esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Composition and Structure
### General View  
In order to complete the task, AAOISE is schemed to work collaboratively in five function subsystems. They are the structure and temperature control system, the power system, the site master control system, the PC monitoring with data storage system, and the communication with remote monitoring center system. Besides these five subsystems, another important part is the scientific observation connector subsystem. The relationships between them are shown in Figure 1. The remote monitoring center system is located in China's Antarctic astronomical center, while other systems and all scientific observation instruments are placed at the South Pole \"Dome A\".  
The electric power is currently only from the diesel launch unit, known as the \"diesel engine power generation (DP) system\".  
The alternating current coming from DP is first rectified into 220 V dc, then, transported to the equipment module through the connection bus cable which links the power module and the equipment module with the name of tension bus bar. In the equipment module, the 220 V dc is converted into two power ways, 220 V ac and 24 V dc, so that different types of equipment can be powered. In the power module, the super capacitors need 24 V dc in the course of charging to meet the engine start motor requirement, so a 24 V dc bus comes back from the equipment module to the power module.  
The \"site master control (SMC) system\" executes the measurement and control of the electric power generation, conversion, transport, power supply protection, battery pack (BB) charge, and discharge through the redundant control nets when automatically running.  
The \"PC monitor (PCM) system\" keeps in touch with the SMC science equipment and network storage system at any time through the redundancy Ethernet, realizing the flow of site data, except the data exchange with the satellite communication.  
The \"iridium satellite communication (IMC) and remote monitoring center (RMC) system\" guarantee the realization of the satellite communications, including data receiver and data sender, which carry out the remote monitoring of AAOISE.  
The \"structure (SW) and temperature control (STD in power module and STI in instrument module) system\" are responsible for designing and manufacturing the two modules' cabins. Both the equipment installation and the internal environment heat preservation are taken into consideration. The two cabins have several heaters and fans inside controlled by the SMC.  
The \"scientific observation interface (SOI) system\" installs the science instruments and connects them with AAOISE through the developed interfaces.
## 2 Informative Frequency Band Selectors
### Conditional Variance Statistic  
The conditional variance statistic is described in details in [49]. It originates from the statistical phenomenon commonly referred to as the 20/60/20 Rule [64; 65]. It is based on the conditional variance measurement. This rule says that if the population is divided into three groups, according to some arbitrary reference criterion, (e.g., 20% of the smallest, 60% of the middle and 20% of the largest values), this particular relationship often means some kind of balance. In the paper [64], the mathematical illustration that justifies this rule in many real-  
\[\hat{A}_{5} := (\hat{x}_{692},\ \hat{x}_{938}],\] \[\hat{A}_{6} := (\hat{x}_{938},\ \hat{x}_{996}],\] \[\hat{A}_{7} := (\hat{x}_{996},\ \infty),\]  
where \(\hat{x}_{q}\) is the empirical quantile of order \(q\) calculated for vector \(x\). Assuming the Gaussian distribution the following equation is fulfilled [65]:  
\[\sigma_{A_{1}}^{2}=\sigma_{A_{2}}^{2}=\sigma_{A_{3}}^{2}=\sigma_{A_{4}}^{2}= \sigma_{A_{5}}^{2}=\sigma_{A_{6}}^{2}=\sigma_{A_{7}}^{2}. \tag{6}\]  
As noted in [64], the condition (6) creates a dispersion balance for the conditional populations and a different number of partitioning sets could be considered.  
After time-frequency signal decomposition the estimator \(\hat{C}_{7}(\cdot)\) applied to the individual frequency band \(f_{i}\): \(\hat{C}_{7}(f_{i})\) is called conditional variance-based selector (CVB selector). The CVB selector is able to distinguish different impulses occurring based on the distribution of their amplitudes [49]. However, it does not take into account the periodicity of the impulses. It is a general problem for all listed selectors demonstrated above.
## 3 System Design
### System Model
#### 3.3.2 Optimal Che Approximation
Thus, we write the hit conditional probability of node \(i\) from the given node \(j\) as:  
\[P_{hit}^{i}(c|j)=\Big{[}P_{hit}^{i}(c)+p\Big{(}1-P_{hit}^{i}(c)\Big{)}\Big{]} \big{(}1-e^{-A_{ij}}\big{)} \tag{5}\]  
where  
\[A_{i,j}=\ \overline{\lambda}_{c}(j)\big{(}1-P_{in}^{j}(c)\big{)}\Big{(}T_{M}^{ i}-T_{M}^{j}\Big{)}+(1-p)\overline{\lambda}_{c}(j)\big{(}1-P_{in}^{j}(c) \big{)}T_{M}^{i}+\sum_{k\in J}^{k\
eq j}\overline{\lambda}_{c}(k)\Big{(}1-P_{ in}^{k}(c)\Big{)}T_{M}^{i}\]  
Note that Equation (5) can be simplified to the following equation for deducing LCE when \(p=1\):  
\[P_{hit}^{i}(c|j)=\Big{(}1-e^{-A_{i,j}}\Big{)} \tag{6}\]  
where  
\[A_{i,j}=\overline{\lambda}_{c}(j)\big{(}1-P_{in}^{j}(c)\Big{)}\Big{(}T_{M}^{ i}-T_{M}^{j}\Big{)}+\sum_{k\in J}^{k\
eq j}\overline{\lambda}_{c}(k)\big{(}1-P_{ in}^{k}(c)\big{)}T_{M}^{i}\]  
Equation (6), which we deduced for LCE, was same as the conclusion from [23]. The final result of \(P_{hit}^{i}(c)\) could be obtained by de-conditioning the form iteration of each node \(j\) in \(J\). Leave-Copy-Down  
For LCD, the content is cached only in the next node of the hit node for each time, which avoids the existence of multiple copies of the same content in the transmission path. Assume that \(P_{in}^{i}(c)\approx\Big{(}1-e^{-\overline{\lambda}_{c}(i)T_{M}^{i}}\Big{)}\). There is a known strong assumption that NDO \(c\) can be inserted in \(j\) only if \(c\) has cached in \(i\) already. Hence, the two possible conditions to make NDC \(c\) cache in node \(j\) at time \(t\) are the previous request either hit at \(i\) forwarded from \(j\) or hit in the \(j\) at the time duration of \(\Big{[}t-T_{M}^{j},t\Big{]}\). We deduce:  
\[P_{in}^{J}(c)\approx\Big{[}\big{(}1-P_{in}^{J}(c)\big{)}P_{hit}^{i}+P_{in}^{J }(c)\Big{]}\Big{(}1-e^{-\overline{\lambda}_{c}(j)T_{M}^{j}}\Big{)}\]  
Moreover, to ensure that the request of \(c\) forwards to \(i\) at time \(t\), there are 3 possible events that could have happened in the duration of \(\Big{[}t-T_{M}^{i},t\Big{]}\). The last request of \(c\) either arrives \(i\) from given \(j\) at the time duration of \(\Big{[}t-T_{M}^{i},t-T_{M}^{j}\Big{]}\) (provided that \(T_{M}^{i}>T_{M}^{j}\)) when NDO \(c\) is known not to be cached in node \(j\) before, it arrives \(i\) at \(\Big{[}t-T_{M}^{j},t\Big{]}\) because \(c\) stores in neither \(i\) nor in \(j\), or it arrives \(i\) from any node \(k(k\in J)\) different from \(j\). Thus, we write:  
\[P_{hit}^{i}(c|j)=\Big{(}1-e^{-\overline{\lambda}_{c}(i)(1-P_{in}^{j}(c))(T_{M }^{i}-T_{M}^{j})}\Big{)}e^{-\overline{\lambda}_{c}(i)T_{M}^{j}}+\Big{(}1-e^{- A_{i,j}}\Big{)} \tag{7}\]  
where  
\[A_{i,j}=\overline{\lambda}_{c}(j)\Big{(}1-P_{in}^{j}(c)\Big{)}T_{M}^{i}+\sum_ {k\in J}^{k\
eq j}\overline{\lambda}_{c}(k)\Big{(}1-P_{in}^{k}(c)\Big{)}T_{M} ^{i}\]  
We needed to combine de-conditioning and a multi-variable fixed-point approach [34] to solve the entire conditional probability equation.
## 5 SSFXG Architecture and Basic Interactions
### NW Subscription Identity Issuance
#### 5.1.3 The Creation and Management of an NW-Subscriber-Governed SC  
In this phase, the NW broker takes the process a step further by creating a unique and dedicated SC for the requesting NW subscriber (Steps 6 and 7 Figure 2). This SC serves as a digital representation of the subscriber within the NW and acts as a secure container for storing all the relevant NW identity credentials. The information within the SC includes both private and public credentials that are essential for authenticating and identifying the subscriber. By securely storing these credentials, the SC ensures that the subscriber's data remain protected while being accessible for necessary NW operations.  
Once the SC is created and populated with the subscriber's identity credentials, the new subscriber is able to interact with the SC. This interaction allows the subscriber to configure the strategy by which the SC will manage authentication and verification processes on their behalf. The SC plays a pivotal role in maintaining the anonymity of the subscriber during these operations. It ensures that the subscriber's identity and credentials are used only as necessary, without exposing sensitive information to unauthorized entities. This design promotes a balance between security, privacy, and functionality within the federated wireless communication NW.  
The use of SCs provides an efficient and secure mechanism for subscribers to manage their identities and participate in the NW (Step 8, Figure 2). By delegating authentication and verification tasks to the SC, the NW minimizes the risk of exposure of sensitive information and ensures that all interactions are conducted seamlessly and securely. This step is a critical component of the overall onboarding process, as it equips the subscriber with the tools and mechanisms needed to operate within the federated NW while preserving privacy and security.
# Compact UHF Circularly Polarized Multi-Band Quadrifilar Antenna for CubeSat
## 2 Antenna System Design
### Feeding Circuit  
As described in the previous section, the radiating structure is composed of four independent IFAs with sequential rotation. To radiate right-hand circular polarization, each element must be fed equally with a sequential phase shift of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). Various solutions have been proposed to create such a feeding mechanism in the literature. For example, three hybrid branch-line couplers and a 90\({}^{\circ}\) phase shifter are combined, which is presented in [36]. This solution provides a low insertion loss and good balance but requires the integration of three branchline couplers and delay lines. This structure would be difficult to integrate with the limited surface available (due to the ceramic ring in the center).  
In order to miniaturize the circuit, the proposed feeding network consists of a balun and two hybrid couplers, as shown in Figure 3, which is inspired by the Marchand structure in [37], the balun is designed using the microstrip line with a phase shift of 180\({}^{\circ}\) between the two output ports. The center microstrip quarter wave transmission line is terminated with a short circuit. The upper and lower microstrip lines are coupled with the center line thanks to 0.3 mm width slots. The balun output ports are connected to the two hybrid couplers to provide four equal signals with the phase shifts of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). To avoid unwanted reflection effects due to the radiating elements mismatching, the coupler isolation ports are matched to 50 \(\Omega\) using discrete resistors. These ports are mentioned as Port 2 and Port 3 in the block diagram of the feeding network structure (Figure 3).  
The proposed design is illustrated in Figure 4a, with the yellow element being the broadband 180\({}^{\circ}\) balun and the orange parts being the hybrid couplers. In order to minimize the dimension of the feeding circuit, meander microstrip lines are used. In addition, the blue  
Figure 3: Block diagram of feeding network structure.  
Figure 2: Surface current on the radiating element from the top view.  
elements are presented as 50 \(\Omega\) transmission lines that will be connected to the radiating element on the top PCB. The main RF connector (Port 1) is located on the bottom part of the substrate and is directly connected to the unbalanced input of the balun.
## 5 Discussion
### Experimental Setup  
The experimental analysis was conducted on a high-performance server equipped with PyTorch 1.12.0, CUDA 11.3, and four NVIDIA RTX 2080 Ti GPUs (16 GB each), supported by 126 GB of RAM to optimize training and evaluation processes. The input resolution for all experiments was set to 256 \(\times\) 256 pixels, striking a balance between computational efficiency and the preservation of anatomical details critical for accurate segmentation.  
The proposed multi-scale dual-attention network is designed to enhance segmentation performance while maintaining reasonable computational complexity and inference time. The network achieves an average segmentation time of approximately 5.7 s per image, making it suitable for real-time clinical applications. This efficiency is achieved through the integration of advanced techniques, including residual connections and a dual-attention mechanism, which significantly improve segmentation accuracy without imposing excessive computational overhead. During inference, the network requires approximately 65 MB of GPU memory, which is well within the capacity of modern GPUs.
_Applied_  
_sciences_  
_Article_  
**In Vitro Antioxidant Activity and In Vivo Topical Efficacy of Lipid Nanoparticles Co-Loading Idebenone and Tocpheryl Acetate**  
**[PERSON] 1,*(c)**, **[PERSON] 2(c)**, **[PERSON] 2(c)**,  
**[PERSON] 1, [PERSON] 3(c)**, **[PERSON] 3(c)**, **[PERSON] 4, [PERSON] 2 and [PERSON] 2,5(c)**  
1 Department of Drug Sciences, Pharmaceutical Technology Section, University of Catania, Viale A. Doria 6, 95125 Catania, Italy; lmontene@unicitit (L.M.); [EMAIL_ADDRESS] (L.M.S.)  
2 Dipartimento di Scienze della Terra e del Mare DiSteM, Laboratorio di Biochimica Marina ed Ecolossicologia, Universita degli Studi di Palermo, Via G. Barlotta 4, 91100 Trapani, Italy; [PERSON](C.M.M.); [EMAIL_ADDRESS] (S.M.); [EMAIL_ADDRESS] (R.A.); [EMAIL_ADDRESS] (A.S.)  
3 Department of Drug Sciences, Medicinal Chemistry Section, University of Catania, Viale A. Doria 6, 95125 Catania, Italy; [EMAIL_ADDRESS] (L.P.); [EMAIL_ADDRESS] (R.T.)  
4 Department of Drug Sciences, Pharmacology and Toxicology Section, University of Catania, Viale A. Doria 6, 95125 Catania, Italy; [EMAIL_ADDRESS]  
5 Consorzio Universitario della Provincia di Trapani, Istituto di Biologia marina, Via G. Barlotta 4, 91100 Trapani, Italy  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +39-095-738-4010  
**Featured Application: Development of topical formulations based on lipid nanoparticles for the treatment of skin aging and diseases involving cutaneous dryness and scaling such as atopic and exfoliative dermatitis and psoriasis.**  
**Abstract:** Idebenone (IDE) is a strong antioxidant that has been proposed for the treatment of skin disorders, including skin ageing. Unfavorable physico-chemical properties make IDE a poor skin permeant where effectiveness could be improved by its loading into suitable delivery systems such as solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC). In this work, we designed novel IDE-loaded NLC containing tocopheryl acetate (VitE) as a liquid component to obtain a synergic effect between IDE and VitE. The resulting NLC showed small particle sizes (24-42 nm), low polydispersity indices (!0.300), good stability, and were assessed for their in vitro antioxidant activity and in vivo topical effects. IDE-loaded SLN and NLC showed a high antioxidant activity in in vitro assays (DPPH and reducing power method) and provided a similar and significant protection from oxidative stress of fibroblast cells, HS-68, exposed to UV light. After a two-week topical treatment of human volunteers with gels containing IDE-loaded SLN or NLC, a similar increase in skin hydration was observed, while IDE NLC reduced skin pigmentation to a greater extent than IDE SLN. These results suggest that co-loading IDE and VitE into NLC could be a promising strategy to obtain topical formulations with improved photo-protection.  
**Keywords:** Idebenone; tocpheryl acetate; lipid nanoparticles; antioxidants; topical administration; skin hydration; photo-protective effect
## 1 Introduction
Ramp and sinusoidal signals are used to model the uneven characteristics of the surface, and the relation between the disturbances and the velocity along the surface is presented. Since we are considering only the regulation of the force in the normal direction, the magnitude of the slope (values of frequency) is bounded to avoid steep slopes producing force in a different direction than the normal one. Then, we propose a method to compute the controller considering the performance in terms of force error and attenuation of disturbances in the normal direction. Additionally, we include the gain margin analysis to estimate how much the control gain/magnitude can be modified without creating instability. In addition, the gain margin is used to predict how much uncertainty in the stiffness the system can tolerate. Furthermore, considering the proportional relation between the velocity along the surface, and the ramp magnitude (or frequency of the sinusoidal), we provide a method to estimate the maximum velocity at which the task can be executed. The proposed methods are validated via simulations. (Preliminary results linked with this paper were presented in [16]). The structure of the paper is the following. Section 2 presents the problem statement. The methods of analysis and design are presented in Section 3. Section 4 contains the simulations, and the conclusions are presented in Section 5.
# Self-Supervised Learning across the Spectrum
## 6 Experiments and Results
### Curated Pretrain Datasets  
We demonstrate the efficacy of multimodal self-supervised pretraining by gathering a large unlabeled dataset of aligned optical and radar SITS. Although labeling satellite images is difficult, there is an abundant amount of unlabeled multimodal satellite SITS.  
Our main motivation for curating this dataset is to illustrate how geographically specific aligned multimodal satellite data are easy to acquire, allowing for greater opportunities to benefit from pretraining. Given the geo-tagged characteristic of satellite images, we can also collect data from geographically specific locations and study how the geographic location of images from certain regions can have an impact on the performance of downstream segmentation. Given that our pretrain data come from the same geographic location as the fine-tuned sets, our curated dataset is suitable and a geographically representative set of imagery for pretraining.  
**Motivation:** We collected our own pretraining dataset because there is no large-scale SITS dataset available in which radar and optical satellite imagery are spatially aligned, i.e., all images of the same location have the same number of pixels, and the same pixel in all images corresponds to the exact same geographic coordinate. We curate this dataset by collecting and aligning Sentinel-1 (radar) and Sentinel-2 (optical) images. Furthermore, a constantly shifting satellite orbit requires stitching multiple different images that each capture a given location partially. This pixel-level alignment of m2s2-SITS is crucial for our self-supervised model which requires pixel-wise contrastive and reconstruction loss in pretraining.  
**Curation:** Images in our dataset were collected from Sentinel 1 and 2 satellites and were aligned using the Microsoft FarmVibes SpaceEye workflow [63]. m2s2-SITS consists of satellite imagery from randomly sampled geographic locations within France and South Sudan, where the fine-tune datasets (PASTIS-R and Africa Crop Type Mapping, respectively) are captured from, and the time period of m2s2-SITS is approximately one year. We ensure that the images of m2s2-SITS are taken at least a year prior to the images in the fine-tuned datasets to prevent the chance of duplicates. Although in our evaluation we use only 3 RGB bands to ensure a fair comparison with baseline approaches, our dataset contains the full 12-band multispectral imagery from optical S2 and the 2 polarizations from radar S1. Specifically, we collect a pretraining dataset over France that contains 5314 time series with a total of 731 k Sentinel 1 images and 90 k Sentinel 2 images. After pretraining on this dataset, the models are fine-tuned on the PASTIS-R dataset. Our Africa pretrain dataset contains 5941 time series, with 193 k Sentinel 1 images and 70 k Sentinel 2 images. After pretraining on this dataset, the models are fine-tuned on the Africa Crop Type Mapping dataset. We plan to release our custom-curated pretrain datasets.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 2 Related Works
### Gaussian Processes (GP) Model  
Gaussian processes (GP), which are also known as kernel-based learning algorithms, have become more popular in spatiotemporal research due to their ability to effectively model complex, nonlinear relationships [36]. GP models are very effective tools for investigating implicit correlations between parameters, which makes them particularly effective for complex nonlinear classification and regression analysis [37]. A highly appealing feature of GP models is that they are developed using a Bayesian framework, which enables probabilistic predictions based on the model's parameters. Furthermore, Bayesian learning may be utilized to determine the parameters of GP models. It has been shown in a variety of research areas, including geostatistics, general regression, time series, and spatial statistics that GP models outperform other standard techniques. In terms of traffic-related studies, GP models have been successfully applied in different studies, such as traffic congestion [38], travel times [39], public transportation flow [40], etc.  
Additionally, GP provides excellent accuracy in learning and is relatively straightforward to apply. However, the GP model computational cost is expensive, due to the covariance matrix, where its computational complexity is \(O(N^{3})\) and its memory complexity is \(O(N^{2})\)[41]. This may limit the use of the GP model when N is large, where N is the number of observations [37]. To overcome this issue, a number of studies have suggested reducing the run-time complexity by reducing the number of the parameters, which can be achieved by producing sub-samples of the observations using hierarchical structures [42].