernicus Services in addressing regional challenges related to Environmental, Food Security, Climate Change, Security, and Risk Management through geospatial technologies. By tailoring training sessions in Ecuador to enhance stakeholders' capabilities and conducting panel discussions in Bolivia to promote these services among public entities, this research highlights the successes and challenges of these initiatives. We emphasize the importance of flexible event design, alignment with local contexts, and the integration of interactive methodologies to enhance stakeholder engagement and learning outcomes. Additionally, differences and similarities between the event formats are discussed in terms of purposes and objectives, audience engagement, content delivery, attendance, and post-event outcomes. Finally, we outline the convergences and divergences in strategic priorities for future Copernicus Services training initiatives in both countries.  
Copernicus services; capacity building; Earth observation; environmental management; food security; climate change; risk management; Ecuador; Bolivia
# Supply-Scalable High-Speed I/O Interfaces
## 4 Survey on State-Of-The-Art Supply-Scalable I/O
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Process** \\ **(nm)** \\ \end{tabular} & \
\begin{tabular}{c} **Min.** \\ **Rate** \\ **(cfb/s)** \\ \end{tabular} &
\begin{tabular}{c} **Max.** \\ **Rate** \\ **(cfb/s)** \\ \end{tabular} & **Clocking** & **Clock Rate** & **\# of PLLs** & **Channel** & **Supply** & **Min.** & **Max.** & **Area** & **FoM (pl/p)** & **FoM (pl/p)** \\  & & & & & & & & & & & **Scaling** & **(V)** & **Supply (V)** & **Area** & **@Min Rate** & **@Max Rate** \\ \hline
[24] & 350 & 0.2 & 0.8 & Mesochronous & Half rate & External & N/A & None & DC-DC & 1.3 & 3.2 & 1.625 & N/A & 65.625 \\
[22] & 250 & 0.65 & 5 & Presochronous & 1/5 rate & 1 & N/A & None & DC-DC & 0.9 & 2.5 & 0.63 & 15 & 76 \\
[25] & 65 & 5 & 15 & Mesochronous & Half rate & External & 10 & \
\begin{tabular}{c} TX FFE + \\ CITE \\ \end{tabular} &
\begin{tabular}{c} External \\ source \\ \end{tabular} & 0.68 & 1.05 & 0.088 & 2.7 & 5 \\
[26] & 45 & 5 & 25 & Mesochronous & Half rate & External & N/A & \
\begin{tabular}{c} TX FFE + \\ DFE \\ \end{tabular} &
\begin{tabular}{c} External \\ source \\ \end{tabular} & 0.75 & 1.1 & 0.21 & 1.6 & 2.6 \\
[15] & 32 & 2 & 16 & Mesochronous & \begin{tabular}{c} Quarter \\ rate \\ \end{tabular} & External & 11 & \
\begin{tabular}{c} TX FFE + \\ CITE \\ \end{tabular} &
\begin{tabular}{c} External \\ source \\ \end{tabular} & 0.6 & 1.08 & 0.039 & 0.99 & 2.56 \\
[27] & 65 & 4.8 & 8 & Mesochronous &
\begin{tabular}{c} Quarter \\ rate \\ \end{tabular} & External & 8.4 & CTLE & External & 0.6 & 0.8 & 0.057 & 0.51 & 0.66 \\
[16] & 22 & 8 & 32 & Presochronous & \begin{tabular}{c} Quarter \\ rate \\ \end{tabular} & 1 & 16 & \
\begin{tabular}{c} TX FFE + \\ CITE + \\ DFE \\ \end{tabular} &
\begin{tabular}{c} External \\ source \\ \end{tabular} & 0.72 & 1.07 & 0.079 & 3.25 & 6.41 \\
[31] & 65 & 3 & 10 &
\begin{tabular}{c} Source \\ synchronous \\ \end{tabular} & Half rate & 1 & N/A & TX FFE + \\
[34] & 65 & 3 & 16 & N/A (no CDR) & \begin{tabular}{c} Quarter \\ rate \\ \end{tabular} & External & 24 & \
\begin{tabular}{c} TX FPM + \\ RX passive \\ \end{tabular} &
\begin{tabular}{c} External \\ source \\ \end{tabular} & 0.5 & 0.9 & 0.13 & 1.65 & 3.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Survey of supply-scalable transceivers. CDR: clock and data recovery.
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 4 Literature Review
### IoT Healthcare Applications Based on Image Processing  
Medical images used for IoT healthcare are many. One example is described in [63]. It illustrates that the image may be utilized to mask a textual version of diagnostic data. Images are employed to cover data. Since data has been encrypted using industry-standard techniques such as AES and RSA, which stands for Advanced Encryption Standard. Using a 2D discrete wavelet transform (1 level) image, the ciphertext is concealed. The text scale has been suppressed to conceal its contents. To accomplish this, two distinct cover images made from gray-scale and color images have been created, resulting in two distinct font sizes.  
Another example of this concept can be found in [64], in which pre-encrypted medical images have been provided, producing an additional layer of protection by having a one-time password (OTP) embedded into them. The encrypted image with the OTP will be  
Figure 6: IoT location detection applications based on image processing: localization mechanism elements proposed by [61].  
transmitted to the same user. After that, the encrypted medical image will be decrypted to reveal the encoded OTP. Finally, the two OTPs, previously retrieved from the medical image, are compared. This verification step is necessary to check the encrypted medical image's integrity. Once the verification procedure is completed, original medical data will be extracted. An illustration related to IoT healthcare applications based on image processing is shown in Figure 7.  
Another example may be discussed in [65]. People in less developed countries, where medical care is scarce, may benefit greatly from IoTSs that place a high priority on human vision. The proposed system in [65] proposes a hybrid IoT healthcare architecture that analyzes retinal images. By considering the retinal images, the proposed super-resolution (SR) work makes use of multi-kernel support vector regression (SVR) to improve the overall image quality that is recorded. This is done to better diagnose retinal diseases. By producing high-resolution retinal images, the hybrid architecture enables ophthalmologists to establish more accurate diagnoses more rapidly.  
The healthcare industry has gained considerably from digital image processing technology, notably medical images and health-related information. This use may be noticeable when there is a requirement to send the information securely via communication channels such as the cloud for storage and/or processing reasons. Zigzag encryption of medical images is one of these advantages [66]. The proposed algorithm exceeds the competition and may be put to good use in diagnosing medical images [66; 67].
## 4 Experimental Results  
### Crystal Structure Analysis of Cu-Based Absorbents on \(\Gamma\)-Alumina and Macro-Porous Alumina  
The crystal structures of the prepared CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and CuO/macro-porous Al\({}_{2}\)O\({}_{3}\) absorbents were analyzed by XRD. The CuO phases were present on the \(\gamma\)-alumina and macro-porous alumina surfaces (see Figure 6). The XRD patterns of the CuO crystal showed peaks at 35\({}^{\circ}\), 39.4\({}^{\circ}\), 49\({}^{\circ}\), 54\({}^{\circ}\), 58\({}^{\circ}\), and 61.8\({}^{\circ}\) 20. The XRD peak intensity of CuO supported on the \(\gamma\)-alumina surface was stronger than that on the macro-porous alumina surface. The peak patterns of CuO supported on the surface of the macroporous alumina did not appear clearly. This suggests that CuO is distributed widely on the alumina surface. As the content of CuO supported on alumina was decreased, the intensity of the crystalline  
Figure 5: Top and side views of the (**a**) CuO(111) surface. The cus and sat on CuO(111) represent a coordinatively unsaturated site and saturated site, respectively. The vertical and horizontal arrows on the CuO(111) surface in (a) represent the [\(\overline{1}\overline{1}2\)] and [\(\overline{1}\overline{1}0\)] crystallographic directions. (**b–d**) The top views of O vacancy, surface S atom, and both O vacancy and surface S atom employed on CuO(111) surfaces, respectively. Each O vacancy (white) and surface S atom (black) are indicated by a dashed circle.  
peak became lower because a low CuO content was well dispersed over a large surface of the alumina supports, which resulted in little CuO crystal growth on the supports. In contrast to CuO, the XRD pattern of alumina revealed an amorphous structure, and the peak intensity of \(\gamma\)-alumina was stronger than that of macro-porous Al\({}_{2}\)O\({}_{3}\).
## 3 Experiments and Results
### Experiments with BEE1

Table 3: Performance of top hand-designed ConvNet, ResNet32, and VGG16 models (trained for 100 epochs), RFs, and SVMs on BEE1. The number following the RF abbreviation denotes the number of trees. The two top performing auto-designed ConvNets on BEE1 (ConvNetGS-3 and ConvNetGS-5) are included for comparison. The validation accuracies are sorted from highest to lowest. Figure 9: The architecture of the best performing hand-designed ConvNet (ConvNet 1 in Table 3) on BEE1. with a validation accuracy of 99.48%. The best auto-designed ConvNet (ConvNetGS-3) came a close second with a validation accuracy of 99.09%. Thus, the top two ConvNets performed only slightly better than the top performing auto-designed ConvNet. ConvNetGS-3 tied for the second place with ConvNet 2 and outperformed all other ConvNets, VGG16, RFs, and SVMs. The RFs performed much better than the linear kernel SVMs but were \(\approx\)6% below the best hand- and auto-designed ConvNets.
###### Abstract  
Peer-to-Peer (P2P) energy trading is a new financial mechanism that can be adopted to incentivize the development of distributed energy resources (DERs), by promoting the selling of excess energy to other peers on the network at a negotiated rate. Current incentive programs, such as net metering (NEM) and Feed-in-Tariff (FiT), operate according to a centralized policy framework, where energy is only traded with the utility, the state-owned grid authority, the service provider, or the power generation/distribution company, who also have the upper hand in deciding on the rates for buying the excess energy. This study presents a comparative analysis of three energy trading mechanisms, P2P energy trading, NEM, and FiT, within a rural microgrid consisting of two prosumers and four consumers. The microgrid serves as a practical testbed for evaluating the economic impacts of these mechanisms, through simulations considering various factors such as energy demand, production variability, and energy rates, and using key metrics such as economic savings, annual energy bill, and wasted excess energy. Results indicate that while net metering and FiT offer stable financial returns for prosumers, P2P trading demonstrates superior flexibility and potentially higher economic benefits for both prosumers and consumers by aligning energy trading with real-time market conditions. The findings offer valuable insights for policymakers and stakeholders seeking to optimize rural energy systems through innovative trading mechanisms.  
energy; energy trading; blockchain; peer-to-peer; distributed energy resources; financing mechanism; feed-in tariff; net metering Article
# Study on the Light Field Regulation of UVC-LED Disinfection for Cold Chain Transportation
## 4 Results
### Simulation of the Proposed Optical System  
In this proposed system, a pair of microlens arrays and a piece of focusing lens were designed and used as the uniformity elements to achieve a compact structure and uniform illumination. To get uniformity on the same plane, the full width of the light surface must be illuminated by equal axial and divergent rays. By achieving uniformity via the microlens arrays and focusing lens, the power loss of luminance caused by the uneven distribution of luminance in the illumination plane is reduced, and a uniform illumination spot is obtained.  
The effect of ordinary UVC-LED illumination was compared with the effect of uniform UVC-LED illumination based on microlens arrays. Figure 6 presents the light distribution of a single LED chip, and Figure 6 exhibits the light distribution effect, which reveals high brightness in the center (8.7 \(\mu\)W-cm\({}^{-2}\)), surrounded by low brightness (1.8 \(\mu\)W-cm\({}^{-2}\)). The central part with higher luminance is characterized by energy waste, while the surrounding part has the risk of substandard luminance. Figure 6 presents the uniform lighting effect of the UVC-LED based on microlens arrays designed in this paper.  
The illumination intensity of the illuminated surface is calculated as follows:  
\[\gamma=1-\frac{\sum|E_{i}-\overline{E}|}{n\cdot\overline{E}} \tag{8}\]  
Figure 6: **(a)** The light distribution of a single LED chip; (**b**) the illuminance diagram of a single LED; (**c**) the light distribution of the proposed LED microlens arrays; (**d**) the illuminance diagram of the microlens arrays.  
where \(n\) is the number of sampling points, \(E_{i}\) is the illumination intensity of the sampling points, and \(E\) is the average illumination intensity of the sampling points.  
Compared with that shown in Figure 6b, a better lighting effect was achieved. In addition, the illumination uniformity shown in Figure 6d is 96.4%, and the luminance of the central part of the illuminated area (7.8 uW\(\cdot\)cm\({}^{-2}\)) is not much different from that of the surrounding area (1.5 uW\(\cdot\)cm\({}^{-2}\)). From the results of simulation, we can see that the proposed system can improve the illumination uniformity while ensuring the using requirements, which is consistent with our design. In the proposed system, the light field is regulated as we design, with less light in the center area and more light in the edge area; therefore, the energy is uniform in the whole area and reduces the risk of excessive disinfection and incomplete disinfection, ensuring the disinfection quality.
## 3 Methodology
### Features Extraction and Classification  
As a final step, the Xception model was fine-tuned, and multiple classifiers were applied to predict the true labels. In our proposed model, the transfer learning approach is utilized as it performs better than training completely from beginning [40; 41]. The Xception model, which was pre-trained on the ImageNet dataset, was used and fine-tuned on our dataset by applying a dropout layer with 0.4 probability. The input of the Xception model is the images with contours, and the output is the features. These features are used for classification by applying multiple classifiers, like softmax, linear SVM, quadratic SVM, and Bayesian.  
The Xception model is based on CNN with depth-wise separable convolutional layers. This model has 36 convolutional layers that are arranged into 14 modules. In simple terms, the Xception model is a depth-wise separable CNN with a residual connection. The architecture of Xception is shown in Figure 6. The authors of [42] proved through experimentation that Xception outperforms other CNN models like VGG-16, ResNet-152, and Inception V3 on the ImageNet dataset.  
For experimentation, Python was used, and other settings are shown here in order to reproduce the results. During model training, the Adam optimizer was applied. Because of its outstanding outcomes and adaptable learning gain, the Adam optimizer is frequently used by researchers for CNNs [37]. Categorical cross-entropy (CCE) was also employed as a loss function. During the training of the DL model, the loss function determines the difference between the original class and the anticipated class. It also adjusts the weights of the CNN to produce a better-fitting model [43]. The set batch size was 50 and the model was trained on 250 epochs.  
CCE loss is an excellent measure for calculating loss by computing how distinguished two discrete probabilities are from each other. The mathematical equation of this loss is as follows:  
\[CCE=-\sum_{n=1}^{Outcome\ \ Size}\ \mathcal{S}_{i}.\ log\ \widehat{\mathcal{S}}_{i} \tag{7}\]  
Figure 5: Heat map visualization of input image before and after segmentation using the Xception-Net model.  
Figure 6: XceptionNet architecture [42].  
where \(\mathcal{S}_{i}\) is the original valuation, and \(\widehat{\mathcal{S}_{i}}\) is the anticipated valuation of the model.
###### Abstract  
The aim of the present cohort study was to evaluate the possible occurrence of central sleep apnea (CSA) in patients with obstructive sleep apnea (OSA) after treatment with a mandibular advancement device (MAD). In this case, 56 patients with OSA treated with MAD were enrolled. Inclusion criteria were age over 20 years, body mass index (BMI) less than 34 kg/m\({}^{2}\), diagnosis of OSA confirmed by polysomnography (PSG), and MAD therapy for OSA. Exclusion criteria were smoking, medications for neurological disorders or a history of cervical head injury, and comorbidities (arrhythmias, congenital heart disease, stroke, heart failure or lung disease). Apnea-Hypopnea Index (AHI), Oxygen Destaturation Index (ODI), Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA) and Mixed Apnea were extracted from the PSGs before (T0) and after three months of treatment (T1). Paired-sample \(t\)-tests and the Wilcoxon Signature Rank test were performed to evaluate differences in PSG indices at T1 and T0. OSA, CSA, AHI, ODI showed a noticeable reduction after MAD therapy, but a limited number of patients showed a dramatic increase in TCSA. The presence of TECSA in the course of MAD treatment is a condition that should be taken into consideration when needing to treat an OSA patient.  
OSA (obstructive sleep apnea); TECSA (treatment emergent central sleep apnea); MAD (mandibular advancement device) Article
# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 6 Discussion
### Threats and Validity  
Despite the strong results, this study acknowledges certain limitations related to the dataset and experimental setup. The dataset used in this study was derived from seven open-source projects, which, while diverse, may not fully represent all types of software projects. The structures, data distributions, and reporting conventions of these projects could differ significantly from other open-source or proprietary software systems. This poses a limitation to the generalizability of the results, as the model's performance in other domains remains untested. Future studies should incorporate datasets with broader characteristics to ensure the proposed model is applicable across various contexts.  
Another potential limitation lies in the scalability of the model for larger or real-time datasets. Although the proposed model achieved competitive accuracy and processing times in the current study, its efficiency under high-throughput or real-time conditions has not been evaluated. Testing its performance in such scenarios would be critical for determining its suitability for real-world deployment, particularly in dynamic development environments.  
Additionally, standard hyperparameter values were used for training and evaluation, which may not always provide optimal results. At the same time, this ensured consistency across experiments, fine-tuning hyperparameters using advanced optimization techniques could further enhance the model's performance. Future research should focus on refining these parameters to improve the model's adaptability and accuracy.  
The study also employed traditional evaluation metrics, including precision, recall, F1-measure, and accuracy. Although these metrics provide a robust assessment of model performance, additional criteria, such as robustness under varying data distributions or the ability to handle streaming data, could offer deeper insights into the model's practical applicability. Testing the model's robustness in more complex and variable scenarios would further validate its reliability.  
By addressing these limitations in future research, the model can be further optimized, and its applicability extended to a wider range of software projects, ensuring its scalability and relevance in real-world scenarios.