esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Duplicate Announcements for BGP Anomaly Detection
## 2 Related Work
### Duplicate Announcements
#### 2.2.2 Factors Affecting Duplicate Announcements  
The primary reason for Duplicate announcements is non-purposeful communication between the routers of eBGP and iBGP. Duplicate messages are created because the routers are getting updates through iBGP [35]. These updates are different from the values of iBGP attributes, and hence, the router considers these updates to be rare, as well as the newly created message. Meanwhile, after the router processes the update message, it opens the values of the attributes of iBGP, and the update is dispatched to its corresponding neighbor or peer of eBGP [13]. Both the updates figure out to be similar when it is observed from the eBGP neighborship. The reason behind the occurrence of duplicate messages in eBGP is the modification of some of the attributes of iBGP. For example, if the cluster list attribute changes, then a duplicate message is generated, and the alteration in the originator-id attribute leads to the creation of duplicate update messages in eBGP.  
When a router is unable to access the network prefix that is meant for it, this failure is referred to as flapping. The flapping event leads to the creation of updates where dis-similarity between the BGP update message and duplicate update packet lies in the Next-hop attribute for the analyzed set of network prefixes. When these update packets are dispatched to the eBGP neighborship, most of the received update packets are duplicates [36]. By the utilization of iBGP and eBGP collected data, it is investigated that duplicate messages pre-exist by an occurrence of failure in the route of iBGP neighborship. Similar autonomous systems can also alter the number of duplicate messages that are received. The variation in MRAI time settings between the available networks is one possible cause of its generation. The other cause is when the internal routing policy gets altered, and it leads to the creation of duplicates [37]. When routing policy varies, update packets come in the form of flood, and hence, by application of MRAI timers, the flood of the update packets can be clogged from being dispatched. The timer variation in MRAI is unable to give complete elaboration on the magnitude of inspected duplicate packets when they are received from one autonomous system to another.
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 2 Related Work
### Anomaly Detection  
Anomaly detection (or outlier detection) is an important research field with broad application directions, c.f., [30; 31]. In general, it targets the identification and detection of irregular or exceptional points and/or structures, while there exist different definitions. According to the classical definition of [32], \"an outlier is an observation that differs so much from other observations as to arouse suspicion that it was generated by a different mechanism\". Here, there exists a variety of techniques, e.g., using subspace clustering [33; 34], tensor factorization [35], community detection [36], adapting deep learning (classification/condition monitoring) techniques [37; 38; 39], or graph/signal processing methods [40; 41]. Identifying anomalies in network data is a prominent novel research area. It features many important applications, such as identifying new and/or emerging user behavior, as well as the identification of detrimental or malicious activities, c.f., [42; 43; 44; 45; 46; 47].  
The general graph anomaly detection problem can be defined as follows: \"Given a [...] graph database, find the graph objects [...] that are rare and that differ significantly from the majority of the reference objects in the graph\" [48]. Thus, for complex networks, we can focus on different types of graph objects. We can consider individual nodes, the respective links/edges between nodes, or more complex substructures of nodes and/or links. In our context, this extends to multi-layer networks naturally. Currently, in the literature, there are mainly approaches for handling individual point anomalies corresponding to detecting individual nodes, as discussed in [48]. Additionally, in graph anomaly detection scenarios [49; 50; 51], typically, static plain graphs are considered. However, in real-world networks, the situation is typically more complex than only considering point anomalies and static graphs: for these, it is difficult to capture the multi-relations of the complex heterogeneous networks. Therefore, we extend our focus from point anomalies to groups and more complex structures, i.e., towards multi-layer networks; in particular, this also covers multiplex networks if we consider the same set of nodes in each layer. Regarding group anomaly detection, for example, the authors of [52] defined the general group anomaly detection problem as follows: \"We are interested in finding the groups which exhibit a pattern that does not conform to the majority of other groups\". Then, for example, groups can be detected based on mixture models [53] or graphical models, e.g., [52]. In this paper, we focus on the group anomaly detection problem, applying multi-objective optimization using centrality measures as well as applying a scoring method, which is implemented in a human-centered approach--in contrast to methods relying on purely automatic methods. With this human-in-the-loop approach, we also enable computational sensemaking, e.g., [11; 54].
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 3 System Modeling and Problem Statement
### Problem Statement  
The main purpose of the present paper is to design a robust control for attitude tracking that would be able to ensure the position control of a multirotor UAV, and specifically of a very small quadrotor helicopter. Then, one has the following objectives to accomplish:  
\[\mathbf{q}(t)\rightarrow\mathbf{q}^{d}(t)\in\mathbb{S}^{3},\:\mathbf{\omega} \rightarrow\mathbf{\omega}_{d}(t)\in\mathbb{R}^{3} \tag{9}\]  
Let us consider the error variables \(\mathbf{\check{q}}=\left(\mathbf{q}^{d}\right)^{-1}\odot\mathbf{q}\), \(\mathbf{\omega}_{e}=\mathbf{\omega}-\mathbf{\omega}_{r}\) with \(\mathbf{\omega}_{r}=\mathbf{R}^{T}(\mathbf{\check{q}})\mathbf{\omega}_{d}\). As a result, the dynamics and kinematics errors are calculated using:  
\[\Sigma_{R_{c}}:\begin{cases}&\mathbf{\check{q}}=\frac{1}{2}\Bigg{(}\begin{array} []{c}-\mathbf{\check{q}}_{v}^{T}\\ I_{3}\mathbf{\check{q}}_{0}+[\mathbf{\check{q}}_{v}^{\times}]\end{array}\Bigg{)} \mathbf{\omega}_{e}\\ &\\ \mathbf{J}\dot{\mathbf{\omega}}_{e}=-[(\mathbf{\omega}_{e}+\mathbf{\omega}_{r})^{\times}] \mathbf{J}(\mathbf{\omega}_{e}+\mathbf{\omega}_{r})+[\mathbf{\omega}_{e}^{\times}]\mathbf{ \omega}_{r}-\mathbf{R}^{T}(\mathbf{\check{q}})\dot{\mathbf{\omega}}_{d}+\mathbf{ \Gamma}+\mathbf{\Gamma}_{d}\end{cases} \tag{10}\]  
In this work, an active disturbance rejection control (ADRC) to track a desired attitude despite external disturbances will be designed. For that, an extended state observer (ESO) is applied to estimate online the unknown disturbances and cancel them by injecting the output of the ESO into the feedback loop. Furthermore, the proposed feedback controls have to take into account the physical constraints and limitations of the body's structure and actuation. This is ensured by a saturation of the control torque to avoid unwanted damage and maximize the effectiveness of the system's actuators. This can be formulated as:  
\[\Gamma_{j}\in[-\bar{\Gamma}_{j},\bar{\Gamma}_{j}],\quad j\in\{1,2,3\}\]  
where \(\bar{\Gamma}_{j}\) represents the bounds of the \(j\)th control torque component.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 4 Methods
### Signal Pre-Processing  
The recording of EEG signals according to the protocol described in Section 3.2 was performed. The pronunciation of individual words was alternated by longer pauses of inactivity. Parts with pronunciation are considered to be an active state of the brain when speech is produced, and pauses between words are considered to be the non-speech state. The average pronunciation time for one word was 0.75 s, while the average pause time lasted up to 4.25 s (Figure 2) and the average number of words in one recording was 120. In summary, it is clear that the speech time in one recording was about 90 s and the silence time was about 510 s. As a result, the overall length of the silence segments dominates significantly. Such a disproportion in the data would causes the model to be trained on the dominant target (non-speech) which would result in the creation of an erroneous and unacceptable speech detection model. To overcome this issue the data were balanced by random undersampling of majority labels (non-speech state labels) before further processing. Indexes of samples belonging to the zero labels were selected based on the indexes of the selected zero samples, the samples from both the EEG and the label data were marked and deleted, thus shortening the total length of the signal, but the data were balanced.
## 2 Materials and Methods  
### Microseismic Monitoring Signals  
Microseismic events refer to weak seismic activities caused by minor displacements and stress changes within underground rock formations [18]. In mining environments, factors such as mining activities and rock movements can lead to minor fractures and deformations in rocks, resulting in microseismic events. These events can occur naturally or be triggered by human activities like mining operations and blasting. Monitoring and analyzing microseismic events are crucial for mine safety. Using the example of MMS in a metal mine, the system collects underground microseismic signals. These signals include seismic waveforms from microseismic events, which record the seismic signals generatedwhen underground rock formations undergo slight changes. These signals may encompass different types of events such as rock fractures and blasting. The monitoring system captures these signals using sensors and transmits them to the surface or data centers for further analysis. These signals exhibit unique characteristics in terms of waveform, amplitude, frequency, etc., which can be used to identify various types of microseismic events and provide essential information and warnings for mine safety [19].  
For instance, microseismic monitoring systems play a crucial role in ensuring coal mine safety by capturing MS from coal-rock fractures and blasting activities. However, the challenge lies in distinguishing between these signals due to their waveform similarities. Scholars have extensively studied this issue to enable accurate identification of authentic MS within the monitoring system [20; 21]. Seismic data analysis methods, commonly used for assessing seismic activity in volatile mines, encounter challenges due to localization errors and incomplete data catalogs caused by unfavorable seismic detector layouts [22]. Furthermore, dynamic disasters like stress-type and fracture-type rock bursts significantly impact mine safety. To address this, a study integrates spatiotemporal parameters through a big data platform and employs the AdaBoost algorithm to predict rock burst risks, contributing to accurate and timely warnings [23]. [PERSON] et al. examined the features and classification of MS in coal mine workings, as well as its importance in the early detection of gas and coal outbursts [24]. In addition, [PERSON] et al. employed a data-driven approach based on deep learning to successfully predict coal seam floor water inrush using microseismic monitoring data [25]. These applications highlight the significance of MMS and data analysis in enhancing safety production in coal mining.
## 4 Discussion
For deciduous forest sites, S1 data showed a higher accuracy of phenological estimation than S2 data. Although seasonal variations could not be extracted stably from all study sites based on S1 signal profiles, they still showed better continuity in time series than S2 NDVI results because optical remote sensing satellites can be affected by cloud coverage and cause the loss of data for a continuous period. Generally, although S2 performs better than S1 on coniferous forest phenological estimation, the continuity of S1 data was clearly better than S2 and will have better research potential. In this research, the SOS extracted from satellite data had a satisfying correlation with the rising transition date of Gcc, but the correlation of EOS was relatively poor. We suggest that this may be caused by the difference in seasonal variation drivers between SAR backscattering, NDVI, and Gcc, which may have nonlinear relationships. To be clear, C-band backscatters are affected by canopy structures [17; 18; 19], NDVI is affected by leaf microstructures, while Gcc is influenced by leaf greenness, leaf area index, and pigmentation [49]. Previous work also discovered the inconsistency between Gcc time series and VI time series at the end of the growing season and listed some possible explanations [32; 49; 50]. This could be the main reason for the low accuracy of EOS estimation in this research but cannot completely explain why the EOS correlation was significantly lower than that in other studies. The noise in the S1 time series and the difference in the smoothing algorithm used (this research applied FT and DL, while PhenoCam applied spline interpolation) could also play a minor role. Although the same smoothing algorithm and phenological extraction algorithm were applied, the coherence between the S1 polarization combination and S2 NDVI was not satisfactory for EOS and evergreen needleleaf forests (Table 3), which might also be because of the difference in the driving factors of seasonal variation. However, limited by research conditions, no further investigation or development was carried out on this aspect. Affected by environmental variables such as weather and temperature, this research did not obtain sufficient remote sensing data of coniferous forest, while the phenological measurements in PhenoCam products were also missing. Therefore, the statistical analysis for the accuracy of coniferous forest phenology estimation is not reliable to some degree. In future research, more near-surface remote sensing data can be introduced for quantitative analysis, and in-situ data can be collected to enrich the phenological measurements when necessary. Further smoothing methods such as the Savitzky-Golay filter would be considered in future studies to improve the curve fitting results.
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 4 Results
### Ablation  
To demonstrate the efficiency of our method, we employ a conventional tracking module as a baseline, which uses the public detector DPM, yolov5 seg, appearance features, and motion features for tracking. Yolov5 is only used for target cutting. Baseline + GSP adds the grayscale spatial-temporal features (GSP). The tracking algorithms are performed in the CPU without GPU accelerating. The ablation experiment results are shown in Table 2.  
As can be seen from Table 2, after adding grayscale spatial-temporal features, everything improved. The TFPS increased to 17.6, and the FPS not only did not decrease but increased. This is because, in the TBD paradigm, the number of IDs often increases when the detection time increases, which will result in more time to match the ID. This is the case for the baseline method, which spends a lot of time on ID matching. After adding grayscale spatial-temporal features, most targets only need to match between two IDs, which greatly reduces the matching time. It can be seen that our algorithm reduces the time of tracking, and the tracking accuracy is improved. At the same time, the accuracy is also improved. First, because the range of target matching is reduced, the interference of similar targets is removed. Second, as shown in Figure 6, the grayscale spatial-temporal features are robust for long-term occlusion.  
Grayscale spatial-temporal features can solve the problems of ID switching and tracking target loss caused by occlusion. As shown in Figure 6, our method can effectively handle occlusion. The top row is the visualization result of the tracker on MOT16-09. The bottom row is the visualization result of the tracker on MOT16-02. When matching, we give priority to matching continuously detected targets and, secondly, match re-detected targets, which effectively avoids ID switching between targets. When occlusion occurs, the occluded target is recorded in the region where it belongs. When the occluded target is re-detected, matching IDs are found based on the region where the target belongs. This reduces the interference of other similar targets.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 3 Background
### Comparison of LoRa and Other LPWAN Technologies
#### 3.3.3 LoRa versus Sigfox  
Of the wireless communication technologies that have been classified for IoT applications, the two that are commonly deployed in the ISM band (LoRaWAN and Sigfox) are long range. Sigfox is a proprietary ultra-narrowband technology, developed by an eponymous company based in Labgee, France, which uses differential binary phase-shift keying (DBPSK) modulation. In contrast, modulation with LoRa is based on proprietary spread spectrum techniques and Gaussian frequency shift keying [21]. Communication in Sigfox is performed in the 868-MHz frequency band, with the spectrum divided into 400 channels of 100 Hz. Sigfox claims that up to a million end-devices can be handled by only one access point, and the coverage can reach 50 km in rural areas and 10 km in urban areas [12].  
With the Sigfox network, data packages are transmitted three times at random frequencies, to maximize the probability of successfully receiving at least one packet, as it is more sensitive to interference than LoRa networks. In [21], coverage and capacity for Sigfox and LoRaWAN on area covering 150 km\({}^{2}\) of urban areas Northern Denmark tested, and found that uplink indoor coverage (20 dB penetration loss), the impact from interference is even worse, as the link budget is reduced, where LoRaWAN has 78% coverage and Sigfox less than 50% coverage.
# NetAP: Adaptive Polling Technique for Network Packet Processing in Virtualized Environments
## 3 Design
### Adaptation Using Golden-Section Search  
We use the golden-section search algorithm to find the near-optimal polling interval for various workloads dynamically. The golden-section search technique searches the maximum or minimum value within a given range in a unimodal function [25]. The method to find the maximum value for the target function \(f(x)\) is as follows: First, we set two x-coordinates \(x_{l}\) and \(x_{u}\) to search. We assume that the maximum value is between \(x_{l}\) and \(x_{u}\). Second, we choose \(x_{1}\), where \(x_{l}<x_{1}<x_{u}\) and the \((x_{u}-x_{1})/(x_{u}-x_{l})\) is \(R\), a reciprocal of the golden ratio (\(R\approx 0.618\)). These three x-coordinates are golden-section triplets. Third, we set the new x-coordinate, \(x_{2}\), where \(x_{l}<x_{2}<x_{u}\) and the \((x_{2}-x_{l})/(x_{u}-x_{l})\) is \(R\). Subsequently, we evaluate the \(f(x_{l}),f(x_{1}),f(x_{2})\), and \(f(x_{u})\). If \(f(x_{2})<f(x_{1})\), then \(x_{l}\), \(x_{1}\), \(x_{2}\) are chosen as new triplets, else if \(f(x_{2})>f(x_{1})\) then \(x_{1}\), \(x_{2}\), \(x_{u}\) are chosen. Subsequently, we iterate this process until the outer x-coordinates are closer than \(\epsilon\), which is the predetermined minimal distance to terminate the algorithm.  
The main advantage of the golden-section search algorithm is the efficiency. The evaluation of \(f(x)\) requires a considerable time for the real-world workloads. The golden-section algorithm only requires one additional evaluation for each iteration. Moreover, the complexity of the algorithm is logarithmic. The big-O notation of the algorithm is shown in Equation (1), where \(N\) is the number of iterations required to find the solution [26; 27].  
\[N=O(log\frac{1}{\epsilon}) \tag{1}\]
# Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images
## 4 Experimental Results
### Evaluation Metrics
#### 4.2.4 Mean Average Precision (mAP)  
The mean average precision, also referred to as mAP score, is calculated by averaging maximum precision over various recall thresholds. Mathematically, it is explained in [52] as follows:  
\[\text{mAP}=\frac{1}{N}\sum_{r=1}^{N}AP_{r} \tag{10}\]  
where \(AP_{r}\) is the average precision on a recall level \(r\).
## 5 Finite Element Modeling
### Comparison of Local-Level Deformations  
To further validate the numerical models, measurements from string potentiometers (string pots for short) are compared with predictions obtained from the calibrated models in this section. The string pots were deployed to measure the deflection of the cage at the local level. A comparison of the strong pot measurements with the numerical predictions can provide a better understanding of the accuracy of the model in simulating the local-level deflection of the rebar cage. The string pot measurements from the experiment and the corresponding FE model predictions obtained from test scenario 3 for all specimens are presented in Table 5. The string pot labels are provided in Figure 7. Additionally, a graphical representation of the string pot measurements (after loading) is depicted in Figure 19a (related to C3-S3). It is assumed that the start and end points of the diagonal string pots are collocated with the intersection of the horizontal and vertical string pots, forming a closed quadrilateral. The angles between string pots (after deformation) are then calculated using the law of cosines in a triangle. In triangle ABC, as depicted in Figure 19b, it can be seen that  
\[a^{2}=b^{2}+c^{2}-2 bc\cos\bigl{(}\hat{A}\bigr{)} \tag{3}\]  
where angle \(\hat{A}\) is opposite to side \(a\). The shear deformation angle of the segment can be defined as \(\gamma=\frac{\pi}{2}-\hat{A}\); therefore, Equation (3) can be written as follows:  
\[\gamma=\sin^{-1}\left(\frac{b^{2}+c^{2}-a^{2}}{2 bc}\right) \tag{4}\]  
Similarly, the shear deformation angle, \(\gamma\), can be calculated using the law of cosines in triangles \(ABD\), \(ACD\), and \(BCD\). Table 5 lists both the experimentally measured (\(\gamma_{exp}\)) and model-predicted (\(\gamma_{m}\)) shear deformation angles, which are calculated by averaging the resulting shear angles from the four triangles. The results show that \(\gamma_{exp}\) and \(\gamma_{m}\) are in reasonable agreement for all test specimens, indicating that the model has acceptable accuracy in simulating both the structural and local-level deflection of the rebar cage. This model can now be utilized for parametric studies to further investigate the effect of U-bolt connectors on the behavior of the rebar cage. It should be noted that, in Case #5, the diagonal string pots interfered with the horizontal elements of the inner rings during the  
Figure 18: Comparison between the experimentally measured and numerically simulated displacement profiles of Case #5: (**a**) scenario 1, (**b**) scenario 2, (**c**) scenario 3, and (**d**) scenario 4. (Note: The deflection of some points in scenarios 3 and 4 of the experimental tests could not be captured with DIC). tests because of the large deformation in the rebar cage. Therefore, the measurement data are unreliable and excluded from Table 5.