esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Clothing Framework for Health Monitoring Applications
## 4 Challenges and Future Development
### Design and Integration
#### 4.4.2 Sensor Integration  
The operational reliability of smart clothing depends on the appropriate integration of the sensor on and within textiles. Sensors can be developed in situ or as an extra piece for attachment with textiles. For in situ textile-based sensors, it is important to carefully consider the compatibility of the nontextile component with textile components. For example, while choosing a conductive chemical for coating or printing on textiles, or doping or impregnating with a fibre polymer system, the natural bonding and fixation between those chemicals and textiles should be first considered to ensure high wash fastness. Otherwise, with frequent washing, the connectivity might be lost in the case of sensors coated onto fabrics. Similarly, for an external sensor of electronics being attached to a textile, it is also important to judge the physical and mechanical behaviour such weight, stretch, shrinkage, etc., before selection.
# Hybrid Load Forecasting Using Gaussian Process Regression and Novel Residual Prediction
## 2 Data Analysis  
This paper tests how different factors impact energy consumption behavior. To develop the forecasting algorithms, load data were gathered from the Romanian energy transmission and system operator [28] and correlated with weather measurements for the city of Bucharest [29]. The weather data were correlated with energy load data to provide information at hourly intervals on the same time samples. Energy load data consist of energy consumption for every hour, starting with 1 January 2018 and ending on 1 June 2019. The weather data that were taken into account consist of five variables that were sampled in the same time intervals. The weather factors that were considered to have the greatest impact on load consumption were temperature, changes in atmospheric pressure, dew point temperature, the level of precipitation in mm, and air humidity, all measured at two meters above ground reference. Another important factor considered was whether or not the day was a working day. This data type is binary and is 1 if the day is a weekend day or a national holiday and is 0 otherwise. The final factor to be considered was the date (month, day, day of the week); this criterion was added as, upon data analysis, this principle was observed to create patterns in load demand. Prior to algorithm implementation, all data were processed, and an outlier test was performed to improve the datasets.  
Figure 2 illustrates how energy load fluctuates in accordance with day type; as expected a significant increase in energy load for working days could be observed. Figure 3 represents temperature influence on load demand. A clear dependency of the load with respect to the temperature could be observed. Further interesting pattern-forming characteristics for the datasets are presented in Figures 4 and 5, which illustrate the influences of humidity and dew point temperature on power consumption, respectively. These characteristics are studied in order to determine if a feature selection would benefit the algorithm and which of the predictors influence less the energy load data.  
Figure 1: Schematic flow of the proposed method.  
Figure 3: Temperature influence on energy load.  
Figure 2: Energy consumption based on day type.
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 3 Results and Discussion
### S-Parameters  
To compare the simulated results with the measured ones, Figure 5 depicts the S-parameters of port 1 to port 5, that is, S11, S22, S33, S44, and S55. As seen in the plot, the measured values differ from the simulation results due to SMA connector soldering problems and the inaccuracies that are inevitable during the fabrication and measurement of electronic circuits. As illustrated in Figure 5, the achieved \(-6\) dB impedance bandwidth does cover some of the desired bands, such as n79 (4400-5000 MHz) and the future upcoming band n102/n104 5.9-7.1 GHz. These deviations between the results obtained through measurement and simulation demonstrate the effect of practical issues that were experienced while designing the actual physical antenna.  
Figure 4: Parametric analysis of a proposed antenna.  
Figure 5: Reflection coefficient of proposed ten-port MIMO antennas.  
The simulated and measured transmission curves (isolation) also reveal the working of the antenna and highlight that S21, S23, S43, and S54 are below \(-17\) dB over the total operating bandwidth depicted in Figure 6. This low S21 value suggests that there is good isolation between the antenna ports that is required to avoid interference and ensure proper MIMO performance. These seemingly show the practicality of fabrication and measurement and indicate that the designed antenna is adequate to provide dual-band performance and high isolation. The obtained data provide evidence that the suggested configuration is appropriate for providing frequencies required in the contemporary 5G NR field.
## 1 Introduction
The Loess Plateau in Northwest China features complex geological structures and thick loess deposits, characterized by high porosity, high moisture content, and low shear strength, resulting in poor soil stability and increased susceptibility to landslides triggered by rainfall, snowmelt, or seismic activity. Additionally, the region's arid climate, sparse vegetation, and limited hydrological regulation intensify the potential for landslides, especially following abrupt rainfall events. Due to terrain constraints, populations and infrastructure predominantly cluster at slope bases in high-risk areas, exacerbating geological hazards due to intensified human activities [12; 13; 14; 15]. However, most existing studies focus on single-sensor data or simple overlays of multi-source imagery, often lacking systematic integration frameworks that account for the spatiotemporal discrepancies and complementary characteristics among optical, SAR, and topographic data. Therefore, there remains a significant research gap in developing a unified and robust methodology for effectively integrating multi-source remote-sensing data to enhance the accuracy and reliability of landslide hazard identification. Considering current technological capabilities and the critical need for landslide hazard identification, this study focuses on the central region of Tianshui City, a landslide-prone area on the Loess Plateau. Sentinel-1 C-band SAR satellite data were employed alongside the SBAS-InSAR technique to acquire ground deformation information. An integrated remote-sensing method identified potential hazard sites, and validation was performed on the Google Earth platform, generating a definitive dataset of landslide hazard points. This research provides a valuable reference for similar regions, enhancing China's landslide hazard-identification capabilities and contributing essential guidance to disaster prevention and mitigation strategies on the Loess Plateau.
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
## 3 Local TTL Coupling  
Angular and lateral jitter of the RX beam during its propagation within the optical bench, termed local TTL coupling, will be addressed in this section. In the simulation model shown in Figure 4[7], the RX beam is tilted in yaw and pitch degrees of freedom and LPS variation is derived, and the local TTL coupling is then calculated. For more description of TTL coupling, see [9; 14].  
To begin with, offsets due to assembly errors of the optical elements and the piston effect [14] will be considered.  
Figure 4: Illustration of the local TTL coupling noise in the simulation. The RX beam is rotated around the RP point in different rotational degrees of freedom [7].  
Figure 3: The schematic drawing of a reference point correction setup. Two more FSM mirrors (FSM2, FSM3) are added, and the position of the RP point can be corrected by steering the FSM mirrors.
## 5 Advantages, Limitations, and Future Research Directions of the Model  
### Advantages of the Model  
(1) Our model can use text to generate images and leverage the generated images to assist text classification, enabling it to learn more knowledge from different modalities' information. Unlike approaches that expand the model structure or increase training data, our model improves classification accuracy without increasing data demand, instead extracting more information from existing data.  
(2) Our model is more general than traditional multimodal models, as it does not need to be trained on multimodal datasets. Existing multimodal models require multimodal datasets for training, whereas our model can be trained using text-only datasets to achieve similar multimodal capabilities.  
(3) The training resources required for our model are fewer. For the image generation module, we leverage the pre-trained Stable diffusion model; by directly loading the pre-trained weights, we achieve good performance without retraining. Moreover, both the text and image encoders are adopted from the CLIP model--a multimodal pre-trained model with strong zero-shot performance--allowing direct transfer to our task. Thus, only the parameters of the model's classifier need to be learned through training.  
Although our model has these advantages, there are also some limitations.
## 3 Computed Tomography and Mitral Valve
### Mitral Regurgitation and Transcatheter Interventions  
Mitral regurgitation (MR) is the most common valvular pathological condition in the Western world [65]. It can be classified as primary or secondary. Therefore, a primary valve defect such as a flail or prolapse causes a primary MR. Secondary MR is due to the incomplete coaptation of the valve leaflets caused by the malposition of the subvalvular apparatus; it can be both ischemic and not [66].  
Today, surgery remains the standard gold treatment for symptomatic MR patients. However, in recent years, transcatheter mitral valve surgery (TMVI) has emerged as a safe option for patients with high surgical risk [67]. According to the latest ESC 2021 guidelines on valvulopathies, transcatheter mitral valve repair can be considered in patients with chronic mitral regurgitation, symptomatic, with high surgical risk or inoperable, carefully avoiding futile treatments (IIA for secondary mitral regurgitation and IIB for primary mitral regurgitation) [19].  
Recently, various techniques for the transcatheter mitral valve have been devised, often with reference to pre-existing surgical techniques. The MitraClip NT(r) System (Abbott Vascular, Santa Clara, CA, USA) is a repair system that conceptually emulates the \"edge-to-edge\" repair described by [PERSON] et al. [68]. It consists of a clip that hooks the two mitral leaflets simultaneously, reducing the width of the regurgitant orifice. Several randomized trials demonstrated the benefit of MitraClip in reducing mortality and hospitalization rate in patients with primary and functional MR [69; 70; 71]. Some other devices have been designed for the percutaneous treatment of MR, such as the Cardioband System(r) (Valtech Cardio, Or Yehuda, Israel) and the CARILLON Mitral Contour System(r) (Cardiac Dimensions, Inc., Kirkland, WA, USA), which mimic annuloplasty procedures. Unlike the MitraClip system, the latter technique can only be used for secondary etiology [72]. Finally, great interest revolves around transcatheter mitral valve replacement (TMVR); however, the growth of this technique, due to the complex anatomy of the mitral valve apparatus, the heterogeneity of pathology, and mitral annular dynamism, is undoubtedly slower than the explosive development of TAVI. As anticipated, several devices have been developed for this purpose, each with a different valve-anchoring mechanism (e.g., native flap engagement, mitral annulus clamping, apical tether, mitral annulus clamping, radial force, outer anchorage, flaps annular, subannular mitral annulus).
# Satellite Network Task Deployment Method Based on SDN and ICN
## 5 The Simulation Result
### Analysis of Task Deployment Method Based on Genetic Algorithm
With the doubling of the size of the mission, the transmission of the data required by the satellite network not only increases the response delay of the mission, but also increases the communication burden of the satellite network. When a task is deployed in a satellite network, due to the limited computing and caching resources of the satellite network, and when the task size is more than 10 times, it will cause a higher task response delay because the task is deployed to a remote resource available node.
# Multi-Modal Low-Data-Based Learning for Video Classification
## 4 SS-CADA-VAE Model
### Variational Inference and Autoencoder  
Variational inference aims to approximate intractable true posterior distribution, \(p_{\phi}(z|x)\), using a parameterized tractable proxy posterior distribution, \(q_{\phi}(z|x)\), while trying to maximize evidence lower bound (ELBO), or in other words, minimize the Kullback-Leibler divergence [50]. In particular, the posterior distribution of a latent variable model is obtained by the Bayes rule, which is given in Equation (1):  
\[p_{\phi}(z|x)=\frac{p_{\phi}(x|z)\cdot p_{\phi}(z)}{p_{\phi}(x)}\enspace, \tag{1}\]  
where \(z\) is a latent variable, and under these circumstances, the evidence term, \(p_{\phi}(x)\), is intractable, and variational inference aims to approximate the posterior with another distribution in Equation (2):  
\[p_{\phi}(z|x)\approx q_{\theta}(z|x)\enspace, \tag{2}\]  
Within the scope of this equation, the approximation can be formulated as an optimization problem where \(\theta\) refers to learnable parameters, and the objective function, which is a variational bound on the marginal likelihood, is presented in Equation (3):  
\[ELBO=E_{q_{\theta}(z|x)}[\log p_{\theta}(x|z)]-D_{KL}(q_{\phi}(z|x)||p_{\theta }(z))\enspace, \tag{3}\]  
The objective function in Equation (3) consists of two terms. The first one refers to reconstruction loss that is based on the latent variable and the second one refers to KL divergence between a learnable parameter-based approximated distribution and prior distribution. In our proposed method, a multivariate normal Gaussian distribution is used as a prior distribution.
# Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
## 6 Conclusions  
In this paper, the RS overhead problem is analyzed, and we proposed a BGRR scheme for the reduction of RS overhead in LS-MIMO systems. We first analyzed the RS overhead in the LS-MIMO  
Figure 14: SE (\(\text{bps}/\text{Hz}\)) versus SNR (dB), when \(N_{t}=640,K=64\), \(\chi_{DM-RS}=10\), \(v_{BGRR}=32\), \(\xi=1,0.98,0.95,0.90,0.85\). Red lines are the ZF precoding and blue lines are the MF precoding.  
Figure 15: SE performance based on the space separations among UEs, when \(N_{t}=320,K=32\), \(\chi_{DM-RS}=10\), \(v_{BGRR}=16\), ZF precoding.  
system by using the RS overhead model based on the 3 GPP LTE-A standard, and provided the performance improvement of the proposed BGRR scheme for two scenarios, sparse UE distribution and dense UE distribution. The dense UE distribution scenario provides more benefits than the sparse UE distribution scenario, due to the multi-UE selection gain. In the sparse UE distribution scenario, we can choose between UE based grouping and angle based grouping schemes because the improvement of performance depends on the situation. We validated that the proposed scheme can remarkably increase spectral efficiency, and could be a core technology for the realization of LS-MIMO systems.  
This research was supported by the MISP(Ministry of Science, ICT & Future Planning), Korea, under National program for Excellence in Software program (the SW oriented college support grogram) (R7718-16-1005) supervised by the IITP (Institute for Information & communications Technology Promotion). This work was supported by the faculty research fund of Sejong University in 2016. This work was conducted by the research Grant of Kwangwoon University in 2016.  
[PERSON] designed the algorithm, performed the simulations, and prepared the manuscript; [PERSON] was responsible for coordinating, writing, and revising the paper. Both authors discussed the results and approved the publication.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Density, Contamination, and Magnetic Properties
Consequently, regarding the contamination results and particularly magnetic properties, feedstock B was selected as the best overall formulation. The powder loading was increased in feedstock B to improve the densification rate and the magnetic properties. Increasing the powder content from 55 to 57 and 58 vol.% led to a continuous increase in density due to a significant reduction in open porosity, as seen in Table 4. However, as the powder content increased beyond 58 vol.% to 59 and 60 vol.%, the density continuously decreased because the open porosity increased. The very high powder content in these feedstocks (59 and 60 vol.%) could result in powder-binder separation, inhomogeneity, and agglomeration in the feedstock. In addition, the pressure fluctuations are visible during the viscosity measurement at different shear rates for 59 and 60 vol.% (see Appendix A Figure A1). Even if higher magnetic performances can be observed in these two samples compared to the B58 (Table 4 and Figure 7), the inhomogeneity of the feedstock evidenced by the rheological measurements is an issue for the processability of these feedstocks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feedstocks**} & **Density** & **Oxygen** & **Carbon** & **Remanence** & **Coercivity** \\  & **(g/cm\({}^{3}\))** & **(ppm)** & **(ppm)** & **(Br) (T)** & **(HcG) (kA/m)** \\ \hline B55 & 6.83 & 4468 & 2012 & 0.591 & 744.6 \\ B57 & 7.27 & 8384 & 1045 & 0.556 & 418.8 \\ B58 & 7.54 & 7177 & 982 & 0.618 & 740.1 \\ B59 & 7.47 & 6917 & 997 & 0.637 & 828.3 \\ B60 & 7.42 & 6825 & 1064 & 0.578 & 852.8 \\As expected, using less binder led to less carbon contamination in the final parts and, thus, to improved physical and magnetic properties. Nonetheless, there was an optimum powder loading regarding the proper distribution of the powder particles in the binder system that affects the final quality of the sintered magnet. Therefore, the highest powder content could not be processed appropriately and 58 vol.% was the optimal powder content for feedstock B. Comparing the carbon contamination of the B58 with the study conducted by [PERSON] et al. (2018), in which they used the same powder content and 45 vol.% backbone (30 vol.% HDPE and 15 vol.% EVA), revealed that using 45 vol.% LLDPE in the B58 formulation led to lower carbon content (982 ppm) compared to employing 30 vol.% HDPE and 15 vol.% EVA as the backbone (1250 ppm), thus indicating that LLDPE is much better than HDPE as a backbone.
## 2 Related Works and the Problem Formulation
The TrackNet main scheme is presented in Figure 2, where one sees two stacked recurrent layers with GRU units [10] as base neural blocks. There are two fully connected layers on the top of the network to predict the parameters of the ellipse (coordinates of the center x and y, semi-axis of the ellipse R1 and R2 as shown in Figure 2. The TrackNETv2 model can be treated as a Kalman filter analogue powered by neural networks, although without the finite module KF, which performs spatial spiral fitting for hits recognized as track candidates. Unfortunately, there is a drawback to the TrackNET due to its local nature. The locality means that the model sees only one particular track candidate during the prediction phase and cannot rebuild it in time. Therefore, it can give a lot of false positives, which leads to the creation of an unacceptably large number of false tracks. There are two ways to eliminate such false tracks. The first one is proposed in [9] and consists of applying the combined approach, when the track candidates from the TrackNET output consider as an input of the graph neural network (GNN) described in [8]. A GNN can now observe the whole event but by looking only at hits from the potential track candidates, which are presented in the form of a graph. A combination of the two models significantly improves the overall precision of tracking without a high decrease in the recall, and at the same time, it greatly reduces the memory consumption of the GNN model. The second alternative method for identifying false tracks involves applying a statistical criterion that considers the proximity of the track to a smooth spatial curve. This approach was studied in a joint effort [11] by researchers from al-Farabi Kazakh National University and JINR, who proposed algorithms for filtering out false tracks based on approximating the detected points of the particle trajectory with a spatial helix line and calculating a metric that describes the quality of the approximation. If this metric exceeds a certain threshold, it indicates a poor fit of the candidate track to the particle's trajectory, suggesting that it is a false track. Therefore, the main objective of this study is to develop algorithms for the most handy approximation and account for data contamination by fake measurements while also employing parallelization techniques to overcome computational limitations. The study will contribute to this area by providing a new and effective solution to this problem.
## 2 Materials and Methods
### The Hybrid Platform Overview  
The main idea is to take advantage of modularity and self-reconfigurability to design a new versatile multi-purpose Hybrid Platform, which will consist of three systems: a mobile Main Base and two twin Crawler Units, as shown in Figure 1. These robots are designed to perform inspections either independently or in collaboration, as described in [18; 19]. Thanks to the small size, the two Crawler Units are especially suitable for inspection in constrained or cluttered environments and narrow spaces, as discussed in [20; 21]. On the contrary, the Main Base presents a larger size than the Crawler Units in order to cover long distances as fast as possible. Therefore, such a system is more effective in patrolling and monitoring wide areas, such as the aforementioned plant inspection robots.  
Whenever the inspection target or the surroundings are too complex to reach or too challenging to deal with a single robot, the systems can reconfigure autonomously without requiring direct human intervention. In this way, a human operator can tele-operate the system, limiting the risks of being in close proximity of inspection targets. For instance, the Crawler Units can dock together forming a snake robot, see Figure 1. Assuming this configuration, the system can deal with complex operations such as: reaching components placed in higher places, climbing stairs, or crossing very rough terrains. In addition, the Crawler Units can couple with the Main Base, turning the Hybrid Platform into a dual arm system that can execute manipulation and maintenance tasks, as shown in Figure 1. Adopting such a configuration, the Main Base becomes a true marsupial system, which can carry, deploy and recover the Crawler Units near and from different inspection sites. Ultimately, this Hybrid Platform provides the end user with a suite of systems capable of dealing with multiple tasks that span from the inspection of confined spaces to the monitoring of an entire plant, passing through the exploration of hard-to-reach areas or,eventually, the manipulation of small objects. To develop such a versatile system, the design of the Crawler Units assumes particular relevance.