esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Beam Welding of CubeSat 1U Structure Parts Obtained by Powder Bed Fusion
###### Abstract  
This study contributes to a possible methodology for manufacturing CubeSats using additive manufacturing and laser beam welding. Titanium connectors were constructed by selective laser melting and electron beam melting and characterized from a topological point of view. The connectors can be joined to titanium tubes for the construction of CubeSats via laser spot welding. The fiber laser welds exhibited full penetration using pulses with 400 jf energy. The welds showed titanium acicular martensite grains with recesses and pores. The average hardness of the cast zone was 350 HV, which is close to the hardness of the connectors (400 HV) and more rigid than that of the tubes (100 HV). Spot welding has proven to be useful in resisting forces above 2000 N, which is sufficient for CubeSat frame space applications.  
laser materials processing; laser beam welding; titanium alloys; space structures +
Footnote †: journal: materials  
0000-0002-4891-  
large finished products. The time and costs involved would be extremely high, and the examples available in the market are just proofs of concept and, except for rare excesses, are not relevant in the manufacturing scenario. As examples, turbine blades by directed energy deposition AM have been repaired [8], and cooling channels by AM in plastic injection molds have been constructed [9]. This is also the case in the present study of CubeSat structures, as it is not logical to build an integral structure using AM [10]. On the other hand, connectors are somewhat customizable and can be joined to supporting structures at the edges produced by conventional means. Two AM techniques are competitive for titanium parts: selective laser melting (SLM) and electron beam melting (EBM) [11]. Therefore, this study analyzed both alternatives.  
Few authors have studied the union between the parts manufactured conventionally and those by AM. The specific case of titanium components is even more limited. For example, [PERSON] et al. [12] reported some results on SLM parts joined to wrought Ti-6 Al-4V by laser beam welding (LBW). The authors utilized a fiber laser with a power of 4 kW and welding speeds between 1 and 2 m/min to weld two 5 mm plates, wrought, and SLM plates. The fusion zone is marked by acicular \(\alpha\)' martensite inside the prior \(\beta\) grains. The hardness reached 450 HV in the fusion zone and was slightly depleted in the heat-affected zone.  
The objective of the present study is to investigate whether SLM and EBM connectors can be properly joined via LBW to titanium tubes as an original contribution to the CubeSat designs.
###### Abstract  
The Himalayan main frontal thrust (MFT) accommodates most of the present-day Indo-Asia convergence with related periodic earthquakes. The seismicity and deformation mechanism varies considerably across the frontal Himalayas. We mapped a segment (Manzai Ranges) of the MFT at the western margin of the Himalayas and analyzed its deformation mechanism and active tectonics using geomorphic indices and the Interferometric Synthetic Aperture Radar (InSAR) Small Baseline Subset (SBAS) technique. Two frontal thrust faults (Khirgi and Jandola) were mapped using Sentinel-2B band ratios in the study area. Water gaps were present in the form of deflected streams at the tip of the growing anticlines. The C-band RADAR interferometry (Sentinel-1A) showed an average uplift of 5-9 mm/year in the satellite line of sight (LOS) from May 2018 to October 2019. The velocity profiles show an uplift variation across the anticlines and may be related to the displacement transfer from the zone of compression in the Manzai Ranges to the zone of transpression in the Pezu-Bhittani Ranges. Four types of morphometric analyses were carried out to assess the relative tectonic activity, namely mountain front sinuosity index (Smf), valley floor width to height ratio (V\({}_{t}\)), normalized longitudinal river profile, and normalized channel steepness index (Ksn). The landscape response to active tectonics in the study area was recorded as a deep fluvial incision in V-shaped valleys, convex river profiles, topographic breaks as knickpoints, and a high Ksn index. The geomorphic parameters show a relative increase in tectonic uplift and deformation from the Kundi anticline to the Khirgi and Manzai anticline. We concluded that the frontal structures in the western Himalayas are still going through an active phase of deformation and landscape development with both seismic and aseismic creep.  
technic geomorphology; Himalayas; InSAR; SBAS; Manzai Ranges; active tectonics +
Footnote †: journal: remote sensing
## 3 Database of Water Content and Hyperspectral Information
### Analysis of Hyperspectral Information
#### 3.3.2 Atmospheric Correction through White and Black References  
In order to obtain unique hyperspectral information, the influence of solar altitude and light brightness, including weather conditions, must be removed. To achieve this, the normalization of hyperspectral information through white and black balance must be performed, as shown in Equation (3). Here, _Raw_ is hyperspectral information before correction, \(R_{B}\) is the black reference--hyperspectral information obtained by completely covering the camera lens with an opaque cap, \(R_{W}\) is the white reference--reflectance of calibration tarp installed within the shooting range, and \(a\) is the specific reflectance of calibration tarp installed in the shooting area. At this time, the white reference selects a relatively clear object among the four installed tarp, and the unique reflectance (a) is divided into the \(R_{W}\) in order of brightness (white \(\rightarrow\) black) as 84%, 48%, 24%, and 3%. The overall process is shown in Figure 10.  
\[\text{Reflectance}=[(Raw-R_{B})/((R_{W}/a)-R_{B})]\times 100\% \tag{3}\]  
Figure 9: Geometric correction: (**a**) Before; (**b**) After.
# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 4 Autonomous Tugboat Element Technologies
### Control Technology
In the early 2000s, research on control methods for tugboats evolved from simple directional control to adaptive control, which compensates for the positional problem of tugboats and the uncertainty of differential coefficients of hydrodynamic force, as well as robust control methods that take into account environmental disturbances. Recently, research on control methods using artificial intelligence has gained traction. As the actual berthing method is gradually considered, the control methods are also being applied in various ways.
## 1 Introduction
Additionally, a nonlinear dynamical system can be analyzed by studying their dynamics using a topological partition of the phase space [19] that is known as the Symbolic Dynamics (SD). When dealing with signal processing applications, SD enables the transformation of the signals into sequences of symbols that enhance the non-linear features useful for detection or classification. Although several research studies for classifying faults in reciprocating compressors have been reported, most of the research has been focused on studying a small set of valves faults. This has motivated to set up a test-bed platform that enables investigation of a more extensive set of faults in valves as well as combined faults of valves and roller bearings. In the previous research [20], a deep learning approach based on long short-term memory (LSTM) models has been used for classifying 17 different faulty conditions in valves of a two-stage reciprocating compressor. The multi-fault dataset considers combinations of faults in the intake and discharge valves of the first and second stages. Each of the valves is affected by one of the following simulated valve faults: seat wear, plate corrosion, plate fracture and broken spring. The proposed approach attained a classification accuracy up to 93%. This paper proposes the use of Symbolic Dynamics (SD) and Complex Correlation Measure (CCM) as novel features for fault diagnosis in rotating machinery, as an alternative regarding complex computational architectures such as deep learning approaches. We used the same test-bed platform in [20] to investigate the classification of combined faults of valves and roller bearing by using vibration signals, also called \"multi-fault scenario\". The dataset of combined faults of valves and roller bearings considers the same set of four basic fault conditions for the discharge valve of the second stage of the compressor, in combination with three common faults of the roller bearings. Additionally, we also test the proposed features with the valve faults dataset in [20] and perform the comparison to that previous research, in order to highlight the proper classification accuracy regarding the use of deep learning architectures. The original contributions of this paper are the following:  
* Novel application in PHM of the Complex Correlation Measure (CCM) derived from the Poincare plot of the vibration signal for extracting useful features for accurate classification of multi-fault in valves and roller bearings. * Novel application of Symbolic Dynamics (SD) for classification of multi-fault of valves and roller bearings. * Accurate classification of 13 different combined fault conditions (multi-fault scenario) of valves and roller bearing. * Accurate classification of 17 fault conditions of valves in a reciprocating compressor. * Comparison of three different set of features extracted from vibration signal for classifying the set of multi-fault previously mentioned. * Comparison of two high performance Random Forest (RF) models applied to the problem of multi-fault classification of valves and roller bearings in a reciprocating compressor.
# Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
###### Abstract  
We are currently experiencing a revolution in data production and artificial intelligence (AI) applications. Data are produced much faster than they can be consumed. Thus, there is an urgent need to develop AI algorithms for all aspects of modern life. Furthermore, the medical field is a fertile field in which to apply AI techniques. Breast cancer is one of the most common cancers and a leading cause of death around the world. Early detection is critical to treating the disease effectively. Breast density plays a significant role in determining the likelihood and risk of breast cancer. Breast density describes the amount of fibrous and glandular tissue compared with the amount of fatty tissue in the breast. Breast density is categorized using a system called the ACR BI-RADS. The ACR assigns breast density to one of four classes. In class A, breasts are almost entirely fatty. In class B, scattered areas of fibroglandular density appear in the breasts. In class C, the breasts are heterogeneously dense. In class D, the breasts are extremely dense. This paper applies pre-trained Convolutional Neural Network (CNN) on a local mammogram dataset to classify breast density. Several transfer learning models were tested on a dataset consisting of more than 800 mammogram screenings from King Abdulaziz Medical City (KAMC). Inception V3, EfficientNet 280, and Xception gave the highest accuracy for both four- and two-class classification. To enhance the accuracy of density classification, we applied weighted average ensembles, and performance was visibly improved. The overall accuracy of ACR classification with weighted average ensembles was 78.11%.  
deep learning; mammography; breast density; breast cancer; CAD systems +
Footnote †: journal: Medical Image Processing
# Study of Sea Fog Environment Polarization Transmission Characteristics
## 4 Experimental Device  
The experimental device for studying polarization transmission characteristics in an indoor sea fog environment consists primarily of three components [17]: a transmitter that can control the wavelength and polarization of incident light; a sea fog environment simu  
Figure 2: \(I\) and \(V\) component values in the 90\({}^{\circ}\) direction of the bottom circumferential angle of the atmosphere [16].  
lator that can adjust the particle size and salt concentration in sea fog; and a polarization state and optical power-receiving device, or the receiving end.  
The transmitting end consists primarily of three instruments: 450 nm, 532 nm, and 671 nm lasers, attenuators that adjust the intensity of the incident laser, visible-light-band polarizers that polarize the incident laser, and wave plates that are circularly polarized light with an additional linearly polarized light phase. The main component of the apparatus for simulating a sea fog environment is the sea fog box. The top box contains the atmospheric medium, whereas the bottom box contains marine fog. The laser is emitted from the top of the box, and the primary optical channel is dispersed longitudinally. Together with the usual air medium and sea fog particles, it disperses and settles to the bottom of the box. In the experiment, the constructed simulation apparatus was used to recreate the environment of marine fog. The experiment-required sea fog environment with varying concentrations is created by adjusting the fog filling time, and the sea fog concentration is related to the fog filling time. The receiving end consists primarily of two instruments: a polarization state measurement device for measuring the polarization state of the emitted light and an optical power measurement instrument, i.e., an optical power meter. Figures 3 and 4 depict the block design and physical diagram of the polarization transmission experiment, respectively.  
Figure 3: Schematic representation of the polarization transmission experiment.
## 5 Summary and Outlook
### Future Direction  
In recent years, with the advancement of computer and optical communication technologies, silicon photonics has rapidly developed. For silicon photonics, the most popular methods include the use of the electro-optic effect or thermo-optic effect. The use of the thermo-optic effect results in almost no additional loss, making it a preferred choice. Silicon photonics has many advantages, such as ultra-high refractive index contrast and large transparent window, and is widely used in the design, manufacture, and packaging of devices, and the sensor technology is moving towards integrated and industrial applications [165]. To obtain a highly integrated sensing system, it is necessary to integrate light sources, sensors, and detectors into one chip, and silicon photonics provide a good platform for their integration.  
Photonic integration technology is a promising research field in photonics and will be significant in the field of sensing in the future. In photonic integration technology, the integration of multiple devices on multiple thin films of different materials is crucial. It is also necessary to improve chip integration and performance and reduce production costs. However, the rapid development and excellent application potential of silicon photonics have further promoted research on photonic integration [166].  
MIR sensing systems that have broadband light sources can be packaged into handheld devices; however, systems based on quantum cascade lasers (QCLs) can be further scaled down to the chip level. The level of integration achievable with MIR photonic devices is expected to facilitate biomedical and diagnostic applications, as the label-free detection of biomolecular interactions, such as DNA or protein content assays, can be detected by selective MIR signatures. MIR spectroscopy is promising for the rapid screening of specific diseases of the nervous system, respiratory tract, kidney, metabolism, and genetics; however, it currently remains a research technique.  
The development of technologies such as optical waveguides, silicon-based photonics, and photonic integration technology and the application of new materials have enabled the size of sensors to downscale from millimeters to microns or even nanometers, driving the trend of miniaturization and integration of sensors.
# An Indoor Smart Parking Algorithm Based on Fingerprinting
## 3 Background  
### Outdoor and Indoor Localization  
Nowadays, in outdoor environments, the global position system constitutes an established instrument to localize vehicles, pedestrians, and so on. When one moves to indoor contexts, one has to deal with several challenging aspects, as they say in [18]. Indeed, the continuous interruption of connection with satellites makes indoor localization much harder. See [19] for an overview of the existing localization system technologies and algorithms. The classical positioning techniques used for indoor environments are [18]:* Time of arrival: based on the computation of the time needed by the signal to go from the unlocated device (UD) to the base station. To detect the position of UD, three base stations are needed.
* Time difference of arrival: it evaluates the time difference at which the signal arrives at many measuring units.
* RSS-based fingerprinting: after a collection phase of several RSS fingerprints, it is based on matching online measurements with the closest possible location in the database.
* Angle of arrival: based on the computation of the angle at which the signal arrives from the UD to the base station.
## 7 Conclusions  
We demonstrate that wavelength-resolved surface solar irradiance can be estimated with a high degree of accuracy using machine learning, by combining data from an ensemble of low-cost sensors with remotely sensed surface reflectance inputs. By leveraging affordable, widely deployable sensors integrated with edge computing capabilities, our framework can be put to use to enable fine-grained quantification of surface solar irradiance  
Figure 19: \(R^{2}\) values on each of the 421 spectral bins evaluated on the testing set for the model with SR data vs. with no SR data.  
Figure 20: MSE values on each of the 421 spectral bins evaluated on the testing set for the model with SR data vs. with no SR data.  
at high spatial and temporal resolutions, effectively capturing its natural variability for applications in energy and climate research.  
Conceptualization, D.J.L., V.S. and Y.Z.; methodology, V.S. and D.J.L.; software, V.S., L.O.H.W. and Y.Z.; validation, V.S., D.J.L. and L.O.H.W.; formal analysis, V.S.; investigation, V.S.; resources, D.J.L., L.O.H.W., Y.Z., V.S., J.W. and R.P.; data curation, V.S., L.O.H.W.; writing--original draft preparation, V.S.; writing--review and editing, D.J.L. and V.S.; visualization, V.S.; supervision, D.J.L.; project administration, D.J.L.; funding acquisition, D.J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the following grants: The Texas National Security Network Excellence Fund award for Environmental Sensing Security Sentinels; the SOFWERX award for Machine Learning for Robotic Teams and NSF Award OAC-2115094; support from the University of Texas at Dallas Office of Sponsored Programs, Dean of Natural Sciences and Mathematics, and Chair of the Physics Department is gratefully acknowledged; TRECIS CC* Cyberteam (NSF #2019135); NSF OAC-2115094 Award; and EPA P3 grant number 84057001-0.  
Not applicable.  
The data used for the training and testing of the model are publicly available at [[https://github.com/mi3](https://github.com/mi3) nts/SSI_Estimation]([https://github.com/mi3](https://github.com/mi3) nts/SSI_Estimation), accessed on 30 July 2025.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
SSISurface solar irradiance UV Ultraviolet SR Surface reflectance GEE Google Earth Engine ML Machine learning RFRandom forest regressor R2 Coefficient of determination MSE Mean squared error RAE Relative absolute error
## 1 Introduction
The results of thermal analysis (TGA) of fuels are well suited as additional characteristics of the above methods, allowing one to expand the theoretical and experimental scientific base [17]. The conditions and characteristics of the physical and chemical processes occurring within the TGA differ from the conditions of solid fuel combustion in the boiler furnace. First of all, the TGA method allows us to establish more information about the fuel itself, which is important for conducting a comprehensive assessment of the possibility of converting a power plant to the combustion of non-standard solid fuel. In contrast to the above methods, the TGA method makes it possible to study thermal effects and mass changes during the occurrence of physical and chemical processes under conditions of relatively slow fuel heating and can determine both qualitative and quantitative changes [18]. [PERSON] et al. [19] studied the combustion characteristics of individual particles of solid fuel mixtures based on coal and biomass and found that one of the key factors affecting the combustion process of solid fuels is size, surface area, and fuel particle density. With an increase in particle size, the intensity of their burnout decreases. [PERSON] et al. [20] investigated co-combustion and interaction between fuel particles of low-grade coal and biomass. They concluded that an increase in the proportion of biomass in the mixture has a greater effect on reducing the burn-up temperature than on the fuel particle ignition temperature. [PERSON] et al. [21] made an economic assessment of the efficiency of biomass with coal co-combustion and found that the use of biomass improves economic performance. However, due to the fibrous structure of biomass particles and their strength, the cost of fuel preparation (grinding) may increase. One of the solutions to this problem can be the use of large biomass particles (compared to coal), which will minimize the fuel preparation cost. However, there is only limited information on the effect of particle size and composition of solid fuel mixtures based on coal and biomass on combustion performance. The purpose of the work is to study the effect of particle size and composition of solid fuel mixtures based on coal and biomass on ignition and combustion characteristics, as well as to establish the possibility of achieving a positive effect when coal is burned with larger biomass particles (compared to coal particles). The results of the study can serve as a theoretical basis for the development of measures for the conversion of coal-fired boilers to the combustion of non-design fuel mixtures based on coal and biomass.