esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization of Experimental Parameters  
Comparison of Optical-optical Double Resonance (OODR) and Double-resonance Optical Pumping (DROP) Spectroscopic Schemes  
In the experiment, we compared two spectroscopic signals by detecting different beams corresponding to the OODR and DROP spectra. We used the 852 nm laser as the probe beam, and its frequency was locked by the polarization spectrum, through which atoms were distributed  
Figure 1: The relevant hyperfine energy levels of cesium (Cs) atoms. A double-resonance optical pumping (DROP) spectroscopic scheme using the ladder-type three-level system (5\(p^{6}\)6s \({}^{2}\)S\({}_{1/2}\)-5\(p^{6}\)6\(p\)\({}^{2}\)P\({}_{3/2}\)-5\(p^{6}\)7d \({}^{2}\)D\({}_{5/2}\)) was employed to measure the hyperfine splitting of the Cs 5\(p^{6}\)7d \({}^{2}\)D\({}_{5/2}\) excited state. The 852 nm probe laser drives the 5\(p^{6}\)6s \({}^{2}\)S\({}_{1/2}\) (\(F=4\))-5\(p^{6}\)6p \({}^{2}\)P\({}_{3/2}\) (\(F^{\prime}=5\)) transition, as indicated by the red arrows, while the 698 nm coupling laser drives the 5\(p^{6}\)6\(p\) \({}^{2}\)P\({}_{3/2}\) (\(F^{\prime}=5\))-5\(p^{6}\)7d \({}^{2}\)D\({}_{5/2}\) (\(F^{\prime\prime}=6\)), (\(F^{\prime\prime}=5\)), and (\(F^{\prime\prime}=4\)) transitions, as indicated by the three blue arrows. The wavy arrows indicate the spontaneous decay channels. The energy at infinity is zero (the energy axis is not to scale).  
Figure 2: Experimental setup for hyperfine splitting measurement. Red solid arrows represent the counter-propagating (CTP) configuration with the probe and coupling laser beams, whereas the gray dashed arrows represent the co-propagating (CP) configuration. \(\mu\)-metal, magnetic shielding device; EOPM, waveguide-type electro-optic phase modulator; PBS, polarization beam splitter cube; BS, beam splitter plate; \(\uplambda\)/2, half-wave plate; \(\uplambda\)/4, quarter-wave plate; PD, photodetector; DPD, differential photodiode; DM, dichroic mirror; HR, high-reflectivity mirror; servo, frequency-locking servo system.  
from the ground state of \(5p^{6}6s\ ^{2}S_{1/2}\) to the intermediate state of \(5p^{6}6p\ ^{2}P_{3/2}\). The 698 nm laser served as the coupling beam, and, when it was scanned over the range of \(5p^{6}6p\ ^{2}P_{3/2}\) to \(5p^{6}7d\ ^{2}D_{5/2}\) transitions, the absorption spectrum was obtained. The 852 nm probe laser transmitted the atoms from the ground state \(5p^{6}6s\ ^{2}S_{1/2}\) (\(F=4\)) to the intermediate state \(5p^{6}6p\ ^{2}P_{3/2}\) (\(F^{\prime}=5\)). The 698 nm coupling beam excited atoms into the \(5p^{6}7d\ ^{2}D_{5/2}\) (\(F^{\prime\prime}=4\), 5, 6) state. However, some atoms in the \(5p^{6}7d\ ^{2}D_{5/2}\) (\(F^{\prime\prime}=4\), 5, 6) state could be pumped into another ground \(5p^{6}6s\ ^{2}S_{1/2}\) (\(F=3\)) state via other intermediate states, as shown in Figure 1, which led to a change in the population due to DROP. It can be seen in Figure 3 that the resolution of the DROP spectra were better than that of the OODR spectra, and there was basically no coincidence among the three peaks, which was helpful for measuring the hyperfine splitting interval.
###### Abstract  
Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, based on a novel optimization method to enhance the connectivity of the vehicular networks, is proposed. The presented model optimizes the inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. Based on the system quality-of-service (QoS) being achieved, a decision will be taken whether the transmitting AV communicates directly to the destination or through cooperative communication. To achieve the given objectives, the best position of the relay-vehicle issue was mathematically formulated as a constrained optimization problem to enhance the communication between AV2X under different environmental conditions. To illustrate the effectiveness of the proposed model, the following factors are considered: distribution of vehicles, vehicle density, vehicle mobility and speed. Simulation results show how the proposed model outperforms other previous models and enhances system performance in terms of four benchmark aspects: throughput (S), packet loss rate (PLR), packet delivery ratio (PDR) and average delivery latency (DL).  
autonomous vehicles; vehicular communication; vehicles-to-vehicles (V2V); vehicle-to-everything (V2X); cooperative communication; throughput; packet loss rate; packet delivery ratio; average delivery latency +
Footnote †: journal: Applied  
1 Basic and Applied Science Department, College of Engineering and Technology,  
Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt 1  
A.K. A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication. _Appl. Sci._**2021**, _11_, 9089. [[https://doi.org/10.3390/appl1199089](https://doi.org/10.3390/appl1199089)]([https://doi.org/10.3390/appl1199089](https://doi.org/10.3390/appl1199089))
## 6 Experimental Results and Discussion
### Efficiency Experiment
#### 6.2.2 Tracking Efficiency  
The computational efficiency of our algorithm was evaluated by a statistical analysis of the tracking time of each frame using the warehouse dataset. The performance of ORB-SLAM2 on this dataset was also evaluated. The number of tracking keypoints of both algorithms was set to 2000.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Ours**} & \multicolumn{2}{c}{**ORB-SLAM2**} \\ \cline{2-5}  & **Time (ms)** & **STD** & **Time (ms)** & **STD** \\ \hline
2000 & 12.39 & 1.74 & 30.07 & 4.09 \\
1500 & 11.28 & 1.73 & 29.40 & 3.90 \\
1000 & 8.97 & 1.40 & 29.00 & 4.02 \\
500 & 6.73 & 1.31 & 28.17 & 3.95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of extraction times.  
Figure 8: Feature extraction results of the same image by two feature extraction algorithms. (**a**) The proposed two step dynamic threshold feature extraction. (**b**) The method that divides the image into \(m\times n\) cells and extracts features in each cell.  
Figure 7: The effect of different numbers of expected feature points on the efficiency of two feature extraction algorithms.  
As shown in Figure 9 and Table 2, our algorithm only required 17 ms to track a new frame. The fastest time used to track a new frame using the direct method was 6.53 ms. The worst result using the feature matching method was still less than 100 ms. Therefore, our algorithm could be easily run in real time using a normal CPU core. In contrast, ORB-SLAM required an average of more than 100 ms to track a new frame, and the worst case was 288 ms. Obviously, ORB-SLAM was difficult to run in real time on our dataset.  
The remarkable improvement in the efficiency of our algorithm was attributed to three main reasons. First, ORB-SLAM extracted and matched keypoints for each frame, whereas our algorithm used image alignment to track each frame and then only extracted and matched keypoints for keyframes. Because the number of keyframes was far less than all frames, image alignment could be realized with a very fast calculation speed as a result of its linear calculation complexity. Thus, our hybrid method could significantly improve the calculation efficiency. Second, we adopted the above discussed two step dynamic threshold feature extraction method, so the feature extraction time (the most time consuming step of tracking) was greatly reduced while ensuring the uniform distribution of feature points. Finally, the pose estimation based on the forklift motion model could provide a more reasonable initial value for the new frame, thereby reducing the number of calculation iterations.
# Edge Computing Approach for Vessel Monitoring System
## 5 Results  
This project is an addition to the existent proprietary system developed by XSealence--the Integrated Control Center. In 2018, the system recorded 1,048,576 events based on 85,024 vessel trips data from Portuguese fishing fleet. Table 2 shows a picture with a small sample of these records with the following variables: (1) NAME: the name of the vessel. The system was installed in 310 different vessels; (2) DATETIME: Date and time in format (DD-MM-AAAA HH:MM); (3) GPS coordinates of Latitude and Longitude; (4) COURSE: represents movement direction and ranges from 0 (north) to 360 degrees. The average measurement was of 138.4 with a deviation of 116.55; (5) SPEED: vessel speed in knots. Data collected ranged from 0 to 23, with a mean of 1.13, and a standard deviation of 2.7. These data were created automatically, as opposed to the manual reports generated by the captain of the vessel, which is produced at the end of each trip.  
At deep sea, the only communication system available is satellite--which is very expensive. Consequently, this cost heavily restricts the amount of data to be transmitted. In 2018, we tested the edge computing approach in 50 vessels out of a population of 310, divided by 10 in each fishing gear: (1) trawls; (2) surrounding nets with a purse seine; (3) hooks and lines; (4) traps; and (5) hillnets and entangling nets. During the initial semester, they transmitted once every 10 min, and after this period (second semester), they started using the proposed approach of edge computing at VSM box. This allowed comparing the data transmission performed. In the second semester (when this approach went live and was already implemented on the 50 vessels) the transmission frequency was only reduced by 20%, on average. Further analysis allowed identifying that this low reduction in the transmission frequency was due to the fact that new fishing positions were found. The final results showed that in the last months of 2018, there was already a reduction of 70% on the communication frequency. Data for the first three months of 2019 showed savings of up to 90% in five of the analyzed vessels. All of these five vessels use a fishing technique named trawl fishing and are not adapted to conduct other techniques. It is then possible to state that consistency in fishing technique allied with a regular fishing behavior (use of regular fishing areas and overall behavior) leads to reduced abnormal behavior, which results in a reduction in the need to transmit data and, therefore, the associated costs.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 5 Energy Harvested and Cooperative-Enabled Routing Protocol (EHCRP) Scheme
### Sensor Nodes Deployment Phase  
Deployment of all SNs along with coordinator nodes is the very first phase of WBAN. Total ten (10) nodes having eight (08) SNs and two (02) coordinator nodes are position on/in various location of human body as exposed in Figure 3, also described in Table 2. By using RSSI, the SNs and both the coordinator nodes start to calculate their location and distance from their neighbors and the coordinators node. After distance calculation all SNs including along coordinators transmit a BEACONmessage in network. The BEACON message includes of Node ID, distance from its neighbor (d), destination node ID, and Residual Energy (\(R.E\)) and node location.
## 5 Total Tree Net Primary Production  
Several studies (Table 2) have calculated the total tree net primary production (aboveground + belowground) with contrasting results, mainly due to the increasing information over time, especially changes in the accuracy of the global area of mangroves and the rapid rise in estimates of belowground production. Total mangrove NPP (Mg C ha\({}^{-1}\) y\({}^{-1}\)) estimates have ranged from 11 to 21 Mg C ha\({}^{-1}\) y\({}^{-1}\) with the most recent calculations suggesting a value at the lower end of the range. Likewise, global mangrove NPP estimates range from 143 to 383 Tg C y\({}^{-1}\) with current calculations of a global mean of 183 Tg C y\({}^{-1}\) (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Liter** & **Wood** & **Root** & **Total** & **Global** & **Global** & **Magnrove NPP** & **Reference** \\
**Production** & **Production** & **Production** & **Mangrove NPP** & **Mangrove Area** & **Mangrove NPP** & **Reference** \\ \hline
9.0 & 12.1 & NA & 21.1 & 181,900 & 383.4 & [168] \\ \hline
12.8 & NA & NA & 16.3 & 160,000 & 260.5 & [169] \\ \hline
4.2 & 4.2 & 5.2 & 13.6 & 160,000 & 218.0 & [123] \\ \hline
4.3 & 4.2 & 10.8 & 19.3 & 160,000 & 308.7 & [8] \\ \hline
10.0 & 9.0 & 2.0 & 21.0 & 170,000 & 356.9 & [9] \\ \hline
4.3 & 4.2 & 4.7 & 13.1 & 138,000 & 210.0 & [125] \\ \hline
4.4 & 4.9 & 5.9 & 15.2 & 160,000 \({}^{*}\) & 228.0 & [170] \\ \hline
4.5 & 3.6 & 3.0 & 11.0 & 137,600 & 143.0 & [159] \\ \hline
4.0 & 4.7 & 3.0 & 11.7 & 147,359 & 182.7 & [124] \\ \hline \end{tabular}
\end{table}
Table 2: Estimates of mean mangrove tree net primary production (Mg C ha\({}^{-1}\) y\({}^{-1}\)) and of global total mangrove NPP (Tg C y\({}^{-1}\)) using different estimates of global mangrove area (km\({}^{2}\)). NA = not available. \({}^{*}\) = area correction factor (\(\times\)1.98) applied.  
Global NPP estimates for seagrass meadows, salt marshes, coral reefs, mangrove forests, and the entire tropical coastal ocean average 294, 27 78, 183, and 4254 Tg C y\({}^{-1}\) for a total global NPP of the low-latitude regions of 4836 Tg C y\({}^{-1}\)[5, 171]. Mangroves are thus by area about 1.5, 2.4, 8, and 2 times more productive than seagrass, salt marshes, coral reefs, and tropical coastal ocean ecosystems, respectively. Mangroves account for nearly 4% of the total global NPP although they represent only 1.9% of the total coastal ocean area, illustrating their disproportionate role in coastal carbon dynamics [123, 125]. When these estimates were calculated, however, the importance of macroalgal beds in blue carbon dynamics was still not fully acknowledged.
## 4 Experimental Objects and Analysis of Results
### Analysis of Fitting Results  
Five images were collected at the same location for the first piston part, and after extracting the edges using the improved Canny algorithm, the RANSAC algorithm was  
Figure 4: The results of edge image extraction: (**a**) edges extracted by the traditional Canny algorithm; (**b**) edges extracted by the improved Canny algorithm; (**c**) inner and outer edge images obtained by removing interference points with the traditional Canny algorithm; and (**d**) inner and outer edge images obtained by removing interference points with the improved Canny algorithm.  
Figure 3: (**a**) Piston component; (**b**) image of the part to be detected.  
applied to fit them 10 times, respectively. The average value of the processed results was used as the detection value of concentricity of the first piston part after the improved algorithm. The average value of concentricity after the improved algorithm was obtained as 0.6475 mm and the data are shown in Table 1.  
After extracting the edges using the traditional Canny algorithm, the same fitting was carried out, and the average value of concentricity was obtained as 0.6024 mm. The concentricity of this part was measured as 0.6405 mm using the coordinate measuring machine.  
Taking the concentricity results measured by the coordinate measuring machine as the benchmark, the error value between the results of the traditional Canny algorithm and the benchmark is more than 0.035 mm, and the relative error is more than 5%, while the error value between the results of the improved Canny algorithm and the benchmark is within 0.01 mm, and the relative error is less than 0.5%, which verifies the validity of the improved Canny algorithm. The improved Canny algorithm reduces the concentricity error by 4%, which has high accuracy, and also proves that the improved Canny algorithm can obtain good solution accuracy in engineering applications.
# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 4 Experimental Results
### Dataset Description and Experiments Setup  
The Merced dataset comprises 21 categories of Earth scenes, each containing 100 RGB images. These images, acquired from the United States Geological Survey (USGS) National Map, have a ground resolution of 0.3 m and a size of 256 \(\times\) 256 pixels [17]. Meanwhile, the AID dataset encompasses 10,000 RGB images sourced from Google Earth, with 30 scene classifications and a size of 600 \(\times\) 600 pixels. The ground resolution for AID images varies from 0.5 to 8 m [18]. The NWPU-RESISC45 dataset consists of 31,500 RS images grouped into 45 categories, with each class containing 700 images extracted from Google Earth imagery. These images have a size of 256 \(\times\) 256 pixels, and their spatial resolution ranges from 30 m to 0.2 m, with exceptions for certain classes [19]. Figure 2 displays samples from these three datasets, and Table 2 summarizes the class count and images per class.  
To assess the proposed approach's effectiveness, we conducted a set of experiments with all classes considered as unseen. Specifically, we regarded 21, 30, and 45 classes as unseen for the Merced, AID, and NWPU datasets, respectively.  
In this study, we explored 13 distinct models based on CNNs and Transformers. We present the classification results in terms of overall accuracy (OA), representing the percentage of correct classifications relative to the total number of images in each dataset, along with class-specific accuracy.  
The experiments were carried out using a computer with an Intel i9 processor, 64 GB RAM, and an NVIDIA GeForce RTX GPU with 11 GB of memory.  
\begin{table}
\begin{tabular}{c c} \hline Prompt 1 & class\_name \\ \hline Prompt 2 & a photo of + class\_name \\ \hline Prompt 3 & a remote sensing image of + class\_name \\ \hline Prompt 4 & a top view image of + class\_name \\ \hline Prompt 5 & a satellite image of + class\_name \\ \hline Prompt 6 & a scene of + class\_name \\ \hline \end{tabular}
\end{table}
Table 1: Prompts used for the language model.  
Figure 2: Examples from (**a**) Merced, (**b**) AID, and (**c**) NWPU datasets.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 6 Related Work
[35] use a wireless sensor network to monitor road tunnels; they find that the LQI is more suitable than the RSSI in estimating the link performance, the link asymmetry is permanent at most times, and temporary link disruptions will occur when vehicular traffic exists. [PERSON] [36] investigate the throughput variation of a wireless sensor network deployed in forest and present the temporal dynamics of low-power links. The works [37; 38] studied the effect of temperature on the performance of low-power radio links. [PERSON] et al. [39] evaluate the aggregated performance of all of the links and study the effect of outdoor environments on the physical layer of the link; especially, the authors analyze the effect of seasonal variations on the wireless links. Furthermore, the author in [40] collects the link measurements in target fields and then presents models for estimating the run-time link quality. These two papers offer a guide for our future work investigating the low-power link performance in forest environment. To monitor a Canadian boreal forest ecosystem, [PERSON] et al. [41] operate a wireless sensor network; they report that the forest environment is so complex that it is very hard to accurately model the link performance; they mainly focus on investigating the variation of link RSSI, and they believe that the canopy structure, the wind speed and the off leaves possibly impact the link performance. There have been some efforts at investigating the physical layer characteristics of in-forest wireless links [42; 43; 44; 45]. [PERSON] et al. [42] measure the physical layer of wireless mesh networks and indicate that the interference, rather than the multipath fading effect, is the major cause of dynamic links. In [44], a propagation model for the ZigBee link with the transmit power of 18 dBm is fit according to the experiment data obtained in a forest with relatively sparse trees. The work [46] studies the wireless sensor network link over a vegetarian area and then proposes two path loss models with empirical parameters. [PERSON] et al. [47] conduct a wireless sensor network experiment under the inhomogeneous vegetation scenario and they qualitatively confirm the effect of vegetation topology on the link performance. The works in [48; 49] model the path loss of VHF band wireless links penetrating forests. The error properties of IEEE 802.15.4 links in industrial environments are studied by [PERSON] et al. [50]; the authors show that for the low-power links, the link errors caused by multipath fading and attenuation are different, in terms of pattern, from that caused IEEE 802.11 interference. Those physical-layer works attempt to deduce the radio propagation parameters in practical scenarios; the results might be helpful for simulation experiments, but cannot be easily employed by the upper layer protocols at runtime, especially for the capability-limited wireless sensor networks.
## 3 Basic Concepts
### Learning Automata  
Learning automata [11] are probabilistic decision-making tools that iteratively adapt to the environment and learn the optimal action. A widespread type of learning automata is variable structure learning automata, which are defined by a quadruple [\(\alpha\), \(\beta\), P, T], where:  
* \(\alpha=\alpha_{1}\), \(\alpha_{2}\),..., \(\alpha_{r}\) is the set of actions where r is the number of actions.
* \(\beta\) is the reinforcement signals where, in a P-model environment, \(\beta\in[0,1]\).
* P = {p\({}_{1}\), p\({}_{2}\),..., p\({}_{r}\)} is the set of actions' probability, P \(\in[0,1]\).
* T is the learning algorithm where, in the \(n_{th}\) step, \(p(n+1)=\) T[p(n), \(\alpha\)(n), \(\beta\)(n)] is linear, if \(p(n+1)\) is a linear function of \(p(n)\), or nonlinear if \(p(n+1)\) is a nonlinear function of \(p(n)\).  
In the \(n_{th}\) step of a linear learning algorithm, if the \(i_{th}\) selected action \(\alpha_{i}(n)\) receives the reward reinforcement signal \(\beta(n)=0\), the corresponding probability vector of learning automaton, \(p(n+1)\), is updated using 1. If it receives the penalty reinforcement signal \(\beta(n)=1\), the corresponding probability vector of learning automaton \(p(n+1)\) is updated using 2 [40]:  
\[p_{j}(n-1)=\left\{\begin{matrix}p_{j}(n)+a\big{(}1-p_{j}(n)\big{)}&j=i\\ p_{j}(n)(1-a)&\forall j\big{|}&j\
eq i\end{matrix}\right. \tag{1}\]  
\[p_{j}(n-1)=\left\{\begin{matrix}p_{j}(n)-(1-b)&j=i\\ \frac{b}{(r-1)}+(1-b)p_{j}(n)&\forall j\big{|}&j\
eq i\end{matrix}\right. \tag{2}\]  
where in Equations (1) and (2), a and b are learning parameters (reward and penalty parameters), and different values for a and b create different learning algorithms:  
* If a = b, the learning algorithm will be of the linear reward penalty (LRP) type.
* If b = 0, the learning algorithm will be of the linear reward inaction (LRI) type.
* If a >> b (a is much larger than b), the learning algorithm will be of the reward epsilon penalty (LREP) type.