esa-sceva/satcom-chunk-collection huggingface dataset:
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 7 Conclusions  
In this work, we propose FusionTrack, a transformer-based model for long-term tracking under scenes featuring uniform appearance and complex motions. As discussed in Sections 4 and 5, the proposed joint track-detection decoder, score-guided multi-level query fuser and query propagation module can effectively fuse in-frame and cross-frame information into the track queries to obtain richer representations of tracking objects. With these modules and a new tracklet management scheme as shown in Table 2, FusionTrack achieves 65.3 in HOTA metric on the DanceTrack dataset, which is +11.1 higher than the baseline model MOTR [11]. More importantly, the association accuracy and IDF1 metric are improved by more than 40%. This suggests that the proposed modules improve the performance of the proposed model by better associating objects with uniform appearance  
Figure 9: Performance on part of DanceTrack validation dataset under different configurations.  
and complex motions. We hope that the proposed modules and design choices can help researchers to develop stronger multi-object tracking models.  
Conceptualization, Y.Y., Z.H. and J.W.; methodology, Z.H. and J.W.; software, Z.H.; investigation, H.L.; resources, H.Z., Y.Y., D.Y. and X.L.; writing--original draft preparation, Z.H. and J.W.; writing--review and editing, Y.Y., Z.H. and J.W.; visualization, Z.H.; supervision, H.Z.; project administration, H.Z.; funding acquisition, Y.Y., D.Y. and X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grants 62002005, 61972015 and 62002008.  
Not applicable.  
The code implementation of the proposed model will be available at [[https://github.com/APassbyDreg/FusionTrack](https://github.com/APassbyDreg/FusionTrack)]([https://github.com/APassbyDreg/FusionTrack](https://github.com/APassbyDreg/FusionTrack)), accessed on 29 May 2023.  
We sincerely thank OpenMMLab and IDEA Research for providing useful code frameworks for model development.  
The authors declare no conflict of interest.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 1 Introduction
The emitting source is also considered a point source, and the propagation from each source point to the field point is determined from the ray. After integrating each ray's propagation, antenna information at the receiver, and vector superposition of each arriving ray, calculations such as line-plane intersection are utilized to explain the channel propagation characteristics. [PERSON] et al. [14] examined an indoor Geometry-based Stochastic Channel Model-Ray Tracing (GSCM-RT) hybrid channel model, which does not account for long-distance path loss and air attenuation. [PERSON] et al. [15] created a hybrid near-far field channel model for massive reconfigurable-intelligent-surface-assisted communication by introducing a weighting factor. The theoretical foundation for our study is provided by the above references. In this study, SSCM-RT channel modeling and simulation are used to examine the UAV dynamic non-terrestrial transmission channel with a focus on the issues with long-distance  
Figure 1: Millimeter-wave band UAV application scenarios. communication and dynamic scenario challenges. The main research contents of this paper are as follows:  
* A long-distance non-terrestrial transmission channel model for UAVs is established. The channel parameters chosen for the simulation are based on the spatial statistical channel model. To confirm the validity of the SSCM-RT channel model, the path loss simulation results are compared with those from other models. * The relative motion of the UAVs is simulated using various motion models in symmetric and asymmetric scenarios, and the spatial consistency's impact on the channel is examined. The channel characteristics of several motion models are simulated to further confirm the reliability of the SSCM-RT model. The remainder of this study is organized as follows. Section 2 introduces the channel fading model parameters and the concept of spatial consistency. Section 3 establishes a long-distance non-terrestrial transmission channel model for UAVs and analyzes the non-terrestrial transmission channel using the SSCM-RT model. Section 4 introduces spatial consistency using various motion models and simulates dynamic scenarios using the SSCM-RT model. Section 5 provides a conclusion.
## 2 Network Model
### Q-Learning Technique  
Machine learning is very popular and applied to many fields. As a subset of machine learning, reinforcement learning obtains specific objectives by interacting with the environment [26]. Q-Learning is one of the reinforcement-learning techniques and it does not need to know prior information of environment [27]. It eventually converges on the optimal strategy by iteratively learning the information gained from environmental feedback. In this context, it is suitable for the dynamic underwater environment.  
The node is described with a tuple (\(s,a,r\)), denoting the state of sensor nodes, action taken by nodes, and direct reward, respectively.  
In UASNs, when node \(i\) processes a data packet, the state of it is changed to busy; otherwise, \(s_{i}\) is idle. The neighbors selected as the next hop are the actions made by the node. The agent performs action \(a_{i}\) from strategy \(\pi\) before proceeding to state \(s_{i}\) from state \(s_{i}\). Reward is the evaluation of an agent's actions.  
\(Q_{\pi}(s_{i},a_{i})\) is the reward that constitutes the direct reward and discounted future rewards, as defined below:  
\[Q^{\pi}(s_{i},a_{i})=r_{i}+\gamma\sum_{s_{i}\in X}p_{  
optimal policy. Furthermore, the Bellman equation can be used to determine at least one optimal strategy \(\pi^{\star}\)[28]. Under the policy, the optimal value is defined as:  
\[\begin{array}{l}V^{*}(s)=\max_{a}(Q^{*}(s,a))\\ Q^{*}(s_{i},a_{j})=r_{i}+\gamma\sum\limits_{s_{j}\in X}P_{s_{i}s_{j}}^{a_{i}}V^ {*}(s_{j})\end{array} \tag{10}\]  
\(Q^{*}(s_{i},a_{j})\) is the expected reward obtained by performing action \(a_{j}\) in accordance with the optimal policy at state \(s_{i}\). Therefore, the optimal action \(a_{i}^{*}\) can be described as:  
\[a_{i}^{*}=\operatorname*{argmax}_{a_{i}\in A(s_{i})}Q(s_{i},a_{i}) \tag{11}\]  
The design of reward function in the SROA will be introduced in Section 3.4.
# Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment
## 4 Proposed Work: Secure Key Exchange Model for MPTCP (SKEXMTCP) Using Identity-Based Encryption
* Host [PERSON] sends request to PKG with IPBob as a parameter by authenticating itself using digitally signed IP address and port combination. * PKG calculates the share of [PERSON] PRBob_master, and it will be sent is back to [PERSON]. * [PERSON] can calculate private key PRBob from the PRBob_master. PRBob = Generate (PRBob_master, IDBob). The messages encrypted by PUBob can be decrypted using PRBob. **Module 2. Initial Handshake using MP_CAPABLE with SKEXMTCP (MPC_SKEXMTCP)**  
**Step 1. SYN [MP_CAPABLE]**  
* Encryption of [PERSON]'s key KAlice. [PERSON] encrypts the session key KAlice with public key of [PERSON] (PUBob = IPBob) using IBE. * Key Transmission of [PERSON]. [PERSON] sends the encrypted key EKAlice = En(PUBob, KAlice) to [PERSON] with MP_CAPABLE. **Step 2. SYN+ACK [MP_CAPABLE]**  
* Encryption of [PERSON]'s key KBob. [PERSON] encrypts the session key KBob with public key of [PERSON] PUAlice using IBE. * Key transmission of [PERSON]. [PERSON] sends the encrypted key EKBob = En (PUAlice, KBob) to [PERSON] with MP_CAPABLE. **Step 3. ACK [MP_CAPABLE]**  
* Key Echoing [PERSON] sends the EKAlice and EKBob again to complete the connection establishment. Figure 11 shows the packet sequence and the parameters passed with each packet of the initial three-way handshake according to the proposed scheme. The whole scenario of module 1, which is about the key generation using IBE, and module 2, which is about the three-way handshake process of MPTCP, is represented in Figure 12. [PERSON] needs to authenticate himself by proving his identity to PKG at the time of requesting the private key to decrypt the message received from [PERSON]. Here, [PERSON] proves his identity through the combination of his IP address and port number. The request for the private key will be encrypted by the session key generated using ECC and HMAC by PKG and digitally signed by the private key of [PERSON] (PRBob). In order to  
Figure 11: Three-way handshake with proposed scheme. the attacker needs to obtain the private key of [PERSON], which is extremely difficult. If the size of the private key is \(n\) bits, the attacker needs to take at least \(2^{n}\) trials to break the security.
## 3 Results and Discussion
### Shelf-Life Study
#### 3.1.1 Microbial Growth
Finally, it is essential to mention the potential antimicrobial resistance of rose extract, with the RSE samples presenting the lowest growth rate for all the microorganisms tested under different temperature conditions. The antibacterial properties of rose petal extract can be ascribed to the presence of flavonoids such as naringenin, quercetin, and kaempferrol [19; 36]. These phenolic compounds were identified as the primary constituents by [PERSON] et al. (2023) [26], who conducted an analysis of the extract utilized in the manufacturing of bacon. Extensive reviews on the antimicrobial mechanism of action of flavonoids is presented by [PERSON] et al. [37], as well as by [PERSON] and [PERSON], 2014 [38]. Flavonoids operate \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Maximum Specific Growth Rate (\(\mu_{\text{max}}\))**} \\ \hline Samples & \(\text{E}_{\mu_{\text{th}}}\) (J/mole) & \(\mu_{\text{ref}}\) (d\({}^{-1}\)), at T = 4 \({}^{\circ}\)C & SSE & R\({}^{2}\) \\ \hline CNT & 103,225.90 & 0.1213 & 0.0033 & 0.9436 \\ RN & 86,422.33 & 0.1578 & 0.0026 & 0.9574 \\ RSE & 88,792.87 & 0.1305 & 0.0004 & 0.9922 \\ RSE-RSME & 91,065.15 & 0.1366 & 0.0006 & 0.9907 \\ \hline  & \multicolumn{4}{c}{**Lag phase (\(\lambda\))**} \\ \hline Samples & \(\text{E}_{\lambda_{\text{A}}}\) (J/mole) & \(\lambda_{\text{ref}}\) (d), at T = 4 \({}^{\circ}\)C & SSE & R\({}^{2}\) \\ \hline CNT & \(-\)109,798.65 & 9.8 & 0.5782 & 0.9642 \\ RN & \(-\)159,416.30 & 6.3 & 0.0394 & 0.9879 \\ RSE & \(-\)117,055.14 & 10.5 & 0.7335 & 0.9589 \\ RSE-RSME & \(-\)98,938.67 & 6.6 & 0.8305 & 0.9165 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of secondary models derived from Arrhenius equations to describe the temperature dependence of the maximum specific growth rate and the lag phase of lactic acid bacteria in the different bacon samples examined. as antimicrobials through various mechanisms. Quercetin induces cell death through the disruption of microbial cell membranes, which results in increased permeability and the subsequent leakage of vital cellular contents. Additionally, it may hinder the production of proteins and nucleic acids (specifically DNA and RNA). In relation to naringenin, it alters the electrochemical membrane potential by decreasing the fluidity of hydrophilic and hydrophobic regions of the cell membrane. Regarding kaempfer, it exerts antibacterial effects through cell membrane disruption and the inactivation of critical enzymes (gyrase, helicase) for the synthesis of proteins and DNA [39].
###### Abstract  
Here we demonstrate that human neural stem cells (NSCs) proliferate while in space and they express specific NSC markers after being in space. NSCs displayed both higher oxygen consumption and glycolysis than ground controls. These cells also kept their ability to become young neurons. Electrophysiological recordings of space NSC-derived neurons showed immature cell membrane properties characterized by small capacitance and very high input resistance. Current injections elicited only an incipient action potential. No spontaneous synaptic events could be detected, suggesting their immature status even though most recorded cells displayed complex morphology and numerous cell processes. Ascertaining the origin of the NSCs' increased energy requirement is of the essence in order to design effective measures to minimize health risks associated with long-duration human spaceflight missions.  
Keywords:neural stem cells; pluripotency; space flight; microgravity; proliferation; neuronal specification; neurons; energetics; glycolysis +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Data Processing
#### 2.5.3 Muscle Activity  
Raw EMG signals are exported from muscle monitor into Matlab. The quality of the MVC signals is controlled based on the noise of the baseline signal when the muscles are fully relaxed. The following requirements are made: the amplitude spikes cannot exceed 15 \(\mu\)V, and the average noise level cannot exceed 5 \(\mu\)V. The baseline has to stay at the same level before and after contractions. When the muscles are contracted, the EMG signal can reach up to maximally 5000 \(\mu\)V. The muscle monitor automatically corrects baseline offsets. Additionally, it reflects all negative amplitudes by the baseline to positives ones. The EMG signals are normalized to the MVC, which is calculated as the mean amplitude of the highest signal portion with a duration of 500 ms. It is determined utilizing the moving average technique. Due to the neural interplay and coordination between muscle agonists and antagonists, EMG curves present great standard deviation ranges and can only be interpreted after averaging out multiple cycles to an AVC.
## 3 Results
### Comparison of the Extraction Performance of Seven Porous Nanomaterials  
The extraction efficiency of seven porous nanomaterials--SBA-15, SBA-16, MCM-41, UiO-66-NH\({}_{2}\)-50%, Meso-UiO-66, HCl-Meso-UiO-66, and 4-Meso-UiO-66--coated on SPME Arrows (with PAN as the adhesive agent) was evaluated for extracting five veterinary drugs in the mixed standard solution (3 \(\upmu\)g mL\({}^{-1}\)) [26; 27; 31]. Among these materials, SBA-15 exhibited the highest adsorption capacity and superior selectivity for all analytes (Figure 3a). This performance can be attributed to SBA-15's large surface area (~720 m\({}^{2}\) g\({}^{-1}\)), well-ordered mesoporous structure, and large pore volume (~0.94 cm\({}^{3}\) g\({}^{-1}\)), which provide numerous active sites for adsorption and facilitate efficient diffusion of analytes into the pores [27]. Additionally, the silica surface of SBA-15 promotes hydrogen bonding and van der Waals interactions with the functional groups of the veterinary drugs, further enhancing selective adsorption [35]. Compared to MCM-41 and UiO-66, SBA-15's superior adsorption performance is likely due to its optimal pore size and stability, allowing for repeated use without performance loss [27]. SBA-15's enhanced extraction capacity offers a significant advantage for detecting trace amounts of veterinary drugs in complex matrices. By improving the sensitivity and selectivity of the SPME Arrow, SBA-15 enhances the accuracy and reliability of on-site detection methods, making it a valuable tool for food safety monitoring. These results highlight SBA-15's potential for broader applications in analytical chemistry.  
Figure 3: (a) Comparison of coating materials: (1) UiO-66-NH2-50%, (2) MCM-41, (3) 4-meso-UiO-66, (4) SBA-15, (5) SBA-16, (6) HCl-meso-UiO-66, (7) meso-UiO-66, and (8) PAN; (b) absolute recoveries of five veterinary drugs and two competing compounds by SBA-15-SPME Arrow. The mixed solution contains 3 \(\upmu\)g mL\({}^{-1}\) of amantadine, thiabendazole, sulfamethazine, clenbuterol, ractopamine, and 300 \(\upmu\)g mL\({}^{-1}\) of bisacodyl and metronidazoleazole.  
Further selectivity testing of the SBA-15-SPME Arrow in the presence of two additional antibiotics, bisacodyl, and metronidazolezole, showed no significant impact on the adsorption of the five target veterinary drugs, despite the higher concentrations of these competing compounds (Figure 3b). These findings demonstrate the superior extraction capacity and selectivity of SBA-15 coatings, even in the presence of potential interferences. As a result, SBA-15 was selected for use in subsequent experiments.
## 3 Results and Discussion
### Influence of Motor Frame/Housing on Natural Frequencies and Modal Shapes  
The modal analysis of stator and windings with the presence of the motor frame was also performed in order to investigate its influence on the natural frequencies of the inner structures as well as to investigate the resulting natural frequency of the whole motor assembly. The results displayed in Figure 8 show that the presence of the motor frame, front and back ending, increases the natural frequencies of the whole motor assembly (i.e., the motor frame, front and back ending, stator stack and windings). The results were obtained with the final mesh density consisting of 324,440 finite elements and 597,323 nodes. Mechanical vibration energy is transmitted from the inner structures (predominantly stator stack) to the motor frame and fins, and can be detected as audible noise and vibration. From Figure 8b, the vibrations can be visually seen. This probably means that they can be easily detected by measurement on the real machine. The first frequency at which the motor frame will start vibrating occurs at \(f=1382\) Hz (Figure 8b), which is also the first detectable natural frequency of the stator stack vibration. Our results show that the motor frame will vibrate only if the stator stack vibrates. The presence of the motor frame will increase the natural frequency of the whole assembly; however, it cannot prevent the separate vibrations of the end-windings at lower frequencies, which are not detectable from the motor frame, as shown in Figure 8a. These results shown in Figure 8a show that at the frequency \(f=616\) Hz, the end-windings vibrate while the motor frame does not vibrate due to low energy transmitted from the inner structures (stator and windings). Therefore, the end-winding in close proximity to the machine cover vibrates at the frequency \(f=616\) Hz, which is not detectable if only the stator frame vibration is observed. This effect may result in undesirable mechanical damage of the windings and premature failure, although it is not detectable on the motor frame. This also means that the vibration, which is not in the audible specter, needs to be adequately mitigated. It is therefore necessary to perform detailed modal analysis of the inner structures as well in order to evaluate the vibration extent of all components and thus to design a safe and low vibration motor structure.
# Fault-Tolerant Resolvability and Extremal Structures of Graphs
## 2 Preliminaries
### Relation between Resolving Sets and Fault-Tolerant Resolving Sets of Graphs  
In Theorem 4, [PERSON] et al. [18] showed that the fault-tolerant metric dimension is bounded by a function of metric dimension. They also showed a relation between a resolving set and a fault-tolerant resolving set for an arbitrary graph. Now, let \(N(w)\) (resp. \(N[w]\)) represent the open and close neighborhood of a vertex \(w\in V(\Gamma)\), where \(N(w):=\{u\in V(\Gamma)\mid uw\in E(\Gamma)\}\) and \(N[w]:=\{w\}\cup N(w)\).  
**Lemma 2**.: [18] _Let \(R\) be a resolving set of graph \(\Gamma\). For all \(w\in R\), let \(T(w):=\{x\in V(\Gamma):N(w)\subseteq N(x)\}\). Then, \(R^{\prime}:=\cup_{w\in R}(N[w]\cup T(w))\) is a fault-tolerant resolving set of \(\Gamma\)._  
Now, the following lemma will help us to obtain upper bounds on the fault-tolerant metric dimension of a given graph. It uses \(R\) in a graph to produce a fault-tolerant resolving set within it. In view of Lemma 2, for a given resolving set \(R\) of a graph \(\Gamma\), finding the set \(R^{\prime}\) to evaluate the corresponding fault-tolerant resolving set seems tedious due to the calculation of the set \(T(w)\) for a vertex \(w\in R\). [PERSON] et al. [21] further simplify this lemma so that one does not have to check every vertex \(x\) of \(\Gamma\) to verify whether or not it belongs to \(T(w)\) for some \(w\in R\). Now, for vertices \(x\) and \(y\) in \(\Gamma\), we let \(\lambda(x,y)\) be a set of common neighbors of these vertices and, for some \(Q\subset V(\Gamma)\), let \(\lambda(Q)\) be the set of common neighbors of each vertex in \(Q\). The following lemma is a key result for finding upper bounds on the fault-tolerant metric dimension of a given graph.  
**Lemma 3**.: [21] _For a graph \(\Gamma\), let \(R\) be a distinguishing or resolving set, and \(R^{\prime}:=\cup_{w\in R}\big{(}N[w]\cup\lambda(N(w))\big{)}\). Then, \(\beta^{\prime}(G)\leq\mid R^{\prime}\mid\)._  
Proof.: Let \(R\) be a resolving set of graph \(\Gamma\). For \(v\in R\), let \(T(v):=\{x\in V(\Gamma):N(v)\subseteq N(x)\}\). Then, for any \(x\in T(v)\), we notice that \(d(x,v)=2\) (see Figure 2).  
Moreover, for \(y,z\in N(v)\), we obtain \(\lambda(y,z)=x\) for some \(x\in T(v)\). This implies that \(T(v)=\lambda\big{(}N(v)\big{)}\setminus\{v\}\) for any \(v\in R\). Now, by Lemma 2, \(R^{\prime}:=\cup_{v\in R}(N[v]\cup T(v))\) is a fault-tolerant resolving set of \(\Gamma\). Since \(v\) is contained in \(N[v]\), then, for any \(v\in R\),  
\[N[v]\cup T(v)=N[v]\cup\lambda\big{(}N(v)\big{)}.\]  
Figure 2: A depiction of the proof of Lemma 3.  
This shows the lemma.  
We use Lemma 3 in later subsections to calculate upper bounds on the fault-tolerant metric dimension for certain families of regular graphs.
## 4 Results and Discussion
### Results of the Simplified Fire Model  
Figure 3 shows the results for the heat flow rate through the ventilation systems, obtained with FDS using the simplified fire model with the four combinations of \(T_{\rm in}\) and \(\dot{m}_{\rm in}\) of cases G-1 to G-4 (Table 1).  
Note that, despite the broad range of inlet temperatures considered (2436-1500 K), maintaining the same convective heat release rate (calculated from Equation (2)) in all the cases yields results that compare very well with each other. The results for the normalized CO\({}_{2}\) mass flow rate (\(\dot{m}_{\rm CO_{2}}/\dot{m}_{\rm in,CO_{2}}\), where \(\dot{m}_{\rm in,CO_{2}}\) is the mass flow rate of CO\({}_{2}\) introduced in the domain at the fire focus) are qualitatively very similar to those for the heat flow rate shown in Figure 3. Although not shown here, the results for the mass flow rate extracted by the forced ventilation system and net mass flow rate leaving the compartment are practically independent of \(T_{\rm in}\).  
Table 3 shows the fractions of the heat and CO\({}_{2}\) mass introduced in the domain from the beginning of the fire that had been extracted by the ventilation systems at \(t=90\) and \(720\) s,  
\[\mathcal{I}_{h}(t)=\frac{1}{(\HRR)t}\int_{0}^{t}\dot{q}\,\dd t;\quad\mathcal{I }_{c}(t)=\frac{1}{\dot{m}_{\rm in,CO_{2}}t}\int_{0}^{t}\dot{m}_{\rm CO_{2}}\dd t. \tag{6}\]  
Figure 3: Heat flow rate through the forced extraction grilles and west vent, predicted by FDS and the simplified fire model with \(T_{\rm in}=2436,2124,1812\) and \(1500\) K (cases G-1 to G-4 in Table 1).  
Note the low dependence of \(\mathcal{I}_{h}\) and \(\mathcal{I}_{c}\) on the inlet temperature \(T_{\text{in}}\).  
Figure 4 shows the temperature contours at \(t=90\) s on horizontal planes at heights \(z=2.95\) and \(2.05\) m, and at \(t=720\) s and \(z=2.95\) m, predicted by FDS using the simplified fire model with \(T_{\text{in}}=2436\) and \(1500\) K. It can be observed that, sufficiently far from the fire focus, the temperature distributions obtained using different \(T_{\text{in}}\) values compare very well, behavior that is consistent with the very slight dependence of the results of Figure 3 and Table 3 on \(T_{\text{in}}\). A similar behavior is observed for the distributions of the normalized CO\({}_{2}\) concentration and velocity magnitude, which are not shown for brevity.