esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Software System
### Three-Dimensional Modeling of the Lasted Upper
#### 4.1.2 Precise Laser-Line Localization  
When the laser line is projected onto the lasted upper, it is necessary to adjust the camera's aperture and exposure time to capture a sufficiently clear laser image while maintaining compatibility with the required production line speed. As shown in Figure 11a, although the laser image appears clear, the laser stripe remains too thick to enable precise localization. To improve accuracy, adaptive thresholding is first applied for binarization, followed by morphological closing and contour-tracing techniques [24]. These image processing steps extract the contour edge points of the laser stripe, as illustrated in Figure 11b,c.  
Figure 10: (**a**) Three-sided laser imaging results in one shot. (**b**) Definition of the reference plane of the upper.  
Subsequently, based on the extracted upper and lower contour edge points, the laser stripe's centerline is computed by determining the midpoint between the two edges in each image row. This approach assumes a symmetrical laser intensity distribution, allowing the centerline to represent the geometric center of the stripe's visible width. The resulting centerline provides a subpixel-accurate estimation of the stripe position and serves as the basis for calculating displacement values, as illustrated in Figure 11d. In cases where the lasted upper exhibits complex textures or surface patterns, small gaps may appear along the extracted contours. As these discontinuities are typically minor, they can be effectively filled using linear interpolation.
## 3 Results and Discussion  
### GC-MS Analyses  
The achieved findings of the GC-MS study of the SPME extraction revealed the existence of esters, alcohols, etc., (Table 1, Figure 2). A total of 29 compounds were identified. Isopentyl alcohol was the compound that was detected in major concentration (18.19%) in the alcoholic beer and such a compound is the one that mostly affects the aroma in alcohol. It influences the drinkability because the aroma of the beer becomes more pronounced if the concentration of isoamyl alcohol increases. Notably, isobutyl alcohol (0.95%) in the alcoholic beer has an adverse impact on the quality of beer if its concentration exceeds 20% of the total amount of \(n\)-propanol, isobutyl alcohol, and isoamyl alcohol. In humans, isoamyl alcohol causes sedative, hypnotic, and anticonvulsant effects similar to the effects of ethanol by ingestion or inhalation and was earlier utilized in medicine for these reasons [32].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & \
\begin{tabular}{c} **Non-Alcoholic** \\ **Beer** \\ \end{tabular} &
\begin{tabular}{c} **Alcoholic** \\ **Beer** \\ \end{tabular} \\ \hline
**Peak** & **Compound** & **LRIexp** & **LRIIib** & **Similarity** & **Area\%** & **Area\%** \\ \hline
**1** & Acetaldehyde & 611 & nd & 89 & 0.58 & 0.08 \\
**2** & Ethanol & 616 & nd & 98 & 2.65 & 6.14 \\
**3** & Propyl alcohol & 633 & 638 & 91 & nd & 0.19 \\
**4** & Acetic acid ethyl ester & 649 & 606 & 97 & 1.57 & 4.29 \\
**5** & Isobutyl alcohol & 656 & 621 & 89 & 0.21 & 0.95 \\
**6** & Ethyl propanoate & 705 & 708 & 89 & nd & 0.08 \\
**7** & Isopentyl alcohol & 727 & 729 & 92 & 11.48 & 18.19 \\
**8** & sec-Butylcarbinol & 730 & 733 & 93 & 2.97 & 8.52 \\
**9** & Ethyl butyrate & 803 & 807 & 90 & nd & 0.18 \\
**10** & Isoamyl acetate & 871 & 871 & 96 & 1.19 & 13.81 \\
**11** & Ethyl hexanoate & 995 & 1003 & 93 & 0.43 & 4.77 \\
**12** & Octanal & 1001 & 1006 & 91 & 0.71 & 0.4 \\
**13** & 2-Ethyl hexanol & 1026 & 1030 & 88 & 2.03 & 0.24 \\
**14** & _n_-Octanol & 1069 & 1076 & 89 & 0.54 & 0.27 \\
**15** & _p_-Tolualdehyde & 1082 & 1086 & 91 & 0.21 & nd \\
**16** & Linalool & 1097 & 1100 & 94 & 0.69 & nd \\
**17** & Nonanal & 1102 & 1107 & 96 & 3.83 & 2.06 \\
**18** & Phenethyl alcohol & 1111 & 1113 & 96 & 1.39 & 10.6 \\
**19** & _n_-Octanoic acid & 1168 & 1192 & 90 & nd & 0.44 \\
**20** & (Z)-3-Nonen-1-ol & 1171 & 1173 & 89 & 0.31 & 0.21 \\
**21** & Naphthene & 1184 & 1188 & 92 & 32.88 & 2.27 \\
**22** & Ethyl octanoate & 1194 & 1202 & 96 & 0.17 & 4.83 \\
**23** & _n_-Decanal & 1203 & 1208 & 96 & 1.62 & 2.24 \\
**24** & 2-Phenethyl acetate & 1252 & 1257 & 88 & 0.46 & 5.74 \\
**25** & Bornyl acetate & 1283 & 1285 & 93 & 0.41 & 0.31 \\
**26** & Ethyl decanoate & 1391 & 1399 & 91 & nd & 0.34 \\
**27** & _n_-Dodecanal & 1407 & 1410 & 92 & 0.48 & 0.37 \\
**28** & Verdyl acetate & 1421 & 1424 & 88 & 0.51 & nd \\
**29** & Dodecanol & 1472 & 1480 & 98 & 24.35 & 2.9 \\  & TOTAL IDENTIFIED & & & 91.69 & 90.44 \\ \hline \hline \end{tabular} nd: not detectable.  
\end{table}
Table 1: List of compounds identified in alcoholic and non-alcoholic beer samples achieved by GC-MS.
# Experimental Study of a Novel Prototype of Dual-Cycle Two-Phase Cooling System for a Data Cabinet
## 3 Results and Discussion
### The Effects of the Flow Rate of Each Loop on the Cooling System
#### 3.2.2 The Effects of PCTCS External Loop Flow Rate  
Figure 7 provides a detailed description of the effects of varying the external loop flow rate on system temperature and pressure differences. During the experimental process, the internal loop remained stable while the external loop flow rate gradually increased from 1.5 L/min to 3.1 L/min. Throughout the testing period, the internal loop and chip core temperature TCPU exhibited minimal fluctuations, and the pressure differences across various sections remained stable, indicating that adjusting the external loop flow rate has a limited impact on the internal loop pressure. For the external loop, an increase in flow rate led to continuous pressure fluctuations. At the moment of increasing the flow rate, a temporary decrease in reservoir temperature was observed. This is attributed to the fact that an increase in flow rate amplifies the pressure differences across various parts of the external loop. Notably, the pressure difference across the PHE, denoted as \(\Delta\)P\({}_{201-202}\), increased less than the pressure difference across the piping and condenser \(\Delta\)P\({}_{202-203}\). An increase in total pressure difference reduces the reservoir pressure. Since the control target is the reservoir temperature, this results in greater temperature fluctuations in the reservoir. Overall, the external loop flow rate has a relatively small impact on the temperatures at various nodes of the cooling system.  
By analyzing the effects of flow rate changes in both internal and external loops, significant decoupling characteristics between the two loops of the PCTCS are revealed. Altering the flow rate of one loop primarily affects that loop itself, with a relatively small impact on the temperature and pressure drop of the other loop. It is not feasible to effectively control the operating conditions of one loop solely by adjusting the flow rate of the other loop, indicating a considerable degree of independence, and decoupling between the two loops.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Error Ellipse  
Set \(\sigma_{\mathbf{\theta}}=0.5^{\circ}\), \(\sigma_{\mathbf{\varphi}}=0.75^{\circ}\), \(\sigma_{\mathbf{h}}=2\) km, \(\sigma_{\mathbf{r}}=0.5\) km, and \(\sigma_{\mathbf{s}}=1.5\) km. The localization results and the corresponding uncertainty error ellipses are given in Figure 4, with ellipse probabilities of 0.5 and 0.9, respectively.  
As can be seen from Figure 4, the localization results are consistent with the ellipse, proving the effectiveness of the proposed ICWLS algorithm. In addition, the ellipse area of the fusion result is smaller compared to the single-method localization, which demonstrates that the fusion method improves the localization accuracy.
## 4 Empirical Results
### Project Recommendation Results
\begin{table}
\begin{tabular}{l c c c c c c c}
**ID** &
\begin{tabular}{c} **Developer Metrics** \\ **(experience approach)** \\ \end{tabular} & \multicolumn{3}{c}{**Context-free similarity**} & \multicolumn{3}{c}{**Context-based similarity**} \\
**1** & **compit\_authored** & **top1** & **top5** & **top10** & **top1** & **top5** & **top10** & _MRR_ \\
1 & commit\_authored** & 23.00 & 35.60 & 36.30 & 65.00 & 58.80 & 57.00 & _0.05_ \\
2 & commit\_commmented** & **74.00** & 57.60 & 51.90 & 72.00 & 66.60 & 63.90 & _0.10_ \\
3 & commit\_omitted** & 23.00 & 35.60 & 35.30 & 65.00 & 57.60 & 55.70 & _0.04_ \\
4 & issue\_assigned & 64.00 & 52.20 & 53.80 & 59.00 & 57.80 & 53.30 & _0.05_ \\
5 & issue\_dosed** & 68.00 & 59.60 & 55.40 & 50.00 & 49.40 & 46.40 & _0.05_ \\
6 & issue\_naspR** & 72.00 & 62.60 & 61.10 & **73.00** & 70.80 & 69.00 & _0.09_ \\
7 & issue\_commmented** & 66.00 & 66.80 & 65.60 & **65.60** & 70.60 & 70.20 & _0.12_ \\
8 & issue\_opened & 68.00 & 67.60 & 66.40 & **75.00** & **71.80** & 70.70 & _0.14_ \\
9 & pr\_assigned & 44.00 & 26.60 & 20.50 & 36.00 & 30.00 & 29.80 & _0.04_ \\
10 & pr\_commmented & 48.00 & 47.80 & 48.00 & 60.00 & 61.40 & 60.10 & _0.05_ \\
11 pr\_merged & **74.00** & 50.40 & 45.00 & 65.00 & 62.20 & 60.30 & _0.08_ \\
12 pr\_opened & 70.00 & 61.80 & 61.10 & 70.00 & 69.20 & 68.30 & _0.08_ \\
13 comments & 67.00 & 63.40 & 63.70 & 64.00 & **72.00** & 70.60 & _0.18_ \\
14 code contributions & **13.00** & 67.00 & 64.90 & 70.00 & 70.40 & 70.80 & _0.15_ \\
15 commit\_related & 69.00 & 61.80 & 60.10 & 72.00 & 67.60 & 64.50 & _0.07_ \\
16 pr\_related & 63.00 & 62.80 & 64.00 & 60.90 & 71.20 & 67.60 & _0.08_ \\
17 issue\_related & **74.00** & **71.80** & **67.30** & **72.00** & 71.40 & 69.60 & _0.20_ \\
18 binary\_commit\_related & 72.00 & 64.60 & 60.40 & 70.00 & 69.20 & 65.90 & _0.08_ \\
**19 binary\_pr\_related** & 70.00 & 67.00 & **66.50** & **70.00** & **72.00** & **72.00** & _0.45_ \\
**20 binary\_issue\_related** & **25.00** & **25.00** & **25.00** & **25.00** & **25.00** & **25.00** & _0.65_ \\
**21 binary\_code\_contributions** & **73.00** & **68.60** & **25.00** & **25.00** & **25.00** & **25.00** & _0.43_ \\
**22 binary\_comments** & **76.00** & **68.60** & **25.00** & **25.00** & **25.00** & **25.00** & _0.43_ \\
23 code\_2\_comment & 52.00 & 61.00 & 61.20 & 65.00 & 70.20 & 68.90 & _0.07_ \\
24 O\_issue\_closedPR & **73.00** & 55.80 & 51.20 & 60.00 & 56.60 & 57.20 & _0.06_ \\
**25 Sun metric** & **73.00** & **73.00** & **72.00** & 68.00 & 71.40 & _**72.00** & _0.22_ \\ \hline \end{tabular}
\end{table} 
Table 10: Developer metrics scores according to evaluate with the language experience approach. Figure 9: The most successful five developer metrics all hit scores (experience). Analyzing both Tables 9 and 10 together, we saw that binary metrics were quite successful. It is also a noteworthy that comment-based metrics achieved higher scores than code activity-based metrics.
## 2 Modeling of TSDs
### Efficiency Considerations  
A typical setup for a TSD might assume the wave motions to be dominantly unidirectional. For more complex movement patterns, a coupling between various directions will be more realistic. Additionally, for such case,s the shape of the container needs to be reconsidered, as sharp edges at the corners could be detrimental. This consideration not only holds in the horizontal plane, but also in the vertical one. Consequently, some use cases will lead to the adoption of sloped bottoms and avoid all kinds of sharp corners where possible. These measures intend to increase the amount of liquid in motion. Further improvements will lead to the usage of three-dimensional screens. Such elements, placed in the middle of the container, have the intention of increasing the friction (and thus dissipating kinetic energy through damping) of the sloshing water. The ideal placement will aim to locate the regions with the highest kinetic energy.
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 2 Materials and Methods
### Hardware-in-the-Loop Test  
The effectiveness of the proposed LKAS control strategy was verified by the hardware-in-the-loop test, where the test bench consisted of six main components: upper computer, lower computer, interface system, steering column, steering motor controller, and servo motor controller. In the upper computer, the TruckSim 2019 (Mechanical Simulation Corp., Ann Arbor, MI, USA) vehicle dynamics, road environment, and Matlab/Simulink R2016a (MathWorks, Natick, MA, USA) control models were established. The linear two-degrees-of-freedom vehicle dynamics model and the vehicle lateral tracking deviation model were used in the control algorithm built in Matlab/Simulink. The vehicle dynamic model was selected from the model base provided by TruckSim, the parameters of the vehicle model are listed in Table 1, and the road model was designed as discussed in Section 2.1.1. The longitudinal vehicle speed, lateral acceleration, and preview deviation were obtained from the TruckSim vehicle model. Matlab/Simulink and TruckSim were connected and interacted in the VeriStand 2017 environment (National Instruments Corp., Austin, TX, USA). The lower computer utilized the National Instruments PXI system to run the real-time simulation program created by the upper computer. The interface system transmitted the steering angle calculated by the LKAS control strategy to the servo motor controller through controller area network signals. The servo motor controller converted the received data into hexadecimal format and sent this to the steering motor to control the steering angle. Simultaneously, the steering angle signal acquired by the steering wheel angle sensor was input to the PXI real-time system. The hardware-in-the-loop process flowchart is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Vehicle mass, \(m\) (kg) & 5760 \\ Moment of inertia along \(z\)-axis, \(l_{z}\) (kg\(\cdot\)m\({}^{2}\)) & 348,23.2 \\ Equivalent side stiffness of front tires, \(C_{f}\) (N\(\cdot\)rad\({}^{-1}\)) & 259,752 \\ Equivalent side stiffness of rear tires, \(C_{r}\) (N\(\cdot\)rad\({}^{-1}\)) & 259,752 \\ Distance from center of mass to front axle, \(a\) (m) & 1.25 \\ Distance from center of mass to rear axle, \(b\) (m) & 3.75 \\ \end{tabular}
\end{table}
Table 1: Vehicle parameters.
## 2 Related Work
### Overlay Networks  
[PERSON] _et al._[9] define an overlay network as a network that runs on top of another network. The overlay networks build a logical end-to-end delivery infrastructure, which is mounted on the existing transport networks. A possible drawback is that an overlay network could involve an additional cost, which refers to two main problems: duplication of features and the likelihood of adding too much overhead by encapsulation.  
Overlay networks have been widely popularised in recent years because of the advantages in many areas of networks, ranging from multicasting and packet routing to the location of resources or objects in a distributed environment [10]. Overlay networks have been used to support a number of different applications: from their origins supporting file sharing, they have expanded to include more real-time applications and interactive features, such as streaming multimedia, voice over IP, and real-time gaming. Each of these applications requires different levels of QoS. For example, a file transfer application requires a path that has more bandwidth available, while real-time interactive applicationshave latency requirements (delay) and jitter [11]. The overlay networks can help to solve some problems, in particular, end to end QoS [12].  
The Peer-to-Peer (P2P) architecture is often used to implement overlay networks. Unlike what occurs in a client-server architecture, nodes in a P2P network behave as equals and can act simultaneously as clients or as servers for the other nodes in the network. An extensive compilation and comparison of P2P overlay network models is provided by [PERSON] _et al_. [13]. Depending on the optimisation technique used, the approaches that seek to provide QoS by P2P overlay networks can be classified into two main groups:  
* Approaches targeting path optimisation. There are several proposals that seek to make optimal use of the network, by finding the best overlay network topology to minimise the overhead. Within this group are the following solutions: additional auxiliary nodes [14], hierarchy of nodes [15], organisational groups [16], simplification of the network [17], replication [18], semantics/users [19], symbiotic networks [20], multiple paths [21], a variety of routes [22], adaptive/unstructured approach [23], bandwidth reservation [12], flexibility in the underlying layer [8], and traffic prioritisation [24];
* Approaches focusing on information optimisation. This approach attempts to perform some processing of information to make a simplification. Within this group are the following solutions: distributed infrastructure [25], simplification of goods [26], adaptation based on context [27] or QoS requirements [28], and urgency-based scheduling [29]. Although the size of the problem has been limited due to the fact that the amount of information has been reduced, this type of solution is still exposed to the problems that occur in the network.
## 5 WBAN and the Communications Protocol Stack
### Transport Layer
The ESRT algorithm tracks the event reporting frequency (_f_) of the received packets and matches it with the required reliability metric. Congestion is managed by decreasing reporting frequency without affecting reliability.
## 5 Experiment Results and Discussion
### Comparison of Subjective Ratings of Interactions in SG1 and SG2
First, for each user, the average of the ratings assigned to interactions for pairs of tasks (Translation 1, Scaling 1, Rotation 1) in SG1 was calculated and these were compared with the ratings of interactions in the same tasks in SG2 (Translation 2, Scaling 2, Rotation 2). The results of the test are shown in Table 12.  
Figure 17: The difference in mean values of interactions’ ratings I\({}_{2}\)–I\({}_{1}\) for each task. As can be seen from the table, the difference in the subjective ratings of the interactions for the Translation task is not statistically significant (W = 978; \(p\) = 0.227). Therefore, no conclusions can be drawn regarding the method of assigning the interaction mechanism to an individual participant for the translation task. On the other hand, the difference in the subjective ratings of the interactions for the Scaling (W = 390; \(p\) < 0.001; \(r\) = \(-\)0.44) and Rotation (W = 913.5; \(p\) = 0.010; \(r\) = \(-\)0.25) tasks is statistically significant. Therefore, it can be concluded that the participants were generally more satisfied with the interaction mechanisms in the rotation and scaling tasks in SG2 than in SG1, i.e., the method of assigning the interaction mechanism for these tasks is appropriate.
# A Geospatial Framework of Food Demand Mapping
## 3 Results
### Food Regional Mapping
Farmers, on the other hand, stand to gain from tailoring their crop production to align with the granular insights unearthed by food demand mapping, focusing on cultivating crops that cater to the specific dietary needs and preferences of diverse regions. This precision in farming can be augmented through partnerships with local governments and businesses to  
Figure 3: Eating habits score Vilnius city map (capital of Lithuania). forge direct-to-consumer avenues, enhancing the direct flow of nutritious food. In parallel, retail and e-grocery sectors are encouraged to leverage food demand maps to fine-tune their inventory and marketing efforts, ensuring that their product offerings resonate with the unique needs of their demographic and regional customer base. By adopting flexible pricing and delivery frameworks, these platforms can make healthy food alternatives more accessible to a broader audience, potentially offsetting the challenges posed by food deserts. Warehouse and transportation providers play a pivotal role in this ecosystem by optimizing their logistics and distribution strategies based on spatial food demand analyses. A focus on reducing delivery times and costs in areas identified as high-demand can significantly improve food access, underpinned by investments in technology to anticipate demand fluctuations and adapt logistical operations accordingly. This concerted effort across sectors is key to mitigating food accessibility issues and fostering healthier communities.
# Cloning and Functional Identification of _Sipg49_ in _Solanum Lyopersicum_
## 3 Results
### Subcellular Localization of SIPG49  
Onion epidermal cells are a good material for the transient expression of exogenous genes because of their clear structure, lack of chloroplasts, and convenient materials. A transient expression assay in onion epidermal cells with _CaMV355::SIPG49-GFP_ plasmids was conducted using particle bombardment to examine the subcellular localization of SIPG49. The results showed that green fluorescence signals were widely distributed in onion epidermal cells transformed with SIPG49-GFP (Figure 3A-C), and the fusion protein signals of SIPG49-GFP could be detected in the cell membrane, cell wall, and extracellular space in the plasma wall-isolated onion epidermal cells (Figure 3D,F). These results indicate that SIPG49 is a secreted protein, and localizes in the cell membrane and the cell wall.  
Figure 1: Gene structure, transmembrane region, multiple alignment, and phylogenetic analysis of SIPG49: (**A**) gene structure of SIPG49; (**B**) transmembrane region of SIPG49; (**C**) phylogenetic analysis of SIPG49; (**D**) multiple alignment analysis of SIPG49.  
Figure 2: Expression pattern analysis of SIPG49 by qRT-PCR. R: roots; S: stems; L: leaves; F: flowers; MG: mature green fruit; B: breaker fruit; RR: red ripening fruit. Data are presented as mean \(\pm\) SE with at least three biological replicates. Significant differences are indicated by different letters (\(p<0.05\)). * indicates undetected expression level.
# Integration of the 3D Environment for UAV Onboard Visual Object Tracking
###### Abstract  
Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for traffic monitoring, video surveillance, and navigation.  
Keywords:single object tracking; deep learning; unmanned aerial vehicle; structure-from-motion +
Footnote †: journal: Applied
# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 3 Materials and Methods  
In this section, we develop a method to compute optimal weightings for the filters within our filter bank. We start by defining the cost function that defines this optimal weighting scheme and then derive the analytical solution that minimizes this cost function. Next, we discuss the practical application of this weighting scheme including its strengths and weaknesses, and how the method may be adjusted to improve its performance.