esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Hardware and Data Acquisition  
The video data for this investigation were captured by BeePi monitors, multi-sensor EBM systems we designed and built in 2014 [4], and have been iteratively modifying [6; 21] since then. Each BeePi monitor consists of a raspberry pi 3 model B v1.2 computer, a pi T-Cobbler, a breadboard, a waterproof DS18B20 temperature sensor, a pi v2 8-megapixel camera board, a v2.1 ChronoDot clock, and a Neewer 3.5 mm mini lapel microphone placed above the landing pad. All hardware components fit in a single Langstroth super. BeePi units are powered either from the grid or rechargeable batteries.  
BeePi monitors thus far have had six field deployments. The first deployment was in Logan, UT (September 2014) when a single BeePi monitor was placed into an empty hive and ran on solar power for two weeks. The second deployment was in Garland, UT (December 2014-January 2015), when a BeePi monitor was placed in a hive with overwintering honeybees and successfully operated for nine out of the fourteen days of deployment on solar power to capture \(\approx\)200 MB of data. The third deployment was in North Logan, UT (April 2016-November 2016) where four BeePi monitors were placed into four beehives at two small apiaries and captured \(\approx\)20 GB of data. The fourth deployment was in Logan and North Logan, UT (April 2017-September 2017), when four BeePi units were placed into four beehives at two small apiaries to collect \(\approx\)220 GB of audio, video, and temperature data. The fifth deployment started in April 2018, when four BeePi monitors were placed into four beehives at an apiary in Logan, UT. In September 2018, we decided to keep the monitors deployed through the winter to stress test the equipment in the harsh weather conditions of northern Utah. By May 2019, we had collected over 400 GB of video, audio, and temperature data. The sixth field deployment started in May 2019 with four freshly installed bee packages and is ongoing with \(\approx\)150 GB of data collected so far.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Proposed Pansharpening Technique  
This study proposes a pansharpening technique to improve the spatial resolution of Worldview-3 satellite images that is performed in two phases [21]. The first phase sharpens the SWIR bands with 7.2 m spatial resolution using VNIR bands with 1.2 m spatial resolution, and the second phase sharpens the Worldview-3 bands with 1.2 m spatial resolution using panchromatic images with 0.3 m spatial resolution. The flowchart in Figure 2 describes the pansharpening technique proposed in this study and used for Worldview-3 satellite images.
## 4 Discussion
(3) Shortcomings and prospects  
In order to verify the accuracy of the D-InSAR technique in extracting the depth of mine subsidence, this paper uses RTK to measure the amount of surface subsidence at 50 leveling points from 5 March 2021 to 10 April 2021 and compares and analyses the amount of subsidence with three sets of interferometric image pairs in 2021 (5 March 2021-17 March 2021, 17 March 2021-29 March 2021, 29 March 2021-10 April 2021. However, the RTK technique has a relatively large error compared to the total station and level, and a higher accuracy method such as total station or level can be selected for measurement at a later stage to verify the accuracy with the D-InSAR technique measurement results. In addition, this paper only studies the collapse situation of the Zhaogu Mining Area in the past 5 years, which can provide methods for reference for future subsidence area extraction and land use information classification. In future research work, the research time can be extended to study more complete collapse situation of the Zhaogu Mining Area.
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 4 Throughput Analysis of String and Grid Topology Considering Inter-Network Interference
### Analysis of Grid Network Topology
#### 4.2.2 Three Carrier-Sensing Networks  
As shown in Figure 4, Network 2, 4, 6 and 8 are in accord with this case. Due to the position similarity, we only consider Network 2. In this case, \(v(2)\) includes Network 1, 3, 5. Hence, when a \(\
eq\) 5, the probability of Network 2 being in the idle state is  
\[U_{2,a} = 1-\frac{X_{b}}{1-X_{2}}-\frac{X_{5}}{1-Y_{a}}+\frac{X_{b}X_{5}}{( 1-Y_{a})(1-Y_{b})}; \tag{20}\] \[a,b\in v(2),a\
eq b,b\
eq 5.\]  
We know Network 2 is within Network \(a\) and Network \(b\)'s carrier-sensing range. Because Network 2 does not transmit, the probability of Network \(b\) transmitting in this condition is \(\frac{X_{b}}{1-X_{2}}\). Network 2 and one other (Network \(c\)) are within the carrier-sensing range of Network \(a\) and Network 5. When Network 2 and Network \(a\) are in the idle state, Network \(c\) does not transmit either. In this case, the probability \(\frac{X_{5}}{1-X_{2}-X_{c}}\) is not enough, because of considering the simultaneous transmission time of Network a and Network 5. The correct expression is \(\frac{X_{5}}{1-Y_{a}}\). The final component of the right-hand side in (20) denotes the overlap time between Network \(b\) and Network 5. When a = 5, \(U_{2,5}\) is  
\[U_{2,5} = 1-\frac{X_{1}}{1-Y_{1}}-\frac{X_{3}}{1-Y_{3}}+\frac{X_{1}X_{3}}{ (1-Y_{1})(1-Y_{3})}. \tag{21}\]  
Similar to in (20), the rightmost component of (21) shows the probability of overlap time between Network 1 and Network 3. We know Network \(\{2,4\}\), Network \(\{2,6\}\) are within the carrier-sensing range of Network \(\{1,5\}\), Network \(\{3,5\}\), respectively. In the former condition, the probability of Network 5 transmitting is \(\frac{X_{1}}{1-Y_{1}}\). In the latter, the probability is \(\frac{X_{3}}{1-Y_{5}}\).  
From (2) we derive the carrier-sensing airtime of Network 2 as follows.  
\[Y_{2} = \sum\limits_{i\in v(2)}X_{i}(1-\gamma_{2,i})-\frac{\prod\limits _{\begin{subarray}{c}k\in v(2)\\ k\
eq 5\end{subarray}}X_{k}(1-\gamma_{2,k})}{1-X_{2}} \tag{22}\] \[- \sum\limits_{\begin{subarray}{c}k\in v(2)\\ k\
eq 5\end{subarray}}\frac{X_{k}(1-\gamma_{2,k})X_{5}(1-\gamma_{2,5})}{1-Y_{k}}+ \prod\limits_{i\in v(2)}\frac{X_{i}(1-\gamma_{2,i})}{(1-Y_{i})}.\]  
Figure 4: Carrier-sensing range of each network in grid-topology.  
\(\prod\limits_{\begin{subarray}{c}k\in v(2)\\ k\
eq 5\end{subarray}}\)\(\frac{\prod\limits_{k\in v(2)}X_{k}(1-\gamma_{2k})}{1-X_{2}}\)  
In (22) the simultaneous time of Network 1 and Network 3 is \(\frac{k\
eq 5}{1-X_{2}}\), because in such condition there is no transmission in Network 2. Because of no transmission in Network \(\{2,4\}\) and Network \(\{2,6\}\), the simultaneous time of Network 1 (or Network 3) and Network 5 can be expressed as \(\frac{X_{k}(1-\gamma_{2k})X_{5}(1-\gamma_{25})}{1-Y_{k}}\). The last line in (22) denotes the overlap time between Network 1, Network 3, and Network 5.
## 2 Mechanism behind Thermal-Induced Measurement Errors
### Relationship between Image Error and Measurement Error  
The influences of the investigated image errors on the measurement errors in image analysis and fringe analysis of photo mechanics methods need to be considered, separately. For image analysis, the thermal-induced strain measurement error \(\varepsilon_{e}\) is consistent with the image distortion error \(\alpha_{e}\), and the thermal-induced translation measurement errors \(t_{e}^{x}\) and \(t_{e}^{y}\) are consistent with the image translation errors \(\Delta x_{e}\) and \(\Delta y_{e}\), respectively. The relationships are expressed as:  
\[\left\{\begin{array}{l}\varepsilon_{e}=\alpha_{e}\\ t_{e}^{x}=\Delta x_{e}\\ t_{e}^{y}=\Delta y_{e}\end{array}\right. \tag{14}\]  
In this case, taking the DIC method as an example, the measurement errors in this method induced by the distortion error and translation error of speckle images were investigated. As shown in Figure 9, a speckle image was subjected to longitudinal stretching of \(1\times 10^{3}\)\(\upmu\)e and translation of the \(1\times 10^{-1}\) pixel to obtain a distorted image and translated image. Furthermore, DIC was used to obtain the strain result (\(1.009\times 10^{3}\)\(\upmu\)e) of the distorted image relative to the initial image and translation result (\(0.891\times 10^{-1}\) pixel) of the translated image relative to the initial image. The results obtained using DIC are consistent with the theoretical values considering the numerical error induced by this  
Figure 8: Experimental verification of the established image error models: (**a**) experimental arrangement and (**b**) experimental results: image drift (multiplied by 200), image distortion error, and image translation error.
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
## 3 Impact of Temperature Drift on a Fiber Optic Current Sensor  
Temperature drift has a long duration but slow rate of change, where the duration is measured in seconds. Transient mutation of the magnetic field is fast and may be caused by external overvoltage interference, where the duration is measured in microseconds.  
A fiber optic current sensor has a closed circle structure, which satisfies [PERSON]'s circuital law. After integration, the derived current is similar to the current being measured, so the transient magnetic field mutation produces a weak effect:  
\[\oint\limits_{l}H\cdot dl\approx I \tag{8}\]  
Temperature drift lasts for a long time. A fiber optic current sensor is wound with a multi-turn coil, so the optical path is long. The temperature change produces strong interference in the fiber optic current sensor, and the temperature disturbance generates birefringence in the optical fiber, thus changing the rotation angle.  
[PERSON]'s effect and [PERSON]'s circuital law can be used to derive the rotation angle in the sensing as:  
\[\theta_{1}=NVi \tag{9}\]  
where \(N\) is the number of winding turns in the sensing fiber, \(V\) is the Verdet constant of the optical fiber, and \(i\) is the measured current.  
A multi-turn optical fiber forms a closed light path, so the fiber signal is shielded from external magnetic field interference. However, the fiber is sensitive to temperature fluctuations, producing changes in the Verdet constant [16]:  
\[V(T)=V_{0}[1+0.7\times 10^{-4}\Delta T] \tag{10}\]  
where \(\Delta T=T-T_{0}\), \(T\) is the actual temperature, \(T_{0}=25\)\({}^{\circ}\)C, and \(V_{0}=1.0\times 10^{-6}\) rad/A at \(25\)\({}^{\circ}\)C.  
On the other hand, the installation of a sensing ring inevitably causes the fiber to bend, and temperature fluctuations generate a stress difference along the cross section of the curved fiber, thereby generating stress-induced birefringence. \(Q\) is the temperature-induced linear birefringence coefficient per unit length [17]:  
\[Q=\frac{\pi n^{3}}{\lambda}(p_{12}-p_{11})\frac{(1+\
u)(2-3\
u)}{(1-\
u)}\frac {A}{R}\alpha \tag{11}\]  
where \(\lambda\) is the wavelength of light, \(\
u\) is Poisson's ratio, \(p_{12}\) and \(p_{11}\) are components of the photoelastic tensor, \(A\) is the fiber radius, \(R\) is the fiber bending radius, and \(\alpha\) is the fiber thermal expansion coefficient.  
Figure 2: Open loop in a polarization-type fiber optic current sensor.  
When temperature drift occurs, the actual rotation angle is:  
\[\theta_{1}=NV_{0}i\cdot(1+0.7\times 10^{-4}\Delta T)\frac{\sin(\delta_{0}l+Ql \Delta T)}{\delta_{0}l+Ql\Delta T} \tag{12}\]  
where \(\delta_{0}\) is the residual linear birefringence per unit length, and \(l\) is the fiber length.
## 3 LQ Controller
### Control: Closed Loop System
#### 3.2.3 Emergency Braking Manoeuvre  
In this scenario, the truck platoon travels at 80 km/h when the leader vehicle suddenly brakes on a high road friction condition. Tests were performed to analyse the performance of the platooning control system with different control calibrations during an emergency braking manoeuvre. The test cases are the same as in Table 1. The simulation stops when the first collision is reached (i.e., when a distance curve crosses the zero line).  
Figure 7 shows the results of vehicle velocities (top) and inter-vehicle distance (bottom) during the emergency braking test for different values of \(R_{0}\) (for both followers): The time headway set of 1.5 s represents the most challenging condition. As seen during the driving cycle, the highest value of ( \(R_{0}=10^{-3}\)) leads to the greatest inter-vehicle distance error, thus leading to a rear-end collision between follower 1 and the lead vehicle (the impact occurs at \(\sim\)22 km/h). For \(R_{0}=10^{-5}\) and \(R_{0}=10^{-7}\) both vehicles (follower 1 and follower 2) can stop safely (red line).  
Figure 6: WLTP Class 3 simulation with LQC: velocity response, inter-vehicle distance error, electric motor torque, and longitudinal acceleration for three different values of \(t_{h}=[1.5\,s,3\,s,4.5\,s]\) with \(R_{0}=10^{-5}\).  
Figure 7: Emergency braking manoeuvre results: follower 1 and follower 2 velocity (**top**) and inter-vehicle distance (**bottom**) \(R_{0}=[10^{-7},10^{-5},10^{-3}]\) with \(t_{h}=1.5\) s.  
Figure 8 shows the beneficial effect of higher time headway on safety since for \(t_{h}=3\) s and \(t_{h}=4.5\) s the vehicles stop without any collisions (at the same speed the inter-vehicle distance increases) if meanwhile \(R_{0}=10^{-5}\).  
Table 2 summarizes the results of the sensitivity analysis. To avoid the collision, the value of \(R_{0}\) should be set as low as possible (in fact, for \(R_{0}=10^{-3}\) the follower 1 vehicle collides with the leader for all the analysed time headways) and the time headway the highest possible (but a low time headway is required to increase road capacity). All the computed acceleration and jerk values are well below the comfort limits for the tested driving cycle.  
Given that for \(R_{0}>10^{-5}\) the safety requirement is satisfied for all the time headway values, \(R_{0}=10^{-5}\) has been selected for the comparison with the RL control. The final calibration for the matrices \(Q\) and \(R\) is:  
\[Q=\text{diag}\Big{(}100,\;10^{-5},100,\;10^{-5},\;20,\;20\Big{)}\]  
\[R=\text{diag}\Big{(}10^{-5},10^{-5}\Big{)}\]
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 4 HSV Color-Space-Based Automated Object Feature Extraction  
### Motivation  
In a previous study [7], HSV color-space-based robot grasping was conducted by measuring object detection and size. However, the HSV color-space-based product detection algorithm requires human intervention and preset settings (color designation, setting threshold value for color identification, etc.), in addition to product size measurement, whenever a product is changed. In addition, even if the user designates a specific color for the product to be recognized in advance, the color of the target product appears to be deformed due to external environmental changes (weather, illumination interference, shadows, reflections, etc.). In a complex and highly volatile factory environment, users have many difficulties in automating object recognition based on color. To solve this problem, we propose a robust and simple HSV color-space-based automated object localization algorithm that can respond in real time to changes in the external environment without prior knowledge by improving the contents of previous work. The previous work is shown in Figure 4.
# Expanding Ground Vehicle Autonomy into Unstructured, Off-Road Environments: Dataset Challenges
## 2 Off-Road Dataset Challenges
### Annotating Off-Road Data: Importance and Challenges  
Annotating off-road data is a challenging yet crucial task in the development of autonomous navigation systems. Off-road environments pose unique challenges due to the complexity of their imagery, which is filled with diverse features at the pixel level. This demands meticulous attention to how annotations are applied, unlike urban settings where lane boundaries are well-defined and consistent. Off-road terrains lack these clear demarcations, making it difficult to train machine learning models.  
This raises the question: how should we annotate off-road data for computer vision-based autonomous navigation? This is not a simple task, as it involves balancing the trade-offs between the level of detail, consistency, and usefulness of the annotations. On the  
Figure 3: Sample TartanDrive 2.0 imagery [18].  
Figure 2: Sample RELLIS-3D imagery and its corresponding semantic segmentation annotation [16].  
one hand, we could capture the diversity and complexity of off-road environments by annotating every pixel with a specific class label, such as grass, gravel, bush, tree, etc. On the other hand, we could simplify the annotation process by using more general and abstract categories, such as path, non-path, wooded area, etc. These choices have implications for the performance and reliability of the autonomous systems that use the annotated data for learning and inference. Are we overcomplicating the task of producing a useful dataset for off-road autonomy by forcing such complicated annotations? Are we hindering the progress of off-road autonomy by ignoring the subtleties and nuances of off-road scenes? Do we truly need to differentiate between a patch of weeds in the middle of a gravel path? Or, should we consider the ontology from a broader perspective, such as path, non-path, wooded area, etc.? Understanding these scenes at a higher level could enable the development of more sophisticated autonomy behaviors to address how the UGV handles the perceived region.
## 2 Literature Review  
### Mechanisms of Urban Rail Transit Noise  
The noise generation mechanisms in urban rail transit systems are relatively complex, with multiple noise sources interacting to produce diverse noise characteristics [24]. The overview of metro noise research and mitigation strategies is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
One of the primary noise sources is the friction between the rails and the wheelsets [10]. Rail-wheel friction noise encompasses both high-frequency vibration noise from direct contact and impact noise due to track irregularities, as well as rail and wheelset wear [33]. These noise sources exhibit markedly distinct spectral characteristics, necessitating comprehensive spectral analysis to accurately delineate and identify their specific properties [30].  
Aerodynamic noise is particularly pronounced in high-speed trains and becomes markedly intensified during tunnel ingress and egress, where fluctuations in airflow precipitate substantial increases in noise levels [34; 35]. The substantial increase in aerodynamic drag at high speeds generates noise originating from the friction between the airflow and the train's surface, turbulence at the front and rear of the train, and sudden changes in air pressure as the train moves through tunnels [31]. The intensity and spectral characteristics of aerodynamic noise are influenced by factors such as the shape of the train body, train speed, tunnel design, and weather conditions [23]. Structural resonance noise of train carriages constitutes a pivotal factor [26]. During train operation, the carriage structure resonates due to external vibrations and internal sound waves, engendering structural noise [20]. The spectral characteristics of this noise are intricately linked to factors such as the materials composing the carriages, the structural design, and the train speed [32; 36].  
The aforementioned analysis illustrates that urban rail transit noise encompasses intricate generation mechanisms and is influenced by a multitude of factors. Evaluating and mitigating this noise based solely on overall sound pressure levels cannot provide enough evidence for customizing mitigation strategies. Hence, effective noise abatement strategies necessitate meticulous spectral analysis and a profound understanding of the characteristics of various noise sources.
## 4 Conclusions  
The combination of HS-SPME, SPE, and SBSE sample preparation methods coupled with GC\(\times\)GC-TOFMS analysis enabled us to (tentatively and positively) identify 606 compounds in HAB. Many low content compounds that have never been reported before were identified for the first time. Especially for terpen, 41 more compounds were identified than previously reported, which are important physiologically active substances in HAB. Furthermore, the contributions of some important compounds were studied in terms of aroma characteristics and odor thresholds. Meanwhile, the three extraction methods show distinct differences and biases for specific analytes. HS-SPME preferred the analysis of alcohols, sulfur-containing, and terpen compounds; SPE generally revealed more high-boiling compounds, such as lactones and nitrogenous compounds; SBSE showed general extraction ability for all types of compounds, but too much adsorption led to chromatogram overload, making it suitable for the identification of trace content substances. Therefore, the analysis of volatiles in such a complex sample requires multiple pretreatment methods coupled with GC\(\times\)GC-TOFMS. Importantly, the method can be applied to other alcoholic beverage systems for the determination of the specific kinds of volatile compounds. This approach proved beneficial for the analysis of terpen, lactones, and sulfur containing compounds, which are important flavor contributors of Baijiu. In addition, the development of this technique laid a foundation for the quantitative determination of the content substances at very low levels (in the region of \(\upmu\)g L\({}^{-1}\) and lower). Based on the feasibility of accurate quantification, it is hoped that this method can be used to monitor the formation of key aroma substances in the production process of Baijiu.  
The following are available online, Table S1: Volatile compounds identified in Chinese herbaceous aroma-type Baijiu by HS-SPME-GC\(\times\)GC-TOFMS, SPE-GC\(\times\)GC-TOFMS and SBSE-GC\(\times\)GC-TOFMS, Table S2: Peak area of 606 volatile compounds identified in Chinese herbaceous aroma-type Baijiu.  
L.W., Conceptualization, methodology, formal analysis, visualization, writing--review & editing; M.G., Validation, data curation; Z.L., investigation, writing--original draft; S.C., Conceptualization, validation, supervision, review & editing; Y.X., Supervision, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 31530055), National Key R&D Program of China (no. 2018 YFC1604100), Project funded by China Postdoctoral Science Foundation (no.2018M631971), National First-class Discipline Program of Light Industry Technology and Engineering (no. LITE2018-12), 111 Program of Introducing Talents (no. 111-2-06), and Sichuan Science and Technology Program (no. 2018 JZ0033).  
The authors declare no conflict of interest.