esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 2 Materials and Methods
### Data Pre-Process  
A program with graphic user interface (GUI) developed in Matlab (The MathWorks, Inc.) environment was used to process spectral images (Figure 3). For spectral data of a scan pass, the TIFF images corresponding to each scan line were combined as a three-dimensional data cube (x, y, \(\lambda\)) where x, y, and \(\lambda\) represent spatial and spectral components respectively. Due to the low light output of lamps in the VIS \(<\) 468 nm and low quantum efficiency of the CMOS in the NIR \(>\) 950 nm, only hyperspectral image data in the wavelength range between 468 nm and 950 nm were used for analysis in this work. A region of interest (ROI) was applied to the data cube to select the relevant subset of data for further process. The locally estimated scatterplot smoothing (LOESS) filter was applied to selected data in the spectral direction, and then the relative reflectance was calculated using white and dark references.  
Pseudo RGB images constructed from calibrated data by combining three wavelengths were used for feature labeling of jujube surface. Due to the smooth and waxy skin of the sphere-like jujube surface, the mirror-like reflection or glare of incident light cannot be completely removed by adjusting the angle of incident light. In HSI, glare alters the spectral fingerprint, which could introduce artifacts and hence deteriorate classification results. Therefore, glare pixels need to be detected and removed before feature extraction and classification [24]. In this study, glare was considered as one type of surface feature and thus pixels of seven types of jujube skin conditions (i.e., normal, rusty spots, decay, white fungus, black fungus, crack, and glare) were visually identified and labeled on the pseudo RGB images for the use of supervised machine learning methods.
## 2 Test Design and Result Analysis
### Analysis of Monitoring Results
#### 2.3.1 Deformation of the Foundation Pit Retaining Structure
To further obtain the horizontal deformation law of the retaining pile in deep foundation pit excavation construction, the horizontal displacement monitoring data of the retaining pile at each time point of ZQT-4, ZQT-9, ZQT-15 and ZQT-21 in four directions and four measuring points in the southeast and northwest of the retaining pile are analyzed, as shown in Figure 7. The deformation trend of each retaining pile measuring point position at different excavation time nodes is large in the middle and small at both ends. With the passage of time, the maximum horizontal deformation position of the retaining pile gradually moves downward along the pile body. Figure 6: Time\(-\)history curve of the horizontal displacement of the pile top of the retaining pile. Figure 7: _Cont._  
Comparing the pile displacement of the four measuring points at different construction times, it can be seen that the horizontal deformation of the ZQT-9 retaining pile on the south side of the foundation pit is slightly smaller than that on the north side of the ZQT-21 measuring point. The main reason is that the excavation interface of the north side of the foundation pit is large, and the contact surface between the retaining pile and the lateral active earth pressure is large during the excavation of the foundation pit, resulting in large deformation of the retaining pile on the north side. The retaining pile at measuring point ZQT-4 on the west side of the foundation pit is close to the side of the subway station, and the pile deformation is slightly smaller than that at measuring point ZQT-15 on the east side of the foundation pit. The main reason for the deformation of retaining piles is that the stiffness of subway station structure is large, and the disturbance of station structure caused by foundation pit excavation is far less than that of the stratum.
## 3 Background and Overview of LEACH  
In WSNs, recharging the batteries of sensor nodes is an infeasible task [3]. Hence, the network lifetime is a primary concern in WSNs. In order to prolong the network lifetime, several routing protocols exist and could be classified into two types depending on the network topology: Flat routing protocols and Hierarchical routing protocols. Since flat routing protocols require maintaining routing table data and cannot aggregate the sensed data, they are not applicable for large scale WSNs. However, hierarchical routing  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline CH & Cluster Head \\ CM & Cluster Member \\ BS & Base Station \\ \(l\) & Length of Message in Bits \\ \(p\) & Probability to Select a Node as CH \\ \(N_{i}\) & \(i_{th}\) Node \\ \(T(N_{i})\) & Threshold Function Value against \(i_{th}\) Node \\ \(R(N_{i})\) & Random Number against \(i_{th}\) Node \\ DT & Direct Transmission \\ AP & Access Point \\ \(G\) & Set of Nodes not selected as CH \\ RSS & Received Signal Strength \\ TDMA & Time Division Multiple Access \\ \(r\) & Number of Rounds \\ \(d\) & Remote Distance to BS \\ \(E_{b}/N_{0}\) & Bit to Noise Ratio \\ \(E_{AVG}\) & Average Energy of System \\ \(E_{CH}\) & Current Energy of CH \\ \(E_{CM}\) & Current Energy of CM \\ \(D_{CM}\) & Distance of CM to BS \\ \(D_{AVG}\) & Average Distance to BS \\ \(E_{Tx-dec}\) & Transmitter Electronics \\ \(E_{Rx-dec}\) & Receiver Electronics \\ \(\in_{amp}\) & Transmitter Amplifier \\ ADVPKT & Advertisement Message Packet \\ CTRKT & Control Packet \\ DRN & Death Rate of Nodes \\ FND & First Node Died \\ HND & Half of the Nodes Died \\ SRE & System Remaining Energy \\ C\({}_{id}\) & Cluster ID \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the used notations.  
protocols can solve this issue to some extent. Direct Transmission (DT) [4] is the basic hierarchical routing protocol used for transferring data from source node to the destination directly without any intermediate node. Due to this, DT consumes more power for sending information towards the BS, especially when this latter is located far away from the sensing field. This will quickly degrade nodes' battery and reduce the overall lifetime of the network [4].
## 2 Materials and Methods
### Methodology
#### 2.2.2 Analysis of Wind Characteristics  
For the analysis of wind characteristics in the study area, wind velocity and direction data were collected from two sources: the ERA-5 mission [35] covering the period from 1979 to 2021 and the METEO platform [36] for the ground meteorological station of Lavrio for the period 2009 to 2021. The ERA-5 Land reanalysis data provided the \(U\) and \(V\) wind components on an hourly basis, offering a spatial resolution of 9 km. Wind speed was calculated using the Pythagoras theorem [37]:  
\[WS=\sqrt{U^{2}+V^{2}} \tag{1}\]  
where \(WS\) is the wind speed, \(U\) is the horizontal speed of air moving towards the east, at a height of ten meters above the surface of the Earth, in meters per second and \(V\) is the horizontal speed of air moving towards the north, at a height of ten meters above the surface of the Earth, in meters per second.  
Wind direction (\(\theta\)) was determined by applying the following:  
\[\theta=\text{atan2}(V/U) \tag{2}\]  
All these calculations and subsequent analyses were performed utilizing the Python 3.10.6 programming environment.  
The METEO ground station time series data can effectively capture actual weather conditions within the study area. In this dataset, daily averages and maximum wind intensities are recorded, along with prevailing wind directions.  
Due to the limited operational time of the meteorological station in the area, a historical statistical wind analysis for the whole region was performed by incorporating the large timeseries of the gridded ERA-5 data and the observed wind conditions on a local-scale. The data from ERA-5 and METEO were integrated using a quantile mapping technique [38]. This merging process ensured that the gridded data were aligned with the distribution of point observations from the meteorological station at Lavrio, thus enhancing the overall reliability and accuracy of the wind data on a finer spatial and temporal scale.
# Radiation-Balanced Lasers: History, Status, Potential
## 3 Theory of Radiation-Balanced Lasing
### Pringsheim's Cooling  
The idea to remove the thermal energy from a system optically using anti-Stokes fluorescence was first proposed by [PERSON] in 1929 [22]. In 1995, laser cooling with anti-Stokes fluorescence was demonstrated for RE-doped solids [23]. In this first proof-of-principle experiment, a high-purity ytterbium (Yb\({}^{3+}\))-doped fluorozirconate ZrF\({}_{4}\)-BaF\({}_{2}\)-LaF\({}_{3}\)-AlF\({}_{3}\)-NaF-PbF\({}_{2}\) (ZBLANP) glass sample was cooled down to only 0.3 K below room temperature.  
Each energy level of the RE ions doped into the crystal or glass host splits into a set of sublevels as a result of the Stark effect. These sets of sublevels are known as Stark level manifolds. In order to realize cooling with anti-Stokes fluorescence in an RE-doped sample, electrons must be excited from the top of the ground manifold to the bottom of the excited manifold of the RE ions. This implies that the pump wavelength, \(\lambda_{P}=2\pi c/\omega_{P}\), must be in the long wavelength tail of the absorption spectrum. After thermalization accompanied by phonon absorption, anti-Stokes fluorescence photons remove energy from the system (Figure 3).  
The efficiency of laser cooling can be estimated as a difference between the energy of the mean fluorescence photon, \(\hbar\omega_{F}\), and the energy of the pump photon, \(\hbar\omega_{P}\), normalized by the energy of the pump photon [24]:  
\[\eta_{cool}=\frac{\hbar\omega_{F}-\hbar\omega_{P}}{\hbar\omega_{P}}=\frac{ \lambda_{P}}{\lambda_{F}}-1, \tag{3}\]  
where \(\omega_{P}\) and \(\lambda_{P}\) are the frequency and the wavelength of the pump photon, respectively.  
\[\lambda_{F}=\frac{2\pi c}{\omega_{F}}=\frac{\int\lambda I_{F}(\lambda)d \lambda}{\int I_{F}(\lambda)d\lambda}, \tag{4}\]  
where \(\lambda_{F}\) and \(\omega_{F}\) are the mean fluorescence wavelength and the mean fluorescence frequency, respectively. \(I_{F}(\lambda)\) is the fluorescence intensity at the wavelength \(\lambda\).  
Figure 3: Energy levels and relevant excitation and decay processes for the laser-cooled RE-doped sample. \(\omega_{P}\) and \(\omega_{F}\) are the pump and the mean fluorescence frequencies, respectively.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation  
### Simulation Settings  
Simulations were conducted in MATLAB, while branch-and-cut was implemented in CPLEX to solve the optimization problem. In the experiments, 50 sensor nodes were deployed in Layer 1. All sensor nodes predict their future positions for \(N\) seconds with the IMM algorithm. The statistics are obtained from 100 runs. To guarantees the maximum coverage and connectivity in both time  
Figure 4: Initial deployment algorithm.  
and space for underwater sensor networks. We consider the dynamic of the sensors, the dynamic means that the underwater sensor nodes are typically mobile with the water current. So, in simulation, we consider dynamic deployment rather than static which is inconsistent with the real underwater environment. To ensure a fair comparison, we performed an entirely controlled simulation to make sure at the beginning of each round simulation, the positions of sensor nodes and surface gateways are the same. The parameters of the simulation are shown in Table 1.  
The mobility behavior of the sensor nodes is characterized by the kinematic model [40], in which the node velocities are:  
\[\begin{cases}V_{x}=k_{1}\lambda v\sin(k_{2}x)\cos(k_{3}y)+k_{1}\lambda\cos(2k_ {1}t)+k_{4}\\ V_{y}=-\lambda v\cos(k_{2}x)\sin(k_{3}y)+k_{5}\end{cases} \tag{16}\]  
where \(V_{x}\) and \(V_{y}\) represent the velocity along the \(x\) and \(y\) axis, respectively. \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(\lambda,v\) are variables related to environmental factors such as tides and bathymetry. These parameters will vary in different settings. Random variables \(k_{4}\) and \(k_{5}\) represent additional random physical factors.  
The queuing and channel access delays are assumed to be constant and represented as \(D_{q}\) and \(D_{c}\) to estimate delays. The bit rate (\(T_{r}\)) of 3.1 kbps is based on our OFDM model, and the ranging message (\(P_{r}\)) is 40 bytes, which makes the transmission delay 0.103 s. The propagation speed is 1500 m/s. In ocean, a maximum random speed of nodes under different mobility models is usually set to 5 m/s on a random direction. A 6 s interval was selected for nodes to predict their future position. It will give a limit on the maximum traveled distance of 30 m. This distance can affect the reachability of gateways by the underwater node and can be a reasonable deciding factor for gateway redeployment.
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 3 Modeling
### Structural Finite Element Dynamic Analysis Equations
#### 3.2.3 Random Vibration Analysis Equations  
Random vibration analysis uses statistical methods to study the excitation or response of a random vibration system, and to calculate the displacement, stress, strain and reaction of the model based on power spectral density (PSD). PSD describes the variance of excitation or response varying with frequency.  
Random vibration is uncertain, and the dynamic equation is solved by approximately converting the random excitation into the simple harmonic excitation:  
\[M\ddot{y}(t)+C\dot{y}(t)+Ky(t)=-ME\ddot{x}(t) \tag{14}\]  
where \(\ddot{x}(t)\) is the second derivative of the displacement vector.  
The steady-state solution of the above equation is:  
\[\dot{y}(t)=\sum_{j=1}^{n}q_{j}\[PERSON]_{j}=-\gamma_{j}H_{j}\[PERSON]_{j}\sqrt{k_{xx( \omega)}}e^{i\omega t} \tag{15}\]  
and the power spectral density is obtained by solving the following equation:  
\[K(\omega)=\dot{y}(t)\dot{y}(t)^{T}\approx\sum_{j=1}^{n}\gamma_{j}^{2}\[PERSON]_{j} \[PERSON]_{j}^{T}\big{|}H_{j}(\omega)\big{|}^{2}K_{xx}(\omega)d\omega \tag{16}\]  
thus, the Grms response can be obtained as:  
\[A=\left[\int_{-\infty}^{+\infty}K(\omega)d\omega\right]^{\frac{1}{2}}=\left[ \sum_{j=1}^{n}\gamma_{j}^{2}\Phi_{j}\Phi_{j}^{T}\big{|}H_{j}(\omega)\big{|}^{2 }K_{xx}(\omega)d\omega\right]^{\frac{1}{2}} \tag{17}\]  
The PSD of the structure in the prescribed frequency domain is calculated by random vibration analysis to measure the response.
## 4 Results
### Centralized Learning vs. FL Based on Edge-Fog-Cloud Architecture
#### 4.2.4 FL on Cloud Layer Performance  
After analyzing the detection performance in the fog layer, we proceed to examine the detection performance in the cloud layer or global model. The results of the detection performance in the cloud layer or global model are illustrated in Figure 10. The aggregation of the model in the cloud is also influenced by conditions in the fog layer. Notably, the accuracy of the global model drops to near 0% in round 4, while the other parameters remain relatively stable at over 90% in each round. After five rounds of training, the final results for accuracy, precision, recall, and F1-score consistently stand at 97.65%, 97.65%, 100%, and 98.81%, respectively.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 6 Conclusions  
This paper proposed an ML-based algorithm to perform adaptive modulation in drone-user communication systems to overcome ill effects due to atmospheric disturbances. The adaptive modulation scheme proposed in the paper achieves reliable communication at the cost of spectral efficiency. Monte Carlo simulations were carried out to analyze the  
Figure 6: BER performance for adaptive modulation.  
Figure 7: ML-assisted adaptive modulation.  
system performance. As a result, a reliable system that achieves the maximum possible data rate without compromising error performance is developed using the k-means clustering algorithm. The algorithm requires the position of users to perform the k-means algorithm. As future work, we consider a case where the atmospheric disturbances vary for locations. A swarm of drones deployed for communication will have to use AM schemes decentralized to achieve an overall best performance.  
Conceptualization, S.P.G. and M.M.M.; methodology, S.P.G.; software, S.P.G.; validation, S.P.G.; writing--original draft preparation, S.P.G. and S.H.A.; writing--review and editing, S.P.G., S.H.A. and M.M.M.; visualization, S.P.G.; supervision, M.M.; project administration, A.V.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We acknowledge the support by research grant from Science596 Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm), and Marie Sklodowska-Curie597 grant agreement No. 847577 co-funded by the European Regional Development Fund.  
The authors declare no conflict of interest.
## 4 EMTARP: System Model and Protocol Description
### Operation of EMTARP
#### 4.3.2 Next Hop Selection Algorithm  
The main component of the proposed protocol is the next hop selection scheme. Actually, the next hop selection scheme does not require complicated algorithms. According to Algorithm 1, each node selects the best next hop based on the routing cost. At line 6, the next hop which has the highest routing cost is selected, such as greedy forwarding. The routing cost is calculated in the \(CostFunction\), and it requires three routing metrics: \(H_{i}\), \(T_{i}\), \(Link_{i}\). The objective of this function is to find the best next hop considering multiple metrics, and it computes a single routing cost. The detailed procedures for calculating routing cost are described in the above subsection.  
```
1:\(H_{i}\) = Hop count from node \(i\) to destination
2:\(T_{i}\) = Normalized temperature of node \(i\)
3:\(Link_{i}\) = Normalized link quality between a source node and node \(i\)
4:\(CostFunction\) = A Function to compute a routing cost using MCDM method
5:for\(i\gets 1,n\)do
6:\(Next\ Hop\gets argMax\ (CostFunction(H_{i},T_{i},Link_{i}))\)
7:\(i\in n\)
8:endfor
```  
**Algorithm 1** Next hop selection process
# An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks
## 3 The Proposed Approach
### Subgraph Construction
#### 3.2.1 User-Follow Subgraph  
We define the user-follow graph as a directed graph \(G_{f}=(V,E_{f})\), where each node \(u_{i}\in V\) represents a user in social networks, and each edge \((u_{i},u_{j})\in E_{f}\) represents a follow relationship in the network. We use \(E_{1}\) to represent a set of unidirectional edges, e.g., \(E_{1}=\left\{(u_{i},u_{j})\big{|}(u_{i},u_{j})\in E_{f}and(u_{j},u_{i})\
otin E _{f}\right\}\)and \(E_{2}\) is a bidirectional edge set, i.e., \(E_{2}=\big{\{}(u_{i},u_{j})\big{|}(u_{i},u_{j})\in E_{f}and(u_{j},u_{i})\in E_{f} \big{\}}\). For each user \(u_{i}\in V\), Figure 1 shows an example of a follow relationship in a simple social graph.  
As shown in Figure 1, unidirectional edges \((u_{1},u_{2})\) represent that user \(u_{1}\) is following user \(u_{2}\), and bidirectional edges \((u_{2},u_{3})\) represent that user \(u_{2}\) and user \(u_{3}\) are following each other. Based on Twitter definitions, user \(u_{1}\) is a follower of user \(u_{2}\), user \(u_{2}\) is a friend of user \(u_{1}\), and user \(u_{2}\) and user \(u_{3}\) are mutual friends. There is no direct relationship between user \(u_{1}\) and user \(u_{3}\). In this paper, we denote the set of all neighbor relations of user \(u_{i}\) as \(E_{f}=E_{1}\cup E_{2}\).  
There are several explicit natural patterns in user following relationships that we can use to further enhance our model [19]. Different types of neighbors have different effects on the label of an unknown user. In social networks, according to the homogeneity of the social network, assuming that account \(u_{j}\) and account \(u_{i}\) are bidirectional neighbors, \(u_{i}\) usually has the same label as \(u_{j}\), because legitimate accounts are not linked to anomaly accounts with the bidirectional edge in most cases. In the unidirectional relationship, if the user \(u_{i}\) has many unidirectional incoming neighbors, the user \(u_{i}\) tends to be marked as a legitimate account because the anomaly accounts in OSNs are unlikely to attract a large number of other accounts, especially legitimate accounts. If the user \(u_{i}\) has many unidirectional outgoing neighbors, then \(u_{i}\) is likely to be an anomaly account because the anomaly accounts need to pay attention to a large number of other accounts to realize the dissemination of spam information, while the legitimate account is selectively following other account.
# Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning
###### Abstract  
In manufacturing automation, accurately determining the optimal tool replacement timing is critical yet challenging. Tool condition monitoring (TCM) has been widely studied to address this issue. Cutting force is a key parameter for evaluating tool wear, but conventional force sensors are costly and difficult to implement. This study proposes a cost-effective alternative by estimating cutting forces using spindle vibration data through a long short-term memory (LSTM)-based machine learning model. First, the correlation between cutting force and tool wear is analyzed to emphasize the need for accurate force estimation. Then, vibration data collected from the spindle are used to train an LSTM model, which is effective for time-series data processing. The model is trained with vibration signals from various machining positions, with structured time-series datasets improving performance and generalization. Experimental results show that the developed model accurately estimates cutting forces using short segments of vibration data from a single tool revolution. Additionally, the observed relationship between cutting force and tool wear remains consistent across different machining conditions. This study validates real-time cutting force estimation via spindle vibration monitoring and suggests its potential for tool wear prediction. The proposed method offers a practical, low-cost solution for improving tool condition monitoring in automated machining.  
m +
Footnote †: journal: applied sciences