esa-sceva/satcom-chunk-collection huggingface dataset:
# Determining Rotor Blade Multi-Mode Vibration Components +
## 2 Methodology for Determining Multi-Mode Blade Vibration Components  
The algorithm presented in this paper determines the multi-mode blade vibration components: frequencies \(\omega_{1}\) [Hz], \(\mathrm{a_{i}}\) and \(\mathrm{b_{i}}\), \(\mathrm{i-1,\ldots}\), k in Equation (1) where the velocity at the time of blade arrival \(\mathrm{t_{v}}\) = (\(\mathrm{t_{v1,n}}\), \(\mathrm{t_{v2,n}}\), \(\ldots\), \(\mathrm{t_{v(s-1),n}}\)), and n = 1, 2, \(\ldots\), N, N is the number of rotations and s is the number of sensors (see Appendix A Equation (A11)) is known from the experiment.  
\[\mathrm{V(t_{v})}=\sum_{i=1}^{k}\left(\mathrm{a_{i}sin}(2\pi\omega_{i}\mathrm{ t_{v}})+\mathrm{b_{i}cos}(2\pi\omega_{i}\mathrm{t_{v}})\right) \tag{1}\]  
Therefore, the measured times of blade arrival are used to determine the number of harmonics on the assumption that the one-mode blade vibration velocity is as follows:  
\[\mathrm{V(t_{v})}=\mathrm{a}\ \sin(2\pi\omega_{w}\mathrm{t_{v}})+\mathrm{b} \ \mathrm{cos}(2\pi\omega_{w}\mathrm{t_{v}}) \tag{2}\]  
In our numerical calculations, we noticed that by maintaining the single mode (Equation (2)) whilst varying the \(\omega_{w}\) and using the algorithm presented in the Appendix A and [11], we can obtain local extremes: multiples of the rotation speed frequencies \(\Omega\), \(0.5\Omega\), \(2\Omega\), \(3\Omega\), \(4\Omega\), \(5\Omega\), \(\ldots\), \(\omega_{w}=\mathrm{w_{i}}+\mathrm{j}\ \Omega\), \(\mathrm{j}=\pm 0\), 1, 2, \(\ldots\), \(\omega_{w}=\mathrm{j}\ \Omega\ -\ \omega_{i}\mathrm{j}=1\), 2, \(\ldots\) For us, the \(\omega_{i}\) frequencies are important. By assuming a single-mode solution (i.e., Equation (2)), we can obtain the amplitude calculated as a first approximation.  
With \(\omega_{i}\) and Equation (1), the same algorithm (Appendix A) can be used to calculate the blade amplitude and phase for each mode. If the amplitudes for \(\omega_{i}\) are of a similar order, they show mode coupling. This is a novel approach, which is demonstrated later on in this paper.
# Multiphase Conjugate Heat Transfer Analyses on the Assembly Situation of Rotary Shaft Seals
## 2 Materials and Methods
### Modeling
#### 2.2.1 Computational Domain  
The tests used to validate the simulation model were performed on a high-speed friction torque test bench. The rotationally symmetrical geometry model used in the simulations was based on this test bench and consisted of a total of nine domains, which are illustrated in Figure 3. The domains _pressure chamber_, _closure cover_, _adapter_, _scaling ring housing_, _rotary shaft seal_, _shuff sleeve_ and _shaft_ were considered as solid bodies. The geometry of the sealing ring used on the test bench was simplified in the simulations: neither the spring nor the metal insert of the sealing ring were considered, and the entire volume of the sealing ring was assumed to be made of elastomer. Both the sealing edge and the shaft as the counterface were assumed to be ideally smooth. The sealing gap itself was not simulated, but the direct contact of the sealing edge to the shaft was assumed. The fluid domain _oil chamber_ contained both oil and air, which is why a multiphase model was necessary to describe the two-phase flow. The fluid domain _environment_ contained only the air.  
The geometry was meshed in the Ansys Workbench meshing software, Ansys CFX 2021 R2. An element size was specified for each domain. The sealing ring was meshed with the finest element size of 0.2 mm. To ensure a smooth transition to the surrounding domains and, thus, to other element sizes, the mesh size at the interfaces of the sealing ring was also set to 0.2 mm. Over the 0.2 mm wide contact width between the sealing edge and the shaft sleeve, the number of elements was set to 8 elements. The domains _environment_ and _oil chamber_ were meshed with an element size of 1 mm. The largest mesh size of 4 mm was defined for the domains _pressure chamber_, _adapter_ and _closure cover_. The used mesh contained 60.5 million elements and 13.7 million nodes. Due to the limited computing power available, the mesh was not further refined. Figure 4 shows the mesh in the area of the sealing edge. The domains _rotary shaft seal_, _oil chamber_ and _environment_ were meshed with tetrahedrons; all other domains were meshed with hexahedrons.  
Figure 4: Mesh in the area of the sealing edge: Sectional view of the 3-dimensional model.  
Figure 3: Geometry of the reference variant: A sectional view of the 3-dimensional model.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Fine-Tuning Deep CNN
#### 3.4.1 Fine-AlexNet  
To facilitate effective extraction of image features and considering the ship-image size, the size of the convolution kernel was fine-tuned to 3 pixels \(\times\) 3 pixels, and the number of filters contained within convolution layers Conv1-Conv5 equaled 32, 64, 64, 128, and 256, respectively. The size of the pooling kernel equaled \(2\times 2\) and the number output layers equaled 4 or 6 corresponding to the number of ship classes contained within the two self-built and VAIS datasets. The ReLU nonlinear activation function was employed after each convolution and fully connected layer. The specific network architecture has been described in Table 3.
## 4 Methods
### Main Processing
#### 4.2.2 Spectral Unmixing  
The \(n=5\) endmembers were used in the SU process, which was applied separately to each dataset. In this study, we adopted the linear mixing model defined as:  
\[\mathbf{y}=\sum\
olimits_{i=1}^{n}a_{i}\mathbf{x}_{i} \tag{1}\]  
where the spectral signature \(\mathbf{y}\) of a certain pixel is assumed to be expressed as a linear combination of the spectral signatures of the selected endmembers \(\mathbf{x}_{i}\)s (\(a_{i}\)s denotes the respective coefficients).  
\begin{table}
\begin{tabular}{c c c} \hline
**ASTER**[103,104] & **Landsat-8/OLI**[61] & **Sentinel-2 MSI**[61] \\ \hline \(\frac{(1.656~{}\upmu\text{m}\text{-}2.167~{}\upmu\text{m})}{(1.656~{}\upmu \text{m}\text{-}2.167~{}\upmu\text{m})}\) & \(\frac{(1.609~{}\upmu\text{m}\text{-}2.201~{}\upmu\text{m})}{(1.609~{}\upmu \text{m}\text{+}2.201~{}\upmu\text{m})}\) & \(\frac{(1.610~{}\upmu\text{m}\text{-}2.190~{}\upmu\text{m})}{(1.610~{}\upmu \text{m}\text{+}2.190~{}\upmu\text{m})}\) \\ \hline \end{tabular}
\end{table}
Table 2: The normalized hydrothermal alteration spectral index (NHASI) formulation for ASTER, Landsat-8/OLI, and Sentinel-2 MSI data, expressed in wavelengths.  
Figure 4: Plot of the five endmembers used in the SU procedure for (**a**) ASTER, (**b**) Landsat-8/OLI, and (**c**) Sentinel-2 MSI datasets.  
Then, the abundances \(a_{i}\) of the \(n=5\) endmembers were estimated via the unmixing process, which in our case utilized the least squares error criterion [(65; 105; 106)] in its unconstrained form.  
Let us discuss briefly the geometrically perspective of the above criterion. The number of bands \(l\) specifies the dimensionality of the Euclidean space where the spectral signatures live. The unconstrained unmixing process projects the vector \(\mathbf{y}\) of the spectral signature of the pixel under consideration to the \(n\)-dimensional space \(S\) spanned by the vectors of the spectral signatures of the selected endmembers \(\mathbf{x}_{1},\ldots,\mathbf{x}_{n}\) and the abundances are the coefficients of the linear combination of \(\mathbf{x}_{1},\ldots,\mathbf{x}_{n}\) that defines the projection \(\mathbf{y}^{\prime}\) of \(\mathbf{y}\) on \(S\). Since the abundances are indicative of the correlation of \(\mathbf{y}\) with the respective \(\mathbf{x}_{i}\)s, a negative abundance is likely to indicate reduced correlation between \(\mathbf{y}\) and the respective \(\mathbf{x}_{i}\). From this perspective, we set it equal to zero.  
The unconstrained form of SU was selected because, in contrast to constrained SU forms (sum-to-one and/or non-negativity), it resulted in smaller residues than the constrained model.
## 4 HSI in Fresh-Cut Product Quality Assessment
### Fresh-Cut Green Leafy Vegetables
#### 4.1.2 Spinach Leaves  
[PERSON] et al. (2019) investigated the adequacy of hyperspectral imaging for the evaluation of the freshness of spinach leaves during storage at different temperatures. They applied visible-SWNIR (380-1030 nm) and NIR reflectance (874-1734 nm) HSI systems. After data preprocessing and wavelength selection, the following models were applied and evaluated: PLS-DA, SVM, and ELM (extreme learning machine). All three models achieved good results, with above 92% accuracies for both spectra. Although good results were achieved, the data processing techniques were time-consuming, which is a barrier to practical deployment. Moreover, to improve the models' robustness, the sample size should be increased, and new types of samples should be used in future investigations [33].  
[PERSON] et al. (2011) proposed an HSI device (400-1000 nm) to measure the contamination degree of _Escherichia coli_ in packaged fresh spinach. PCA was applied for wavelength selection, and ANNs were used to predict the number of colonies of _E. coli_ (R\({}^{2}\)p = 0.97) and obtain a prediction map of the microorganism number to pixel spatial information, allowing a rapid and useful data interpretation [58].  
Instead, [PERSON] et al. (2014) used both reflectance (Vis-NIR) and fluorescence (UV and violet) HSI devices to detect fecal contamination (in different concentrations) on spinach leaves. PLS-DA and two indexes based on wavelength ratio were used to compare the two HSI devices (Figure 8). The results showed that UV and violet fluorescence had better performances (from 87% to 100% accuracy depending on the degree of dilution) than the Vis/NIR reflectance device, where the accuracy decreased sharply with increasing dilution [30].  
Figure 8: Examples of fecal contamination detection on _Spinacia oleraca_ leaves for each HSI image configuration (UV at 370 nm, violet “V” at 410 nm, Vis/NIR) paired with PLS-DA (on the (**left**)) and wavelength ratio (on the (**right**)) analyses. Adapted with permission from Ref. [30]. 2014, Elsevier.
# Experimental Analysis on Pre-Stress Friction Loss of Crushed Limestone Sand Concrete Beams
###### Abstract  
This paper presents an experimental research work that evaluates the pre-stress loss caused by friction in crushed limestone sand (CLS) concrete members with post-tensioning. A total of 26 full-scale pre-stressed concrete beams were constructed and tested for the friction loss experiment. The considered variables mainly included the duct-forming materials, wires of tendons and arrangement of ducts. The tensile forces at both active and passive ends of specimen were recorded by steps, and then the pre-stress friction loss for each case was calculated. The result shows that the proportion of pre-stress friction loss in specimen with multi-wire tendons is in the range of 10-40%, with the trend first increasing before decreasing. The pre-stress friction loss in specimen with curve duct accounts for 10-30%. The pressure on the curved part definitely increases the friction when compared with the straight duct. The pre-stress friction loss in specimen with rubber hose reaches nearly 40%, which is larger than the metal bellow and plastic bellow. The suggested values for each case are proposed for a deviation coefficient \(\kappa\) of 0.0017-0.007 and a friction coefficient \(\mu\) of 0.108-0.858. This can provide reliable theoretical support for the design and construction.  
pre-stress loss; friction factor; pre-stressed concrete; curved duct +
Footnote †: journal: Applied Sciences
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### Nonpreemptive Priority and Multiclass Model  
The multiclass queue (see Figure 2(a)) serves messages on the basis of a first-come first-served policy regardless of the class to which each message belongs. However, IoT systems handle the data of different application types such as emergency response, real-time, and video streaming. The nonpreemptive priority and multiclass (NPPM) enables the prioritization of messages belonging to different classes when being served (transmitted or processed). The NPPM is different from the nonpreemptive priority (NPP) queue, where classes of messages are created on the basis of the priority assigned to each message. As depicted in Figure 2(b), messages belonging to \(n\) classes arrive according to Poisson processes and are then classified to different queues on the basis of their assigned priority. At the exit of each priority queue, messages are classified to be served (on the basis of exponential service times) depending on the class to which they belong (video-streaming data vs sensor data).  
Figure 3: Multiclass and priority queues.  
A nonpreemptive priority and multiclass queue (\(q_{\text{mclpr}}\)) is defined by the tuple:  
\[q_{\text{mclpr}}=(\lambda,\mu,C,Y) \tag{10}\]  
where \(\lambda=\{\lambda_{c_{n}}:c_{n}\in C\}\) is the input rate of messages to the queue of each class \(c_{n}\in C\), \(\mu=\{\mu_{c_{n}}:c_{n}\in C\}\) is the service rate for the processing of messages of \(c_{n}\in C\) and \(Y=\{y_{c_{n}}:c_{n}\in C\}\) is the assigned priority to each class \(c_{n}\in C\). Given a message that belongs to a class \(c_{k}\) and is assigned with priority \(y_{c_{k}}\), the time that it remains is given by:  
\[\Delta^{\text{mcl}}\ =\ \frac{L_{c_{k},y_{c_{k}}}(\lambda,\mu)}{\lambda_{c_{k}}} \tag{11}\]  
where \(L_{c_{k},y_{c_{k}}}(\lambda,\mu)\) is the number of messages classified to \(c_{k}\) and assigned with priority \(y_{c_{k}}\) in the system (queue + server) of \(q_{\text{mclpr}}\). We omitted the proof of (11) due to space constraints, but analysis is similar to that in Section 3.4.2 in [8] (provided in [23]).
# Role of Irisin in Endocrine and Metabolic Disorders--Possible New Therapeutic Agent?
## 8 Polycystic Ovary Syndrome  
According to the anticipated function of irisin in reducing insulin resistance, the study of its role in the pathogenesis of PCOS is very promising. PCOS represents a complex endocrine disorder affecting 6-10% of reproductive women. The clinical manifestations are heterogenous with possible severe metabolic complications; nevertheless, the pathogenesis of PCOS has not yet been fully elucidated.  
Peroxisome proliferatorator-activated receptor gamma coactivator 1-alpha (PGC1\(\alpha\)), which is a transcription factor upstream of FNDC5, plays an important role in ovarian function regulating cellular metabolism and the production of sex hormones. Assessment of the expression of these genes in granulosa cells of a PCOS mouse model showed that the expression of FNDC5 was significantly reduced in granulosa cells of PCOS mice, compared with the control group, while there were no significant differences in PGC1\(\alpha\) expression among different groups. The authors concluded that the downregulation of FNDC5 transcript level may contribute to the metabolic disturbance of granulosa cells derived from ovaries suffering from PCOS, apart from PGC1\(\alpha\) levels, which remained unchanged [41].  
Several studies have indicated elevated insulin in PCOS women and have also related it to insulin sensitivity [42]. The mean serum concentrations of irisin before and after insulin infusion are higher in PCOS women, in comparison to the control group. Insulin infusion resulted in a decrease in serum irisin concentration only in the PCOS group. These results lead to the following question: is there an \"irisin resistance\" in PCOS women similar to \"insulin resistance\" or \"leptin resistance\"? [42].  
It is possible that increased irisin levels predict insulin resistance, metabolic syndromes, and hyperandrogenemia [43]. Follicular fluid irisin has been correlated with serum irisin in PCOS women. In contrast, there was no significant correlation between serum and follicular fluid irisin levels in poor responders undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) [44]. The elevated serum irisin level can be considered as a protection factor in the pre-diabetic state of PCOS (i.e., before diabetes mellitus develops) [45].  
All these data support the assumption that metabolic imbalance triggers a \"vicious circle\" in PCOS women. As such, the relationship between the adipose tissue, hyperinsulin-linemia, and hyperandrogenism in PCOS may include a new player; namely, irisin.
## 2 Overview of SWARM IoT Constellation
### Capabilities of Constellation
Each data packet is limited to 192 bytes, making it ideal for transmitting small time-sensitive data like sensor readings or status updates. The latency and throughput are influenced by the number of satellite passes per day, which may limit two-way communication. However, SWARM continues to expand its satellite constellation to improve coverage and frequency of access. This system's combination of low power, small size, and simplicity makes it particularly well suited for remote IoT deployments, where cost and energy efficiency are crucial. It has become increasingly popular for applications in agriculture, logistics, environmental monitoring, and emergency response. The SWARM device includes the main application of data transmission and two-way communication between the device and the satellite. However, SWARM Technologies does not provide transmission between two different devices at a relative distance while using a satellite as the connection point with the SWARM cloud. For communication between two devices, a satellite and a SWARM cloud, a telecommunication network should be used for message receiving, storage, notification receiving, and other subsystem work. As illustrated in Figure 2, SWARM Device 1 (Dev 1) transmits a message containing specific information to a satellite (the maximum amount allowed is 192 bytes per one transfer [27]). The satellite, which is connected to the SWARM cloud platform known as HIVE, forwards the message to HIVE for storage. To efficiently monitor and receive notifications for incoming messages without the need to manually check HIVE repeatedly, we utilize the AWS platform. Specifically, we leverage AWS features such as APIs and Webhooks. We have implemented a Webhook-based system that periodically checks HIVE for new messages. When a message is detected, it is extracted and processed within the AWS environment. This method is discussed in detail in Section 3. Notifications of received messages are sent via email or text message, depending on user preferences. After processing, the system is configured to generate an automatic or manual response. This response is then transmitted back to the satellite. The satellite subsequently relays the response message to the second SWARM device (Dev 2) via a nearby satellite.
## 2 Construction Method of EMR-SF along the Road Collection Data  
Since the EMR data sampled along the road have a largely empty region and uneven distribution overall and the data have a linear distribution locally, the EMR-SF cannot be constructed accurately by using the traditional spatial interpolation method. In this paper, the LRBF interpolation method is proposed for the space field construction of this type of EMR sampling data. The basic process is as follows. (1) Construction of the grid points in the LRBF: the regular grid points are constructed within the study area. (2) The layered grid points in the LRBF: the grid points are gradually separated into multiple layers from the outside to the inside at a certain interval. (3) Construction of EMR-SF by the LRBF: based on the grid point being layered, the EMR value of the grid point is generated by layered interpolation using the LRBF interpolation method. Finally, the EMR-SF is constructed according to the original sampling data points and the grid point. The process is shown in Figure 1.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Network Topology Prediction  
The proposed framework provides a global view of the NTN system by integrating satellite and network operations. Combined with a satellite operations simulator and orbit propagation models, this enables the CMS to predict the network topology effectively. This capability is crucial for addressing integrated management challenges, such as mobility management, resource management, and uncertainty mitigation. A global understanding of network topology also enables the seamless integration of network operations into satellite operations and vice versa. This exchange of information provides MANO with critical insights into the future states of spaceborne nodes and resources. Regarding operational dynamics challenges, accurate network topology predictions are key in optimizing space routing algorithms. They also support SDN controllers in addressing traffic offloading and unstable link challenges while enabling the efficient deployment and maintenance of VNFs.
## 4 Results
### Decision-Making Analysis  
Figure 7 consists of two pie charts summarizing the decisions related to the installation of inspection technologies in the analyzed automotive case study. Figure 7a shows the installed proportion between the different types of technologies. Figure 7b depicts, instead, the percentage of stations where technology has been implemented versus those without any technological installations.  
According to Figure 7a, most installed technologies belong to the machine vision category, accounting for 75% of all installations. In contrast, human and human inspections assisted with digital technologies are installed in only 12.5% of cases. This distribution suggests a strong preference for machine vision-based systems in the examined case study, likely due to their higher efficiency and detection accuracy reported in Figures 1 and 2. Instead, Figure 7b illustrates the extent of technology adoption across stations. Only 33.3% of stations are equipped with one technology, while the remaining 66.7% operate without any technological support. This indicates that despite the availability of multiple technologies, their deployment is limited to a subset of the station network. The results  
Figure 6: Dependence of solving time on the cardinality of the problem sets.  
reflect a selective installation strategy driven by resource limitations, targeted inspection needs, and strategic allocation of high-performing technologies to specific stations.  
Additional decision-making insights related to the examined case study are provided in Figure 8. Here, the figure shows a bar chart representing the distribution of the sampling rate for the different technologies, plotted on a logarithmic scale on the \(y\)-axis. The \(x\)-axis displays the different values of the sampling rate (which can vary from 0 to 1), while the \(y\)-axis shows the logarithmic count of occurrences for each value. Bars are color-coded by technology.  
The results indicate that in most cases where inspection is performed, the model opts to inspect the full available demand using vision-based systems. However, while the sampling rate of 1.0 dominates across all technologies, a non-negligible number of instances exhibit intermediate or partial sampling rates, especially for human inspections and those assisted by digital tools. This suggests that the model leverages variable sampling rates selectively, favoring full inspection only when cost, performance, or time constraints are justified. The presence of partial sampling configurations confirms that, under certain conditions, inspecting a subset of units is the more cost-effective strategy, particularly when using less efficient or more resource-constrained technologies.