esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Relationship between Meteorological Elements and GPP
This gradual response can mitigate the impact of the meteorological changes on ecosystems, reducing their vulnerability to such changes. This helps prevent ecosystems from suffering adverse effects due to short-term meteorological variations. Furthermore, the lagged response of the vegetation to meteorological factors can serve as a regulating mechanism for the climate. For instance, the delayed vegetation response can slow down temperature fluctuations in the climate system, preventing rapid temperature increases or decreases. This climate-regulating effect contributes to maintaining the relative stability of the climate system. This paper elucidates the lagged responses of the GPP to meteorological elements in different regions and vegetation types across China. It also reveals that regions in China, especially Regions V and VII, exhibit the highest proportion of the GPP with no lag effects in response to temperature and radiation. We propose that this observation may be attributed to the high prevalence of the single vegetation types and a significant proportion of barren land and grassland in these regions (Figure 11). The Grasslands and barren lands typically consist of the less vegetation or exposed soil, which have relatively lower heat capacity and higher surface reflectance [65]. The high surface reflectance allows them to more effectively reflect heat, reducing their heat absorption and temperature increase capabilities. Additionally, the low heat capacity means that they can quickly warm up or cool down when exposed to heat radiation, without requiring an extended period to accumulate or release heat. Consequently, these areas do not exhibit significant lag effects.
# Security Risk Analysis of LoRaWAN and Future Directions
## 5 Security Threat and Risk Assessment of LoRaWAN v1.1
### LoRaWAN v1.1 Threat Catalog
#### 5.2.10. Routing Attacks  
Mainly two types of attacks can be considered under this category:  
* **Selective Forwarding Attack:** In this attack type, an attacker can selectively forward packets and can cause one or several nodes in the network to be totally blocked, or totally overwhelmed.
* **Sinkhole or Blackhole Attack:** In these attacks, an attacker attracts the traffic through itself by falsely advertising modified routing information. As a result, whole network traffic might collapse.  
In LoRaWAN, in order routing attacks to be successful, an attacker needs to capture a \(GW\) or a server, which is a non-trivial and complicated task. These attacks cannot be perpetrated from the \(EDs\)s, as they do not participate in routing. Therefore, the probability of this attack to happen is very low.
# Respiration Monitoring for Premature Neonates in NICU
## 3 Methods  
### Material  
Our study was conducted with videos recorded at the Maxima Medical Center in Veldhoven, The Netherlands, by a fixed-position high-definition camera (Camera model IDS uEye monochrome) filming the infant's entire body in the direction of the foot to the head. Figure 1 shows an example of a captured video frame. We decided on the recording position of the camera by considering: (1) little or no interruption to daily routine care; and (2) a good viewpoint for observing vertical movement of the infant chest, where the movement is assumed to be with maximum respiratory motion energy in the vertical direction. For all infant recordings, written consent was obtained from the parents. The resolution of each video frame is 736 \(\times\) 480 pixels, while the frame rate is 8 fps. The videos were recorded under uncontrolled, regular hospital lighting conditions. Five infants with an average gestational age of 29.6 \(\pm\) 2.8 weeks (range 27\({}^{+0}\)-33\({}^{+6}\) weeks), an average postnatal age of 1.2 \(\pm\) 0.6 weeks (range 0\({}^{+4}\)-2\({}^{+1}\) weeks), and an average weight of 1555 \(\pm\) 682.4 g (range 755-2410 g) were filmed. Parallel with the video capturing, standard chest impedance (CI) signals were recorded simultaneously as reference standards.
## 2 Artificial Orthogonal Gland (AOG) Mechanism
### Control Mechanism  
The processing unit of \(CI\) is the control mechanism (Figure 1). \(CI\) input can have four different states:  
1. \(CI=0\) or \(CI=1\): zero-state (inhibitory state) is activated when the gland threshold is not achieved (\(0\leq CI<\) threshold_value), implying a gland not to be active;
2. \(CI=2\): when the excitatory manner of work is activated;
3. \(CI=3\): when the negative feedback logic of work is activated;
4. \(CI=4\): when the positive feedback logic of work is activated.  
The state of inhibition (a zero state) is active when the gland threshold is not achieved and results in the output of the control mechanism equal to \(CM_{out}(t)=0\). The threshold value can be set in accordance with control needs: if there is a requirement to build an extra sensitive system that will register even the most minor environmental disturbances, the threshold_value should be set to a value close to 0. If the requirement is to provide a robust system that will activate glands on some significant disturbances or peak occurrences, the threshold_value should be large.  
The following introduced feature is the excitatory feature, active when threshold_value is achieved and can be described with the following equation:  
\[CM_{out}=f_{CM}(SM_{out}(t-1)). \tag{2}\]  
\(fCM\) from Equation (2) can be selected as a linear function, bipolar, sigmoid, step, etc. The selection should be made according to a specific use case. The third possibility that the control mechanism can exploit is a negative feedback logic that can be described as follows:  
\[CM_{out}(t)=f_{CM}\bigg{(}\frac{SM_{out}(t-1)}{1+CI(t)}\bigg{)}. \tag{3}\]  
When the negative feedback feature (Equation (3)) is active, the hormone production will be suppressed. The final option is to use the positive feedback logic to calculate \(CM_{out}\):  
\[CM_{out}=f_{CM}\bigg{(}\frac{SM_{out}(t-1)}{1-CI(t)}\bigg{)}. \tag{4}\]  
When the positive feedback feature (Equation (4)) is active, the hormone production intensifies. Negative (Equation (3)) and positive (Equation (4)) feedback controls should be activated once the specified hormone concentrations are achieved (low or high), and the equilibrium points are reached. Feedback controls can rapidly increase or decrease hormone stimulations, enabling quick responses of artificial glands to changes occurring in the environment. Traditional artificial glands need a significant number of iterations to adjust their parameters to sudden changes in environmental conditions and large volatilities. Negative and positive feedback features can make these adjustments significantly faster.
# Gait Phase Identification and Damping Control for Knee Orthosis Using Time Series Forest Classifier
## 4 Machine Learning Algorithm for Gait Phase Identification: Training, Evaluation, and Results
### Time Series Forest Classifier (TSFC)
#### 4.1.1 Number of Features  
The dataset created has 12 different features that correspond to absolute orientation in degrees and acceleration associated with two IMUs. The acceleration data are requisite for stable prediction, and the absolute orientation is to counter the long-term deviations incurred as it uses the output from the onboard fusion engine in the Adafruit BNO085 chipset.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 4 Field Test
### Data Processing  
UAVs are a commonly used technique for obtaining 3D terrain information. Through image reconstruction processing, it can generate Digital Surface Model (DSM) data from UAV images. The UAV images are generally processed using SfM software to produce a high-resolution DSM to study surface movement [9]. The SfM software calculates the intrinsic and extrinsic parameters of the images, and these values are used to calculate the positions of the points in the overlapping images to generate the 3D data. This study used the SfM software package @Agisoft Metashape [29] to produce point clouds, DSM, and orthophotos from UAV images.  
This software follows a general workflow with some phases of data processing. The phases include:  
1. Importing the images into software.
2. Alignment between overlapping images, parameter setting high accuracy, 40,000 key point limit, 4000 tie point limit.
3. Georeferencing images using GCPs to optimize the camera position and orientation.
4. Generation of the dense point cloud, parameter setting high quality.
5. Building 3D meshes from point clouds.
6. Above the 3D meshes parameter, a DSM and orthomosaic are created.  
The georeferencing of the UAV images was performed using ground control points (GCPs). Since setting attributes in the forest area is complex, GCPs were placed on the bare surface with better transmittance. The GCPs were evenly distributed over a surveyed area to achieve optimal accuracy of results. We created 11 GCPs and 8 check points (CPs) distribution of the study areas, as shown in Figure 10. The GCP distribution of the study areas is shown in Figure 10. Each point used the VRS (Virtual Reference Station), and the locational accuracy was about 3 cm. In addition, the UAV images were processed by software to generate point clouds, and GCPs were added to calculate the actual location.  
Figure 9: The flight route positions of the UAV images captured in the Liugui study area. (**a**) Grid-type flying mission, (**b**) contour-type flying mission.
## 3 Proposed Ensemble Deep Neural Network for Individual Identification Using ECG Signals
### Evaluation Methods  
For performance evaluation, the following metrics were used: Accuracy, Precision, Recall, F1-Score, False Rejection Rate (FRR), and False Acceptance Rate (FAR). Accuracy measures the proportion of correctly identified instances among all instances. Precision indicates the proportion of correctly identified positive instances of all predicted positive instances. Recall measures the ratio of correctly identified positive instances to all actual positive instances. F1-Score is the harmonic mean of the precision and recall, providing a balance between the two metrics. The FRR is the proportion of genuine acceptances that are incorrectly rejected. The FAR is the proportion of impostor attempts that are incorrectly accepted. These metrics are commonly used in classification tasks to assess the performance of the classifiers based on their predictions and true data labels. These evaluation methods are calculated using the following formulas based on True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) [47]:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{3}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{F1-Score}=\frac{2\times(\text{Precision}\times\text{Recall})}{\text{ Precision}+\text{Recall}} \tag{6}\]  
\[\text{FRR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{7}\]  
\[\text{FAR}=\frac{\text{FN}}{\text{TP}+\text{FN}} \tag{8}\]  
We have essentially trained the network to improve accuracy and are making performance improvements accordingly. Since precision, sensitivity, and other metrics are also important for personal identification, they have been included as well.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 2 Materials and Methods
### Data and Experiment Details
#### 2.5.4 Dataset Construction  
Existing datasets are dominated by ISPRS-Vaihingen and Potsdam ([[https://www.isprrs.org/education/benchmarks/UrbanSemLab/semantic-labeling.aspx](https://www.isprrs.org/education/benchmarks/UrbanSemLab/semantic-labeling.aspx)]([https://www.isprrs.org/education/benchmarks/UrbanSemLab/semantic-labeling.aspx](https://www.isprrs.org/education/benchmarks/UrbanSemLab/semantic-labeling.aspx)), accessed on 24 January 2023), mostly for land cover use. There are few datasets about UGS and most of them are not publicly available for UGS related research. Meanwhile, most of the existing studies use MODIS, Landsat, and other low- and medium-resolution remote sensing image data, which have low precision in UGS exposure level evaluation and cannot express the structure, quality, morphology, and other aspects of UGS characteristics. Therefore, we selected an HRS image (GF-2) as the data to construct the UGS dataset. With the help of Google Earth, all acquired images are marked at the pixel level, and the original image pixels are divided into three categories: low vegetation, tree, and background. The RGB values of the background, tree and low vegetation pixels are set to (0, 0, 0), (0, 255, 0) and (0, 255, 255), respectively, as shown in Figure 7. Among them, low vegetation is dominated by grass and shrub. In order to adapt to the limited computing resources, we adopt the image cropping method to segment both the original image and the labeled image into 256 \(\times\) 256-pixel images, and finally the UGS labeled dataset UGS-1 is obtained, including the training, validation, and test set. UGS-1 is shown in the original bands (1-2-3) of the GF-2 image. The sets are 1109, 336, and 336, respectively. At the same time, the original bands (1-2-3) of UGS-1 are modified to display the false color image in the order of 4-3-2 bands sequentially. The same method as above is used to construct UGS-2 with false color image, as shown in Figure 8, where the training, validation, and test set are 1109, 336, and 336, respectively.
## 1 Introduction
However, the increasing abundance of data arising from several platforms has not been used well within NWP models. In fact, the 89-GHz frequency is more sensitive to cloud information compared with other channels of the AMSR-E, but it also contains information of land surface emission. Obtaining atmospheric information from combined land-atmosphere signals is very challenging because the strong heterogeneous land surface emissions mask the very weak atmospheric signals. When land surface emissions are estimated as accurately as possible, then the weak signal of the cloud fields from the combined signals can be converted into realistic representations of the cloud fields for the NWP models. Consequently, the Coupled Atmosphere and Land Data Assimilation System (CALDAS) was developed as an improvement of the LDAS-A. CALDAS assimilates lower frequency data of AMSR-E/AMSR-2 to improve the representation of land surface conditions and it merges them with higher frequency data of AMSR-E/AMSR-2 to improve the representation of cloud conditions over land surfaces. CALDAS results from the Tibetan Plateau have shown that simultaneous assimilation of land and cloud conditions improved the biases in land surface states, cloud representation, and forcing to land surface models, which enhanced land-atmosphere interactions [12]. As a continuation from our previous study [12], this work addresses the application of CALDAS as a weather forecasting system, simulating short-term weather conditions over a mesoscale domain of Niger in West Africa. Model performance is investigated using available ground-based data and satellite observations.
# Study on Band Gap and Sound Insulation Characteristics of an Adjustable Helmholtz Resonator
## 4 Discussion
### Influence of Cavity Structure on Band Gap  
In order to control variables for comparative analysis, when analyzing the influence of the length of telescopic screw on the low-frequency band gap, the structural parameters \(a=62\) mm, \(l=60\) mm, \(s=0.35\) mm, \(l_{1}=254.8\) mm and \(h=24.5\) mm remained unchanged. When the length of the telescopic screw \(b\) decreased from 25 mm to 0, the change of the band gap structure was calculated. The influence of the length of the telescopic screw \(b\) on the band gap structure is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **The Lower Limit of** & **The Upper Limit of** & **The Lower Limit of** & **The Upper Limit of** \\  & **the First Band Gap** & **the First Band Gap** & **the Second Band Gap** & **the Second Band Gap** \\ \hline FEM/Hz & 39.27 & 86.18 & 133.12 & 209.44 \\ Equivalent model/Hz & 37.73 & 88.45 & 134.80 & 212.89 \\ Error & \(-3.92\%\) & 2.63\% & 1.26\% & 1.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of FEM and equivalent model method.  
From the mechanism of band gap generation, it can be seen that the first band gap is mainly the result of the local resonance between the cavity II and the labyrinth tube, while the second band gap is the result of the local resonance between the cavity I and the labyrinth tube. According to the band diagram shown in Figure 10, when the length of the telescopic screw is reduced from 25 mm to 0, cavity I keeps increasing and cavity II keeps decreasing. With the increase of cavity I, the second band gap keeps moving down, while cavity II keeps decreasing, which makes the first band gap move up slowly. When the length of the telescopic screw is 0, the volume of the two cavities is the same, and the first band gap superimposed with the second band gap to form a band gap. In general, the first band gap changes little in the process of adjusting the length of the telescopic screw, but the second band gap keeps moving down when the length of telescopic screw keeps decreasing, covering a wide low-frequency band gap. In engineering applications, this design has good adaptability for noise crest elimination.  
In order to further explore the adaptation range of this structure, the influence of the change of the inner cavity structure on the band structure was analyzed. As shown in Figure 11, the upper and lower limits of the first and second band gaps varied with the position of diaphragm were obtained. As the length of the telescopic screw \(b\) changed, the inner cavity structure changed and the band structure changed accordingly. As shown in the figure, the width of the first band gap decreases as the diaphragm continues to move down, and the lower limit of the first band gap also keeps increasing. Correspondingly, the width of the second band gap increases as the diaphragm continues to move down, and the lower limit of the second band gap decreases. On the whole, the two band gaps converge to the same band gap during the process of diaphragm movement.
## 4 Case Study  
According to the collected data, the evaluation levels were divided, the evaluation index system was established, and the evaluation indexes were quantified and graded. The hierarchical analysis method was applied to obtain the index weights, and the fuzzy C-mean clustering algorithm was used to complete the classification, and the classification results were input into GIS for visualization according to the centroid data derived from the raster to obtain the final zoning map for the evaluation of underground space resources development. The thresholds of the comprehensive scoring results were all divided into four levels, namely: excellent zone (I), good zone (II), medium zone (III), and poor zone (IV) for the suitability of underground space development and utilization. The final zoning map for the evaluation of the geological suitability of underground space resources development  
According to the comprehensive suitability evaluation map and the evaluation distribution table, we can know that  
(1) Combined with the 0-30 m, 30-100 m, and 100-200 m strata, the overall suitability of underground space utilization in Huancui District of Weihai City is good, with the sum of the area of Zone I and Zone II accounting for 79%, 94%, and 100% respectively.  
(2) In the 0-30 m strata, the areas of poor suitability are mainly located in Shuangdao Bay, Zhengqi Mountain, Funding, Nangli, Soapbu, the international seawater bathing beach, the coastal protection zone, the scenic tourist spot and the area around the shopping mall. The area of Shuangdao Bay is artificially filled, prone to uneven ground settlement, and is an unstable foundation for buildings; the area around Zhengqi Mountain and Funding has a large undulating terrain, making it difficult and expensive to develop underground space, and historical quarry pits can be seen everywhere, with geological hazards of collapse and landslides developing; Zhanli is prone to ground collapse; the International Beach is a tourist geological resource in the area and should be moderately protected; and the scenic tourist area, the is a key geological resource protection zone. Therefore, the development and use of underground space are not recommended in the above-mentioned areas.  
(3) The space between 30-100 m and 100-200 m is unexploited, and because it is located in the bedrock distribution area, it can be regarded as a suitable area for underground space development and exploitation.
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers
## 5 Non-Rest-to-Rest Trajectory with a Single-Switch Torque Input  
The problem becomes more complicated for non-rest initial conditions, where \(\dot{\theta}_{1}(t_{0})\), \(\theta_{2}(t_{0})\), and \(\dot{\theta}_{2}(t_{0})\) have non-zero values. As mentioned previously, we assume that the initial non-rest conditions are known. In these cases, the minimum number of switches for the non-rest-to-rest control problem is not limited to \(2n+1\). As mentioned earlier, the minimum required number of switches changes with the initial conditions and the torque limit. We propose to solve the problem using a single switch reference trajectory with input shaping. Figure 5: Rest-to-rest case with conventional ZVDD input shaping. The plot units are degree (\(\theta_{1}(t)\) and \(\theta_{2}(t)\)), degree/second (\(\dot{\theta}_{1}(t)\) and \(\dot{\theta}_{2}(t)\)) and N.m (\(u(t)\)). Suppose that \(\dot{\theta}_{1}(t_{0})=\dot{e}_{1}\), \(\theta_{2}(t_{0})=e_{2}\), and \(\dot{\theta}_{2}(t_{0})=\dot{e}_{2}\), so the initial state takes the form \(\theta(t_{0})=[\theta_{0},\dot{e}_{1},e_{2},\dot{e}_{2}]^{T}\) and the final desired state is \(\theta(t_{f})=[0,0,0,0]^{T}\). Using Equation (11), the time increments \(t_{21}\) and \(t_{10}\) for the single-switch reference trajectory can be obtained:  
\[t_{21} = \frac{(c_{3}^{2}\dot{\theta}_{1,1}+c_{1}\dot{\theta}_{2,1})-(c_{3 }^{2}\dot{e}_{1}+c_{1}\dot{e}_{2})}{(b_{4}c_{1}u_{2}+b_{2}c_{3}^{2}u_{2})} \tag{34}\] \[t_{10} = \frac{(c_{3}^{2}\dot{\theta}_{1,0}+c_{1}\dot{\theta}_{2,0})-(c_{3 }^{2}\dot{\theta}_{1,1}+c_{1}\dot{\theta}_{2,1})}{(b_{4}c_{1}u_{1}+b_{2}c_{3} ^{2}u_{1})} \tag{35}\]  
Similar to Equation (14), if \((c_{3}^{2}\dot{\theta}_{1,0}+c_{1}\dot{\theta}_{2,0})=0\) (or \(\approx 0\)), then the following equation can be written:  
\[t_{10}\approx\frac{(c_{3}^{2}\dot{\theta}_{1,1}+c_{1}\dot{\theta}_{2,1})}{(b_ {4}c_{1}u_{2}+b_{2}c_{3}^{2}u_{2})} \tag{36}\]  
Therefore, using Equations (34) and (36),  
\[t_{21}=t_{10}-\frac{(c_{3}^{2}\dot{e}_{1}+c_{1}\dot{e}_{2})}{(b_{4}c_{1}u_{2} +b_{2}c_{3}^{2}u_{2})}=t_{10}-f_{1} \tag{37}\]  
where \(f_{1}=\frac{(c_{3}^{2}\dot{e}_{1}+c_{1}\dot{e}_{2})}{(b_{4}c_{1}u_{2}+b_{2}c_ {3}^{2}u_{2})}\) is a constant. Like the single-switch case with initial resting conditions, there will be vibrations at the final time for this case.