esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Data Collection Utilizing Mobile Sink and Unmanned Aerial Vehicle (UAV)  
A different paradigm for the traditional setup of data gathering in WSN, which relies on a single or multiple static sink(s) towards which all traffic needs to travel, is the utilization of one or multiple mobile sinks (MSs) that traverse the terrain and collect the reports from the devices. Such mobile sink(s) can be the traffic's final destination or just an accessory that collects the data and transmits it to the sink. Note that in the latter case, the final destination is not necessarily located within the wireless network realm and can be located outside it (e.g., within the Internet or the cloud). Since these mobile sinks are unmanned aerial vehicles in many cases, these systems are sometimes called unmanned aerial vehicle-wireless sensor networks (UAV-WSNs). This section reviews several state-of-the-art developments in mobile sink(s). Since sometimes the MS routing challenges are interleaved with the MAC layer's challenges, the solution suggested in some papers, and accordingly their description, incorporated both layers.  
Typically, data-gathering protocols that utilize moving sink(s) aim at optimizing some performance metrics, such as overall power consumption, average or worst latency of the data, trajectory traversed by the mobile sink, awake time of the sensors, maximizing the life cycle of the network, and so on. There are different options to classify these protocols: they can be classified according to this aforementioned performance objective, or other categories, such as characteristics of the moving sink, its speed and constraints, network model, communication standard utilized, and so on. In this section, we will classify the protocol into two classes: protocols that solely rely on the moving sink with no intra-trafficbetween the nodes (all the traffic is directly transmitted to the moving sink), and protocols that combine routing between the nodes and traffic forwarding to the sink. This latter class will mostly include cluster-based protocols in which the nodes are clustered and cluster-heads (CHs) are selected, but rather than the CHs routing the traffic towards the sink, they forward the traffic directly to the mobile sink.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
## 4 Experimental Research
### Comparison Study Based on a Multi-Subject MI-EEG Database  
In order to verify the self-adaptive characteristic of the WPD + SE-isomap method, a comparison study is completed based on the BCI Competition 2008 Datasets 2b [29]. The database is composed of two classes, namely the motor imagery of left hand and right hand, and it consisted of the MI-EEG data from nine subjects. For each subject, five sessions are provided, whereby the first two sessions contain training data without feedback, and the last three sessions were recorded with feedback. Each session consisted of six runs with 10 trials per class and two classes of imagery. This resulted in 20 trials per run and 120 trials per person. In the following, a comparative experiment is conducted on the same  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Training Time (s)** & **Test Time (s)** & **Classification Rate (\%)** \\ \hline PCA & 152.2 & 1.7 & 68.1 \(\pm\) 6.3 \\ MDS & 35.3 & 1.5 & 73.6 \(\pm\) 7.4 \\ LLE & 207.2 & 2.7 & 86.3 \(\pm\) 4.2 \\ WPD & 99.5 & 2.3 & 82.7 \(\pm\) 8.8 \\ SE-isomap & 325.7 & 0.61 & 88.5 \(\pm\) 6.5 \\ WPD + PCA & 258.7 & 7.9 & 73.1 \(\pm\) 10.3 \\ WPD + ICA & 195.1 & 6.4 & 87.3 \(\pm\) 6.2 \\ WPD + MDS & 140.5 & 4.3 & 77.4 \(\pm\) 12.8 \\ WPD + LLE & 312.6 & 5.3 & 91.8 \(\pm\) 3.5 \\ WPD + SE-isomap & 515.3 & **0.68** & **92.7 \(\pm\) 3.9** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the computational cost.  
training set and test set as [22]. The classification accuracies of two feature extraction methods for each subject are shown in Table 5.  
Table 5 indicates that the average recognition accuracy over nine subjects of WPD + SE-isomap has been improved slightly compared with WPD + DEFS [22]. Especially, the experiment results of the proposed method are better than WPD + DEFS for Subjects B01, B06 and B09, and they are as good as that of WPD + DEFS for Subjects B02, B03, B04 and B07. It is regretful that WPD + SE-isomap is not better than WPD + DEFS for Subjects B05 and B08. This is mainly because the proposed feature extraction needs sufficient training samples, and Sessions 1, 2 and 3 are provided as the training set only for Subjects B04, B06 and B09. It can be predicted that WPD + SE-isomap will show a clear advantage when the training set is adequate.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## 3 Scatter Phy
Rx control messages are employed to configure Rx gain, the Rx channel, the maximum number of turbo decoder iterations, enable or disable Rx combining, and Rx PHY BW of a specific PHY, addressed through the PHY ID parameter. The PHY ID parameter is used to specify for each one of the two PHYs a control message. Tx statistics messages inform the MAC layer about transmission statistics like data coding-time and the total number of transmitted subframes of each independent PHY. Rx statistics messages transport the received data, PHY ID, and reception statistics regarding the received data such as Received Signal Strength Indication (RSSI), Channel Quality Indicator (CQI), decoded MCS, decoding time, subframe error counter, number of turbo decoder iterations, etc. The real-time configurable parameters and statistics offered by SCATTER PHY are summarized in Table 3. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Message** & \multicolumn{1}{c}{**Parameter**} & **Type** & **Unit** & **Range** \\ \hline \multirow{8}{*}{**Tx control**} & PHY ID & uint32 & - & 0–1 \\  & MCS & uint8 & - & 0–28 \\  & Tx gain & uint32 & dB & depends on HW 1 \\  & Tx channel & uint32 & - & \(\geq 0\) \\  & Tx PHY BW & uint8 & MHz & 0–3 2 \\  & Transmission timestamp & uint64 & s & \(\geq 0\) \\  & User Data length & uint32 & - & \(>0\) \\  & User data & uchar[] & - & uchar range \\ \hline \multirow{8}{*}{**Rx control**} & PHY ID & uint32 & - & 0–1 \\  & Rx channel & uint32 & - & \(\geq 0\) \\  & Rx gain & uint32 & dB & depends on HW 1 \\  & Rx PHY BW & uint8 & MHz & 0–3 2 \\  & Max. \# of turbo decoder iterations & uint32 & - & uint32 range \\  & Enable Rx combining & Boolean & - & True/false \\ \hline \multirow{8}{*}{**Rx statistics**} & PHY ID & uint32 & - & 0–1 \\  & CSI & uint8 & - & 0–15 \\  & RSSI & float & dBW & float range \\  & Noise & float & dBW & float range \\  & Decoded MCS & uint8 & - & 0–28 \\  & Subframe error counter & uint32 & - & \(\geq 0\) \\  & Decoding time & uint32 & ms & \(\geq 0\) \\  & Data length & uint32 & - & \(\geq 0\) \\  & \# of turbo decoder iterations & uint32 & - & uint32 range \\  & Received data & uchar[] & - & uchar range \\ \hline \multirow{8}{*}{**Tx statistics**} & PHY ID & uint32 & - & 0–1 \\  & Coding time & uint32 & ms & \(\geq 0\) \\ \cline{1-1}  & Number of transmitted subframes & uint32 & - & \(\geq 0\) \\ \hline \hline \end{tabular}  
* This parameter depends on the installed USRP daughter board model [16]. 2 The numbers correspond to the following bandwidths: 1.26, 2.7, 4.5, and 9 MHz, respectively.  
\end{table} 
Table 3: List of real-time configurable parameters and statistics provided by SCATTER PHY.
###### Abstract  
The reconstruction for limited-view scanning, though often the case in practice, has remained a difficult issue for photoacoustic imaging (PAI). The incompleteness of sampling data will cause serious artifacts and fuzziness in those missing views and it will heavily affect the quality of the image. To solve the problem of limited-view PAI, a compensation method based on the Gerchberg-Papoulis (GP) extrapolation is applied into PAI. Based on the known data, missing detectors elements are estimated and the image in the missing views is then compensated using the Fast Fourier Transform (FFT). To accelerate the convergence speed of the algorithm, the total variation (TV)-based iterative algorithm is incorporated into the GP extrapolation-based FFT-utilized compensation method (TV-GPEF). The effective variable splitting and Barzilai-Borwein based method is adopted to solve the optimization problem. Simulations and in vitro experiments for both limited-angle circular scanning and straight-line scanning are conducted to validate the proposed algorithm. Results show that the proposed algorithm can greatly suppress the artifacts caused by the missing views and enhance the edges and the details of the image. It can be indicated that the proposed TV-GPEF algorithm is efficient for limited-view PAI.  
photoacoustic imaging; limited-view reconstruction; Gerchberg-Papoulis extrapolation; total variation +
Footnote †: journal: Applied  
Article
# Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
## 3 Proposed Computation Method for Structured Gramians  
LMIs' versatility and computational efficiency for handling a wide range of system design challenges have made them an effective computational design tool in systems and control engineering [32; 42; 43; 44; 45].  
In general, an LMI has the form:  
\[F(x)\triangleq F_{0}+\sum_{i=1}^{m}x_{i}F_{i}>0, \tag{7}\]  
where \(x=(x_{1},\ldots,x_{m})\) is a vector of \(m\) real numbers called the decision variables, i.e., \(x_{i}\in\mathbb{R}^{m}\), for \(i=0,\ldots,m\) and \(F_{i}\in\mathbb{R}^{n\times n}\) are given symmetric matrices. The problem is to determine if there is a vector \(x\) that exists and satisfies the matrix inequality.  
In [46; 47], it has been shown that for certain special cases, the rank minimization problem can be reduced to a semi-definite problem. Under these hypotheses, it is possible to say that the solution can be obtained by solving the associated LMI. One effective heuristic, applicable when the matrix variable is symmetric and positive semi-definite, is to minimize the trace instead of the rank [32; 42]. This results in a semi-definite problem (SDP), which can be efficiently solved. This heuristic obviously does not apply to problems in which the matrix is non-square, since the trace is not even defined [32; 42; 48]. In the following, we use an LMI-based algorithm to compute the Gramian matrices \(P^{s}\) and \(Q^{s}\) of a 2-D discrete system represented by [PERSON]'s model.  
Given a 2-D system in state-space form represented by [PERSON]'s model \(\Sigma=(A,b,c,d)\), then the block-diagonal positive-definite solutions \(P^{s}\) and \(Q^{s}\) of the Lyapunov inequalities given by (5) and (6) are the structured controllability and structured observability Gramians. The existence of such \(P^{s}\) and \(Q^{s}\) ensures the stability of the system \(\Sigma\), but the converse does not hold, as shown in [6,25]. Finding \(P^{s}\) and \(Q^{s}\) satisfying (5) and (6) can be solved by formulating an optimization problem using the traces of \(P^{s}\) and \(Q^{s}\).  
Given a 2-D system realization \(\Sigma=(A,b,c,d)\), one can find \(P^{s}\) by minimizing  
\[min\ tr(P^{s}) \tag{8}\]  
subject to  
\[AP^{s}A^{T}-P^{s}+bb^{T}<0\] \[P^{s}>0 \tag{9}\] \[P^{s}=P^{s^{T}}\] (10) \[P^{s}=diag(p_{1},p_{2})\]  
and  
\[min\ tr(Q^{s}) \tag{11}\]  
subject to  
\[A^{T}Q^{s}A-Q^{s}+c^{T}c<0\] \[Q^{s}>0 \tag{12}\] \[Q^{s}=Q^{s^{T}}\] (13) \[Q^{s}=diag(q_{1},q_{2})\]  
This problem can be solved as a linear objective minimization problem under LMI constraints. The controllability and observability Gramian matrices \(P^{s}\) and \(Q^{s}\) obtained by using this method are block-diagonal positive-definite matrices.  
We refer the reader to Appendix A, where several numerical examples, including separable and non-separable-denominator 2-D systems, are presented. These examples illustrate the use of the LMI to solve 2-D Lyapunov inequalities.
## 5 Discussion
### Toward Full Automation  
While the MAMRS achieves partial automation, certain aspects of its operation, such as robot initialization and error validation, still require human supervision. To address this limitation, a roadmap toward full automation is proposed:  
**(1) Self-Calibration:**  
Developing self-calibrating mechanisms to automate the robot's setup and alignment in inspection areas is suggested.  
**(2) Real-Time Adaptation:**  
Implementing reinforcement learning-based algorithms to enable the system to detect and correct anomalies during operation without human intervention should be considered.  
**(3) Advanced Navigation:**  
Enhancing the robot's navigation capabilities using improved SLAM algorithms and obstacle detection technologies to handle complex, dynamic environments autonomously is worth exploring.  
Future experiments will quantify progress in reducing human involvement through metrics such as inspection time savings, error correction rates, and system recovery capabilities. These improvements aim to achieve fully autonomous, unmanned inspections.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 4 State of the Art Detection and Mitigation Strategies
### Machine Learning and AI-Based Techniques  
ML/AI-based IFA defenses leverage pattern recognition and learning algorithms to detect attacks more adaptively. Instead of fixed thresholds, these methods train on traffic features to distinguish normal vs. attack behavior. Standard features include interest sending rate, PIT usage patterns, temporal correlation of requests, or entropy of interest names [37]. Supervised learning has been applied: a binary classifier at NDN routers in a vehicular scenario to detect attacker vehicles was deployed [41]. The study evaluated multiple classifiers (logistic regression, decision tree, K Nearest Neighbor (kNN), random forest, etc.) at roadside units and found that machine learning (ML) could reliably separate legitimate traffic from flooding attacks with high accuracy. Other works have explored deep learning or ensemble methods. Unsupervised anomaly detection has also been studied, e.g., using clustering or outlier detection on traffic statistics to flag abnormal surges of unsatisfied interests. A reconstruction error-based approach (using techniques such as auto-encoders or isolation forests) can identify unusual traffic of interest that does not match the normal patterns learned. Researchers have even proposed graph-based learning: treating the NDN forwarding nodes and traffic flows as a graph and using Graph Neural Networks to spot suspicious patterns in interest propagation [42].  
ML-based detectors can recognize complex or subtle attack patterns that evade simple rules. ML classifiers can be trained to detect advanced IFAs (e.g., low rate or periodic pulses) by learning the differences from normal usage. Once trained, a lightweight classifier can potentially make decisions quickly (e.g., a decision tree or neural network forward pass per packet). Such systems also offer the ability to continuously improve, where retraining on new data allow for adaptation to evolving attacker strategies. In evaluations, AI-based approaches (or deep learning) often report high detection rates (e.g., >95% true positive) with manageable false positive rates under various attack intensities. TAI/ML detectors can also combine multiple features (rates, entropy, PIT occupancy, etc.) to improve accuracy beyond any single metric [43].
## 5 Applications of Thermal Images to Future Space Missions
### Guidance and Navigation Control of Spacecraft  
In the Hayabusa2 mission, the spacecraft is basically kept on the day side of Ryugu in the field of view of the wide angle camera (except for the escape from the ejecta excavated by the SCI impact), because the optical images should be used for its navigation control all the time. As an application to future missions, such as European Space Agency's Hera mission to asteroid Didymos [32], the spacecraft will be operated at the proximity of the asteroid, especially at the large solar phase angles (or SPE angles), where detailed navigation control is required even with the night-side area to precisely determine the center of the asteroid. Thermal image will be useful for the navigation and guidance control to include the night-side area. Precise positioning is needed to know the asteroid whole shape, which is easily detectable by TIR using the function of subtraction of dark frame images and binarization.
# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
## 4 Evaluation of FCRP
### Simulation in the ONE Simulator
Latency results are shown in Figure 7d. When the number of nodes is low, the FCRP demonstrates good performance with an average latency of 2290 s, whereas MaxProp has an average latency of over 4668 s, and Epidemic's latency is over 1622 s. However, as the number of nodes increases to 100, the FCRP's latency increases to 3408 s, which is higher than both Epidemic with 998 s and MaxProp with 2485 s. The reason for the increase lies in the design of the protocols. Epidemic, for example, floods the messages in the network by sending a copy of each message to every encounter. This increases the chances that the  
Figure 7: Performance evaluation of the FCRP in the ONE simulator and comparison with Epidemic and MaxProp. (**a**) Delivery probability. (**b**) Overhead ratio. (**c**) Average hop count. (**d**) Average latency. message arrives to the destination as soon as possible. The more nodes, the more chances the messages arrive quickly to the destination, thus the low latency. On the other hand, sending a copy of the message to every encounter drastically increases the overhead, which in turn reduces the chances to deliver newer messages as the buffers are filled and new packets are dropped. Instead of sending a message to every encounter, the FCRP sends copies only to the nodes that do have the potential to successfully deliver them to the destination. This reduces overhead significantly, but it comes at the cost of increased latency. The results show that our proposed approach is suitable for delay tolerant applications but needs improvements for delay sensitive ones. We have highlighted this limitation in the paper and will further improve our work by developing a more balanced approach that will not compromise the latency as much while still lowering overhead.
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 2 Materials and Methods  
### DOP Indicator in a Snapshot Least Squares Solution  
In this section, the considered positioning algorithm is the SPP algorithm, consisting in the estimation of the 3D position and the receiver clock bias from GNSS code observations at a single epoch \(k\). \(j\) is used to index the scalar states within the state vector: \(\mathbf{x}_{k}=[x_{j,k}]_{j\in[1,4]}\)  
The linearized observation model around an approximate position is:  
\[\mathbf{y}_{k}=\mathbf{H}_{k}\mathbf{x}_{k}+\mathbf{n}_{k},\quad\mathbf{n}_{ k}\sim\mathcal{N}(\mathbf{0},\mathbf{R}_{k}) \tag{1}\]  
where \(\mathbf{y}_{k}\) is observation vector comprising all GNSS code observations at epoch \(k\), \(\mathbf{x}_{k}\) is the unknown state vector, \(\mathbf{H}_{k}\) is the Jacobian matrix of the code observation model and \(\mathbf{n}_{k}\) is the observation error vector, assumed normally-distributed, centered with a covariance \(\mathbf{R}_{k}\).  
The WLS estimate of \(\mathbf{x}_{k}\)[7], referred to as the _snapshot solution_ is given by Equation (2) and its covariance matrix is given by Equation (3):  
\[\hat{\mathbf{x}}_{k}^{(snap)} = (\mathbf{H}_{k}{}^{T}\mathbf{R}_{k}{}^{-1}\mathbf{H}_{k})^{-1} \mathbf{H}_{k}{}^{T}\mathbf{R}_{k}{}^{-1}\mathbf{y}_{k} \tag{2}\] \[\mathbf{p}_{k}^{(snap)} = (\mathbf{H}_{k}{}^{T}\mathbf{R}_{k}{}^{-1}\mathbf{H}_{k})^{-1} \tag{3}\]  
Assuming that the observation error is independent and identically-distributed accross all available observations, i.e., \(\mathbf{R}_{k}=\sigma_{obs}^{2}\mathbf{I}\), we obtain:  
\[\mathbf{P}_{k}^{(snap)}=\sigma_{obs}^{2}(\mathbf{H}_{k}{}^{T}\mathbf{H}_{k}) ^{-1} \tag{4}\]  
where \(\sigma_{obs}\) is the standard deviation of the GNSS code observations.  
This expression separates the contribution of the measurement noise from the geometry of the constellation. We note \(\mathbf{D}_{k}=(\mathbf{H}_{k}{}^{T}\mathbf{H}_{k})^{-1}\) the DOP matrix. The different DOP indicators are computed from the diagonal elements of \(\mathbf{D}_{k}\)[1]. For example, assuming that the 3D position is placed on the first 3 elements of the state vector \(\mathbf{x}_{k}\):  
* Global DOP: \(GDOP=\text{Trace}(\mathbf{D}_{k})\)Position DOP: \(PDOP=(\mathbf{D}_{k})_{1,1}+(\mathbf{D}_{k})_{2,2}+(\mathbf{D}_{k})_{3,3}\)  
where \((\mathbf{A})_{i,j}\) refers to the element at row \(i\) and column \(j\) of a matrix \(\mathbf{A}\).
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 2 Theoretically Background
### Principal Component Analysis (PCA)  
Dimensionality Reduction is a technique to take a high-dimensional dataset (data objects with many features/attributes), replacing it with a much smaller dimensional dataset, preserving the similarities between the data objects [31].  
Principal Components (PC) were first found by [PERSON] [32] and gained consideration by the statistical community when [PERSON] [33] proposed them as an estimate of linear combinations of a set of random variables that retained the greatest possible variance.  
[PERSON] et al. [27], [PERSON] et al. [34], and [PERSON] et al. [35] argue that it is an unsupervised linear transformation algorithm and that it uses a statistical procedure through an orthogonal transformation to map a set of observations of possibly correlated variables into a set of linear values of uncorrelated variables called Principal Components.  
In short (Table 1), PCA is a method that seeks to find linear combinations of predictors, known as principal components (PCs), that capture the greatest possible variance [36]. The first PC is defined as the linear combination of predictors that captures the greatest variability of all possible linear combinations. Subsequent PCs are derived, so these linear combinations capture the greatest remaining variability, although they are not correlated with all previous PCs.
## 2 Overview of Measurement Setup and Used Data-Extraction Method
### Case 3: Indoor Office Environment at 28 GHz  
In [6], for studying spatial characteristics of 28-GHz mmWave, a channel measurement campaign is conducted in ETRI Daejeon, Korea by using direction scan-sounding in an indoor office environment consists of aligned cubicles offices separated by cardboards and a wide entrance hall and corridors. The Tx was positioned inside the entrance hall and measurements were taken by fixing a transmit horn antenna pointing in the corridor. Three scenarios, a LoS and two NLoS, were considered by changing the location of receiver. The detailed map and measurement setup details are provided in [6]. The measured data is provided as contour-maps for joint azimuth AoA and time-of-arrival (ToA).