esa-sceva/satcom-chunk-collection huggingface dataset:
# Ocular Implications in Patients with Sleep Apnea
## 3 Results
### Diabetic Retinopathy (DR)  
It is the most common microvascular complication of diabetes and a major cause of vision loss. In general, the prevalence of DR, the proliferative type and diabetic macular edema in the general population is estimated at 34.6%, 7% and 6.8%, respectively [23]. SAS affects between 58% and 86% of patients diagnosed with diabetes. The common element between SAS and type 2 diabetes mellitus (DM2) is obesity. The connection between type 2 diabetes and SAS has been highlighted in several studies suggesting that the prevalence of SAS is doubled in patients with ophthalmic complications of type 2 diabetes, compared to those with type 2 diabetes without complications. One of the mechanisms proposed for this is the alteration of glycemic control, caused by inflammation and oxidative stress characteristic of SAS, the type of final glycosylation products, elements that are also found in the pathophysiology of vascular complications characteristic of diabetes [24]. The most recent study was conducted in 2020 by [PERSON] et al., according to which they concluded that patients with diabetes, especially with type 2 diabetes, have a higher risk of developing microvascular and cardiovascular complications than those with type 1 diabetes. There is a 50% higher frequency of developing comorbidities (congestive heart failure, myocardial infarction, stroke, ischemic transient accident, 18% of developed BCR, and other comorbidities), compared to the group control that consisted of patients with diabetes, but who were not diagnosed with SAS [25]. [PERSON] et al. also confirmed the influence of SAS on the complications of type 2 diabetes [26] regarding the effect of CPAP on DR, studies which have reached different conclusions: One study conducted in the UK has revealed a significant improvement in macular edema or fundus examination and another one that highlighted the fact that people who received CPAP treatment were less likely to develop pre-proliferative or proliferative DR [3].
## 6 Conclusions  
The three experiments highlighted several important design considerations for both the mechanism itself and future experimental testing. During testing, it quickly became apparent that it is very difficult to push an array of slender wire roots through the anchor plug, especially in sediment conditions 1 and 2, and even more so with a partial (four-root) array. We recommend that the flexible wire roots be sufficiently rigid to be pushed directly, unsupported, into the expected sediment for the full depth without excessive flexion or kinking. The short prototype anchor plug also did not provide sufficient support to the wire roots during the repeated deployments to prevent bending and kinking, which required considerable effort to straighten and repair between pulls. A complete RAATM anchor plug prototype must provide sufficient tunnel length to prevent this damage to the wire roots. The root tunnel bores within the anchor plug should be sufficiently large and smooth to provide stability to the wire roots with minimum friction during insertion and retraction, when bristles are likely to be fouled. Over repeated testing, we observed that the nylon bristles did become quite fouled by the silt and clay sediments, although this did not appear to appreciably degrade anchoring strength during the testing period. Additionally, the total anchor plug height should be kept as low as possible to maximize strength under angled tension, as described in Section 5.3.  
Future experimental test environments should be designed to ensure greater independence of individual \"pull\" data by ensuring consistency of the sediments. This is difficult for condition 1 and 2 sediments, and we recommend that sufficient settling time be allowed after collecting and submerging the samples, as discussed in Section 5.2. Furthermore, a container providing a large enough sediment surface area to enable individual pulls to be conducted in separate (isolated) sediment locations would reduce data errors related to the independence of the anchoring strength data while eliminating the need for re-tamping the sediment after each uprooting.  
Planned future development includes the construction, largely by additive manufacturing, and the testing of a complete RAATM prototype, which will eventually be integrated with the WIEVLE AUV. Continued iterative refinement of the design of the mechanism to optimize anchoring strength is also necessary, particularly for the wire root array. Finally, significant research efforts will be required to develop the antenna portion of the design to enable communications between two or more vehicles and external communication to the vehicle while anchored at various depths.
# Things2 People Interaction toward Energy Savings in Shared Spaces Using BIM
## 4 Personalized Context Data Prediction
### Modelling
#### 4.2.2 Holt-Winters Exponential Smoothing  
Holt-Winters exponential smoothing is a widely used model for predictions that can handle seasonal and trend variation. This model has two variations for seasonality--(1) additive and (2) multiplicative. The additive Holt-Winters model takes into consideration that the seasonal variation is the same size throughout the periods and can be explained by Equation (3)--Holt-Winters Additive Model Equation.  
\[\hat{y}_{t+hlt}=\ \ell_{t}+hb_{t}+\ s_{t+h-m(k+1)} \tag{3}\]  
The multiplicative Holt-Winters models consider that the seasonal variation changes in proportion with the time series in the analysis. This model can be explained by Equation (4)--Holt-Winters Multiplicative Model Equation.  
\[\hat{y}_{t+hlt}=\ \left(\ell_{t}+hb_{t}\right)s_{t+h-m(k+1)} \tag{4}\]  
Figure 5: Decomposition of the hourly kWh consumption from September 2016 to September 2019.  
With the application of this algorithm, we seek to develop a predictive model that has the best evaluation scores in order to guarantee that the predicting values are almost identical with the real values. The \(b_{t}\) expresses the trend component, while \(s_{t}\) expresses the seasonal component.
## 4 Result and Discussion
### Model Training
#### 4.1.1 Determination of Optimal WAOA-ANN Models  
In multi-output ANN models, different outputs share the output layer and the hidden layer, and thus the adjustment of the weights and biases of the hidden layer neurons during the training process is jointly affected by all outputs. The differences between different outputs are mainly caused by the differences in the weights and biases adopted by their respective output layer neurons. The fitness curves of the five WAOA-ANN models under different pop sizes are shown in Figure 8a,b. From the figure, for the prediction of \(\theta\), the optimal fitness of the model is stable between \(7.06\times 10^{-4}\) and \(1.14\times 10^{-3}\), while for the prediction of \(r_{b}/r\), the optimal fitness of the model is stable between \(8\times 10^{-4}\) and \(1.2\times 10^{-3}\). There is a significant decrease in fitness compared to when iterating the initial model.  
Even though the model fitness is evaluated by 5-fold cross-validation during the iteration process, overfitting of the model is largely avoided. However, during each iteration, the data in the training set are directly or indirectly involved in the assessment of model fitness, and because it is a multi-output model, the model may not perform equally on two predicted objects, so the ranking of the final model fitness cannot completely reflect the model prediction performance. To completely evaluate the model prediction performance and generalization performance to select the optimal WAOA-ANN model, the resulting models are further compared on the training and test sets. Table 5 lists the prediction performance and ranking scores of the five WAOA-RF models on the training and test sets. As can be seen from Table 5, there is no model that can rank first in both \(\theta\) and \(r_{b}/r\) prediction on both the training and test sets at the same time. For example, the WAOA-RF model obtained with a pop size setting of 10 ranks first in all four metrics for the prediction of \(\theta\) on the training set and test set, but ranks poorly for the prediction of \(r_{b}/r\) on test set. This suggests that multi-output ANN models with different hyperparameter settings have their own focus on different outputs. For the sake of model comprehensiveness, a superior multi-output model should be balanced in the prediction performance of different outputs. Therefore, further the predictive performance of different WAOA-ANN models for \(\theta\) and \(r_{b}/r\) on the training set and the test set is shown in Figure 9a. As can be seen in Figure 9a, for the five WAOA-ANN models obtained, the models with the pop size setting of 10 and 80 accounted for the highest scores for \(\theta\) and \(r_{b}/r\) predictions, respectively, and the model with a pop size of 10 (Num dense layers = 4, Num dense nodes = 100, Learning rate = \(4.8307\times 10^{-4}\)) had the highest combined score. Therefore, in the subsequent study, the WAOA-ANN model with a pop size of 10 was used as a representative of the WAOA-ANN model.
# Metal Additive Manufacturing for Satellites and Rockets
## 7 Other Spacecraft Components  
While the aforementioned components are recently most often prepared by metal additive manufacturing, there are some other possibilities to use these techniques for satellites or rockets and in future manned space missions.  
[PERSON] et al., e.g., used selective laser melting of permalloy to prepare magnetic shields for fiber optic gyroscopes in spacecraft [103]. They found recrystallization annealing to improve the soft magnetic performance and the overall magnetic performance of the additively manufactured parts to be similar to traditional processing methods.  
Taking a flow connector as an example, [PERSON] et al. suggested a design for additive manufacturing method especially for space applications [104]. While design for additive manufacturing usually takes into account the limitations of the 3D printing process, here also the functionality was discussed in different workshops and interviews with participating companies, resulting in a cost-efficient design and production method which is suggested to further reduce future development time and costs.  
Other aspects taken into account are the general possibility to reduce space debris by proper additive manufacturing design of small satellites [105] or the idea of 3D printing in space, especially in the ISS or in future manned space missions to repair components [106]. Combining such promising attempts as the special design for additive manufacturing for space [104] with the potential advantages of reduced weight, time, and space debris can be expected to lead to an increase of 3D printing approaches for diverse spacecraft components.
# Collaborative System Usability in Spaceflight Analog Environments through Remote Observations
## 1 Introduction  
The conventional design cycle in human-computer interaction (HCI) involves understanding a specific domain, gathering feedback from end users, refining prototypes through usability testing, and ultimately releasing a product developed with significant end-user participation [1]. This process, however, faces significant access challenges when applied to user populations in isolated, confined environments. Our research revolves around enabling human space exploration, with astronauts as our user population. Space is an extreme environment inhabited by only a select few who live in spaceships like the International Space Station (ISS). Astronaut crews heavily rely on numerous computer systems to support critical tasks and ensure mission success during spaceflight. The advanced technologies used in space missions demand meticulous human-in-the-loop design and development, but achieving this is challenging due to the limited number of astronauts, the very limited time they have to be part of the HCI design process, and their presence in remote and inaccessible environments.  
To address some of these limitations, NASA has invested resources in developing spaceflight analogs. Analog missions simulate aspects of spaceflight; for instance, crews may experience isolation, an extreme environment, or interact in a physical environment resembling space. These analog missions recruit participants with backgrounds similar to astronauts for simulated long-duration space missions. Analogous user populations provide an opportunity to not only refine prototypes and conduct usability evaluations but also allow exploration of new methods to apply HCI in the spaceflight domain. This paper summarizes how our research team conducted remote usability observations in aspaceflight analog for an integrated timeline software tool used in operations. We demonstrate that conversation analysis of naturalistic voice recordings significantly contributes to characterizing the usability of collaborative software.
# Attosecond Pulse Shaping by Multilayer Mirrors
## 4 Achieved Results and Examples of Multilayer Mirrors for Attosecond Pulses  
This section focuses on results which have been achieved so far in different spectral ranges. Ever shorter isolated attosecond pulses enable experimental access to the absolute or relative timing of electronic processes with never-achieved temporal resolution [26; 48; 49]. The filtering, reflection, and shaping of attosecond pulses require phase-correct broadband optics. Especially in normal incidence setups, the question of the shortest possible attosecond pulses is thus directly connected to the availability of appropriate optics, namely, EUV multilayer mirrors and filters. One can estimate the bandwidth \(\Delta E\) of an \(N\)-period multilayer stack with central energy \(E\) from Equation (3): \(E/\Delta E=N\). An interference coating consists of at least two periods (\(N\geq 2\)); thus, in principle, the maximum bandwidth of a periodic multilayer mirror is about half of its central energy. Higher central energies relax the constraints on the bandwidth of optics and thus enable shorter reflected pulses, but they usually suffer from lower reflectivity.
## 3 Results and Discussion
### Impregnation Methodology
#### 3.1.5 Effect of Drying Temperature  
The drying step is a relevant stage for solvent removal. The drying temperature can influence the distribution of TiO\({}_{2}\) suspension over Leca particles due to the turbulence that can promote inside the contact vessel. The solvent used to disperse the TiO\({}_{2}\) nanoparticles was ethanol, with a boiling temperature of about 78 \({}^{\circ}\)C. In order to evaluate the impact of drying temperature in the TiO\({}_{2}\) impregnated mass, two different temperatures were tested: above (105 \({}^{\circ}\)C) and below (65 \({}^{\circ}\)C) ethanol boiling point. The procedure for testing the drying temperature consists of placing the TiO\({}_{2}\) suspension contacted with Leca for 15 h at 105 \({}^{\circ}\)C. When a lower drying temperature was tested, the procedure was divided into two steps: the suspension plus Leca were placed at 65 \({}^{\circ}\)C for 15 h, and then the temperature was increased to 105 \({}^{\circ}\)C for 3 h to guarantee the complete solvent removal. For these experiments, suspensions with 3.6% TiO\({}_{2}\) were prepared and contacted with 10 g of Leca using an ultrasonic bath for 15 min. Using 105 \({}^{\circ}\)C for 15 h led to about 23.9 mg/g of impregnation, while at 65 \({}^{\circ}\)C followed by 105 \({}^{\circ}\)C led to 20.3 mg/g. This allows us to conclude that keeping the oven at 105 \({}^{\circ}\)C is the most effective method since a greater amount of TiO\({}_{2}\) covers the surface of Leca compared to the two stages method. For the drying temperature of 65 \({}^{\circ}\)C, the difference in the uniformity of coating is noticed. Some particles of Leca remained uncovered with TiO\({}_{2}\), while others presented a huge accumulation of TiO\({}_{2}\). The difference is because at 105 \({}^{\circ}\)C, ethanol boils, and this process promotes the redistribution of TiO\({}_{2}\) on the Leca surface due to the turbulence improvement. This favors the adsorption of TiO\({}_{2}\) onto the Leca particles. At 65 \({}^{\circ}\)C, this effect is limited  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Agitation Time (min)** & **Load of TiO\({}_{2}\) Using a Shaker** & **Load of TiO\({}_{2}\) Using an** \\  & **Bath (mg/g)** & **Ultrasonic Bath (mg/g)** \\ \hline Without mix & 18.1 & 18.1 \\ \hline
15 & - & 21.9 \\ \hline
30 & 12.0 & 22.0 \\ \hline
60 & 13.7 & 21.5 \\ \hline
120 & 11.6 & 23.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect of contact time and type of agitation on the amount of TiO\({}_{2}\) impregnated.  
due to the slow evaporation of ethanol. Thus, it can be inferred that drying at 105 \({}^{\circ}\)C had a better effect on the amount of TiO\({}_{2}\) impregnated in Leca.
## 2 Literature Review
By utilizing data normalization, dimensionality reduction (Fisher Discriminant Analysis), and the k-nearest neighbor method, data preparation is enhanced, and instance-based learners are used to detect attack vectors, achieving 99% accuracy and detection rates. This approach provides valuable insights for aquaculture identification projects, particularly in handling imbalanced data and classification challenges. In the study by [33], it is mentioned that IoT systems consist of multiple layers, including the perception layer, network layer, and application layer, each facing different security challenges. To protect these layers, the research proposes the use of encryption technologies, intrusion detection systems, and secure communication protocols to defend against attacks. Moreover, machine learning and deep learning models can enhance system security by analyzing attack patterns in IoT data for real-time anomaly detection. These findings may offer valuable insights for aquaculture identification projects, particularly in ensuring data integrity and system security. AI-driven security models can improve the ability to detect and address potential threats.
## 5 Convolutional Neural Network for Indoor Positioning  
For CIR samples, the structure and dimension of the CIR input are similar to image input, so AI methods used for image processing are considered as our positioning schemes. The most commonly used neural network models for image processing are based on the CNN and self-attention mechanism. Although the concept of deep neural networks is stacking neural networks together, which is observed as a simple process, the performance of these networks can vary greatly due to different network architectures and choices of hyperparameters.  
With regard to the models based on CNN, there are several mainstream network structures. AlexNet [51] introduced the concept of deep CNNs and addressed the vanishing gradient problem by utilizing the Rectified Linear Unit (ReLU) activation function while it employed a dropout regularization to prevent overfitting. GoogLeNet [52] further advances the field with its inception module architecture, which allows for the simultaneous use of different-sized convolutional kernels and pooling layers, enabling the extraction of features at multiple scales. On the other hand, ResNet [53] introduces the concept of residual learning. It addresses the degradation problem that arises when deep neural networks suffer from a diminishing performance with increasing depth. By employing skip connections, ResNet allows for the direct flow of input to the output layer, facilitating the learning of residual mappings.  
The self-attention mechanism [54] focuses on the correlation of vectors in input sequences, and it was first used for semantic comprehension. When applying the self-attention to image processing tasks, images are divided into small parts and input as sequences. The most typically used model is the Vision Transformer (ViT) [55]. ViT is an effective tool for handling large images and complex visual scenes. This model can also enhance its performance through pre-training.  
To effectively extract features for positioning, we consider a ResNet structure as the basic model for AMT. It is applied in both student and teacher models. The ResNet has several advantages, including its extremely deep network structure, which enhances the network's ability for feature extraction. Additionally, it introduces the residual blocks, which prevent network degradation, gradient vanishing, or gradient explosion in deeper networks.
## 1 Introduction
The deployment of the STACER is influenced by its own configuration design and the deployment environment. Factors of the environment of space include its being a vacuum, microgravity, extreme cold and high temperatures, etc. Extreme environmental conditions tend to reduce the stability of the space-bound mechanism [24; 25; 26; 27]. In the study of the impact arm, [PERSON] [28] designed a prototype flight experiment and established a vertical vacuum laboratory. It is applied to verify whether the vibration frequency and deployment force of the impact arm meet the requirements during the deployment process in the space environment. This test well considers the influence of microgravity and vacuum environment on STACER during its deployment process. The test conditions are close to the space environment, and the reliability is high, but the cost is huge. The research of above-mentioned with thin-walled expansion mechanism mainly focuses on the mechanical properties analysis of the deployment process. For STACER, which requires repeatable deployment, the stability analyses are equally important during the gathering process and after  
Figure 1: The deploying process of the spring reel of STACER. gathering. STACER needs to complete the process of packaging and deploying alternately due to different working conditions [29; 30]. When the packaging process occurs, different layers are misaligned. If the outer layer finally gathers higher than the inner layer, while the size of the outer layer exceeds the storage bin, the subsequent work of the space equipment may not be started or the STACER cannot be expanded again. It will directly lead to the failure of the task. Self-preservation is defined as the state of the STACER structure being completely stored in the storage bucket and not tending to deploy with a stable state. Therefore, it is necessary to conduct research on the self-preservation of STACER during its gathering process and after gathering. The thin-walled deployable mechanism shows the characteristics of large deformation, large displacement, and large rotation, and there is a large amount of overlapping contact and friction between the layers, which is difficult for the dynamic simulation [31]. Based on the work mentioned above with thin-walled deployable mechanism, the finite element model of nonlinear explicit dynamics of STACER mechanism is established by finite element software Abaqus. The mechanical properties and self-preservation mechanism of the STACER are studied during its deploying process. It will provide both theoretical and application guidance for its further development.