esa-sceva/satcom-chunk-collection huggingface dataset:
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 5 Experiment
### Dataset and Labeling  
The dataset was provided by the computer vision group of the Technical University of Munich (TUM) [34]. We used the dataset's RGB-D image sequence, which includes Fr2/rpy, Fr2/large_with_loop, Fr2/pioneer_slam, Fr2/pioneer_slam2 and Fr3/long_office_household. However, the loop closure is not labeled in the TUM dataset and must be manually marked. Because of the 30 Hz/s sample frequency, there are too many images in the same place. Therefore, we selected the key frames with the ground truth of trajectory.  
Keyframe selection includes the following steps.  
For each image sequence, a key frame list \(F=\{\}\) is set, and the first frame is added to \(F\).  
Sequentially compare the image \(f_{j}\) in the image sequence with the last frame  
\[\overline{f_{i}}\]  
in the key frame list. Assuming that their corresponding poses are \(T_{j},T_{i}\) which can be found in the ground truth, the relative translation of the two frames is \(M_{j,i}=trans(T_{j}^{-1}T_{i})\), where \(trans(\bullet)\) represents the 2 norm of the translational part of the transformation matrix. If \(0.1<M_{j,i}<1\), the frame \(f_{j}\) is added to the end of the key frame list.  
Finally, the loop closure is manually labeled with the selected key frame sequence.  
Figure 5(a) shows the trajectory ground truth of the Fr3/long_office_household sequence, and in Figure 5(b) red dots are the selected key frames and the black line segments are the loop closure. Figure 5(c),d are one pair of loop closure images.  
Figure 5: Keyframe selection and loop closure example in FR3/long_office_household.
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 2 Problem Formulation
### Diesel Generator Model  
The fuel cost function of the diesel generator \(n\) (\(FC_{n}\)) can be expressed by a quadratic equation [2], as shown in Equation (2).  
\[FC_{n}(P_{n}(t))=a_{n}+b_{n}P_{n}(t)+c_{n}P_{n}^{2}(t) \tag{2}\]  
where \(a_{n}\), \(b_{n}\), and \(c_{n}\) are the fuel cost constants of the unit and \(P_{n}(t)\) is the output power of the diesel generator at a specific hour.  
A non-linear characteristic curve is used to describe the relationship between the output power of the diesel generator and the fuel consumption cost, and there will only be a one-line segment at each given time. The linearized fuel cost of the diesel generators [1] is, therefore, in Equation (3).  
\[C(t)=\sum_{n=1}^{N}U_{n}(t)[P_{n}(t)\alpha_{n,i}+\beta_{n,i}B_{n,i}(t)] \tag{3}\]  
In Equation (3), \(P_{n}\) is the power output of the generator corresponding to each line segment \(i\) of the characteristic curve, represented by a set of linear segments. The cost \(C(t)\) of generating diesel power \(P_{n}\) is obtained from the non-linear characteristic curve. For every generator \(n\), there is a gradient \(\alpha_{n,i}\) of segment \(i\), an intercept on the ordinate axis \(\beta_{n,i}\) of segment \(i\), and a binary variable \(B_{n,i}\) representing the start of the segment \(i\).  
\[\sum_{i=1}^{I}B_{n,i}(t)\leq 1 \tag{4}\]  
\[B_{n}(t)P_{n,i,min}\leq P_{n}(t)\leq B_{n}(t)P_{n,i,max} \tag{5}\]  
For the diesel generator model, it must be ensured that only one segment can be analyzed at a time, so Inequality (4) is used to describe this constraint. Equations (3)-(5) are used to ensure the output power of the diesel generator is in the range of the minimum \(P_{n,i,min}\) and maximum \(P_{n,i,max}\) of the segment. The startup cost of diesel generators \(C_{st}\) is shown in Equation (6), where \(st_{price}\) is the startup cost of the generator. \(U_{n}\) is a binary variable of the operating state of the diesel generator \(n\).  
\[C_{st}=st_{price,n}(U_{n}(t)-U_{n}(t-1)) \tag{6}\]
## 3 Results and Discussion
To qualitatively assess the model's performance, we examine a randomly selected timestamp from the test set corresponding to the IEEE 13-bus system--the smallest test case considered. Table 3 reports the actual and predicted values of voltage magnitude, voltage angle, current magnitude, and current angle for each bus. As observed, the model demonstrates excellent accuracy in estimating voltage magnitudes and angles across all buses. In terms of current estimation, the performance remains satisfactory, though some deviations are observed--primarily in buses where the actual current injections are zero. This is expected, as minor numerical deviations can yield large relative or angular errors when current magnitudes are near zero, due to the amplification of small differences in both magnitude and phase when dividing or taking angles of near-zero complex numbers. However, these errors are of limited practical significance, as the corresponding current \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**PINN**} & \multicolumn{2}{c}{**Improvement (\%)**} \\ \hline  & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAE**} \\ \cline{2-7}  & **Voltage** & **Current** & **Voltage** & **Current** & **Voltage** & **Current** \\ \cline{2-7}
**IEEE13** & 0.28 & 24.04 & 0.18 & 0.62 & 37.28 & 97.43 \\
**IEEE34** & 2.03 & 17.45 & 1.73 & 3.1 & 14.82 & 82.27 \\
**IEEE37** & 0.85 & 7.34 & 0.29 & 0.43 & 66.2 & 94.07 \\
**IEEE123** & 0.28 & 5.46 & 0.21 & 0.45 & 22.92 & 91.72 \\ \hline  & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{2-7}  & **Voltage** & **Current** & **Voltage** & **Current** & **Voltage** & **Current** \\ \cline{2-7}
**IEEE13** & 0.44 & 52.6 & 0.28 & 1.12 & 37.13 & 97.87 \\
**IEEE34** & 2.87 & 52.92 & 2.47 & _7.7_ & 13.77 & 85.45 \\
**IEEE37** & 1.06 & 26.45 & 0.39 & 0.56 & 63.67 & 97.88 \\
**IEEE123** & 0.37 & 21.8 & 0.28 & 0.64 & 22.44 & 97.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test set results. injections are negligible in absolute terms and have minimal impact on the overall system behavior. This issue can be mitigated by explicitly incorporating prior knowledge about load and zero-injection buses, e.g., via masking or regularization.
## Materials and Methods
### Characterization and Selection of the Prototype MCs
#### 2.3.2 Dimension of MCs: Particle Size Distribution (PSD) Analysis  
After manufacturing, the MCs were passed through 100 \(\mu\)m and 250 \(\mu\)m sieves to obtain a MC fraction within this Particle Size Distribution (PSD) range. The PSD was measured using a Sympatec HELOS Laser equipped with a cuvette dispersing system in distilled water at room temperature (magnetic stirring required at 800 rpm). An R5 lens was used (size range 4.5 to 875 \(\mu\)m), and the trigger conditions were set as follows: timebase: 1000 ms, start: Ch.10 \(\geq\) 3, valid: 10% \(\leq\) Ch.10 \(\leq\) 25%, duration: 10 s.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 4 Methodology
### Correlation Problem  
In the previous section, we introduced a cost-sensitive learning scheme that filters out noncritical alerts and outputs only those that require further investigation. This method significantly reduces the number of alerts to be processed; however, it only operates on individual alerts and lacks the ability to reduce redundancy caused by duplicate and correlated critical alerts. To address this issue, we employ alert correlation--based on the compactness in terms of behavioral, spatial, and temporal factors--to group alerts into events and summarize them as an \"event profile\". These groups can be considered a collection of alerts detected in the same attack campaign, thereby enabling a single-point investigation of the entire event.  
When implementing correlation, behavioral similarity is a necessary but insufficient condition for grouping alerts into an attack event. To illustrate this, we consider two Emotet (Emotet, also known as Heodo,is a notorious malware strain and cybercrime operation that has caused havoc in the cybersecurity world since 2014 and is believed to have originated from Ukraine [68]) warnings: one detected a month ago on a finance department server and the other triggered this morning on an HR department server. Although the behavior of these alerts is similar and they should be categorized into the same group, the differences in communication targets and timing require they be treated as separate incidents. Thus, alert correlation requires careful consideration and tuning to ensure that only genuinely related alerts are grouped as part of the same event.  
**Definition 3** (**Correlation Problem**).: _Let \(\mathbf{X}=\{\mathbf{x}_{i}|i=1,\ldots,N\}\) denote the alert set, and for each alert in \(\mathbf{X}\), the three-tuple \(([\mathbf{S}_{i},D_{i},P_{i}])\) represents the source IP address, destination IP address, and service port. Let \(t_{i}\) denote the issuing timestamp associated with alert \(\mathbf{x}\) in \(\mathbf{X}\) and let \(\tau\) represent the maximum duration (in seconds) for an event to exist before being terminated. A correlation problem involves grouping alerts in \(\mathbf{X}\) based on their compactness in terms of behavioral, spatial, and temporal factors. Here, behavioral compactness is achieved by satisfying similarity conditions based on numerical representation of the alerts. Spatial compactness is achieved by satisfying the three-tuple condition defined in (3), where alerts in each event share the same source IP address, destination IP address, and service port. Temporal compactness is realized by satisfying the timeout condition defined in (4), which limits the duration of each event to a maximum value \(\tau\)._  
\[\forall\mathbf{x}_{i},\mathbf{x}_{j}\in\mathbf{E},S_{i}\equiv S_{j},D_{i}\equiv D_{j},P_{i} \equiv P_{j}, \tag{3}\]  
\[\forall\mathbf{x}_{i},\mathbf{x}_{j}\in\mathbf{E},|t_{i}-t_{j}|\leq\tau. \tag{4}\]
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 5 General Discussion  
### Do Listeners' Preferences Optimise Intelligibility?  
When provided with a simple instruction to adjust some characteristic of a speech signal in order to recognise as many words as possible, much of the time listeners responded by choosing a setting that maximised intelligibility. This finding suggests that a listener-centric approach can be used to design algorithmically modified speech in a way to optimise intelligibility. A further benefit of the experimental paradigm is in delivering findings at a finer level of granularity than is normal in a traditional experiment that tests a limited number of conditions. This is exemplified by the ability to fine tune centre frequency (Expt. 2), leading to a more refined estimate of the optimal choice than would be possible using the smaller number of octave bands typically studied (e.g., [79]). In addition, preference distributions indicate how sensitive the choice of adjustment is to small perturbations.  
However, while much of the time listeners' choices did maximise intelligibility, this outcome has to be tempered by the finding that in a substantial number of trials, listeners' preferences were sub-optimal as evidenced by the reduced intelligibility at values chosen by some listeners on some trials (see Figures 3 and 6). The difficulty in choosing the optimal value is most evident when adjusting the centre frequency of an octave band of speech in Expt. 2, especially in the quiet condition. In that condition, listeners spent nearly twice as long attempting to optimise the band centre than for any of the other seven features tested in the two experiments. The extreme sensitivity to the precise location of an octave band of speech is an unexpected finding and merits further study using a more fine-grained adjustment scale.
# Mechanism of HCB-Modified Asphalt and Dynamic Properties of Mixtures
## 5 Analysis of HCB-Modified Asphalt Properties
### Rheological Properties  
Asphalt is a typical viscoelastic material with significant rheological properties. Asphalt binder has more viscous components at high temperatures, enhancing resistance to putting, and it has more elastic components at low temperatures, enhancing resistance to cracking. The complex shear modulus (G*), phase angle (\(\delta\)), and rut factor (G*/sin) values of base asphalt (BA), CB-modified asphalt, and HCB-modified asphalt are shown in Figures 6-8.  
A similar tendency was observed for modified asphalt and BA to that shown in Figure 6, indicating that three asphalt binders exhibited simple linear viscoelastic behavior. At temperatures below 40 \({}^{\circ}\)C, the G* values of the two modified asphalts were larger than that of the BA, with a slightly larger G* for the HCB-modified asphalt. The G* of asphalt can be used to evaluate the softness and hardness of asphalt. The larger the G*, the harder the asphalt. Therefore, CB and HCB can stiffen asphalt, but the effect was not obvious. When the temperature exceeded 40 \({}^{\circ}\)C, the curves of G* of CB- and HCB-modified asphalt approximately coincided with that of BA, indicating a small effect of CB and HCB on asphalt at higher temperatures.  
As depicted in Figure 7, the phase angles of the three asphalt binders gradually increased with increasing temperature. When the temperature was less than 40 \({}^{\circ}\)C, the order produced by phase angles was BA > CB-modified asphalt > HCB-modified asphalt. Thus, CB and HCB weakened the viscosity and enhanced the elasticity of asphalt; in particular, HCB had a better effect on improving the resistance to shear deformation of asphalt. For temperatures exceeding 40 \({}^{\circ}\)C, the phase angle curves of the modified asphalt were close to that of the BA. Therefore, the CB and HCB had slight effects on the antideformation capability of asphalt at high temperature.  
The rutting factor (G*/sin \(\delta\)) can be used to evaluate resistance to rutting deformation. A large G*/sin \(\delta\) means a greater capacity of the asphalt to resist rutting deformation. From Figure 8, the improvement effect of HCB on G*/sin \(\delta\) of asphalt was more obvious than that of CB, so HCB-modified  
Figure 8: Effect of CB and HCB on asphalt rutting factor.  
Figure 7: Effect of CB and HCB on phase angle of asphalt.  
asphalt had better resistance to rutting deformation. However, the difference of G*/sin \(\delta\) between the modified asphalt and BA was small with increasing temperature.  
Oxygen-containing functional groups such as -OH are formed on the surface of HCB, increasing the polarity of the carbon black surface. As a result, the adsorption between HCB particles is weakened, and the dispersion capability in asphalt is improved. The interaction effect between HCB and asphalt and the particle effect were enhanced, which increased the viscosity and strengthened the resistance to cracking and shear-slip deformation of HCB-modified asphalt at low temperatures.
## 4 Operation of Proposed Scheme
### Network Partitioning Phase  
First, the AUV employs the concept of a Voronoi region [28] to compute generator points which are used by sensor nodes to identify the cluster to which they belong. Suppose that region \(i\) of plane \(A\) has _N_\({}_{i}\) nodes of which locations are denoted by the two-dimensional vector _x_\({}_{k}\),\(k\) = 1,2,3,...,_N_\({}_{i}\). Then, generator point _z_\({}_{i}\)  for the region is defined by [22, 29]:  
\[z_{{}_{i}}=\frac{1}{N_{{}_{i}}}\sum\limits_{k=1}^{N_{{}_{i}}}x_{{}_{k}}\ \ \ \ \ {\rm where}\ \ \ i=1,2,3...,K \tag{9}\]  
Figure 3: Timeline of the proposed scheme.  
where \(E_{\mapsto}\) is the amount of energy consumption for data delivery incurred between node \(i\) and node \(j\), and \(M\) is the number of MNs within a subcluster. Comparing the energy of Equation (11) for all cases of \(i=1,2,...M\), CH selects the node \(m\) that will cost the minimum energy as  
\[m=\arg\ \ \min_{i}\left(E_{MNs\to PN\to AUV}^{Delivery}(i)\right)_{i=1}^{M} \tag{12}\]  
Then, the CH announces the final PN selections using the information of associated MNs. The PN starts the data-gathering step MN\(\rightarrow\)PN.
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for QoS Preemption  
Preemption techniques can be broadly divided into two categories, implicit preemption and explicit preemption. Implicit preemption is the result of performing periodic admission control and resource reservation. Most protocols use this mechanism to maintain QoS condition. Only two protocols [11, 17] use explicit preemption mechanism to detect and recover the QoS violation.  
In QAMNet [11], each node monitors its real-time traffic class periodically. When QoS violations are detected, a flow is randomly selected and congestion experienced (CE) bit is set in all packets belonging to that flow. This implies that downstream nodes treat the packet as best effort.  
In the scheme proposed by [PERSON] [17], a receiver monitors the receiving packets delivered from the source to check whether the bandwidth requirement is obtained. If it detects a QoS violation (the route is unsatisfied), the node will find a new bandwidth-satisfied route to the source.
## 5 Evaluation  
This section provides a detailed quantitative analysis comparing the performance of the MSCWSNs-PC protocol using a variety of deployment sink node numbers. We precisely define three metrics for MSCWSNs-PC performance: connectivity, power efficient and clustering interference. The connectivity illustrates the resilience of the algorithm. Because of the rigidly energy constraint in each sensor node, the power efficient is the most important metric for comparison when evaluating WSN protocol schemes. In this paper, power efficiency is denoted as the total transmission power of a sink. The other obvious metric for comparison when evaluating WSN schemes is the clustering interference due to the very high deployment density of sensor nodes. For all of our experiments, sensor nodes are randomly distributed in a 100 \(\times\) 100 m\({}^{2}\) rectangular region, the connected sub-network consist of \(N\) sensor nodes and \(M\) sink nodes act as the centric nodes.
## 3 Results
### Comparision to Reference Test and Other Cell Configurations  
The calorimetric test procedure defined in the materials section validates the results given by the TIS schedule. Also, a comparison to an HE cell using the same geometrical format as the observed cell is investigated.  
In Figure 9, the resulting impedance spectra of both cells are shown. Here, the higher the temperature response per generated heat is, the larger the resulting semi-circle becomes. That mainly results from a smaller heat capacity of the cell, causing a more elevated temperature increase. As expected, the HE cell also shows a smaller cross-plane heat conductivity resulting from the different layer thicknesses. Especially the thinner current collectors of HE cells made of proper thermal conductive materials (aluminium and copper) and thicker active materials decrease the overall cross-plane heat conductivity.  
Comparing the results of both cells in Table 2 shows the expected behaviour, using a standard ambient configuration of \(T_{amb}\) = 25 \({}^{\circ}\)C and a SOC of 50% each. The specific heat capacity shows a difference of nearly 13%, while the cross-plane heat conductivity differs by approximately 16%. For both values, the HP cell delivers the surplus. When comparing both cell types using the calorimetric test setup, the difference between the cells is reduced, showing a difference of only 3%. A possible reason is the setup used, which is highly dependent on the polystyrene casing. Moreover, the influence of the surrounding air in the climate chamber is more prominent here. Also, a deviation of less than 2% for the further investigated HP cell between the TIS and the calorimetric test can be observed. Considering the HE cell, the difference is almost 9%.
# Evaluation of Mechanical Properties of Recycled Material for Utilization in Asphalt Mixtures
## 3 Experimental Work
### Laboratory Tests
#### 3.2.3 Flakiness Index Test  
Some aggregates, on account of their shape, would be unsuitable for asphalt mixture as they would have low potential for developing inter-particle interlock. The percentage by mass of this type of aggregates, namely flaky aggregates is determined by the most commonly used test, called Flakiness Index Test (Figure 5). In this test, the flakiness index is determined by direct measurement using a special slotted sieve, from the ratio of the mass of material passing the slotted sieve to the total mass of the size fraction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample Number**} & \multicolumn{3}{c}{**Misshapen Particles (\%)**} & \multicolumn{2}{c}{**Australian Standards Misshapen**} \\ \cline{2-5}  & **RCA** & **RAP** & **Basalt** & **Percentage Limits (\%)** \\ \hline Sample 1 & 5.3 & 12.0 & 19.4 & \\ Sample 2 & 6.2 & 7.7 & 18.3 & 35\% (max) \\ Sample 3 & 7.0 & 8.7 & 17.3 & For heavy and very heavy traffic \\
**Average Misshapen** & **6.2** & **9.5** & **18.3** & \\
**Particles Percentage (\%)** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Results of Particle Shape Test for Coarse Aggregates and Misshapen Percentage Limits for Dense Graded Asphalt Based on Australian Standards.  
Figure 4: Classification of Aggregate Based on Particle Shape Test.  
The flakiness index test is performed based on AS 1141.15 (1999) and the results of this test on three samples for each aggregate type (i.e., RCA, RAP and basalt) and the average flakiness index for each aggregate type are given in Table 3.  
The results of flakiness index test shows that RCA has less flakiness index than basalt and RAP which can positively affect the inter-particle interlock in asphalt mixture.
# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 3 Materials and Methods
### Speaker  
Speakers function as the final output component of sound systems; they convert filtered and amplified audio signals into physical sound waves and deliver them to listeners.  
The types of speakers used in vehicle sound systems are specially designed to provide optimal performance across different frequency ranges. Essentially, these speakers are categorized into four main types to cover low, mid, and high frequencies: subwoofer/woofer, mid-range, tweeter, and full-range speakers. Each type of speaker is optimized for a specific frequency range, and the frequency ranges of these speakers are detailed in Table 2[6].  
The type of speaker used in this study is the full-range speaker, which covers a wide frequency range. Figure 6 shows the frequency response of the full-range speaker. The manufacturer states that the effective operating frequency of this speaker is between 85 Hz and 12.5 kHz. This wide frequency range indicates that the speaker can adequately produce both low-frequency bass sounds and high-frequency treble sounds. In this study, the modeling of the speaker was based on this frequency response curve.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Speaker Type** & **Frequency Range** \\ \hline Subwoofer & 20 Hz–200 Hz \\ \hline Woofer & 04 Hz–500 Hz \\ \hline Mid-Range & 250 Hz–4000 Hz \\ \hline Tweeter & 2000 Hz–20,000 Hz \\ \hline Full-Range & 80 Hz–12,500 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency ranges of speakers.  
Figure 5: Class-D amplifier load response.