esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Uncertainty Discussions for Phenology Regularity in Northeast China  
Phenology, as a comprehensive indicator of climate and natural environment changes, has been widely used worldwide in many assessment projects about environmental change impact. In this study, we determined the SOS, that is to say, the date of green-up date, in the spring phenology, and EOS in the winter phenology of forests based on the statistics references of the NPP time series every eight days, from 2001 to 2019, in Northeast China. Our study found that the whole growth period of forest in Northeast China became extended with the change of phenology. Concretely, our results performed that there is an advanced SOS and a significant delayed EOS in forest ecosystem in Northeast China, from 2001 to 2019. In addition, other scholars have developed many phenology retrieving methods based on remoting-sensing data, such as machine learning and vegetation indices from time series based on software TIMESAT, but the strength and weakness between different retrieving methods are still unclear [68; 69; 70; 71]. A latest study in Northeast China also found that there is an advanced at the start of the growing season (SOS) with in forest ecosystems based on statistics and analysis [72]. However, there are some uncertainties in exploring the phenological period. For example, this study only explored the EOS and SOS of forests roughly based on the eight-day NPP instead of some photo-temperature driven phenological model. Moreover, since we have not yet found the more precise way to study the growth period of each forest type in detail, this also creates uncertainty.  
The study of phenology regularity not only improves the understanding of vegetation response to climate change, but also influences the forest construction [16; 73; 74]. Of course, there are some limitations in the exploration of vegetation phenology. Firstly, we did not explore the spatial phenology since we pay more attention to the analysis from multiple time series. In addition, we recommend that the multi-model retrieving methods based on satellite data should be compared to explore the vegetation phenology in the different ecosystems in the future. To conclude, to realize the sustainable development of forest ecosystem, it is suggested that the forest management plan should be scientifically formulated based on understanding and mastering the growth regularity and phenological periods of different forests in Northeast China.
###### Abstract  
This article presents the results of applied research focused on the development of a hybrid software solution, which is based on the ABB RobotStudio 2021 suite, because even in the latest version, RobotStudio 10 does not have a dedicated virtual commissioning module. The aim of the study is to explore the potential communication capabilities between ABB RobotStudio and Siemens Simatic Robot Integrator and to unify control systems for performing virtual commissioning tasks. The proposed solution offers valuable procedures for any end user responsible for implementing robotic cell virtual commissioning tasks and ensures the compatibility between hardware and software components from ABB and Siemens manufacturers integrated into any robotic cell type. Siemens, one of the leading PLC manufacturers, offers the complex PLM platform Process Simulate, which can be integrated with the Totally Integrated Automation (TIA) Portal and Mechatronics Concept Designer (MCD) for developing virtual commissioning (VC) applications. The TIA Portal is recognized by robotics application integrators as the most efficient work environment for VC. However, there is currently no direct compatibility between the ABB RobotStudio (RS) platform and Siemens TIA Portal. To address integration challenges, Siemens has introduced the Simatic Robot Integrator (SRI) platform, which ensures compatibility with certain proprietary software from various robot manufacturers, including KUKA, Motoman, and Staubli. While Siemens has collaborated with manufacturers like KUKA and Kawasaki, major industry manufacturers such as ABB and Fanuc have yet to establish a standardized interface within the Siemens SRI platform. A mathematical model was also developed, which serves as a key perspective and a valuable tool for the future integration of double-port-type robots into the working environments of ABB and Siemens. This integration aims to facilitate virtual commissioning in scenarios where such robots need to be implemented. Currently, ABB's library does not include double-portal-type robots, despite the fact that leading manufacturers of double-portal robot types utilize ABB's IRC5 controllers.  
virtual commissioning; simulation; integration; methodology; automotive systems; industrial robots; practical approach
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 3 Theoretical Model
### Localization Model  
#### 3.1.1 One Dimensional TD-PD  
The localization method presented in this paper is based on the tag phase shift measured by each antenna at different points in time (TD-PD) [36]. In a homogeneous medium, the phase shift \(\phi_{i,j}-\phi_{0,j}\) between the initial and the \(i\)-th (unwrapped) phase measurement, is directly proportional to the radial displacement \(\delta r_{i,j}\) between the tag and antenna \(j\) (see Equation (1)).  
Assuming an initial radial distance \(r_{0,j}\), we obtain a series of radial distances \(r_{i,j}\) from a measured phase series \(\phi_{i,j}\):  
\[r_{i,j}=r_{0,j}+\delta r_{i,j} \tag{2}\]  
where \(\delta r_{i,j}\) is obtained directly through Equation (1). This localization method is, hence, relative to the initial position, as it does not allow for absolute positioning without further information about the system (e.g., when \(r_{0,j}\) is not known).
## 2 FANET Architecture and Routing Protocol
### Routing Protocols
#### 2.2.6 Heterogeneous-Based  
FANETs serve as relay networks in SAGINs and must connect with a variety of platforms, including ground stations, satellites, manned aircraft, and near-space platforms, in the practical use of UAVs [53]. Typically, FANETs communicate with a variety of networks, particularly those that are fixed nodes or those that are positioned on the ground. Researchers have therefore developed the idea of heterogeneous routing protocols.  
[PERSON] et al. [54] propose a new routing protocol for UAV-assisted vehicle self-assembly networks. The UAV improves the overall connectivity between vehicles by collecting information about ground traffic density and vehicle connectivity status in order to relay data when the only vehicles on the ground cannot be connected to each other, thus effectively improving the routing process. By adding a routing component to the UAV, [PERSON] et al. [55] has further extended UVAR. The extended version consists of two protocols: UVAR-G for ground-to-air communication and UVAR-S for air-to-air communication. however, this protocol still suffers from high latency and energy failures. [PERSON] et al. [56] propose a routing protocol using distributed priority that provides a solution to the sparsity of FANETs and ground-based networks by using the characteristics of red-black trees.  
Existing heterogeneous-based routing protocols generally focus more attention on other networks cooperating with the FANET and can cause higher latency and energy consumption. In addition, most of these protocols only consider static networks and do not take into account the mobility of network nodes. The next step still requires continued research on heterogeneous-based routing protocols if the efficient collaboration of individual self-organizing networks under SAGINs is to be achieved.
## 2 Materials and Methods
### The Designation of Experiments  
We designed two scenarios to compare the OBIA labels and the Manual labels. The designation of experiments for the two scenarios can be found in Figure 7.  
In Scenario I, we designed four strategies to train models with the OBIA labels from past humanitarian operations. The first strategy is to train models with data from one camp and one season (OCOS). The other three training strategies include intermixing data from (1) one camp during two seasons (OCTS), (2) two camps during the same season (TCOS), and (3) two camps during two seasons (TCTS). The output results of Scenario I are expected to illustrate whether we can use existing OBIA-generated labels to replace the Manual labels. The three intermixing strategies may help reveal the influences of combining different data sources on the performance of the selected model.  
In Scenario II, we randomly selected different percentages (10%, 20%, 30%, 40%, and 50%) of the total training patches of the Manual labels to fine-tune models pretrained by  
Figure 7: The designation of experiments for Scenario I and Scenario II. KuleDry means data from the Kule refugee camp during the dry season. AccKD means the accuracy of the model tested by data from KuleDry. Ngu denotes the Ngueny-iel refugee camp. Other abbreviations in Scenario I follow analogous rules. The FT in Scenario II denotes fine-tuning.  
the four strategies in Scenario I. The reason that we made a random selection rather than a manual selection of patches with a high density of target built-up structures was to keep the data distribution of fine-tuning data similar to that of the training and testing data. The output results from Scenario II are expected to reveal whether we can use a smaller amount of the Manual labels based on models pretrained by the OBIA labels to replace a large number of the Manual labels.  
Additionally, we designed baseline experiments where models were trained with purely Manual labels by the same strategies in Scenario I. The results from the two scenarios for the OBIA labels were compared with the performance of the baseline models for each camp during each season.
## 4 Results and Discussion
### Balanced and Imbalanced Data Stream with Incremental Drift  
An incremental drift takes place when the concept changes gradually with a constant speed, and this is the most difficult drift to detect. The performance of the base classifier on balanced and imbalanced multi-class data streams with increment drift is presented in Figure 9. It can be observed that it is hard to learn from the data steam with incremental drift and the classifier has produced poor results as compared to streams with gradual drift and streams with sudden drift. The class imbalance further decreases the performance compared to the balanced data stream. Figure 10 shows the actual effect of each drift detection method on base classifier performance in terms of percentage increase or decrease. The performance of HT as a base classifier on balanced and imbalanced data streams was again decreased using HDDM_W, STEPD, and EWMAC. The HDDM_W also caused a decrease in performance  
Figure 8: Number of false drifts detected by each method on balanced and imbalanced data streamd. Figure 9: F1 score on balanced and imbalanced data streams with incremental drift. on the balanced data stream. The performance of the base classifier with GMA remained unchanged again, i.e., neither increased nor decreased on balanced as well as imbalanced data streams. Whereas, with DDM and HDDM_A, the performance was slightly improved on an imbalanced data stream. The DDM and HDDM_A cause a decrease in performance whereas they produced an improved performance on the imbalanced data stream. Overall, PH produced a high F1 score on a balanced data stream with incremental drift whereas DDM produced a high F1 score on an imbalanced data stream with incremental drift. For streams with incremental drift, we cannot calculate the false alarms because the position of increment drift is always unknown. The overall performance of the base classifier on each data stream is presented in Figure 11a-f. It can be observed that the performance of the base classifier is decreased at the time of drift occurrence. Another point to observe here is that class imbalance has further affected the performance of most drift detection methods. Table 5 shows the performance in terms of the overall F1 score on each data stream. The bold values represent the best performances on a particular data stream. The results show that none of these methods has shown consistent performance on all data stream types. Figure 11: _Cont._  
Figure 10: Effect of drift detection methods on base classifier’s performance in case of incremental drift: (**a**) incremental drift without class imbalance; (**b**) incremental Drift with class imbalance. Figure 11: F1 score on each time step for all data streams: (**a**) performance of HT on balanced data with sudden drift; (**b**) performance of HT on imbalanced data with sudden drift; (**c**) performance of HT on balanced data with gradual drift; (**d**) performance of HT on imbalanced data with gradual drift; (**e**) performance of HT on balanced data with incremental drift; (**f**) performance of HT on imbalanced data with incremental drift.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 6 Performance Evaluation
### Performance of the PM2M Overlay Network without Backoff Error  
We evaluate the performance of the PM2M overlay network for two traffic classes and for the contention-based RAP, with and without the backoff error, for H2H traffic intensity set to 100 calls/s. We consider the non-saturation condition, which means that the UE buffer will not always have a data frame to transmit. We assume that the data packet size is 30 bytes, including MAC headers of 10 bytes (where the cell and node ID should be) and the remaining 20 bytes used for MAC data.  
We first investigate the capacity of the PM2M overlay network on PRACH by neglecting the error in clear channel assessment. The packet arrival rate per M2M node was set in the range between 0.4 and two packets/s, while the number of PM2M nodes was varied between 300 and 1560. The upper bound for the number of nodes was selected so as to capture reasonable decline in transmission success probability.  
Figure 8: Performance of H2H traffic for \(PF=2\), \(c_{f}=5\).  
Figures 9a and 10a show the probability of successful packet transmission for Traffic Classes 0 and 1. We notice that Traffic Class 1 achieves a 5% higher success probability than Traffic Class 0 for 1560 nodes. The mean backoff time for Classes 0 and 1, for 1560 nodes, was 98 and 35 backoff periods, respectively, as shown in Figures 9b and 10b. This is reasonable since lower the priority traffic class with a higher contention window value has to backoff for a longer period of time than the higher priority class with shorter contention window. Medium access probabilities are shown in Figures 9c and 10c. We notice that Traffic Class 1 has up to a 15% higher access probability.  
Figure 9: Performance of the PM2M overlay for Traffic Class 0 without backoff error.  
We calculate the normalized throughput for each node, which was defined as the fraction of time in which the channel is used to transmit the frames' payload. Figures 9d and 10d show that the throughput of \(TC_{1}\) is almost three-times higher than \(TC_{0}\) for 1560 PM2M nodes and the highest data arrival rate. The mean number of backoff attempts is shown in Figures 9e and 10e for Traffic Classes 0 and 1, respectively; again, we note the slight disadvantage of Traffic Class 0 with respect to Class 1.
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 3 Results and Analysis
### Impact of Sheet Thickness  
#### 3.2.1 Experimental Conditions  
In this experiment, the influence of PET sheet thickness on the crawling performance of kirigami-patterned soft robotic crawlers is evaluated. Five different thicknesses--50 \(\upmu\)m (red), 100 \(\upmu\)m, 150 \(\upmu\)m, 200 \(\upmu\)m (transparent), and 250 \(\upmu\)m (green)--were tested using both triangular and trapezoidal kirigami patterns. Each sample incorporated a 3 \(\times\) 14 cut configuration over a fixed contact area of 51 mm \(\times\) 140 mm, ensuring consistent cut density across all variants. To assess locomotion, each crawler underwent five complete extension-retraction cycles driven by a PM linear actuator with a 30 mm stroke at a constant speed of 15 mm/s on a compliant polyurethane (PU) foam substrate. The goal was to determine the optimal sheet thickness that provides an effective trade-off between material flexibility for out-of-plane deformation and mechanical stability for surface anchoring across both triangular and trapezoidal kirigami configurations.
## 2 Theoretical Background  
### Determinants of Navigational Obstacle Hazards  
Article 24 of the Enforcement Rules of the Maritime Traffic Safety Act (Assessment of the Danger of Navigational Obstacles) specifies the factors for determining the danger posed by navigational obstacles, which are categorized into twelve classes [30], as follows: (1) size, shape, and structure, (2) condition and type of damage, (3) nature and quantity of cargo onboard the obstacle, including the type and quantity of oil (fuel oil, lubricating oil, etc.), (4) state of submersion (for sunken ships), (5) water depth and seabed topography of the area, (6) hydrographic survey results, including the tidal range, currents, and weather conditions, (7) proximity to nearby marine facilities, (8) proximity to navigation zones and routes used for international ship navigation, (9) ship traffic density and frequency, (10) ship navigation methods, (11) safety of port facilities, and (12) Particularly Sensitive Sea Areas (PSSAs) as designated by the International Maritime Organization (IMO).
## 3 Fluctuation Function and Decoding of the Proposed System
### Codeword Extraction and Matching
 \tag{15}\]  
In the proposed fluctuation functions, \(f_{\rm K}(a,b)^{(0)}\) and \(f_{\rm K}(a,b)^{(1)}\), vertical and horizontal average value and boundary pixels for calculating the distance are used. Moreover, pixels in the right and left corner are used to determine the distance from the horizontal average, and similarly pixels in the top and bottom corner are used to determine the distance from the vertical average. In the Experimental Results, the performance comparison among three fluctuation functions will be discussed. Let \(f(a,b)^{(0)}\) be one of the fluctuation functions (11), (13), (15), and similarly, let \(f(a,b)^{(1)}\) be one of the fluctuation functions (12), (14), (16). Then, if the \(f(a,b)^{(0)}\) is smaller than the \(f(a,b)^{(1)}\), the corresponding codeword bit is zero. If otherwise, the corresponding codeword bit is one. Therefore, the estimated bit embedded into the \((a,b)\) block, \(\hat{y}(a,b)\), is given as:  
\[\hat{y}(a,b)= \begin{cases}0,&f(a,b)^{(0)}<f(a,b)^{(1)}\\ 1,&\text{otherwise.}\end{cases} \]  
The estimated bit \(\hat{y}(a,b)\) corresponds to \(y(a,b)\) in (5). Then, the \(\left(a\left\lfloor\frac{M}{s}\right\rfloor+b\right)\)-th element \(\hat{z}^{\prime}\left(a\left\lfloor\frac{M}{s}\right\rfloor+b\right)\) of \(\hat{\mathbf{z}}^{\prime}\) can be defined as:  
\[\hat{z}^{\prime}\left(a\left\lfloor\frac{M}{s}\right\rfloor+b\right)=\hat{y}( a,b).\]  
The length of \(\hat{\mathbf{z}}^{\prime}\) is \(kqN_{\rm c}\). From the permutation \(\pi\) in (4), \(\hat{\mathbf{z}}\) is defined as:  
\[\hat{\mathbf{z}}=\pi^{-1}(\hat{\mathbf{z}}^{\prime}),\]  
where \(\pi^{-1}\) is the inverse function of \(\pi\). By using the mappings \(B_{\rm C}\) and \(B_{\rm F2B}\) in (3), (2) and (1), the estimated codeword \(\hat{\mathbf{c}}_{v}\) for \(v=0,\cdots,N_{\rm c}-1\) can be determined as:  
\[\hat{\mathbf{c}}_{v}=B_{\rm F2B}^{-1}\left(B_{\rm C}^{-1}\left(\hat{\mathbf{z} }(nqv:(nq(v+1)-1))\right)\right), \tag{17}\]  
where \(B_{\rm F2B}^{-1}\) and \(B_{\rm C}^{-1}\) are inverse mappings of \(B_{\rm F2B}\) and \(B_{\rm C}\), respectively, and \(\hat{\mathbf{z}}(i:j)\) denotes the vectors with from the \(i\)-th elements to the \(j\)-th elements in \(\hat{\mathbf{z}}\).
## 4 New Propagation Model
### Modelling Line of Sight Probability  
The LoS probability on the street is obtained based on elevation angle for four environments with UAV altitudes up to 500 m and azimuthal angles between 0 and 360 degrees. The LoS probability proposed in our previous work [34] is given by  
\[P_{LoS}=\frac{1}{a_{3}+e^{-(-a_{1}+a_{2}(\theta-a_{4}))}} \tag{2}\]  
where \(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\) are the empirical parameters. The empirical parameter values for the suburban environment are (2.1778, 0.3557, 1, 0), the urban environment (3.0734, 0.1565, 0.9989, 0.158), the dense urban environment (3.4912, 0.1304, 1.007, 0.3344), the high-rise urban environment below elevation angle 45 degrees (4.2234, 0.8815, 1.5747, 0.114), and the high-rise urban environment above elevation angle 45 degrees (4.7313, 0.1209, 0.9801, 13.144).
# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 5 Limitation of Current Methodologies
We have summarized the brief comparison among the various glucose-sensing technologies in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Technologies** & **Advantages** & **Disadvantages** & **Measuring Site** & **Sensitivity** & **Cost** & **Ref.** \\ \hline Electrochemical glucose sensors & \begin{tabular}{c} Simpler concept, \\ predictive accuracy, and \\ stability upon changing \\ conditions \\ \end{tabular} & \begin{tabular}{c} Pain and invasiveness, \\ risk of infections, and \\ biocompatibility \\ \end{tabular} & \begin{tabular}{c} Skin \\ \end{tabular} & 1.3 mM & Low & [6; 34; 118] \\ \hline Microdialysis & \begin{tabular}{c} Improved real-time \\ monitoring \\ \end{tabular} & Pain and invasiveness & Skin & 21 \(\upmu\)M & Moderate & [20; 39] \\ \hline Hexokinase method & High specificity & Enzyme instability & In vitro & \begin{tabular}{c} 3.3 mM or \\ 60 mg/dL \\ \end{tabular} & High & [42; 105] \\ \hline Ion-Selective Field-Effect Transistor (ISFET) & Fast reproducibility with repeat measurements & Low sensitivity and slow response, electrical noise and signal interference, and sensoritiv to & In vitro & 0.1 mM & High & [119; 120; 121; 122] \\ \hline Extended gate electrode field-effect transistor (EGFET) & Improved sensitivity as compared to ISFET & \
\begin{tabular}{c} Electrical noise and \\ signal interference and \\ sensitivity to \\ environmental factors \\ \end{tabular} &
\begin{tabular}{c} Potential \\ application in \\ saliva and sweat \\ \end{tabular} & 20 \(\upmu\)M & High & [29; 45] \\ \hline \end{tabular}
\end{table} 
Table 1: Key metrics of glucose-sensing technologies. In conclusion, while optical sensing technologies, especially fluorescence, Raman sensing, and OCT offer significant advantages, challenges still remain in maintaining their signal strength, sensor lifespan, probe biocompatibility, and interference with other biological signals. Continuous development and refinement of these technologies are essential to meet the demand for more reliable, non-invasive glucose-monitoring systems.