esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Level Joint Feature Learning for Person Re-Identification
## 4 Results
### Comparison with CUHK03  
Comparison between the proposed method and CUHK03 is detailed in Tables 4 and 5. We conduct experiments on a CUHK03-detected dataset and a CUHK03-labeled dataset, respectively. We only use the single-query method for person re-identification on CUHK03-detected and CUHK03-labeled datasets. In this paper, our model is compared with many methods, such as LOMO+KISSME [6] using a horizontal occurrence model, pedestrian alignment network [41] and HA-CNN [25] using harmonious attention network. In this experiment, we use Rank-1 accuracy and mAP as evaluation indicators. As shown in Table 4, the MFF model achieves Rank-1 accuracy of 67.4% which is 0.6% higher than the best experimental result on CUHK03-detected data. Additionally, the mAP reaches 66.7%, which is 0.7% better than the best experimental result. Comparison results obtained on CUHK03-labeled are as follows: we surpass MGN by 1.6% in Rank-1 accuracy for the single-query setting. The MFF model reaches mAP of 68.8%. Compared with other deep learning methods, our  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Market1501**} \\ \cline{2-5}  & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline BoW + KISSME [33] & 44.4 & 63.9 & 72.2 & 20.8 \\ WARCA [36] & 45.2 & 68.1 & 76.0 & - \\ SVDNet [34] & 82.3 & 92.3 & 95.2 & 62.1 \\ PDC [38] & 84.4 & 92.7 & 94.9 & 63.4 \\ Triplet Loss [39] & 84.9 & 94.2 & - & 69.1 \\ DML [40] & 87.7 & - & - & 68.8 \\ PAR [13] & 81.0 & 92.0 & 94.7 & 63.4 \\ \hline MFF (Ours) & 96.0 & 98.7 & 99.3 & 87.9 \\ \hline \hline \end{tabular}  
* The above shows Rank-1 to Rank-5 accuracy (%) and mean Average Precision (mAP) (%).  
\end{table}
Table 3: Comparison with existing methods on Market1501.  
model is even more discriminative, which is attributed to our global feature extraction and each-part feature extraction. We believe that local feature extraction benefits from PMN, which is because PMN can extract low-to-high level features more comprehensively.
## 2 Materials and Methods
### Connectivity Analysis  
We used graph theory to study structural connectivity. Graph theory is a good way to find conservation targets because unlike most other conservation priority methods, it does not need demographic data [26]. According to [PERSON] and [PERSON] [33], this approach provides a comprehensive, robust analysis of connectivity with minimal data requirements. Graphad 2.6 software [34] was used to evaluate the functional connectivity. Functional connectivity was calculated using _Pumu vagouaroundi_ (jaguarondi) as the reference species (medium-sized mammal), for the four scenarios indicated in Section 2.3[35]. First, these scenarios were converted to raster layers (30 \(\times\) 30 m) and each land cover was assigned a cost (Table 1). This cost is an estimated resistance value for the reference species to move through each land-cover type. Native forest areas were considered \"habitat\" (cost value of 1). Subsequently, links between patches were created that accumulated the cost values between patches. Links between patches only occur if the accumulated cost is less than 166 (approximately 5 km of distance with a cost value of 1). This approach allowed us to assess the significance of individual planted patches (i.e., nodes in the network) within each regional network (via node-level metrics that can identify which patches facilitate landscape connectivity [36]. In this method, a component is a group of connected nodes. Species can move (link) between patches (nodes) across the same component, but not across different components [37]. We calculated global connectivity metrics (Flux, Equivalent Connectivity, Probability of Connectivity and Number of Components) and metric per patch (Current Flow) (Table 2). Global connectivity metrics show different features, with probability of connectivity (PC) being the most useful (value between 0 and 1) to compare connectivity among ecosystems. Current flow (CF) is based on circuit theory and analysis of the links between patches at landscape scale. These metrics have already shown their usefulness in analyzing connectivity in fragmented landscapes [38; 39]. The number, distance in cost and distance of the links for the four scenarios were also calculated. Finally, corridors were calculated as the accumulated number of links between patches given the maximum cost distance of 166 [34]. Corridors show the area that can be traversed between two patches of habitat (i.e., the area representing the set of possible paths connecting two patches) [34] and were used to establish potential areas representing a set of possible \"new forested paths\" for connecting natural forest areas. The corridor was divided into six categories: no corridor (0), very low (1-13), low (14-55), medium (56-138), high (139-291) and very high (>292).
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### What Are the Main Technological Uses of Audio Features in Educational Research? (RQ1)
#### 3.2.1 Acoustic Features  
Acoustic features operate directly on the raw audio waveform, capturing attributes such as pitch, energy, spectral properties, and Mel-frequency cepstral coefficients (MFCCs). These features allow researchers to analyze classroom environments without relying on text transcriptions, making them particularly useful in noisy settings or when privacy concerns limit transcription. Time-frequency representations (e.g., Mel-spectrograms) are often computed using established signal processing libraries such as Praat, PyAudio, OpenSmile, or Librosa [31; 36; 46; 47; 48].  
The main applications of acoustic features relate to classroom climate and emotion and behavior analysis. Variations in prosodic features, such as pitch, energy, or spectral envelopes, have been associated with teacher enthusiasm or student disengagement [49], as well as with shifts in instructional delivery [34; 50]. In some cases, acoustic indicators are combined with NLP techniques to refine the detection of emotional states [51] or used as the basis for diarization techniques [52].  
Extraction workflows for acoustic features typically rely on digital signal processing (DSP) techniques to compute metrics such as MFCCs, pitch, energy, and zero-crossing rate (ZCR). More recent approaches leverage deep learning models, such as wav2 vec2 or OpenL3, to generate acoustic embeddings that capture complex prosodic and paralinguistic information [36; 53]. These features have also proven effective for tasks such as teaching methodology classification [10; 54] or as a basis for voice activity detection (VAD), which supports downstream processes like diarization [55].
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm
### Pseudocode of JRCO Algorithm  
The pseudocode of the JRCO algorithm is shown in Algorithm 2.  
```
01 Initialize \(\vec{\textbf{r}}^{0}\), \(\vec{\textbf{\omega}}^{0}\), \(\textbf{C}_{R}^{0}\)for\(t=1,2,\cdots,T\)for\(i=1,2,\cdots,M\)for\(j=1,2,\cdots,N\) calculate \(\left(\vec{\textbf{\omega}}^{t}_{i,j}\right)^{2}\) based on \(\omega_{i}^{t}\) get \(\vec{\textbf{r}}_{i}^{t}\), \(\vec{\textbf{C}}_{\theta,i}^{0}\), \(\vec{\textbf{\omega}}_{\phi,j}^{t}\) from neighboring \(M_{i}\) UAVs estimate the position of the target \(\hat{\textbf{u}}\) based on \(\theta_{i}^{t}\) and \(\vec{\textbf{r}}_{i}^{t}\) get \(\vec{\textbf{\omega}}_{\phi,j}^{t}\), \(\vec{\textbf{\omega}}_{\phi,j}^{t}\), based on \(\eta_{i}^{t}\), \(\vec{\textbf{\omega}}_{\phi,j}^{t}\), \(\vec{\textbf{\omega}}_{\phi,j}^{t}\), \(\sigma_{i}^{2}\), \(\hat{\textbf{u}}\) and Monte Carlo method if\(M_{i}>1\) update the memory in \(\textbf{m}_{i,j}^{t}\), \(\textbf{m}_{\phi,i}^{t}\) and \(\textbf{m}_{u,i}^{t}\) else retrieve the memory from \(\textbf{m}_{i,j}^{t}\), \(\textbf{m}_{\phi,i}^{t}\) and \(\textbf{m}_{u,i}^{t}\) endif get the objective function \(\mathcal{J}_{i}\left(\vec{\textbf{r}}_{i}^{t},\omega_{i}^{t},\hat{\textbf{u}}, \sigma_{\theta,j}^{t},\sigma_{\theta,j}^{t},\sigma_{\theta,j}^{t}\right)\) get the optimal policy \(\vec{\textbf{r}}_{i}^{t+1}\) for the \(i\)-th UAV based on current \(\omega_{i}^{t}\) endfor select the optimal coverage radius \(\omega_{i}^{t}\) and optimal \(\vec{\textbf{r}}_{i}^{t+1}\) for the \(i\)-th UAV endfor update \(\vec{\textbf{r}}_{i}^{t+1}\) and \(\vec{\textbf{\omega}}^{t+1}\) for all \(M\) UAVs
20 endfor
```  
**Algorithm 2** The pseudocode of the JRCO algorithm.  
\begin{table}
\begin{tabular}{l l} \hline \hline
01 & Initialize \(\vec{\textbf{r}}^{0}\), \(\vec{\textbf{\omega}}^{0}\), \(\textbf{C}_{R}^{0}\) \\
02 & for\(t=1,2,\cdots,T\) \\
03 & for\(i=1,2,\cdots,M\) \\
04 & for\(j=1,2,\cdots,N\) \\
05 & calculate \(\left(\vec{\textbf{\omega}}^{t}_{i,j}\right)^{2}\) based on \(\omega_{i}^{t}\) \\
06 & get \(\vec{\textbf{r}}_{i}^
## 3 Results
### Comparative Analysis
#### 3.1.2 Cognitive Function  
The MoCA test consists of seven segments that evaluate visuospatial executive function, naming, attention, language, abstraction, delayed recall, and orientation. The scores for each segment and their respective \(p\)-values are presented in Table 2.  
In the case of LC, the interquartile range for the MoCA score was 6.25, whereas in the case of HC it was lower, with a value of 5. The results are significantly different for every segment, except for the orientation segment, with the HC group scoring higher in each of the segments compared to the LC group.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Score (/Max)** & **ALL (\(n\) = 57)** & **HC (\(n\) = 39)** & **LC (\(n\) = 18)** & \multirow{2}{*}{_p-Value *_} \\  & **MEAN (SD)** & **MEAN (SD)** & **MEAN (SD)** & \\ \hline Age (years) & 74.42 (4.56) & 73.46 (4.51) & 76.5 (4.04) & 0.01 * \\ Height (cm) & 166.96 (5.53) & 167.15 (5.91) & 166.56 (4.73) & 0.708 \\ Weight (kg) & 67.34 (8.42) & 67.4 (8.8) & 67.19 (7.76) & 0.932 \\ Education (years) & 13.07 (3.41) & 13.1 (3.35) & 13.0 (3.63) & 0.917 \\ \hline \hline \end{tabular}  
* HC, Healthy Control; LC, Lower Cognition; SD, Standard Deviation;
* significance level \(p\)-value < 0.05.  
\end{table}
Table 1: Demographics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Score (/Max)** & **HC (\(n\) = 39)** & **LC (\(n\) = 18)** & \multirow{2}{*}{_p-Value *_} \\  & **MEAN (SD)** & **MEAN (SD)** & **MEAN (SD)** \\ \hline MoCA score (/30) & 23.3 (2.7) & 17.44 (3.4) & \textless{}0.001 * \\ Visuospatial executive function (/5) & 4.13 (1.0) & 2.39 (0.9) & \textless{}0.001 * \\ Naming (/3) & 2.89 (0.3) & 2.67 (0.5) & 0.035 * \\ Attention (/6) & 5.28 (0.76) & 4.44 (1.1) & 0.005 * \\ Language (/3) & 2.36 (0.67) & 1.44 (1.15) & 0.003 * \\ Abstraction (/2) & 1.02 (0.81) & 0.56 (0.51) & 0.038 * \\ Delayed recall (/5) & 1.79 (1.28) & 0.56 (0.78) & 0.001 * \\ Orientation (/6) & 5.82 (2.72) & 5.39 (3.4) & 0.055 \\ \hline \hline \end{tabular}  
* HC, Healthy Control; LC, Lower Cognition; SD, Standard Deviation; MoCA, Montreal Cognitive Assessment;
* significance level \(p\)-value < 0.05.  
\end{table}
Table 2: Cognitive scores of lower cognition and healthy control groups.
###### Abstract  
Visible light communication (VLC) and visible light positioning (VLP) systems are usually designed separately to prevent mutual interference, while terminal mobility often introduces challenges that can degrade their performance. In this paper, we propose an integrated visible light communication and positioning (VLCP) scheme designed for mobile scenarios, encompassing both multiple-input-single-output (MISO) and multiple-input-multiple-output (MIMO) configurations. This scheme integrates both functionalities into a unified system. Utilizing decision feedback channel estimation (DFCE), we effectively estimate the dynamic channel state information (CSI) for both VLC and VLP, thereby potentially enhancing both communication and positioning performance. Simulation results across various routes verify the effectiveness of the proposed scheme. It is observed that when the terminal moves at 1 m/s, the VLCP scheme with DFCE can maintain reliable transmission quality, ensuring bit error rates (BERs) consistently below 1.3 \(\times\) 10\({}^{-2}\). Additionally, the mean positioning errors remain within the centimeter range in different routes, not exceeding 4.3 cm and 15.5 cm in the MISO and MIMO scenarios, respectively.  
visible light communication and positioning (VLCP); decision feedback channel estimation (DFCE); multiple-input-single-output (MISO); multiple-input-multiple-output (MIMO) +
Footnote †: journal: _Photonics_
## 3 Results and Discussion
### Results of Preliminary Characterization Tests  
Results obtained from penetration and viscosity tests carried out on the considered binders are listed in Table 4.  
In the case of penetration, different effects were observed for the two types of employed polymers. As expected, addition of SBR and w-SBR caused a gradual increase in consistency, with a corresponding reduction in penetration. On the contrary, PBR modification resulted in a slight increase of measured penetration. In both cases, no significant differences were found between the effects caused by polymers of different quality. These outcomes are consistent with the hypothesized differences in polymer solubility and resulting microstructure, which were discussed in Section 3.1. In particular, PBR may have completely dissolved in bitumen due to the high content of aromatics, whereas it  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Binder Code**} & **Penetration at** & **Viscosity at** & **Viscosity at** \\  & **25 °C [dmm]** & **135 °C [Pa-s]** & **165 °C [Pa-s]** \\ \hline NB & 62 & 0.48 & 0.13 \\ \hline SW3 & 60 & 1.11 & 0.35 \\ SQ3 & 59 & 1.16 & 0.36 \\ SW4 & 56 & 1.5 & 0.51 \\ SQ4 & 56 & 1.52 & 0.51 \\ SW5 & 54 & 2.01 & 0.72 \\ \hline PW3 & 64 & 1.29 & 0.42 \\ PQ3 & 65 & 1.26 & 0.38 \\ PW4 & 62 & 1.88 & 0.65 \\ PQ4 & 61 & 1.65 & 0.53 \\ PW5 & 63 & 2.61 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of penetration and viscosity tests.  
may be hypothesized that SBR may have formed inner polymeric domains distributed within the bituminous matrix.  
Both types of polymer caused a significant increase in viscosity, which was found to be sensitive to polymer dosage but did not depend upon polymer quality. With respect to the effect of polymer type, it was found that the use of PBR had a greater effect on the resistance to flow, with viscosity values which were greater for all dosages and test temperatures. This is also shown in Figure 3, which highlights the existence of clear relationships between polymer dosage and measured viscosity. These results seem to be consistent with the bitumen-polymer interactions investigated by means of FTIR analyses since a greater viscosity increase was expected in the case of dissolved PBR polymers in comparison to SBR polymers forming dispersed domains.  
It should be underlined that viscosity values obtained at 135 \({}^{\circ}\)C were in all cases lower than the maximum limit specified in AASHTO M332-14 (equal to 3 Pa-s), thus suggesting that the considered PMBs may be used with no concerns in ordinary operations of hot mix asphalt plants. Furthermore, at both test temperatures and for all PMBs, viscosity values recorded after initial temperature conditioning were extremely stable, thereby justifying the adoption of their average value as representative of the steady-state response.
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 5 ESCAPE Algorithm
```
1:Input:
2:\(\Rightarrow\) Set of humans who selected the route: \(N_{e}\)
3:\(\Rightarrow\) Capacity of the route \(C_{e}\)
4:\(\Rightarrow\) Set of humans inside the route \(|M|_{e}\)
5:Output:
6:\(\Rightarrow\) Set of humans that go and do not go to the route \(N_{e}^{go}\), \(N_{e}^{ngo}\)
7:Initialization:
8: Each human \(m\), \(m\in N_{e}\) has equal probability to go or not go \(pr_{m,s_{m}}^{(0)}=0.5\), \(\forall m\in N_{e}\), \(\forall s_{m}\in S_{m}\)\(\Rightarrow\)\(ite=0\), number of iterations for minority-game convergence \(\Rightarrow\)\(\pi_{m,s_{m}}^{(0)}=0\), \(\forall s_{m}\in S_{m}\), the score of each action \(\Rightarrow\)Convergence \(=0\)
9:Iterative Procedure:
10:whileConvergence\(==0\)do
11:\(N_{e}^{go}=\varnothing\), \(N_{e}^{ngo}=\varnothing\)
12:for\(m=1\) to \(|N_{e}|\)do
13:\(human\)\(m\) chooses their action \(s_{m}^{(ite)}\) based on probabilities: \(pr_{m}^{(ite)}=[pr_{m,s_{m}=0}^{(ite)},pr_{m,s_{m}=1}^{(ite)}]\)
14:if\((s_{m}^{(ite)}=1)\)then
15:\(N_{e}^{go}=N_{e}^{go}\cup\{m\}\);
16:else
17:\(N_{e}^{ngo}=N_{e}^{ngo}\cup\{m\}\);
18:endif
19:endfor
20:if\((|N_{e}^{go}|\leq C_{e}-|M|_{e})\)then
21:\(w^{(ite)}=1\); go is the winning action
22:else
23:\(w^{(ite)}=0\);do not go is the winning action
24:endif
25:\(\pi_{m,s_{m}}^{(ite+1)}=\pi_{m,s_{m}}^{(ite)}+f_{s_{m}}(m)\)\(\forall s_{m}\in S_{m}\), \(\forall m\in N_{e}\)
26:\(p_{m,s_{m}}^{(ite+1)}=\exp\left(\gamma\cdot\pi_{m,s_{m}}^{(ite+1)}\right)/ \sum_{\forall s_{m}\in S_{m}}\exp\left(\gamma\cdot\pi_{m,s_{m}}^{(ite+1)}\right)\), \(\forall s_{m}\in S_{m}\), \(\forall m\in N_{e}\)
27:if\((\forall m\in N_{e}\), \(\exists s_{m}\in S_{m}\): \(|pr_{m,s_{m}}(t+1)-1|\leq\epsilon,\epsilon\to 0)\)then
28:\(Convergence=1\);
29:else
30:\(ite=ite+1\);
31:endif
32:endwhile
```  
**Algorithm 2** Distributed Exponential Learning Algorithm for Minority Games (DEMG)
## 2 Materials and Methods
### Experimental Setup  
A greenhouse experiment was carried out at the University of Hohenheim (Stuttgart, Germany) between 10 December and 28 March 2021. The phytocannabinoid-rich _Camabis sativa_ L. strains A4, Kanada (AI FAME, Wald-Schonengrund, Switzerland) (KAN) and E19 (Super Strains, Bladel, The Netherlands) (Figure 2) were grown under three light sources: two LEDs, namely, a Solray385\({}^{\text{\textregistered}}\) (SOL) and an AP67 (both obtained from Valoya Oy, Helsinki, Finland), and one ceramic metal-halide lamp, a CHD Agro 400 (DH Licht GmbH, Wulfrath, Germany). Each light source was replicated three times. Light sources were randomized to tables according to a randomized complete block design. The plants of the three strains were randomized according to a row-column design (3 \(\times\) 4) within each table and thus to each light-source-by-replicate combination. Thus, there were four plants for each strain in each light-source-by-replicate combination. Plants were harvested at three harvest times, with two remaining plants harvested at the final harvest. The design has similarities to a split-plot design, in the sense that light sources as the main-plot factors and strains-by-harvest as sub-plot factors were randomized to different randomization units. The design varies from a split-plot design, however, as there were two plants harvested at the final harvest for each strain-by-main-plot combination. Throughout the experiment, the night temperature was 18 \({}^{\circ}\)C and mean day temperatures showed minor variations depending on the light source, with 23.5 \({}^{\circ}\)C under AP67, 22.4 \({}^{\circ}\)C under SOL and 22.8 \({}^{\circ}\)C under CHD, respectively. Humidity varied from 40.5 to 80.1%, and the CO\({}_{2}\) concentration was between 390 and 450 ppm. Water was provided as needed, according to horticultural standards.  
Figure 2: Exemplary plants of the strains A4, E19 and KAN at final harvest, grown under the SOL light spectra (White bar at bottom right = 10 cm).
## 3 Establishment of the Scale Factor Model
### Finite Element Method Simulation Models  
According to the same simulation conditions of the magnetic dipole model, a rectangular permanent magnet with dimensions of 20 cm \(\times\) 10 cm \(\times\) 5 cm was selected as the magnetic target. In order to calculate the magnetic field in the entire space accurately, the finite element simulation software COMSOL Multiphysics 6.0 was utilized to establish the model. The material properties of the magnetic target were set as iron with a relative magnetic permeability of \(\mu_{0}=4\pi\times 10^{-7}\)H/m (since the propagation characteristics of static magnets in air and water are essentially the same). The background magnetic field was set as the geomagnetic vector for the Xi'an region [41,529 \(-\)1218 2183]nT (in the north-east-down coordinate system) to simulate the surrounding environment of the magnetic target. Figure 6 illustrates the geometric model of the permanent magnet.  
After completing the geometric modeling of the permanent magnet body, the next step was to model the surrounding computational space. The dimensions of this computational space were 20 m \(\times\) 10 m \(\times\) 5 m, as shown in Figure 7.  
Figure 5: The magnetic field components of the magnetic dipole model.  
Figure 6: Geometrical model of the magnetic target.  
Based on the simulation results of these two models, we concluded that both models demonstrate similar effectiveness in reflecting magnetic anomaly signals under the experimental conditions. However, considering that the magnetic dipole model is a simple and widely used target model with specific expressions for the radiation field, it has been extensively applied. Therefore, in engineering practice, within a spatial range greater than 2.5 times the length of the magnetic target, the magnetic target can be treated as a magnetic dipole for analysis, which is more practically meaningful.
# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 2 Materials and Methods
### The Used Machine Learning Methods
#### 2.2.2 Naive Bayes  
Naive Bayes is a supervised machine learning algorithm used for classification tasks. This classifier uses principles of probability in order to perform classification tasks [58]. This algorithm has been incorporated in many research areas, such as document classification [59], traffic risk management [60], network intrusion detection [61], etc. Also, naive Bayes has been used for forest fire issues in a series of papers. For example, [PERSON] et al. proposed a system for forest fire prevention using a combination of a wireless sensor network and a naive Bayes classifier [62]. A classification of hotspots causing forest fires using the naive Bayes algorithm is proposed in the work of [PERSON] et al. [63]. [PERSON] et al. proposed a methodology to classify wildfires using feature selection and naive Bayes among other machine learning methods [64]. Also, a variant of the naive Bayes algorithm was suggested by [PERSON] et al. for forest fire prediction [65].
## 4 Local Updates to Handle Mobility of Sensors for Geocasting
### New Parent Node Selection of MN  
If [PERSON] receives an ACK_BEACON packet from its parent, [PERSON] concludes that it is still in the communication range of its parent. Although [PERSON] is still in the communication range of the current parent node, it may change its _hop count level_ (HCL) due to its mobility, e.g., comes closer to the sink. However, as long as the current parent of MN is in the communication range, [PERSON] keeps the same parent node, although there exist other parent candidates having shorter distances to the sink. With this approach, we avoid sending of extra HCL update messages (_i.e._, HCL_UPDATE) to the child nodes of MN, which are needed if [PERSON] connects to a parent with a different HCL. Also, [PERSON] still remains connected to the tree.  
Figure 6: Fundamental operations of local updating mechanism.  
If [PERSON] does not receive an ACK_BEACON packet from its parent, MN concludes that it is not in the communication range of its parent anymore. Therefore, [PERSON] checks the HCLs (_i.e._, hops to sink entries in ACK packets) of ACK_BEACON packets sent by other neighbor nodes. When [PERSON] receives an ACK_BEACON, it records the sender node as a candidate for the parent node. After the predefined time interval expires, [PERSON] checks its candidate parents list. Since its current parent is not in the list, it chooses the node with the smallest HCL from the candidate parents list as the new parent node. In Figure 5 at time t14, MN chooses node N as the new parent.