esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
This may be due to these activities share similar movements. The result showed these three classifiers have quite different misclassifications. DT contributed less misclassified instances of stand-to-sit and sit-to-stand compared to SVM. KNN also contributed less misclassified instances of lie-to-sit. The result illustrated that the single classifiers cannot perform best in all evaluation measures, thus combining three single classifiers could contribute better classification performance. Based on this, SVM, KNN and DT were selected as ensemble members due to their excellent performance in evaluation measures. These three classifiers had their own strengths on classifying different activities, they also provided different disagreement with each other, which can used by majority voting to obtain a final prediction. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Performance** & **A1** & **A2** & **A3** & **A4** & **A5** & **A6** & **A7** & **A8** & **A9** & **A10** & **A11** & **A12** \\ \hline Accuracy & 99.21 & 98.80 & 98.96 & 97.56 & 99.56 & 98.76 & 72.33 & 75.76 & 61.28 & 69.11 & 77.08 & 68.80 \\ Sensitivity & 0.995 & 0.987 & 0.982 & 0.973 & 0.995 & 0.987 & 0.735 & 0.757 & 0.610 & 0.691 & 0.768 & 0.684 \\ Specificity & 1.000 & 1.000 & 1.000 & 0.998 & 1.000 & 0.986 & 0.723 & 0.756 & 0.622 & 0.688 & 0.770 & 0.690 \\ F-measure & 0.991 & 0.979 & 0.984 & 0.970 & 0.997 & 0.988 & 0.773 & 0.792 & 0.635 & 0.716 & 0.788 & 0.688 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation for the heterogeneous ensemble model (A1 = Standing, A2 = Sleeping, A3 = Watching TV, A4 = Walking, A5 = Running, A6 = Sweeping, A7 = Stand-to-sit, A8 = Sit-to-stand, A9 = Stand-to-walk, A10 = Walk-to-stand, A11 = Lie-to-sit, A12 = Sit-to-lie). Figure 2: The sensitivity of each activity class obtained from three segmentation methods by applying SVM classifier. We evaluated the classification performance of the ensemble model constructed with SVM, DT and KNN by applying it on the feature subspaces. The overall accuracy on twelve activity classes achieved 96.87%. Table 4 presented high sensitivity and specificity (sensitivity was from 0.898 to 0.995, specificity was from 0.993 to 1.000) on each activity class, which illustrated that ensemble model performed well both in identifying the positive class and negative class. The proposed ensemble model performed extremely well in identifying running and achieved the highest F-measure score of 0.997. The results showed that not only adopting adaptive data segmentation method, but also combining the heterogeneous base classifiers by majority voting could significantly improve the performance of activity recognition.
## 2 Material and Methods
### Statistical Analyses  
Descriptive data are presented as means and standard deviation for continuous variables and frequencies and percentages for categorical variables. Chi-squared was applied to test for differences between categorical variables. An analysis of covariance (ANCOVA) was used to estimate differences between mean values of zBMI and zGFS across PA intensity in PE classes (high or low), and PA intensity in school recess (high or low) groups. Furthermore, ANCOVA with post hoc pairwise comparisons using Bonferroni was used to test for differences between mean values of zBMI and zGFS across combined intensity in PE classes and school recesses categories, using four groups for each combination: (1) \"High-intensity PE and High-intensity recess\" (high intensity of PA in PE, and high intensity of PA in school recess); (2) \"High-intensity PE and Low-intensity recess\" (high intensity of PA in PE, and low intensity of PA in school recess); (3) \"Low-intensity PE and High-intensity recess\" (low intensity of PA in PE, and high intensity of PA in school recess); (4) \"Low-intensity PE and Low-intensity recess\" (high intensity of PA in PE, and high intensity of PA in school recess). Since we did not find significant interactions between sex and different intensities of PA in PE or school recesses in relation to zBMI (PE: \(p\) = 0.839; and school recesses: \(p\) = 0.593) and zGFS (PE: \(p\) = 0.593; and school recesses: \(p\) = 0.457) in the initial analyses, both girls and boys were analyzed together for greater statistical power. Furthermore, binary logistic regression analyses were carried out to verify the association between excess weight (no excess weight versus excess weight) and GFS tertiles (low/medium GFS tertiles versus high GFS tertile) and the different PA intensity in PE classes, school recesses, and both settings. All analyses were adjusted for age, sex, type of schooling, area of residence, daily screen time, and weekly sport activities. Data analyses were performed using the Statistical Package for Social Sciences (v25.0) software. A \(p\)-value < 0.05 was considered as statistically significant.
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 3 Materials and Methods  
According to the implementation program of the 5G SENSOR@SEA project [1], this section presents the materials and methods employed for the design activity of the prototype of the CTD. They are summarized as follows:  
* Identification of the CTD technical requirements and functions;
* Definition of the CTD's architecture, including the specification of the employed radio transceivers and the internal interfaces;
* Specification of the hardware design;
* Specification of the software design.
# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 4 Barriers to Incorporating Artificial Intelligence Technology in Smart Greenhouses
For example, even though digital twins offer immense potential to balance accuracy, accuracy, and computational speeds in agriculture, there are limited data on their applications. [PERSON] et al. noted that \"state-of-the-art for DTs is limited to specific sub-processes modeled with highly specialized models\" [136] (p. 7). Further experimental data on the cost-benefits of digital twins was needed before deploying the systems in greenhouses to simulate and optimize production. The issues relating to digital twins are but a microcosm of the broader gaps in the body of knowledge, which does not sufficiently delineate the impacts of AI and IT in agriculture. Drawing from experimental data compiled by researchers in Europe, North America, and Asia [86; 90; 104; 105; 108], there were region-specific barriers and enablers for AI integration in agriculture depending on the pace of knowledge and technology transfer. The intermittent integration of R&D innovations could be due to the higher costs, knowledge and resource gaps between advanced and developing countries, the inability to entirely replace human labor on farms with robots, and variations between the simulated and actual outcomes in the field/smart greenhouses [8]. The criticism of AI systems in smart greenhouses and farms by [8] was corroborated by [PERSON] et al. [131], who claimed that mechanical robots were unreliable because they relied on satellite triangulation to guide movement--this technology was less reliable compared to LIDAR and camera sensor innovations. On a positive note, the constraints associated with satellite triangulation have been addressed using light imaging, detection, and ranging (LIDAR) optical and electro-optical cameras [131]. The advances highlighted by [131] were consistent with [162], which noted that algorithm systems had led to the development of advanced sensor systems,including fusion algorithms that facilitated the separation of colors into the hue, saturation, and value formats. However, the case for electro-optical cameras made by [162; 131] was not in line with the research of [PERSON] et al. on latency issues in an image collection using multispectral cameras [164]. The latter concerns reinforced the case for LIDAR systems in agricultural robots [162]. The technology milestones in robotics shaped industry trends in the adoption of robotics for harvesting and greenhouse management.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
###### Abstract  
The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network.  
Keywords:geographic routing protocol; fuzzy logic; selection criteria; parameters for assessment; routing overhead +
Footnote †: journal:  
_Article_
## 2 Traditional A* Algorithm with Improvements
### Improved A* Algorithm
#### 2.3.3 Optimization of Key Points Extraction  
Upon refining the conventional A* algorithm's single search mode in accordance with Section 2.3.2, the resulting path is visually depicted as the solid black line in Figure 3. As observed in this illustration, the planned path exhibits redundant collinear nodes and turning points. Following such a path would subject the AGV to excessive turning maneuvers, thereby increasing the vehicle's operational load and consequently diminishing overall efficiency. Therefore, to solve this problem, this study optimizes the planned path, selects and retains the necessary key nodes, and deletes unnecessary nodes. The specific methods are as follows.  
First, all nodes are put into the set \(\{P_{k}|k=1,2,3,\cdots,n\}\); the starting point is denoted by \(P_{1}\) and the target point is denoted by \(P_{t}\). Then, starting from P1, the included angle between vectors \(P_{1}\) to \(P_{2}\) and vectors \(P_{2}\) to \(P_{3}\) are solved. If the included angle is equal to zero, it means that the two vectors are collinear and \(P_{2}\) is a redundant node that needs to be deleted. If the included angle is not equal to zero, it means that the two vectors are not collinear. At this point, \(P_{1}\) is connected to \(P_{3}\) and then the positional relationship between the line and the nearest obstacle is judged. If the distance from the center of the  
Figure 2: Obstacle location and search method.  
obstacle to the line segment is greater than the preset safe distance, it means that node \(P_{2}\) is a redundant turning point and can be deleted; otherwise, it cannot be deleted. After deleting redundant nodes, the nodes in the set are updated. When key nodes that cannot be deleted are encountered, they are used as a new optimization starting point until the target point is connected. The black line represents the path searched by the traditional A* algorithm. Finally, by eliminating redundant points and retaining key points, a static global path that contains only the starting node, key points, and target points is optimized, as shown by the red dotted line in Figure 3.
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 2 Uncertainty Estimation Model
A widespread practice is to consider that if  
\[\Big{\{}S_{n}^{2}\Big{\}}=\sigma^{2}, \tag{7}\]  
then,  
\[E\{S_{n}\}=\sigma. \tag{8}\]  
Nonetheless, defining by z a random variable,  
\[z(\omega)=1,\ \forall\ \omega\in\Omega, \tag{9}\]  
and applying Cauchy-Bunyakowskii-Schwartz inequality [20],  
\[E\{S_{n}\}=E\{S_{n}z\}\leq\sqrt{E\Big{\{}S_{n}^{2}\Big{\}}E\{z^{2}\}}=\sqrt{ E\Big{\{}S_{n}^{2}\Big{\}}}, \tag{10}\]that is  
\[E[S_{n}]\leq\sigma, \tag{11}\]  
this inequality depends on all these three: sample size \(n\), distribution function \(F_{X}\), and correlation dependence \(\rho_{X_{i}X_{j}}\). For a sample, \(X=(X_{1},\cdots,X_{N})\) corresponds a density function \(f_{X}(X,\theta)\) for which the score \(V(\theta)\) is defined  
\[V(\theta)\equiv V_{X}(\theta)logf_{X}(X;\theta), \tag{12}\]  
and the Fisher information matrix  
\[S_{n}:=\sqrt{S_{n}^{2}}, \tag{4}\]0  
The [PERSON] theorem is fulfilled [20] where if \(\hat{\theta}_{n}=\hat{\theta}(X_{1},\ldots,X_{n})\) is an unbiased estimator of \(\theta^{\infty}\) then  
\[S_{n}:=\sqrt{S_{n}^{2}}, \tag{4}\]1  
and also depend on sample size \(n\), distribution \(F_{X}\), and correlation dependence \(\rho_{X_{i}X_{j}}\). This means that in the case of the fetal monitor where there are few samples, it is possible to determine the boundaries for the reliability estimation and its uncertainty using the Monte-Carlo method.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 4 Results
### Walker Constellation  
The walker configuration, which comes from [41], has been widely used in LEO augmentation navigation systems due to its symmetrical distribution and favorable global coverage. In this section, we take Globalstar as an example and conduct the survey. The Globalstar constellation is a walker 52\({}^{\circ}\):48/8/1 constellation [21]. This means that there are 48 satellites in 8 planes inclined at 52 degrees, evenly spanning the 360\({}^{\circ}\) around the equator. The value \"1\" defines the phasing between the planes. The configuration can be seen in  
Figure 11: The critical state of two satellites observed by the same user. O is the center of the Earth, and G1, G2, G3, and G4 are the locations of users. A, B, C, and D are satellites with the same orbital altitudes, which are uniformly distributed.  
Figure 10: Graph of the orbital altitude and approximate minimum number of PRN codes. The blue line shows the approximate minimum number of PRN codes, and the orange line shows the number of conflicts. This is a polar-orbiting constellation at low altitudes of 300–3000 km, which is divided into 6 orbits with 11 satellites each. The number of conflicts is equal to the number of instances of 1 in the conflict matrix \(R\).  
Figure 4a. The result of satellite PRN code assignment is shown in Figure 12, and these 8 PRN codes are shown to be sufficient for CDMA and ranging of the Globalstar constellation.  
We studied the effect of the satellite orbital altitude and inclination of the walker constellation on the number of the minimum PRN codes. Figures 13 and 14 show the results for each altitude and inclination. On one hand, once again, we proved the conclusion that if other satellite parameters remain the same, the higher the altitude is, the more PRN codes needed. On the other hand, if the constellation configuration and satellite altitude remain the same, the minimum number of PRN codes is independent of the orbital inclination: no matter what the orbital inclination is, the minimum number of PRN codes remains the same.  
Figure 12: Graph of Globalstar grouping multiplexed PRN codes. The Globalstar constellation is a walker \(52^{\circ}48/8/1\) constellation with an altitude of 1414 km. Eight colors and numbers represent eight PRN codes, which are enough to realize CDMA for Globalstar constellation.  
Figure 13: Graph of the orbital altitude and approximate minimum number of groups. The blue line shows the approximate minimum number of PRN codes, and the orange line shows the number of conflicts. This is a walker constellation similar to Globalstar, which is divided into 8 orbits with \(11\) satellites each. The orbital inclination is 52 degrees. The number of conflicts is equal to the number of instances of 1 in the conflict matrix \(R\).
# Beam Formation and Vernier Steering Off of a Rough Surface
## 3 Non-Mechanical Beam Steering
### Results and Discussion
The paint sample’s decrease is shown in (**c**,**d**). In each scatterplot, the profile of the cross section passing through the overall center maximum is identified. overall trends shown in Figure 6c,d are only attributable to the singly scattered light from the paint sample. The other notable feature of the paint sample is that vertical scanning has a steeper decline than horizontal scanning. This was not due to any non-uniformity of the paint but must be attributed to the fact that the horizontal plane is the plane of incidence. Additionally, the central cross sections in Figure 6 show that the largest relative decrease occurs after the first circular shift, both horizontally and vertically. Using this view of the data, it is easy to see the discontinuous dropoff in the paint as well as a larger than average dropoff in the metal sample. The likely explanation is that the beam steering technique only holds for singly scattered light. The relative enhancement for the central cross section below that of the other cross sections is then attributable to some function of the proportion of singly scattered light and multiply scattered light in addition to the relative optimality of the phase mask. As a surface reflector, the metal sample should cause less multiple scattering than the paint sample. This is believed to be why the decreases in relative enhancement for the metal sample's central cross section are less prominent than the paint sample's. The presented technique for beam steering could provide a vernier beam steering capability but would need to be paired with another technique if scanning is required over large solid angles, such as in LiDAR. Additionally, the technique is highly dependent on the focal plane setup, which places design constraints on the use cases. For instance, it would not be practical to use this technique with an OPA in LiDAR because the OPA itself could act as a steerable illumination source. Other LiDAR illumination techniques, such as flash LiDAR, utilize diffractive elements such as metasurfaces to create point clouds [22; 27]. At a minimum, there is a possibility of using a focal plane system with these approaches when the diffractive element can be separated from the laser. The goal of such applications could be to perform vernier rotations of the point cloud.
# Hybrid-Margin Softmax for the Detection of Trademark Image Similarity
## 3 Results
### Discussion  
Metric learning is an appropriate research idea for the TMISD task since the requirement for annotation data during the training is reduced. With the same numbers of similar TM pairs, the triplet network and fine-tuning data-driven methods can improve performance greatly compared to a simple SNN model. The triplet model enhances the discrimination ability with an additional input during training. The fine-tuning method transfers the learned information from other tasks and alleviates the pressure of data annotation.  
The advantage of the detection models based on the data-driven methods is not prominent compared to the typical loss-driven models since the training is complicated  
Figure 6: The accuracy of the detection model (resnet18) trained with different hyper-parameters in HMS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SNN (Contrastive Loss) Method**} & \multicolumn{2}{c}{**Triplet Network Method**} & \multicolumn{2}{c}{**Fine-Tuning Method**} \\ \cline{2-7}  & **Accuracy (\%)** & **F1** & **Accuracy (\%)** & **F1** & **Accuracy (\%)** & **F1** \\ \hline ResNet18 & 41.53 & 0.4237 & 85.05 & 0.8449 & 53.61 & 0.5588 \\ ResNet34 & 46.73 & 0.4535 & **92.27** & **0.9282** & **70.65** & **0.7149** \\ ResNet50 & 47.18 & 0.4654 & 58.25 & 0.6897 & 56.19 & 0.5685 \\ ResNet101 & 46.53 & 0.4549 & 54.13 & 0.6642 & 47.42 & 0.5049 \\ ResNet152 & 46.84 & 0.4431 & 59.28 & 0.6802 & 46.39 & 0.4851 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of data-driven methods on the TMISD task.  
and expensive. The performance gaps between the SphereFace model and the other two models, CosFace and ArcFace, are huge, but the performance cannot be sustained when SNN depth is increased or a new type of image is input for detection. The SphereFace model can be damaged by the diversity of TM images.  
The HMS model outperformed other methods in the following aspects: (1) the compactness of similar TM pairs is tightened obviously; (2) the discrimination ability for another type of image, i.e., face data, is improved, which indicates the model trained on HMS is robust; (3) the training cost is reduced as a result of the requirements of annotated data and deep SNN depth being loosened.  
In general, the introduction of the loss-driven model training idea is meaningful to the TMISD task. The challenges of training data-building and generalization on new data are dealt with in a low-cost way.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 2 Materials and Methods
### Corrosion Inhibition  
#### 2.5.1 Weight Loss Method  
For finding the exact condition preventing corrosion in mild steel against 1 M HCl, weight loss methods in two different parameters (concentration and temperature) were examined. The percentage of inhibition efficiency (\(\eta\%\)) and surface coverage (\(\theta\)) were calculated individually for both the parameters using the following equations [28; 29; 30]:  
\[\text{Inhibition efficiency (\(\eta\%\))}=(\text{W}_{0}-\text{W}_{\text{i}}/\text{W}_{0}) \times 100 \tag{5}\]  
\[\text{Surface coverage }=\text{ (\theta) }=\text{ \frac{\eta(\%)}{100}} \tag{6}\]  
where \(\text{W}_{0}\) and \(\text{W}_{\text{i}}\) are the weight loss of the mild steel in the absence and the presence of the inhibitor, respectively.  
Effect of Concentration on Corrosion Inhibition  
The percentage inhibition efficiency (\(\eta\%\)) and surface coverage (\(\theta\)) of the plant extract were recognized by the difference between the initial and after weight of mild steel immersed in corroduct solution. The mild steel specimens were immersed in 1 M HCl without and with different concentrations (100, 200, 300, 400, 500, and 600 ppm) of inhibitor at 303 \(\pm\) 1 K for 3 h. Then, the specimens were washed thoroughly and weighed.  
Effect of Temperature on Corrosion Inhibition  
The effect of different temperatures (313, 323, 333, and 343 \(\pm\) 1 K) on different concentrations (100, 200, 300, 400, 500, and 600 ppm) of _C. tagal_ extract was tested for 3 h. The same method as in concentration was followed. The weight of each specimen was noted before and after immersion to calculate the corrosion inhibition.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 2 Datasets and Methods
### Gps  
GPS data were collected in the Quetta Valley by the University of Balochistan from 2006-2016. Data were acquired using four GPS monitoring sites spread out in the valley: the Balochistan University IT Department (QTIT), the Balochistan University Geology Department (QTAG), Sardar Bahadur Khan University (SBKW), and Ismail House (IHAK) (Figure 1). As the stations were not continuously operating, GPS data were recorded at an interval of once per month or greater.  
Raw data were first converted into a Rinex file format. The GPS data were then processed using the Automatic Precise Positioning Service (APPS) to get the GPS solution referenced in the International Terrestrial Reference Frame 2008 (ITRF 2008). Then, this output was rotated into an Indian-Plate-fixed reference frame with a pole-of-rotation calculation using a python code.