esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Evaluations on MOT17  
Similarly to the experiment on the MOT15 dataset, we used the public detection results of the MOT17 benchmark dataset as the input detections of the algorithm, and compared it with other excellent methods. For a fair comparison, these baseline methods are all online and they are also dependent on the appearance cue. Parts of the experimental results are shown in Table 5. Identically, we only choose those accessible published methods for fair comparison.  
Compared to the MOT15 dataset, the detections provided in the MOT17 dataset were much precise, leading to our relatively higher MT score and lower FN score. It proved that our method relies heavily on detections as well. The more accurate the detection, the better the performance of our method. It is worth mentioning that GMPHD_DAL [12] also utilized pedestrian re-identification technology. The difference is that we trained our appearance extraction network on a pedestrian re-ID dataset while GMPHD_DAL was applied on the data association part for object re-matching. Although our MOTA was only 1% higher than GMPHD_DAL, there was still an obvious gap in IDS, Frag, and FPS, which further highlights the effectiveness of our method.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Method** & **MOTA(\(\uparrow\))** & **MT(\(\downarrow\))** & **ML(\(\downarrow\))** & **FP(\(\downarrow\))** & **FN(\(\downarrow\))** & **IDS(\(\downarrow\))** & **Frag(\(\downarrow\))** & **FPS(\(\uparrow\))** \\ \hline \multirow{4}{*}{A} & GM\_PHD [39] & 36.4\% & 4.1\% & 57.3\% & 23,723 & 330,767 & 4607 & 11,317 & 38.4 \\ \cline{2-10}  & GMPHD\_KCF [40] & 39.6\% & 8.8\% & 43.3\% & 50,903 & 284,228 & 5811 & 7414 & 3.3 \\ \cline{2-10}  & GMPHD\_NITr [41] & 42.1\% & 11.9\% & 42.7\% & 18,214 & 287,646 & 10,698 & 10,864 & 9.9 \\ \cline{2-10}  & EAMTT [35] & 42.6\% & 12.7\% & 42.7\% & 30,711 & 288,474 & 4488 & 5720 & 12.0 \\ \cline{2-10}  & GMPHD\_HDA [32] & 43.7\% & 11.7\% & 43.0\% & 25,935 & 287,758 & 3838 & 4046 & 9.2 \\ \hline \multirow{4}{*}{B} & GMPHD\_DAL [12] & 44.4\% & 14.9\% & 39.4\% & 19,170 & 283,380 & 11,137 & 13,900 & 3.4 \\ \cline{2-10}  & FPSN [42] & 44.9\% & 16.5\% & 35.8\% & 33,757 & 269,952 & 7136 & 14,491 & 10.1 \\ \cline{1-1} \cline{2-10}  & YOONKJ17 [11] & 51.4\% & 21.2\% & 37.3\% & 29,051 & 243,202 & 2118 & 3072 & 3.4 \\ \cline{1-1} \cline{2-10}  & Tracktor++ [38] & 53.5\% & 19.5\% & 36.6\% & 12,201 & 248,047 & 2072 & 4611 & 1.5 \\ \cline{1-1} \cline{2-10}  & HMB\_DAF (Ours) & 45.5\% & 14.6\% & 40.4\% & 21,161 & 282,901 & 3592 & 7696 & 35.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tracking performance on the MOT17 dataset. Best results in each category appear in bold. (A) Traditional method, (B) deep learning method. Our method is denoted by HMB_DAF.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### Cut Problems
#### 4.5.2 Minimum Bisection and Balanced Separator  
Given a graph \(G=(V,E)\), Minimum Edge Bisection (resp. Minimum Vertex Bisection) asks to remove the fewest number of edges such that the graph is partitioned into two parts \(A\) and \(B\) with \(||A|-|B||\leq 1\). Balanced Edge Separator (resp. Balanced Vertex Separator) is a more relaxed version of the problem where the goal is to bound the size of the largest component by \(\alpha n\) for some \(1/2<\alpha<1\). It has been actively studied from approximation algorithms, culminating in \(O(\sqrt{\log n})\)-approximation algorithms for both Balanced Edge Separator and Balanced Vertex Separator[218; 234], and an \(O(\log n)\)-approximation algorithm for Minimum Edge Bisection[235].  
If we parameterize by the size of optimal separator \(k\), Minimum Edge Bisection admits an exact parameterized algorithm [222]. While Minimum Vertex Bisection is W[1]-hard [219], Feige and Mahdian [236] gave an algorithm that given \(2/3\leq\alpha<1\) and \(\varepsilon>0\), in time \(2^{O(k)}n^{O(1)}\) returns an \((\alpha+\varepsilon)\) separator of size at most \(k\).
## 1 Introduction
The above studies focused on identifying the differences in driving style between groups but did not create a model to quantify individual risk indices. The driving style plays an important role in improving the safety and the ride comfort of autonomous vehicles although the driving style is difficult to perceive and describe accurately. Decomposing complex driver behaviors into simple, smaller behaviors can facilitate identifying and analyzing driving styles. In this paper, a novel framework to identify driving style with a quantitative method is proposed (Figure 2). The main contributions of this paper are as follows. (I) the Bayesian nonparametric method, i.e., HDP-HSMM, is innovatively applied to segment the time series driving data. It can decompose the time series driving data into fragment clusters with similar characteristics effectively. (II) The novel State-Action semantic plane is proposed to analyze and quantify the driving style, expressing driving preference simply and intuitively. (III) The transition probabilities are extracted based on the semantic plane to reveal interrelations among driving behaviors. The transition probabilities are used to improve the identification accuracy of driving styles. The benefit of the proposed method is further verified by a comparison with the conventional statistical feature's method. The remainder of this paper is organized as follows: Section 2 introduces the simulator platform in detail, including the data collection, participants, and data analysis. Section 3 presents the framework of the driving style and the basic methods. Section 4 demonstrates the segment results using HDP-HSMM and the State-Action semantic plane. Section 5 presents the discussion of the results in detail and possible applications. Lastly, the conclusions are summarized in Section 6.
# Low-Complexity Hybrid Optical OFDM with High Spectrum Efficiency for Dimming Compatible VLC System
## 3 Proposed Dimming Compatible Hybrid O-OFDM
### Dcho-Ofdm
\begin{split} I_{ave}&=\frac{T_{ON}}{T}\left(I_{h}+ \mathbb{E}\left[s_{\text{conv},n}^{\text{on}}\right]\right)+\frac{T-T_{ON}}{T }\left(I_{l}+\mathbb{E}\left[s_{\text{conv},n}^{\text{off}}\right]\right)\\ &=I_{l}+E_{1}+\rho(I_{h}-I_{l}+E_{2}-E_{1})\end{split}  \tag{15}\]  
where \(\mathbb{E}[\bullet]\) denoted the expectation operation. Here, we have \(E_{1}\) and \(E_{2}\) to denote the expectation result of signal \(s_{\text{conv},n}^{\text{off}}\) and \(s_{\text{conv},n}^{\text{on}}\), respectively, which can be readily obtained by numerical simulations. Recalling the Equation (5), the dimming level can then be further expressed as:  
\[\eta=\frac{E_{1}}{I_{h}-I_{l}}+\rho\left[1+\frac{E_{2}-E_{1}}{I_{h}-I_{l}} \right]. \tag{16}\]  
Figure 3: Block diagram of the transmitter and receiver for the proposed DCHO-OFDM. The Equation (16) shows that the dimming level \(\eta\) has a linear relationship with the duty cycle \(\rho\). It is essential to discuss the receiver structure for the proposed DCHO-OFDM scheme. The clipping distortion caused by down/upper-clipped PAM-DMT branch in DCHO-OFDM is orthogonal to the information-carrying subcarriers. Moreover, in the light of Proposition 1, the odd-indexed subcarriers carrying ACO-OFDM and the imaginary-part of the even-indexed subcarriers carrying PAM-DMT is capable of remaining uncontaminated during the polarity conversion process relying on the introduced time-varying bias, which is demonstrated in Figure 1c. When the converted signal is amalgamated with PWM for dimming control, the interference caused by PWM falls only on the 0-th subcarrier, which does not introduce additional interference to the transmitted signal. As a benefit, the DCHO-OFDM signal is obtained in an interference-orthogonal approach. Therefore, DCHO-OFDM symbols can be readily recovered upon relying on a standard OFDM receiver, as shown in Figure 3.
## 3 Case Study
### Multi-Targets Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **112 mm/1 h** & **234.5 mm/3 h** & **350 mm/6 h** \\ \hline \multicolumn{4}{c}{**Reduction of the Inundation (equal to the recharge of the unconfined aquifer)**} \\ \hline
**Original Runoff** & 93,744 m\({}^{3}\) & 188,352 m\({}^{3}\) & 270,360 m\({}^{3}\) \\
**General Permeable** & 47,196 m\({}^{3}\) (49.1%) & 94,788 m\({}^{3}\) (49.6\%) & 140,616 m\({}^{3}\) (48\%) \\
**JW Ecological Technology** & 33,624 m\({}^{3}\) (64.1\%) & 70,920 m\({}^{3}\) (62.3\%) & 105,120 m\({}^{3}\) (61\%) \\ \hline \multicolumn{4}{c}{**Economic benefit (NTD)**} \\ \hline
**Original loss** & 3,000,000 & 7,900,000 & 9,000,000 \\
**General Permeable** & 1,675,000 (44.2\%) & 3,000,000 (62.0\%) & 6,500,000 (27.8\%) \\
**JW Ecological Technology** & 1,250,000 (58.3\%) & 2,250,000 (71.5\%) & 4,400,000 (51.1\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparisons of multi-targets. Figure 6: Simulation result for 3 h/234.5 mm. an economic loss of nearly NTD 9 million. After applying JW ecological technology, its economic benefits can be reduced by about NTD 4.4 million (51.1%).
# Optical Fiber Current Sensors Based on FBG and Magnetotstrictive Composite Materials
## 2 Working Principle of the Sensor  
The proposed sensor is composed of a magnetic circuit sensing system and photoelectric testing system. The magnetic circuit sensing system is composed of magnetostrictive materials (magnetostrictive composite materials), fiber gratings, magnetically permeable materials, and permanent magnets. The photoelectric test system consists of a light source, a circulator, and a spectrum analyzer. The magnetostrictive material is combined with the fiber grating and placed in the magnetic field generated by the current, and the change of the current can be obtained by demodulating the wavelength drift of the fiber grating [6]. This paper studies the magnetostriction phenomenon and its causes, the working principle of fiber grating, and the role of permanent magnets.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 4 Results and Discussion
### Polarization Calibration Accuracy Inspection and Analysis  
After completing the polarization calibration of SAR data, the polarization matrix of the trihedral CR was extracted according to the method of extracting polarization matrices for point targets. The polarization isolation \(\Delta\phi_{hrc}\), amplitude imbalance \(\Delta f\), and phase imbalance \(\Delta\phi_{f}\) were calculated using Equations (20)-(22), respectively, to evaluate the accuracy of polarization calibration (Tables 7 and 8).
###### Abstract  
The Electric Energy Consumption Prediction (EECP) is a complex and important process in an intelligent energy management system and its importance has been increasing rapidly due to technological developments and human population growth. A reliable and accurate model for EECP is considered a key factor for an appropriate energy management policy. In recent periods, many artificial intelligence-based models have been developed to perform different simulation functions, engineering techniques, and optimal energy forecasting in order to predict future energy demands on the basis of historical data. In this article, a new metaheuristic based on a Long Short-Term Memory (LSTM) network model is proposed for an effective EECP. After collecting data sequences from the Individual Household Electric Power Consumption (IHEPC) dataset and Appliances Load Prediction (AEP) dataset, data refinement is accomplished using min-max and standard transformation methods. Then, the LSTM network with Butterfly Optimization Algorithm (BOA) is developed for EECP. In this article, the BOA is used to select optimal hyperparametric values which precisely describe the EEC patterns and discover the time series dynamics in the energy domain. This extensive experiment conducted on the IHEPC and AEP datasets shows that the proposed model obtains a minimum error rate relative to the existing models.  
s 2020 2020 23 November 2021 27 November 2021 2020 23 November 2021 27 November 2021  
non-objective factors and electric load series are affected in practical applications, and it is difficult to control these with multi-factor forecasting models [10; 11; 12]. Hence, the multi-factor forecasting models simply evaluate the relations between forecasting variables and influencing factors [13; 14; 15]. In this research, a novel metaheuristic based on an LSTM model is developed to generate a more effective EECP. The main contributions are specified below:  
* Input data sequences are collected from IHEPC and AEP datasets, and data refinement is accomplished using min-max along with standard transformation methods in order to eliminate redundant, missing, and outlier variables.
* Next, the EECP is generated using the proposed metaheuristic based on the LSTM model. The proposed model superiorly handles the irregular tendencies of energy consumption relative to other deep learning models and conventional LSTM networks.
* The effectiveness of the proposed metaheuristic based on the LSTM model is evaluated in terms of mean squared error (MSE), root MSE (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) on both IHEPC and AEP datasets.  
This article is structured as follows. Previous existing research studies on the topic of EECP are reviewed in Section 2. The mathematical explanations of the proposed metaheuristic based on the LSTM model and a quantitative study including experimental results are specified in Sections 3 and 4, respectively. Finally, the conclusion of this work is stated in Section 5.
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 7 Conclusions  
As a result of this work, an architecture of an IoT technological stack in hard-to-reach areas was presented. Information encoding methods were identified that allow for the ability to solve the problem of collecting information from remote sensors located in the absence of traditional communication channels, and practical verification of received results was obtained. The paper used simulation modelling to study the applicability of different methods of presenting information in the case of transmitting IoT data over low-speed satellite communications channels.  
We see the following main contributions of this paper:  
Figure 8: Container utilization with packaging applied.  
1. A novel architecture of a heterogeneous data collection network for remote areas of the world, which includes energy-efficient technologies of the Internet of things at the data collection level, with low-speed satellite communication channels targeted at M2M and IoT being proposed.
2. A new method (GDEP) of encoding information at the OSI presentation layer of a heterogeneous IoT network was developed, which by combining several encoding and packaging techniques, increases the efficiency of data transmission by 4.8 times.
3. The proposed GDEP method was validated on a simulation model and on experimental equipment.  
The GDEP method proposed in the paper allows for the use of IoT technology stack in remote regions by integrating it with the SBD satellite short message service. The GDEP method allowed for the reduction in the volume and number of SBD messages during data transmission via low-speed satellite communication channels, which made it possible to reduce the size of transmitted data by almost five times. Reducing the cost of data transmission leads to an increase in the economic efficiency of SATCOM for organizing data transmission networks in remote areas, which do not have a telecommunication infrastructure.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology
### Hyperparameter Tuning on HDBSCAN
#### 3.5.3 Cluster_Selection_Epsilon  
Unlike DBSCAN, HDBSCAN also includes the _cluster_selection_epsilon_ parameter. The _cluster_selection_epsilon_ extends the hierarchical cluster selection process in HDBSCAN by merging clusters that are in close proximity to each other within the spanning tree. It essentially defines a distance threshold that determines how far apart clusters can be before being considered for merging. A smaller _cluster_selection_epsilon_ results in sparse boundaries, preserving more distinct and separate clusters, whereas a larger value allows for broader merging, potentially combining closely related clusters into a single, larger cluster. In relation to _min_cluster_size_, this mechanism is particularly useful in applications where the smaller cluster is considered but where we want to avoid micro-clusters inside the large cluster, as it provides additional control to adapt the clustering results to the specific needs of the dataset or the analysis objectives.  
In the experiment, we systematically evaluated HDBSCAN's performance by modifying individual hyperparameters while maintaining all other parameters at their scikit-learn default values ([[https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.HDBSCAN.html](https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.HDBSCAN.html)]([https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.HDBSCAN.html](https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.HDBSCAN.html)) (accessed on 17 March 2025)), as shown in Table 3.
## 3 Theoretical Model
### Governing Equations
}\)), denoted as \(x_{s}^{\eta,l}\), \(\kappa_{s}^{\eta,m}\) under loading and unloading conditions, respectively, which may be formally written as:  
\[\frac{\partial\varepsilon_{v}}{\partial p^{\prime}}=x_{s}^{p^{\prime}J}\quad \partial p^{\prime}>0\quad\text{ or }\quad\frac{\partial\varepsilon_{v}}{\partial p^{\prime}}=x_{s}^{p^{\prime} Jun}\quad\partial p^{\prime}<0, \tag{7}\]  
\[\frac{\partial\varepsilon_{v}}{\partial\eta}=x_{s}^{\eta J}\quad\partial\eta >0\quad\text{ or }\quad\frac{\partial\varepsilon_{v}}{\partial\eta}=x_{s}^{\eta,um}\quad \partial\eta<0 \tag{8}\]  
Applying Equation (6), the change in the volumetric strain of the soil skeleton, \(\varepsilon_{v}\), can be formally presented in the following form:  
\[\text{d}\varepsilon_{v}=\left(\frac{\partial\varepsilon_{v}}{\partial p^{ \prime}}\right)\text{d}p^{\prime}+\left(\frac{\partial\varepsilon_{v}}{ \partial\eta}\right)\text{d}\eta. \tag{9}\]  
Equation (9) is similar in its form to two incremental equations proposed by [PERSON] [59] describing the development of volumetric and deviatoric deformations in dry sand through the superposition of the effects of stress tensor invariants: deviator and isotropic stress. Partial derivatives in Equation (9) can be replaced by corresponding functions (recall Equations (7) and (8)) and formally rewritten as:  
\[\text{d}\varepsilon_{v}=\kappa_{s}^{p^{\prime}}\text{d}p^{\prime}+\kappa_{s}^ {\eta}\text{d}\eta. \tag{10}\]  
In the present case of partially saturated soils, it is assumed that the condition regarding the equality of potential changes in the volume of the soil skeleton structure and voids remains valid:  
\[\Delta V_{p}=\Delta V_{s}, \tag{11}\]  
where \(V_{p}\) is the volume of pores and \(V_{s}\) is the total volume of the soil skeleton structure. Using a definition of soil porosity in the form:  
\[V_{p}=nV_{s}, \tag{12}\]  
the corresponding increments of pore fluid and soil skeleton volumetric strains \(\text{d}\varepsilon_{v}^{f}\), \(\text{d}\varepsilon_{v}^{s}\), respectively, are  
\[\text{d}\varepsilon_{v}^{f}=-\frac{\Delta V_{p}}{V_{p}}=-n\frac{\Delta V_{s} }{V_{s}}, \tag{13}\]  
\[\varepsilon_{v}=f\big{(}p^{\prime},\eta\big{)}, \tag{5}\]0  
Combining Equations (11)-(13), we obtain:  
\[\varepsilon_{v}=f\big{(}p^{\prime},\eta\big{)}, \tag{5}\]1  
According to the definition of pore fluid compressibility \(\kappa_{f}\), we can write:  
\[\varepsilon_{v}=f\big{(}p^{\prime},\eta\big{)}, \tag{5}\]2  
A general model equation describing changes in pore pressure caused by triaxial monotonic loading under undrained conditions is obtained by substituting Formulas (15) and (16) into Equation (10):  
\[\varepsilon_{v}=f\big{(}p^{\prime},\eta\big{)}, \tag{5}\]3Equation (17) contains three variables and cannot be solved by numerical methods. However, in the case of a standard triaxial compression stress path, when a constant cell pressure is maintained, we can easily eliminate one of the unknowns [38].
# Offline-Online Change Detection for Sentinel-1 InSAR Time Series
## 4 Discussion
A future improvement on the method could be to tune the gradient window through an iterative test to the application of interest in a specific region. Or to search for changes in several window sizes in order to capture different mechanisms at work. While the purpose of our method is rapid online detections with the aim of providing alerts for anomalous ground deformation, an additional benefit is that the algorithm works as an offline detector as well. This means that the method can be used to look for anomalous changes that have happened in the past given an existing InSAR time series (Figure 7). Although we attempt to correct for signals caused by tropospheric delays, no currently available method can remove all of this noise, especially delays arising from the turbulent component of the atmosphere [42; 43; 44]. Previous studies have shown that in general, weather-model derived corrections can reduce the noise by 2-16% [45] resulting in an RMSE of around 2-3 cm [42] in individual interferograms. Atmospheric noise is generally uncorrelated in time [46]. These are seen as noisy peaks in the time series, and are the source of most of the variability in the displacement time series. The purpose of using a long 5 year displacement time series is to characterise the dispersion in the test statistic arising mostly due to this noise. Atmospheric noise-induced peaks in the time series are the main source of false positives in our offset detector. The gradient detector is less prone to these as long as the detection window is sufficiently large (Figure 9).