esa-sceva/satcom-chunk-collection huggingface dataset:
# A Gene Expression Programming Model for Predicting Tunnel Convergence
###### Abstract  
Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson's ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model's output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner.  
tunneling; convergence; new Austrian tunneling method; gene expression programming; predictive model +
Footnote †: journal: Applied Sciences
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
## 4 Experimental Results
### Benchmarking Platform  
The benchmarking platform comprises two systems: a quadrotor and a motion capture system. The quadrotor employed in this work is s Qball 2 from Quanser(r) (Markham, ON, Canada), which is an off-the-shelf platform widely used in related research [24; 25; 29; 30]. Figure 2 depicts a picture of the Qball 2. Figure 3 shows the motion capture system Optitrack(r) (Corvallis, OR, USA), which is employed to measure the cartesian position of the quadrotor and the yaw angle through an array of six Flex 3 cameras. Under this setup, the data are sent to a ground station through Wi-Fi. The benchmarking platform was configured and arranged in the same manner as in previous studies [24; 25]. See [24; 25] for a detailed description.  
Figure 2: Experimental platform: Qball 2 quadrotor from Quanser®.  
The parameters of the QBall 2 with the controller (3)-(5) are [24,25]:  
\[K_{u} =\text{diag}\{15.812\;15.812\;10.6\;2.38\},\quad m=1.79\;\text{[kg]},\] \[K_{\wp} =\text{diag}\{0.02\;0.02\;1.4136\;1.4036\},\quad g=9.81\;\text{[m/s ^{2}]}, \tag{29}\] \[\omega_{\theta} =\omega_{\phi}=13.944,\zeta_{\theta}=\xi_{\phi}=1.593,\quad\tau_{ 2}=\tau_{\phi}=0.728.\]  
All the tested controllers were implemented using MATLAB(r) R2014b and Simulink(r) 8.4 with a sample frequency of 500 [Hz].
## 4 Proposed SARP Routing Protocol for BASNs
### Working Principle of Sensors in SARP
#### 4.4.2 Dynamic Cluster Formation  
At this stage, each biosensor node has updated information on the neighboring nodes as well as the coordinator in BASNs. Consequently, a node which has a high residual energy and lower distance to its neighboring biosensors, as well as to the sink, declares itself a group leader and broadcasts this message (\(reader\_msg\)) to its neighboring nodes located in the region. The key aim of this message is to request neighboring biosensors for joining the sender as member nodes. The \(\ell reader\_msg\) message contains short information, like sender node identity and its priority, which is set to 1 for the cluster head in BASNs. Upon receiving the \(\ell reader\_msg\) message, the neighboring biosensors that already have the sender information update their status to 1 as a leader node in their routing table. Then, the interested biosensor nodes reply to the potential cluster leader via a leader acceptance message (\(reader\_accept\_msg\)) with its identity and location on the body. Then, each head node, upon receiving the \(\ell reader\_accept\_msg\) acceptance message from the neighboring node, updates its information and sets the priority value to 0 for the member node in the routing table. Later, each cluster leader broadcasts a message (\(c\ell water\_member\)) that contains the information of the member node's identity and a certain time slot for each member node to communicate in BASNs. Thus, the entire region is partitioned into a set of predefined, highly stable, dynamic clusters in BASNs. Throughout this process, it is possible that a node may join two cluster leaders; however, it must have different time slots for communication in order to avoid packet loss and interference in BASNs.
## 2 Data Generation and Ingestion Pipeline  
All data sets used in the creation of this linked data platform were initially classified as non-RDF based and RDF based data sources. The first step in our data ingestion pipeline was to convert non-RDF based data sources to RDF based data graphs. Later these RDF data graphs were integrated together and published as a linked data platform. Figure 1 illustrates the data generation and ingestion pipeline used by the pbg-ld platform. Geno- and phenotypic data from three _Solanaceae_ species: (i) reference sequence tomato (_S. Iycopersicum_), (ii) wild tomato (_S. pentellii_) and (iii) the reference sequence potato (_S. tuberosum_) is collected and integrated into this pipeline.  
Figure 1: Data generation and ingestion pipeline. All data originate from either non-RDF or RDF sources. Several tools are used to retrieve and transform non-RDF data into RDF graphs: QTLTableMiner++ (QTM) [28] is used to extract tomato and potato quantitative trait loci (QTLs) from Europe PubMed Central (PMC) articles; OpenRefine [29] is used to transform the QTLs into RDF according to the specified data model. Similarly, SIGA.py tool [30] converts the genome annotations, as provided by the Sol Genomics Network (SGN) in GFF files into RDF graphs with gene models and markers. In addition, the UniProt (proteomes) and Ensembl Plants (gene models) distribute their data in RDF format. All RDF graphs including domain-specific ontologies (in OWL) and database cross-references were stored and integrated with Virtuoso RDF Quad Store. The resulting linked datasets are made available for queries and analyses through data-access layer: (i) Linked data browser, (ii) SPARQL endpoint, (iii) grlc-based Web API [31] and (iv) FAIR Data Point (FDP) metadata service [32].
## 5 Novel Proposed Method Application for Planetary Gearbox Fault Diagnosis
### Sun Gear Fault Experiment  
For this experiment, we used a sun gear wear fault mode to test the proposed method while the remaining gears were all in good condition, undamaged. Similar to the case study in Section 4, in order to ensure a stable signal, the data acquisition system is adjusted to use the data sample rate based on the tachometer signal. The adjusted sampling rate is a synchronous sampling rate and donates the resampled vibration signal. During the experiment, the mean rotating frequency of the  
Figure 19: A schematic of the experimental planetary gearbox test rig.  
Figure 20: Implanted wear fault: (a) ring gear; (b) planet gear; (c) sun gear.  
input shaft connecting the sun gear of the planetary gearbox was 20.0889 Hz, while a load of 405 Nm was applied to the output shaft connecting the planet carrier. An accelerometer was mounted on the top of the gearbox casing (as shown in Figure 19), and the vibration signals were collected from the accelerometer at position S3. According to the planetary gearbox configuration and its running speed, the characteristic frequencies are calculated and listed in Table 3.  
According to the framework of the proposed method, the squared envelope signal after DC offset cancellation is acquired. For this case, the CK value shows that the first envelope has the biggest PIS, as shown in Figure 21. The squared envelope spectrum after the first DC offset cancellation and the envelope spectrum of only the time synchronous signal are illustrated in Figure 22. Compared to the envelope spectrum of the original signal, the amplitudes of the sun gear fault frequency and its harmonics are much higher compared to the envelope spectrum of the original signal. However, it can also be noticed that the noise amplitudes unrelated to the sun gear fault frequency are also higher compared to the envelope spectrum of the original signal.  
The time-frequency contour of the squared envelope signal using the S transform is shown in Figure 23. In contrast, the time-frequency contour of the original en  
Figure 21: CK value variation vs. iteration number.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Meshing Frequency**} & \multicolumn{3}{c}{**Rotating Frequency**} & \multicolumn{3}{c}{**Local Damage**} \\ \cline{2-7}  & **Sun** & **Carrier** & **Planet** & **Sun** & **Ring** \\ \hline
239.8183 & 20.0889 & 1.6425 & 3.7472 & 7.4944 & 55.3427 & 4.9278 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristic frequencies (Hz).  
Figure 22: (**a**) Squared envelope spectrum after the first DC offset cancellation; (**b**) envelope spectrum of the original time synchronous signal.  
transform is shown in Figure 24. From these two figures and their local amplifications, the sun gear fault frequency in the frequency domain and the time-frequency domain can be seen. However, the time-frequency contour of the squared envelope signal has less useful sun gear fault information than the time-frequency contour of the original envelope signal.
# A Review of Smart Grid Anomaly Detection Approaches
## 3 Results and Analysis  
Table 3 lists the papers that were reviewed. The dates range from 2011 to 2023, and they provide solutions for the following: distortions in data in measurements of energy consumption, distortions in measurements of electrical signal values, network intrusions, false events in the network, anomalies that can go unnoticed, deviations in state estimates in the network, distortions in consumer prices, injection of false data, distortions in event timestamps, and device configuration changes.  
The investigations rely on real or artificially created data obtained through the utilization of information and communications technology. This data is derived from network events and time series, which are saved and processed via various methods with the goal of uncovering unforeseen patterns or behaviors in the SG. In this review, seven study objects related to the detection of anomalies in SGs were identified (Table 4).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Study Object** & **Paper** \\ \hline Data integrity attacks & [13; 21; 25; 29; 40; 41; 48; 49; 53; 55; 59; 60; 61; 62; 70; 75; 76] \\ \hline Unusual consumption behaviors and measurements & [6; 24; 27; 32; 34; 35; 38; 46; 52; 67; 68; 71; 72; 73] \\ \hline Network intrusions & [16; 18; 19; 56; 63; 69] \\ \hline Network infrastructure anomalies & [14; 15; 17; 20; 22; 33; 39; 47; 58; 64] \\ \hline Electrical data anomalies & [7; 23; 26; 36; 43; 44; 45; 50; 54; 65; 66; 74] \\ \hline Cyberattack detection & [8, 30; 37; 57] \\ \hline Devices for detecting anomalies & [5; 28; 31; 42; 51] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Grouping of issues studied for the detection of anomalies in SGs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**N.** & **Criteria** \\ \hline
1 & Knowledge of existing related literature is evident \\
2 & Provides answers to research questions \\
3 & The work enables practical applications or explores new design options \\
4 & The solution to a real problem is being addressed \\
5 & The work presents evidence of the validity of the findings \\
6 & The work contrasts alternative solutions \\
7 & The experiments are explained and reproducible \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of quality criteria.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Paper** \\ \hline
2011 & [13; 14; 15] \\
2012 & [16; 17] \\
2013 & [18] \\
2014 & [19] \\
2015 & [20; 21; 22] \\
2016 & [23; 24; 25; 26; 27] \\
2017 & [7; 2
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 4 Discussion
### Challenges
#### 4.1.2 Model-Specific Challenges  
Developing robust AI models for PV system monitoring and fault detection involves several key challenges related to accuracy, adaptability, and computational efficiency. While many models demonstrate strong performance in controlled environments, they often falter when exposed to real-world variability--such as fluctuating weather conditions, sensor noise, and unanticipated fault scenarios. A common limitation is overfitting, where models are overly tailored to training data and fail to generalize across diverse PV installations. Computational demands pose another barrier, particularly for deep learning architectures. These models often require significant processing power, making them unsuitable for deployment on resource-constrained edge devices where low latency and energy efficiency are critical. Balancing model complexity with lightweight, real-time performance is essential for effective fault detection and system responsiveness. Additionally, a lack of transparency in many AI models--especially deep learning approaches--undermines operator trust. Without explainable outputs, it becomes difficult to interpret predictions, verify results, or justify automated decisions. Overcoming these limitations calls for the development of adaptable, energy-efficient AI architectures that maintain high accuracy in diverse operational contexts. Incorporating explainable AI (XAI) techniques will also be key to building user trust and facilitating broader adoption in solar energy applications.
## 3 Analyzing Experimental Data: An Illustrative Example
They obtain \(\overline{Y}_{E}-\overline{Y}_{C}=-1.04+0.18=-0.86\), \(S_{p}^{2}=0.946\), and the \(t\) statistic \(t=\frac{-0.86}{\sqrt{0.946\times 4/50}}=-3.15\), with the two-sided \(p\)-value = 0.0028. (Note that for the ANCOVA approach the \(t\)-statistic was more extreme: \(t=-3.24\); and the two-sided \(p\)-value was slightly smaller: \(p=0.0022\), indicating greater evidence against \(H_{0}\)). A 95% confidence interval for \(\Delta_{\overline{x}_{E}}\) based on the two-sample \(t\)-test is \((-1.42,-0.31)\). Overall, while the two-sample \(t\)-test leads to a similar conclusion as the ANCOVA test, the inference for the former approach is only limited to the contrast \(\mu_{E}+\gamma\overline{x}_{E}-\mu_{C}\), whereas ANCOVA allows investigators to address more research questions.
###### Abstract  
This paper presents a compact spectral detection system for common fluorescent and colorimetric assays. This system includes a gradient grating period guided-mode resonance (GGP-GMR) filter and charge-coupled device. In its current form, the GGP-GMR filter, which has a size of less than 2.5 mm, can achieve a spectral detection range of 500-700 nm. Through the direct measurement of the fluorescence emission, the proposed system was demonstrated to detect both the peak wavelength and its corresponding intensity. One fluorescent assay (albumin) and two colorimetric assays (albumin and creatinine) were performed to demonstrate the practical application of the proposed system for quantifying common liquid assays. The results of our system exhibited suitable agreement with those of a commercial spectrometer in terms of the assay sensitivity and limit of detection (LOD). With the proposed system, the fluorescent albumin, colorimetric albumin, and colorimetric creatinine assays achieved LODs of 40.99 and 398 and 25.49 mg/L, respectively. For a wide selection of biomolecules in point-of-care applications, the spectral detection range achieved by the GGP-GMR filter can be further extended and the simple and compact optical path configuration can be integrated with a lab-on-a-chip system.  
Subwavelength grating; spectral detection; guided-mode resonance; fluorescence detection; colorimetric assays; albumin; creatinine +
Footnote †: journal: Physics Letters B
# Inverse Kinematics of a Serial Manipulator with a Free Joint for Aerial Manipulation
## 4 Simulation Setup  
The algorithm described in the previous sections was implemented in a MATLAB simulator (MATLAB release 2024a), which recursively solves the kinematics and dynamics of free-base robotic systems. The simulator solves the system through a time discretization; a timestep of 0.001 s was used for the solution of the differential inverse kinematics.  
In particular, at each timestep \(k\), generalized forces and inertia matrices are calculated based on the current state of the UAM. The generalized forces acting on the system are: controls \(U_{1}\) and \(U_{2}\), forces and torques due to gravity, forces due to the payload, and Coriolis and centrifugal terms \(c_{b}\) and \(c_{m}\). Base acceleration is calculated using Equation (13). The acceleration of actuated joints are calculated through the inverse kinematics Equation (20), while the acceleration of the non-actuated joints can be retrieved from Equation (19). The state of the UAM at timestep \(k+1\) is computed by numerical integration of \(\ddot{\mathbf{x}}_{b}\) and \(\ddot{\mathbf{q}}\). The diagram describing the simulation procedure is depicted in Figure 2.  
For each manipulator, the inverse kinematic algorithm was tested by considering two scenarios: the tracking of a trajectory with the end-effector, and a load picking task.
## 2 Experiments and Results
### Front-End and Regenerative Amplifier
In this work, a reflecting volume Bragg grating (VBG) is inserted as one of the mirrors to achieve the temporal stretching. The VBG we utilize (OptiGrate, RBG-1064-99, Oviedo, FL, USA) has an aperture of 8.0 mm \(\times\) 5.0 mm and a thickness of 12 mm. The VBG has a bandwidth of 0.1 nm centered at 1064 nm with a reflection efficiency as high as 99.7%. Narrower reflected bandwidth will result in stretching in the time-domain. To obtain a maximum, saturated output with a fixed round-trip time, the input pulse energy needs to be maintained constant. If we want to change the duration of the output pulse, we need to vary the energy of the injected pulse and make the regenerative amplifier at the saturated state with different round-trips. We precisely control the input pulse energy, and after 20 round trips in the regenerative cavity, laser pulses are stretched to 115 ps, which is shown in Figure 2a. Using stretched pulses has two advantages: on the one hand, it will match the width of the signal pulses in the OPA process; on the other hand, stretched pulses lower the peak power of laser pulses and promise a higher energy gain without bringing about damage to optical devices. After 20 round trips, a total 2.2 mJ of pulse energy is achieved via the regenerative amplifier. The beam distribution is shown in Figure 2b. The laser pulses of the regenerative amplifier are then expanded to 6.5 mm and aligned with a pair of plano-convex and plano-concave lenses. In the OPA process, a top-hat distribution of pump pulses is preferred due to its uniform amplification gain. To achieve a top-hat distribution of the laser  
Figure 2: (**a**) Temporal profile and (**b**) beam distribution of amplified pulses from the regenerative amplifier. beam, a serrated aperture with a diameter of 6 mm is inserted behind the regenerative amplifier to select the uniform part of the laser beam, which also reduces the pulse energy from 2.2 to 1.6 mJ. The beam at the serrated aperture is then relay-imaged to the high-reflection mirror 1 (HR1) with a pair of plano-convex lenses. A pinhole is set at the focal point in the relay-imaging setup to achieve spatial filtering.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell eNodeB  
In this section, the HetNet deployment scenario with small cell deployed near macro cell eNodeB inside a macro cell is considered. One interfering ring of small cells is considered around the central small cell. Figure 3 depicts the deployment scenario. The central small cell is prone to interference offered by the small cells in the interfering ring as well as the macro cell in which the small cells are deployed. The macro cell interference is considered from the macro cell containing the small cells as well as the neighboring macro cells.
# LAN Intrusion Detection Using Convolutional Neural Networks
## 1 Introduction
#### Objectives  
The objective of our research was to develop behavioral patterns that deviate from the system's regular behavior, known as an anomaly detection-based attack. Although Intrusion Detection System can be used to identify previously undisclosed harmful activity, an innovative CNN architecture type was designed to learn content properties of anomalies and then locate the individual anomaly. The following questions are addressed in our work:  
* What characteristics best describe various attacks?
* What kind of data is most suited for identifying specific attacks?
* Which deep learning methods are best suited to a certain data type?  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No** & **Attack Category** & **Attack Name** \\ \hline \multirow{3}{*}{1} & \multicolumn{2}{c}{U2R:} & Buffer Overflow, Httpuneel, Rootkit, LoadModule, Perl, Stern, Ps, SQL attack \\  & \multicolumn{2}{c}{} & \\  & \multicolumn{2}{c}{} & \\  & \multicolumn{2}{c}{} & \\
2 & Unauthorized access to a vulnerable & Guess Password, Xsnoop, WareMaster, Phf, Spy, Imap, Sendmail, Multihop, Apache2, Neptune, Pod, Land, Smurf, Mailbomb \\  & \multicolumn{2}{c}{} & \\
3 & \multicolumn{2}{c}{} & \\  & \multicolumn{2}{c}{} & \\
4 & \multicolumn{2}{c}{} & \\ \end{tabular}
\end{table}
Table 1: NSL-KDD dataset attack categories.  
This paper is organized as follows. This article is organized as follows. Chapter 2 provides the Literature review. Section 3 provides information on the proposed system, dataset selection, data preprocessing, and feature reduction. Chapter 4 contains information about the CNN Intrusion Detection Model. Performance evaluation of the proposed system and comparisons in the literature are given in Chapter 5. Finally, Chapter 6 concludes with conclusions and future work.