esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model
### Measuring Similarity Based on Probability Distribution of User Interactions
\[\text{Similarity}\ \propto\ \frac{1}{\text{Distance}} \tag{5}\]  
In terms of the KL divergence, the negative distance between similarities can be defined as the negative distance between \(P_{u_{i}}(\text{I})\) and \(P_{u_{j}}(\text{I})\), which is defined as the measure of similarity between \(u_{i}\) and \(u_{j}\) as shown in Equation (6). \[Sim\left(P_{u_{i}}(\text{I}),\ P_{u_{j}}(\text{I})\right)=-\ D_{KL}\left(P_{u_{ i}}(\text{I}),\ P_{u_{j}}(\text{I})\right) \tag{6}\]  
The similarity measure is normalized between 0 and 1 for simplicity. After estimating the similarity between all the users, we set a similarity score threshold to select the neighbors. Neighbor sampling is a critical yet vital task for graphs. The value of the similarity score ranges between 0 and 1. If the node's probability distribution is 100% the same as that of other nodes, the score will be 1; otherwise, 0. We defined threshold values for the similarity scores to estimate the level of the neighboring node. In Figure 5, different colors represent different nodes, and there are a total of six nodes. If the score of similarity between two nodes is \(\geq\)0.8, they are considered first-order neighbors and are at a distance of only one click from each other. If the similarity score is \(\geq\)0.6 but less than 0.8, then these nodes are second-order neighbors. Figure 4: Similarity score estimation for users based on the probability distribution. Consider a scenario where there are multiple first-order neighbors and then how we can select to which node the second-level neighbor might become attached. In such scenarios, the similarity score is calculated in loops. The closer a node is in terms of the score, the more likely it is to get selected as a direct connection. For example, in Figure 5, the similarity score of node 5 and node 4 is 0.9, node 6 and node 5 is 0.7, and node 4 and node 6 is 0.6. In this case, node 4 and node 5 are direct neighbors according to the rule. On the other hand, node 6 is a second-order neighbor with both node 4 and node 5, as the similarity score between node 5 and node 6 is greater than the score between node 4 and 6. Therefore, node 6 will relate to node 5. If the score is the same, then the node can be attached to any node. All the other nodes do not have the similarity score criteria and, hence, are considered as isolated clusters. Now, we have generated clusters of nodes that have similar item-click patterns and learned the neighborhood sampling. We regenerate session graphs based on this knowledge and use these graphs as input for our GCNN model. Previously, the GCNN model learned neighbors' sampling directly from the raw session graphs, but now, we use updated session graphs where we already know about the node neighbors. This knowledge helps to decrease the computational complexity of the GCNN. The next section explains the proposed model's complete architecture and describes the GCNN model used for training.
## 3 Results and Discussion
### Modelling Results
#### 3.2.2 Pd/Zn vs. Pd/C Catalyst  
The experimental and process-modelling results obtained using the novel 2%Pd\({}_{6}\)Zn\({}_{4}\) catalyst were compared with a commercial 5 wt.% Pd/C catalyst using a batch reactor [5]. In addition, the catalytic performance of the material presented in this work was compared with published data on 1% Pd/HHT [4,17]. The decomposition of formic acid under mild conditions using a Pd/C and 1% Pd/HHT catalyst occurred under similar conditions to those of the current work. The study was conducted using a two-necked 100 mL round-bottom flask, which was immersed in an oil bath alongside a reflux condenser and a magnetic stirrer. The reaction took place at 30 \({}^{\circ}\)C, and 0.5 M of aqueous formic acid solution was placed into the reactor. The substrate/metal molar ratio was 2000:1, and the stirring rate was 1400 rpm [4,5,17].  
The process-simulation studies took place under constant reaction conditions for the comparison between the two catalysts to assess their performance for the reaction. From Table 2, it can be observed that, under constant reaction conditions, the performance of the novel Pd/Zn catalyst is significantly higher than that of the commercial Pd/C catalyst and of the monometallic counterpart, underlining the role of Zn in the catalytic process. As a result, the novel Pd/Zn catalyst could be utilised to enhance the decomposition of formic acid reaction. Furthermore, there is good agreement (less than 5% difference) between the process-simulation modelling and experimental data. Therefore, the theoretical models created in this study have the ability to successfully predict the decomposition of formic acid under mild conditions. A gas analysis was performed on the system using  
Figure 4: Conversion of formic acid against reaction temperature. Reaction conditions: HCOOH/metal molar ratio is 2000:1, inlet [HCOOH] = 0.5 M, rpm = 1400, catalyst 2%Pd\({}_{6}\)Zn\({}_{4}\).  
Figure 5: Conversion of formic acid against inlet concentration. Reaction conditions: temperature = 30 \({}^{\circ}\)C, HCOOH/metal molar ratio is 2000:1, rpm = 1400, catalyst 2%Pd\({}_{6}\)Zn\({}_{4}\).  
an online micro-GC in order to calculate CO/CO\({}_{2}\) ratio, and a selectivity of 76% for the dehydrogenation reaction was obtained.
# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Simulation Results
#### 5.1.2 Network Overhead Simulation Results
Using VCDNoCAM, the effect of the coverage area is null on the overhead related to the data. It only affects the overhead related to the interests. GeoVCDN is the unique approach, tending to decrease the throughput when increasing the RSUs' range because fewer V2V exchanges are needed to spread the data. Table 13 presents a comparison between the expected values and the observed values with the RSU's range of 150 m. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Expected Value** & **Observed Value** & **Difference** \\ \hline NDN & 3,075,840.00 & 3,240,097.20 & +5.34\% \\ RENE & 19,672.56 & 19,734.53 & +0.32\% \\ VCDN & 151,559.88 & 151,847.26 & +0.19\% \\ VCDNoCAM & 9709.23 & 9680.13 & \(-\)0.30\% \\ GeoVCDN & 30,725.85 & 31,360.93 & +2.07\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Network Overhead Comparison for RSUs’ Range. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Expected Value** & **Observed Value** & **Difference** \\ \hline NDN & 890,000.00 & 956,702.13 & +7.49\% \\ RENE & 10,298.97 & 10,469.80 & +1.69\% \\ VCDN & 100,984.96 & 103,212.86 & +2.2\% \\ VCDNoCAM & 5889.28 & 6001.46 & +1.90\% \\ GeoVCDN & 27,812.43 & 25,939.06 & \(-\)6.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Network Overhead Comparison for Number of RSUs. Figure 22: RSUs’ range. Again, the table confirms the analytic model when the RSU's range changes. Indeed, we can see that the difference between the model and the simulation is very low (0-2%). For VCDN, RENE, and VCDNoCAM, the increase of the data generation frequency leads to a similar behavior for the overhead related to it, as we can see in Figure 23: with the increase of the data generation frequency, we increase the data amount transiting in the network. Using VCDNoCAM, we are limited by the CAM frequency (10 Hz), which is why the network overhead is constantly passed. We can see that with GeoVCDN, the higher frequency is, the fewer exchanges between vehicles. Passed 10 Hz, the exchanges between vehicles decrease (because the maximum hop limit is computed based on the data generation frequency). For this reason, from this point, the higher the frequency, the lower the consumed bandwidth within the GeoVCDN approach. Table 14 confirms that the difference between the expected and measured values for a frequency X is very low (between 1 and 2%).
## 4 Results and Discussion
### Influence of Pile Head Depth (With Fixed Pile Toe Depth)  
The depth of the pile toe was fixed at 40 m. Under the influence of the mechanisms described above, the evolutions of vertical and horizontal control efficiencies with the pile head depth for cases with x = 2, x = 5.5, and x = 9 m are presented in Figure 19. From a global perspective, as the pile head depth increased, the vertical control efficiency decreased from a positive value to a negative value and then began to rise again. Notably, the maximum vertical control efficiencies for all cases occurred when the pile head depth was approximately 0-5 m. This indicates that isolation piles with a pile head depth of 0 m could be more effective in controlling vertical displacement of the tunnel.  
It can also be observed in Figure 19 that at x = 2 m, the horizontal control efficiency generally showed a trend of first decreasing and then increasing as the pile head depth increased. At x = 5.5 m, the horizontal control efficiency initially decreased, then increased, and finally decreased again as the pile head depth increased. At x = 9 m, the horizontal control efficiency first increased and then decreased with increasing pile head depth. Additionally, as shown in Figure 19, it is observed that as the distance between the isolation pile and the excavation increased, the horizontal control efficiency of the isolation pile gradually shifts from a negative value to a positive one.
## 5 Conclusions  
The aim of this research was to obtain a unified and established criteria for the determination of the additional clearance over obstacles in mountainous areas. This clearance is constituted by the barometric altimeter error due to strong winds and by the loss of aircraft altitude caused by turbulences due to the presence of mountain waves. The following conclusions were reached:  
* A loss of altitude due to mountain wave turbulence depends not only on the wind intensity, but also on aircraft characteristics such as weight, wing area, angle of attack, and flight speed.
* The lower aircraft categories (A and B) are more susceptible to turbulence than the higher categories (C and D), resulting in greater vertical displacements.
* Aircrafts of the same category assume lower vertical displacements the higher the wing loading is, meaning a greater the weight or smaller the wing area. Therefore, an identical aircraft will be less affected by turbulence at high weights.
* An aircraft flying under the same conditions of weight, airspeed, wind, and altitude experiences greater vertical displacements due to turbulence effects when operating at higher angles of attack.
* The maximum vertical displacement due to mountain wave turbulence for a category A aircraft is approximately 30 m (100 ft), while for categories B, C, and D, it is around 20 m (67 ft).
* The altitude loss due to turbulence is less significant than the altimeter error. This difference increases the higher the wind speed is, which means that at strong winds, the altimeter error margin is the main one, since the turbulence clearance is almost negligible.
* The value obtained by adding the altimeter error and the aircraft altitude loss due to turbulence is proposed as the additional clearance to be applied in mountainous terrains.  
Conceptualization, L.P.S.; methodology, L.P.S. and A.F.-S.G.; software, A.F.-S.G.; validation, J.A.P.-C., L.S.-M., and E.S.A.; formal analysis, L.P.S., A.F.-S.G., and D.R.F.; investigation, L.P.S. and A.F.-S.G.; resources, L.P.S., A.F.-S.G., and D.R.F.; data curation, A.F.-S.G. and E.S.A.; writing--original draft preparation, L.P.S. and A.F.-S.G.; writing--review and editing, J.A.P.-C., L.S.-M., and E.S.A.; visualization, L.P.S. and A.F.-S.G.; supervision, L.P.S.; project administration, L.P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Iberia Airlines. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Nomenclature  
### Symbols  
\(\mathrm{D_{ref}}\) [m] & Runner outlet diameter  
E & [J\(\cdot\)kg\({}^{-1}\)] & Specific energy  
g & [m\(\cdot\)s\({}^{-2}\)] & Gravity  
H & [m] & Head  
N & [min\({}^{-1}\)] & Runner rotational speed  
\(\mathrm{N_{ED}}\) & [-] & Speed factor  
P & [W] & Power  
P\({}^{*}\)\({}_{\mathrm{ED}}\) & [-] & Power factor (based on  
mechanical/electrical power)  
Q & [m\({}^{3}\cdot\)s\({}^{-1}\)] & Discharge  
Q\({}_{\mathrm{ED}}\) & [-] & Discharge factor  
T\({}_{\mathrm{mec}}\) & [N\(\cdot\)m] & Runner mechanical torque  
\(\mathrm{\eta}\) & [%] & Efficiency  
\(\mathrm{\eta}^{*}\) & [-] & Dimensionless efficiency  
\(\mathrm{\eta}^{*\prime}\) & [-] & Dimensionless efficiency  
fluctuation  
\(\mathrm{\eta}^{*}\)\({}_{\mathrm{fit}}\) & [-] & Fitted dimensionless efficiency  
\(\mathrm{\eta}^{*\prime}\)\({}_{\mathrm{STD}}\) & [-] & Efficiency fluctuation Standard  
Deviation  
\(\mathrm{\rho}\) & [kg\(\cdot\)m\({}^{-3}\)] & Water density  
\(\omega\) & [rad\(\cdot\)s\({}^{-1}\)] & Runner angular speed  
Subscripts  
A,B & 1 st, 2 nd independent runners  
e & Energetic losses  
elec & Electrical  
h & Hydraulic  
m & Bearing losses  
mec & Mechanical  
q & Volumetric losses  
rm & Disc friction losses
## 2 Powertrain Modeling
### Motor Model  
We use a look-up table containing the power losses of the machine for different torque and shaft speed combinations to describe the electric machine behavior. This approach is advantageous for the following reasons:  
1. The loss map gives means for a generic representation of the machine, which is able to fit any motor used in powertrain systems.
2. Focus is given to conversion efficiency through motoring and regenerative quadrants.
3. Power losses can be accurately characterized through electromagnetic finite-element models without the need to represent the machine through a lumped-parameter model.
4. Electromagnetic dynamics are represented through a first-order low-pass filter with a time constant of 50 ms. The electric machine loss map used in this case is illustrated in Figure 3.  
Assuming perfect wheel rolling (no slip) and an ideally efficient gearbox, the powertrain mechanical power can be defined either through vehicle variables or through motor variables, i.e.,  
\[P_{m}=F_{xf}\dot{x}-P_{rr}=T_{m}\omega_{m}-P_{rr} \tag{14}\]  
where \(T_{m}\) is the motor torque, \(\omega_{m}\) is its angular speed, and \(P_{rr}\) is the vehicle dissipated power due to rolling resistance.  
During operation, the motor will attempt to impose a torque--positive or negative--on the vehicle based on the longitudinal control's request. On the other hand, the vehicle will impose angular velocity on the motor shaft. Part of the mechanical power in Equation (14) will have an impact on the electrical domain of the motor, as it will drive the absorption or regeneration of electrical power, defined as:  
\[P_{e}=V_{dc}i_{dc} \tag{15}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Distance from COG to front axle & \(l_{f}\) & 1.04 & m \\ Distance from COG to rear axle & \(l_{r}\) & 1.56 & m \\ Mass & \(m\) & 1377 & kg \\ Yaw polar moment of inertia & \(l_{zz}\) & 1343.1 & kgm\({}^{2}\) \\ Front tire cornering stiffness & \(C_{af}\) & 12 & kN/ rad \\ Rear tire cornering stiffness & \(C_{ar}\) & 11 & kN/ rad \\ Total rolling resistance force & \(F_{c}\) & 5 & N \\ \hline \hline \end{tabular}
\end{table}
Table 2: B-class hatchback electric vehicle model parameters.  
with \(V_{dc},i_{dc}\) being the voltage and current of the battery.  
In motoring mode, the efficiency of the powertrain is defined as  
\[\eta_{mat}=\frac{P_{m}}{P_{e}}=\frac{P_{m}}{P_{m}+P_{loss}(\omega_{m},T_{m})} \tag{16}\]  
where \(P_{loss}(\cdot)\) represents the loss map function of the machine evaluated at a specific operating point.  
Conversely, the powertrain has a regenerative efficiency given by  
\[\eta_{reg}=\frac{P_{e}}{P_{m}}=1-\frac{P_{loss}(\omega_{m},T_{m})}{P_{m}} \tag{17}\]  
Note that in any instance, a generic efficiency term must guarantee \(\eta<1\).
## 3 Experiments and Analysis
### Runtime Analysis  
We conducted runtime comparisons across Seq3, Seq4, and Seq6, including multi-scale sequences. UIUNet was excluded due to its GPU-dependent implementation, ensuring fair CPU-based benchmarking. As shown in Table 4, the proposed method demonstrates competitive runtime performance, significantly outperforming computationally intensive iterative optimization methods while remaining comparable to lightweight single-domain filtering approaches. Though marginally slower than TVF and DNGM--which achieve speed prioritization through specialized acceleration--our approach delivers substantially superior background suppression (BSF) and target contrast enhancement (SCRG), as evidenced by the comprehensive metrics in Table 1. This moderate computational overhead is well-justified given the critical improvements in detection reliability and clutter rejection capability, particularly in cluttered scenarios where conventional methods exhibit severe performance degradation (e.g., Seq3 D-statistic comparisons in Section 3.5).  
In terms of memory utilization, our method exhibits a moderate increase in consumption (34.74 MB-171.14 MB) compared with TVF (4.82 MB-226.42 MB) and DNGM (5.12 MB-216.64 MB), yet demonstrates significantly greater efficiency than memory-intensive approaches like IMNNLWC (291.48 MB-293.05 MB). Notably, our method shows superior scalability on larger images, as evidenced by its performance on Seq4 and Seq6. While DNGM and TLLCM experience substantial memory growth (DNGM: 5.12 MB to 216.64 MB; TLLCM: 13.64 MB to 218.73 MB) and IPI suffers from extreme memory inefficiency (213.68 MB-213.72 MB), our approach maintains a more controlled memory footprint (34.74 MB-171.14 MB) with better computational stability. This elevated memory footprint primarily arises from the temporal fusion mechanisms essential for robust target detection in complex backgrounds. While lightweight methods achieve reduced memory usage through simplified processing pipelines, they fail to maintain comparable detection accuracy, as demonstrated in Table 3. The memory trade-off in our approach is thus well-justified by its superior performance metrics, particularly in challenging scenarios where conventional methods struggle to balance detection capability and computational efficiency. This design optimization ensures reliable detection across diverse infrared scenarios while maintaining practical resource requirements, establishing an effective compromise between memory utilization and detection performance.
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 5 JDT Algorithms Based Deep Learning
### Fusion Algorithm of Single Object Tracking  
The single object tracking task and the visual multi-object tracking task have a strong relationship. The expected kinematic and visual characteristics of a single object tracking are included in the single object tracking algorithm. Due to the swift advancement of the single object tracking field, the fusion of single object tracking for multi-object tracking has persisted in recent years.  
The single object tracking (SOT) method itself has information such as placement and identification thanks to the twin network structure. As a result, several iterations of multi-object tracking algorithms have emerged that leverage the single object tracking technique to replace the motion model and the appearance model. In theory, the multi-object tracking algorithm based on single-object tracking can be compared to the detection-based tracking algorithm, since the problem of lack of observation has some robustness and temporary object positioning information can be gained by regional search.  
Zhu et al. [76] proposed the DMAN algorithm to integrate the advantages of single-object tracking and data association methods in a unified framework, and in incorporating SOT into MOT, they introduced cost-sensitive tracking loss for visual tracking to deal with mutual occlusion and interference between objects. Feng et al. [77] proposed the LSST algorithm for the problem of occlusion leading to trajectory features with residuals and even ID switches, and the base tracker is a SiamRPN for fast and accurate detection in the field of single-object tracking, which achieves long-term stable tracking based on the epistatic information extracted by the ReID algorithm. Chu et al. [78] proposed a KCFalgorithm with a relatively complex structure, and designed an Instance-aware SOT tracker by encoding awareness both within and between the object model. Then, the joint model was tested and corrected, the dynamic model was refreshed, and the goal management was among the best in the MOT challenge.  
In accordance with the earlier introduction, we discovered that the technology that combines single object tracking and multi-object tracking has both benefits and glaring drawbacks. When the scene composition is modest, the quick and precise feature extraction and positioning capabilities of single object tracking technology are somewhat adaptable for the issues of false detection and missed detection. This is good, but when there are numerous scenario items, an object tracker needs to be added to every item, leading to major efficiency and real-time performance issues that call for a deeper investigation of the method.
## 2 Materials and Methods
### Performance Characterization  
The structure of the flexible sensor is depicted in Figure 2a. The pressure sensor is composed of five layers of hydrophobic conductive fabric, each measuring 1 cm \(\times\) 1 cm, with conductive copper tape utilized as the top and bottom electrodes. This size was chosen considering the size of the fingers, while the thickness was determined to achieve the best balance for wearer comfort. To mitigate the impact of contact resistance during testing, the outer sides of the electrodes are wrapped in medical-grade polyurethane (PU) film, ensuring stable electrical performance signals from the sensor. The performance of the fabricated sensor was tested using an electrochemical workstation (CHI660E, Shanghai Chenhua Instrument Co., Ltd., Shanghai, China). The sensor exhibits different signal responses under pressures of 0.1 kPa, 0.2 kPa, 0.5 kPa, 1 kPa, 2 kPa, and 5 kPa, demonstrating its capability to detect a range of pressure levels (Figure 2b). Under a constant pressure of 0.5 kPa, the sensor was tested at frequencies of 0.5 Hz, 1 Hz, 2 Hz, and 3 Hz, revealing that the different frequencies had minimal impact on the sensor's relative current change (Figure 2c). Current-voltage curves were obtained at pressures of 0 kPa, 0.1 kPa, 0.2 kPa, 0.5 kPa, 1 kPa, 2 kPa, and 5 kPa. Within the limits of the sensor's functionality, the resistance exhibited a linear change, which amply demonstrated that the material possessed practical piezoresistive properties (Figure 2d). Under a pressure of 0.5 kPa, loading and unloading tests were conducted, yielding response and recovery times of 119 ms and 68 ms, respectively, as shown in the inset of Figure 2e, indicating a rapid response. The sensor exhibited stability after 1000 loading and unloading cycles at a pressure of 0.5 kPa. The magnified view in the inset displays the relative current changes from the 35 th to 39 th and the 957 th to 961 st cycles, which are nearly identical, thereby verifying its long-term stability (Figure 2f).  
Compared to the sensor in reference [54], the sensor fabricated in this study has a greater advantage in low-pressure detection, whereas the sensor in reference [54] is more suitable for detection within the medium to high pressure range. Additionally, the sensor in this study is also more appropriate for routine static or low-frequency dynamic pressure monitoring.  
Figure 1: Preparation process diagram.