esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Analysis of OLR Anomalies  
One of the main parameters used to characterize the Earth's radiation environment is the OLR. The latter has been associated with the top of the atmosphere (TOA), integrating the longwave radiation (LWR) emissions between 10 and 13 microns from the ground, lower atmosphere, and clouds. Hence, it has primarily been used to study Earth's radiative budget and climate. An increase in radiation and a transient change in OLR was proposed to be related to thermodynamic processes in the atmosphere over major geohazard regions [69; 70; 71]), and the anomalous trend was measured as an OLR anomaly. The latter was defined similarly to the anomalous thermal field definition proposed by [PERSON] et al. [72; 73] and represents the different amplitude for a specific spatial location and predefined times:  
\[\mathit{OLR\;anomaly}\;(x,y,t)=\frac{S(x,y,t)-\overline{S}(x,y,t)}{\tau(x,y,t)} \tag{2}\]  
\[\overline{S}(x,y,t)=\frac{1}{\mathrm{N}}\sum_{i=1}^{\mathrm{N}}S(x_{i},y_{i}, t_{i}) \tag{3}\]  
\[\tau(x,y,z)=\sqrt{\frac{\Sigma\Big{(}S(x,y,t)-\overline{S}(x,y,t)\Big{)}^{2}}{ \mathrm{N}}} \tag{4}\]  
where \(S(x,y,t)\) is the current OLR value; \(\overline{S}(x,y,t)\) is the computed mean of the background field, defined as the daily mean value of OLR \(S\Big{(}x_{i},y_{i},t_{j}\Big{)}\) over an area of longitude \(x\) and latitude \(y\) in the \(t\)-\(th\) day of \(M\) years; and \(\tau(x,y,z)\) is the standard deviation over the same location \((x,y)\) and local time (\(t\)) as the OLR value.  
The first result in connecting the LWR variations in relation to volcanic activity was obtained for the Mt. Kirishima (Japan) eruption of 26-30 January 2011 [6; 71]. An abnormal increase in the thermalradiation was detected around 22 December 2010 (i.e., about one month before the aforementioned eruption), and the maximum was observed from 22 to 30 January 2011, which coincided with the peak of volcanic activity.
## 3 Experiments
### Use Cases  
Based on the above experiments and the results obtained, we can consider several use cases where the system can be applied:  
* Smart agriculture, where we could add soil moisture sensors using LoRaWAN for longer range instead of BLE.
* Industrial automation, where we could replace the BLE communication protocol with MODBUS/MQTT in order to provide multinode communications.
* Disaster monitoring, where we could add CO\({}_{2}\) sensors using ZigBee in case of multiple nodes used within a single mesh.
###### Abstract  
In 2015, the plan for global coverage by the Chinese BeiDou Navigation Satellite System was launched. Five global BeiDou experimental satellites (BeiDou-3e) are in orbit for testing. To analyze the performances of precise orbit determination (POD) and precise point positioning (PPP) of onboard BeiDou satellites, about two months of data from 24 tracking stations were used. According to quality analysis of BeiDou-2/BeiDou-3e data, there is no satellite-induced code bias in BeiDou-3e satellites, which has been found in BeiDou-2 satellites. This phenomenon indicates that the quality issues of pseudorange data in BeiDou satellites have been solved well. POD results indicate that the BeiDou-3e orbit precision is comparable to that of BeiDou-2 satellites. The ambiguity fixed solution improved the orbit consistency of inclined geosynchronous orbit satellites in along-track and cross-track directions, but had little effect in the radial direction. Satellite laser ranging of BeiDou-3e medium Earth orbit satellites (MEOs) achieved a standard deviation of about 4 cm. Differences in clock offset series after the removal of reference clock in overlapping arcs were used to assess clock quality, and standard deviation of clock offset could reach 0.18 ns on average, which was in agreement with the orbit precision. For static PPP, when BeiDou-3e satellites were included, the positioning performance for horizontal components was improved slightly. For kinematic PPP, when global positioning satellites (GPS) were combined with BeiDou-2 and BeiDou-3e satellites, the convergence time was 13.5 min with a precision of 2-3 cm for horizontal components, and 3-4 cm for the vertical component.  
BeiDou-3e; code bias; precise orbit determination; ambiguity fixing; precise point positioning +
Footnote †: journal: Sensors
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 2 Materials and Methods
### Epipolar Geometry-Based Extrinsic Parameters Determination
#### 2.3.3 SVD Based Extrinsic Parameters Initialization  
For any matrix, it is always possible to decompose it into the product of a unitary matrix \(\mathbf{U}\), a diagonal matrix \(\mathbf{\Sigma}\) and a transpose of another unitary matrix \(\mathbf{V}^{\text{T}}\). As soon as the essential matrix is obtained, the singular value decomposition (SVD) of the essential matrix \(\mathbf{E}\) is expressed as:  
\[\mathbf{E}=\mathbf{U\Sigma V^{\text{T}}},\ \ \mathbf{\Sigma}=\text{diag}(1,1,0). \tag{10}\]  
The \(\mathbf{R}\) and \(\mathbf{t}\) describing the rigid-body transformation from the left camera to the right camera can be obtained as follows:  
\[\begin{array}{l}\mathbf{R}=\mathbf{UWV^{\text{T}}}\text{ {or} }\mathbf{R}=\mathbf{UW^{\text{T}}V^{\text{T}}}\\ \mathbf{t}=\mathbf{U_{2}}\text{ {or} }\mathbf{t}=-\mathbf{U_{2}}\end{array} \tag{11}\]where \(\mathbf{W}=\begin{pmatrix}0&-1&0\\ 1&0&0\\ 0&0&1\end{pmatrix}\), \(\mathbf{U}_{2}\) is the last column of the unitary matrix \(\mathbf{U}\).  
From geometric considerations, there are four possibilities for the combination of \(\mathbf{R}\) and \(\mathbf{t}\). The positive depth constraint [43] is used to eliminate the redundant solutions from all possible solutions and extract the true solution. It retrieves a point in the world coordinate system using these four possible extrinsic matrices and selects the reconstructed world point located in front of the two cameras, i.e., the point should be in the positive direction of the z-axis [44].
# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 2 Materials and Methods
### Face Classification
#### 2.6.2 Cascading Haar Classifier
It is impossible to avoid errors or misclassifications, so features with the minimum error rate are chosen, meaning those that classify face images most accurately. Each image initially has the same weight. After each classification, the weight of misclassified images is increased. The process is then repeated, new error rates and weights are calculated, and the process continues until the required accuracy is achieved or the required minimum number of features is found [13]. Each single Haar feature is only a \"weak classifier\". They are called weak because they cannot correctly classify an image. Therefore, a large number of Haar features are necessary to describe an object with enough accuracy to identify the image correctly. A cascade of weak classifiers (single Haar features) is introduced to form a single \"strong\" classifier. The cascade classifier consists of a series of stages, each being a set of weak learning stimuli, i.e., simple classifiers called decision stumps. Each stage is trained using a technique called boosting. Boosting enables the training of a highly accurate classifier by taking a weighted average of the decisions made by the decision stumps. Each stage of the classifier designates the area defined by the current location of the sliding window as positive or negative. Positive indicates that an object has been found, and negative means no objects have been found. If the factor is negative, the classification of this area is completed, and the detector moves the window to the next location. The classifier passes the area to the next stage if the factor is positive. The detector then re-examines the same window at a later stage for reverification. If the window is positively verified, the final stage saves the window with a positive outcome. The stages aim to reject negative samples as quickly as possible. For the classifier to function correctly, it must have a low rate of false negatives--samples incorrectly classified as negative. If a stage incorrectly marks an object as negative, classification is halted, and the error cannot be corrected. However, each stage can have many false positives--negative samples are incorrectly classified as positive. Subsequent stages correct the errors of false positive samples, so the greater the number of stages, the fewer windows incorrectly classified as positive. The final classifier is thus a weighted sum of weak classifiers. According to the creators of the classifier, even 200 functions can ensure face detection with 95% accuracy (Figure 8). By using AdaBoost learning algorithms, we can reduce and select as significant about 6000 functions out of over 160,000 in a 24 \(\times\) 24-pixel window without reducing the accuracy of the classifier [14]. Figure 8: Haar classifier training scheme [68].
## 4 Case Study  
A tailings pond located in Gansu Province, China, was used to validate the method proposed in this paper. The region is in the temperate monsoon season, with moderate rainfall and less cloud cover, which is conducive to satellite photography. The terrain where the tailings pond is located is dominated by mountains and hills, and the surrounding vegetation coverage is relatively sparse, and is mainly composed of grassland and shrubs. The tailings pond is of the valley type. The site location and landform is shown in Figure 12. There are three monitoring points, A, B, and C, for dry beach length on-site. Monitoring points A and B are positioned at one-third and two-thirds of the primary beach crest line,  
Figure 11: Calculation of pixel distance of dry beach length.  
Figure 10: Straight-line fitting treatment. (**a**) Preprocessing image; (**b**) linear fitting to beach crest line.  
while monitoring point C is situated at the midpoint of the secondary beach crest line, as seen in Figure 13.
## 1 Introduction
An interesting alternative to modelling multiway data has been proposed in the chemometrics literature, the multiway partial least squares regression (N-PLS). This is a modelling strategy introduced by [PERSON] [17] and further advanced by [PERSON] [19]. N-PLS aims at building a model between a high-order array **X** of independent variables and a response array **y**[20]. In this paper, PLS regression is extended to multiway data (N-PLS), with the main emphasis on three-way data, by including time as a dimension. It is important to note that N-PLS imposes a trilinear structure on the data [21] and when introducing time into the analysis, this implies that the emphasis is exclusively on the development of a correlational structure over time [22], i.e., time will determine both the variability between individuals and the variability between variables (wavelengths). The N-PLS methodology allows a joint evaluation of time and variables, in order to determine the interaction between them, to know if and how the variables modify their \"behaviour\" at different occasions. To the authors' knowledge, such an approach has not been proposed in the analysis of remotely sensed satellite imagery. Therefore, the objectives of this study are (i) to propose a formalism to apply the N-PLS approach to a time-series of images at the regional scale in order to predict a variable of interest with a small ground truth dataset, (ii) to show the value of the approach for its potential to generate knowledge in an agricultural case study, and (iii) to identify the possible limitations of the approach when dealing with image time-series on large scales with series that may be incomplete for certain areas. The work is organized as follows: Section 2 introduces the proposed NPL-S method and the development of the model as well as the description of the case study that the methodology is applied to. The results are presented in Section 3, with the discussion in Section 4.
## 5 Conclusions  
Through the impacts analysis of summer droughts to GPP and ET, we found that the impacts are diverse and depend on the biome types, the significant relationship between the carbon-water process and drought, and drought timescale. Generally, during summer drought, grassland, barren/sparse vegetation and shrub reduce the GPP (or ET), while cropland and forest regions improve GPP (or ET) due to different main limited factors. And the arid barren/sparse vegetation exhibit smaller sensitivity to droughts than semi-arid grassland due to some compensation mechanisms, which weakens the summer drought impacts on the carbon-water process. Whereas, compared with abrupt wet-dry shifts, persistent or multiple droughts intensify the impacts on the carbon-water process in NADRs with higher change rates. Notably, the wet winter and warm spring weaken the summer drought impacts on GPP in some parts of grasslands. In summary, the response mechanism of carbon-water processes to summer drought is complex but could help us to analyze and account for the dynamics of ecosystem water use efficiency during drought disturbance in further research. Also, this study is conducive to making measures or actionable recommendations for policymakers or land managers dealing with dryland ecosystems.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs17040589/s1](https://www.mdpi.com/article/10.3390/rs17040589/s1)]([https://www.mdpi.com/article/10.3390/rs17040589/s1](https://www.mdpi.com/article/10.3390/rs17040589/s1)), Figure S1: Comparison of (a) GPP_model data and (b) ET_model data with flux tower measurements. Dashed line is the 1:1 line and solid line indicates the best linear fit curve.; Table S1: Summary of climate and vegetation characteristics of the 8 tower sites; Table S2: Statistics for the modeled GPP, ET (GPP_Model, ET_Model) with tower-measured GPP (GPP_Obs) and ET (ET_Obs) at the 8 study sites.  
Writing--original draft preparation, W.K.; writing--review and editing, S.L.; supervision, S.K. and T.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 42107499.  
The MODIS data products could be found in MODIS Web [[https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)]([https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)) (accessed on 27 January 2025); The SPEI dataset can be found from [[https://spei.csic.es/database.html](https://spei.csic.es/database.html)]([https://spei.csic.es/database.html](https://spei.csic.es/database.html)) (accessed on 27 January 2025).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GPP & Gross Primary Productivity \\ ET & Evapotranspiration \\ NADR & Northeast Asia Dryland Regions \\ VPM & Vegetation Photosynthesis Model \\ \end{tabular}
## 4 Experimental Outcomes
### Results: Post-Installation System P2
The main results are summarized in Table 4, as in the previous Section 4.1. It is possible to observe that the scatter significantly decreases for measurements provided by both ceramic and capacitive stress sensors. Moreover, the peak stress values recorded by ceramic sensors are closer to the values provided by the load cell, with an underestimation of about the 6% for calcarenite specimens, and about the 32% for clay brick specimens. The average results and the values of standard variation are reported in Figure 20. In the case of the post-installation system P2, the measurements provided by the capacitive stress sensors in terms of capacitance variation resulted in the highest scattering (COV is 35.6%), while the average measurements by ceramic sensors are closer to the average peak stress values recorded by the load cell and reported in the same order of magnitude.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 1 Introduction
This ensures full coverage of the target area by UAV-BS while minimizing coverage redundancy. Subsequently, with the goal of minimizing system transmission power, the number of UAV-BS deployments and the site planning are optimized. This study integrates a multi-UAV coverage model with the Coverage and Connectivity Problem (CCP) to guarantee that any point can complete communication. On this basis, a function to minimize system transmission power is constructed, and site planning strategies for different target areas under full coverage are provided. The method uses a circular coverage strategy to establish a coverage model, which can maximize the reduction in overlap in UAV coverage areasand enhance deployment efficiency. In future UAV communication scenarios, this strategy can provide a reference for the planning and deployment of UAV-BS and also support the development of UAV communication technology.
# Experimental Study on the Soil-Structure Responses Induced by Tunnelling in Limited Space
## 3 Model and Test Descriptions  
### Similarity of the Model Test  
Many researchers pointed out that model tests can be an irreplaceably effective way of investigating complex engineering problems. Therefore, laboratory model tests were widely adopted to conduct investigations about tunneling in a congested environment. In order to reproduce the actual geotechnical condition and to reflect the interaction between the tunnel and the existing underground structures, the models must satisfy the main requirements (e.g., the geometry and physical-mechanical characteristics) [32, 36]. Based on the similarity theory and the well-documented model test cases, the similarity ratios of the main parameters in this study can be written as follows:  
\[\left\{\begin{array}{l}C_{\sigma}=C_{\gamma}\times C_{l}\\ C_{\delta}=C_{\epsilon}\times C_{l}\\ C_{\sigma}=C_{\epsilon}\times C_{\epsilon}\\ C_{\epsilon}=C_{\varphi}=C_{\mu}=1\end{array}\right. \tag{1}\]  
where \(C\) represents the similarity ratio between the model and the prototype; and \(\sigma\), \(\gamma\), \(E\), \(l\), \(\delta\), \(\varphi\), \(\epsilon\) and \(\mu\) refer to the stress, gravity, elastic modulus, length, deformation, friction angle, strain and Poisson ratio, respectively.  
In light of the effect of the self-weight stress field in this study, the gravity in the model test was equal to that of the field condition. Therefore, the \(C_{\gamma}\) was 1. Considering the operating conditions and the size of the experimental devices, the similarity ratio of the length, \(C_{L}\), was set as 1:30.  
In order to minimize the adverse boundary effect of the model tank, the dimension of the model tank was determined as 1800 mm \(\times\) 600 mm \(\times\) 1800 mm. As shown in Figure 3, the model tank was made of steel. To achieve better observation of the soil movement during the test process, a high-strength glass plate was set at the front side of the model tank. Hence, the ground settlement and the soil deformation during the whole process can be monitored well by a digital image acquisition system. In general, the size of the test tank met the experimental requirements in this study.  
Figure 2: Typical soil profile along with measured geotechnical parameters at site.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 4 Results
### Evaluation Metrics  
To evaluate the model's predictive performance, several metrics should be selected. The model predictions represent \(\hat{y_{ij}}\), and include predicted RSRP and predicted NR-RSRP. The true value is represented as \(y_{ij}\). Mean Absolute Error (MAE) represents the average absolute difference between the true values and the predicted values.  
\[MAE=\frac{1}{nm}\sum_{i=1}^{n}\sum_{j=1}^{m}|y_{ij}-\hat{y}_{ij}| \tag{5}\]  
Figure 9: Prediction result of centralized LSTM model.  
Mean Squared Error (MSE) calculates the average of the squared differences between the predicted values and the true values. The closer the model's predictions are to the true values, the smaller the MSE.  
\[MSE=\frac{1}{nm}\sum_{i=1}^{n}\sum_{j=1}^{m}(y_{ij}-\hat{y}_{ij})^{2} \tag{6}\]  
Root Mean Squared Error (RMSE) is the square root of the mean squared differences between the predicted values and the true values. Similar to MSE, the closer the model's predictions are to the true values, the smaller the RMSE.  
\[RMSE=\sqrt{\frac{1}{nm}\sum_{i=1}^{n}\sum_{j=1}^{m}(y_{ij}-\hat{y}_{ij})^{2}} \tag{7}\]  
Table 2 presents a comparison of model prediction performance across three different approaches: Heterogeneous Federated Learning Hete_FL, Homogeneous Federated Learning Homo_FL, and Centralized LSTM Model Cen_LSTM. The performance metrics considered are Mean Absolute Error (MAE), Mean Squared Error MSE, and Root Mean Squared Error (RMSE). The results highlight several advantages: The Hete_FL approach demonstrates superior accuracy in predictions with the lowest MAE of 3.5021, the smallest overall squared error with an MSE of 5.2394, and the least deviation from the actual values with an RMSE of 2.2883. Similarly, the Homo_FL approach shows significantly better performance compared to the centralized LSTM model, with an MAE of 3.3223, an MSE of 4.6171, and an RMSE of 2.1476, indicating more accurate predictions and better prediction accuracy. Overall, the table indicates that federated learning approaches, both heterogeneous and homogeneous, outperform the centralized LSTM model, with Hete_FL standing out as the most effective method, delivering the highest accuracy and lowest error rates across all metrics. First, the three methods are categorized into federated learning and centralized machine learning. For model aggregation in federated learning, we employ the FedAvg algorithm, which helps guide model training away from local minima. As a result, federated learning tends to outperform centralized machine learning. In the case of heterogeneous federated learning, where different devices are connected via the network, model aggregation can be affected by factors such as network latency, connection stability, or variations in device computing capabilities. Consequently, across all three performance metrics, homogeneous federated learning outperforms heterogeneous federated learning.