esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this work, the digital assessment of loose, damaged and intact fasteners in riveted lab-joints of commercial aircraft fuselage panels are investigated with three NDT methods. Besides well-established methods being SLDV and ULT, a novel inspection technique based on in-plane propagation of heatwaves, named In-plane Heatwave Thermography is explored and compared with the aforementioned techniques.  
The measurements on a serviced aircraft panel serve as a feasibility study under conditions being close to the ones in an industrial environment. In comparison to the thermographic inspection methods, the SLDV measurement provides the clearest results that enable the distinction between loose, damaged and intact rivets. However, this method is also the slowest and most expensive inspection method. In the presented experimental configuration, ULT only detects sheared but tight rivets which appear in the phase as well as amplitude image of the post-processing. The advantage of this method is the full-field character with an equally distributed SNR. In contrast to this, the novel IPHWT has heterogeneous SNR being high close to the heat source and decreasing with increasing distance from the heat source. Nevertheless, the full-field character enables the detection of all kinds of fasteners in a large area and shows a clear signature distinguishing loose from intact and damaged rivets.  
Conceptualization, M.S. and P.K.; Methodology, M.S. and P.K.; Software, M.S. and P.K.; Formal Analysis, M.S. and P.K.; Investigation, M.S. and P.K.; Resources, B.K. and M.W.; Writing--Original Draft Preparation, M.S.; Writing--Review & Editing, B.K., H.P. and M.W.; Visualization, M.S.; Supervision, J.R. and M.W.; Project Administration, H.P., J.R. and M.W.; Funding Acquisition, B.K., H.P., J.R. and M.W. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the \"NDTonAIR\" project (Training Network in Non-Destructive Testing and Structural Health Monitoring of Aircraft structures) under the action: H2020-MSCA-ITN-2016-GRANT 722134. The work leading to this publication has been funded by the ICON project \"DETECT-ION\", which fits in the MacroModelMat (M3) research program, coordinated by Siemens (Siemens Digital Industries Software, Belgium) and funded by SIM (Strategic Initiative Materials in Flanders) and VLAIO (Flemish government agency Flanders Innovation & Entrepreneurship).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The presented work was conducted in close cooperation with Optomet GmbH, Germany who provided measurement equipment as well as technical support. The Brussels Airlines M&E department made this work possible by giving advises and providing the test specimen.  
The authors declare no conflict of interest. The sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
###### Abstract  
Global Positioning Systems (GPS) are successfully used in many fields such as navigation, meteorology, military tasks, mapping, virtual fencing, and more. Smart collars are currently the most convenient device for determining animal location in virtual fencing systems, however; these systems are still suffering from environmental effects and propagation in direct visibility. These types of side effects may reduce the work of GPS receivers. The current article defines a method for improving animal location accuracy using a virtual fence smart collar worn around the animal's neck by the aid of maximum probability of movement from one point to another. The proposed approach first checks the current position of the animal, and after receiving a GPS signal from satellites it calculates the distance between the two GPS signals. Secondly, the method checks the animal's behavior for the receiving period of the two points. Finally, the approach calculates a probability of maximum animal movement for the two-point receiving period. If the animal can pass the distance in the time frame of the two signals, then the second signal is taken as the correct position; otherwise, the point is taken which the animal could pass. Real-time animal behavior is classified using Support Vector Machines (SVM). The proposed method was verified within seven days of experiments. Consequently, the proposed approach experiments were sufficiently successful. The recreated locations from our approach appeared very close to the real point. The mean average of passed distance by the marked line decreased to 16.2, 5, 0 m for running, walking, and resting conditions, respectively. On the other hand, the unfiltered geolocations of the GPS receiver, give results significantly further from the animal's actual position such as 148.8, 182.7, 136.2 m for running, walking, and resting conditions.  
GPS error; filter; correction; virtual fence; livestock; smart collar; IoT; machine learning; SVM +
Footnote †: journal: Applied  
_Article_
# ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
## 4 Implementation  
### Datasets  
**Public Datasets.** Logpai [54] is a log parser benchmark that uses 16 real-world log datasets from distributed systems, supercomputers, operating systems, mobile systems, and server applications, as well as standalone software such as HDFS, Hadoop, Spark, Zookeeper, BGL, HPC, Thunderbird, Windows, Linux, Android, HealthApp, Apache, Proxifier, OpenSSH, OpenStack, and Mac. LogHub [55] provides the above log datasets. Each dataset comprises 2000 log samples, each of which has been tagged by a rule-based log parser. In addition to sampling datasets, we chose datasets from two exemplary systems to assess the proposed ADAL-NN technique. Table 2 displays the specifics.  
The HDFS log collection was obtained from a cluster of 203 nodes on the Amazon EC2 platform [3], and it contains 11,176,738 raw log messages. The aberrant HDFS actions were categorized manually by researching HDFS code and by talking with Hadoop professionals, including sequential-order anomalies such as \"Replica quickly removed\" and certain exception logs such as \"Receive block error\". More information may be found in the original publication [3].  
Lawrence Livermore National Labs (LLNL) gathered the BGL dataset, which is a supercomputing system log dataset [56]. BGL's abnormalities are manually identified by its system administrators. The log messages in these anomalies most likely contain exception descriptions. \"ciod: Error generating node map from file [...]\" is an example of a log message. More information may be found in the original publication [56].  
We also gathered real Papertrail log data, which we used for anomaly detection and localization following log parsing. We utilized Papertrail's search and filtering capabilities to identify the instances or components that may be causing the anomaly. We used the information obtained from the process of anomaly detection and localization to identify and fix the problem's root cause.
## 3 Results and Discussion
### Validation of Numerical Model  
To ensure the reliability of the numerical simulation, the CFD model was validated using the air velocity and temperature measured by the sampling lines in the environmental chamber. Detailed results are shown in Figure 7. Comparing the temperature and velocity predicted by three turbulence models with the experimental results along Sampling Lines 1 and 4 with supply air conditions of 1.62 m/s and 22.8 \({}^{\circ}\)C and an air inlet spacing of 650 mm, the simulation results were all close to the experimental results. With an increase in the height of the measurement point from the floor, the temperature of the measurement points along Line 1 and Line 4 decreased slowly, reached a minimum at a height of 1.1 m, and then increased rapidly. The temperature of both sampling lines at a height of 1.7 m exceeded 27 \({}^{\circ}\)C. The air velocity at the measurement point exhibited the opposite trend as the height increased. The peak value of air velocity along Line 1 and Line 4 appeared at heights of 0.6 m and 1.1 m, respectively.  
The root mean square error (RMSE) of the three numerical simulation results was calculated to quantitatively analyze the consistency between the predicted and experimental results. The calculation equation is  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{j=1}^{n}(P_{j}-Q_{j})^{2}} \tag{19}\]  
where \(P_{j}\) is the simulation value (temperature or velocity) of the corresponding point, \(Q_{j}\) is the measured value of the measurement point, \(n\) is the number of measurement points.  
The calculation results are presented in Table 6. Compared with the RNG and SST models, the RMSE of temperature and velocity between the results obtained by the SKE model and experiments was the smallest, 0.72 \({}^{\circ}\)C and 0.107 m/s, respectively. Thus, the SKE model was selected to optimize the supply air inlet spacing of the SV. Previous studies have also shown that the SKE model has good accuracy, numerical stability, and calculation efficiency for predicting indoor airflow [51]. In a simulation study of a typical office with SV, the SKE model performed slightly better than the RNG model [52].
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results
### Datasets and Metrics
#### 4.1.2 YouTube-VOS  
YouTube-VOS 2019 [14] is a benchmark for multi-object video segmentation, providing a total of 3924 videos, with 507 designated for the validation set and 3471 for the training set. The validation set includes categories not present in the training set, allowing for a further assessment of the model's generalization performance.
## 3 Results
### Seasonal and Interannual Variations of Observed C Fluxes and ET before and after Fertilization
In addition, compared with the pre-fertilization (9-years) mean CUEe (17%) in DF49, annual average values of CUEe during 2007-2010 were higher by about 9%, 8%, 9% and 12%, respectively. Annual ET during the post-fertilization period did not show an apparent difference from the previous 9-year means in all three stands (Figure 3d). Annual values of WUE varied greatly among the three stands (Figure 3e). Annual WUE of DF49 was the highest, ranging from 4.5 to 5.4 g\(\cdot\)C (kg water)\({}^{-1}\), and steadily decreased during 2008-2010. Annual LUE values in DF49 were also the highest among the three stands, ranging from 0.0216 to 0.0254 mol\(\cdot\)C\(\cdot\)mol\({}^{-1}\) photons (Figure 3f). Figure 3: Interannual variations of eddy-covariance tower measured carbon fluxes, evapotranspiration (ET), water-use efficiency (WUE) and light-use efficiency (LUE) in the three Douglas-fr stands. (**a**) gross primary productivity (GPP); (**b**) ecosystem respiration (\(R\)); (**c**) net ecosystem productivity (NEP); (**d**) ET; (**e**) WUE and (**f**) LUE. Note: The open symbols in all three stands indicates the annual pre-fertilization values. MLR simulations for DF49 for the model calibration period, however, explained about 97%, 92% and 76% of the variance of monthly GPP, \(R\), and NEP, respectively (Figure 4a and Table 1), which are somewhat lower than for the ANN simulations. The linear regression analysis showed RMSE = 21, 29, and 19 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\) for GPP, \(R\) and NEP, respectively, which are significantly higher than those for the comparison of ANN-simulated and EC-measured values. In addition, for the model verification period, linear regression between MLR-simulated and observed values showed RMSE = 30, 34 and 24 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)month\({}^{-1}\) for GPP, \(R\), and NEP, respectively, which are all significantly higher than those for the calibration period. For the other two stands, the performance of the ANN model was also superior to that of the MLR model in the estimates of GPP, \(R\) and NEP (Table 1). Regarding ET, similar results were observed in the calibration and verification periods in all three stands (Table 1). As a consequence, we focused on describing and discussing the results of ANN modeling in the prediction period to ascertain the responses of C fluxes and ET to fertilization.
###### Abstract  
This paper presents an active phase shifter for phased array system applications, implemented using 0.18 \(\upmu\)m SiGe BiCMOS technology. The phase shifter circuit consists of a wideband quadrature signal generator, a vector modulator, an input balun, and an output balun. To enhance the bandwidth, a polyphase filter is employed as the quadrature signal generator, and a two-stage RC-CR filter with a highly symmetrical miniaturized layout is cascaded to create multiple resonant points, thus extending the phase shifter's bandwidth to cover the required range. The gain of the variable-gain amplifier within the vector modulator is adjustable by varying the tail current, thereby enlarging the range of selectable points, improving phase-shifting accuracy, and reducing gain fluctuations. The measurement results show that the proposed active phase shifter achieves an RMS phase error of less than \(2^{\circ}\) and a gain variation ranging from \(-1.2\) dB to 0.1 dB across a 20 GHz to 30 GHz bandwidth at room temperature. The total chip area is 0.4 mm\({}^{2}\), with a core area of 0.165 mm\({}^{2}\), and consumes 19.5 mW of power from a 2.5 V supply.  
SiGe BiCMOS; phase shifter; quadrature signal generator; vector modulator Article
# Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel
## Appendix A  
The derivation of MCRLB for the symbol timing estimator is as follows. Typically, the Matched Filter in the receiver can be expressed in  
\[p(\tau)=\frac{\sin(\pi\tau)}{\pi\tau}\cdot\frac{\cos(\alpha\tau\tau)}{1-\left(2 \alpha\tau\right)^{2}}. \tag{1}\]  
Equation (2) can be developed from Equation (1) by Taylor series expansion. In addition, the fourth, fifth, and remaining terms on the right-hand side of the equation can be denoted as \(o(\tau^{2})\).  
We have  
\[p(\tau)=p(0)+p^{\prime}(0)\tau+\frac{p^{\prime\prime}(0)}{2}\tau^{2}+o(\tau^{2 }), \tag{2}\]  
where the first-order derivative of \(p(\tau)\) can be denoted as \(p^{\prime}(0)\) and the second-order derivative of \(p(\tau)\) can be \(p^{\prime\prime}(0)\).  
The development of \(p(\tau)\) can be written as  
\[p(\tau)=\left[1-\frac{(\pi\tau)^{2}}{6}\right]\times\left[1-\frac{(\alpha\pi \tau)^{2}}{2}\right]\times\left[1+(2\alpha\tau)^{2}\right]+o(\tau^{2}). \tag{3}\]  
According to the Taylor expansion, Equation (3) can be derived from Equation (1) using Equations (4)-(6) when \(\tau\) is close to 0, which are given respectively by  
\[\frac{\sin(\pi\tau)}{\pi\tau}=1-\frac{(\pi\tau)^{2}}{6}+o(\tau^{2}) \tag{4}\]  
\[\cos(\alpha\pi\tau)=1-\frac{(\alpha\pi\tau)^{2}}{2}+o(\tau^{2}) \tag{5}\]\[\frac{1}{1-(2\pi\tau)^{2}}=1+(2\pi\tau)+o(\tau^{2}). \tag{10}\]  
Equation (11) corresponds to Equation (12) as follows:  
\[p(\tau)=\underbrace{1}_{p(0)}+\underbrace{\left\{\alpha^{2}\left(4-\frac{\pi^{2 }}{2}\right)-\frac{\pi^{2}}{6}\right\}}_{\frac{p^{\prime\prime}(0)}{2}}\tau^{2 }+o(\tau^{2}) \tag{11}\]  
\[E\left[\left(\frac{\hat{\tau}-\tau}{T}\right)^{2}\right]\geq MCRB=\frac{1}{2 \left(\frac{E_{s}}{N_{o}}\right)LC_{1}}, \tag{12}\]  
where \(L\) is the estimation length and \(C_{1}=\int\left(p^{\prime}(t)\right)^{2}=-\)\(p^{\prime\prime}\left(0\right)\) in [13; 14]. The MCRLB can be derived as Equation (10), given by  
\[MCRLB=2\left(\frac{E_{s}}{N_{o}}\right)LC_{1}=4\left(\frac{E_{s}}{N_{o}}\right) L\left\{\frac{\pi^{2}}{3}-\alpha^{2}\left(8-\pi^{2}\right)\right\}. \tag{13}\]
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
## 5 Computational Study
### Runtime Considerations  
The runtime of the optimization-simulation framework mainly depends on the number of iterations needed for the framework to converge to a solution. To speed up this process, we can reduce the number of times to run the optimization model by returning multiple feasible solutions for simulation instead of only the optimal solution. This has the advantage of possibly simulating the solution that performs best under uncertainty at early iterations. On the other hand, extracting too many solutions implies simulating solutions that may never be needed. Thus, it would be interesting to study the trade-off between the number of solutions extracted in each iteration and the number of iterations of the optimization-simulation framework.  
We extract the \(n\) best solutions found while solving the master problem. The results for the convergence speed of the solution method for different values of \(n\) are presented in Table 1. The first column indicates the maximum number of extracted solutions \(n\) in each iteration. The column Itr. needed states how many times the master problem has been solved, i.e., the number of iterations. The third column, Route Gen. Time, is the time needed to generate the routes. Note that this time is constant, as the routes are only generated once. The total time spent by Xpress to solve the master problems is given in column Opt. Time, whereas the total time used by MATLAB to simulate the solutions is given in column Sim. Time. Column Total Time shows the total runtime spent by the solution approach. The last column, Solutions Simulated, contains the total number of simulated solutions.  
As can be seen from Table 1, the number of iterations of solving the master problem decreases with higher values of \(n\). In contrast, the time needed to simulate all solutions increases by \(n\). For our case, we select \(n=20\) for which the best total time is obtained. Finally, we point out that the approach is not sensitive to \(n\) when considering the solution quality. The same best solution is found for all different values of \(n\).  
Figure 8: Data grid with time series observation on each cell used for the weather simulation.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Opportunistic Routing Protocols
#### 5.2.7 Leor  
In 2020, [PERSON] et al. proposed Load-balanced and Energy-aware Opportunistic Routing (LEOR) [17], an opportunistic routing protocol for multi-channel asynchronous duty-cycled WSNs. The main purpose of this protocol is to increase network life-time by reducing energy consumption. The protocol works with scalability by considering network density, and remaining energy is used for the adaptive duty-cycling mechanism. LEOR handles data on two levels: the inter-nodes level and the inter-channels level. The adaptive asynchronous scheduling helps to balance energy load among different nodes. In addition, it uses multiple channels and ranks them based on channel utilization, which results in the avoidance of network interferences. Decreasing interferences, on the other hand, leads to a minimization of E2E delay and throughput. LEOR controls redundant data transmission by restricting forwarder selection-zone, which minimizes the number of forwarders.  
It chooses a best forwarder node based on the distance from the receiver, the remaining energy of the forwarder and the number of its connections. LEOR works via two stages: the contention stage and the synchronization stage. When a sender has a packet to send, it will compete for access to the channel, and once it wins the channel it will synchronize with the selected channel and transmit the packet.  
Figure 18 shows the competition among three nodes on three different channels. Each node will wait for a specific amount of time to get a CTS reply from the receiver node. Once the winner node gets the CTS reply, it will instantly send the data packet and finish the transmission procedure. The simulation results provided in the paper in [17] were compared with MORR, and showed significant improvements.  
Figure 18: Contention and channel-hopping in LEOR [17].
## 5 Studies and Mechanism about n-3 PUFAs and Melanoma
### Discrepancy in Results and Approaches to Studies in Humans  
On the other hand, other authors found contradictory results. [PERSON] et al. showed that EPA treatment increased both the growth and metastasis of B16 melanoma cells by suppressing the cytolytic function of both T cells and macrophages [174]. Moreover, in human studies it is challenging to standardize the levels of n-3 PUFA intake based on the amounts and frequency of fish ingested and this may be an explanation for the conflicting results in human observational studies [175; 176; 177] (Table 1). Despite contradictory results, this could be attributed to discrepancies in the doses of n-3 PUFA and in the different experimental protocols used in the respective studies, which renders these findings not yet successful in clinical practice. However, various clinical trials have demonstrated that the combination of DHA with other solid cancer chemotherapeutics such as paclitaxel is well tolerated by patients [178; 179; 180], justifying further studies about melanoma resistance to treatment and lipidomics due to the recent advances in the scientific field. Another challenge is the scarcity of human studies on supplementation with n-3 PUFA in melanoma and other cancers. Over the past decades, there has been a shift in dietary patterns in the current human population compared to our ancestors, and modulating the n-6/n-3 ratio has been considered an intriguing point in the research field across various pathologies [181; 182; 183; 184]. However, a randomized controlled trial showed that supplementation with n-3 PUFA did not result in a lower incidence of major cardiovascular events or cancer than placebo [185]. In contrast, another clinical trial demonstrated that dietary supplementation with n-3 PUFA led to a clinically important and statistically significant benefit about cardiovascular death [186] (Table 1). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Dose** & **Results** & **Reference** \\ \hline In vitro (metastatic B16F10 murine melanoma cells and metastatic WM266-4 human melanoma cells) & CDDP (1, 2.5, and 5 umol/L), DHA or EPA (10–30 umol/L) alone or in combination at different time points (24–72 h) & - Growth-inhibitory effect was enhanced with DHA + CDDP - Inhib melanoma cell migration was enhanced with DHA + CDDP - Both DHA and EPA + CDDP revert ERCC1 expression. - Reduction of pERK1/2 was observed in both cell lines in a DHA & [128] \\ melanoma cells & different time points (24–72 h) & dose-dependent manner - DHA or EPA, given alone significantly increased DUSP6 & - DHA or EPA, given alone significantly increased DUSP6 \\  & & expression with respect to control cells & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main findings from different studies with n-3 PUFA in melanoma.