esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ET Partitioning  
ET partitioning is carried out according to the approach based on _uWUE_[18]. In this method, maximum potential underlying water use efficiency (_uWUE\({}_{p}\)_) is related to transpiration, while the apparent underlying water use efficiency (_uWUE\({}_{a}\)_) is related to evapotranspiration. On an ecosystem scale, the relationship is defined as follows:  
\[\mu WUE_{p}=\frac{GPP\sqrt{VPD}}{T} \tag{4}\]  
\[\mu WUE_{a}=\frac{GPP\sqrt{VPD}}{ET}, \tag{5}\]  
where _uWUE\({}_{p}\)_ is the potential water use efficiency; _uWUE\({}_{a}\)_ is the apparent water use efficiency; _GPP_ is the gross primary productivity, which represents carbon assimilation on an ecosystem scale; and _VPD_ is the atmosphere vapor pressure deficit.  
The T/ET ratio can be determined as follows:  
\[\frac{T}{ET}=\frac{\mu WUE_{a}}{\mu WUE_{p}}. \tag{6}\]  
Figure 2: Sketch representing the identification of growing season.  
GPP, VPD, and ET can be observed from eddy covariance measurements, whereas \(uWUE_{a}\) and \(uWUE_{p}\) can be observed through regression analysis.  
In this approach, it is assumed that T/ET follows a single-peak diurnal pattern, and the diurnal peak is close to 1 when vegetation coverage is high and soil evaporation can be negligible. Under such circumstances, \(uWUE_{p}\) is equal to the maximum value of \(uWUE_{a}\). \(uWUE_{p}\) is assumed to be constant for each site for each year. [PERSON] (2015) and [PERSON] et al. (2016) reported that extreme quantiles such as 5% and 95% are widely used to describe the lower and upper limits, respectively, for the regression slope. Therefore, in this study, 95 th percentile quantile regression is applied to derive the upper slope of GPP VPD\({}^{0.5}\) over ET and \(uWUE_{p}\). For each site, an annual \(uWUE_{p}\) for a given year is derived using half-hourly GPP, ET, and VPD data with an intercept of zero. For instance, at the Arou site, we can see from Figure 3 that the slope of the 95 th percentile quantile regression (red line) with the annual \(uWUE_{p}\) is 12.25 g C hPa\({}^{0.5}\)/kg H\({}_{2}\)O.  
\(uWUE_{a}\) is estimated using linear regression with half-hourly GPP, ET, and VPD measurements for two particular periods of one month and one week. The regression slope of GPP VPD\({}^{0.5}\) over ET with an intercept of zero represents the \(uWUE_{a}\) for each site of given period. As shown in Figure 4, for the Arou site, the linear regression slope is 7.56 g C hPa\({}^{0.5}\)/kg H\({}_{2}\)O in August and 3.73 g C hPa\({}^{0.5}\)/kg H\({}_{2}\)O in the 23 rd week in 2015. Therefore, the T/ET is 0.62 (=7.56/12.25) for August and 0.30 (=3.73/12.25) for the 23 rd week in 2015. The performance of linear regression is evaluated using \(R^{2}\) and \(p\)-value.  
Figure 3: The 95 th quantile linear regression using the half-hourly data for each site in 2015.  
Figure 4: Linear regression using the half-hourly data for each site on a monthly and weekly scale. (**a**) is linear regression using the data of August in 2015, (**b**) is linear regression using data of the 23 rd week in 2015.
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 4 Numerical Results and Discussion
### Number of Alive Nodes  
In this section, we compare the EFRP and DSR routing protocols in terms of alive nodes in the network after different round times. Figure 16 portrays the number of alive nodes in the field. The proposed EFRP protocol is alive with 28 nodes after 11,484 rounds, while the number of alive nodes in the network is 0 using the DSR routing protocol. The main reason behind this noticeable gap between the two routing protocols is the parameters and constraints that are included in the objective function used in EFRP protocol, which considers not only the distances between nodes and the BS, but also the SoC for sensor nodes, the SLD to the BS, and the other system constraints that were discussed before in the previous section, Section 3.1. Thus, the lifetime of IoT networks using our proposed routing protocols is the same as in the previous section.  
Figure 14: Residual energy after sending a packet using DSR.  
Figure 15: A comparison between the EFRP and DSR algorithm in terms of the total energy consumption of sensor nodes participated in the path.  
protocol will last longer than that of the DSR routing algorithm, and even the throughput of the system will be more efficient and reliable when using EFRP protocol compared to the DSR routing protocol.  
Table 10 shows the number of alive nodes over the given number of rounds for both protocols EFRP and the DSR. It is easy to notice that when the system uses the EFRP for data transmission, there are more alive nodes than the DSR at all the transmission rounds. This is because the EFPR routing protocol always tries to consider the threshold of residual energy at each sensor node before considering it in the transmission process, which in turn prolongs the network of IoT lifetime. Moreover, it is also obvious that the gap between the two routing protocols has increased during this time. The reason behind this is the technique that the DSR follows to select the next hop. The DSR continuously chooses the same sensor nodes to transmit data to the next nodes while disregarding their remaining energy, which causes a progressive increase in the number of dead nodes. Constantly using the same nodes for transmission of data will significantly drain their energy. Using such a technique will negatively affect the lifetime and the throughput of the network. As shown in Table 10, the number of alive sensor nodes in our proposed protocol has increased by more than 25% compared to the other approaches. Furthermore, energy consumption is reduced by about 33% in one round of transmission.
## 4 Results and Analysis
### Influence of Lateral Pressure Coefficient
#### 4.2.2 Horizontal Displacement of the Roadway Sidewall  
The horizontal displacement distribution of a rock mass varies with the \(\lambda\) value, as illustrated in Figure 15. As the \(\lambda\) value increases, the horizontal displacement of the sidewall of a roadway first decreases and then rises. With a \(\lambda\) value of 0.1, the corresponding horizontal displacement of a roadway sidewall reaches the value of 22.15 mm. When the \(\lambda\) value rises to 0.3, the horizontal displacement of a roadway sidewall is reduced to 12.66 mm. When the \(\lambda\) value increases to 0.5, the horizontal displacement of the roadway sidewall fluctuates up and down to 12.98 mm. The horizontal displacement increases as the \(\lambda\) value continues to increase from 0.5. When the \(\lambda\) value rises to 1.0, the horizontal displacement of the roadway sidewall rises to 19.57 mm. In general, the horizontal displacement and lateral pressure coefficient of the roadway sidewall show a quadratic polynomial relationship.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### In-Depth Analysis
#### 5.3.7 Effect of Camera Type  
A closely related factor that also affects rPPG analysis is the camera type, which determines the signal acquisition quality. Figure 13 provides a comparison of HR and HRV accuracy for different camera types used in VIPL-HR and MoLi-PPG datasets. As can be seen, the HD cameras provide the best performance in both datasets, likely due to their superior sensor, low internal compression, and higher frame-rate. However, we can also see that a modern webcam can closely match the performance of an HD camera under realistic condition (especially in VIPL-HR). The mobile front camera (Huawei P9) produces the worst performance in VIPL-HR, likely caused by its significantly lower video acquisition quality.
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 3 Method
### Problem Formulation
#### 3.1.2 Extrinsic Parameters Calibration Problem Formulation  
The extrinsic calibration problem is demonstrated in Figure 2a. During once calibration, the camera and LiDAR are fixed firmly. The calibration target is in a suitable position, where it can be captured by the camera and LiDAR simultaneously. The core of the extrinsic calibration problem is to estimate the relative pose between the camera coordinate system and LiDAR coordinate system. Assuming that the coordinates of arbitrary point \(P\) in the camera coordinate system and LiDAR coordinate system are \(P^{C}\) and \(P^{L}\), the transformation of them can be shown as follows:  
\[P^{C}=\mathbf{R}P^{L}+T, \tag{3}\]  
where rotation matrix \(\mathbf{R}\in SO(3)\) and translation vector \(T\) are extrinsic parameters of the camera and LiDAR system. The details of our extrinsic calibration method are shown in Section 3.2.2.
## 5 Experimentation, Results, and Analysis
### Performance Metrics
#### 5.2.3 Custom Metrics  
The Contextual Accuracy (CA) metric is a novel approach to assess the depth of contextual understanding in the generated captions. It quantifies how well the context of each word in the generated caption aligns with that in the reference caption. CA is calculated as follows:  
\[CA=\frac{\sum_{i=1}^{N}\text{sim}(y_{i},y^{\prime}_{i})}{N} \tag{26}\]  
In this equation, \(Y\) represents the generated caption, \(Y^{\prime}\) the reference caption, and \(N\) the length of the caption. The function \(\text{sim}(y_{i},y^{\prime}_{i})\) computes the similarity between the corresponding words \(y_{i}\) in the generated caption and \(y^{\prime}_{i}\) in the reference caption.  
The similarity function \(\text{sim}(y_{i},y^{\prime}_{i})\) is a pivotal component of the CA metric. It evaluates the degree of contextual congruence between the two captions at the word level. The computation of \(\text{sim}(y_{i},y^{\prime}_{i})\) involves the following steps:  
* **Semantic Similarity:** We employ advanced semantic analysis techniques to assess the similarity. This involves using pre-trained word embeddings (e.g., Word2 Vec, GloVe) that encapsulate the semantic meaning of words. The semantic similarity score is obtained by calculating the cosine similarity between the embeddings of \(y_{i}\) and \(y^{\prime}_{i}\).
* **Lexical Matching:** Alongside semantic similarity, lexical matching is also considered, especially in cases where semantic similarity alone may not fully capture the contextual nuances. This involves checking for synonyms, antonyms, and other lexical relations using natural language processing libraries.
* **Normalization:** The similarity scores obtained from both semantic and lexical analyses are normalized to ensure that they contribute equally to the overall similarity calculation. This is important to balance the contributions from both semantic depth and lexical accuracy.  
The final similarity score \(\text{sim}(y_{i},y^{\prime}_{i})\) is a weighted average of both semantic similarity and lexical matching scores, ensuring a comprehensive evaluation of contextual accuracy. By incorporating these multifaceted similarity measures, CA provides a robust assessment of how effectively the generated caption captures the contextual essence of the reference caption.
# Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection
###### Abstract  
The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include the following aspects--first, they have low adaptability to significant appearance changes in long-term tracking and are prone to tracking failure; second, these trackers lack a practical re-detection module to find the target again after tracking failure. In our work, we propose a new long-term tracking strategy to solve these issues. First, we make the best of the static and dynamic information of the target by introducing the motion features to our long-term tracker and obtain a more robust tracker. Second, we introduce a low-rank sparse dictionary learning method for re-detection. This re-detection module can exploit a correlation among these training samples and alleviate the impact of occlusion and noise. Third, we propose a new reliability evaluation method to model an adaptive update, which can switch expediently between the tracking module and the re-detection module. Massive experiments demonstrate that our proposed approach has an obvious improvement in precision and success rate over these state-of-the-art trackers.  
visual tracking; long-term; correlation filter; low-rank sparse; motion boundary; re-detection; reliability evaluation +
Footnote †: journal: Applied sciences  
12 January 2021  
Accepted: 18 February 2021  
Published: 23 February 2021
## 2 Formulation for Damage Identification
### Objective Function  
The presented NDE method for identifying damages is founded on the stiffness method, which is an effective method for structural analysis. Since the physical parameters of the structure (such as the moment of inertia, cross-sectional area, and elastic modulus) are contained within the stiffness matrix, any damage to the structure may alter these parameters and can, consequently, affect the structural response. Therefore, any changes in the structural response can be utilized to identify these parameters. If the external force applied to the structure during non-destructive testing is known and some displacements have been accurately measured, then the stiffness method can be utilized to determine the values of unknown parameters. This process is also known as inverse analysis [36; 37]. Assuming that some physical parameters in Equation (1) are unknown, the relationship between the structural stiffness matrix, displacements, and forces can be expressed as follows:  
\[\mathbf{Q}=\mathbf{K}\mathbf{D} \tag{3}\]  
where \(\mathbf{Q}\) represents the global forces; \(\mathbf{K}\) is the global stiffness matrix of the entire structure, which is obtained by assembling the stiffness matrices, \(k^{\prime}\), of all elements; and \(\mathbf{D}\) represents the global displacements.  
The objective function for damage identification can be defined as the discrepancy between analytical displacement and measured displacement, and the objective function can be expressed as  
\[f=\sum_{i=1}^{n}\Bigl{(}D_{i}^{\;i}-D_{a}^{\;i}\Bigr{)}^{2} \tag{4}\]In Equation (4), \(D_{m}^{i}\) represents the \(i\)th measured displacement and \(D_{a}^{i}\) represents the \(i\)th analytical displacement. The measured displacements and analytical displacements can be obtained by solving Equation (3). Furthermore, the unknown parameter can be obtained by minimizing the objective function. In this study, the interior point method was employed for optimizing the objective function, which is known for its superior computational speed, precision, and stability in tackling complex optimization problems [38].
## 4 Protocol for Source Location Protection Based on Limited Flooding
### _h-Directed Routing_  
After the _h_-hops limited flooding process, the packet will be transmitted for _h_-hops, where each hop goes farther away from the source. The _h_-directed routing aims at generating phantom locations that are widely distributed and also as far as possible from the source. To this end, each packet should be transmitted far away from the source for each hop. SLP always sends the packet to a random sensor of \(D\). Therefore, the phantom locations generated are scattered and far away from the source.  
Specifically, the source generates a packet to be transmitted for _h_-hops during this phase as {_EVENT_, _E__K__pub_(_m_), _hop_rand_, _Next_hop_id_}, where _EVENT_ is the packet type and _E__K__pub_(_m_) denotes the event data \(m\), which has been encrypted by the public key \(K_{b}\). _hop_rand_ is used to record the packet transmission times and initialized as 0. It will be increased by 1 after each transmission. The _h_-directed routing phase will terminate as soon as _hop_rand_ reaches \(h\), _Next_hop_id_ is the unique identifier of the next forwarding sensor. Once a sensor (_i.e._, the current sensor) receives a packet and finds the packet satisfies: (1) the _Next_hop_id_ equals to its ID; (2) the packet type is _EVENT_; and (3) _hop_rand_\(<\)_h_, the sensor will choose a sensor randomly from \(D\) and forward the packet to the sensor. The packet forwarding process repeats until _hop_rand_ has increased to \(h\). If the current sensor \(u\) selects a neighbor \(v\) as its next forwarding sensor for a packet and the two sensors satisfies _Hop__v_,_\(s\) - _Hop__u_,_\(s\) = 1, the packet is believed to have been sent far away from the source \(s\). Thus, if the next forwarding sensor is selected from \(D\) of \(u\), the packet is assured to be sent far away from the source. In addition, since the next forwarding sensor is chosen randomly from \(D\), the phantom locations obtained from SLP are widely and evenly distributed in the probabilistic sense. SLP ensures it is difficult for an attacker to trace to the source even if he has reached to a phantom location.
## 1 Introduction
### Study Area  
Within western Arabia lies an exposed Neoproterozoic block (Figure 1A) known as the Arabian Shield, with a crystalline Precambrian basement, Cenozoic alkaline volcanic fields (Harrat), and small sedimentary basins and grabens (e.g., [22]). The Al-Yutamah Valley (Figure 1B) is located in western Arabia (23.8852 N, 39.7524 E) within the Arabian shield, in the vicinity of Harrat Rashid [23] or Al-Madinah [24]; which is the northern part of the Harrat Rahat volcanic field, about 100 km from the Red Sea coast. The study area (Figure 1B) is dominated by basaltic lava flows of the middle-Pleistocene age that bound the area from the eastern and the northern sides. Precambrian basement is exposed in the western and southern regions, while surficial deposits (Alluvium--Quaternary) present as mud/loam flat around the periphery of volcanic rocks [25]. The Al-Yutamah Valley was formed by E-W Cenozoic dextral strike slip faulting (Figure 1B) [26], which has been suggested to be associated with opening of the Red Sea [27]. The other major structural features of the area generally trend in NW-SE direction, consistent with the regional structural direction and believed to be part of the Najd strike-slip fault system (NFS) [26]. However, the pattern of small-scale faulting changes strike in the Al-Yutamah basin to a N-S direction, as seen in the field within the Quaternary deposits [8].  
Figure 1: Geologic and geographic context of the study area: (**A**) Tectonic setting and major structural features of the Arabian Shield and surrounding area modified after [28,29]. Our study area is highlighted in a green box. This region is characterized by diffuse extensional grabens, dike intrusions, and massive volcanic fields. (**B**) Geologic and major structural features of Al-Yutamah Valley is shown as NW sinistral Najd fault system (NFS) which intersects with Cenozoic dextral strike slip (CDM) superimposed on the general bedrock terrain. (**C**) Worldview satellite image of the eastern part of Al-Yutamah Valley showing primary infrastructure and farms. The red circle in (**B**,**C**) denotes the city of Naageeah.
## 4 Conclusions  
The paddy soil in karst areas exhibited a higher geochemical background of As, with the concentration significantly surpassing that found in non-karst regions. However, the As concentration in the rice grains within the karst area remained low, with only 0.62% of the rice samples exceeding the permissible value for inorganic As (0.2 mg/kg). The SEP results show that As in the karstic paddy soil is present mostly in the residual fraction, and the water-soluble and exchangeable fractions of As, readily absorbed by rice, are extremely rare. The high As content but low bioavailability of As in the karstic paddy soil can be primarily attributed to the abundant Fe-Mn nodules, which adsorbed and immobilized a significant portion of As, contributing to 64.45% of the As content in the host soil. The bioavailable fraction of As was found to be less than 0.01% of the total As in the Fe-Mn nodules. Selective extractions revealed that As was primarily sequestered within Fe-(oxyhydr)oxides, which could release As into the soil under certain reduction conditions through the reductive dissolution of Fe-(oxyhydr)oxides. pH-dependent leaching tests showed that only under extremely acidic (pH = 2) and alkaline (pH = 11) conditions was there a trend of As leaching from the Fe-Mn nodules. In the natural pH range of the karstic paddy soil (pH 4.97-8.38), As remained steadily adhered to the Fe-Mn nodules. Therefore, agricultural soil quality management in karst areas should not only be based on the total As content in the soil but should also consider the bioavailability of As, thereby tailoring adaptations to fully utilize arable land.  
X.L. ([PERSON]): Conceptualization, Methodology, Formal analysis, Writing original--draft, Writing--review and editing, and Visualization. X.M.: Methodology, Formal analysis, and Visualization. Q.H.: Writing--review and editing, and Supervision. X.X.: Supervision. B.L.: Investigation and Resources. K.L.: Investigation and Resources. X.L. ([PERSON]): Investigation and Resources. Z.W.: Investigation and Resources. W.J.: Data curation. L.W.: Resources. T.Y.: Conceptualization, Writing--review and editing, Supervision, Project administration, and Funding acquisition. Z.Y.: Conceptualization, Writing--review and editing, Supervision, and Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The pH-dependent leaching of As, Fe, Mn, and Al from Fe–Mn nodules. The blue area is the portion with a pH of 4.97-8.38.  
**Funding:** This work was financially supported by the Geological Survey Project of China (Grant No. DD20211414) and the National Natural Science Foundation of China (42330703).  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the confidentiality of the project.  
**Acknowledgments:** The authors appreciate the helpful and useful suggestions of the editors and reviewers.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Non-Parameterized Time-Frequency Analysis
### Wavelet Transform
#### 2.2.3 Strength and Weakness
Although it has a low-order vanishing moment, it is applicable for rapid fault detection and the detection of signal mutation points. However, its resolution ability for complex fault signals is relatively weak. In practical applications, experts in the field of SHM need to make careful selections based on specific circumstances. Second, the choice of parameters in wavelet transform (such as scale and position) has a substantial influence on the results. However, a rigorous methodology for this process is currently lacking. This may lead to instability and inaccuracy in analysis results, necessitating further research and exploration to refine this method. Finally, the sensitivity of wavelets to noise is also a concern for their application. In SHM, signals are often contaminated with noise due to environmental noise and measurement errors. Wavelet transform may face interference from noise when processing these noisy signals, leading to distorted analysis results. Therefore, complementary denoising techniques need to be integrated to mitigate the impact of noise on analysis results and improve the accuracy and reliability of wavelet transform in SHM.